atividade_heart.ipynb

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      "name": "atividade_heart.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/arthurnovello/ECM514-Ciencia_dos_Dados/blob/master/atividade_heart.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "hrR_fNXuvgKG",
        "colab_type": "text"
      },
      "source": [
        "## Atividade Heart.csv"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tYyHGRoyvO-N",
        "colab_type": "code",
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        "colab": {
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          "height": 88
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      "source": [
        "#Bibliotecas utilizadas para classificação e exportação da árvore gerada\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, \\\n",
        "                             GradientBoostingClassifier\n",
        "#Biblioteca para facilitar a separação dos dataset em dados de traino e teste\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "#Pandas é uma biblioteca que facilita a ingestão e manipulação de dados\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set(color_codes=True)\n",
        "\n",
        "\n",
        "#enabling to plot using plotlib in the notebook\n",
        "%matplotlib inline\n",
        "%pylab inline"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
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        "id": "v2D0BDNDwoUb",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "df = pd.read_csv(\n",
        "    \"/content/drive/My Drive/Colab Notebooks/ecm514_datasets/heart_.csv\")\n",
        "df"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
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              "      <th>slope</th>\n",
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              "      <th>target</th>\n",
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              "      <th>0</th>\n",
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              "      <td>female</td>\n",
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              "      <td>354</td>\n",
              "      <td>0</td>\n",
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              "      <th>299</th>\n",
              "      <td>45</td>\n",
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              "      <td>3</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
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              "      <td>no</td>\n",
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              "      <td>57</td>\n",
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              "      <td>130</td>\n",
              "      <td>131</td>\n",
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              "      <td>115</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>57</td>\n",
              "      <td>female</td>\n",
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              "      <td>130</td>\n",
              "      <td>236</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>no</td>\n",
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              "<p>303 rows × 14 columns</p>\n",
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            ],
            "text/plain": [
              "     age     sex  cp  trestbps  chol  ...  oldpeak  slope  ca  thal  target\n",
              "0     63    male   3       145   233  ...      2.3      0   0     1     yes\n",
              "1     37    male   2       130   250  ...      3.5      0   0     2     yes\n",
              "2     41  female   1       130   204  ...      1.4      2   0     2     yes\n",
              "3     56    male   1       120   236  ...      0.8      2   0     2     yes\n",
              "4     57  female   0       120   354  ...      0.6      2   0     2     yes\n",
              "..   ...     ...  ..       ...   ...  ...      ...    ...  ..   ...     ...\n",
              "298   57  female   0       140   241  ...      0.2      1   0     3      no\n",
              "299   45    male   3       110   264  ...      1.2      1   0     3      no\n",
              "300   68    male   0       144   193  ...      3.4      1   2     3      no\n",
              "301   57    male   0       130   131  ...      1.2      1   1     3      no\n",
              "302   57  female   1       130   236  ...      0.0      1   1     2      no\n",
              "\n",
              "[303 rows x 14 columns]"
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            "tags": []
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      "source": [
        "df['target'] = df['target'].map({'yes':0, 'no':1})\n",
        "df['sex'] = df['sex'].map({'male':0, 'female':1})\n",
        "df"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<p>303 rows × 14 columns</p>\n",
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            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    0   3       145   233    1  ...      0      2.3      0   0     1       0\n",
              "1     37    0   2       130   250    0  ...      0      3.5      0   0     2       0\n",
              "2     41    1   1       130   204    0  ...      0      1.4      2   0     2       0\n",
              "3     56    0   1       120   236    0  ...      0      0.8      2   0     2       0\n",
              "4     57    1   0       120   354    0  ...      1      0.6      2   0     2       0\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    1   0       140   241    0  ...      1      0.2      1   0     3       1\n",
              "299   45    0   3       110   264    0  ...      0      1.2      1   0     3       1\n",
              "300   68    0   0       144   193    1  ...      0      3.4      1   2     3       1\n",
              "301   57    0   0       130   131    0  ...      1      1.2      1   1     3       1\n",
              "302   57    1   1       130   236    0  ...      0      0.0      1   1     2       1\n",
              "\n",
              "[303 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ElNIPoXF02Sz",
        "colab_type": "text"
      },
      "source": [
        "# Todas as features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "13G5j6FhyIEd",
        "colab_type": "code",
        "outputId": "cd77e030-229c-4118-e2d6-daeff2b35d6c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "nomes_features = list(df.columns[:13])\n",
        "nomes_features"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age',\n",
              " 'sex',\n",
              " 'cp',\n",
              " 'trestbps',\n",
              " 'chol',\n",
              " 'fbs',\n",
              " 'restecg',\n",
              " 'thalach',\n",
              " 'exang',\n",
              " 'oldpeak',\n",
              " 'slope',\n",
              " 'ca',\n",
              " 'thal']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dpjpp6v6zP4S",
        "colab_type": "code",
        "outputId": "450d8335-e471-4e39-e1fc-9bbd6caaf786",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "x = df[  nomes_features ]\n",
        "x.head()"
      ],
      "execution_count": 5,
      "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",
              "    .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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  thalach  exang  oldpeak  slope  ca  thal\n",
              "0   63    0   3       145   233    1  ...      150      0      2.3      0   0     1\n",
              "1   37    0   2       130   250    0  ...      187      0      3.5      0   0     2\n",
              "2   41    1   1       130   204    0  ...      172      0      1.4      2   0     2\n",
              "3   56    0   1       120   236    0  ...      178      0      0.8      2   0     2\n",
              "4   57    1   0       120   354    0  ...      163      1      0.6      2   0     2\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjE0Qlz3zYmN",
        "colab_type": "code",
        "outputId": "27cf678b-c796-4076-a827-b1df617b315a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "y = df[  df.columns[13] ]\n",
        "y.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zWerXSmzbFT",
        "colab_type": "code",
        "outputId": "f209b190-98ea-4eb4-92e9-091bfe5f1fee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.3, \n",
        "                                                        random_state = 2556)\n",
        "x_treino.head()"
      ],
      "execution_count": 7,
      "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",
              "    .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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>267</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>125</td>\n",
              "      <td>213</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>1</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>270</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111</td>\n",
              "      <td>1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>125</td>\n",
              "      <td>273</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>152</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>138</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>169</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  ...  exang  oldpeak  slope  ca  thal\n",
              "198   62    0   0       120   267  ...      1      1.8      1   2     3\n",
              "34    51    0   3       125   213  ...      1      1.4      2   1     2\n",
              "202   58    0   0       150   270  ...      1      0.8      2   0     3\n",
              "33    54    0   2       125   273  ...      0      0.5      0   1     2\n",
              "92    52    0   2       138   223  ...      0      0.0      2   4     2\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yDpcP571FzJ",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5wG5JbTYzi3_",
        "colab_type": "code",
        "outputId": "c349bab0-af1e-423d-e593-a790bc1887a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#classificador\n",
        "classificador = DecisionTreeClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 8,
      "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=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dmM0uJ_ezmLT",
        "colab_type": "code",
        "outputId": "fff72a85-04e5-495a-92b1-2cee10d068aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ml1Kzj6l0RTJ",
        "colab_type": "code",
        "outputId": "e350c968-2fb6-499c-9661-a68a274605d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7582417582417582"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIVClISI0Tiu",
        "colab_type": "code",
        "outputId": "9fd3fc96-3671-4c8b-d1cd-57089ed6f2bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583cf334e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JVVw37dxxBLp",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g60l0TSx0Xzi",
        "colab_type": "code",
        "outputId": "15c8af78-9338-49c4-e73b-682b7a6d0bb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "#classificador\n",
        "classificador = RandomForestClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k6C-qx4w36d",
        "colab_type": "code",
        "outputId": "8b4d92dc-95a6-4510-a307-9f1006aabd56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4QEYbB5w6bU",
        "colab_type": "code",
        "outputId": "9d4dffe8-3951-4d1b-d11a-960b3f02720b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8571428571428571"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BAGqnCc6w8cK",
        "colab_type": "code",
        "outputId": "3cba4235-9143-4d6f-b75a-3c1e0fdcc714",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583cf348d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WcrhGjRxELW",
        "colab_type": "text"
      },
      "source": [
        "# Gradient Boost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osVIxlZqw-z3",
        "colab_type": "code",
        "outputId": "cdfd3678-38e6-48c7-d6fa-7b36b8b6dfb9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#classificador\n",
        "classificador = GradientBoostingClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
              "                           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, n_estimators=100,\n",
              "                           n_iter_no_change=None, presort='deprecated',\n",
              "                           random_state=None, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOs9-yvKxIrJ",
        "colab_type": "code",
        "outputId": "cc005bbd-41f0-491f-b2ef-a127ac0a1f94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUWqq1fyxLFn",
        "colab_type": "code",
        "outputId": "ddd23ce8-24d2-4c8c-9a00-d5b4ec32e687",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8131868131868132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8tf1BdxTxNcc",
        "colab_type": "code",
        "outputId": "0e4372a4-7f24-4dae-d09c-6d934bbc588e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c958da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsBDRBX_xVyn",
        "colab_type": "text"
      },
      "source": [
        "# Usando 5 Features\n",
        "\n",
        "\n",
        "\n",
        "1.   age\n",
        "2.   sex\n",
        "3.   trestbps\n",
        "4.   restecg\n",
        "5.   thal\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gxfz0di4xZUL",
        "colab_type": "code",
        "outputId": "410dc3ab-aae3-45a2-8cdb-d4190151788a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "x2 = df[ ['age', 'sex', 'trestbps', 'restecg', 'thal'] ]\n",
        "x2.head()"
      ],
      "execution_count": 20,
      "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",
              "    .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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  trestbps  restecg  thal\n",
              "0   63    0       145        0     1\n",
              "1   37    0       130        1     2\n",
              "2   41    1       130        0     2\n",
              "3   56    0       120        1     2\n",
              "4   57    1       120        1     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vu3-u1nIyfgN",
        "colab_type": "code",
        "outputId": "646fd339-9ea7-4169-93e4-ca7dbec898c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x2, y, test_size = 0.3, \n",
        "                                                        random_state = 2556)\n",
        "x_treino.head()"
      ],
      "execution_count": 21,
      "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",
              "    .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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "      <td>125</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>138</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  trestbps  restecg  thal\n",
              "198   62    0       120        1     3\n",
              "34    51    0       125        0     2\n",
              "202   58    0       150        0     3\n",
              "33    54    0       125        0     2\n",
              "92    52    0       138        1     2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agSSciimzvIA",
        "colab_type": "text"
      },
      "source": [
        "# Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aq8CUKMCzGTI",
        "colab_type": "code",
        "outputId": "25e824cf-4d7a-4e3c-cd95-df7a414d6e3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "#classificador\n",
        "classificador = DecisionTreeClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 22,
      "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=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4_Hib5GzJdF",
        "colab_type": "code",
        "outputId": "1bb81c84-6c79-412c-9522-fdd37aca21b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9952830188679245"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybksX0ZRzLqD",
        "colab_type": "code",
        "outputId": "a4f355ec-2c5a-4bf3-e222-b2576a2d6867",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6813186813186813"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AFINPOxnzP1y",
        "colab_type": "code",
        "outputId": "fafdbb22-00ca-41cc-c3b1-4add62159f29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c8eb6a0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJjQoLgKzx5Y",
        "colab_type": "text"
      },
      "source": [
        "# Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AGYfdAGOzSik",
        "colab_type": "code",
        "outputId": "d5f85b20-47af-4ee3-b965-94d94b280727",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        }
      },
      "source": [
        "#classificador\n",
        "classificador = RandomForestClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cUwMFwlXzV6y",
        "colab_type": "code",
        "outputId": "71c32bd5-0c50-457d-cfdf-7ee45dc5ff15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9952830188679245"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mc17t28PzZHw",
        "colab_type": "code",
        "outputId": "61353a87-a5da-4fed-bd3a-4d1a08518c62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7142857142857143"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wk2xbTDSzbTJ",
        "colab_type": "code",
        "outputId": "66d10699-8c8a-4ef5-bbad-c7842a1aca2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c80df98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jxxfpYxLz0ro",
        "colab_type": "text"
      },
      "source": [
        "# Gradient Boost"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVZP2hXQzfSN",
        "colab_type": "code",
        "outputId": "d9ffaa27-e35e-47b2-879e-170415225063",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "#classificador\n",
        "classificador = GradientBoostingClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
              "                           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, n_estimators=100,\n",
              "                           n_iter_no_change=None, presort='deprecated',\n",
              "                           random_state=None, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5T88WOKzisi",
        "colab_type": "code",
        "outputId": "b8069451-c955-4ed9-84c1-e430cb91f03c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9198113207547169"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z9O7djozzmSo",
        "colab_type": "code",
        "outputId": "7adf1055-b9d2-4eb3-ea7d-b2dc0bbb54bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6923076923076923"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlX76y6JzoMG",
        "colab_type": "code",
        "outputId": "4d9ad095-69d6-4fd0-eabd-17253eca6c70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c6c9e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Tue4dUAgE_A",
        "colab_type": "text"
      },
      "source": [
        "# Usando df.corr()\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdTGp8uzgAOV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "23393da6-974c-431e-b461-9e8c89806f8b"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 35,
      "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",
              "    .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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.098447</td>\n",
              "      <td>-0.068653</td>\n",
              "      <td>0.279351</td>\n",
              "      <td>0.213678</td>\n",
              "      <td>0.121308</td>\n",
              "      <td>-0.116211</td>\n",
              "      <td>-0.398522</td>\n",
              "      <td>0.096801</td>\n",
              "      <td>0.210013</td>\n",
              "      <td>-0.168814</td>\n",
              "      <td>0.276326</td>\n",
              "      <td>0.068001</td>\n",
              "      <td>0.225439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <td>0.098447</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.049353</td>\n",
              "      <td>0.056769</td>\n",
              "      <td>0.197912</td>\n",
              "      <td>-0.045032</td>\n",
              "      <td>0.058196</td>\n",
              "      <td>0.044020</td>\n",
              "      <td>-0.141664</td>\n",
              "      <td>-0.096093</td>\n",
              "      <td>0.030711</td>\n",
              "      <td>-0.118261</td>\n",
              "      <td>-0.210041</td>\n",
              "      <td>-0.280937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cp</th>\n",
              "      <td>-0.068653</td>\n",
              "      <td>0.049353</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>-0.433798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>trestbps</th>\n",
              "      <td>0.279351</td>\n",
              "      <td>0.056769</td>\n",
              "      <td>0.047608</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>0.144931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chol</th>\n",
              "      <td>0.213678</td>\n",
              "      <td>0.197912</td>\n",
              "      <td>-0.076904</td>\n",
              "      <td>0.123174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>0.085239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbs</th>\n",
              "      <td>0.121308</td>\n",
              "      <td>-0.045032</td>\n",
              "      <td>0.094444</td>\n",
              "      <td>0.177531</td>\n",
              "      <td>0.013294</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>0.028046</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>restecg</th>\n",
              "      <td>-0.116211</td>\n",
              "      <td>0.058196</td>\n",
              "      <td>0.044421</td>\n",
              "      <td>-0.114103</td>\n",
              "      <td>-0.151040</td>\n",
              "      <td>-0.084189</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>-0.137230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thalach</th>\n",
              "      <td>-0.398522</td>\n",
              "      <td>0.044020</td>\n",
              "      <td>0.295762</td>\n",
              "      <td>-0.046698</td>\n",
              "      <td>-0.009940</td>\n",
              "      <td>-0.008567</td>\n",
              "      <td>0.044123</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>-0.421741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>exang</th>\n",
              "      <td>0.096801</td>\n",
              "      <td>-0.141664</td>\n",
              "      <td>-0.394280</td>\n",
              "      <td>0.067616</td>\n",
              "      <td>0.067023</td>\n",
              "      <td>0.025665</td>\n",
              "      <td>-0.070733</td>\n",
              "      <td>-0.378812</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>0.436757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>oldpeak</th>\n",
              "      <td>0.210013</td>\n",
              "      <td>-0.096093</td>\n",
              "      <td>-0.149230</td>\n",
              "      <td>0.193216</td>\n",
              "      <td>0.053952</td>\n",
              "      <td>0.005747</td>\n",
              "      <td>-0.058770</td>\n",
              "      <td>-0.344187</td>\n",
              "      <td>0.288223</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>0.430696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>slope</th>\n",
              "      <td>-0.168814</td>\n",
              "      <td>0.030711</td>\n",
              "      <td>0.119717</td>\n",
              "      <td>-0.121475</td>\n",
              "      <td>-0.004038</td>\n",
              "      <td>-0.059894</td>\n",
              "      <td>0.093045</td>\n",
              "      <td>0.386784</td>\n",
              "      <td>-0.257748</td>\n",
              "      <td>-0.577537</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>-0.345877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ca</th>\n",
              "      <td>0.276326</td>\n",
              "      <td>-0.118261</td>\n",
              "      <td>-0.181053</td>\n",
              "      <td>0.101389</td>\n",
              "      <td>0.070511</td>\n",
              "      <td>0.137979</td>\n",
              "      <td>-0.072042</td>\n",
              "      <td>-0.213177</td>\n",
              "      <td>0.115739</td>\n",
              "      <td>0.222682</td>\n",
              "      <td>-0.080155</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>0.391724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thal</th>\n",
              "      <td>0.068001</td>\n",
              "      <td>-0.210041</td>\n",
              "      <td>-0.161736</td>\n",
              "      <td>0.062210</td>\n",
              "      <td>0.098803</td>\n",
              "      <td>-0.032019</td>\n",
              "      <td>-0.011981</td>\n",
              "      <td>-0.096439</td>\n",
              "      <td>0.206754</td>\n",
              "      <td>0.210244</td>\n",
              "      <td>-0.104764</td>\n",
              "      <td>0.151832</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.344029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <td>0.225439</td>\n",
              "      <td>-0.280937</td>\n",
              "      <td>-0.433798</td>\n",
              "      <td>0.144931</td>\n",
              "      <td>0.085239</td>\n",
              "      <td>0.028046</td>\n",
              "      <td>-0.137230</td>\n",
              "      <td>-0.421741</td>\n",
              "      <td>0.436757</td>\n",
              "      <td>0.430696</td>\n",
              "      <td>-0.345877</td>\n",
              "      <td>0.391724</td>\n",
              "      <td>0.344029</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age       sex        cp  ...        ca      thal    target\n",
              "age       1.000000  0.098447 -0.068653  ...  0.276326  0.068001  0.225439\n",
              "sex       0.098447  1.000000  0.049353  ... -0.118261 -0.210041 -0.280937\n",
              "cp       -0.068653  0.049353  1.000000  ... -0.181053 -0.161736 -0.433798\n",
              "trestbps  0.279351  0.056769  0.047608  ...  0.101389  0.062210  0.144931\n",
              "chol      0.213678  0.197912 -0.076904  ...  0.070511  0.098803  0.085239\n",
              "fbs       0.121308 -0.045032  0.094444  ...  0.137979 -0.032019  0.028046\n",
              "restecg  -0.116211  0.058196  0.044421  ... -0.072042 -0.011981 -0.137230\n",
              "thalach  -0.398522  0.044020  0.295762  ... -0.213177 -0.096439 -0.421741\n",
              "exang     0.096801 -0.141664 -0.394280  ...  0.115739  0.206754  0.436757\n",
              "oldpeak   0.210013 -0.096093 -0.149230  ...  0.222682  0.210244  0.430696\n",
              "slope    -0.168814  0.030711  0.119717  ... -0.080155 -0.104764 -0.345877\n",
              "ca        0.276326 -0.118261 -0.181053  ...  1.000000  0.151832  0.391724\n",
              "thal      0.068001 -0.210041 -0.161736  ...  0.151832  1.000000  0.344029\n",
              "target    0.225439 -0.280937 -0.433798  ...  0.391724  0.344029  1.000000\n",
              "\n",
              "[14 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0TbF1CKBgDSm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "a8d08ba0-81a0-4e00-a82c-709e960feed5"
      },
      "source": [
        "x3 = df[ ['ca', 'thal', 'oldpeak', 'exang', 'cp'] ]\n",
        "x3.head()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>exang</th>\n",
              "      <th>cp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ca  thal  oldpeak  exang  cp\n",
              "0   0     1      2.3      0   3\n",
              "1   0     2      3.5      0   2\n",
              "2   0     2      1.4      0   1\n",
              "3   0     2      0.8      0   1\n",
              "4   0     2      0.6      1   0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CTl-UM07g-rE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "80469e12-14a7-41cc-e40a-19738f512742"
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x3, y, test_size = 0.3, \n",
        "                                                        random_state = 2556)\n",
        "x_treino.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>exang</th>\n",
              "      <th>cp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1.4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>202</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     ca  thal  oldpeak  exang  cp\n",
              "198   2     3      1.8      1   0\n",
              "34    1     2      1.4      1   3\n",
              "202   0     3      0.8      1   0\n",
              "33    1     2      0.5      0   2\n",
              "92    4     2      0.0      0   2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBkkLG2thty0",
        "colab_type": "text"
      },
      "source": [
        "## Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VxFcwozhEdO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        },
        "outputId": "a5e6c307-ddaa-44f2-c5c2-0961f142b367"
      },
      "source": [
        "#classificador\n",
        "classificador = DecisionTreeClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 48,
      "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=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YdmvZT3lhKj-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "08727a34-449d-41c3-c3b5-24d10fa95fe4"
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9669811320754716"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AACjiZCphMo4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "720d2596-7f15-4b47-c94a-b356c67ed5f5"
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7252747252747253"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2WLzZ3hhlQc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "89068886-b042-4a79-adf2-09fd0fa1ca20"
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c724ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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glpkNdPc1W9tOyL68bQGbl7e1y92PyFUmDrN/MY0PN5Ve7tSbr/9xVzchLz55aWne/rlwxlEl992G9N2O+dMPi6Wujgx7q6urh1VVVWUernP3urT3w4Fad28AcPcGM1se7s8MfpPM7BhgJTDD3R/J1o5sPb/fZf0GIiIZkuSeRW0+XlNT09YI8iLgwq049RzgUnffFD5j/DYzG+3ub7X3gWzL2+ZuRQNEpIx1pOc3adKkcVVVVcsyDtdlvF8KVJhZKuz1pYChbH6aJADuvjLt9T/NbCmwL/BAe+3Yqmt+IiJtSRIhq0v4s7KyclllZeWSbGXdfbWZPQ1MBuaFPxdmXu8zswp3rw1fHwjsBni2uhX8RCQ2ySQRJjw6XO1UYK6ZXQCsB6YAmNl84AJ3fwK4zMw+DjQQPE/81PTeYFsU/EQkNvnI5+fuLwOHtLF/Ytrr0zpWq4KfiMSo5FJamVlP4AKC8fYAd+8bTimPcvdZ+WygiBSP5vW7ucoUgo5kddkXOJnN9/69AHwzH40SkeKUYPPtLu1thRH6oge/E4CvhjcNNgKEMysV+WqYiBSfUkxssDGzrJkNJFhkLCICEDyaMseMRqpAol/Unt+fCKaaRwKY2RCCh5bX5KthIlJ8mmd7c22FIGrw+xGwGHgO2AlYRJA6Rg85EpEWiVyJTCOsAOksUVNabSTI2nx2ONxd6+45kx6ISHkpxVtdMh9StKOZAeDur8fdKBEpTqXy0PJ0rxLc4pLe7OaeXyrWFolI0UqEf3KVKQRRh72trg2a2S7ADEBJTUWkRSoJ3XLMJKQ6vrY3L7aqGeGC4enAT+NtjogUs+aUVrm2QrAta3sN2D6uhohI8etISquuFnXCIzOl/fbAPsDF+WiUiBSnkpvtZcuU9u8Dz2Q+Pk5EylsxJTbIGfzCtNFHAZXuviH/TRKRYhUkM81dphDkbEb41KRjCBMaiIi0J0ki0lYIOpLS6iIz657PxohIcSuZrC5mNtndbwKmAbsA3zWzNaRNfrj7iPw2UUSKRSmt8LiK4Anpp3RCW0SkyCUSuSc0iqLnR7iczd3bffaliEizBBFudemUluSWK/ilzOxIsrTX3e+Lt0kiUqxSyQjJTAtk3Jsr+PUErqH94NcEZGZ8EZEy1fycjlxlCkGu4Pe+uyu4iUg0UdbuFshFPz23V0RikyD3Nb3CCH0RJzxERKIomeVt7r5jZzVERIpfKfX8REQiSyYTJHPM5uY6nsnMRgFzgQEEj8ud0l5SFQuer7EQuNLdv5+1HR1qhYhIFgk2z/i2t21Fz28OMNvdRwGzCRZfbCFMwnIVcGuUStXzE5HYRMnU3Hy8urp6WFVVVebhOneva35jZoOAMcCEcNdNwCwzG+juazI++0PgTqB3uGWlnp+IxCYRcQOoqalZQPA88PRtekaVw4HaMLtUc5ap5eH+FmZ2APBpgiQskSj4iUhsEkR4hkcY/iZNmjQOGJmxzezoOcNsU9XA1OYgGYWGvSISm1QiQSrHsLf5eGVl5bLKysolOapcClSYWcrdG8LrekPD/c2GAHsA88Pnie8EJMysj7tXtlexgp+IxCbuW13cfbWZPQ1MBuaFPxemX+9z9zeBnZvfm9mFQG/N9opI54mSyLTj071TgWlm9gpBbtGpAGY238wO3tqmqucnIrEJbmfJcZ9fB+t095eBQ9rYP7Gd8hdGqVfBT0RiU4qPrhQRySnB5tncbGUKgYKfiMSmI7O9XU3BT0TiE+XpbIUR+xT8RCQ+uuYnImVJ1/xEpCyV0nN7RUQiSxIhk7N6fiJSajTsFZ5+9lWum3c3jY1NfGr8QRx/3H+1Ov6P+57koosX8t7GRrbr2YNvnPFZhlUMpL6+gTnX3MniN1bQ2NDIEYfvzwnHHd5F36J8HLp7f6YfsxepRILbn17BHx55Y4synxo9iA31cEPlJ3h11XvMuO1F9hrcm3OONXbomaKxEX7/8BLufWl1F3yDwpCIMOwtqwkPM/s58EVgN2A/d3++M87bVRobG7nm+r9z/rknM6B/H86b8TsOHjOKYRUDW8oc/sl9+c13JnLHc6t44iln7o3/5H/P+SqPPvYi9fX1VF02lQ0bNvHd837Lfx26L4MG7tSF36i0JRPwvWON79y4kNXvbODaMw5mwaI1LFn7QUuZYf16MeWwXemRgpOrH6Pf9t0B+GhTAxff/iLL1n/Izr17cN2ZY/nP6+t4b0N9V32dLlVMPb/OSmxwK3AEsOU/pyXo1deWs8ugfgwe1I9u3VIcdug+PP6Utyqzfa+eLa8/2rBp87+GiQQfbdhEQ0MjGzduolsq1aqsxG/voX1Ytu4Dltd9RH1jE/e8uJojRg1sVeYLBw3lz08ua/l7Wv/BJgCWrvuQZes/BGDtextZ//5GdgoDYznKldQgyq0wnaVTen7u/hBAmGur5K1b/w4DBvRpeT+gfx8WvVa7Rbk5f3yAy6+9h/r6Bi744SkAHDp2NE885VSe9Us2btjEaScfQ+/evTqt7eVo4I49Wf3uhpb3q9/ZwD4VfVqVGd5/ewA21MPVp3+cax5czKOvr2tVZu+hO9I9laQ2DIblSE9vy4OjPzYwd6ECsWnlctYt68Vx+w0G4N03+rKpbn3L+xb7DWbqV8ZTc9fj3PPgE/zuJ1P499OvMbz/9vz93p+y/t0POPqMXzL9xLGMHLZzG2cqXFt81wLW0AiNTXDSwUNbvT/l0GEtZTaG+YG7J2HMiL7sO/lAeqY292KamoIy3VPw1IyjOvsrFAwtb8uDe15ew4ebGru6GZEsfreRha+u4o7nVgFwz7NLgVTL+2bH7TeYO55bRa+hw7nl3hq+cNIqfjdvAaP2rODvL68FoGLEEKr//iyHHbJPZ3+NbXLBLS90dRMi27eiD2eOG8nZNc8AMOWwXQG4/t+br9Kc+xnjhdp3uOSE0Yy56D5+89UDufL+13hpxbts3yPFlaeOYe7DS7j/5cxn6hS+IX23Y/70w+KprIi6fkpmmgd77D6UFavWsXrNeurrG/j3oy9w8EGjWpVZsfKtltdPPbOIIYP7A7DzgD48/+ISAD7asJFFr9VSMaS4en3F5qXl7zK8//YM6bsd3ZIJjt57EAteWduqzIO+hjG7BpNOfXt1Z/iA7amt+5BuyQT/96X9uOvZFUUZ+PIhkeNPoSianl8xSaWSnDHlWC694kYam5o48ogDGD5sEH/8y7/YY+QQDh5j/P2eJ5gz5xY+qG+i9w7b8a3KzwNw7NFjufLq2/nueb+lqQmOHHcAu44oniFkMWpoaqLq7leYOflAkskEdz6znMVr3+frR4zkpRXv8tCitTz6+jo+sXt/NtTD7FMOYta9r/LOh/V8et/BHDhiJ/ps352JBwwB4JI7XmLRqve6+Ft1jWJa25toamrK+0nM7NfAicAuwFrgLXePOo7bDVhcTMPeqJqHvaWomIa9HbFwxlEcdNF9Xd2MWKUNe0cCS7aymt2Axc+8+Q4b67PHlB7dEhwwos+2nm+bddZs71nAWZ1xLhHpQkV0zU/DXhGJTTKRiJDYoDCin4KfiMSmiDp+Cn4iErNCiW45KPiJSGyi3MxSKLe7KPiJSGyK6VYXBT8RiY8eYCQi5UjDXhEpS/kY9prZKGAuMAB4C5ji7osyyvwPcDbQCKSAq93919nq1dpeEYlNIuLWQXOA2e4+CpgNXNVGmb8AB7j7gcBhwPfMbP9slarnJyLx6cCNftXV1cOqqqoyj9a5e13zGzMbBIwBJoS7bgJmmdlAd2/JJOHu76TVsT3QHci6zk49PxGJTa6MLumZXWpqahYAizO26RlVDgdq3b0BIPy5PNzfipl93sxeIMgY/zN3fy5bWxX8RCQ2zc/tzbUBTJo0aRxBcoP0bebWntvdbw8TpowCTrUcqeM17BWR+HRg2FtZWbmssrJySY7SS4EKM0u5e4OZpYCh4f42ufubZvYY8DnA2yunnp+IxKYjw94o3H018DQwOdw1GViYfr0PwMxGp73eGTgSyDrsVc9PROKTn5ucpwJzzewCYD0wBcDM5gMXuPsTQKWZHQNsCs8wy93/ka1SBT8RiU0+srq4+8vAIW3sn5j2+uwOVqvgJyIxK4wFHDkp+IlIbJTMVETKkpKZikh5KqLop+AnIrFRVhcRKUtKZioiZUnBT0TKVqEMa3NR8BOR2KjnJyJlqYgmexX8RCQ+6vmJSJkqkMgWgYKfiMQmmYCmnMvbOqctuSj4iUhsEkQY9nZKS3JT8BOR2ERb4VEYFPxEJD5RIluBRD8FPxGJVYHEtpwU/EQkNolEhPv8CiQ6KviJSGwSiQjX/BT8RKTUaIWHiJQlDXtFpCzpVhcRKUvq+YlIWVLwE5EyVSypTBX8RCRGUXp16vmJSMnJx+o2MxsFzAUGAG8BU9x9UUaZHwOTgAZgE/Ajd787W73JDrZDRCS7RI6t4+YAs919FDAbuKqNMo8BY919f+AM4I9m1itbpQp+IhKbRMQ/UZnZIGAMcFO46yZgjJkNTC/n7ne7+wfh22cJwuyAbHVr2CsisUkmoClHmebQV11dPayqqirzcJ2716W9Hw7UunsDgLs3mNnycP+adk4xBXjN3Zdla4eCn4jEpwPD2pqamgVt7L4IuHBrT29m44GfABNylS2G4JcC2K57aY7Qe5Xo9xrSd7uubkLelNp3G9SnZ/PL1LbWlUwkaMrR9Wue7Z00adK4qqqqzN5ZXcb7pUCFmaXCXl8KGBrub8XMPgnMA77g7p6rrYmmXC3teocDbf0LISLxGgc8tJWf7Q+8CvSLWH49sCewLldBM/sX8Dt3n2dmpwBnuvuRGWXGAn8Bvuzu/4nSgGIIfj2BscAKgmlsEYlXChgCPA5s2IZ6+gN9IpZ9hwiBD8DMPkZwq0s/gqA5xd3dzOYDF7j7E2b2OLAbUJv20VPd/bn26i2G4CciErvSvOAkIpKDgp+IlCUFPxEpSwp+IlKWFPxEpCwp+IlIWVLwE5GyVAzL20pOlPxkUjjM7OfAFwluot3P3Z/v2hZJHNTz6xpR8pNJ4bgVOAJ4o6sbIvFR8OtkUfOTSeFw94fcfYuF9FLcFPw63xb5yYDm/GQi0kkU/ESkLCn4db6W/GQA2fKTiUj+KPh1MndfDTwNTA53TQYWunt7KblFJA+U0qoLtJefrGtbJe0xs18DJwK7AGuBt9x9n65tlWwrBT8RKUsa9opIWVLwE5GypOAnImVJwU9EypKCn4iUJQU/wcx+b2aXhK/HmVmn3HZjZk1mtmc7x/5lZl+LWM8SMzt6K9uw1Z+V4qaUVkXCzJYAgwmeXfw+cBfwbXd/L87zuPsCwCK053Tga+5+eJznF+ks6vkVl+PcvTdBVpiDgfMzC5iZ/kETiUD/oxQhd681s7uAfSEYPgLfBqYT/J2ONLPPAZcQJOB8EZjq7s+G5Q8CrgH2AuYDLXe6m9l/A/PcfVj4fjjwK2AcwT+WNxHkIJwDdDez94B6d9/JzHoClwInAT2BvwJnu/uHYV3nAN8Nz7dF4G6Pme0BXA0cEH72buBb7l6XVmxsuBJjCEH+vW+6+0fh59v9XUj5Us+vCIUBaSKwMG338cAhwN5hcLsW+AZBtuirgNvNrKeZ9SAIDn8A+gN/IshS3NZ5UsCdBEk8dwMqgBp3fwmYCjzi7r3dfafwI5cDo4ADgT3D8heEdR0LfB+YQBB0O3KdLQH8lCABxGiC9F8XZpQ5Gfg0sEfYhvPD87b7u+jA+aUEqedXXG41s3rgbeBvwGVpx37q7usAzKwSuMrd/xMem2tmPwIOJeg5dQdmunsT8Gcz+2475/sEQcA5x93rw30PtVXQzBJAJbB/WjsuA24EziPoDV7XnALezC5kc3KHrNz9VeDV8O0aM/sFMCOj2KzmhKNmdinwG4IAmO138UCU80tpUvArLse7+z3tHEtPibUrcJqZTUvb14MgkDURJFNNX9TdXnr24cAbaYEvm4HA9sCTZi3zJQkgFb4eCjwZ4ZxbMLPBbB5670gwYlmfUSz9+78Rng+y/y6kjCn4lY70YLYUuNTdL80sZGbjCfIJJtIC4AjgtTbqXAqMMLNubQTAzIwYa4EPgX3cvbaNulbQOlv1iPa/yhYuC1NV1zEAAAE1SURBVM+3n7uvM7PjgVkZZTLrXp72Hdr8XUh5U/ArTVcDfzWze4DHCHpk/w08CDwC1ANnmdmVwHEEw9v726jnMYKgdbmZzSC4zebj7v4wsAoYZmY93H2juzea2dXAL83s2+6+2swqgH3d/W7gZuA6M7seWMKWw9ZsdiQY6r8d1nlOG2W+ZWZ3Ah8A/wv8Mdfvwt3f7UAbpMRowqMEufsTwNcJekfrCa6XnR4e20iQm+50YB3wFeCWduppIAiOewJvAsvC8gD3AS8AK81sbbjvB+G5HjWzd4B7CO8ZdPe7gJnh514Nf0Z1EcHtPc3XOttq743AP4DXCXqxl+T6XUh5Uz4/ESlL6vmJSFlS8BORsqTgJyJlScFPRMqSgp+IlCUFPxEpSwp+IlKWFPxEpCwp+IlIWfr/wdRt7LaQh7EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OOV5sqLvh2Qm",
        "colab_type": "text"
      },
      "source": [
        "## Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iak9_0ALhOuq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "ef66dddd-97a0-4bcf-aa8a-4973e7d07359"
      },
      "source": [
        "#classificador\n",
        "classificador = RandomForestClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features='auto',\n",
              "                       max_leaf_nodes=None, max_samples=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, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kxmgARLuhSXA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "40c5c251-ac22-4dc7-f7eb-ef6923bd7e3c"
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9669811320754716"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ot_I0ULehVAr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0a826193-e3b8-49d4-de30-24d255dbbf0d"
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7692307692307693"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Uv5sLe4hoKO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "399b1138-139c-44ad-f8a1-d346d537f2ba"
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c1c5d30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xANFSuLhxL8",
        "colab_type": "text"
      },
      "source": [
        "## Gradient Boosting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QH34bHmMhWTH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "7b14c2d0-0f4c-4a0c-c7dc-200842b331e3"
      },
      "source": [
        "#classificador\n",
        "classificador = GradientBoostingClassifier()\n",
        "#treinando o modelo\n",
        "classificador.fit( x_treino, y_treino )"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,\n",
              "                           learning_rate=0.1, loss='deviance', max_depth=3,\n",
              "                           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, n_estimators=100,\n",
              "                           n_iter_no_change=None, presort='deprecated',\n",
              "                           random_state=None, subsample=1.0, tol=0.0001,\n",
              "                           validation_fraction=0.1, verbose=0,\n",
              "                           warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BTeHOm30hacM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "26868d6d-e1a6-44af-b6f7-b2f4b743a8a3"
      },
      "source": [
        "classificador.score( x_treino, y_treino)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9528301886792453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Z1PwSTRhcez",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4d67f255-95e4-4af0-c850-f12d7e392d01"
      },
      "source": [
        "classificador.score(x_teste, y_teste)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8131868131868132"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D14Ko_03hd5h",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "4f38f3e4-72b0-487c-9257-c6010a8b7e87"
      },
      "source": [
        "plot_confusion_matrix(estimator=classificador, X=x_teste, y_true=y_teste, \n",
        "                      display_labels=df['target'].unique(), cmap=plt.cm.Blues, \n",
        "                      normalize='true')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f583c1bfb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}