imersaodados3_aula5.ipynb

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      "name": "imersaodados3_aula5.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/leonardo-melniski/imersaodados-3/blob/main/imersaodados3_aula5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        ""
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0Tj_SYXgDNi"
      },
      "source": [
        "# Aula 5: Machine learning, sci-kit learning e desafios envolvidos.\n",
        "\n",
        "Nesta aula, finalmente vamos criar o modelo de **Machine Learning** para classificar um novo composto com o intuito de prever se o MoA foi ativado ou não. Para isso, vamos usar a famosa biblioteca [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) para testar a **regressão logística** e entender o funcionamento de uma árvore de decisão (AD). Além disso, discutiremos problemas **importantes** como o **overfit**.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vu6fdhkj1-4r"
      },
      "source": [
        "## Ref\n",
        "\n",
        "- [scikit-learn.org/linear_model.html](https://scikit-learn.org/stable/modules/linear_model.html)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoQ5wizhg8v0"
      },
      "source": [
        "## Links extras bacanas\n",
        "\n",
        "- Assista esse [vídeo](https://www.youtube.com/watch?v=K6HmoMnqeI8) para entender mais sobre o Overfit\n",
        "\n",
        "- Quer entender como funciona o [Machine Learning](https://www.youtube.com/watch?v=CAu97npy7zQ)?\n",
        "\n",
        "- Que tal conhecer como é trabalhar com [Machine Learning na Europa](https://www.youtube.com/watch?v=dFLrwi4Oz_o&t=1s)?\n",
        "\n",
        "- [Otimização de hiperparâmetros](https://www.alura.com.br/artigos/otimizacao-de-parametros?utm_source=gnarus&utm_medium=timeline)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sma9bbnuzy2e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "46ofoO8YwcbC"
      },
      "source": [
        "\n",
        "dados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_experimentos.zip?raw=true', compression = 'zip')\n",
        "dados_resultados = pd.read_csv('https://github.com/alura-cursos/imersaodados3/blob/main/dados/dados_resultados.csv?raw=true')\n",
        "\n",
        "dados_resultados['n_moa'] = dados_resultados.drop('id', axis=1).sum(axis=1)\n",
        "dados_resultados['ativo_moa'] = (dados_resultados['n_moa'] != 0)\n",
        "\n",
        "dados_combinados = pd.merge(dados, dados_resultados[['id','n_moa', 'ativo_moa']], on='id')\n",
        "dados_combinados.rename(columns={'droga': 'composto'}, inplace=True)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
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          "base_uri": "https://localhost:8080/",
          "height": 253
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        "id": "hum8ERNQxKIX",
        "outputId": "1400d48f-db87-4aa4-fc24-1335fa826aff"
      },
      "source": [
        "dados_combinados.head()"
      ],
      "execution_count": 11,
      "outputs": [
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              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.3820</td>\n",
              "      <td>-3.7350</td>\n",
              "      <td>-2.9740</td>\n",
              "      <td>-1.4930</td>\n",
              "      <td>-1.6600</td>\n",
              "      <td>-3.1660</td>\n",
              "      <td>0.2816</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id_001626bd3</td>\n",
              "      <td>com_droga</td>\n",
              "      <td>72</td>\n",
              "      <td>D2</td>\n",
              "      <td>7cbed3131</td>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>...</td>\n",
              "      <td>0.1535</td>\n",
              "      <td>-0.4640</td>\n",
              "      <td>-0.5943</td>\n",
              "      <td>0.3973</td>\n",
              "      <td>0.1500</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5159</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 879 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id tratamento  tempo dose  ...    c-98    c-99  n_moa  ativo_moa\n",
              "0  id_000644bb2  com_droga     24   D1  ...  0.3801  0.4176      1       True\n",
              "1  id_000779bfc  com_droga     72   D1  ...  0.6077  0.7371      0      False\n",
              "2  id_000a6266a  com_droga     48   D1  ... -1.4080  0.6931      3       True\n",
              "3  id_0015fd391  com_droga     48   D1  ... -0.3876 -0.8154      0      False\n",
              "4  id_001626bd3  com_droga     72   D2  ... -0.3786  0.7125      1       True\n",
              "\n",
              "[5 rows x 879 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fXHnW0JE4wq8"
      },
      "source": [
        "## Regressão linear"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ALmMzZGj2N-O",
        "outputId": "ca01c2c5-67a1-4ff8-a379-f7a2959de16a"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "x = dados_combinados.select_dtypes('float64')\n",
        "y = dados_combinados['ativo_moa']\n",
        "\n",
        "modelo_rl = LogisticRegression()\n",
        "modelo_rl.fit(x, y)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EWeAJzqR4z3y"
      },
      "source": [
        "## Separando amostrar entre treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHwLQyou3ju0"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, stratify=y, random_state=376)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A4OfpybK3XAE",
        "outputId": "8f156f90-6d59-47ad-bb0e-f98cc24acb41"
      },
      "source": [
        "modelo_rl = LogisticRegression(max_iter=1000) ## max_iter high to avoid warning\n",
        "modelo_rl.fit(x_train, y_train)\n",
        "\n",
        "modelo_rl.score(x_test, y_test)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6267058576527399"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyrmYUFC5onS",
        "outputId": "905d11b3-96b0-45f8-85f8-12806c6d0b2f"
      },
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "model_dummy = DummyClassifier(\"most_frequent\")\n",
        "model_dummy.fit(x_train, y_train)\n",
        "\n",
        "# model_dummy.score(x_test, y_test) \n",
        "\n",
        "y_predict_dummy = model_dummy.predict(x_test)\n",
        "accuracy_score(y_test, y_predict_dummy)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6067604450976275"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4me1mcnP8JF8",
        "outputId": "4f31d0f8-f681-42e2-fd98-595c15bdcdc3"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model_dt = DecisionTreeClassifier(max_depth=3)\n",
        "model_dt.fit(x_train, y_train)\n",
        "\n",
        "model_dt.score(x_test, y_test)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.686122191895864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "lN0GBE08GWY8",
        "outputId": "ed1cd1f9-6a6d-4973-bf9d-1256f4eef35d"
      },
      "source": [
        "from sklearn import tree\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 10), facecolor='k')\n",
        "tree.plot_tree(model_dt,\n",
        "               ax=ax,\n",
        "               fontsize=10,\n",
        "               rounded=True,\n",
        "               filled=True,\n",
        "               feature_names=x_train.columns,\n",
        "               class_names=['Não Ativado', 'Ativado'])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O2LkJUlo86KP"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "trains_score = []\n",
        "tests_score = []\n",
        "for i in range(1, 15):\n",
        "  model_dt = DecisionTreeClassifier(max_depth=i)\n",
        "  model_dt.fit(x_train, y_train)\n",
        "  \n",
        "  tests_score.append(model_dt.score(x_test, y_test))\n",
        "  trains_score.append(model_dt.score(x_train, y_train))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bD3ivOaT-0ug",
        "outputId": "ffc2079c-5479-4596-9eaf-7c61998c5baa"
      },
      "source": [
        "tests_score"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6067604450976275,\n",
              " 0.6067604450976275,\n",
              " 0.6105395758975436,\n",
              " 0.6206172580306529,\n",
              " 0.62019735460844,\n",
              " 0.6193575477640143,\n",
              " 0.6216670165861852,\n",
              " 0.6197774511862272,\n",
              " 0.6166281755196305,\n",
              " 0.6120092378752887,\n",
              " 0.6096997690531177,\n",
              " 0.6004618937644342,\n",
              " 0.5968927146756247,\n",
              " 0.6010917488977535]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQGrXJg8-3Ok",
        "outputId": "c0bb9adc-138d-4bfc-d525-deeb80ab37b3"
      },
      "source": [
        "trains_score"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6066348223190384,\n",
              " 0.6066348223190384,\n",
              " 0.6211222508004829,\n",
              " 0.6360820954280615,\n",
              " 0.6412261823526324,\n",
              " 0.6489423127394888,\n",
              " 0.6606477350270327,\n",
              " 0.6707259461445594,\n",
              " 0.689570101307018,\n",
              " 0.7068395359823632,\n",
              " 0.7294105296309905,\n",
              " 0.752926355571886,\n",
              " 0.7769145976589156,\n",
              " 0.7990131751614088]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "BDIFtJ15_Cas",
        "outputId": "1fdc9597-94a5-4042-d9b7-df4509a78ca1"
      },
      "source": [
        "sns.lineplot(x=range(1,15), y=tests_score, label='tests')\n",
        "sns.lineplot(x=range(1,15), y=trains_score, label='trains')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f82bb5885d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "AQgkpb18CvX1",
        "outputId": "3a54cb73-7898-4f6b-a483-f6057fb04413"
      },
      "source": [
        "pd.get_dummies(dados_combinados['tratamento'])"
      ],
      "execution_count": 32,
      "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>com_controle</th>\n",
              "      <th>com_droga</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       com_controle  com_droga\n",
              "0                 0          1\n",
              "1                 0          1\n",
              "2                 0          1\n",
              "3                 0          1\n",
              "4                 0          1\n",
              "...             ...        ...\n",
              "23809             0          1\n",
              "23810             0          1\n",
              "23811             1          0\n",
              "23812             0          1\n",
              "23813             0          1\n",
              "\n",
              "[23814 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "xAf-RWFJC-Ya",
        "outputId": "c6f5ff99-5db6-4af6-8194-766f91ce3e3e"
      },
      "source": [
        "pd.get_dummies(dados_combinados, columns=['tratamento', 'tempo', 'dose']).drop(['composto', 'id'], axis=1)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>g-0</th>\n",
              "      <th>g-1</th>\n",
              "      <th>g-2</th>\n",
              "      <th>g-3</th>\n",
              "      <th>g-4</th>\n",
              "      <th>g-5</th>\n",
              "      <th>g-6</th>\n",
              "      <th>g-7</th>\n",
              "      <th>g-8</th>\n",
              "      <th>g-9</th>\n",
              "      <th>g-10</th>\n",
              "      <th>g-11</th>\n",
              "      <th>g-12</th>\n",
              "      <th>g-13</th>\n",
              "      <th>g-14</th>\n",
              "      <th>g-15</th>\n",
              "      <th>g-16</th>\n",
              "      <th>g-17</th>\n",
              "      <th>g-18</th>\n",
              "      <th>g-19</th>\n",
              "      <th>g-20</th>\n",
              "      <th>g-21</th>\n",
              "      <th>g-22</th>\n",
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              "      <th>g-28</th>\n",
              "      <th>g-29</th>\n",
              "      <th>g-30</th>\n",
              "      <th>g-31</th>\n",
              "      <th>g-32</th>\n",
              "      <th>g-33</th>\n",
              "      <th>g-34</th>\n",
              "      <th>g-35</th>\n",
              "      <th>g-36</th>\n",
              "      <th>g-37</th>\n",
              "      <th>g-38</th>\n",
              "      <th>g-39</th>\n",
              "      <th>...</th>\n",
              "      <th>c-69</th>\n",
              "      <th>c-70</th>\n",
              "      <th>c-71</th>\n",
              "      <th>c-72</th>\n",
              "      <th>c-73</th>\n",
              "      <th>c-74</th>\n",
              "      <th>c-75</th>\n",
              "      <th>c-76</th>\n",
              "      <th>c-77</th>\n",
              "      <th>c-78</th>\n",
              "      <th>c-79</th>\n",
              "      <th>c-80</th>\n",
              "      <th>c-81</th>\n",
              "      <th>c-82</th>\n",
              "      <th>c-83</th>\n",
              "      <th>c-84</th>\n",
              "      <th>c-85</th>\n",
              "      <th>c-86</th>\n",
              "      <th>c-87</th>\n",
              "      <th>c-88</th>\n",
              "      <th>c-89</th>\n",
              "      <th>c-90</th>\n",
              "      <th>c-91</th>\n",
              "      <th>c-92</th>\n",
              "      <th>c-93</th>\n",
              "      <th>c-94</th>\n",
              "      <th>c-95</th>\n",
              "      <th>c-96</th>\n",
              "      <th>c-97</th>\n",
              "      <th>c-98</th>\n",
              "      <th>c-99</th>\n",
              "      <th>n_moa</th>\n",
              "      <th>ativo_moa</th>\n",
              "      <th>tratamento_com_controle</th>\n",
              "      <th>tratamento_com_droga</th>\n",
              "      <th>tempo_24</th>\n",
              "      <th>tempo_48</th>\n",
              "      <th>tempo_72</th>\n",
              "      <th>dose_D1</th>\n",
              "      <th>dose_D2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.5577</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-0.6208</td>\n",
              "      <td>-0.1944</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>-1.0220</td>\n",
              "      <td>-0.0326</td>\n",
              "      <td>0.5548</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>1.1830</td>\n",
              "      <td>0.1530</td>\n",
              "      <td>0.5574</td>\n",
              "      <td>-0.4015</td>\n",
              "      <td>0.1789</td>\n",
              "      <td>-0.6528</td>\n",
              "      <td>-0.7969</td>\n",
              "      <td>0.6342</td>\n",
              "      <td>0.1778</td>\n",
              "      <td>-0.3694</td>\n",
              "      <td>-0.5688</td>\n",
              "      <td>-1.1360</td>\n",
              "      <td>-1.1880</td>\n",
              "      <td>0.6940</td>\n",
              "      <td>0.4393</td>\n",
              "      <td>0.2664</td>\n",
              "      <td>0.1907</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>-0.2853</td>\n",
              "      <td>0.5819</td>\n",
              "      <td>0.2934</td>\n",
              "      <td>-0.5584</td>\n",
              "      <td>-0.0916</td>\n",
              "      <td>-0.3010</td>\n",
              "      <td>-0.1537</td>\n",
              "      <td>0.2198</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.5055</td>\n",
              "      <td>-0.5119</td>\n",
              "      <td>-0.2162</td>\n",
              "      <td>...</td>\n",
              "      <td>1.2250</td>\n",
              "      <td>-0.0553</td>\n",
              "      <td>0.7351</td>\n",
              "      <td>0.5810</td>\n",
              "      <td>0.9590</td>\n",
              "      <td>0.2427</td>\n",
              "      <td>0.0495</td>\n",
              "      <td>0.4141</td>\n",
              "      <td>0.8432</td>\n",
              "      <td>0.6162</td>\n",
              "      <td>-0.7318</td>\n",
              "      <td>1.2120</td>\n",
              "      <td>0.6362</td>\n",
              "      <td>-0.4427</td>\n",
              "      <td>0.1288</td>\n",
              "      <td>1.4840</td>\n",
              "      <td>0.1799</td>\n",
              "      <td>0.5367</td>\n",
              "      <td>-0.1111</td>\n",
              "      <td>-1.0120</td>\n",
              "      <td>0.6685</td>\n",
              "      <td>0.2862</td>\n",
              "      <td>0.2584</td>\n",
              "      <td>0.8076</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.1912</td>\n",
              "      <td>0.6584</td>\n",
              "      <td>-0.3981</td>\n",
              "      <td>0.2139</td>\n",
              "      <td>0.3801</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0743</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.2991</td>\n",
              "      <td>0.0604</td>\n",
              "      <td>1.0190</td>\n",
              "      <td>0.5207</td>\n",
              "      <td>0.2341</td>\n",
              "      <td>0.3372</td>\n",
              "      <td>-0.4047</td>\n",
              "      <td>0.8507</td>\n",
              "      <td>-1.1520</td>\n",
              "      <td>-0.4201</td>\n",
              "      <td>-0.0958</td>\n",
              "      <td>0.4590</td>\n",
              "      <td>0.0803</td>\n",
              "      <td>0.2250</td>\n",
              "      <td>0.5293</td>\n",
              "      <td>0.2839</td>\n",
              "      <td>-0.3494</td>\n",
              "      <td>0.2883</td>\n",
              "      <td>0.9449</td>\n",
              "      <td>-0.1646</td>\n",
              "      <td>-0.2657</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>-0.4316</td>\n",
              "      <td>0.4773</td>\n",
              "      <td>0.2075</td>\n",
              "      <td>-0.4216</td>\n",
              "      <td>-0.1161</td>\n",
              "      <td>-0.0499</td>\n",
              "      <td>-0.2627</td>\n",
              "      <td>0.9959</td>\n",
              "      <td>-0.2483</td>\n",
              "      <td>0.2655</td>\n",
              "      <td>-0.2102</td>\n",
              "      <td>0.1656</td>\n",
              "      <td>0.5300</td>\n",
              "      <td>-0.2568</td>\n",
              "      <td>-0.0455</td>\n",
              "      <td>...</td>\n",
              "      <td>0.2887</td>\n",
              "      <td>-0.0765</td>\n",
              "      <td>0.2539</td>\n",
              "      <td>0.4443</td>\n",
              "      <td>0.5932</td>\n",
              "      <td>0.2031</td>\n",
              "      <td>0.7639</td>\n",
              "      <td>0.5499</td>\n",
              "      <td>-0.3322</td>\n",
              "      <td>-0.0977</td>\n",
              "      <td>0.4329</td>\n",
              "      <td>-0.2782</td>\n",
              "      <td>0.7827</td>\n",
              "      <td>0.5934</td>\n",
              "      <td>0.3402</td>\n",
              "      <td>0.1499</td>\n",
              "      <td>0.4420</td>\n",
              "      <td>0.9366</td>\n",
              "      <td>0.8193</td>\n",
              "      <td>-0.4236</td>\n",
              "      <td>0.3192</td>\n",
              "      <td>-0.4265</td>\n",
              "      <td>0.7543</td>\n",
              "      <td>0.4708</td>\n",
              "      <td>0.0230</td>\n",
              "      <td>0.2957</td>\n",
              "      <td>0.4899</td>\n",
              "      <td>0.1522</td>\n",
              "      <td>0.1241</td>\n",
              "      <td>0.6077</td>\n",
              "      <td>0.7371</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.6280</td>\n",
              "      <td>0.5817</td>\n",
              "      <td>1.5540</td>\n",
              "      <td>-0.0764</td>\n",
              "      <td>-0.0323</td>\n",
              "      <td>1.2390</td>\n",
              "      <td>0.1715</td>\n",
              "      <td>0.2155</td>\n",
              "      <td>0.0065</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.4797</td>\n",
              "      <td>-0.5631</td>\n",
              "      <td>-0.0366</td>\n",
              "      <td>-1.8300</td>\n",
              "      <td>0.6057</td>\n",
              "      <td>-0.3278</td>\n",
              "      <td>0.6042</td>\n",
              "      <td>-0.3075</td>\n",
              "      <td>-0.1147</td>\n",
              "      <td>-0.0570</td>\n",
              "      <td>-0.0799</td>\n",
              "      <td>-0.8181</td>\n",
              "      <td>-1.5320</td>\n",
              "      <td>0.2307</td>\n",
              "      <td>0.4901</td>\n",
              "      <td>0.4780</td>\n",
              "      <td>-1.3970</td>\n",
              "      <td>4.6240</td>\n",
              "      <td>-0.0437</td>\n",
              "      <td>1.2870</td>\n",
              "      <td>-1.8530</td>\n",
              "      <td>0.6069</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.1783</td>\n",
              "      <td>0.0018</td>\n",
              "      <td>-1.1800</td>\n",
              "      <td>0.1256</td>\n",
              "      <td>-0.1219</td>\n",
              "      <td>5.4470</td>\n",
              "      <td>1.0310</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.0604</td>\n",
              "      <td>0.1682</td>\n",
              "      <td>-0.4436</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.1363</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.9760</td>\n",
              "      <td>-0.0427</td>\n",
              "      <td>-0.1235</td>\n",
              "      <td>0.0959</td>\n",
              "      <td>0.0690</td>\n",
              "      <td>-0.9416</td>\n",
              "      <td>-0.7548</td>\n",
              "      <td>-0.1109</td>\n",
              "      <td>-0.6272</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>0.1172</td>\n",
              "      <td>0.1093</td>\n",
              "      <td>-0.3113</td>\n",
              "      <td>0.3019</td>\n",
              "      <td>-0.0873</td>\n",
              "      <td>-0.7250</td>\n",
              "      <td>-0.6297</td>\n",
              "      <td>0.6103</td>\n",
              "      <td>0.0223</td>\n",
              "      <td>-1.3240</td>\n",
              "      <td>-0.3174</td>\n",
              "      <td>-0.6417</td>\n",
              "      <td>-0.2187</td>\n",
              "      <td>-1.4080</td>\n",
              "      <td>0.6931</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.5138</td>\n",
              "      <td>-0.2491</td>\n",
              "      <td>-0.2656</td>\n",
              "      <td>0.5288</td>\n",
              "      <td>4.0620</td>\n",
              "      <td>-0.8095</td>\n",
              "      <td>-1.9590</td>\n",
              "      <td>0.1792</td>\n",
              "      <td>-0.1321</td>\n",
              "      <td>-1.0600</td>\n",
              "      <td>-0.8269</td>\n",
              "      <td>-0.3584</td>\n",
              "      <td>-0.8511</td>\n",
              "      <td>-0.5844</td>\n",
              "      <td>-2.5690</td>\n",
              "      <td>0.8183</td>\n",
              "      <td>-0.0532</td>\n",
              "      <td>-0.8554</td>\n",
              "      <td>0.1160</td>\n",
              "      <td>-2.3520</td>\n",
              "      <td>2.1200</td>\n",
              "      <td>-1.1580</td>\n",
              "      <td>-0.7191</td>\n",
              "      <td>-0.8004</td>\n",
              "      <td>-1.4670</td>\n",
              "      <td>-0.0107</td>\n",
              "      <td>-0.8995</td>\n",
              "      <td>0.2406</td>\n",
              "      <td>-0.2479</td>\n",
              "      <td>-1.0890</td>\n",
              "      <td>-0.7575</td>\n",
              "      <td>0.0881</td>\n",
              "      <td>-2.7370</td>\n",
              "      <td>0.8745</td>\n",
              "      <td>0.5787</td>\n",
              "      <td>-1.6740</td>\n",
              "      <td>-1.6720</td>\n",
              "      <td>-1.2690</td>\n",
              "      <td>3.0900</td>\n",
              "      <td>-0.3814</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.2990</td>\n",
              "      <td>-1.1870</td>\n",
              "      <td>-0.5044</td>\n",
              "      <td>-1.7750</td>\n",
              "      <td>-1.6120</td>\n",
              "      <td>-0.9215</td>\n",
              "      <td>-1.0810</td>\n",
              "      <td>-3.0520</td>\n",
              "      <td>-3.4470</td>\n",
              "      <td>-2.7740</td>\n",
              "      <td>-1.8460</td>\n",
              "      <td>-0.5568</td>\n",
              "      <td>-3.3960</td>\n",
              "      <td>-2.9510</td>\n",
              "      <td>-1.1550</td>\n",
              "      <td>-3.2620</td>\n",
              "      <td>-1.5390</td>\n",
              "      <td>-2.4600</td>\n",
              "      <td>-0.9417</td>\n",
              "      <td>-1.5550</td>\n",
              "      <td>0.2431</td>\n",
              "      <td>-2.0990</td>\n",
              "      <td>-0.6441</td>\n",
              "      <td>-5.6300</td>\n",
              "      <td>-1.3780</td>\n",
              "      <td>-0.8632</td>\n",
              "      <td>-1.2880</td>\n",
              "      <td>-1.6210</td>\n",
              "      <td>-0.8784</td>\n",
              "      <td>-0.3876</td>\n",
              "      <td>-0.8154</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.3254</td>\n",
              "      <td>-0.4009</td>\n",
              "      <td>0.9700</td>\n",
              "      <td>0.6919</td>\n",
              "      <td>1.4180</td>\n",
              "      <td>-0.8244</td>\n",
              "      <td>-0.2800</td>\n",
              "      <td>-0.1498</td>\n",
              "      <td>-0.8789</td>\n",
              "      <td>0.8630</td>\n",
              "      <td>-0.2219</td>\n",
              "      <td>-0.5121</td>\n",
              "      <td>-0.9577</td>\n",
              "      <td>1.1750</td>\n",
              "      <td>0.2042</td>\n",
              "      <td>0.1970</td>\n",
              "      <td>0.1244</td>\n",
              "      <td>-1.7090</td>\n",
              "      <td>-0.3543</td>\n",
              "      <td>-0.5160</td>\n",
              "      <td>-0.3330</td>\n",
              "      <td>-0.2685</td>\n",
              "      <td>0.7649</td>\n",
              "      <td>0.2057</td>\n",
              "      <td>1.3720</td>\n",
              "      <td>0.6835</td>\n",
              "      <td>0.8056</td>\n",
              "      <td>-0.3754</td>\n",
              "      <td>-1.2090</td>\n",
              "      <td>0.2965</td>\n",
              "      <td>-0.0712</td>\n",
              "      <td>0.6389</td>\n",
              "      <td>0.6674</td>\n",
              "      <td>-0.0783</td>\n",
              "      <td>1.1740</td>\n",
              "      <td>-0.7110</td>\n",
              "      <td>-1.4470</td>\n",
              "      <td>1.0620</td>\n",
              "      <td>0.7888</td>\n",
              "      <td>-0.0848</td>\n",
              "      <td>...</td>\n",
              "      <td>0.6091</td>\n",
              "      <td>0.1813</td>\n",
              "      <td>-0.4249</td>\n",
              "      <td>0.7832</td>\n",
              "      <td>0.6529</td>\n",
              "      <td>0.5648</td>\n",
              "      <td>0.4817</td>\n",
              "      <td>0.0587</td>\n",
              "      <td>0.5303</td>\n",
              "      <td>0.6376</td>\n",
              "      <td>-0.3966</td>\n",
              "      <td>-1.4950</td>\n",
              "      <td>-0.9625</td>\n",
              "      <td>-0.0541</td>\n",
              "      <td>0.6273</td>\n",
              "      <td>0.4563</td>\n",
              "      <td>0.0698</td>\n",
              "      <td>0.8134</td>\n",
              "      <td>0.1924</td>\n",
              "      <td>0.6054</td>\n",
              "      <td>-0.1824</td>\n",
              "      <td>0.0042</td>\n",
              "      <td>0.0048</td>\n",
              "      <td>0.6670</td>\n",
              "      <td>1.0690</td>\n",
              "      <td>0.5523</td>\n",
              "      <td>-0.3031</td>\n",
              "      <td>0.1094</td>\n",
              "      <td>0.2885</td>\n",
              "      <td>-0.3786</td>\n",
              "      <td>0.7125</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23809</th>\n",
              "      <td>0.1394</td>\n",
              "      <td>-0.0636</td>\n",
              "      <td>-0.1112</td>\n",
              "      <td>-0.5080</td>\n",
              "      <td>-0.4713</td>\n",
              "      <td>0.7201</td>\n",
              "      <td>0.5773</td>\n",
              "      <td>0.3055</td>\n",
              "      <td>-0.4726</td>\n",
              "      <td>0.1269</td>\n",
              "      <td>0.2531</td>\n",
              "      <td>0.1730</td>\n",
              "      <td>-0.4532</td>\n",
              "      <td>-1.0790</td>\n",
              "      <td>0.2474</td>\n",
              "      <td>-0.4550</td>\n",
              "      <td>0.3588</td>\n",
              "      <td>0.1600</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>-0.1103</td>\n",
              "      <td>0.8550</td>\n",
              "      <td>-0.4139</td>\n",
              "      <td>0.5541</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.5573</td>\n",
              "      <td>-0.4397</td>\n",
              "      <td>-0.9260</td>\n",
              "      <td>-0.2424</td>\n",
              "      <td>-0.6686</td>\n",
              "      <td>0.2326</td>\n",
              "      <td>0.6456</td>\n",
              "      <td>0.0136</td>\n",
              "      <td>-0.5141</td>\n",
              "      <td>-0.6320</td>\n",
              "      <td>0.7166</td>\n",
              "      <td>-0.1736</td>\n",
              "      <td>0.3686</td>\n",
              "      <td>-0.1565</td>\n",
              "      <td>-0.7362</td>\n",
              "      <td>0.1318</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.6158</td>\n",
              "      <td>-0.2977</td>\n",
              "      <td>0.0992</td>\n",
              "      <td>0.6838</td>\n",
              "      <td>0.5259</td>\n",
              "      <td>0.7882</td>\n",
              "      <td>0.3119</td>\n",
              "      <td>-0.7697</td>\n",
              "      <td>0.2203</td>\n",
              "      <td>-1.0710</td>\n",
              "      <td>0.5979</td>\n",
              "      <td>0.0848</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.6293</td>\n",
              "      <td>1.1660</td>\n",
              "      <td>0.3329</td>\n",
              "      <td>0.2754</td>\n",
              "      <td>0.4108</td>\n",
              "      <td>-0.1252</td>\n",
              "      <td>-0.2340</td>\n",
              "      <td>0.2267</td>\n",
              "      <td>0.1969</td>\n",
              "      <td>0.0262</td>\n",
              "      <td>-0.8121</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>-0.3246</td>\n",
              "      <td>0.0631</td>\n",
              "      <td>0.9171</td>\n",
              "      <td>0.5258</td>\n",
              "      <td>0.4680</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23810</th>\n",
              "      <td>-1.3260</td>\n",
              "      <td>0.3478</td>\n",
              "      <td>-0.3743</td>\n",
              "      <td>0.9905</td>\n",
              "      <td>-0.7178</td>\n",
              "      <td>0.6621</td>\n",
              "      <td>-0.2252</td>\n",
              "      <td>-0.5565</td>\n",
              "      <td>0.5112</td>\n",
              "      <td>0.6727</td>\n",
              "      <td>-0.1851</td>\n",
              "      <td>2.8650</td>\n",
              "      <td>-0.2140</td>\n",
              "      <td>-0.6153</td>\n",
              "      <td>0.8362</td>\n",
              "      <td>0.5584</td>\n",
              "      <td>-0.2589</td>\n",
              "      <td>0.1292</td>\n",
              "      <td>0.0148</td>\n",
              "      <td>0.0949</td>\n",
              "      <td>-0.2182</td>\n",
              "      <td>-0.9235</td>\n",
              "      <td>0.0749</td>\n",
              "      <td>-1.5910</td>\n",
              "      <td>-0.8359</td>\n",
              "      <td>-0.9217</td>\n",
              "      <td>0.3013</td>\n",
              "      <td>0.1716</td>\n",
              "      <td>0.0880</td>\n",
              "      <td>0.1842</td>\n",
              "      <td>0.1835</td>\n",
              "      <td>0.5436</td>\n",
              "      <td>-0.0533</td>\n",
              "      <td>-0.0491</td>\n",
              "      <td>0.9543</td>\n",
              "      <td>0.4626</td>\n",
              "      <td>0.0819</td>\n",
              "      <td>0.1586</td>\n",
              "      <td>1.2050</td>\n",
              "      <td>0.0384</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0570</td>\n",
              "      <td>-0.3355</td>\n",
              "      <td>-0.2555</td>\n",
              "      <td>0.8219</td>\n",
              "      <td>0.6995</td>\n",
              "      <td>-0.2942</td>\n",
              "      <td>0.2408</td>\n",
              "      <td>-0.7781</td>\n",
              "      <td>-0.0929</td>\n",
              "      <td>-0.0329</td>\n",
              "      <td>0.0781</td>\n",
              "      <td>-1.4440</td>\n",
              "      <td>-0.0921</td>\n",
              "      <td>0.3188</td>\n",
              "      <td>-1.1080</td>\n",
              "      <td>0.4895</td>\n",
              "      <td>-0.2144</td>\n",
              "      <td>1.0960</td>\n",
              "      <td>0.3335</td>\n",
              "      <td>0.4444</td>\n",
              "      <td>-1.1130</td>\n",
              "      <td>0.4286</td>\n",
              "      <td>0.4426</td>\n",
              "      <td>0.0423</td>\n",
              "      <td>-0.3195</td>\n",
              "      <td>-0.8086</td>\n",
              "      <td>-0.9798</td>\n",
              "      <td>-0.2084</td>\n",
              "      <td>-0.1224</td>\n",
              "      <td>-0.2715</td>\n",
              "      <td>0.3689</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23811</th>\n",
              "      <td>0.3942</td>\n",
              "      <td>0.3756</td>\n",
              "      <td>0.3109</td>\n",
              "      <td>-0.7389</td>\n",
              "      <td>0.5505</td>\n",
              "      <td>-0.0159</td>\n",
              "      <td>-0.2541</td>\n",
              "      <td>0.1745</td>\n",
              "      <td>-0.0340</td>\n",
              "      <td>0.4865</td>\n",
              "      <td>-0.1854</td>\n",
              "      <td>0.0716</td>\n",
              "      <td>0.1729</td>\n",
              "      <td>-0.0434</td>\n",
              "      <td>0.1542</td>\n",
              "      <td>-0.2192</td>\n",
              "      <td>-0.0302</td>\n",
              "      <td>-0.4218</td>\n",
              "      <td>0.4057</td>\n",
              "      <td>-0.5372</td>\n",
              "      <td>0.1521</td>\n",
              "      <td>-0.2651</td>\n",
              "      <td>0.2310</td>\n",
              "      <td>-0.8101</td>\n",
              "      <td>0.4943</td>\n",
              "      <td>0.6905</td>\n",
              "      <td>-0.3720</td>\n",
              "      <td>-1.4110</td>\n",
              "      <td>0.4516</td>\n",
              "      <td>1.2300</td>\n",
              "      <td>-0.1949</td>\n",
              "      <td>-1.3280</td>\n",
              "      <td>-0.4276</td>\n",
              "      <td>-0.0040</td>\n",
              "      <td>-0.3086</td>\n",
              "      <td>-0.2355</td>\n",
              "      <td>-0.5222</td>\n",
              "      <td>-1.1470</td>\n",
              "      <td>0.2844</td>\n",
              "      <td>0.0813</td>\n",
              "      <td>...</td>\n",
              "      <td>1.4690</td>\n",
              "      <td>-0.6142</td>\n",
              "      <td>0.6068</td>\n",
              "      <td>0.3434</td>\n",
              "      <td>0.9880</td>\n",
              "      <td>-0.0468</td>\n",
              "      <td>-0.1882</td>\n",
              "      <td>-0.0087</td>\n",
              "      <td>-0.0356</td>\n",
              "      <td>0.5718</td>\n",
              "      <td>0.4971</td>\n",
              "      <td>0.1057</td>\n",
              "      <td>0.2111</td>\n",
              "      <td>0.6992</td>\n",
              "      <td>0.0708</td>\n",
              "      <td>0.6169</td>\n",
              "      <td>0.2248</td>\n",
              "      <td>0.5994</td>\n",
              "      <td>0.2689</td>\n",
              "      <td>0.0305</td>\n",
              "      <td>1.2320</td>\n",
              "      <td>0.5409</td>\n",
              "      <td>0.3755</td>\n",
              "      <td>0.7343</td>\n",
              "      <td>0.2807</td>\n",
              "      <td>0.4116</td>\n",
              "      <td>0.6422</td>\n",
              "      <td>0.2256</td>\n",
              "      <td>0.7592</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.3808</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23812</th>\n",
              "      <td>0.6660</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.4392</td>\n",
              "      <td>0.2044</td>\n",
              "      <td>0.8531</td>\n",
              "      <td>-0.0343</td>\n",
              "      <td>0.0323</td>\n",
              "      <td>0.0463</td>\n",
              "      <td>0.4299</td>\n",
              "      <td>-0.7985</td>\n",
              "      <td>0.5742</td>\n",
              "      <td>0.1421</td>\n",
              "      <td>2.2700</td>\n",
              "      <td>0.2046</td>\n",
              "      <td>0.5363</td>\n",
              "      <td>-1.7330</td>\n",
              "      <td>0.1450</td>\n",
              "      <td>0.6097</td>\n",
              "      <td>0.2024</td>\n",
              "      <td>0.9865</td>\n",
              "      <td>-0.7805</td>\n",
              "      <td>0.9608</td>\n",
              "      <td>0.3440</td>\n",
              "      <td>2.7650</td>\n",
              "      <td>0.4925</td>\n",
              "      <td>0.6698</td>\n",
              "      <td>0.2374</td>\n",
              "      <td>-0.3372</td>\n",
              "      <td>0.8771</td>\n",
              "      <td>-2.6560</td>\n",
              "      <td>-0.2000</td>\n",
              "      <td>-0.2043</td>\n",
              "      <td>0.6797</td>\n",
              "      <td>-0.0248</td>\n",
              "      <td>-0.0927</td>\n",
              "      <td>1.8480</td>\n",
              "      <td>-2.7180</td>\n",
              "      <td>-5.3160</td>\n",
              "      <td>-1.0750</td>\n",
              "      <td>-0.1437</td>\n",
              "      <td>...</td>\n",
              "      <td>0.4524</td>\n",
              "      <td>0.4135</td>\n",
              "      <td>0.1356</td>\n",
              "      <td>-0.5801</td>\n",
              "      <td>0.0411</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>1.0340</td>\n",
              "      <td>-0.0270</td>\n",
              "      <td>-0.4194</td>\n",
              "      <td>0.7403</td>\n",
              "      <td>-0.6793</td>\n",
              "      <td>-0.1423</td>\n",
              "      <td>0.7307</td>\n",
              "      <td>0.7946</td>\n",
              "      <td>-0.0650</td>\n",
              "      <td>0.9038</td>\n",
              "      <td>0.2324</td>\n",
              "      <td>0.9676</td>\n",
              "      <td>1.0940</td>\n",
              "      <td>0.4290</td>\n",
              "      <td>0.5187</td>\n",
              "      <td>-0.1105</td>\n",
              "      <td>0.4258</td>\n",
              "      <td>-0.2012</td>\n",
              "      <td>0.1506</td>\n",
              "      <td>1.5230</td>\n",
              "      <td>0.7101</td>\n",
              "      <td>0.1732</td>\n",
              "      <td>0.7015</td>\n",
              "      <td>-0.6290</td>\n",
              "      <td>0.0740</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23813</th>\n",
              "      <td>-0.8598</td>\n",
              "      <td>1.0240</td>\n",
              "      <td>-0.1361</td>\n",
              "      <td>0.7952</td>\n",
              "      <td>-0.3611</td>\n",
              "      <td>-3.6750</td>\n",
              "      <td>-1.2420</td>\n",
              "      <td>0.9146</td>\n",
              "      <td>3.0790</td>\n",
              "      <td>1.2460</td>\n",
              "      <td>1.9460</td>\n",
              "      <td>1.4370</td>\n",
              "      <td>2.9780</td>\n",
              "      <td>2.2370</td>\n",
              "      <td>-0.6818</td>\n",
              "      <td>0.6870</td>\n",
              "      <td>-1.1060</td>\n",
              "      <td>0.0182</td>\n",
              "      <td>-0.9247</td>\n",
              "      <td>-0.0738</td>\n",
              "      <td>-0.1919</td>\n",
              "      <td>-0.7722</td>\n",
              "      <td>-1.4050</td>\n",
              "      <td>-1.0050</td>\n",
              "      <td>-1.1170</td>\n",
              "      <td>-0.5293</td>\n",
              "      <td>-1.1720</td>\n",
              "      <td>-0.2885</td>\n",
              "      <td>0.1599</td>\n",
              "      <td>-0.4250</td>\n",
              "      <td>0.3591</td>\n",
              "      <td>-0.1420</td>\n",
              "      <td>-0.9530</td>\n",
              "      <td>-0.2005</td>\n",
              "      <td>-1.8340</td>\n",
              "      <td>-0.5768</td>\n",
              "      <td>0.5145</td>\n",
              "      <td>-0.9913</td>\n",
              "      <td>3.1350</td>\n",
              "      <td>-0.2644</td>\n",
              "      <td>...</td>\n",
              "      <td>-1.1690</td>\n",
              "      <td>-4.1460</td>\n",
              "      <td>-1.2670</td>\n",
              "      <td>-1.1300</td>\n",
              "      <td>-2.4390</td>\n",
              "      <td>0.1591</td>\n",
              "      <td>-2.2490</td>\n",
              "      <td>-2.5860</td>\n",
              "      <td>-1.9520</td>\n",
              "      <td>-2.1810</td>\n",
              "      <td>-4.6690</td>\n",
              "      <td>-3.9450</td>\n",
              "      <td>-2.9560</td>\n",
              "      <td>-2.7930</td>\n",
              "      <td>-2.1560</td>\n",
              "      <td>-2.4100</td>\n",
              "      <td>-1.8190</td>\n",
              "      <td>-3.3480</td>\n",
              "      <td>-0.1414</td>\n",
              "      <td>-2.6430</td>\n",
              "      <td>-2.5810</td>\n",
              "      <td>-3.3890</td>\n",
              "      <td>-1.7450</td>\n",
              "      <td>-6.6300</td>\n",
              "      <td>-4.0950</td>\n",
              "      <td>-7.3860</td>\n",
              "      <td>-1.4160</td>\n",
              "      <td>-3.5770</td>\n",
              "      <td>-0.4775</td>\n",
              "      <td>-2.1500</td>\n",
              "      <td>-4.2520</td>\n",
              "      <td>0</td>\n",
              "      <td>False</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23814 rows × 881 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          g-0     g-1     g-2     g-3  ...  tempo_48  tempo_72  dose_D1  dose_D2\n",
              "0      1.0620  0.5577 -0.2479 -0.6208  ...         0         0        1        0\n",
              "1      0.0743  0.4087  0.2991  0.0604  ...         0         1        1        0\n",
              "2      0.6280  0.5817  1.5540 -0.0764  ...         1         0        1        0\n",
              "3     -0.5138 -0.2491 -0.2656  0.5288  ...         1         0        1        0\n",
              "4     -0.3254 -0.4009  0.9700  0.6919  ...         0         1        0        1\n",
              "...       ...     ...     ...     ...  ...       ...       ...      ...      ...\n",
              "23809  0.1394 -0.0636 -0.1112 -0.5080  ...         0         0        0        1\n",
              "23810 -1.3260  0.3478 -0.3743  0.9905  ...         0         0        0        1\n",
              "23811  0.3942  0.3756  0.3109 -0.7389  ...         1         0        0        1\n",
              "23812  0.6660  0.2324  0.4392  0.2044  ...         0         0        1        0\n",
              "23813 -0.8598  1.0240 -0.1361  0.7952  ...         0         1        1        0\n",
              "\n",
              "[23814 rows x 881 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMtV8cCdAHoL"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "x = dados_combinados.drop(['id', 'n_moa', 'ativo_moa', 'composto'], axis=1)\n",
        "x = pd.get_dummies(x, columns=['tratamento', 'dose', 'tempo'])\n",
        "y = dados_combinados['ativo_moa'] \n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=.2, stratify=y, random_state=376)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHBMJ6kQEwNb",
        "outputId": "a87fdfcd-b506-4890-b609-0b687c773635"
      },
      "source": [
        "modelo_rf = RandomForestClassifier()\n",
        "modelo_rf.fit(x_train, y_train)\n",
        "modelo_rf.score(x_test , y_test)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.688011757295822"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVil7bO0HhhS"
      },
      "source": [
        "# Desafios"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2QUpjrg1Hlnn"
      },
      "source": [
        "## Desafio 01: Tente outros modelos ou mude os parâmetros dos modelos que testamos em aula."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jaosJPPCHoDC",
        "outputId": "26efbdc6-501e-4d5f-ea24-43a63d866423"
      },
      "source": [
        "modelo_rlogistica = LogisticRegression(max_iter=1000)\n",
        "modelo_rlogistica.fit(x_train, y_train)\n",
        "modelo_rlogistica.score(x_test, y_test)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6640772622296872"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFROIDCvHx6-",
        "outputId": "cba92dda-9cfd-4513-e6bb-dce789cc4a9d"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "n_neighbors= 3\n",
        "\n",
        "clf = KNeighborsClassifier(n_neighbors)\n",
        "clf.fit(x_treino, y_treino)\n",
        "clf.score(x_teste, y_teste)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6155784169640982"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDa6nKCuIIzx"
      },
      "source": [
        "## Desafio 02: Teste outros problemas (perguntas). 0, 1 ou +"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3An2zgVIGGd"
      },
      "source": [
        "x = dados_combinados.loc[:,'g-0':'c-99']\n",
        "y = dados_combinados['tempo'].map({24:0,48:1,72:2})\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y, random_state=376)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RbUud6eMIYNk",
        "outputId": "ea436fbc-58e9-4843-bbb1-c22714febdb9"
      },
      "source": [
        "modelo_rlogistica2 = LogisticRegression(solver='newton-cg')\n",
        "modelo_rlogistica2.fit(x_treino, y_treino)\n",
        "modelo_rlogistica2.score(x_teste, y_teste)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.686122191895864"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xhh-JjppIWKY",
        "outputId": "45a28dcc-5053-4005-e35b-1f669e885fc7"
      },
      "source": [
        "from sklearn.dummy import DummyClassifier\n",
        "\n",
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "modelo_dummy.score(x_teste, y_teste)\n",
        "# previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "# accuracy_score(y_teste, previsao_dummy)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3464203233256351"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGlxl2Z4IctF"
      },
      "source": [
        "## Desafio 3: Escolha a droga mais utilizada e tente criar um modelo para prever está label."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xkKEVxRaIf9F"
      },
      "source": [
        "dados_combinados['composto'].value_counts()\n",
        "\n",
        "dt_temporario = dados_combinados.query('composto in [\"8b87a7a83\", \"5628cb3ee\"]')\n",
        "\n",
        "x = dt_temporario.loc[:,'g-0':'g-10']\n",
        "y = dt_temporario['composto'].map({'8b87a7a83': 0, '5628cb3ee': 1})"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "g5tMa7IUInOf",
        "outputId": "dc9b8552-902e-45bd-90fe-5d7b0ef6b8f9"
      },
      "source": [
        "sns.countplot(y=y)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8294463650>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjNA82aeIpbU"
      },
      "source": [
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.2, stratify=y,random_state=376) "
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WM5F_sxIqwg",
        "outputId": "ab232fd1-da08-4879-892e-181b26dc0753"
      },
      "source": [
        "modelo_rlogistica2 = LogisticRegression()\n",
        "modelo_rlogistica2.fit(x_treino, y_treino)\n",
        "modelo_rlogistica2.score(x_teste, y_teste)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9382716049382716"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OW-CLXPIrZ3",
        "outputId": "8fd99114-dd65-4be8-8892-36c70e4d5a8d"
      },
      "source": [
        "modelo_dummy = DummyClassifier('most_frequent')\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "modelo_dummy.score(x_teste, y_teste)\n",
        "# previsao_dummy = modelo_dummy.predict(x_teste)\n",
        "# accuracy_score(y_teste, previsao_dummy)"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5061728395061729"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    }
  ]
}