Regresion/Regresion_Logistica.ipynb

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      "name": "Regresion_Logistica.ipynb",
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      "authorship_tag": "ABX9TyM+3oq7Eazq4/NmL/X8hnPR"
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4YNzggo4Ps9"
      },
      "source": [
        "from google.colab import files\n",
        " \n",
        "files.upload()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "98_c2SnC2iUC"
      },
      "source": [
        "# Regresión logística con Scikit-Learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iX12Ho9J35wf"
      },
      "source": [
        "La regresión logística es un tipo de análisis de regresión utilizado para predecir el resultado de una variable categórica (variable que puede adoptar un número limitado de categorías o etiquetas) en función de las variables independientes o predictoras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hpB8fjLu3-fw"
      },
      "source": [
        "En este caso supongamos que tenemos un cierto número de pacientes con ciertas características que nos van a permitir determinar si determinado paciente tiene diabetes o no. \n",
        "\n",
        "Adicionalmente, la información con la que tendremos que trabajar para este caso tendrá que estar previamente ordenada o \"limpia\".\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3ljW4dl3_1z"
      },
      "source": [
        "import pandas as pd\n",
        "from sklearn import metrics # Para validar el modelo\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Fy7-MjQ4gfE"
      },
      "source": [
        "### Carguemos el dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "eAs2JpXO4KNL",
        "outputId": "9053abaf-038a-47a6-bd3f-05b7bb13ef89"
      },
      "source": [
        "dataset = pd.read_csv(\"diabetes.csv\")\n",
        "dataset"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
              "      <th>Glucose</th>\n",
              "      <th>BloodPressure</th>\n",
              "      <th>SkinThickness</th>\n",
              "      <th>Insulin</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DiabetesPedigreeFunction</th>\n",
              "      <th>Age</th>\n",
              "      <th>Outcome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "      <td>1</td>\n",
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              "      <td>70</td>\n",
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              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  ...  Age  Outcome\n",
              "0              6      148  ...   50        1\n",
              "1              1       85  ...   31        0\n",
              "2              8      183  ...   32        1\n",
              "3              1       89  ...   21        0\n",
              "4              0      137  ...   33        1\n",
              "..           ...      ...  ...  ...      ...\n",
              "763           10      101  ...   63        0\n",
              "764            2      122  ...   27        0\n",
              "765            5      121  ...   30        0\n",
              "766            1      126  ...   47        1\n",
              "767            1       93  ...   23        0\n",
              "\n",
              "[768 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i5Qe7dEp4Km5",
        "outputId": "cd69f8d9-658b-4c2b-ef72-c96b76969b3b"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(768, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QK8pqmAv6bpt"
      },
      "source": [
        "### Obtenemos las columnas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3htk1S64fKG",
        "outputId": "8691990c-749e-4091-db19-04522f72390b"
      },
      "source": [
        "feature_cols = [i for i  in dataset.columns ]\n",
        "feature_cols.remove('Outcome')\n",
        "print(feature_cols)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5DPcBmbk6VXk"
      },
      "source": [
        "### Creamos nuestras variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "YGR6UFu86X11",
        "outputId": "92574c1d-60a2-49c8-e765-2e1b29d64b3a"
      },
      "source": [
        "X = dataset[feature_cols]\n",
        "X"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Pregnancies</th>\n",
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              "      <th>SkinThickness</th>\n",
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              "      <th>DiabetesPedigreeFunction</th>\n",
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              "      <td>23.3</td>\n",
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              "      <td>28.1</td>\n",
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              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>10</td>\n",
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              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>764</th>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>765</th>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>766</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>767</th>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>768 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Pregnancies  Glucose  BloodPressure  ...   BMI  DiabetesPedigreeFunction  Age\n",
              "0              6      148             72  ...  33.6                     0.627   50\n",
              "1              1       85             66  ...  26.6                     0.351   31\n",
              "2              8      183             64  ...  23.3                     0.672   32\n",
              "3              1       89             66  ...  28.1                     0.167   21\n",
              "4              0      137             40  ...  43.1                     2.288   33\n",
              "..           ...      ...            ...  ...   ...                       ...  ...\n",
              "763           10      101             76  ...  32.9                     0.171   63\n",
              "764            2      122             70  ...  36.8                     0.340   27\n",
              "765            5      121             72  ...  26.2                     0.245   30\n",
              "766            1      126             60  ...  30.1                     0.349   47\n",
              "767            1       93             70  ...  30.4                     0.315   23\n",
              "\n",
              "[768 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0a5S46T4_cD",
        "outputId": "6e901bdd-eff3-4927-ce43-98d09444054d"
      },
      "source": [
        "y = dataset.Outcome\n",
        "y"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1\n",
              "1      0\n",
              "2      1\n",
              "3      0\n",
              "4      1\n",
              "      ..\n",
              "763    0\n",
              "764    0\n",
              "765    0\n",
              "766    1\n",
              "767    0\n",
              "Name: Outcome, Length: 768, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vCbKiRqP58ex"
      },
      "source": [
        "### Dividimos datos de entrenamiento y de prueba"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZJ3xCG0y6m9C"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size = 0.25, random_state = 0 )"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgdrAW5p6z_e"
      },
      "source": [
        "### Llamamos al modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4KNISRt6p_r"
      },
      "source": [
        "# Ajustamos el maximo de iteraciones para que no salga un Warning\n",
        "log_reg = LogisticRegression(max_iter=1000)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJkF59Io6v4X"
      },
      "source": [
        "### Entrenamos el modelo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myiqsWN-6raC",
        "outputId": "65229a63-9397-4dd9-f5ed-3c90317fea1d"
      },
      "source": [
        "log_reg.fit(X_train, y_train)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "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=1000,\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": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKPUIBjl62fA"
      },
      "source": [
        "### Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4ssPDYi6sos",
        "outputId": "d76191c3-c5ed-47cd-b6b4-8f2d9d3cfaed"
      },
      "source": [
        "log_reg.score(X_train, y_train)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7604166666666666"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJq_xLzM6uSo",
        "outputId": "550a9729-b38e-4509-d053-416f9e390afb"
      },
      "source": [
        "log_reg.score(X_test, y_test)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8020833333333334"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTGGFz2i68D7"
      },
      "source": [
        "Al parecer obtuvimos buenos resultados."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "prAvXQBp7CBT",
        "outputId": "25f90d35-7e2f-41ee-ac4a-5daa2d3c3b78"
      },
      "source": [
        "# Obtenemos los valores predichos\n",
        "y_pred = log_reg.predict(X_test)\n",
        "y_pred"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
              "       0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVPC4HL97Dgv"
      },
      "source": [
        "### Evaluando con una matriz de confusión"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8snk9eCT7PHQ"
      },
      "source": [
        "#### Generamos nuestra matriz de confusión"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KyELDONB7LxY",
        "outputId": "f1c0e6b4-5137-4f2c-ae86-b66bb5a14d47"
      },
      "source": [
        "# Recibe 2 paráetros, la información que usamos para testear y la predicción que arrojó el modelo\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[118,  12],\n",
              "       [ 26,  36]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "01h_0hs97R94"
      },
      "source": [
        "Vamos a hacerlo un poco más visual\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "9xBxZTiK7UiF",
        "outputId": "80470b51-d0ab-4c3b-cb3b-d9cbf1f701d7"
      },
      "source": [
        "# seaborn,heatmap nos ayuda a trazar los datos en forma de matriz como buscamos\n",
        "# Crea un DataFrame a partir de la matriz de confusión\n",
        "# https://seaborn.pydata.org/generated/seaborn.heatmap.html --> Para ver el resto de parámetros\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot = True, cmap = 'magma', fmt = 'g') # cmap nos ayuda a extablecer los colores\n",
        "\n",
        "# Ajustamos la posición de los labels\n",
        "ax.xaxis.set_label_position('top')\n",
        "# Ajuste el relleno entre y alrededor de los subplots.\n",
        "plt.tight_layout()\n",
        "\n",
        "# Le ponemos el titulo y las etiquetas de los ejes\n",
        "plt.title('Matriz de confusión', y = 1.1) # Empezamos en la posición 1.1\n",
        "plt.ylabel('Etiqueta actual')\n",
        "plt.xlabel('Etiqueta de predicción')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Etiqueta de predicción')"
            ]
          },
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CEY2BGu87V4t",
        "outputId": "82943c11-d0cf-4ed5-f89c-c3fb360992e5"
      },
      "source": [
        "print('Exactitud:', round( metrics.accuracy_score(y_test, y_pred), 3) )"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Exactitud: 0.802\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aTzBfdUZ7ayd"
      },
      "source": [
        "---"
      ]
    }
  ]
}