Ezquerro_Ruiz_Mario_PIA03_1_Tarea.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Tarea 3.1. Programación de Inteligencia Artificial\n",
        "\n",
        "Tarea correspondiente a la UD03 del módulo Programación de Inteligencia Artificial. "
      ],
      "metadata": {
        "id": "-KJxaOzw55Fx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importación de librerías necesarias\n"
      ],
      "metadata": {
        "id": "sLWUx_jY55F3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#añade las librerías básicas que consideres necesarias para el proyecto (pongo 3). Posteriormente podrás añadir más librerías. \n",
        "import os\n",
        "import numpy as np \n",
        "import pandas as pd "
      ],
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
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      "execution_count": 1,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "# Descarga del dataset\n",
        "\n",
        "A continuación se procede a descargar el dataset y la explicación de sus atributos. "
      ],
      "metadata": {
        "id": "sU07EOFL8Ibv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#https://drive.google.com/file/d/1I7WagDoN1AuVYoUTaonEOHF_gw_UCb16\n",
        "!gdown --id 1I7WagDoN1AuVYoUTaonEOHF_gw_UCb16\n",
        "\n",
        "## Carga del fichero en Python\n",
        "df_train = pd.read_csv('heart_disease.csv', delimiter=',')\n",
        "\n",
        "df_train.head(5)"
      ],
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          "height": 330
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        "id": "KJ7dVE4F55F4",
        "outputId": "0ffee0b0-de3b-4218-d413-7554e1638676"
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      "execution_count": 2,
      "outputs": [
        {
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          "text": [
            "/usr/local/lib/python3.8/dist-packages/gdown/cli.py:127: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1I7WagDoN1AuVYoUTaonEOHF_gw_UCb16\n",
            "To: /content/heart_disease.csv\n",
            "100% 11.3k/11.3k [00:00<00:00, 16.4MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
              "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
              "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
              "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
              "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   0     1       1  \n",
              "1   0     2       1  \n",
              "2   0     2       1  \n",
              "3   0     2       1  \n",
              "4   0     2       1  "
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      "source": [
        "El nombre de los atributos del dataset es intuitivo. Sin embargo, hay ciertas columnas que necesitan explicación.\n",
        "\n",
        "1. **age**: Edad de la persona en años\n",
        "\n",
        "2. **sex**: Sexo de la persona (1 = varon, 0 = mujer)\n",
        "\n",
        "3. **cp**: Tipo de dolor en el pecho (Value 1: tipico de angina, Value 2: atipico angina, Value 3: no hay dolor de angina, Value 4: asintomático)\n",
        "\n",
        "4. **trestbps**: La presión sanguínea de la persona en reposo(mm Hg en la admisión al hospital)\n",
        "\n",
        "5. **chol**: El colesterol de la persona en mg/dl\n",
        "\n",
        "6. **fbs**: Nivel de azúcar en sangre en ayunas de la pesona (> 120 mg/dl, 1 = true; 0 = false)\n",
        "\n",
        "7. **restecg**: Medición electrocardiográfica en reposo (0 = normal, 1 = anormalidad de la onda ST-T, 2 = hipertrofia probable o definitiva del ventrículo izquierdo según los criterios de Estes)\n",
        "\n",
        "8. **thalach**: Frecuencia cardiaca máxima alcanzada por la persona. \n",
        "\n",
        "9. **exang**: Angina inducida por ejercicio (1 = yes; 0 = no)\n",
        "\n",
        "10. **oldpeak**: Descenso del ST inducido por el ejercicio en relación con el reposo ('ST' se relaciona con las posiciones en el gráfico de ECG)\n",
        "\n",
        "11. **slope**: la pendiente del segmento ST del ejercicio máximo (Valor 1: pendiente ascendente, Valor 2: plano, Valor 3: pendiente descendente)\n",
        "\n",
        "12. **ca**: El número de buques principales (0-4)\n",
        "\n",
        "13. **thal**: un trastorno de la sangre llamado talasemia (3 = normal; 6 = defecto fijo; 7 = defecto reversible)\n",
        "\n",
        "14. **target**: Enfermedad cardiaca (0 = no, 1 = yes)\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 1. Información del dataset"
      ],
      "metadata": {
        "id": "jhMret0cqbA2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 1.1. Visualización básica del dataset\n",
        "\n",
        "*   Muestra el número de ocurrencias que presentan enfermedades cardiovasculares y que no. Utiliza un diagrama de barras. \n",
        "\n",
        "*   Balancea ambos conjuntos de tal forma que no se añada ninguna ocurrencia artificial. Es decir, recorta aquel conjunto que contenga menos ocurrencias. \n",
        "\n"
      ],
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#visualización\n",
        "not_healthy = df_train[df_train['target'] == 1].shape[0]\n",
        "healthy = df_train[df_train['target'] == 0].shape[0]\n",
        "plt.bar(['Has heart problems', 'No heart problems'], [not_healthy, healthy])"
      ],
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        "outputId": "238d296c-49f0-4a08-8eb7-bd9e61884090"
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      "execution_count": 3,
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            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import resample\n",
        "#balanceamiento\n",
        "df_may = df_train[df_train['target'] == 1]\n",
        "df_min = df_train[df_train['target'] == 0]\n",
        "\n",
        "df_majority_downsampled = resample(df_min, \n",
        "                                 n_samples=len(df_may),\n",
        "                                 replace=True,      \n",
        "                                 random_state=42) \n",
        "\n",
        "df_train = pd.concat([df_may, df_majority_downsampled])"
      ],
      "metadata": {
        "id": "yvq6_hK7Gkd0"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 1.2. Correlación de variables\n",
        "\n",
        "* Muestra la matríz de correlación de todas las variables.\n",
        "* Crea una variable llamada **corr_variables** con el nombre de las variables más correlacionadas con target (más de 0,3). \n",
        "\n",
        "**Recuerda que un valor muy próximo a -1 también indica una alta correlación, por lo que habrá que tomar los valores absolutos**\n"
      ],
      "metadata": {
        "id": "wTVkckZk55F6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "#visualizar la matriz de correlación (utiliza una librería de visualización)\n",
        "plt.figure(figsize = (12,6))\n",
        "sns.heatmap(df_train.corr(), annot = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "trusted": true,
        "id": "f7ANTIol55F6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6eeab73d-ea00-47f1-9711-b4d6cd4c33e2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#obtener el array con las variables que tengan una correlación con target mayor que 0.3\n",
        "variables = df_train.corr().loc[:, 'target']\n",
        "corr_variables = variables[np.abs(variables) > 0.3].index\n",
        "\n",
        "print(corr_variables)"
      ],
      "metadata": {
        "id": "QfDRgIRAhUaM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be151f44-6415-4915-af6e-f33d4281e534"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['sex', 'cp', 'thalach', 'exang', 'oldpeak', 'ca', 'thal', 'target'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 2. Preprocesamiento de datos"
      ],
      "metadata": {
        "id": "5PniPN3bo4ib"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 2.1. One hot encoding\n",
        "\n",
        "Hasta ahora hemos utilizado one hote encoding para transformar variables categóricas en numéricas. Pero también podemos hacerlo para crear nuevas columnas a partir de los diferentes valores categóricos (aunque sean números) de una columna en concreto. \n",
        "\n",
        "Por ejemplo, la columna cp define el tipo de dolor en el pecho y sus valores pueden ser 1, 2, 3 o 4. Esta columna la podemos dividir en cuatro columnas nuevas cp_0, cp_1, cp_2, cp_3 y cp_4 con valores binarios 0 o 1. "
      ],
      "metadata": {
        "id": "XpcrmUye55GB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Identifica aquellas columnas cuyos valores sean categóricos (aunque sean enteros) y dividelas en columnas binarias con valores 0 y 1. Elimina la columna original.**"
      ],
      "metadata": {
        "id": "1VzFbHZggMRK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# nos fijamos en valores minimos y maximos que seran enteros de 0 a x\n",
        "df_train.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "UyxDo1xYZDTU",
        "outputId": "225f89c0-10b9-467a-807c-2e4ab9101674"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  330.000000  330.000000  330.000000  330.000000  330.000000  330.000000   \n",
              "mean    54.321212    0.706061    0.927273  130.524242  244.318182    0.160606   \n",
              "std      9.101914    0.456256    1.031796   17.332815   51.796234    0.367725   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.000000    0.000000    0.000000  120.000000  208.000000    0.000000   \n",
              "50%     55.500000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     62.000000    1.000000    2.000000  140.000000  276.000000    0.000000   \n",
              "max     76.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  330.000000  330.000000  330.000000  330.000000  330.000000  330.000000   \n",
              "mean     0.512121  148.948485    0.339394    1.044545    1.424242    0.760606   \n",
              "std      0.506647   22.588302    0.474223    1.129832    0.605462    1.022673   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  132.000000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  152.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  165.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "             thal      target  \n",
              "count  330.000000  330.000000  \n",
              "mean     2.339394    0.500000  \n",
              "std      0.623715    0.500759  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    0.500000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec952029-d889-4135-bead-b740c68c810a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "      <td>330.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.321212</td>\n",
              "      <td>0.706061</td>\n",
              "      <td>0.927273</td>\n",
              "      <td>130.524242</td>\n",
              "      <td>244.318182</td>\n",
              "      <td>0.160606</td>\n",
              "      <td>0.512121</td>\n",
              "      <td>148.948485</td>\n",
              "      <td>0.339394</td>\n",
              "      <td>1.044545</td>\n",
              "      <td>1.424242</td>\n",
              "      <td>0.760606</td>\n",
              "      <td>2.339394</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.101914</td>\n",
              "      <td>0.456256</td>\n",
              "      <td>1.031796</td>\n",
              "      <td>17.332815</td>\n",
              "      <td>51.796234</td>\n",
              "      <td>0.367725</td>\n",
              "      <td>0.506647</td>\n",
              "      <td>22.588302</td>\n",
              "      <td>0.474223</td>\n",
              "      <td>1.129832</td>\n",
              "      <td>0.605462</td>\n",
              "      <td>1.022673</td>\n",
              "      <td>0.623715</td>\n",
              "      <td>0.500759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>208.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>152.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>62.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>276.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>76.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec952029-d889-4135-bead-b740c68c810a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ec952029-d889-4135-bead-b740c68c810a button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ec952029-d889-4135-bead-b740c68c810a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_columns = ['ca', 'sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'thal']"
      ],
      "metadata": {
        "trusted": true,
        "id": "c8RAEhGa55GC"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Descategorizar variables utilizando one hot enconding\n",
        "def onehot_encode(df, column):\n",
        "    for name in column:\n",
        "      df = df.copy()\n",
        "      dummies = pd.get_dummies(df[name], prefix=name) \n",
        "\n",
        "      df = pd.concat([df, dummies], axis=1)\n",
        "      df = df.drop(name, axis=1)         \n",
        "    return df\n",
        "    \n",
        "\n",
        "df_train = onehot_encode(df_train, categorical_columns)\n",
        "df_train.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "xNm2U0aQbv2w",
        "outputId": "2d3bdc21-c093-4ef5-b545-5ee93c9a5123"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   age  trestbps  chol  thalach  oldpeak  target  ca_0  ca_1  ca_2  ca_3  ...  \\\n",
              "0   63       145   233      150      2.3       1     1     0     0     0  ...   \n",
              "1   37       130   250      187      3.5       1     1     0     0     0  ...   \n",
              "2   41       130   204      172      1.4       1     1     0     0     0  ...   \n",
              "3   56       120   236      178      0.8       1     1     0     0     0  ...   \n",
              "4   57       120   354      163      0.6       1     1     0     0     0  ...   \n",
              "\n",
              "   restecg_2  exang_0  exang_1  slope_0  slope_1  slope_2  thal_0  thal_1  \\\n",
              "0          0        1        0        1        0        0       0       1   \n",
              "1          0        1        0        1        0        0       0       0   \n",
              "2          0        1        0        0        0        1       0       0   \n",
              "3          0        1        0        0        0        1       0       0   \n",
              "4          0        0        1        0        0        1       0       0   \n",
              "\n",
              "   thal_2  thal_3  \n",
              "0       0       0  \n",
              "1       1       0  \n",
              "2       1       0  \n",
              "3       1       0  \n",
              "4       1       0  \n",
              "\n",
              "[5 rows x 31 columns]"
            ],
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              "      <th>age</th>\n",
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              "      <th>...</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
              "      <th>slope_0</th>\n",
              "      <th>slope_1</th>\n",
              "      <th>slope_2</th>\n",
              "      <th>thal_0</th>\n",
              "      <th>thal_1</th>\n",
              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
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              "      <td>2.3</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>130</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>120</td>\n",
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              "      <td>0.6</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 31 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a8a65dc-10d2-416b-a20a-52447b7af77d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a8a65dc-10d2-416b-a20a-52447b7af77d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9a8a65dc-10d2-416b-a20a-52447b7af77d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 3. División del dataset\n",
        "\n"
      ],
      "metadata": {
        "id": "NDVlC--Jq0OY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 3.1. División en entrenamiento y test\n",
        "**Divide el dataset de tal forma que el 80% sea de entrenamiento y el 20% para test**.\n",
        "\n"
      ],
      "metadata": {
        "id": "AyQWTXA1hQLb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "# División del dataset en las variables X_train, X_test, y_train, y_test\n",
        "df_train_y = df_train['target']\n",
        "df_train_x = df_train.drop('target', axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(df_train_x, df_train_y, train_size=0.80, shuffle=True, random_state=1, stratify=df_train['target'])"
      ],
      "metadata": {
        "trusted": true,
        "id": "4THrrojv55GD"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 3.2. Normalización del conjunto de entrenamiento y test\n",
        "\n",
        "**Normaliza los datos tanto de test como de entrenamiento**"
      ],
      "metadata": {
        "id": "fuM-HBI4h1-p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "#normalización\n",
        "scaler = MinMaxScaler()                                                 \n",
        "nomalized_train = scaler.fit_transform(X_train)                        \n",
        "nomalized_test = scaler.transform(X_test)                        \n",
        "X_train = pd.DataFrame(nomalized_train, columns=X_train.columns)\n",
        "X_test = pd.DataFrame(nomalized_test, columns=X_test.columns)"
      ],
      "metadata": {
        "trusted": true,
        "id": "aYfBzvas55GE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "MktIpj3XIKBu",
        "outputId": "b0d57541-f283-4d48-e9ed-27f4ac8b653f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  trestbps      chol   thalach   oldpeak  ca_0  ca_1  ca_2  ca_3  \\\n",
              "0  0.733333  0.415094  0.383562  0.267176  0.306452   0.0   0.0   0.0   1.0   \n",
              "1  0.800000  0.575472  0.326484  0.587786  0.129032   1.0   0.0   0.0   0.0   \n",
              "2  0.288889  0.509434  0.269406  0.816794  0.129032   0.0   0.0   1.0   0.0   \n",
              "3  0.755556  0.481132  0.244292  0.603053  0.370968   1.0   0.0   0.0   0.0   \n",
              "4  0.644444  0.245283  0.360731  0.679389  0.290323   1.0   0.0   0.0   0.0   \n",
              "\n",
              "   ca_4  ...  restecg_2  exang_0  exang_1  slope_0  slope_1  slope_2  thal_0  \\\n",
              "0   0.0  ...        0.0      1.0      0.0      0.0      1.0      0.0     0.0   \n",
              "1   0.0  ...        0.0      1.0      0.0      0.0      0.0      1.0     0.0   \n",
              "2   0.0  ...        0.0      1.0      0.0      0.0      0.0      1.0     0.0   \n",
              "3   0.0  ...        0.0      1.0      0.0      1.0      0.0      0.0     0.0   \n",
              "4   0.0  ...        0.0      1.0      0.0      0.0      1.0      0.0     0.0   \n",
              "\n",
              "   thal_1  thal_2  thal_3  \n",
              "0     0.0     1.0     0.0  \n",
              "1     0.0     1.0     0.0  \n",
              "2     0.0     1.0     0.0  \n",
              "3     1.0     0.0     0.0  \n",
              "4     0.0     1.0     0.0  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>chol</th>\n",
              "      <th>thalach</th>\n",
              "      <th>oldpeak</th>\n",
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              "      <th>ca_1</th>\n",
              "      <th>ca_2</th>\n",
              "      <th>ca_3</th>\n",
              "      <th>ca_4</th>\n",
              "      <th>...</th>\n",
              "      <th>restecg_2</th>\n",
              "      <th>exang_0</th>\n",
              "      <th>exang_1</th>\n",
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              "      <th>slope_1</th>\n",
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              "      <th>thal_2</th>\n",
              "      <th>thal_3</th>\n",
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              "      <th>0</th>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.415094</td>\n",
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              "      <td>0.575472</td>\n",
              "      <td>0.326484</td>\n",
              "      <td>0.587786</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.509434</td>\n",
              "      <td>0.269406</td>\n",
              "      <td>0.816794</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.755556</td>\n",
              "      <td>0.481132</td>\n",
              "      <td>0.244292</td>\n",
              "      <td>0.603053</td>\n",
              "      <td>0.370968</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.644444</td>\n",
              "      <td>0.245283</td>\n",
              "      <td>0.360731</td>\n",
              "      <td>0.679389</td>\n",
              "      <td>0.290323</td>\n",
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          "metadata": {},
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    {
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      "source": [
        "X_test.head()"
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      "metadata": {
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          "height": 235
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        "id": "mv8xqSBYLmy2",
        "outputId": "b9d8d638-3be4-4fae-9274-5eb3c37bcd7d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  trestbps      chol   thalach   oldpeak  ca_0  ca_1  ca_2  ca_3  \\\n",
              "0  0.488889  0.056604  0.219178  0.549618  0.193548   1.0   0.0   0.0   0.0   \n",
              "1  0.333333  0.339623  0.212329  0.893130  0.000000   1.0   0.0   0.0   0.0   \n",
              "2  0.755556  0.339623  0.292237  0.580153  0.225806   0.0   1.0   0.0   0.0   \n",
              "3  0.666667  0.377358  0.178082  0.694656  0.129032   0.0   0.0   1.0   0.0   \n",
              "4  0.733333  0.320755  0.187215  0.526718  0.000000   1.0   0.0   0.0   0.0   \n",
              "\n",
              "   ca_4  ...  restecg_2  exang_0  exang_1  slope_0  slope_1  slope_2  thal_0  \\\n",
              "0   0.0  ...        0.0      0.0      1.0      0.0      1.0      0.0     0.0   \n",
              "1   0.0  ...        0.0      1.0      0.0      0.0      0.0      1.0     0.0   \n",
              "2   0.0  ...        0.0      1.0      0.0      0.0      1.0      0.0     0.0   \n",
              "3   0.0  ...        0.0      1.0      0.0      0.0      0.0      1.0     0.0   \n",
              "4   0.0  ...        0.0      1.0      0.0      0.0      0.0      1.0     0.0   \n",
              "\n",
              "   thal_1  thal_2  thal_3  \n",
              "0     0.0     1.0     0.0  \n",
              "1     0.0     1.0     0.0  \n",
              "2     0.0     0.0     1.0  \n",
              "3     0.0     1.0     0.0  \n",
              "4     0.0     1.0     0.0  \n",
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              "      <td>0.339623</td>\n",
              "      <td>0.292237</td>\n",
              "      <td>0.580153</td>\n",
              "      <td>0.225806</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.377358</td>\n",
              "      <td>0.178082</td>\n",
              "      <td>0.694656</td>\n",
              "      <td>0.129032</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.733333</td>\n",
              "      <td>0.320755</td>\n",
              "      <td>0.187215</td>\n",
              "      <td>0.526718</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a1e08f4c-ae8e-42fe-bc9d-a58c898a9764')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a1e08f4c-ae8e-42fe-bc9d-a58c898a9764 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a1e08f4c-ae8e-42fe-bc9d-a58c898a9764');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 4. Generación de modelos"
      ],
      "metadata": {
        "id": "6F5yk0dRj6Lq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ejercicio 4.1. Definición de función que muestre informe sobre una predicción\n"
      ],
      "metadata": {
        "id": "-hmLE_c-j9iS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Crea una función que reciba (1) un array con los valores que se deberían obtener de la predicción (y_test); y (2) un array con los valores predichos por un modelo concreto (y_pred). La función deberá generar un informe con:\n",
        "\n",
        "  1.1.   Muestra la exactitud, precisión, y recall del modelo generado. \n",
        "\n",
        "  1.2.   Visualiza la matriz de confusión del modelo.\n",
        "\n",
        "  1.3.   Visualiza un grafo con su curva ROC y área AUC\n",
        "\n",
        "Una posible cabecera de la función podría ser: \n",
        "\n",
        "```python\n",
        "def informe_modelo(y_test, y_pred):\n",
        "```\n"
      ],
      "metadata": {
        "id": "6vAuKkDnkb5z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.2 Funcion para mostar la matriz de confusion "
      ],
      "metadata": {
        "id": "_lvrMMaH7AUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def mostar_matriz_confusion(pred, y_test):\n",
        "  cm = confusion_matrix(pred, y_test)\n",
        "  ax = plt.subplot()\n",
        "\n",
        "  sns.heatmap(cm, annot=True, ax=ax, cmap='Blues');\n",
        "  ax.set_xlabel('Valores Predichos');\n",
        "  ax.set_ylabel('Valores Reales');\n",
        "  ax.xaxis.set_ticklabels([1, 0]); \n",
        "  ax.yaxis.set_ticklabels([1, 0]);\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "_n_gDg8H0rIt"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.3 Funcion para mostar las curvas ROC y AUC"
      ],
      "metadata": {
        "id": "qlASC7ga7D94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc, RocCurveDisplay\n",
        "\n",
        "def mostrar_curva_roc_y_auc(pred, y_test):\n",
        "  #calculo el ratio de verdaderos positivos y falsos positivos. \n",
        "  fpr, tpr, _ = roc_curve(y_test, pred)\n",
        "  \n",
        "  #con estos valores obtengo el valor para la AUC\n",
        "  roc_auc = auc(fpr, tpr)\n",
        "  \n",
        "  display = RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name='RandomForestClassifier')\n",
        "  display.plot()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "zywJv9tt6_Hc"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Informe completo del modelo"
      ],
      "metadata": {
        "id": "S9oi1jbUBhAt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def informe_modelo(y_test, predict):\n",
        "  clf_report = pd.DataFrame(classification_report(y_test, predict, output_dict=True))\n",
        " \n",
        "  print(\"Test Result:\\n================================================\")        \n",
        "  print(f\"1.1 Reporte de clasificacion:\\n{clf_report}\")\n",
        "  print(\"================================================\")\n",
        "  print(\"1.2 Matriz de confusion\")\n",
        "  mostar_matriz_confusion(predict, y_test)\n",
        "  print(\"================================================\")\n",
        "  print(\"1.3 Curva ROC y área AUC\")\n",
        "  mostrar_curva_roc_y_auc(predict, y_test)\n",
        "  "
      ],
      "metadata": {
        "id": "yrX3oKDtlUJa"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 4.2. Crear modelo: RandomForestClassifier\n",
        "\n",
        "1.  Crea un modelo utilizando **RandomForestClassifier** (anterior Unidad Didáctica). \n",
        "\n",
        "2.  Utilizando la función del ejercicio 4.1, muestra el informe de este modelo entrenado con el conjunto de entrenamiento obtenido en el ejercicio 3.1."
      ],
      "metadata": {
        "id": "NiRHQwzvqHtD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import  RandomForestClassifier\n",
        "# Creo el modelo\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "ggizHUROk7Sa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ffe3672-d792-4876-bd9a-f2cac165e2d1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#muestro el informe\n",
        "informe_modelo(y_test, model.predict(X_test))"
      ],
      "metadata": {
        "id": "TZzerq71uxPx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 749
        },
        "outputId": "2b6ae50c-3834-4afd-d090-23a129ceeabd"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Result:\n",
            "================================================\n",
            "1.1 Reporte de clasificacion:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.875000   0.852941  0.863636   0.863971      0.863971\n",
            "recall      0.848485   0.878788  0.863636   0.863636      0.863636\n",
            "f1-score    0.861538   0.865672  0.863636   0.863605      0.863605\n",
            "support    33.000000  33.000000  0.863636  66.000000     66.000000\n",
            "================================================\n",
            "1.2 Matriz de confusion\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================\n",
            "1.3 Curva ROC y área AUC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 4.3. Validación cruzada del anterior modelo"
      ],
      "metadata": {
        "id": "GSc_7ySGnlsM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Valida en anterior modelo utilizando Cross Validation (con KFold). Muestra la exactitud, precisión, y recall del modelo tras la validación cruzada. \n",
        "\n"
      ],
      "metadata": {
        "id": "JCF0kJ3gnMIa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn import model_selection\n",
        "\n",
        "scoring = {'accuracy' : make_scorer(accuracy_score), \n",
        "           'precision' : make_scorer(precision_score),\n",
        "           'recall' : make_scorer(recall_score)}\n",
        "\n",
        "kfold = model_selection.KFold(n_splits=5)\n",
        "\n",
        "results = model_selection.cross_validate(estimator=model,\n",
        "                                          X=X_test,\n",
        "                                          y=y_test,\n",
        "                                          cv=kfold,\n",
        "                                          scoring=scoring)\n",
        "\n",
        "print(f\"Accuracy: {results['test_accuracy'].mean():.3f}\")\n",
        "print(f\"Precission: {results['test_precision'].mean():.3f}\")\n",
        "print(f\"Recall: {results['test_recall'].mean():.3f}\")"
      ],
      "metadata": {
        "id": "pDQKAZ95rhJi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "355e2014-9b7b-4055-b568-4fc52d5b5cbb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.727\n",
            "Precission: 0.715\n",
            "Recall: 0.736\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.   Valida en anterior modelo utilizando Cross Validation (con StratifiedKFold). Muestra la exactitud, precisión, y recall del modelo tras la validación cruzada. \n"
      ],
      "metadata": {
        "id": "GswkGRmXnkUr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "kfold = model_selection.StratifiedKFold(n_splits=5)\n",
        "\n",
        "results = model_selection.cross_validate(estimator=model,\n",
        "                                          X=X_test,\n",
        "                                          y=y_test,\n",
        "                                          cv=kfold,\n",
        "                                          scoring=scoring)\n",
        "\n",
        "print(f\"Accuracy: {results['test_accuracy'].mean():.3f}\")\n",
        "print(f\"Precission: {results['test_precision'].mean():.3f}\")\n",
        "print(f\"Recall: {results['test_recall'].mean():.3f}\")"
      ],
      "metadata": {
        "id": "ud1JXqRXu7Tv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a17dc758-47d2-4e0c-8fda-797220bc1ab4"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.729\n",
            "Precission: 0.725\n",
            "Recall: 0.719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "3.   Comenta qué diferencias han existido entre uno y otro ¿Por qué?\n",
        "\n",
        "La principal caracteristica que diferencia KFold y StratifiedKFold es que StratifiedKFold asegura que, en el proceso de validacion los grupos empleados (folds) en el test tienen la misma proporcion de datos con cierta etiqueta.\n",
        "\n",
        "Es decir en un caso con 16 filas y (12 con etiqueta \"A\" y 4 con etiqueta \"B\") en cada iteracion usara la misma proporcion de clases.\n",
        "2/3 de la clase \"A\" y 1/3 de la clase \"B\" en la validacion\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "rH8m42E4nlub"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![10-K-Fold-Cross-Validation-424618033.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "Ku13NNzZpS7z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ejercicio 5. Generación de modelos utilizando redes neuronales"
      ],
      "metadata": {
        "id": "Q0w3p5Mk55GF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importa las librerías necesarias\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers.core import Dense"
      ],
      "metadata": {
        "trusted": true,
        "id": "kkedd1gJ55GG"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 5.1. Desarrolla la arquitectura de la red neuronal. \n",
        "\n",
        "Desarrolla la arquitectura necesaria para crear una red neuronal que pueda predecir el riesgo de sufrir un problema cardiaco ante una entrada determinada. \n",
        "\n"
      ],
      "metadata": {
        "id": "lJQiv-p655GH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQmtxxOd5-k4",
        "outputId": "456509c5-ce48-4873-b6e5-3d732a2f2716"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(330, 31)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#arquitectura red neuronal y compilación\n",
        "neural_network = Sequential()\n",
        "\n",
        "neural_network.add(Dense(14, activation=\"relu\", input_shape=(30,)))\n",
        "neural_network.add(Dense(1, activation=\"sigmoid\"))\n",
        "\n",
        "neural_network.compile(loss=\"binary_crossentropy\",\n",
        "                       optimizer=\"adam\",\n",
        "                       metrics=\"binary_accuracy\")\n",
        "\n",
        "neural_network.summary()"
      ],
      "metadata": {
        "trusted": true,
        "id": "yu36ybRz55GI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac6b348b-5d00-47bc-c7a3-a8b502af032e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 14)                434       \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 15        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 449\n",
            "Trainable params: 449\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 5.2. Entrena la red neuronal con el conjunto de test\n",
        "\n",
        "Entrena la red utilizando 0.1 para la validación del modelo"
      ],
      "metadata": {
        "id": "fXuBogL455GL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Entrenamiento de la red neuronal\n",
        "history = neural_network.fit(X_train, y_train, batch_size=64, epochs=8, validation_split=0.1)"
      ],
      "metadata": {
        "id": "d8yHbIBPDMOB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e40f1b98-e733-4f1c-e99e-4d7cb99b9634"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "4/4 [==============================] - 1s 101ms/step - loss: 0.6334 - binary_accuracy: 0.7046 - val_loss: 0.5854 - val_binary_accuracy: 0.8889\n",
            "Epoch 2/8\n",
            "4/4 [==============================] - 0s 23ms/step - loss: 0.6217 - binary_accuracy: 0.7511 - val_loss: 0.5769 - val_binary_accuracy: 0.8889\n",
            "Epoch 3/8\n",
            "4/4 [==============================] - 0s 19ms/step - loss: 0.6106 - binary_accuracy: 0.7806 - val_loss: 0.5685 - val_binary_accuracy: 0.8889\n",
            "Epoch 4/8\n",
            "4/4 [==============================] - 0s 22ms/step - loss: 0.5993 - binary_accuracy: 0.7975 - val_loss: 0.5607 - val_binary_accuracy: 0.8889\n",
            "Epoch 5/8\n",
            "4/4 [==============================] - 0s 17ms/step - loss: 0.5881 - binary_accuracy: 0.7932 - val_loss: 0.5530 - val_binary_accuracy: 0.8889\n",
            "Epoch 6/8\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 0.5775 - binary_accuracy: 0.8143 - val_loss: 0.5448 - val_binary_accuracy: 0.8889\n",
            "Epoch 7/8\n",
            "4/4 [==============================] - 0s 18ms/step - loss: 0.5664 - binary_accuracy: 0.8228 - val_loss: 0.5366 - val_binary_accuracy: 0.8889\n",
            "Epoch 8/8\n",
            "4/4 [==============================] - 0s 26ms/step - loss: 0.5555 - binary_accuracy: 0.8228 - val_loss: 0.5287 - val_binary_accuracy: 0.8889\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 4))\n",
        "plt.title('Model performance')\n",
        "plt.plot(np.arange(1, 9), history.history['loss'], label='Loss', lw=3)\n",
        "plt.plot(np.arange(1, 9), history.history['binary_accuracy'], label='Accuracy', lw=3)\n",
        "plt.plot(np.arange(1, 9), history.history['val_binary_accuracy'], label='Validation accuracy', lw=3)\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "sLN-SJIq8cU1",
        "outputId": "c59f1a71-6f5c-4fb3-df69-afe7e9d21453"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3d5031bdc0>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ejercicio 5.3. Evaluación del modelo de red neuronal\n",
        "\n",
        "1.   Realiza una predicción para el conjunto de datos reservados para test. \n"
      ],
      "metadata": {
        "id": "iurIOxPdEoLN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pred = neural_network.predict(X_test)\n",
        "pred = np.round(pred)"
      ],
      "metadata": {
        "trusted": true,
        "id": "MwjKdRJm55GM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1680a2ca-b7bf-41ea-bdf2-8c332792d640"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 3ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "2.   Utiliza la función ```informe_modelo``` para mostrar las diferentes métricas para validar el modelo. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "r6YxrBjpCXnr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#mostrar informe\n",
        "informe_modelo(y_test, pred)"
      ],
      "metadata": {
        "id": "ddSMF_AkETZU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 753
        },
        "outputId": "683c3cec-1bdb-46bc-d719-610c19a7547a"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Result:\n",
            "================================================\n",
            "1.1 Reporte de clasificacion:\n",
            "                   0          1  accuracy  macro avg  weighted avg\n",
            "precision   0.833333   0.900000  0.863636   0.866667      0.866667\n",
            "recall      0.909091   0.818182  0.863636   0.863636      0.863636\n",
            "f1-score    0.869565   0.857143  0.863636   0.863354      0.863354\n",
            "support    33.000000  33.000000  0.863636  66.000000     66.000000\n",
            "================================================\n",
            "1.2 Matriz de confusion\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEKCAYAAAA/2c+EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAY40lEQVR4nO3de5xVZb3H8c93ZiAJ5WIgIVpokqalqKByLPOSop5jqalJHjP0RDdTux2xLLUrvvLSsUMWJt4Okpia5umkRCiaVxAUEAVDMwglFQQMZGb4nT/2GtrSzOw9M/vy7OH77rVes/faez37N0pffj57rWcpIjAzs/TUVbsAMzNrnQPazCxRDmgzs0Q5oM3MEuWANjNLlAPazCxRDmgzsxKStI2kxyQ9KWmhpEuy/btIelTSc5JukdSz0FgOaDOz0noTODwi9gGGA0dLOgi4FLgyInYDVgFnFRrIAW1mVkKRsy572iPbAjgc+FW2/wbg+EJjNZSlwhLote/ZvsTR/smzMy6vdgmWoHdt/zZ1dYyOZM6GeRM/C4zL2zUpIia1PJFUD8wBdgMmAn8CVkdEU/aWZcCQQp+TbECbmaUqC+NJ7bzeDAyX1A+4A9ijM5/jgDYzA1DpZ3wjYrWkmcAooJ+khqyL3glYXuh4z0GbmQHU1Re/tUPSwKxzRlIv4EhgETATOCl72xnAnYVKcgdtZgagLk9jtxgM3JDNQ9cB0yLibklPA7+U9D1gLnBtoYEc0GZmULIpjoh4Cti3lf1LgQM6MpYD2swMStlBl4wD2swMyvIlYVc5oM3MwB20mVmyCpydUQ0OaDMz8BSHmVmyPMVhZpYod9BmZolyQJuZJareXxKamaXJc9BmZonyFIeZWaLcQZuZJcodtJlZotxBm5klypd6m5klylMcZmaJ8hSHmVmi3EGbmSXKAW1mlih/SWhmlijPQZuZJcpTHGZmiXIHbWaWJjmgzczS5IA2M0uU6hzQZmZJcgdtZpYoB7SZWaIc0GZmqUovnx3QZmaQZged3qUzZmZVUFdXV/TWHkk7S5op6WlJCyWdm+2/WNJySfOy7dhCNbmDNjOjpB10E/DViHhC0nbAHEnTs9eujIjLih3IAW1mBiWbg46IFcCK7PFaSYuAIZ0Zy1McZmbkOuhitw6MORTYF3g023W2pKckTZbUv9DxDmgzMzoW0JLGSZqdt41rZbxtgduA8yJiDXA18B5gOLkO+/JCNXmKw8yMjl3qHRGTgEltjiX1IBfOUyLi9uyYl/Nevwa4u9DnOKDNzCjdl4TKDXQtsCgirsjbPzibnwY4AVhQaCwHtJkZJT2L42DgdGC+pHnZvm8AYyQNBwJ4AfhsoYEc0GZmlC6gI+JBWj8n5LcdHcsBbWZGmlcSOqDNzMBrcZiZparQJdzV4IA2M8NTHGZm6Uovnx3QqXlbzwZ+f+159OzZQEN9PXf8fi7f+9lvefeO7+CmCWPZvm9v5i56kTMvvJHGpuZql2tVsm7tGq744cW88KfnQOJr3/wOe35gn2qXVdPcQVtBb25s4uhxV/HG+o00NNTxh8lf4d4/Ps05/344P5kyk1vvmcNV3zyVT58wimtufbDa5VqV/PTKSxlx0MF8+wdX0NjYyJsb1le7pJqXYkCnNytuvLF+IwA9GuppaKgnIvjwyPdy++/nAjDlN49y3KHulrZWb6xby/x5czjmuBMB6NGjB9tu16fKVdW+ciyW1FXuoBNUVyceuvl83rPzQH5+yyyWLnuF19eup7l5EwDLX17Fjjv0rXKVVi0r/rqcvv2250ff+xZLlyxm2B7v4wtfPp9evd5e7dJqWkfW4qiUinfQksa289rmFaKaXllYybKSsmlTcNCpE9ht9IWMeP+72X3ooGqXZAlpbm5myeJFHHfiKfzsxmls06sXt9w4udpl1bwUO+hqTHFc0tYLETEpIkZExIiGAXtVsqYkvb5uPffPXsyBe+9C3+16UV+f+9c1ZFB//rry9SpXZ9UycIdBDBw4iPfttTcAhxx2JEsWL6pyVbVvqwnobEHq1rb5gNvBdgzovy19t+0FwDZv68ERB+7BM8+/zKzZiznxI/sCcNpxB3L3fU9Vs0yrou3fMYCBgwbxlz8/D8Dc2Y/y7qG7Vrmq2icVv1VKueagBwGjgVVb7BfwUJk+s1t454A+XPOd06mvq6OuTtw2/Qn+74EFLFq6gpsmjOWiL/wbTz77F67/9cPVLtWq6ItfuYAfXnwBTY2NDB6yE1/75nerXVLNS/EsjnIF9N3AthExb8sXJN1Xps/sFhYs+Sujxlz6T/tfWP4qHzq96HtNWje323v34KfX/bLaZXQrdQl+SViWgI6Is9p57ZPl+Ewzs65IsIH2aXZmZrAVddBmZrXGHbSZWaK2pi8JzcxqSoL57IA2MwMv2G9mlix30GZmifIctJlZohLMZwe0mRm4gzYzS1aC+eyANjMDX0loZpYsT3GYmSUqwXx2QJuZQZoddMFLZySdK6mPcq6V9ISkoypRnJlZpaR4R5Virm08MyLWAEcB/YHTgQllrcrMrMLq6lT0VinFTHG0VHMscFNELFSK/y1gZtYFKcZaMR30HEn3kgvoeyRtB2wqb1lmZpVVqrt6S9pZ0kxJT0taKOncbP/2kqZLWpL97F+opmIC+ixgPDAyIv4O9ATGFnGcmVnNKOEcdBPw1YjYEzgI+KKkPcnl6IyIGAbMyJ63q5iADmBP4JzseW9gmyKOMzOrGaXqoCNiRUQ8kT1eCywChgAfA27I3nYDcHyhmooJ6J8Co4Ax2fO1wMQijjMzqxkd6aAljZM0O28b1/qYGgrsCzwKDIqIFdlLLwGDCtVUzJeEB0bEfpLmAkTEKkk9i/mFzcxqRUfOzoiIScCk9t4jaVvgNuC8iFiT33lHREiKQp9TTEA3SqonN9WBpIH4S0Iz62bqSngWh6Qe5MJ5SkTcnu1+WdLgiFghaTCwsmBNRXzWVcAdwA6Svg88CPygk3WbmSWpVF8SZqchXwssiogr8l66Czgje3wGcGehmgp20BExRdIc4Ahy50QfHxGLCh1nZlZLSnge9MHkLuibL2letu8b5C7wmybpLODPwCmFBmozoCVtn/d0JTA1/7WIeK0ThZuZJalUFwhGxIP84wK/LR3RkbHa66DnkJt3zv+glucB7NqRDzIzS1lNrQcdEbtUshAzs2pSm01v9RS13Gh2SeIw8i5QiYhZ5SrKzKzSEmygCwe0pP8AzgV2AuaRu3TxYeDw8pZmZlY5tbpY0rnASODPEXEYuatiVpe1KjOzCktxPehipjg2RMSG7Br0t0XEM5J2L3tlZmYVVMoLVUqlmIBeJqkf8GtguqRV5M7hMzPrNmrqLI4WEXFC9vBiSTOBvsDvylqVmVmFJdhAF30WxweBYRFxXbYWxxDg+bJWZmZWQTU5xSHpImAEsDtwHdAD+B9ylzOamXUL6cVzcR30CeTO3GhZgPqv2W2vzMy6jRRPsysmoDfmr10qqXeZazIzq7gEvyMsKqCnSfo50E/SZ8jdo/AX5S3LzKyyavUsjsskHQmsITcP/a2ImF72yszMKqjmpjiyO6n0zwJ5enarq09LWhQR76tIhWZmFZBgA932pd6STgVeA56SdL+ko4ClwDHAaRWqz8ysIkp1V+9Saq+DvhDYPyKek7QfuQWSToqI31SmNDOzykmwgW43oDdGxHMAEfGEpCUOZzPrruoTnONoL6B3kPSVvOf98p9vcTNEM7OaVmtfEl4DbNfOczOzbiPBfG73lleXVLIQM7Nqqsm1OMzMtgYJ5nO6Ab3q8f+udgmWoP4nXl3tEixB6+/6fJfHqLU5aDOzrUZ9ggFd8J6Eks6V1Ec510p6Irtoxcys26hT8VvFairiPWdGxBrgKKA/cDowoaxVmZlVWIoBXcwUR0s5xwI3RcRCpThZY2bWBSnGWjEBPUfSvcAuwAXZYv2byluWmVllJXghYVEBfRYwHFgaEX+X9A5gbHnLMjOrrAQb6KLmoAPYEzgne94b2KZsFZmZVUGDVPRWKcUE9E+BUcCY7PlaYGLZKjIzqwKp+K1SignoAyPii8AGgIhYBfQsa1VmZhVWJxW9FSJpsqSVkhbk7btY0nJJ87Lt2II1FVF3Y3ZnlZabxg7EXxKaWTdT4g76euDoVvZfGRHDs+23hQYpJqCvAu4gt/zo94EHgR8UVaKZWY0o5XnQETGL3B2puqTQPQnrgOeB/wSOIHdO9PERsairH2xmlpKOLNgvaRwwLm/XpIiYVMShZ0v6FDAb+Go2ZdymdgM6IjZJmhgR+wLPFPHhZmY1qSPnQWdhXEwg57sa+C656eLvApcDZ7ZbUxGDzpD0cV89aGbdmTrwv86IiJcjojkiNpG7AcoBhY4pJqA/C9wKbJS0NtvWdKpCM7NElXstDkmD856eACxo670tCl5JGBG+zZWZdXulvNRb0lTgUGCApGXARcChkoaTm+J4gVzz266i1oOW9FHgkOzpfRFxdydqNjNLVilncSNiTCu7r+3oOAUDWtIEYCQwJdt1rqSDI+KCjn6YmVmq6ouZ8K2wYjroY4Hh2cQ2km4A5gIOaDPrNmr5prH9+MdJ133LVIuZWdXU6nKjPwTmSppJ7kKVQ4DxZa3KzKzCEmygizqLY6qk+8jNQwOcHxEvlbUqM7MKq+vk+c3l1GZAS9pvi13Lsp87StoxIp4oX1lmZpVVax305e28FsDhJa7FzKxqGhKchG4zoCPisEoWYmZWTbXWQW8m6f3kbnu1+VZXEXFjuYoyM6u0mjzNTtJF5C5Z3BP4LXAMuTWhHdBm1m0kmM9FLZZ0Erm1oF+KiLHAPvhcaDPrZuo6sFVKMVMc67N1oZsk9QFWAjuXuS4zs4qqySkOYLakfuTWL50DrAMeLmtVZmYVVlMBLWkicHNEfCHb9TNJvwP6RMRTFanOzKxC0ovn9jvoxcBl2SLT04CpETG3MmWZmVVWgg102/PdEfFfETEK+DDwKjBZ0jOSLpL03opVaGZWAZKK3iql4BeSEfHniLg0u3HsGOB4wHf1NrNuJcWzOAp+lqQGScdJmgL8H/AscGLZKzMzq6A6qeitUtr7kvBIch3zscBjwC+BcRHxRoVqMzOrmEpOXRSrvS8JLwBuBr4aEasqVI+ZWVUkeMerdhdL8mp1ZrbVqLUO2sxsq5FePDugzcwAqHcHbWaWpgTz2QFtZgagBCc5HNBmZriDNjNLVk3d1dvMbGviDtrMLFE1tR60mdnWpC69fHZAm5lBmmdxpHj5uZlZxUnFb4XH0mRJKyUtyNu3vaTpkpZkP/sXGscddMLefPNNxn7qNBo3bqSpuZkjjxrNF84+p9plWYXtNKA3vzjvCHbo14sAJt/zNBN/M5+bvn4kw4b0A6Bf756sfmMjB513a3WLrWEl7qCvB/4buDFv33hgRkRMkDQ+e35+e4M4oBPWs2dPfjH5Bt7euzeNjY18+vRP8sEPHcLe+wyvdmlWQU3NwfjJDzFv6Sts26sHD11xEjPmLeP0H03f/J4JZ47i9Tc2VrHK2lfKOeiImCVp6Ba7PwYcmj2+AbiPAgHtKY6ESeLtvXsD0NTURFNTU5rnAllZvbTq78xb+goA69Y38syyVez4jt5vec/HD96NabOeq0Z53UZHFuyXNE7S7LxtXBEfMSgiVmSPXwIGFTqgbB20pD3I/Y0xJNu1HLgrIny7rA5obm5mzMkn8uKLL/KJMZ9k7733qXZJVkXv2mE7hu86gMeffXnzvoP3GszLq//On1a8XsXKal9HWp+ImARM6uxnRURIikLvK0sHLel8cndgEbm7sTyWPZ6azb20ddzmv5WuvabTv3u3Ul9fz7Tb7+TeP9zPgvlPsWTJ4mqXZFXSe5sGpo4fzdd/8UfWrm/cvP+UQ4Zx6wPunruqAre8elnSYIDs58pCB5Srgz4L2CsiGvN3SroCWAhMaO2g/L+VNjRR8G+XrUmfPn0YecCBPPTgAwwb5puqb20a6uuYOn40t9y/mDsffn7z/vo68bFRu3Dwl39Vxeq6hwpMHt4FnEEu/84A7ix0QLnmoDcBO7ayf3D2mhXhtddeY82aNQBs2LCBRx5+iKG77FrlqqwafvalQ3l22WquuvOpt+w/fPhOLF62muWv+lahXaYObIWGkqYCDwO7S1om6SxywXykpCXAR2ijUc1Xrg76PGBGVshfsn3vAnYDzi7TZ3Y7r/xtJRd+YzybNjWzaVNw1Oij+fChh1W7LKuwf3nfOznt8N2Z/8KrPPLjkwG46KZHuWfOi5z8od2YNmtJlSvsHkp5qXdEjGnjpSM6Mo4iyjOTIKkOOIC3fkn4eEQ0F3O8pzisNf1PvLraJViC1t/1+S6n6+NLXy86c0bu2rcip1OV7SyOiNgEPFKu8c3MSirBM1h9oYqZGWmuxeGANjMjzWvAHNBmZiQ5w+GANjOD3NIKqXFAm5nhKQ4zs2QlmM8OaDMzIMmEdkCbmeHT7MzMkuU5aDOzRDmgzcwS5SkOM7NEuYM2M0tUgvnsgDYzA5JMaAe0mRmlXbC/VBzQZmYk2UA7oM3MgCQT2gFtZoZPszMzS1aCU9AOaDMzSHKGwwFtZgZesN/MLFkJ5rMD2swMPMVhZpauBBPaAW1mhk+zMzNLluegzcwSVeeANjNLVXoJ7YA2M8NTHGZmyUownx3QZmZQ2g5a0gvAWqAZaIqIEZ0ZxwFtZkZZLvU+LCJe6coADmgzM9Kc4qirdgFmZimQOrJpnKTZedu4LYYL4F5Jc1p5rWjuoM3M6NiVhBExCZjUzls+GBHLJe0ATJf0TETM6mhN7qDNzCA3x1HsVkBELM9+rgTuAA7oTEkOaDMzSpfPknpL2q7lMXAUsKAzNXmKw8wMqCvdWRyDgDuys0IagJsj4nedGcgBbWZG6c6DjoilwD6lGMtTHGZmiXIHbWaG1+IwM0uWF+w3M0uUO2gzs0Q5oM3MEuUpDjOzRLmDNjNLVIL57IA2MwOSTGgHtJkZJb3Uu2QUEdWuwQqQNC5b3tBsM/+56P58qXdt6PSC39at+c9FN+eANjNLlAPazCxRDuja4HlGa43/XHRz/pLQzCxR7qDNzBLlgDYzS5QDOmGSJktaKalTN5y07kvS0ZKelfScpPHVrsfKwwGdtuuBo6tdhKVFUj0wETgG2BMYI2nP6lZl5eCATlhEzAJeq3YdlpwDgOciYmlEbAR+CXysyjVZGTigzWrPEOAvec+XZfusm3FAm5klygFtVnuWAzvnPd8p22fdjAParPY8DgyTtIuknsCpwF1VrsnKwAGdMElTgYeB3SUtk3RWtWuy6ouIJuBs4B5gETAtIhZWtyorB1/qbWaWKHfQZmaJckCbmSXKAW1mligHtJlZohzQZmaJckBv5STNlDR6i33nSbq6nWPukzSi/NWBpIslLZc0T9ICSR/twlhDW1YGlDRC0lUF3r+us59lVgoOaJtK7kKHfKdm+0siW32tK66MiOHAycBkSW/5cyupoaMDRsTsiDini3WZlZUD2n4F/Gt2RRqShgI7Ag9IulrSbEkLJV3S2sGSxkian3W3l+btXyfpcklPAqMk/bukx7JO+OeS6rPt+uzY+ZK+3F6hEbEIaAIGZF38jyXNBs6VtL+k+yXNkXSPpMFZHftLejKr44t59R0q6e7s8baSrstqeErSx/Pe9/3s+EckDWr5ZyTpD9l7Z0h6V7b/5Ox3eVLSrI7+izDbkgN6KxcRrwGPkVtbGHLd87TIXcH0zYgYAewNfFjS3vnHStoRuBQ4HBgOjJR0fPZyb+DRiNgHeBX4BHBw1gk3A6dlxwyJiPdHxAeA69qrVdKBwCbgb9munll9VwE/AU6KiP2BycD3s/dcB3wpq6Mt3wJej4gPRMTewB/yfodHsmNnAZ/J9v8EuCF775Ts8wG+DYzO3t/pqRizFg5og7dOc+RPb5wi6QlgLrAXucXh840E7ouIv2WXH08BDsleawZuyx4fAewPPC5pXvZ8V2ApsKukn0g6GljTRn1fzo67DPhE/OPy11uyn7sD7wemZ++7ENhJUj+gX7auNsBNbYz/EXIL4AMQEauyhxuBu7PHc4Ch2eNRwM15Y34we/xH4HpJnwG6Oq1jRofn7qxbuhO4UtJ+wNsjYo6kXYCvASMjYpWk64FtOjDmhohozh6LXMd5wZZvkrQPMBr4HHAKcGYrY10ZEZe1sv+NvPEXRsSoLcbu14F6W9OY95dBMwX+/xIRn8u6/H8F5kjaPyJe7WINthVzB21ExDpgJrmpgZbuuQ+5AHw9m3s9ppVDHyM39TEg+yJwDHB/K++bAZwkaQcASdtLerekAUBdRNxGruvdr5O/wrPAQEmjsvF7SNorIlYDqyW1dLintXH8dN46P92/wOc9xD/+i+M04IHsuPdExKMR8W1y0zA7t3G8WVHcQVuLqcAdZMETEU9Kmgs8Q+7uHX/c8oCIWJHdsHQmuS72fyPizlbe97SkC4F7szMwGskF4nrguryzMv6pwy5GRGyUdBJwlaS+5P5c/xhYCIwld+ZHAPe2McT3gInZKXjNwCXA7e185Jeyur9OLojHZvt/JGkYuX8WM4AnO/P7mLXwanZmZonyFIeZWaIc0GZmiXJAm5klygFtZpYoB7SZWaIc0GZmiXJAm5kl6v8Bv0MxNoRgivoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================\n",
            "1.3 Curva ROC y área AUC\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}