PPA_Trabajo_Práctico_N°6_Proyecto_3_ML_Juan_Manuel_Roccia.ipynb

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        "<a href=\"https://colab.research.google.com/github/JuanRoccia/Google-Colab/blob/main/PPA_Trabajo_Pr%C3%A1ctico_N%C2%B06_Proyecto_3_ML_Juan_Manuel_Roccia.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "### El siguiente Trabajo Práctico consiste en completar el notebook para resolver dos problemas de clasificación con Machine Learning Supervisado\n",
        "\n"
      ],
      "metadata": {
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      "source": [
        "## 1. Clasificación Múltiple con 'N' Features:  Regresión Logística con Scikit-Learn\n",
        "\n",
        "* En este Notebook vamos a resolver un problema de ***Clasificación Múltiple con las Regresión Logística usando la librería de Scikit-Learn*** para el caso en el que tengamos en nuestro Dataset ***más de dos Features (o atributos)***.\n",
        "\n",
        "*   DataSet: \"iris.csv\"\n",
        "\n",
        "* Para resolver el problema de Clasificación vamos a utilizar la clase **\"LogisticRegression()\"**, cuya documentación esta en el siguiente enlace:<br>\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
        "\n",
        "\n",
        "* El ejemplo con el que vamos a trabajar, consiste en ***clasificar tres tipos de flores, conociendo los tamaños (alto y ancho) del petalo y sépalo*** de la flor; es decir, que tendremos 4 atributos (o Features).\n",
        "\n",
        "\n",
        "* Al ser un problema ***de Clasificación múltiple obtenemos tantos modelos como clases tengamos que clasificar***, por lo que para esta caso tendremos que encontrar los parámetros $\\beta_0, \\beta_1, \\beta_2, \\beta_3, \\beta_4$ para ***tres funciones de decisión*** que sean capaces de discernir una clase con el resto:\n",
        "\n",
        "<span style=\"font-size:16px\">$$Flor\\;1 = \\beta^{1}_0 + \\beta^{1}_1 \\cdot SepalLenght + \\beta^{1}_2 \\cdot SepalWith + \\beta^{1}_3 \\cdot PetalLenght + \\beta^{1}_4 \\cdot PetalWith$$</span>\n",
        "\n",
        "<span style=\"font-size:16px\">$$Flor\\;2 = \\beta^{2}_0 + \\beta^{2}_1 \\cdot SepalLenght + \\beta^{2}_2 \\cdot SepalWith + \\beta^{2}_3 \\cdot PetalLenght + \\beta^{2}_4 \\cdot PetalWith$$</span>\n",
        "\n",
        "<span style=\"font-size:16px\">$$Flor\\;3 = \\beta^{3}_0 + \\beta^{3}_1 \\cdot SepalLenght + \\beta^{3}_2 \\cdot SepalWith + \\beta^{3}_3 \\cdot PetalLenght + \\beta^{3}_4 \\cdot PetalWith$$</span>\n",
        "    \n",
        "    \n",
        "* Aplicando estas funciones de decisión a una logística podremos saber la probabilidad de pertenencia de cada flor a cada una de las clases de la siguiente manera:\n",
        "\n",
        "<span style=\"font-size:16px\">$$P(Y=Flor\\;1|SepalLenght,SepalWith,PetalLenght,PetalWith) = \\frac{1}{1 + e^{-(\\beta^{1}_0 + \\beta^{1}_1 \\cdot SepalLenght + \\beta^{1}_2 \\cdot SepalWith + \\beta^{1}_3 \\cdot PetalLenght + \\beta^{1}_4 \\cdot PetalWith)}}$$</span>\n",
        "\n",
        "<span style=\"font-size:16px\">$$P(Y=Flor\\;2|SepalLenght,SepalWith,PetalLenght,PetalWith) = \\frac{1}{1 + e^{-(\\beta^{2}_0 + \\beta^{2}_1 \\cdot SepalLenght + \\beta^{2}_2 \\cdot SepalWith + \\beta^{2}_3 \\cdot PetalLenght + \\beta^{2}_4 \\cdot PetalWith)}}$$</span>\n",
        "\n",
        "<span style=\"font-size:16px\">$$P(Y=Flor\\;3|SepalLenght,SepalWith,PetalLenght,PetalWith) = \\frac{1}{1 + e^{-(\\beta^{3}_0 + \\beta^{3}_1 \\cdot SepalLenght + \\beta^{3}_2 \\cdot SepalWith + \\beta^{3}_3 \\cdot PetalLenght + \\beta^{3}_4 \\cdot PetalWith)}}$$</span>\n",
        "    \n",
        "\n",
        "* ***Daremos como predicción para una nueva flor, aquella clase que mayor probabilidad dé***.\n",
        "\n",
        "\n",
        "* Para resolver este problema vamos a realizar los siguientes pasos:\n",
        "\n",
        "    1. [Carga de datos](#M1)\n",
        "\n",
        "    2. [Cambio de estructura de datos a numpy y transformación de las etiquetas a números](#M2)\n",
        "\n",
        "    3. [Visualización de los datos](#M3)\n",
        "\n",
        "    4. [Creacción del modelo (hipótesis) y ajuste](#M4)\n",
        "\n",
        "    5. [Obtención del modelo (hipótesis)](#M5)\n",
        "\n",
        "    6. [Cálculo de predicción](#M6)\n",
        "\n",
        "    7. [Graficar Matriz de confusión](#M7)\n",
        "\n",
        "\n",
        "* En primer lugar vamos a importar las librería necesarias:\n",
        "\n"
      ]
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      "source": [
        "# Importar librerias\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import math\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "# from sklearn.metrics import accuracy_score\n",
        "# from sklearn.metrics import precision_score\n",
        "# from sklearn.metrics import recall_score\n",
        "# from sklearn.metrics import f1_score\n",
        "\n",
        "import itertools\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Para leer datos de Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/Drive/')"
      ],
      "metadata": {
        "id": "ciwhWeD2K7An",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e0a7547-0ed5-4edf-8f6b-4c44b22ee91e"
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      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "Drive already mounted at /content/Drive/; to attempt to forcibly remount, call drive.mount(\"/content/Drive/\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Path de la carpeta donde tenemos los datos\n",
        "archivo = \"/content/Drive/MyDrive/PPA/Data/\""
      ],
      "metadata": {
        "id": "Qk1szIiZK8KV"
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      "execution_count": null,
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        "### 1. Carga de datos\n",
        "\n",
        "\n",
        "* El primer paso que vamos a realizar es el de cargar los datos que los tenemos en el siguiente fichero:\n",
        "    - ***DataSet***: \"iris.csv\"\n",
        "\n",
        "\n",
        "* Para ello ***leeremos el csv con pandas*** (pasandolo a un dataframe), teniendo las siguientes columnas:\n",
        "    - ***class***: Clase de la flor (Iris-versicolor, Iris-setosa)\n",
        "    - ***sepal_length***: El \"largo\" del Sepalo\n",
        "    - ***sepal_width***: El \"ancho\" del Sepalo\n",
        "    - ***petal_length***: El \"largo\" del Pétalo\n",
        "    - ***petal_width***: El \"ancho\" del Pétalo\n",
        "    \n"
      ]
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              "     sepal_length  sepal_width  petal_length  petal_width           class\n",
              "144           6.7          3.3           5.7          2.5  Iris-virginica\n",
              "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
              "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
              "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
              "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
              "149           5.9          3.0           5.1          1.8  Iris-virginica"
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              "      <th>144</th>\n",
              "      <td>6.7</td>\n",
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              "      <td>Iris-virginica</td>\n",
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              "      <td>Iris-virginica</td>\n",
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              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
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        }
      ],
      "source": [
        "# Carga de datos\n",
        "df = pd.read_csv(archivo + \"iris.csv\")\n",
        "df.tail(6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXrwRfB8JVex"
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      "source": [
        "### <a name=\"M2\">2. Cambio de estructura de datos a numpy y transformación de las etiquetas a números</a>\n",
        "\n",
        "\n",
        "* Scikit necesita como entrada para el ajuste (o entrenamiento) del modelo los datos del Dataset de la siguiente forma:\n",
        "<span></span><br><br>\n",
        "    - ***Variables de entrada X***: ***Array de Arrays de numpy*** en el que cada array tiene las variables de entrada de un elemento.\n",
        "<span></span><br><br>\n",
        "    - ***Variable de salida y*** (solo para aprendizaje supervisado): ***Array de numpy*** en el que cada posición del array contiene la salida o el valor esperado del elemento del Dataset.\n",
        "\n",
        "\n",
        "* Por tanto, vamos a pasar los datos las flores a una nueva estructura de datos para que scikit-learn puedan trabajar con los datos:\n",
        "<span></span><br><br>\n",
        "    - Las ***columnas sepal_length y sepal_width*** representan las variables de entrada y las vamos a pasar a un array de array (***numpy array***).\n",
        "<span></span><br><br>\n",
        "    - La ***columna class*** que representa la clase (o variable de salida) y la vamos a pasar a un ***numpy array***.\n",
        "    \n",
        "    \n",
        "* Por otro lado tenemos que transformar la clase de la flor a números, por lo que utilizaremos la clase ***LabelEncoder()*** para transformar las etiquetas de las clases a números, siendo esta transformación:\n",
        "\n",
        "    - ***0***: ***Iris-Setosa***\n",
        "    - ***1***: ***Iris-Versicolor***\n",
        "    - ***2***: ***Iris-Virginica***"
      ]
    },
    {
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      "execution_count": null,
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              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
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      "source": [
        "# Estructura de datos a numpy\n",
        "x = df[['sepal_length', 'sepal_width', \n",
        "        'petal_length', 'petal_width']].values   # Obtengo el alto y ancho del sepalo\n",
        "y_labels = df['class'].values                    # Obtengo las etiquetas\n",
        "\n",
        "# Transformación de las etiquetas a números\n",
        "le = preprocessing.LabelEncoder()                # Instancio un objeto de la clase LabelEncoder()\n",
        "y  = le.fit_transform(y_labels)                  # Transformo las etiquetas a números\n",
        "\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dividir los datos en datos de entrenamiendo y test (X_train, X_test, y_train, y_test)\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2)"
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      "source": [
        "### <a name=\"M3\">3. Visualización de los datos</a>\n",
        "\n",
        "\n",
        "* A continuación vamos a ver la relación que hay entre cada par de atributos de los tres tipos de flores."
      ]
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# usar de seaborn la función pairplot\n",
        "sns.pairplot(df, hue=\"class\", markers=\"o\", palette=\"husl\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G2P1FlBZJVez"
      },
      "source": [
        "### <a name=\"M4\">4. Creación del modelo (hipótesis) y ajuste</a>\n",
        "\n",
        "\n",
        "* A continuación pasamos a crear un objeto de la clase \"***LogisticRegression()***\" para la resolución del problema de clasificación.\n",
        "\n",
        "\n",
        "* Una vez instanciado, procedemos a ajustar (entrenar) el modelo (o hipótesis) llamando al método ***fit(X,y)*** que recibe como parámetros el conjunto de datos de las variables de entrada 'X' y la variable de salida 'Y'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0atK5OdJVez",
        "outputId": "fbc00829-9b2f-4ba2-a4cb-06f826a644a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=50, multi_class='ovr', solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ],
      "source": [
        "# Creamos un objeto de la clase LogisticRegression pasandole una serie de parámetros\n",
        "# logReg = LogisticRegression(solver=\"liblinear\", multi_class=\"ovr\", max_iter=50)\n",
        "\n",
        "logReg = LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
        "          intercept_scaling=1, max_iter=50, multi_class='ovr', n_jobs=None,\n",
        "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
        "          verbose=0, warm_start=False)\n",
        "\n",
        "# Entrenamos el modelo siendo la 'X' el tamaño del sepalo e 'y' el tipo de flor\n",
        "logReg.fit(x_train, y_train)\n",
        "# logReg.fit(x,y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLEKKr2ZJVez"
      },
      "source": [
        "### <a name=\"M5\">5. Obtención del modelo (hipótesis)</a>\n",
        "\n",
        "\n",
        "* El objeto de la clase ***LogisticRegression*** nos proporciona los resultados de los parámetros $\\beta_n$ por medio de los atributos de la clase:\n",
        "<span></span><br><br>\n",
        "    - ***coef_***: Nos devuelve en una matriz los parámetros del $\\beta_1$ al $\\beta_n$ para cada una de las funciones de decisión.\n",
        "<span></span><br><br>\n",
        "    - ***intercept_***: Nos devuelve una lista con los parámetros ***$\\beta_0$*** para cada una de las funciones de decisión.\n",
        "    \n",
        "\n",
        "* Obteniendo estos valores podemos obtener el modelo generado:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3O7ikre0JVe0",
        "outputId": "894e96c0-ad3f-42a9-c694-d0c8f6f7c5c3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Iris-setosa: Z = 0.26 + 0.38 · sepal_length + 1.39 · sepal_width + -2.13 · petal_length + -0.93 · petal_width\n",
            "\n",
            "Iris-versicolor: Z = 0.94 + 0.54 · sepal_length + -1.68 · sepal_width + 0.42 · petal_length + -1.19 · petal_width\n",
            "\n",
            "Iris-virginica: Z = -1.14 + -1.66 · sepal_length + -1.17 · sepal_width + 2.26 · petal_length + 2.37 · petal_width\n"
          ]
        }
      ],
      "source": [
        "# Obtención del modelo\n",
        "b0 = logReg.intercept_\n",
        "b1 = logReg.coef_\n",
        "\n",
        "# Mostrarlo (print)\n",
        "print(\"\\nIris-setosa: Z = {b0:0.2f} + {b1:0.2f} · sepal_length + {b2:0.2f} · sepal_width + {b3:0.2f} · petal_length + {b4:0.2f} · petal_width\"\n",
        "      .format(b0=b0[0], b1=b1[0,0], b2=b1[0,1], b3=b1[0,2], b4=b1[0,3]))\n",
        "\n",
        "print(\"\\nIris-versicolor: Z = {b0:0.2f} + {b1:0.2f} · sepal_length + {b2:0.2f} · sepal_width + {b3:0.2f} · petal_length + {b4:0.2f} · petal_width\"\n",
        "      .format(b0=b0[1], b1=b1[1,0], b2=b1[1,1], b3=b1[1,2], b4=b1[1,3]))\n",
        "\n",
        "print(\"\\nIris-virginica: Z = {b0:0.2f} + {b1:0.2f} · sepal_length + {b2:0.2f} · sepal_width + {b3:0.2f} · petal_length + {b4:0.2f} · petal_width\"\n",
        "      .format(b0=b0[2], b1=b1[2,0], b2=b1[2,1], b3=b1[2,2], b4=b1[2,3]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OJZ26JT0JVe0"
      },
      "source": [
        "### <a name=\"M6\">6. Cálculo de Predicción</a>\n",
        "\n",
        "\n",
        "* A continuación vamos a ver como predecir la clase de una Flor usando la librería de Scikit con el método \".predict(flor)\" y como obtener esa misma predicción aplicando del modelo obtenido.\n",
        "\n",
        "\n",
        "* Para ello seleccionemos una flor del Dataset, que para este ejemplo vamos a seleccionar la flor:\n",
        "    - Class: Iris-Setosa\n",
        "    - Sepal_lenght: 4.9\n",
        "    - Sepal_With: 3.0\n",
        "    - Petal_lenght: 1.4\n",
        "    - Petal_With: 0.2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "okwIddLEJVe0",
        "outputId": "4aca77c2-b63b-4abb-e57b-071720a28cd2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Flor: X=[5.1 3.5 1.4 0.2] - y=0 - Clase = ['Iris-setosa']\n"
          ]
        }
      ],
      "source": [
        "# Valores de X y Y (X[n], Y[n])\n",
        "x_flor = x[0]\n",
        "y_flor = y[0]\n",
        "\n",
        "print('Flor: X={} - y={} - Clase = {}'.format(x_flor, y_flor, le.inverse_transform([y_flor])))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uqpmE4gJVe0"
      },
      "source": [
        "* A continuación vamos a ver cual es la predicción que nos devuelve Scikit-Learn.\n",
        "\n",
        "\n",
        "* Veamos también cuales son los parámetros de la función de decisión y cual es el resultado de esa función de decisión para cada clase:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eg6okBzlJVe1",
        "outputId": "953b626e-61da-4a6e-fdbb-38cad9ab2b81",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicción -> Pertenece a la clase [0]\n",
            "Predicción Etiquetada -> Pertenece a la clase ['Iris-setosa']\n",
            "Probabilidad de pertenencia a cada clase: \n",
            "\tIris-Setosa: 3.92\n",
            "\tIris-Versicolor: -1.85\n",
            "\tIris-Virginica: -10.09\n"
          ]
        }
      ],
      "source": [
        "# Predicción y función de decisión\n",
        "pred = logReg.predict([x_flor])\n",
        "prob = logReg.decision_function([x_flor])\n",
        "\n",
        "print('Predicción -> Pertenece a la clase {}'.format(pred))\n",
        "print('Predicción Etiquetada -> Pertenece a la clase {}'.format(le.inverse_transform(pred)))\n",
        "print('Probabilidad de pertenencia a cada clase: ' + \\\n",
        "      '\\n\\tIris-Setosa: {:0.2f}\\n\\tIris-Versicolor: {:0.2f}\\n\\tIris-Virginica: {:0.2f}' \\\n",
        "     .format(prob[0,0], prob[0,1], prob[0,2]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrOpraArJVe1"
      },
      "source": [
        "* A continuación vamos a calcular la función de decisión de esa flor para cada clase usando el modelo y posteriormente la probabilidad de pertenencia a cada clase:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ohtr3dalJVe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e809f52b-c071-4da3-c9af-1348b6c00517"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultado de funcion de decision de cada clase: \n",
            "\tIris-Setosa: 3.92\n",
            "\tIris-Versicolor: -1.85\n",
            "\tIris-Virginica: -10.09\n",
            "\n",
            "Probabilidad de pertenencia a cada clase: \n",
            "\tIris-Setosa: 0.98\n",
            "\tIris-Versicolor: 0.14\n",
            "\tIris-Virginica: 0.00\n"
          ]
        }
      ],
      "source": [
        "# Obtenemos los parámetros\n",
        "b0 = logReg.intercept_\n",
        "b1 = logReg.coef_\n",
        "\n",
        "# Función de decisión\n",
        "z_setosa = b0[0] + (b1[0,0] * x_flor[0]) + (b1[0,1] * x_flor[1]) + (b1[0,2] * x_flor[2]) + (b1[0,3] * x_flor[3])\n",
        "z_versicolor = b0[1] + (b1[1,0] * x_flor[0]) + (b1[1,1] * x_flor[1]) + (b1[1,2] * x_flor[2]) + (b1[1,3] * x_flor[3])\n",
        "z_virginica = b0[2] + (b1[2,0] * x_flor[0]) + (b1[2,1] * x_flor[1]) + (b1[2,2] * x_flor[2]) + (b1[2,3] * x_flor[3])\n",
        "\n",
        "\n",
        "# Imprimimos las funciones de decisión\n",
        "print('Resultado de funcion de decision de cada clase: ' + \\\n",
        "      '\\n\\tIris-Setosa: {:0.2f}\\n\\tIris-Versicolor: {:0.2f}\\n\\tIris-Virginica: {:0.2f}\\n' \\\n",
        "     .format(z_setosa, z_versicolor, z_virginica))\n",
        "\n",
        "# Calculamos las probabilidades\n",
        "prob_setosa = 1 / (1 + math.exp(-1 * (z_setosa)))\n",
        "prob_versicolor = 1 / (1 + math.exp(-1 * (z_versicolor)))\n",
        "prob_virginica = 1 / (1 + math.exp(-1 * (z_virginica)))\n",
        "\n",
        "# Imprimimos las probabilidades\n",
        "print('Probabilidad de pertenencia a cada clase: ' + \\\n",
        "      '\\n\\tIris-Setosa: {:0.2f}\\n\\tIris-Versicolor: {:0.2f}\\n\\tIris-Virginica: {:0.2f}' \\\n",
        "     .format(prob_setosa, prob_versicolor, prob_virginica))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KXP92STqJVe2"
      },
      "source": [
        "* Veamos como realizamos esos cálculos con detalle:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e7eSu3KYJVe2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2381df1f-e0c3-4bf4-c14f-f3b8fc34122f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Iris-Setosa \n",
            "  Z =  0.26 + 0.38·5.10 + 1.39·3.50 + -2.13·1.40 + -0.93·0.20 = 3.92\n",
            "  g(Z) =  1 / 1 + e^-(3.92) = 0.98\n",
            "\n",
            "Iris-Versicolor \n",
            "  Z =  0.94 + 0.54·5.10 + -1.68·3.50 + 0.42·1.40 + -1.19·0.20 = -1.85\n",
            "  g(Z) =  1 / 1 + e^-(-1.85) = 0.14\n",
            "\n",
            "Iris-Virginica \n",
            "  Z =  -1.14 + -1.66·5.10 + -1.17·5.10 + 2.26·3.50 + 2.37·0.20 = -10.09\n",
            "  g(Z) =  1 / 1 + e^-(-10.09) = 0.00\n"
          ]
        }
      ],
      "source": [
        "# Imprimimos por pantalla la información de cada cálculo\n",
        "print('\\nIris-Setosa ')\n",
        "print('  Z =  {:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} = {:0.2f}'.\n",
        "      format(b0[0], b1[0,0], x_flor[0], b1[0,1], x_flor[1], b1[0,2], x_flor[2], b1[0,3], x_flor[3], z_setosa))\n",
        "print('  g(Z) =  1 / 1 + e^-({:0.2f}) = {:0.2f}'.format(z_setosa, 1 / (1 + math.exp(-1 * z_setosa))))\n",
        "\n",
        "print('\\nIris-Versicolor ')\n",
        "print('  Z =  {:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} = {:0.2f}'.\n",
        "      format(b0[1], b1[1,0], x_flor[0], b1[1,1], x_flor[1], b1[1,2], x_flor[2], b1[1,3], x_flor[3], z_versicolor))\n",
        "print('  g(Z) =  1 / 1 + e^-({:0.2f}) = {:0.2f}'.format(z_versicolor, 1 / (1 + math.exp(-1 * z_versicolor))))\n",
        "\n",
        "print('\\nIris-Virginica ')\n",
        "print('  Z =  {:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} + {:0.2f}·{:0.2f} = {:0.2f}'.\n",
        "      format(b0[2], b1[2,0], x_flor[0], b1[2,1], x_flor[0], b1[2,2], x_flor[1], b1[2,3], x_flor[3], z_virginica))\n",
        "print('  g(Z) =  1 / 1 + e^-({:0.2f}) = {:0.2f}'.format(z_virginica, 1 / (1 + math.exp(-1 * z_virginica))))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M7\">7. Matriz de confusión</a>\n",
        "\n",
        "\n",
        "* Finalmente, calculamos y graficamos la matriz de confusión"
      ],
      "metadata": {
        "id": "9CzOfhiZky3w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Métricas con y_test y y_predict\n",
        "y_predict = logReg.predict(x_test)\n",
        "# y_real =    [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2]\n",
        "# y_predict = [1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 2, 1, 1, 0, 2]\n",
        "\n",
        "# classification_report\n",
        "print(classification_report(y_true=y_test, y_pred=y_predict))\n",
        "# print(classification_report(y_true=y_real, y_pred=y_predict))"
      ],
      "metadata": {
        "id": "owdFWZGxkcIb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "57636de7-d805-4c5c-f1e1-64c8ceee8941"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        10\n",
            "           1       1.00      0.73      0.84        11\n",
            "           2       0.75      1.00      0.86         9\n",
            "\n",
            "    accuracy                           0.90        30\n",
            "   macro avg       0.92      0.91      0.90        30\n",
            "weighted avg       0.93      0.90      0.90        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matriz de confusión\n",
        "def plot_confusion_matrix(cm, classes, title, cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], 'd'), horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "    \n",
        "plt.figure()\n",
        "matriz_confusion = confusion_matrix(y_true=y_test, y_pred=y_predict, labels=[0, 1, 2])\n",
        "plot_confusion_matrix(matriz_confusion, classes=['Clase 0', 'Clase 1', 'Clase 2'], title='Matriz de Confusión')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TGVqpo8MlkXz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "40f9363e-145a-494d-b9a5-6c4f1cf2f337"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Clasificación de pacientes con enfermedades coronarias\n",
        "\n",
        "* El objetivo de este ejercicio es el de obtener un modelo capaz de predecir si un paciente tiene o no una enfermedad coronaria.\n",
        "\n",
        "*   **DataSet**: \"heart_disease.csv\"\n",
        "\n",
        "* Sobre el Dataset se tienen las siguientes características:\n",
        "\n",
        "    - age: Edad en años\n",
        "    - sex: (1 = hombre; 0 = mujer)\n",
        "    - cp: Tipo de dolor en el pecho\n",
        "    - trestbps: Presión arterial\n",
        "    - chol: Colesterol\n",
        "    - fbs: (azúcar en sangre en ayunas> 120 mg / dl) (1 = verdadero; 0 = falso)\n",
        "    - restecg: Resultados electrocardiográficos en reposo\n",
        "    - thalach: Frecuencia cardíaca máxima alcanzada\n",
        "    - exang: Angina inducida por ejercicio (1 = sí; 0 = no)\n",
        "    - oldpeak: Depresión del ST inducida por el ejercicio relativo al descanso\n",
        "    - slope: Pendiente del segmento pico del ejercicio ST\n",
        "    - ca: Número de vasos principales (0-3) coloreados por flourosopía\n",
        "    - thal: 3 = normal; 6 = defecto fijo; 7 = defecto reversible\n",
        "    - target: 1 = Enfermo, 0 = Sano\n",
        "\n",
        "\n",
        "* En este ejercicio se le pide que genere un modelo que sea capaz de **clasificar** a un paciente indicando si sufre o no una enfermendad coronaria.\n",
        "\n",
        "* Para ello hay que tomar las variables de entrada que se consideren y como variable de salida el target.\n",
        "\n",
        "* Para resolver este problema vamos a realizar los siguientes pasos:\n",
        "\n",
        "    1. [Carga de datos](#M8) \n",
        "    2. [***Visualización de los datos***, de esta manera podremos seleccionar las variables que consideremos más relevantes visualmente.](#M9)\n",
        "    3. [***Cambio de estructura de datos a numpy y transformación de las etiquetas***.](#M10)\n",
        "    4. [***Creación del modelo (hipótesis) y ajuste***.](#M11)\n",
        "    5. [***Obtención del modelo (hipótesis)***.](#M12)\n",
        "    6. [***Acierto del Modelo***. Obtener el porcentaje de acierto del modelo.](#M13)\n",
        "    7. [Matriz de confusión](#M14)"
      ],
      "metadata": {
        "id": "0dpsxq9zg6Hi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M8\">1. Carga de datos</a>\n",
        "\n",
        "* El primer paso a realizar es el de cargar los datos que los tenemos en el siguiente fichero:\n",
        "    - ***DataSet***: \"heart_disease.csv\" *\n",
        "\n",
        "\n",
        "* Para ello ***leeremos el csv con pandas*** (pasandolo a un dataframe), con sus correspondientes columnas."
      ],
      "metadata": {
        "id": "3pJnApqbh2l-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Carga de datos\n",
        "df = pd.read_csv(archivo + \"heart_disease.csv\")\n",
        "df.head(6)"
      ],
      "metadata": {
        "id": "EpKtcPWYhLrr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c4c5d4a-3112-45e9-97cc-a5120c9ea5ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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",
              "5   57    1   0       140   192    0        1      148      0      0.4      1   \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  \n",
              "5   0     1       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bf9a5d5d-d464-40d2-8417-4ce650f35398\">\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>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>192</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>148</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bf9a5d5d-d464-40d2-8417-4ce650f35398')\"\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",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-bf9a5d5d-d464-40d2-8417-4ce650f35398 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-bf9a5d5d-d464-40d2-8417-4ce650f35398');\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": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M9\">2. Visualización de los datos</a>\n",
        "\n",
        "*  De esta manera podremos seleccionar las variables que consideremos más relevantes visualmente."
      ],
      "metadata": {
        "id": "zX4HOWDmrWmq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lj0cVI47IaCB",
        "outputId": "78f9d380-95ed-4304-e3d0-e409d6072645"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "6S7k58k5Jmzq",
        "outputId": "73540e2c-91c9-4bd3-b029-be1ffd2c0a6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              age         sex          cp    trestbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    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  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1180afac-7679-476f-8aff-d51279d6f28d\">\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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</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>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</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>55.000000</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>153.000000</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>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</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>77.000000</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-1180afac-7679-476f-8aff-d51279d6f28d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\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",
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              "\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",
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              "    }\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-1180afac-7679-476f-8aff-d51279d6f28d 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-1180afac-7679-476f-8aff-d51279d6f28d');\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": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# recuento de enfermedades del corazón\n",
        "df['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Toh4sF2FrXBP",
        "outputId": "40b4cc4e-d433-4cc4-a970-98cb90da3dd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    165\n",
              "0    138\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['target'].value_counts().plot(kind='bar', color = ['cyan', 'crimson'], grid = True);\n",
        "plt.xticks(rotation = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "Oe1XBYKNmGi5",
        "outputId": "ccaf0f89-c486-4f5c-fd1e-3664940f5bc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 1]), <a list of 2 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 172
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEDCAYAAAAx/aOOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAN6UlEQVR4nO3dfYwc9XnA8e8dpjbGrmLMqT1ERRJCnhIFCQoEhUIF6ZuKVF4KVFErExU1SEEtKKSBVNBCQ18UQAIlpFVK0kY0qgpxVUgTpalI6wqSJhBTQ4nEEwUBSWpLPa6Q2gY74Lv+sXvVPsva3jNzN7vn7+efWc/8dvd39srf25nZnYn5+XkkSVow2fYEJEmjxTBIkgrDIEkqDIMkqTAMkqRiVdsTeINWA2cCO4B9Lc9FksbFEcA08Biwt3/juIfhTODhtichSWPqXOCR/pXjHoYdAC++uJu5OT+P0YSNG9cxO7ur7WlIr+NrszmTkxNs2HA0dP8P7TfuYdgHMDc3bxga5N+lRpWvzcYN3AXvwWdJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQV4/45hrFx9MajWTs5Hh2emlrf9hQO6uW5OXbP7m57GtKKZBiWydrJSSbansQKMj85iVmQlsZ4/AorSVo2hkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSMdR3JUXENHAtcBZwBrAOOD8zt/SNew44YcBDfCwzP9I39k3AbcAlwFrgm8B1mbltUT+BJKlRw36JXgA3AN8FngTOPsDYrcBdfeueKg8WMQl8CTgFuAOYBa4GtkTE6Zn5zJDzkiQ1bNgwbAWOzczZiLgY+IcDjP1BZn7uII93GZ24XJKZDwBExP3Ad4CbgSuGnJckqWFDHWPIzJ2ZOTvsg0bE6ohYe4AhlwHbgQd7nmMGuB+4OCKOHPa5JEnNWoqDz78E7AZ2R8QzEXHVgDGnAVszc75v/aPAeuBtSzAvSdIQmg7Dk3R2BV0KvB94AfhURHykb9w0sGPA/RfWHdfwvCRJQ2r0Cm6ZeWHvnyPir4FHgD+IiL/IzB92Nx0F7B3wEHt6tg9t48Z1i52qVoBxuASpmuW/+fJY0kt7Zua+iLgL+Dvg3cA/dTe9AqwecJc1PduHNju7i7m5/r1So8UXdPNmZna2PQUto6mp9f6bN2RycuKAv1Avxwfcvt9dHtOzbged3Un9FtZtX9IZSZL2aznC8NbucqZn3Tbg9IiY6Bt7FrCLzuclJEktaCwMEXFM94NrvevWAB8GdgL/3rNpM50DzBf1jD0WuBx4MDNfbWpekqTFGfoYQ0Tc1L15cne5KSLOAV7KzLuBC4EbI2Iz8BywEXgf8HbgA5m5q+fhNgPfAO6NiDvonL10NZ1Q3XLIP40k6Q1bzMHnW/v+fGV3+TxwN/CfwNPAJmCKzllHjwMfyswv9t6xe1D6AuB24Bo6ZyE9ClyRme5GkqQWTczPj/bZPAfxZuDZcTkrqf+Aig7dPJ6VdLjxrKTm9JyV9BY6e3jq9uWekCRptBkGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEnFkn67qqTRt+HoI1m1ds3BB46AcfiW4tde3sOLu8f7W30Mg3SYW7V2Dc9Mndv2NFaME2cehjEPg7uSJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSsWqYQRExDVwLnAWcAawDzs/MLQPGXgjcArwD+G/gM8CfZOZrfePeBNwGXAKsBb4JXJeZ2w7xZ5EkNWDYdwwB3AAcDzy530ERvwI8APwP8Lvd238I3Nk3bhL4EvBe4BPA9cBPAFsi4sTF/QiSpCYNG4atwLGZeRJw+wHG3QH8B/DLmXlPZl4D/BlwdUSc1DPuMuBs4IrM/GhmfhI4D5gHbl7kzyBJatBQYcjMnZk5e6AxEfEOOruPPpWZ+3o2/Xn3eS7tWXcZsB14sOc5ZoD7gYsj4sjhpi9JalqTB59P6y6/1bsyM7cDP+jZvjB2a2bO9z3Go8B64G0NzkuStAhNhmG6u9wxYNsO4Li+sfsbR99YSdIyGuqspCEd1V3uHbBtD50zj3rH7m9c72MNZePGdYsZrhViamp921OQBhr312aTYXilu1w9YNuanu0LY/c3jr6xBzU7u4u5uf69UqNl3F8oo2hmZmfbU1gRfG02b9Rfm5OTEwf8hbrJXUkLu4GmB2ybpnOwuXfs/sbRN1aStIyaDMPCB9PO6F0ZEcfR+fzDtr6xp0fERN9jnAXsAr7b4LwkSYvQWBgy89vA08BVEXFEz6YPAHPA3/es20znAPNFCysi4ljgcuDBzHy1qXlJkhZn6GMMEXFT9+bJ3eWmiDgHeCkz7+6u+zDwBeArEXEf8E7gd+h8tuE7PQ+3GfgGcG9E3AG8AFxNJ1S3HOLPIklqwGIOPt/a9+cru8vngbsBMvOLEfFrdD69/AlgBvjj/vtm5r6IuIDOp6ivoXMW0qN0PgntbiRJatHE/Pxon81zEG8Gnh2Xs5L6D6jo0M0z+md+jIupqfU8M3Vu29NYMU6ceXjkX5s9ZyW9BXjudduXe0KSpNFmGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVKxqskHi4jzgH/dz+aTM/PpnrFnA7cBPwP8L3Af8PuZ+XKTc5IkLU6jYehxF7C1b932hRsRcSrwVeDbwHXA8cDvAW8FfnWJ5iRJGsJSheHfMvOBA2z/U2AWOC8zdwFExHPAPRHxnsz8lyWalyTpIJbsGENErI+I14UnIn4c+EXg3oUodN0L7AJ+fanmJEk6uKUKw9/QOW7wSkT8c0Sc0rPtFDrvVL7Ve4fM/BGwDThtieYkSRpC02H4EbAZuBa4CPgj4F3AIxHx9u6Y6e5yx4D77wCOa3hOkqRFaPQYQ2Z+Hfh6z6ovRMQ/0nl3cDPwm8BR3W17BzzEnp7tQ9u4cd1i76IVYGpqfdtTkAYa99fmUh18/n+Z+UREPAT8fHfVK93l6gHD1/RsH9rs7C7m5uYPcYbLY9xfKKNoZmZn21NYEXxtNm/UX5uTkxMH/IV6uT7g9n3gmO7thV1I0wPGTdNzWqskafktVxjeCsx0bz8FvAac0TsgIn4MOJXOAWhJUksaDUNETA1Ydw5wPvAVgMz8IfAQsCkiet/LbALWAZ9vck6SpMVp+hjDfRHxMp0D0C8A7wSu6t6+pWfcjd0xWyLi03Q++fwh4MuZ+VDDc5IkLULTu5IeAKbo/Cf/SeBS4G+BMzPzewuDMvNx4BfonJl0J/B+4B7g8obnI0lapKZPV/048PEhxz4C/GyTzy9JeuP82m1JUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUmEYJEmFYZAkFYZBklQYBklSYRgkSYVhkCQVhkGSVBgGSVJhGCRJhWGQJBWGQZJUGAZJUrGqrSeOiNXAR4FNwAbgCeDGzPxqW3OSJLX7juGzwAeBzwHXAnPAlyPi3S3OSZIOe628Y4iIdwHvBT6YmXd1190LPAV8DPi5NuYlSWrvHcNlwKvApxdWZOYe4DPAOREx3dK8JOmw19YxhtOApzNzV9/6R4EJ4FRgxxCPcwTA5OREs7NbIie0PYEVZlz+3cfBqp/6ybansKKM+muzZ35HDNreVhimgf8asH4hBsct4nHYsOHoJua05J5rewIrzMaN69qewopxwuOfb3sKK8oYvTangWf6V7YVhqOAvQPW7+nZPozHgHPpBGVfA/OSpMPBEXSi8NigjW2F4RVg9YD1a3q2D2Mv8EgjM5Kkw8vr3iksaOvg8w66u4H6LKzbvoxzkST1aCsM24Cfjoj+HXFndZdPLPN8JEldbYVhM3Ak8NsLK7qfhP4t4GuZ6TsGSWrJxPz8fCtPHBH3AxcDd9LZ1/U+4Ezg/Mz8WiuTkiS1911JwBXArd3lBuBJ4AKjIEntau0dgyRpNPm125KkwjBIkoo2jzFoBHS/sPBaOqcKnwGso3MCwJY25yWB121pi+8YFMANwPF0TgCQRsln8boty84waCtwbGaeBNze9mSkBT3Xbbk+M6/PzL8E3gN8j851W7REDMNhLjN3ZuZs2/OQBvC6LS0xDJJG1TDXbdESMAySRtU0gy/YtdjrtmiRDIOkUdXUdVu0SIZB0qhq6rotWiTDIGlUed2WlhgGSaPK67a0xDBIGlVet6UlfruqiIibujdPBn4D+CvgWeClzLy7tYnpsOd1W9rhdyUJOtfF6HVld/k8YBjUJq/b0gLfMUiSCo8xSJIKwyBJKgyDJKkwDJKkwjBIkgrDIEkqDIMkqTAMkqTCMEiSCsMgSSr+D9EK4JWODcgjAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# valores no null\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jJj8iUnwKTlZ",
        "outputId": "fb090528-109c-4872-ed1e-ebd053284336"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Notas:***\n",
        "> * Tenemos 165 personas con enfermedades cardíacas y 138 personas sin enfermedades cardíacas, por lo que el problema está equilibrado.\n",
        "* No hay valores perdidos en el dataset"
      ],
      "metadata": {
        "id": "69e8CjEkGyMa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1 = hombre 0 = mujer\n",
        "print(df.sex.value_counts(),'\\n')\n",
        "# Comparo la columna target con la columna sex.\n",
        "# 1 = efermo 0 = no\n",
        "pd.crosstab(df.target,df.sex)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "8NonPCjlXZXC",
        "outputId": "eb8e41b8-3a20-4b9b-fc41-be38de564e70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    207\n",
            "0     96\n",
            "Name: sex, dtype: int64 \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex      0    1\n",
              "target         \n",
              "0       24  114\n",
              "1       72   93"
            ],
            "text/html": [
              "\n",
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              "    <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",
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              "    .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>sex</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
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              "      <th>0</th>\n",
              "      <td>24</td>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>72</td>\n",
              "      <td>93</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c145002-a0d7-4e51-9208-1d5aa32df200')\"\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",
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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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-1c145002-a0d7-4e51-9208-1d5aa32df200 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-1c145002-a0d7-4e51-9208-1d5aa32df200');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Notas:***\n",
        "* De 96 mujeres 72 pertenecen al target 1, basado en esta variable si es mujer, hay un 70% aproximadamente de probabilidad de que tenga una enfermedad cardíaca. \n",
        "* De 207 hombres 93 (casi la mitad) pertenecen al target 1. Entonces podriamos predecir que si es hombre, el 50% de las veces tendra una enfermedad cardíaca.\n",
        "* Promediando estos dos valores, podemos suponer, sin basarnos en otros parámetros, que si hay una persona, existe un 60% de probabilidad de que tenga una enfermedad cardíaca."
      ],
      "metadata": {
        "id": "iYao9mQPZIQq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.target,df.sex).plot(kind='bar',\n",
        "                                  figsize=(10,6),\n",
        "                                  color=['cyan','crimson']);\n",
        "\n",
        "plt.title('Heart Disease Frequency for Sex')\n",
        "plt.xlabel('0 = No Disease, 1 = Disease')\n",
        "plt.ylabel('Amount')\n",
        "plt.legend(['Female','Male'])\n",
        "plt.grid()\n",
        "plt.xticks(rotation = 0);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "fJYb5EKTWVBU",
        "outputId": "35c565bd-0ce0-450e-842f-68653bfd0596"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df.target,df.fbs).plot(kind='bar',\n",
        "                                  figsize=(10,6),\n",
        "                                  color=['cyan','crimson']);\n",
        "\n",
        "plt.title('Frecuency Blood Sugar')\n",
        "plt.xlabel('0 = No Disease, 1 = Disease')\n",
        "plt.ylabel('Amount')\n",
        "plt.legend(['Female','Male'])\n",
        "plt.grid()\n",
        "plt.xticks(rotation = 0);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "walS_-zU6TIV",
        "outputId": "097aa5b5-ef91-4e22-9565-7495ad21a7e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Valores categoricos y continuos\n",
        "categorical_val = []\n",
        "continous_val = []\n",
        "for column in df.columns:\n",
        "    if len(df[column].unique()) <= 10:\n",
        "        categorical_val.append(column)\n",
        "    else:\n",
        "        continous_val.append(column)\n",
        "categorical_val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNwaFbLIF5gU",
        "outputId": "88012f29-108e-41ac-ce3e-804ed2f3a159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['sex', 'cp', 'fbs', 'restecg', 'exang', 'slope', 'ca', 'thal', 'target']"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "continous_val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tnWrwNnuHpdn",
        "outputId": "a2ef58d2-9142-434c-a604-8300b1b711ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['age', 'trestbps', 'chol', 'thalach', 'oldpeak']"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualización y análisis de los valores categóricos\n",
        "plt.figure(figsize=(15, 15))\n",
        "\n",
        "for i, column in enumerate(categorical_val, 1):\n",
        "    plt.subplot(3, 3, i)\n",
        "    df[df[\"target\"] == 0][column].hist(bins=35, color='blue', label='Have Heart Disease = NO', alpha=0.6)\n",
        "    df[df[\"target\"] == 1][column].hist(bins=35, color='red', label='Have Heart Disease = YES', alpha=0.6)\n",
        "    plt.legend()\n",
        "    plt.xlabel(column)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "G1S2kClNCtzr",
        "outputId": "034fcad2-6116-4611-ffc3-212fecb1534d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* cp: las personas con cp igual a 1, 2, 3 tienen más probabilidades de tener enfermedades del corazón que las personas con cp igual a 0.\n",
        "* restecg: las personas con valor 1 tienen más probabilidades de tener una enfermedad cardíaca.\n",
        "* exang: las personas con valor 0 tienen más enfermedades cardíacas que las personas con valor 1.\n",
        "* slope: las personas con un valor de pendiente igual a 2 tienen más probabilidades de tener una enfermedad cardíaca que las personas con un valor de pendiente igual a 0 o 1.\n",
        "* ca: las personas con ca igual a 0 tienen más probabilidades de tener enfermedades del corazón.\n",
        "* thal: las personas con un valor de thal igual a 2 tienen más probabilidades de tener enfermedades del corazón."
      ],
      "metadata": {
        "id": "caBFvvoSLevQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualización y análisis de los valores continuos\n",
        "plt.figure(figsize=(15, 15))\n",
        "\n",
        "for i, column in enumerate(continous_val, 1):\n",
        "    plt.subplot(3, 2, i)\n",
        "    df[df[\"target\"] == 0][column].hist(bins=35, color='blue', label='Have Heart Disease = NO', alpha=0.6)\n",
        "    df[df[\"target\"] == 1][column].hist(bins=35, color='red', label='Have Heart Disease = YES', alpha=0.6)\n",
        "    plt.legend()\n",
        "    plt.xlabel(column)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "gBkrgrmlIz6I",
        "outputId": "3599447f-5d75-4093-e774-8a4d3a2b3d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 5 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* trestbps: cualquier valor por encima de 130-140 suele ser motivo de preocupación.\n",
        "* chol: por encima de 200 es motivo de preocupación.\n",
        "* thalach: Las personas que alcanzaron un máximo de más de 140 tienen más probabilidades de tener enfermedades del corazón.\n",
        "* oldpeak: La depresión de ST máxima inducida por el ejercicio en relación con el descanso observa el estrés del corazón durante el ejercicio. El corazón no saludable se estresará más."
      ],
      "metadata": {
        "id": "cFC5JfEE3sH9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Otros Gráficos"
      ],
      "metadata": {
        "id": "OvaMPQl--HK3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1--- AGE OF PATIENTE PLOT\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['age'])\n",
        "plt.hist(bins[:-1], bins, weights=counts)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Patients')\n",
        "plt.grid(True)\n",
        "plt.title('Age Histogram')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H0wejaPY82p2",
        "outputId": "f0451dce-7ac0-4e00-e149-27b3f85e035e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Age Histogram')"
            ]
          },
          "metadata": {},
          "execution_count": 181
        },
        {
          "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": "code",
      "source": [
        "# 2--- SEX OF PATIENTE PLOT\n",
        "plt.figure()\n",
        "list_sex = df['sex']\n",
        "length = len(list_sex)\n",
        "male = np.count_nonzero(list_sex == 1)\n",
        "pie_sex = [male, length-male]\n",
        "labels = ['male', 'female']\n",
        "colors = ['tab:cyan','r']\n",
        "explode = (0.1, 0)\n",
        "plt.pie(pie_sex,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('Sex of pacient');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lf6ioC5N9Mrt",
        "outputId": "8ac4a43b-7a31-4dbb-f72a-fb0f350d9ef6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3--- CHEST PAIN TYPE PLOT\n",
        "plt.figure()\n",
        "list_cp = df['cp']\n",
        "length = len(list_cp)\n",
        "cp0 = np.count_nonzero(list_cp == 0)\n",
        "cp1 = np.count_nonzero(list_cp == 1)\n",
        "cp2 = np.count_nonzero(list_cp == 2)\n",
        "cp3 = np.count_nonzero(list_cp == 3)\n",
        "pie_cp = [cp0,cp1,cp2,cp3]\n",
        "labels = ['cp=0', 'cp=1', 'cp=2', 'cp=3']\n",
        "colors = ['palegreen','moccasin','coral','r']\n",
        "explode = (0.1, 0,0,0)\n",
        "plt.pie(pie_cp,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('chest pain type');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hb6nR0dt9QZV",
        "outputId": "a19313f4-89fe-4619-c0fd-dd6946ee5a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4--- RESTING BLOOD PRESSURE PLOT\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['trestbps'])\n",
        "plt.hist(bins[:-1], bins, weights=counts,color='k', alpha=0.5)\n",
        "plt.xlabel('mm Hg')\n",
        "plt.ylabel('Patients')\n",
        "plt.title('Histogram of trestbps')\n",
        "plt.grid(True)\n",
        "plt.title('Resting Blood Pressure Histogram');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR4F9VKi9TvV",
        "outputId": "e6f9d74f-2081-4182-e0d7-f0994c392d5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "source": [
        "# 5--- SERUM CHOLESTORAL PLOT\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['chol'])\n",
        "plt.hist(bins[:-1], bins, weights=counts)\n",
        "plt.xlabel('mg/dl')\n",
        "plt.ylabel('Patients')\n",
        "#plt.xlim(40, 160)\n",
        "#plt.ylim(0, 0.03)\n",
        "plt.grid(True)\n",
        "plt.title('Serum Cholestoral Histogram')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bx3uvdLk9X0g",
        "outputId": "c4263bdf-d7f4-47e8-ef92-ae26a29b82a8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Serum Cholestoral Histogram')"
            ]
          },
          "metadata": {},
          "execution_count": 185
        },
        {
          "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": "code",
      "source": [
        "# 6--- FASTING BLOOD SUGAR PLOT\n",
        "plt.figure()\n",
        "list_fbs = df['fbs']\n",
        "length = len(list_fbs)\n",
        "fbs = np.count_nonzero(list_fbs == 1)\n",
        "pie_fbs = [fbs, length-fbs]\n",
        "labels = ['fbs=1', 'fbs=0']\n",
        "colors = ['coral','palegreen']\n",
        "explode = (0.1, 0)\n",
        "plt.pie(pie_fbs,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('Fasting blood sugar');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pv2i52O59Z4m",
        "outputId": "6168c273-38bf-4700-f7cb-3a4ac60f820e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 7--- RESTING ELECTROCARDIOGRAPHIC PLOT\n",
        "plt.figure()\n",
        "list_restecg = df['restecg']\n",
        "re0 = np.count_nonzero(list_restecg == 0)\n",
        "re1 = np.count_nonzero(list_restecg == 1)\n",
        "re2 = np.count_nonzero(list_restecg == 2)\n",
        "pie_rest = [re0,re1,re2]\n",
        "labels = ['0','1','2']\n",
        "colors = ['tab:cyan','pink','lightgrey']\n",
        "explode = (0.1, 0.1,0)\n",
        "plt.pie(pie_rest,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('Resting Electrocardiographic Results');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXxPPoKo9cXu",
        "outputId": "5f9c9190-2291-420c-89b0-5481ce57481f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 8--- MAXIMUM HEART RATE ACHIEVED PLOT\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['thalach'])\n",
        "plt.hist(bins[:-1], bins, weights=counts)\n",
        "plt.xlabel('bps')\n",
        "plt.ylabel('Patients')\n",
        "#plt.xlim(40, 160)\n",
        "#plt.ylim(0, 0.03)\n",
        "plt.grid(True)\n",
        "plt.title('Maximum Heart Rate Achived Histogram')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0OYEMfR89fPV",
        "outputId": "fa474aff-6d84-4851-958f-eca8f99d5df6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Maximum Heart Rate Achived Histogram')"
            ]
          },
          "metadata": {},
          "execution_count": 188
        },
        {
          "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": "code",
      "source": [
        "# 9--- EXERCISE INDUCED ANGINA PLOT  exang => exercise induced angina\n",
        "plt.figure()\n",
        "list_ex = df['exang']\n",
        "length = len(list_ex)\n",
        "ex = np.count_nonzero(list_ex == 1)\n",
        "pie_ex = [length-ex,ex]\n",
        "labels = ['0', '1']\n",
        "colors = ['tab:cyan','r']\n",
        "explode = (0.1, 0)\n",
        "plt.pie(pie_ex,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('Exercise Induced Angina');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcxwv8aH9hTc",
        "outputId": "5beabb40-d546-4a81-9c3b-91ec799a1779"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 10-- ST DEPRESSION INDUCED BY EXERCISE RELATIVE TO REST ST PLOT\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['oldpeak'])\n",
        "plt.hist(bins[:-1], bins, weights=counts)\n",
        "plt.xlabel('oldpeak')\n",
        "plt.ylabel('Patients')\n",
        "#plt.xlim(40, 160)\n",
        "#plt.ylim(0, 0.03)\n",
        "plt.grid(True)\n",
        "plt.title('ST depression induced by exercise relative to rest Histogram')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTWwEacu9lC5",
        "outputId": "c00e7341-a8f5-4d61-e736-11cdd96c3dd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'ST depression induced by exercise relative to rest Histogram')"
            ]
          },
          "metadata": {},
          "execution_count": 190
        },
        {
          "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": "code",
      "source": [
        "# 11-- THE SLOPE OF THE PEAK EXERCISE ST SEGMENT PLOT   slope => the slope of the peak exercise ST segment\n",
        "plt.figure()\n",
        "list_slope = df['slope']\n",
        "sl0 = np.count_nonzero(list_slope == 0)\n",
        "sl1 = np.count_nonzero(list_slope == 1)\n",
        "sl2 = np.count_nonzero(list_slope == 2)\n",
        "pie_slope = [sl0,sl1,sl2]\n",
        "labels = ['0','1','2']\n",
        "colors = ['tab:cyan','pink','lightgrey']\n",
        "explode = (0.1, 0,0)\n",
        "plt.pie(pie_slope,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('The Slope of the Peak Exercise ST Segment');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c2E4VCR19nW4",
        "outputId": "e3d967d1-03a7-447f-d1b6-05b29c245ace"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 12-- NUMBER OF MAJOR VESSELS PLOT  ca => number of major vessels (0-3) colored by flourosopy\n",
        "plt.figure()\n",
        "counts, bins = np.histogram(df['ca'])\n",
        "plt.hist(bins[:-1], bins, weights=counts)\n",
        "plt.xlabel('Number of Major Vessels')\n",
        "plt.ylabel('Patients')\n",
        "#plt.xlim(40, 160)\n",
        "#plt.ylim(0, 0.03)\n",
        "plt.grid(True)\n",
        "plt.title('Number of Major Vessels Histogram');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HPewn7P9pP4",
        "outputId": "13a78baa-a2eb-4598-b3ee-6313dfc07600"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "code",
      "source": [
        "# 13-- THAL PLOT\n",
        "plt.figure()\n",
        "list_thal = df['thal']\n",
        "thal0 = np.count_nonzero(list_thal == 0)\n",
        "thal1 = np.count_nonzero(list_thal == 1)\n",
        "thal2 = np.count_nonzero(list_thal == 2)\n",
        "thal3 = np.count_nonzero(list_thal == 3)\n",
        "pie_thal = [thal0,thal1,thal2,thal3]\n",
        "labels = ['0','1','2','3']\n",
        "colors = ['tab:cyan','pink','lightgrey','r']\n",
        "explode = (0.1, 0,0,0)\n",
        "plt.pie(pie_thal,labels=labels,colors=colors,explode=explode,autopct='%1.1f%%',shadow=True, startangle=90);\n",
        "plt.title('Thal PLOT');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1HRtJdQw9snq",
        "outputId": "83214c15-7c1c-4a74-9982-8957b088ceed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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izVWf7lxI05Gk+CjJG6+9Ki+cEo8jt0gSEUMnYuhYJIkeTWNVt59DcoZHa3DbX/5mr3n6H+VaKPRfYLzZ9iSCYSnOjRs3fq+8vPzFhoYGu9/vN82OUChEMBQymFCx78K7oamjnZL8gi+fF+fm0+Tz7VxI0wEJl6+Nhvp6Zo0eA8ARefm83NrCI/W1LCoo4sXWZhYVFCFLw6d/WveP56xVj/6lSAuFPmEYrm4ZduL8z3/+c9qYMWMera2ttXZ3d5ttTnyt56gEBiXo8dy1jv9+gOeII43t+xoV2OxcP3YCN42rxC5L+GJRSh1OHq6v4YG6ahojw2Otc+MrKyxbHnw4XwuF3gXGmm3PUDKsxPnPf/5z0Zw5c/7W0NBgHaqIn8HS1dWFARKlRf0+tjg3n8b2ti+fN3W0U5y3S39R00CS4JP3+ebRR+92dPjZliZOLirmDV8bC3LzOW1UCS+2Dp8Eec1veaVtf/17rhYMvccwCpofNuJ8/vnnDzniiCP+0dzcbEsFj7kjLS0txEYX99t7zhw3garmRmpam4nEYrz8yYccMeuAnQtpOlu2boWeADP22+9r51B7AuRabRTbHUR0HYn4RQ8PYVRUKtDw0suWhlf+NSrW0+Mlvt9M2jMscgj98Y9/VE477bRXfT6fqX3MPdHR0UFxcbFEjhs6A30+zmqxsPTM73PR/T9H03VO+cZCJpVV8JsXnmHG2PEcOTsu1Jdffpncgxcg7dKfNAyDl1qbWVweD8RfmJvHQ/W16BicU1w2dB8wRdj22F/t9vy8yvwD579izcg4kvgO4GlL2k+l3HTTTaOuuOKKVRaLpailpSVl83UUFxcb+Q4Xls/XD/mIjP6N2Xx/XTW/Glek+R9/0tL4yr+Guor0QZaZtvSmnuwpyisWl+t00jgLfVo3a0877TTnOeec85bT6SxMZWECtLe3S1JmhoR96Bsrhm6Qa0vpj588dJ319/w8I9TcfKweiVxvtjmDIW2btR6Px3LPPfc8XV5erlRXV6f854hGo3R1dRlZlWM46sTjJbfDiSzLWCwWnr3prp3KdgYC3PjoQ1S3NOGw2bjnvEuYXD6a9i4/V/zh13QFe/jhCadx1Jy5AFxx5ZW4z78MyvPN+Ggphx6JsO6Oe9xz/u++pbLd/iHxfVzSjpT/Ue8Oj8cjnX322dfvv//+x1ZVVVnSpWne2toqZY4dB8Cj19xMflbWbsst+9cLTB09ht9fvoTNDfXc8fifefRHN/LPjz/gjIVH8q0587jkt7/gqDlzeWvVZ0xVJhMpKU7iJ0l9Iq2trP/5L11Tb7z+OYvTOYM0jCJKy2bttGnTjjj99NNvaWpqssRi6ZPlPxgMEolGDPaRZ3ZzfR0HTZkOQGVpGXWtLbT6O7FarIQiESKxGLIsE9M0Hn1zBeeffY6e6rlrzaBz1Wpq//FcZqyn52V69yBNJ9JOnB6Pp3jx4sUPh8Nha1dXl9nm9JuWlhYJq5UL7/8ZJ991E0++89bXykwZPYbXPvsEiIfw1be30uhr5/j5h/Dmyk85//6fsvjY7/B37+uccNA3cNnsZKZZesxkUfv0P6xd6oZJWjD4f2bb0l/SarTW4/HYLr744r+deOKJJ1VVVSV86VeiyMvLo9wfpm3TVs6//2fccsa5zJv8VfRZd7CHu5/8C+uqq5hcPpotjQ3cde6FTB097ssynYEAP3zo//jdZUu467XnjY1d3dLl556jjV37v5E9WrsbLO4M9v/9//XY8/JOB142256+kla328MOO+y8U0455aSGhoa0FSaALMtGbEypXpCdwzdnz2V11Zad3s90ZfDT71/KC0t/yr0XXIav28/owlE7lfnDy8+x+LgTePmTD9h/xkzOv/lW408PPZhW1zNZaIEeNvzq/gwtFHqMNFoHmjYX8zvf+Y5y6aWX/ry9vV0OhUJmmzNgQqEQ9fX1kuxyyD0WiffXrWFS2c6B8f6eAJHevvTT7/2buZOmkOnK+PL9qqZGGn3tHKhMIxiJYAHcFplwODx8otqHmM41X9DyzrsZsZ6e35ttS19Ji9Faj8fjvvHGG5dnZWVl19TUpM0NZXd0dHRw7733YrPZMDSN4/eby4IZs3j87TcAOHPhUWxuqOcny5eBJDGprJy7z71kp3P8+vmnWHJiPDn6t+cdzOV/+r3U+uTjXHrpYp3m5C0oTzeq/vRnZ/78eSeSkXEMsMJse/ZFyvc5PR6PdOSRR9563XXX3bx169a0Gp3dG3a7nYkTJyL/ZxXEtEGdyxhXxobcbD1fMowRHyG0D3Jm7cfUG69vszidlUCn2fbsjXS4y84766yzLm9tbZWHizBhaBNQS5qOaxit00wknatW0/re++5YT89vzLZlX6S0OD0eT+bxxx9/S0lJSa5Z2QwSSTwB9RCMTwzT3LWJYuuf/uwkvmnSLLNt2RspLU6n07no/PPP97S0tAzL7cp7E1Az6ATUmo591yUpgj2iBXqo/tvjjlig5wFSOA9uyorT4/EUn3322VdZrVZHOgYb9JUhSUCt6diE5+wXjf96VdZ6evYDjjXblj2RkuL0eDxScXHxWaeccsrc5ubmYek1tzMkCah1HatwnP3C0DS2PPSwWwsGHyBFZy1S0ihg4sUXX3xBT09PWs9p9oXtCagLxpdjbR/g9hCahkU0a/tN+8f/pae6piBz0sSLJVl+oL/HK4oyF7gJ2B8YRXz0dyVwh6qqHwzWvpTznB6PR54yZcplCxYsUFpaWlL15jGktLe3S3KGU8I5wF0GdB05XZNEm8zmZQ+59Wj0pwxsq8FK4g7uYeBK4BfERfqOoijfHKxtqfjj3/+SSy45qb29fVhNnewNTdPo7Og0sieOxjKQBNS9uWsF/SewZStd/1tvzdlv5vf76z1VVX0SeHLH1xRFeQDYAlwNvD4Y21LqduvxeBxjxoy5eObMmaN9Pl9K2ZZoWttaB56AWosn6xJN24FR/cRTbj0cXgoMet2dqqo9xLeIGPQcWap5zm+cccYZ8zs7Ow1NG1zUTLoRDocJhUKGe0KFxKbq/h2sxxNLW4b5bmOJout/6wk2NGZmThh/CvBUf49XFCWL+M5nBcB5wAzgjsHalTJX0+PxWJxO57cPP/zwqT6fL9VuGklhwAmoNR0kCcsw3qcz0dQ8/kRmLBC4g4HNey4n7i3XA9cAy4B7BmtTyogTmHrCCSfsH4lEpHB4eGQj7y9dXV3ohiFR1s8E1LoOsoRVDAoNmPZPPiUWCJQDRwzg8NuBbwEXAO8T96KDngJMpat5zEknnTSjs7Mz7dJJDCUtLS3EKor7l/HZMMAAWXjOgWMY1Dz5tDvWHbi1v4eqqrpGVdXXVVVdDhwNHAD8ebAmpYQ4PR5P6fz58xfk5uZmp2JS6GTS0dGBZLfJ5PQvabmh61iE5xwUre+8J0lWyzxgwKsRVFWNAi8AJyuKMpDpmS9Jlau58Hvf+970kdrX3BHDMGhvbzdiEyr61/fUR9YAWiLQIxFa3nkXPRq9cJCnchHvu+4+vWIfMV2cHo/HXVpaetz06dPHdHR0iGYZvUEJmRkS9r53WwxNT+2FuWlC44rXnEYsdjl90IaiKF8bHFAUJRs4DahRVXVQu0Wlgqeae9JJJyl+v3/ETZ/siWg0Spffb2RNHI28bkufbliGpiGJDHyDJrB5C1G/32lxuQ4B3ttH8ScVRQkBHwCNxJvD5wMVwBmDtcXUq+nxeGTg2wsWLBjf1dU1rAPc+0trW5tk5Of0uSVhaMNr1zAzaXz1dbcWDF7Uh6J/BTKAq4AHgMuBVcDhqqr2e750V8z2nOPKy8srCgoK8jds2GCyKalFMBgkEokYrvHlElv7kKxc0wxSeG1iOtHy9rvymDNOPxW4mL3sVKaq6p+APyXKDrPbQbMWLVo0xu/366mey8gMWlpapFhJQd++mEHmIRJ8RaS1lVBTswYcYqYdponT4/FIwGELFiwYHwgERJN2N/j9fpBlieI+bFA0zDbDNZu2D/7j1sLhE8y0wUzPOTonJ6ewpKRkVKrtRJ1KtLS0GLExpftUnqTpokk7hLT/91OLEYudbKYNZopz5uGHH17a3d0d08Vdf4/4fD5JdjpkMvc+ny2JAaEhpXvTZiRZLgbGmGWDmeI8cMGCBaXBYHBEh+vtC13XaW9vN7TK0Xvte8q6ITznUKLr+D79XMPEHEOmjNZ6PJ5cSZIqFEUZX19fL35U+6C9vV3KnzgRrJY9DvxIhh6PsU0RorrOz6q3EjUMdMNgblY2JxYVsy7QzVPNjRiAQ5a5sLScYvvXM0DUhEI81lhHUI8vJF86thKA39ZV44tGOTwvnyPyCgD4c0Mdh+flM9Y5qGi5r9H24UfunFkzz7BlZT04pCfuI2ZNpUysrKzMtFqttpG6AqU/RCIRAt3dhrtytCSrVbsvlGLNWqskce2YcThlCzHD4KfbtjAzM4u/NNbzg4oxlDmcvOVr45+tLVy4y14xmmHwcEMNF5VWMMbpoluLYZEkVnV3McmVwaKKIn66bQtH5BVQHQqiYwy5MAE6V6/G4nQeSLyFmfQv2Kxm7QGzZ8/OCgQCqfWLSmFa29oko3Avi+u11PKckiThlOOJBTTDQNtumwTB3jGGoK6Ta/u6f1gb6KbC4WRMr+AyLVZkScIiSUQMHc0w2P5Jn29t5qTCxOzqHe30Ewv06MCkhFSwD5LuOXunUKbPmDEjOxqNiv5mHwkEAsRiMSxjSqG64esFUnBQTTcMbq/aTHMkwhF5+VS6Mji/pJz7a7Zhl+PivXnshK8d1xgJIwG/qqmiKxbjwOwcji0oYro7k//4O7h72xaOyS/k8y4/YxxO8myJm4nr3rBRz58/dx6gJqySPWCG58wCMidMmFAutqzrHy2trcTKCnfvHlOsWQsgSxK3j5/IryYqbA0FqQ2HeK29jR+OHsuvJk7h0Jxcnmhu/NpxugEbgz1cUlrBDWMn8FmXn3WBbiySxKVlo7lt/ETmZufwuq+No/MLeaKpgd/XVfN519AvN+xcuzZTC4UOHfIT9wEzxFkCGIWFhaOCwaAJ1acvnZ2dSFarRMFuElCnoOfcTobFwpQMN2u6u6gJB6ns3Wt0fnYOm4I9XyufZ7My2eUmy2rFIcvMzMxiW2jn38q/fW0ckp3LllAQl8XCZWWjebW9dcht7964SdKj0QVDfuI+YIY4S4uLi502m80eje4xbFGwG7YnoI6NK/+690yxFT3+WIyeXpsius7aQDelDgdBXacxEh8EXBvopmw3I7Uz3FnUhkOE9Xj/Uu0JUOZwfvl+QNNY1d3FITm5RHT9y4DiaAL63N2bt2BxuSYyBGlH+osZo7WTDzjggKyenp4YQ5CKcKTR3t4uFRUVgcsBwR1GulOsWdsZi/FIQy06BoYB87JzmJ2ZzXkl8Pu6aiQk3LLM+aXxkdrPu/xUhYKcVFSM22Lh6PxC7qzajATMzMxiVuZX65ZfbG3m2wVFyJLEDHcmb/naWOrvxJPbhzDHfqKHQkTafSHnqKLpxLO5Jw0zxDlx+vTpWeFw2OwVMWlJPAF1h5E9cQyWNRu/6rOnWLN2tNPJbeMnfu31A7KyOSAr+2uvz8nKZs4Orx+ck8vBObsfnT6zuPTL/22yzDVjxg+BxXsmVF9vOEcVVZJkcSa1WevxeJxA0aRJk8oikYjwmgOkta1NknIyd05AnWKeczjRU1vnBBJ7B9gNye5zlgDGqFGjiof7BkWJJBwOEwwGDSp3yEOlxRNLC4aeUH29PdbTMy3Z9ZohTtnpdDpHyj4oiaKltVWKFeV+NQKi60KbCSLU1IwRi01Jdr3JFmeZLMsxu91uF/mCBkd3d3c8AXX5qPgLKRQdNNwINTUjWSxjk11vssWZV1BQIGuaJjIfDAHxBNSjvvoixZeaEMJNTViczlEkWS9JF2dJSYk1FhM5NYaCjo4OJJtNIic+zaDrIj1mItAjEfT4bzYvmfUmW5zZxcXF1mg0KoYWhwDDMGhrazNiE3qDEoQ2E4YeDscYZJLo/pJsceYUFhbaY7GYGLoYIr5KQG0XzdoEogVDGvD1CdoEkjRx9q5GySwoKHDoum521r9hQywWw+/3G/rECkMMCiUOLdhjMFzFCTgBKfWiV6wAABhfSURBVDc3N0PXdRGAMIS0bU9ALbSZMGKBHhjGzdoMwMjOzs4UnnNoCQaDhMNhZIdN3PQShBYIyAxjz+kGDEmSJNE1GnpaW1uF40wgsZ4emWHsOZ0AkUgkIot9JIccv9+PAUiyGGtLBJIkQZLzCCVTJTpANBoVcXsJosPvN8pPOQlbTlJbXyMCyWYz2Mu+KYkgmeLUAKLRqCZJkpjnTABNzc1SRJaNabffash2kZ5pKJGt1mEtzhh86TlF9yhBbKutlSyFBUy+/scGovswZPR6zkgy6zTLcwpxJpCtNTVS1tQpjLvw++J7HiKGu+fcLk7hOROMrutsqamRio88Qir59nHiux4CJKsVhrE4Y4AUiUSE50wCsViM6oYGxp1zlpQ/f67Z5qQ9FpcTIJDMOpPuOSORSEyIMzn09PTQ0NrK5GuWkDmx0mxz0hpbbq4F2E0278SRdM/Z1dUVtlhEIEuy6OjooM3vZ9ptS3GMKjLbnLTFmpnpZBiLUwPYtm1bp91uFzPlSaS5uZnuaMSYcdcdWNxus81JOyzuDDAMjWHcrA0B+qZNm7ocDodIi5lk6urrJc3pNKbderPRO7gh6CP23Dz0SKQ92fUmTZxer9cA2jo7O2VN0zTRtE0+W2uqJXt5GZOWXCX6/P3Anp+HoWlNya432bPULYAjEAh020UEiylsqa6WcufMZvRZZwqB9hF7fh5IUn2y6022OBsBZ3t7e5sQpznous7Wujqp7PhFUtGRhwuB9gFXRYVucbk+T3a9ZojTUVdXV2+328UPwyQikQg1jY1UXnKRlDNrP7PNSXkyJ03slq3WVcmu14xmrV5dXd1qtVrFFmMmEggEaGpvZ8oN15ExdozZ5qQ07nFjZWBNsutNtjhbAWPjxo2tDodDeE6TaW9vx9fdzfQ7b4v3qwRfQ3Y4sGZlOYBNSa87yfW1ApbVq1e3OZ1OW+8CVoGJNDU1ETQMY/qdtyE7nfs+YISRMboCPRSqoXdVVTJJqji9Xm8Y6PD5fJLf7+9wuVzJrF6wB2pqayWyc4wpN14vlpntQsbYsZDkrf+2Y8aV2Axkbd68eVNGRoZo2qYIW6q3SRmVE6i87FJxTXYgc/LEsMXt/sCMus0Q52og47PPPtvidDqTunhVsHe21tZKBYceIpWdfKIQaC95s2eHJUl614y6zRDnVsB46623tmVmZoo4shQiFouxrb6eMd89TSr4xiFmm2M61sxM7AX5DiDpc5xgjjjrgVhTU1Osq6urS/Q7U4tQKERdczOTrrqCrCmK2eaYSva0KWih0CqSvMh6O0kXp9fr1YB1QM7mzZs3ud1u0YRKMfx+Py0dHUxbejPO0lKzzTGN3P3nRKwZGc+ZVb9ZQ3MrAffKlSs3OxwO0e9MQVpbW/GHgsaMu2/Hmj0yU23mz50bliyW18yq3yxxVgHGv//97+qsrKyk9DsXL17MkiVLuOaaa7juuut2eu/FF1/klFNOwe/37/bYxx57jKuvvpqrrrqKRx55BMMwiEaj3Hnnnfzwhz9kxYoVX5Z94IEH2LJlS0I/S7Kob2iQohaLMf32W0Zcqk17YSG2nGwZk6ZRAMwakKkDYnV1dZGuri5/RkZGXk9PT8Irvf3228nexQu0traycuVKCgsLd3vM+vXrWb9+Pffddx8AN998M2vXriUYDDJ16lROPvlkbrrpJo455hiqqqrQdZ0JEyYk/LMki6raWmni2LHG5Gt/ZKy/5+fSSNnJrODgAw09GntJtttNy7Fsiuf0er0xYD2Q/emnn36enZ1tWhb45cuXc+6557KnaCVJkohGo8RiMWKxGJqmkZubi8ViIRwOo2nal9tiPv7445x55pnJND8pbKmpkbKmT2PcBeeNDGUCo448otvqznjUTBvMDAf5DMh85plnVmdnZ+9RHEOFJEnccccdXHvttbz2Wrwb8fHHH5Ofn8+4ceP2eJyiKMyYMYOLLrqIiy66iFmzZlFRUcGsWbNobm7mhhtuYNGiRXzyySdMmDCB/Pz8hH4OM9B1na21tVLxN4+Sio87ZtgL1FFUhKusTAbeNNMOM+cZVwGsX7++q62trTkrK6tsT32+oeCuu+6ioKCAzs5Obr/9dsrLy3n22We55ZZb9npcQ0MDtbW1PPTQQwDccccdrFu3jmnTprFkyRIgPj9455138pOf/ITly5fT2tqKx+Nh3rx5Cfs8ySYajVLd0MD4886VIk3N+D79zGyTEkbBIQfphhZ7HuymrpwyzXN6vd4O4stw8t9+++1PsrKyEjpqW1BQAEBOTg4HHngg69ato6mpiWuuuYbFixfT1tbGtddei8/n2+m4jz76iMmTJ+NyuXC5XMyZM4cNGzbsVGbFihUsXLiQDRs24Ha7+dGPfsSLL76YyI9jCvFUmy0o112Du3L49Kt3ZdSRRwSsGeY2acHcZi3A24D7qaeeWpeZmSknKq9QKBQiGAx++f+qVauYOHEiy5cvZ9myZSxbtoyCggJ+8YtfkJe389KpoqIi1q5di6ZpxGIx1q1bR3l5+Zfvd3d38+mnn+LxeAiHw0iShCRJRCLDc4aoo6OD9q4upt++FPseBtHSGceoUThLiiXg32bbYnb43Dog0t7ebmzdunVTTk7OlPb2oU9y1tHRwb333guApmkcdthhzJkzZ4/lN23axGuvvcbll1/OQQcdxJo1a1iyZAmSJDF79uydmqtPP/00p5xyCrIsM3v2bFasWMGSJUs4+uijh/xzpApNTU3YysqMGXffLq360bVogcSPtCeL4qO/GUM3/ooJS8R2xfRdpj0ez7nAN0499VT3eeedd+q2bdscphok6DPjR482YvUNrL35VsmImf5bHjSSzcb8x/4UsmZkzAI27POABGN2sxbgQ8D+wgsvbLHZbIjEX+nD1poayTm6golXXzksRnALD/0G6ManpIAwITXEuRnojEajjs8///yzvLw8zWyDBH1nc3W1lDf3ACrO/G7aC7T85BMC1kz3T822Yzumi7M3EP5NoOjBBx/8ICcnx7CKjORpg67rVNXVSeUnfkcqOnyh2eYMmMzJk3AWFXUDK/ZZOEmYLs5ePgSoqqoKrl69emV+fr7wnmlEOBymtrGRysWXkDNzhtnmDIiyE44PyXb7L+jd0ycVSAlxer3eFuLTKsUPPPDAO3l5eYbYriG96O7uptnnY8qN1+MaPdpsc/qFs6yU/LlzNcliecRsW3YkJcTZywrAtnnz5sAXX3yxWnjP9KOtrY2OQMCYcddt2HJzzTanz4z+3hlhSZbvBTrMtmVHUkacXq+3CXgXKFm2bNnb+fn5wnumIY1NTVIIjOl33makQ6rNjDGjyZ83Nyrbbb8225ZdSRlx9vIvwKaqave6devW5ufnm7ZcRzBwqmtrJSkvlyk3XJfyqTYrzv5eWLZY7gC6zLZlV1Lqm/N6vQ3AB0Dxgw8+6M3Ly9OF90xPtlZXSxkTK5lw6cUpO8XirpxA3n4zQ7LN9nuzbdkdKSXOXl4BHOvWrfNv2LDhf6LvmZ4YhsHW2lqpaOFhUukJx6ekQMece3bIYrffDKRk/GHKidPr9dYRn1op/vWvf/16bm6uLqKG0pPtqTbHfu8MKf/gg8w2Zyfy588ja9LEVsliechsW/ZE0mb7FUWZB3wfOBwYC7QRb8LerKrqrpvE/BM4cPPmzT1vvPHGW4ceeugRNTU1tmTZKhg6gsEgdc3NTP7hD1jb3k6Xan5knOx0Mv6yS8I2t/scIGWXDyXTc14PnAy8AVwNPAR4gM8VRZm6Y0Gv11sLvAqU3X///R/rut6Vk5OTRFMFQ4nf76e1s5Npt96Ms6TYbHOoOON0TbLZXga8ZtuyN5IpzvuAsaqqXqWq6h9VVb0LOAywERfurrwEBKLRqOsPf/jDc6NGjYrKKT7yJ9gzLS0t+EMhY/pdd2DNyjTNjoyxYyk55lthR1bWZaYZ0UeS9mtXVfUDVVUju7y2EVgLTN21vNfrDQB/AUa9/vrrtevWrfti1KhR6b8uaQRT39Agxew2Y9ptSw3JZkIvRZKYcOVlYYvN9iOgOfkG9A9TXZGiKBJQTHzfzt3xKfAFUHzHHXescLlc4cxM8+66gsFTVVMj2YpHGZN/vMQgyfuzlhx3rOEsKd4kW60PJ7XiAWJ2O/EsoBx4andver1eHXgUsPp8Punhhx9+tqSkRDRv05wtNTVyzsyZjD337KRNsWSMG8uYs88MO7KzTwDSIrjFtF+5oihTgN8D7xFvvu4Wr9fbDDwOlD3//PNbNm7cuK64uFg0b9MYXdfZWlcrlRx7tFR89DcTLlDZ4WDSdT+OYrVeSnz9cFpgijgVRSkBXgZ8wGmqqu7rTvY28STUxTfffPPLsix35eXlpeTEtqBvRCIRahobGX/h+VLu/nvO5zQUjLnw+xoOxwqb3f5YQisaYpIuTkVRcojH0OYAR6uq2rivY3oXZC8HLJ2dnbalS5f+pbCwMJqRkZFgawWJJBAI0NDSwpTrf4x7/LiE1FFwyMEUHHyQL7Ow4KyEVJBAkipORVGcxKdIJgPfVlVV7euxXq+3EVgGlKxZs8b/4IMPPlVeXh6zmTHqJxgyOjo6aPP7mX7HbdgLC4b03M6SYiZcfmnUlpFxLCkY2L4vkiZORVEswJPAwcSbsh/29xxer/cz4HlgzLPPPrv57bfffru8vDya6K0cBImlubmZHi1mzLjzdixD1BqyuN0oS2+KRTTteovV+t8hOWmSSVpqTEVR7iceGfQSXx+d7VZV9fm+nMfj8ViAq4BpkiTVPfzww2fm5eVNaGhoEImH0pwJY8bo0Zpaae0tt0mGNvD1DpLFgnLbLTEK8p8sKC8/ewhNTCrJFKcX2FMGqG2qqo7r67k8Hk8WsBRw5uXldS1fvnxxd3d3bkdHh3Chac6kceOMjo//y6bf/HbA13L85Ys154xpq/LLy+eTQjmB+ovpSaUHisfjGQ3cCrTNmjXLde+9915aX19vDwQCZpsmGAQWi4WJY8dS/9wLRu1Tz/RboKUnHG8UHf/tJsNmnZydm5t2/cwdSdvZfK/XW0N8gKh01apVnb/73e8eLysrEyO4aY6maVTV1VFxyklS4cLD+nVswcEHUXrqycGYxEHpLkxIY3ECeL3e/wIvAmNeeumlbQ888MATFRUVQqBpTjgcprapiYmXX0b29Gl9Oib/wPmMu2JxxOfrODKvsHBbgk1MCmktzl6eIx6kMO7555/fumzZsicrKiqiLpfLbLsEg6Crq4tmXztTb74BV0X5XsvmzZvLuCsvi25SNxxTNnZMv2cBUpW0F2dvgMKjwDvAuGeffXbLQw899NTo0aNjQqDpTVtbG509Pcb0u27Htof1vHkH7M+Eq66IfvH5yhNmzD3A9G37hpK0FyfsJND3gHHPPPPM5j/+8Y9CoMOAhsZGKSJJxvQ7bzXkXdLV5M6ZzYQf/iC6ZuXKk+ctXPAvk0xMGMNCnABerzdGPMTvfWDcU089temRRx55uqKiIupMg/ypgj2zrbZWkvPzUW649stUm/kHzafyR1dHv1i56tR5Cxb802QTE0LaTqXsCY/HYwUuBA4Bqs4888zJ559//qn19fXW7u5uk60TDBRZlpk4dqzR6n2Hnppays44Lfrpfz484bBjj0mZjYeGmmEnTvhSoBcDBwLbjj322NFXXXXVmT6fz97e3j5sWgsjDZvNRuWYMYR6ekIrnn/hqFMvvOB9s21KJMNSnAAej8fGVx60esqUKe577rnnHMMwchobG63D9XMPV2RZpqS4WAsFAi3vrnj16O9ecvFqs21KNMNWnAAej0cGjgNOA5qzs7Mjv/zlL08pKyubUFtba4sNg63SRwI2m42ysjJtwxdfVD3+u98f9Mu//XVPaW2GFcNanNvxeDyzgCuBkCRJbTfddNOCww477LDa2lprMBg02zzBXnC73ZSWlmoff/zxS7/97W/PfPrpp0Nm25QsRoQ4ATweTwXx1Sx5QN13v/vdSRdccMGpzc3Nts7OThEwn2JIkkRRUZHmdru1l1566e5ly5bd1ZtTasQwYsQJX65muQTYD6g+4IAD8m699dZzotFoRlNTk1XXR9S1T1mcTielpaWxqqqq+kceeeTC++677w2zbTKDESVO+HIk91TifdH6nJwc7dZbbz162rRp+zU2NtrEdIu5FBQU6Hl5efozzzzz4mOPPXbVa6+9Vme2TWYx4sQJ4PF4JOAg4HziaRIbjz/++HGLFy8+NRQKOYQXTT52u52SkpKoz+fr+MMf/nDDRx999JjX642abZeZjEhxbsfj8RQRF+h0oD4/P99YunTpMVOnTp3R1NRk8/v9Jls4MsjNzTWKior0V1999d1ly5Zd8sorr2w026ZUYESLE75Me7KAeILrGNB45JFHll955ZWnyLKc2dTUZItEUnYjqrQmMzOToqKiqM/n61q+fPmv3nzzzfu9Xm9K7pVpBiNenNvxeDzFwDnEB4uaHA5HcMmSJQcfccQRCzs6OuT29naLmBcdGpxOJ0VFRRFd16N//etfvS+88MKNsVhsjdfrFT/GHRDi3IHevuhc4DzABdRPmDDBdfnlly+cNWvWbJ/PJ7W1tVm0QSSfGsnYbDaKioqiTqfT+Mc//vHfv/3tb8tCodCLXq+302zbUhEhzt3g8XgygROBI+ht6lZWVrqvuOKKhTNnzpzV3t4utbe3C5H2EYvFQkFBgZaTk2O8+eabax955JG/tLW1/d3r9TaYbVsqI8S5F3qbuscR30c0BjROmjQp84orrvBMmzZtps/nEyLdC3a7ndzcXC03N9f47LPPqh588MF/bN269Y/AZtGE3TdCnH3A4/GUAIuAQ4lvU96kKErm5ZdffvjUqVNndHR0SH6/3xIKjZjIsr2SnZ1NTk5OxOFw8N577214+umn31dVdRmweqRF+QwGIc5+4PF4SoFvE1/pEgUap0yZknXmmWfuP3fu3LmGYVj9fr+ts7NTGmne1Gq1kpubq+fm5mo+n6/zhRdeWPvCCy+sDQaDTwEfjPQ5y4EgxDkAPB5PGXA88UAGA2iTZbln0aJFYxctWjSvsrJycldXl97V1WXv6kr7DI17RJIk3G432dnZ0aysLOnzzz/f8sQTT/zvs88++wB4FVjbm6FCMACEOAeBx+MpAA4AvgkUEvemLQUFBfIZZ5wxbcGCBQfm5eUVdHR0SIFAwNLT00O6f9+yLJOZmYnb7Y5mZ2fLPp+v86233trw5JNPrvP5fK8C73m93nqz7RwOCHEOAb3rRscTb+4eCtiBbqB99uzZeSeddNKsadOmTc3Ly8sPBAKxYDBoDwQCUrosV3M6nbjdbsPlckUyMzOtDQ0Nje+8807VihUramtqajYCrwArvV5venygNEGIc4jxeDxOYAbxaZgpvS8HgY78/HzpqKOOGnvAAQdUTpo0aXJWVlZ2d3d3LBQK2QOBgBQOh033rDabDafTicPhMOx2ezQzM1MOh8Oh9evXb33//febvF5vY0dHRyvxRGqfAVvFyGtiEOJMIB6PpxCYBMwmHnlkByQgAHSUlZXZjzrqqHFz5syZVFlZWel2uzNDoVAsHA4TjUZtkUhEikQiRCIRhnqASZZlHA4HTqcTu90eczgcWkZGhjUajUZbW1tbtm3b1rB+/fqujz76qH3jxo0BoBb4APgCqBOjrolHiDNJ9MbwlgITgDnANMDKV2Ltcbvd0f322y9/6tSpo8aNG1dUXl5elpubm5eVlZVtsVgsoVAoGo1GDcMwJF3XMQxD6v2TAXn76xAXnyRJmizLmsViMSwWi2G1WuXeP6umaZrf7+9oaGho2LhxY9O6deu6V65c2dPS0tLTa1MPcc+4ivi8pIjiSTJCnCbRu650NFAJKMBY4oNKOnFxyEAYCAGh/Px8Q1GUnMLCwgyXy2VzOp02l8tlczgcVofDYdv+53Q6HRaLxeL3+7u7u7uDnZ2doY6Ojmh7e3uktbU11traGmtpaZEikYhEfKRZApqALcAmoLH3uU80V81FiDOF8Hg8DuICLQTygTLi3ra497mFuHgNvhLWni7gdoHrxAenuolvvd4F1AE1xEXY7PV6xbKbFESIM03oDcqXAVvvn3WH/7c/txKPYAru8BcRHjA9EeIUCFIUkf1cIEhRhDgFghRFiFMgSFGEOAWCFEWIUyBIUYQ4BYIURYhTIEhRhDgFghRFiFMgSFGEOAWCFEWIUyBIUYQ4BYIURYhTIEhRhDgFghRFiFMgSFGEOAWCFEWIUyBIUYQ4BYIURYhTIEhRhDgFghRFiFMgSFGEOAWCFEWIUyBIUYQ4BYIURYhTIEhR/h/6B7Q3qPhkDgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M10\">3. Cambio de estructura de datos a numpy y transformación de las etiquetas</a>\n",
        "\n",
        "* Vamos a pasar los datos de los pacientes a una nueva estructura de datos para que scikit-learn puedan trabajar con los datos:\n",
        "<span></span><br><br>\n",
        "    - Las ***columnas age\tsex\tcp\ttrestbps\tchol\tfbs\trestecg\tthalach\texang\toldpeak\tslope\tca\tthal*** representan las variables de entrada y las vamos a pasar a un ***numpy array***.\n",
        "<span></span><br><br>\n",
        "    - La ***columna target*** que representa la clase (o variable de salida) y la vamos a pasar a un ***numpy array***."
      ],
      "metadata": {
        "id": "EhxJC06x8bc0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLe0MP3j8r2G",
        "outputId": "b2225fb2-dcfc-4c15-868f-62847fd9b9b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba3b6860-4cf6-46af-bf6a-d454c2857a4c\">\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",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 194
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dividimos los datos en X e y\n",
        "X = df.drop('target',axis = 1)\n",
        "y = df.target.values"
      ],
      "metadata": {
        "id": "LdlMY3OR-MP8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Utilizamos una semilla aleatoria para \n",
        "# garantizar que los resultados sean reproducibles\n",
        "np.random.seed(42)\n",
        "# Dividimos los datos en train y test\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,\n",
        "                                                    y,\n",
        "                                                    test_size = 0.2)"
      ],
      "metadata": {
        "id": "9yNqH7Dv-TXs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
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        "colab": {
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        "id": "h8spqlmdsYGq",
        "outputId": "bdc5731a-0061-4246-9fca-fa4c397f058b"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "132   42    1   1       120   295    0        1      162      0      0.0   \n",
              "202   58    1   0       150   270    0        0      111      1      0.8   \n",
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              "176   60    1   0       117   230    1        1      160      1      1.4   \n",
              "\n",
              "     slope  ca  thal  \n",
              "132      2   0     2  \n",
              "202      2   0     3  \n",
              "196      1   0     2  \n",
              "75       1   0     2  \n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "y_train, len(y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NAGVaGkesbdq",
        "outputId": "249ffa20-7c20-4db5-c0bf-694af49e694a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1,\n",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "179   57    1   0       150   276    0        0      112      1      0.6   \n",
              "228   59    1   3       170   288    0        0      159      0      0.2   \n",
              "111   57    1   2       150   126    1        1      173      0      0.2   \n",
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              "60    71    0   2       110   265    1        0      130      0      0.0   \n",
              "\n",
              "     slope  ca  thal  \n",
              "179      1   1     1  \n",
              "228      1   0     3  \n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>276</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>59</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>170</td>\n",
              "      <td>288</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>159</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>57</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>126</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>134</td>\n",
              "      <td>409</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>1.9</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>71</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>110</td>\n",
              "      <td>265</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c2754fbf-50cd-4d2f-8620-19749e4a5e89')\"\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",
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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",
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              "    }\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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-c2754fbf-50cd-4d2f-8620-19749e4a5e89 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-c2754fbf-50cd-4d2f-8620-19749e4a5e89');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 199
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test, len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTnG864Js0ri",
        "outputId": "df7f5e65-38c3-4684-8714-92a0c0ce2386"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "        0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0]), 61)"
            ]
          },
          "metadata": {},
          "execution_count": 200
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M11\">4. Creación del modelo (hipótesis) y ajuste</a>\n",
        "\n",
        "\n",
        "* A continuación pasamos a crear un objeto de la clase \"***LogisticRegression()***\" para la resolución del problema de clasificación.\n",
        "\n",
        "\n",
        "* Una vez instanciado, procedemos a ajustar (entrenar) el modelo (o hipótesis) llamando al método ***fit(X,y)*** que recibe como parámetros el conjunto de datos de las variables de entrada 'X' y la variable de salida 'Y'."
      ],
      "metadata": {
        "id": "zSQGmN2pW-v2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creamos un objeto de la clase LogisticRegression\n",
        "model = LogisticRegression(solver=\"liblinear\", multi_class=\"ovr\", max_iter=50)\n",
        "\n",
        "# Entrenamos el modelo\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9koVNiucKbo",
        "outputId": "2ff61d13-c1f4-4b97-a671-3147d4013999"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=50, multi_class='ovr', solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 201
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M12\">5. Obtención del modelo (hipótesis)</a>\n",
        "\n",
        "\n",
        "* El objeto de la clase ***LogisticRegression*** nos proporciona los resultados de los parámetros $\\beta_n$ por medio de los atributos del target:\n",
        "<span></span><br><br>\n",
        "    - ***Obteniendo*** estos valores podemos obtener el modelo generado:\n",
        "<span></span><br><br>\n",
        "    "
      ],
      "metadata": {
        "id": "VQ4Oj3GopgM8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtener los parámetros de la funcion de decisión\n",
        "# age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal\n",
        "\n",
        "b0 = model.intercept_[0]\n",
        "b1 = model.coef_[0,0]\n",
        "b2 = model.coef_[0,1]\n",
        "b3 = model.coef_[0,2]\n",
        "b4 = model.coef_[0,3]\n",
        "b5 = model.coef_[0,4]\n",
        "b6 = model.coef_[0,5]\n",
        "b7 = model.coef_[0,6]\n",
        "b8 = model.coef_[0,7]\n",
        "b9 = model.coef_[0,8]\n",
        "b10 = model.coef_[0,9]\n",
        "b11 = model.coef_[0,10]\n",
        "b12 = model.coef_[0,11]\n",
        "b13 = model.coef_[0,12]\n",
        "\n",
        "print(\"\"\"  Z = {b0:0.2f} + {b1:0.2f} · age + {b2:0.2f} · sex + {b3:0.2f} · cp + {b4:0.2f} ·\n",
        "  trestbps + {b5:0.2f} · chol + {b6:0.2f} · fbs + {b7:0.2f} · restecg + {b8:0.2f} ·\n",
        "   thalach + {b9:0.2f} · exang + {b10:0.2f} · oldpeak + {b11:0.2f} · slope + {b12:0.2f} · \n",
        "        ca + {b13:0.2f} · thal  \"\"\".format(b0=b0, b1=b1, b2=b2, b3=b3, b4=b4, b5=b5, b6=b6, \n",
        "                                   b7=b7, b8=b8, b9=b9, b10=b10, b11=b11, b12=b12, b13=b13))"
      ],
      "metadata": {
        "id": "fmTtRojf30t4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6dd65fc8-53b9-4ced-eecc-7fb748e2a7eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Z = 0.37 + 0.01 · age + -1.35 · sex + 0.79 · cp + -0.01 ·\n",
            "  trestbps + -0.00 · chol + 0.13 · fbs + 0.52 · restecg + 0.03 ·\n",
            "   thalach + -0.89 · exang + -0.61 · oldpeak + 0.66 · slope + -0.77 · \n",
            "        ca + -0.86 · thal  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M13\">6. Acierto del Modelo. Obtener el porcentaje de acierto del modelo.</a>\n"
      ],
      "metadata": {
        "id": "Bz7JBWxJ0SNR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hacemos un diccionario para guardar el puntaje del modelo\n",
        "model_scores = {}\n",
        "\n",
        "# Evaluamos el modelo y apendizamos el puntaje a model_scores\n",
        "model_scores = model.score(X_test, y_test)\n",
        "\n",
        "# Mostramos el score\n",
        "model_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S21uNY4-vgkz",
        "outputId": "64d756a1-02d4-4e53-884b-907be966c822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8688524590163934"
            ]
          },
          "metadata": {},
          "execution_count": 208
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <a name=\"M7\">7. Matriz de confusión</a>\n",
        "* Finalmente, calculamos y graficamos la matriz de confusión"
      ],
      "metadata": {
        "id": "7C3ISiT94iln"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make preidctions on test data\n",
        "y_preds = model.predict(X_test)\n",
        "\n",
        "y_preds"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GAbqtDaT6MPv",
        "outputId": "dbc10b9c-c100-4a22-e737-77357f3f057b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 209
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pg0MiyaK6gEw",
        "outputId": "c379601f-4619-46c4-c74c-0ee7bde7fa33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 210
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matriz de confusión\n",
        "print(confusion_matrix(y_test, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFq5PVZ4635g",
        "outputId": "a663f36e-c6fa-4377-f259-3b71e87edf43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[25  4]\n",
            " [ 4 28]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matriz de confusión con Seaborn\n",
        "sns.set(font_scale=1.5)\n",
        "\n",
        "def plot_conf_mat(y_test, y_preds):\n",
        "    \"\"\"\n",
        "    Plots a confusion matrix using Seaborn's heatmap().\n",
        "    \"\"\"\n",
        "    fig, ax = plt.subplots(figsize=(3, 3))\n",
        "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
        "                     annot=True,  \n",
        "                     cbar=False)\n",
        "    plt.xlabel(\"true label\")\n",
        "    plt.ylabel(\"predicted label\")\n",
        "    \n",
        "plot_conf_mat(y_test, y_preds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "AMEyGMw_ynuj",
        "outputId": "85627437-2bbb-4505-ed84-3c50c705a1ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 216x216 with 1 Axes>"
            ],
            "image/png": 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pQBRO5lA4mUUPW1M4GUPhZBYjD1sTQvqW1LF20dHR4HA4eOedd8DlcsVPp3SHw+Fg+fLljBZIyGAl9bLW1tYWHA4HWVlZUFNTk2lOWg6Hg7y8PMaL7C26rGUOXdYyq0cD38+ePQsA4tkNOn4nhPQNqeE0MTHp9ndCiGJRhxAhLNVth5C8qEOIEOZ02yEksTOHAwAScwl1zC9EHUIDH3UIMatXHUId6uvrsW7dOqioqCA4OFg8096dO3ewd+9etLW14ZNPPmGoZEKIzCOENm3ahNzcXHz77bcSU5E0NzcjMDAQ9vb2eP/99xVSaG/QmZM5dOZkFiMjhE6ePImZM2d2OUeQqqoqZs6cCaFQ2LMKCSESZJ6N68mTJ6itrZXaXltb2207+XMaGurIunYOlpZmiNm5BytXse8qRNl+v1eE46fO4dJPv+B+8UM8fdoMU5NRmPGaGxb7z4Gmhnqn/a/n5uE/+w4h79YdVFXXwnC4PiY7jcdbi/1hajJKSZ9CNjKfOceOHYv4+Hjx0yjPKiwsRHx8PF588UVGixtsPti4FoaGBsoug9VSTnyPfYcOw9RkFN4ODsA/li+FOX80omL3IfDtNWh8+lS8b/rlqwh6Jwx37xVhoZ831q9+B9PcpiD1dBrmL30XJWXS1/5hA5nPnGFhYQgJCcGsWbMgEAhgYWEBACgoKMDZs2fB4XBY+zxnfzBhvB3effcthK//GJHbNiq7HNZyf9UNby2eDx1tLfG2+XNmwczUGLFxCUg+dgoBc70BAPsOpYDL5eLbXZ9imJ6ueH8rCzP8e+vn+P7cBSyeP6fPP4OsZD5zTpw4Efv378e4ceNw8uRJ7Ny5Ezt37oRQKMS4ceOwb98+TJw4UZG1DlhcLhe7dm3DqVPnkXI4VdnlsJrdWOtOwezgOb19GZDbBYXibXV19RiqpgqejnanfY2G6wNo/xrBZnLNAO3o6IiEhARUVlbi/v37AIDRo0fDwIAuxXpj1cpQ2Nq8AP/5ocoupd8qKW2/RDXQ1xNvc53shKxfb2DDpk+xJGAuhunxcKegENui/wNLc1O8Pv0vyipXJj2anl1fXx/6+vpM1zIomZubYuO/wrDp4+0oLCyCmdloZZfU77S2tmLX3oMYoqKCWe7TxNtDF/uj8nEVUk58jxPfp4m3v+LijG0frIOWlqYyypWZXGNrW1tbcfjwYYSFhWHJkiX47bffAADV1dU4fPgwSkpKFFIkAMTHx2P69OkKO76y7IyOQMHdQmzfEavsUvqtrZ9/hazcPCx/azEsnvnnxuWqwMjQAFMmTsAH4auwY/P7eHOhLy5fvYa1/4pAc0uLEqv+czKfORsaGhASEoJr165BQ0MDjY2NqK6uBgBoa2sjMjISfn5+WL16tUIKrampQXFxsUKOrSwBAb4QCKZi2mu+aGH5HwpbRcXuw4GkY5jn8zpCg+Z3anvv40+RlZuHw9/ugvofK+AJ/vIy+CbG+CgyGkdSz2Cut6cyypaJzOGMiopCbm4uoqOj8dJLL8HV1VXcpqKighkzZiA9PV2ucMqzTH1RUZHM+/YHampqiPxkI06ePIdHJWWwsjIHAJgYjwQA6OryYGVljvLySlRX1yixUvaK+fpbfBV3ELNnueNfa//eqe3ho1Kc+D4NAX5e4mB28HjtFXwUGY2r13MGRjiFQiHmz58PgUCAx48fS7Tz+XykpsrX07h48WLxYPo/0zGwfqDQ0FCHkdFwzJolwKxZAon2wEV+CFzkh3+u+xCfbf9KCRWyW8zX3+LLb+Lh87oAH4avkvjbKCmvAAC0trVJvLblj9XZu1qlnU1kDmdpaWm3M7praGigrq5OrjfX1NSEra0tQkJC/nRfoVCIEydOyHV8Nqurq4f/gr9KbDccboCY6C0QCs/hm70JyMlh31M+yvblN/H48pt4eHlOx0cbVne5DIgFfzRUVLg492MGVi4L7nQ75UjqaQDtt2XYTOZw6unpddvhc/v2bRgZGcn15nZ2digpKYFAIHnm6Or4A0lLSwuSkyX/2XT01uYXFHbZPtgdTDqGmK+/xagRRpgycTxOnD7fqd1gmB5cJ70EXZ4OAv1nI+5gMuYtWQE/L0/o8nRwLec3nPg+DaYmo+Dn5aGcDyEjmcPp4uKC5ORkLF26VKLt/v37SEpKgo+Pj1xv7uDggK+//hrV1dXQ1dXtdl+RSCTxHCkZfHLzbgEAHpaU4r1Nn0q0T5xgD9dJLwEAwpa/BQv+aCQdO4Xd+w+huakZRoYGmD9nFv4WsgjaWpKDGdhE5kfGCgsL4efnhxEjRmDWrFmIiorCkiVLoKKigoSEBHC5XBw+fBijRsk+mLisrAx3796FnZ0dNDUVd8+JHhljDj0yxizGZnzPzc3Fhg0bcOvWrU7bx4wZg23btsk0faYyUDiZQ+FkFuPLMdy6dQv5+fkQiUQwNzdn/dMoFE7mUDiZ1etw1tXVwcfHB4GBgQgODmaytj5B4WQOhZNZvZ4JQUtLC1VVVdBi+RdoQgYSmcfWOjo6IicnR5G1EEKeIXM4w8LCIBQKkZSURLc0COkDMncIBQUFobi4GA8ePICuri74fD7U1Ts/rMrhcBAXF6eQQnuDvnMyh75zMqtH89Y+r2Pgecd9zPJyds+/Qkh/RytbE7nQmZNZtLI1If2Q3NOUNDU14cqVK+I5hExNTTFp0iQMfe6ZOUJI78gVzsOHD2PLli2oqakR99hyOBzweDysW7cOvr6+CimSkMFI5nCmpqYiPDwcxsbGWLp0aaeFjBISEvDee+9BXV0dM2fOVFixhAwmMncIeXt7o6WlBd999x20tTvPA1pbW4t58+ZBTU0NR48eVUihvUEdQsyhDiFmMdIhdPfuXfj6+koEEwB0dHTg6+uL33//vUcFEkIkyRxOQ0PDbts5HA6GDx/e64IIIe1kDuecOXOQnJzc5TxBT548QXJyMnUIEcIgmTuEJk6ciLS0NHh5eSEgIACWlu3Xyvn5+Th48CCGDRsGJycniekunZ2dma2YkEFC5g6h52c56JiK8NmXPzs9YcdUlnl5yp89jjqEmEMdQsxiZGztli1bGCmGECIbGltL5EJnTmbR2FpC+iEKJyEsReEkhKUonISwFIWTEJaicBLCUhROQliKwkkIS1E4CWEpCichLEXhJISlKJyEsNSgGPhOSH9EZ05CWIrCSQhLUTgJYSkKJyEsReEkhKUonISwFIWTEJaicBLCUhROQliKwkkIS1E4CWEpCqcSNTU1Ydu2bXBzc4ODgwP8/f2RkZGh7LL6rdLSUkRGRmLx4sWYMGECbGxscOXKFWWX1WMUTiUKDw9HXFwcvL298d5774HL5SI0NBTXrl1Tdmn90t27d7F7926UlJTAxsZG2eX0nogoRVZWlsja2lq0Z88e8bbGxkaRQCAQBQQEKK+wfqy2tlZUWVkpEolEotOnT4usra1Fly9fVnJVPUdnTiURCoVQVVXFvHnzxNuGDh2KuXPnIjMzE6WlpUqsrn/S1tbGsGHDlF0GYyicSpKXlwcLCwtoaWl12u7g4ACRSMSKpROJclE4laSsrAxGRkYS2w0NDQGAzpyEwqksjY2NUFVVldg+dOhQAMDTp0/7uiTCMhROJVFXV0dzc7PE9o5QdoSUDF4UTiUxNDTs8tK1rKwMALq85CWDC4VTSWxtbXH37l3U1dV12p6VlSVuJ4MbhVNJPD090dzcjMTERPG2pqYmJCcn46WXXsKIESOUWB1hgyHKLmCwcnR0hKenJyIjI1FWVgY+n4+UlBQUFxdjy5Ytyi6v39q5cycAID8/HwBw5MgRZGZmgsfjITAwUJmlyY3mrVWip0+fYseOHTh27Biqq6thY2ODNWvWwNXVVdml9VvShu2ZmJjg3LlzfVxN71A4CWEp+s5JCEtROAlhKQonISxF4SSEpSichLAUhZMQlqJwEsJSFE4it6ioKNjY2KCoqKhHr09OTlbY5Fvh4eEDY/4gUDhZIy8vD1FRUT3+gycDD4WTJfLy8hAdHY0HDx4ouxTCEhTOfqq1tRUNDQ3KLoMoEIWTBaKiorB+/XoAQFBQEGxsbGBjY4Pw8HAA//uOdunSJcTExEAgEMDBwQEnT54EgE77Pkvad7va2lps27YN7u7usLOzw5QpU7BmzRrcv3+/x5+hpKQEERER8PHxgbOzM+zt7TFz5kzExsaitbW1y9e0trYiKioK06ZNg52dHby8vHDixIku983JycHy5csxefJk2NnZwcPDA19++SVaWlp6XDPb0SNjLODu7o6ysjIcOnQIb7/9NiwtLQEAfD6/035bt25FS0sL/P39oaWlBQsLC7nfq7a2FgsWLEBxcTH8/PwwZswYlJWV4cCBA5g3bx6SkpJgYmIi93Fv3ryJ77//Hu7u7uDz+WhubsaFCxfw6aefoqioCB9++KHEayIjI1FfX4+FCxcCaP9nsmbNGjx9+hS+vr7i/c6fP48VK1bAzMwMISEh0NXVxfXr1/HFF18gLy8PX3zxhdz19gtKnTWXiCUlJUmdBLmjbcaMGaL6+nqJdmtra9G6detkOuZHH30ksre3F+Xl5XXat6ioSDRhwoQuj/O8L774QmRtbS26f/++eFtDQ4Oora1NYt+wsDCRra2tqKSkRKKuV199VVRTUyPeXlNTI3r11VdFzs7OooaGBpFI1D7RtqurqyggIEDU3Nzc6dh79uyR+Hzr1q0TWVtb/+ln6A/osrYfWbhwITQ0NHr8epFIhGPHjsHZ2RlGRkaorKwU/2hoaGD8+PFIT0/v0bHV1dXB4XAAtM/oUFVVhcrKSri5uaGtrQ25ubldfh4dHR3x7zo6OliwYAGqq6vFl+IXL15EeXk5fH19UVNT06nmqVOnivcZiOiyth/pyWXssyorK1FVVYX09HS4uLh0uQ+X27P/1y0tLYiNjcWRI0dQWFgI0XOPCdfU1Ei8puPy/VlWVlYAIL6l1DGjwYYNG6S+d3l5eY9qZjsKZz+irq4u1/7Pd8R0BMbV1RWhoaGM1QUAERER2L9/P2bOnIm3334b+vr6UFVVxa+//orIyEi0tbX16LgdNf/zn//E2LFju9xnoM5USOFkiY5Lwp7Q09NDVVWVxPbne1/19fXB4/Hw5MkTxqdCOXLkCJydnbF9+/ZO2wsLC6W+pqCgQGJbx5ly9OjRAABzc3MAgIaGxqCbvoW+c7KEpqYmAKC6ulru15qbm+P69eud7ntWV1cjOTm5035cLhdeXl7Izs6GUCjs8lgVFRVyv3/HsZ+/lK2vr8fevXulvubgwYOora0V/15bW4uEhATweDxMmjQJAODm5gYDAwPs3r27y39AjY2NePLkSY9qZjs6c7KEvb09uFwudu3aherqamhqamL06NFwdHT809cuWrQIa9euxZtvvgkfHx/U1NQgMTERxsbG4kmqO6xevRq//PILVq1ahddffx2Ojo5QVVVFcXExfvzxR4wbNw4RERFy1+/h4YFDhw5h1apVcHV1RXl5OZKSkqCnpyf1NcOGDcO8efPEt02Sk5NRXFyMTZs2iTu+NDU1sXXrVixfvhyenp7w8/ODmZkZampqUFBQgNOnTyM6OhqTJ0+Wu2a2o3CyhLGxMTZv3ozdu3fjgw8+QHNzM+bMmSNTOL29vVFaWor4+Hhs2bIFpqam+Nvf/gYulyuepLqDjo4ODh48iG+++QZCoRBnz56FiooKRo4cCScnp05LEspj/fr10NLSEh9z1KhRmD9/Puzt7REcHNzla8LCwnD16lUcOHAA5eXlsLCwQGRkJLy8vDrt98orr+C///0vYmNjcfToUTx+/Bg8Hg98Ph/BwcEDZqD782j2PUJYir5zEsJSFE5CWIrCSQhLUTgJYSkKJyEsReEkhKUonISwFIWTEJaicBLCUv8PQKsSWxoiSzEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Informe de clasificación\n",
        "print(classification_report(y_test, y_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWQsnG-r6wvX",
        "outputId": "c6f3fd66-917c-4789-ab99-2e4f1e550698"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.86      0.86        29\n",
            "           1       0.88      0.88      0.88        32\n",
            "\n",
            "    accuracy                           0.87        61\n",
            "   macro avg       0.87      0.87      0.87        61\n",
            "weighted avg       0.87      0.87      0.87        61\n",
            "\n"
          ]
        }
      ]
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      "source": [
        "## Enlaces (documentacion y contenido consultado)\n",
        "* https://repositorio.unal.edu.co/bitstream/handle/unal/79641/1152700918.2020.pdf?sequence=5&isAllowed=y\n",
        "\n",
        "* http://repositorio.uan.edu.co/bitstream/123456789/4803/5/2021DavidAlejandroGallegoMonografi%CC%81a.pdf\n",
        "\n",
        "* https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\n",
        "\n",
        "* https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
        "\n",
        "* https://scikit-learn.org/stable/modules/preprocessing.html\n",
        "\n",
        "* https://www.analyticsvidhya.com/blog/2021/12/what-does-numpy-random-seed-do/\n",
        "\n",
        "* https://bokeh.org/\n",
        "\n",
        "* https://www.kaggle.com/code/faressayah/predicting-heart-disease-using-machine-learning\n",
        "\n",
        "* https://colab.research.google.com/github/sujikathir/Heart-disease-Identification/blob/master/HEART_DISEASE.ipynb#scrollTo=KC3dwoE0w_oC\n",
        "\n",
        "* https://github.com/RicardoMoya/Data_Science_Introduction_With_Python/blob/master/31_Ejercicio6_Clasificacion_Enfermedad_Coronaria.ipynb\n",
        "\n",
        "* https://www.kaggle.com/code/dovah0/notebookc4f0fa41a8\n",
        "\n",
        "* https://github.com/RicardoMoya/Data_Science_Introduction_With_Python/blob/master/30_Clasificacion_Multiple_N_Features_Regresion_Logistica_Scikit_Iris.ipynb\n",
        "\n",
        "* <a href=\"https://www.google.com/search?q=matriz+de+correlacion&sxsrf=ALiCzsaL5oBM5h6Xm-E82THTLncjE-Ju6g%3A1669068519045&ei=5_Z7Y5-pAtnV1sQP7p6C4Ac&oq=matriz+de+correl&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAxgAMgUIABCABDIFCAAQgAQyBQgAEIAEMgUIABCABDIFCAAQgAQyBQgAEIAEMgUIABCABDIFCAAQgAQyCAgAEIAEEMsBMgUIABCABDoKCAAQRxDWBBCwAzoHCAAQsAMQQzoECCMQJzoECAAQQ0oECEEYAEoECEYYAFCUBFiGCmCNFWgBcAF4AIABXogBhgSSAQE2mAEAoAEByAEKwAEB&sclient=gws-wiz-serp\">Matriz de correlacion</a>\n",
        "\n",
        "* <a href=\"https://www.google.com/search?q=matriz+de+confusion&sxsrf=ALiCzsaci0yG5qNoe6NsilkETpsBfXAEeQ%3A1669068435967&ei=k_Z7Y7DKOrrS1sQPzIia0Ao&ved=0ahUKEwjw3-y5pMD7AhU6qZUCHUyEBqoQ4dUDCBA&uact=5&oq=matriz+de+confusion&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIECCMQJzIFCAAQgAQyBQgAEIAEMgUIABCABDIFCAAQgAQyBQgAEIAEMgUIABCABDIFCAAQgAQyBQgAEIAEMgUIABCABDoKCAAQRxDWBBCwAzoECAAQQzoKCAAQsQMQgwEQQzoHCAAQsQMQQzoKCAAQgAQQhwIQFDoICAAQgAQQywFKBAhBGABKBAhGGABQ9A5YmB5goCJoAXABeACAAaYBiAHzCJIBBDEwLjKYAQCgAQHIAQjAAQE&sclient=gws-wiz-serp\">Matriz de confusión</a>\n",
        "\n",
        "* <a href=\"https://www.google.com/search?q=matriz+de+nash&sxsrf=ALiCzsYjtQ3kPeXzWe4ry3AeYSEvtSa6_A%3A1669068408689&ei=ePZ7Y-XeKdbb1sQP952EkAY&ved=0ahUKEwjl-uuspMD7AhXWrZUCHfcOAWIQ4dUDCBA&uact=5&oq=matriz+de+nash&gs_lcp=Cgxnd3Mtd2l6LXNlcnAQAzIFCAAQgAQ6CggAEEcQ1gQQsAM6BwgAELADEEM6CggAEIAEEIcCEBQ6BggAEBYQHjoICAAQFhAeEApKBAhBGABKBAhGGABQ1QZY9Qlgxw5oAXABeACAAaQBiAGrBJIBAzEuM5gBAKABAcgBCsABAQ&sclient=gws-wiz-serp\">matriz de nash</a>\n",
        "\n",
        "* https://pesquisa.bvsalud.org/portal/resource/pt/biblio-1289245\n",
        "\n",
        "* http://rstudio-pubs-static.s3.amazonaws.com/514121_43c005ab7bb049bbaee19aaf9dec29cd.html\n",
        "\n",
        "* https://gist.github.com/shushrutsharma/f51a8f9ce0692c040d379d0b2382118f\n",
        "\n",
        "* https://github.com/vedantyetekar7/heart-disease-prediction/blob/main/heart-disease-prediction.ipynb"
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