Tarea05_ML_Febrero_AprendizajaNoSupervisado.ipynb

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      "source": [
        "🤖 📓\n",
        "# Tarea en clase  #05\n",
        "#### Nombre :Joel Alejandro Rodarte Rivera \n",
        "#### Matrícula: 2173260\n",
        "\n",
        "El siguiente cuaderno contiene:\n",
        "* Algoritmo aprendizaje automático no supervisado No visto en clase y que se adecua a mi set de datos.\n",
        "* Estrategia para determinar número de grupos no visto en clase y que se adecua a mi set de datos. \n",
        "\n",
        "\n",
        "\n"
      ],
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      "source": [
        "## Algoritmos no supervisados (Información Binaria)\n",
        "\n",
        "**IMPORTANTE: Por el momento, considero que la explicación del modelo No me queda muy claro dado existen conceptos de probabilidad con los que no estoy familiarizado. Sin emabargo, me es importante al menos saber de su existencia para dominarlo más adelante en el transcurso del curso.**\n",
        "\n",
        "[Benroulli Mixture Model](https://stats.stackexchange.com/questions/89535/what-algorithm-should-i-use-to-cluster-a-huge-binary-dataset-into-few-categories#:~:text=A%20classic%20algorithm%20for%20binary,powerful%20but%20also%20more%20difficult.)\n",
        "\n",
        "* **¿Qué es?**\n",
        "  * La distribución Bernoulli es una distribución discreta que predice la probablidad de éxito (1) o fracaso (0) de un evento. El término \"mixture\" se incluye dado que este método combina multiples distribuciónes de probabilidad para modelar una distribucón más compleja. \n",
        "  * Respecto al punto anterior, este algoritmo asumme que la información se genera de K mezclas de distribuciónes de Bernoulli, en donde cada componente de la mezcla representa a un cluster.\n",
        "  * Cada cluster es caracterizado por un vector de probabilidades que predice la probabilidad de encontrar un 1 en ese cluster. La distribución de la mezcla general es entonces una suma ponderada de las distribuciones de K Bernoulli, donde los pesos especifican la proporción de los datos que pertenecen a cada grupo. (?) \n",
        "    * Este último punto es el que no me queda muy claro.\n",
        "\n",
        "\n",
        "* **¿Porqué es conveniente?**\n",
        "  * K means asume que la información se generó con una distribución Normal (Gaussiana) mientras que la Bernouilli mixiture model puede manejar datos binarios que No están normalmente distribuidos. \n"
      ],
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      "source": [
        "## Clustering para datos binarios\n",
        "\n",
        "[Akaike Information Criterion](https://builtin.com/data-science/what-is-aic)\n",
        "\n",
        "El Akaike Information Criterion es un un solo numero que se utiliza para determinar cual dentro de múltiples opciónes de modelos mejor se ajusta para un set de datos. Este numero se puede utilizar en combinación con el Bernoulli Mixture Model para determinar la cantidad de cluster óptima. "
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      "source": [
        "\n",
        "🤖 📓\n",
        "# Tarea #05\n",
        "#### Nombre :Joel Alejandro Rodarte Rivera \n",
        "#### Matrícula: 2173260\n",
        "\n",
        "El siguiente cuaderno contiene:\n",
        "\n",
        "\n",
        "\n",
        " \n",
        "\n",
        "\n"
      ],
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          "text": [
            "Cloning into 'MachineLearning_EM23'...\n",
            "remote: Enumerating objects: 48, done.\u001b[K\n",
            "remote: Counting objects: 100% (48/48), done.\u001b[K\n",
            "remote: Compressing objects: 100% (44/44), done.\u001b[K\n",
            "remote: Total 48 (delta 19), reused 4 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (48/48), 663.92 KiB | 1.22 MiB/s, done.\n"
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      "source": [
        "! git clone https://github.com/JoeAM11/MachineLearning_EM23.git\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "import statistics\n",
        "import matplotlib.pyplot as plt\n",
        "df = pd.read_csv('/content/MachineLearning_EM23/Training.csv') #Importar el csv desde mi repositorio en Guthub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "4sKni2_uHYR8"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "pd.set_option('display.max_columns', 10)\n",
        "pd.set_option('display.max_rows', 10) #Para evitar que me trunque informacióno o que no despliegue tanta, segun sea necesario \n",
        "df = df[df.columns[::-1]] # Me es más logico tener la enfermedad en la primera columna por motivos de organización. \n",
        "pd.set_option('display.float_format', '{:.3f}'.format) #que los resultados se impriman con tres decimales\n"
      ],
      "metadata": {
        "id": "039XGzN9Ro7X"
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      "execution_count": 4,
      "outputs": []
    },
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      "source": [
        "df.drop(columns=['Unnamed: 133'],inplace=True) #no entiendo porque aparecio una nueva columna, por eso la quito. "
      ],
      "metadata": {
        "id": "hkEkPfvSHE9O"
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "id": "YZqqZfKMVtKv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Los sintomas más comunes \n",
        "\n",
        "common_prognosis = df.mean().sort_values(ascending=False) \n",
        "common_prognosis"
      ],
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            "<ipython-input-224-972ae5685221>:3: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
            "  common_prognosis = df.mean().sort_values(ascending=False)\n"
          ]
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            "text/plain": [
              "fatigue                  0.393\n",
              "vomiting                 0.389\n",
              "high_fever               0.277\n",
              "loss_of_appetite         0.234\n",
              "nausea                   0.233\n",
              "                          ... \n",
              "extra_marital_contacts   0.022\n",
              "swollen_blood_vessels    0.022\n",
              "sunken_eyes              0.022\n",
              "foul_smell_of urine      0.021\n",
              "fluid_overload           0.000\n",
              "Length: 132, dtype: float64"
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          "execution_count": 224
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        "# #Para calcular cuantos pacientes tengo paar cada enfermedad, en este caso parece se rque tengo 120 por enfermedad \n",
        "\n",
        "prognosis_counts = df.groupby('prognosis').count() \n",
        "prognosis_counts"
      ],
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              "                                         Unnamed: 133  yellow_crust_ooze  \\\n",
              "prognosis                                                                  \n",
              "(vertigo) Paroymsal  Positional Vertigo             0                120   \n",
              "AIDS                                                0                120   \n",
              "Acne                                                0                120   \n",
              "Alcoholic hepatitis                                 0                120   \n",
              "Allergy                                             0                120   \n",
              "...                                               ...                ...   \n",
              "Tuberculosis                                        0                120   \n",
              "Typhoid                                             0                120   \n",
              "Urinary tract infection                             0                120   \n",
              "Varicose veins                                      0                120   \n",
              "hepatitis A                                         0                120   \n",
              "\n",
              "                                         red_sore_around_nose  blister  \\\n",
              "prognosis                                                                \n",
              "(vertigo) Paroymsal  Positional Vertigo                   120      120   \n",
              "AIDS                                                      120      120   \n",
              "Acne                                                      120      120   \n",
              "Alcoholic hepatitis                                       120      120   \n",
              "Allergy                                                   120      120   \n",
              "...                                                       ...      ...   \n",
              "Tuberculosis                                              120      120   \n",
              "Typhoid                                                   120      120   \n",
              "Urinary tract infection                                   120      120   \n",
              "Varicose veins                                            120      120   \n",
              "hepatitis A                                               120      120   \n",
              "\n",
              "                                         inflammatory_nails  ...  shivering  \\\n",
              "prognosis                                                    ...              \n",
              "(vertigo) Paroymsal  Positional Vertigo                 120  ...        120   \n",
              "AIDS                                                    120  ...        120   \n",
              "Acne                                                    120  ...        120   \n",
              "Alcoholic hepatitis                                     120  ...        120   \n",
              "Allergy                                                 120  ...        120   \n",
              "...                                                     ...  ...        ...   \n",
              "Tuberculosis                                            120  ...        120   \n",
              "Typhoid                                                 120  ...        120   \n",
              "Urinary tract infection                                 120  ...        120   \n",
              "Varicose veins                                          120  ...        120   \n",
              "hepatitis A                                             120  ...        120   \n",
              "\n",
              "                                         continuous_sneezing  \\\n",
              "prognosis                                                      \n",
              "(vertigo) Paroymsal  Positional Vertigo                  120   \n",
              "AIDS                                                     120   \n",
              "Acne                                                     120   \n",
              "Alcoholic hepatitis                                      120   \n",
              "Allergy                                                  120   \n",
              "...                                                      ...   \n",
              "Tuberculosis                                             120   \n",
              "Typhoid                                                  120   \n",
              "Urinary tract infection                                  120   \n",
              "Varicose veins                                           120   \n",
              "hepatitis A                                              120   \n",
              "\n",
              "                                         nodal_skin_eruptions  skin_rash  \\\n",
              "prognosis                                                                  \n",
              "(vertigo) Paroymsal  Positional Vertigo                   120        120   \n",
              "AIDS                                                      120        120   \n",
              "Acne                                                      120        120   \n",
              "Alcoholic hepatitis                                       120        120   \n",
              "Allergy                                                   120        120   \n",
              "...                                                       ...        ...   \n",
              "Tuberculosis                                              120        120   \n",
              "Typhoid                                                   120        120   \n",
              "Urinary tract infection                                   120        120   \n",
              "Varicose veins                                            120        120   \n",
              "hepatitis A                                               120        120   \n",
              "\n",
              "                                         itching  \n",
              "prognosis                                         \n",
              "(vertigo) Paroymsal  Positional Vertigo      120  \n",
              "AIDS                                         120  \n",
              "Acne                                         120  \n",
              "Alcoholic hepatitis                          120  \n",
              "Allergy                                      120  \n",
              "...                                          ...  \n",
              "Tuberculosis                                 120  \n",
              "Typhoid                                      120  \n",
              "Urinary tract infection                      120  \n",
              "Varicose veins                               120  \n",
              "hepatitis A                                  120  \n",
              "\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ff44c5dd-ea10-4d47-96e8-da64773d30ca 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-ff44c5dd-ea10-4d47-96e8-da64773d30ca');\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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Fatiga es el sintoma más comun, por enfermedad en cuantos de los 120 casos SI está presente fatiga\n",
        "\n",
        "prognosis_sum = df.groupby('prognosis')[['fatigue']].sum() \n",
        "prognosis_sum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "dM9Xox7kPJur",
        "outputId": "d591267c-9cc4-451e-f501-fb9a1b6d3994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         fatigue\n",
              "prognosis                                       \n",
              "(vertigo) Paroymsal  Positional Vertigo        0\n",
              "AIDS                                           0\n",
              "Acne                                           0\n",
              "Alcoholic hepatitis                            0\n",
              "Allergy                                        0\n",
              "...                                          ...\n",
              "Tuberculosis                                 114\n",
              "Typhoid                                      120\n",
              "Urinary tract infection                        0\n",
              "Varicose veins                               114\n",
              "hepatitis A                                    0\n",
              "\n",
              "[41 rows x 1 columns]"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>fatigue</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prognosis</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(vertigo) Paroymsal  Positional Vertigo</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIDS</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acne</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcoholic hepatitis</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allergy</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tuberculosis</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Typhoid</th>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urinary tract infection</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Varicose veins</th>\n",
              "      <td>114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hepatitis A</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a408133-b4cb-454f-b520-03edc5c5575c')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a408133-b4cb-454f-b520-03edc5c5575c 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-0a408133-b4cb-454f-b520-03edc5c5575c');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 226
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Lo mismo que el anterior pero en porcentaje. En realidad no debería ser porcentaje pero como tengo los datos con 0 y 1, el promedio si es es.\n",
        "\n",
        "df_prognosis = df.groupby('prognosis')[['fatigue']].mean() \n",
        "df_prognosis"
      ],
      "metadata": {
        "id": "4Qi823sMSh6g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "e5f175ea-e8ea-4daa-86e5-4e303b7cd22f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         fatigue\n",
              "prognosis                                       \n",
              "(vertigo) Paroymsal  Positional Vertigo    0.000\n",
              "AIDS                                       0.000\n",
              "Acne                                       0.000\n",
              "Alcoholic hepatitis                        0.000\n",
              "Allergy                                    0.000\n",
              "...                                          ...\n",
              "Tuberculosis                               0.950\n",
              "Typhoid                                    1.000\n",
              "Urinary tract infection                    0.000\n",
              "Varicose veins                             0.950\n",
              "hepatitis A                                0.000\n",
              "\n",
              "[41 rows x 1 columns]"
            ],
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              "\n",
              "  <div id=\"df-39422123-206c-485d-a19c-6d986161d72d\">\n",
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              "    .dataframe tbody tr th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fatigue</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>prognosis</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(vertigo) Paroymsal  Positional Vertigo</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AIDS</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acne</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alcoholic hepatitis</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Allergy</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tuberculosis</th>\n",
              "      <td>0.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Typhoid</th>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Urinary tract infection</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Varicose veins</th>\n",
              "      <td>0.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hepatitis A</th>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39422123-206c-485d-a19c-6d986161d72d')\"\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",
              "    }\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-39422123-206c-485d-a19c-6d986161d72d 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-39422123-206c-485d-a19c-6d986161d72d');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 227
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## K means"
      ],
      "metadata": {
        "id": "59qo_RSsUBGq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Me deshago de la columna prognosis dado el algoritmo de Kmeans no puede trabajar con variables categóricas.\n",
        "# Utilizaré los dos síntomas más comunes para ver la combinación de aparición entre ellas para los pacientes.\n",
        "\n",
        "df_noProg = df.drop(columns=['prognosis']) \n",
        "df_noProg_fatigue_vomiting= df_noProg[['fatigue','vomiting']]\n",
        "df_noProg_fatigue_vomiting"
      ],
      "metadata": {
        "id": "MlhJuLd0XwlQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Coloco cuatro clusters proque sé que sólo tengo cuatro resultados posibles para X enfermedad \n",
        "  #Que paciente no tenga ninguno de los dos síntomas\n",
        "  #Que paciente tenga fatiga pero no vómito\n",
        "  #Que paciente tenga vómito pero no fatiga\n",
        "  #Que paciente tenga ambos síntomas\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "kmeans = KMeans(n_clusters = 4)\n",
        "\n",
        "kmeans.fit(df_noProg_fatigue_vomiting) #Utilizando el df que solo tiene fatiga y vómito"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0ZIXFRxUEbI",
        "outputId": "6ac6b1bc-e3b9-4ac5-8b4b-84c51118a378"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=4)"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# The predict() method is then used to predict the cluster labels for each data point. \n",
        "labels = kmeans.predict(df_noProg_fatigue_vomiting) \n",
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SKVmc6Z5ZEZ0",
        "outputId": "359e2d73-b895-4ecb-b7f0-92e3bd6c777c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the clusters\n",
        "# Dada se cuenta con infromación binaria, se espera que el plot del K medias sea una cuadrado porque solo hay 4 posibles resultados. \n",
        "\n",
        "plt.figure(figsize = (10,10))\n",
        "plt.scatter(df_noProg_fatigue_vomiting.iloc[:, 0], df_noProg_fatigue_vomiting.iloc[:, 1], c=labels, cmap='viridis')\n",
        "plt.xlabel('Vomiting')\n",
        "plt.ylabel('Fatigue')\n",
        "plt.title('KMeans Clustering with 4 Clusters')\n",
        "plt.show()\n",
        "plt.savefig(\"test1.jpg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 638
        },
        "id": "pePkJVYjZIYm",
        "outputId": "a5ae1eeb-885e-4400-bff0-eb7b78682e54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Coordenadas de los centros de los clusters\n",
        "# Cómo se esperaba sólo se tienen 4 coordenadas\n",
        "\n",
        "kmeans.cluster_centers_\n",
        "df_centers = pd.DataFrame(kmeans.cluster_centers_, columns = ['x', 'y'])\n",
        "df_centers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "KqRBdtGTaOH6",
        "outputId": "c8981578-9136-41ce-f979-87b9488ff442"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       x     y\n",
              "0  0.000 0.000\n",
              "1  1.000 0.000\n",
              "2 -0.000 1.000\n",
              "3  1.000 1.000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a4e70d8-80e4-4129-a070-48cb24c6d0dc\">\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>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.000</td>\n",
              "      <td>0.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a4e70d8-80e4-4129-a070-48cb24c6d0dc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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-3a4e70d8-80e4-4129-a070-48cb24c6d0dc 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-3a4e70d8-80e4-4129-a070-48cb24c6d0dc');\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": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# En kmeans, el tamaño de los grupos es variable y depende SÓLO de la distancia entre sus características\n",
        "df_centroids_freq = pd.DataFrame(np.unique(kmeans.labels_, return_counts = True)).T\n",
        "df_centroids_freq.columns = ['ck', 'nk']\n",
        "df_centroids_freq\n",
        "\n",
        "#interpretación\n",
        "  #Se tienen 1826 pacientes que para alguna enfermedad NO tienen ni vomito ni fatiga\n",
        "  #Se tienen 1152 pacientes que para alguna enfermedad NO tienen fatiga pero SI vómito\n",
        "  #Se tienen 1170 pacientes que para alguna enfermedad SI tienen fatiga pero NO vómito\n",
        "  #Se tienen 762 pacientes que para alguna enfermedad SI tienen fatiga y SI vómito\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "OIApMYubbasq",
        "outputId": "acf8b1e3-0a51-4763-b3b6-ea119e3f81d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ck    nk\n",
              "0   0  1836\n",
              "1   1  1170\n",
              "2   2  1152\n",
              "3   3   762"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb483179-9818-4226-ba7d-197e5d9b8d23\">\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>ck</th>\n",
              "      <th>nk</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>762</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb483179-9818-4226-ba7d-197e5d9b8d23')\"\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",
              "      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-cb483179-9818-4226-ba7d-197e5d9b8d23 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-cb483179-9818-4226-ba7d-197e5d9b8d23');\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": 183
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bernouilli Mixture Model\n",
        "\n"
      ],
      "metadata": {
        "id": "9CPJr1aHuu7H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.mixture import BayesianGaussianMixture\n",
        "\n",
        "# Fit Bernoulli mixture model with 4 components\n",
        "model = BayesianGaussianMixture(n_components=4, weight_concentration_prior_type=\"dirichlet_distribution\")\n",
        "model.fit(df_noProg_fatigue_vomiting)\n",
        "\n",
        "# Get cluster assignments\n",
        "y = model.predict(df_noProg_fatigue_vomiting)\n",
        "\n",
        "# Get parameters of each component\n",
        "means = model.means_\n",
        "\n",
        "# Plot data and clusters\n",
        "colors = ['red', 'blue']\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(means[:, 0], means[:, 1], color='black', marker='x', label=\"Cluster centers\")\n",
        "plt.xlabel(\"Fatigue\")\n",
        "plt.ylabel(\"Vomiting\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "fNBiGAcereJp",
        "outputId": "8bcea6e2-08dd-4c15-f965-899e95e3c614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Seleccion # de clusters adecuado"
      ],
      "metadata": {
        "id": "VFBaX8QwpUFP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Método del codo \n",
        "  # Entender que sucedería que si se itera entre 2 y 8 clusters \n",
        "  # Se sabe que con debería ser suficiente con 4 pero, se tratará de dar respuesta en ver cómo cambia la dispersión añadiendo más de 4. \n",
        "    # Se espera que la diferencia sea pequeña o nula dsepués de 4. Se pera sea muy grande para menos de 4. \n",
        "\n",
        "inertia = []\n",
        "for n_clusters in range(2, 9):\n",
        "  kmeans = KMeans(n_clusters=n_clusters).fit(df_noProg_fatigue_vomiting)\n",
        "  inertia.append(kmeans.inertia_ / n_clusters)"
      ],
      "metadata": {
        "id": "4w6yX7ZPktqE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Como se esperaba después de 4 la inercia es 0, indicando que 4 es el punto óptimo para hacer el corte.\n",
        "\n",
        "df_inertias = pd.DataFrame({'n_clusters': range(2, 9), 'inertia': inertia})\n",
        "df_inertias"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "vqq8mmqYlZm4",
        "outputId": "aa441a83-4b4f-43e3-acc7-db3f6e2c2c7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   n_clusters  inertia\n",
              "0           2  584.658\n",
              "1           3  152.878\n",
              "2           4    0.000\n",
              "3           5    0.000\n",
              "4           6    0.000\n",
              "5           7    0.000\n",
              "6           8    0.000"
            ],
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              "\n",
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              "          const element = document.querySelector('#df-c005e2cd-0a37-4f0b-ad38-90ba23b9f09c');\n",
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              "                                                     [key], {});\n",
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              "\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",
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            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (10,10))\n",
        "plt.plot(df_inertias.n_clusters, df_inertias.inertia)\n",
        "plt.xlabel('Número de grupos')\n",
        "plt.ylabel('Media de las distancias cuadradas entre grupos')\n",
        "plt.ylim((0, 1.1 * df_inertias.inertia.max()))\n",
        "plt.xticks(range(2, 15))\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        },
        "id": "wCCKzTlJmXbq",
        "outputId": "a3e757f8-9d21-4680-f855-2b7e2770266b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
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    {
      "cell_type": "markdown",
      "source": [
        "#Revista - Publicaciónes Médicas relacionadas con datos \n",
        "\n",
        "Para el desarollo de este curso, he encontrado la siguiente revista que se relaciona con la temática de mis set de datos. \n",
        "\n",
        "[BMC Medical Informatics and Decision Making](https://bmcmedinformdecismak.biomedcentral.com/)\n",
        "\n",
        "**Áreas de conocimiento y categorías (Quartil):**\n",
        "* Health Policy (Q1); Computer Science Applications (Q2); Health Informatics (Q2)\n",
        "\n",
        "**Tipo de acceso:**\n",
        "* Open Access\n",
        "\n",
        "**Enfoque del journal:**\n",
        "* Journal para publicación de investigaciones sobre diseño, desarrollo, implementación, uso y evaluación de tecnologías de información y toma de decisiones para la salud humana.\n",
        "\n",
        "**Impact factor:**\n",
        "* 2.398\n",
        "\n",
        "**H-Index:**\n",
        "* 79\n",
        "\n",
        "**Ranking:**\n",
        "* 5405\n",
        "\n",
        "**SCImago Journal Rank (SJR):** \n",
        "* .833\n",
        "* [SCImagoJournal](https://www.scimagojr.com/journalsearch.php?q=23602&tip=sid&clean=0)\n",
        "\n",
        "\n",
        "## Justificación \n",
        "\n",
        "El journal BMC medical informatics and decision making es un journal relevante para el tema de Disease prediction using machine learning bajo las categorías de Health Informatics o Computer Science aplication. La audiencia de este journal consiste en investigadores, médicos y reguladores de leyes interesados en los útlimos desarrollos de tecnologias de la información con aplicación médica y para la toma de decisiones. La investigación propuesta es relevante para esta audiencia, ya que tiene potencial para mejorar los resultados de salud de los pacientes. Además, el journal es open access, lo que promueve el uso de esta investigación en futuros proyectos relacionados. \n",
        "\n",
        "Por otro lado, las métricas del journal ayudan a evaluar la callidad, visibilidad y reputación del journal, por lo que fueron consideradas para su selección. En este caso el factor de impacto del 2020 fue de 2.398, según JCR. Esto es útil para evaluar la importancia relativa de un journal en su campo y medir la frecuencia en la que un articulo del journal ha sido citado. Además del factor de impacto, otra métrica son los cuartiles, donde los rankings de los journals en cada categoría son divididos en Q1, Q2, Q3, Q4 de mayor a menor según su JCR y SJR. Para este journal en las áreas de Health Informatics y Computer Science aplication fue de Q2. Esto quiere decir que el journal tiene alto impacto y visibilidad en el campo de la investigación. \n",
        "\n",
        "## Algunos artículos relacionados con mi set de datos\n",
        "[Comparing different supervised machine learning algorithms for disease prediction](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-019-1004-8)\n",
        "\n",
        "[Design, implementation, and evaluation of the computer-aided clinical decision support system based on learning-to-rank: collaboration between physicians and machine learning in the differential diagnosis process](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02123-5)\n",
        "\n",
        "[Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01318-4)\n",
        "\n",
        "\n",
        "[Machine learning based efficient prediction of positive cases of waterborne diseases](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-022-02092-1)\n",
        "\n",
        "[Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis](https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01266-z\n",
        ")\n",
        "\n",
        "\n",
        "\n",
        "# Lineamientos para publicación - Template de LateX\n",
        "\n",
        "El artículo seguirá los lineamientos de la siguiente liga segun el Journal. \n",
        "\n",
        "[Research article](https://bmcmedinformdecismak.biomedcentral.com/submission-guidelines/preparing-your-manuscript/research-article\n",
        ")\n",
        "\n",
        "\n"
      ],
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