Prediccion_TM_Tesis_USMP.ipynb

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      "source": [
        "# **PREDICCIÓN DE TEMA DE INVESTIGACIÓN PARA HACER TU TESIS USANDO ALGORITMOS DE MACHINE LEARNING**\n",
        "#USMP\n",
        "## Logística Regresión con Python\n",
        "\n",
        "# Objetivos\n",
        "Con este proyecto innovador ayudamos a todos los egresados a cumplir su sueño de ser titulados de la USMP.\n",
        "Con este Proyectos innovador los egresados aprenderan:\n",
        "* Utilizar scikit Logistic Regression para clasificar\n",
        "* Entender la matriz de confusión que es una metrica de evaluación.\n",
        "\n",
        "En este notebook, aprenderá Regresión logística y, luego, creará un modelo para predecir el tema de investigación de una tesis, predecirá la tecnologia moderna que usara en su tesis como también que problema resolverá en su tesis, de acuerdo al perfil de cada estudiante, tema de interes, años de experiencia, etc. Para la toma de desiciones de los egresados y no perder tiempo o incluso a veces no realizan sus tesis ya que no saben que tecnología usar.\n",
        "\n",
        "# Tabla de contenidos\n",
        "1. Sobre el dataset\n",
        "2. Selección y Pre-procesamiento de Data\n",
        "3. Modelado (Regresión Logística con scikit learn)\n",
        "4. Evaluación\n",
        "5. Práctica\n",
        "\n",
        "# ¿Cuál es la diferencia entre Regresió Lineal y Regresión Logística\n",
        "Si bien la regresión lineal es adecuada para estimar valores continuos (por ejemplo, estimar el precio de la vivienda), no es la mejor herramienta para predecir la clase de un punto de datos observado. Para estimar la clase de un punto de datos, necesitamos algún tipo de orientación sobre cuál sería la clase más probable para ese punto de datos. Para ello utilizamos Regresión logística.\n",
        "\n",
        "# Recuerde la regresión lineal:\n",
        "\n",
        "Como sabe, la regresión lineal encuentra una función que relaciona una variable dependiente continua, y, con algunos predictores (variables independientes 𝑥1, 𝑥2, etc.). Por ejemplo, la regresión lineal simple asume una función de la forma:\n",
        "$$ y= \\theta_0+ \\theta_1 x_1 + \\theta_2 x_2 + \\cdots $$\n",
        "y encuentra los valores de los parámetros $ \\theta_0, \\theta_1, \\theta_2$\n",
        " , etc., donde el término $\\theta_0$ es la \"intersección\". Generalmente se puede mostrar como: \n",
        "$$ h_\\theta(x) = \\theta^TX $$\n",
        "La regresión logística es una variación de la regresión lineal, útil cuando la variable dependiente observada, y, es categórica. Produce una fórmula que predice la probabilidad de la etiqueta de clase en función de las variables independientes.\n",
        "\n",
        "La regresión logística se ajusta a una curva especial en forma de s tomando la regresión lineal y transformando la estimación numérica en una probabilidad con la siguiente función, que se llama función sigmoidea 𝜎:\n",
        "\n",
        "$$ h_\\theta(x) = \\sigma({\\theta^TX}) = \\frac{e^{(\\theta_0 + \\theta_1 x_1 +\\theta_2 x_2 + ...)}}{1+e^{(\\theta_0 + \\theta_1 x_1 +\\theta_2 x_2 + \\cdots)}} \n",
        "$$ or:\n",
        "$$\n",
        "ProbabilityOfaClass_1 =  P(Y=1|X) = \\sigma({\\theta^TX}) = \\frac{e^{\\theta^TX}}{1+e^{\\theta^TX}} \n",
        "$$\n",
        "En esta ecuación, ${\\theta^TX}$ es el resultado de la regresión (la suma de las variables ponderadas por los coeficientes), exp es la función exponencial y $\\sigma(\\theta^TX)$ es la [función sigmoidea o logística](https://en.wikipedia.org/wiki/Logistic_function?cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ&cm_mmc=Email_Newsletter-_-Developer_Ed%2BTech-_-WW_WW-_-SkillsNetwork-Courses-IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork-20718538&cm_mmca1=000026UJ&cm_mmca2=10006555&cm_mmca3=M12345678&cvosrc=email.Newsletter.M12345678&cvo_campaign=000026UJ), también llamada curva logística. Es una forma de \"S\" común (curva sigmoidea).\n",
        "\n",
        "Entonces, brevemente, la regresión logística pasa la entrada a través del logístico / sigmoide, pero luego trata el resultado como una probabilidad:\n",
        "<img\n",
        "src=\"https://ibm.box.com/shared/static/kgv9alcghmjcv97op4d6onkyxevk23b1.png\" width=\"400\" align=\"center\">\n",
        "\n",
        "\n",
        "El objetivo del algoritmo de Regresión Logística, es encontrar los mejores parámetros θ, para **$ℎ_\\theta(𝑥)$ = $\\sigma({\\theta^TX})$**, de tal manera que el modelo prediga mejor la clase de cada caso.\n",
        "\n",
        "# Técnica Moderna para Egresados con regresión logística\n",
        "A muchos rectores le preocupa la cantidad minima de estudiantes egresados de USMP que no realizan su tesis de grado porque no saben que tema o que tecnología usarán en su tesis o qué problema resolveran, por ello este proyecto de tesis ayudara a mas de xxx egresados a cumplir su sueño de ser titulados usando tecnología moderna de Machine Learning.\n",
        "\n",
        "# 1. Primero importemos las bibliotecas necesarias:"
      ]
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      },
      "source": [
        "# 1. Importemos las librerias necesarias"
      ]
    },
    {
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      "execution_count": 3,
      "metadata": {
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      "source": [
        "import pandas as pd\n",
        "import pylab as pl\n",
        "import numpy as np\n",
        "import scipy.optimize as opt\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline \n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Sobre el conjunto de datos o DataSet\n",
        "Usaremos un conjunto de datos de una encuesta realizada por una APP creada por nuestra autoria para predecir la técnica moderna para su tesis de investigación de los egresados. Este es un conjunto de datos de egresados históricos donde cada fila representa un egresado. Los datos son relativamente fáciles de entender y es posible que descubra información que puede utilizar de inmediato. \n",
        "Por lo general, el tema de investigacion esta ligado a sus intereses tecnológicos, tema de interes, años de experiencia, por lo que el enfoque de este análisis es predecir qué técnica moderna usarán los egresados para su tema de investigación de su tesis.\n",
        "Este conjunto de datos proporciona información para ayudarlo a predecir qué tema de investigación aplicarán en su tesis,  lo ayudará a que los egresados puedan hacer su tesis de acuerso a sus perfiles. Puede analizar todos los datos relevantes de los egresados y desarrollar programas de temas de investigación y que problema resolveran los egresados.\n",
        "\n",
        "El conjunto de datos incluye información sobre:\n",
        "\n",
        "* Codigo de alumno, esta es un identificador de cada egresado.\n",
        "\n"
      ],
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    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# 2. Cargue los datos de egresados\n",
        "DATASET COMBINADO FINAL es un archivo de datos hipotéticos sobre los EGRESADO DE LA USMP de la carrera profesional XXXX el esfuerzo de los tesistas para aumentar la base de datos de realización de tesis de los egresados."
      ]
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              "    CODALUMNO  BD  PROGRA  ML  BI  ...  AEX  PROBLEMA  GENERO  EDAD  ANIOEGRESO\n",
              "0  1844989078   3       3   3   2  ...    5         7       M    22        2015\n",
              "1  4447792027   3       3   4   1  ...    1        44       M    23        2019\n",
              "2  4812902185   3       3   4   1  ...    4        27       M    24        2016\n",
              "3  9971932859   4       5   5   2  ...    1        43       M    26        2015\n",
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              "\n",
              "[5 rows x 15 columns]"
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      "source": [
        "df = pd.read_csv('DATASET COMBINADO FINAL.csv')\n",
        "df.head()"
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              "(13999, 15)"
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              "      <th>mean</th>\n",
              "      <td>5.514888e+09</td>\n",
              "      <td>3.126509</td>\n",
              "      <td>3.191728</td>\n",
              "      <td>3.609758</td>\n",
              "      <td>2.107293</td>\n",
              "      <td>2.490106</td>\n",
              "      <td>2.494607</td>\n",
              "      <td>4.719623</td>\n",
              "      <td>3.996714</td>\n",
              "      <td>5.430174</td>\n",
              "      <td>2.993357</td>\n",
              "      <td>26.923995</td>\n",
              "      <td>23.483249</td>\n",
              "      <td>2018.038074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.570368e+09</td>\n",
              "      <td>1.151508</td>\n",
              "      <td>1.235616</td>\n",
              "      <td>1.216731</td>\n",
              "      <td>1.255104</td>\n",
              "      <td>1.490322</td>\n",
              "      <td>1.671375</td>\n",
              "      <td>2.960671</td>\n",
              "      <td>2.003566</td>\n",
              "      <td>2.870341</td>\n",
              "      <td>1.417654</td>\n",
              "      <td>13.658875</td>\n",
              "      <td>1.689199</td>\n",
              "      <td>1.999405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.111239e+09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.269600e+09</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.514104e+09</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.728166e+09</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2020.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.999496e+09</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fffd994f-febb-42ed-bbaa-6d77acd1debf')\"\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-fffd994f-febb-42ed-bbaa-6d77acd1debf 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-fffd994f-febb-42ed-bbaa-6d77acd1debf');\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",
              "  "
            ],
            "text/plain": [
              "          CODALUMNO            BD  ...          EDAD    ANIOEGRESO\n",
              "count  1.399900e+04  13999.000000  ...  13999.000000  13999.000000\n",
              "mean   5.514888e+09      3.126509  ...     23.483249   2018.038074\n",
              "std    2.570368e+09      1.151508  ...      1.689199      1.999405\n",
              "min    1.111239e+09      1.000000  ...     21.000000   2015.000000\n",
              "25%    3.269600e+09      2.000000  ...     22.000000   2016.000000\n",
              "50%    5.514104e+09      3.000000  ...     23.000000   2018.000000\n",
              "75%    7.728166e+09      4.000000  ...     25.000000   2020.000000\n",
              "max    9.999496e+09      5.000000  ...     26.000000   2021.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2zdliK2WLE-",
        "outputId": "708cedc8-4845-4a35-ffdc-0c91c5c8ca62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              count          mean  ...           75%           max\n",
            "CODALUMNO   13999.0  5.514888e+09  ...  7.728166e+09  9.999496e+09\n",
            "BD          13999.0  3.126509e+00  ...  4.000000e+00  5.000000e+00\n",
            "PROGRA      13999.0  3.191728e+00  ...  4.000000e+00  5.000000e+00\n",
            "ML          13999.0  3.609758e+00  ...  4.000000e+00  5.000000e+00\n",
            "BI          13999.0  2.107293e+00  ...  3.000000e+00  5.000000e+00\n",
            "RV          13999.0  2.490106e+00  ...  3.000000e+00  5.000000e+00\n",
            "RA          13999.0  2.494607e+00  ...  4.000000e+00  5.000000e+00\n",
            "TM          13999.0  4.719623e+00  ...  7.000000e+00  9.000000e+00\n",
            "SL          13999.0  3.996714e+00  ...  6.000000e+00  7.000000e+00\n",
            "TIN         13999.0  5.430174e+00  ...  8.000000e+00  1.000000e+01\n",
            "AEX         13999.0  2.993357e+00  ...  4.000000e+00  5.000000e+00\n",
            "PROBLEMA    13999.0  2.692399e+01  ...  3.900000e+01  4.500000e+01\n",
            "EDAD        13999.0  2.348325e+01  ...  2.500000e+01  2.600000e+01\n",
            "ANIOEGRESO  13999.0  2.018038e+03  ...  2.020000e+03  2.021000e+03\n",
            "\n",
            "[14 rows x 8 columns]\n"
          ]
        }
      ],
      "source": [
        "print(df.describe().T)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2RUMY2k8RJ7"
      },
      "source": [
        "# 3. Data Preprocesamiento y selección"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "_J_E7GGNo7W3",
        "outputId": "4f44c73b-9aa3-4f22-fafe-d7f739352a38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-945768c9-95d5-442b-8223-fe94773e76cb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .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>BD</th>\n",
              "      <th>PROGRA</th>\n",
              "      <th>ML</th>\n",
              "      <th>BI</th>\n",
              "      <th>RV</th>\n",
              "      <th>RA</th>\n",
              "      <th>TM</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <th>2</th>\n",
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              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
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              "      <td>5</td>\n",
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              "      <td>5</td>\n",
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              "      <td>9</td>\n",
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              "      <th>4</th>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13994</th>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>13995</th>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13996</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>13997</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
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              "      <td>2</td>\n",
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              "      <th>13998</th>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13999 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-945768c9-95d5-442b-8223-fe94773e76cb')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-945768c9-95d5-442b-8223-fe94773e76cb 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-945768c9-95d5-442b-8223-fe94773e76cb');\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",
              "  "
            ],
            "text/plain": [
              "       BD  PROGRA  ML  BI  RV  RA  TM\n",
              "0       3       3   3   2   4   5   9\n",
              "1       3       3   4   1   2   1   5\n",
              "2       3       3   4   1   2   1   5\n",
              "3       4       5   5   2   5   5   9\n",
              "4       3       3   4   1   2   1   5\n",
              "...    ..     ...  ..  ..  ..  ..  ..\n",
              "13994   4       5   3   3   3   1   1\n",
              "13995   1       4   4   2   4   5   8\n",
              "13996   4       2   4   5   4   3   8\n",
              "13997   1       5   2   2   3   3   2\n",
              "13998   4       4   5   4   2   4   1\n",
              "\n",
              "[13999 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df1 = df[[\"BD\",\t'PROGRA',\t'ML',\t'BI',\t'RV','RA',\"TM\"\t]]\n",
        "df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UGBI33xT7HF4",
        "outputId": "e8ad5c2c-c4bf-4178-8652-cdc498a4b1da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<p>13999 rows × 12 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33be3e33-1de4-49bc-8a8f-91f511460950')\"\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "          const element = document.querySelector('#df-33be3e33-1de4-49bc-8a8f-91f511460950');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       BD  PROGRA  ML  BI  RV  RA  SL  TIN  AEX GENERO  EDAD  PROBLEMA\n",
              "0       3       3   3   2   4   5   2    3    5      M    22         7\n",
              "1       3       3   4   1   2   1   1    6    1      M    23        44\n",
              "2       3       3   4   1   2   1   6    1    4      M    24        27\n",
              "3       4       5   5   2   5   5   1   10    1      M    26        43\n",
              "4       3       3   4   1   2   1   4    1    4      M    26        16\n",
              "...    ..     ...  ..  ..  ..  ..  ..  ...  ...    ...   ...       ...\n",
              "13994   4       5   3   3   3   1   7    3    1      F    21        45\n",
              "13995   1       4   4   2   4   5   1    5    3      M    25         5\n",
              "13996   4       2   4   5   4   3   2    5    4      M    21        10\n",
              "13997   1       5   2   2   3   3   5    2    3      M    25        25\n",
              "13998   4       4   5   4   2   4   5    2    1      M    21        42\n",
              "\n",
              "[13999 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df2 = df[['BD',\t'PROGRA',\t'ML',\t'BI',\t'RV',\t'RA',\t'SL',\t'TIN',\t'AEX','GENERO',\t'EDAD', 'PROBLEMA']]\n",
        "df2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "iL5HSHx88-2Y",
        "outputId": "3d76e9b6-5edb-4c13-e9bc-f98e7c470aab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BD</th>\n",
              "      <th>PROGRA</th>\n",
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              "      <th>TM</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.126509</td>\n",
              "      <td>3.191728</td>\n",
              "      <td>3.609758</td>\n",
              "      <td>2.107293</td>\n",
              "      <td>2.490106</td>\n",
              "      <td>2.494607</td>\n",
              "      <td>4.719623</td>\n",
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              "      <th>std</th>\n",
              "      <td>1.151508</td>\n",
              "      <td>1.235616</td>\n",
              "      <td>1.216731</td>\n",
              "      <td>1.255104</td>\n",
              "      <td>1.490322</td>\n",
              "      <td>1.671375</td>\n",
              "      <td>2.960671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>7.000000</td>\n",
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              "      <th>max</th>\n",
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              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.000000</td>\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-87f46b54-9ee2-41df-9e04-e9e93fa312af 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-87f46b54-9ee2-41df-9e04-e9e93fa312af');\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",
              "  "
            ],
            "text/plain": [
              "                 BD        PROGRA  ...            RA            TM\n",
              "count  13999.000000  13999.000000  ...  13999.000000  13999.000000\n",
              "mean       3.126509      3.191728  ...      2.494607      4.719623\n",
              "std        1.151508      1.235616  ...      1.671375      2.960671\n",
              "min        1.000000      1.000000  ...      1.000000      1.000000\n",
              "25%        2.000000      2.000000  ...      1.000000      2.000000\n",
              "50%        3.000000      3.000000  ...      2.000000      5.000000\n",
              "75%        4.000000      4.000000  ...      4.000000      7.000000\n",
              "max        5.000000      5.000000  ...      5.000000      9.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df1.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "SAwYRTCv-Kj7",
        "outputId": "50defac7-fa35-4b7b-8e72-a8b8635b722f"
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        {
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          "data": {
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              "\n",
              "  <div id=\"df-5aad8150-4f91-4a92-858a-2fcccfea766b\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>BD</th>\n",
              "      <th>PROGRA</th>\n",
              "      <th>ML</th>\n",
              "      <th>BI</th>\n",
              "      <th>RV</th>\n",
              "      <th>RA</th>\n",
              "      <th>SL</th>\n",
              "      <th>TIN</th>\n",
              "      <th>AEX</th>\n",
              "      <th>EDAD</th>\n",
              "      <th>PROBLEMA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "      <td>13999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.126509</td>\n",
              "      <td>3.191728</td>\n",
              "      <td>3.609758</td>\n",
              "      <td>2.107293</td>\n",
              "      <td>2.490106</td>\n",
              "      <td>2.494607</td>\n",
              "      <td>3.996714</td>\n",
              "      <td>5.430174</td>\n",
              "      <td>2.993357</td>\n",
              "      <td>23.483249</td>\n",
              "      <td>26.923995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.151508</td>\n",
              "      <td>1.235616</td>\n",
              "      <td>1.216731</td>\n",
              "      <td>1.255104</td>\n",
              "      <td>1.490322</td>\n",
              "      <td>1.671375</td>\n",
              "      <td>2.003566</td>\n",
              "      <td>2.870341</td>\n",
              "      <td>1.417654</td>\n",
              "      <td>1.689199</td>\n",
              "      <td>13.658875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>15.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5aad8150-4f91-4a92-858a-2fcccfea766b')\"\n",
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              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5aad8150-4f91-4a92-858a-2fcccfea766b 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-5aad8150-4f91-4a92-858a-2fcccfea766b');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
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            "text/plain": [
              "                 BD        PROGRA  ...          EDAD      PROBLEMA\n",
              "count  13999.000000  13999.000000  ...  13999.000000  13999.000000\n",
              "mean       3.126509      3.191728  ...     23.483249     26.923995\n",
              "std        1.151508      1.235616  ...      1.689199     13.658875\n",
              "min        1.000000      1.000000  ...     21.000000      1.000000\n",
              "25%        2.000000      2.000000  ...     22.000000     15.000000\n",
              "50%        3.000000      3.000000  ...     23.000000     30.000000\n",
              "75%        4.000000      4.000000  ...     25.000000     39.000000\n",
              "max        5.000000      5.000000  ...     26.000000     45.000000\n",
              "\n",
              "[8 rows x 11 columns]"
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          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df2.describe()"
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    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "0GCjelTKnEDC",
        "outputId": "688565ce-e4ff-47d1-b17e-e24239f4165b"
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        {
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          "data": {
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              "      <th>TM</th>\n",
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              "    <tr>\n",
              "      <th>BD</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.104635</td>\n",
              "      <td>0.082102</td>\n",
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              "      <td>-0.021742</td>\n",
              "      <td>0.397006</td>\n",
              "      <td>0.388243</td>\n",
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              "    <tr>\n",
              "      <th>ML</th>\n",
              "      <td>0.104635</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>-0.193335</td>\n",
              "      <td>0.187625</td>\n",
              "      <td>0.100786</td>\n",
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              "      <td>0.203604</td>\n",
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              "      <td>0.187625</td>\n",
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              "      <td>0.541752</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.187206</td>\n",
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              "      <td>-0.115501</td>\n",
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              "          document.querySelector('#df-e15797ac-d1b0-4010-a162-1dcb09bffb30 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-e15797ac-d1b0-4010-a162-1dcb09bffb30');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              BD    PROGRA        ML        BI        RV        RA        TM\n",
              "BD      1.000000  0.141161  0.104635  0.082102  0.203604  0.222826  0.343267\n",
              "PROGRA  0.141161  1.000000  0.207054 -0.021742  0.397006  0.388243  0.302247\n",
              "ML      0.104635  0.207054  1.000000 -0.193335  0.187625  0.100786  0.354132\n",
              "BI      0.082102 -0.021742 -0.193335  1.000000  0.203024  0.304557 -0.115501\n",
              "RV      0.203604  0.397006  0.187625  0.203024  1.000000  0.541752  0.624516\n",
              "RA      0.222826  0.388243  0.100786  0.304557  0.541752  1.000000  0.187206\n",
              "TM      0.343267  0.302247  0.354132 -0.115501  0.624516  0.187206  1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df1.corr()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.corr()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "egiPkTYifFmt",
        "outputId": "dd4e7ecb-58ab-4a6d-81bd-acf6c3913671"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
              "      <th>BD</th>\n",
              "      <th>PROGRA</th>\n",
              "      <th>ML</th>\n",
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              "    <tr>\n",
              "      <th>BD</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.104635</td>\n",
              "      <td>0.082102</td>\n",
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              "      <td>-0.021742</td>\n",
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              "      <th>ML</th>\n",
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              "      <td>-0.193335</td>\n",
              "      <td>0.187625</td>\n",
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              "      <td>0.003621</td>\n",
              "      <td>-0.005189</td>\n",
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              "      <td>0.004947</td>\n",
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              "      <th>BI</th>\n",
              "      <td>0.082102</td>\n",
              "      <td>-0.021742</td>\n",
              "      <td>-0.193335</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.203024</td>\n",
              "      <td>0.304557</td>\n",
              "      <td>0.006447</td>\n",
              "      <td>-0.000260</td>\n",
              "      <td>0.004135</td>\n",
              "      <td>0.000275</td>\n",
              "      <td>-0.006013</td>\n",
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              "    <tr>\n",
              "      <th>RV</th>\n",
              "      <td>0.203604</td>\n",
              "      <td>0.397006</td>\n",
              "      <td>0.187625</td>\n",
              "      <td>0.203024</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.541752</td>\n",
              "      <td>0.005875</td>\n",
              "      <td>-0.011213</td>\n",
              "      <td>-0.027944</td>\n",
              "      <td>-0.015822</td>\n",
              "      <td>0.012892</td>\n",
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              "    <tr>\n",
              "      <th>RA</th>\n",
              "      <td>0.222826</td>\n",
              "      <td>0.388243</td>\n",
              "      <td>0.100786</td>\n",
              "      <td>0.304557</td>\n",
              "      <td>0.541752</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.003077</td>\n",
              "      <td>0.004043</td>\n",
              "      <td>-0.013477</td>\n",
              "      <td>-0.012601</td>\n",
              "      <td>0.002517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SL</th>\n",
              "      <td>-0.000718</td>\n",
              "      <td>-0.001188</td>\n",
              "      <td>0.002815</td>\n",
              "      <td>0.006447</td>\n",
              "      <td>0.005875</td>\n",
              "      <td>-0.003077</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.006718</td>\n",
              "      <td>-0.007201</td>\n",
              "      <td>0.006316</td>\n",
              "      <td>0.448659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TIN</th>\n",
              "      <td>0.000414</td>\n",
              "      <td>0.001318</td>\n",
              "      <td>0.003621</td>\n",
              "      <td>-0.000260</td>\n",
              "      <td>-0.011213</td>\n",
              "      <td>0.004043</td>\n",
              "      <td>0.006718</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.003827</td>\n",
              "      <td>-0.003715</td>\n",
              "      <td>0.002693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AEX</th>\n",
              "      <td>-0.003336</td>\n",
              "      <td>-0.012813</td>\n",
              "      <td>-0.005189</td>\n",
              "      <td>0.004135</td>\n",
              "      <td>-0.027944</td>\n",
              "      <td>-0.013477</td>\n",
              "      <td>-0.007201</td>\n",
              "      <td>0.003827</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.002206</td>\n",
              "      <td>-0.701086</td>\n",
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              "      <th>EDAD</th>\n",
              "      <td>-0.019202</td>\n",
              "      <td>-0.010714</td>\n",
              "      <td>-0.012096</td>\n",
              "      <td>0.000275</td>\n",
              "      <td>-0.015822</td>\n",
              "      <td>-0.012601</td>\n",
              "      <td>0.006316</td>\n",
              "      <td>-0.003715</td>\n",
              "      <td>0.002206</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.005088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PROBLEMA</th>\n",
              "      <td>0.012216</td>\n",
              "      <td>0.011378</td>\n",
              "      <td>0.004947</td>\n",
              "      <td>-0.006013</td>\n",
              "      <td>0.012892</td>\n",
              "      <td>0.002517</td>\n",
              "      <td>0.448659</td>\n",
              "      <td>0.002693</td>\n",
              "      <td>-0.701086</td>\n",
              "      <td>0.005088</td>\n",
              "      <td>1.000000</td>\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                BD    PROGRA        ML  ...       AEX      EDAD  PROBLEMA\n",
              "BD        1.000000  0.141161  0.104635  ... -0.003336 -0.019202  0.012216\n",
              "PROGRA    0.141161  1.000000  0.207054  ... -0.012813 -0.010714  0.011378\n",
              "ML        0.104635  0.207054  1.000000  ... -0.005189 -0.012096  0.004947\n",
              "BI        0.082102 -0.021742 -0.193335  ...  0.004135  0.000275 -0.006013\n",
              "RV        0.203604  0.397006  0.187625  ... -0.027944 -0.015822  0.012892\n",
              "RA        0.222826  0.388243  0.100786  ... -0.013477 -0.012601  0.002517\n",
              "SL       -0.000718 -0.001188  0.002815  ... -0.007201  0.006316  0.448659\n",
              "TIN       0.000414  0.001318  0.003621  ...  0.003827 -0.003715  0.002693\n",
              "AEX      -0.003336 -0.012813 -0.005189  ...  1.000000  0.002206 -0.701086\n",
              "EDAD     -0.019202 -0.010714 -0.012096  ...  0.002206  1.000000  0.005088\n",
              "PROBLEMA  0.012216  0.011378  0.004947  ... -0.701086  0.005088  1.000000\n",
              "\n",
              "[11 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kl9hpc0gdtmm",
        "outputId": "dab07bf4-33c2-4204-98b3-ab860c047e64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 13999 entries, 0 to 13998\n",
            "Data columns (total 15 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   CODALUMNO   13999 non-null  int64 \n",
            " 1   BD          13999 non-null  int64 \n",
            " 2   PROGRA      13999 non-null  int64 \n",
            " 3   ML          13999 non-null  int64 \n",
            " 4   BI          13999 non-null  int64 \n",
            " 5   RV          13999 non-null  int64 \n",
            " 6   RA          13999 non-null  int64 \n",
            " 7   TM          13999 non-null  int64 \n",
            " 8   SL          13999 non-null  int64 \n",
            " 9   TIN         13999 non-null  int64 \n",
            " 10  AEX         13999 non-null  int64 \n",
            " 11  PROBLEMA    13999 non-null  int64 \n",
            " 12  GENERO      13999 non-null  object\n",
            " 13  EDAD        13999 non-null  int64 \n",
            " 14  ANIOEGRESO  13999 non-null  int64 \n",
            "dtypes: int64(14), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Seleccionemos algunas características para el modelado. También cambiamos el tipo de datos de destino para que sea entero, ya que es un requisito del algoritmo skitlearn:"
      ],
      "metadata": {
        "id": "OqIDKJeMg2L6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "5u6_3NcYtQ4o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "outputId": "d2705c0f-b40a-471d-d038-a204b666c63d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ]
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              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ada26eb3-5a38-4f33-813a-e8e79002ba38')\"\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-ada26eb3-5a38-4f33-813a-e8e79002ba38 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-ada26eb3-5a38-4f33-813a-e8e79002ba38');\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",
              "  "
            ],
            "text/plain": [
              "   BD  PROGRA  ML  BI  RV  RA  TM\n",
              "0   3       3   3   2   4   5   9\n",
              "1   3       3   4   1   2   1   5\n",
              "2   3       3   4   1   2   1   5\n",
              "3   4       5   5   2   5   5   9\n",
              "4   3       3   4   1   2   1   5"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df= df[[\"BD\",\t'PROGRA',\t'ML',\t'BI',\t'RV','RA',\"TM\"\t]]\n",
        "df['TM'] = df['TM'].astype('int')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Práctica\n",
        "¿Cuántas filas y columnas hay en este conjunto de datos en total? ¿Cómo se llaman las columnas?"
      ],
      "metadata": {
        "id": "CITln8Tzh5u7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Z6CuKAmhkNe",
        "outputId": "e09910fe-4bdc-43cc-bd6b-6cc0a8e0660a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(13999, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definamos X e y para nuestro conjunto de datos:"
      ],
      "metadata": {
        "id": "OJRPyJN7iJpn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.asarray(df[[\"BD\",'PROGRA',\t'ML',\t'BI',\t'RV','RA']])\n",
        "X[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5AMKeOCziCRq",
        "outputId": "149dd91b-779a-44b3-963b-0e4accb8aca8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3, 3, 3, 2, 4, 5],\n",
              "       [3, 3, 4, 1, 2, 1],\n",
              "       [3, 3, 4, 1, 2, 1],\n",
              "       [4, 5, 5, 2, 5, 5],\n",
              "       [3, 3, 4, 1, 2, 1]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = np.asarray(df['TM'])\n",
        "y [0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "brViKniujyCX",
        "outputId": "d4a80bc0-abc8-46cd-d26f-f6fa16267df9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([9, 5, 5, 9, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Además, normalizamos el conjunto de datos:"
      ],
      "metadata": {
        "id": "jxCAGL8IkR0k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cVfOzXdJkS1f",
        "outputId": "0a93c4a1-9292-4555-9c82-3cd7998c81e0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.10986767, -0.15517348, -0.50116238, -0.08548872,  1.01316885,\n",
              "         1.49905465],\n",
              "       [-0.10986767, -0.15517348,  0.32074158, -0.88226406, -0.32887124,\n",
              "        -0.89426968],\n",
              "       [-0.10986767, -0.15517348,  0.32074158, -0.88226406, -0.32887124,\n",
              "        -0.89426968],\n",
              "       [ 0.75858944,  1.46351023,  1.14264553, -0.08548872,  1.68418889,\n",
              "         1.49905465],\n",
              "       [-0.10986767, -0.15517348,  0.32074158, -0.88226406, -0.32887124,\n",
              "        -0.89426968]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4. Train /Test dataset\n",
        "Bien, dividimos nuestro conjunto de datos en tren y conjunto de prueba:"
      ],
      "metadata": {
        "id": "SdP6ctgdklpb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
        "print ('Train set:', X_train.shape,  y_train.shape)\n",
        "print ('Test set:', X_test.shape,  y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44GAfSQCkcp1",
        "outputId": "4706cebc-b49f-406f-fafb-1a42b3ebf9ed"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (11199, 6) (11199,)\n",
            "Test set: (2800, 6) (2800,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Modelado Regresión Logística con scikit-learn\n",
        "\n",
        "Construyamos nuestro modelo usando **LogisticRegression** del paquete Scikit-learn. Esta función implementa la regresión logística y puede usar diferentes optimizadores numéricos para encontrar parámetros, incluidos los solucionadores \"newton-cg\", \"lbfgs\", \"liblinear\", \"sag\" y \"saga\". Puede encontrar amplia información sobre los pros y los contras de estos optimizadores si lo busca en Internet.\n",
        "\n",
        "La versión de Regresión logística en Scikit-learn, admite regularización. La regularización es una técnica utilizada para resolver el problema de sobreajuste en modelos de Machine Learning. El parámetro C indica el **inverso de la fuerza de regularización**, que debe ser un valor flotante positivo. Los valores más pequeños especifican una regularización más fuerte. Ahora ajustemos nuestro modelo con el conjunto de entrenamiento:"
      ],
      "metadata": {
        "id": "sR6wSyZdliTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix\n",
        "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
        "LR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tMnCKCq0uLAM",
        "outputId": "eec603eb-70b4-406e-aa23-9c53ec05ce2e"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=0.01, solver='liblinear')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ahora podemos predecir usando nuestro conjunto de prueba:"
      ],
      "metadata": {
        "id": "gjShloD0wBMw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yhat = LR.predict(X_test)\n",
        "yhat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jkIVO-LMwAyg",
        "outputId": "dc27fec2-40d3-4dbc-aca9-89ca962776bf"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 6, 2, ..., 3, 9, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "predict_proba devuelve estimaciones para todas las clases, ordenadas por la etiqueta de clases. Entonces, la primera columna es la probabilidad de la clase 1, P (Y = 1 | X), y la segunda columna es la probabilidad de la clase 0, P (Y = 0 | X):"
      ],
      "metadata": {
        "id": "pq2eA3MFZmF4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "yhat_prob = LR.predict_proba(X_test)\n",
        "yhat_prob"
      ],
      "metadata": {
        "id": "BQEo7u_awFDG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e3c38b7-b809-48b3-b98c-68a78e47d61b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.96484805e-01, 3.22327118e-03, 3.22934133e-04, ...,\n",
              "        2.98343248e-02, 3.95406792e-01, 4.88692389e-02],\n",
              "       [8.15884458e-03, 8.78131380e-02, 1.17553114e-01, ...,\n",
              "        7.61720395e-02, 9.83479474e-02, 3.83218789e-02],\n",
              "       [1.85072689e-01, 2.07783909e-01, 2.03525336e-01, ...,\n",
              "        6.51978923e-02, 1.52704092e-01, 5.46887474e-03],\n",
              "       ...,\n",
              "       [3.59126946e-02, 1.46464216e-01, 4.99432781e-01, ...,\n",
              "        9.34219349e-02, 9.67611532e-03, 4.74929206e-03],\n",
              "       [8.62098548e-03, 6.88226092e-02, 1.61426092e-03, ...,\n",
              "        1.96120168e-03, 8.31229062e-02, 7.37794143e-01],\n",
              "       [2.53186596e-03, 1.90870867e-03, 2.19440247e-03, ...,\n",
              "        3.77298450e-02, 1.16352852e-01, 3.47557510e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Evaluación \n",
        "\n",
        "# Indice de jaccard\n",
        "Probemos el índice jaccard para evaluar la precisión podemos definir jaccard como el tamaño de la intersección dividido por el tamaño de la unión de dos conjuntos de etiquetas. Si todo el conjunto de etiquetas predichas para una muestra coincide estrictamente con el conjunto verdadero de etiquetas, entonces la precisión del subconjunto es 1.0; de lo contrario, es 0.0."
      ],
      "metadata": {
        "id": "fUZ7VZsJaOEE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip uninstall scikit-learn\n",
        "#Found existing installation: scikit-learn 0.23.2\n",
        "#Uninstalling scikit-learn-0.23.2:\n",
        "'''\n",
        "  Would remove:\n",
        "    /opt/conda/lib/python3.7/site-packages/scikit_learn-0.23.2.dist-info/*\n",
        "    /opt/conda/lib/python3.7/site-packages/sklearn/*\n",
        "Proceed (y/n)? y\n",
        "  Successfully uninstalled scikit-learn-0.23.2\n",
        "daveccampbell@tensorflow-115-vm:~/projects$ pip install scikit-learn==0.20.4'''"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209
        },
        "id": "zWjblcKYdFJB",
        "outputId": "b5dfdb1c-11c9-4a12-bd3e-33a8acc2354c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found existing installation: scikit-learn 1.0.2\n",
            "Uninstalling scikit-learn-1.0.2:\n",
            "  Would remove:\n",
            "    /usr/local/lib/python3.7/dist-packages/scikit_learn-1.0.2.dist-info/*\n",
            "    /usr/local/lib/python3.7/dist-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0\n",
            "    /usr/local/lib/python3.7/dist-packages/sklearn/*\n",
            "Proceed (y/n)? Y\n",
            "  Successfully uninstalled scikit-learn-1.0.2\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\n  Would remove:\\n    /opt/conda/lib/python3.7/site-packages/scikit_learn-0.23.2.dist-info/*\\n    /opt/conda/lib/python3.7/site-packages/sklearn/*\\nProceed (y/n)? y\\n  Successfully uninstalled scikit-learn-0.23.2\\ndaveccampbell@tensorflow-115-vm:~/projects$ pip install scikit-learn==0.20.4'"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U scikit-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-w6zVfGxqad",
        "outputId": "d7d0e485-d975-476e-e622-975678456ba6"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (1.0.2)\n",
            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (3.1.0)\n",
            "Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.metrics import jaccard_similarity_score\n",
        "#jaccard_similarity_score(y_test, yhat)"
      ],
      "metadata": {
        "id": "nZEDbObsyL6k"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#from sklearn.metrics import jaccard_score\n",
        "#jaccard_score(y_test, yhat)"
      ],
      "metadata": {
        "id": "yMcUZKCUaFUh"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7. matriz de confusión \n",
        "Otra forma de ver la precisión del clasificador es mirar la matriz de confusión."
      ],
      "metadata": {
        "id": "YPd6OthOyY1s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import itertools\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    Esta función imprime y traza la matriz de confusión.\n",
        "     La normalización se puede aplicar configurando 'normalize=True'.\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    print(cm)\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",
        "    fmt = '.2f' if normalize else 'd'\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], fmt),\n",
        "                 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",
        "print(confusion_matrix(y_test, yhat, labels=[1,0]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJxEcnFbcx2T",
        "outputId": "3c9a3dda-7926-444b-dde8-7c183321d3c6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[427   0]\n",
            " [  0   0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute confusion matrix\n",
        "cnf_matrix = confusion_matrix(y_test, yhat, labels=[1,0])\n",
        "np.set_printoptions(precision=2)\n",
        "\n",
        "# Plot non-normalized confusion matrix\n",
        "plt.figure()\n",
        "plot_confusion_matrix(cnf_matrix, classes=['TM=1','TM=0'],normalize= False,  title='Confusion matrix')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "VSHKkH7rzM62",
        "outputId": "efba440c-e97b-4c63-f400-c6bd18c56d35"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[427   0]\n",
            " [  0   0]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mira la primera fila. La primera fila es para los egresados cuyo valor predicho de TM real en el conjunto de prueba es 1. Como puede calcular, de 427 EGRESADOS, el valor predicho de TM  de 427 de ellos es 1. Y de estos 427, el clasificador predijo correctamente 427 de ellos como 0 y 0 de ellos como 0.\n",
        "\n",
        "Significa que nuestro clasificador no tiene error ya que para predecir la TM con un valor de 1 los clasifica todo.\n",
        "\n",
        "El clasificador predijo correctamente 427 de ellos como 1. Lo bueno de la matriz de confusión es que muestra la capacidad del modelo para predecir correctamente o separar las clases. En un caso específico de clasificador binario, como este ejemplo, podemos interpretar estos números como el recuento de verdaderos positivos, falsos positivos, verdaderos negativos y falsos negativos."
      ],
      "metadata": {
        "id": "Mdh3ef4c0Ifv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print (classification_report(y_test, yhat))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2MjSb2p93KDg",
        "outputId": "8a282999-9604-4a92-ce4d-c0e3785cff85"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           1       0.89      0.90      0.90       473\n",
            "           2       0.89      0.65      0.75       509\n",
            "           3       0.61      1.00      0.76       376\n",
            "           5       1.00      0.01      0.03       200\n",
            "           6       0.92      1.00      0.96       241\n",
            "           7       0.78      1.00      0.88       307\n",
            "           8       0.82      0.63      0.71       216\n",
            "           9       0.87      0.93      0.90       478\n",
            "\n",
            "    accuracy                           0.81      2800\n",
            "   macro avg       0.85      0.77      0.74      2800\n",
            "weighted avg       0.84      0.81      0.78      2800\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Basándonos en el recuento de cada sección, podemos calcular la precisión y la recuperación (recall) de cada etiqueta:\n",
        "\n",
        "* La precisión es una medida de la precisión siempre que se haya predicho una etiqueta de clase. Está definido por: precision = TP / (TP + FP)\n",
        "\n",
        "* El recall es una verdadera tasa positiva. Se define como: Recall = TP / (TP + FN)\n",
        "\n",
        "Entonces, podemos calcular la precisión y la recuperación de cada clase.\n",
        "\n",
        "**F1 Score** : ahora estamos en condiciones de calcular los  F1 Scores para cada etiqueta en función de la precision y el recall de esa etiqueta.\n",
        "\n",
        "La puntuación F1 es el promedio armónico de la precisión y la recuperación, donde una puntuación F1 alcanza su mejor valor en 1 (precisión y recuperación perfecta) y el peor en 0. Es una buena forma de demostrar que un clasificador tiene un buen valor para ambos recuerdo y precisión.\n",
        "\n",
        "Y finalmente, podemos decir que la precisión promedio para este clasificador es el promedio de la puntuación F1 para ambas etiquetas, que es 0,78 en nuestro caso.\n",
        "\n",
        "# Log loss\n",
        "Ahora, intentemos la pérdida de registros para su evaluación. En la regresión logística, el resultado puede ser que la probabilidad de que el cliente abandone sea sí (o igual a 1). Esta probabilidad es un valor entre 0 y 1. La pérdida logarítmica (pérdida logarítmica) mide el rendimiento de un clasificador donde la salida prevista es un valor de probabilidad entre 0 y 1."
      ],
      "metadata": {
        "id": "gx-xFXPv5XGB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import log_loss\n",
        "log_loss(y_test, yhat_prob)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qrko21a93Kty",
        "outputId": "b455564b-bd09-49a5-ae5d-4f0b7570aee3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8117959753181987"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LR2 = LogisticRegression(C=0.01, solver='sag').fit(X_train,y_train)\n",
        "yhat_prob2 = LR2.predict_proba(X_test)\n",
        "print (\"LogLoss: : %.2f\" % log_loss(y_test, yhat_prob2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YRv9lis6ezP",
        "outputId": "595231ef-cc87-4829-eb29-59fa3d36dd23"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "LogLoss: : 0.50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MBRo5tW56r73"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Prediccion_TM_Tesis_USMP.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPom/xchOKnfW09Bd8G5hi/",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}