tarea_redes_neuronales_breast_cancer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gatoborrachon/AdvancedAlloyIngot/blob/master/tarea_redes_neuronales_breast_cancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **LIBRERIAS PARA EL EJEMPLO Y LA ACTIVIDAD**"
      ],
      "metadata": {
        "id": "BbnrFDCuaaHy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "39PA-9Tg1ZCL"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **DATASET IRIS** - EJEMPLO DEMOSTRATIVO PARA LA REALIZACION DE LA ACTIVIDAD\n",
        "\n",
        "> MODELO DE REDES NEURONALES (RN)\n",
        "\n"
      ],
      "metadata": {
        "id": "opZCzharaift"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "iris = load_iris()"
      ],
      "metadata": {
        "id": "01z6vh45LP-u"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(data=iris.data, columns=iris.feature_names)"
      ],
      "metadata": {
        "id": "rqJCsTAeLSbC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GTNPszsTM24Z",
        "outputId": "9838de22-c3d7-40b6-88b6-2170228104d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                  5.1               3.5                1.4               0.2\n",
              "1                  4.9               3.0                1.4               0.2\n",
              "2                  4.7               3.2                1.3               0.2\n",
              "3                  4.6               3.1                1.5               0.2\n",
              "4                  5.0               3.6                1.4               0.2\n",
              "..                 ...               ...                ...               ...\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8\n",
              "\n",
              "[150 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d7b27510-69c6-45e9-823e-6a8342d4cde8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7b27510-69c6-45e9-823e-6a8342d4cde8')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-d7b27510-69c6-45e9-823e-6a8342d4cde8 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-d7b27510-69c6-45e9-823e-6a8342d4cde8');\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",
              "\n",
              "\n",
              "<div id=\"df-31e53cc0-0e40-4ca3-a466-1943813e5f97\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-31e53cc0-0e40-4ca3-a466-1943813e5f97')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-31e53cc0-0e40-4ca3-a466-1943813e5f97 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9b87a55d-fafe-40c6-825b-6a12baad9564\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9b87a55d-fafe-40c6-825b-6a12baad9564 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['species'] = iris.target\n",
        "df"
      ],
      "metadata": {
        "id": "j7y772qZLU0w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "078e53d2-0843-40c0-a085-cd83cba63816"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                  5.1               3.5                1.4               0.2   \n",
              "1                  4.9               3.0                1.4               0.2   \n",
              "2                  4.7               3.2                1.3               0.2   \n",
              "3                  4.6               3.1                1.5               0.2   \n",
              "4                  5.0               3.6                1.4               0.2   \n",
              "..                 ...               ...                ...               ...   \n",
              "145                6.7               3.0                5.2               2.3   \n",
              "146                6.3               2.5                5.0               1.9   \n",
              "147                6.5               3.0                5.2               2.0   \n",
              "148                6.2               3.4                5.4               2.3   \n",
              "149                5.9               3.0                5.1               1.8   \n",
              "\n",
              "     species  \n",
              "0          0  \n",
              "1          0  \n",
              "2          0  \n",
              "3          0  \n",
              "4          0  \n",
              "..       ...  \n",
              "145        2  \n",
              "146        2  \n",
              "147        2  \n",
              "148        2  \n",
              "149        2  \n",
              "\n",
              "[150 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5bd7ad54-3539-4b1f-ba65-540323a7186c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5bd7ad54-3539-4b1f-ba65-540323a7186c')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-5bd7ad54-3539-4b1f-ba65-540323a7186c 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-5bd7ad54-3539-4b1f-ba65-540323a7186c');\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",
              "\n",
              "\n",
              "<div id=\"df-3828e892-af6b-4c1d-94ae-b0e245736caf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3828e892-af6b-4c1d-94ae-b0e245736caf')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3828e892-af6b-4c1d-94ae-b0e245736caf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4358662849366982,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7652982332594662,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7622376689603465,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('species', axis=1)\n",
        "y = df['species']"
      ],
      "metadata": {
        "id": "meBq2XSHLpa5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "35hexFl9MvMk",
        "outputId": "fadf2cbc-db41-481f-e52f-d946df9ac4e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "145                6.7               3.0                5.2               2.3\n",
              "146                6.3               2.5                5.0               1.9\n",
              "147                6.5               3.0                5.2               2.0\n",
              "148                6.2               3.4                5.4               2.3\n",
              "149                5.9               3.0                5.1               1.8"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c8e6a63c-684f-4d56-b49a-d254d4420a95\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8e6a63c-684f-4d56-b49a-d254d4420a95')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-c8e6a63c-684f-4d56-b49a-d254d4420a95 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-c8e6a63c-684f-4d56-b49a-d254d4420a95');\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",
              "\n",
              "\n",
              "<div id=\"df-c2de849a-d923-4e97-a4be-483aef04a5ab\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2de849a-d923-4e97-a4be-483aef04a5ab')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2de849a-d923-4e97-a4be-483aef04a5ab button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30331501776206193,\n        \"min\": 5.9,\n        \"max\": 6.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.3,\n          5.9,\n          6.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.31937438845342625,\n        \"min\": 2.5,\n        \"max\": 3.4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3.0,\n          2.5,\n          3.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14832396974191348,\n        \"min\": 5.0,\n        \"max\": 5.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5.0,\n          5.1,\n          5.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.23021728866442667,\n        \"min\": 1.8,\n        \"max\": 2.3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.9,\n          1.8,\n          2.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujRe4o3RMxnW",
        "outputId": "4d2199a3-4143-4f88-cb1b-9fd4a8125127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "145    2\n",
              "146    2\n",
              "147    2\n",
              "148    2\n",
              "149    2\n",
              "Name: species, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.unique() #<-Muestrars los valores unicos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqXL3-UMN5ZT",
        "outputId": "c18f5fae-eb1d-4a65-cd84-8e3ff3313c65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQD7vIv8N3XA",
        "outputId": "1e905337-66ef-4bd5-f0bb-b4d23f1f4dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = OneHotEncoder(sparse=False)\n",
        "y_encoded = encoder.fit_transform(y.values.reshape(-1, 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ht4HBQfVLrfh",
        "outputId": "11cbd9dd-9e71-41cb-c119-0b2f8318c757"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_encoders.py:868: FutureWarning: `sparse` was renamed to `sparse_output` in version 1.2 and will be removed in 1.4. `sparse_output` is ignored unless you leave `sparse` to its default value.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y_encoded, test_size=0.2, random_state=42)\n",
        "X_train.shape[1]\n"
      ],
      "metadata": {
        "id": "2V73pFLLLXB9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "510bfbe0-2a13-4943-d531-3fc7d5eb4af6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "oculta1 = tf.keras.layers.Dense(units=3, input_shape=(X_train.shape[1],))\n",
        "oculta2 = tf.keras.layers.Dense(units=3)\n",
        "salida = tf.keras.layers.Dense(units=3)\n",
        "modelo = tf.keras.Sequential([oculta1, oculta2, salida])"
      ],
      "metadata": {
        "id": "fEjqwlBddC6L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelo.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(0.1),\n",
        "    loss='mean_squared_error'\n",
        ")\n"
      ],
      "metadata": {
        "id": "arnrDywpRyl5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"comenzando entrenamiento\")\n",
        "historial = modelo.fit(X_train, y_train, epochs=50, batch_size=5, verbose=True)\n",
        "print(\"modelo entrenado\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bnuLd4pIL-Lz",
        "outputId": "6331598f-01d0-4901-f250-cba91a7438d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "comenzando entrenamiento\n",
            "Epoch 1/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1369\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1121\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1421\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1254\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1344\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1368\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1381\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1223\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1143\n",
            "Epoch 10/50\n",
            "24/24 [==============================] - 0s 2ms/step - loss: 0.1118\n",
            "Epoch 11/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1224\n",
            "Epoch 12/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1113\n",
            "Epoch 13/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1301\n",
            "Epoch 14/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1216\n",
            "Epoch 15/50\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.1217\n",
            "Epoch 16/50\n",
            "24/24 [==============================] - 0s 8ms/step - loss: 0.1162\n",
            "Epoch 17/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1617\n",
            "Epoch 18/50\n",
            "24/24 [==============================] - 0s 7ms/step - loss: 0.1449\n",
            "Epoch 19/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1190\n",
            "Epoch 20/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1325\n",
            "Epoch 21/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1327\n",
            "Epoch 22/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1273\n",
            "Epoch 23/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1209\n",
            "Epoch 24/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1228\n",
            "Epoch 25/50\n",
            "24/24 [==============================] - 0s 11ms/step - loss: 0.1475\n",
            "Epoch 26/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1444\n",
            "Epoch 27/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1318\n",
            "Epoch 28/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1497\n",
            "Epoch 29/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1372\n",
            "Epoch 30/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1181\n",
            "Epoch 31/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1242\n",
            "Epoch 32/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1230\n",
            "Epoch 33/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1377\n",
            "Epoch 34/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1413\n",
            "Epoch 35/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1521\n",
            "Epoch 36/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1305\n",
            "Epoch 37/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1071\n",
            "Epoch 38/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1221\n",
            "Epoch 39/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1306\n",
            "Epoch 40/50\n",
            "24/24 [==============================] - 0s 3ms/step - loss: 0.1288\n",
            "Epoch 41/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1287\n",
            "Epoch 42/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1267\n",
            "Epoch 43/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1223\n",
            "Epoch 44/50\n",
            "24/24 [==============================] - 0s 6ms/step - loss: 0.1039\n",
            "Epoch 45/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1239\n",
            "Epoch 46/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1204\n",
            "Epoch 47/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1155\n",
            "Epoch 48/50\n",
            "24/24 [==============================] - 0s 4ms/step - loss: 0.1450\n",
            "Epoch 49/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1398\n",
            "Epoch 50/50\n",
            "24/24 [==============================] - 0s 5ms/step - loss: 0.1349\n",
            "modelo entrenado\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = modelo.evaluate(X_test, y_test)\n",
        "print(f'MSE: {loss}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mXy0DhApMCv6",
        "outputId": "7c3cdfce-d910-48fb-90c0-1efe6af71f8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 156ms/step - loss: 0.1094\n",
            "MSE: 0.10942471772432327\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Epoca')\n",
        "plt.ylabel('Magnitud de perdida')\n",
        "plt.plot(historial.history['loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "2bHv_jpPPfll",
        "outputId": "73e5d16e-fdb4-499e-db2c-4dcb848328b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x794d75c58790>]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#New data\n",
        "#sepal length (cm), sepal width (cm), petal length (cm),\tpetal width (cm)\n",
        "new_data = np.array([[5.9,\t3.0,\t5.1\t,1.8]])\n",
        "prediction = modelo.predict(new_data)\n",
        "predicted_class = np.argmax(prediction, axis=1)\n",
        "species_labels = iris.target_names[predicted_class]\n",
        "print(f'Predicción: {species_labels[0]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ29auWxMG97",
        "outputId": "c47fe83c-2c54-4d3a-9356-52ce5ee929a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 117ms/step\n",
            "Predicción: versicolor\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset Iris - Ejemplo Demostrativo para realizar la actividad**"
      ],
      "metadata": {
        "id": "4ZQAyLhHMy4T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATASET IRIS - EJEMPLO DEMOSTRATIVO PARA LA REALIZACION DE LA ACTIVIDAD**"
      ],
      "metadata": {
        "id": "hz0PucsKQR2Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the libraries\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "PhenmLvHNoK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load dataset\n",
        "iris = load_iris()\n",
        "\n",
        "# Separate features and target variable\n",
        "X = iris.data\n",
        "y = iris.target"
      ],
      "metadata": {
        "id": "FzyU4Q36UOD1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "OddSQNpUUZK1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a logistic regression model\n",
        "model = LogisticRegression(max_iter=1000)\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "leiuevGpUdtI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "9f7c5b8b-8f37-43f2-db18-355e71793946"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test) # Use the trained model to make predictions on the testing set"
      ],
      "metadata": {
        "id": "yuTxReKbUrSt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model performance\n",
        "print(f'Precisión: {accuracy_score(y_test, y_pred)}') # show precision in console"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UE2BbmW5U3cf",
        "outputId": "17d716ac-eee7-4e82-ad47-aa62ecfdf300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model performance\n",
        "print(classification_report(y_test, y_pred, target_names=iris.target_names))\n",
        "# It's a detailed breakdown of your model's performance on the testing data for a classification task"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fnq1RmU9P46e",
        "outputId": "9f1e8ef8-655f-4927-83b1-28593bf1ec45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "      setosa       1.00      1.00      1.00        10\n",
            "  versicolor       1.00      1.00      1.00         9\n",
            "   virginica       1.00      1.00      1.00        11\n",
            "\n",
            "    accuracy                           1.00        30\n",
            "   macro avg       1.00      1.00      1.00        30\n",
            "weighted avg       1.00      1.00      1.00        30\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#New data\n",
        "#sepal length (cm), sepal width (cm), petal length (cm),\tpetal width (cm)\n",
        "#new_data = np.array([[5.9,\t3.0,\t5.1\t,1.8]])\n",
        "new_data = np.array([[1.9,\t2.0,\t3.1\t,1.]])\n",
        "prediction = model.predict(new_data)\n",
        "print(f'Predicción: {iris.target_names[prediction[0]]}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aE29wBuZVAc3",
        "outputId": "7409ace7-444a-492d-ce5f-231dd19d360e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicción: setosa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFJV2r3ELs1K",
        "outputId": "f2e32b55-214e-470e-e689-f42b58afab72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **AHORA ENTRENA UN MODELO CON LA BASE DE DATOS CANCER**\n",
        "\n",
        "**RECUERDA HACERLO EN UNA NUEVA PESTAÑA DE GOOGLE COLAB**\n",
        "\n",
        "---\n",
        "\n",
        "Objetivo: No es aprender a programar cómo tal, es aprender los pasos para desarollar un modelo computacional con el uso de la inteligencia artificial y cómo usarlo realizando predicciones.\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Pasos a seguir:\n",
        "\n",
        "1.   Cargar las bibliotecas y los datos\n",
        "\n",
        "2.   Divida los datos en conjuntos de entrenamiento y prueba\n",
        "\n",
        "3.   Entrenar el modelo\n",
        "\n",
        "4.   Evaluar el modelo\n",
        "\n",
        "5.   Realizar predicciones sobre nuevos datos (Opcional)\n",
        "\n",
        "---\n",
        "\n",
        "Fecha limite de entrega: 01/07/24"
      ],
      "metadata": {
        "id": "SLYvuGdHRWf6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the libraries\n",
        "import numpy as np\n",
        "from sklearn.datasets import load_breast_cancer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Librerias Adicionales\n",
        "import pandas as pd\n",
        "\n",
        "# Load the breast cancer dataset\n",
        "breast_cancer = load_breast_cancer()"
      ],
      "metadata": {
        "id": "fxVV7LhlWgG9"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "breast_cancer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltmTozuJNHkC",
        "outputId": "bc4216a1-0721-4ccf-fe44-b2c540701559"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "         1.189e-01],\n",
              "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "         8.902e-02],\n",
              "        [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "         8.758e-02],\n",
              "        ...,\n",
              "        [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "         7.820e-02],\n",
              "        [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "         1.240e-01],\n",
              "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "         7.039e-02]]),\n",
              " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
              "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "        1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
              "        1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
              "        0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "        1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
              "        0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
              "        1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
              "        1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "        0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
              "        0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
              "        0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
              "        1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
              "        1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
              "        1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
              "        1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
              "        1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
              "        1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]),\n",
              " 'frame': None,\n",
              " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
              " 'DESCR': '.. _breast_cancer_dataset:\\n\\nBreast cancer wisconsin (diagnostic) dataset\\n--------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 569\\n\\n    :Number of Attributes: 30 numeric, predictive attributes and the class\\n\\n    :Attribute Information:\\n        - radius (mean of distances from center to points on the perimeter)\\n        - texture (standard deviation of gray-scale values)\\n        - perimeter\\n        - area\\n        - smoothness (local variation in radius lengths)\\n        - compactness (perimeter^2 / area - 1.0)\\n        - concavity (severity of concave portions of the contour)\\n        - concave points (number of concave portions of the contour)\\n        - symmetry\\n        - fractal dimension (\"coastline approximation\" - 1)\\n\\n        The mean, standard error, and \"worst\" or largest (mean of the three\\n        worst/largest values) of these features were computed for each image,\\n        resulting in 30 features.  For instance, field 0 is Mean Radius, field\\n        10 is Radius SE, field 20 is Worst Radius.\\n\\n        - class:\\n                - WDBC-Malignant\\n                - WDBC-Benign\\n\\n    :Summary Statistics:\\n\\n    ===================================== ====== ======\\n                                           Min    Max\\n    ===================================== ====== ======\\n    radius (mean):                        6.981  28.11\\n    texture (mean):                       9.71   39.28\\n    perimeter (mean):                     43.79  188.5\\n    area (mean):                          143.5  2501.0\\n    smoothness (mean):                    0.053  0.163\\n    compactness (mean):                   0.019  0.345\\n    concavity (mean):                     0.0    0.427\\n    concave points (mean):                0.0    0.201\\n    symmetry (mean):                      0.106  0.304\\n    fractal dimension (mean):             0.05   0.097\\n    radius (standard error):              0.112  2.873\\n    texture (standard error):             0.36   4.885\\n    perimeter (standard error):           0.757  21.98\\n    area (standard error):                6.802  542.2\\n    smoothness (standard error):          0.002  0.031\\n    compactness (standard error):         0.002  0.135\\n    concavity (standard error):           0.0    0.396\\n    concave points (standard error):      0.0    0.053\\n    symmetry (standard error):            0.008  0.079\\n    fractal dimension (standard error):   0.001  0.03\\n    radius (worst):                       7.93   36.04\\n    texture (worst):                      12.02  49.54\\n    perimeter (worst):                    50.41  251.2\\n    area (worst):                         185.2  4254.0\\n    smoothness (worst):                   0.071  0.223\\n    compactness (worst):                  0.027  1.058\\n    concavity (worst):                    0.0    1.252\\n    concave points (worst):               0.0    0.291\\n    symmetry (worst):                     0.156  0.664\\n    fractal dimension (worst):            0.055  0.208\\n    ===================================== ====== ======\\n\\n    :Missing Attribute Values: None\\n\\n    :Class Distribution: 212 - Malignant, 357 - Benign\\n\\n    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\\n\\n    :Donor: Nick Street\\n\\n    :Date: November, 1995\\n\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\n.. topic:: References\\n\\n   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \\n     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \\n     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\\n     San Jose, CA, 1993.\\n   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \\n     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \\n     July-August 1995.\\n   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\\n     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \\n     163-171.',\n",
              " 'feature_names': array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
              "        'mean smoothness', 'mean compactness', 'mean concavity',\n",
              "        'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
              "        'radius error', 'texture error', 'perimeter error', 'area error',\n",
              "        'smoothness error', 'compactness error', 'concavity error',\n",
              "        'concave points error', 'symmetry error',\n",
              "        'fractal dimension error', 'worst radius', 'worst texture',\n",
              "        'worst perimeter', 'worst area', 'worst smoothness',\n",
              "        'worst compactness', 'worst concavity', 'worst concave points',\n",
              "        'worst symmetry', 'worst fractal dimension'], dtype='<U23'),\n",
              " 'filename': 'breast_cancer.csv',\n",
              " 'data_module': 'sklearn.datasets.data'}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creamos una dataframe para ver como quedan los datos en forma agrupada\n",
        "BreatCancerDataFrame = pd.DataFrame(data=breast_cancer.data, columns=breast_cancer.feature_names)\n",
        "BreatCancerDataFrame\n"
      ],
      "metadata": {
        "id": "_xVL68xMPLqD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "outputId": "db8c14b8-fa16-4f99-8929-a0ec366dff04"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst radius  worst texture  \\\n",
              "0                   0.07871  ...        25.380          17.33   \n",
              "1                   0.05667  ...        24.990          23.41   \n",
              "2                   0.05999  ...        23.570          25.53   \n",
              "3                   0.09744  ...        14.910          26.50   \n",
              "4                   0.05883  ...        22.540          16.67   \n",
              "..                      ...  ...           ...            ...   \n",
              "564                 0.05623  ...        25.450          26.40   \n",
              "565                 0.05533  ...        23.690          38.25   \n",
              "566                 0.05648  ...        18.980          34.12   \n",
              "567                 0.07016  ...        25.740          39.42   \n",
              "568                 0.05884  ...         9.456          30.37   \n",
              "\n",
              "     worst perimeter  worst area  worst smoothness  worst compactness  \\\n",
              "0             184.60      2019.0           0.16220            0.66560   \n",
              "1             158.80      1956.0           0.12380            0.18660   \n",
              "2             152.50      1709.0           0.14440            0.42450   \n",
              "3              98.87       567.7           0.20980            0.86630   \n",
              "4             152.20      1575.0           0.13740            0.20500   \n",
              "..               ...         ...               ...                ...   \n",
              "564           166.10      2027.0           0.14100            0.21130   \n",
              "565           155.00      1731.0           0.11660            0.19220   \n",
              "566           126.70      1124.0           0.11390            0.30940   \n",
              "567           184.60      1821.0           0.16500            0.86810   \n",
              "568            59.16       268.6           0.08996            0.06444   \n",
              "\n",
              "     worst concavity  worst concave points  worst symmetry  \\\n",
              "0             0.7119                0.2654          0.4601   \n",
              "1             0.2416                0.1860          0.2750   \n",
              "2             0.4504                0.2430          0.3613   \n",
              "3             0.6869                0.2575          0.6638   \n",
              "4             0.4000                0.1625          0.2364   \n",
              "..               ...                   ...             ...   \n",
              "564           0.4107                0.2216          0.2060   \n",
              "565           0.3215                0.1628          0.2572   \n",
              "566           0.3403                0.1418          0.2218   \n",
              "567           0.9387                0.2650          0.4087   \n",
              "568           0.0000                0.0000          0.2871   \n",
              "\n",
              "     worst fractal dimension  \n",
              "0                    0.11890  \n",
              "1                    0.08902  \n",
              "2                    0.08758  \n",
              "3                    0.17300  \n",
              "4                    0.07678  \n",
              "..                       ...  \n",
              "564                  0.07115  \n",
              "565                  0.06637  \n",
              "566                  0.07820  \n",
              "567                  0.12400  \n",
              "568                  0.07039  \n",
              "\n",
              "[569 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b640f238-dfb8-49e8-bceb-03ceef7dea72\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst radius</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>25.380</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b640f238-dfb8-49e8-bceb-03ceef7dea72')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-b640f238-dfb8-49e8-bceb-03ceef7dea72 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-b640f238-dfb8-49e8-bceb-03ceef7dea72');\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",
              "\n",
              "\n",
              "<div id=\"df-d341ecbc-d762-4d1e-a95f-215486a7c715\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d341ecbc-d762-4d1e-a95f-215486a7c715')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d341ecbc-d762-4d1e-a95f-215486a7c715 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "BreatCancerDataFrame"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creamos una columna para corroborar si es benigno o no, mas por seguir el\n",
        "#ejemplo de la clase que otra cosa\n",
        "BreatCancerDataFrame['isBenign'] = breast_cancer.target\n",
        "BreatCancerDataFrame"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "DpvSGyxiCnEV",
        "outputId": "f4002555-8a3c-4b21-c7be-40bb69bec302"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "0                   0.07871  ...          17.33           184.60      2019.0   \n",
              "1                   0.05667  ...          23.41           158.80      1956.0   \n",
              "2                   0.05999  ...          25.53           152.50      1709.0   \n",
              "3                   0.09744  ...          26.50            98.87       567.7   \n",
              "4                   0.05883  ...          16.67           152.20      1575.0   \n",
              "..                      ...  ...            ...              ...         ...   \n",
              "564                 0.05623  ...          26.40           166.10      2027.0   \n",
              "565                 0.05533  ...          38.25           155.00      1731.0   \n",
              "566                 0.05648  ...          34.12           126.70      1124.0   \n",
              "567                 0.07016  ...          39.42           184.60      1821.0   \n",
              "568                 0.05884  ...          30.37            59.16       268.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "0             0.16220            0.66560           0.7119   \n",
              "1             0.12380            0.18660           0.2416   \n",
              "2             0.14440            0.42450           0.4504   \n",
              "3             0.20980            0.86630           0.6869   \n",
              "4             0.13740            0.20500           0.4000   \n",
              "..                ...                ...              ...   \n",
              "564           0.14100            0.21130           0.4107   \n",
              "565           0.11660            0.19220           0.3215   \n",
              "566           0.11390            0.30940           0.3403   \n",
              "567           0.16500            0.86810           0.9387   \n",
              "568           0.08996            0.06444           0.0000   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  isBenign  \n",
              "0                  0.2654          0.4601                  0.11890         0  \n",
              "1                  0.1860          0.2750                  0.08902         0  \n",
              "2                  0.2430          0.3613                  0.08758         0  \n",
              "3                  0.2575          0.6638                  0.17300         0  \n",
              "4                  0.1625          0.2364                  0.07678         0  \n",
              "..                    ...             ...                      ...       ...  \n",
              "564                0.2216          0.2060                  0.07115         0  \n",
              "565                0.1628          0.2572                  0.06637         0  \n",
              "566                0.1418          0.2218                  0.07820         0  \n",
              "567                0.2650          0.4087                  0.12400         0  \n",
              "568                0.0000          0.2871                  0.07039         1  \n",
              "\n",
              "[569 rows x 31 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38145e13-243f-4329-9f6c-100e99163ddf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>...</th>\n",
              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
              "      <th>worst fractal dimension</th>\n",
              "      <th>isBenign</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.30010</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>...</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.16220</td>\n",
              "      <td>0.66560</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>...</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\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>564</th>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>...</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>...</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>...</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>...</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>...</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 31 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38145e13-243f-4329-9f6c-100e99163ddf')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-38145e13-243f-4329-9f6c-100e99163ddf 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-38145e13-243f-4329-9f6c-100e99163ddf');\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",
              "\n",
              "\n",
              "<div id=\"df-ccb14efd-7730-4120-95b5-b7eb05027eae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ccb14efd-7730-4120-95b5-b7eb05027eae')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ccb14efd-7730-4120-95b5-b7eb05027eae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "BreatCancerDataFrame"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#firstDataGroupX = BreatCancerDataFrame.drop('isBenign', axis=1)\n",
        "#secondDataGroupY = BreatCancerDataFrame['isBenign']\n",
        "BreatCancerDataFrame.drop('isBenign', axis=1)\n",
        "\n",
        "datosDeTrabajoX = breast_cancer.data\n",
        "datosDeEvaluacionY = breast_cancer.target\n",
        "\n",
        "datosDeTrabajoX"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N7mxdg8cDseM",
        "outputId": "3f85c05c-6e7b-4c11-c82a-b7342c999909"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n",
              "        1.189e-01],\n",
              "       [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
              "        8.902e-02],\n",
              "       [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n",
              "        8.758e-02],\n",
              "       ...,\n",
              "       [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n",
              "        7.820e-02],\n",
              "       [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n",
              "        1.240e-01],\n",
              "       [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
              "        7.039e-02]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(datosDeTrabajoX, datosDeEvaluacionY, test_size=0.2, random_state=42)\n",
        "\n",
        "\n",
        "\n",
        "#LogisticRegression es un tipo de modelo, existen mas, pero en el ejemplo que\n",
        "#nos pidieron, por practividad podemos usar este\n",
        "modelo = LogisticRegression(max_iter=1000)\n",
        "\n",
        "#Entrenar el modelo\n",
        "modelo.fit(X_train, Y_train)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "3eoFJQ45EfzO",
        "outputId": "1bf08dd3-c1d4-460b-d058-439301f086fa"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generacion de datos predictorios para posterior evaluacion\n",
        "datosPredichosY = modelo.predict(X_test)\n",
        "\n",
        "#Evaluacion de la capacidad de prediccion del modelo contra datos verdaderos\n",
        "print(f'Precisión: {accuracy_score(Y_test, datosPredichosY)}')\n",
        "print(classification_report(Y_test, datosPredichosY, target_names=breast_cancer.target_names))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSwxr3b_lUUM",
        "outputId": "1b485c73-4456-4cf2-d2e1-9e65f29131ee"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión: 0.956140350877193\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "   malignant       0.97      0.91      0.94        43\n",
            "      benign       0.95      0.99      0.97        71\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.96      0.95      0.95       114\n",
            "weighted avg       0.96      0.96      0.96       114\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#declaramos el tipo de estas variables porque nos lo pide el codigo\n",
        "valores_aleatorios = []\n",
        "valores_celulares_aleatorios = []\n",
        "\n",
        "#Creamos valores aleatorios en la lista \"valores_aleatorios\"\n",
        "for _ in range(30):\n",
        "  valores_aleatorios.append(np.random.uniform(0, 200))\n",
        "\n",
        "#Convertimos esos valores aleatorios a tipo float, ademas de darles formato a\n",
        "#3 decimales y meterlos en otra lista llamada \"valores_celulares_aleatorios\"\n",
        "for x in valores_aleatorios:\n",
        "  valores_celulares_aleatorios.append(float(\"{:.3f}\".format(x)))\n",
        "\n",
        "#convertimos la lista a una array\n",
        "array_valores_celulares_aleatorios = np.array([valores_celulares_aleatorios])\n",
        "\n",
        "#hacemos un intento de prediccion con esos valores\n",
        "ejercicio_de_prediccion = modelo.predict(array_valores_celulares_aleatorios)\n",
        "\n",
        "print(f'Predicción: {breast_cancer.target_names[ejercicio_de_prediccion[0]]}')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30Hqxp7WmBuq",
        "outputId": "3daf898d-1ffe-4c10-f735-296b0a93a880"
      },
      "execution_count": 343,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicción: malignant\n"
          ]
        }
      ]
    }
  ]
}