SIM_II_PF.ipynb

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  "metadata": {
    "colab": {
      "name": "SIM-II-PF.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPQ5H6IKQZrMCRHLDy6VITa",
      "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/cctorres/SIM-II/blob/main/SIM_II_PF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cardiotocographic data**\n",
        "\n",
        "Santiago Montoya\n",
        "\n",
        "Cristian Camilo Torres Alzate"
      ],
      "metadata": {
        "id": "6Q_4ydqwKAq5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importacion de Librerias**"
      ],
      "metadata": {
        "id": "IanqH_HvKcZa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6JyqyuHE-Mkm",
        "outputId": "db6f6af3-60e9-4233-9022-98f8988cac3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting xlrd==1.2.0\n",
            "  Downloading xlrd-1.2.0-py2.py3-none-any.whl (103 kB)\n",
            "\u001b[?25l\r\u001b[K     |███▏                            | 10 kB 20.2 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 20 kB 21.5 MB/s eta 0:00:01\r\u001b[K     |█████████▌                      | 30 kB 15.4 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 40 kB 10.5 MB/s eta 0:00:01\r\u001b[K     |███████████████▉                | 51 kB 7.4 MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 61 kB 8.4 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 71 kB 8.0 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 81 kB 7.9 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▋   | 92 kB 8.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▊| 102 kB 9.6 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 103 kB 9.6 MB/s \n",
            "\u001b[?25hInstalling collected packages: xlrd\n",
            "  Attempting uninstall: xlrd\n",
            "    Found existing installation: xlrd 1.1.0\n",
            "    Uninstalling xlrd-1.1.0:\n",
            "      Successfully uninstalled xlrd-1.1.0\n",
            "Successfully installed xlrd-1.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install xlrd==1.2.0\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import KFold\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Cargando el DataSet**"
      ],
      "metadata": {
        "id": "vyZzt6-nKh-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00193/CTG.xls'\n",
        "\n",
        "df = pd.read_excel(url, sheet_name='Data', skipfooter=3)\n",
        "df = df.drop(columns=df.filter(like='Unnamed').columns)\n",
        "df.columns = df.iloc[0].to_list()\n",
        "df = df[1:].reset_index(drop=True)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "LYjrJ_xuME_1",
        "outputId": "d4bae11e-b8ba-49a5-8b8d-955dd4e3773b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    LB        AC FM        UC        DL DS DP ASTV MSTV ALTV  ...  Max Nmax  \\\n",
              "0  120         0  0         0         0  0  0   73  0.5   43  ...  126    2   \n",
              "1  132   0.00638  0   0.00638   0.00319  0  0   17  2.1    0  ...  198    6   \n",
              "2  133  0.003322  0  0.008306  0.003322  0  0   16  2.1    0  ...  198    5   \n",
              "3  134  0.002561  0  0.007682  0.002561  0  0   16  2.4    0  ...  170   11   \n",
              "4  132  0.006515  0  0.008143         0  0  0   16  2.4    0  ...  170    9   \n",
              "\n",
              "  Nzeros Mode Mean Median Variance Tendency CLASS NSP  \n",
              "0      0  120  137    121       73        1     9   2  \n",
              "1      1  141  136    140       12        0     6   1  \n",
              "2      1  141  135    138       13        0     6   1  \n",
              "3      0  137  134    137       13        1     6   1  \n",
              "4      0  137  136    138       11        1     2   1  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <td>1</td>\n",
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              "      <td>134</td>\n",
              "      <td>0.002561</td>\n",
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              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Entradas faltantes**"
      ],
      "metadata": {
        "id": "6uRlIgYCKosW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.astype(int)\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd9HEPrBQGCF",
        "outputId": "68fa8fe9-88a1-452d-8e03-2ea19b566ea0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2126 entries, 0 to 2125\n",
            "Data columns (total 23 columns):\n",
            " #   Column    Non-Null Count  Dtype\n",
            "---  ------    --------------  -----\n",
            " 0   LB        2126 non-null   int64\n",
            " 1   AC        2126 non-null   int64\n",
            " 2   FM        2126 non-null   int64\n",
            " 3   UC        2126 non-null   int64\n",
            " 4   DL        2126 non-null   int64\n",
            " 5   DS        2126 non-null   int64\n",
            " 6   DP        2126 non-null   int64\n",
            " 7   ASTV      2126 non-null   int64\n",
            " 8   MSTV      2126 non-null   int64\n",
            " 9   ALTV      2126 non-null   int64\n",
            " 10  MLTV      2126 non-null   int64\n",
            " 11  Width     2126 non-null   int64\n",
            " 12  Min       2126 non-null   int64\n",
            " 13  Max       2126 non-null   int64\n",
            " 14  Nmax      2126 non-null   int64\n",
            " 15  Nzeros    2126 non-null   int64\n",
            " 16  Mode      2126 non-null   int64\n",
            " 17  Mean      2126 non-null   int64\n",
            " 18  Median    2126 non-null   int64\n",
            " 19  Variance  2126 non-null   int64\n",
            " 20  Tendency  2126 non-null   int64\n",
            " 21  CLASS     2126 non-null   int64\n",
            " 22  NSP       2126 non-null   int64\n",
            "dtypes: int64(23)\n",
            "memory usage: 382.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Desbalance**"
      ],
      "metadata": {
        "id": "Ai4QvULeLoNk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "values = df[\"NSP\"].value_counts()\n",
        "font = {'size'   : 12}\n",
        "matplotlib.rc('font', **font)\n",
        "\n",
        "\n",
        "pd.value_counts(df[\"NSP\"]).plot.bar(rot=0)\n",
        "plt.title('Distribución NSP')\n",
        "plt.xlabel('NSP')\n",
        "plt.ylabel('Frecuencia')\n",
        "\n",
        "df[\"NSP\"].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "hOlcO5vm_sJx",
        "outputId": "b665324b-ecce-403a-b1f4-60951884bed2"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    1655\n",
              "2     295\n",
              "3     176\n",
              "Name: NSP, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Entrenamiento**"
      ],
      "metadata": {
        "id": "afW60wETL3N3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:, 0:21].values\n",
        "y = df.iloc[:, 22].values\n",
        "y=y.astype('int')\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=0)\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(x_train)\n",
        "x_train = scaler.transform(x_train)\n",
        "x_test = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "3Z6O4P63HMQG"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Analisis discriminante Cuadratico**"
      ],
      "metadata": {
        "id": "BXfzXK5XNe3d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis \n",
        "arr = np.array([['Kernel','Score']])\n",
        "QDA = QuadraticDiscriminantAnalysis()\n",
        "QDA.fit(x_train, y_train)\n",
        "\n",
        "qda_score=QDA.score(x_train, y_train)\n",
        "y_pred = QDA.predict(x_test)\n",
        "qda_accuracy_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "row = np.array([qda_score,qda_accuracy_score])\n",
        "arr = np.append(arr,[row],axis= 0)\n",
        "table = pd.DataFrame(data=arr)\n",
        "table.head(table.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "wVe8JZxexv0b",
        "outputId": "876c1e6f-6452-4f0a-ee15-e0223a81769f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/discriminant_analysis.py:878: UserWarning: Variables are collinear\n",
            "  warnings.warn(\"Variables are collinear\")\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    0                   1\n",
              "0              Kernel               Score\n",
              "1  0.7553324968632371  0.7481203007518797"
            ],
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              "\n",
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              "      <th>0</th>\n",
              "      <td>Kernel</td>\n",
              "      <td>Score</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.7553324968632371</td>\n",
              "      <td>0.7481203007518797</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4ea8b82b-8234-44c3-be22-87013d995ef8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4ea8b82b-8234-44c3-be22-87013d995ef8 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-4ea8b82b-8234-44c3-be22-87013d995ef8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Ventana de Parzen - Kernel**"
      ],
      "metadata": {
        "id": "CtCQTh8JO4v1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KernelDensity\n",
        "arr = np.array([['Kernel','Score']])\n",
        "kernel = ['gaussian', 'tophat', 'epanechnikov', 'exponential', 'linear', 'cosine']\n",
        "\n",
        "for k in kernel:\n",
        "\n",
        "  PW = KernelDensity(kernel=k)\n",
        "  PW.fit(x_train, y_train)\n",
        "\n",
        "  pw_score=PW.score(x_train, y_train)\n",
        "\n",
        "  row = np.array([k,pw_score])\n",
        "  arr = np.append(arr,[row],axis= 0)\n",
        "\n",
        "table = pd.DataFrame(data=arr)\n",
        "table.head(table.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "4GG8TCMLO9DR",
        "outputId": "0f08908a-eb84-42a0-d3c4-b27c388acff6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              0                    1\n",
              "0        Kernel                Score\n",
              "1      gaussian  -31502.757100147184\n",
              "2        tophat    5834.672084129061\n",
              "3  epanechnikov    8271.717053168648\n",
              "4   exponential    -66978.6273663641\n",
              "5        linear    8529.046975007172\n",
              "6        cosine    4879.487365226221"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca5b1b20-9e3d-4070-abd1-aad41f97d3f4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "      <th>0</th>\n",
              "      <td>Kernel</td>\n",
              "      <td>Score</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>gaussian</td>\n",
              "      <td>-31502.757100147184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tophat</td>\n",
              "      <td>5834.672084129061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>epanechnikov</td>\n",
              "      <td>8271.717053168648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>exponential</td>\n",
              "      <td>-66978.6273663641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>linear</td>\n",
              "      <td>8529.046975007172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>cosine</td>\n",
              "      <td>4879.487365226221</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca5b1b20-9e3d-4070-abd1-aad41f97d3f4')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca5b1b20-9e3d-4070-abd1-aad41f97d3f4 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-ca5b1b20-9e3d-4070-abd1-aad41f97d3f4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Gradient Boosting Tree**"
      ],
      "metadata": {
        "id": "S-Ekm3q6Ntb2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "#GradientBoostingTree\n",
        "arr = np.array([['# Trees','Score','Accuracy']])\n",
        "number_trees = [10, 50, 100, 200]\n",
        "\n",
        "for t in number_trees:\n",
        "  reg = GradientBoostingClassifier(n_estimators=t)\n",
        "  reg.fit(x_train, y_train)\n",
        "\n",
        "  gb_score=reg.score(x_train, y_train)\n",
        "  y_pred = reg.predict(x_test)\n",
        "  gb_accuracy_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "  row = np.array([t,gb_score,gb_accuracy_score])\n",
        "  arr = np.append(arr,[row],axis= 0)\n",
        "table = pd.DataFrame(data=arr)\n",
        "table.head(table.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "th14SrI1xXng",
        "outputId": "1aabebe9-67a2-4bb8-cef1-bb8a28dd4037"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         0                   1                   2\n",
              "0  # Trees               Score            Accuracy\n",
              "1     10.0  0.9259723964868256  0.9154135338345865\n",
              "2     50.0  0.9730238393977415  0.9285714285714286\n",
              "3    100.0   0.986198243412798  0.9266917293233082\n",
              "4    200.0  0.9981179422835633  0.9323308270676691"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td># Trees</td>\n",
              "      <td>Score</td>\n",
              "      <td>Accuracy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.9259723964868256</td>\n",
              "      <td>0.9154135338345865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.9730238393977415</td>\n",
              "      <td>0.9285714285714286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.986198243412798</td>\n",
              "      <td>0.9266917293233082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>200.0</td>\n",
              "      <td>0.9981179422835633</td>\n",
              "      <td>0.9323308270676691</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-536c58f9-919d-482e-abdc-3664f3ec36ee')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-536c58f9-919d-482e-abdc-3664f3ec36ee 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-536c58f9-919d-482e-abdc-3664f3ec36ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Redes Neuronales Artificales**"
      ],
      "metadata": {
        "id": "znsgqVwvPEh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "arr = np.array([['# Hidden Layers','Activation','Score','Accuracy']])\n",
        "number_layer = [2, 5,10]\n",
        "activation_list = ['identity', 'logistic', 'tanh', 'relu']\n",
        "for n in number_layer:\n",
        "  for a in activation_list:\n",
        "    reg = MLPClassifier(hidden_layer_sizes=(n,), activation=a)\n",
        "    reg.fit(x_train, y_train)\n",
        "\n",
        "    gb_score=reg.score(x_train, y_train)\n",
        "    y_pred = reg.predict(x_test)\n",
        "    gb_accuracy_score = accuracy_score(y_test, y_pred)\n",
        "\n",
        "    row = np.array([n,a,gb_score,gb_accuracy_score])\n",
        "    arr = np.append(arr,[row],axis= 0)\n",
        "table = pd.DataFrame(data=arr)\n",
        "table.head(table.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 874
        },
        "id": "fQTpRJ-BPH-7",
        "outputId": "7cb66d8c-9ee2-4ec9-a66c-83f4a9ed2da4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0           1                   2                   3\n",
              "0   # Hidden Layers  Activation               Score            Accuracy\n",
              "1                 2    identity  0.8506900878293601  0.8421052631578947\n",
              "2                 2    logistic  0.7810539523212046  0.7706766917293233\n",
              "3                 2        tanh  0.7879548306148055  0.7763157894736842\n",
              "4                 2        relu  0.8262233375156838  0.8176691729323309\n",
              "5                 5    identity  0.8500627352572145  0.8458646616541353\n",
              "6                 5    logistic  0.7917189460476788  0.7744360902255639\n",
              "7                 5        tanh  0.8381430363864492  0.8383458646616542\n",
              "8                 5        relu  0.8299874529485571  0.8157894736842105\n",
              "9                10    identity  0.8720200752823086  0.8609022556390977\n",
              "10               10    logistic  0.8005018820577164  0.7819548872180451\n",
              "11               10        tanh  0.8651191969887077  0.8590225563909775\n",
              "12               10        relu  0.8682559598494354  0.8609022556390977"
            ],
            "text/html": [
              "\n",
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              "      <th>0</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td># Hidden Layers</td>\n",
              "      <td>Activation</td>\n",
              "      <td>Score</td>\n",
              "      <td>Accuracy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>identity</td>\n",
              "      <td>0.8506900878293601</td>\n",
              "      <td>0.8421052631578947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>logistic</td>\n",
              "      <td>0.7810539523212046</td>\n",
              "      <td>0.7706766917293233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>tanh</td>\n",
              "      <td>0.7879548306148055</td>\n",
              "      <td>0.7763157894736842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>relu</td>\n",
              "      <td>0.8262233375156838</td>\n",
              "      <td>0.8176691729323309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>identity</td>\n",
              "      <td>0.8500627352572145</td>\n",
              "      <td>0.8458646616541353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>5</td>\n",
              "      <td>logistic</td>\n",
              "      <td>0.7917189460476788</td>\n",
              "      <td>0.7744360902255639</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>5</td>\n",
              "      <td>tanh</td>\n",
              "      <td>0.8381430363864492</td>\n",
              "      <td>0.8383458646616542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>5</td>\n",
              "      <td>relu</td>\n",
              "      <td>0.8299874529485571</td>\n",
              "      <td>0.8157894736842105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>identity</td>\n",
              "      <td>0.8720200752823086</td>\n",
              "      <td>0.8609022556390977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>10</td>\n",
              "      <td>logistic</td>\n",
              "      <td>0.8005018820577164</td>\n",
              "      <td>0.7819548872180451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>10</td>\n",
              "      <td>tanh</td>\n",
              "      <td>0.8651191969887077</td>\n",
              "      <td>0.8590225563909775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>relu</td>\n",
              "      <td>0.8682559598494354</td>\n",
              "      <td>0.8609022556390977</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e83dae91-afb3-49e9-bdb4-408db2ef566c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e83dae91-afb3-49e9-bdb4-408db2ef566c 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-e83dae91-afb3-49e9-bdb4-408db2ef566c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Maquinas de Soporte Vectorial**\n"
      ],
      "metadata": {
        "id": "CsvNQFpxNzqf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM\n",
        "from sklearn import svm\n",
        "\n",
        "C_list = [ 0.1, 1, 10]\n",
        "kernel = ['linear', 'poly', 'rbf', 'sigmoid']\n",
        "arr = np.array([['Kernel','C','Score','Accuracy']])\n",
        "\n",
        "for k in kernel:\n",
        "  for c in C_list:\n",
        "\n",
        "    kf = KFold(n_splits=10)\n",
        "\n",
        "    svm_reg = svm.SVC(C = c, kernel = k)\n",
        "    svm_reg.fit(x_train, y_train)\n",
        "\n",
        "    svm_score=svm_reg.score(x_train, y_train)\n",
        "    y_pred = svm_reg.predict(x_test)\n",
        "    svm_accuracy_score = accuracy_score(y_test, y_pred)\n",
        "    row = np.array([k,c,svm_score,svm_accuracy_score])\n",
        "    arr = np.append(arr,[row],axis= 0)\n",
        "\n",
        "table = pd.DataFrame(data=arr)\n",
        "table.head(table.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "Y1dDNFzTySst",
        "outputId": "9c12ff1f-13aa-49ac-bd45-2aa93eff4b5c"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          0    1                   2                   3\n",
              "0    Kernel    C               Score            Accuracy\n",
              "1    linear  0.1  0.8293601003764115  0.8139097744360902\n",
              "2    linear    1  0.8801756587202008  0.8778195488721805\n",
              "3    linear   10  0.8883312421580929  0.8796992481203008\n",
              "4      poly  0.1  0.8726474278544541  0.8703007518796992\n",
              "5      poly    1  0.9121706398996235  0.9060150375939849\n",
              "6      poly   10  0.9335006273525721   0.900375939849624\n",
              "7       rbf  0.1  0.8343789209535759  0.8195488721804511\n",
              "8       rbf    1   0.890840652446675  0.8928571428571429\n",
              "9       rbf   10  0.9240903387703889  0.9022556390977443\n",
              "10  sigmoid  0.1  0.7810539523212046   0.768796992481203\n",
              "11  sigmoid    1  0.7013801756587202  0.7030075187969925\n",
              "12  sigmoid   10  0.6725219573400251  0.6804511278195489"
            ],
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              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Kernel</td>\n",
              "      <td>C</td>\n",
              "      <td>Score</td>\n",
              "      <td>Accuracy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>linear</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.8293601003764115</td>\n",
              "      <td>0.8139097744360902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>linear</td>\n",
              "      <td>1</td>\n",
              "      <td>0.8801756587202008</td>\n",
              "      <td>0.8778195488721805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>linear</td>\n",
              "      <td>10</td>\n",
              "      <td>0.8883312421580929</td>\n",
              "      <td>0.8796992481203008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>poly</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.8726474278544541</td>\n",
              "      <td>0.8703007518796992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>poly</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9121706398996235</td>\n",
              "      <td>0.9060150375939849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>poly</td>\n",
              "      <td>10</td>\n",
              "      <td>0.9335006273525721</td>\n",
              "      <td>0.900375939849624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>rbf</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.8343789209535759</td>\n",
              "      <td>0.8195488721804511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>rbf</td>\n",
              "      <td>1</td>\n",
              "      <td>0.890840652446675</td>\n",
              "      <td>0.8928571428571429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>rbf</td>\n",
              "      <td>10</td>\n",
              "      <td>0.9240903387703889</td>\n",
              "      <td>0.9022556390977443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>sigmoid</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.7810539523212046</td>\n",
              "      <td>0.768796992481203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>sigmoid</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7013801756587202</td>\n",
              "      <td>0.7030075187969925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>sigmoid</td>\n",
              "      <td>10</td>\n",
              "      <td>0.6725219573400251</td>\n",
              "      <td>0.6804511278195489</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-558ecd0a-17c2-4bb4-9f9c-fca008b781cf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-558ecd0a-17c2-4bb4-9f9c-fca008b781cf 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-558ecd0a-17c2-4bb4-9f9c-fca008b781cf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Correlacion Caracteristicas**"
      ],
      "metadata": {
        "id": "OhuhuCxAiLla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.heatmap(df.corr())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "ZbkE43U7KXIv",
        "outputId": "a2f61121-dfba-4a0c-f3d6-5229ef1dec92"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f73fe5329d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}