Proyecto_de_Clasificación_Addison_Reyes_2021_2026.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Librerias"
      ],
      "metadata": {
        "id": "9RTN57B4RAhE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip -q install sklearn"
      ],
      "metadata": {
        "id": "x2C9lsBZTQ5_"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip -q install dataprep"
      ],
      "metadata": {
        "id": "ny99DiPQb5Yi"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.compose import make_column_transformer\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from pandas.core.common import random_state\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from dataprep.eda import create_report\n",
        "from sklearn.utils import resample\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import svm\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle"
      ],
      "metadata": {
        "id": "XFp4IITAC3cr"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1 - Recoleccion de data"
      ],
      "metadata": {
        "id": "4nHWzI1gRu56"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 40,
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          "height": 317
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        "outputId": "ec48810f-95a3-4e50-b9a0-a13e07d92490"
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      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "   age  height(cm)  weight(kg)  waist(cm)  eyesight(left)  eyesight(right)  \\\n",
              "0   35         170          85       97.0             0.9              0.9   \n",
              "1   20         175         110      110.0             0.7              0.9   \n",
              "2   45         155          65       86.0             0.9              0.9   \n",
              "3   45         165          80       94.0             0.8              0.7   \n",
              "4   20         165          60       81.0             1.5              0.1   \n",
              "\n",
              "   hearing(left)  hearing(right)  systolic  relaxation  ...  HDL  LDL  \\\n",
              "0              1               1       118          78  ...   70  142   \n",
              "1              1               1       119          79  ...   71  114   \n",
              "2              1               1       110          80  ...   57  112   \n",
              "3              1               1       158          88  ...   46   91   \n",
              "4              1               1       109          64  ...   47   92   \n",
              "\n",
              "   hemoglobin  Urine protein  serum creatinine   AST   ALT  Gtp  \\\n",
              "0        19.8              1               1.0    61   115  125   \n",
              "1        15.9              1               1.1    19    25   30   \n",
              "2        13.7              3               0.6  1090  1400  276   \n",
              "3        16.9              1               0.9    32    36   36   \n",
              "4        14.9              1               1.2    26    28   15   \n",
              "\n",
              "   dental caries  smoking  \n",
              "0              1        1  \n",
              "1              1        0  \n",
              "2              0        0  \n",
              "3              0        0  \n",
              "4              0        0  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <th>age</th>\n",
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              "      <th>LDL</th>\n",
              "      <th>hemoglobin</th>\n",
              "      <th>Urine protein</th>\n",
              "      <th>serum creatinine</th>\n",
              "      <th>AST</th>\n",
              "      <th>ALT</th>\n",
              "      <th>Gtp</th>\n",
              "      <th>dental caries</th>\n",
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              "      <td>114</td>\n",
              "      <td>15.9</td>\n",
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              "      <td>1090</td>\n",
              "      <td>1400</td>\n",
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              "      <td>80</td>\n",
              "      <td>94.0</td>\n",
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              "      <td>16.9</td>\n",
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              "      <td>...</td>\n",
              "      <td>47</td>\n",
              "      <td>92</td>\n",
              "      <td>14.9</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>26</td>\n",
              "      <td>28</td>\n",
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              "      <td>0</td>\n",
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          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "#https://www.kaggle.com/datasets/gauravduttakiit/smoker-status-prediction?select=train_dataset.csv\n",
        "df = pd.read_csv(\"/content/train_dataset.csv\")\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2 - Preparacion de la data / Preprocesamiento de la data"
      ],
      "metadata": {
        "id": "d-YqLLXGR4Ne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['waist(cm)', 'eyesight(left)', 'eyesight(right)', 'hearing(left)', 'hearing(right)'], axis=1, inplace=True)\n",
        "\n",
        "df = df.rename(columns={'height(cm)':'height', 'weight(kg)':'weight', 'fasting blood sugar':'sugar',\n",
        "       'Urine protein':'UrineProtein', 'serum creatinine':'Creatinine', 'dental caries':'Caries'})"
      ],
      "metadata": {
        "id": "0KOXGZ-h_ckP"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().any()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O939IP_ySh2Z",
        "outputId": "c87f84d4-d987-4190-f8a6-d9673f034a1a"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age             False\n",
              "height          False\n",
              "weight          False\n",
              "systolic        False\n",
              "relaxation      False\n",
              "sugar           False\n",
              "Cholesterol     False\n",
              "triglyceride    False\n",
              "HDL             False\n",
              "LDL             False\n",
              "hemoglobin      False\n",
              "UrineProtein    False\n",
              "Creatinine      False\n",
              "AST             False\n",
              "ALT             False\n",
              "Gtp             False\n",
              "Caries          False\n",
              "smoking         False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['smoking'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "9xRmX3o_Y8wT",
        "outputId": "29bf85c1-71eb-4f6a-bb27-c7bef27d97cb"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24666\n",
              "1    14318\n",
              "Name: smoking, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "no_fumador = resample(df[df['smoking'] == 0], replace=False, random_state=2, n_samples=10000)\n",
        "fumador = resample(df[df['smoking'] == 1], replace=False, random_state=2, n_samples=10000)\n",
        "\n",
        "df = pd.concat([no_fumador, fumador])\n",
        "\n",
        "df.head(5)"
      ],
      "metadata": {
        "id": "Zc3KXSt5ZlBR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d19e0d4e-3116-487a-dc64-2e571ba989ea"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  height  weight  systolic  relaxation  sugar  Cholesterol  \\\n",
              "34126   50     160      60       119          70    103          223   \n",
              "32637   55     155      50        92          61     91          189   \n",
              "27570   45     165      65       140          89    129          150   \n",
              "15714   40     155      65       142          89     98          199   \n",
              "24697   40     155      50       120          90     97          244   \n",
              "\n",
              "       triglyceride  HDL  LDL  hemoglobin  UrineProtein  Creatinine  AST  ALT  \\\n",
              "34126            94   64  140        13.8             1         0.7   20   14   \n",
              "32637            51   64  115        13.2             3         0.9   19   12   \n",
              "27570            91   55   77        16.9             1         0.8  182  311   \n",
              "15714            74   59  125        14.3             1         0.5   15   15   \n",
              "24697           141   83  132        14.5             1         0.7   22   20   \n",
              "\n",
              "       Gtp  Caries  smoking  \n",
              "34126   13       0        0  \n",
              "32637   13       0        0  \n",
              "27570  231       0        0  \n",
              "15714   22       0        0  \n",
              "24697   35       0        0  "
            ],
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              "      <th>hemoglobin</th>\n",
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              "      <td>129</td>\n",
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              "      <td>311</td>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['smoking'] == 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zOkASN4IPJup",
        "outputId": "40e85528-6a2c-4879-a3cc-202f08bd1e33"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  height  weight  systolic  relaxation  sugar  Cholesterol  \\\n",
              "5368    40     175      70       110          68     89          202   \n",
              "16547   50     175      65       130          93     97          212   \n",
              "27912   50     170      60       114          66    101          173   \n",
              "31154   40     165      65       118          84    104          195   \n",
              "25231   25     175      80       110          70     92          168   \n",
              "...    ...     ...     ...       ...         ...    ...          ...   \n",
              "26821   50     170      70       112          74    101          271   \n",
              "4814    25     175      75       108          62     89          133   \n",
              "29130   55     175      60       120          80    100          175   \n",
              "32792   40     165      60       109          73    185          161   \n",
              "6020    60     175      70       150         100    131          218   \n",
              "\n",
              "       triglyceride  HDL  LDL  hemoglobin  UrineProtein  Creatinine  AST  ALT  \\\n",
              "5368            162   58  112        14.4             1         1.1   17   26   \n",
              "16547           186   49  126        16.2             2         1.1   35   34   \n",
              "27912           185   51   85        14.4             1         0.8   24   12   \n",
              "31154           261   45   98        14.2             1         1.3   27   33   \n",
              "25231            47   53  106        15.8             1         1.2   20   36   \n",
              "...             ...  ...  ...         ...           ...         ...  ...  ...   \n",
              "26821           188   54  179        16.0             1         1.1   25   23   \n",
              "4814             39   62   63        15.1             1         1.0   29   30   \n",
              "29130            72   69   92        14.0             1         0.9   49   41   \n",
              "32792           115   71  910        15.3             1         1.0   32   70   \n",
              "6020            314   75   80        15.4             1         1.0   21   17   \n",
              "\n",
              "       Gtp  Caries  smoking  \n",
              "5368    28       0        1  \n",
              "16547   35       0        1  \n",
              "27912   45       1        1  \n",
              "31154  103       0        1  \n",
              "25231   61       0        1  \n",
              "...    ...     ...      ...  \n",
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              "29130   62       0        1  \n",
              "32792   43       0        1  \n",
              "6020   155       0        1  \n",
              "\n",
              "[10000 rows x 18 columns]"
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              "       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-5a34dab4-d178-4299-bff8-e2f2ed3f01a7 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-5a34dab4-d178-4299-bff8-e2f2ed3f01a7');\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": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3 - Análisis descriptivo de la data (EDA)"
      ],
      "metadata": {
        "id": "_Ke3-GIlThw4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rep = create_report(df)\n",
        "rep.save(\"Reporte\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4G6bGt16URyY",
        "outputId": "3abdb5b9-b5a5-4dd3-cea9-d2028db3339b"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Report has been saved to Reporte.html!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1CfvO0vVTAN",
        "outputId": "77372ec7-4c59-4e32-bbda-d68d11f9e630"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'height', 'weight', 'systolic', 'relaxation', 'sugar',\n",
              "       'Cholesterol', 'triglyceride', 'HDL', 'LDL', 'hemoglobin',\n",
              "       'UrineProtein', 'Creatinine', 'AST', 'ALT', 'Gtp', 'Caries', 'smoking'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['age'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Io790J3f56_T",
        "outputId": "590d7b3c-9b21-48d1-97be-62306c8b6aa9"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['height'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "WgUiyAgP6U6h",
        "outputId": "703d9ba5-0733-4755-cbb3-f12ba1a3eeb1"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['weight'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "otM9sMNM6XZi",
        "outputId": "cedcf7e7-322b-453e-80a3-5865d4279f6f"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['systolic'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "wWompCgC6Y-O",
        "outputId": "24bceff8-de7c-46a5-cbab-ce1c6da61ae6"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['relaxation'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "oALgS3rG6bi7",
        "outputId": "2a41d8eb-3fda-4b4b-efba-c775fcff9a9b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['sugar'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "SSA2z56g6d8j",
        "outputId": "0a6d44ca-8d4c-4a84-a8a8-b3660db08128"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Cholesterol'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "yYdARUzy6fij",
        "outputId": "7201be52-0871-47b2-af54-b88554d584d2"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['triglyceride'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YCfELhvA6hef",
        "outputId": "dcf30af1-e70d-4312-c675-bf1026aa6d7d"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['HDL'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "hSQTlIyT6jR7",
        "outputId": "571b54d4-3239-4aea-e425-e25ccdb6239c"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['LDL'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "pXAGh_YK6lk0",
        "outputId": "a78203ae-7134-4832-aae1-39726da19d8e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD4CAYAAAAO9oqkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAV9UlEQVR4nO3df4xd9Znf8fenJqAoCcWEqeW1Se2kJpITtQZGxNKGKC0bMOw2JtuK2qqCw6I4KCAFZaut2UgFJUUiu5tEQk2JnMXCVATCLkFYu1DiWFFQpZowEC9gfsQDAWHL2LOYhrRE7Jo8/eN+Z/dgZsYzc+cX8vslHd1zn/PjPveMx5+533PuvakqJEkntn8y3w1IkuafYSBJMgwkSYaBJAnDQJIEnDTfDUzXGWecUStWrJjvNiTpHeXRRx/926oaOLb+jg2DFStWMDQ0NN9tSNI7SpIXx6ofd5goyZlJfpzkqSR7k3yp1U9PsjPJvna7uNWT5OYkw0keT3JOZ1+b2vr7kmzq1M9N8kTb5uYk6f8pS5ImazLnDI4Cf1hVq4G1wNVJVgNbgF1VtQrY1e4DXAysatNm4BbohQdwPfAx4Dzg+tEAaet8vrPduv6fmiRpso4bBlV1sKoea/O/Ap4GlgHrge1tte3ApW1+PXB79ewGTkuyFLgI2FlVR6rqVWAnsK4tO7Wqdlfv7dC3d/YlSZoDU7qaKMkK4GzgYWBJVR1si14GlrT5ZcBLnc32t9pE9f1j1Md6/M1JhpIMjYyMTKV1SdIEJh0GSd4L3ANcW1WvdZe1v+hn/UOOqmprVQ1W1eDAwNtOhkuSpmlSYZDkXfSC4I6q+kErH2pDPLTbw61+ADizs/nyVpuovnyMuiRpjkzmaqIAtwJPV9U3O4t2AKNXBG0C7uvUL29XFa0FftmGkx4ELkyyuJ04vhB4sC17Lcna9liXd/YlSZoDk3mfwW8DnwWeSLKn1f4YuAm4O8mVwIvAZW3Z/cAlwDDwOnAFQFUdSfI14JG23ler6kib/yJwG/Bu4IE2SZLmSN6p32cwODhYvulMkqYmyaNVNXhs/R37DuR+rNjy1/PyuC/c9Lvz8riSdDx+UJ0kyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJKYRBgk2ZbkcJInO7XvJ9nTphdGvxs5yYokv+4s+05nm3OTPJFkOMnNSdLqpyfZmWRfu108G09UkjS+ybwyuA1Y1y1U1X+oqjVVtQa4B/hBZ/Fzo8uq6qpO/Rbg88CqNo3ucwuwq6pWAbvafUnSHDpuGFTVQ8CRsZa1v+4vA+6caB9JlgKnVtXuqirgduDStng9sL3Nb+/UJUlzpN9zBucDh6pqX6e2MsnPkvwkyfmttgzY31lnf6sBLKmqg23+ZWDJeA+WZHOSoSRDIyMjfbYuSRrVbxhs5K2vCg4CH6iqs4EvA99Lcupkd9ZeNdQEy7dW1WBVDQ4MDEy3Z0nSMU6a7oZJTgJ+Hzh3tFZVbwBvtPlHkzwHnAUcAJZ3Nl/eagCHkiytqoNtOOnwdHuSJE1PP68Mfgd4pqr+YfgnyUCSRW3+g/ROFD/fhoFeS7K2nWe4HLivbbYD2NTmN3XqkqQ5MplLS+8E/jfw4ST7k1zZFm3g7SeOPwE83i41/UvgqqoaPfn8ReDPgWHgOeCBVr8J+FSSffQC5qY+no8kaRqOO0xUVRvHqX9ujNo99C41HWv9IeCjY9RfAS44Xh+SpNnjO5AlSYaBJMkwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJLE5L72cluSw0me7NRuSHIgyZ42XdJZdl2S4STPJrmoU1/XasNJtnTqK5M83OrfT3LyTD5BSdLxTeaVwW3AujHq36qqNW26HyDJanrfjfyRts1/T7IoySLg28DFwGpgY1sX4OttX/8CeBW48tgHkiTNruOGQVU9BBw53nrNeuCuqnqjqn4BDAPntWm4qp6vqr8D7gLWJwnwb4C/bNtvBy6d4nOQJPWpn3MG1yR5vA0jLW61ZcBLnXX2t9p49fcD/6eqjh5TlyTNoemGwS3Ah4A1wEHgGzPW0QSSbE4ylGRoZGRkLh5Skk4I0wqDqjpUVW9W1W+A79IbBgI4AJzZWXV5q41XfwU4LclJx9THe9ytVTVYVYMDAwPTaV2SNIZphUGSpZ27nwFGrzTaAWxIckqSlcAq4KfAI8CqduXQyfROMu+oqgJ+DPz7tv0m4L7p9CRJmr6TjrdCkjuBTwJnJNkPXA98MskaoIAXgC8AVNXeJHcDTwFHgaur6s22n2uAB4FFwLaq2tse4j8DdyX5r8DPgFtn7NlJkibluGFQVRvHKI/7H3ZV3QjcOEb9fuD+MerP84/DTJKkeeA7kCVJhoEkyTCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEliEmGQZFuSw0me7NT+NMkzSR5Pcm+S01p9RZJfJ9nTpu90tjk3yRNJhpPcnCStfnqSnUn2tdvFs/FEJUnjm8wrg9uAdcfUdgIfrap/CfwcuK6z7LmqWtOmqzr1W4DPA6vaNLrPLcCuqloF7Gr3JUlz6LhhUFUPAUeOqf2wqo62u7uB5RPtI8lS4NSq2l1VBdwOXNoWrwe2t/ntnbokaY7MxDmDPwAe6NxfmeRnSX6S5PxWWwbs76yzv9UAllTVwTb/MrBkvAdKsjnJUJKhkZGRGWhdkgR9hkGSrwBHgTta6SDwgao6G/gy8L0kp052f+1VQ02wfGtVDVbV4MDAQB+dS5K6Tpruhkk+B/wecEH7T5yqegN4o80/muQ54CzgAG8dSlreagCHkiytqoNtOOnwdHuSJE3PtF4ZJFkH/BHw6ap6vVMfSLKozX+Q3oni59sw0GtJ1rariC4H7mub7QA2tflNnbokaY4c95VBkjuBTwJnJNkPXE/v6qFTgJ3tCtHd7cqhTwBfTfL3wG+Aq6pq9OTzF+ldmfRueucYRs8z3ATcneRK4EXgshl5ZpKkSTtuGFTVxjHKt46z7j3APeMsGwI+Okb9FeCC4/UhSZo9vgNZkmQYSJIMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJTDIMkmxLcjjJk53a6Ul2JtnXbhe3epLcnGQ4yeNJzulss6mtvy/Jpk793CRPtG1ubt+TLEmaI5N9ZXAbsO6Y2hZgV1WtAna1+wAXA6vatBm4BXrhQe/7kz8GnAdcPxogbZ3Pd7Y79rEkSbNoUmFQVQ8BR44prwe2t/ntwKWd+u3Vsxs4LclS4CJgZ1UdqapXgZ3Aurbs1KraXVUF3N7ZlyRpDvRzzmBJVR1s8y8DS9r8MuClznr7W22i+v4x6pKkOTIjJ5DbX/Q1E/uaSJLNSYaSDI2MjMz2w0nSCaOfMDjUhnhot4db/QBwZme95a02UX35GPW3qaqtVTVYVYMDAwN9tC5J6uonDHYAo1cEbQLu69Qvb1cVrQV+2YaTHgQuTLK4nTi+EHiwLXstydp2FdHlnX1JkubASZNZKcmdwCeBM5Lsp3dV0E3A3UmuBF4ELmur3w9cAgwDrwNXAFTVkSRfAx5p6321qkZPSn+R3hVL7wYeaJMkaY5MKgyqauM4iy4YY90Crh5nP9uAbWPUh4CPTqYXSdLM8x3IkiTDQJJkGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJNFHGCT5cJI9nem1JNcmuSHJgU79ks421yUZTvJskos69XWtNpxkS79PSpI0NZP6DuSxVNWzwBqAJIuAA8C9wBXAt6rqz7rrJ1kNbAA+AvwW8KMkZ7XF3wY+BewHHkmyo6qemm5vkqSpmXYYHOMC4LmqejHJeOusB+6qqjeAXyQZBs5ry4ar6nmAJHe1dQ0DSZojM3XOYANwZ+f+NUkeT7ItyeJWWwa81Flnf6uNV3+bJJuTDCUZGhkZmaHWJUl9h0GSk4FPA3/RSrcAH6I3hHQQ+Ea/jzGqqrZW1WBVDQ4MDMzUbiXphDcTw0QXA49V1SGA0VuAJN8F/qrdPQCc2dlueasxQV2SNAdmYphoI50hoiRLO8s+AzzZ5ncAG5KckmQlsAr4KfAIsCrJyvYqY0NbV5I0R/p6ZZDkPfSuAvpCp/wnSdYABbwwuqyq9ia5m96J4aPA1VX1ZtvPNcCDwCJgW1Xt7acvSdLU9BUGVfX/gPcfU/vsBOvfCNw4Rv1+4P5+epEkTZ/vQJYkGQaSJMNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSSJGQiDJC8keSLJniRDrXZ6kp1J9rXbxa2eJDcnGU7yeJJzOvvZ1Nbfl2RTv31JkiZvpl4Z/OuqWlNVg+3+FmBXVa0CdrX7ABcDq9q0GbgFeuEBXA98DDgPuH40QCRJs2+2honWA9vb/Hbg0k799urZDZyWZClwEbCzqo5U1avATmDdLPUmSTrGTIRBAT9M8miSza22pKoOtvmXgSVtfhnwUmfb/a02Xv0tkmxOMpRkaGRkZAZalyQBnDQD+/h4VR1I8s+AnUme6S6sqkpSM/A4VNVWYCvA4ODgjOxTkjQDrwyq6kC7PQzcS2/M/1Ab/qHdHm6rHwDO7Gy+vNXGq0uS5kBfYZDkPUneNzoPXAg8CewARq8I2gTc1+Z3AJe3q4rWAr9sw0kPAhcmWdxOHF/YapKkOdDvMNES4N4ko/v6XlX9zySPAHcnuRJ4EbisrX8/cAkwDLwOXAFQVUeSfA14pK331ao60mdvkqRJ6isMqup54F+NUX8FuGCMegFXj7OvbcC2fvqRJE2P70CWJBkGkiTDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kSfYRBkjOT/DjJU0n2JvlSq9+Q5ECSPW26pLPNdUmGkzyb5KJOfV2rDSfZ0t9TkiRNVT9fe3kU+MOqeizJ+4BHk+xsy75VVX/WXTnJamAD8BHgt4AfJTmrLf428ClgP/BIkh1V9VQfvUmSpmDaYVBVB4GDbf5XSZ4Glk2wyXrgrqp6A/hFkmHgvLZsuH2fMknuausaBpI0R2bknEGSFcDZwMOtdE2Sx5NsS7K41ZYBL3U2299q49XHepzNSYaSDI2MjMxE65IkZiAMkrwXuAe4tqpeA24BPgSsoffK4Rv9PsaoqtpaVYNVNTgwMDBTu5WkE14/5wxI8i56QXBHVf0AoKoOdZZ/F/irdvcAcGZn8+WtxgR1SdIc6OdqogC3Ak9X1Tc79aWd1T4DPNnmdwAbkpySZCWwCvgp8AiwKsnKJCfTO8m8Y7p9SZKmrp9XBr8NfBZ4IsmeVvtjYGOSNUABLwBfAKiqvUnupndi+ChwdVW9CZDkGuBBYBGwrar29tGXJGmK+rma6H8BGWPR/RNscyNw4xj1+yfaTpI0u3wHsiTJMJAkGQaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAk0ed3IGtqVmz563l77Bdu+t15e2xJC5+vDCRJCycMkqxL8myS4SRb5rsfSTqRLIgwSLII+DZwMbAa2Jhk9fx2JUknjgURBsB5wHBVPV9VfwfcBayf554k6YSxUE4gLwNe6tzfD3zs2JWSbAY2t7v/N8mz03y8M4C/nea2c2VGe8zXZ2pPb3HCHcdZYo/9W+j9wcLp8Z+PVVwoYTApVbUV2NrvfpIMVdXgDLQ0a+xxZtjjzFjoPS70/mDh97hQhokOAGd27i9vNUnSHFgoYfAIsCrJyiQnAxuAHfPckySdMBbEMFFVHU1yDfAgsAjYVlV7Z/Eh+x5qmgP2ODPscWYs9B4Xen+wwHtMVc13D5KkebZQhokkSfPIMJAknVhhsFA+8iLJmUl+nOSpJHuTfKnVb0hyIMmeNl3S2ea61vezSS6aoz5fSPJE62Wo1U5PsjPJvna7uNWT5ObW4+NJzpmD/j7cOVZ7kryW5Nr5Po5JtiU5nOTJTm3Kxy3Jprb+viSb5qDHP03yTOvj3iSntfqKJL/uHM/vdLY5t/0bGW7PI7Pc45R/trP5ez9Oj9/v9PdCkj2tPi/HcdKq6oSY6J2Yfg74IHAy8DfA6nnqZSlwTpt/H/Bzeh/DcQPwn8ZYf3Xr9xRgZXsei+agzxeAM46p/Qmwpc1vAb7e5i8BHgACrAUenoef78v03lAzr8cR+ARwDvDkdI8bcDrwfLtd3OYXz3KPFwIntfmvd3pc0V3vmP38tPWd9jwunuUep/Szne3f+7F6PGb5N4D/Mp/HcbLTifTKYMF85EVVHayqx9r8r4Cn6b0Lezzrgbuq6o2q+gUwTO/5zIf1wPY2vx24tFO/vXp2A6clWTqHfV0APFdVL06wzpwcx6p6CDgyxmNP5bhdBOysqiNV9SqwE1g3mz1W1Q+r6mi7u5ve+33G1fo8tap2V+9/tNs7z2tWepzAeD/bWf29n6jH9tf9ZcCdE+1jto/jZJ1IYTDWR15M9B/wnEiyAjgbeLiVrmkv07eNDiUwf70X8MMkj6b3USAAS6rqYJt/GVgyzz2O2sBbf+kW0nGEqR+3+T6ef0DvL9RRK5P8LMlPkpzfastaX6Pmqsep/Gzn8zieDxyqqn2d2kI6jm9xIoXBgpPkvcA9wLVV9RpwC/AhYA1wkN5LzPn08ao6h96nyV6d5BPdhe2vmHm/Njm9Nyp+GviLVlpox/EtFspxG0+SrwBHgTta6SDwgao6G/gy8L0kp85Tewv6Z3uMjbz1D5SFdBzf5kQKgwX1kRdJ3kUvCO6oqh8AVNWhqnqzqn4DfJd/HMKYl96r6kC7PQzc2/o5NDr8024Pz2ePzcXAY1V1qPW7oI5jM9XjNi+9Jvkc8HvAf2yhRRt6eaXNP0pvDP6s1k93KGnWe5zGz3a+juNJwO8D3x+tLaTjOJYTKQwWzEdetLHEW4Gnq+qbnXp3jP0zwOgVCjuADUlOSbISWEXvhNNs9vieJO8bnad3cvHJ1svolS2bgPs6PV7ero5ZC/yyMywy297yF9hCOo4dUz1uDwIXJlnchkIubLVZk2Qd8EfAp6vq9U59IL3vHCHJB+kdt+dbn68lWdv+TV/eeV6z1eNUf7bz9Xv/O8AzVfUPwz8L6TiOaa7PWM/nRO/KjZ/TS+SvzGMfH6c3TPA4sKdNlwD/A3ii1XcASzvbfKX1/SxzcKUBvasv/qZNe0ePF/B+YBewD/gRcHqrh94XFD3XnsPgHB3L9wCvAP+0U5vX40gvmA4Cf09v/PfK6Rw3euP2w226Yg56HKY3vj76b/I7bd1/1/4N7AEeA/5tZz+D9P5Dfg74b7RPNZjFHqf8s53N3/uxemz124Crjll3Xo7jZCc/jkKSdEINE0mSxmEYSJIMA0mSYSBJwjCQJGEYSJIwDCRJwP8HCAYXjdcYuLoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['hemoglobin'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ALPPchvk6oAu",
        "outputId": "da9013e1-1ee9-43b2-ad12-4d65d30a402e"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['UrineProtein'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "AfwiGjPD6pqd",
        "outputId": "6864e470-9c92-44d9-ed2e-2d7c0e99e50c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Creatinine'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "q4srNezk6rcu",
        "outputId": "a1da7e18-5f36-482f-af17-e2934c3f9669"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['AST'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "NUV33mEh6txj",
        "outputId": "7cfe570f-b42a-4d9b-a23e-bf9fcf41d08d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['ALT'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "A8B0X53c6wVC",
        "outputId": "b7973bbd-5c60-4676-c584-b0a0206010b8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Gtp'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YxUp3tMF6xvW",
        "outputId": "798cf3ef-d76e-495c-a0b3-8d3cd0f31c2e"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(df['Caries'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "ELadIYl560_D",
        "outputId": "5d51e7c4-eadf-4cbe-c689-4505b79f2e39"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4 - Entrenamiento del modelo."
      ],
      "metadata": {
        "id": "iP1s5dnfULma"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df.iloc[:, :-1]\n",
        "y = df.iloc[:, -1]"
      ],
      "metadata": {
        "id": "2LhXHH_pjN83"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "D4b7CZi09byj",
        "outputId": "825c284b-7635-46fb-8f7a-a7ae8da2b53b"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       age  height  weight  systolic  relaxation  sugar  Cholesterol  \\\n",
              "34126   50     160      60       119          70    103          223   \n",
              "32637   55     155      50        92          61     91          189   \n",
              "27570   45     165      65       140          89    129          150   \n",
              "15714   40     155      65       142          89     98          199   \n",
              "24697   40     155      50       120          90     97          244   \n",
              "...    ...     ...     ...       ...         ...    ...          ...   \n",
              "26821   50     170      70       112          74    101          271   \n",
              "4814    25     175      75       108          62     89          133   \n",
              "29130   55     175      60       120          80    100          175   \n",
              "32792   40     165      60       109          73    185          161   \n",
              "6020    60     175      70       150         100    131          218   \n",
              "\n",
              "       triglyceride  HDL  LDL  hemoglobin  UrineProtein  Creatinine  AST  ALT  \\\n",
              "34126            94   64  140        13.8             1         0.7   20   14   \n",
              "32637            51   64  115        13.2             3         0.9   19   12   \n",
              "27570            91   55   77        16.9             1         0.8  182  311   \n",
              "15714            74   59  125        14.3             1         0.5   15   15   \n",
              "24697           141   83  132        14.5             1         0.7   22   20   \n",
              "...             ...  ...  ...         ...           ...         ...  ...  ...   \n",
              "26821           188   54  179        16.0             1         1.1   25   23   \n",
              "4814             39   62   63        15.1             1         1.0   29   30   \n",
              "29130            72   69   92        14.0             1         0.9   49   41   \n",
              "32792           115   71  910        15.3             1         1.0   32   70   \n",
              "6020            314   75   80        15.4             1         1.0   21   17   \n",
              "\n",
              "       Gtp  Caries  \n",
              "34126   13       0  \n",
              "32637   13       0  \n",
              "27570  231       0  \n",
              "15714   22       0  \n",
              "24697   35       0  \n",
              "...    ...     ...  \n",
              "26821   63       0  \n",
              "4814    30       0  \n",
              "29130   62       0  \n",
              "32792   43       0  \n",
              "6020   155       0  \n",
              "\n",
              "[20000 rows x 17 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7f1f0d0-02d4-404a-9b08-fe8bed1b88ec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>height</th>\n",
              "      <th>weight</th>\n",
              "      <th>systolic</th>\n",
              "      <th>relaxation</th>\n",
              "      <th>sugar</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>triglyceride</th>\n",
              "      <th>HDL</th>\n",
              "      <th>LDL</th>\n",
              "      <th>hemoglobin</th>\n",
              "      <th>UrineProtein</th>\n",
              "      <th>Creatinine</th>\n",
              "      <th>AST</th>\n",
              "      <th>ALT</th>\n",
              "      <th>Gtp</th>\n",
              "      <th>Caries</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34126</th>\n",
              "      <td>50</td>\n",
              "      <td>160</td>\n",
              "      <td>60</td>\n",
              "      <td>119</td>\n",
              "      <td>70</td>\n",
              "      <td>103</td>\n",
              "      <td>223</td>\n",
              "      <td>94</td>\n",
              "      <td>64</td>\n",
              "      <td>140</td>\n",
              "      <td>13.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32637</th>\n",
              "      <td>55</td>\n",
              "      <td>155</td>\n",
              "      <td>50</td>\n",
              "      <td>92</td>\n",
              "      <td>61</td>\n",
              "      <td>91</td>\n",
              "      <td>189</td>\n",
              "      <td>51</td>\n",
              "      <td>64</td>\n",
              "      <td>115</td>\n",
              "      <td>13.2</td>\n",
              "      <td>3</td>\n",
              "      <td>0.9</td>\n",
              "      <td>19</td>\n",
              "      <td>12</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27570</th>\n",
              "      <td>45</td>\n",
              "      <td>165</td>\n",
              "      <td>65</td>\n",
              "      <td>140</td>\n",
              "      <td>89</td>\n",
              "      <td>129</td>\n",
              "      <td>150</td>\n",
              "      <td>91</td>\n",
              "      <td>55</td>\n",
              "      <td>77</td>\n",
              "      <td>16.9</td>\n",
              "      <td>1</td>\n",
              "      <td>0.8</td>\n",
              "      <td>182</td>\n",
              "      <td>311</td>\n",
              "      <td>231</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15714</th>\n",
              "      <td>40</td>\n",
              "      <td>155</td>\n",
              "      <td>65</td>\n",
              "      <td>142</td>\n",
              "      <td>89</td>\n",
              "      <td>98</td>\n",
              "      <td>199</td>\n",
              "      <td>74</td>\n",
              "      <td>59</td>\n",
              "      <td>125</td>\n",
              "      <td>14.3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.5</td>\n",
              "      <td>15</td>\n",
              "      <td>15</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24697</th>\n",
              "      <td>40</td>\n",
              "      <td>155</td>\n",
              "      <td>50</td>\n",
              "      <td>120</td>\n",
              "      <td>90</td>\n",
              "      <td>97</td>\n",
              "      <td>244</td>\n",
              "      <td>141</td>\n",
              "      <td>83</td>\n",
              "      <td>132</td>\n",
              "      <td>14.5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>22</td>\n",
              "      <td>20</td>\n",
              "      <td>35</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26821</th>\n",
              "      <td>50</td>\n",
              "      <td>170</td>\n",
              "      <td>70</td>\n",
              "      <td>112</td>\n",
              "      <td>74</td>\n",
              "      <td>101</td>\n",
              "      <td>271</td>\n",
              "      <td>188</td>\n",
              "      <td>54</td>\n",
              "      <td>179</td>\n",
              "      <td>16.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.1</td>\n",
              "      <td>25</td>\n",
              "      <td>23</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4814</th>\n",
              "      <td>25</td>\n",
              "      <td>175</td>\n",
              "      <td>75</td>\n",
              "      <td>108</td>\n",
              "      <td>62</td>\n",
              "      <td>89</td>\n",
              "      <td>133</td>\n",
              "      <td>39</td>\n",
              "      <td>62</td>\n",
              "      <td>63</td>\n",
              "      <td>15.1</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29130</th>\n",
              "      <td>55</td>\n",
              "      <td>175</td>\n",
              "      <td>60</td>\n",
              "      <td>120</td>\n",
              "      <td>80</td>\n",
              "      <td>100</td>\n",
              "      <td>175</td>\n",
              "      <td>72</td>\n",
              "      <td>69</td>\n",
              "      <td>92</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>49</td>\n",
              "      <td>41</td>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32792</th>\n",
              "      <td>40</td>\n",
              "      <td>165</td>\n",
              "      <td>60</td>\n",
              "      <td>109</td>\n",
              "      <td>73</td>\n",
              "      <td>185</td>\n",
              "      <td>161</td>\n",
              "      <td>115</td>\n",
              "      <td>71</td>\n",
              "      <td>910</td>\n",
              "      <td>15.3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>32</td>\n",
              "      <td>70</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6020</th>\n",
              "      <td>60</td>\n",
              "      <td>175</td>\n",
              "      <td>70</td>\n",
              "      <td>150</td>\n",
              "      <td>100</td>\n",
              "      <td>131</td>\n",
              "      <td>218</td>\n",
              "      <td>314</td>\n",
              "      <td>75</td>\n",
              "      <td>80</td>\n",
              "      <td>15.4</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>21</td>\n",
              "      <td>17</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>20000 rows × 17 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7f1f0d0-02d4-404a-9b08-fe8bed1b88ec')\"\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",
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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",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7f1f0d0-02d4-404a-9b08-fe8bed1b88ec 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-a7f1f0d0-02d4-404a-9b08-fe8bed1b88ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_columns = x.columns[(x.dtypes == \"float64\") | (x.dtypes == \"int64\")]\n",
        "\n",
        "col_transf = make_column_transformer(\n",
        "    (StandardScaler(), numerical_columns),\n",
        ")"
      ],
      "metadata": {
        "id": "uRT2BtKUxhOj"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "col_transf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5i8iWW4DdrT",
        "outputId": "3a4d6423-6781-4515-c74a-31af96927c78"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('standardscaler', StandardScaler(),\n",
              "                                 Index(['age', 'height', 'weight', 'systolic', 'relaxation', 'sugar',\n",
              "       'Cholesterol', 'triglyceride', 'HDL', 'LDL', 'hemoglobin',\n",
              "       'UrineProtein', 'Creatinine', 'AST', 'ALT', 'Gtp', 'Caries'],\n",
              "      dtype='object'))])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(x, y, random_state=2, train_size = 0.75)"
      ],
      "metadata": {
        "id": "W7glP3xhdx7j"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "modelos = []\n",
        "modelos.append((\"KNC\", KNeighborsClassifier()))\n",
        "modelos.append((\"RFC\", RandomForestClassifier()))\n",
        "modelos.append((\"SVC\", svm.SVC()))\n",
        "modelos.append((\"MLP\", MLPClassifier()))\n",
        "modelos.append((\"GNB\", GaussianNB()))"
      ],
      "metadata": {
        "id": "n3Z8ew1H1Jw1"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipelines  = {nombre: make_pipeline(col_transf, modelo) for nombre, modelo in modelos}"
      ],
      "metadata": {
        "id": "ZBCZMLD00oJT"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = []\n",
        "for name, pipeline in pipelines.items():\n",
        "\tcv_results = cross_val_score(pipeline, x_train, y_train, cv=5, scoring='accuracy', error_score='raise')\n",
        "\tresults.append({name: cv_results})"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "24YgE6eUzHb8",
        "outputId": "f6a4ec8d-8355-4a18-af88-b23e122ba954"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:692: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSOtggUs6t9S",
        "outputId": "1c238f96-730d-4006-fcef-b5698d855f27"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'KNC': array([0.706     , 0.69933333, 0.71066667, 0.71933333, 0.709     ])},\n",
              " {'RFC': array([0.77366667, 0.77266667, 0.76933333, 0.77533333, 0.767     ])},\n",
              " {'SVC': array([0.75133333, 0.73966667, 0.74266667, 0.76066667, 0.75466667])},\n",
              " {'MLP': array([0.745     , 0.74033333, 0.743     , 0.758     , 0.74333333])},\n",
              " {'GNB': array([0.66133333, 0.65366667, 0.66366667, 0.66666667, 0.66966667])}]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = sorted(results, key=lambda x: list(x.values())[0].mean(), reverse=True)[0]\n",
        "best_pipeline = pipelines[list(best_model.keys())[0]]"
      ],
      "metadata": {
        "id": "3CPsFMfH6yPX"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5. Validación y testeo del modelo."
      ],
      "metadata": {
        "id": "xutYxUbouJbT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "best_pipeline.fit(x_train, y_train)\n",
        "best_pipeline.predict(x_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbPb5t2U68t1",
        "outputId": "30994999-3c2e-4211-c1eb-2a20a48ab55c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_true = y_val\n",
        "y_pred = best_pipeline.predict(x_val)"
      ],
      "metadata": {
        "id": "IG9BLB6qs4Wl"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cr = classification_report(y_true, y_pred)\n",
        "print(cr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kbQlXAx5tx8O",
        "outputId": "28b7a3a6-d1d2-442a-fd0e-f3a2a85a9260"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.69      0.75      2433\n",
            "           1       0.74      0.85      0.79      2567\n",
            "\n",
            "    accuracy                           0.77      5000\n",
            "   macro avg       0.78      0.77      0.77      5000\n",
            "weighted avg       0.78      0.77      0.77      5000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_true, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k21x87M6uDoZ",
        "outputId": "594853bb-cc45-4679-9dcc-d3febd254871"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1681  752]\n",
            " [ 378 2189]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6. Despliegue del modelo y comprobación con data recién creada."
      ],
      "metadata": {
        "id": "I20WJkJnuWL3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(best_pipeline, open('bp.pkl', mode = 'wb')) "
      ],
      "metadata": {
        "id": "MAC5gcKuzWmf"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import sklearn\n",
        "import pickle\n",
        "\n",
        "if __name__ == '__main__':\n",
        "  bp_mod = pickle.load(open('bp.pkl', mode = 'rb'))\n",
        "\n",
        "  print(\"Bienvenid@!\\n\\tDigita los datos del paciente...\\n\")\n",
        "  \n",
        "  edad = input('  Edad: ')\n",
        "  altura = input('  Altura(cm): ')\n",
        "  peso = input('  Peso(kg): ')\n",
        "  sys = input('  Presion sistolica: ')\n",
        "  sangre = input('  Relajacion o presion en sangre: ')\n",
        "  glucemia = input('  Glucosa en ayunas: ')\n",
        "  colesterol = input('  Colesterol total: ')\n",
        "  triglicelidos = input('  Triglicelidos: ')\n",
        "  hdl = input('  Colesterol bueno(HDL): ')\n",
        "  ldl = input('  Colesterol malo(LDL): ')\n",
        "  hemoglobina = input('  Hemoglobina: ')\n",
        "  proteina_orina = input('  Proteina en orina: ')\n",
        "  sc = input('  Creatinina Serica: ')\n",
        "  ast = input('  Aspartato Aminotransferasa(AST): ')\n",
        "  alt = input('  Alanina Aminotransferasa(ALT): ')\n",
        "  gtp = input('  Guanosina trisfosfato(GTP): ')\n",
        "  cd = input('  Caries Dentales: ')\n",
        "\n",
        "  a = pd.DataFrame([edad, altura, peso, sys, sangre, glucemia, colesterol, triglicelidos, hdl, ldl, hemoglobina, proteina_orina, sc, ast, alt, gtp, cd])\n",
        "  a = a.transpose().rename(columns={0:'age', 1:'height', 2:'weight', 3:'systolic', 4:'relaxation', 5:'sugar', 6:'Cholesterol', 7:'triglyceride', 8:'HDL', 9:'LDL', 10:'hemoglobin', 11:'UrineProtein', 12:'Creatinine', 13:'AST', 14:'ALT', 15:'Gtp', 16:'Caries'})\n",
        "\n",
        "  prediccion = bp_mod.predict(a)\n",
        "\n",
        "  if prediccion[0] == 1:\n",
        "    print(\"\\n\\tEl paciente es fumador \",prediccion[0])\n",
        "  else:\n",
        "    print(\"\\n\\tEl paciente no es fumador \",prediccion[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UB-eCKXL0reQ",
        "outputId": "e4313b7d-7ea6-419a-b1ad-602aa10cd705"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bienvenid@!\n",
            "\tDigita los datos del paciente...\n",
            "\n",
            "  Edad: 20\n",
            "  Altura(cm): 174\n",
            "  Peso(kg): 90\n",
            "  Presion sistolica: 80\n",
            "  Relajacion o presion en sangre: 100\n",
            "  Glucosa en ayunas: 60\n",
            "  Colesterol total: 200\n",
            "  Triglicelidos: 160\n",
            "  Colesterol bueno(HDL): 70\n",
            "  Colesterol malo(LDL): 70\n",
            "  Hemoglobina: 15\n",
            "  Proteina en orina: 3\n",
            "  Creatinina Serica: 1.4\n",
            "  Aspartato Aminotransferasa(AST): 30\n",
            "  Alanina Aminotransferasa(ALT): 30\n",
            "  Guanosina trisfosfato(GTP): 30\n",
            "  Caries Dentales: 1\n",
            "\n",
            "\tEl paciente es fumador  1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bp_mod"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yvGmFR5GHa7Z",
        "outputId": "ce0734e1-5e12-44ba-806b-c10814444cc6"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('columntransformer',\n",
              "                 ColumnTransformer(remainder='passthrough',\n",
              "                                   transformers=[('standardscaler',\n",
              "                                                  StandardScaler(),\n",
              "                                                  Index(['age', 'height', 'weight', 'systolic', 'relaxation', 'sugar',\n",
              "       'Cholesterol', 'triglyceride', 'HDL', 'LDL', 'hemoglobin',\n",
              "       'UrineProtein', 'Creatinine', 'AST', 'ALT', 'Gtp', 'Caries'],\n",
              "      dtype='object'))])),\n",
              "                ('randomforestclassifier', RandomForestClassifier())])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bp_mod[1].feature_importances_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MyH8SA0lHcMI",
        "outputId": "ba8a87f2-4609-4ac1-ea8e-0e6ee605e2cf"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.04407627, 0.11648351, 0.05176017, 0.04818675, 0.04703316,\n",
              "       0.05154389, 0.05395464, 0.07146395, 0.05463896, 0.05376567,\n",
              "       0.13287303, 0.00465236, 0.04898384, 0.04651945, 0.05425479,\n",
              "       0.11054624, 0.00926332])"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    }
  ]
}