test_stroke.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ef729066",
   "metadata": {},
   "source": [
    "# EDA GRUPO PULMÓN\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6315b991-5211-411a-8e26-b67a6d353901",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Librerías de análisis de datos:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "import plotly.graph_objects as go\n",
    "import pickle\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "#from xgboost import XGBClassifier\n",
    "#from lightgbm import LGBMClassifier\n",
    "#from catboost import CatBoostClassifier\n",
    "\n",
    "from pickle import dump\n",
    "from pickle import load\n",
    "\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix, recall_score, precision_score, roc_auc_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(action='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "632ddc3f-6b0d-447e-8f04-d3947e5f49f9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (1.6.2)\n",
      "Requirement already satisfied: lightgbm in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (3.3.2)\n",
      "Requirement already satisfied: catboost in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (1.0.6)\n",
      "Requirement already satisfied: numpy in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from xgboost) (1.23.3)\n",
      "Requirement already satisfied: scipy in /home/anghi/.local/lib/python3.10/site-packages (from xgboost) (1.9.1)\n",
      "Requirement already satisfied: wheel in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from lightgbm) (0.37.1)\n",
      "Requirement already satisfied: scikit-learn!=0.22.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from lightgbm) (1.1.2)\n",
      "Requirement already satisfied: matplotlib in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from catboost) (3.6.0)\n",
      "Requirement already satisfied: plotly in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from catboost) (5.10.0)\n",
      "Requirement already satisfied: graphviz in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from catboost) (0.20.1)\n",
      "Requirement already satisfied: pandas>=0.24.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from catboost) (1.5.0)\n",
      "Requirement already satisfied: six in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from catboost) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from pandas>=0.24.0->catboost) (2022.2.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
      "Requirement already satisfied: joblib>=1.0.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from scikit-learn!=0.22.0->lightgbm) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/anghi/.local/lib/python3.10/site-packages (from scikit-learn!=0.22.0->lightgbm) (3.1.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (0.11.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (9.2.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (1.4.4)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (4.37.2)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from matplotlib->catboost) (3.0.9)\n",
      "Requirement already satisfied: tenacity>=6.2.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from plotly->catboost) (8.0.1)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install xgboost lightgbm catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58e0f923-c79a-4b25-bc62-11ad414836d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from catboost import CatBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b12b4307-088f-4bd1-9b45-92b534896994",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Buenas prácticas.\n",
    "path_to_data = \"./stroke_dataset.csv\"\n",
    "data_stroke = pd.read_csv(path_to_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e65561db-da56-45f2-8df5-fb802f42eb80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4981, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cantidad de entradas y columnas del dataset.\n",
    "data_stroke.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1cf5c3e3-6eed-4328-9c74-17bfa2ac7e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0          Urban             228.69  36.6  formerly smoked       1  \n",
       "1          Rural             105.92  32.5     never smoked       1  \n",
       "2          Urban             171.23  34.4           smokes       1  \n",
       "3          Rural             174.12  24.0     never smoked       1  \n",
       "4          Urban             186.21  29.0  formerly smoked       1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizamos las 5 primeras entradas de nuestro dataset\n",
    "data_stroke.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00456c55-856c-4c81-9d28-39c26c72aeec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4981 entries, 0 to 4980\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   gender             4981 non-null   object \n",
      " 1   age                4981 non-null   float64\n",
      " 2   hypertension       4981 non-null   int64  \n",
      " 3   heart_disease      4981 non-null   int64  \n",
      " 4   ever_married       4981 non-null   object \n",
      " 5   work_type          4981 non-null   object \n",
      " 6   Residence_type     4981 non-null   object \n",
      " 7   avg_glucose_level  4981 non-null   float64\n",
      " 8   bmi                4981 non-null   float64\n",
      " 9   smoking_status     4981 non-null   object \n",
      " 10  stroke             4981 non-null   int64  \n",
      "dtypes: float64(3), int64(3), object(5)\n",
      "memory usage: 428.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Información del tipo de variables de nuestro dataset\n",
    "data_stroke.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e9da3db4-3054-4dc1-9724-f401520c3e26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                  2\n",
       "age                   104\n",
       "hypertension            2\n",
       "heart_disease           2\n",
       "ever_married            2\n",
       "work_type               4\n",
       "Residence_type          2\n",
       "avg_glucose_level    3895\n",
       "bmi                   342\n",
       "smoking_status          4\n",
       "stroke                  2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos el número de observaciones distintas de cada variable\n",
    "data_stroke.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "accc97de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Private          2860\n",
       "Self-employed     804\n",
       "children          673\n",
       "Govt_job          644\n",
       "Name: work_type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualización del número de observaciones distintas en la variable \"Work_type\"\n",
    "data_stroke [\"work_type\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b07bb527-89db-4c32-998b-e19bca0a62a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender               0\n",
       "age                  0\n",
       "hypertension         0\n",
       "heart_disease        0\n",
       "ever_married         0\n",
       "work_type            0\n",
       "Residence_type       0\n",
       "avg_glucose_level    0\n",
       "bmi                  0\n",
       "smoking_status       0\n",
       "stroke               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Buscamos datos nulos \n",
    "data_stroke.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a148f016",
   "metadata": {},
   "source": [
    "### Vemos que nuestro dataset no tiene datos nulos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "52474972-3dbc-42d2-8fa6-63f5948c122d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clasificación de variables\n",
    "categoricas=[\"gender\", \"ever_married\",\"heart_disease\", \"hypertension\",\"work_type\",\"Residence_type\",\"smoking_status\", \"stroke\"]\n",
    "numericas=[\"age\",\"avg_glucose_level\",\"bmi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcffdc87-5cdc-4623-b67e-6574d5b7acfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "      <td>4981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.419859</td>\n",
       "      <td>105.943562</td>\n",
       "      <td>28.498173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>22.662755</td>\n",
       "      <td>45.075373</td>\n",
       "      <td>6.790464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.080000</td>\n",
       "      <td>55.120000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>77.230000</td>\n",
       "      <td>23.700000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>91.850000</td>\n",
       "      <td>28.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>113.860000</td>\n",
       "      <td>32.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>271.740000</td>\n",
       "      <td>48.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age  avg_glucose_level          bmi\n",
       "count  4981.000000        4981.000000  4981.000000\n",
       "mean     43.419859         105.943562    28.498173\n",
       "std      22.662755          45.075373     6.790464\n",
       "min       0.080000          55.120000    14.000000\n",
       "25%      25.000000          77.230000    23.700000\n",
       "50%      45.000000          91.850000    28.100000\n",
       "75%      61.000000         113.860000    32.600000\n",
       "max      82.000000         271.740000    48.900000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Descripción de los datos numéricos con sus métricas estadísticas más relevantes.\n",
    "\n",
    "data_stroke[numericas].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "485d7877-1932-4a7d-9ce4-6fc4c84147ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comprobamos que no hay datos duplicados\n",
    "data_stroke.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14129852",
   "metadata": {},
   "source": [
    "## Preprocesamiento: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3b286332",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creamos una función con Onehot encode para poder hacer dummies.\n",
    "\n",
    "def onehot_encode(df,column):\n",
    "    df = df.copy()\n",
    "    \n",
    "    dummies = pd.get_dummies( df [column],prefix=column)\n",
    "    df = pd.concat([df,dummies], axis=1)\n",
    "    df = df.drop(column,axis=1)\n",
    "    \n",
    "    return df \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e94e8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hacemos función que hace una copia como primer paso\n",
    "\n",
    "def preprocess_inputs(df):\n",
    "    df = df.copy()\n",
    "\n",
    "#2.- Si quicieramos eliminar la columna Id, pero en este dato no existe.\n",
    "    # df = df.drop(\"id\", axis=1)\n",
    "    \n",
    "#3.- Despues de identificar las clases  dentro de cada variable y transformamos los datos en binarios\n",
    "    df[\"ever_married\"] = df[\"ever_married\"].replace({\"No\":0,\"Yes\":1})\n",
    "    df[\"gender\"] = df[\"gender\"].replace({\"Male\":0,\"Female\":1})\n",
    "    df[\"Residence_type\"] = df[\"Residence_type\"].replace({\"Urban\":0,\"Rural\":1})\n",
    "    \n",
    "#4.-Haciendo Onehot_encode con las variables categóricas\n",
    "    for column in [\"work_type\",\"smoking_status\"]:\n",
    "        df = onehot_encode(df,column = column)\n",
    "        \n",
    "        \n",
    "#5.- Separamos la columna stroke (variable a predecir) \n",
    "    y = df['stroke']\n",
    "    X = df.drop('stroke', axis=1)\n",
    "    \n",
    "#6.- Definiendo Train-test split \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=1)\n",
    "    \n",
    "#7.- Escalamos las variables Xtest, Xtrain  \n",
    "    scaler = StandardScaler()\n",
    "    scaler.fit(X_train)\n",
    "    X_train = pd.DataFrame(scaler.transform(X_train), index=X_train.index, columns=X_train.columns)\n",
    "    X_test = pd.DataFrame(scaler.transform(X_test), index=X_test.index, columns=X_test.columns)\n",
    "    \n",
    "   \n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5d582fb5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Definimos la variable preprocess_inputs \n",
    "X_train, X_test, y_train, y_test = preprocess_inputs(data_stroke)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5e1911d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2117    0\n",
       "332     0\n",
       "3915    0\n",
       "4610    0\n",
       "4485    0\n",
       "       ..\n",
       "2895    0\n",
       "2763    0\n",
       "905     0\n",
       "3980    0\n",
       "235     0\n",
       "Name: stroke, Length: 3486, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Veridficamos el funcionamiento de preprocess_inputs \n",
    "y_train "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4158f97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                            1.000287\n",
       "age                               1.000287\n",
       "hypertension                      1.000287\n",
       "heart_disease                     1.000287\n",
       "ever_married                      1.000287\n",
       "Residence_type                    1.000287\n",
       "avg_glucose_level                 1.000287\n",
       "bmi                               1.000287\n",
       "work_type_Govt_job                1.000287\n",
       "work_type_Private                 1.000287\n",
       "work_type_Self-employed           1.000287\n",
       "work_type_children                1.000287\n",
       "smoking_status_Unknown            1.000287\n",
       "smoking_status_formerly smoked    1.000287\n",
       "smoking_status_never smoked       1.000287\n",
       "smoking_status_smokes             1.000287\n",
       "dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculamos la varianza para comprobar la escala en X_train\n",
    "X_train.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ad8dc85-31bb-4568-a7e6-f061f826b368",
   "metadata": {},
   "source": [
    "## ENTRENAMOS\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab28829f-f333-4a7b-a545-3576a4a4cfa2",
   "metadata": {},
   "source": [
    "### Primer entrenamiento sin balanceo \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ef283273-fdb9-47a8-8496-4f2f4ef48f6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Logistic Regression trained.\n",
      "                   K-Nearest Neighbors trained.\n",
      "                         Decision Tree trained.\n",
      "Support Vector Machine (Linear Kernel) trained.\n",
      "   Support Vector Machine (RBF Kernel) trained.\n",
      "                         Random Forest trained.\n",
      "                     Gradient Boosting trained.\n",
      "                               XGBoost trained.\n",
      "                              LightGBM trained.\n",
      "                              CatBoost trained.\n"
     ]
    }
   ],
   "source": [
    "# Modelos elegidos para nuestro entrenamiesto de clasificación \n",
    "models = {\n",
    "    \"                   Logistic Regression\": LogisticRegression(),\n",
    "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
    "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
    "   # \"                        Neural Network\": MLPClassifier(),\n",
    "    \"                         Random Forest\": RandomForestClassifier(),\n",
    "    \"                     Gradient Boosting\": GradientBoostingClassifier(),\n",
    "    \"                               XGBoost\": XGBClassifier(eval_metric='mlogloss'),\n",
    "    \"                              LightGBM\": LGBMClassifier(),\n",
    "    \"                              CatBoost\": CatBoostClassifier(verbose=0)\n",
    "}\n",
    "\n",
    "# entrenamiento:\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    print(name + \" trained.\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "41a6dd5d-9943-464d-ad6a-a453cedf6f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3315\n",
       "1     171\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cantidad de datos que tienen casos de stroke afirmativos y negativos donde 0=No y 1=Si\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68767fc7",
   "metadata": {},
   "source": [
    "### Nos damos cuenta que el dataset esta muy desbalanceado puesto que hay muchos casos negativos y esto puede influir sesgando los datos , pero lo solucionaremos luego\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a8e4e76f-9e8a-4edd-83e3-62281b018eb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "-----------------\n",
      "\n",
      "                   Logistic Regression Accuracy: 94.916%\n",
      "\t\t\t\t       F1-Score: 0.02564\n",
      "\t\t\t\t              Recall: 0.01299\n",
      "\t\t\t\t       Precision: 1.00000\n",
      "\t\t\t\t       Roc_Au: 0.50649\n",
      "\t\t\t\t\n",
      "[[1418    0]\n",
      " [  76    1]]\n",
      "\n",
      "                   Logistic Regression Accuracy_train: 95.095%\n",
      "\t\t\t\t       F1-Score_train: 0.00000\n",
      "\t\t\t\t              Recall_Train: 0.00000\n",
      "\t\t\t\t       Precision_Train: 0.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.50000\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [ 171    0]]\n",
      "0.18782469048650188\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy: 94.716%\n",
      "\t\t\t\t       F1-Score: 0.04819\n",
      "\t\t\t\t              Recall: 0.02597\n",
      "\t\t\t\t       Precision: 0.33333\n",
      "\t\t\t\t       Roc_Au: 0.51158\n",
      "\t\t\t\t\n",
      "[[1414    4]\n",
      " [  75    2]]\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy_train: 95.209%\n",
      "\t\t\t\t       F1-Score_train: 0.07735\n",
      "\t\t\t\t              Recall_Train: 0.04094\n",
      "\t\t\t\t       Precision_Train: 0.70000\n",
      "\t\t\t\t       Roc_Au_Train: 0.52002\n",
      "\t\t\t\t\n",
      "[[3312    3]\n",
      " [ 164    7]]\n",
      "0.5212334406228691\n",
      "\n",
      "                         Decision Tree Accuracy: 91.839%\n",
      "\t\t\t\t       F1-Score: 0.19737\n",
      "\t\t\t\t              Recall: 0.19481\n",
      "\t\t\t\t       Precision: 0.20000\n",
      "\t\t\t\t       Roc_Au: 0.57625\n",
      "\t\t\t\t\n",
      "[[1358   60]\n",
      " [  62   15]]\n",
      "\n",
      "                         Decision Tree Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t              Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [   0  171]]\n",
      "8.885651857246904\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy: 94.849%\n",
      "\t\t\t\t       F1-Score: 0.00000\n",
      "\t\t\t\t              Recall: 0.00000\n",
      "\t\t\t\t       Precision: 0.00000\n",
      "\t\t\t\t       Roc_Au: 0.50000\n",
      "\t\t\t\t\n",
      "[[1418    0]\n",
      " [  77    0]]\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy_train: 95.095%\n",
      "\t\t\t\t       F1-Score_train: 0.00000\n",
      "\t\t\t\t              Recall_Train: 0.00000\n",
      "\t\t\t\t       Precision_Train: 0.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.50000\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [ 171    0]]\n",
      "0.25847900973226334\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy: 94.849%\n",
      "\t\t\t\t       F1-Score: 0.00000\n",
      "\t\t\t\t              Recall: 0.00000\n",
      "\t\t\t\t       Precision: 0.00000\n",
      "\t\t\t\t       Roc_Au: 0.50000\n",
      "\t\t\t\t\n",
      "[[1418    0]\n",
      " [  77    0]]\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy_train: 95.095%\n",
      "\t\t\t\t       F1-Score_train: 0.00000\n",
      "\t\t\t\t              Recall_Train: 0.00000\n",
      "\t\t\t\t       Precision_Train: 0.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.50000\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [ 171    0]]\n",
      "0.25847900973226334\n",
      "\n",
      "                         Random Forest Accuracy: 94.716%\n",
      "\t\t\t\t       F1-Score: 0.00000\n",
      "\t\t\t\t              Recall: 0.00000\n",
      "\t\t\t\t       Precision: 0.00000\n",
      "\t\t\t\t       Roc_Au: 0.49929\n",
      "\t\t\t\t\n",
      "[[1416    2]\n",
      " [  77    0]]\n",
      "\n",
      "                         Random Forest Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t              Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [   0  171]]\n",
      "5.579096045197747\n",
      "\n",
      "                     Gradient Boosting Accuracy: 94.983%\n",
      "\t\t\t\t       F1-Score: 0.07407\n",
      "\t\t\t\t              Recall: 0.03896\n",
      "\t\t\t\t       Precision: 0.75000\n",
      "\t\t\t\t       Roc_Au: 0.51913\n",
      "\t\t\t\t\n",
      "[[1417    1]\n",
      " [  74    3]]\n",
      "\n",
      "                     Gradient Boosting Accuracy_train: 95.697%\n",
      "\t\t\t\t       F1-Score_train: 0.21875\n",
      "\t\t\t\t              Recall_Train: 0.12281\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.56140\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [ 150   21]]\n",
      "0.7514969334076702\n",
      "\n",
      "                               XGBoost Accuracy: 93.913%\n",
      "\t\t\t\t       F1-Score: 0.04211\n",
      "\t\t\t\t              Recall: 0.02597\n",
      "\t\t\t\t       Precision: 0.11111\n",
      "\t\t\t\t       Roc_Au: 0.50735\n",
      "\t\t\t\t\n",
      "[[1402   16]\n",
      " [  75    2]]\n",
      "\n",
      "                               XGBoost Accuracy_train: 99.742%\n",
      "\t\t\t\t       F1-Score_train: 0.97297\n",
      "\t\t\t\t              Recall_Train: 0.94737\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.97368\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [   9  162]]\n",
      "6.206572321030149\n",
      "\n",
      "                              LightGBM Accuracy: 93.980%\n",
      "\t\t\t\t       F1-Score: 0.00000\n",
      "\t\t\t\t              Recall: 0.00000\n",
      "\t\t\t\t       Precision: 0.00000\n",
      "\t\t\t\t       Roc_Au: 0.49542\n",
      "\t\t\t\t\n",
      "[[1405   13]\n",
      " [  77    0]]\n",
      "\n",
      "                              LightGBM Accuracy_train: 99.828%\n",
      "\t\t\t\t       F1-Score_train: 0.98214\n",
      "\t\t\t\t              Recall_Train: 0.96491\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.98246\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [   6  165]]\n",
      "6.2225516197989705\n",
      "\n",
      "                              CatBoost Accuracy: 94.716%\n",
      "\t\t\t\t       F1-Score: 0.04819\n",
      "\t\t\t\t              Recall: 0.02597\n",
      "\t\t\t\t       Precision: 0.33333\n",
      "\t\t\t\t       Roc_Au: 0.51158\n",
      "\t\t\t\t\n",
      "[[1414    4]\n",
      " [  75    2]]\n",
      "\n",
      "                              CatBoost Accuracy_train: 97.676%\n",
      "\t\t\t\t       F1-Score_train: 0.68966\n",
      "\t\t\t\t              Recall_Train: 0.52632\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 0.76316\n",
      "\t\t\t\t\n",
      "[[3315    0]\n",
      " [  81   90]]\n",
      "3.1258812489668135\n"
     ]
    }
   ],
   "source": [
    "# Sacamos las métricas en test y train \n",
    "print(\"Model Performance\\n-----------------\")\n",
    "for name, model in models.items():\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    y_pred_train = model.predict(X_train)\n",
    "    \n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy: {:.3f}%\\n\\t\\t\\t\\t       F1-Score: {:.5f}\\n\\t\\t\\t\\t              Recall: {:.5f}\\n\\t\\t\\t\\t       Precision: {:.5f}\\n\\t\\t\\t\\t       Roc_Au: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_test, y_pred) * 100, f1_score(y_test, y_pred),recall_score(y_test, y_pred),precision_score(y_test, y_pred),roc_auc_score(y_test, y_pred) ))\n",
    "    \n",
    "    print( confusion_matrix(y_test, y_pred))\n",
    "    \n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy_train: {:.3f}%\\n\\t\\t\\t\\t       F1-Score_train: {:.5f}\\n\\t\\t\\t\\t              Recall_Train: {:.5f}\\n\\t\\t\\t\\t       Precision_Train: {:.5f}\\n\\t\\t\\t\\t       Roc_Au_Train: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_train, y_pred_train) * 100, f1_score(y_train, y_pred_train),recall_score(y_train, y_pred_train),precision_score(y_train, y_pred_train),roc_auc_score(y_train, y_pred_train) ))\n",
    "\n",
    "# Imprimiendo matriz de confusión \n",
    "    print(confusion_matrix(y_train, y_pred_train))\n",
    "    \n",
    "# Medimos el overfitting\n",
    "    print(\n",
    "    np.abs(((((accuracy_score(y_train, y_pred_train))-accuracy_score(y_test, y_pred))/(accuracy_score(y_test, y_pred)) *100)))\n",
    "       )\n",
    "           "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa8298fe-2ddd-4ef4-a54b-9f84a9ee9e10",
   "metadata": {},
   "source": [
    "### Primer intento de balanceo de datos con técnica de Oversampling\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e0018fb-964b-4e3d-8c0e-fdec65fc195f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    3315\n",
       "1     171\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comprobamos en numero de casos positivos y negativos en y_train\n",
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "71434f41-fb10-4a7a-bc61-59f108375010",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Definimos el oversample en los datos reservados para train y hacemos una copia para mayor seguridad\n",
    "oversampled_data = pd.concat([X_train, y_train], axis=1).copy()\n",
    "\n",
    "# igualamos la cantidad de datos \n",
    "num_samples = y_train.value_counts()[0] - y_train.value_counts()[1]\n",
    "new_samples = oversampled_data.query(\"stroke == 1\").sample(num_samples, replace=True, random_state=1)\n",
    "\n",
    "# definimos para oversamplear\n",
    "oversampled_data = pd.concat([oversampled_data, new_samples], axis=0).sample(frac=1.0, random_state=1).reset_index(drop=True)\n",
    "\n",
    "# Definimos X e y con la data banceada \n",
    "y_oversampled = oversampled_data['stroke']\n",
    "X_oversampled = oversampled_data.drop('stroke', axis=1)\n",
    "\n",
    "#Def Train-Test-Split\n",
    "X_oversampled_train, X_oversampled_test, y_oversampled_train, y_oversampled_test = train_test_split(X_oversampled, y_oversampled, train_size=0.7, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f5ddf7ae-4721-492c-9031-e6c95e9d9931",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3253    1\n",
       "4972    0\n",
       "2408    0\n",
       "6594    1\n",
       "2761    0\n",
       "       ..\n",
       "905     1\n",
       "5192    0\n",
       "3980    0\n",
       "235     0\n",
       "5157    0\n",
       "Name: stroke, Length: 4641, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificamos que se ha hecho bien el split\n",
    "y_oversampled_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4d536996-627f-47dd-a6e0-048cdc488129",
   "metadata": {},
   "outputs": [
    {
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       "        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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>work_type_Govt_job</th>\n",
       "      <th>work_type_Private</th>\n",
       "      <th>work_type_Self-employed</th>\n",
       "      <th>work_type_children</th>\n",
       "      <th>smoking_status_Unknown</th>\n",
       "      <th>smoking_status_formerly smoked</th>\n",
       "      <th>smoking_status_never smoked</th>\n",
       "      <th>smoking_status_smokes</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.860964</td>\n",
       "      <td>0.760070</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>4.153470</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>1.009801</td>\n",
       "      <td>0.301358</td>\n",
       "      <td>1.210494</td>\n",
       "      <td>2.5208</td>\n",
       "      <td>-1.147292</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>2.380208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.860964</td>\n",
       "      <td>-0.296505</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>-0.192419</td>\n",
       "      <td>-1.199677</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>0.871618</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>1.309562</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.860964</td>\n",
       "      <td>1.200310</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>1.957183</td>\n",
       "      <td>0.813698</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>-1.147292</td>\n",
       "      <td>2.285826</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>2.147912</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.161489</td>\n",
       "      <td>0.451902</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>-1.409662</td>\n",
       "      <td>1.009801</td>\n",
       "      <td>-0.005382</td>\n",
       "      <td>-0.170945</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>0.871618</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>2.147912</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.860964</td>\n",
       "      <td>1.508478</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>1.009801</td>\n",
       "      <td>-1.014280</td>\n",
       "      <td>-0.215034</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>-1.147292</td>\n",
       "      <td>2.285826</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>2.147912</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>1</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>6625</th>\n",
       "      <td>0.860964</td>\n",
       "      <td>0.319830</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>1.009801</td>\n",
       "      <td>-0.209362</td>\n",
       "      <td>-1.038019</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>-1.147292</td>\n",
       "      <td>2.285826</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>1.309562</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6626</th>\n",
       "      <td>-1.161489</td>\n",
       "      <td>1.068238</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>-0.636906</td>\n",
       "      <td>-0.156249</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>-1.147292</td>\n",
       "      <td>2.285826</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>2.380208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6627</th>\n",
       "      <td>-1.161489</td>\n",
       "      <td>1.332382</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>-0.743847</td>\n",
       "      <td>0.402205</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>0.871618</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>2.380208</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6628</th>\n",
       "      <td>-1.161489</td>\n",
       "      <td>-0.428577</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>-1.409662</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>0.037526</td>\n",
       "      <td>0.269940</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>0.871618</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>-0.465568</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>2.380208</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6629</th>\n",
       "      <td>-1.161489</td>\n",
       "      <td>0.672022</td>\n",
       "      <td>-0.329816</td>\n",
       "      <td>-0.240763</td>\n",
       "      <td>0.709390</td>\n",
       "      <td>-0.990294</td>\n",
       "      <td>0.094077</td>\n",
       "      <td>0.505078</td>\n",
       "      <td>-0.3967</td>\n",
       "      <td>0.871618</td>\n",
       "      <td>-0.437479</td>\n",
       "      <td>-0.395245</td>\n",
       "      <td>-0.660113</td>\n",
       "      <td>2.147912</td>\n",
       "      <td>-0.763614</td>\n",
       "      <td>-0.420131</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6630 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        gender       age  hypertension  heart_disease  ever_married  \\\n",
       "0     0.860964  0.760070     -0.329816       4.153470      0.709390   \n",
       "1     0.860964 -0.296505     -0.329816      -0.240763      0.709390   \n",
       "2     0.860964  1.200310     -0.329816      -0.240763      0.709390   \n",
       "3    -1.161489  0.451902     -0.329816      -0.240763     -1.409662   \n",
       "4     0.860964  1.508478     -0.329816      -0.240763      0.709390   \n",
       "...        ...       ...           ...            ...           ...   \n",
       "6625  0.860964  0.319830     -0.329816      -0.240763      0.709390   \n",
       "6626 -1.161489  1.068238     -0.329816      -0.240763      0.709390   \n",
       "6627 -1.161489  1.332382     -0.329816      -0.240763      0.709390   \n",
       "6628 -1.161489 -0.428577     -0.329816      -0.240763     -1.409662   \n",
       "6629 -1.161489  0.672022     -0.329816      -0.240763      0.709390   \n",
       "\n",
       "      Residence_type  avg_glucose_level       bmi  work_type_Govt_job  \\\n",
       "0           1.009801           0.301358  1.210494              2.5208   \n",
       "1          -0.990294          -0.192419 -1.199677             -0.3967   \n",
       "2          -0.990294           1.957183  0.813698             -0.3967   \n",
       "3           1.009801          -0.005382 -0.170945             -0.3967   \n",
       "4           1.009801          -1.014280 -0.215034             -0.3967   \n",
       "...              ...                ...       ...                 ...   \n",
       "6625        1.009801          -0.209362 -1.038019             -0.3967   \n",
       "6626       -0.990294          -0.636906 -0.156249             -0.3967   \n",
       "6627       -0.990294          -0.743847  0.402205             -0.3967   \n",
       "6628       -0.990294           0.037526  0.269940             -0.3967   \n",
       "6629       -0.990294           0.094077  0.505078             -0.3967   \n",
       "\n",
       "      work_type_Private  work_type_Self-employed  work_type_children  \\\n",
       "0             -1.147292                -0.437479           -0.395245   \n",
       "1              0.871618                -0.437479           -0.395245   \n",
       "2             -1.147292                 2.285826           -0.395245   \n",
       "3              0.871618                -0.437479           -0.395245   \n",
       "4             -1.147292                 2.285826           -0.395245   \n",
       "...                 ...                      ...                 ...   \n",
       "6625          -1.147292                 2.285826           -0.395245   \n",
       "6626          -1.147292                 2.285826           -0.395245   \n",
       "6627           0.871618                -0.437479           -0.395245   \n",
       "6628           0.871618                -0.437479           -0.395245   \n",
       "6629           0.871618                -0.437479           -0.395245   \n",
       "\n",
       "      smoking_status_Unknown  smoking_status_formerly smoked  \\\n",
       "0                  -0.660113                       -0.465568   \n",
       "1                  -0.660113                       -0.465568   \n",
       "2                  -0.660113                        2.147912   \n",
       "3                  -0.660113                        2.147912   \n",
       "4                  -0.660113                        2.147912   \n",
       "...                      ...                             ...   \n",
       "6625               -0.660113                       -0.465568   \n",
       "6626               -0.660113                       -0.465568   \n",
       "6627               -0.660113                       -0.465568   \n",
       "6628               -0.660113                       -0.465568   \n",
       "6629               -0.660113                        2.147912   \n",
       "\n",
       "      smoking_status_never smoked  smoking_status_smokes  stroke  \n",
       "0                       -0.763614               2.380208       1  \n",
       "1                        1.309562              -0.420131       0  \n",
       "2                       -0.763614              -0.420131       1  \n",
       "3                       -0.763614              -0.420131       0  \n",
       "4                       -0.763614              -0.420131       1  \n",
       "...                           ...                    ...     ...  \n",
       "6625                     1.309562              -0.420131       0  \n",
       "6626                    -0.763614               2.380208       1  \n",
       "6627                    -0.763614               2.380208       1  \n",
       "6628                    -0.763614               2.380208       0  \n",
       "6629                    -0.763614              -0.420131       1  \n",
       "\n",
       "[6630 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Visualización de la data oversampleada\n",
    "oversampled_data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7903d322-6624-4c9d-9249-aa8225d4daca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2322\n",
       "0    2319\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# comprobamos el balanceo de la data\n",
    "y_oversampled_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "91a83d3d-b451-40f1-8700-bdc03f055115",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Logistic Regression Entrenado.\n",
      "                   K-Nearest Neighbors Entrenado.\n",
      "                         Decision Tree Entrenado.\n",
      "Support Vector Machine (Linear Kernel) Entrenado.\n",
      "   Support Vector Machine (RBF Kernel) Entrenado.\n",
      "                         Random Forest Entrenado.\n",
      "                     Gradient Boosting Entrenado.\n",
      "                               XGBoost Entrenado.\n",
      "                              LightGBM Entrenado.\n",
      "                              CatBoost Entrenado.\n"
     ]
    }
   ],
   "source": [
    "# Entrenando los modelos con la data balanceada\n",
    "models = {\n",
    "    \"                   Logistic Regression\": LogisticRegression(),\n",
    "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
    "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
    "    #\"                        Neural Network\": MLPClassifier(),\n",
    "    \"                         Random Forest\": RandomForestClassifier(),\n",
    "    \"                     Gradient Boosting\": GradientBoostingClassifier(),\n",
    "    \"                               XGBoost\": XGBClassifier(eval_metric='mlogloss'),\n",
    "    \"                              LightGBM\": LGBMClassifier(),\n",
    "    \"                              CatBoost\": CatBoostClassifier(verbose=0)\n",
    "}\n",
    "\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_oversampled_train, y_oversampled_train)\n",
    "    print(name + \" Entrenado.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b41f4c67-e65f-4ebf-a2a8-2c8331c8ba6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "-----------------\n",
      "\n",
      "                   Logistic Regression Accuracy: 78.482%\n",
      "\t\t\t\t       F1-Score: 0.79383\n",
      "\t\t\t              Recall: 0.82981\n",
      "\t\t\t\t       Precision: 0.76085\n",
      "\t\t\t\t       Roc_Au: 0.78488\n",
      "\t\t\t\t\n",
      "[[737 259]\n",
      " [169 824]]\n",
      "\n",
      "                   Logistic Regression Accuracy_train: 77.591%\n",
      "\t\t\t\t       F1-Score_train: 0.78819\n",
      "\t\t\t\t        Recall_Train: 0.83333\n",
      "\t\t\t\t       Precision_Train: 0.74768\n",
      "\t\t\t\t       Roc_Au_Train: 0.77587\n",
      "\t\t\t\t\n",
      "[[1666  653]\n",
      " [ 387 1935]]\n",
      "\n",
      "                   Logistic Regression Overfitting:\n",
      "\t\t\t\t 1.1348036972636548\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy: 90.447%\n",
      "\t\t\t\t       F1-Score: 0.91268\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.83939\n",
      "\t\t\t\t       Roc_Au: 0.90462\n",
      "\t\t\t\t\n",
      "[[806 190]\n",
      " [  0 993]]\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy_train: 94.441%\n",
      "\t\t\t\t       F1-Score_train: 0.94737\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 0.90000\n",
      "\t\t\t\t       Roc_Au_Train: 0.94437\n",
      "\t\t\t\t\n",
      "[[2061  258]\n",
      " [   0 2322]]\n",
      "\n",
      "                   K-Nearest Neighbors Overfitting:\n",
      "\t\t\t\t 4.415151274517585\n",
      "\n",
      "                         Decision Tree Accuracy: 96.782%\n",
      "\t\t\t\t       F1-Score: 0.96878\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.93945\n",
      "\t\t\t\t       Roc_Au: 0.96787\n",
      "\t\t\t\t\n",
      "[[932  64]\n",
      " [  0 993]]\n",
      "\n",
      "                         Decision Tree Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[2319    0]\n",
      " [   0 2322]]\n",
      "\n",
      "                         Decision Tree Overfitting:\n",
      "\t\t\t\t 3.324675324675324\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy: 77.979%\n",
      "\t\t\t\t       F1-Score: 0.78963\n",
      "\t\t\t              Recall: 0.82779\n",
      "\t\t\t\t       Precision: 0.75482\n",
      "\t\t\t\t       Roc_Au: 0.77986\n",
      "\t\t\t\t\n",
      "[[729 267]\n",
      " [171 822]]\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy_train: 76.858%\n",
      "\t\t\t\t       F1-Score_train: 0.78135\n",
      "\t\t\t\t        Recall_Train: 0.82644\n",
      "\t\t\t\t       Precision_Train: 0.74093\n",
      "\t\t\t\t       Roc_Au_Train: 0.76855\n",
      "\t\t\t\t\n",
      "[[1648  671]\n",
      " [ 403 1919]]\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Overfitting:\n",
      "\t\t\t\t 1.4368610113290914\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy: 83.459%\n",
      "\t\t\t\t       F1-Score: 0.84371\n",
      "\t\t\t              Recall: 0.89426\n",
      "\t\t\t\t       Precision: 0.79856\n",
      "\t\t\t\t       Roc_Au: 0.83468\n",
      "\t\t\t\t\n",
      "[[772 224]\n",
      " [105 888]]\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy_train: 85.887%\n",
      "\t\t\t\t       F1-Score_train: 0.86673\n",
      "\t\t\t\t        Recall_Train: 0.91731\n",
      "\t\t\t\t       Precision_Train: 0.82144\n",
      "\t\t\t\t       Roc_Au_Train: 0.85883\n",
      "\t\t\t\t\n",
      "[[1856  463]\n",
      " [ 192 2130]]\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Overfitting:\n",
      "\t\t\t\t 2.908777969018922\n",
      "\n",
      "                         Random Forest Accuracy: 99.346%\n",
      "\t\t\t\t       F1-Score: 0.99350\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.98708\n",
      "\t\t\t\t       Roc_Au: 0.99347\n",
      "\t\t\t\t\n",
      "[[983  13]\n",
      " [  0 993]]\n",
      "\n",
      "                         Random Forest Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[2319    0]\n",
      " [   0 2322]]\n",
      "\n",
      "                         Random Forest Overfitting:\n",
      "\t\t\t\t 0.6578947368421029\n",
      "\n",
      "                     Gradient Boosting Accuracy: 86.677%\n",
      "\t\t\t\t       F1-Score: 0.87623\n",
      "\t\t\t              Recall: 0.94461\n",
      "\t\t\t\t       Precision: 0.81707\n",
      "\t\t\t\t       Roc_Au: 0.86688\n",
      "\t\t\t\t\n",
      "[[786 210]\n",
      " [ 55 938]]\n",
      "\n",
      "                     Gradient Boosting Accuracy_train: 88.903%\n",
      "\t\t\t\t       F1-Score_train: 0.89615\n",
      "\t\t\t\t        Recall_Train: 0.95693\n",
      "\t\t\t\t       Precision_Train: 0.84262\n",
      "\t\t\t\t       Roc_Au_Train: 0.88899\n",
      "\t\t\t\t\n",
      "[[1904  415]\n",
      " [ 100 2222]]\n",
      "\n",
      "                     Gradient Boosting Overfitting:\n",
      "\t\t\t\t 2.5687769307258774\n",
      "\n",
      "                               XGBoost Accuracy: 97.185%\n",
      "\t\t\t\t       F1-Score: 0.97258\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.94662\n",
      "\t\t\t\t       Roc_Au: 0.97189\n",
      "\t\t\t\t\n",
      "[[940  56]\n",
      " [  0 993]]\n",
      "\n",
      "                               XGBoost Accuracy_train: 99.957%\n",
      "\t\t\t\t       F1-Score_train: 0.99957\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 0.99914\n",
      "\t\t\t\t       Roc_Au_Train: 0.99957\n",
      "\t\t\t\t\n",
      "[[2317    2]\n",
      " [   0 2322]]\n",
      "\n",
      "                               XGBoost Overfitting:\n",
      "\t\t\t\t 2.8527085950779707\n",
      "\n",
      "                              LightGBM Accuracy: 96.481%\n",
      "\t\t\t\t       F1-Score: 0.96595\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.93415\n",
      "\t\t\t\t       Roc_Au: 0.96486\n",
      "\t\t\t\t\n",
      "[[926  70]\n",
      " [  0 993]]\n",
      "\n",
      "                              LightGBM Accuracy_train: 99.310%\n",
      "\t\t\t\t       F1-Score_train: 0.99316\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 0.98641\n",
      "\t\t\t\t       Roc_Au_Train: 0.99310\n",
      "\t\t\t\t\n",
      "[[2287   32]\n",
      " [   0 2322]]\n",
      "\n",
      "                              LightGBM Overfitting:\n",
      "\t\t\t\t 2.9330752624134604\n",
      "\n",
      "                              CatBoost Accuracy: 95.726%\n",
      "\t\t\t\t       F1-Score: 0.95896\n",
      "\t\t\t              Recall: 1.00000\n",
      "\t\t\t\t       Precision: 0.92115\n",
      "\t\t\t\t       Roc_Au: 0.95733\n",
      "\t\t\t\t\n",
      "[[911  85]\n",
      " [  0 993]]\n",
      "\n",
      "                              CatBoost Accuracy_train: 98.880%\n",
      "\t\t\t\t       F1-Score_train: 0.98893\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 0.97810\n",
      "\t\t\t\t       Roc_Au_Train: 0.98879\n",
      "\t\t\t\t\n",
      "[[2267   52]\n",
      " [   0 2322]]\n",
      "\n",
      "                              CatBoost Overfitting:\n",
      "\t\t\t\t 3.293817527010804\n"
     ]
    }
   ],
   "source": [
    "# Sacamos las métricas más importantes ya con los datos de Oversampled en X e y .\n",
    "\n",
    "print(\"Model Performance\\n-----------------\")\n",
    "for name, model in models.items():\n",
    "    y_oversampled_pred = model.predict(X_oversampled_test)\n",
    "    \n",
    "    y_oversampled_pred_train = model.predict(X_oversampled_train)\n",
    "\n",
    "\n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy: {:.3f}%\\n\\t\\t\\t\\t       F1-Score: {:.5f}\\n\\t\\t\\t              Recall: {:.5f}\\n\\t\\t\\t\\t       Precision: {:.5f}\\n\\t\\t\\t\\t       Roc_Au: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_oversampled_test, y_oversampled_pred) * 100, f1_score(y_oversampled_test, y_oversampled_pred),recall_score(y_oversampled_test, y_oversampled_pred),precision_score(y_oversampled_test, y_oversampled_pred),roc_auc_score(y_oversampled_test, y_oversampled_pred) ))\n",
    "    \n",
    "    print( confusion_matrix(y_oversampled_test, y_oversampled_pred))\n",
    "    \n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy_train: {:.3f}%\\n\\t\\t\\t\\t       F1-Score_train: {:.5f}\\n\\t\\t\\t\\t        Recall_Train: {:.5f}\\n\\t\\t\\t\\t       Precision_Train: {:.5f}\\n\\t\\t\\t\\t       Roc_Au_Train: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_oversampled_train, y_oversampled_pred_train) * 100, f1_score(y_oversampled_train, y_oversampled_pred_train),recall_score(y_oversampled_train, y_oversampled_pred_train),precision_score(y_oversampled_train, y_oversampled_pred_train),roc_auc_score(y_oversampled_train, y_oversampled_pred_train) ))\n",
    " \n",
    "    print( confusion_matrix(y_oversampled_train, y_oversampled_pred_train))\n",
    "    \n",
    "    print(\n",
    "    \"\\n\" + name + \" Overfitting:\\n\\t\\t\\t\\t\",\n",
    "    np.abs(((((accuracy_score(y_oversampled_train, y_oversampled_pred_train))-accuracy_score(y_oversampled_test, y_oversampled_pred))/(accuracy_score(y_oversampled_test, y_oversampled_pred)) *100)))\n",
    "       )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8a841a9",
   "metadata": {},
   "source": [
    "### Al haber usado el metodo de oversampling para balancear , no podemos confiar en que haya duplicado datos y las predicciones no esten sesgadas. \n",
    "### Para elegir un modelo , vamos a probar otro método de balanceo y comparamos las métricas ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de2e76a6",
   "metadata": {},
   "source": [
    "## Segunda técnica de balanceo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ee644d26-fc6f-42b8-be3c-ed0ef9fa5e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>hypertension</th>\n",
       "      <th>heart_disease</th>\n",
       "      <th>ever_married</th>\n",
       "      <th>work_type</th>\n",
       "      <th>Residence_type</th>\n",
       "      <th>avg_glucose_level</th>\n",
       "      <th>bmi</th>\n",
       "      <th>smoking_status</th>\n",
       "      <th>stroke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>228.69</td>\n",
       "      <td>36.6</td>\n",
       "      <td>formerly smoked</td>\n",
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       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>105.92</td>\n",
       "      <td>32.5</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>49.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>171.23</td>\n",
       "      <td>34.4</td>\n",
       "      <td>smokes</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4976</th>\n",
       "      <td>Male</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>70.15</td>\n",
       "      <td>29.8</td>\n",
       "      <td>formerly smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4977</th>\n",
       "      <td>Male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>191.15</td>\n",
       "      <td>31.1</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4978</th>\n",
       "      <td>Female</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Govt_job</td>\n",
       "      <td>Rural</td>\n",
       "      <td>95.02</td>\n",
       "      <td>31.8</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4979</th>\n",
       "      <td>Male</td>\n",
       "      <td>40.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Rural</td>\n",
       "      <td>83.94</td>\n",
       "      <td>30.0</td>\n",
       "      <td>smokes</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4980</th>\n",
       "      <td>Female</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>83.75</td>\n",
       "      <td>29.1</td>\n",
       "      <td>never smoked</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4981 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0       Male  67.0             0              1          Yes        Private   \n",
       "1       Male  80.0             0              1          Yes        Private   \n",
       "2     Female  49.0             0              0          Yes        Private   \n",
       "3     Female  79.0             1              0          Yes  Self-employed   \n",
       "4       Male  81.0             0              0          Yes        Private   \n",
       "...      ...   ...           ...            ...          ...            ...   \n",
       "4976    Male  41.0             0              0           No        Private   \n",
       "4977    Male  40.0             0              0          Yes        Private   \n",
       "4978  Female  45.0             1              0          Yes       Govt_job   \n",
       "4979    Male  40.0             0              0          Yes        Private   \n",
       "4980  Female  80.0             1              0          Yes        Private   \n",
       "\n",
       "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
       "0             Urban             228.69  36.6  formerly smoked       1  \n",
       "1             Rural             105.92  32.5     never smoked       1  \n",
       "2             Urban             171.23  34.4           smokes       1  \n",
       "3             Rural             174.12  24.0     never smoked       1  \n",
       "4             Urban             186.21  29.0  formerly smoked       1  \n",
       "...             ...                ...   ...              ...     ...  \n",
       "4976          Rural              70.15  29.8  formerly smoked       0  \n",
       "4977          Urban             191.15  31.1           smokes       0  \n",
       "4978          Rural              95.02  31.8           smokes       0  \n",
       "4979          Rural              83.94  30.0           smokes       0  \n",
       "4980          Urban              83.75  29.1     never smoked       0  \n",
       "\n",
       "[4981 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Segundo intento de balanceo de datos con técnica de SmoteTomek\n",
    "\n",
    "data_stroke"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f7279a1f-df75-4f88-8442-4d1160879011",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Aqui sacamos las X y y para procesarlas fuera de la función\n",
    "#Eliminamos Transported de X porque es el valor a predecir. La X es mayúscula\n",
    "y = data_stroke['stroke']\n",
    "X = data_stroke.drop('stroke', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ceef2160-3258-4863-87c6-90be79b7680d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Self-employed</td>\n",
       "      <td>Rural</td>\n",
       "      <td>174.12</td>\n",
       "      <td>24.0</td>\n",
       "      <td>never smoked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Private</td>\n",
       "      <td>Urban</td>\n",
       "      <td>186.21</td>\n",
       "      <td>29.0</td>\n",
       "      <td>formerly smoked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
       "0    Male  67.0             0              1          Yes        Private   \n",
       "1    Male  80.0             0              1          Yes        Private   \n",
       "2  Female  49.0             0              0          Yes        Private   \n",
       "3  Female  79.0             1              0          Yes  Self-employed   \n",
       "4    Male  81.0             0              0          Yes        Private   \n",
       "\n",
       "  Residence_type  avg_glucose_level   bmi   smoking_status  \n",
       "0          Urban             228.69  36.6  formerly smoked  \n",
       "1          Rural             105.92  32.5     never smoked  \n",
       "2          Urban             171.23  34.4           smokes  \n",
       "3          Rural             174.12  24.0     never smoked  \n",
       "4          Urban             186.21  29.0  formerly smoked  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Comprobamos que se ha eliminado la variable Stroke del eje X\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ef733ec3-78cd-498d-a4cd-ca3194aefcb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobamos que Y es la variable del stroke.\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ee8f9ad-d499-44af-bc12-5931953c6a95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clasificación de las variables\n",
    "categoricas=[\"gender\", \"ever_married\",\"heart_disease\", \"hypertension\",\"work_type\",\"Residence_type\",\"smoking_status\"]\n",
    "numericas=[\"age\",\"avg_glucose_level\",\"bmi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ab7bde78-eec2-4e64-bdb0-d32c0398f32c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importanción de Sklearn\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, LabelEncoder\n",
    "from sklearn.compose import ColumnTransformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "64ff0e77-e198-482d-9df7-15c90763243d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creamos transformers para cada categoría de variables.\n",
    "transformer_numerico = (\"transformer_numerica\", MinMaxScaler(), numericas)\n",
    "transformer_categorico = (\"transformer_categorica\", OneHotEncoder(), categoricas)\n",
    "transformer = ColumnTransformer([transformer_numerico, transformer_categorico], remainder=\"passthrough\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5a73a3f2-5aa2-4a91-9bca-006c77b6c42d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Denominamos la X para transformer\n",
    "X = transformer.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "60852dc3-1c8f-4876-8f40-fdf695d04988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se crea un DataFrame con los transformers.\n",
    "transformer_final= pd.DataFrame(X, columns = transformer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6d5bf9be-379e-4ec6-948e-79874a33cc2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\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>transformer_numerica__age</th>\n",
       "      <th>transformer_numerica__avg_glucose_level</th>\n",
       "      <th>transformer_numerica__bmi</th>\n",
       "      <th>transformer_categorica__gender_Female</th>\n",
       "      <th>transformer_categorica__gender_Male</th>\n",
       "      <th>transformer_categorica__ever_married_No</th>\n",
       "      <th>transformer_categorica__ever_married_Yes</th>\n",
       "      <th>transformer_categorica__heart_disease_0</th>\n",
       "      <th>transformer_categorica__heart_disease_1</th>\n",
       "      <th>transformer_categorica__hypertension_0</th>\n",
       "      <th>...</th>\n",
       "      <th>transformer_categorica__work_type_Govt_job</th>\n",
       "      <th>transformer_categorica__work_type_Private</th>\n",
       "      <th>transformer_categorica__work_type_Self-employed</th>\n",
       "      <th>transformer_categorica__work_type_children</th>\n",
       "      <th>transformer_categorica__Residence_type_Rural</th>\n",
       "      <th>transformer_categorica__Residence_type_Urban</th>\n",
       "      <th>transformer_categorica__smoking_status_Unknown</th>\n",
       "      <th>transformer_categorica__smoking_status_formerly smoked</th>\n",
       "      <th>transformer_categorica__smoking_status_never smoked</th>\n",
       "      <th>transformer_categorica__smoking_status_smokes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.597168</td>\n",
       "      <td>0.536008</td>\n",
       "      <td>0.584527</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>0.286533</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.987793</td>\n",
       "      <td>0.605161</td>\n",
       "      <td>0.429799</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>0.133044</td>\n",
       "      <td>0.458453</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4981 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      transformer_numerica__age  transformer_numerica__avg_glucose_level  \\\n",
       "0                      0.816895                                 0.801265   \n",
       "1                      0.975586                                 0.234512   \n",
       "2                      0.597168                                 0.536008   \n",
       "3                      0.963379                                 0.549349   \n",
       "4                      0.987793                                 0.605161   \n",
       "...                         ...                                      ...   \n",
       "4976                   0.499512                                 0.069384   \n",
       "4977                   0.487305                                 0.627966   \n",
       "4978                   0.548340                                 0.184194   \n",
       "4979                   0.487305                                 0.133044   \n",
       "4980                   0.975586                                 0.132167   \n",
       "\n",
       "      transformer_numerica__bmi  transformer_categorica__gender_Female  \\\n",
       "0                      0.647564                                    0.0   \n",
       "1                      0.530086                                    0.0   \n",
       "2                      0.584527                                    1.0   \n",
       "3                      0.286533                                    1.0   \n",
       "4                      0.429799                                    0.0   \n",
       "...                         ...                                    ...   \n",
       "4976                   0.452722                                    0.0   \n",
       "4977                   0.489971                                    0.0   \n",
       "4978                   0.510029                                    1.0   \n",
       "4979                   0.458453                                    0.0   \n",
       "4980                   0.432665                                    1.0   \n",
       "\n",
       "      transformer_categorica__gender_Male  \\\n",
       "0                                     1.0   \n",
       "1                                     1.0   \n",
       "2                                     0.0   \n",
       "3                                     0.0   \n",
       "4                                     1.0   \n",
       "...                                   ...   \n",
       "4976                                  1.0   \n",
       "4977                                  1.0   \n",
       "4978                                  0.0   \n",
       "4979                                  1.0   \n",
       "4980                                  0.0   \n",
       "\n",
       "      transformer_categorica__ever_married_No  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "...                                       ...   \n",
       "4976                                      1.0   \n",
       "4977                                      0.0   \n",
       "4978                                      0.0   \n",
       "4979                                      0.0   \n",
       "4980                                      0.0   \n",
       "\n",
       "      transformer_categorica__ever_married_Yes  \\\n",
       "0                                          1.0   \n",
       "1                                          1.0   \n",
       "2                                          1.0   \n",
       "3                                          1.0   \n",
       "4                                          1.0   \n",
       "...                                        ...   \n",
       "4976                                       0.0   \n",
       "4977                                       1.0   \n",
       "4978                                       1.0   \n",
       "4979                                       1.0   \n",
       "4980                                       1.0   \n",
       "\n",
       "      transformer_categorica__heart_disease_0  \\\n",
       "0                                         0.0   \n",
       "1                                         0.0   \n",
       "2                                         1.0   \n",
       "3                                         1.0   \n",
       "4                                         1.0   \n",
       "...                                       ...   \n",
       "4976                                      1.0   \n",
       "4977                                      1.0   \n",
       "4978                                      1.0   \n",
       "4979                                      1.0   \n",
       "4980                                      1.0   \n",
       "\n",
       "      transformer_categorica__heart_disease_1  \\\n",
       "0                                         1.0   \n",
       "1                                         1.0   \n",
       "2                                         0.0   \n",
       "3                                         0.0   \n",
       "4                                         0.0   \n",
       "...                                       ...   \n",
       "4976                                      0.0   \n",
       "4977                                      0.0   \n",
       "4978                                      0.0   \n",
       "4979                                      0.0   \n",
       "4980                                      0.0   \n",
       "\n",
       "      transformer_categorica__hypertension_0  ...  \\\n",
       "0                                        1.0  ...   \n",
       "1                                        1.0  ...   \n",
       "2                                        1.0  ...   \n",
       "3                                        0.0  ...   \n",
       "4                                        1.0  ...   \n",
       "...                                      ...  ...   \n",
       "4976                                     1.0  ...   \n",
       "4977                                     1.0  ...   \n",
       "4978                                     0.0  ...   \n",
       "4979                                     1.0  ...   \n",
       "4980                                     0.0  ...   \n",
       "\n",
       "      transformer_categorica__work_type_Govt_job  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "...                                          ...   \n",
       "4976                                         0.0   \n",
       "4977                                         0.0   \n",
       "4978                                         1.0   \n",
       "4979                                         0.0   \n",
       "4980                                         0.0   \n",
       "\n",
       "      transformer_categorica__work_type_Private  \\\n",
       "0                                           1.0   \n",
       "1                                           1.0   \n",
       "2                                           1.0   \n",
       "3                                           0.0   \n",
       "4                                           1.0   \n",
       "...                                         ...   \n",
       "4976                                        1.0   \n",
       "4977                                        1.0   \n",
       "4978                                        0.0   \n",
       "4979                                        1.0   \n",
       "4980                                        1.0   \n",
       "\n",
       "      transformer_categorica__work_type_Self-employed  \\\n",
       "0                                                 0.0   \n",
       "1                                                 0.0   \n",
       "2                                                 0.0   \n",
       "3                                                 1.0   \n",
       "4                                                 0.0   \n",
       "...                                               ...   \n",
       "4976                                              0.0   \n",
       "4977                                              0.0   \n",
       "4978                                              0.0   \n",
       "4979                                              0.0   \n",
       "4980                                              0.0   \n",
       "\n",
       "      transformer_categorica__work_type_children  \\\n",
       "0                                            0.0   \n",
       "1                                            0.0   \n",
       "2                                            0.0   \n",
       "3                                            0.0   \n",
       "4                                            0.0   \n",
       "...                                          ...   \n",
       "4976                                         0.0   \n",
       "4977                                         0.0   \n",
       "4978                                         0.0   \n",
       "4979                                         0.0   \n",
       "4980                                         0.0   \n",
       "\n",
       "      transformer_categorica__Residence_type_Rural  \\\n",
       "0                                              0.0   \n",
       "1                                              1.0   \n",
       "2                                              0.0   \n",
       "3                                              1.0   \n",
       "4                                              0.0   \n",
       "...                                            ...   \n",
       "4976                                           1.0   \n",
       "4977                                           0.0   \n",
       "4978                                           1.0   \n",
       "4979                                           1.0   \n",
       "4980                                           0.0   \n",
       "\n",
       "      transformer_categorica__Residence_type_Urban  \\\n",
       "0                                              1.0   \n",
       "1                                              0.0   \n",
       "2                                              1.0   \n",
       "3                                              0.0   \n",
       "4                                              1.0   \n",
       "...                                            ...   \n",
       "4976                                           0.0   \n",
       "4977                                           1.0   \n",
       "4978                                           0.0   \n",
       "4979                                           0.0   \n",
       "4980                                           1.0   \n",
       "\n",
       "      transformer_categorica__smoking_status_Unknown  \\\n",
       "0                                                0.0   \n",
       "1                                                0.0   \n",
       "2                                                0.0   \n",
       "3                                                0.0   \n",
       "4                                                0.0   \n",
       "...                                              ...   \n",
       "4976                                             0.0   \n",
       "4977                                             0.0   \n",
       "4978                                             0.0   \n",
       "4979                                             0.0   \n",
       "4980                                             0.0   \n",
       "\n",
       "      transformer_categorica__smoking_status_formerly smoked  \\\n",
       "0                                                   1.0        \n",
       "1                                                   0.0        \n",
       "2                                                   0.0        \n",
       "3                                                   0.0        \n",
       "4                                                   1.0        \n",
       "...                                                 ...        \n",
       "4976                                                1.0        \n",
       "4977                                                0.0        \n",
       "4978                                                0.0        \n",
       "4979                                                0.0        \n",
       "4980                                                0.0        \n",
       "\n",
       "      transformer_categorica__smoking_status_never smoked  \\\n",
       "0                                                   0.0     \n",
       "1                                                   1.0     \n",
       "2                                                   0.0     \n",
       "3                                                   1.0     \n",
       "4                                                   0.0     \n",
       "...                                                 ...     \n",
       "4976                                                0.0     \n",
       "4977                                                0.0     \n",
       "4978                                                0.0     \n",
       "4979                                                0.0     \n",
       "4980                                                1.0     \n",
       "\n",
       "      transformer_categorica__smoking_status_smokes  \n",
       "0                                               0.0  \n",
       "1                                               0.0  \n",
       "2                                               1.0  \n",
       "3                                               0.0  \n",
       "4                                               0.0  \n",
       "...                                             ...  \n",
       "4976                                            0.0  \n",
       "4977                                            1.0  \n",
       "4978                                            1.0  \n",
       "4979                                            1.0  \n",
       "4980                                            0.0  \n",
       "\n",
       "[4981 rows x 21 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizamos el DataFrame con transformers\n",
    "transformer_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "55b3d783-ea26-4be3-a102-a0a44591725d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'transformer_numerica': slice(0, 3, None),\n",
       " 'transformer_categorica': slice(3, 21, None),\n",
       " 'remainder': slice(0, 0, None)}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vemos las variables numéricas y categóricas.\n",
    "transformer.output_indices_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3408a641-8466-443b-be45-8dcc60f51875",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cargando el archivo de transformer a pickle para subirlo a streamlit\n",
    "\n",
    "file_trans = open('transformer1.pkl', 'wb')\n",
    "pickle.dump(transformer, file_trans)\n",
    "file_trans.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1cabccd5-2f97-4554-b533-62dd3f15b92a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sumamos la variable Y en positivos para después tratarlo.\n",
    "positivo = y.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "82a85204-8ff7-40bb-95ff-bacd1d91fc51",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Para sacar los valores negativos hemos de restar los positivos.\n",
    "negativo = y.shape[0]-positivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d793181c-ed5a-417d-87d3-5934960ac7eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rpta Positivos:  248 Rpta Negativos: 4733\n"
     ]
    }
   ],
   "source": [
    "# Mostramos los valores positivos y negativos.\n",
    "print(\"Rpta Positivos: \", positivo, \"Rpta Negativos:\", negativo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a3c94068-24ba-45fd-b735-036b9f7018d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (0.0)\n",
      "Requirement already satisfied: imbalanced-learn in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from imblearn) (0.9.1)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /home/anghi/.local/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.9.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/anghi/.local/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (3.1.0)\n",
      "Requirement already satisfied: scikit-learn>=1.1.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.1.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.23.3)\n",
      "Requirement already satisfied: joblib>=1.0.0 in /home/anghi/miniconda3/envs/pulmon/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.2.0)\n"
     ]
    }
   ],
   "source": [
    "# Instalación de imblearn para utilizar el método de SmoteTomek (no desde terminal)\n",
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d67a234b-1af8-4af6-a611-eca5c62af802",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importación de SmomteTomek\n",
    "from imblearn.combine import SMOTETomek\n",
    "\n",
    "smoteT = SMOTETomek()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "87eeeaad-afa0-431e-bd09-9cc1c105ab94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rpta Positivas: 4678 ,Rpta Negativas: 4678\n"
     ]
    }
   ],
   "source": [
    "# Definiendo las variables para balancear los datos\n",
    "\n",
    "X_smoteT, y_smoteT = smoteT.fit_resample(X, y)\n",
    "Positivo_smoteT = y_smoteT.sum()\n",
    "Negativo_smoteT = y_smoteT.shape[0]-Positivo_smoteT\n",
    "\n",
    "\n",
    "print(\"Rpta Positivas:\",Positivo_smoteT,\",Rpta Negativas:\", Negativo_smoteT)\n",
    "X_smoteT \n",
    "\n",
    "X_smoteT_train, X_smoteT_test, y_smoteT_train, y_smoteT_test = train_test_split(X_smoteT, y_smoteT, train_size=0.7, shuffle=True, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "735cf6f7-3737-4c08-8bc8-b2ad2b9dafa7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Logistic Regression Entrenado.\n",
      "                   K-Nearest Neighbors Entrenado.\n",
      "                         Decision Tree Entrenado.\n",
      "Support Vector Machine (Linear Kernel) Entrenado.\n",
      "   Support Vector Machine (RBF Kernel) Entrenado.\n",
      "                         Random Forest Entrenado.\n",
      "                     Gradient Boosting Entrenado.\n",
      "                               XGBoost Entrenado.\n",
      "                              LightGBM Entrenado.\n",
      "                              CatBoost Entrenado.\n"
     ]
    }
   ],
   "source": [
    "## Corriendo los modelos con balanceo SMOTETomek\n",
    "models = {\n",
    "    \"                   Logistic Regression\": LogisticRegression(),\n",
    "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
    "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
    "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
    "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
    "   # \"                        Neural Network\": MLPClassifier(),\n",
    "    \"                         Random Forest\": RandomForestClassifier(),\n",
    "    \"                     Gradient Boosting\": GradientBoostingClassifier(),\n",
    "    \"                               XGBoost\": XGBClassifier(eval_metric='mlogloss'),\n",
    "    \"                              LightGBM\": LGBMClassifier(),\n",
    "    \"                              CatBoost\": CatBoostClassifier(verbose=0)\n",
    "}\n",
    "\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_smoteT_train, y_smoteT_train)\n",
    "    print(name + \" Entrenado.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2c7ef1",
   "metadata": {},
   "source": [
    "### Tras entrenar con los datos balanaceados aplicando SmoteTomek , comparamos y el que nos da una mejor métrica es XGBoost. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c2864c43-c960-4d8f-bc89-6d7fbe1e27cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    3292\n",
       "0    3257\n",
       "Name: stroke, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recuento de valores positivos y negativos despues de balancear con SmoteTomek\n",
    "y_smoteT_train.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2cca2934-91c2-48f9-a08c-76115afd6ad9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Performance\n",
      "-----------------\n",
      "\n",
      "                   Logistic Regression Accuracy: 79.480%\n",
      "\t\t\t\t       F1-Score: 0.80152\n",
      "\t\t\t              Recall: 0.83911\n",
      "\t\t\t\t       Precision: 0.76715\n",
      "\t\t\t\t       Roc_Au: 0.79534\n",
      "\t\t\t\t\n",
      "[[1068  353]\n",
      " [ 223 1163]]\n",
      "\n",
      "                   Logistic Regression Accuracy_train: 79.279%\n",
      "\t\t\t\t       F1-Score_train: 0.80387\n",
      "\t\t\t\t        Recall_Train: 0.84478\n",
      "\t\t\t\t       Precision_Train: 0.76675\n",
      "\t\t\t\t       Roc_Au_Train: 0.79251\n",
      "\t\t\t\t\n",
      "[[2411  846]\n",
      " [ 511 2781]]\n",
      "\n",
      "                   Logistic Regression Overfitting accuracy:\n",
      "\t\t\t\t 0.2523814715656867\n",
      "\n",
      "                   Logistic Regression Overfitting recall:\n",
      "\t\t\t\t 0.6757046186121405\n",
      "\n",
      "                   Logistic Regression Overfitting presicion:\n",
      "\t\t\t\t 0.05227346918744975\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy: 89.847%\n",
      "\t\t\t\t       F1-Score: 0.90478\n",
      "\t\t\t              Recall: 0.97691\n",
      "\t\t\t\t       Precision: 0.84256\n",
      "\t\t\t\t       Roc_Au: 0.89943\n",
      "\t\t\t\t\n",
      "[[1168  253]\n",
      " [  32 1354]]\n",
      "\n",
      "                   K-Nearest Neighbors Accuracy_train: 92.594%\n",
      "\t\t\t\t       F1-Score_train: 0.93063\n",
      "\t\t\t\t        Recall_Train: 0.98815\n",
      "\t\t\t\t       Precision_Train: 0.87943\n",
      "\t\t\t\t       Roc_Au_Train: 0.92561\n",
      "\t\t\t\t\n",
      "[[2811  446]\n",
      " [  39 3253]]\n",
      "\n",
      "                   K-Nearest Neighbors Overfitting accuracy:\n",
      "\t\t\t\t 3.0579578893400314\n",
      "\n",
      "                   K-Nearest Neighbors Overfitting recall:\n",
      "\t\t\t\t 1.1506790554425776\n",
      "\n",
      "                   K-Nearest Neighbors Overfitting presicion:\n",
      "\t\t\t\t 4.375109564922933\n",
      "\n",
      "                         Decision Tree Accuracy: 87.353%\n",
      "\t\t\t\t       F1-Score: 0.87460\n",
      "\t\t\t              Recall: 0.89322\n",
      "\t\t\t\t       Precision: 0.85675\n",
      "\t\t\t\t       Roc_Au: 0.87377\n",
      "\t\t\t\t\n",
      "[[1214  207]\n",
      " [ 148 1238]]\n",
      "\n",
      "                         Decision Tree Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[3257    0]\n",
      " [   0 3292]]\n",
      "\n",
      "                         Decision Tree Overfitting accuracy:\n",
      "\t\t\t\t 14.47797716150082\n",
      "\n",
      "                         Decision Tree Overfitting recall:\n",
      "\t\t\t\t 11.954765751211628\n",
      "\n",
      "                         Decision Tree Overfitting presicion:\n",
      "\t\t\t\t 16.720516962843295\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy: 79.159%\n",
      "\t\t\t\t       F1-Score: 0.80000\n",
      "\t\t\t              Recall: 0.84416\n",
      "\t\t\t\t       Precision: 0.76023\n",
      "\t\t\t\t       Roc_Au: 0.79224\n",
      "\t\t\t\t\n",
      "[[1052  369]\n",
      " [ 216 1170]]\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Accuracy_train: 79.432%\n",
      "\t\t\t\t       F1-Score_train: 0.80710\n",
      "\t\t\t\t        Recall_Train: 0.85601\n",
      "\t\t\t\t       Precision_Train: 0.76348\n",
      "\t\t\t\t       Roc_Au_Train: 0.79399\n",
      "\t\t\t\t\n",
      "[[2384  873]\n",
      " [ 474 2818]]\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Overfitting accuracy:\n",
      "\t\t\t\t 0.34453284998678824\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Overfitting recall:\n",
      "\t\t\t\t 1.4048041873072359\n",
      "\n",
      "Support Vector Machine (Linear Kernel) Overfitting presicion:\n",
      "\t\t\t\t 0.4268178313152688\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy: 86.284%\n",
      "\t\t\t\t       F1-Score: 0.86865\n",
      "\t\t\t              Recall: 0.91847\n",
      "\t\t\t\t       Precision: 0.82395\n",
      "\t\t\t\t       Roc_Au: 0.86353\n",
      "\t\t\t\t\n",
      "[[1149  272]\n",
      " [ 113 1273]]\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Accuracy_train: 87.219%\n",
      "\t\t\t\t       F1-Score_train: 0.88048\n",
      "\t\t\t\t        Recall_Train: 0.93651\n",
      "\t\t\t\t       Precision_Train: 0.83077\n",
      "\t\t\t\t       Roc_Au_Train: 0.87185\n",
      "\t\t\t\t\n",
      "[[2629  628]\n",
      " [ 209 3083]]\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Overfitting accuracy:\n",
      "\t\t\t\t 1.083781930259842\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Overfitting recall:\n",
      "\t\t\t\t 1.9643898560532422\n",
      "\n",
      "   Support Vector Machine (RBF Kernel) Overfitting presicion:\n",
      "\t\t\t\t 0.8283477307755592\n",
      "\n",
      "                         Random Forest Accuracy: 95.155%\n",
      "\t\t\t\t       F1-Score: 0.95194\n",
      "\t\t\t              Recall: 0.97186\n",
      "\t\t\t\t       Precision: 0.93283\n",
      "\t\t\t\t       Roc_Au: 0.95180\n",
      "\t\t\t\t\n",
      "[[1324   97]\n",
      " [  39 1347]]\n",
      "\n",
      "                         Random Forest Accuracy_train: 100.000%\n",
      "\t\t\t\t       F1-Score_train: 1.00000\n",
      "\t\t\t\t        Recall_Train: 1.00000\n",
      "\t\t\t\t       Precision_Train: 1.00000\n",
      "\t\t\t\t       Roc_Au_Train: 1.00000\n",
      "\t\t\t\t\n",
      "[[3257    0]\n",
      " [   0 3292]]\n",
      "\n",
      "                         Random Forest Overfitting accuracy:\n",
      "\t\t\t\t 5.09172594533882\n",
      "\n",
      "                         Random Forest Overfitting recall:\n",
      "\t\t\t\t 2.8953229398663685\n",
      "\n",
      "                         Random Forest Overfitting presicion:\n",
      "\t\t\t\t 7.201187824795839\n",
      "\n",
      "                     Gradient Boosting Accuracy: 88.457%\n",
      "\t\t\t\t       F1-Score: 0.88687\n",
      "\t\t\t              Recall: 0.91631\n",
      "\t\t\t\t       Precision: 0.85927\n",
      "\t\t\t\t       Roc_Au: 0.88497\n",
      "\t\t\t\t\n",
      "[[1213  208]\n",
      " [ 116 1270]]\n",
      "\n",
      "                     Gradient Boosting Accuracy_train: 89.922%\n",
      "\t\t\t\t       F1-Score_train: 0.90382\n",
      "\t\t\t\t        Recall_Train: 0.94198\n",
      "\t\t\t\t       Precision_Train: 0.86863\n",
      "\t\t\t\t       Roc_Au_Train: 0.89899\n",
      "\t\t\t\t\n",
      "[[2788  469]\n",
      " [ 191 3101]]\n",
      "\n",
      "                     Gradient Boosting Overfitting accuracy:\n",
      "\t\t\t\t 1.655822119039791\n",
      "\n",
      "                     Gradient Boosting Overfitting recall:\n",
      "\t\t\t\t 2.8019728092919123\n",
      "\n",
      "                     Gradient Boosting Overfitting presicion:\n",
      "\t\t\t\t 1.089084452678712\n",
      "\n",
      "                               XGBoost Accuracy: 95.404%\n",
      "\t\t\t\t       F1-Score: 0.95328\n",
      "\t\t\t              Recall: 0.94949\n",
      "\t\t\t\t       Precision: 0.95709\n",
      "\t\t\t\t       Roc_Au: 0.95399\n",
      "\t\t\t\t\n",
      "[[1362   59]\n",
      " [  70 1316]]\n",
      "\n",
      "                               XGBoost Accuracy_train: 99.710%\n",
      "\t\t\t\t       F1-Score_train: 0.99711\n",
      "\t\t\t\t        Recall_Train: 0.99544\n",
      "\t\t\t\t       Precision_Train: 0.99878\n",
      "\t\t\t\t       Roc_Au_Train: 0.99711\n",
      "\t\t\t\t\n",
      "[[3253    4]\n",
      " [  15 3277]]\n",
      "\n",
      "                               XGBoost Overfitting accuracy:\n",
      "\t\t\t\t 4.512931812586243\n",
      "\n",
      "                               XGBoost Overfitting recall:\n",
      "\t\t\t\t 4.839262170057649\n",
      "\n",
      "                               XGBoost Overfitting presicion:\n",
      "\t\t\t\t 4.355902872669297\n",
      "\n",
      "                              LightGBM Accuracy: 95.262%\n",
      "\t\t\t\t       F1-Score: 0.95186\n",
      "\t\t\t              Recall: 0.94877\n",
      "\t\t\t\t       Precision: 0.95497\n",
      "\t\t\t\t       Roc_Au: 0.95257\n",
      "\t\t\t\t\n",
      "[[1359   62]\n",
      " [  71 1315]]\n",
      "\n",
      "                              LightGBM Accuracy_train: 98.595%\n",
      "\t\t\t\t       F1-Score_train: 0.98596\n",
      "\t\t\t\t        Recall_Train: 0.98117\n",
      "\t\t\t\t       Precision_Train: 0.99080\n",
      "\t\t\t\t       Roc_Au_Train: 0.98598\n",
      "\t\t\t\t\n",
      "[[3227   30]\n",
      " [  62 3230]]\n",
      "\n",
      "                              LightGBM Overfitting accuracy:\n",
      "\t\t\t\t 3.499155380422577\n",
      "\n",
      "                              LightGBM Overfitting recall:\n",
      "\t\t\t\t 3.4141991877994293\n",
      "\n",
      "                              LightGBM Overfitting presicion:\n",
      "\t\t\t\t 3.751195502577631\n",
      "\n",
      "                              CatBoost Accuracy: 94.763%\n",
      "\t\t\t\t       F1-Score: 0.94707\n",
      "\t\t\t              Recall: 0.94877\n",
      "\t\t\t\t       Precision: 0.94536\n",
      "\t\t\t\t       Roc_Au: 0.94764\n",
      "\t\t\t\t\n",
      "[[1345   76]\n",
      " [  71 1315]]\n",
      "\n",
      "                              CatBoost Accuracy_train: 97.633%\n",
      "\t\t\t\t       F1-Score_train: 0.97645\n",
      "\t\t\t\t        Recall_Train: 0.97631\n",
      "\t\t\t\t       Precision_Train: 0.97660\n",
      "\t\t\t\t       Roc_Au_Train: 0.97633\n",
      "\t\t\t\t\n",
      "[[3180   77]\n",
      " [  78 3214]]\n",
      "\n",
      "                              CatBoost Overfitting accuracy:\n",
      "\t\t\t\t 3.0287468556870847\n",
      "\n",
      "                              CatBoost Overfitting recall:\n",
      "\t\t\t\t 2.9019307088505735\n",
      "\n",
      "                              CatBoost Overfitting presicion:\n",
      "\t\t\t\t 3.3045302720982326\n"
     ]
    }
   ],
   "source": [
    "#Test con valores smoteT\n",
    "print(\"Model Performance\\n-----------------\")\n",
    "for name, model in models.items():\n",
    "    y_smoteT_pred = model.predict(X_smoteT_test)\n",
    "    \n",
    "    y_smoteT_pred_train = model.predict(X_smoteT_train)\n",
    "\n",
    "\n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy: {:.3f}%\\n\\t\\t\\t\\t       F1-Score: {:.5f}\\n\\t\\t\\t              Recall: {:.5f}\\n\\t\\t\\t\\t       Precision: {:.5f}\\n\\t\\t\\t\\t       Roc_Au: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_smoteT_test, y_smoteT_pred) * 100, f1_score(y_smoteT_test, y_smoteT_pred),recall_score(y_smoteT_test, y_smoteT_pred),precision_score(y_smoteT_test, y_smoteT_pred),roc_auc_score(y_smoteT_test, y_smoteT_pred) ))\n",
    "    \n",
    "    print( confusion_matrix(y_smoteT_test, y_smoteT_pred))\n",
    "    \n",
    "    print(\n",
    "        \"\\n\" + name + \" Accuracy_train: {:.3f}%\\n\\t\\t\\t\\t       F1-Score_train: {:.5f}\\n\\t\\t\\t\\t        Recall_Train: {:.5f}\\n\\t\\t\\t\\t       Precision_Train: {:.5f}\\n\\t\\t\\t\\t       Roc_Au_Train: {:.5f}\\n\\t\\t\\t\\t\"\\\n",
    "        .format(accuracy_score(y_smoteT_train, y_smoteT_pred_train) * 100, f1_score(y_smoteT_train, y_smoteT_pred_train),recall_score(y_smoteT_train, y_smoteT_pred_train),precision_score(y_smoteT_train, y_smoteT_pred_train),roc_auc_score(y_smoteT_train, y_smoteT_pred_train) ))\n",
    "   \n",
    "    \n",
    "    print( confusion_matrix(y_smoteT_train, y_smoteT_pred_train))\n",
    "    \n",
    "    print(\n",
    "    \"\\n\" + name + \" Overfitting accuracy:\\n\\t\\t\\t\\t\",\n",
    "    np.abs(((((accuracy_score(y_smoteT_train, y_smoteT_pred_train))-accuracy_score(y_smoteT_test, y_smoteT_pred))/(accuracy_score(y_smoteT_test, y_smoteT_pred)) *100)))\n",
    "       )\n",
    "    \n",
    "    print(\n",
    "    \"\\n\" + name + \" Overfitting recall:\\n\\t\\t\\t\\t\",\n",
    "    np.abs(((((recall_score(y_smoteT_train, y_smoteT_pred_train))-recall_score(y_smoteT_test, y_smoteT_pred))/(recall_score(y_smoteT_test, y_smoteT_pred)) *100)))\n",
    "       )\n",
    "# Overfitting        \n",
    "    print(\n",
    "    \"\\n\" + name + \" Overfitting presicion:\\n\\t\\t\\t\\t\",\n",
    "    np.abs(((((precision_score(y_smoteT_train, y_smoteT_pred_train))-precision_score(y_smoteT_test, y_smoteT_pred))/(precision_score(y_smoteT_test, y_smoteT_pred)) *100)))\n",
    "       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "c197881d-50a6-44c2-bf4a-484889f662bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Elegimos el modelo y lo guardamos en una variable para poder utilizar Pickle.\n",
    "modelo_elegido = XGBClassifier()\n",
    "modelo_elegido.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c8195f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9fdc8a33-23b7-4e29-960e-49c61a51ec86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utilizamos pickle para guardar el modelo y poder utilizar en streamlit.\n",
    "modelo_pickle = 'XGBC_modelo.pkl'\n",
    "pickle.dump(modelo_elegido, open(modelo_pickle, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "621c2112-63cb-412b-9757-b8a42ed9def4",
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "938123fa",
   "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "881c906b",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c25bfa9d",
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   "cell_type": "code",
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   "id": "a3f8100f",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8aaf6e4",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eac63572",
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