22_Redes Neuronales Artificiales - Detección de transacciones bancarias fraudulentas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/drive/1ibh29tsjCfm8xoaSnSoG5bbVRC674h32\">Abre este Jupyter en Google Colab</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Caso Práctico: Redes Neuronales Artificiales (RNA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conjunto de datos: Detección de transacciones bancarias fraudulentas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descripción\n",
    "The datasets contains transactions made by credit cards in September 2013 by european cardholders. \n",
    "This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
    "\n",
    "It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
    "\n",
    "### Descarga de los ficheros de datos\n",
    "https://www.kaggle.com/mlg-ulb/creditcardfraud#creditcard.csv\n",
    "\n",
    "### Referencias adicionales sobre el conjunto de datos\n",
    "_The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection.\n",
    "More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.gridspec as gridspec\n",
    "from collections import Counter\n",
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from matplotlib.colors import LogNorm\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funciones auxiliares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construcción de una función que realice el particionado completo\n",
    "def train_val_test_split(df, rstate=42, shuffle=True, stratify=None):\n",
    "    strat = df[stratify] if stratify else None\n",
    "    train_set, test_set = train_test_split(\n",
    "        df, test_size=0.4, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    strat = test_set[stratify] if stratify else None\n",
    "    val_set, test_set = train_test_split(\n",
    "        test_set, test_size=0.5, random_state=rstate, shuffle=shuffle, stratify=strat)\n",
    "    return (train_set, val_set, test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_labels(df, label_name):\n",
    "    X = df.drop(label_name, axis=1)\n",
    "    y = df[label_name].copy()\n",
    "    return (X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Lectura del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"datasets/creditcard.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Visualización del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
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       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
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       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1.548718</td>\n",
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       "      <td>0.817739</td>\n",
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       "      <td>0.219422</td>\n",
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       "      <td>69.99</td>\n",
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       "      <td>-0.392048</td>\n",
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       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
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       "      <td>93.20</td>\n",
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       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
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       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
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       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Número de características: 31\n",
      "Longitud del conjunto de datos: 284807\n"
     ]
    }
   ],
   "source": [
    "print(\"Número de características:\", len(df.columns))\n",
    "print(\"Longitud del conjunto de datos:\", len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class\n",
       "0    284315\n",
       "1       492\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 492 transacciones fraudulentas, 284315 transacciones legitimas\n",
    "# El conjunto de datos se encuntra desequilabrado\n",
    "df[\"Class\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284807 entries, 0 to 284806\n",
      "Data columns (total 31 columns):\n",
      " #   Column  Non-Null Count   Dtype  \n",
      "---  ------  --------------   -----  \n",
      " 0   Time    284807 non-null  float64\n",
      " 1   V1      284807 non-null  float64\n",
      " 2   V2      284807 non-null  float64\n",
      " 3   V3      284807 non-null  float64\n",
      " 4   V4      284807 non-null  float64\n",
      " 5   V5      284807 non-null  float64\n",
      " 6   V6      284807 non-null  float64\n",
      " 7   V7      284807 non-null  float64\n",
      " 8   V8      284807 non-null  float64\n",
      " 9   V9      284807 non-null  float64\n",
      " 10  V10     284807 non-null  float64\n",
      " 11  V11     284807 non-null  float64\n",
      " 12  V12     284807 non-null  float64\n",
      " 13  V13     284807 non-null  float64\n",
      " 14  V14     284807 non-null  float64\n",
      " 15  V15     284807 non-null  float64\n",
      " 16  V16     284807 non-null  float64\n",
      " 17  V17     284807 non-null  float64\n",
      " 18  V18     284807 non-null  float64\n",
      " 19  V19     284807 non-null  float64\n",
      " 20  V20     284807 non-null  float64\n",
      " 21  V21     284807 non-null  float64\n",
      " 22  V22     284807 non-null  float64\n",
      " 23  V23     284807 non-null  float64\n",
      " 24  V24     284807 non-null  float64\n",
      " 25  V25     284807 non-null  float64\n",
      " 26  V26     284807 non-null  float64\n",
      " 27  V27     284807 non-null  float64\n",
      " 28  V28     284807 non-null  float64\n",
      " 29  Amount  284807 non-null  float64\n",
      " 30  Class   284807 non-null  int64  \n",
      "dtypes: float64(30), int64(1)\n",
      "memory usage: 67.4 MB\n"
     ]
    }
   ],
   "source": [
    "# Visualizamos los tipos de cada uno de los atributos\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Time      False\n",
       "V1        False\n",
       "V2        False\n",
       "V3        False\n",
       "V4        False\n",
       "V5        False\n",
       "V6        False\n",
       "V7        False\n",
       "V8        False\n",
       "V9        False\n",
       "V10       False\n",
       "V11       False\n",
       "V12       False\n",
       "V13       False\n",
       "V14       False\n",
       "V15       False\n",
       "V16       False\n",
       "V17       False\n",
       "V18       False\n",
       "V19       False\n",
       "V20       False\n",
       "V21       False\n",
       "V22       False\n",
       "V23       False\n",
       "V24       False\n",
       "V25       False\n",
       "V26       False\n",
       "V27       False\n",
       "V28       False\n",
       "Amount    False\n",
       "Class     False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comprobamos si alguna columna tiene valores nulos\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>1.168375e-15</td>\n",
       "      <td>3.416908e-16</td>\n",
       "      <td>-1.379537e-15</td>\n",
       "      <td>2.074095e-15</td>\n",
       "      <td>9.604066e-16</td>\n",
       "      <td>1.487313e-15</td>\n",
       "      <td>-5.556467e-16</td>\n",
       "      <td>1.213481e-16</td>\n",
       "      <td>-2.406331e-15</td>\n",
       "      <td>...</td>\n",
       "      <td>1.654067e-16</td>\n",
       "      <td>-3.568593e-16</td>\n",
       "      <td>2.578648e-16</td>\n",
       "      <td>4.473266e-15</td>\n",
       "      <td>5.340915e-16</td>\n",
       "      <td>1.683437e-15</td>\n",
       "      <td>-3.660091e-16</td>\n",
       "      <td>-1.227390e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>1.332271e+00</td>\n",
       "      <td>1.237094e+00</td>\n",
       "      <td>1.194353e+00</td>\n",
       "      <td>1.098632e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.345240e-01</td>\n",
       "      <td>7.257016e-01</td>\n",
       "      <td>6.244603e-01</td>\n",
       "      <td>6.056471e-01</td>\n",
       "      <td>5.212781e-01</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.616051e+01</td>\n",
       "      <td>-4.355724e+01</td>\n",
       "      <td>-7.321672e+01</td>\n",
       "      <td>-1.343407e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>-3.483038e+01</td>\n",
       "      <td>-1.093314e+01</td>\n",
       "      <td>-4.480774e+01</td>\n",
       "      <td>-2.836627e+00</td>\n",
       "      <td>-1.029540e+01</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-7.682956e-01</td>\n",
       "      <td>-5.540759e-01</td>\n",
       "      <td>-2.086297e-01</td>\n",
       "      <td>-6.430976e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.283949e-01</td>\n",
       "      <td>-5.423504e-01</td>\n",
       "      <td>-1.618463e-01</td>\n",
       "      <td>-3.545861e-01</td>\n",
       "      <td>-3.171451e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-2.741871e-01</td>\n",
       "      <td>4.010308e-02</td>\n",
       "      <td>2.235804e-02</td>\n",
       "      <td>-5.142873e-02</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.945017e-02</td>\n",
       "      <td>6.781943e-03</td>\n",
       "      <td>-1.119293e-02</td>\n",
       "      <td>4.097606e-02</td>\n",
       "      <td>1.659350e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>3.985649e-01</td>\n",
       "      <td>5.704361e-01</td>\n",
       "      <td>3.273459e-01</td>\n",
       "      <td>5.971390e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>1.863772e-01</td>\n",
       "      <td>5.285536e-01</td>\n",
       "      <td>1.476421e-01</td>\n",
       "      <td>4.395266e-01</td>\n",
       "      <td>3.507156e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>7.330163e+01</td>\n",
       "      <td>1.205895e+02</td>\n",
       "      <td>2.000721e+01</td>\n",
       "      <td>1.559499e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>2.720284e+01</td>\n",
       "      <td>1.050309e+01</td>\n",
       "      <td>2.252841e+01</td>\n",
       "      <td>4.584549e+00</td>\n",
       "      <td>7.519589e+00</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  1.168375e-15  3.416908e-16 -1.379537e-15  2.074095e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5            V6            V7            V8            V9  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   9.604066e-16  1.487313e-15 -5.556467e-16  1.213481e-16 -2.406331e-15   \n",
       "std    1.380247e+00  1.332271e+00  1.237094e+00  1.194353e+00  1.098632e+00   \n",
       "min   -1.137433e+02 -2.616051e+01 -4.355724e+01 -7.321672e+01 -1.343407e+01   \n",
       "25%   -6.915971e-01 -7.682956e-01 -5.540759e-01 -2.086297e-01 -6.430976e-01   \n",
       "50%   -5.433583e-02 -2.741871e-01  4.010308e-02  2.235804e-02 -5.142873e-02   \n",
       "75%    6.119264e-01  3.985649e-01  5.704361e-01  3.273459e-01  5.971390e-01   \n",
       "max    3.480167e+01  7.330163e+01  1.205895e+02  2.000721e+01  1.559499e+01   \n",
       "\n",
       "       ...           V21           V22           V23           V24  \\\n",
       "count  ...  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean   ...  1.654067e-16 -3.568593e-16  2.578648e-16  4.473266e-15   \n",
       "std    ...  7.345240e-01  7.257016e-01  6.244603e-01  6.056471e-01   \n",
       "min    ... -3.483038e+01 -1.093314e+01 -4.480774e+01 -2.836627e+00   \n",
       "25%    ... -2.283949e-01 -5.423504e-01 -1.618463e-01 -3.545861e-01   \n",
       "50%    ... -2.945017e-02  6.781943e-03 -1.119293e-02  4.097606e-02   \n",
       "75%    ...  1.863772e-01  5.285536e-01  1.476421e-01  4.395266e-01   \n",
       "max    ...  2.720284e+01  1.050309e+01  2.252841e+01  4.584549e+00   \n",
       "\n",
       "                V25           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean   5.340915e-16  1.683437e-15 -3.660091e-16 -1.227390e-16      88.349619   \n",
       "std    5.212781e-01  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.029540e+01 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -3.171451e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%    1.659350e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    3.507156e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    7.519589e+00  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representación gráfica de dos características\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 0], df[\"V14\"][df['Class'] == 0], c=\"g\", marker=\".\")\n",
    "plt.scatter(df[\"V10\"][df['Class'] == 1], df[\"V14\"][df['Class'] == 1], c=\"r\", marker=\".\")\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Preparación del conjunto de datos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para este tipo de algoritmos es importante que todos los datos se encuentren en un rango similar, por lo tanto, podemos aplicar una función de escalado o normalización. Otra opción, es eliminar las características que no se encuentran en un rango similar siempre y cuando no sean muy influyentes para la predicción."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop([\"Time\", \"Amount\"], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. División del conjunto de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dividimos el conjunto de datos\n",
    "train_set, val_set, test_set = train_val_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = remove_labels(train_set, 'Class')\n",
    "X_val, y_val = remove_labels(val_set, 'Class')\n",
    "X_test, y_test = remove_labels(test_set, 'Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Redes Neuronales Artificiales con un conjunto de datos de dos dimensiones"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de comenzar con la detección de transacciones fraudulentas mediante Redes Neuronales Artificiales para todos los atributos del conjunto de datos, se realiza una prueba para dos atributos con el objetivo de comprender como construye el límite de decisión."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_reduced = X_train[[\"V10\", \"V14\"]].copy()\n",
    "X_val_reduced = X_val[[\"V10\", \"V14\"]].copy()\n",
    "X_test_reduced = X_test[[\"V10\", \"V14\"]].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V10</th>\n",
       "      <th>V14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164406</th>\n",
       "      <td>-0.975273</td>\n",
       "      <td>-0.115675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220383</th>\n",
       "      <td>0.919355</td>\n",
       "      <td>-0.505054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49922</th>\n",
       "      <td>-0.094516</td>\n",
       "      <td>-0.439746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181642</th>\n",
       "      <td>0.107004</td>\n",
       "      <td>0.565399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210715</th>\n",
       "      <td>-0.179247</td>\n",
       "      <td>-0.433966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119879</th>\n",
       "      <td>0.079128</td>\n",
       "      <td>0.600937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259178</th>\n",
       "      <td>-0.654029</td>\n",
       "      <td>0.384625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131932</th>\n",
       "      <td>-0.212753</td>\n",
       "      <td>-0.019493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146867</th>\n",
       "      <td>-0.027572</td>\n",
       "      <td>0.927786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>0.543016</td>\n",
       "      <td>-0.346209</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170884 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V10       V14\n",
       "164406 -0.975273 -0.115675\n",
       "220383  0.919355 -0.505054\n",
       "49922  -0.094516 -0.439746\n",
       "181642  0.107004  0.565399\n",
       "210715 -0.179247 -0.433966\n",
       "...          ...       ...\n",
       "119879  0.079128  0.600937\n",
       "259178 -0.654029  0.384625\n",
       "131932 -0.212753 -0.019493\n",
       "146867 -0.027572  0.927786\n",
       "121958  0.543016 -0.346209\n",
       "\n",
       "[170884 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_reduced"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Entrenamiento del algoritmo con un conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tensorflow in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (2.15.0)\n",
      "Requirement already satisfied: tensorflow-intel==2.15.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow) (2.15.0)\n",
      "Requirement already satisfied: absl-py>=1.0.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.1.0)\n",
      "Requirement already satisfied: astunparse>=1.6.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.6.3)\n",
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      "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.5.4)\n",
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      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.26.3)\n",
      "Requirement already satisfied: opt-einsum>=2.3.2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (3.3.0)\n",
      "Requirement already satisfied: packaging in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (23.1)\n",
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      "Requirement already satisfied: setuptools in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (68.2.2)\n",
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      "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (0.31.0)\n",
      "Requirement already satisfied: grpcio<2.0,>=1.24.3 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (1.60.0)\n",
      "Requirement already satisfied: tensorboard<2.16,>=2.15 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: keras<2.16,>=2.15.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorflow-intel==2.15.0->tensorflow) (2.15.0)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.15.0->tensorflow) (0.41.2)\n",
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      "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.2.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.5.2)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.31.0)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.7.2)\n",
      "Requirement already satisfied: werkzeug>=1.0.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.0.1)\n",
      "Requirement already satisfied: cachetools<6.0,>=2.0.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (5.3.2)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.3.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (4.9)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.3.1)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.4)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (1.26.18)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2023.11.17)\n",
      "Requirement already satisfied: MarkupSafe>=2.1.1 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (2.1.3)\n",
      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.5.1)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.2.2)\n",
      "Requirement already satisfied: keras in c:\\users\\santiago\\anaconda3\\envs\\machinelearning\\lib\\site-packages (2.15.0)\n"
     ]
    }
   ],
   "source": [
    "# Instalamos tensorflow\n",
    "!pip install tensorflow\n",
    "!pip install keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\santiago\\anaconda3\\envs\\MachineLearning\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\santiago\\anaconda3\\envs\\MachineLearning\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\santiago\\anaconda3\\envs\\MachineLearning\\Lib\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Definición de la arquitectura de la red neuronal\n",
    "from tensorflow.keras import models\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(128, activation='relu', input_shape=(X_train_reduced.shape[1],)))\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(32, activation='relu'))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='rmsprop',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 128)               384       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 11265 (44.00 KB)\n",
      "Trainable params: 11265 (44.00 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Resumen de las capas del modelo\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "WARNING:tensorflow:From C:\\Users\\santiago\\anaconda3\\envs\\MachineLearning\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\santiago\\anaconda3\\envs\\MachineLearning\\Lib\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "334/334 [==============================] - 2s 3ms/step - loss: 0.0376 - acc: 0.9982 - val_loss: 0.0042 - val_acc: 0.9994\n",
      "Epoch 2/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0046 - acc: 0.9992 - val_loss: 0.0038 - val_acc: 0.9993\n",
      "Epoch 3/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0041 - acc: 0.9992 - val_loss: 0.0036 - val_acc: 0.9993\n",
      "Epoch 4/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0040 - acc: 0.9992 - val_loss: 0.0037 - val_acc: 0.9993\n",
      "Epoch 5/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0039 - acc: 0.9992 - val_loss: 0.0044 - val_acc: 0.9994\n",
      "Epoch 6/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0039 - acc: 0.9992 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 7/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 8/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0033 - val_acc: 0.9994\n",
      "Epoch 9/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 10/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 11/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0036 - val_acc: 0.9994\n",
      "Epoch 12/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0033 - val_acc: 0.9994\n",
      "Epoch 13/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9992 - val_loss: 0.0036 - val_acc: 0.9994\n",
      "Epoch 14/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0038 - acc: 0.9993 - val_loss: 0.0036 - val_acc: 0.9993\n",
      "Epoch 15/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 16/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 17/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0039 - val_acc: 0.9992\n",
      "Epoch 18/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 19/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 20/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9992 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 21/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 22/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0033 - val_acc: 0.9994\n",
      "Epoch 23/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 24/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 25/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 26/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 27/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0039 - val_acc: 0.9994\n",
      "Epoch 28/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 29/40\n",
      "334/334 [==============================] - 1s 3ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 30/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0033 - val_acc: 0.9994\n",
      "Epoch 31/40\n",
      "334/334 [==============================] - 1s 3ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 32/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 33/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 34/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 35/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 36/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 37/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 38/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0037 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 39/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 40/40\n",
      "334/334 [==============================] - 1s 2ms/step - loss: 0.0036 - acc: 0.9993 - val_loss: 0.0035 - val_acc: 0.9994\n"
     ]
    }
   ],
   "source": [
    "# Entrenamos el modelo\n",
    "history = model.fit(X_train_reduced,\n",
    "                   y_train,\n",
    "                   epochs=40,\n",
    "                   batch_size=512,\n",
    "                   validation_data=(X_val_reduced, y_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación del límite de decisión**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ann_decision_boundary(X, y, model, steps=1000):\n",
    "    xmin, xmax = X[:,0].min() - 1, X[:,0].max() + 1\n",
    "    ymin, ymax = X[:,1].min() - 1, X[:,1].max() + 1\n",
    "    steps = 1000\n",
    "    \n",
    "    x_span = np.linspace(xmin, xmax, steps)\n",
    "    y_span = np.linspace(ymin, ymax, steps)\n",
    "    xx, yy = np.meshgrid(x_span, y_span)\n",
    "\n",
    "    labels = model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = labels.reshape(xx.shape)\n",
    "    \n",
    "    plt.contourf(xx, yy, Z, cmap=\"RdBu\", alpha=0.5)\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], 'k.', markersize=2)\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], 'r.', markersize=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31250/31250 [==============================] - 28s 888us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "plot_ann_decision_boundary(X_train_reduced.values, y_train, model)\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Representación de los ejemplos catalogados como maliciosos**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5341/5341 [==============================] - 5s 925us/step\n"
     ]
    }
   ],
   "source": [
    "# Predicción con el conjunto de datos de entrenamiento\n",
    "pred = model.predict(X_train_reduced) \n",
    "y_pred = (pred > 0.5).astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31250/31250 [==============================] - 27s 879us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 6))\n",
    "plt.plot(X_train_reduced[y_pred==1][\"V10\"], X_train_reduced[y_pred==1][\"V14\"], 'go', markersize=4)\n",
    "plot_ann_decision_boundary(X_train_reduced.values, y_train, model)\n",
    "plt.xlabel(\"V10\", fontsize=14)\n",
    "plt.ylabel(\"V14\", fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Predicción con el conjunto de datos reducido**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 2s 891us/step\n"
     ]
    }
   ],
   "source": [
    "# Predicción con el conjunto de datos de pruebas\n",
    "pred = model.predict(X_test_reduced) \n",
    "y_pred = (pred > 0.5).astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Redes Neuronales Artificiales con un conjunto de datos multidimensional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aplicamos selección de características con Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50, n_jobs=-1, random_state=42)</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=50, n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=50, n_jobs=-1, random_state=42)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Utilizamos Random Forest para realizar selección de características\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf_rnd = RandomForestClassifier(n_estimators=50, random_state=42, n_jobs=-1)\n",
    "clf_rnd.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "V17    0.243084\n",
       "V14    0.132483\n",
       "V12    0.093218\n",
       "V11    0.066528\n",
       "V16    0.066408\n",
       "V10    0.059194\n",
       "V18    0.034266\n",
       "V9     0.030143\n",
       "V4     0.025466\n",
       "V7     0.020946\n",
       "dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seleccionamos las características más importantes\n",
    "feature_importances = {name: score for name, score in zip(list(X_train), clf_rnd.feature_importances_)}\n",
    "feature_importances_sorted = pd.Series(feature_importances).sort_values(ascending=False)\n",
    "feature_importances_sorted.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>V17</th>\n",
       "      <th>V14</th>\n",
       "      <th>V12</th>\n",
       "      <th>V11</th>\n",
       "      <th>V16</th>\n",
       "      <th>V10</th>\n",
       "      <th>V18</th>\n",
       "      <th>V9</th>\n",
       "      <th>V4</th>\n",
       "      <th>V7</th>\n",
       "      <th>V1</th>\n",
       "      <th>V15</th>\n",
       "      <th>V20</th>\n",
       "      <th>V6</th>\n",
       "      <th>V21</th>\n",
       "      <th>V3</th>\n",
       "      <th>V26</th>\n",
       "      <th>V8</th>\n",
       "      <th>V27</th>\n",
       "      <th>V5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>164406</th>\n",
       "      <td>-1.209313</td>\n",
       "      <td>-0.115675</td>\n",
       "      <td>-0.589246</td>\n",
       "      <td>-1.850664</td>\n",
       "      <td>0.954582</td>\n",
       "      <td>-0.975273</td>\n",
       "      <td>0.640636</td>\n",
       "      <td>0.226930</td>\n",
       "      <td>-0.779271</td>\n",
       "      <td>0.176700</td>\n",
       "      <td>-1.509348</td>\n",
       "      <td>0.337068</td>\n",
       "      <td>0.502244</td>\n",
       "      <td>-0.250687</td>\n",
       "      <td>0.369591</td>\n",
       "      <td>1.089287</td>\n",
       "      <td>0.288080</td>\n",
       "      <td>0.211197</td>\n",
       "      <td>-0.023211</td>\n",
       "      <td>1.213151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220383</th>\n",
       "      <td>-0.685169</td>\n",
       "      <td>-0.505054</td>\n",
       "      <td>-0.440400</td>\n",
       "      <td>-0.752344</td>\n",
       "      <td>0.794032</td>\n",
       "      <td>0.919355</td>\n",
       "      <td>-0.031408</td>\n",
       "      <td>-0.965050</td>\n",
       "      <td>2.972199</td>\n",
       "      <td>0.497715</td>\n",
       "      <td>-0.855208</td>\n",
       "      <td>-0.981423</td>\n",
       "      <td>-0.358988</td>\n",
       "      <td>0.195791</td>\n",
       "      <td>1.074965</td>\n",
       "      <td>2.563111</td>\n",
       "      <td>-0.011914</td>\n",
       "      <td>-0.915033</td>\n",
       "      <td>-0.087454</td>\n",
       "      <td>-0.187680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49922</th>\n",
       "      <td>-1.121411</td>\n",
       "      <td>-0.439746</td>\n",
       "      <td>-0.361721</td>\n",
       "      <td>-0.942655</td>\n",
       "      <td>1.077567</td>\n",
       "      <td>-0.094516</td>\n",
       "      <td>0.342143</td>\n",
       "      <td>-0.612186</td>\n",
       "      <td>0.455518</td>\n",
       "      <td>-0.402337</td>\n",
       "      <td>-1.082417</td>\n",
       "      <td>-0.652224</td>\n",
       "      <td>-0.028980</td>\n",
       "      <td>4.381102</td>\n",
       "      <td>-0.069963</td>\n",
       "      <td>1.375652</td>\n",
       "      <td>-0.119972</td>\n",
       "      <td>1.227511</td>\n",
       "      <td>-0.072594</td>\n",
       "      <td>2.471959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181642</th>\n",
       "      <td>-0.107078</td>\n",
       "      <td>0.565399</td>\n",
       "      <td>-0.035721</td>\n",
       "      <td>-1.144233</td>\n",
       "      <td>-0.494280</td>\n",
       "      <td>0.107004</td>\n",
       "      <td>-0.658594</td>\n",
       "      <td>0.450298</td>\n",
       "      <td>0.672408</td>\n",
       "      <td>0.612810</td>\n",
       "      <td>1.898722</td>\n",
       "      <td>-0.368894</td>\n",
       "      <td>-0.082106</td>\n",
       "      <td>-1.267347</td>\n",
       "      <td>0.015111</td>\n",
       "      <td>-1.771837</td>\n",
       "      <td>0.378225</td>\n",
       "      <td>-0.441070</td>\n",
       "      <td>-0.106042</td>\n",
       "      <td>0.115019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210715</th>\n",
       "      <td>0.349047</td>\n",
       "      <td>-0.433966</td>\n",
       "      <td>0.355078</td>\n",
       "      <td>1.124602</td>\n",
       "      <td>0.505890</td>\n",
       "      <td>-0.179247</td>\n",
       "      <td>0.251416</td>\n",
       "      <td>0.375881</td>\n",
       "      <td>0.354859</td>\n",
       "      <td>0.016307</td>\n",
       "      <td>2.019857</td>\n",
       "      <td>-0.397238</td>\n",
       "      <td>-0.218017</td>\n",
       "      <td>-0.688151</td>\n",
       "      <td>-0.312851</td>\n",
       "      <td>-1.623959</td>\n",
       "      <td>0.146084</td>\n",
       "      <td>-0.043346</td>\n",
       "      <td>-0.072767</td>\n",
       "      <td>0.291940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119879</th>\n",
       "      <td>-0.356782</td>\n",
       "      <td>0.600937</td>\n",
       "      <td>0.777009</td>\n",
       "      <td>1.585195</td>\n",
       "      <td>0.239566</td>\n",
       "      <td>0.079128</td>\n",
       "      <td>-0.410923</td>\n",
       "      <td>-0.114199</td>\n",
       "      <td>0.461596</td>\n",
       "      <td>-0.165254</td>\n",
       "      <td>1.173488</td>\n",
       "      <td>0.658885</td>\n",
       "      <td>-0.157534</td>\n",
       "      <td>-0.213165</td>\n",
       "      <td>-0.186027</td>\n",
       "      <td>0.490512</td>\n",
       "      <td>0.109235</td>\n",
       "      <td>0.119221</td>\n",
       "      <td>-0.020922</td>\n",
       "      <td>-0.296377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259178</th>\n",
       "      <td>-0.462528</td>\n",
       "      <td>0.384625</td>\n",
       "      <td>0.346437</td>\n",
       "      <td>-0.421422</td>\n",
       "      <td>0.057999</td>\n",
       "      <td>-0.654029</td>\n",
       "      <td>-0.063197</td>\n",
       "      <td>0.400697</td>\n",
       "      <td>-1.241113</td>\n",
       "      <td>-0.466107</td>\n",
       "      <td>-0.775981</td>\n",
       "      <td>0.785702</td>\n",
       "      <td>-0.295730</td>\n",
       "      <td>3.912076</td>\n",
       "      <td>0.037078</td>\n",
       "      <td>-1.142399</td>\n",
       "      <td>-0.770941</td>\n",
       "      <td>1.360620</td>\n",
       "      <td>0.066006</td>\n",
       "      <td>1.940358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131932</th>\n",
       "      <td>-0.539887</td>\n",
       "      <td>-0.019493</td>\n",
       "      <td>0.022804</td>\n",
       "      <td>-0.382362</td>\n",
       "      <td>-0.144778</td>\n",
       "      <td>-0.212753</td>\n",
       "      <td>-0.068375</td>\n",
       "      <td>-0.491303</td>\n",
       "      <td>0.485774</td>\n",
       "      <td>1.076640</td>\n",
       "      <td>-0.146609</td>\n",
       "      <td>0.690200</td>\n",
       "      <td>0.007155</td>\n",
       "      <td>-0.815198</td>\n",
       "      <td>0.052649</td>\n",
       "      <td>1.524591</td>\n",
       "      <td>-0.398467</td>\n",
       "      <td>-0.395316</td>\n",
       "      <td>-0.121139</td>\n",
       "      <td>0.349308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146867</th>\n",
       "      <td>-0.396148</td>\n",
       "      <td>0.927786</td>\n",
       "      <td>0.570400</td>\n",
       "      <td>-0.418939</td>\n",
       "      <td>0.426113</td>\n",
       "      <td>-0.027572</td>\n",
       "      <td>-0.946837</td>\n",
       "      <td>0.580199</td>\n",
       "      <td>-3.798905</td>\n",
       "      <td>-0.471769</td>\n",
       "      <td>-2.948638</td>\n",
       "      <td>-0.090065</td>\n",
       "      <td>0.417396</td>\n",
       "      <td>2.727695</td>\n",
       "      <td>-0.332759</td>\n",
       "      <td>-2.521201</td>\n",
       "      <td>0.426309</td>\n",
       "      <td>2.217537</td>\n",
       "      <td>0.496912</td>\n",
       "      <td>1.866302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121958</th>\n",
       "      <td>-0.040565</td>\n",
       "      <td>-0.346209</td>\n",
       "      <td>-0.407529</td>\n",
       "      <td>-0.546288</td>\n",
       "      <td>1.447832</td>\n",
       "      <td>0.543016</td>\n",
       "      <td>-1.314021</td>\n",
       "      <td>-0.749637</td>\n",
       "      <td>-0.698559</td>\n",
       "      <td>-0.502369</td>\n",
       "      <td>1.233174</td>\n",
       "      <td>0.893667</td>\n",
       "      <td>0.337732</td>\n",
       "      <td>-0.637028</td>\n",
       "      <td>0.027634</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.393074</td>\n",
       "      <td>-0.188057</td>\n",
       "      <td>0.001217</td>\n",
       "      <td>-1.034018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>170884 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             V17       V14       V12       V11       V16       V10       V18  \\\n",
       "164406 -1.209313 -0.115675 -0.589246 -1.850664  0.954582 -0.975273  0.640636   \n",
       "220383 -0.685169 -0.505054 -0.440400 -0.752344  0.794032  0.919355 -0.031408   \n",
       "49922  -1.121411 -0.439746 -0.361721 -0.942655  1.077567 -0.094516  0.342143   \n",
       "181642 -0.107078  0.565399 -0.035721 -1.144233 -0.494280  0.107004 -0.658594   \n",
       "210715  0.349047 -0.433966  0.355078  1.124602  0.505890 -0.179247  0.251416   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "119879 -0.356782  0.600937  0.777009  1.585195  0.239566  0.079128 -0.410923   \n",
       "259178 -0.462528  0.384625  0.346437 -0.421422  0.057999 -0.654029 -0.063197   \n",
       "131932 -0.539887 -0.019493  0.022804 -0.382362 -0.144778 -0.212753 -0.068375   \n",
       "146867 -0.396148  0.927786  0.570400 -0.418939  0.426113 -0.027572 -0.946837   \n",
       "121958 -0.040565 -0.346209 -0.407529 -0.546288  1.447832  0.543016 -1.314021   \n",
       "\n",
       "              V9        V4        V7        V1       V15       V20        V6  \\\n",
       "164406  0.226930 -0.779271  0.176700 -1.509348  0.337068  0.502244 -0.250687   \n",
       "220383 -0.965050  2.972199  0.497715 -0.855208 -0.981423 -0.358988  0.195791   \n",
       "49922  -0.612186  0.455518 -0.402337 -1.082417 -0.652224 -0.028980  4.381102   \n",
       "181642  0.450298  0.672408  0.612810  1.898722 -0.368894 -0.082106 -1.267347   \n",
       "210715  0.375881  0.354859  0.016307  2.019857 -0.397238 -0.218017 -0.688151   \n",
       "...          ...       ...       ...       ...       ...       ...       ...   \n",
       "119879 -0.114199  0.461596 -0.165254  1.173488  0.658885 -0.157534 -0.213165   \n",
       "259178  0.400697 -1.241113 -0.466107 -0.775981  0.785702 -0.295730  3.912076   \n",
       "131932 -0.491303  0.485774  1.076640 -0.146609  0.690200  0.007155 -0.815198   \n",
       "146867  0.580199 -3.798905 -0.471769 -2.948638 -0.090065  0.417396  2.727695   \n",
       "121958 -0.749637 -0.698559 -0.502369  1.233174  0.893667  0.337732 -0.637028   \n",
       "\n",
       "             V21        V3       V26        V8       V27        V5  \n",
       "164406  0.369591  1.089287  0.288080  0.211197 -0.023211  1.213151  \n",
       "220383  1.074965  2.563111 -0.011914 -0.915033 -0.087454 -0.187680  \n",
       "49922  -0.069963  1.375652 -0.119972  1.227511 -0.072594  2.471959  \n",
       "181642  0.015111 -1.771837  0.378225 -0.441070 -0.106042  0.115019  \n",
       "210715 -0.312851 -1.623959  0.146084 -0.043346 -0.072767  0.291940  \n",
       "...          ...       ...       ...       ...       ...       ...  \n",
       "119879 -0.186027  0.490512  0.109235  0.119221 -0.020922 -0.296377  \n",
       "259178  0.037078 -1.142399 -0.770941  1.360620  0.066006  1.940358  \n",
       "131932  0.052649  1.524591 -0.398467 -0.395316 -0.121139  0.349308  \n",
       "146867 -0.332759 -2.521201  0.426309  2.217537  0.496912  1.866302  \n",
       "121958  0.027634  0.386784 -0.393074 -0.188057  0.001217 -1.034018  \n",
       "\n",
       "[170884 rows x 20 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reducimos el conjunto de datos a las 20 características más importantes\n",
    "features = list(feature_importances_sorted.head(20).index)\n",
    "X_train_select = X_train[features].copy()\n",
    "X_val_select = X_val[features].copy()\n",
    "X_test_select = X_test[features].copy()\n",
    "X_train_select"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entrenamiento del algoritmo RNA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Entrenamiento del algoritmo\n",
    "model = models.Sequential()\n",
    "\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(128, activation='relu', input_shape=(X_train_select.shape[1],)))\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(32, activation='relu'))\n",
    "model.add(layers.Dense(16, activation='relu'))\n",
    "model.add(layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_5 (Dense)             (None, 128)               2688      \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " dense_8 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dense_9 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 13569 (53.00 KB)\n",
      "Trainable params: 13569 (53.00 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2671/2671 [==============================] - 5s 2ms/step - loss: 0.0061 - acc: 0.9993 - val_loss: 0.0036 - val_acc: 0.9993\n",
      "Epoch 2/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0034 - acc: 0.9993 - val_loss: 0.0049 - val_acc: 0.9992\n",
      "Epoch 3/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0033 - acc: 0.9994 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 4/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0029 - acc: 0.9994 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 5/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0028 - acc: 0.9994 - val_loss: 0.0035 - val_acc: 0.9994\n",
      "Epoch 6/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0027 - acc: 0.9994 - val_loss: 0.0031 - val_acc: 0.9994\n",
      "Epoch 7/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0026 - acc: 0.9994 - val_loss: 0.0029 - val_acc: 0.9994\n",
      "Epoch 8/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0025 - acc: 0.9994 - val_loss: 0.0035 - val_acc: 0.9995\n",
      "Epoch 9/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0024 - acc: 0.9994 - val_loss: 0.0034 - val_acc: 0.9994\n",
      "Epoch 10/10\n",
      "2671/2671 [==============================] - 4s 2ms/step - loss: 0.0021 - acc: 0.9994 - val_loss: 0.0033 - val_acc: 0.9994\n"
     ]
    }
   ],
   "source": [
    "# Entrenamos el algoritmo\n",
    "history = model.fit(X_train_select,\n",
    "                   y_train,\n",
    "                   epochs=10,\n",
    "                   batch_size=64,\n",
    "                   validation_data=(X_val_select, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1781/1781 [==============================] - 1s 809us/step\n"
     ]
    }
   ],
   "source": [
    "# Predicción con el conjunto de datos de pruebas\n",
    "pred = model.predict(X_test_select) \n",
    "y_pred = (pred > 0.5).astype(\"int32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score: 0.8087431693989071\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score:\", f1_score(y_test, y_pred))"
   ]
  },
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