02_Preprocesado_y_modelos_e_iteraciones.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Moorea-AI/AI4ENG_UDEA/blob/main/02_Preprocesado_y_modelos_e_iteraciones.ipynb)"
      ],
      "metadata": {
        "id": "YmKqjksL6Twh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#EXPLORACIÓN DE DATOS PARA EL DATASET \"Fraude en Transacciones Electrónicas\" <br>\n",
        "###TOMADO DE: https://www.kaggle.com/competitions/ieee-fraud-detection/data?select=test_transaction"
      ],
      "metadata": {
        "id": "Y6-KU8P-w4nB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Presentado por: </br>\n",
        "Aura Luz Moreno Díaz, Estudiante de Ingeniería Industrial </br>\n",
        "Para la materia:<br>\n",
        "INTRODUCCIÓN A LA INTELIGENCIA ARTIFICIAL PARA LAS CIENCIAS Y LA INGENIERIA<BR>\n",
        "Con el profesor: RAÚL RAMOS POLLAN"
      ],
      "metadata": {
        "id": "jcZxrE1XxC-z"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gCYrctRVwvPv"
      },
      "outputs": [],
      "source": [
        "#PASO 1: Importación de librerias que serán necesarias para la exploración de datos\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import requests\n",
        "import json\n",
        "import seaborn as sns\n",
        "\n",
        "#Importacion de librerias para manipulación de datos para Machine Learning\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import cross_validate, ShuffleSplit\n",
        "\n",
        "\n",
        "from sklearn.model_selection import learning_curve\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import FunctionTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn import utils\n",
        "from sklearn import preprocessing\n",
        "from sklearn.model_selection import train_test_split,KFold,GroupKFold\n",
        "\n",
        "from scipy.stats import norm\n",
        "from scipy import stats\n",
        "\n",
        "\n",
        "import lightgbm as lgb\n",
        "import gc\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##1. Lectura y concatenación de los Dataset"
      ],
      "metadata": {
        "id": "2HFItEBzt8lm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cargando credenciales de Kaggle\n",
        "data = {\"username\":\"mooreamed\",\"key\":\"a253e996d6247d9093be1edd604d0125\"}\n",
        "with open('kaggle.json', 'w') as file:\n",
        "    json.dump(data, file, indent=4)\n",
        "\n",
        "#!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "gzj11gdT03In"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ByfCyklBSnGi",
        "outputId": "a480b21c-26dd-4dec-c069-5f26a2838815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.13)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2022.12.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.15)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! mkdir ~/.kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k86IPF6RSqqV",
        "outputId": "fc7e6e19-35f0-41ac-c484-063805b06168"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "tIgyKJJySxpK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "-pSiM8zISzCQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Se descarga el dataset\n",
        "!kaggle datasets download -d niangmohamed/ieeecis-fraud-detection\n",
        "\n",
        "# Se descomprime el dataset\n",
        "!unzip ieeecis-fraud-detection.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BkJ2yvf1Rxa",
        "outputId": "4f5bdeee-2e61-4516-bcfc-2b7f90738444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ieeecis-fraud-detection.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "Archive:  ieeecis-fraud-detection.zip\n",
            "replace sample_submission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace test_identity.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace test_transaction.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace train_identity.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n",
            "replace train_transaction.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: n\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Verificamos que se este funcionando la conexión de la API\n",
        "!kaggle datasets list"
      ],
      "metadata": {
        "id": "wrdMIUP_1Ohi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7a607d7-fc74-4561-deb8-c56c7714c8d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                                   title                                                size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "arnabchaki/data-science-salaries-2023                                 Data Science Salaries 2023 💸                         25KB  2023-04-13 09:55:16          21882        607  1.0              \n",
            "mauryansshivam/netflix-ott-revenue-and-subscribers-csv-file           Netflix OTT Revenue and Subscribers (CSV File)        2KB  2023-05-13 17:40:23            950         23  1.0              \n",
            "fatihb/coffee-quality-data-cqi                                        Coffee Quality Data (CQI May-2023)                   22KB  2023-05-12 13:06:39           2386         57  1.0              \n",
            "darshanprabhu09/stock-prices-for                                      Stock prices of Amazon , Microsoft , Google, Apple   85KB  2023-05-16 15:17:16           1038         31  1.0              \n",
            "adityaramachandran27/world-air-quality-index-by-city-and-coordinates  World Air Quality Index by City and Coordinates     372KB  2023-05-07 07:29:26            984         28  1.0              \n",
            "anxods/spotify-top-50-playlist-songs-anxods                           Spotify Top 50 Playlist Songs | @anxods              66KB  2023-05-28 11:40:44            897         29  0.9411765        \n",
            "bilalwaseer/google-stocks-complete                                    Google Stocks Complete                               98KB  2023-05-16 18:17:17            530         23  1.0              \n",
            "iammustafatz/diabetes-prediction-dataset                              Diabetes prediction dataset                         734KB  2023-04-08 06:11:45          11337        166  1.0              \n",
            "tanisha1416/aqi-bangalore-2023                                        Smog City Diaries: Unveiling Air Quality Trends📈      4KB  2023-05-25 04:18:15            337         23  1.0              \n",
            "mohithsairamreddy/salary-data                                         Salary_Data                                          17KB  2023-05-18 14:05:19           1466         38  0.88235295       \n",
            "desalegngeb/students-exam-scores                                      Students Exam Scores: Extended Dataset              695KB  2023-04-14 00:15:38           9075        179  1.0              \n",
            "utkarshx27/lovoo-dating-app-dataset                                   Dating App Fame & Behavior                          572KB  2023-05-14 12:26:00            624         21  1.0              \n",
            "ashpalsingh1525/imdb-movies-dataset                                   IMDB movies dataset                                   3MB  2023-04-28 23:18:15           2537         56  1.0              \n",
            "radheshyamkollipara/bank-customer-churn                               Bank Customer Churn                                 307KB  2023-04-28 16:32:01           2570         42  1.0              \n",
            "danbraswell/temporary-us-births                                       US Births 👶 by Year, State, and Education Level      60KB  2023-05-08 23:15:33           1043         23  1.0              \n",
            "utkarshx27/fashion-ecommerce-user-data                                Fashion E-commerce User Data                          2MB  2023-05-19 11:04:09            826         29  1.0              \n",
            "riybot/riyadh-cafes                                                   Riyadh Cafes                                        251KB  2023-05-22 22:24:22            577         25  1.0              \n",
            "arnabchaki/tripadvisor-reviews-2023                                   Tripadvisor Reviews 2023 ⭐                           22MB  2023-05-17 08:26:35            420         25  1.0              \n",
            "utkarshx27/inflation-rate-in-asia                                     Inflation Rate in Asia                                3KB  2023-05-13 17:41:29           1052         34  1.0              \n",
            "utkarshx27/breast-cancer-wisconsin-diagnostic-dataset                 Breast Cancer Wisconsin Diagnostic Dataset           48KB  2023-05-10 04:40:33            686         27  1.0              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ANALISIS DE LOS DATOS\n",
        "\n",
        "####**TABLA IDENTITY** <BR>\n",
        "Las variables en esta tabla son información de identidad: \n",
        "<BR><BR>\n",
        "información de conexión de red (IP, ISP, Proxy, etc.) y firma digital (UA/\n",
        "navegador/OS/versión, etc.) asociada con las transacciones.<BR><BR>\n",
        "Son recopilados por el sistema de protección contra fraudes de Vesta y los socios de seguridad digital.<BR><BR>\n",
        "(Los nombres de los campos están enmascarados y no se proporcionará el diccionario por pares para la protección de la privacidad y el acuerdo del contrato)<BR><BR>\n",
        "\n",
        "TransactionID<br>\n",
        "id_12 - id_38<BR>\n",
        "DeviceType<BR>\n",
        "DeviceInfo<BR>\n",
        "<BR>\n",
        "####**TABLA TRANSACCIONES:** <BR>\n",
        "\n",
        "TransactionDT: timedelta de una fecha y hora de referencia determinada (no una marca de tiempo real). timedelta de una fecha y hora de referencia dada (no una marca de tiempo real). El primer valor de TransactionDT es 86400, que corresponde a la cantidad de segundos en un día (60 * 60 * 24 = 86400), así que creo que la unidad es segundos. Usando esto, sabemos que los datos abarcan 6 meses, ya que el valor máximo es 15811131, que correspondería al día 183”\n",
        "<BR>\n",
        "\n",
        "TransactionAMT: monto del pago de la transacción en USD<BR>\n",
        "\n",
        "ProductCD: código de producto, el producto para cada transacción<BR>\n",
        "\n",
        "card1 - card6: información de la tarjeta de pago, como tipo de tarjeta, categoría de tarjeta, banco emisor, país, etc.<BR>\n",
        "\n",
        "dirección: dirección addr1 como región de facturación, addr2 como país de facturación<BR>\n",
        "\n",
        "distancia: distancias entre (no limitadas) la dirección de facturación, la dirección postal, el código postal, la dirección IP, el área telefónica, etc\n",
        "<BR>\n",
        "\n",
        "P_ y (R__) emaildomain: dominio de correo electrónico del comprador y del destinatario<BR>\n",
        "\n",
        "C1-C14: conteo, como cuántas direcciones se encuentran asociadas con la tarjeta de pago, etc. El significado real está enmascarado.<BR>\n",
        "\n",
        "D1-D15: timedelta, como días entre transacciones anteriores, etc.<BR>\n",
        "\n",
        "M1-M9: coincidencia, como nombres en la tarjeta y dirección, etc.<BR>\n",
        "\n",
        "Vxxx: características completas diseñadas por Vesta, que incluyen clasificación, conteo y otras relaciones de entidad.<BR><BR>\n",
        "\n",
        "Características categóricas:<BR>\n",
        "\n",
        "ProductCD<BR>\n",
        "card1 - card6<BR>\n",
        "addr1, addr2<BR>\n",
        "P_emaildomain<BR>\n",
        "R_emaildomain<BR>\n",
        "M1 - M9<BR>\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jPj6EZVAAd3N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Lectura  los datos que tiene el dataset para empezar con el análisis exploratorio\n",
        "#Lo llamaremos df por dataframe, tid por transactions id para que quede dftid\n",
        "\n",
        "dftid = pd.read_csv('train_identity.csv')\n",
        "dftid.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "Xg3tKPB11V6l",
        "outputId": "64e48fc2-131c-45ea-fe64-549b8574b1ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  id_08  \\\n",
              "0        2987004    0.0   70787.0    NaN    NaN    NaN    NaN    NaN    NaN   \n",
              "1        2987008   -5.0   98945.0    NaN    NaN    0.0   -5.0    NaN    NaN   \n",
              "2        2987010   -5.0  191631.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
              "3        2987011   -5.0  221832.0    NaN    NaN    0.0   -6.0    NaN    NaN   \n",
              "4        2987016    0.0    7460.0    0.0    0.0    1.0    0.0    NaN    NaN   \n",
              "5        2987017   -5.0   61141.0    3.0    0.0    3.0    0.0    NaN    NaN   \n",
              "6        2987022  -15.0       NaN    NaN    NaN    NaN    NaN    NaN    NaN   \n",
              "7        2987038    0.0   31964.0    0.0    0.0    0.0  -10.0    NaN    NaN   \n",
              "8        2987040  -10.0  116098.0    0.0    0.0    0.0    0.0    NaN    NaN   \n",
              "9        2987048   -5.0  257037.0    NaN    NaN    0.0    0.0    NaN    NaN   \n",
              "\n",
              "   id_09  ...                id_31  id_32      id_33           id_34  id_35  \\\n",
              "0    NaN  ...  samsung browser 6.2   32.0  2220x1080  match_status:2      T   \n",
              "1    NaN  ...   mobile safari 11.0   32.0   1334x750  match_status:1      T   \n",
              "2    0.0  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
              "3    NaN  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
              "4    0.0  ...          chrome 62.0   24.0   1280x800  match_status:2      T   \n",
              "5    3.0  ...          chrome 62.0   24.0   1366x768  match_status:2      T   \n",
              "6    NaN  ...                  NaN    NaN        NaN             NaN    NaN   \n",
              "7    0.0  ...          chrome 62.0   32.0  1920x1080  match_status:2      T   \n",
              "8    0.0  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
              "9    NaN  ...          chrome 62.0    NaN        NaN             NaN      F   \n",
              "\n",
              "  id_36 id_37  id_38  DeviceType                     DeviceInfo  \n",
              "0     F     T      T      mobile  SAMSUNG SM-G892A Build/NRD90M  \n",
              "1     F     F      T      mobile                     iOS Device  \n",
              "2     F     T      T     desktop                        Windows  \n",
              "3     F     T      T     desktop                            NaN  \n",
              "4     F     T      T     desktop                          MacOS  \n",
              "5     F     T      T     desktop                        Windows  \n",
              "6   NaN   NaN    NaN         NaN                            NaN  \n",
              "7     F     T      T      mobile                            NaN  \n",
              "8     F     T      T     desktop                        Windows  \n",
              "9     F     T      T     desktop                        Windows  \n",
              "\n",
              "[10 rows x 41 columns]"
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              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>mobile safari 11.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>match_status:1</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987010</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>191631.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987011</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>221832.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-6.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987016</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7460.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2987017</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>61141.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1366x768</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2987022</td>\n",
              "      <td>-15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2987038</td>\n",
              "      <td>0.0</td>\n",
              "      <td>31964.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1920x1080</td>\n",
              "      <td>match_status:2</td>\n",
              "      <td>T</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>mobile</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2987040</td>\n",
              "      <td>-10.0</td>\n",
              "      <td>116098.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2987048</td>\n",
              "      <td>-5.0</td>\n",
              "      <td>257037.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>F</td>\n",
              "      <td>F</td>\n",
              "      <td>T</td>\n",
              "      <td>T</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 41 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c6b6a94f-ac17-45ed-a04b-10f538f0abc9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c6b6a94f-ac17-45ed-a04b-10f538f0abc9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c6b6a94f-ac17-45ed-a04b-10f538f0abc9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se imprimen las columnas para poder identificarlas y seleccionar cuáles serán susceptibles de análisis. \n",
        "#Para esto, tomaremos solamente la tabla de identidad\n",
        "dftid.columns"
      ],
      "metadata": {
        "id": "mTUaN9or8GG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Para cumplir el requerimiento solicitado por el trabajo académico, tenemos 41 columnas y 141.907 instancias\n",
        "dftid.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzR-kqEA8ICw",
        "outputId": "5c960e69-18c6-4774-8cb2-abbc5cb5a465"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144233, 41)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Y la info exacta del DF\n",
        "dftid.info() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Han3GLxt8JjU",
        "outputId": "30fdc455-c6c0-473d-9192-75d940276bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 144233 entries, 0 to 144232\n",
            "Data columns (total 41 columns):\n",
            " #   Column         Non-Null Count   Dtype  \n",
            "---  ------         --------------   -----  \n",
            " 0   TransactionID  144233 non-null  int64  \n",
            " 1   id_01          144233 non-null  float64\n",
            " 2   id_02          140872 non-null  float64\n",
            " 3   id_03          66324 non-null   float64\n",
            " 4   id_04          66324 non-null   float64\n",
            " 5   id_05          136865 non-null  float64\n",
            " 6   id_06          136865 non-null  float64\n",
            " 7   id_07          5155 non-null    float64\n",
            " 8   id_08          5155 non-null    float64\n",
            " 9   id_09          74926 non-null   float64\n",
            " 10  id_10          74926 non-null   float64\n",
            " 11  id_11          140978 non-null  float64\n",
            " 12  id_12          144233 non-null  object \n",
            " 13  id_13          127320 non-null  float64\n",
            " 14  id_14          80044 non-null   float64\n",
            " 15  id_15          140985 non-null  object \n",
            " 16  id_16          129340 non-null  object \n",
            " 17  id_17          139369 non-null  float64\n",
            " 18  id_18          45113 non-null   float64\n",
            " 19  id_19          139318 non-null  float64\n",
            " 20  id_20          139261 non-null  float64\n",
            " 21  id_21          5159 non-null    float64\n",
            " 22  id_22          5169 non-null    float64\n",
            " 23  id_23          5169 non-null    object \n",
            " 24  id_24          4747 non-null    float64\n",
            " 25  id_25          5132 non-null    float64\n",
            " 26  id_26          5163 non-null    float64\n",
            " 27  id_27          5169 non-null    object \n",
            " 28  id_28          140978 non-null  object \n",
            " 29  id_29          140978 non-null  object \n",
            " 30  id_30          77565 non-null   object \n",
            " 31  id_31          140282 non-null  object \n",
            " 32  id_32          77586 non-null   float64\n",
            " 33  id_33          73289 non-null   object \n",
            " 34  id_34          77805 non-null   object \n",
            " 35  id_35          140985 non-null  object \n",
            " 36  id_36          140985 non-null  object \n",
            " 37  id_37          140985 non-null  object \n",
            " 38  id_38          140985 non-null  object \n",
            " 39  DeviceType     140810 non-null  object \n",
            " 40  DeviceInfo     118666 non-null  object \n",
            "dtypes: float64(23), int64(1), object(17)\n",
            "memory usage: 45.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Debemos verificar cuántos valores nulos tenemos en la tabla, para esto hacemos el chequeo asi:\n",
        "check_for_any_nan= dftid.isna().any().any()#Vamos a encontrar cuantos NaN existen en el dataframe\n",
        "total_nan_values = dftid.isna().sum().sum()#Vamos a sumar el total NaN presente en el dataframe\n",
        "fil ,col = dftid.shape\n",
        "num_datos = fil*col\n",
        "print(\"Existen valores NaN: \"+str(check_for_any_nan))\n",
        "print(\"Total de valores NaN: \"+str(total_nan_values))\n",
        "#print(\"El porcentaje de valores NaN: \" +str((total_nan_values*100)/num_datos) + \"%\")\n",
        "print(\"El porcentaje de valores NaN: {:.1f}%\".format(round((total_nan_values*100)/num_datos, 1)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "811PfKZgP7A8",
        "outputId": "160dbd60-b34a-4a55-c99b-2df438c4aa4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Existen valores NaN: True\n",
            "Total de valores NaN: 2104107\n",
            "El porcentaje de valores NaN: 35.6%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Según el dato anterior, tenemos 2.104.107 de datos nulos, es decir, superior al 5%, ya que tenemos un total de 5.913.553 datos. <br>\n",
        "Con esto se cumple la condición solicitada en el trabajo académico ya que tenemos el 35.6% de datos nulos"
      ],
      "metadata": {
        "id": "seUMXK30Tvkv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftid.isnull().sum() #Columnas con valores nulos"
      ],
      "metadata": {
        "id": "g483FzWS8Vaq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8836c599-2a9b-485d-d6cc-13700006d8c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransactionID         0\n",
              "id_01                 0\n",
              "id_02              3361\n",
              "id_03             77909\n",
              "id_04             77909\n",
              "id_05              7368\n",
              "id_06              7368\n",
              "id_07            139078\n",
              "id_08            139078\n",
              "id_09             69307\n",
              "id_10             69307\n",
              "id_11              3255\n",
              "id_12                 0\n",
              "id_13             16913\n",
              "id_14             64189\n",
              "id_15              3248\n",
              "id_16             14893\n",
              "id_17              4864\n",
              "id_18             99120\n",
              "id_19              4915\n",
              "id_20              4972\n",
              "id_21            139074\n",
              "id_22            139064\n",
              "id_23            139064\n",
              "id_24            139486\n",
              "id_25            139101\n",
              "id_26            139070\n",
              "id_27            139064\n",
              "id_28              3255\n",
              "id_29              3255\n",
              "id_30             66668\n",
              "id_31              3951\n",
              "id_32             66647\n",
              "id_33             70944\n",
              "id_34             66428\n",
              "id_35              3248\n",
              "id_36              3248\n",
              "id_37              3248\n",
              "id_38              3248\n",
              "DeviceType         3423\n",
              "DeviceInfo        25567\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Queremos saber cual es el promedio de cada columna con valores nulos.\n",
        "dftid.isnull().mean() * 100 "
      ],
      "metadata": {
        "id": "dc9wcD868W3F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4cef3e3-39b8-4735-8afd-ded9e4dc8fab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransactionID     0.000000\n",
              "id_01             0.000000\n",
              "id_02             2.330257\n",
              "id_03            54.016071\n",
              "id_04            54.016071\n",
              "id_05             5.108401\n",
              "id_06             5.108401\n",
              "id_07            96.425922\n",
              "id_08            96.425922\n",
              "id_09            48.052110\n",
              "id_10            48.052110\n",
              "id_11             2.256765\n",
              "id_12             0.000000\n",
              "id_13            11.726165\n",
              "id_14            44.503685\n",
              "id_15             2.251912\n",
              "id_16            10.325654\n",
              "id_17             3.372321\n",
              "id_18            68.722137\n",
              "id_19             3.407681\n",
              "id_20             3.447200\n",
              "id_21            96.423149\n",
              "id_22            96.416215\n",
              "id_23            96.416215\n",
              "id_24            96.708798\n",
              "id_25            96.441868\n",
              "id_26            96.420375\n",
              "id_27            96.416215\n",
              "id_28             2.256765\n",
              "id_29             2.256765\n",
              "id_30            46.222432\n",
              "id_31             2.739318\n",
              "id_32            46.207872\n",
              "id_33            49.187079\n",
              "id_34            46.056034\n",
              "id_35             2.251912\n",
              "id_36             2.251912\n",
              "id_37             2.251912\n",
              "id_38             2.251912\n",
              "DeviceType        2.373243\n",
              "DeviceInfo       17.726179\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Del resultado anterior, podemos ver que existen varias columnas con un gran porcentaje de datos nulos, por ende podrían ser susceptibles de ser borradas una vez sean analizados para saber si se tienen en cuenta o no para los fines del trabajo académico."
      ],
      "metadata": {
        "id": "vUQMC2G1VUFk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftid_sinNaN = dftid.dropna() #eliminamos todas las columnas con valores en NaN\n",
        "dftid_sinNaN.isnull().sum() #Verificamos si las filas en NaN fueron eliminadas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xM5a_3cXYnzf",
        "outputId": "8d7f388b-08e5-4c6e-ab9b-2cb28afb3a4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransactionID    0\n",
              "id_01            0\n",
              "id_02            0\n",
              "id_03            0\n",
              "id_04            0\n",
              "id_05            0\n",
              "id_06            0\n",
              "id_07            0\n",
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              "id_14            0\n",
              "id_15            0\n",
              "id_16            0\n",
              "id_17            0\n",
              "id_18            0\n",
              "id_19            0\n",
              "id_20            0\n",
              "id_21            0\n",
              "id_22            0\n",
              "id_23            0\n",
              "id_24            0\n",
              "id_25            0\n",
              "id_26            0\n",
              "id_27            0\n",
              "id_28            0\n",
              "id_29            0\n",
              "id_30            0\n",
              "id_31            0\n",
              "id_32            0\n",
              "id_33            0\n",
              "id_34            0\n",
              "id_35            0\n",
              "id_36            0\n",
              "id_37            0\n",
              "id_38            0\n",
              "DeviceType       0\n",
              "DeviceInfo       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftid_sinNaN"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2rwpt-mXv92n",
        "outputId": "4b900f03-3259-45b0-d311-9b00548f1898"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TransactionID  id_01     id_02  id_03  id_04  id_05  id_06  id_07  \\\n",
              "19            2987099  -10.0  129080.0    0.0    0.0    9.0  -43.0   22.0   \n",
              "370           2988702  -20.0  171610.0    0.0    0.0   13.0  -34.0    2.0   \n",
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              "375           2988714  -55.0   83328.0    0.0    0.0    9.0 -100.0   17.0   \n",
              "604           2990059 -100.0  773938.0    0.0    0.0    0.0   -1.0   18.0   \n",
              "...               ...    ...       ...    ...    ...    ...    ...    ...   \n",
              "143395        3572892  -30.0  267420.0    0.0    0.0    9.0  -34.0   16.0   \n",
              "143476        3573334   -5.0   34007.0    0.0    0.0   -6.0  -30.0   25.0   \n",
              "143477        3573336   -5.0   33041.0    0.0    0.0   -6.0  -30.0   27.0   \n",
              "143591        3573907  -20.0    1408.0    0.0    0.0   15.0  -33.0   20.0   \n",
              "144207        3577384  -20.0   50886.0    0.0    0.0   13.0  -34.0   39.0   \n",
              "\n",
              "        id_08  id_09  ...                  id_31  id_32      id_33  \\\n",
              "19      -34.0    0.0  ...  mobile safari generic   32.0   1334x750   \n",
              "370     -33.0    0.0  ...     mobile safari 11.0   32.0  2208x1242   \n",
              "372     -33.0    0.0  ...     mobile safari 11.0   32.0  2208x1242   \n",
              "375     -13.0    0.0  ...    ie 11.0 for desktop   24.0   1440x900   \n",
              "604     -50.0    0.0  ...            chrome 62.0   24.0  1280x1024   \n",
              "...       ...    ...  ...                    ...    ...        ...   \n",
              "143395  -43.0    0.0  ...                 google   32.0  2048x1536   \n",
              "143476  -34.0    0.0  ...     mobile safari 11.0   32.0  2436x1125   \n",
              "143477  -34.0    0.0  ...     mobile safari 11.0   32.0  2436x1125   \n",
              "143591 -100.0    0.0  ...    samsung browser 4.0   32.0  1920x1080   \n",
              "144207 -100.0    0.0  ...     mobile safari 11.0   32.0  2436x1125   \n",
              "\n",
              "                 id_34  id_35 id_36 id_37  id_38  DeviceType  \\\n",
              "19      match_status:2      T     F     T      F      mobile   \n",
              "370     match_status:1      T     F     T      T      mobile   \n",
              "372     match_status:1      T     F     T      T      mobile   \n",
              "375     match_status:2      T     T     T      T     desktop   \n",
              "604     match_status:2      T     F     T      T     desktop   \n",
              "...                ...    ...   ...   ...    ...         ...   \n",
              "143395  match_status:2      T     F     T      F      mobile   \n",
              "143476  match_status:2      T     F     T      T      mobile   \n",
              "143477  match_status:1      T     F     T      T      mobile   \n",
              "143591  match_status:2      T     F     T      T      mobile   \n",
              "144207  match_status:1      T     F     T      T      mobile   \n",
              "\n",
              "                           DeviceInfo  \n",
              "19                         iOS Device  \n",
              "370                        iOS Device  \n",
              "372                        iOS Device  \n",
              "375                       Trident/7.0  \n",
              "604                           Windows  \n",
              "...                               ...  \n",
              "143395                     iOS Device  \n",
              "143476                     iOS Device  \n",
              "143477                     iOS Device  \n",
              "143591  SAMSUNG-SM-G900A Build/MMB29M  \n",
              "144207                     iOS Device  \n",
              "\n",
              "[935 rows x 41 columns]"
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              "      <td>12.0</td>\n",
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              "      <td>-33.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>mobile safari 11.0</td>\n",
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              "      <td>match_status:1</td>\n",
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              "      <td>T</td>\n",
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              "      <td>desktop</td>\n",
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              "      <td>mobile</td>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a76d4dd1-3108-4fa3-b646-575631533cab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a76d4dd1-3108-4fa3-b646-575631533cab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dftid_sinNaN.info() #comprobamos que ya no hay datos nulos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-P4_GCtWZcYj",
        "outputId": "b3e732c7-959a-4eac-9978-ae9c3ad54a56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 935 entries, 19 to 144207\n",
            "Data columns (total 41 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   TransactionID  935 non-null    int64  \n",
            " 1   id_01          935 non-null    float64\n",
            " 2   id_02          935 non-null    float64\n",
            " 3   id_03          935 non-null    float64\n",
            " 4   id_04          935 non-null    float64\n",
            " 5   id_05          935 non-null    float64\n",
            " 6   id_06          935 non-null    float64\n",
            " 7   id_07          935 non-null    float64\n",
            " 8   id_08          935 non-null    float64\n",
            " 9   id_09          935 non-null    float64\n",
            " 10  id_10          935 non-null    float64\n",
            " 11  id_11          935 non-null    float64\n",
            " 12  id_12          935 non-null    object \n",
            " 13  id_13          935 non-null    float64\n",
            " 14  id_14          935 non-null    float64\n",
            " 15  id_15          935 non-null    object \n",
            " 16  id_16          935 non-null    object \n",
            " 17  id_17          935 non-null    float64\n",
            " 18  id_18          935 non-null    float64\n",
            " 19  id_19          935 non-null    float64\n",
            " 20  id_20          935 non-null    float64\n",
            " 21  id_21          935 non-null    float64\n",
            " 22  id_22          935 non-null    float64\n",
            " 23  id_23          935 non-null    object \n",
            " 24  id_24          935 non-null    float64\n",
            " 25  id_25          935 non-null    float64\n",
            " 26  id_26          935 non-null    float64\n",
            " 27  id_27          935 non-null    object \n",
            " 28  id_28          935 non-null    object \n",
            " 29  id_29          935 non-null    object \n",
            " 30  id_30          935 non-null    object \n",
            " 31  id_31          935 non-null    object \n",
            " 32  id_32          935 non-null    float64\n",
            " 33  id_33          935 non-null    object \n",
            " 34  id_34          935 non-null    object \n",
            " 35  id_35          935 non-null    object \n",
            " 36  id_36          935 non-null    object \n",
            " 37  id_37          935 non-null    object \n",
            " 38  id_38          935 non-null    object \n",
            " 39  DeviceType     935 non-null    object \n",
            " 40  DeviceInfo     935 non-null    object \n",
            "dtypes: float64(23), int64(1), object(17)\n",
            "memory usage: 306.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creamos una nueva tabla 'identidad' con las columnas seleccionadas, ya que no necesitamos todo el dataset original para hacer los análisis que nos interesan.\n",
        "\n",
        "identidad = dftid[['TransactionID', 'id_23', 'id_30', 'id_31', 'id_33', 'DeviceType', 'DeviceInfo']].copy()"
      ],
      "metadata": {
        "id": "WJLQ0PfEZsMl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#cambiamos los nombres de las columnas en la tabla 'transacciones' por los que nos interesan para que sean identificables\n",
        "identidad = identidad.rename(columns={        'id_23': 'Proxy',\n",
        "                                              'id_30': 'SistemaOperativo',\n",
        "                                              'id_31': 'Navegador',\n",
        "                                              'id_33': 'Resolucion',\n",
        "                                              'DeviceType': 'TipoDispositivo',\n",
        "                                              'DeviceInfo': 'InfoDispositivo'})\n",
        "identidad"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "AijDhVNsZ2QS",
        "outputId": "43115603-6069-442b-9f43-09d969a9ef6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        IdTransaccion Proxy  SistemaOperativo                Navegador  \\\n",
              "0             2987004   NaN       android 7.0      samsung browser 6.2   \n",
              "1             2987008   NaN        ios 11.1.2       mobile safari 11.0   \n",
              "2             2987010   NaN               NaN              chrome 62.0   \n",
              "3             2987011   NaN               NaN              chrome 62.0   \n",
              "4             2987016   NaN  mac os x 10.11.6              chrome 62.0   \n",
              "...               ...   ...               ...                      ...   \n",
              "144228        3577521   NaN               NaN  chrome 66.0 for android   \n",
              "144229        3577526   NaN     android 7.1.1  chrome 55.0 for android   \n",
              "144230        3577529   NaN               NaN  chrome 65.0 for android   \n",
              "144231        3577531   NaN   mac os x 10.9.5              chrome 66.0   \n",
              "144232        3577534   NaN               NaN  chrome 66.0 for android   \n",
              "\n",
              "       Resolucion TipoDispositivo                     InfoDispositivo  \n",
              "0       2220x1080          mobile       SAMSUNG SM-G892A Build/NRD90M  \n",
              "1        1334x750          mobile                          iOS Device  \n",
              "2             NaN         desktop                             Windows  \n",
              "3             NaN         desktop                                 NaN  \n",
              "4        1280x800         desktop                               MacOS  \n",
              "...           ...             ...                                 ...  \n",
              "144228        NaN          mobile             F3111 Build/33.3.A.1.97  \n",
              "144229    855x480          mobile                 A574BL Build/NMF26F  \n",
              "144230        NaN          mobile  Moto E (4) Plus Build/NMA26.42-152  \n",
              "144231  2560x1600         desktop                               MacOS  \n",
              "144232        NaN          mobile         RNE-L03 Build/HUAWEIRNE-L03  \n",
              "\n",
              "[144233 rows x 7 columns]"
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              "\n",
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              "      <th>IdTransaccion</th>\n",
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              "      <th>SistemaOperativo</th>\n",
              "      <th>Navegador</th>\n",
              "      <th>Resolucion</th>\n",
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              "      <td>chrome 62.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>chrome 66.0 for android</td>\n",
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              "      <th>144229</th>\n",
              "      <td>3577526</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>chrome 55.0 for android</td>\n",
              "      <td>855x480</td>\n",
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              "      <td>A574BL Build/NMF26F</td>\n",
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              "      <th>144230</th>\n",
              "      <td>3577529</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 65.0 for android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mobile</td>\n",
              "      <td>Moto E (4) Plus Build/NMA26.42-152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144231</th>\n",
              "      <td>3577531</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mac os x 10.9.5</td>\n",
              "      <td>chrome 66.0</td>\n",
              "      <td>2560x1600</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
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              "    <tr>\n",
              "      <th>144232</th>\n",
              "      <td>3577534</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 66.0 for android</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mobile</td>\n",
              "      <td>RNE-L03 Build/HUAWEIRNE-L03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>144233 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb892f8b-c08b-4cdf-a2cd-40d3aa7908b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb892f8b-c08b-4cdf-a2cd-40d3aa7908b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cb892f8b-c08b-4cdf-a2cd-40d3aa7908b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Empezamos a explorar los contenidos de las variables para estandarizarlos\n",
        "identidad['Proxy'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Ku5wZ6Gbs5X",
        "outputId": "03c66bde-b15f-41c0-b245-87f9aefe0d87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IP_PROXY:TRANSPARENT    3489\n",
              "IP_PROXY:ANONYMOUS      1071\n",
              "IP_PROXY:HIDDEN          609\n",
              "Name: Proxy, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#hacemos los cambios pertinentes para Proxy\n",
        "identidad['Proxy'] = identidad['Proxy'].replace(['IP_PROXY:TRANSPARENT'], 'Transparent')\n",
        "identidad['Proxy'] = identidad['Proxy'].replace(['IP_PROXY:ANONYMOUS'], 'Anonymous')\n",
        "identidad['Proxy'] = identidad['Proxy'].replace(['IP_PROXY:HIDDEN'], 'Hidden')"
      ],
      "metadata": {
        "id": "88fJyeoLb5nV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Ahora hacemos lo mismo para S.O.\n",
        "identidad['SistemaOperativo'].value_counts().sort_values(ascending=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EG2fgEocJvX",
        "outputId": "b7cc8022-3956-4eb4-b3a8-6f00d12ed3e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "iOS 11.4.1              1\n",
              "Windows                 3\n",
              "Mac OS X 10_13_5        4\n",
              "iOS 11.4.0              5\n",
              "func                   10\n",
              "                    ...  \n",
              "Android 7.0          2871\n",
              "iOS 11.1.2           3699\n",
              "iOS 11.2.1           3722\n",
              "Windows 7           13110\n",
              "Windows 10          21155\n",
              "Name: SistemaOperativo, Length: 75, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Organizamos lo de iOS\n",
        "identidad['SistemaOperativo'] = identidad['SistemaOperativo'].str.replace('_', '.')\n",
        "identidad['SistemaOperativo'] = identidad['SistemaOperativo'].replace(['Ios', 'iOs', 'ioS', 'IOs', 'iOS', 'i0S'], 'IOS')\n",
        "identidad['SistemaOperativo'] = identidad['SistemaOperativo'].apply(lambda x: x.lower().strip() if pd.notnull(x) else x )"
      ],
      "metadata": {
        "id": "n8oJeD_CctCD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Hacemos lo mismo para la columna Navegador pero vemos que son demasiado asi que lo dejamos asi\n",
        "identidad['Navegador'].value_counts().sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o72QRSkFfE-s",
        "outputId": "90eebb3b-088e-40a5-9296-4831369c04f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "chrome 67.0 for android        1\n",
              "firefox mobile 61.0            1\n",
              "chrome 69.0                    1\n",
              "chromium                       1\n",
              "android                        1\n",
              "                           ...  \n",
              "safari generic              8195\n",
              "ie 11.0 for desktop         9030\n",
              "mobile safari generic      11474\n",
              "mobile safari 11.0         13423\n",
              "chrome 63.0                22000\n",
              "Name: Navegador, Length: 130, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "identidad['TipoDispositivo'].value_counts().sort_values(ascending=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SipYMEvwfJMP",
        "outputId": "6ce69605-05b9-40cd-c8dd-882a3679feb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "mobile     55645\n",
              "desktop    85165\n",
              "Name: TipoDispositivo, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###############################################################################################\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#           CON ESTO YA TENEMOS SOLAMENTE LOS DATOS QUE NOS INTERESAN PARA EL DASHBOARD EN LA TABLA DE IDENTIDAD\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "###############################################################################################"
      ],
      "metadata": {
        "id": "rrUwg1tK6vR4"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8sawR7CN62FN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "AHORA HACEMOS LO MISMO PARA LA OTRA TABLA\n",
        "##TRANSACTIONS\n",
        "\n",
        "\n",
        "TABLA TRANSACCIONES:\n",
        "TransactionDT: timedelta de una fecha y hora de referencia determinada (no una marca de tiempo real). timedelta de una fecha y hora de referencia dada (no una marca de tiempo real). El primer valor de TransactionDT es 86400, que corresponde a la cantidad de segundos en un día (60 * 60 * 24 = 86400), así que creo que la unidad es segundos. Usando esto, sabemos que los datos abarcan 6 meses, ya que el valor máximo es 15811131, que correspondería al día 183”\n",
        "\n",
        "TransactionAMT: monto del pago de la transacción en USD\n",
        "\n",
        "ProductCD: código de producto, el producto para cada transacción\n",
        "\n",
        "card1 - card6: información de la tarjeta de pago, como tipo de tarjeta, categoría de tarjeta, banco emisor, país, etc.\n",
        "\n",
        "dirección: dirección addr1 como región de facturación, addr2 como país de facturación\n",
        "\n",
        "distancia: distancias entre (no limitadas) la dirección de facturación, la dirección postal, el código postal, la dirección IP, el área telefónica, etc\n",
        "\n",
        "P_ y (R__) emaildomain: dominio de correo electrónico del comprador y del destinatario\n",
        "\n",
        "C1-C14: conteo, como cuántas direcciones se encuentran asociadas con la tarjeta de pago, etc. El significado real está enmascarado.\n",
        "\n",
        "D1-D15: timedelta, como días entre transacciones anteriores, etc.\n",
        "\n",
        "M1-M9: coincidencia, como nombres en la tarjeta y dirección, etc.\n",
        "\n",
        "Vxxx: características completas diseñadas por Vesta, que incluyen clasificación, conteo y otras relaciones de entidad."
      ],
      "metadata": {
        "id": "rNKKrOYJm7uB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Vamos a leer los datos que tiene el dataset para empezar con el análisis explorartorio\n",
        "#El nombre será df por DataFrame, ttr por Tabla de TRansacciones\n",
        "dfttr = pd.read_csv('train_transaction.csv')\n",
        "dfttr.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "DghlbhrZnEaw",
        "outputId": "b89e3b32-d57f-4b36-9ffb-6a7ebf0401dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TransactionID  isFraud  TransactionDT  TransactionAmt ProductCD  card1  \\\n",
              "0        2987000        0          86400            68.5         W  13926   \n",
              "1        2987001        0          86401            29.0         W   2755   \n",
              "2        2987002        0          86469            59.0         W   4663   \n",
              "3        2987003        0          86499            50.0         W  18132   \n",
              "4        2987004        0          86506            50.0         H   4497   \n",
              "5        2987005        0          86510            49.0         W   5937   \n",
              "6        2987006        0          86522           159.0         W  12308   \n",
              "7        2987007        0          86529           422.5         W  12695   \n",
              "8        2987008        0          86535            15.0         H   2803   \n",
              "9        2987009        0          86536           117.0         W  17399   \n",
              "\n",
              "   card2  card3       card4  card5  ... V330  V331  V332  V333  V334 V335  \\\n",
              "0    NaN  150.0    discover  142.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "1  404.0  150.0  mastercard  102.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "2  490.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "3  567.0  150.0  mastercard  117.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "4  514.0  150.0  mastercard  102.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
              "5  555.0  150.0        visa  226.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "6  360.0  150.0        visa  166.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "7  490.0  150.0        visa  226.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "8  100.0  150.0        visa  226.0  ...  0.0   0.0   0.0   0.0   0.0  0.0   \n",
              "9  111.0  150.0  mastercard  224.0  ...  NaN   NaN   NaN   NaN   NaN  NaN   \n",
              "\n",
              "  V336  V337  V338  V339  \n",
              "0  NaN   NaN   NaN   NaN  \n",
              "1  NaN   NaN   NaN   NaN  \n",
              "2  NaN   NaN   NaN   NaN  \n",
              "3  NaN   NaN   NaN   NaN  \n",
              "4  0.0   0.0   0.0   0.0  \n",
              "5  NaN   NaN   NaN   NaN  \n",
              "6  NaN   NaN   NaN   NaN  \n",
              "7  NaN   NaN   NaN   NaN  \n",
              "8  0.0   0.0   0.0   0.0  \n",
              "9  NaN   NaN   NaN   NaN  \n",
              "\n",
              "[10 rows x 394 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>TransactionDT</th>\n",
              "      <th>TransactionAmt</th>\n",
              "      <th>ProductCD</th>\n",
              "      <th>card1</th>\n",
              "      <th>card2</th>\n",
              "      <th>card3</th>\n",
              "      <th>card4</th>\n",
              "      <th>card5</th>\n",
              "      <th>...</th>\n",
              "      <th>V330</th>\n",
              "      <th>V331</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>W</td>\n",
              "      <td>13926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>150.0</td>\n",
              "      <td>discover</td>\n",
              "      <td>142.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>W</td>\n",
              "      <td>2755</td>\n",
              "      <td>404.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>W</td>\n",
              "      <td>4663</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>166.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>W</td>\n",
              "      <td>18132</td>\n",
              "      <td>567.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>117.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>H</td>\n",
              "      <td>4497</td>\n",
              "      <td>514.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>102.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2987005</td>\n",
              "      <td>0</td>\n",
              "      <td>86510</td>\n",
              "      <td>49.0</td>\n",
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              "      <td>555.0</td>\n",
              "      <td>150.0</td>\n",
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              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2987006</td>\n",
              "      <td>0</td>\n",
              "      <td>86522</td>\n",
              "      <td>159.0</td>\n",
              "      <td>W</td>\n",
              "      <td>12308</td>\n",
              "      <td>360.0</td>\n",
              "      <td>150.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2987007</td>\n",
              "      <td>0</td>\n",
              "      <td>86529</td>\n",
              "      <td>422.5</td>\n",
              "      <td>W</td>\n",
              "      <td>12695</td>\n",
              "      <td>490.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>226.0</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2987008</td>\n",
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              "      <td>86535</td>\n",
              "      <td>15.0</td>\n",
              "      <td>H</td>\n",
              "      <td>2803</td>\n",
              "      <td>100.0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>visa</td>\n",
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              "    <tr>\n",
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              "      <td>2987009</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cd08c4d8-7c21-46f8-9386-f5d6c6ef9bea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-cd08c4d8-7c21-46f8-9386-f5d6c6ef9bea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se imprimen las columnas para poder identificarlas y seleccionar cuáles serán susceptibles de análisis. \n",
        "dfttr.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUNPXh3an-aq",
        "outputId": "a3086a35-c309-4a47-e139-fa460f83b610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['TransactionID', 'isFraud', 'TransactionDT', 'TransactionAmt',\n",
              "       'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5',\n",
              "       ...\n",
              "       'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338',\n",
              "       'V339'],\n",
              "      dtype='object', length=394)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Para cumplir el requerimiento solicitado por el trabajo académico, tenemos 394 columnas y 590.540 instancias\n",
        "dfttr.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dkQZ2NSGjok3",
        "outputId": "58b09c51-ac02-490e-9e25-d7d5e989b37c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(590540, 394)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Debemos verificar cuántos valores nulos tenemos en la tabla, para esto hacemos el chequeo asi:\n",
        "check_for_any_nan= dfttr.isna().any().any()#Vamos a encontrar cuantos valores NaN existen en el dataframe\n",
        "total_nan_values = dfttr.isna().sum().sum()#Vamos a sumar el total de valores NaN presentes en el dataframe\n",
        "fil ,col = dfttr.shape\n",
        "num_datos = fil*col\n",
        "print(\"Existen valores NaN: \"+str(check_for_any_nan))\n",
        "print(\"Total de valores NaN: \"+str(total_nan_values))\n",
        "#print(\"El porcentaje de valores NaN: \" +str((total_nan_values*100)/num_datos) + \"%\")\n",
        "print(\"El porcentaje de valores NaN: {:.1f}%\".format(round((total_nan_values*100)/num_datos, 1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_q_oyTDjsyz",
        "outputId": "cdb3899c-da65-42a7-8a5d-ec451bd43021"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Existen valores NaN: True\n",
            "Total de valores NaN: 95566686\n",
            "El porcentaje de valores NaN: 41.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfttr.isnull().sum() #Columnas con valores nulos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MaGiU6MOjwI2",
        "outputId": "b48b51a2-133f-4d08-96bf-7985a745fabd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransactionID          0\n",
              "isFraud                0\n",
              "TransactionDT          0\n",
              "TransactionAmt         0\n",
              "ProductCD              0\n",
              "card1                  0\n",
              "card2               8933\n",
              "card3               1565\n",
              "card4               1577\n",
              "card5               4259\n",
              "card6               1571\n",
              "addr1              65706\n",
              "addr2              65706\n",
              "dist1             352271\n",
              "dist2             552913\n",
              "P_emaildomain      94456\n",
              "R_emaildomain     453249\n",
              "C1                     0\n",
              "C2                     0\n",
              "C3                     0\n",
              "C4                     0\n",
              "C5                     0\n",
              "C6                     0\n",
              "C7                     0\n",
              "C8                     0\n",
              "C9                     0\n",
              "C10                    0\n",
              "C11                    0\n",
              "C12                    0\n",
              "C13                    0\n",
              "C14                    0\n",
              "D1                  1269\n",
              "D2                280797\n",
              "D3                262878\n",
              "D4                168922\n",
              "D5                309841\n",
              "D6                517353\n",
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              "D10                76022\n",
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              "D15                89113\n",
              "M1                271100\n",
              "M2                271100\n",
              "M3                271100\n",
              "M4                281444\n",
              "M5                350482\n",
              "M6                169360\n",
              "M7                346265\n",
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              "V279                  12\n",
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              "V281                1269\n",
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              "V322              508189\n",
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              "V329              508189\n",
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              "V333              508189\n",
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              "V335              508189\n",
              "V336              508189\n",
              "V337              508189\n",
              "V338              508189\n",
              "V339              508189\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Analizamos las variables que nos interesan.\n",
        "unique_values = dfttr['isFraud'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dr9BIsyYfzpi",
        "outputId": "011f5875-a318-4ccc-c139-03358775c362"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['TransactionDT'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydNCKo7Gg7M3",
        "outputId": "dec5d72e-1452-4cf9-acfd-d16e6171eb66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[   86400    86401    86469 ... 15811079 15811088 15811131]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['TransactionAmt'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ud-Dt8K1f5Ya",
        "outputId": "f1203ffb-9f79-4a99-bc09-e4a05e6c8eab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  68.5    29.     59.   ...  557.54 1659.95  400.78]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['card4'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1u4Adublg9Sd",
        "outputId": "5c73b3e9-bb63-488e-d6c9-1da72704abc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['discover' 'mastercard' 'visa' 'american express' nan]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['card6'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aAggRmkahRvi",
        "outputId": "87c306f5-a98e-4403-cb7e-3de73d124f24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['credit' 'debit' nan 'debit or credit' 'charge card']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['P_emaildomain'].unique() \n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQK8qGgRhVrx",
        "outputId": "429b4e1d-3485-44c7-f5df-904e596b1213"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nan 'gmail.com' 'outlook.com' 'yahoo.com' 'mail.com' 'anonymous.com'\n",
            " 'hotmail.com' 'verizon.net' 'aol.com' 'me.com' 'comcast.net'\n",
            " 'optonline.net' 'cox.net' 'charter.net' 'rocketmail.com' 'prodigy.net.mx'\n",
            " 'embarqmail.com' 'icloud.com' 'live.com.mx' 'gmail' 'live.com' 'att.net'\n",
            " 'juno.com' 'ymail.com' 'sbcglobal.net' 'bellsouth.net' 'msn.com' 'q.com'\n",
            " 'yahoo.com.mx' 'centurylink.net' 'servicios-ta.com' 'earthlink.net'\n",
            " 'hotmail.es' 'cfl.rr.com' 'roadrunner.com' 'netzero.net' 'gmx.de'\n",
            " 'suddenlink.net' 'frontiernet.net' 'windstream.net' 'frontier.com'\n",
            " 'outlook.es' 'mac.com' 'netzero.com' 'aim.com' 'web.de' 'twc.com'\n",
            " 'cableone.net' 'yahoo.fr' 'yahoo.de' 'yahoo.es' 'sc.rr.com' 'ptd.net'\n",
            " 'live.fr' 'yahoo.co.uk' 'hotmail.fr' 'hotmail.de' 'hotmail.co.uk'\n",
            " 'protonmail.com' 'yahoo.co.jp']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = dfttr['R_emaildomain'].unique() \n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j4eIYP80ic3l",
        "outputId": "99592c98-f2df-4f8c-81ca-7ac131fdb597"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nan 'gmail.com' 'hotmail.com' 'outlook.com' 'anonymous.com' 'charter.net'\n",
            " 'prodigy.net.mx' 'comcast.net' 'live.com.mx' 'icloud.com' 'yahoo.com'\n",
            " 'aol.com' 'juno.com' 'att.net' 'verizon.net' 'yahoo.com.mx'\n",
            " 'bellsouth.net' 'servicios-ta.com' 'ymail.com' 'hotmail.es' 'msn.com'\n",
            " 'optonline.net' 'live.com' 'gmx.de' 'aim.com' 'mail.com' 'me.com'\n",
            " 'outlook.es' 'cox.net' 'earthlink.net' 'embarqmail.com' 'web.de'\n",
            " 'sbcglobal.net' 'scranton.edu' 'mac.com' 'twc.com' 'roadrunner.com'\n",
            " 'yahoo.fr' 'yahoo.de' 'yahoo.es' 'frontiernet.net' 'gmail' 'live.fr'\n",
            " 'q.com' 'yahoo.co.uk' 'windstream.net' 'suddenlink.net' 'hotmail.fr'\n",
            " 'ptd.net' 'hotmail.de' 'frontier.com' 'cfl.rr.com' 'hotmail.co.uk'\n",
            " 'netzero.com' 'netzero.net' 'yahoo.co.jp' 'rocketmail.com'\n",
            " 'centurylink.net' 'protonmail.com' 'cableone.net' 'sc.rr.com']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear una nueva tabla 'transacciones' con las columnas seleccionadas\n",
        "transacciones = dfttr[['TransactionID', 'isFraud', 'TransactionDT', 'TransactionAmt', 'card4', 'card6', 'P_emaildomain']].copy()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "QGmYlnGxj0jF",
        "outputId": "4a140235-c4ec-4e5f-86c4-d59a4b2ed419"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-8f091e4c6157>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Crear una nueva tabla 'transacciones' con las columnas seleccionadas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtransacciones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfttr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'TransactionID'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'isFraud'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'TransactionDT'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'TransactionAmt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'card4'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'card6'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'P_emaildomain'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3811\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3812\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3813\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3814\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3815\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[0;34m(self, key, axis_name)\u001b[0m\n\u001b[1;32m   6068\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6070\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6072\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[0;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[1;32m   6131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6132\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6133\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{not_found} not in index\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6135\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0moverload\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['card4', 'card6', 'P_emaildomain'] not in index\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cambiar los nombres de las columnas en la tabla 'transacciones'\n",
        "transacciones = transacciones.rename(columns={'TransactionDT': 'LineaDeTiempo',\n",
        "                                              'TransactionAmt': 'MontoTransaccion',\n",
        "                                              'card4': 'Franquicia',\n",
        "                                              'card6': 'TipoTarjeta',\n",
        "                                              'P_emaildomain': 'DominioCompra'})\n",
        "transacciones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6TE-gNrglOF3",
        "outputId": "56e55766-76c1-4343-8fa0-98d3a7293bc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        IdTransaccion  isFraud  LineaDeTiempo  MontoTransaccion  Franquicia  \\\n",
              "0             2987000        0          86400             68.50    discover   \n",
              "1             2987001        0          86401             29.00  mastercard   \n",
              "2             2987002        0          86469             59.00        visa   \n",
              "3             2987003        0          86499             50.00  mastercard   \n",
              "4             2987004        0          86506             50.00  mastercard   \n",
              "...               ...      ...            ...               ...         ...   \n",
              "590535        3577535        0       15811047             49.00        visa   \n",
              "590536        3577536        0       15811049             39.50  mastercard   \n",
              "590537        3577537        0       15811079             30.95  mastercard   \n",
              "590538        3577538        0       15811088            117.00  mastercard   \n",
              "590539        3577539        0       15811131            279.95  mastercard   \n",
              "\n",
              "       TipoTarjeta    DominioCompra  \n",
              "0           credit  Sin Información  \n",
              "1           credit        gmail.com  \n",
              "2            debit      outlook.com  \n",
              "3            debit        yahoo.com  \n",
              "4           credit        gmail.com  \n",
              "...            ...              ...  \n",
              "590535       debit  Sin Información  \n",
              "590536       debit        gmail.com  \n",
              "590537       debit        gmail.com  \n",
              "590538       debit          aol.com  \n",
              "590539      credit        gmail.com  \n",
              "\n",
              "[590540 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8be136c5-459d-45bd-b202-b1626696c3db\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IdTransaccion</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>LineaDeTiempo</th>\n",
              "      <th>MontoTransaccion</th>\n",
              "      <th>Franquicia</th>\n",
              "      <th>TipoTarjeta</th>\n",
              "      <th>DominioCompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.50</td>\n",
              "      <td>discover</td>\n",
              "      <td>credit</td>\n",
              "      <td>Sin Información</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.00</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.00</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.00</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.00</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590535</th>\n",
              "      <td>3577535</td>\n",
              "      <td>0</td>\n",
              "      <td>15811047</td>\n",
              "      <td>49.00</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>Sin Información</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590536</th>\n",
              "      <td>3577536</td>\n",
              "      <td>0</td>\n",
              "      <td>15811049</td>\n",
              "      <td>39.50</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590537</th>\n",
              "      <td>3577537</td>\n",
              "      <td>0</td>\n",
              "      <td>15811079</td>\n",
              "      <td>30.95</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590538</th>\n",
              "      <td>3577538</td>\n",
              "      <td>0</td>\n",
              "      <td>15811088</td>\n",
              "      <td>117.00</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>aol.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>590539</th>\n",
              "      <td>3577539</td>\n",
              "      <td>0</td>\n",
              "      <td>15811131</td>\n",
              "      <td>279.95</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>590540 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8be136c5-459d-45bd-b202-b1626696c3db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8be136c5-459d-45bd-b202-b1626696c3db button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8be136c5-459d-45bd-b202-b1626696c3db');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transacciones.isnull().sum() #Columnas con valores nulos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s1r7bM0YkXZH",
        "outputId": "597d4b7a-d920-4d5c-ef41-74974a00c755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IdTransaccion       0\n",
              "isFraud             0\n",
              "LineaDeTiempo       0\n",
              "MontoTransaccion    0\n",
              "Franquicia          0\n",
              "TipoTarjeta         0\n",
              "DominioCompra       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transacciones['DominioCompra'] = transacciones['DominioCompra'].fillna('Sin Información')\n",
        "transacciones['Franquicia'] = transacciones['Franquicia'].fillna('Sin Información')\n",
        "transacciones['TipoTarjeta'] = transacciones['TipoTarjeta'].fillna('Sin Información')"
      ],
      "metadata": {
        "id": "ikxUizqck-qO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transacciones.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n511AhZCwTrN",
        "outputId": "5fda77b2-ed92-47a7-fa43-9588a20ebecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   IdTransaccion  isFraud  LineaDeTiempo  MontoTransaccion  Franquicia  \\\n",
              "0        2987000        0          86400              68.5    discover   \n",
              "1        2987001        0          86401              29.0  mastercard   \n",
              "2        2987002        0          86469              59.0        visa   \n",
              "3        2987003        0          86499              50.0  mastercard   \n",
              "4        2987004        0          86506              50.0  mastercard   \n",
              "\n",
              "  TipoTarjeta    DominioCompra  \n",
              "0      credit  Sin Información  \n",
              "1      credit        gmail.com  \n",
              "2       debit      outlook.com  \n",
              "3       debit        yahoo.com  \n",
              "4      credit        gmail.com  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4b26fa06-25f3-487a-878c-2ca7a0c03517\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IdTransaccion</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>LineaDeTiempo</th>\n",
              "      <th>MontoTransaccion</th>\n",
              "      <th>Franquicia</th>\n",
              "      <th>TipoTarjeta</th>\n",
              "      <th>DominioCompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987000</td>\n",
              "      <td>0</td>\n",
              "      <td>86400</td>\n",
              "      <td>68.5</td>\n",
              "      <td>discover</td>\n",
              "      <td>credit</td>\n",
              "      <td>Sin Información</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987001</td>\n",
              "      <td>0</td>\n",
              "      <td>86401</td>\n",
              "      <td>29.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987002</td>\n",
              "      <td>0</td>\n",
              "      <td>86469</td>\n",
              "      <td>59.0</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>outlook.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987003</td>\n",
              "      <td>0</td>\n",
              "      <td>86499</td>\n",
              "      <td>50.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>yahoo.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987004</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.0</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b26fa06-25f3-487a-878c-2ca7a0c03517')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4b26fa06-25f3-487a-878c-2ca7a0c03517 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b26fa06-25f3-487a-878c-2ca7a0c03517');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###############################################################################################\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "#           CON ESTO YA TENEMOS SOLAMENTE LOS DATOS QUE NOS INTERESAN PARA EL DASHBOARD EN LA TABLA DE TRANSACCIONES\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "###############################################################################################"
      ],
      "metadata": {
        "id": "kGls8APc7L-Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "AHORA CONCATENAMOS AMBAS TABLAS CON DATOS LIMPIOS"
      ],
      "metadata": {
        "id": "Hx556Xd1uNU2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Concatenamos los datos de dfi sin NAN con dft\n",
        "df = identidad.merge(transacciones,on = 'IdTransaccion',how = 'left')"
      ],
      "metadata": {
        "id": "myTxSVKWuUi1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PonotVbauqaR",
        "outputId": "3629bd19-9a9b-4c61-e67d-295762a52d0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   IdTransaccion Proxy  SistemaOperativo            Navegador Resolucion  \\\n",
              "0        2987004   NaN       android 7.0  samsung browser 6.2  2220x1080   \n",
              "1        2987008   NaN        ios 11.1.2   mobile safari 11.0   1334x750   \n",
              "2        2987010   NaN               NaN          chrome 62.0        NaN   \n",
              "3        2987011   NaN               NaN          chrome 62.0        NaN   \n",
              "4        2987016   NaN  mac os x 10.11.6          chrome 62.0   1280x800   \n",
              "\n",
              "  TipoDispositivo                InfoDispositivo  isFraud  LineaDeTiempo  \\\n",
              "0          mobile  SAMSUNG SM-G892A Build/NRD90M        0          86506   \n",
              "1          mobile                     iOS Device        0          86535   \n",
              "2         desktop                        Windows        0          86549   \n",
              "3         desktop                            NaN        0          86555   \n",
              "4         desktop                          MacOS        0          86620   \n",
              "\n",
              "   MontoTransaccion  Franquicia TipoTarjeta  DominioCompra  \n",
              "0            50.000  mastercard      credit      gmail.com  \n",
              "1            15.000        visa       debit  anonymous.com  \n",
              "2            75.887  mastercard      credit      gmail.com  \n",
              "3            16.495  mastercard       debit    hotmail.com  \n",
              "4            30.000        visa       debit        aol.com  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e0be434-37c7-4474-b4e0-0cabe4ad1ee6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>IdTransaccion</th>\n",
              "      <th>Proxy</th>\n",
              "      <th>SistemaOperativo</th>\n",
              "      <th>Navegador</th>\n",
              "      <th>Resolucion</th>\n",
              "      <th>TipoDispositivo</th>\n",
              "      <th>InfoDispositivo</th>\n",
              "      <th>isFraud</th>\n",
              "      <th>LineaDeTiempo</th>\n",
              "      <th>MontoTransaccion</th>\n",
              "      <th>Franquicia</th>\n",
              "      <th>TipoTarjeta</th>\n",
              "      <th>DominioCompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>android 7.0</td>\n",
              "      <td>samsung browser 6.2</td>\n",
              "      <td>2220x1080</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.000</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ios 11.1.2</td>\n",
              "      <td>mobile safari 11.0</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "      <td>0</td>\n",
              "      <td>86535</td>\n",
              "      <td>15.000</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>anonymous.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>86549</td>\n",
              "      <td>75.887</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>86555</td>\n",
              "      <td>16.495</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>hotmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987016</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mac os x 10.11.6</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "      <td>0</td>\n",
              "      <td>86620</td>\n",
              "      <td>30.000</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>aol.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3e0be434-37c7-4474-b4e0-0cabe4ad1ee6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3e0be434-37c7-4474-b4e0-0cabe4ad1ee6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3e0be434-37c7-4474-b4e0-0cabe4ad1ee6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df['isFraud'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "817P8_UPht_U",
        "outputId": "23f048fb-958c-451a-f82b-e2862457bd9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape  #Tenemos 144.233 transacciones con 13 columnas que nos informan varias cosas sobre la transacción"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "phY43SCfm_bJ",
        "outputId": "310d6993-acbb-4bac-f178-011cf6a39806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(144233, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_counts = df.isnull().sum()\n",
        "print(null_counts)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qgxvgMAg7bEM",
        "outputId": "4180e551-843d-4572-fc9e-afbe41b8f9d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "IdTransaccion           0\n",
            "Proxy                   0\n",
            "SistemaOperativo    66668\n",
            "Navegador               0\n",
            "Resolucion              0\n",
            "TipoDispositivo         0\n",
            "InfoDispositivo         0\n",
            "EsFraude                0\n",
            "LineaDeTiempo           0\n",
            "MontoTransaccion        0\n",
            "Franquicia              0\n",
            "TipoTarjeta             0\n",
            "DominioCompra           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns_to_fillna = ['Proxy', 'Navegador', 'Resolucion', 'TipoDispositivo', 'InfoDispositivo']\n",
        "for column in columns_to_fillna:\n",
        "    df[column] = df[column].fillna('Sin Información')"
      ],
      "metadata": {
        "id": "FJRqVPTF7swo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##2. Análisis de la variable objetivo (isFraud)"
      ],
      "metadata": {
        "id": "ZiC4jDIwxZie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Distribución de la variable objetivo\n",
        "sns.distplot(df['isFraud'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "16fRowIbwbRc",
        "outputId": "2e37447c-f2c6-4c54-a052-0964e06f409e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='isFraud', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df['isFraud'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l7u28x3DwXUH",
        "outputId": "16bf3ef3-23ac-4084-9a61-16ccc6c8994f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ahora mediremos el sesgo de nuestra variable objetivo\n",
        "print('Skewness de la variable objetivo {:.1f}%'.format(round((df['isFraud'].skew()),1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnfikG-9xza1",
        "outputId": "74d20912-a932-49b5-9de3-9a6be4eeb5ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Skewness de la variable objetivo 3.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observación:\n",
        "La variable objetivo presenta una asimetria muy pronunciada hacia la izquierda.\n",
        "Se puede arreglar esta asimetria aplicando una transformación logaritmica."
      ],
      "metadata": {
        "id": "KBqlO13yyyLJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6F8FjPg92F6l",
        "outputId": "afe08600-b118-4321-fdf8-0e7902d8cff0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   IdTransaccion Proxy  SistemaOperativo            Navegador Resolucion  \\\n",
              "0        2987004   NaN       android 7.0  samsung browser 6.2  2220x1080   \n",
              "1        2987008   NaN        ios 11.1.2   mobile safari 11.0   1334x750   \n",
              "2        2987010   NaN               NaN          chrome 62.0        NaN   \n",
              "3        2987011   NaN               NaN          chrome 62.0        NaN   \n",
              "4        2987016   NaN  mac os x 10.11.6          chrome 62.0   1280x800   \n",
              "\n",
              "  TipoDispositivo                InfoDispositivo  EsFraude  LineaDeTiempo  \\\n",
              "0          mobile  SAMSUNG SM-G892A Build/NRD90M         0          86506   \n",
              "1          mobile                     iOS Device         0          86535   \n",
              "2         desktop                        Windows         0          86549   \n",
              "3         desktop                            NaN         0          86555   \n",
              "4         desktop                          MacOS         0          86620   \n",
              "\n",
              "   MontoTransaccion  Franquicia TipoTarjeta  DominioCompra  \n",
              "0            50.000  mastercard      credit      gmail.com  \n",
              "1            15.000        visa       debit  anonymous.com  \n",
              "2            75.887  mastercard      credit      gmail.com  \n",
              "3            16.495  mastercard       debit    hotmail.com  \n",
              "4            30.000        visa       debit        aol.com  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f6a80501-6e81-4ec3-a81e-bdc22f01b25f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IdTransaccion</th>\n",
              "      <th>Proxy</th>\n",
              "      <th>SistemaOperativo</th>\n",
              "      <th>Navegador</th>\n",
              "      <th>Resolucion</th>\n",
              "      <th>TipoDispositivo</th>\n",
              "      <th>InfoDispositivo</th>\n",
              "      <th>EsFraude</th>\n",
              "      <th>LineaDeTiempo</th>\n",
              "      <th>MontoTransaccion</th>\n",
              "      <th>Franquicia</th>\n",
              "      <th>TipoTarjeta</th>\n",
              "      <th>DominioCompra</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2987004</td>\n",
              "      <td>NaN</td>\n",
              "      <td>android 7.0</td>\n",
              "      <td>samsung browser 6.2</td>\n",
              "      <td>2220x1080</td>\n",
              "      <td>mobile</td>\n",
              "      <td>SAMSUNG SM-G892A Build/NRD90M</td>\n",
              "      <td>0</td>\n",
              "      <td>86506</td>\n",
              "      <td>50.000</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2987008</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ios 11.1.2</td>\n",
              "      <td>mobile safari 11.0</td>\n",
              "      <td>1334x750</td>\n",
              "      <td>mobile</td>\n",
              "      <td>iOS Device</td>\n",
              "      <td>0</td>\n",
              "      <td>86535</td>\n",
              "      <td>15.000</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>anonymous.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2987010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>desktop</td>\n",
              "      <td>Windows</td>\n",
              "      <td>0</td>\n",
              "      <td>86549</td>\n",
              "      <td>75.887</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>credit</td>\n",
              "      <td>gmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2987011</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>desktop</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>86555</td>\n",
              "      <td>16.495</td>\n",
              "      <td>mastercard</td>\n",
              "      <td>debit</td>\n",
              "      <td>hotmail.com</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2987016</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mac os x 10.11.6</td>\n",
              "      <td>chrome 62.0</td>\n",
              "      <td>1280x800</td>\n",
              "      <td>desktop</td>\n",
              "      <td>MacOS</td>\n",
              "      <td>0</td>\n",
              "      <td>86620</td>\n",
              "      <td>30.000</td>\n",
              "      <td>visa</td>\n",
              "      <td>debit</td>\n",
              "      <td>aol.com</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f6a80501-6e81-4ec3-a81e-bdc22f01b25f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f6a80501-6e81-4ec3-a81e-bdc22f01b25f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f6a80501-6e81-4ec3-a81e-bdc22f01b25f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = df['isFraud'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZXxjHHjhW5u",
        "outputId": "3d11a018-ccf4-45c2-b20c-00f6f3485906"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fraud_count = df['isFraud'].value_counts()\n",
        "fraud_percentage = fraud_count / len(df) * 100\n",
        "plt.bar(fraud_count.index, fraud_count.values)\n",
        "plt.xlabel('isFraud')\n",
        "plt.ylabel('Cantidad de transacciones')\n",
        "plt.xticks(fraud_count.index, ['No Fraude', 'Fraude'])\n",
        "plt.title('Distribución de transacciones fraudulentas y no fraudulentas')\n",
        "plt.show()\n",
        "print(\"Porcentaje de transacciones fraudulentas: {:.2f}%\".format(fraud_percentage[1]))\n",
        "print(\"Porcentaje de transacciones no fraudulentas: {:.2f}%\".format(fraud_percentage[0]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 510
        },
        "id": "XoAjnxc6-4wt",
        "outputId": "90a91af6-7cfe-426b-acd2-9b01812a55e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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rVq0gCIJ4+0/XfVub0NBQ3LhxQy3udevWwcLCAr169dKpjf/9739q7/39/XHnzh3xPKU6dz7e/3TcuHEAoLFP66O887sux7E+du/eDRcXF/Tu3Vsss7S0xPDhw58tcC2ys7ORlJSEPn36iDHfvn0bd+7cQWBgIC5duqRxW/dZzv0qcrlcPMeUlpbizp07YteBR49BOzs7XL9+XaNbhj6YbFWw/Px8tcTmcX379kXr1q0xdOhQODk5ISQkBJs2bdLri6BmzZp6dcatV6+e2nuZTAYPD49nGgfp8uXLqF+/vl6d/m/duoX79++jfv36GtMaNmyIsrIyjb4hj3+Zq5KOJ514VQfj4+sLQGPZly5dQm5uLhwdHeHg4KD2ys/PFzuwPit3d3et5WvXrkXjxo1hbm6O6tWrw8HBAbt27UJubq5G3ad9BnK5HPPnz8eePXvg5OSEtm3bIiYmBpmZmWrznTt3Dj169ICtrS0UCgUcHBzEL0vVclX97Ro1alTuOqm+TJ9URxvVdnl8G5iZmaFOnToaJ9HXXntNI3mzt7dX2/aXLl3CuXPnNLbd66+/DuC/DsiGON60McT2fRp99ufncenSJQiCgHr16ml8nufPn9c4FnTZPk+SkZGBQYMGoVq1arC2toaDgwPatWsH4L/9Udd9W5u3334bLi4uWLduHYCHie///d//4d13333iuflRTzv2rl69CiMjI3h4eKjVc3Z2hp2dnU6JQXnKO7/rchzr4+rVq/Dw8NDYlobct/78808IgoDIyEiNfWv69OkAoLF/Pcu5X6WsrAyLFy9GvXr1IJfLUaNGDTg4OOD06dNqn9GkSZNgbW2NN998E/Xq1UNYWNgTuxZowz5bFej69evIzc3VOAAfZWFhgYMHD2Lfvn3YtWsXEhISsHHjRrz11lv4+eefdXqKS99+Vrp40lWpiniyrLxlCoJgkPbLysrg6OgonpAf9/hVC31p20bfffcdBg0ahODgYEyYMAGOjo4wNjZGdHS02sMFKrp8BmPGjEG3bt2wfft2/PTTT4iMjER0dDSSkpLQtGlT5OTkoF27dlAoFJg1axbq1q0Lc3NzHD9+HJMmTTLo1R5D0WW9y8rK4OXlhUWLFmmt6+rqCsAwx5s2z7N9n3SsPavnabOsrAwymQx79uzR+nlYW1urvX+eY7O0tBRvv/02srOzMWnSJDRo0ABWVlb4+++/MWjQILX98Wn7dnmMjY3Rv39/fPnll1ixYgUOHz6MGzduqF2Nexpd11GKwWG17Vv6HMdSnMuftU1VXOPHj0dgYKDWOo9/Xz7P/jVv3jxERkbiww8/xOzZs1GtWjUYGRlhzJgxap9Rw4YNceHCBezcuRMJCQnYsmULVqxYgaioKMycOfOpywGYbFUoVWfp8nYqFSMjI3Ts2BEdO3bEokWLMG/ePEydOhX79u1DQECAwQ/gS5cuqb0XBAF//vmn2nhg9vb2Wkcwvnr1qtqtv7p16yIlJQXFxcXldiB/nIODAywtLXHhwgWNaWlpaTAyMhK/HJ+Hm5sbAM31BaCx7Lp16+KXX35B69atnyl5fZZt9P3336NOnTrYunWr2vyq//CeVd26dTFu3DiMGzcOly5dgre3NxYuXIjvvvsO+/fvx507d7B161a0bdtWnCc9PV2jDQA4e/ZsueP+qPaDs2fP6hWfartcuHBBbV8qKipCenr6M40zVLduXZw6dQodO3Z86rZ42vFmKLpuX9V/6o8fb49fDdFnf9a1TW3q1q0LQRDg7u4uXhl8XuVtkzNnzuDixYtYu3YtQkNDxfLExMRyYytv336S0NBQLFy4EDt27MCePXvg4ODw1POyPtzc3FBWVoZLly6hYcOGYnlWVhZycnLEbWcouh7HgO7ncjc3N5w9exaCIKhtL23naV3bfJxqmqmpqUGPtfL2r++//x4dOnTAV199pVaek5ODGjVqqJVZWVmhb9++6Nu3L4qKitCzZ0/MnTsXU6ZMKXfookfxNmIFSUpKwuzZs+Hu7o4BAwaUWy87O1ujTPWUkuqRW9VIwob6+YZvvvlGrR/Z999/j3/++Ud8sgt4eFL77bffUFRUJJbt3LlT4/Zer169cPv2bSxbtkxjOeX952FsbIxOnTrhhx9+ULt1mZWVhfXr16NNmzZQKBTPunoiFxcXeHt7Y+3atWqXjBMTE/HHH3+o1e3Tpw9KS0sxe/ZsjXZKSkqe+tk/yzZS/cf26OeUkpKC5ORkndt41P3791FYWKhWVrduXdjY2Ij7krZlFhUVYcWKFWrzNWvWDO7u7oiNjdVYJ9W8Dg4OaNu2Lb7++mtkZGRoraNNQEAAzMzMsHTpUrV6X331FXJzcxEUFKTjGv+nT58++Pvvv/Hll19qTPv3339RUFAAQLfjzVB03b5ubm4wNjbGwYMH1cof3yb67M+6tqlNz549YWxsjJkzZ2psR0EQcOfOnae28bjyjg9tn5EgCBrDi+iybz9J48aN0bhxY6xevRpbtmxBSEiIQcc77NKlC4CHT8Y9SnWl9Vn26SfR9TgGdD+Xd+nSBTdu3FD7Obf79+9j1apVz9zm4xwdHdG+fXt88cUX+OeffzSm6zqcz+OsrKy0nnuNjY019uHNmzdr9At7fJ82MzODp6cnBEFAcXGxTjHwytYLsGfPHqSlpaGkpARZWVlISkpCYmIi3Nzc8OOPPz4xK541axYOHjyIoKAguLm54ebNm1ixYgVee+01tGnTBsDDHdvOzg5xcXGwsbGBlZUVfH19y+0n8jTVqlVDmzZtMHjwYGRlZSE2NhYeHh5qw1MMHToU33//PTp37ow+ffrg8uXL+O677zSGcggNDcU333yDiIgIHD16FP7+/igoKMAvv/yCjz/+GO+++67WGObMmSOOd/Txxx/DxMQEX3zxBR48eKB1DKVnFR0djaCgILRp0wYffvghsrOzxfFU8vPzxXrt2rXDiBEjEB0djZMnT6JTp04wNTXFpUuXsHnzZixZskSt4+jjmjdvDgAYNWoUAgMDYWxsjJCQkCfG1rVrV2zduhU9evRAUFAQ0tPTERcXB09PT7XYdHXx4kV07NgRffr0gaenJ0xMTLBt2zZkZWWJsbRq1Qr29vYYOHAgRo0aBZlMhm+//VbjhGRkZISVK1eiW7du8Pb2xuDBg+Hi4oK0tDScO3dO/MHwpUuXok2bNmjWrBmGDx8Od3d3XLlyBbt27Sr3p1kcHBwwZcoUzJw5E507d0b37t1x4cIFrFixAi1atNDr9o7KBx98gE2bNuF///sf9u3bh9atW6O0tBRpaWnYtGkTfvrpJ/j4+Oh0vBmKrtvX1tYW7733Hj7//HPIZDLUrVsXO3fu1NpPUNf9WZ82H1e3bl3MmTMHU6ZMwZUrVxAcHAwbGxukp6dj27ZtGD58OMaPH6/XZ1HeOaxBgwaoW7cuxo8fj7///hsKhQJbtmzR6I+jy779NKGhoWLcz7KPPUmTJk0wcOBArFq1SrzFd/ToUaxduxbBwcHo0KGDQZen63EM6H4uHzZsGJYtW4bQ0FCkpqbCxcUF3377rdYBVHVtU5vly5ejTZs28PLywrBhw1CnTh1kZWUhOTkZ169f1zoG1tM0b94cK1euxJw5c+Dh4QFHR0e89dZb6Nq1K2bNmoXBgwejVatWOHPmDNatW6dx9a1Tp05wdnZG69at4eTkhPPnz2PZsmUICgrSuV8fh36QkOrRVNXLzMxMcHZ2Ft5++21hyZIlasMrqDz+mPXevXuFd999V1AqlYKZmZmgVCqFfv36CRcvXlSb74cffhA8PT0FExMTtUdptT2OrlLe0A//93//J0yZMkVwdHQULCwshKCgII1H9wVBEBYuXCjUrFlTkMvlQuvWrYXff/9do01BePgI8tSpUwV3d3fB1NRUcHZ2Fnr37q02rAMeG/pBEB4+fh0YGChYW1sLlpaWQocOHYQjR45o/YwffyS6vEfbtdmyZYvQsGFDQS6XC56ensLWrVuFgQMHqj0qr7Jq1SqhefPmgoWFhWBjYyN4eXkJEydOFG7cuPHEZZSUlAgjR44UHBwcBJlMJm5j1aPP2h5ZLysrE+bNmye4ubkJcrlcaNq0qbBz506N2J7UxqOf6+3bt4WwsDChQYMGgpWVlWBrayv4+vqqPcotCIJw+PBhoWXLloKFhYWgVCqFiRMnCj/99JPWz/PQoUPC22+/LdjY2AhWVlZC48aNhc8//1ytztmzZ4UePXoIdnZ2grm5uVC/fn0hMjJSnP740A8qy5YtExo0aCCYmpoKTk5OwkcffaQxzER5+7e27VdUVCTMnz9feOONNwS5XC7Y29sLzZs3F2bOnCnk5uYKgqD78abNk4Z+eJ7tKwiCcOvWLaFXr16CpaWlYG9vL4wYMUI4e/as1uEJdN2fdW3z8XPSo8tp06aNYGVlJVhZWQkNGjQQwsLChAsXLjzxMxEE7dunvHPYH3/8IQQEBAjW1tZCjRo1hGHDhgmnTp1Sq6Prvv0k//zzj2BsbCy8/vrrOs+j+mweHypE2z5dXFwszJw5UzwPurq6ClOmTFEb4qA8Txr6obzzuz7Hsa7n8qtXrwrdu3cXLC0thRo1agijR48Wh795lja1Df0gCIJw+fJlITQ0VHB2dhZMTU2FmjVrCl27dhW+//77p34m2s79mZmZQlBQkGBjYyMAEGMoLCwUxo0bJ7i4uAgWFhZC69atheTkZI04v/jiC6Ft27ZC9erVBblcLtStW1eYMGGCeN7QhUwQDNSDmIiIqJK6ffs2XFxcEBUVhcjIyIoOh6oY9tkiIqJXXnx8PEpLS/HBBx9UdChUBbHPFhERvbKSkpLwxx9/YO7cuQgODi73p32IngdvIxIR0Surffv2OHLkCFq3bo3vvvtOp99CJNIXky0iIiIiCbHPFhEREZGEmGwRERERSYgd5F+gsrIy3LhxAzY2NpL8RhYREREZniAIuHfvHpRKJYyM9L9OxWTrBbpx44ZBftOPiIiIXrxr167htdde03s+JlsvkGpY/2vXrhnkt/2IiIhIenl5eXB1ddX953kew2TrBVLdOlQoFEy2iIiIKpln7QLEDvJEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEmKyRURERCQhJltEREREEjKp6ADIMGpP3lXRIRC91K58GlTRIRDRK4pXtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkVKHJ1sGDB9GtWzcolUrIZDJs375dnFZcXIxJkybBy8sLVlZWUCqVCA0NxY0bN9TayM7OxoABA6BQKGBnZ4chQ4YgPz9frc7p06fh7+8Pc3NzuLq6IiYmRiOWzZs3o0GDBjA3N4eXlxd2796tNl0QBERFRcHFxQUWFhYICAjApUuXDPdhEBERUZVUoclWQUEBmjRpguXLl2tMu3//Po4fP47IyEgcP34cW7duxYULF9C9e3e1egMGDMC5c+eQmJiInTt34uDBgxg+fLg4PS8vD506dYKbmxtSU1OxYMECzJgxA6tWrRLrHDlyBP369cOQIUNw4sQJBAcHIzg4GGfPnhXrxMTEYOnSpYiLi0NKSgqsrKwQGBiIwsJCCT4ZIiIiqipkgiAIFR0EAMhkMmzbtg3BwcHl1jl27BjefPNNXL16FbVq1cL58+fh6emJY8eOwcfHBwCQkJCALl264Pr161AqlVi5ciWmTp2KzMxMmJmZAQAmT56M7du3Iy0tDQDQt29fFBQUYOfOneKyWrZsCW9vb8TFxUEQBCiVSowbNw7jx48HAOTm5sLJyQnx8fEICQnRaR3z8vJga2uL3NxcKBSKZ/mYylV78i6DtkdU1Vz5NKiiQyCiSup5v78rVZ+t3NxcyGQy2NnZAQCSk5NhZ2cnJloAEBAQACMjI6SkpIh12rZtKyZaABAYGIgLFy7g7t27Yp2AgAC1ZQUGBiI5ORkAkJ6ejszMTLU6tra28PX1Feto8+DBA+Tl5am9iIiI6NVSaZKtwsJCTJo0Cf369ROzyszMTDg6OqrVMzExQbVq1ZCZmSnWcXJyUqujev+0Oo9Of3Q+bXW0iY6Ohq2trfhydXXVa52JiIio8qsUyVZxcTH69OkDQRCwcuXKig5HZ1OmTEFubq74unbtWkWHRERERC+YSUUH8DSqROvq1atISkpSu1fq7OyMmzdvqtUvKSlBdnY2nJ2dxTpZWVlqdVTvn1bn0emqMhcXF7U63t7e5cYul8shl8v1WV0iIiKqYl7qK1uqROvSpUv45ZdfUL16dbXpfn5+yMnJQWpqqliWlJSEsrIy+Pr6inUOHjyI4uJisU5iYiLq168Pe3t7sc7evXvV2k5MTISfnx8AwN3dHc7Ozmp18vLykJKSItYhIiIi0qZCk638/HycPHkSJ0+eBPCwI/rJkyeRkZGB4uJi9O7dG7///jvWrVuH0tJSZGZmIjMzE0VFRQCAhg0bonPnzhg2bBiOHj2Kw4cPIzw8HCEhIVAqlQCA/v37w8zMDEOGDMG5c+ewceNGLFmyBBEREWIco0ePRkJCAhYuXIi0tDTMmDEDv//+O8LDwwE8fFJyzJgxmDNnDn788UecOXMGoaGhUCqVT3x6koiIiKhCh37Yv38/OnTooFE+cOBAzJgxA+7u7lrn27dvH9q3bw/g4aCm4eHh2LFjB4yMjNCrVy8sXboU1tbWYv3Tp08jLCwMx44dQ40aNTBy5EhMmjRJrc3Nmzdj2rRpuHLlCurVq4eYmBh06dJFnC4IAqZPn45Vq1YhJycHbdq0wYoVK/D666/rvL4c+oGo4nDoByJ6Vs/7/f3SjLP1KmCyRVRxmGwR0bN6pcbZIiIiIqpsmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGEmGwRERERSYjJFhEREZGE9E62jh8/jjNnzojvf/jhBwQHB+OTTz5BUVGRQYMjIiIiquz0TrZGjBiBixcvAgD++usvhISEwNLSEps3b8bEiRMNHiARERFRZaZ3snXx4kV4e3sDADZv3oy2bdti/fr1iI+Px5YtWwwdHxEREVGlpneyJQgCysrKAAC//PILunTpAgBwdXXF7du3DRsdERERUSWnd7Ll4+ODOXPm4Ntvv8WBAwcQFBQEAEhPT4eTk5PBAyQiIiKqzPROtmJjY3H8+HGEh4dj6tSp8PDwAAB8//33aNWqlcEDJCIiIqrMTPSdoXHjxmpPI6osWLAAxsbGBgmKiIiIqKp4pnG2cnJysHr1akyZMgXZ2dkAgD/++AM3b97Uq52DBw+iW7duUCqVkMlk2L59u9p0QRAQFRUFFxcXWFhYICAgAJcuXVKrk52djQEDBkChUMDOzg5DhgxBfn6+Wp3Tp0/D398f5ubmcHV1RUxMjEYsmzdvRoMGDWBubg4vLy/s3r1b71iIiIiIHqd3snX69GnUq1cP8+fPx2effYacnBwAwNatWzFlyhS92iooKECTJk2wfPlyrdNjYmKwdOlSxMXFISUlBVZWVggMDERhYaFYZ8CAATh37hwSExOxc+dOHDx4EMOHDxen5+XloVOnTnBzc0NqaioWLFiAGTNmYNWqVWKdI0eOoF+/fhgyZAhOnDiB4OBgBAcH4+zZs3rFQkRERPQ4mSAIgj4zBAQEoFmzZoiJiYGNjQ1OnTqFOnXq4MiRI+jfvz+uXLnybIHIZNi2bRuCg4MBPLySpFQqMW7cOIwfPx4AkJubCycnJ8THxyMkJATnz5+Hp6cnjh07Bh8fHwBAQkICunTpguvXr0OpVGLlypWYOnUqMjMzYWZmBgCYPHkytm/fjrS0NABA3759UVBQgJ07d4rxtGzZEt7e3oiLi9MpFl3k5eXB1tYWubm5UCgUz/Q5laf25F0GbY+oqrnyaVBFh0BEldTzfn/rfWXr2LFjGDFihEZ5zZo1kZmZqXcA5UlPT0dmZiYCAgLEMltbW/j6+iI5ORkAkJycDDs7OzHRAh4mg0ZGRkhJSRHrtG3bVky0ACAwMBAXLlzA3bt3xTqPLkdVR7UcXWLR5sGDB8jLy1N7ERER0atF72RLLpdrTRouXrwIBwcHgwQFQEzcHh9OwsnJSZyWmZkJR0dHtekmJiaoVq2aWh1tbTy6jPLqPDr9abFoEx0dDVtbW/Hl6ur6lLUmIiKiqkbvZKt79+6YNWsWiouLATy8/ZeRkYFJkyahV69eBg+wMpsyZQpyc3PF17Vr1yo6JCIiInrB9E62Fi5ciPz8fDg6OuLff/9Fu3bt4OHhARsbG8ydO9dggTk7OwMAsrKy1MqzsrLEac7OzhpPQJaUlCA7O1utjrY2Hl1GeXUenf60WLSRy+VQKBRqLyIiInq16J1s2draIjExETt27MDSpUsRHh6O3bt348CBA7CysjJYYO7u7nB2dsbevXvFsry8PKSkpMDPzw8A4Ofnh5ycHKSmpop1kpKSUFZWBl9fX7HOwYMHxStxAJCYmIj69evD3t5erPPoclR1VMvRJRYiIiIibfQe1FSlTZs2aNOmzXMtPD8/H3/++af4Pj09HSdPnkS1atVQq1YtjBkzBnPmzEG9evXg7u6OyMhIKJVK8YnFhg0bonPnzhg2bBji4uJQXFyM8PBwhISEQKlUAgD69++PmTNnYsiQIZg0aRLOnj2LJUuWYPHixeJyR48ejXbt2mHhwoUICgrChg0b8Pvvv4vDQ8hksqfGQkRERKTNMyVbe/fuxd69e3Hz5k3xR6lVvv76a53b+f3339GhQwfxfUREBABg4MCBiI+Px8SJE1FQUIDhw4cjJycHbdq0QUJCAszNzcV51q1bh/DwcHTs2BFGRkbo1asXli5dKk63tbXFzz//jLCwMDRv3hw1atRAVFSU2lhcrVq1wvr16zFt2jR88sknqFevHrZv345GjRqJdXSJhYiIiOhxeo+zNXPmTMyaNQs+Pj5wcXGBTCZTm75t2zaDBliVcJwtoorDcbaI6Fk97/e33le24uLiEB8fjw8++EDvhRERERG9avTuIF9UVIRWrVpJEQsRERFRlaN3sjV06FCsX79eiliIiIiIqhy9byMWFhZi1apV+OWXX9C4cWOYmpqqTV+0aJHBgiMiIiKq7PROtk6fPg1vb28AwNmzZ9WmPd5ZnoiIiOhVp3eytW/fPiniICIiIqqS9O6z9ajr16/j+vXrhoqFiIiIqMrRO9kqKyvDrFmzYGtrCzc3N7i5ucHOzg6zZ8/WGOCUiIiI6FWn923EqVOn4quvvsKnn36K1q1bAwAOHTqEGTNmoLCw0KA/Rk1ERERU2emdbK1duxarV69G9+7dxbLGjRujZs2a+Pjjj5lsERERET1C79uI2dnZaNCggUZ5gwYNkJ2dbZCgiIiIiKoKvZOtJk2aYNmyZRrly5YtQ5MmTQwSFBEREVFVofdtxJiYGAQFBeGXX36Bn58fACA5ORnXrl3D7t27DR4gERERUWWm95Wtdu3a4eLFi+jRowdycnKQk5ODnj174sKFC/D395ciRiIiIqJKS+8rWwCgVCrZEZ6IiIhIBzolW6dPn0ajRo1gZGSE06dPP7Fu48aNDRIYERERUVWgU7Ll7e2NzMxMODo6wtvbGzKZDIIgaNSTyWQoLS01eJBERERElZVOyVZ6ejocHBzEv4mIiIhINzolW25ublr/JiIiIqIn0/tpxOjoaHz99dca5V9//TXmz59vkKCIiIiIqgq9k60vvvhC6wjyb7zxBuLi4gwSFBEREVFVoXeylZmZCRcXF41yBwcH/PPPPwYJioiIiKiq0DvZcnV1xeHDhzXKDx8+DKVSaZCgiIiIiKoKvQc1HTZsGMaMGYPi4mK89dZbAIC9e/di4sSJGDdunMEDJCIiIqrM9E62JkyYgDt37uDjjz9GUVERAMDc3ByTJk3C5MmTDR4gERERUWWmd7Ilk8kwf/58REZG4vz587CwsEC9evUgl8uliI+IiIioUtM72crNzUVpaSmqVauGFi1aiOXZ2dkwMTGBQqEwaIBERERElZneHeRDQkKwYcMGjfJNmzYhJCTEIEERERERVRV6J1spKSno0KGDRnn79u2RkpJikKCIiIiIqgq9k60HDx6gpKREo7y4uBj//vuvQYIiIiIiqir0TrbefPNNrFq1SqM8Li4OzZs3N0hQRERERFWF3h3k58yZg4CAAJw6dQodO3YE8HCcrWPHjuHnn382eIBERERElZneV7Zat26N5ORkuLq6YtOmTdixYwc8PDxw+vRp+Pv7SxEjERERUaWl95UtAPD29sa6desMHQsRERFRlfNMyZZKYWGhOIq8CsfZIiIiIvqP3rcR79+/j/DwcDg6OsLKygr29vZqLyIiIiL6j97J1oQJE5CUlISVK1dCLpdj9erVmDlzJpRKJb755hspYiQiIiKqtPS+jbhjxw588803aN++PQYPHgx/f394eHjAzc0N69atw4ABA6SIk4iIiKhS0vvKVnZ2NurUqQPgYf+s7OxsAECbNm1w8OBBw0ZHREREVMnpnWzVqVMH6enpAIAGDRpg06ZNAB5e8bKzszNocERERESVnd7J1uDBg3Hq1CkAwOTJk7F8+XKYm5tj7NixmDBhgsEDJCIiIqrM9O6zNXbsWPHvgIAApKWlITU1FR4eHmjcuLFBgyMiIiKq7PS+svU4Nzc3vPXWW5IkWqWlpYiMjIS7uzssLCxQt25dzJ49G4IgiHUEQUBUVBRcXFxgYWGBgIAAXLp0Sa2d7OxsDBgwAAqFAnZ2dhgyZAjy8/PV6qhGwDc3N4erqytiYmI04tm8eTMaNGgAc3NzeHl5Yffu3QZfZyIiIqpa9E625s+fj40bN4rv+/Tpg+rVq6NmzZri7UVDmT9/PlauXIlly5bh/PnzmD9/PmJiYvD555+LdWJiYrB06VLExcUhJSUFVlZWCAwMRGFhoVhnwIABOHfuHBITE7Fz504cPHgQw4cPF6fn5eWhU6dOcHNzQ2pqKhYsWIAZM2ao/eD2kSNH0K9fPwwZMgQnTpxAcHAwgoODcfbsWYOuMxEREVUtMuHRy0Q6cHd3x7p169CqVSskJiaiT58+2LhxIzZt2oSMjAyD/hh1165d4eTkhK+++kos69WrFywsLPDdd99BEAQolUqMGzcO48ePBwDk5ubCyckJ8fHxCAkJwfnz5+Hp6Yljx47Bx8cHAJCQkIAuXbrg+vXrUCqVWLlyJaZOnYrMzEyYmZkBeNgfbfv27UhLSwMA9O3bFwUFBdi5c6cYS8uWLeHt7Y24uDid1icvLw+2trbIzc01+Ej7tSfvMmh7RFXNlU+DKjoEIqqknvf7W+8rW5mZmXB1dQUA7Ny5E3369EGnTp0wceJEHDt2TO8AnqRVq1bYu3cvLl68CAA4deoUDh06hHfeeQcAkJ6ejszMTAQEBIjz2NrawtfXF8nJyQCA5ORk2NnZiYkW8LCvmZGREVJSUsQ6bdu2FRMtAAgMDMSFCxdw9+5dsc6jy1HVUS1HmwcPHiAvL0/tRURERK8WvZMte3t7XLt2DcDDK0SqBEQQBJSWlho0uMmTJyMkJAQNGjSAqakpmjZtijFjxogDp2ZmZgIAnJyc1OZzcnISp2VmZsLR0VFtuomJCapVq6ZWR1sbjy6jvDqq6dpER0fD1tZWfKmSVCIiInp16J1s9ezZE/3798fbb7+NO3fuiFeZTpw4AQ8PD4MGt2nTJqxbtw7r16/H8ePHsXbtWnz22WdYu3atQZcjlSlTpiA3N1d8qZJUIiIienXoPfTD4sWLUbt2bVy7dg0xMTGwtrYGAPzzzz/4+OOPDRrchAkTxKtbAODl5YWrV68iOjoaAwcOhLOzMwAgKysLLi4u4nxZWVnw9vYGADg7O+PmzZtq7ZaUlCA7O1uc39nZGVlZWWp1VO+fVkc1XRu5XA65XK7vahMREVEVoveVLVNTU4wfPx5LlixB06ZNxfKxY8di6NChBg3u/v37MDJSD9HY2BhlZWUAHnbWd3Z2xt69e8XpeXl5SElJgZ+fHwDAz88POTk5SE1NFeskJSWhrKwMvr6+Yp2DBw+iuLhYrJOYmIj69evD3t5erPPoclR1VMshIiIi0kbvK1sAcOnSJezbtw83b94UEx+VqKgogwQGAN26dcPcuXNRq1YtvPHGGzhx4gQWLVqEDz/8EAAgk8kwZswYzJkzB/Xq1YO7uzsiIyOhVCoRHBwMAGjYsCE6d+6MYcOGIS4uDsXFxQgPD0dISAiUSiUAoH///pg5cyaGDBmCSZMm4ezZs1iyZAkWL14sxjJ69Gi0a9cOCxcuRFBQEDZs2IDff/9dbXgIIiIiosfpPfTDl19+iY8++gg1atSAs7MzZDLZf43JZDh+/LjBgrt37x4iIyOxbds23Lx5E0qlEv369UNUVJT45KAgCJg+fTpWrVqFnJwctGnTBitWrMDrr78utpOdnY3w8HDs2LEDRkZG6NWrF5YuXSreAgUeDmoaFhaGY8eOoUaNGhg5ciQmTZqkFs/mzZsxbdo0XLlyBfXq1UNMTAy6dOmi8/pw6AeiisOhH4joWT3v97feyZabmxs+/vhjjUSEno7JFlHFYbJFRM/qhY+zdffuXbz33nt6L4iIiIjoVaR3svXee+8ZdJR4IiIioqpM7w7yHh4eiIyMxG+//QYvLy+YmpqqTR81apTBgiMiIiKq7J7ptxHLbUwmw19//fXcQVVV7LNFVHHYZ4uIntXzfn/rfWUrPT1d74UQERERvar07rNFRERERLp7pkFNr1+/jh9//BEZGRkoKipSm7Zo0SKDBEZERERUFeidbO3duxfdu3dHnTp1kJaWhkaNGuHKlSsQBAHNmjWTIkYiIiKiSkvv24hTpkzB+PHjcebMGZibm2PLli24du0a2rVrx/G3iIiIiB6jd7J1/vx5hIaGAgBMTEzw77//wtraGrNmzcL8+fMNHiARERFRZaZ3smVlZSX203JxccHly5fFabdv3zZcZERERERVgN59tlq2bIlDhw6hYcOG6NKlC8aNG4czZ85g69ataNmypRQxEhEREVVaeidbixYtQn5+PgBg5syZyM/Px8aNG1GvXj0+iUhERET0GL2SrdLSUly/fh2NGzcG8PCWYlxcnCSBEREREVUFevXZMjY2RqdOnXD37l2p4iEiIiKqUvTuIN+oUSP+/iERERGRjvROtubMmYPx48dj586d+Oeff5CXl6f2IiIiIqL/6N1BvkuXLgCA7t27QyaTieWCIEAmk6G0tNRw0RERERFVcnonW/v27ZMiDiIiIqIqSe9ky93dHa6urmpXtYCHV7auXbtmsMCIiIiIqgK9+2y5u7vj1q1bGuXZ2dlwd3c3SFBEREREVYXeyZaqb9bj8vPzYW5ubpCgiIiIiKoKnW8jRkREAABkMhkiIyNhaWkpTistLUVKSgq8vb0NHiARERFRZaZzsnXixAkAD69snTlzBmZmZuI0MzMzNGnSBOPHjzd8hERERESVmM7JluopxMGDB2PJkiVQKBSSBUVERERUVej9NOKaNWukiIOIiIioStK7gzwRERER6Y7JFhEREZGEmGwRERERSYjJFhEREZGEninZ+vbbb9G6dWsolUpcvXoVABAbG4sffvjBoMERERERVXZ6J1srV65EREQEunTpgpycHJSWlgIA7OzsEBsba+j4iIiIiCo1vZOtzz//HF9++SWmTp0KY2NjsdzHxwdnzpwxaHBERERElZ3eyVZ6ejqaNm2qUS6Xy1FQUGCQoIiIiIiqCr2TLXd3d5w8eVKjPCEhAQ0bNjRETERERERVht4jyEdERCAsLAyFhYUQBAFHjx7F//3f/yE6OhqrV6+WIkYiIiKiSkvvZGvo0KGwsLDAtGnTcP/+ffTv3x9KpRJLlixBSEiIFDESERERVVp6J1sAMGDAAAwYMAD3799Hfn4+HB0dDR0XERERUZXwTMmWiqWlJSwtLQ0VCxEREVGVo1Oy1bRpU8hkMp0aPH78+HMFRERERFSV6JRsBQcHi38XFhZixYoV8PT0hJ+fHwDgt99+w7lz5/Dxxx9LEiQRERFRZaVTsjV9+nTx76FDh2LUqFGYPXu2Rp1r164ZNjoiIiKiSk7vcbY2b96M0NBQjfL3338fW7ZsMUhQj/r777/x/vvvo3r16rCwsICXlxd+//13cbogCIiKioKLiwssLCwQEBCAS5cuqbWRnZ2NAQMGQKFQwM7ODkOGDEF+fr5andOnT8Pf3x/m5uZwdXVFTEyMRiybN29GgwYNYG5uDi8vL+zevdvg60tERERVi97JloWFBQ4fPqxRfvjwYZibmxskKJW7d++idevWMDU1xZ49e/DHH39g4cKFsLe3F+vExMRg6dKliIuLQ0pKCqysrBAYGIjCwkKxzoABA3Du3DkkJiZi586dOHjwIIYPHy5Oz8vLQ6dOneDm5obU1FQsWLAAM2bMwKpVq8Q6R44cQb9+/TBkyBCcOHECwcHBCA4OxtmzZw26zkRERFS1yARBEPSZ4dNPP8XMmTMxbNgwvPnmmwCAlJQUfP3114iMjMTkyZMNFtzkyZNx+PBh/Prrr1qnC4IApVKJcePGYfz48QCA3NxcODk5IT4+HiEhITh//jw8PT1x7Ngx+Pj4AHg42n2XLl1w/fp1KJVKrFy5ElOnTkVmZibMzMzEZW/fvh1paWkAgL59+6KgoAA7d+4Ul9+yZUt4e3sjLi5Op/XJy8uDra0tcnNzoVAonvlz0ab25F0GbY+oqrnyaVBFh0BEldTzfn/rfWVr8uTJWLt2LVJTUzFq1CiMGjUKx48fx5o1awyaaAHAjz/+CB8fH7z33ntwdHRE06ZN8eWXX4rT09PTkZmZiYCAALHM1tYWvr6+SE5OBgAkJyfDzs5OTLQAICAgAEZGRkhJSRHrtG3bVky0ACAwMBAXLlzA3bt3xTqPLkdVR7UcbR48eIC8vDy1FxEREb1a9E62AKBPnz44fPgwsrOzkZ2djcOHD6NPnz6Gjg1//fUXVq5ciXr16uGnn37CRx99hFGjRmHt2rUAgMzMTACAk5OT2nxOTk7itMzMTI1BV01MTFCtWjW1OtraeHQZ5dVRTdcmOjoatra24svV1VWv9SciIqLK75mSrRelrKwMzZo1w7x589C0aVMMHz4cw4YN0/m2XUWbMmUKcnNzxRef1iQiInr1vNTJlouLCzw9PdXKGjZsiIyMDACAs7MzACArK0utTlZWljjN2dkZN2/eVJteUlKC7OxstTra2nh0GeXVUU3XRi6XQ6FQqL2IiIjo1fJSJ1utW7fGhQsX1MouXrwINzc3AIC7uzucnZ2xd+9ecXpeXh5SUlLEAVf9/PyQk5OD1NRUsU5SUhLKysrg6+sr1jl48CCKi4vFOomJiahfv7745KOfn5/aclR1VMshIiIi0ualTrbGjh2L3377DfPmzcOff/6J9evXY9WqVQgLCwMAyGQyjBkzBnPmzMGPP/6IM2fOIDQ0FEqlUhz1vmHDhujcuTOGDRuGo0eP4vDhwwgPD0dISAiUSiUAoH///jAzM8OQIUNw7tw5bNy4EUuWLEFERIQYy+jRo5GQkICFCxciLS0NM2bMwO+//47w8PAX/rkQERFR5fFcP0QttRYtWmDbtm2YMmUKZs2aBXd3d8TGxmLAgAFinYkTJ6KgoADDhw9HTk4O2rRpg4SEBLUxv9atW4fw8HB07NgRRkZG6NWrF5YuXSpOt7W1xc8//4ywsDA0b94cNWrUQFRUlNpYXK1atcL69esxbdo0fPLJJ6hXrx62b9+ORo0avZgPg4iIiColncbZevQKz9MsWrTouQKqyjjOFlHF4ThbRPSsnvf7W6crWydOnFB7f/z4cZSUlKB+/foAHvajMjY2RvPmzfUOgIiIiKgq0ynZ2rdvn/j3okWLYGNjg7Vr14qdx+/evYvBgwfD399fmiiJiIiIKim9O8gvXLgQ0dHRar9PaG9vjzlz5mDhwoUGDY6IiIiostM72crLy8OtW7c0ym/duoV79+4ZJCgiIiKiqkLvZKtHjx4YPHgwtm7diuvXr+P69evYsmULhgwZgp49e0oRIxEREVGlpffQD3FxcRg/fjz69+8vDgJqYmKCIUOGYMGCBQYPkIiIiKgy0zvZsrS0xIoVK7BgwQJcvnwZAFC3bl1YWVkZPDgiIiKiyu6ZBzW1srJC48aNDRkLERERUZXzTMnW77//jk2bNiEjIwNFRUVq07Zu3WqQwIiIiIiqAr07yG/YsAGtWrXC+fPnsW3bNhQXF+PcuXNISkqCra2tFDESERERVVp6J1vz5s3D4sWLsWPHDpiZmWHJkiVIS0tDnz59UKtWLSliJCIiIqq09E62Ll++jKCgh78xZmZmhoKCAshkMowdOxarVq0yeIBERERElZneyZa9vb04eGnNmjVx9uxZAEBOTg7u379v2OiIiIiIKjm9O8i3bdsWiYmJ8PLywnvvvYfRo0cjKSkJiYmJ6NixoxQxEhEREVVaeidby5YtQ2FhIQBg6tSpMDU1xZEjR9CrVy9MmzbN4AESERERVWZ6J1vVqlUT/zYyMsLkyZMNGhARERFRVaJTspWXl6dzgwqF4pmDISIiIqpqdEq27OzsIJPJdGqwtLT0uQIiIiIiqkp0Srb27dsn/n3lyhVMnjwZgwYNgp+fHwAgOTkZa9euRXR0tDRREhEREVVSOiVb7dq1E/+eNWsWFi1ahH79+oll3bt3h5eXF1atWoWBAwcaPkoiIiKiSkrvcbaSk5Ph4+OjUe7j44OjR48aJCgiIiKiqkLvZMvV1RVffvmlRvnq1avh6upqkKCIiIiIqgq9h35YvHgxevXqhT179sDX1xcAcPToUVy6dAlbtmwxeIBERERElZneV7a6dOmCixcvolu3bsjOzkZ2dja6deuGixcvokuXLlLESERERFRp6X1lC3h4K3HevHmGjoWIiIioytEp2Tp9+jQaNWoEIyMjnD59+ol1GzdubJDAiIiIiKoCnZItb29vZGZmwtHREd7e3pDJZBAEQaOeTCbjoKZEREREj9Ap2UpPT4eDg4P4NxERERHpRqdky83NTfz76tWraNWqFUxM1GctKSnBkSNH1OoSERERver0fhqxQ4cOyM7O1ijPzc1Fhw4dDBIUERERUVWhd7IlCILWH6W+c+cOrKysDBIUERERUVWh89APPXv2BPCwE/ygQYMgl8vFaaWlpTh9+jRatWpl+AiJiIiIKjGdky1bW1sAD69s2djYwMLCQpxmZmaGli1bYtiwYYaPkIiIiKgS0znZWrNmDQCgdu3aGD9+PG8ZEhEREelA7xHkp0+fLkUcRERERFWS3h3ks7Ky8MEHH0CpVMLExATGxsZqLyIiIiL6j95XtgYNGoSMjAxERkbCxcVF65OJRERERPSQ3snWoUOH8Ouvv8Lb21uCcIiIiIiqFr1vI7q6umr9XUQiIiIi0qR3shUbG4vJkyfjypUrEoRDREREVLXofRuxb9++uH//PurWrQtLS0uYmpqqTdf2Uz5EREREryq9k63Y2FgJwiAiIiKqmvS+jThw4MAnvqT06aefQiaTYcyYMWJZYWEhwsLCUL16dVhbW6NXr17IyspSmy8jIwNBQUGwtLSEo6MjJkyYgJKSErU6+/fvR7NmzSCXy+Hh4YH4+HiN5S9fvhy1a9eGubk5fH19cfToUSlWk4iIiKoQvZOtRxUWFiIvL0/tJZVjx47hiy++QOPGjdXKx44dix07dmDz5s04cOAAbty4If6OI/DwdxuDgoJQVFSEI0eOYO3atYiPj0dUVJRYJz09HUFBQejQoQNOnjyJMWPGYOjQofjpp5/EOhs3bkRERASmT5+O48ePo0mTJggMDMTNmzclW2ciIiKq/GSCno8WFhQUYNKkSdi0aRPu3LmjMb20tNRgwank5+ejWbNmWLFiBebMmQNvb2/ExsYiNzcXDg4OWL9+PXr37g0ASEtLQ8OGDZGcnIyWLVtiz5496Nq1K27cuAEnJycAQFxcHCZNmoRbt27BzMwMkyZNwq5du3D27FlxmSEhIcjJyUFCQgIAwNfXFy1atMCyZcsAAGVlZXB1dcXIkSMxefJkndYjLy8Ptra2yM3NhUKhMORHhNqTdxm0PaKq5sqnQRUdAhFVUs/7/a33la2JEyciKSkJK1euhFwux+rVqzFz5kwolUp88803egegi7CwMAQFBSEgIECtPDU1FcXFxWrlDRo0QK1atZCcnAwASE5OhpeXl5hoAUBgYCDy8vJw7tw5sc7jbQcGBoptFBUVITU1Va2OkZERAgICxDraPHjw4IVd+SMiIqKXk94d5Hfs2IFvvvkG7du3x+DBg+Hv7w8PDw+4ublh3bp1GDBggEED3LBhA44fP45jx45pTMvMzISZmRns7OzUyp2cnJCZmSnWeTTRUk1XTXtSnby8PPz777+4e/cuSktLtdZJS0srN/bo6GjMnDlTtxUlIiKiKknvK1vZ2dmoU6cOAEChUIhDPbRp0wYHDx40aHDXrl3D6NGjsW7dOpibmxu07RdhypQpyM3NFV/Xrl2r6JCIiIjoBdM72apTpw7S09MBPLxlt2nTJgAPr3g9foXpeaWmpuLmzZto1qwZTExMYGJiggMHDmDp0qUwMTGBk5MTioqKkJOTozZfVlYWnJ2dAQDOzs4aTyeq3j+tjkKhgIWFBWrUqAFjY2OtdVRtaCOXy6FQKNReRERE9GrRO9kaPHgwTp06BQCYPHkyli9fDnNzc4wdOxYTJkwwaHAdO3bEmTNncPLkSfHl4+ODAQMGiH+bmppi79694jwXLlxARkYG/Pz8AAB+fn44c+aM2lODiYmJUCgU8PT0FOs82oaqjqoNMzMzNG/eXK1OWVkZ9u7dK9YhIiIi0kbvPltjx44V/w4ICEBaWhpSU1Ph4eGhMSzD87KxsUGjRo3UyqysrFC9enWxfMiQIYiIiEC1atWgUCgwcuRI+Pn5oWXLlgCATp06wdPTEx988AFiYmKQmZmJadOmISwsDHK5HADwv//9D8uWLcPEiRPx4YcfIikpCZs2bcKuXf894RcREYGBAwfCx8cHb775JmJjY1FQUIDBgwcbdJ2JiIioatE72Xqcm5sb3NzcDBHLM1m8eDGMjIzQq1cvPHjwAIGBgVixYoU43djYGDt37sRHH30EPz8/WFlZYeDAgZg1a5ZYx93dHbt27cLYsWOxZMkSvPbaa1i9ejUCAwPFOn379sWtW7cQFRWFzMxMeHt7IyEhQaPTPBEREdGjdB5nKykpCeHh4fjtt980+h7l5uaiVatWiIuLg7+/vySBVgUcZ4uo4nCcLSJ6Vi9snK3Y2FgMGzZM60JsbW0xYsQILFq0SO8AiIiIiKoynZOtU6dOoXPnzuVO79SpE1JTUw0SFBEREVFVoXOylZWVBVNT03Knm5iY4NatWwYJioiIiKiq0DnZqlmzptpvBz7u9OnTcHFxMUhQRERERFWFzslWly5dEBkZicLCQo1p//77L6ZPn46uXbsaNDgiIiKiyk7noR+mTZuGrVu34vXXX0d4eDjq168PAEhLS8Py5ctRWlqKqVOnShYoERERUWWkc7Ll5OSEI0eO4KOPPsKUKVOgGjFCJpMhMDAQy5cv55hTRERERI/Ra1BTNzc37N69G3fv3sWff/4JQRBQr1492NvbSxUfERERUaX2TCPI29vbo0WLFoaOhYiIiKjK0fuHqImIiIhId0y2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQky2iIiIiCTEZIuIiIhIQi91shUdHY0WLVrAxsYGjo6OCA4OxoULF9TqFBYWIiwsDNWrV4e1tTV69eqFrKwstToZGRkICgqCpaUlHB0dMWHCBJSUlKjV2b9/P5o1awa5XA4PDw/Ex8drxLN8+XLUrl0b5ubm8PX1xdGjRw2+zkRERFS1vNTJ1oEDBxAWFobffvsNiYmJKC4uRqdOnVBQUCDWGTt2LHbs2IHNmzfjwIEDuHHjBnr27ClOLy0tRVBQEIqKinDkyBGsXbsW8fHxiIqKEuukp6cjKCgIHTp0wMmTJzFmzBgMHToUP/30k1hn48aNiIiIwPTp03H8+HE0adIEgYGBuHnz5ov5MIiIiKhSkgmCIFR0ELq6desWHB0dceDAAbRt2xa5ublwcHDA+vXr0bt3bwBAWloaGjZsiOTkZLRs2RJ79uxB165dcePGDTg5OQEA4uLiMGnSJNy6dQtmZmaYNGkSdu3ahbNnz4rLCgkJQU5ODhISEgAAvr6+aNGiBZYtWwYAKCsrg6urK0aOHInJkyfrFH9eXh5sbW2Rm5sLhUJhyI8GtSfvMmh7RFXNlU+DKjoEIqqknvf7+6W+svW43NxcAEC1atUAAKmpqSguLkZAQIBYp0GDBqhVqxaSk5MBAMnJyfDy8hITLQAIDAxEXl4ezp07J9Z5tA1VHVUbRUVFSE1NVatjZGSEgIAAsY42Dx48QF5entqLiIiIXi2VJtkqKyvDmDFj0Lp1azRq1AgAkJmZCTMzM9jZ2anVdXJyQmZmpljn0URLNV017Ul18vLy8O+//+L27dsoLS3VWkfVhjbR0dGwtbUVX66urvqvOBEREVVqlSbZCgsLw9mzZ7Fhw4aKDkVnU6ZMQW5urvi6du1aRYdEREREL5hJRQegi/DwcOzcuRMHDx7Ea6+9JpY7OzujqKgIOTk5ale3srKy4OzsLNZ5/KlB1dOKj9Z5/AnGrKwsKBQKWFhYwNjYGMbGxlrrqNrQRi6XQy6X67/CREREVGW81Fe2BEFAeHg4tm3bhqSkJLi7u6tNb968OUxNTbF3716x7MKFC8jIyICfnx8AwM/PD2fOnFF7ajAxMREKhQKenp5inUfbUNVRtWFmZobmzZur1SkrK8PevXvFOkRERETavNRXtsLCwrB+/Xr88MMPsLGxEftH2drawsLCAra2thgyZAgiIiJQrVo1KBQKjBw5En5+fmjZsiUAoFOnTvD09MQHH3yAmJgYZGZmYtq0aQgLCxOvOv3vf//DsmXLMHHiRHz44YdISkrCpk2bsGvXf0/4RUREYODAgfDx8cGbb76J2NhYFBQUYPDgwS/+gyEiIqJK46VOtlauXAkAaN++vVr5mjVrMGjQIADA4sWLYWRkhF69euHBgwcIDAzEihUrxLrGxsbYuXMnPvroI/j5+cHKygoDBw7ErFmzxDru7u7YtWsXxo4diyVLluC1117D6tWrERgYKNbp27cvbt26haioKGRmZsLb2xsJCQkaneaJiIiIHlWpxtmq7DjOFlHF4ThbRPSsXqlxtoiIiIgqGyZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBJiskVEREQkISZbRERERBIyqegAiIhId7Un76roEIheWlc+DaroELTilS0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZIiIiIpIQky0iIiIiCTHZ0tPy5ctRu3ZtmJubw9fXF0ePHq3okIiIiOglxmRLDxs3bkRERASmT5+O48ePo0mTJggMDMTNmzcrOjQiIiJ6STHZ0sOiRYswbNgwDB48GJ6enoiLi4OlpSW+/vrrig6NiIiIXlImFR1AZVFUVITU1FRMmTJFLDMyMkJAQACSk5O1zvPgwQM8ePBAfJ+bmwsAyMvLM3h8ZQ/uG7xNoqpEiuOuIvBYJyqfVMe5ql1BEJ5pfiZbOrp9+zZKS0vh5OSkVu7k5IS0tDSt80RHR2PmzJka5a6urpLESETls42t6AiISGpSH+f37t2Dra2t3vMx2ZLQlClTEBERIb4vKytDdnY2qlevDplMVoGRkdTy8vLg6uqKa9euQaFQVHQ4RCQBHuevDkEQcO/ePSiVymean8mWjmrUqAFjY2NkZWWplWdlZcHZ2VnrPHK5HHK5XK3Mzs5OqhDpJaRQKHgSJqrieJy/Gp7lipYKO8jryMzMDM2bN8fevXvFsrKyMuzduxd+fn4VGBkRERG9zHhlSw8REREYOHAgfHx88OabbyI2NhYFBQUYPHhwRYdGRERELykmW3ro27cvbt26haioKGRmZsLb2xsJCQkaneaJ5HI5pk+frnEbmYiqDh7npCuZ8KzPMRIRERHRU7HPFhEREZGEmGwRERERSYjJFhEREZGEmGwRVULx8fEcs42oEho0aBCCg4MrOgx6wZhsUZUyaNAgyGQyfPrpp2rl27dvf+5R++Pj4yGTyTReq1evfq52iUh6qnPD468///yzokOjVwCHfqAqx9zcHPPnz8eIESNgb29v0LYVCgUuXLigVqZtVOGioiKYmZkZdNlE9Hw6d+6MNWvWqJU5ODioveexS1LglS2qcgICAuDs7Izo6Ogn1tuyZQveeOMNyOVy1K5dGwsXLnxq2zKZDM7OzmovCwsLzJgxA97e3li9ejXc3d1hbm4OAEhISECbNm1gZ2eH6tWro2vXrrh8+bLY3v79+yGTyZCTkyOWnTx5EjKZDFeuXBHL4uPjUatWLVhaWqJHjx64c+eORmw//PADmjVrBnNzc9SpUwczZ85ESUnJU9eJ6FUhl8s1jt+OHTsiPDwcY8aMQY0aNRAYGAgAWLRoEby8vGBlZQVXV1d8/PHHyM/PF9tSHfOPio2NRe3atcX3paWliIiIEI//iRMn4vHRlsrKyhAdHQ13d3dYWFigSZMm+P777yX7DKhiMNmiKsfY2Bjz5s3D559/juvXr2utk5qaij59+iAkJARnzpzBjBkzEBkZifj4+Gde7p9//oktW7Zg69atOHnyJACgoKAAERER+P3337F3714YGRmhR48eKCsr07ndlJQUDBkyBOHh4Th58iQ6dOiAOXPmqNX59ddfERoaitGjR+OPP/7AF198gfj4eMydO/eZ14foVbF27VqYmZnh8OHDiIuLAwAYGRlh6dKlOHfuHNauXYukpCRMnDhRr3YXLlyI+Ph4fP311zh06BCys7Oxbds2tTrR0dH45ptvEBcXh3PnzmHs2LF4//33ceDAAYOtH70EBKIqZODAgcK7774rCIIgtGzZUvjwww8FQRCEbdu2CY/u7v379xfefvtttXknTJggeHp6ltv2mjVrBACClZWV+HJychIEQRCmT58umJqaCjdv3nxifLdu3RIACGfOnBEEQRD27dsnABDu3r0r1jlx4oQAQEhPTxcEQRD69esndOnSRa2dvn37Cra2tuL7jh07CvPmzVOr8+233wouLi5PjIfoVTFw4EDB2NhY7fjt3bu30K5dO6Fp06ZPnX/z5s1C9erVxffTp08XmjRpolZn8eLFgpubm/jexcVFiImJEd8XFxcLr732mniOKiwsFCwtLYUjR46otTNkyBChX79++q8kvbTYZ4uqrPnz5+Ott97C+PHjNaadP38e7777rlpZ69atERsbi9LSUhgbG2tt08bGBsePHxffGxn9d3HYzc1No//HpUuXEBUVhZSUFNy+fVu8opWRkYFGjRrptB7nz59Hjx491Mr8/PyQkJAgvj916hQOHz6sdiWrtLQUhYWFuH//PiwtLXVaFlFV1qFDB6xcuVJ8b2VlhX79+qF58+YadX/55RdER0cjLS0NeXl5KCkp0et4ys3NxT///ANfX1+xzMTEBD4+PuKtxD///BP379/H22+/rTZvUVERmjZt+qyrSS8hJltUZbVt2xaBgYGYMmUKBg0aZJA2jYyM4OHhoXWalZWVRlm3bt3g5uaGL7/8EkqlEmVlZWjUqBGKiorE9gCo9eMoLi7WO678/HzMnDkTPXv21Jim6j9G9KqzsrLSevw+fuxeuXIFXbt2xUcffYS5c+eiWrVqOHToEIYMGYKioiJYWlrCyMhIo/+Vvseuqg/Yrl27ULNmTbVp/L3FqoXJFlVpn376Kby9vVG/fn218oYNG+Lw4cNqZYcPH8brr79e7lUtfd25cwcXLlzAl19+CX9/fwDAoUOH1OqoroT9888/4pOTqv5ej8aakpKiVvbbb7+pvW/WrBkuXLhQbiJIRLpLTU1FWVkZFi5cKP5DtGnTJrU6Dg4OyMzMhCAI4rAyjx67tra2cHFxQUpKCtq2bQsAKCkpQWpqKpo1awYA8PT0hFwuR0ZGBtq1a/cC1owqCpMtqtK8vLwwYMAALF26VK183LhxaNGiBWbPno2+ffsiOTkZy5Ytw4oVKwy2bHt7e1SvXh2rVq2Ci4sLMjIyMHnyZLU6Hh4ecHV1xYwZMzB37lxcvHhR46nIUaNGoXXr1vjss8/w7rvv4qefflK7hQgAUVFR6Nq1K2rVqoXevXvDyMgIp06dwtmzZzU60xPRk3l4eKC4uBiff/45unXrptZxXqV9+/a4desWYmJi0Lt3byQkJGDPnj1QKBRindGjR+PTTz9FvXr10KBBAyxatEjtyWMbGxuMHz8eY8eORVlZGdq0aYPc3FwcPnwYCoUCAwcOfFGrTFKr2C5jRIb1aAd5lfT0dMHMzEx4fHf//vvvBU9PT8HU1FSoVauWsGDBgie2vWbNGrVO6Y/S1llWEAQhMTFRaNiwoSCXy4XGjRsL+/fvFwAI27ZtE+scOnRI8PLyEszNzQV/f39h8+bNah3kBUEQvvrqK+G1114TLCwshG7dugmfffaZRiwJCQlCq1atBAsLC0GhUAhvvvmmsGrVqieuE9GrQtu5QRAEoV27dsLo0aM1yhctWiS4uLgIFhYWQmBgoPDNN99oPMyycuVKwdXVVbCyshJCQ0OFuXPnqnWQLy4uFkaPHi0oFArBzs5OiIiIEEJDQ9XiKCsrE2JjY4X69esLpqamgoODgxAYGCgcOHDAcCtPFU4mCI/ddCYiIiIig+E4W0REREQSYrJFREREJCEmW0REREQSYrJFREREJCEmW0REREQSYrJFREREJCEmW0REREQSYrJFREREJCEmW0REL6n27dtjzJgxFR0GET0nJltEVOUMGjQIMplM49W5c+enztu+fXut85aUlLyAyImoKuIPURNRldS5c2esWbNGrUwul+s077BhwzBr1iy1MhMTzdNlUVERzMzMnj1IInol8MoWEVVJcrkczs7Oai97e3sIgoAZM2agVq1akMvlUCqVGDVqlNq8lpaWGvMCQO3atTF79myEhoZCoVBg+PDhAIBJkybh9ddfh6WlJerUqYPIyEgUFxeL7Q0aNAjBwcFqyxgzZgzat28vvi8oKEBoaCisra3h4uKChQsXaqzTgwcPMH78eNSsWRNWVlbw9fXF/v37DfOBEZFkeGWLiF4pW7ZsweLFi7Fhwwa88cYbyMzMxKlTp3Se/7PPPkNUVBSmT58ultnY2CA+Ph5KpRJnzpzBsGHDYGNjg4kTJ+rc7oQJE3DgwAH88MMPcHR0xCeffILjx4/D29tbrBMeHo4//vgDGzZsgFKpxLZt29C5c2ecOXMG9erV03lZRPRiMdkioipp586dsLa2Viv75JNPYG5uDmdnZwQEBMDU1BS1atXCm2++qVZvxYoVWL16tfh+xIgR4pWmt956C+PGjVOrP23aNPHv2rVrY/z48diwYYPOyVZ+fj6++uorfPfdd+jYsSMAYO3atXjttdfEOhkZGVizZg0yMjKgVCoBAOPHj0dCQgLWrFmDefPm6bQsInrxmGwRUZXUoUMHrFy5Uq2sWrVqKCgoQGxsLOrUqYPOnTujS5cu6Natm1qfrAEDBmDq1Kniezs7O/FvHx8fjWVt3LgRS5cuxeXLl5Gfn4+SkhIoFAqdY718+TKKiorg6+urFmv9+vXF92fOnEFpaSlef/11tXkfPHiA6tWr67wsInrxmGwRUZVkZWUFDw8PjfJq1arhwoUL+OWXX5CYmIiPP/4YCxYswIEDB2BqagoAsLW11Tqvqt1HJScnY8CAAZg5cyYCAwNha2uLDRs2qPW5MjIygiAIavM92qdLF/n5+TA2NkZqaiqMjY3Vpj1+BY+IXi5MtojolWNhYYFu3bqhW7duCAsLQ4MGDXDmzBk0a9ZM77aOHDkCNzc3tSthV69eVavj4OCAs2fPqpWdPHlSTO7q1q0LU1NTpKSkoFatWgCAu3fv4uLFi2jXrh0AoGnTpigtLcXNmzfh7++vd5xEVHGYbBFRlfTgwQNkZmaqlZmYmGDnzp0oLS2Fr68vLC0t8d1338HCwgJubm7PtJx69eohIyMDGzZsQIsWLbBr1y5s27ZNrc5bb72FBQsW4JtvvoGfnx++++47nD17Fk2bNgXw8MrUkCFDMGHCBFSvXh2Ojo6YOnUqjIz+e2D89ddfx4ABAxAaGoqFCxeiadOmuHXrFvbu3YvGjRsjKCjomeInIulx6AciqpISEhLg4uKi9mrTpg3s7Ozw5ZdfonXr1mjcuDF++eUX7Nix45n7PXXv3h1jx45FeHg4vL29ceTIEURGRqrVCQwMRGRkJCZOnIgWLVrg3r17CA0NVauzYMEC+Pv7o1u3bggICECbNm3QvHlztTpr1qxBaGgoxo0bh/r16yM4OBjHjh0Tr4YR0ctJJjzekYCIiIiIDIZXtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgkxGSLiIiISEJMtoiIiIgk9P+b3N9KsWohtwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Porcentaje de transacciones fraudulentas: 7.85%\n",
            "Porcentaje de transacciones no fraudulentas: 92.15%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Exploración de variables"
      ],
      "metadata": {
        "id": "CwFKNEWtzLJE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mLdib6DN_bED",
        "outputId": "debc518e-8748-498a-ef68-367bb106ec5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       IdTransaccion       EsFraude  LineaDeTiempo  MontoTransaccion\n",
              "count   1.442330e+05  144233.000000   1.442330e+05     144233.000000\n",
              "mean    3.236329e+06       0.078470   6.166958e+06         83.554533\n",
              "std     1.788496e+05       0.268911   4.807714e+06         99.850258\n",
              "min     2.987004e+06       0.000000   8.650600e+04          0.251000\n",
              "25%     3.077142e+06       0.000000   1.885289e+06         25.453000\n",
              "50%     3.198818e+06       0.000000   4.913738e+06         50.000000\n",
              "75%     3.392923e+06       0.000000   1.025794e+07        100.000000\n",
              "max     3.577534e+06       1.000000   1.581103e+07       1800.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f3ad524-6ada-40f5-854c-4f0b9e8a9988\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>IdTransaccion</th>\n",
              "      <th>EsFraude</th>\n",
              "      <th>LineaDeTiempo</th>\n",
              "      <th>MontoTransaccion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.442330e+05</td>\n",
              "      <td>144233.000000</td>\n",
              "      <td>1.442330e+05</td>\n",
              "      <td>144233.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.236329e+06</td>\n",
              "      <td>0.078470</td>\n",
              "      <td>6.166958e+06</td>\n",
              "      <td>83.554533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.788496e+05</td>\n",
              "      <td>0.268911</td>\n",
              "      <td>4.807714e+06</td>\n",
              "      <td>99.850258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.987004e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.650600e+04</td>\n",
              "      <td>0.251000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.077142e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.885289e+06</td>\n",
              "      <td>25.453000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.198818e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.913738e+06</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.392923e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.025794e+07</td>\n",
              "      <td>100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>3.577534e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.581103e+07</td>\n",
              "      <td>1800.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9f3ad524-6ada-40f5-854c-4f0b9e8a9988');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tenemos 144.233 datos en Fraude\n",
        "\n",
        "La media de las transacciones es de 83 dolares, con un minimo de 0.25 centavos de dolar y un máximo de 1.800 dolares.\n",
        "\n",
        "Podriamos decir que los montos para hacer fraudes son inferiores a 1.800 dólares"
      ],
      "metadata": {
        "id": "PlJBx630_emK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1. Franquicias"
      ],
      "metadata": {
        "id": "nIecbH_fzW7f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "transacciones.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqm17V1Hw0pM",
        "outputId": "8ad997a5-92dd-46a9-eb2c-a779460bd294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['IdTransaccion', 'EsFraude', 'LineaDeTiempo', 'MontoTransaccion',\n",
              "       'Franquicia', 'TipoTarjeta', 'DominioCompra'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "count_franquicias = df['Franquicia'].value_counts()\n",
        "fig = go.Figure(data=[go.Pie(labels=count_franquicias.index, values=count_franquicias)])\n",
        "fig.update_layout(\n",
        "    title='Distribución de Transacciones por Franquicia',\n",
        "    showlegend=True,\n",
        "    legend=dict(\n",
        "        orientation=\"h\",\n",
        "        yanchor=\"bottom\",\n",
        "        y=1.02,\n",
        "        xanchor=\"center\",\n",
        "        x=0.5\n",
        "    )\n",
        ")\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "VdkWjbWR0ZqM",
        "outputId": "68e4ab68-5174-4cc8-c547-3bd53fe955e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aquí podemos notar lo siguiente:\n",
        "\n",
        "La franquicia más usada es Visa con un 61.9%\n",
        "Luego mastercard con un 30.6%\n",
        "\n",
        "Con esta dos ya tenemos más de un 90% de las transacciones totales del dataset."
      ],
      "metadata": {
        "id": "xbBXi2m10iVZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.2. Tipo de dispositivo"
      ],
      "metadata": {
        "id": "dGYez9Dk3V6P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "conteo_dispositivo = df['TipoDispositivo'].value_counts()\n",
        "fig = go.Figure(data=[go.Pie(\n",
        "    labels=conteo_dispositivo.index,\n",
        "    values=conteo_dispositivo.values,\n",
        "    hole=0.4\n",
        ")])\n",
        "fig.update_layout(\n",
        "    title='Distribución de Transacciones por Tipo de Dispositivo',\n",
        "    showlegend=True\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "CetY_Syn1iX1",
        "outputId": "0111b4dd-c255-451e-9ab4-a224554adc2f"
      },
      "execution_count": null,
      "outputs": [
        {
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos observar que se hacen más transacciones desde los equipos de escritorio que desde los celulares."
      ],
      "metadata": {
        "id": "N3MW3CFj16BF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.3. Sistema Operativo"
      ],
      "metadata": {
        "id": "QFI-FmOi3YuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "valores = df['SistemaOperativo'].value_counts().values\n",
        "etiquetas = df['SistemaOperativo'].value_counts().index\n",
        "fig = go.Figure(data=[go.Bar(\n",
        "    y=etiquetas,\n",
        "    x=valores,\n",
        "    orientation='h'\n",
        ")])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Distribución de Transacciones por Sistema Operativo',\n",
        "    xaxis_title='Cantidad de Transacciones',\n",
        "    yaxis_title='Sistema Operativo',\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "0RJdaEr805pv",
        "outputId": "5d97b3e6-7d30-4209-c1ed-b98d0b99032b"
      },
      "execution_count": null,
      "outputs": [
        {
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El sistema operativo más usado es Windows 10 con más de 20.000 datos\n",
        "y de segundo es android 7.0\n",
        "\n",
        "Cabe anotar que esta base de datos es anterior a la aparición de Windows 11"
      ],
      "metadata": {
        "id": "SbREp9rj1VgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "utDqX6vT3fty"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.4. Tipo de dispositivo vs Monto de la transacción"
      ],
      "metadata": {
        "id": "_RQZVP7x3css"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "monto_total = df.groupby('TipoDispositivo')['MontoTransaccion'].sum()\n",
        "fig = go.Figure(data=[go.Bar(\n",
        "    x=monto_total.index,\n",
        "    y=monto_total.values\n",
        ")])\n",
        "\n",
        "fig.update_layout(\n",
        "    title='Monto Total de Transacciones por Tipo de Dispositivo',\n",
        "    xaxis_title='Tipo de Dispositivo',\n",
        "    yaxis_title='Monto Total',\n",
        "    showlegend=False\n",
        ")\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "FuxUikEO2T59",
        "outputId": "4f6d8c83-9dec-46ca-a916-0568e6d88607"
      },
      "execution_count": null,
      "outputs": [
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Total de Transacciones por Tipo de Dispositivo\"},\"xaxis\":{\"title\":{\"text\":\"Tipo de Dispositivo\"}},\"yaxis\":{\"title\":{\"text\":\"Monto Total\"}},\"showlegend\":false},                        {\"responsive\": true}                    ).then(function(){\n",
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos ver que desde los dispositivos tipo desktop o laptop se hacen muchisimas más transacaciones en monto de dinero que desde el teléfono celular-."
      ],
      "metadata": {
        "id": "rzOcfHxr2aRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fraud_by_tipo_dispositivo = df[df['isFraud'] == True]['TipoDispositivo'].value_counts()\n",
        "\n",
        "fig = px.bar(fraud_by_tipo_dispositivo, x=fraud_by_tipo_dispositivo.index, y=fraud_by_tipo_dispositivo.values)\n",
        "fig.update_layout(title='Cantidad de fraudes por tipo de dispositivo',\n",
        "                  xaxis_title='Tipo de Dispositivo',\n",
        "                  yaxis_title='Cantidad de fraudes')\n",
        "\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "xcpec4hNjGB_",
        "outputId": "ff51be08-b9dc-46ad-ea45-ef92f4b90786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['MontoTransaccion'].hist()\n",
        "plt.xlabel('Monto de la Transacción')\n",
        "plt.ylabel('Frecuencia')\n",
        "plt.title('Distribución del Monto de la Transacción')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "SNm_ZZaJAFxX",
        "outputId": "fffd182e-4626-431b-8b33-93b8d0013104"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Distribución de las variables categóricas\n",
        "df['Franquicia'].value_counts().plot(kind='bar')\n",
        "plt.xlabel('Franquicia')\n",
        "plt.ylabel('Frecuencia')\n",
        "plt.title('Distribución de la Franquicia')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "MsPjFlIcAPQt",
        "outputId": "d8ca35d1-0db8-4cdb-a1b2-f09a5d02d806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraudes_por_franquicia = df[df['isFraud'] == True]['Franquicia'].value_counts()\n",
        "\n",
        "plt.bar(fraudes_por_franquicia.index, fraudes_por_franquicia.values)\n",
        "plt.xlabel('Franquicia')\n",
        "plt.ylabel('Cantidad de fraudes')\n",
        "plt.title('Cantidad de fraudes por franquicia')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "oMTYN_92iW0C",
        "outputId": "c1112972-e2a9-401b-df42-efeadaff8f24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fraude_por_franquicia = df.groupby('Franquicia')['isFraud'].value_counts().unstack().fillna(0)\n",
        "\n",
        "fraude_por_franquicia.plot(kind='bar', stacked=True)\n",
        "plt.xlabel('Franquicia')\n",
        "plt.ylabel('Cantidad de transacciones')\n",
        "plt.title('Cantidad de transacciones por franquicia')\n",
        "plt.legend(['No Fraude', 'Fraude'])\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 555
        },
        "id": "DiPxyr_6iyJg",
        "outputId": "8bd96e6c-cea0-4e70-b405-3f9e8c1ba732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.4. Correlación entre las diferentes variables"
      ],
      "metadata": {
        "id": "PjpiPpQGzlSh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Matriz de correlaciones\n",
        "corr_matrix = df.corr()"
      ],
      "metadata": {
        "id": "5IACug-JzrOp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Gráfico de la matriz de correlaciones\n",
        "plt.figure(figsize=(18,18))\n",
        "sns.heatmap(corr_matrix, annot = True, vmin = -1, vmax = 1, cmap = \"YlGnBu\").set_title('Correlation Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "heLqqv2RzwLo",
        "outputId": "2c1b31c0-86c3-43c1-da99-f05b2caf0a5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlaciones de las variables con la variable objetivo\n",
        "target_corr_df = pd.DataFrame(corr_matrix[\"isFraud\"].sort_values(ascending=False))\n",
        "target_corr_df "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "EHRkDv6Sz3H8",
        "outputId": "5815105a-358e-4458-85e0-bfee0d603f90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   isFraud\n",
              "isFraud           1.000000\n",
              "IdTransaccion     0.092935\n",
              "LineaDeTiempo     0.089427\n",
              "MontoTransaccion  0.015360"
            ],
            "text/html": [
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-ca5634db-ecb9-439c-bc49-0f92b496be1e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.5 Distribución de las variables numéricas"
      ],
      "metadata": {
        "id": "lveqlBsSz9fp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Gráficos de los histogramas de las distribuciones de las variables\n",
        "df.hist(bins=50, figsize=(24,15))\n",
        "plt.suptitle('Histograms For the Different Features', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aUJWcql9z6tx",
        "outputId": "d5653144-97de-4887-9956-5a580f2d151b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2400x1500 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x='MontoTransaccion', y='Franquicia', data=df)\n",
        "plt.xlabel('Monto de Transacción')\n",
        "plt.ylabel('Franquicia')\n",
        "plt.title('Relación entre Monto de Transacción y Franquicia')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ZxU55KKaAYlN",
        "outputId": "9e850cc4-be1c-4f2d-e297-077ed3121294"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "grouped_df = df.groupby(['Franquicia', 'TipoDispositivo'])['MontoTransaccion'].sum().reset_index()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Franquicia', y='MontoTransaccion', hue='TipoDispositivo', data=grouped_df)\n",
        "plt.xlabel('Franquicia')\n",
        "plt.ylabel('Monto de Transacción')\n",
        "plt.title('Monto de Transacción por Franquicia y Dispositivo')\n",
        "plt.legend(title='Tipo de Dispositivo')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "M-ky0rzmA0J7",
        "outputId": "f3bbcae9-6fbb-4a11-de66-a480477c94ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ahora solo cuando es sospechoso de fraude\n",
        "fraud_df = df[df['isFraud'] == 1]\n",
        "\n",
        "grouped_df = fraud_df.groupby(['Franquicia', 'TipoDispositivo'])['MontoTransaccion'].sum().reset_index()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Franquicia', y='MontoTransaccion', hue='TipoDispositivo', data=grouped_df)\n",
        "plt.xlabel('Franquicia')\n",
        "plt.ylabel('Monto de Transacción')\n",
        "plt.title('Monto de Transacción por Franquicia y Dispositivo (Fraudes)')\n",
        "plt.legend(title='Tipo de Dispositivo')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "9WCNWwaSA_cE",
        "outputId": "c3fbc1ea-e224-4634-cdf2-97f1d8dc53d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sub = pd.read_csv('sample_submission.csv')\n",
        "sub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "gY8w3wXGDeC3",
        "outputId": "c22f7896-e6ad-4e1d-c2d1-6f4ab837d0e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TransactionID  isFraud\n",
              "0             3663549      0.5\n",
              "1             3663550      0.5\n",
              "2             3663551      0.5\n",
              "3             3663552      0.5\n",
              "4             3663553      0.5\n",
              "...               ...      ...\n",
              "506686        4170235      0.5\n",
              "506687        4170236      0.5\n",
              "506688        4170237      0.5\n",
              "506689        4170238      0.5\n",
              "506690        4170239      0.5\n",
              "\n",
              "[506691 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c5332a00-ee25-4057-b659-e248c6dd6016\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TransactionID</th>\n",
              "      <th>isFraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3663549</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3663550</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3663551</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3663552</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3663553</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506686</th>\n",
              "      <td>4170235</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506687</th>\n",
              "      <td>4170236</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506688</th>\n",
              "      <td>4170237</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506689</th>\n",
              "      <td>4170238</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>506690</th>\n",
              "      <td>4170239</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506691 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c5332a00-ee25-4057-b659-e248c6dd6016')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c5332a00-ee25-4057-b659-e248c6dd6016 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c5332a00-ee25-4057-b659-e248c6dd6016');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_values = sub['isFraud'].unique()\n",
        "print(unique_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYl3XfNfjiet",
        "outputId": "64b7de65-8dfd-4548-b72e-c7b12088ad68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZuXa5Yx1DsPO",
        "outputId": "2b742c0e-d4ad-48cc-83ca-93f51ddc403e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['IdTransaccion', 'Proxy', 'SistemaOperativo', 'Navegador', 'Resolucion',\n",
              "       'TipoDispositivo', 'InfoDispositivo', 'EsFraude', 'LineaDeTiempo',\n",
              "       'MontoTransaccion', 'Franquicia', 'TipoTarjeta', 'DominioCompra'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "transacciones.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Ji4KDhonyOW",
        "outputId": "8d8b3dc3-685b-431b-eab3-b7cfb33d7044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['IdTransaccion', 'EsFraude', 'LineaDeTiempo', 'MontoTransaccion',\n",
              "       'Franquicia', 'TipoTarjeta', 'DominioCompra'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['isFraud'], fit=norm)\n",
        "fig = plt.figure()\n",
        "res = stats.probplot(df['isFraud'], plot=plt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "id": "4tXPolO9sGb7",
        "outputId": "30141ea8-882e-49e8-9209-72e45de36484"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Obtener las probabilidades de la clase positiva del modelo\n",
        "probabilities = df['isFraud']\n",
        "\n",
        "# Calcular la curva ROC\n",
        "fpr, tpr, thresholds = roc_curve(df['isFraud'], probabilities)\n",
        "\n",
        "# Calcular el área bajo la curva ROC\n",
        "auc_roc = roc_auc_score(df['isFraud'], probabilities)\n",
        "\n",
        "# Graficar la curva ROC\n",
        "plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc_roc)\n",
        "plt.plot([0, 1], [0, 1], 'k--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('Tasa de falsos positivos')\n",
        "plt.ylabel('Tasa de verdaderos positivos')\n",
        "plt.title('Curva ROC (Receiver Operating Characteristic)')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()\n",
        "\n",
        "# Imprimir el valor del área bajo la curva ROC\n",
        "print(\"Área bajo la curva ROC (AUC-ROC): {:.4f}\".format(auc_roc))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 491
        },
        "id": "jybPyZ60pkNV",
        "outputId": "5ad0e38b-5536-4514-d884-a4881e129732"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Área bajo la curva ROC (AUC-ROC): 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Reemplazar \"Sin Información\" con NaN\n",
        "df.replace('Sin Información', np.nan, inplace=True)"
      ],
      "metadata": {
        "id": "5WfmBJsPESDg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiVZhEsxF-hb",
        "outputId": "4b6a090d-7800-494a-ce7f-c316ec51640e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IdTransaccion         int64\n",
              "Proxy                object\n",
              "SistemaOperativo     object\n",
              "Navegador            object\n",
              "Resolucion           object\n",
              "TipoDispositivo      object\n",
              "InfoDispositivo      object\n",
              "EsFraude              int64\n",
              "LineaDeTiempo         int64\n",
              "MontoTransaccion    float64\n",
              "Franquicia           object\n",
              "TipoTarjeta          object\n",
              "DominioCompra        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[['Proxy', 'SistemaOperativo', 'Navegador', 'Resolucion',\n",
        "       'TipoDispositivo', 'InfoDispositivo', 'LineaDeTiempo',\n",
        "       'MontoTransaccion', 'Franquicia', 'TipoTarjeta', 'DominioCompra']]\n",
        "y = df['isFraud']\n"
      ],
      "metadata": {
        "id": "_0SX3sCKtVEO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_encoded = pd.get_dummies(X)\n"
      ],
      "metadata": {
        "id": "8QoJkR4EGwWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "PB0W6QQzG0QC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "id": "EEiQXXmPG3ri",
        "outputId": "8038e306-fa3f-4b69-cc11-4f035ee31c9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)\n"
      ],
      "metadata": {
        "id": "uVgQvI-KG8lq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(y_test, y_pred))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I9ud-_ezHBWs",
        "outputId": "885b0705-d0c5-44ca-ebc4-42a5c7b3cf16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      1.00      0.96     26587\n",
            "           1       0.00      0.00      0.00      2260\n",
            "\n",
            "    accuracy                           0.92     28847\n",
            "   macro avg       0.46      0.50      0.48     28847\n",
            "weighted avg       0.85      0.92      0.88     28847\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Con esto tenemos que los resultados obtenidos son muy bajos para lo que necesitamos. "
      ],
      "metadata": {
        "id": "lc9IX8ZsHyUy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "o la técnica de balanceo de datos mediante el sobremuestreo de la clase minoritaria (fraude) con la biblioteca imbalanced-learn. Además, se utiliza la validación cruzada estratificada para evaluar el rendimiento del modelo de regresión logística:"
      ],
      "metadata": {
        "id": "vfitKRD7IMb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "#Dividimos los datos en características (variables independientes) y la variable objetivo (variable dependiente)\n",
        "X = df.drop('isFraud', axis=1)\n",
        "y = df['isFraud']\n",
        "\n",
        "#Identificamos las columnas categóricas y numéricas\n",
        "categorical_cols = [col for col, dtype in zip(X.columns, X.dtypes) if dtype == 'object']\n",
        "numeric_cols = [col for col, dtype in zip(X.columns, X.dtypes) if dtype != 'object']\n",
        "\n",
        "#Aplicamos la codificación one-hot encoding a las variables categóricas\n",
        "ct = ColumnTransformer([('encoder', OneHotEncoder(), categorical_cols)], remainder='passthrough')\n",
        "X_encoded = ct.fit_transform(X)\n",
        "\n",
        "#Realizamos sobremuestreo de la clase minoritaria utilizando SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "X_resampled, y_resampled = smote.fit_resample(X_encoded, y)\n",
        "\n",
        "#Realizamos validación cruzada estratificada\n",
        "skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\n",
        "\n",
        "#Inicializamos una lista para almacenar los resultados de evaluación en cada iteración de validación cruzada\n",
        "results = []\n",
        "\n",
        "#Iteramos sobre cada fold de la validación cruzada\n",
        "for train_index, test_index in skf.split(X_resampled, y_resampled):\n",
        "    X_train, X_test = X_resampled[train_index], X_resampled[test_index]\n",
        "    y_train, y_test = y_resampled[train_index], y_resampled[test_index]\n",
        "    \n",
        "    #Creamos y entrenamos el modelo de regresión logística\n",
        "    model = LogisticRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "    \n",
        "    #Realizamos predicciones en el conjunto de prueba\n",
        "    y_pred = model.predict(X_test)\n",
        "    \n",
        "    #Evaluamos el rendimiento del modelo\n",
        "    report = classification_report(y_test, y_pred)\n",
        "    results.append(report)\n",
        "\n",
        "# Imprimir los resultados de evaluación promediados en cada iteración de validación cruzada\n",
        "print(\"Resultados de evaluación promediados en validación cruzada:\")\n",
        "print(\"--------------------------------------------------------\")\n",
        "print(pd.DataFrame(results).mean())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjr0J3clIPV4",
        "outputId": "d3e66afd-2c5e-4b18-e734-12b3fc78a674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Resultados de evaluación promediados en validación cruzada:\n",
            "--------------------------------------------------------\n",
            "Series([], dtype: float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Este código utiliza la biblioteca imbalanced-learn para aplicar la técnica de sobremuestreo SMOTE, que genera nuevas instancias sintéticas de la clase minoritaria para equilibrar la distribución de las clases. Luego, se realiza la validación cruzada estratificada utilizando StratifiedKFold para dividir los datos en conjuntos de entrenamiento y prueba en cada iteración.\n",
        "\n",
        "Dentro del bucle de validación cruzada, se crea y entrena un modelo de regresión logística en el conjunto de entrenamiento y se evalúa su rendimiento en el conjunto de prueba utilizando classification_report. Finalmente, los resultados se promedian en todas las iteraciones de validación cruzada para obtener una medida general del rendimiento del modelo."
      ],
      "metadata": {
        "id": "8wUUsbZTIZSq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dftid.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O3vn7KJZxWx9",
        "outputId": "c4ca53bd-ba49-47e2-87ed-a65e5efa2044"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TransactionID      int64\n",
              "id_01            float64\n",
              "id_02            float64\n",
              "id_03            float64\n",
              "id_04            float64\n",
              "id_05            float64\n",
              "id_06            float64\n",
              "id_07            float64\n",
              "id_08            float64\n",
              "id_09            float64\n",
              "id_10            float64\n",
              "id_11            float64\n",
              "id_12             object\n",
              "id_13            float64\n",
              "id_14            float64\n",
              "id_15             object\n",
              "id_16             object\n",
              "id_17            float64\n",
              "id_18            float64\n",
              "id_19            float64\n",
              "id_20            float64\n",
              "id_21            float64\n",
              "id_22            float64\n",
              "id_23             object\n",
              "id_24            float64\n",
              "id_25            float64\n",
              "id_26            float64\n",
              "id_27             object\n",
              "id_28             object\n",
              "id_29             object\n",
              "id_30             object\n",
              "id_31             object\n",
              "id_32            float64\n",
              "id_33             object\n",
              "id_34             object\n",
              "id_35             object\n",
              "id_36             object\n",
              "id_37             object\n",
              "id_38             object\n",
              "DeviceType        object\n",
              "DeviceInfo        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dfttr = dfttr.dropna() #Esto hace que me quede sin datos y por eso falló el modelo anterior! Entonces mejor rellenamos los datos con la media\n",
        "dfttr = dfttr.fillna(dfttr.mean())\n",
        "dftid = dftid.fillna(dftid.mean())"
      ],
      "metadata": {
        "id": "12UDoKCFx1Ni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.fillna(df.mean())"
      ],
      "metadata": {
        "id": "CLU3R6IC2hk6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfttr = pd.get_dummies(dfttr)\n",
        "dftid = pd.get_dummies(dftid)\n",
        "df = pd.get_dummies(df)"
      ],
      "metadata": {
        "id": "hyw_-GxVyBAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ScaM8ax4-YCL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "La métrica Accuracy representa el procentaje total de los valores correctamente clasificados, tanto positivos como negativos\n",
        "\n",
        "Accuracy = (TP + TN)/(TP+TN+FP+FN)\n",
        "\n",
        "Como se debe usar en datos que están balanceados y el nuestro claramente no lo está, se usa pero se descarta"
      ],
      "metadata": {
        "id": "ZgcO0MowALfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "X = df.drop('isFraud', axis=1)\n",
        "y = df['isFraud']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "rf_model = RandomForestClassifier()\n",
        "\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = rf_model.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(\"Precisión del modelo: {:.2f}%\".format(accuracy * 100))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cxF-XRtytRp",
        "outputId": "1094cfc6-b37b-4712-a9a2-52ef8ed7e1cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precisión del modelo: 97.06%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El F1-score es una medida de  precisión y recall combinadas. Es útil cuando se tiene un desbalance entre las clases, como en el caso de detección de fraudes donde la clase positiva (fraude) es menos frecuente."
      ],
      "metadata": {
        "id": "3MTvfsMy_7Bg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import f1_score\n",
        "\n",
        "X_train = df.drop('isFraud', axis=1)\n",
        "y_train = df['isFraud']\n",
        "\n",
        "modelo = RandomForestClassifier()\n",
        "\n",
        "modelo.fit(X_train, y_train)\n",
        "\n",
        "test_identity = pd.read_csv('test_identity.csv')\n",
        "test_transaction = pd.read_csv('test_transaction.csv')\n",
        "\n",
        "test_data = pd.merge(test_transaction, test_identity, on='TransactionID', how='left')\n",
        "\n",
        "X_test = test_data\n",
        "\n",
        "y_pred = modelo.predict(X_test)\n",
        "\n",
        "y_true = test_identity['isFraud']\n",
        "\n",
        "f1 = f1_score(y_true, y_pred)\n",
        "print(\"F1-score:\", f1)\n"
      ],
      "metadata": {
        "id": "RJ3yvUiK46m-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "La matriz de confusión muestra la cantidad de casos clasificados correctamente e incorrectamente para cada clase."
      ],
      "metadata": {
        "id": "1faBWQms-T3x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "X_train = df.drop('isFraud', axis=1)\n",
        "y_train = df['isFraud']\n",
        "modelo = RandomForestClassifier()\n",
        "modelo.fit(X_train, y_train)\n",
        "test_identity = pd.read_csv('test_identity.csv')\n",
        "test_transaction = pd.read_csv('test_transaction.csv')\n",
        "test_data = pd.merge(test_transaction, test_identity, on='TransactionID', how='left')\n",
        "X_test = test_data\n",
        "y_pred = modelo.predict(X_test)\n",
        "y_true = test_identity['isFraud']\n",
        "confusion_mat = confusion_matrix(y_true, y_pred)\n",
        "print(\"Matriz de Confusión:\")\n",
        "print(confusion_mat)\n"
      ],
      "metadata": {
        "id": "pxvybfQI9p4c"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}