Proyecto_Enunciado.ipynb

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        "# Proyecto: Innovación Tecnológica en Bodoque Bank\n",
        "\n",
        "**MDS7202: Laboratorio de Programación Científica para Ciencia de Datos**\n",
        "\n",
        "### Cuerpo Docente:\n",
        "\n",
        "- Profesor: Pablo Badilla, Ignacio Meza De La Jara\n",
        "- Auxiliar: Sebastián Tinoco\n",
        "- Ayudante: Diego Cortez M., Felipe Arias T.\n",
        "\n",
        "*Por favor, lean detalladamente las instrucciones de la tarea antes de empezar a escribir.*\n"
      ]
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        "----\n",
        "\n",
        "## Reglas\n",
        "\n",
        "- Fecha de entrega: 01/06/2021\n",
        "- **Grupos de 2 personas.**\n",
        "- Cualquier duda fuera del horario de clases al foro. Mensajes al equipo docente serán respondidos por este medio.\n",
        "- Estrictamente prohibida la copia. \n",
        "- Pueden usar cualquier material del curso que estimen conveniente."
      ]
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        "# Motivación\n",
        "\n",
        "![](https://www.geekmi.news/__export/1621969098810/sites/debate/img/2021/05/25/juan-carlos-bodoque-1.jpg_554688468.jpg)\n",
        "\n",
        "Juan Carlos Bodoque, el famoso periodista y empresario, decidió diversificar su portafolio de negocios y crear su propio banco. Después de varios años de investigar y analizar el mercado financiero, finalmente logró fundar su entidad bancaria con el objetivo de ofrecer a sus clientes una experiencia personalizada y confiable en sus transacciones financieras. \n",
        "\n",
        "Sin embargo, con las nuevas tecnologías, aparecen nuevos desafíos para la joven entidad bancaria. Por ello, Bodoque decide invertir en un equipo de expertos en tecnología y finanzas, para que Bodoque Bank implemente las últimas innovaciones en seguridad y servicio al cliente para garantizar la satisfacción y fidelización de sus clientes.\n",
        "\n",
        "El primer objetivo de la entidad bancaria será la detección de potenciales clientes fraudulentos, para ello Bodoque Bank les hace entrega de un extenso dataset en el que se registran las actividades que han realizado sus clientes durante los últimos meses. Uno de los puntos que resaltan al pasar el conjunto de datos es que el nombre de los usuarios está protegido y que consideren cada una de las filas como una muestra independiente de la otra.\n",
        "\n",
        "Tras la solicitud, uno de los mayores accionistas del banco llamado Mario Hugo, les sugiere que al momento de realizar el proyecto tomen las siguientes consideraciones:\n",
        "\n",
        "- Realicen un análisis exhaustivo de cada variable, ya que los directores tienden a ser quisquillosos con puntos o definiciones sin algún fundamento claro.\n",
        "- Deben ser muy claros de cómo encontrar los outliers y si es posible expliquen cómo están determinando que un cliente es fraudulento.\n",
        "- Es muy probable que existan clientes con diferentes comportamientos bancarios, por lo que sería muy buena decisión de separar a los clientes en grupos etarios.\n"
      ]
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        "### Definición Formal del Problema\n",
        "\n",
        "El dataset con el que se trabajará en este primer proyecto será [_The Bank Account Fraud (BAF)_](https://www.kaggle.com/datasets/sgpjesus/bank-account-fraud-dataset-neurips-2022). Este consiste en un dataset para evaluar métodos de detección de fraudes bancarios, el cuál según sus autores es:\n",
        "\n",
        "- Realista: Se basa en un actualizado dataset de casos reales de detección de fraude bancario.\n",
        "- Sesgado: Poseen distintos tipos de bias.\n",
        "- Desbalanceado: Clase positiva extremadamente pequeña.\n",
        "- Dinámico: Presenta datos temporales y cambios de distribución.\n",
        "- Preserva la privacidad: Protege a los potenciales clientes a través de la aplicación de privacidad diferencial.\n",
        "\n",
        "El proyecto tiene por objetivo evaluar los conocimientos adquiridos en la primera mitad del curso, consistente en manejo de datos tabulares (I/O, manipulación, agregaciones, merge y visualizaciones) más la primera parte de modelos consistente en preprocesamiento de datos y detección de anomalías. Por ende, el proyecto consiste en dos tareas principales:\n",
        "\n",
        "1. Generar un Análisis exploratorio de Datos (EDA) que describa completamente el dataset.\n",
        "2. Buscar anomalías de forma automatizada.\n",
        "\n",
        "**Importante:** Esta permitido el uso de librerías externas a las vistas en clases para profundizar aún mas en los análisis. Sin embargo, al momento de utilizar cualqueir metodo deberán explicar que hace y el porque de su aplicación.\n"
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        "---\n",
        "\n",
        "## Secciones requeridas en la entrega\n",
        "\n",
        "La siguiente lista detalla las secciones que debe contener su notebook para resolver el proyecto. Es importante que al momento de desarrollar cada una de las secciones, estas sean escritas en un formato tipo **informe**, donde describan detalladamente cada uno de los puntos realizados.\n",
        "\n",
        "### 1. Introducción [0.5 Puntos]\n",
        "\n",
        "Elaborar una breve introducción con todo lo necesario para entender qué realizarán durante su proyecto. La idea es que describan de manera formal el proyecto con sus propias palabras y logren describir algunos aspectos básicos tanto del dataset como del análisis a realizar sobre los datos.\n",
        "\n",
        "Por lo anterior, en esta sección ustedes deberán ser capaces de:\n",
        "\n",
        "- Describir la tarea asociada al dataset.\n",
        "- Describir brevemente los datos de entrada que les provee el problema.\n",
        "- Plantear hipótesis de cómo podrían abordar el problema.\n",
        "\n",
        "### 2. Lectura y Manejo de Datos [1 Punto]\n",
        "\n",
        "Este punto tiene por objetivo evaluar la capacidad de leer y manejar distintas fuentes de datos y unirlas.\n",
        "Para esto, se les provee de 3 datasets distintos: \n",
        "\n",
        "- `df_1.parquet` que contiene una cierta cantidad de ejemplos.\n",
        "- `df_2.parquet` que contiene el resto de los ejemplos.\n",
        "- `df_email_phone.parquet` que contiene características exclusivas relacionadas a los emails y teléfonos de los ejemplos.\n",
        "\n",
        "Tanto `df_1` como `df_2` son dataframes que contienen conjuntos (no necesariamente disjuntos) de ejemplos obtenidos a partir de distintas fuentes (la primera más confiable que la segunda)  mientras que `df_email_phone` contiene nuevas características que deben ser fusionadas con el conjunto de ejemplos.\n",
        "Dicho esto, para este punto se le solicita:\n",
        "\n",
        "1. Cargar los datasets usando pandas.\n",
        "2. Explorar superficialmente los datsets por medio de `.head(5)` e `.info()` y reportar número de filas y columnas de cada dataset.\n",
        "3. Unir los datasets con los conjuntos de ejemplos en un único dataset.\n",
        "4. Combinar el datasets con ejemplos con las nuevas características usando la estrategia `outer`.\n",
        "5. Explorar nulos y duplicados de datos (i.e., generar conteos de cada uno y ver algunos de sus ejemplos), explicar su origen y entregar una solución sencilla a estos casos.\n",
        "6. Limpiar datos y reportar modificaciones a los datasets originales (cuántos ejemplos se eliminaron, cuántos ejemplos quedaron finalmente, etc...).\n",
        "7. Realizar una segmentación etaria de los datos creando una columna llamada `segmentacion_etaria` para clasificar a los diferentes usuarios. Para la segmentación utilice los siguientes rangos:\n",
        "   - Joven (Menor a 18 años)\n",
        "   - Adulto-Joven (18 - 26 años)\n",
        "   - Adulto (27- 59 años)\n",
        "   - Persona Mayor (60 años o mas).\n",
        "\n",
        "\n",
        "### 3. Análisis Exploratorio de Datos [1 Punto]\n",
        "\n",
        "Esta sección se busca evaluar la capacidad de generar reportes y conclusiones a partir de los patrones observados en estos.\n",
        "\n",
        "- Utilizando `pandas-profiling` (nombre de la librería: `ydata-profiling`), explore:\n",
        "\n",
        "    * Analizar cantidad de datos nulos, tipos de datos, duplicados, distribuciones de las variables a través de histogramas.\n",
        "    * Generar visualizaciones de las interacciones (como por ejemplo, una scatter matrix) en las distintas variables.\n",
        "    * Ver las correlaciones entre las distintas variables y los valores faltantes de cada una de estas. \n",
        "    * Reportar los patrones interesantes observados. Ejemplos:\n",
        "        - Se observó que las variables numéricas A, B y C siguen una distribución normal / exponencial / logarítmica / no presenta distribución conocida debido a que (usar información sobre el descripción de cada variable)....\n",
        "        - La mayoría de los ejemplos de las variables I, J y K tienen valor 0.\n",
        "        - La variable M presenta gran cantidad de outliers.\n",
        "        - Se observó que las variables categóricas E y F están bien balanceadas / presentan un gran desbalance.\n",
        "        - Del gráfico de correlaciones se puede inferir que X, Y y Z tienen una correlación alta/baja entre ellas.\n",
        "\n",
        "\n",
        "\n",
        "### 4. Preparación de Datos [1 Punto]\n",
        "\n",
        "*Esta sección consiste en generar los distintos pasos para preparar sus datos con el fin de luego poder crear su modelo.*\n",
        "\n",
        "Dentro de los aspectos minimos que se les solicitarán en este desarrollo estan:\n",
        "\n",
        "- Definir preprocesadores para datos categóricos y ordinales.\n",
        "- Setear las transformaciones en un `ColumnTransformer`.\n",
        "- Transformar todo el datset.\n",
        "\n",
        "**Notas**: \n",
        "\n",
        "- Las variables `id` y `fraud_bool` no deben ser transformadas, pero si pasadas a la siguiente etapa. (Estudiar la transformación `passthrough`).\n",
        "- La salida de la transformación debe ser un pandas dataframe. Para esto, investiguen acerca de la nueva [`set_output` API](https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_set_output.html). Si se levanta un error por matriz sparse, desactiven su uso en la transformación correspondiente.\n",
        "\n",
        "### 5. Visualización en baja dimensionalidad [1 Punto]\n",
        "\n",
        "Programar un script para proyectar los datos en baja dimensionalidad usando `PCA` o `UMAP`.\n",
        "\n",
        "**Notas**: \n",
        "\n",
        "- Para realizar las proyecciones no consideren la variable `fraud_bool`, ya que esto provocaría un _data leakage_ (i.e., usar información del futuro para analizar el mismo fenómeno que quieren descubrir).\n",
        "- Utilice un muestreo significativo del dataframe (10k a 25k ejemplos) para generar las proyecciones. De lo contrario, probablemente el proyector no sea capaz de asignar todos los recursos necesarios y lance alguna excepción.\n",
        "\n",
        "Luego, por cada segmento etario generado en la sección 2, implemente un gráfico de dispersión (idealmente en plotly express) la proyección en 2D generada y coloree cada punto según la etiqueta `fraud_bool`.\n",
        "\n",
        "Reportar si existen patrones y/o relaciones interesantes con respecto a variables de interés.\n",
        "\n",
        "\n",
        "### 6. Explorar Anomalías [1.5 Puntos]\n",
        "\n",
        "Proponer una técnica para detectar clientes fraudulentos en base a detección de anomalías. \n",
        "Al igual que el punto anterior, la técnica que proponga debe ser aplicada por separado en los diferentes segmentos etarios descritos en el punto 2 (es decir, debe entrenar un detector de anomalías para cada uno de estos grupos). \n",
        "\n",
        "Luego, al igual que el punto anterior, genere un gráfico de dispersión con las proyecciones 2D en donde el coloreado sea ahora las etiquetas predichas por el detector de anomalías.\n",
        "\n",
        "Por último, usando queries de numpy/pandas, calcule dos ratios (P y R):\n",
        "\n",
        "- P: Cantidad de ejemplos predichos correctamente como fraude / cantidad total de datos predichos como fraude.\n",
        "- R: Cantidad de ejemplos predichos correctamente como fraude / cantidad total de ejemplos que eran realmente fraude.\n",
        "\n",
        "\n",
        "De los resultados obtenidos responda las siguientes preguntas:\n",
        "\n",
        "- ¿Qué significan los ratios y sus valores?\n",
        "- ¿Qué tan correctas fueron las predicciones realizadas por su modelo según los ratios calculados?\n",
        "- ¿Son coherentes los resultados obtenidos?.\n",
        "- ¿Cómo se comportan los casos de fraudes para los diferentes rangos etarios?\n",
        "\n",
        "Justifique cada una de sus respuestas."
      ]
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        "---\n",
        "\n",
        "## Esquema de la Tarea\n",
        "\n",
        "\n",
        "Pueden usar el siguiente esquema para organizar la tarea (y borrar todo lo anterior).\n",
        "Obviamente **no deben limitarse a lo que está escrito en esta**: puede incrementar en caso de más técnicas y obviar algunas partes en caso que alguna y otro punto no aplique a su problema.\n",
        "\n",
        "Pueden borrar las instrucciones anteriores y quedarse solo con lo que viene a continuación.\n",
        "\n",
        "---\n",
        "<br>\n",
        "\n",
        "<br>\n",
        "\n",
        "<br>"
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        "\n",
        "\n",
        "# Proyecto\n",
        "\n",
        "### Equipo:\n",
        "\n",
        "- Isabel Marx\n",
        "- Canela Orellana\n",
        "\n",
        "\n",
        "### Link de repositorio de GitHub: https://github.com/CanelaOrellana/MDS7202-IsabelMarx-y-CanelaOrellana\n",
        "\n"
      ]
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        "\n",
        "## 1. Introducción\n",
        "\n",
        "En el siguiente documento se presenta la primera entrega del proyecto del curso MDS7202 Laboratorio de Programación Científica para Ciencia de Datos en el semestre de Otoño 2023, en el cual se busca aplicar todo lo visto en el curso hasta el momento a un problema real.\n",
        "\n",
        "Para ello, se trabajará con en el dataset \"The Bank Account Fraud (BAF)\" para evaluar métodos de detección de fraudes bancarios. El dataset es realista, sesgado, desbalanceado y dinámico, y preserva la privacidad de los clientes. El dataset incluye diversas columnas que capturan información relevante, como edad, ingresos, propiedades, características de transacciones, historial bancario, entre otros. La hipótesis de abordaje consiste en realizar un preprocesamiento de datos y un análisis exploratorio de ellos, para finalmente implementar algún modelo de detección de anomalías y utilizarlo para detectar fraudes bancarios."
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        "## 2. Lectura y Manejo de Datos\n",
        "\n",
        "Este punto tiene por objetivo evaluar la capacidad de leer y manejar distintas fuentes de datos y unirlas. Para esto, se les provee de 3 datasets distintos:\n",
        "\n",
        "-  df_1.parquet que contiene una cierta cantidad de ejemplos. \n",
        "- df_2.parquet que contiene el resto de los ejemplos.\n",
        "- df_email_phone.parquet que contiene características exclusivas relacionadas a los emails y teléfonos de los ejemplos.\n",
        "\n",
        "Tanto df_1 como df_2 son dataframes que contienen conjuntos (no necesariamente disjuntos) de ejemplos obtenidos a partir de distintas fuentes (la primera más confiable que la segunda) mientras que df_email_phone contiene nuevas características que deben ser fusionadas con el conjunto de ejemplos. Dicho esto, para este punto se le solicita:\n",
        "\n",
        "- Cargar los datasets usando pandas.\n",
        "- Explorar superficialmente los datsets por medio de .head(5) e .info() y reportar número de filas y columnas de cada dataset.\n",
        "- Unir los datasets con los conjuntos de ejemplos en un único dataset.\n",
        "- Combinar el datasets con ejemplos con las nuevas características usando la estrategia outer.\n",
        "- Explorar nulos y duplicados de datos (i.e., generar conteos de cada uno y ver algunos de sus ejemplos), explicar su origen y entregar una solución sencilla a estos casos.\n",
        "- Limpiar datos y reportar modificaciones a los datasets originales (cuántos ejemplos se eliminaron, cuántos ejemplos quedaron finalmente, etc...).\n",
        "- Realizar una segmentación etaria de los datos creando una columna llamada segmentacion_etaria para clasificar a los diferentes usuarios. Para la segmentación utilice los siguientes rangos:\n",
        "  - Joven (Menor a 18 años)\n",
        "  - Adulto-Joven (18 - 26 años)\n",
        "  - Adulto (27- 59 años)\n",
        "  - Persona Mayor (60 años o mas).\n",
        "\n"
      ]
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      "source": [
        "# Libreria Core del lab.\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_rows', 500)\n",
        "pd.set_option('display.max_columns', 500)\n",
        "pd.set_option('display.width', 1000)\n",
        "import datetime\n",
        "from IPython.display import HTML"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Si usted está utilizando Colabolatory le puede ser útil este código para cargar los archivos.\n",
        "try:\n",
        "    from google.colab import drive\n",
        "    drive.mount(\"/content/drive\")\n",
        "    path1 = '/content/drive/MyDrive/df_1.parquet'\n",
        "    path2 = '/content/drive/MyDrive/df_2.parquet'\n",
        "    pathemail = '/content/drive/MyDrive/df_email_phone.parquet'\n",
        "except: \n",
        "    print('Ignorando conexión drive-colab')"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqktI8i0amBu",
        "outputId": "93f88dc8-711b-4d37-f4c9-cb2bbb2748dd"
      },
      "id": "jqktI8i0amBu",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
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      "metadata": {
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      },
      "source": [
        "### 2.1 Cargar datos en dataframes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Se cargan los datos usando pandas\n",
        "df_1 = pd.read_parquet(path1)\n",
        "df_2 = pd.read_parquet(path2)\n",
        "df_email = pd.read_parquet(pathemail)"
      ],
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      },
      "source": [
        "### 2.2 Exploración inicial"
      ]
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      "source": [
        "A continuación se realiza una exploración superficial de los datos"
      ],
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      "cell_type": "code",
      "source": [
        "#Mostrar primeras 5 filas \n",
        "display(df_1.head(5))\n",
        "#Observar la información del dataframe\n",
        "display(df_1.info())"
      ],
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        {
          "output_type": "display_data",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 389782 entries, 104446 to 193209\n",
            "Data columns (total 29 columns):\n",
            " #   Column                        Non-Null Count   Dtype  \n",
            "---  ------                        --------------   -----  \n",
            " 0   id                            389782 non-null  int64  \n",
            " 1   fraud_bool                    389782 non-null  int64  \n",
            " 2   income                        389782 non-null  float64\n",
            " 3   prev_address_months_count     389782 non-null  int64  \n",
            " 4   current_address_months_count  389782 non-null  int64  \n",
            " 5   customer_age                  389782 non-null  int64  \n",
            " 6   days_since_request            389782 non-null  float64\n",
            " 7   intended_balcon_amount        389782 non-null  float64\n",
            " 8   payment_type                  389782 non-null  object \n",
            " 9   zip_count_4w                  389782 non-null  int64  \n",
            " 10  velocity_6h                   389782 non-null  float64\n",
            " 11  velocity_24h                  389782 non-null  float64\n",
            " 12  velocity_4w                   389782 non-null  float64\n",
            " 13  bank_branch_count_8w          389782 non-null  int64  \n",
            " 14  employment_status             389782 non-null  object \n",
            " 15  credit_risk_score             389782 non-null  int64  \n",
            " 16  housing_status                389782 non-null  object \n",
            " 17  bank_months_count             389782 non-null  int64  \n",
            " 18  has_other_cards               389782 non-null  int64  \n",
            " 19  proposed_credit_limit         389782 non-null  float64\n",
            " 20  foreign_request               389782 non-null  int64  \n",
            " 21  source                        389782 non-null  object \n",
            " 22  session_length_in_minutes     389782 non-null  float64\n",
            " 23  device_os                     389782 non-null  object \n",
            " 24  keep_alive_session            389782 non-null  int64  \n",
            " 25  device_fraud_count            389782 non-null  int64  \n",
            " 26  month                         389782 non-null  int64  \n",
            " 27  x1                            389782 non-null  float64\n",
            " 28  x2                            389782 non-null  float64\n",
            "dtypes: float64(10), int64(14), object(5)\n",
            "memory usage: 89.2+ MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset ```df_1```cuenta con 389.782 filas y 29 columnas. Además dado que la cuenta de valores no nulos por columna es igual a la cantidad de filas en cada una de las columnas, se puede concluir que el dataset no contiene valores nulos."
      ],
      "metadata": {
        "id": "7sZ5RIggoqum"
      },
      "id": "7sZ5RIggoqum"
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostrar primeras 5 filas \n",
        "display(df_2.head(5))\n",
        "#Observar la información del dataframe\n",
        "display(df_2.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "bFjHmK16d8LE",
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      },
      "id": "bFjHmK16d8LE",
      "execution_count": null,
      "outputs": [
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              "      <th>foreign_request</th>\n",
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              "      <th>month</th>\n",
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              "      <td>5624.897018</td>\n",
              "      <td>6771.795222</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          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",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 747410 entries, 699954 to 384611\n",
            "Data columns (total 29 columns):\n",
            " #   Column                        Non-Null Count   Dtype  \n",
            "---  ------                        --------------   -----  \n",
            " 0   id                            747410 non-null  int64  \n",
            " 1   fraud_bool                    747410 non-null  int64  \n",
            " 2   income                        747410 non-null  float64\n",
            " 3   prev_address_months_count     747410 non-null  int64  \n",
            " 4   current_address_months_count  747410 non-null  int64  \n",
            " 5   customer_age                  747410 non-null  int64  \n",
            " 6   days_since_request            747410 non-null  float64\n",
            " 7   intended_balcon_amount        747410 non-null  float64\n",
            " 8   payment_type                  747410 non-null  object \n",
            " 9   zip_count_4w                  747410 non-null  int64  \n",
            " 10  velocity_6h                   747410 non-null  float64\n",
            " 11  velocity_24h                  747410 non-null  float64\n",
            " 12  velocity_4w                   747410 non-null  float64\n",
            " 13  bank_branch_count_8w          747410 non-null  int64  \n",
            " 14  employment_status             747410 non-null  object \n",
            " 15  credit_risk_score             747410 non-null  int64  \n",
            " 16  housing_status                747410 non-null  object \n",
            " 17  bank_months_count             747410 non-null  int64  \n",
            " 18  has_other_cards               747410 non-null  int64  \n",
            " 19  proposed_credit_limit         747410 non-null  float64\n",
            " 20  foreign_request               747410 non-null  int64  \n",
            " 21  source                        747410 non-null  object \n",
            " 22  session_length_in_minutes     747410 non-null  float64\n",
            " 23  device_os                     747410 non-null  object \n",
            " 24  keep_alive_session            747410 non-null  int64  \n",
            " 25  device_fraud_count            747410 non-null  int64  \n",
            " 26  month                         747410 non-null  int64  \n",
            " 27  x1                            747410 non-null  float64\n",
            " 28  x2                            747410 non-null  float64\n",
            "dtypes: float64(10), int64(14), object(5)\n",
            "memory usage: 171.1+ MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset ```df_2```cuenta con 747.410 filas y las mismas 29 columnas que ```df_1```. Del mismo modo que con ```df_1```, la cuenta de valores no nulos en cada columna, es igual al número de filas, por lo que se puede concluir que el dataset no contiene valores nulos."
      ],
      "metadata": {
        "id": "AE06RPVbpyu4"
      },
      "id": "AE06RPVbpyu4"
    },
    {
      "cell_type": "code",
      "source": [
        "#Mostrar primeras 5 filas \n",
        "display(df_email.head(5))\n",
        "#Observar la información del dataframe\n",
        "display(df_email.info())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "0fh_uXhkd8W2",
        "outputId": "8057f98c-82ae-4c8e-8dce-2a631f3718b7"
      },
      "id": "0fh_uXhkd8W2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            id  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "987231  624588               0.555653                                15              0                          1                 0                   1\n",
              "79954   620810               0.849718                                 2              0                          1                 0                   0\n",
              "567130  580633               0.110898                                 3              0                          1                 1                   0\n",
              "500891    6379               0.671250                                15              0                          1                 0                   1\n",
              "55399   366511               0.772932                                 7              0                          1                 0                   1"
            ],
            "text/html": [
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              "      <th>id</th>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-074eb3a3-fbae-410b-8bbf-e49a4aace327');\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": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 934730 entries, 987231 to 986114\n",
            "Data columns (total 7 columns):\n",
            " #   Column                            Non-Null Count   Dtype  \n",
            "---  ------                            --------------   -----  \n",
            " 0   id                                934730 non-null  int64  \n",
            " 1   name_email_similarity             934730 non-null  float64\n",
            " 2   date_of_birth_distinct_emails_4w  934730 non-null  int64  \n",
            " 3   email_is_free                     934730 non-null  int64  \n",
            " 4   device_distinct_emails_8w         934730 non-null  int64  \n",
            " 5   phone_home_valid                  934730 non-null  int64  \n",
            " 6   phone_mobile_valid                934730 non-null  int64  \n",
            "dtypes: float64(1), int64(6)\n",
            "memory usage: 57.1 MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset ```df_email_phone``` cuenta con 934.730 filas y 7 columnas en total, que contienen información diferente a los datasets anteriores. Al igual que los dataset anteriores, no contiene nulos."
      ],
      "metadata": {
        "id": "rNYKIfnjypGb"
      },
      "id": "rNYKIfnjypGb"
    },
    {
      "cell_type": "markdown",
      "id": "882a9ff0-9399-43c7-9b7d-108e36ddef24",
      "metadata": {
        "id": "882a9ff0-9399-43c7-9b7d-108e36ddef24"
      },
      "source": [
        "### 2.3 Unir dataframes"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A continuación se unen los dataset ```df_1``` y ```df_2```, dado que no necesariamente son disjuntos, luego de unirlos se deben eliminar los duplicados, priorizando la permanencia de aquellos que vienen de ```df_1```ya que se sabe que es más confiable.  "
      ],
      "metadata": {
        "id": "bSjy9XQXuLbt"
      },
      "id": "bSjy9XQXuLbt"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_3 = pd.concat([df_1,df_2], axis=0)\n",
        "df_3.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "17003guveVBS",
        "outputId": "881dcb09-2be7-45da-ec66-d73022ca6c89"
      },
      "id": "17003guveVBS",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "            id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes device_os  keep_alive_session  device_fraud_count  month        x1        x2\n",
              "104446  729517           0     0.7                         -1                           305            60            0.030059               -1.599455           AC           990  5340.131605   3945.871264  5531.610889                     1                CC                147             BB                 -1                0                  500.0                0  INTERNET                   8.865992   windows                   0                   0      2 -0.245425  0.568811\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-542f45a3-5be9-4d01-9421-6c97c3bbde97')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-542f45a3-5be9-4d01-9421-6c97c3bbde97 button.colab-df-convert');\n",
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              "        async function convertToInteractive(key) {\n",
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              "                                                     [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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_3.drop_duplicates(keep='first', inplace=True)#, ignore_index=False)\n",
        "df_3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "I-hC8BZ0fypq",
        "outputId": "ad0c7a61-b0c1-4d53-fe97-d37f1280ec1d"
      },
      "id": "I-hC8BZ0fypq",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w   velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes  device_os  keep_alive_session  device_fraud_count  month        x1        x2\n",
              "104446  729517           0     0.7                         -1                           305            60            0.030059               -1.599455           AC           990   5340.131605   3945.871264  5531.610889                     1                CC                147             BB                 -1                0                  500.0                0  INTERNET                   8.865992    windows                   0                   0      2 -0.245425  0.568811\n",
              "269483  149585           0     0.8                         -1                           140            50            0.015659                3.951994           AA          1269   2844.260015   2789.827355  3141.137362                  1605                CF                105             BA                  9                1                  200.0                0  INTERNET                   4.654872      linux                   1                   0      7  0.009336 -2.096682\n",
              "4102     64486           0     0.9                         -1                           171            50            0.001409               28.159779           AB          4430   5854.065790   4210.039095  5313.233943                    17                CC                208             BA                  1                1                 1500.0                0  INTERNET                   3.720953      linux                   1                   0      1  2.229616 -0.005823\n",
              "351767  825283           0     0.5                         -1                            85            30            0.027292               -1.310498           AB          1698   7938.525709   4963.194619  5070.085151                   872                CA                138             BE                  3                0                  200.0                0  INTERNET                   2.912670    windows                   1                   0      3 -0.193945  0.861207\n",
              "126377    8308           0     0.9                         -1                            39            30            0.010945               -1.450972           AC           569   5268.975264   5573.932997  4931.751648                     1                CA                110             BB                 -1                1                  200.0                0  INTERNET                   2.283680      other                   1                   0      3 -2.440650  0.354986\n",
              "...        ...         ...     ...                        ...                           ...           ...                 ...                     ...          ...           ...           ...           ...          ...                   ...               ...                ...            ...                ...              ...                    ...              ...       ...                        ...        ...                 ...                 ...    ...       ...       ...\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "El dataset ```df_3``` corresponde a la unión entre ```df_1``` y ```df_2```, en vista de que este contiene 934.858 filas, se concluye que existían 202.334 filas duplicadas entre ambos datasets."
      ],
      "metadata": {
        "id": "zvpvpKTkuWGG"
      },
      "id": "zvpvpKTkuWGG"
    },
    {
      "cell_type": "markdown",
      "id": "3359abbb-aa14-4c03-b108-cc7c0baa4031",
      "metadata": {
        "id": "3359abbb-aa14-4c03-b108-cc7c0baa4031"
      },
      "source": [
        "### 2.4 Unir nuevas variables"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "En la siguiente celda, se combinan los dataset ```df_3``` y ```df_email```, en vista de que ambos datasets no contienen las mismas columnas, se debe realizar un _merge_ entre ambos, de modo que se combine la información que contienen. Para que permanezcan los datos de ambos dataset se utiliza el método _outer_."
      ],
      "metadata": {
        "id": "1qr4dgzgyzAj"
      },
      "id": "1qr4dgzgyzAj"
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.merge(\n",
        "    left=df_3,\n",
        "    right=df_email,\n",
        "    on=\"id\",\n",
        "    sort=True,\n",
        "    how=\"outer\",\n",
        ")\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "r6dx6NkAfoQO",
        "outputId": "8dac8b13-8600-4fbc-d8b2-fbf1be809fcb"
      },
      "id": "r6dx6NkAfoQO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes  device_os  keep_alive_session  device_fraud_count  month        x1        x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "0   0         0.0     0.2                      326.0                          20.0          60.0            0.022212               -0.758101           AB         647.0  5944.130884   3862.316270  5330.682025                 763.0                CC              186.0             BC               30.0              0.0                 1000.0              0.0  INTERNET                  19.450933      other                 1.0                 0.0    3.0 -0.282223 -0.137122               0.369510                               2.0            0.0                        2.0               0.0                 0.0\n",
              "1   1         NaN     NaN                        NaN                           NaN           NaN                 NaN                     NaN          NaN           NaN          NaN           NaN          NaN                   NaN               NaN                NaN            NaN                NaN              NaN                    NaN              NaN       NaN                        NaN        NaN                 NaN                 NaN    NaN       NaN       NaN               0.858929                               6.0            0.0                        1.0               1.0                 1.0\n",
              "2   2         0.0     0.9                       -1.0                          41.0          50.0            0.002588               17.710636           AA         815.0  7784.667290   4863.579732  3607.350151                   1.0                CA              212.0             BC               31.0              0.0                  500.0              0.0  INTERNET                   3.850864  macintosh                 0.0                 0.0    4.0 -0.029179  1.089166               0.166457                               7.0            1.0                        1.0               1.0                 1.0\n",
              "3   3         0.0     0.8                       23.0                           9.0          50.0            0.004026               18.224113           AA        1204.0  6510.930878   6593.331522  5245.997031                   1.0                CA              130.0             BC                6.0              0.0                  200.0              0.0  INTERNET                   5.182197    windows                 1.0                 0.0    2.0  2.178365  1.301696               0.250290                               1.0            0.0                        1.0               0.0                 1.0\n",
              "4   4         0.0     0.1                       -1.0                          30.0          30.0            0.011687               -1.434154           AB        1182.0  4304.899127   3738.266526  3609.551261                  12.0                CB              164.0             BB               11.0              0.0                  500.0              0.0  INTERNET                   4.346861    windows                 1.0                 0.0    7.0  1.472099  0.367675               0.619087                               8.0            0.0                        1.0               0.0                 1.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fb5a6fe-1667-449a-bceb-5b7d38bf8de2\">\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>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>days_since_request</th>\n",
              "      <th>intended_balcon_amount</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>employment_status</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>housing_status</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>source</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>device_os</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>326.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.022212</td>\n",
              "      <td>-0.758101</td>\n",
              "      <td>AB</td>\n",
              "      <td>647.0</td>\n",
              "      <td>5944.130884</td>\n",
              "      <td>3862.316270</td>\n",
              "      <td>5330.682025</td>\n",
              "      <td>763.0</td>\n",
              "      <td>CC</td>\n",
              "      <td>186.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>19.450933</td>\n",
              "      <td>other</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.282223</td>\n",
              "      <td>-0.137122</td>\n",
              "      <td>0.369510</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</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",
              "      <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",
              "      <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>0.858929</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>41.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.002588</td>\n",
              "      <td>17.710636</td>\n",
              "      <td>AA</td>\n",
              "      <td>815.0</td>\n",
              "      <td>7784.667290</td>\n",
              "      <td>4863.579732</td>\n",
              "      <td>3607.350151</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>212.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>31.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>3.850864</td>\n",
              "      <td>macintosh</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.029179</td>\n",
              "      <td>1.089166</td>\n",
              "      <td>0.166457</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>23.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.004026</td>\n",
              "      <td>18.224113</td>\n",
              "      <td>AA</td>\n",
              "      <td>1204.0</td>\n",
              "      <td>6510.930878</td>\n",
              "      <td>6593.331522</td>\n",
              "      <td>5245.997031</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>130.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>5.182197</td>\n",
              "      <td>windows</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.178365</td>\n",
              "      <td>1.301696</td>\n",
              "      <td>0.250290</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.011687</td>\n",
              "      <td>-1.434154</td>\n",
              "      <td>AB</td>\n",
              "      <td>1182.0</td>\n",
              "      <td>4304.899127</td>\n",
              "      <td>3738.266526</td>\n",
              "      <td>3609.551261</td>\n",
              "      <td>12.0</td>\n",
              "      <td>CB</td>\n",
              "      <td>164.0</td>\n",
              "      <td>BB</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>4.346861</td>\n",
              "      <td>windows</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.472099</td>\n",
              "      <td>0.367675</td>\n",
              "      <td>0.619087</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fb5a6fe-1667-449a-bceb-5b7d38bf8de2')\"\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-8fb5a6fe-1667-449a-bceb-5b7d38bf8de2 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-8fb5a6fe-1667-449a-bceb-5b7d38bf8de2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se puede observar que en el ```id``` 1 se presentó un dato que no estaba presente en los ejemplos originales, pero sí en las características de email y teléfono contenidas en el otro dataset."
      ],
      "metadata": {
        "id": "VQa1mhVay75Q"
      },
      "id": "VQa1mhVay75Q"
    },
    {
      "cell_type": "markdown",
      "id": "813bab66-81d8-421f-921d-109a7e474790",
      "metadata": {
        "id": "813bab66-81d8-421f-921d-109a7e474790"
      },
      "source": [
        "### 2.5 Verificar nulos y duplicados, generar explicaciones sobre sus fuentes y proponer soluciones.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se reciclará la función creada en el laboratorio 4 para hacer un análisis exploratorio inicial del dataframe ```df``` obtenido."
      ],
      "metadata": {
        "id": "ieAb-4qcvoke"
      },
      "id": "ieAb-4qcvoke"
    },
    {
      "cell_type": "code",
      "source": [
        "def exploratory_data_analysis(dataframe):\n",
        "  fil = dataframe.shape[0]\n",
        "  col = dataframe.shape[1]\n",
        "  print('1.- El Dataframe tiene '+ str(fil) + ' filas y ' + str(col) + ' columnas \\n')\n",
        "  columnas = str(dataframe.columns.values)\n",
        "  print('2.- El DataFrame está compuesto por las siguientes columnas: ' + columnas + '\\n')\n",
        "  print('3.- Ejemplos de filas del DataFrame: \\n \\n Primeras 5 filas')\n",
        "  display(dataframe.head())\n",
        "  print('\\n Últimas 5 filas')\n",
        "  display(dataframe.tail())\n",
        "  print('\\n Muestreo aleatorio de 5 filas:')\n",
        "  display(dataframe.sample(5))\n",
        "  print('\\n 4.- Descripción numérica del Dataframe: \\n')\n",
        "  display(dataframe.describe())\n",
        "  nulos = str(dataframe.isna().sum())\n",
        "  print('\\n 5.- Cantidad de valores nulos por columna: \\n' + nulos)\n",
        "  unicos = str(dataframe.nunique())\n",
        "  print('\\n 6.- Cantidad de valores únicos por columna: \\n' + unicos)\n",
        "  duplicados = str(dataframe.duplicated().sum())\n",
        "  print('\\n 7.- Presenta un total de ' + duplicados + ' filas duplicadas.')\n",
        "  print('\\n 8.- Las filas con duplicados son: \\n')\n",
        "  display(dataframe[dataframe.duplicated()])\n",
        "  #print('\\n 9.- Boxplot de notas:')\n",
        "  #fig = px.box(dataframe[['math score', 'reading score', 'writing score']])\n",
        "  #fig.show()\n",
        "\n",
        "\n",
        "exploratory_data_analysis(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "piunFBsTfvQ5",
        "outputId": "d6f8e001-1f14-4457-b1e9-241431b83348"
      },
      "id": "piunFBsTfvQ5",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.- El Dataframe tiene 995817 filas y 35 columnas \n",
            "\n",
            "2.- El DataFrame está compuesto por las siguientes columnas: ['id' 'fraud_bool' 'income' 'prev_address_months_count'\n",
            " 'current_address_months_count' 'customer_age' 'days_since_request'\n",
            " 'intended_balcon_amount' 'payment_type' 'zip_count_4w' 'velocity_6h'\n",
            " 'velocity_24h' 'velocity_4w' 'bank_branch_count_8w' 'employment_status'\n",
            " 'credit_risk_score' 'housing_status' 'bank_months_count'\n",
            " 'has_other_cards' 'proposed_credit_limit' 'foreign_request' 'source'\n",
            " 'session_length_in_minutes' 'device_os' 'keep_alive_session'\n",
            " 'device_fraud_count' 'month' 'x1' 'x2' 'name_email_similarity'\n",
            " 'date_of_birth_distinct_emails_4w' 'email_is_free'\n",
            " 'device_distinct_emails_8w' 'phone_home_valid' 'phone_mobile_valid']\n",
            "\n",
            "3.- Ejemplos de filas del DataFrame: \n",
            " \n",
            " Primeras 5 filas\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes  device_os  keep_alive_session  device_fraud_count  month        x1        x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "0   0         0.0     0.2                      326.0                          20.0          60.0            0.022212               -0.758101           AB         647.0  5944.130884   3862.316270  5330.682025                 763.0                CC              186.0             BC               30.0              0.0                 1000.0              0.0  INTERNET                  19.450933      other                 1.0                 0.0    3.0 -0.282223 -0.137122               0.369510                               2.0            0.0                        2.0               0.0                 0.0\n",
              "1   1         NaN     NaN                        NaN                           NaN           NaN                 NaN                     NaN          NaN           NaN          NaN           NaN          NaN                   NaN               NaN                NaN            NaN                NaN              NaN                    NaN              NaN       NaN                        NaN        NaN                 NaN                 NaN    NaN       NaN       NaN               0.858929                               6.0            0.0                        1.0               1.0                 1.0\n",
              "2   2         0.0     0.9                       -1.0                          41.0          50.0            0.002588               17.710636           AA         815.0  7784.667290   4863.579732  3607.350151                   1.0                CA              212.0             BC               31.0              0.0                  500.0              0.0  INTERNET                   3.850864  macintosh                 0.0                 0.0    4.0 -0.029179  1.089166               0.166457                               7.0            1.0                        1.0               1.0                 1.0\n",
              "3   3         0.0     0.8                       23.0                           9.0          50.0            0.004026               18.224113           AA        1204.0  6510.930878   6593.331522  5245.997031                   1.0                CA              130.0             BC                6.0              0.0                  200.0              0.0  INTERNET                   5.182197    windows                 1.0                 0.0    2.0  2.178365  1.301696               0.250290                               1.0            0.0                        1.0               0.0                 1.0\n",
              "4   4         0.0     0.1                       -1.0                          30.0          30.0            0.011687               -1.434154           AB        1182.0  4304.899127   3738.266526  3609.551261                  12.0                CB              164.0             BB               11.0              0.0                  500.0              0.0  INTERNET                   4.346861    windows                 1.0                 0.0    7.0  1.472099  0.367675               0.619087                               8.0            0.0                        1.0               0.0                 1.0"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
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              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>employment_status</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>housing_status</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>source</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>device_os</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>0.2</td>\n",
              "      <td>326.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.022212</td>\n",
              "      <td>-0.758101</td>\n",
              "      <td>AB</td>\n",
              "      <td>647.0</td>\n",
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              "      <td>3862.316270</td>\n",
              "      <td>5330.682025</td>\n",
              "      <td>763.0</td>\n",
              "      <td>CC</td>\n",
              "      <td>186.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>19.450933</td>\n",
              "      <td>other</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>-0.282223</td>\n",
              "      <td>-0.137122</td>\n",
              "      <td>0.369510</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>1</th>\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",
              "      <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",
              "      <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>0.858929</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-1.0</td>\n",
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              "      <td>50.0</td>\n",
              "      <td>0.002588</td>\n",
              "      <td>17.710636</td>\n",
              "      <td>AA</td>\n",
              "      <td>815.0</td>\n",
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              "      <td>3607.350151</td>\n",
              "      <td>1.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>212.0</td>\n",
              "      <td>BC</td>\n",
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              "      <td>500.0</td>\n",
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              "      <td>INTERNET</td>\n",
              "      <td>3.850864</td>\n",
              "      <td>macintosh</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>-0.029179</td>\n",
              "      <td>1.089166</td>\n",
              "      <td>0.166457</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>3</td>\n",
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              "      <td>0.004026</td>\n",
              "      <td>18.224113</td>\n",
              "      <td>AA</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>130.0</td>\n",
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              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
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              "      <td>windows</td>\n",
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              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.1</td>\n",
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              "      <td>30.0</td>\n",
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              "      <td>-1.434154</td>\n",
              "      <td>AB</td>\n",
              "      <td>1182.0</td>\n",
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              "      <td>3738.266526</td>\n",
              "      <td>3609.551261</td>\n",
              "      <td>12.0</td>\n",
              "      <td>CB</td>\n",
              "      <td>164.0</td>\n",
              "      <td>BB</td>\n",
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              "      <td>500.0</td>\n",
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              "      <td>INTERNET</td>\n",
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              "      <td>windows</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>0.0</td>\n",
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          "metadata": {}
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          "text": [
            "\n",
            " Últimas 5 filas\n"
          ]
        },
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          "data": {
            "text/plain": [
              "            id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes device_os  keep_alive_session  device_fraud_count  month        x1        x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "995812  999995         1.0     0.4                       -1.0                         115.0          50.0            0.016675               -1.277662           AD        1704.0  7574.000604   5401.986247  5194.686284                   0.0                CA               43.0             BB               -1.0              0.0                  200.0              0.0  INTERNET                   4.871168     other                 0.0                 0.0    3.0  0.430381 -0.391984               0.522887                               1.0            1.0                        1.0               0.0                 1.0\n",
              "995813  999996         0.0     0.9                       38.0                           8.0          20.0            0.009012               50.289551           AA        1728.0  3581.552855   2510.056836  4363.077001                   2.0                CA              176.0             BC               29.0              0.0                 1500.0              0.0  INTERNET                  13.960900     linux                 1.0                 0.0    6.0  0.802064 -1.299359               0.475857                              12.0            1.0                        1.0               0.0                 1.0\n",
              "995814  999997         0.0     0.7                       -1.0                         305.0          50.0            0.001224               -1.639289           AB         780.0  4878.864192   3657.691361  5205.214618                   0.0                CA              309.0             BA               30.0              1.0                 1500.0              0.0  INTERNET                   2.007887   windows                 1.0                 0.0    6.0  0.432079 -0.018634               0.512173                               3.0            0.0                        1.0               1.0                 0.0\n",
              "995815  999998         0.0     0.8                       -1.0                          36.0          60.0            0.031814               -1.671697           AB        2140.0   879.645376   4436.203372  4218.324833                  16.0                CC              186.0             BC                1.0              1.0                  200.0              0.0  INTERNET                   0.999235   windows                 0.0                 0.0    4.0  1.686424  0.065182               0.138305                               8.0            0.0                        1.0               0.0                 1.0\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",
              "      <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>0.279632</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>947299</th>\n",
              "      <td>951280</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>172.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.039716</td>\n",
              "      <td>-1.195129</td>\n",
              "      <td>AB</td>\n",
              "      <td>801.0</td>\n",
              "      <td>8511.823387</td>\n",
              "      <td>5119.060421</td>\n",
              "      <td>5451.033971</td>\n",
              "      <td>15.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>162.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>4.301870</td>\n",
              "      <td>windows</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.943931</td>\n",
              "      <td>-1.015107</td>\n",
              "      <td>0.725991</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475336</th>\n",
              "      <td>477373</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>10.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.001711</td>\n",
              "      <td>28.311041</td>\n",
              "      <td>AA</td>\n",
              "      <td>749.0</td>\n",
              "      <td>3903.938762</td>\n",
              "      <td>4063.225967</td>\n",
              "      <td>5454.754341</td>\n",
              "      <td>16.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>92.0</td>\n",
              "      <td>BE</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>2.268031</td>\n",
              "      <td>macintosh</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.038325</td>\n",
              "      <td>-1.523983</td>\n",
              "      <td>0.490516</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>842806</th>\n",
              "      <td>846376</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9</td>\n",
              "      <td>33.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.008755</td>\n",
              "      <td>-1.212781</td>\n",
              "      <td>AC</td>\n",
              "      <td>1136.0</td>\n",
              "      <td>4049.535099</td>\n",
              "      <td>2777.321070</td>\n",
              "      <td>4339.442701</td>\n",
              "      <td>0.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>172.0</td>\n",
              "      <td>BB</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>9.123924</td>\n",
              "      <td>linux</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>-0.855007</td>\n",
              "      <td>-0.154632</td>\n",
              "      <td>0.162979</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42851</th>\n",
              "      <td>43040</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",
              "      <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",
              "      <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>0.238618</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-875002bc-b59f-4a00-84ca-0e14a982d56b')\"\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-875002bc-b59f-4a00-84ca-0e14a982d56b 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-875002bc-b59f-4a00-84ca-0e14a982d56b');\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": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 4.- Descripción numérica del Dataframe: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  id     fraud_bool         income  prev_address_months_count  current_address_months_count   customer_age  days_since_request  intended_balcon_amount   zip_count_4w    velocity_6h   velocity_24h    velocity_4w  bank_branch_count_8w  credit_risk_score  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request  session_length_in_minutes  keep_alive_session  device_fraud_count          month             x1             x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "count  995817.000000  934858.000000  934858.000000              934858.000000                 934858.000000  934858.000000        9.348580e+05           934858.000000  934858.000000  934858.000000  934858.000000  934858.000000         934858.000000      934858.000000      934858.000000    934858.000000          934858.000000    934858.000000              934858.000000       934858.000000            934858.0  934858.000000  934858.000000  934858.000000           9.347300e+05                     934730.000000  934730.000000              934730.000000     934730.000000       934730.000000\n",
              "mean   500037.694456       0.010989       0.578675                  14.674602                     99.223237      41.343605        9.038432e-01                8.549701    1517.628018    5489.966951    4661.073868    4733.988043            201.157147         139.260631          11.138964         0.249306             551.639393         0.023907                   7.814800            0.555611                 0.0       3.657613       0.013954       0.011317           4.876156e-01                          7.771928       0.518645                   1.022067          0.493399            0.856989\n",
              "std    288672.311891       0.104250       0.288248                  43.044012                     94.061999      13.765578        5.006807e+00               20.523497     965.052437    2940.694926    1451.791980     871.170401            473.669745          71.438414          12.125446         0.432612             506.698473         0.152761                   8.237816            0.496898                 0.0       2.116982       1.013219       1.012477           2.914544e-01                          4.815612       0.499653                   0.191637          0.499957            0.350085\n",
              "min         0.000000       0.000000       0.100000                  -1.000000                     -1.000000      10.000000        3.112791e-08              -15.537329       1.000000    -174.109691    1322.325176    2870.591613              0.000000        -177.000000          -1.000000         0.000000             190.000000         0.000000                  -1.000000            0.000000                 0.0       0.000000      -4.977864      -4.846414           5.024707e-08                          0.000000       0.000000                  -1.000000          0.000000            0.000000\n",
              "25%    250051.000000       0.000000       0.300000                  -1.000000                     27.000000      30.000000        7.444951e-03               -1.179790     886.000000    3332.844048    3502.670600    4238.443392              1.000000          90.000000           1.000000         0.000000             200.000000         0.000000                   3.151046            0.000000                 0.0       2.000000      -0.668066      -0.670285           2.145474e-01                          4.000000       0.000000                   1.000000          0.000000            1.000000\n",
              "50%    500053.000000       0.000000       0.600000                  -1.000000                     64.000000      50.000000        1.566869e-02               -0.834570    1208.000000    5189.174358    4640.736346    4813.370088             10.000000         130.000000           6.000000         0.000000             200.000000         0.000000                   5.246366            1.000000                 0.0       4.000000       0.006922       0.005659           4.858929e-01                          7.000000       1.000000                   1.000000          0.000000            1.000000\n",
              "75%    750047.000000       0.000000       0.800000                  -1.000000                    154.000000      50.000000        2.692743e-02               -0.069674    1844.000000    7368.257121    5591.966565    5331.803997             31.000000         188.000000          25.000000         0.000000            1000.000000         0.000000                   9.365532            1.000000                 0.0       5.000000       0.686546       0.683718           7.545305e-01                         11.000000       1.000000                   1.000000          1.000000            1.000000\n",
              "max    999999.000000       1.000000       0.900000                 399.000000                    429.000000      90.000000        7.657750e+01              112.702504    6650.000000   16754.959024    9539.356530    7019.201030           2377.000000         388.000000          32.000000         1.000000            2100.000000         1.000000                  85.567848            1.000000                 0.0       7.000000       6.434867       6.542492           1.000000e+00                         39.000000       1.000000                   2.000000          1.000000            1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b274f79a-68fb-459a-9634-2acf601dc976\">\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>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>days_since_request</th>\n",
              "      <th>intended_balcon_amount</th>\n",
              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>995817.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>9.348580e+05</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.0</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>934858.000000</td>\n",
              "      <td>9.347300e+05</td>\n",
              "      <td>934730.000000</td>\n",
              "      <td>934730.000000</td>\n",
              "      <td>934730.000000</td>\n",
              "      <td>934730.000000</td>\n",
              "      <td>934730.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>500037.694456</td>\n",
              "      <td>0.010989</td>\n",
              "      <td>0.578675</td>\n",
              "      <td>14.674602</td>\n",
              "      <td>99.223237</td>\n",
              "      <td>41.343605</td>\n",
              "      <td>9.038432e-01</td>\n",
              "      <td>8.549701</td>\n",
              "      <td>1517.628018</td>\n",
              "      <td>5489.966951</td>\n",
              "      <td>4661.073868</td>\n",
              "      <td>4733.988043</td>\n",
              "      <td>201.157147</td>\n",
              "      <td>139.260631</td>\n",
              "      <td>11.138964</td>\n",
              "      <td>0.249306</td>\n",
              "      <td>551.639393</td>\n",
              "      <td>0.023907</td>\n",
              "      <td>7.814800</td>\n",
              "      <td>0.555611</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.657613</td>\n",
              "      <td>0.013954</td>\n",
              "      <td>0.011317</td>\n",
              "      <td>4.876156e-01</td>\n",
              "      <td>7.771928</td>\n",
              "      <td>0.518645</td>\n",
              "      <td>1.022067</td>\n",
              "      <td>0.493399</td>\n",
              "      <td>0.856989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>288672.311891</td>\n",
              "      <td>0.104250</td>\n",
              "      <td>0.288248</td>\n",
              "      <td>43.044012</td>\n",
              "      <td>94.061999</td>\n",
              "      <td>13.765578</td>\n",
              "      <td>5.006807e+00</td>\n",
              "      <td>20.523497</td>\n",
              "      <td>965.052437</td>\n",
              "      <td>2940.694926</td>\n",
              "      <td>1451.791980</td>\n",
              "      <td>871.170401</td>\n",
              "      <td>473.669745</td>\n",
              "      <td>71.438414</td>\n",
              "      <td>12.125446</td>\n",
              "      <td>0.432612</td>\n",
              "      <td>506.698473</td>\n",
              "      <td>0.152761</td>\n",
              "      <td>8.237816</td>\n",
              "      <td>0.496898</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.116982</td>\n",
              "      <td>1.013219</td>\n",
              "      <td>1.012477</td>\n",
              "      <td>2.914544e-01</td>\n",
              "      <td>4.815612</td>\n",
              "      <td>0.499653</td>\n",
              "      <td>0.191637</td>\n",
              "      <td>0.499957</td>\n",
              "      <td>0.350085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.112791e-08</td>\n",
              "      <td>-15.537329</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-174.109691</td>\n",
              "      <td>1322.325176</td>\n",
              "      <td>2870.591613</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-177.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-4.977864</td>\n",
              "      <td>-4.846414</td>\n",
              "      <td>5.024707e-08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>250051.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>7.444951e-03</td>\n",
              "      <td>-1.179790</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>3332.844048</td>\n",
              "      <td>3502.670600</td>\n",
              "      <td>4238.443392</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.151046</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-0.668066</td>\n",
              "      <td>-0.670285</td>\n",
              "      <td>2.145474e-01</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>500053.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.566869e-02</td>\n",
              "      <td>-0.834570</td>\n",
              "      <td>1208.000000</td>\n",
              "      <td>5189.174358</td>\n",
              "      <td>4640.736346</td>\n",
              "      <td>4813.370088</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.246366</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.006922</td>\n",
              "      <td>0.005659</td>\n",
              "      <td>4.858929e-01</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>750047.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>2.692743e-02</td>\n",
              "      <td>-0.069674</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>7368.257121</td>\n",
              "      <td>5591.966565</td>\n",
              "      <td>5331.803997</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.365532</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.686546</td>\n",
              "      <td>0.683718</td>\n",
              "      <td>7.545305e-01</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999999.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>7.657750e+01</td>\n",
              "      <td>112.702504</td>\n",
              "      <td>6650.000000</td>\n",
              "      <td>16754.959024</td>\n",
              "      <td>9539.356530</td>\n",
              "      <td>7019.201030</td>\n",
              "      <td>2377.000000</td>\n",
              "      <td>388.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>85.567848</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.434867</td>\n",
              "      <td>6.542492</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 5.- Cantidad de valores nulos por columna: \n",
            "id                                      0\n",
            "fraud_bool                          60959\n",
            "income                              60959\n",
            "prev_address_months_count           60959\n",
            "current_address_months_count        60959\n",
            "customer_age                        60959\n",
            "days_since_request                  60959\n",
            "intended_balcon_amount              60959\n",
            "payment_type                        60959\n",
            "zip_count_4w                        60959\n",
            "velocity_6h                         60959\n",
            "velocity_24h                        60959\n",
            "velocity_4w                         60959\n",
            "bank_branch_count_8w                60959\n",
            "employment_status                   60959\n",
            "credit_risk_score                   60959\n",
            "housing_status                      60959\n",
            "bank_months_count                   60959\n",
            "has_other_cards                     60959\n",
            "proposed_credit_limit               60959\n",
            "foreign_request                     60959\n",
            "source                              60959\n",
            "session_length_in_minutes           60959\n",
            "device_os                           60959\n",
            "keep_alive_session                  60959\n",
            "device_fraud_count                  60959\n",
            "month                               60959\n",
            "x1                                  60959\n",
            "x2                                  60959\n",
            "name_email_similarity               61087\n",
            "date_of_birth_distinct_emails_4w    61087\n",
            "email_is_free                       61087\n",
            "device_distinct_emails_8w           61087\n",
            "phone_home_valid                    61087\n",
            "phone_mobile_valid                  61087\n",
            "dtype: int64\n",
            "\n",
            " 6.- Cantidad de valores únicos por columna: \n",
            "id                                  995817\n",
            "fraud_bool                               2\n",
            "income                                   9\n",
            "prev_address_months_count              373\n",
            "current_address_months_count           416\n",
            "customer_age                             9\n",
            "days_since_request                  925125\n",
            "intended_balcon_amount              930349\n",
            "payment_type                             5\n",
            "zip_count_4w                          6238\n",
            "velocity_6h                         933695\n",
            "velocity_24h                        933964\n",
            "velocity_4w                         933273\n",
            "bank_branch_count_8w                  2320\n",
            "employment_status                        7\n",
            "credit_risk_score                      544\n",
            "housing_status                           7\n",
            "bank_months_count                       33\n",
            "has_other_cards                          2\n",
            "proposed_credit_limit                   12\n",
            "foreign_request                          2\n",
            "source                                   2\n",
            "session_length_in_minutes           930312\n",
            "device_os                                5\n",
            "keep_alive_session                       2\n",
            "device_fraud_count                       1\n",
            "month                                    8\n",
            "x1                                  934858\n",
            "x2                                  934858\n",
            "name_email_similarity               933577\n",
            "date_of_birth_distinct_emails_4w        40\n",
            "email_is_free                            2\n",
            "device_distinct_emails_8w                4\n",
            "phone_home_valid                         2\n",
            "phone_mobile_valid                       2\n",
            "dtype: int64\n",
            "\n",
            " 7.- Presenta un total de 0 filas duplicadas.\n",
            "\n",
            " 8.- Las filas con duplicados son: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, fraud_bool, income, prev_address_months_count, current_address_months_count, customer_age, days_since_request, intended_balcon_amount, payment_type, zip_count_4w, velocity_6h, velocity_24h, velocity_4w, bank_branch_count_8w, employment_status, credit_risk_score, housing_status, bank_months_count, has_other_cards, proposed_credit_limit, foreign_request, source, session_length_in_minutes, device_os, keep_alive_session, device_fraud_count, month, x1, x2, name_email_similarity, date_of_birth_distinct_emails_4w, email_is_free, device_distinct_emails_8w, phone_home_valid, phone_mobile_valid]\n",
              "Index: []"
            ],
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a8a37a1f-ccf6-43d2-903d-f369b1d0b805 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-a8a37a1f-ccf6-43d2-903d-f369b1d0b805');\n",
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              "            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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Respecto a los nulos: \n",
        "- Se observa que la columna ```id``` no contiene nulos.\n",
        "- Se observa que existen 60.959 filas con datos nulos en todas las columnas provenientes del dataset que contenía los ejemplos de datos iniciales.\n",
        "- Se observa que existen 61.087 filas con datos nulos en todas las columnas provinientes del dataset que contenía características extras de los ejemplos.\n",
        "\n",
        "Así, si se asume que no hubo errores en la columna ```id``` , se concluye el dataframe ```df_3``` contiene 61.087 ```id``` que no están presetes en ```df_email``` y que esta última contiene 60.959 ```id``` que no están presentes en ```df_3``` .\n",
        "\n",
        "\n",
        "Respecto a los duplicados: \n",
        "- Se observa que no existen datos duplicados (filas completas), lo cual es esperable, dado que al menos en el caso de los ejemplos iniciales, estos fueron eliminados de antemano.\n",
        "- No se revisa por id duplicados en específico, ya que no existían en los dataframes particulares y el _merge_ se encarga de combinarlos correctamente, de este modo, no se duplican.\n",
        "\n",
        "\n",
        "Como solución al problema de los nulos, al ver que no son muchas filas en relación al total de filas del dataset, se propone eliminar las filas que contienen nulos. "
      ],
      "metadata": {
        "id": "tP5RB-FeyEaf"
      },
      "id": "tP5RB-FeyEaf"
    },
    {
      "cell_type": "markdown",
      "id": "31b39557-4e12-4961-9a96-60d7eb945080",
      "metadata": {
        "id": "31b39557-4e12-4961-9a96-60d7eb945080"
      },
      "source": [
        "### 2.6 Limpiar"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65507c50",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "65507c50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8b5d2c44-11df-4011-e9ea-5a2771aefd8e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.- El Dataframe tiene 873771 filas y 35 columnas \n",
            "\n",
            "2.- El DataFrame está compuesto por las siguientes columnas: ['id' 'fraud_bool' 'income' 'prev_address_months_count'\n",
            " 'current_address_months_count' 'customer_age' 'days_since_request'\n",
            " 'intended_balcon_amount' 'payment_type' 'zip_count_4w' 'velocity_6h'\n",
            " 'velocity_24h' 'velocity_4w' 'bank_branch_count_8w' 'employment_status'\n",
            " 'credit_risk_score' 'housing_status' 'bank_months_count'\n",
            " 'has_other_cards' 'proposed_credit_limit' 'foreign_request' 'source'\n",
            " 'session_length_in_minutes' 'device_os' 'keep_alive_session'\n",
            " 'device_fraud_count' 'month' 'x1' 'x2' 'name_email_similarity'\n",
            " 'date_of_birth_distinct_emails_4w' 'email_is_free'\n",
            " 'device_distinct_emails_8w' 'phone_home_valid' 'phone_mobile_valid']\n",
            "\n",
            "3.- Ejemplos de filas del DataFrame: \n",
            " \n",
            " Primeras 5 filas\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes  device_os  keep_alive_session  device_fraud_count  month        x1        x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "0   0         0.0     0.2                      326.0                          20.0          60.0            0.022212               -0.758101           AB         647.0  5944.130884   3862.316270  5330.682025                 763.0                CC              186.0             BC               30.0              0.0                 1000.0              0.0  INTERNET                  19.450933      other                 1.0                 0.0    3.0 -0.282223 -0.137122               0.369510                               2.0            0.0                        2.0               0.0                 0.0\n",
              "2   2         0.0     0.9                       -1.0                          41.0          50.0            0.002588               17.710636           AA         815.0  7784.667290   4863.579732  3607.350151                   1.0                CA              212.0             BC               31.0              0.0                  500.0              0.0  INTERNET                   3.850864  macintosh                 0.0                 0.0    4.0 -0.029179  1.089166               0.166457                               7.0            1.0                        1.0               1.0                 1.0\n",
              "3   3         0.0     0.8                       23.0                           9.0          50.0            0.004026               18.224113           AA        1204.0  6510.930878   6593.331522  5245.997031                   1.0                CA              130.0             BC                6.0              0.0                  200.0              0.0  INTERNET                   5.182197    windows                 1.0                 0.0    2.0  2.178365  1.301696               0.250290                               1.0            0.0                        1.0               0.0                 1.0\n",
              "4   4         0.0     0.1                       -1.0                          30.0          30.0            0.011687               -1.434154           AB        1182.0  4304.899127   3738.266526  3609.551261                  12.0                CB              164.0             BB               11.0              0.0                  500.0              0.0  INTERNET                   4.346861    windows                 1.0                 0.0    7.0  1.472099  0.367675               0.619087                               8.0            0.0                        1.0               0.0                 1.0\n",
              "5   5         0.0     0.9                       83.0                           6.0          50.0            0.024149               -1.323432           AC         477.0  5526.000890   4576.354823  5042.758337                   1.0                CA              205.0             BA               -1.0              0.0                 1500.0              0.0  INTERNET                   4.427453      other                 0.0                 0.0    3.0  0.330928 -1.505284               0.317856                               5.0            0.0                        1.0               0.0                 1.0"
            ],
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            " Últimas 5 filas\n"
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              "995813  999996         0.0     0.9                       38.0                           8.0          20.0            0.009012               50.289551           AA        1728.0  3581.552855   2510.056836  4363.077001                   2.0                CA              176.0             BC               29.0              0.0                 1500.0              0.0  INTERNET                  13.960900     linux                 1.0                 0.0    6.0  0.802064 -1.299359               0.475857                              12.0            1.0                        1.0               0.0                 1.0\n",
              "995814  999997         0.0     0.7                       -1.0                         305.0          50.0            0.001224               -1.639289           AB         780.0  4878.864192   3657.691361  5205.214618                   0.0                CA              309.0             BA               30.0              1.0                 1500.0              0.0  INTERNET                   2.007887   windows                 1.0                 0.0    6.0  0.432079 -0.018634               0.512173                               3.0            0.0                        1.0               1.0                 0.0\n",
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              "189107  189949         0.0     0.2                       -1.0                          30.0          30.0            0.008891               -0.927732           AB         999.0  6051.112090   3827.374313  4287.931534                  19.0                CA               99.0             BC               25.0              0.0                  200.0              0.0  INTERNET                   1.723781     linux                 1.0                 0.0    4.0  0.328102  0.717683               0.176331                              11.0            1.0                        1.0               1.0                 1.0\n",
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              "    }\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>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>days_since_request</th>\n",
              "      <th>intended_balcon_amount</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>employment_status</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>housing_status</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>source</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>device_os</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>503575</th>\n",
              "      <td>505740</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.032330</td>\n",
              "      <td>-0.827852</td>\n",
              "      <td>AB</td>\n",
              "      <td>1405.0</td>\n",
              "      <td>3588.340501</td>\n",
              "      <td>3348.881437</td>\n",
              "      <td>3108.293207</td>\n",
              "      <td>15.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>142.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>4.783160</td>\n",
              "      <td>linux</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.185180</td>\n",
              "      <td>0.975322</td>\n",
              "      <td>0.895937</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>815519</th>\n",
              "      <td>818992</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.025670</td>\n",
              "      <td>-1.294048</td>\n",
              "      <td>AB</td>\n",
              "      <td>3524.0</td>\n",
              "      <td>3616.850014</td>\n",
              "      <td>7621.886620</td>\n",
              "      <td>5444.498989</td>\n",
              "      <td>16.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>53.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>0.627595</td>\n",
              "      <td>linux</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.739613</td>\n",
              "      <td>-1.478160</td>\n",
              "      <td>0.209078</td>\n",
              "      <td>12.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189107</th>\n",
              "      <td>189949</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.008891</td>\n",
              "      <td>-0.927732</td>\n",
              "      <td>AB</td>\n",
              "      <td>999.0</td>\n",
              "      <td>6051.112090</td>\n",
              "      <td>3827.374313</td>\n",
              "      <td>4287.931534</td>\n",
              "      <td>19.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>99.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>1.723781</td>\n",
              "      <td>linux</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.328102</td>\n",
              "      <td>0.717683</td>\n",
              "      <td>0.176331</td>\n",
              "      <td>11.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>732603</th>\n",
              "      <td>735736</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>0.013032</td>\n",
              "      <td>-1.183003</td>\n",
              "      <td>AB</td>\n",
              "      <td>1487.0</td>\n",
              "      <td>818.843085</td>\n",
              "      <td>3065.989761</td>\n",
              "      <td>3671.233295</td>\n",
              "      <td>1770.0</td>\n",
              "      <td>CA</td>\n",
              "      <td>118.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>21.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>6.410627</td>\n",
              "      <td>linux</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>-0.233655</td>\n",
              "      <td>-0.488159</td>\n",
              "      <td>0.312814</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135238</th>\n",
              "      <td>135830</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0.001445</td>\n",
              "      <td>-0.475639</td>\n",
              "      <td>AB</td>\n",
              "      <td>2166.0</td>\n",
              "      <td>5154.816352</td>\n",
              "      <td>3549.434118</td>\n",
              "      <td>3086.286583</td>\n",
              "      <td>12.0</td>\n",
              "      <td>CB</td>\n",
              "      <td>255.0</td>\n",
              "      <td>BC</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>INTERNET</td>\n",
              "      <td>1.646823</td>\n",
              "      <td>other</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>-0.375626</td>\n",
              "      <td>0.834348</td>\n",
              "      <td>0.894580</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d7633bd-f808-40e9-a7ee-d3991c36c4ac')\"\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",
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              "  <style>\n",
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              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "          document.querySelector('#df-1d7633bd-f808-40e9-a7ee-d3991c36c4ac 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-1d7633bd-f808-40e9-a7ee-d3991c36c4ac');\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",
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              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 4.- Descripción numérica del Dataframe: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                  id     fraud_bool         income  prev_address_months_count  current_address_months_count   customer_age  days_since_request  intended_balcon_amount   zip_count_4w    velocity_6h   velocity_24h    velocity_4w  bank_branch_count_8w  credit_risk_score  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request  session_length_in_minutes  keep_alive_session  device_fraud_count          month             x1             x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid\n",
              "count  873771.000000  873771.000000  873771.000000              873771.000000                 873771.000000  873771.000000        8.737710e+05           873771.000000  873771.000000  873771.000000  873771.000000  873771.000000         873771.000000      873771.000000      873771.000000    873771.000000          873771.000000    873771.000000              873771.000000       873771.000000            873771.0  873771.000000  873771.000000  873771.000000           8.737710e+05                     873771.000000  873771.000000              873771.000000     873771.000000       873771.000000\n",
              "mean   499986.082237       0.011012       0.578727                  14.664555                     99.234073      41.344013        9.026673e-01                8.554425    1517.752437    5490.980468    4660.455323    4734.233331            200.939237         139.257098          11.140093         0.249381             551.569713         0.023951                   7.814552            0.555831                 0.0       3.657684       0.014022       0.011191           4.877150e-01                          7.773291       0.518628                   1.022184          0.493474            0.856930\n",
              "std    288603.604762       0.104359       0.288292                  43.026325                     94.047441      13.762575        4.996813e+00               20.528247     965.194858    2940.464800    1451.283880     870.968411            473.420354          71.432183          12.125940         0.432655             506.763240         0.152898                   8.238858            0.496873                 0.0       2.117050       1.013359       1.012448           2.914227e-01                          4.815301       0.499653                   0.191700          0.499958            0.350144\n",
              "min         0.000000       0.000000       0.100000                  -1.000000                     -1.000000      10.000000        3.112791e-08              -15.537329       1.000000    -174.109691    1322.325176    2870.591613              0.000000        -177.000000          -1.000000         0.000000             190.000000         0.000000                  -1.000000            0.000000                 0.0       0.000000      -4.977864      -4.846414           5.024707e-08                          0.000000       0.000000                  -1.000000          0.000000            0.000000\n",
              "25%    249948.500000       0.000000       0.300000                  -1.000000                     27.000000      30.000000        7.448993e-03               -1.179763     886.000000    3335.150770    3502.713718    4238.476728              1.000000          90.000000           1.000000         0.000000             200.000000         0.000000                   3.150412            0.000000                 0.0       2.000000      -0.668335      -0.670306           2.146826e-01                          4.000000       0.000000                   1.000000          0.000000            1.000000\n",
              "50%    500153.000000       0.000000       0.600000                  -1.000000                     64.000000      50.000000        1.567109e-02               -0.834561    1208.000000    5190.987335    4639.588503    4813.684295             10.000000         130.000000           6.000000         0.000000             200.000000         0.000000                   5.246457            1.000000                 0.0       4.000000       0.006772       0.005482           4.860334e-01                          7.000000       1.000000                   1.000000          0.000000            1.000000\n",
              "75%    749761.500000       0.000000       0.800000                  -1.000000                    154.000000      50.000000        2.693016e-02               -0.065957    1844.000000    7369.109285    5590.460945    5331.919866             31.000000         188.000000          25.000000         0.000000            1000.000000         0.000000                   9.366925            1.000000                 0.0       5.000000       0.686693       0.683431           7.546222e-01                         11.000000       1.000000                   1.000000          1.000000            1.000000\n",
              "max    999999.000000       1.000000       0.900000                 399.000000                    429.000000      90.000000        7.657750e+01              112.702504    6650.000000   16754.959024    9539.356530    7019.201030           2377.000000         387.000000          32.000000         1.000000            2100.000000         1.000000                  85.567848            1.000000                 0.0       7.000000       6.434867       6.542492           1.000000e+00                         39.000000       1.000000                   2.000000          1.000000            1.000000"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>days_since_request</th>\n",
              "      <th>intended_balcon_amount</th>\n",
              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>8.737710e+05</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.0</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>8.737710e+05</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "      <td>873771.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>499986.082237</td>\n",
              "      <td>0.011012</td>\n",
              "      <td>0.578727</td>\n",
              "      <td>14.664555</td>\n",
              "      <td>99.234073</td>\n",
              "      <td>41.344013</td>\n",
              "      <td>9.026673e-01</td>\n",
              "      <td>8.554425</td>\n",
              "      <td>1517.752437</td>\n",
              "      <td>5490.980468</td>\n",
              "      <td>4660.455323</td>\n",
              "      <td>4734.233331</td>\n",
              "      <td>200.939237</td>\n",
              "      <td>139.257098</td>\n",
              "      <td>11.140093</td>\n",
              "      <td>0.249381</td>\n",
              "      <td>551.569713</td>\n",
              "      <td>0.023951</td>\n",
              "      <td>7.814552</td>\n",
              "      <td>0.555831</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.657684</td>\n",
              "      <td>0.014022</td>\n",
              "      <td>0.011191</td>\n",
              "      <td>4.877150e-01</td>\n",
              "      <td>7.773291</td>\n",
              "      <td>0.518628</td>\n",
              "      <td>1.022184</td>\n",
              "      <td>0.493474</td>\n",
              "      <td>0.856930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>288603.604762</td>\n",
              "      <td>0.104359</td>\n",
              "      <td>0.288292</td>\n",
              "      <td>43.026325</td>\n",
              "      <td>94.047441</td>\n",
              "      <td>13.762575</td>\n",
              "      <td>4.996813e+00</td>\n",
              "      <td>20.528247</td>\n",
              "      <td>965.194858</td>\n",
              "      <td>2940.464800</td>\n",
              "      <td>1451.283880</td>\n",
              "      <td>870.968411</td>\n",
              "      <td>473.420354</td>\n",
              "      <td>71.432183</td>\n",
              "      <td>12.125940</td>\n",
              "      <td>0.432655</td>\n",
              "      <td>506.763240</td>\n",
              "      <td>0.152898</td>\n",
              "      <td>8.238858</td>\n",
              "      <td>0.496873</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.117050</td>\n",
              "      <td>1.013359</td>\n",
              "      <td>1.012448</td>\n",
              "      <td>2.914227e-01</td>\n",
              "      <td>4.815301</td>\n",
              "      <td>0.499653</td>\n",
              "      <td>0.191700</td>\n",
              "      <td>0.499958</td>\n",
              "      <td>0.350144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>3.112791e-08</td>\n",
              "      <td>-15.537329</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-174.109691</td>\n",
              "      <td>1322.325176</td>\n",
              "      <td>2870.591613</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-177.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-4.977864</td>\n",
              "      <td>-4.846414</td>\n",
              "      <td>5.024707e-08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>249948.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>7.448993e-03</td>\n",
              "      <td>-1.179763</td>\n",
              "      <td>886.000000</td>\n",
              "      <td>3335.150770</td>\n",
              "      <td>3502.713718</td>\n",
              "      <td>4238.476728</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.150412</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-0.668335</td>\n",
              "      <td>-0.670306</td>\n",
              "      <td>2.146826e-01</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>500153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.567109e-02</td>\n",
              "      <td>-0.834561</td>\n",
              "      <td>1208.000000</td>\n",
              "      <td>5190.987335</td>\n",
              "      <td>4639.588503</td>\n",
              "      <td>4813.684295</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.246457</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.006772</td>\n",
              "      <td>0.005482</td>\n",
              "      <td>4.860334e-01</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>749761.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>154.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>2.693016e-02</td>\n",
              "      <td>-0.065957</td>\n",
              "      <td>1844.000000</td>\n",
              "      <td>7369.109285</td>\n",
              "      <td>5590.460945</td>\n",
              "      <td>5331.919866</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.366925</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.686693</td>\n",
              "      <td>0.683431</td>\n",
              "      <td>7.546222e-01</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999999.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>399.000000</td>\n",
              "      <td>429.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>7.657750e+01</td>\n",
              "      <td>112.702504</td>\n",
              "      <td>6650.000000</td>\n",
              "      <td>16754.959024</td>\n",
              "      <td>9539.356530</td>\n",
              "      <td>7019.201030</td>\n",
              "      <td>2377.000000</td>\n",
              "      <td>387.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>85.567848</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>6.434867</td>\n",
              "      <td>6.542492</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c8b30c09-f714-4ddd-b8e7-cb2b9e9157d7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-c8b30c09-f714-4ddd-b8e7-cb2b9e9157d7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " 5.- Cantidad de valores nulos por columna: \n",
            "id                                  0\n",
            "fraud_bool                          0\n",
            "income                              0\n",
            "prev_address_months_count           0\n",
            "current_address_months_count        0\n",
            "customer_age                        0\n",
            "days_since_request                  0\n",
            "intended_balcon_amount              0\n",
            "payment_type                        0\n",
            "zip_count_4w                        0\n",
            "velocity_6h                         0\n",
            "velocity_24h                        0\n",
            "velocity_4w                         0\n",
            "bank_branch_count_8w                0\n",
            "employment_status                   0\n",
            "credit_risk_score                   0\n",
            "housing_status                      0\n",
            "bank_months_count                   0\n",
            "has_other_cards                     0\n",
            "proposed_credit_limit               0\n",
            "foreign_request                     0\n",
            "source                              0\n",
            "session_length_in_minutes           0\n",
            "device_os                           0\n",
            "keep_alive_session                  0\n",
            "device_fraud_count                  0\n",
            "month                               0\n",
            "x1                                  0\n",
            "x2                                  0\n",
            "name_email_similarity               0\n",
            "date_of_birth_distinct_emails_4w    0\n",
            "email_is_free                       0\n",
            "device_distinct_emails_8w           0\n",
            "phone_home_valid                    0\n",
            "phone_mobile_valid                  0\n",
            "dtype: int64\n",
            "\n",
            " 6.- Cantidad de valores únicos por columna: \n",
            "id                                  873771\n",
            "fraud_bool                               2\n",
            "income                                   9\n",
            "prev_address_months_count              373\n",
            "current_address_months_count           416\n",
            "customer_age                             9\n",
            "days_since_request                  865279\n",
            "intended_balcon_amount              869846\n",
            "payment_type                             5\n",
            "zip_count_4w                          6222\n",
            "velocity_6h                         872758\n",
            "velocity_24h                        873005\n",
            "velocity_4w                         872391\n",
            "bank_branch_count_8w                  2317\n",
            "employment_status                        7\n",
            "credit_risk_score                      543\n",
            "housing_status                           7\n",
            "bank_months_count                       33\n",
            "has_other_cards                          2\n",
            "proposed_credit_limit                   12\n",
            "foreign_request                          2\n",
            "source                                   2\n",
            "session_length_in_minutes           869648\n",
            "device_os                                5\n",
            "keep_alive_session                       2\n",
            "device_fraud_count                       1\n",
            "month                                    8\n",
            "x1                                  873771\n",
            "x2                                  873771\n",
            "name_email_similarity               872778\n",
            "date_of_birth_distinct_emails_4w        40\n",
            "email_is_free                            2\n",
            "device_distinct_emails_8w                4\n",
            "phone_home_valid                         2\n",
            "phone_mobile_valid                       2\n",
            "dtype: int64\n",
            "\n",
            " 7.- Presenta un total de 0 filas duplicadas.\n",
            "\n",
            " 8.- Las filas con duplicados son: \n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, fraud_bool, income, prev_address_months_count, current_address_months_count, customer_age, days_since_request, intended_balcon_amount, payment_type, zip_count_4w, velocity_6h, velocity_24h, velocity_4w, bank_branch_count_8w, employment_status, credit_risk_score, housing_status, bank_months_count, has_other_cards, proposed_credit_limit, foreign_request, source, session_length_in_minutes, device_os, keep_alive_session, device_fraud_count, month, x1, x2, name_email_similarity, date_of_birth_distinct_emails_4w, email_is_free, device_distinct_emails_8w, phone_home_valid, phone_mobile_valid]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>fraud_bool</th>\n",
              "      <th>income</th>\n",
              "      <th>prev_address_months_count</th>\n",
              "      <th>current_address_months_count</th>\n",
              "      <th>customer_age</th>\n",
              "      <th>days_since_request</th>\n",
              "      <th>intended_balcon_amount</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>zip_count_4w</th>\n",
              "      <th>velocity_6h</th>\n",
              "      <th>velocity_24h</th>\n",
              "      <th>velocity_4w</th>\n",
              "      <th>bank_branch_count_8w</th>\n",
              "      <th>employment_status</th>\n",
              "      <th>credit_risk_score</th>\n",
              "      <th>housing_status</th>\n",
              "      <th>bank_months_count</th>\n",
              "      <th>has_other_cards</th>\n",
              "      <th>proposed_credit_limit</th>\n",
              "      <th>foreign_request</th>\n",
              "      <th>source</th>\n",
              "      <th>session_length_in_minutes</th>\n",
              "      <th>device_os</th>\n",
              "      <th>keep_alive_session</th>\n",
              "      <th>device_fraud_count</th>\n",
              "      <th>month</th>\n",
              "      <th>x1</th>\n",
              "      <th>x2</th>\n",
              "      <th>name_email_similarity</th>\n",
              "      <th>date_of_birth_distinct_emails_4w</th>\n",
              "      <th>email_is_free</th>\n",
              "      <th>device_distinct_emails_8w</th>\n",
              "      <th>phone_home_valid</th>\n",
              "      <th>phone_mobile_valid</th>\n",
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              "  "
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Se eliminan las filas que contienen datos nulos\n",
        "df.dropna(axis=0, how='any', inplace=True)\n",
        "\n",
        "exploratory_data_analysis(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se puede observar que ya no se tienen columnas con valores nulos y que la cantidad de filas quedó en 873.771, que es exactamente igual a 995.817-60.959-61.087."
      ],
      "metadata": {
        "id": "WRW2cVB0SJf_"
      },
      "id": "WRW2cVB0SJf_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.7 Segmentación etaria de los datos\n",
        "\n",
        "En esta parte se agregará una nueva columna a nuestro dataframe, que indique el rango etario del usuario descrito en la columna. "
      ],
      "metadata": {
        "id": "I3yQFTcUSJuY"
      },
      "id": "I3yQFTcUSJuY"
    },
    {
      "cell_type": "code",
      "source": [
        "bins = [0,17, 26, 59, df['customer_age'].max()]\n",
        "Edades = ['Joven', 'Adulto Joven', 'Adulto', 'Persona Mayor']\n",
        "df['segmentacion_etaria'] = pd.cut(df['customer_age'], bins, labels=Edades)\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "RrHLaO8_l1x-",
        "outputId": "44ca965d-23d3-41a3-c31b-0bfa592153f2"
      },
      "id": "RrHLaO8_l1x-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id  fraud_bool  income  prev_address_months_count  current_address_months_count  customer_age  days_since_request  intended_balcon_amount payment_type  zip_count_4w  velocity_6h  velocity_24h  velocity_4w  bank_branch_count_8w employment_status  credit_risk_score housing_status  bank_months_count  has_other_cards  proposed_credit_limit  foreign_request    source  session_length_in_minutes  device_os  keep_alive_session  device_fraud_count  month        x1        x2  name_email_similarity  date_of_birth_distinct_emails_4w  email_is_free  device_distinct_emails_8w  phone_home_valid  phone_mobile_valid segmentacion_etaria\n",
              "0   0         0.0     0.2                      326.0                          20.0          60.0            0.022212               -0.758101           AB         647.0  5944.130884   3862.316270  5330.682025                 763.0                CC              186.0             BC               30.0              0.0                 1000.0              0.0  INTERNET                  19.450933      other                 1.0                 0.0    3.0 -0.282223 -0.137122               0.369510                               2.0            0.0                        2.0               0.0                 0.0       Persona Mayor\n",
              "2   2         0.0     0.9                       -1.0                          41.0          50.0            0.002588               17.710636           AA         815.0  7784.667290   4863.579732  3607.350151                   1.0                CA              212.0             BC               31.0              0.0                  500.0              0.0  INTERNET                   3.850864  macintosh                 0.0                 0.0    4.0 -0.029179  1.089166               0.166457                               7.0            1.0                        1.0               1.0                 1.0              Adulto\n",
              "3   3         0.0     0.8                       23.0                           9.0          50.0            0.004026               18.224113           AA        1204.0  6510.930878   6593.331522  5245.997031                   1.0                CA              130.0             BC                6.0              0.0                  200.0              0.0  INTERNET                   5.182197    windows                 1.0                 0.0    2.0  2.178365  1.301696               0.250290                               1.0            0.0                        1.0               0.0                 1.0              Adulto\n",
              "4   4         0.0     0.1                       -1.0                          30.0          30.0            0.011687               -1.434154           AB        1182.0  4304.899127   3738.266526  3609.551261                  12.0                CB              164.0             BB               11.0              0.0                  500.0              0.0  INTERNET                   4.346861    windows                 1.0                 0.0    7.0  1.472099  0.367675               0.619087                               8.0            0.0                        1.0               0.0                 1.0              Adulto\n",
              "5   5         0.0     0.9                       83.0                           6.0          50.0            0.024149               -1.323432           AC         477.0  5526.000890   4576.354823  5042.758337                   1.0                CA              205.0             BA               -1.0              0.0                 1500.0              0.0  INTERNET                   4.427453      other                 0.0                 0.0    3.0  0.330928 -1.505284               0.317856                               5.0            0.0                        1.0               0.0                 1.0              Adulto"
            ],
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              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Persona Mayor</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
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              "\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-37a6159a-33e9-4e9e-801c-74781b28d3e4 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-37a6159a-33e9-4e9e-801c-74781b28d3e4');\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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "576f4495",
      "metadata": {
        "cell_id": "00007-d1ce17da-f6e0-4c12-bbe8-a40a46e7371b",
        "deepnote_cell_type": "markdown",
        "tags": [],
        "id": "576f4495"
      },
      "source": [
        "## 3. Análisis Exploratorio de Datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "943cbe73",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "943cbe73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c543ef5e-dcf5-4119-fb35-e40761e8d4d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.24.0->ydata_profiling) (3.4)\n",
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          ]
        }
      ],
      "source": [
        "!pip install ydata_profiling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "27056a96",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "27056a96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "b3571c02acc74638a7c87bb14d5d2e41",
            "8ab7e56ec9eb46e48d9a1692d2213052",
            "9c277d04698a4d28a3864bb5d0451e7d",
            "14d357de88224858bf00e829fe111d7e",
            "280e3ce28ca9466491cc30e70b183d31",
            "3aa978a7b7fe46af8b6e2e1f35bab0a7",
            "0ad19a43f3804a9e995fc399ff13f353",
            "39078e8389d149e8b34dea04b5a8e757",
            "a4e3c581dd86420295aabd9bd2b63eea",
            "136e8fde152a4d43b786bc2ebf471546",
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            "b20c81d77c5b477d949afccee5cdf867",
            "052b2eac7df44bb5a6b1ff1eed9170d5",
            "180a4777f8eb4f04a88ad44988945881",
            "4a67c42925714d2daa05d742103bbe49",
            "fb655f79a34b42bf8ab59493a65f758a",
            "b18a693318cd4ea9984bd40a9b439a82",
            "b217f07170af43cfa8dd7f5f96ada31a",
            "f6c1008dfe434bf5b1ec7e2efca6d23a",
            "aa7515bad10f47a89c385bfe821b56bc",
            "2ee6cb23b62340938401be1f0f752091",
            "4bfc2b49d1c34ac2acd635865dace56d",
            "988b4b338be24f728b9d1fee95f92d71",
            "781fc84527db43d7a3df279a1a5680c2",
            "72883f00262f42fbbd06ebb85eeb9125",
            "df1b1f98130648348c62ec05cede270d",
            "caadee9bfc2048d0995e2a27f0cb1c1b",
            "b9b34f24d5bf4fe0850f706e6bcc3030",
            "14f0edf8b2604592ba81d0323c97028a",
            "b3e4a3b81df54b94bc5ba977749867b6",
            "5f89d6fd14e7429495a13866a0386c26",
            "45e95a723883403bb0fc3d49c0b8e797",
            "52e094d3b09e445f895eecc9cee0b410",
            "633c1ca412c148a191254b740691daea",
            "d02501ad8182447dab6756a6b9473992",
            "6d9c51e45f44416cba2f414056a2dd6a",
            "fd7ef2e177064691a2f8fe5f63ffad8b",
            "cd100af1927446caa5fbeedfd78eb627",
            "80905e9f9abe490c89bf3e5bd3c86d4a",
            "3614db369405473dae62d12f2ec7fbea",
            "db50db953a1b4524bdee263ec34c7af5",
            "04164db9df59425faed6e4d492c955c8",
            "662ef4f78025402db60c0ae1a61e353d",
            "64ed8f13a49e49d998e57c01731419c6"
          ]
        },
        "outputId": "bb201320-7313-4044-83c3-a9a7f43efba3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b3571c02acc74638a7c87bb14d5d2e41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b20c81d77c5b477d949afccee5cdf867"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "988b4b338be24f728b9d1fee95f92d71"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "633c1ca412c148a191254b740691daea"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from ydata_profiling import ProfileReport\n",
        "\n",
        "profile = ProfileReport(df, title=\"EDA\")\n",
        "profile.to_file(\"eda.html\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9faaf197",
      "metadata": {
        "id": "9faaf197"
      },
      "source": [
        "### 3.1 Análisis del EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Algunos patrones interesantes observados en el reporte son:\n",
        "\n",
        "*   El dataset contiene un total de 36 variables y 873,771 observaciones. Las variables se dividen en 21 numéricas y 15 categóricas.\n",
        "\n",
        "*   La variable \"id\" es una variable numérica con, naturalmente, una distribución uniforme y un rango de valores entre 0 y 999,999.\n",
        "\n",
        "*   La variable objetivo \"fraud_bool\" es categórica y tiene dos categorías distintas (0 y 1). La mayoría de los registros (864,149 o 86.4%) corresponden a la categoría 0, mientras que una pequeña cantidad de registros (9,622 o 0.1%) corresponden a la categoría 1, siendo una variable altamente desbalanceada.\n",
        "\n",
        "*   La variable \"income\" es numérica. Tiene nueve categorías distintas, variando entre 0.1 y 0.9. No hay valores igual a cero en esta variable, luego todas las personas con registro en el dataset tienen un trabajo.\n",
        "\n",
        "* La variable \"employment_status\" es una variable categórica con 7 categorías distintas, siendo \"CA\" la categoría más común.\n",
        "\n",
        "*   Las variables numéricas \"velocity_6h\", \"velocity_24h\" y \"velocity_4w\", tienen valores negativos.\n",
        "\n",
        "*   La variable \"prev_address_months_count\" es numérica y tiene un alto porcentaje del valor -1 (76.2%). Incluso sin los valores negativos, no se observa una clara distribución, pero sí muestras $outliers$. Es una variable desbalanceada.\n",
        "\n",
        "*   La variable \"current_address_months_count\" es numérica y tiene un pequeño porcentaje de ceros (0.8%) y algunos valores negativos (0.3%). Sin contar estos últimos, se observa que el histograma se asemeja a una distribución exponencial.\n",
        "\n",
        "*   La variable \"customer_age\" es una variable numérica discreta con 9 categorías distintas. Obviando una importante cantidad de clientes entre los 20 y 40 años, se puede apreciar una distribución log-normal, con una $skewness$ negativa.\n",
        "\n",
        "*   La variable \"days_since_request\" es una variable numérica, con la mayor cantidad de sus muestras cercanas a 0. Es una variable desbalanceada.\n",
        "\n",
        "*   La variable \"intended_balcon_amount\" es una variable numérica. Gran parte de sus datos son negativos (75.2% de los datos) y en el histograma se observan también distintivas cantidades de muestras $outliers$. Es una variable desbalanceada.\n",
        "\n",
        "*   La variable \"payment_type\" es una variable categórica con 5 categorías distintas, siendo \"AB\" la categoría más común.\n",
        "\n",
        "*   Tanto \"velocity_6h\", \"zip_count_4w\" como \"velocity_24h\" son similares a una distribución log-normal.\n",
        "\n",
        "*   La variable \"bank_branch_count_8w\" es una variable numérica continua con una alta correlación con la cantidad de ceros. El 14.2% de los valores son ceros. Es una variable desbalanceada.\n",
        "\n",
        "*   La variable \"credit_risk_score\" es una variable numérica continua con 1.2% de valores negativos y 384 valores iguales a cero, lo que representa menos del 0.1% del total.\n",
        "\n",
        "*   La variable \"housing_status\" es una variable categórica con 7 categorías distintas, siendo \"BC\" la categoría más común.\n",
        "\n",
        "* La columna 'session_length_in_minutes' es una variable numérica con 0.2% de valores negativos. Sin estos, podría distribuir log-normal con una $skewness$ positiva.\n",
        "\n",
        "*   Se observa que las variables \"x1\" y \"x2\" presentan distribuciones normales.\n",
        "\n",
        "*   La columna \"date_of_birth_distinct_emails_4w\" distribuye log-normal.\n",
        "\n",
        "*   Las variables categóricas, como \"payment_type\", \"employment_status\", \"housing_status\", \"has_other_cards\", \"foreign_request\", \"device_os\" y \"source\", tienen distintas categorías con diferentes frecuencias.\n",
        "\n",
        "*   Las variables \"foreign_request\", \"source\" y \"device_distinct_emails_8w\" son variables desbalanceadas.\n",
        "\n",
        "*   Las variables \"payment_type\" y \"source\" son las que tienen la mayor cantidad de categorías.\n",
        "\n",
        "En las figuras de interacciones se observan los histogramas de las variables numéricas \"vistos desde arriba\", donde la variable independiente corresponde al \"frente\" del histograma, y la variable dependiente a la profundidad de este. Por ejemplo, escogiendo la variable \"x1\", como independiente, y variando el eje y, se puede observar cómo están distribuidas las muestras con respecto a cada una de las demás variables, teniendo algunas una clara distribución gaussiana, como con \"velocity_6h\", o más aleatoria, como con \"name_email_similarity\".\n",
        "\n",
        "En la matriz de correlación, se observa una alta correlación entre las variables \"customer_age\" y \"segmentacion_etaria\", como es de esperar, debido a que una fue creada en base a la otra. También se observa una alta correlación entre la variable \"proposed_credit_limit\" y \"credit_risk_score\". Prestando atención al gráfico de interacción entre estas dos variables, se observa que si bien la mayoría de las muestras se encuentran en una propuesta de límite de crédito(\"proposed_credit_limit\") bajo, se observan también muestras que crecen directamente con el puntaje de riesgo de crédito(\"credit_risk_score\"). También se observa una correlación relevante entre las variables \"velocity_6h\", \"velocity_8h\" y \"velocity_24h\"."
      ],
      "metadata": {
        "id": "easA0yP1zAcS"
      },
      "id": "easA0yP1zAcS"
    },
    {
      "cell_type": "markdown",
      "id": "66c034e4",
      "metadata": {
        "cell_id": "00010-41569a5a-d2db-40c6-a236-99a8d5ef58b7",
        "deepnote_cell_type": "markdown",
        "id": "66c034e4"
      },
      "source": [
        "## 4. Preprocesamiento"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se pre procesarán las columnas de la siguiente manera:\n",
        "\n",
        "- Variables categóricas se pasarán a One Hot Encoding\n",
        "- Variables numéricas se normalizarán\n",
        "- Variables binarias se mantedrán, al igual que las columnas "
      ],
      "metadata": {
        "id": "CoebapB86QoN"
      },
      "id": "CoebapB86QoN"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from sklearn.preprocessing import Normalizer"
      ],
      "metadata": {
        "id": "_3V72YTN5p4v"
      },
      "id": "_3V72YTN5p4v",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mj3AmS5GLktp",
        "outputId": "9a3c2d83-2195-4e6c-dcae-39030ee2665b"
      },
      "id": "Mj3AmS5GLktp",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 873771 entries, 0 to 995816\n",
            "Data columns (total 36 columns):\n",
            " #   Column                            Non-Null Count   Dtype   \n",
            "---  ------                            --------------   -----   \n",
            " 0   id                                873771 non-null  int64   \n",
            " 1   fraud_bool                        873771 non-null  float64 \n",
            " 2   income                            873771 non-null  float64 \n",
            " 3   prev_address_months_count         873771 non-null  float64 \n",
            " 4   current_address_months_count      873771 non-null  float64 \n",
            " 5   customer_age                      873771 non-null  float64 \n",
            " 6   days_since_request                873771 non-null  float64 \n",
            " 7   intended_balcon_amount            873771 non-null  float64 \n",
            " 8   payment_type                      873771 non-null  object  \n",
            " 9   zip_count_4w                      873771 non-null  float64 \n",
            " 10  velocity_6h                       873771 non-null  float64 \n",
            " 11  velocity_24h                      873771 non-null  float64 \n",
            " 12  velocity_4w                       873771 non-null  float64 \n",
            " 13  bank_branch_count_8w              873771 non-null  float64 \n",
            " 14  employment_status                 873771 non-null  object  \n",
            " 15  credit_risk_score                 873771 non-null  float64 \n",
            " 16  housing_status                    873771 non-null  object  \n",
            " 17  bank_months_count                 873771 non-null  float64 \n",
            " 18  has_other_cards                   873771 non-null  float64 \n",
            " 19  proposed_credit_limit             873771 non-null  float64 \n",
            " 20  foreign_request                   873771 non-null  float64 \n",
            " 21  source                            873771 non-null  object  \n",
            " 22  session_length_in_minutes         873771 non-null  float64 \n",
            " 23  device_os                         873771 non-null  object  \n",
            " 24  keep_alive_session                873771 non-null  float64 \n",
            " 25  device_fraud_count                873771 non-null  float64 \n",
            " 26  month                             873771 non-null  float64 \n",
            " 27  x1                                873771 non-null  float64 \n",
            " 28  x2                                873771 non-null  float64 \n",
            " 29  name_email_similarity             873771 non-null  float64 \n",
            " 30  date_of_birth_distinct_emails_4w  873771 non-null  float64 \n",
            " 31  email_is_free                     873771 non-null  float64 \n",
            " 32  device_distinct_emails_8w         873771 non-null  float64 \n",
            " 33  phone_home_valid                  873771 non-null  float64 \n",
            " 34  phone_mobile_valid                873771 non-null  float64 \n",
            " 35  segmentacion_etaria               873771 non-null  category\n",
            "dtypes: category(1), float64(29), int64(1), object(5)\n",
            "memory usage: 273.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95c8cf73",
      "metadata": {
        "id": "95c8cf73"
      },
      "source": [
        "### 4.1 Declarar `ColumnTransformer`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f1799cb4",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "f1799cb4"
      },
      "outputs": [],
      "source": [
        "transformers=[\n",
        "        ('Pass', 'passthrough', ['id','fraud_bool','keep_alive_session', 'foreign_request', 'has_other_cards', 'phone_mobile_valid',\n",
        "                                 'phone_home_valid', 'email_is_free']),\n",
        "        ('OneHotEncoder', OneHotEncoder(),  ['payment_type', 'employment_status', 'housing_status', 'source', 'device_os','segmentacion_etaria']),\n",
        "        ('Normalizar', Normalizer(),['income', 'name_email_similarity', 'prev_address_months_count', 'current_address_months_count',\n",
        "                                     'customer_age', 'days_since_request', 'intended_balcon_amount', 'zip_count_4w', 'velocity_6h',\n",
        "                                     'velocity_24h', 'velocity_4w', 'bank_branch_count_8w', 'date_of_birth_distinct_emails_4w',\n",
        "                                     'credit_risk_score', 'bank_months_count', 'proposed_credit_limit', 'session_length_in_minutes', \n",
        "                                     'device_distinct_emails_8w', 'device_fraud_count','x1','x2'])]\n",
        "\n",
        "\n",
        "#Se crea el objeto Column Transformer\n",
        "preprocessing_transformer = ColumnTransformer(transformers)#.set_output(transform=\"pandas\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1a23ec24",
      "metadata": {
        "id": "1a23ec24"
      },
      "source": [
        "### 4.2 Transformar datos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "749ab504",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "749ab504",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "cd0f07c4-4f1a-4171-c648-4f269cc6944f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    0    1    2    3    4    5    6    7    8    9    10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34   35   36   37        38        39        40        41        42            43        44        45        46        47        48        49        50        51        52        53        54        55   56        57        58\n",
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              "2  3.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.000075  0.000023  0.002146  0.000840  0.004665  3.756401e-07  0.001700  0.112325  0.607425  0.615113  0.489416  0.000093  0.000093  0.012128  0.000560  0.018659  0.000483  0.000093  0.0  0.000203  0.000121\n",
              "3  4.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.000015  0.000090 -0.000146  0.004366  0.004366  1.700897e-06 -0.000209  0.172025  0.626524  0.544058  0.525325  0.001746  0.001164  0.023868  0.001601  0.072769  0.000633  0.000146  0.0  0.000214  0.000054\n",
              "4  5.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.000101  0.000036  0.009312  0.000673  0.005610  2.709505e-06 -0.000148  0.053518  0.620007  0.513458  0.565788  0.000112  0.000561  0.023001 -0.000112  0.168297  0.000497  0.000112  0.0  0.000037 -0.000169"
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            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
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              "      <td>0.661222</td>\n",
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              "      <td>0.084876</td>\n",
              "      <td>0.000222</td>\n",
              "      <td>0.020691</td>\n",
              "      <td>0.003337</td>\n",
              "      <td>0.111239</td>\n",
              "      <td>0.002164</td>\n",
              "      <td>0.000222</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000031</td>\n",
              "      <td>-0.000015</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.005045</td>\n",
              "      <td>2.610796e-07</td>\n",
              "      <td>0.001787</td>\n",
              "      <td>0.082230</td>\n",
              "      <td>0.785437</td>\n",
              "      <td>0.490712</td>\n",
              "      <td>0.363965</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000706</td>\n",
              "      <td>0.021390</td>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.050448</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>0.000110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002146</td>\n",
              "      <td>0.000840</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>3.756401e-07</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.112325</td>\n",
              "      <td>0.607425</td>\n",
              "      <td>0.615113</td>\n",
              "      <td>0.489416</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.012128</td>\n",
              "      <td>0.000560</td>\n",
              "      <td>0.018659</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000203</td>\n",
              "      <td>0.000121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>-0.000146</td>\n",
              "      <td>0.004366</td>\n",
              "      <td>0.004366</td>\n",
              "      <td>1.700897e-06</td>\n",
              "      <td>-0.000209</td>\n",
              "      <td>0.172025</td>\n",
              "      <td>0.626524</td>\n",
              "      <td>0.544058</td>\n",
              "      <td>0.525325</td>\n",
              "      <td>0.001746</td>\n",
              "      <td>0.001164</td>\n",
              "      <td>0.023868</td>\n",
              "      <td>0.001601</td>\n",
              "      <td>0.072769</td>\n",
              "      <td>0.000633</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.000054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.009312</td>\n",
              "      <td>0.000673</td>\n",
              "      <td>0.005610</td>\n",
              "      <td>2.709505e-06</td>\n",
              "      <td>-0.000148</td>\n",
              "      <td>0.053518</td>\n",
              "      <td>0.620007</td>\n",
              "      <td>0.513458</td>\n",
              "      <td>0.565788</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.000561</td>\n",
              "      <td>0.023001</td>\n",
              "      <td>-0.000112</td>\n",
              "      <td>0.168297</td>\n",
              "      <td>0.000497</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>-0.000169</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c846eac-ae90-486b-a87c-0e079f58df9c')\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "#Se transforman las columnas\n",
        "df_trans = preprocessing_transformer.fit_transform(df)\n",
        "\n",
        "df_trans = pd.DataFrame(df_trans)\n",
        "df_trans.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "40b95e20-857d-4e29-8071-0071dfd3f62f",
      "metadata": {
        "id": "40b95e20-857d-4e29-8071-0071dfd3f62f"
      },
      "source": [
        "## 5. Visualización en Baja Dimensionalidad"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8f55ee74",
      "metadata": {
        "id": "8f55ee74"
      },
      "source": [
        "### 5.1 Muestrear dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Primero se separa el dataframe por rango etario."
      ],
      "metadata": {
        "id": "pKtkaRGFZTCu"
      },
      "id": "pKtkaRGFZTCu"
    },
    {
      "cell_type": "code",
      "source": [
        "df_trans['segmentacion_etaria'] = df['segmentacion_etaria'].values\n",
        "df_trans.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "FGj1bNwpczfr",
        "outputId": "bba8d88b-2063-4e81-a658-1b3c739b79a1"
      },
      "id": "FGj1bNwpczfr",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     0    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34   35   36   37        38        39        40        41        42            43        44        45        46        47        48        49        50        51        52        53        54        55   56        57        58 segmentacion_etaria\n",
              "0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.000022  0.000041  0.036264  0.002225  0.006674  2.470866e-06 -0.000084  0.071972  0.661222  0.429642  0.592982  0.084876  0.000222  0.020691  0.003337  0.111239  0.002164  0.000222  0.0 -0.000031 -0.000015       Persona Mayor\n",
              "1  2.0  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.000091  0.000017 -0.000101  0.004137  0.005045  2.610796e-07  0.001787  0.082230  0.785437  0.490712  0.363965  0.000101  0.000706  0.021390  0.003128  0.050448  0.000389  0.000101  0.0 -0.000003  0.000110              Adulto\n",
              "2  3.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.000075  0.000023  0.002146  0.000840  0.004665  3.756401e-07  0.001700  0.112325  0.607425  0.615113  0.489416  0.000093  0.000093  0.012128  0.000560  0.018659  0.000483  0.000093  0.0  0.000203  0.000121              Adulto\n",
              "3  4.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0  0.0  0.0  0.000015  0.000090 -0.000146  0.004366  0.004366  1.700897e-06 -0.000209  0.172025  0.626524  0.544058  0.525325  0.001746  0.001164  0.023868  0.001601  0.072769  0.000633  0.000146  0.0  0.000214  0.000054              Adulto\n",
              "4  5.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  1.0  0.0  0.0  1.0  0.0  0.0  0.0  0.000101  0.000036  0.009312  0.000673  0.005610  2.709505e-06 -0.000148  0.053518  0.620007  0.513458  0.565788  0.000112  0.000561  0.023001 -0.000112  0.168297  0.000497  0.000112  0.0  0.000037 -0.000169              Adulto"
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              "      <th></th>\n",
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              "      <th>8</th>\n",
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              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
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              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
              "      <th>42</th>\n",
              "      <th>43</th>\n",
              "      <th>44</th>\n",
              "      <th>45</th>\n",
              "      <th>46</th>\n",
              "      <th>47</th>\n",
              "      <th>48</th>\n",
              "      <th>49</th>\n",
              "      <th>50</th>\n",
              "      <th>51</th>\n",
              "      <th>52</th>\n",
              "      <th>53</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>segmentacion_etaria</th>\n",
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              "      <td>-0.000015</td>\n",
              "      <td>Persona Mayor</td>\n",
              "    </tr>\n",
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              "      <td>0.021390</td>\n",
              "      <td>0.003128</td>\n",
              "      <td>0.050448</td>\n",
              "      <td>0.000389</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.000003</td>\n",
              "      <td>0.000110</td>\n",
              "      <td>Adulto</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000075</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.002146</td>\n",
              "      <td>0.000840</td>\n",
              "      <td>0.004665</td>\n",
              "      <td>3.756401e-07</td>\n",
              "      <td>0.001700</td>\n",
              "      <td>0.112325</td>\n",
              "      <td>0.607425</td>\n",
              "      <td>0.615113</td>\n",
              "      <td>0.489416</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.012128</td>\n",
              "      <td>0.000560</td>\n",
              "      <td>0.018659</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>0.000093</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000203</td>\n",
              "      <td>0.000121</td>\n",
              "      <td>Adulto</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>-0.000146</td>\n",
              "      <td>0.004366</td>\n",
              "      <td>0.004366</td>\n",
              "      <td>1.700897e-06</td>\n",
              "      <td>-0.000209</td>\n",
              "      <td>0.172025</td>\n",
              "      <td>0.626524</td>\n",
              "      <td>0.544058</td>\n",
              "      <td>0.525325</td>\n",
              "      <td>0.001746</td>\n",
              "      <td>0.001164</td>\n",
              "      <td>0.023868</td>\n",
              "      <td>0.001601</td>\n",
              "      <td>0.072769</td>\n",
              "      <td>0.000633</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000214</td>\n",
              "      <td>0.000054</td>\n",
              "      <td>Adulto</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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>1.0</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>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000101</td>\n",
              "      <td>0.000036</td>\n",
              "      <td>0.009312</td>\n",
              "      <td>0.000673</td>\n",
              "      <td>0.005610</td>\n",
              "      <td>2.709505e-06</td>\n",
              "      <td>-0.000148</td>\n",
              "      <td>0.053518</td>\n",
              "      <td>0.620007</td>\n",
              "      <td>0.513458</td>\n",
              "      <td>0.565788</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.000561</td>\n",
              "      <td>0.023001</td>\n",
              "      <td>-0.000112</td>\n",
              "      <td>0.168297</td>\n",
              "      <td>0.000497</td>\n",
              "      <td>0.000112</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>-0.000169</td>\n",
              "      <td>Adulto</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5af1c74d-dbb2-49d9-b2ed-09aef709602b')\"\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",
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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-5af1c74d-dbb2-49d9-b2ed-09aef709602b 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-5af1c74d-dbb2-49d9-b2ed-09aef709602b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7e169dd",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "b7e169dd"
      },
      "outputs": [],
      "source": [
        "#dataframe jóvenes\n",
        "df_jovenes = df_trans[df_trans['segmentacion_etaria']=='Joven']\n",
        "\n",
        "#dataframe adultos jóvenes\n",
        "df_a_jovenes = df_trans[df_trans['segmentacion_etaria']=='Adulto Joven']\n",
        "\n",
        "#dataframe adultos\n",
        "df_adultos = df_trans[df_trans['segmentacion_etaria']=='Adulto']\n",
        "\n",
        "#dataframe personas mayores\n",
        "df_p_mayores = df_trans[df_trans['segmentacion_etaria']=='Persona Mayor']"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "proporcion = len(df_trans)/30000\n",
        "\n",
        "n_jov = round(len(df_jovenes)/proporcion)\n",
        "n_ajov = round(len(df_a_jovenes)/proporcion)\n",
        "n_adul = round(len(df_adultos)/proporcion)\n",
        "n_pmay = round(len(df_p_mayores)/proporcion)\n",
        "\n",
        "n_jov, n_ajov, n_adul, n_pmay"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sxFmwo6GZoEw",
        "outputId": "d5b6283f-129f-4683-a221-6ab62a466b8a"
      },
      "id": "sxFmwo6GZoEw",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(389, 4408, 21528, 3675)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Muestra personas jóvenes\n",
        "df_jov_sample = df_jovenes.sample(n_jov)\n",
        "#Muestra adultos jóvenes\n",
        "df_ajov_sample = df_a_jovenes.sample(n_ajov)\n",
        "#Muestra personas adultas\n",
        "df_adul_sample = df_adultos.sample(n_adul)\n",
        "#Muestra personas mayores\n",
        "df_pmay_sample = df_p_mayores.sample(n_pmay)"
      ],
      "metadata": {
        "id": "psgUcoFvZbDu"
      },
      "id": "psgUcoFvZbDu",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Se transforman los nombres de las columnas a string, para evitar que ca\n",
        "df_jov_sample.rename(str, axis='columns',inplace=True)\n",
        "df_ajov_sample.rename(str, axis='columns',inplace=True)\n",
        "df_adul_sample.rename(str, axis='columns',inplace=True)\n",
        "df_pmay_sample.rename(str, axis='columns',inplace=True)\n",
        "df_jov_sample.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQxP6bM5eq8Z",
        "outputId": "66d0fc26-e2db-4bda-c282-424a4cb78537"
      },
      "id": "nQxP6bM5eq8Z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '50', '51', '52', '53', '54', '55', '56', '57', '58', 'segmentacion_etaria'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a699143c",
      "metadata": {
        "id": "a699143c"
      },
      "source": [
        "### 5.2 Proyectar y agregar proyecciones al dataframe de muestreo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# hay que instalarlo aparte.\n",
        "!pip install umap-learn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bEDlvwNffWe",
        "outputId": "f6582339-d7e0-4afd-fd6f-6ea7d0236687"
      },
      "id": "6bEDlvwNffWe",
      "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: umap-learn in /usr/local/lib/python3.10/dist-packages (0.5.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.2.2)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (1.10.1)\n",
            "Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (0.56.4)\n",
            "Requirement already satisfied: pynndescent>=0.5 in /usr/local/lib/python3.10/dist-packages (from umap-learn) (0.5.10)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from umap-learn) (4.65.0)\n",
            "Requirement already satisfied: llvmlite<0.40,>=0.39.0dev0 in /usr/local/lib/python3.10/dist-packages (from numba>=0.49->umap-learn) (0.39.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from numba>=0.49->umap-learn) (67.7.2)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from pynndescent>=0.5->umap-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.22->umap-learn) (3.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbbe5843",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "bbbe5843"
      },
      "outputs": [],
      "source": [
        "import umap\n",
        "\n",
        "umap = umap.UMAP()\n",
        "\n",
        "umap_jov = umap.fit_transform(df_jov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "umap_ajov = umap.fit_transform(df_ajov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "umap_adul = umap.fit_transform(df_adul_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "umap_pmay = umap.fit_transform(df_pmay_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "\n",
        "#umap_jov"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "\n",
        "pca_jov = pca.fit_transform(df_jov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "pca_ajov = pca.fit_transform(df_ajov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "pca_adul = pca.fit_transform(df_adul_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "pca_pmay = pca.fit_transform(df_pmay_sample.drop(['1','segmentacion_etaria'], axis=1))\n"
      ],
      "metadata": {
        "id": "EUA25GEgn4Vw"
      },
      "id": "EUA25GEgn4Vw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "491bdaf7",
      "metadata": {
        "id": "491bdaf7"
      },
      "source": [
        "### 5.3 Visualizar según rangos etarios"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d94ac394",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "d94ac394"
      },
      "outputs": [],
      "source": [
        "# Definimos x e y\n",
        "df_jov_sample[\"x_umap\"] = umap_jov[:, 0]\n",
        "df_jov_sample[\"y_umap\"] = umap_jov[:, 1]\n",
        "df_jov_sample[\"x_pca\"] = pca_jov[:, 0]\n",
        "df_jov_sample[\"y_pca\"] = pca_jov[:, 1]\n",
        "\n",
        "df_ajov_sample[\"x_umap\"] = umap_ajov[:, 0]\n",
        "df_ajov_sample[\"y_umap\"] = umap_ajov[:, 1]\n",
        "df_ajov_sample[\"x_pca\"] = pca_ajov[:, 0]\n",
        "df_ajov_sample[\"y_pca\"] = pca_ajov[:, 1]\n",
        "\n",
        "df_adul_sample[\"x_umap\"] = umap_adul[:, 0]\n",
        "df_adul_sample[\"y_umap\"] = umap_adul[:, 1]\n",
        "df_adul_sample[\"x_pca\"] = pca_adul[:, 0]\n",
        "df_adul_sample[\"y_pca\"] = pca_adul[:, 1]\n",
        "\n",
        "df_pmay_sample[\"x_umap\"] = umap_pmay[:, 0]\n",
        "df_pmay_sample[\"y_umap\"] = umap_pmay[:, 1]\n",
        "df_pmay_sample[\"x_pca\"] = pca_pmay[:, 0]\n",
        "df_pmay_sample[\"y_pca\"] = pca_pmay[:, 1]\n",
        "\n",
        "\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.express as px\n",
        "\n",
        "def graficar(dataframe, color, text):\n",
        "  fig1 = (\n",
        "      px.scatter(dataframe,x=\"x_umap\",y=\"y_umap\",color=color)\n",
        "  )\n",
        "  fig2 = (\n",
        "      px.scatter(dataframe,x=\"x_pca\",y=\"y_pca\",color=color)\n",
        "  )\n",
        "  fig = (\n",
        "      make_subplots(rows=1, cols=2)\n",
        "      .add_trace(fig1.data[0], row=1, col=1)\n",
        "      .add_trace(fig2.data[0], row=1, col=2)\n",
        "      .update_layout(height=400, title_text=text)\n",
        "      .show()\n",
        "      )\n",
        "  \n",
        "graficar(df_jov_sample, '1', \"Comparación UMAP - PCA para jóvenes\")\n",
        "graficar(df_ajov_sample, '1', \"Comparación UMAP - PCA, para adultos jóvenes\")\n",
        "graficar(df_adul_sample, '1', \"Comparación UMAP - PCA, para adultos\")\n",
        "graficar(df_pmay_sample, '1', \"Comparación UMAP - PCA, para personas mayores\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Se pueden observar patrones más precisos e identificables al utilizar el método UMAP que al utilizar PCA, esto se debe a la forma de operar que tiene UMAP que resuta más adecuada en este caso. "
      ],
      "metadata": {
        "id": "p_9b7k-CIZyF"
      },
      "id": "p_9b7k-CIZyF"
    },
    {
      "cell_type": "markdown",
      "id": "ca2ac9bc-c472-49d6-81fc-ccfd2a07b92e",
      "metadata": {
        "id": "ca2ac9bc-c472-49d6-81fc-ccfd2a07b92e"
      },
      "source": [
        "## 6. Anomalías"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "723aa232",
      "metadata": {
        "id": "723aa232"
      },
      "source": [
        "### 6.1 Implementar detector de anomalías sobre dataframe de muestreo\n",
        "\n",
        "Se aplica detección de anomalías con Isolation Forest"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "isf_jov = IsolationForest(n_estimators=20)\n",
        "isf_ajov = IsolationForest(n_estimators=20)\n",
        "isf_adul = IsolationForest(n_estimators=20)\n",
        "isf_pmay = IsolationForest(n_estimators=20)\n",
        "\n",
        "outl_jov = isf_jov.fit_predict(df_jov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "outl_ajov = isf_ajov.fit_predict(df_ajov_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "outl_adul = isf_adul.fit_predict(df_adul_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "outl_pmay = isf_pmay.fit_predict(df_pmay_sample.drop(['1','segmentacion_etaria'], axis=1))\n",
        "\n",
        "\n",
        "graficar(df_jov_sample, outl_jov, \"Isolation Forest, conjunto Joven\")\n",
        "graficar(df_ajov_sample, outl_ajov, \"Isolation Forest, conjunto Adulto Joven\")\n",
        "graficar(df_adul_sample, outl_adul, \"Isolation Forest, conjunto Adulto\")\n",
        "graficar(df_pmay_sample, outl_pmay, \"Isolation Forest, conjunto Personas Mayores\")"
      ],
      "metadata": {
        "id": "nQE8q8TD0iOo"
      },
      "id": "nQE8q8TD0iOo",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "61449e87",
      "metadata": {
        "id": "61449e87"
      },
      "source": [
        "### 6.4 Calcular ratios y responder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_jov_sample['predict'] = outl_jov\n",
        "df_ajov_sample['predict'] = outl_ajov\n",
        "df_adul_sample['predict'] = outl_adul\n",
        "df_pmay_sample['predict'] = outl_pmay"
      ],
      "metadata": {
        "id": "R2629Mh264kr"
      },
      "id": "R2629Mh264kr",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df_jov_sample[df_jov_sample['1']==0.0]\n",
        "df_jov_sample['1'].sum()"
      ],
      "metadata": {
        "id": "AHY9mBeK-NU5"
      },
      "id": "AHY9mBeK-NU5",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd07169f",
      "metadata": {
        "vscode": {
          "languageId": "plaintext"
        },
        "id": "fd07169f"
      },
      "outputs": [],
      "source": [
        "#Datos necesarios\n",
        "Fraude_pred_bien_j = len(df_jov_sample[(df_jov_sample['1']==1) & (df_jov_sample['predict']==-1)])\n",
        "Fraude_pred_j = len(df_jov_sample[df_jov_sample['predict']==-1])\n",
        "Fraude_real_j = len(df_jov_sample[df_jov_sample['1']==1])\n",
        "\n",
        "Fraude_pred_bien_aj = len(df_ajov_sample[(df_ajov_sample['1']==1) & (df_ajov_sample['predict']==-1)])\n",
        "Fraude_pred_aj = len(df_ajov_sample[df_ajov_sample['predict']==-1])\n",
        "Fraude_real_aj = len(df_ajov_sample[df_ajov_sample['1']==1])\n",
        "\n",
        "Fraude_pred_bien_a = len(df_adul_sample[(df_adul_sample['1']==1) & (df_adul_sample['predict']==-1)])\n",
        "Fraude_pred_a = len(df_adul_sample[df_adul_sample['predict']==-1])\n",
        "Fraude_real_a = len(df_adul_sample[df_adul_sample['1']==1])\n",
        "\n",
        "Fraude_pred_bien_pm = len(df_pmay_sample[(df_pmay_sample['1']==1) & (df_pmay_sample['predict']==-1)])\n",
        "Fraude_pred_pm = len(df_pmay_sample[df_pmay_sample['predict']==-1])\n",
        "Fraude_real_pm = len(df_pmay_sample[df_pmay_sample['1']==1])\n",
        "\n",
        "#Cálculo de P\n",
        "P_j = Fraude_pred_bien_j/Fraude_pred_j\n",
        "P_aj = Fraude_pred_bien_aj/Fraude_pred_aj\n",
        "P_a = Fraude_pred_bien_a/Fraude_pred_a\n",
        "P_pm = Fraude_pred_bien_pm/Fraude_pred_pm\n",
        "\n",
        "#Cálculo de R\n",
        "R_j = Fraude_pred_bien_j/Fraude_real_j\n",
        "R_aj = Fraude_pred_bien_aj/Fraude_real_aj\n",
        "R_a = Fraude_pred_bien_a/Fraude_real_a\n",
        "R_pm = Fraude_pred_bien_pm/Fraude_real_pm\n",
        "\n",
        "print('El valor obtenido para P en los jóvenes es: '+ str(P_j) + '\\n El valor obtenido para R en los jóvenes es: '+ str(R_j))\n",
        "print('El valor obtenido para P en los adultos jóvenes es: '+ str(P_aj) + '\\n El valor obtenido para R en los adultos jóvenes es: '+ str(R_aj))\n",
        "print('El valor obtenido para P en los adultos es: '+ str(P_a) + '\\n El valor obtenido para R en los adultos es: '+ str(R_a))\n",
        "print('El valor obtenido para P en las personas mayores es: '+ str(P_pm) + '\\n El valor obtenido para R en las personas mayores es: '+ str(R_pm))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los valores P y R que se solicitan corresponden a las métricas _'Precision'_ y _'Recall'_ respectivamente. Estos se interpretan según:\n",
        "\n",
        "- Precision: se encuentra entre 0 y 1. Se puede pensar como el porcentaje de los datos predichos como fraude, que realmente eran fraude. Por ello, mientras más cercano a 0, la predicción realizada es peor, ya que pocos de los datos predichos como fraude son realmente fraude. Por otra parte, mientras más cercano a 1 se tiene que la mayoría de los predichos como fraude, realmente lo son, lo cual es lo que se busca al predecir.\n",
        "\n",
        "- Recall: también se encuentra entre 0 y 1. Del mismo modo que en _Precision_ , mientras más cerca de 0, la predicción es peor. La diferencia es que en este caso, se interpreta como el porcentaje de los fraudes totales, que se logró predecir.\n",
        "\n",
        "Sabiendo lo anterior, los valores obtenidos para Precision y Recall al identificar los outliers (anomalías) como fraudes, son predicciones bastante malas. Esto se puede deber a que si bien se espera que los fraudes no sean comunes, no se debe asumir que aparecerán como outliers en los datos, primero que todo porque por lo general, los outliers son producidos por errores en la medición de los datos, por lo que más que detectar fraudes, se detectan outliers clásicos de los datos. Por ello, es coherente haber obtenido malos resultados al utilizar esta estrategia de predicción. \n",
        "\n",
        "Los mejores resultados se obtuvieron en los segmentos etarios de 'Adultos jóvenes' y de 'Adultos'. Esto podría deberse a que son aquellos cuyas muestras quedaron más grandes, pero por otro lado, cabe mencionar que estos resultados cambian cada vez que se vuelve a correr el modelo y se obtiene una muestra diferente, por lo que es posible que al momento de escribir este párrafo se haya obtenido una 'muestra suertuda' de esos dataframes."
      ],
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