Introdución_a_Machine_learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Introdución a Machine learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.5"
    },
    "toc-showcode": false,
    "toc-showmarkdowntxt": false
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Naren8520/Python/blob/main/Introduci%C3%B3n_a_Machine_learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "yAYg3kOXOXe5",
        "cellView": "form",
        "outputId": "b243cb79-e292-4f76-efc4-9cc87001d945"
      },
      "source": [
        "#@title\n",
        "\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Panama_Honduras-Mexico/3er_congreso_estadistica.jpeg\",width=600)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQQOHVXZPoaT"
      },
      "source": [
        "**Contenido**\n",
        "\n",
        "1. **Introducción a Machine Learning**\n",
        "2. **Aplicaciones de Machine Learning**\n",
        "3. **Inteligencia Aritificial, Machine Learning, Deep Learning y Ciencias de Datos**\n",
        "4. **¿Qué debe saber un cientifico de datos?**\n",
        "5. **Tipo de Machine Learning**\n",
        "6. **Algoritmos**\n",
        "7. **Cómo funcionan los algoritmos de aprendizaje supervisado**\n",
        "8. **Aplicaremos un modelo de regresión lineal multiple con Machine Learning para predecir el costo de seguro médico**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NhOXBE3NwdM"
      },
      "source": [
        "# 1. Introducción a Machine Learning\n",
        "El aprendizaje automático o aprendizaje automatizado o aprendizaje de máquinas (del inglés, machine learning) es el subcampo de las ciencias de la computación y una rama de la inteligencia artificial, cuyo objetivo es desarrollar técnicas que permitan que las computadoras aprendan. Se dice que un agente aprende cuando su desempeño mejora con la experiencia y mediante el uso de datos; es decir, cuando la habilidad no estaba presente en su genotipo o rasgos de nacimiento. \"En el aprendizaje de máquinas un computador observa datos, construye un modelo basado en esos datos y utiliza ese modelo a la vez como una hipótesis acerca del mundo y una pieza de software que puede resolver problemas\".(wikipedia)\n",
        "\n",
        "En muchas ocasiones el campo de actuación del aprendizaje automático se solapa con el de la estadística inferencial, ya que las dos disciplinas se basan en el análisis de datos. Sin embargo, el aprendizaje automático incorpora las preocupaciones de la complejidad computacional de los problemas. Muchos problemas son de clase NP-hard, por lo que gran parte de la investigación realizada en aprendizaje automático está enfocada al diseño de soluciones factibles a esos problemas. El aprendizaje automático también está estrechamente relacionado con el reconocimiento de patrones. El aprendizaje automático puede ser visto como un intento de automatizar algunas partes del método científico mediante métodos matemáticos. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4M5SejcNwdQ"
      },
      "source": [
        "## 2. Aplicaciones de Machine Learning\n",
        "El aprendizaje automático tiene una amplia gama de aplicaciones como:\n",
        "* motores de búsqueda\n",
        "* diagnósticos médicos\n",
        "* detección de fraude en el uso de tarjetas de crédito\n",
        "* análisis del mercado de valores\n",
        "* clasificación de secuencias de ADN\n",
        "* reconocimiento del habla y del lenguaje escrito\n",
        "* juegos y robótica\n",
        "* Sistema de recomendaciones\n",
        "* Recursos Humanano\n",
        "* Segmentación de clientes.\n",
        "* entre otros..etc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Th4pPNmXOb4D",
        "tags": []
      },
      "source": [
        "## 3. Inteligencia Aritificial, Machine Learning, Deep Learning y Ciencias de Datos\n",
        "Para la mayoría de las personas, los términos aprendizaje automático, aprendizaje profundo, inteligencia artificial y ciencia de datos son confusos. De hecho, mucha gente usa un término indistintamente uno del otro.\n",
        "\n",
        "La siguiente gráfico muestra las relaciones entre IA, aprendizaje automático, aprendizaje profundo y ciencia de datos. El aprendizaje automático es un subconjunto de la IA que consta de técnicas que permiten a las computadoras identificar patrones en los datos y entregar aplicaciones de IA. Mientras tanto, el aprendizaje profundo es un subconjunto del aprendizaje automático que permite a las computadoras resolver problemas más complejos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "cellView": "form",
        "id": "qJAYH0pocdXl",
        "outputId": "bc5c2ed3-9c88-4545-a045-438d29b667d4"
      },
      "source": [
        "#@title\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Panama_Honduras-Mexico/AI, machine learning, deep learning, and data science.JPG\",width=600)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 600
            }
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XkfUrHi-QQN8"
      },
      "source": [
        "La ciencia de datos no es exactamente un subconjunto del aprendizaje automático, pero utiliza el aprendizaje automático, el aprendizaje profundo y la inteligencia artificial para analizar datos y llegar a conclusiones procesables. Combina el aprendizaje automático, el aprendizaje profundo y la inteligencia artificial con otras disciplinas como el análisis de big data y la computación en la nube.\n",
        "\n",
        "El siguiente es un resumen de los detalles sobre inteligencia artificial, aprendizaje automático, aprendizaje profundo y ciencia de datos:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FiB5W-gNwdh"
      },
      "source": [
        "|**Inteligencia artificial**|**Machine Learning**|**Deep Learning**|**Ciencia de Datos**\n",
        "|----------------------------|--------------------|-----------------|---------|\n",
        "|Es la creación de patrones o algoritmos, Modelos matemáticos y estadístico de manera que podamos enseñarle a una maquina a hacer lo que hacemos nosostro pero mucho mejor.|El aprendizaje automático es una aplicación de inteligencia artificial que proporciona al sistema de IA la capacidad de aprender automáticamente del entorno y aplicar esas lecciones para tomar mejores decisiones. Existe una variedad de algoritmos que utiliza el aprendizaje automático para aprender, describir y mejorar de forma iterativa los datos, detectar patrones y luego realizar acciones sobre estos patrones.|Es un subconjunto del aprendizaje automático que implica el estudio de algoritmos relacionados con redes neuronales artificiales que contienen muchos bloques (o capas) apilados unos sobre otros. El diseño de modelos de aprendizaje profundo está inspirado en la red neuronal biológica del cerebro humano. Se esfuerza por analizar datos con una estructura lógica similar a la forma en que un humano saca conclusiones.|La ciencia de datos es un campo interdisciplinario similar a la minería de datos que utiliza métodos, procesos y sistemas científicos para extraer conocimiento o información de los datos en diversas formas, ya sea estructuradas o no estructuradas. La ciencia de datos es diferente del ML y la IA porque su objetivo es obtener información y comprensión de los datos mediante el uso de diferentes herramientas y técnicas científicas. Sin embargo, existen varias herramientas y técnicas comunes tanto al ML como a la ciencia de datos, algunas de las cuales mostraremos aquí.|"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffFpJhdfIePA"
      },
      "source": [
        "## ¿Qué es un cientifico de datos?\n",
        "\n",
        "Es un profesional que trabaja con una gran cantidad de datos, y a partir de estos conjuntos de datos puede extraer conocimiento para responder a muchisimas preguntas que se puedan formular."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYtjCR-MIePT"
      },
      "source": [
        "## 4. ¿Qué debe saber un cientifico de datos?\n",
        "\n",
        "**1. Matemática**\n",
        "\n",
        "*Las matemáticas nos ayudara a comprender, a tener un mejor criterioy  tener mayor fluidez para aplicar en nuestro día a día.*\n",
        "\n",
        "- **Algebra lineal:** sobre todo aplicado a Machine Learning.\n",
        "- **Estadisitica:** La estadística y la probabilidad están muy ligadas a la capacidad de un cientifico de datos: axiomas y leyes de la probabilidad, variables aleatorias, distribuciones de probabilidades, varianza, análisis de la varianza etc.\n",
        "- **Cálculo:** Conocimiento de calculo diferencial e integral, funciones vectoriales, funciones de varias variables.\n",
        "- **Analisis funcional:** Espacion normado, espacio de Hilbert etc.\n",
        "- **Algoritmos:** Es importante para poder optimizar la eficiencia computacional y la escalabilidad de nuestro proyecto. Además tener conocimiento sobre la estructura de datos, grafos, métodos estocasticos.\n",
        "\n",
        "**2. Análisis de de datos**\n",
        "\n",
        "*Esta es la parte donde el cientifico de datos reune su mayor esfuerzo y la cual será la más valiosa.\n",
        "Muchos de los software y herramienta que utilizamos ya sea para Big Data y Machine Learning se encargan de hacer la mayor parte de los cálculos matemáticos y estadísticos por nosotros, sin embargo, esto no podrá hacerlo nadie.*\n",
        "\n",
        "*EL 80% del trabajo de un cientifico de datos se basa en preparación de datos (tratamiendo de datos) y su visualización.*\n",
        "\n",
        "**3. Programación**\n",
        "\n",
        "*Como trabajamos con conjuntos de datos inmensos, entonces necesitamos conocer un lenguaje de programación especial diseñados para manipular y extraer datos de sistemas de gestores de bases de datos relacionales como MySQL, PostgresSQL, SQL server entre otros.*\n",
        "\n",
        "*Pero también necesitamos conocer los lenguaje donde el cientifico de datos puede trabajar, y acá tenemos los dos más destacado en el mundo actual, lo que **Python** y **R**, ambas herramientas son usadas por los cientificos de datos.*\n",
        "\n",
        "**3. visualización**\n",
        "\n",
        "*Una vez que los datos se han preparado de la mejor manera, es decir, se hizo un tratamiento de datos exhautivo de arriba abajo, entonces es necesario poder realizar un EDAV (Un análisis exploratorio de los datos visual)*\n",
        "\n",
        "**4. Modelado predictivo**\n",
        "\n",
        "*En este paso es donde podemos diferenciar de un analista de datos de un cientifico de datos. Los cientificos de datos somos los encargados o tenemos la tarea de predecir futuro usando los datos extraidos del pasado y del presente. Explotan una gran cantidad de datos y utilizamos modelo o métodos de Machine learning (aprendizaje automatico).*\n",
        "\n",
        "**5. Conocimiento del Negocio**\n",
        "\n",
        "*El cientifico de datos debe tener conocimiento y habilidades pertenecientes a mundos diferentes como es el mundo de los negocios. Es aquí donde se debe tener la capacidad y los conocimientos necesarios para interpretar y detectar tendencias en el área en que se este trabajando, y traducir estos descubrimientos en acciones que impacten sobre el negocio, crear nuevas oportunidades.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuF-PGlnNwdw"
      },
      "source": [
        "<center><h1 style=\"font-size:200%; font-family:cursive; background:azure; color:black; border-radius:10px 10px; padding:10px;\"> 5. Tipo de Machine Learning </h1></center>\n",
        "\n",
        "Los diferentes algoritmos de Aprendizaje Automático se agrupan en una taxonomía en función de la salida de los mismos. Algunos tipos de algoritmos son:\n",
        "\n",
        "\n",
        "|Aprendizaje Supervisado|Aprendizaje No Supervisado|Aprendizaje por Refuerzo|\n",
        "|-----------------------|---------------------------|-----------------------|\n",
        "|   Regresión           |Reducción de Dimmensiones  |                       |\n",
        "|                       |                           |                       |\n",
        "|   Clasificación       |Clustering                 |                       |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaEu6HT9Nwdx"
      },
      "source": [
        "### Aprendizaje Supervisado\n",
        "Es la tarea de aprendizaje automático de aprender una función que asigna una entrada a una salida en función de pares de entrada-salida de ejemplo. En el aprendizaje supervisado, cada ejemplo es un par que consta de un objeto de entrada (típicamente un vector) y un valor de salida deseado (también llamado señal de supervisión). Un algoritmo de aprendizaje supervisado analiza los datos de entrenamiento y produce una función inferida, que puede usarse para mapear nuevos ejemplos. Un escenario óptimo permitirá que el algoritmo determine correctamente las etiquetas de clase para instancias invisibles. Esto requiere que el algoritmo de aprendizaje generalice a partir de los datos de entrenamiento a situaciones invisibles de una manera \"razonable\". Esta calidad estadística de un algoritmo se mide mediante el llamado error de generalización."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LQUo2mTrNwdy"
      },
      "source": [
        "#### Pasos\n",
        "Para resolver un determinado problema de aprendizaje supervisado, se deben realizar los siguientes pasos:\n",
        "\n",
        "1. Determine el tipo de ejemplos de formación. Antes de hacer cualquier otra cosa, el debemos de decidir qué tipo de datos se utilizarán como conjunto de entrenamiento. En el caso del análisis de escritura a mano, por ejemplo, esto podría ser un solo carácter escrito a mano, una palabra escrita a mano completa, una oración completa de escritura a mano o quizás un párrafo completo de escritura a mano.\n",
        "\n",
        "2. Definir un conjunto de entrenamiento. El conjunto de entrenamiento debe ser representativo del uso real de la función. Por lo tanto, se recopila un conjunto de objetos de entrada y también se recopilan las salidas correspondientes, ya sea de expertos humanos o de mediciones.\n",
        "\n",
        "3. Determine la representación de la característica de entrada de la función aprendida. La precisión de la función aprendida depende en gran medida de cómo se representa el objeto de entrada. Normalmente, el objeto de entrada se transforma en un vector de características, que contiene una serie de características que son descriptivas del objeto. El número de características no debería ser demasiado grande, debido a la maldición de la dimensionalidad; pero debe contener suficiente información para predecir con precisión la salida.\n",
        "\n",
        "4. Determine la estructura de la función aprendida y el algoritmo de aprendizaje correspondiente. Por ejemplo, el ingeniero puede optar por utilizar máquinas de vectores de soporte o árboles de decisión.\n",
        "\n",
        "5. Completa el diseño. Ejecute el algoritmo de aprendizaje en el conjunto de entrenamiento recopilado. Algunos algoritmos de aprendizaje supervisado requieren que el usuario determine ciertos parámetros de control. Estos parámetros se pueden ajustar optimizando el rendimiento en un subconjunto (llamado conjunto de validación) del conjunto de entrenamiento, o mediante validación cruzada.\n",
        "\n",
        "6. Evalúe la precisión de la función aprendida. Después del ajuste y el aprendizaje de los parámetros, el rendimiento de la función resultante debe medirse en un conjunto de prueba que esté separado del conjunto de entrenamiento."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-WoU3P_BNwd1"
      },
      "source": [
        "#### Escoger el Algoritmo\n",
        "Se encuentra disponible una amplia gama de algoritmos de aprendizaje supervisado, cada uno con sus fortalezas y debilidades. No existe un algoritmo de aprendizaje único que funcione mejor en todos los problemas de aprendizaje supervisado.\n",
        "\n",
        "Hay cuatro principales problemas a considerar en el aprendizaje supervisado:\n",
        "\n",
        "**1. Bias-variance tradeoff.** \n",
        "Un primer problema es la relación entre sesgo y varianza. Imagine que tenemos disponibles varios conjuntos de datos de entrenamiento diferentes, pero igualmente buenos. Un algoritmo de aprendizaje está sesgado para una entrada particular $x$ si, cuando se entrena en cada uno de estos conjuntos de datos, es sistemáticamente incorrecto al predecir la salida correcta para $x$. Un algoritmo de aprendizaje tiene una alta varianza para una entrada en particular $x$ si predice diferentes valores de salida cuando se entrena en diferentes conjuntos de entrenamiento. El error de predicción de un clasificador aprendido está relacionado con la suma del sesgo y la varianza del algoritmo de aprendizaje. Generalmente, existe una compensación entre sesgo y varianza. Un algoritmo de aprendizaje con un sesgo bajo debe ser \"flexible\" para que pueda ajustarse bien a los datos. Pero si el algoritmo de aprendizaje es demasiado flexible, se ajustará a cada conjunto de datos de entrenamiento de manera diferente y, por lo tanto, tendrá una gran variación. Un aspecto clave de muchos métodos de aprendizaje supervisado es que pueden ajustar esta compensación entre sesgo y varianza."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eNCSR-VGNwd3"
      },
      "source": [
        "**2. Complejidad de la función y cantidad de datos de entrenamiento.**\n",
        "El segundo problema es la cantidad de datos de entrenamiento disponibles en relación con la complejidad de la función \"verdadera\" (función de clasificación o regresión). Si la función verdadera es simple, entonces un algoritmo de aprendizaje \"inflexible\" con alto sesgo y baja varianza podrá aprenderlo a partir de una pequeña cantidad de datos. Pero si la función verdadera es muy compleja (por ejemplo, porque implica interacciones complejas entre muchas características de entrada diferentes y se comporta de manera diferente en diferentes partes del espacio de entrada), entonces la función solo podrá aprender de una gran cantidad de datos de entrenamiento y el uso de un algoritmo de aprendizaje \"flexible\" con bajo sesgo y alta varianza. Existe una clara demarcación entre la entrada y la salida deseada.\n",
        "\n",
        "**3. Dimensionalidad del espacio de entrada.**\n",
        "Un tercer problema es la dimensionalidad del espacio de entrada. Si los vectores de características de entrada tienen una dimensión muy alta, el problema de aprendizaje puede ser difícil incluso si la función verdadera solo depende de una pequeña cantidad de esas características. Esto se debe a que las muchas dimensiones \"adicionales\" pueden confundir el algoritmo de aprendizaje y hacer que tenga una gran variación. Por lo tanto, una dimensión de entrada alta normalmente requiere ajustar el clasificador para que tenga una baja varianza y un alto sesgo. En la práctica, si el ingeniero puede eliminar manualmente características irrelevantes de los datos de entrada, es probable que esto mejore la precisión de la función aprendida. Además, existen muchos algoritmos para la selección de características que buscan identificar las características relevantes y descartar las irrelevantes. Este es un ejemplo de la estrategia más general de reducción de dimensionalidad, que busca mapear los datos de entrada en un espacio de menor dimensión antes de ejecutar el algoritmo de aprendizaje supervisado."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5iXYQYMNwd5"
      },
      "source": [
        "**4. Ruido en los valores de salida.**\n",
        "Un cuarto problema es el grado de ruido en los valores de salida deseados (las variables objetivo de supervisión). Si los valores de salida deseados son a menudo incorrectos (debido a errores humanos o errores del sensor), entonces el algoritmo de aprendizaje no debe intentar encontrar una función que coincida exactamente con los ejemplos de entrenamiento. Intentar ajustar los datos con demasiado cuidado conduce a un sobreajuste. Puede sobreajustar incluso cuando no hay errores de medición (ruido estocástico) si la función que está tratando de aprender es demasiado compleja para su modelo de aprendizaje. En tal situación, la parte de la función de destino que no se puede modelar \"corrompe\" los datos de entrenamiento; este fenómeno se ha denominado ruido determinista. Cuando está presente cualquier tipo de ruido, es mejor utilizar un estimador de mayor sesgo y menor varianza.\n",
        "\n",
        "En la práctica, existen varios enfoques para aliviar el ruido en los valores de salida, como la parada anticipada para evitar el sobreajuste, así como detectar y eliminar los ejemplos de entrenamiento ruidosos antes de entrenar el algoritmo de aprendizaje supervisado. Hay varios algoritmos que identifican ejemplos de entrenamiento ruidosos y la eliminación de los ejemplos de entrenamiento ruidosos sospechosos antes del entrenamiento ha disminuido el error de generalización con significación estadística."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gdx1k4niNwd8"
      },
      "source": [
        "#### Otro factores a tomar encuenta sobre el Aprendizaje supervisado\n",
        "Otros factores a considerar al elegir y aplicar un algoritmo de aprendizaje incluyen los siguientes:\n",
        "\n",
        "* **Heterogeneidad de los datos.** Si los vectores de características incluyen características de muchos tipos diferentes (discretos, discretos ordenados, recuentos, valores continuos), algunos algoritmos son más fáciles de aplicar que otros. Muchos algoritmos, incluidas las máquinas de vectores de soporte, la regresión lineal, la regresión logística, las redes neuronales y los métodos del vecino más cercano, requieren que las características de entrada sean numéricas y escaladas a rangos similares (por ejemplo, al intervalo [-1,1]). Los métodos que emplean una función de distancia, como los métodos del vecino más cercano y las máquinas de vectores de soporte con núcleos gaussianos, son particularmente sensibles a esto. Una ventaja de los árboles de decisión es que manejan fácilmente datos heterogéneos.\n",
        "\n",
        "* **Redundancia en los datos.** Si las características de entrada contienen información redundante (por ejemplo, características altamente correlacionadas), algunos algoritmos de aprendizaje (por ejemplo, regresión lineal, regresión logística y métodos basados en la distancia) funcionarán mal debido a inestabilidades numéricas. Estos problemas a menudo pueden resolverse imponiendo alguna forma de regularización.\n",
        "\n",
        "* **Presencia de interacciones y no linealidades.** Si cada una de las características hace una contribución independiente a la salida, entonces los algoritmos basados en funciones lineales (por ejemplo, regresión lineal, regresión logística, máquinas de vectores de soporte, Bayes ingenuo) y funciones de distancia (por ejemplo, métodos del vecino más cercano, máquinas de vectores de soporte) con núcleos gaussianos) generalmente funcionan bien. Sin embargo, si hay interacciones complejas entre características, los algoritmos como los árboles de decisión y las redes neuronales funcionan mejor, porque están diseñados específicamente para descubrir estas interacciones. También se pueden aplicar métodos lineales, pero el ingeniero debe especificar manualmente las interacciones al usarlos.\n",
        "\n",
        "Al considerar una nueva aplicación, el ingeniero puede comparar múltiples algoritmos de aprendizaje y determinar experimentalmente cuál funciona mejor en el problema en cuestión (ver validación cruzada). Ajustar el rendimiento de un algoritmo de aprendizaje puede llevar mucho tiempo. Dados los recursos fijos, a menudo es mejor dedicar más tiempo a recopilar datos de entrenamiento adicionales y características más informativas que dedicar más tiempo a ajustar los algoritmos de aprendizaje."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fm7H87FxNwd9"
      },
      "source": [
        "#### 6. Algoritmos\n",
        "\n",
        "    1.1. Linear Models\n",
        "        1.1.1. Ordinary Least Squares\n",
        "        1.1.2. Ridge regression and classification\n",
        "        1.1.3. Lasso\n",
        "        1.1.4. Multi-task Lasso\n",
        "        1.1.5. Elastic-Net\n",
        "        1.1.6. Multi-task Elastic-Net\n",
        "        1.1.7. Least Angle Regression\n",
        "        1.1.8. LARS Lasso\n",
        "        1.1.9. Orthogonal Matching Pursuit (OMP)\n",
        "        1.1.10. Bayesian Regression\n",
        "        1.1.11. Logistic regression\n",
        "        1.1.12. Generalized Linear Regression\n",
        "        1.1.13. Stochastic Gradient Descent - SGD\n",
        "        1.1.14. Perceptron\n",
        "        1.1.15. Passive Aggressive Algorithms\n",
        "        1.1.16. Robustness regression: outliers and modeling errors\n",
        "        1.1.17. Quantile Regression\n",
        "        1.1.18. Polynomial regression: extending linear models with basis functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D_vbbT2oNwd-"
      },
      "source": [
        "    1.2. Linear and Quadratic Discriminant Analysis\n",
        "        1.2.1. Dimensionality reduction using Linear Discriminant Analysis\n",
        "        1.2.2. Shrinkage and Covariance Estimator\n",
        "        1.2.3. Estimation algorithms\n",
        "        \n",
        "    1.3. Kernel ridge regression \n",
        "    \n",
        "    1.4. Support Vector Machines\n",
        "        1.4.1. Classification\n",
        "        1.4.2. Regression\n",
        "        1.4.3. Density estimation, novelty detection\n",
        "        1.4.4. Complexity\n",
        "        \n",
        "        \n",
        "    1.5. Stochastic Gradient Descent\n",
        "        1.5.1. Classification\n",
        "        1.5.2. Regression\n",
        "        1.5.3. Online One-Class SVM\n",
        "        1.5.4. Stochastic Gradient Descent for sparse data\n",
        "        1.5.5. Complexity\n",
        "        1.5.6. Stopping criterion\n",
        "        "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAkdtFIlNwd_"
      },
      "source": [
        "    1.6. Nearest Neighbors\n",
        "        1.6.1. Unsupervised Nearest Neighbors\n",
        "        1.6.2. Nearest Neighbors Classification\n",
        "        1.6.3. Nearest Neighbors Regression\n",
        "        1.6.4. Nearest Neighbor Algorithms\n",
        "        1.6.5. Nearest Centroid Classifier\n",
        "        1.6.6. Nearest Neighbors Transformer\n",
        "        1.6.7. Neighborhood Components Analysis\n",
        "        \n",
        "    1.7. Gaussian Processes\n",
        "        1.7.1. Gaussian Process Regression (GPR)\n",
        "        1.7.2. Gaussian Process Classification (GPC)\n",
        "        1.7.3. Kernels for Gaussian Processes     \n",
        "        \n",
        "    1.8. Cross decomposition\n",
        "        1.8.1. PLSCanonical\n",
        "        1.8.2. PLSSVD\n",
        "        1.8.3. PLSRegression\n",
        "        1.8.4. Canonical Correlation Analysis\n",
        "        \n",
        "    1.9. Naive Bayes\n",
        "        1.9.1. Gaussian Naive Bayes\n",
        "        1.9.2. Multinomial Naive Bayes\n",
        "        1.9.3. Complement Naive Bayes\n",
        "        1.9.4. Bernoulli Naive Bayes\n",
        "        1.9.5. Categorical Naive Bayes\n",
        "        1.9.6. Out-of-core naive Bayes model fitting \n",
        "        \n",
        "    1.10. Decision Trees\n",
        "        1.10.1. Classification\n",
        "        1.10.2. Regression\n",
        "        1.10.3. Tree algorithms: ID3, C4.5, C5.0 and CART\n",
        "        1.10.4. Minimal Cost-Complexity Pruning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gwVw-nXENweA"
      },
      "source": [
        "    1.11. Ensemble methods\n",
        "        1.11.1. Bagging meta-estimator\n",
        "        1.11.2. Forests of randomized trees\n",
        "        1.11.3. AdaBoost\n",
        "        1.11.4. Gradient Tree Boosting\n",
        "        1.11.5. Histogram-Based Gradient Boosting\n",
        "        1.11.6. Voting Classifier\n",
        "        1.11.7. Voting Regressor\n",
        "        1.11.8. Stacked generalization\n",
        "        \n",
        "    1.12. Multiclass and multioutput algorithms\n",
        "        1.12.1. Multiclass classification\n",
        "        1.12.2. Multilabel classification\n",
        "        1.12.3. Multiclass-multioutput classification\n",
        "        1.12.4. Multioutput regression\n",
        "        \n",
        "    1.13. Feature selection\n",
        "        1.13.1. Removing features with low variance\n",
        "        1.13.2. Univariate feature selection\n",
        "        1.13.3. Recursive feature elimination\n",
        "        1.13.4. Feature selection using SelectFromModel\n",
        "        1.13.5. Sequential Feature Selection\n",
        "        1.13.6. Feature selection as part of a pipeline        \n",
        "        \n",
        "    1.14. Semi-supervised learning\n",
        "        1.14.1. Self Training\n",
        "        1.14.2. Label Propagation       \n",
        "        \n",
        "    1.15. Isotonic regression        \n",
        "    \n",
        "    1.16. Probability calibration\n",
        "        1.16.1. Calibration curves\n",
        "        1.16.2. Calibrating a classifier\n",
        "       "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b68DwWSfNweC"
      },
      "source": [
        "    1.17. Neural network models (supervised)\n",
        "        1.17.1. Multi-layer Perceptron\n",
        "        1.17.2. Classification\n",
        "        1.17.3. Regression\n",
        "        1.17.4. Regularization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "haKiBRl_NweD"
      },
      "source": [
        "#### 7. Cómo funcionan los algoritmos de aprendizaje supervisado\n",
        "Dado un conjunto de $N$ ejemplos de entrenamiento de la forma $(x_1, y_1), \\cdots, (x_N, \\; y_N)$ tal que $x_{i}$ es el vector de características del i-ésimo ejemplo y $y_ {i}$ es su etiqueta (es decir, clase), un algoritmo de aprendizaje busca una función  $g: X \\to Y$, donde $X$ es el espacio de entrada y $Y$ es el espacio de salida. La función $g$ es un elemento de algún espacio de posibles funciones $G$, generalmente llamado espacio de hipótesis. A veces es conveniente representar $g$ usando una función de puntuación $f: X \\times Y \\to \\mathbb {R}$ tal que $g$ se define como devolver el valor $y$ que da la puntuación más alta: $g (x) = {\\underset {y} {\\arg \\max}} \\; f (x, y)$. Sea $F$ el espacio de las funciones de puntuación.\n",
        "\n",
        "Aunque $G$ y $F$ pueden ser cualquier espacio de funciones, muchos algoritmos de aprendizaje son modelos probabilísticos donde $g$ toma la forma de un modelo de probabilidad condicional $g (x) =P (y | x)$, o $f$ toma la forma de un modelo de probabilidad conjunta $f (x, y) = P (x, y)$. Por ejemplo, Bayes ingenuo y el análisis discriminante lineal son modelos de probabilidad conjunta, mientras que la regresión logística es un modelo de probabilidad condicional.\n",
        "\n",
        "Hay dos enfoques básicos para elegir $f$ o $g$: minimización del riesgo empírico y minimización del riesgo estructural. La minimización de riesgos empíricos busca la función que mejor se adapta a los datos de entrenamiento. La minimización del riesgo estructural incluye una función de penalización que controla la compensación de sesgo / varianza.\n",
        "\n",
        "En ambos casos, se supone que el conjunto de entrenamiento consiste en una muestra de pares independientes e idénticamente distribuidos, $(x_i, \\; y_i)$. Para medir qué tan bien se ajusta una función a los datos de entrenamiento, una función de pérdida $L: Y \\times Y \\to \\mathbb {R} ^{\\geq 0}$ está definido. Para el ejemplo de entrenamiento $(x_i, \\; y_i)$, la pérdida de la predicción del valor $\\hat {y }$ es $L (y_i, \\hat {y})$.\n",
        "\n",
        "El riesgo $R (g)$ de la función $g$ se define como la pérdida esperada de $g$. Esto se puede estimar a partir de los datos de entrenamiento como\n",
        "$$R_ {emp} (g) = \\frac {1} {N} \\sum_i L (y_i, g (x_i)).$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j2gS8GfaNweF"
      },
      "source": [
        "**Minimización de riesgos empíricos**\n",
        "\n",
        "En la minimización del riesgo empírico, el algoritmo de aprendizaje supervisado busca la función $g$ que minimiza $R (g)$. Por lo tanto, se puede construir un algoritmo de aprendizaje supervisado aplicando un algoritmo de optimización para encontrar $g$.\n",
        "\n",
        "Cuando $g$ es una distribución de probabilidad condicional $P (y | x)$ y la función de pérdida es la probabilidad logarítmica negativa: $L (y, \\hat {y}) = - \\log P (y | x)$, entonces la minimización del riesgo empírico es equivalente a la estimación de máxima verosimilitud.\n",
        "\n",
        "Cuando $G$ contiene muchas funciones candidatas o el conjunto de entrenamiento no es lo suficientemente grande, la minimización del riesgo empírico conduce a una alta varianza y una mala generalización. El algoritmo de aprendizaje es capaz de memorizar los ejemplos de entrenamiento sin generalizar bien. A esto se le llama sobreajuste."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJnfKOlDNweO"
      },
      "source": [
        "**Minimización de riesgos estructurales**\n",
        "\n",
        "La minimización del riesgo estructural busca prevenir el sobreajuste incorporando una penalización de regularización en la optimización. La penalización por regularización puede verse como la implementación de una forma de navaja de Occam que prefiere funciones más simples sobre las más complejas.\n",
        "\n",
        "Se ha empleado una amplia variedad de sanciones que corresponden a diferentes definiciones de complejidad. Por ejemplo, considere el caso en el que la función $g$ es una función lineal de la forma\n",
        "\n",
        "$$g (x) = \\sum_ {j = 1} ^ d \\beta_j x_j.$$\n",
        "Una penalización de regularización popular es $\\sum_j \\beta_j ^ 2$, que es la norma euclidiana al cuadrado de los pesos, también conocida como $L_{2}$. Otras normas incluyen la norma $L_{1}$, $\\sum_j | \\beta_j |$ y $L_{0}$ \"norma\", que es el número de $\\beta_{j}s$ distintos de cero. La penalización se indicará con $C (g).$\n",
        "\n",
        "El problema de optimización del aprendizaje supervisado es encontrar la función $g$ que minimiza\n",
        "\n",
        "$$J (g) = R_ {emp} (g) + \\ lambda C (g).$$\n",
        "El parámetro $\\lambda$ controla la compensación de sesgo-varianza. Cuando $\\lambda = 0$, esto proporciona una minimización del riesgo empírico con bajo sesgo y alta varianza. Cuando $\\lambda$ es grande, el algoritmo de aprendizaje tendrá un alto sesgo y una baja varianza. El valor de $\\lambda$ se puede elegir empíricamente mediante validación cruzada.\n",
        "\n",
        "La penalización por complejidad tiene una interpretación bayesiana como la probabilidad previa logarítmica negativa de $g$, $- \\log P (g)$, en cuyo caso $J (g)$ es la probabilidad posterior de $g$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TasmezruNweP"
      },
      "source": [
        "### Aprendizaje no Supervisado\n",
        "El aprendizaje no supervisado es un tipo de aprendizaje automático en el que el algoritmo no recibe etiquetas o puntuaciones asignadas previamente para los datos de entrenamiento. Como resultado, los algoritmos de aprendizaje no supervisados primero deben descubrir por sí mismos cualquier patrón que ocurra naturalmente en ese conjunto de datos de entrenamiento. Los ejemplos comunes incluyen la agrupación en clústeres, donde el algoritmo agrupa automáticamente sus ejemplos de entrenamiento en categorías con características similares, y el análisis de componentes principales, donde el algoritmo encuentra formas de comprimir el conjunto de datos de entrenamiento identificando qué características son más útiles para discriminar entre diferentes ejemplos de entrenamiento, y descartando el resto. Esto contrasta con el aprendizaje supervisado en el que los datos de entrenamiento incluyen etiquetas de categoría preasignadas (a menudo por un humano, o del resultado de un algoritmo de clasificación de no aprendizaje). Otros niveles intermedios en el espectro de supervisión incluyen el aprendizaje por refuerzo, donde solo están disponibles puntuaciones numéricas para cada ejemplo de capacitación en lugar de etiquetas detalladas, y el aprendizaje semi-supervisado donde solo se ha etiquetado una parte de los datos de capacitación.\n",
        "\n",
        "Las ventajas del aprendizaje no supervisado incluyen una carga de trabajo mínima para preparar y auditar el conjunto de capacitación, en contraste con las técnicas de aprendizaje supervisado donde se requiere una cantidad considerable de trabajo humano experto para asignar y verificar las etiquetas iniciales, y una mayor libertad para identificar y explotar patrones previamente no detectados. que puede que no hayan sido notados por los \"expertos\". Esto a menudo tiene el costo de técnicas no supervisadas que requieren una mayor cantidad de datos de entrenamiento y convergen más lentamente a un rendimiento aceptable, mayores requisitos computacionales y de almacenamiento durante el proceso exploratorio y una mayor susceptibilidad a artefactos o anomalías en los datos de entrenamiento que obviamente podrían ser irrelevantes o reconocidos como erróneos por un humano, pero el algoritmo de aprendizaje no supervisado les asigna una importancia indebida."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IsavZ0hDNweQ"
      },
      "source": [
        "**¿Qué es el aprendizaje no supervisado?**\n",
        "\n",
        "El aprendizaje no supervisado, también conocido como aprendizaje automático no supervisado, utiliza algoritmos de aprendizaje automático para analizar y agrupar conjuntos de datos sin etiquetar. Estos algoritmos descubren patrones ocultos o agrupaciones de datos sin necesidad de intervención humana. Su capacidad para descubrir similitudes y diferencias en la información la convierte en la solución ideal para análisis de datos exploratorios, estrategias de venta cruzada, segmentación de clientes y reconocimiento de imágenes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jj2tYz4mNweQ"
      },
      "source": [
        "**Enfoques comunes de aprendizaje no supervisado**\n",
        "\n",
        "Los modelos de aprendizaje no supervisados se utilizan para tres tareas principales: \n",
        "* Clusteringo o agrupamiento, \n",
        "* asociación y \n",
        "* reducción de dimensionalidad. \n",
        "\n",
        "A continuación, definiremos cada método de aprendizaje y destacaremos los algoritmos y enfoques comunes para llevarlos a cabo de manera eficaz."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ygwog24NweR"
      },
      "source": [
        "**Clustering o Agrupamiento**\n",
        "\n",
        "La agrupación en clústeres es una técnica de minería de datos que agrupa datos sin etiquetar en función de sus similitudes o diferencias. Los algoritmos de agrupación en clústeres se utilizan para procesar objetos de datos sin clasificar y sin procesar en grupos representados por estructuras o patrones en la información. Los algoritmos de agrupación en clústeres se pueden clasificar en unos pocos tipos, específicamente exclusivos, superpuestos, jerárquicos y probabilísticos.\n",
        "\n",
        "**Clustering exclusivo y superpuesto**\n",
        "\n",
        "La agrupación en **clústeres exclusiva** es una forma de agrupación que estipula que un punto de datos solo puede existir en un clúster. Esto también se puede denominar agrupamiento \"duro\". El algoritmo de agrupación en clústeres de K-means es un ejemplo de agrupación en clústeres exclusiva.\n",
        "\n",
        "    - El Cluster de K-means es un ejemplo común de un método de agrupamiento exclusivo en el que los puntos de datos se asignan en K grupos, donde K representa el número de grupos en función de la distancia desde el centroide de cada grupo. Los puntos de datos más cercanos a un centroide determinado se agruparán en la misma categoría. Un valor de K más grande será indicativo de agrupaciones más pequeñas con más granularidad, mientras que un valor de K más pequeño tendrá agrupaciones más grandes y menos granularidad. La agrupación en clústeres de K-means se utiliza comúnmente en la segmentación del mercado, la agrupación de documentos, la segmentación de imágenes y la compresión de imágenes.\n",
        "    \n",
        "Los clústeres superpuestos se diferencian del agrupamiento exclusivo en que permite que los puntos de datos pertenezcan a varios clústeres con grados separados de pertenencia. El agrupamiento \"suave\" o difuso de k-medias es un ejemplo de agrupamiento superpuesto."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rYwVVCm6NweY"
      },
      "source": [
        "**Clustering jerárquica**\n",
        "\n",
        "El agrupamiento jerárquico, también conocido como análisis de agrupamiento jerárquico (HCA), es un algoritmo de agrupamiento no supervisado que se puede categorizar de dos formas; pueden ser aglomerados o divisivos. La agrupación aglomerativa se considera un \"enfoque de abajo hacia arriba\". Inicialmente, sus puntos de datos se aíslan como agrupaciones separadas y luego se fusionan de forma iterativa sobre la base de la similitud hasta que se haya logrado una agrupación. Normalmente se utilizan cuatro métodos diferentes para medir la similitud:\n",
        "\n",
        "1. **Vínculo de Ward:** este método establece que la distancia entre dos conglomerados se define por el aumento de la suma de los cuadrados después de que se fusionan los conglomerados.\n",
        "\n",
        "2. **Vinculación promedio:** este método se define por la distancia media entre dos puntos en cada grupo\n",
        "\n",
        "3. **Enlace completo (o máximo):** este método se define por la distancia máxima entre dos puntos en cada grupo.\n",
        "\n",
        "4. **Enlace único (o mínimo):** este método se define por la distancia mínima entre dos puntos en cada grupo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7cha8P4NweZ"
      },
      "source": [
        "La distancia euclidiana es la métrica más común utilizada para calcular estas distancias; sin embargo, otras métricas, como la distancia de Manhattan, también se citan en la literatura sobre agrupaciones.\n",
        "\n",
        "El agrupamiento divisivo se puede definir como lo opuesto al agrupamiento aglomerativo; en su lugar, adopta un enfoque \"de arriba hacia abajo\". En este caso, un solo grupo de datos se divide en función de las diferencias entre los puntos de datos. La agrupación en clústeres divisiva no se utiliza comúnmente, pero aún vale la pena señalarla en el contexto de la agrupación jerárquica. Estos procesos de agrupamiento generalmente se visualizan usando un dendrograma, un diagrama en forma de árbol que documenta la fusión o división de puntos de datos en cada iteración."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HoUjHZpKNweb"
      },
      "source": [
        "**Cluster probabilística**\n",
        "\n",
        "Un modelo probabilístico es una técnica no supervisada que nos ayuda a resolver problemas de estimación de densidad o de agrupamiento \"suave\". En la agrupación probabilística, los puntos de datos se agrupan en función de la probabilidad de que pertenezcan a una distribución particular. El modelo de mezcla gaussiana (GMM) es uno de los métodos de agrupamiento probabilístico más utilizados.\n",
        "\n",
        "- **Los modelos de mezcla gaussianos(Gaussian Mixture Models)** se clasifican como modelos de mezcla, lo que significa que están compuestos por un número no especificado de funciones de distribución de probabilidad. Los GMM se aprovechan principalmente para determinar a qué distribución de probabilidad gaussiana o normal pertenece un punto de datos dado. Si se conoce la media o la varianza, entonces podemos determinar a qué distribución pertenece un punto de datos dado. Sin embargo, en los GMM, estas variables no se conocen, por lo que asumimos que existe una variable latente u oculta para agrupar puntos de datos de manera adecuada. Si bien no es necesario utilizar el algoritmo de maximización de expectativas (EM), se usa comúnmente para estimar las probabilidades de asignación para un punto de datos dado a un grupo de datos en particular."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7sLO5RiNwee"
      },
      "source": [
        "**Reglas de asociación**\n",
        "\n",
        "Una regla de asociación es un método basado en reglas para encontrar relaciones entre variables en un conjunto de datos determinado. Estos métodos se utilizan con frecuencia para el análisis de la canasta de mercado, lo que permite a las empresas comprender mejor las relaciones entre diferentes productos. Comprender los hábitos de consumo de los clientes permite a las empresas desarrollar mejores estrategias de venta cruzada y motores de recomendación. Se pueden ver ejemplos de esto en la lista de reproducción \"Los clientes que compraron este artículo también compraron\" de Amazon o \"Discover Weekly\" de Spotify. Si bien se utilizan algunos algoritmos diferentes para generar reglas de asociación, como Apriori, Eclat y FP-Growth, el algoritmo Apriori es el más utilizado.\n",
        "\n",
        "   * **Algoritmos a priori** Los algoritmos a priori se han popularizado a través de análisis de la cesta de la compra, lo que ha dado lugar a diferentes motores de recomendación para plataformas de música y minoristas en línea. Se utilizan dentro de conjuntos de datos transaccionales para identificar conjuntos de artículos frecuentes, o colecciones de artículos, para identificar la probabilidad de consumir un producto dado el consumo de otro producto. Por ejemplo, si pongo la radio de Black Sabbath en Spotify, comenzando con su canción \"Orchid\", es probable que una de las otras canciones de este canal sea una canción de Led Zeppelin, como \"Over the Hills and Far Away\". Esto se basa en mis hábitos de escucha anteriores, así como en los de los demás. Los algoritmos a priori utilizan un árbol hash para contar conjuntos de elementos, navegando a través del conjunto de datos de una manera amplia."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fOsJG1fNwef"
      },
      "source": [
        "**Reducción de dimensionalidad**\n",
        "Si bien más datos generalmente arrojan resultados más precisos, también puede afectar el rendimiento de los algoritmos de aprendizaje automático (por ejemplo, sobreajuste) y también puede dificultar la visualización de conjuntos de datos. La reducción de dimensionalidad es una técnica que se utiliza cuando el número de características o dimensiones de un conjunto de datos determinado es demasiado alto. Reduce la cantidad de entradas de datos a un tamaño manejable y al mismo tiempo preserva la integridad del conjunto de datos tanto como sea posible. Se usa comúnmente en la etapa de preprocesamiento de datos, y existen algunos métodos diferentes de reducción de dimensionalidad que se pueden usar, como:\n",
        "\n",
        "* **Análisis de componentes principales:** El análisis de componentes principales (PCA) es un tipo de algoritmo de reducción de dimensionalidad que se utiliza para reducir redundancias y comprimir conjuntos de datos mediante la extracción de características. Este método utiliza una transformación lineal para crear una nueva representación de datos, produciendo un conjunto de \"componentes principales\". El primer componente principal es la dirección que maximiza la varianza del conjunto de datos. Si bien el segundo componente principal también encuentra la varianza máxima en los datos, no está correlacionado por completo con el primer componente principal, lo que produce una dirección que es perpendicular u ortogonal al primer componente. Este proceso se repite en función del número de dimensiones, donde un siguiente componente principal es la dirección ortogonal a los componentes anteriores con la mayor variación.\n",
        "\n",
        "* **Valor singular de descomposición:** La descomposición de valores singulares (SVD) es otro enfoque de reducción de dimensionalidad que factoriza una matriz, A, en tres matrices de bajo rango. SVD se denota mediante la fórmula, $A = USVT$, donde $U$ y $V$ son matrices ortogonales. $S$ es una matriz diagonal y los valores de $S$ se consideran valores singulares de la matriz A. Similar a PCA, se usa comúnmente para reducir el ruido y comprimir datos, como archivos de imagen.\n",
        "\n",
        "* **Autoenconders:** Los codificadores automáticos aprovechan las redes neuronales para comprimir datos y luego recrear una nueva representación de la entrada de datos originales. Al mirar la imagen de abajo, puede ver que la capa oculta actúa específicamente como un cuello de botella para comprimir la capa de entrada antes de reconstruirla dentro de la capa de salida. La etapa de la capa de entrada a la capa oculta se conoce como \"codificación\", mientras que la etapa de la capa oculta a la capa de salida se conoce como \"decodificación\"."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BMWuBr3CNweg"
      },
      "source": [
        "**Aplicaciones del aprendizaje no supervisado**\n",
        "\n",
        "Las técnicas de aprendizaje automático se han convertido en un método común para mejorar la experiencia del usuario de un producto y para probar sistemas para garantizar la calidad. El aprendizaje no supervisado proporciona una ruta exploratoria para ver datos, lo que permite a las empresas identificar patrones en grandes volúmenes de datos más rápidamente en comparación con la observación manual. Algunas de las aplicaciones más comunes del aprendizaje no supervisado en el mundo real son:\n",
        "\n",
        "* **Secciones de noticias:** Google News utiliza el aprendizaje no supervisado para categorizar artículos sobre la misma historia de varios medios de noticias en línea. Por ejemplo, los resultados de una elección presidencial podrían clasificarse bajo su etiqueta para noticias de “Estados Unidos”.\n",
        "\n",
        "* **Visión por computadora:** los algoritmos de aprendizaje no supervisados se utilizan para tareas de percepción visual, como el reconocimiento de objetos.\n",
        "\n",
        "* **Imágenes médicas:** el aprendizaje automático no supervisado proporciona funciones esenciales para los dispositivos de imágenes médicas, como la detección, clasificación y segmentación de imágenes, que se utilizan en radiología y patología para diagnosticar a los pacientes de forma rápida y precisa.\n",
        "\n",
        "* **Detección de anomalías:** los modelos de aprendizaje no supervisados pueden analizar grandes cantidades de datos y descubrir puntos de datos atípicos dentro de un conjunto de datos. Estas anomalías pueden generar conciencia sobre equipos defectuosos, errores humanos o brechas en la seguridad.\n",
        "\n",
        "* **Personas del cliente:** definir las personas del cliente facilita la comprensión de los rasgos comunes y los hábitos de compra de los clientes comerciales. El aprendizaje no supervisado permite a las empresas crear mejores perfiles de compradores, lo que permite a las organizaciones alinear los mensajes de sus productos de manera más adecuada.\n",
        "\n",
        "* **Motores de recomendación:** utilizando datos de comportamiento de compra anteriores, el aprendizaje no supervisado puede ayudar a descubrir tendencias de datos que se pueden utilizar para desarrollar estrategias de venta cruzada más efectivas. Esto se utiliza para hacer recomendaciones complementarias relevantes a los clientes durante el proceso de pago para los minoristas en línea."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90xHjfU3Nweh"
      },
      "source": [
        "### Unsupervised vs. supervised vs. semi-supervised learning\n",
        "El aprendizaje no supervisado y el aprendizaje supervisado con frecuencia se discuten juntos. A diferencia de los algoritmos de aprendizaje no supervisados, los algoritmos de aprendizaje supervisados utilizan datos etiquetados. A partir de esos datos, predice resultados futuros o asigna datos a categorías específicas en función del problema de regresión o clasificación que está tratando de resolver. Si bien los algoritmos de aprendizaje supervisado tienden a ser más precisos que los modelos de aprendizaje no supervisados, requieren una intervención humana inicial para etiquetar los datos de manera adecuada. Sin embargo, estos conjuntos de datos etiquetados permiten que los algoritmos de aprendizaje supervisados eviten la complejidad computacional, ya que no necesitan un gran conjunto de capacitación para producir los resultados previstos. Las técnicas comunes de regresión y clasificación son la regresión lineal y logística, bayes ingenuos, algoritmo KNN y bosque aleatorio.\n",
        "\n",
        "El aprendizaje semi-supervisado ocurre cuando solo se ha etiquetado una parte de los datos de entrada dados. El aprendizaje no supervisado y semi-supervisado puede ser una alternativa más atractiva, ya que puede llevar mucho tiempo y ser costoso confiar en la experiencia del dominio para etiquetar los datos de manera adecuada para el aprendizaje supervisado.\n",
        "\n",
        "Para profundizar en las diferencias entre estos enfoques, consulte \"Aprendizaje supervisado y no supervisado: ¿Cuál es la diferencia?\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ejJtKzBlXLpC"
      },
      "source": [
        "## **Pasos para desarrollar un Modelo**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "id": "zWo1-8NnRv4h",
        "cellView": "form",
        "outputId": "7a2d3ccc-96b1-42bd-d227-f900c82b9d1f"
      },
      "source": [
        "#@title\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Panama_Honduras-Mexico/Model development steps.JPG\",width=900)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 900
            }
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwBt3AZyIePb"
      },
      "source": [
        "## **8. Aplicaremos el modelo de Random Forest de Machine Learning para predecir el costo de seguro médico**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r6FkVzMcZED3"
      },
      "source": [
        "**¿Qué factores influyen en el precio del seguro médico?**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31i2alITZEER"
      },
      "source": [
        "<font size='2'> Existen factores que afectan la cantidad que paga por el seguro médico no están bajo su control. No obstante, es bueno tener una comprensión de lo que son. Estos son algunos factores que afectan el costo de las primas del seguro médico\n",
        " \n",
        "* **age:** edad del asegurado \n",
        " \n",
        "* **sex:** sexo del asegurado , female, male\n",
        " \n",
        "* **bmi:** Índice de masa corporal, que proporciona una comprensión del cuerpo, pesos relativamente altos o bajos en relación con la altura, índice objetivo de peso corporal (kg / m ^ 2) utilizando la relación entre la altura y el peso, idealmente 18,5 a 24,9\n",
        " \n",
        "* **children:** Número de niños cubiertos por el seguro médico / Número de dependientes\n",
        " \n",
        "* **smoker:** fuma (si/no)\n",
        " \n",
        "* **region:** el área residencial del beneficiario en los EE. UU., noreste, sureste, suroeste, noroeste"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5ewSqT_ZEEV"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\"> Análisis EDA y  Visualización</h1></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0brWYA5wZIox",
        "outputId": "e8f4fc76-c52a-4d3f-92d4-82a6b35302c0"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XijpF2wyZEEX"
      },
      "source": [
        "#Operación y manipulación de datos\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "#Visualización de datos\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "2sRckRaCZEEZ",
        "outputId": "5c595b62-3e30-4cd4-8712-2728b551ba37"
      },
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Panama_Honduras-Mexico/insurance.csv\")\n",
        "\n",
        "# El comando head visualizar las primeras 5 lineas \n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex     bmi  children smoker     region      charges\n",
              "0   19  female  27.900         0    yes  southwest  16884.92400\n",
              "1   18    male  33.770         1     no  southeast   1725.55230\n",
              "2   28    male  33.000         3     no  southeast   4449.46200\n",
              "3   33    male  22.705         0     no  northwest  21984.47061\n",
              "4   32    male  28.880         0     no  northwest   3866.85520"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92jOvO6YZEEe",
        "outputId": "11e2875d-8a53-4e01-f59f-04c2edcdf2b0"
      },
      "source": [
        "# shaper me permite visualizar la cantidad de registro que tiene mis datos\n",
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1338, 7)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dW_LYncfZEEf",
        "outputId": "da930da6-b5dc-40f9-c684-41067920b8e6"
      },
      "source": [
        "df.describe()\n",
        "#bmi indice de masa corpora kg/m2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               age          bmi     children       charges\n",
              "count  1338.000000  1338.000000  1338.000000   1338.000000\n",
              "mean     39.207025    30.663397     1.094918  13270.422265\n",
              "std      14.049960     6.098187     1.205493  12110.011237\n",
              "min      18.000000    15.960000     0.000000   1121.873900\n",
              "25%      27.000000    26.296250     0.000000   4740.287150\n",
              "50%      39.000000    30.400000     1.000000   9382.033000\n",
              "75%      51.000000    34.693750     2.000000  16639.912515\n",
              "max      64.000000    53.130000     5.000000  63770.428010"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "tBZfgqJ_bpZ1",
        "outputId": "8c9a0eb1-4baa-4257-eeb1-811d0902977a"
      },
      "source": [
        "df.describe().T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>39.207025</td>\n",
              "      <td>14.049960</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>27.00000</td>\n",
              "      <td>39.000</td>\n",
              "      <td>51.000000</td>\n",
              "      <td>64.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>15.9600</td>\n",
              "      <td>26.29625</td>\n",
              "      <td>30.400</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>53.13000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>charges</th>\n",
              "      <td>1338.0</td>\n",
              "      <td>13270.422265</td>\n",
              "      <td>12110.011237</td>\n",
              "      <td>1121.8739</td>\n",
              "      <td>4740.28715</td>\n",
              "      <td>9382.033</td>\n",
              "      <td>16639.912515</td>\n",
              "      <td>63770.42801</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           count          mean  ...           75%          max\n",
              "age       1338.0     39.207025  ...     51.000000     64.00000\n",
              "bmi       1338.0     30.663397  ...     34.693750     53.13000\n",
              "children  1338.0      1.094918  ...      2.000000      5.00000\n",
              "charges   1338.0  13270.422265  ...  16639.912515  63770.42801\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "m6NR4tIrcCCV",
        "outputId": "154b4a10-a0b0-4d9d-908e-d8a713fd68bb"
      },
      "source": [
        "df.describe(include=\"all\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338.000000</td>\n",
              "      <td>1338</td>\n",
              "      <td>1338</td>\n",
              "      <td>1338.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>676</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1064</td>\n",
              "      <td>364</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>39.207025</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.663397</td>\n",
              "      <td>1.094918</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13270.422265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>14.049960</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6.098187</td>\n",
              "      <td>1.205493</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12110.011237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.960000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1121.873900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26.296250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4740.287150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>39.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.400000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9382.033000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.693750</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16639.912515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>64.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>53.130000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>63770.428010</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                age   sex          bmi  ...  smoker     region       charges\n",
              "count   1338.000000  1338  1338.000000  ...    1338       1338   1338.000000\n",
              "unique          NaN     2          NaN  ...       2          4           NaN\n",
              "top             NaN  male          NaN  ...      no  southeast           NaN\n",
              "freq            NaN   676          NaN  ...    1064        364           NaN\n",
              "mean      39.207025   NaN    30.663397  ...     NaN        NaN  13270.422265\n",
              "std       14.049960   NaN     6.098187  ...     NaN        NaN  12110.011237\n",
              "min       18.000000   NaN    15.960000  ...     NaN        NaN   1121.873900\n",
              "25%       27.000000   NaN    26.296250  ...     NaN        NaN   4740.287150\n",
              "50%       39.000000   NaN    30.400000  ...     NaN        NaN   9382.033000\n",
              "75%       51.000000   NaN    34.693750  ...     NaN        NaN  16639.912515\n",
              "max       64.000000   NaN    53.130000  ...     NaN        NaN  63770.428010\n",
              "\n",
              "[11 rows x 7 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AU0uYwpeZEEh",
        "outputId": "6c49b384-99a2-4e38-d31d-e563357af004"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex          object\n",
              "bmi         float64\n",
              "children      int64\n",
              "smoker       object\n",
              "region       object\n",
              "charges     float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mvSYGdD5ZEEi",
        "outputId": "54fa124c-4c8b-4634-b16e-c185a147565a"
      },
      "source": [
        "#Ver si hay valores valtantes\n",
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "bmi         0\n",
              "children    0\n",
              "smoker      0\n",
              "region      0\n",
              "charges     0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqUt7SLrZEEk"
      },
      "source": [
        "<font size='3' font>Tenemos 0 valores perdidos, lo cual es muy bueno.\n",
        "Ahora hagamos EDA con algunos gráficos geniales :) Primero veremos cómo se distribuyen los cargos de acuerdo con factores dados"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        },
        "id": "lqpgEtL4ZEEl",
        "outputId": "6661e3a5-c09a-4883-8822-1bd5512abd5f"
      },
      "source": [
        "sns.set(style='whitegrid')\n",
        "f, ax = plt.subplots(1,1, figsize=(20, 8))\n",
        "ax = sns.distplot(df['charges'], kde = True, color = 'c')\n",
        "plt.title('Distribution of Charges')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of Charges')"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggendH5VZEEn"
      },
      "source": [
        "<font size='2' font>Esta distribución está sesgada a la derecha. Para hacerlo más cercano a lo normal podemos aplicar log natural"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "BitA3CCiZEEp",
        "outputId": "fdacee3f-4b2a-4ddd-f6ce-39c28fdc4016"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
        "ax = sns.distplot(np.log10(df['charges']), kde = True, color = 'r' )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCo_9WNzZEEq"
      },
      "source": [
        "<font size=2> veamos ahora charges por region"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "T_g7yQSHZEEr",
        "outputId": "714c0534-2983-44f0-93f0-c3b7c1ebb3c3"
      },
      "source": [
        "charges = df['charges'].groupby(df.region).sum().sort_values(ascending = True)\n",
        "f, ax = plt.subplots(1, 1, figsize=(8, 6))\n",
        "ax = sns.barplot(charges.head(), charges.head().index, palette='Blues')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5Lc4hiDZEEs"
      },
      "source": [
        "<font size='2' font>Entonces, en general, los cargos médicos más altos se encuentran en el sureste y los más bajos en el suroeste. Teniendo en cuenta ciertos factores (sexo, tabaquismo, tener hijos) veamos cómo cambia por región"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2SvepC6IeSG",
        "outputId": "3d9d0c2c-b079-45d9-c05f-f5aea1244604"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "a=pd.DataFrame(df['sex'].value_counts())\n",
        "a.reset_index(inplace=True)\n",
        "\n",
        "\n",
        "a.plot(kind='pie',y='sex',labels=a['index'],autopct='%1.2f');\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQg8LQETIeSJ",
        "outputId": "7500b53e-1c75-4cc1-ac3f-09a18f87fb35"
      },
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "a=pd.DataFrame(df['smoker'].value_counts())\n",
        "a.reset_index(inplace=True)\n",
        "\n",
        "\n",
        "a.plot(kind='pie',y='smoker',labels=a['index'],autopct='%1.2f');\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUPY71eTIeSL",
        "outputId": "47992fe0-dc47-46c6-bd4a-349e902c58f6"
      },
      "source": [
        "plt.figure(figsize=(10, 8))\n",
        "\n",
        "\n",
        "plt.bar(df['children'].value_counts().index,df['children'].value_counts().values)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 6 artists>"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YykZSzmsIeSN",
        "outputId": "820d331c-c468-43cb-8dfb-fa808c503fa5"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
        "ax = sns.distplot(np.log10(df['bmi']), kde = True, color = 'g' )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "4_GnA0GpZEEt",
        "outputId": "11046e22-bd82-41a1-d682-723045d05594"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
        "ax = sns.barplot(x='region', y='charges', hue='sex', data=df, palette='cool')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 193
        },
        "id": "TY7pLz_MZEEv",
        "outputId": "02f2ea08-c248-4a33-b65a-7cb48ad54a1a"
      },
      "source": [
        "f, ax = plt.subplots(1,1, figsize=(12,8))\n",
        "ax = sns.barplot(x = 'region', y = 'charges',\n",
        "                 hue='smoker', data=df, palette='Reds_r')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3sXLP660ZEEw",
        "outputId": "b91ad5f6-da2d-48fa-f656-676b6602bcbb"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
        "ax = sns.barplot(x='region', y='charges', hue='children', data=df, palette='Set1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nMXRrXtTZEEx"
      },
      "source": [
        "<font size='2' font> Como podemos ver en estos gráficos de barras, los cargos más altos por fumar todavía se encuentran en el sureste, pero los más bajos están en el noreste. Las personas en el suroeste generalmente fuman más que las personas en el noreste, pero las personas en el noreste tienen cargos más altos por género que en el suroeste y el noroeste en general. Y las personas con niños también tienden a tener costos médicos más altos en general."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_23XhcPZEEy"
      },
      "source": [
        "<font size='2' font>Ahora analicemos los gastos médicos por edad, IMC y niños según el factor tabaquismo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThB9lXhmZEEz",
        "outputId": "53af7450-0534-4fca-ecad-e63ccbc6ae01"
      },
      "source": [
        "ax = sns.lmplot(x = 'age', y = 'charges', data=df, hue='smoker', palette='Set1')\n",
        "ax = sns.lmplot(x = 'bmi', y = 'charges', data=df, hue='smoker', palette='Set2')\n",
        "ax = sns.lmplot(x = 'children', y = 'charges', data=df, hue='smoker', palette='Set3')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 416.6x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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ca6XeWCdhmYMal45EP0hVVEqNIIdsTwahUUIGIolEMq4QWWVcehBl3JFUnN/sepU2KwUMrx8UVHVigRCaqvZpOSYZGWQgkkgk4wYvq4wzB1HGvdlaz/p9b2B7Loai8qlTzmLREPYHKUBED1JiBGQWdAyQgUgikYwLHM8lbmWwvf5FCZ4QPHloR0E/6Oo5tUyJlBV9HU1RiQVCBQpFyegiv2mJRDLmKca4NOVYPLD7L+w6in5QSDOIBoJybtAxRgYiiUQypsk4NnE7zUAtmqPtB+WG10WM4+PIf7IjA5FEIhmzJLPGpQNR11rPH46iHySH1x1/5DcvkUjGHEIIOm1zwJHenhD88dA7vNCwCxhePyicLcWpshR3XJGBSCIZBeTIhuHjCY+4lcF0+1fGpRyLB/a8xq64P8RudmwiV80+l5Ii+0FyeN3YQgYiiWSEkSMbho/reXRY6QGVcT37QR+cPJtPDKEfZKgasUAIQzpmjxlkIJJIRhg5smF4qIZOm5nCHUAZd7T9IDm8bmwiA5FEMsLIkQ1Dx3RtOh2r3yB0tP0g6Zg9tpGBSCIZYeTIhqGRsi0Sdga7n55Qz37QrNgEPj17cdH9oO42PZKxifzNSCQjjBzZUDydlkmn3b979pFUnJ/seCEfhJZUzeK6uRcUFYQUoEQPUhYMyyA0xpEZkUQywsiRDYNTzEjvo+kHSZue8YX8LUkko4Ac2dA/g430Ptp+kLTpGX/IQCSRSI4Zg430Ppp+kLTpGb/IQCSRSI4JgxmXtnsWP9nxQn5/0JKqWSyrmV9UZiNtesY38rcmkUhGnYxjEbcz/RqX1rXW85RVj4vAUFQ+OXMRZ02oKerc0qZn/CMDkeSkR9rxjC4DGZceTT9I2vScOMhAJDmpkXY8o8dgxqUpx+LBPa/xfrYfVKWGuH7+h4vqB0mbnhMLGYgkJzXSjmd0GMy4tCEd59fvd/OLq5rN1A6vqCAUydr0yBHeJw4yEElOaqQdz8gzmHFpXVs96/e+geW56IrKp7L9oLr4WwOeV1VUSo0gQWnTc8IhA5HkpEba8YwstufSYab79IzzhOCp+nd4/sjQ+0HSpufERv5WJSc10o5n5DAdmzYz2WcQSjkW//P+lnwQmhWbwFdO/9CgQUja9JwcyIxIclJzPOx4TkSVXiqrjOtLnd2zH1Ts/iBp03PyIH/DkpOeY2nHcyKq9DqtDKl+lHFvtdXzhz76QYMhbXpOLmQgkkiOISeSSm8g49Ke/aCybD9o6mClOAWiurTpOdmQgUgiOYacKCq9gYxL01m/uPe7+8XNOpeSQYJLyDAoD0SkTc9JyKj+xq+77jpaWlrQdf8y//Iv/8L+/fu55557sG2bL3zhC1x77bUAvPTSS9x2222YpsnFF1/MDTfcAMCOHTtYu3YtiUSC2tpabr31VnRdp76+njVr1tDS0sKsWbNYt24dJSUlo/lxJJKj5kRQ6Q1kXNqQjvObXa/Sag6tHxTWDErQZRA6SRm1AqwQgt27d7Nhw4b8P9XV1dx55538+te/ZsOGDfzud7/j/fffJ5PJcNNNN3H33XezceNG6urqePbZZwFYs2YNN998M5s3b0YIwf333w/ArbfeyjXXXMOmTZtYuHAhd99992h9FIlkxBjvKj3TdWg3030Gobfa6vnpOy/SaqbQFZUrTjmbi6efMWAQUhQoDYQoDYZx3b73HUlOfEYtEO3evRtFUfjyl7/MJz/5Sf7nf/6Hl156iQ984AOUl5cTiURYvnw5mzZtYvv27cycOZPp06ej6zqrVq1i06ZNHDp0iEwmw9lnnw3A6tWr2bRpE7Zts3XrVpYvX15wXCIZ6yysnMrVc86jPBAm5VjZvTTjQ6iQcSw6rFQv92xPCJ489A6/2/0XLM+lLBDmS6dfOKgowVA1KoIl0itOMnqluXg8zpIlS/jud79LJpPhuuuu4+KLL2bSpEn511RVVbF9+3YaGxt7HW9oaOh1fNKkSTQ0NNDW1kY0Gs2X/HLHJZLxQF8qvbEu6e7PuHS4/SBp0yPpzqgFonPOOYdzzjkHgEgkwpVXXsltt93GV7/61YLXKYqC6MMbfjjHh0JdXd2QXj/SbNu27bhefzDk+obPUNe230nwot2ICugoHLFa+cXbL3KhUcUMfeR7R0NZn6qqOJpCp5Xp9f9dh2fxot1AQviChblaKWdZMfbsfL/f8wV0nRItCI7T5//HY/n3CqO/vsWLF4/q+ccqoxaIXn31VWzbZsmSJYDfM5o2bRrNzc351zQ2NlJVVcXkyZOLOt7U1ERVVRWVlZUkEglc10XTtPzxobBw4UKCweMjEd22bduY/gsn1zd8hrO2Z7c/RUQLFwgYTNdhT8Dh8kUj+zkHWl/PrGxFzQKmlpT1aVz6Vls9f9r7Bpbw9wd9cuYizh6kFBdQdWKBIHo/jtlj+fcKY39945lRC0SdnZ386Ec/4re//S22bfOHP/yB22+/nTVr1tDa2ko4HOaJJ57g//v//j9OO+009uzZw759+6ipqeHRRx/liiuuYNq0aQSDwfxfgPXr13PRRRdhGAa1tbVs3LiRVatW5Y9LJOORgSTdueBwKNmOKzx0VWNqpGxES3d1rfX8fs9rHE7F0VSVCiOE43ms3/s6H54yl3ll/kPezo5GXjj8PofTccysbLvY/UGyFCcZiFELRB/5yEd44403uOyyy/A8j2uuuYbFixdzww03cN1112HbNldeeSWLFi0C4Ac/+AFf+9rXME2TpUuXsmLFCgDWrVvH2rVrSSaTLFiwgOuuuw6AW265hRtvvJF77rmHKVOmcMcdd4zWR5FIRpSemUdIN7A8t5ekO6Qb/GbXVmzXI+3aIASW69CYjh+1G0P3AGe6DgKBoigoAgRguw5J1+LFI7uYV1bFzo5GHtm3nZRr5121dUXlE9NOHzAIScdsSTGMqmj/H/7hH/iHf/iHgmOrVq1i1apVvV67ZMkSHn744V7HTz/9dB588MFex6dNm8Z99903YmuVSI4FfVn8pBwTBT9TCKgalufiChddKGiKRqdnouD3azwhSLk25Vpk2G4M3ddgey6e8HARBBWNimCEpGPRbmeYECyhLbsf6E+H3qXTNvGybnIRPUBI1dnWtJ9FldP6vE5A1SmVjtmSIpC7xySSY0hfFj8AuqIQNUIFqrlfv7+ViB7A8bz8PgtFUXA8b0huDN0zsKDtoextyK/BER6qomQNRsMkHJOM6+AJge25VAQjvNV2mEPpjvz5ygJhInoAIUQ+UHVHAcJ6kKgRkKU4SVHIQCSRDMJISqt79oMyjkW7lcbxPOaWhbjm1K5yW86FQVdVXM9DwRf96KpatBtDzwwsbiXoTGaYEIyApqOrKoaiEs5mZ57nIRCoiorruZQYAX6321eKqShUhkry47lzgao7fkALEtRkKU5SPDJnlkgGIHcjb7fSBW7Zda31wzrfxFAUK9tjyTgWrWYK1/MwVK3XuXMuDCHVQOBPPhVCENGMot0YumdgiqJgKCqaqtJuZwCYFIwS1gK0mylcz/W3RwDlwQgRI0hd22EAqkIxYkYQIQQi26vyhMeF1XPy1wppBhWhiAxCkiEjMyLJCcdAGcxQs5uRdsueV1bFpoNv4WZv6OA39EOaRruVwnZdfvbOi3zp9AsLZiXlVHOGqlEVLi06K+tLkVdmhHwbHlQChkGHlUYgCOsBqkIxzqiYwkuNu6lP+aW/D1SdwvKaBeyKN/PikV20mSkqghEurJ7DvLIqdsebqWutpz7VQSwQGnObcSVjHxmIJCcUA837AYY8C2gobtl1rfU8mtnPQ1sO9hnk6lrr+XPjbqJ6kKRjYWX92oKKStLxRymoioLpOQXrOpqbel8mq7qqMbd0EgHN4ECilYmhaD6ovN12mN/vfT0/P+j8SadwONXBv735p4Lgk2N/opXnjrxHxnX8TOsEmK8kOfbIQCQ5oRgogwGGnN1MDEU5koqT8Wwcz0NXVUKqQXWktOB1uQBoC4dSPdrnDTm3tkggSCwQpiEdx3Fd0p6LpiioioInBIaqoSnaiMwoWlYzn9/s2orp+gHUFYIyPcCF1XM4JTYh/zpPCP546B2eO+K7IpQaIS6cPJuXG/f4GZtuELczPLb/TZhxJvPKqgjrAbY27iPp2CfEfCXJ8UMGIskJQa7k9l5HI4aqETNCRAw/k+mewQx1FtC8sire62hEIatYc106XZeLyk4teF0uyKCoKIrSKwDm1qarKuWBMCE9QJkRosVNIhC4QuBkHW/CijFiM4q6l/fazBTVwSgXVJ9aEITSjs2De/7Ce1m/uJnRSj4zezEP7P4LqqLmRzMENB3LddjWtJ/zJs0kpBscTLWfEPOVJMcXGYgk457u5ThdVXE8lzbLlxVHjECBwmyos4B2djRSFgiRcrMZkaYR0Qx2djQWvC5Xwkt3OxZQNQ4l2/NrM1QNx3NpNVNUAiE9QEi3STomOdc1DYWUa6NamV5Z13DJlfdStsmW7a8zu3Ri/meN6U5+vWtrfn7QBZNOYcX0BWiKSpuZItRtI6qKwsRgCWnXyh8/EeYrSY4/MhBJxj3dy3HlgTDNZhJPeDSbCQxbI6TpfHr2uQAFZarcxtGB1GfNmQRRI0QsEM4fE0L0euLP3ZC745/fy68tZoRos1IIIWi30lQoKpbrUKIZpD3fBNRFgBAknAzLai4Yse+o0zJJOSau1zXCoWc/qKdfXEUwQtzOENB0P8vUg7RmErTbGW7asoGJoSjzyqr4c+PuAb/Tse4sLjn+SPm2ZNzTnEkQ6MdIE8i7FgxnFlB3uXWOvp74c1JrW3gFA+90VcuvLWIEqAhE0FUNx/MoD/hGpz1LW+D3bEYCTwg6TN+9ofuxPx56h9/u3ubPDzJCfPG0D/YyLb2weg6e8NAVPwg1puM0ZBIEVD0v9vhz426WVM3u9zsdafl7sdS11nPH9qe4acsG7tj+1KhfT3J0yIxIMu7pXh7qsDOo+I1/TVWZHC7FdJ2CXs1Qnsx7Nvv7y6JyvZiHdrxCyrHy53/i4I6C0lXECKBl+0TfXPQx7tj+FLs7m/01Z61wPOH7vh1tw98THnEzkzcoBbCEy6/e35LvB2mKSpkRJulYvd4/r6yK8Kxz2da0n/3JVpKORcwIURb0s8NcH2xnRyPfXPSxPtcwkvL3YjOrgZSTMhMbm8iMSDLu6T5+23ZdBL5xZ0wPAX4AOZzqGNaT+VCyqIWVU7k0NIN/Pf9TfHPRx1hYOXXQ0eDLaub7G1XxS36eEAj8vT5H0/C3PZd2M10QhBrTnfzRqs8HoZCmMzFYQtK1eGz/mwV9LwWI6EEWT5rBV874MN8//1NE9AClgVDBdQYTJvSVrQ5HzDCUzKrnJt6gpudViJKxicyIJOOe7sqw1kwCFIUyI5xXzVmei+25hPXAsJ7Mj2YvT/e19fUkv7ByKlMipTRmEnhZ+56YHspnTcPBdB3iVqZgpHe+HyTcbJAJUJo9fwC/V5Vz2u7Ppmc4woSREjMMJbMayt4vydhABiLJCUEuWHSVZVTfiiZbStMUteDJPGVbdNoZmtKd3LH9qVFtoA8WyFbPOidfSipWRNEfqexI71yHyROCp+vfze8PCqOhaTrRHqO8DVUjng0YsUAITeldLCm2THm07+mLoQQXqeQbf8jSnOSEor9S2rSS8rzoIGVbtFkpHM9F7+YGcLwa2sMRUfRECEGnlaGzWxBKOza/en9rPgjNjFbyieA0JoWj+ZlCOVRFYVZsIuXBSJ9BaLjrHInPBsWLRoBBy6GSsYfMiCQnHP1lILkn8047A1lBQE65drRuALlGen26hWe3t+eHyRUrjDia8p+XDUIZ184fa0x38ptdr9JiJoGu/UE73t7BhdVzeGz/m1iuQ0DVCGoGmgIfrJ496LWGs86jtSmCoWVWg5VDjzdSzt4bGYgkJwXdb05N6c4ChwMovofQ100EujzsEPB+vIl3Oxr8sQnByIiotvq7ebmeR4eVLshweu4PWjXzTM6ZMD3/83llVTDjTF5u2I2LoMwI8+Epp3LGGL4ZDjW4jETwGw2koq9vZCCSnDTkbk53bH9qWD2E/m4ifkahIYRHEjfvqi0QtFlpKgIRNHX43nF9Xfe32ZvX5HApO9qP8OKRXbRmkiiKQrvtb6wtNUJcPaeWaSXlvc55WnkViyfOyAs6xgNjNbgMhZF2cz9RkIFIMq4YzOG6v/d0f5Iuxg2gr/cl7EyfN5GGVJwpkTIaMykUIKdVE/gy6E4nQ1UoNmzVVs+bV1g3CCghnjz4DosnzfCNSFHIeE5erl0VivGFeR/oJUpQgAOJVrY1H+BAso3KYIksDR1DpKKvb2QgkowbinG47i/odM8mcm4AA/Vw+spCmtKdVGYnm+bIKfEsz8XJ2ucodAUhBXA876hUW91vXrqiUqIHiFsZOqw0Lx7ZhScg4WRws3LtkKYT0YxeQQggpcOLB9/C9gQhzSiqNCR7GiOHVPT1jQxEknHDQA7XhdLtruCx6eBbRPUgkYB/Uy7GDaD7tbpnP5qq0mFniHS7wVuey+RwDNNzUBUFV3ioKLiIfHbkHx++ait384oZQUKaP8gu6VhUBCM0pDtJu1ZeKVcWCBPOBpju5PYGvZFpxDaKLw3JnsbIMlJy9hMNKd+WjBsG26Xf1456V4he9jXFlEL6ulZY1bE9l4PJNhpScTrMNK5wWT3rHK6ecx5V4RgCUFWVUj2IpmoIIagKRYclWc6xrGY+hqKgKAptZoqkY+F6LlEjSCobhFRFYUKwhIgewPZcKoKR/PuDmp4f4d3mmkNyOpAuBSPLSMnZTzRkRiQZN/TncJ0ra/RVfzcUrdeemWJKIT1LKCnbIulYaIqKpijYnosnBCumLChwSfjDK8+yJ+zQnEkwpaR8wDJWsSWvBRXVmM6Z/PHQOyRtk9JACFd4vNnm73tSUVAEtJpJNEUlqOlcPP0MFKDECFLSLYOLKQaW5xZdGpI9jZHnRBBdjDQyI5KMG/pzuM6VNfra9BjSDDRFHfLmxp6bIjuySrSoEUTJZico8JeWAwXvm6FH+eaijxX4zfXFo/ve5N63n+e9jkY67QyN6Xifm2qdrGdcTbScL5z2Af567vkkHYtDqQ4ATiurIqRp/pqyLuMKwh+JEYwUBCGARXrlkDZ7DmUjqUQyXGQgkowbcmWNEkXvs6zR1456Q1NZMX3BkEshPUsoQghKjCBJ25/poyoKnudxONkxZEeGutZ6Nh18C094aKrqb0jNnrd7yctyHdpMf4/Qzo5GfvzWs/z47WdpMZOoisLlM8/Ccl3CepBJ4RiTI6VMiZQxJVLBtqb9+cmq3ZmhR4dUGpIuBZJjgSzNScYVCyunYoZmsHjxYqBr7kyuvDWYGm6o18q9NzeuAfx+DPijw9UhjGvIleJ2xZtwhIcK+RHkXraXlSt5ZRybuJ1GCHi3vYGH9rxGJivNVlEo0QKUGMGCKapBVSdmBEk5FgeSbUV9rmK+g7HsUiA5MZCBSDJuycu5XY+Ma9Nmpdjd2cSKmjMGVMQNh2U18/mPt55FURREdlSDAMr7GdfQs/9TZoR4reUgrvDwsho3D3A9PytSFAXH85gSKSNpmySznnFpx+b3e1/PB6GAqhFWAyRdk9+8v9Ufsif89wU0jU7HJO3YI1o6Ox49DSkZP7mQgUgybnni4A5s1yPhmCiAhoLnCTYdeJtTYhNGdF/MUMY19JQ8N6bjWcsffCWd53a5YyNQs3OISvQAH66eQ8L2p6k2pTv59a5XSWc95CJ6gAAacScD+M4NYV1HU1RStkXGVU4IObCUjJ98yB6RZNzSnEmQcW0U/HKZovhTTl3h9SsvPprR1atnnUNZIMTEUJSqUAzbc2k1E9SnOgrGUfeUPKeygcQDPM8rOKcAXASxQJCPTD2NKZEyAHa0H+En77xAi5n01W96gLJAmKRn5TfKlhhBakoqMRSNVit5wsiBpWT85ENmRJJxy8RQlDYrhZZVi4E/DkFX1aL2xcDQB+Tl+iWHku2YrkPUCBI1QvmAVuuV0+wWSp4dz8u7LXTlQl2cGpvI0ilzmR6txBOCPx3eybOH3wN8v7gPTp7NK417sFwH1/MAQcwIMTkUI+lYGJpORAT41/M/NbQvcIwiJeMnHzIQScYty2rms3tHE54nULO9G/Czh+49ku6luA4rTUUg3Mump9ibXH/GqUJ4dFgZnvTqCWg6judRFvRLdrqqYrldmVAuKKkozCgpJ6DpTI9WknZsHtr7Wn5k94xoJZ+ZfS4xw8/CXjyyi4SVoSwYoUQ3yAhfVn2iyamlDc7JhwxEknHLwsqprKg5g00H3sb1PHTV92HTVDXfI+nZb4jbGVrNFBMUBU/4hqS25xJUdR7d92bRirvuT+0Zx6LVTGVFDIKQptNh+X2c0kCIiGZguy5+V8cPQoaiMrWkHAWFg4n2fD+o+/yg5TUL0FW/ej6vrIrTyqtoSHXyh72vk8rOEsr1hOaVVRWoB49Vc380RAXSBufkQ/aIJOOWutZ6dnY0EtR0gppOSDOoCpcW9Eh69hvKDD9Lac4k/SmtrosiQFVUHttfx5FUvKjeUfeNnh22H3QURUFDIRYIUxYIYXkOKceiKlzKyhkLCWkGKgoxI8is2AQUFOJ2hoCm85/vvEiLmURXVC6beRYrZyzMByEAXdGoCJZw5oRprJ51TsE+oCVVs/lz4+5h9b2OhqPptw2EtME5+ZAZkWRc0j3TqQhGCp6au9+wevYb/Pk7ws88BBiaRpkRosPOoAAZz6ZMGXxqa/endtt1facFIIzv4xY1QqiKVdC3OSU2gYf2/IUSI0TKtkg5/j85aXZ/84NCmkEsEETtZ4T3X5r3H5cZN6M5W0fa4JxcjHog+uEPf0hbWxs/+MEP2LFjB2vXriWRSFBbW8utt96KruvU19ezZs0aWlpamDVrFuvWraOkpIR4PM63vvUtDhw4QGVlJXfddReTJk3Csiz++Z//mbq6OkKhEOvWrWPOnDmj/VEkY4hib4J99Rs0VUNTVKZEyvIBpMVM5ffy5OjZO+pZhsptnm3NJCCXbdk2GceizUojhOBftj0GikLGsamJlFM78RTeaq2n08pgCzcfhGZGK/nM7MUFoxsUfEuh7m7fQxlPMdrNfSkqkIwUo1qa+/Of/8wf/vCH/J/XrFnDzTffzObNmxFCcP/99wNw6623cs0117Bp0yYWLlzI3XffDcBdd91FbW0tjz/+OFdddRXf//73AbjvvvsIh8M8/vjj3HTTTdx4442j+TEkY5DBnLhz9GdRMzlSmi+tZRwLTwgc4eEJQSbr1t29QV7XWs9/73yZ3Z3NtJkpdnc28+zh91hWM5+vLriIsoC/p8j0XFoySTzPI6wZHEnFaUh2UKoHSTgmLxx5jwWVUzA0LS/rPn/STD4/9wPUpzr4+bt/5s7tT3H/7m00ZRIFQQj6kTZnx1N051g096UPnWSkGLVA1N7ezp133slXv/pVAA4dOkQmk+Hss88GYPXq1WzatAnbttm6dSvLly8vOA7wzDPPsGrVKgAuvfRSnnvuOWzb5plnnuGTn/wkAOeddx5tbW3U149uPVwytpgYihK3MjSk4xxKtlOfbKc+1UGHlS7Y09Nfv2H1KWfjCpdOK01LJomSl1ULWjJJOq10QYP893tf990OhEBTVYQQJG2T3+99HfDtdVoyCTpxUBSFimAJlnDRFJXyYIS4Y5JybWzP49H9dTSbSVQUJgZL2NneyL07nuMPe18n6VhUR8pI2ha/7qPf0lcALjdCuJ436n5wOTulm7Zs4I7tTzGvrOqofei6n/PRzP5R72tJxiajVpr7zne+ww033MDhw4cBaGxsZNKkSfmfT5o0iYaGBtra2ohGo+i6XnC853t0XScajdLa2trnuY4cOcLUqbKmfLIwr6yK9zoas8PnRD6MqCgcTLZz79vPE9R0pmVHMfRl+XM15/Gzd15EKGCoOgFFwxIutueScR2+NPfCfJmvIRX3z9/NZ84VgiOpjnypbEqkjIPJNoQQqFmNdkUwTNp16LQzlGSzIoBI1hVcACHdoDmTIKhqlAdC2MLFReQ3cQ5WalRVjSklZUT14Kip5voqCRYz6XYo54xbCemgcJIyKoHogQceYMqUKSxZsoTf//73APk9Ht1Ruu396Hm8P1S17ySuv+P9UVdXN6TXjzTbtm07rtcfjDG/voPvE0Ylk3du8/EQJLOWP8LxOBJv5Rdvv8iFRhUz9N4lI90TRDBQhAICgmgIVCxPYO45zLY9/oNUTprd3RlBIPAE2KYFikoa0LPTWZNWhoqAL4LIuH4fKBeEDBTCroKJi+d6WPi9IE1R2NfZQkwx8tfcn05y64t/oFPYxBSDKUqYei9NJnstB4EH/ucTUdDKwaZg7b2+u2H8bh/N7McWTv5zAtjCY9vB97k0NKOo6w52TkNRsU2Lh3a8ghmaMeQ1HgtG+/+LnJnvycaoBKKNGzfS1NTEpz71KTo6Okil/EZwc3Nz/jVNTU1UVVVRWVlJIpHAdV00TcsfB6iqqqK5uZnq6mocxyGRSFBeXk5VVRVNTU3MnDmz4FxDYeHChQSDwcFfOAps27ZtTP+FG8vrq2ut56Edr3DEy6CrKioqbrZPkdsoSvbfQlEoK4lhug57Ag6XL+r9mZ7d3k5jOk7KtXGye5EiusHUcCmLu73+sW1HOJKK57Ov7sGvNBLNPzyZCRdPU4kYBq4nyLhOwWuDqsbqWefw+P63iOohDFUjZoQ4ku6gPbv3KBIpAaDTSuM4Dm7QoFwNYXku+4TJX1Wdns9CqoeYhQz3d/vQloOU6tGCh0QhBCnHGvbflZ7nTKWSlEaiR3XO0WQs/38x3hmVHtHPf/5zHn30UTZs2MDXv/51PvrRj3LbbbcRDAbzTxTr16/noosuwjAMamtr2bhxY8FxgKVLl7J+/XrAD261tbUYhsHSpUvZsGEDAK+++irBYFCW5U4CcqWcpHAwVA3X87A9N2/w0zO3zu3DGUjJNa+sig4r4+8nAhzXpcPKMK+s8MFm9axzMFQNr4/rtKS7zl1iBAnrBi1mihYrWfDaicESPjh5Di837KHTzpC0TcKajuk5qKgguhwiTNchYVtEtGAvz7WdHY1FDd8bSXr25BrSceJW5qiECVLsIMlxTDe0rlu3jttuu42LL76YdDrNddddB8Att9zC/fffzyWXXMKrr77KP/zDPwDwjW98g9dff52VK1fy61//mu985zsAfO5zn8OyLFauXMn3v/99/s//+T/H8mNIjhM5xZihqMSMEGSfpHsXd31ieggY+Oa2s6ORmBFC1zQEoGt+hpKz2cmxsHIqE8JRtOxeHgXQFRUVSHk2cTONJiCjeLSaSQKKH7QAwprBl0+/kBXTz+D1lgN02hlqSsrRNY33O5vZE2+mw05jqBpV4VheVBHUdEoDoYJ1HC959LyyKjrtwoDdafcO2EOhp6LRFp50UDhJGfV9RKtXr2b16tUAnH766Tz44IO9XjNt2jTuu+++XsfLy8u59957ex0PBoP88Ic/HPnFSgbkeM+Iye1bSZPbmAodVgpbeKgoaFkBgYcgohqEdWNQJVdzJkFpIESZ0jXKQQjBoWR7L8ucjGOjKQoKapdoARVVQFg36LDSdNhpdEUrGGLneh4P7PoLadcmrBlMjpQStzJ0mOl8h0sDAprO6lPOLhjGNxzPtdH4Pe3saKQsEOoqYWoaEc3oFbCHQs+heyWKzhXSQeGkZMiBKJFIEI/HZSnsJGMszIjJKcZyRAzfV05X1QLF2LyyqqKVXH2p0OJWBtN1elnXhDSdNtMfZJdDU1QqghEQfvNeAeysGWlQ1bE8B1u4hHQDy3MIaDqtZopWyx/3rSn+qPApkTJM1ylQyQ3Hc22g3xP4AoGHthwccoBqziSIGiFigcKAfbTZWXcHhW3btskgdJJSVCB68sknefnll7nhhhv45Cc/SWdnJ//7f/9vPv/5z4/2+iRjhKOxcxmpJ/TcjdkWHkKI/I3506ecO+wbWF83+5RrEjWCvT4rucDheaiKQkDTKdGDmJ6DrmokHCtfJizL7ltSAF3VKDVCCAStmSSqquJ4/qjw3NgK6F12G86Y7v5+T7/f+zqma2MLh1I9OuQHCemILRlNiuoR/eQnP+HTn/40TzzxBGeffTZ/+tOfePjhh0d7bZIxRLFOBj0ZSWPM3ObUEkUfshlmz82YA214DWkBolk/uFxzvt1M0WGmWTF9AarqB6GwbtCSSdBmpWjMdCIQKPiecWHNwPFcdEVleklFXoTgCYGdPe5lty6UGf33shZWTh2SMKG/31NjKp7vrw1n2Fx/DhWynyMZCYrKiIQQnHbaafz0pz/loosuIhqN9rn/R3LiMtwn4pE2xlxYORUzNGNIMtrByoo9DTbv2P4UR1Lx/AhyFXA8F08ITolN4JMzFvHckfdoySQLpNyTQlFmO0Eag9BmpijRApQYQQ6n47jCI6aHKA2EyLhOviQXNQIENb+XlXJMdFXlpi0bhp059vd7EvgBKd3ttUOdwzTU7EwiKZaiApGqqmzcuJEXXniBb3/72zz77LOjvS7JGGO4M2KOhTHmYKW/oQbDZTXzufvt53CFr3tT8DdZx4wALx7Zhe152K6Hoqh42dcEVZ0SLcBMSll52hnsS7Ty5IEdHMkGIRSFVjMBKET0ANNKygt6WSFNR8E3Xc0Fy//e+TIxI0jGdYq+8ff3e5ocjh21VFo6YktGi6IC0be//W1+/OMfc8MNNzBp0iTuuece1q5dO9prk4whhvtEPNq9hWJEFH0FQ9dz2RVv6jf7yAWYHJqiMiFYwpF0nA4zTcrt6gedallcvvcNyjJJlFA5bUaKP5idtFopwA9kCD9z0hSFimAkb5GTKy3esf0pHCEKJr4mbZO0a1MdLi26p9Pf7wnos78mS2uSsUBRgai2tpZf/OIXxOO+39Zvf/vbUV2UZGwynCfi0Z62WUy20zMYpmyLNjOFqqp9Bq8nDu5AV7WsZ5yCoWpE9SD1qQ4MTSfp+u7cqqKwMJ3m0++9hqMo2EaIEjtF+UvrqZ4+l9byiUCudCfyDdlcj6b7OnsGy5ybtidEn68fiP5+T1dzHg/teIWUY8nSmmRMUZRYYffu3axcuZKVK1fS0NDAxRdfzK5du0Z7bZITgNGetlmMiKJno73D9jslFYFwn437ivpdfOOdbXzn9ef53zte5ax4G21miqRr5+XjmqJSZoT4xL53sARYqg6KQgcCC4VPHNkPQHfXRA8Kpq52X2dPlwHH88t5/b1+OCysnMqloRnH1JFBIimGojKi733ve9x0003cfvvtTJ48mb/+67/mO9/5Dr/61a9Ge32SE4DR7C0MVvrL9Y9M18XxLN/xWggqgxFC3TKQ3E3e27OdVbvfxEQhowcotU3O3b+TbXMXgeYHvHmlVbRZKZozCcoySUwjgK6ouMLDQeAoChNMP2D1lPTk3B66r7OutZ6mdFcpT89NYhWi4PVxK4PlOUclZpBIxiJFZUTt7e1ceOGF+T9fe+21JBJyCqPk+DOQrLi7dLw8EKY0ECak60yOlKJ2y6IyjkV9qoNWM8k7f/wlpgBX9zOcN2OV/OfcMzE1DRWFCyadQlOmE08IIlqQeDiK7rp4eCiKggIEPI/WYBgNpSAjUlHys4xy65xXVsUvdv6ZDiudf62T7eMYqpZ/fYeZptPOENL0o5bBSyRjjaK95kzTzLvkNjU1FdjhSyTHi4FKf31OM1U0ECIfvNK2SVMmkVfITTTTuLqOKwTPTazmoZnzsDSdmG3xxdM+SGO6EzU77C6iG/xx8gw0z0N1HBCCoOuhC48/Vs8ERfGDDwqlRoiVMxb2WufOjkYyrj9Mz1A1AqqGrqhoqsqEcDT/estzKAv4zgbD2QckkYxliirNXXPNNXzxi1+kpaWF//t//y+PPfYYX/rSl0Z7bRLJkDBdh/2JVn6x82WmRso4lGz37Xe6EVA1Uo7FNaf6yrLd8SbAfyKL6EE6wlE02+L300/lvdIKAKYnO7nk8D6mfWA17WaKqnAMTdXodEzqyifgnXI6Hzm8l8m2SUsgxJZT5tNWORnS/tgIVVGJGUFOiU3g0plnFqzn1+9vxROi4IlQUXx/uoxj851zLwHgpi0bRl0GL5EcL4oKRFdeeSUzZ87kmWeewXEc/uVf/oUPfehDo702iWRQcuU32/VIuzYIgeU6NKbjmK5D3MpQFuzyR8v1ZXJ9q5u2bKDNTBE1gkQ0g4enzKTVCNAc8gNYbUsDl9bv4dl55zINmFU6kVYzhec5RPUgbVaKt8om8E7FJCqCJaQyab6w4ELOhrysPKcW7Et+PTEUJW5nEEJ0jbPIjiPvLnGXFjuSE5miAlF9fT3Tpk3j2muvBfwntra2NioqKkZ1cZLiOZ7O2N6e7YhXN0NHM5RNRKldjjpr0aDvG4k158pvnV7WBUH1HQtSrk3UCJBwTEKu0a90fGIompdpN2eSHImVA6B5Hpce2s3cdIKNs85g/sKLiBkhPlx9Kr/atQVN0QjrBrYXJOWahDSD8kCYc9xYfl9QMZtol9XM5xc7/0zKtnABso4lET1QsM7RlsFLJMeTogLR1VdfTWNjI9GoP02xs7MTTdOoqKjg3/7t3zj33HNHe52SATieztjenu2Ip38Fmg6hCCTbEU//Cu+jDBiMRmrNuf03ORNRTwhfveZ6mNkR3XHLn/UzJVLWK9gtr5nP/btepSGTKBw5rqr8cdYCtodifHTaaZw9oYaAprOgckrBhtHqSCllRog32+p5r6ORPSgY+94s2lFiYeVUvjBvCb/f8xoN6U4URaEqUlowDiL3OmmxIzlRKSoQffCDH+SCCy7gsssuA2Dz5s28+OKLfPazn+WWW27hgQceGM01SgZhpP3choJ4dbMfhIzs2PXsv8Wrm2GAQDRSa86VrHRVxXFd3D7G5FmeL0xIOGbB8YxjEwsE0TW94F0TghEunr4QBXizrZ4nD+7ghSO78jf+7nL0R/e9yWP76/xsTFFwhMdj++soD4SxPLeoUlqx8nZpsSM5USlKNffOO+/kgxDA8uXLqaurY8GCBdi2PVprkxTJcJ2xR4SOZujx5I8e8I8PwEitOSffDqlGn0EIwBUeHv6wu//e+TJvtx4maZvsijfx73XPcjDZDkBIM5gULMH1BM/Uv8tzh9+n1UwhoF+59B8PvYMCaKo/LE/NSrYTjiXdqiXjihtvvJH/+q//Oi7XLioQOY7Dzp0783/euXMnnudhmiaO44za4iTF0XNXPhzDRnbZRHCswmOO5R8fgJFac06+XR0pLer1CoLNB99mW9N+7tnxPPGslU5EM7BcmxYzSdQIYHseR1IdZBxrQLm0mZVeF1xDUXA8d1QdJSSSE4miSnPf+ta3+NznPsfcuXMRQrB3717WrVvHj370Iz7+8Y+P9holg3A8G9lK7XK/RwR+JuRY4DootctHZc11rfU8mtnPb17eh+O5aIqad7LeHW/GE16fmZGCQnkgjBAeOzsaeLejAZE9XhmMELczaKiUBcKYnkubmUYIjw47k3dg6CtjC2o6lutAt2AksualspQmGQ2SyST/9E//xL59+1BVlTPOOIOVK1dy1113UVVVxXvvvUc4HOZrX/sa9913H3v27GHZsmXcdNNNAPzud7/jvvvuQ1VVJk6cyM0338ysWbMKrnHbbbfxzjvvcPfdd2MYBuvWrWPr1q24rsuCBQtYu3Yt0WiUj370oyxatIh3332Xb37zm3ziE58Y1mcqKhBNnDiRzZs38+qrr6JpGueeey5lZWWceeaZRKNSPnq8OZ6NbHXWIryPMmTV3HDWnBM4pD2LTG5DtaJwJBVnd2cTAc3AzEq4u6MrfoBJOzYptyt70xSVymAEXdVQbYXyUIS0Y5G0bb/n5PljGXL0lbF9fNrpPLa/DtfznRW8bHj7+LTTi/wGJZKh8eSTT5JMJtmwYQOu63LLLbdw8OBB3nzzTR588EEWLFjAl770Jf7zP/+TX/7ylyQSCS666CK++MUvsnv3bn72s5/xu9/9jsrKSn7/+9/z93//9zz22GOA/xB166230tTUxE9/+lMCgQA//vGP0TSN3//+9yiKwh133MG6dev47ne/C8DcuXO56667juozFZ0RPf74472yHxmExg7H8+lbnbVoQGFCfwx1zTmBg5W101GzGz/jdgaBwPZMygJhOqyu8W8BVaM0ECZupQtKgQFVoywQxhOCsGZQmR3NYHsuuqoS00O0mf5Y74HGJuQ2qP7x0DuYroOOyvIZZ/TauDpUhiuJl5z4LF68mDvvvJPPfe5zfPCDH+Tzn/88ra2t1NTUsGDBAgBmzJhBLBYjEAhQWVlJSUkJHR0dPP/881xyySVUVlYCsHr1ar7//e9z8OBBAH7xi1/Q0tLC+vXrCQT8SsAzzzxDZ2cnL730EgC2bTNhwoT8empra4/6MxUViE477TQeeeQRFi9eTCTStVO9vLz8qBcgkRRLfaoD07WxESjCH8ydK8Mp+AajSdvEyI5wCGbHebdmktlMxUdFIWaEfDcFI4CqKHie55fYgJgeRFNVIkaA0myPZ6CM7dKZZ+YDz7Zt21g8EkFoGJJ4ycnB9OnTefLJJ3nllVd4+eWXuf7661m7dm0+cOTQ9d63974mawsh8r3+8847j3PPPZd/+qd/4ne/+x2GYeB5HjfddBNLly4F/NKgaXYpULvHhOFSVCB66qmn2LRpU8ExRVHYsUP6XEmODXWt9WRcC8/LTfcBV3QFoVwgyjkUxIwgQpAf550j18mJ6P4m10tnLGLTwbdxEFSXlIEQZFyH8kCYT88+97hkmcOVxEtODn7961+zbds21q1bx4c//GFaWlqKnoTwoQ99iO9+97t8/vOfp7Kykoceeojy8nJmzpwJwMKFC/nrv/5r/vjHP+aHoX7oQx/iV7/6FUuWLEHXdW6++WYikQjf+973RuwzFRWI3nzzzRG74HjleDoXnGgM57t84uAOIlqQhDCz2VAX/ihvlZhm4ApBRDeIGkH2JVoLXqejIIBYIERYD2IoKgsqp7CgcsoofMqjoKPZz4S6U4QkXnJycNlll7FlyxYuueQSwuEwU6dO5brrruPf/u3fBn3vhRdeyBe+8AU+//nP43kelZWV/OQnP0HtNvdKURT+9V//lcsuu4ylS5fyd3/3d/zwhz/k8ssvx3Vd5s+fz4033jiin0kRfeVqPbAsi2effZZkMgmA67rs37+fG264YUQXcywwTZO6ujoWLlxIMBgs6j3dXQC6K7yGK8fdtm0bixcvHvL7jhWjub7hfpc508+0Y9NmJguUcYaqUWaEKQ2EMFQN23HodH1Xbf/natZ5QaEsEKZED2J6Np+ZUzviDxMj8d25D9wOyfaujAjANqGkHO2qNcd9faPFWF4bjP31jWeKyohuuOEGDhw4QFNTEwsWLOCNN97g/PPPH+21jRmOp3PBicZwv8ucg4Kq+CU4PTvgzkXgeR4hVUNXVQ4n20l7DrbnoikKtRNn0pjupNVMEtWD2J5LxAhwWc1ZY/Z3N1xJvEQyXikqEO3YsYMnnniC7373u1x//fV5id/JQrG+YZLBGc536e3Zzpfq/ozVdoSmYIgnJ8/g3YoqUBRiWgBD1TBdm1TaJp618YkZQT4zu5YZ0QoU8N21jeIy4OPNcCXxEsl4pahAVFVVha7rnHLKKezcuZOLL76YdDo9+BtPEKQF/8gx1O8ypyAr0XTUknJKkx18et+7/F7VOFw1g6pwlLRjsT/Rhp0dbje9pILPzF5MaSCEqqiUBoIENeOYfL6RYriSeIlkPFKUxU8kEuGRRx7h9NNP5/HHH+fdd9+lvb19lJc2dhhoHLVkaAz1u+yuIAsbAZRACE/RWNFwkKqwH9T2JlrzQah24gyun/eBfL+oIhge80HI27Md94HbcX/2bdwHbsfbs/14L0kiOaYUFYi+853vsGPHDi688EJUVeWv//qv+V//63+N9trGDAONo5YMjSF/lz1MVUsDIRxNozyT5EgqzqFkO64QqCh8csaZfHLmInRVI6wHKM+6Joxl8nuGku2Fe4ZkMJKcRBRVmjvllFP4x3/8R4CjtnIYr0jfsJFjSN9l2cQCBVlYM4gKhY1TTqHZ9FWcIc3gr089jxnRymw/KETECPR/zjGE3DMkkRQZiLZt28aPf/xjWlpaCnbmPvLII6O2MIkEuhRkGdeh0/OwPIc/1Mzm7XLfYqSmpJzPzq6lNBBCU1RKAyECWlF/rccGcs+QRFJcILr55pv59Kc/zfz583tZ3ksko4k6axF7z2tFbNuM5Xn895wzaQmGADi1dBLXzKlFVzWCqk4s6AejcUWPjA8oaoyGRHIiUVQgCgQCfOELXxjlpUgkvRFCsFnX2D3/fDrsdH4ba4kewPU8dNUfNR41guPyIUnuGZIcLaPl+rJmzRpqa2v5zGc+A8DnPvc5vvWtb3HXXXfR3t5OKBTi5ptvZsGCBTzyyCP87Gc/Q9M0ampquP3224s2DIAiA9Hs2bN58803OfPMoZk5/tu//RubN29GURSuvPJKrr/+el566SVuu+02TNPk4osvzrsz7Nixg7Vr15JIJKitreXWW29F13Xq6+tZs2YNLS0tzJo1i3Xr1lFSUkI8Hudb3/oWBw4coLKykrvuuotJkyYNaX0Sn55Oz7GyOXh7jFHfxzKYw7QrPOKZNHVt9QXO2SE0YkaIjGNTFgjl5wWNR+SeIcnR0N2pJKIH8pOEr+boxVRXXHEF//7v/85nPvMZDh06RGtrK7fddhvf+c53WLBgAe+//z5///d/z+bNm7nrrru4//77mTBhAnfeeSe7d+9m/vziVcUDBqJVq1YBvtvq1VdfzfTp0wscXQfqEW3ZsoWXX36Zhx9+GMdxuOSSS1iyZAk33XQT9913H1OmTOErX/kKzz77LEuXLmXNmjV873vf4+yzz+amm27i/vvv55prruHWW2/lmmuuYeXKlfzHf/wHd999N2vWrOGuu+6itraW//zP/2T9+vV8//vfP2mFFEdDX07P0488jdj9vP/nUXJ/Hsxh2nIdmtIJfr7zzwVBSAVMfNeE6kjZuA5COQbaMyTHQUgGYjRdXy644AJuvvlmDh48yIYNG7j44ou59957+ad/+qf8a1KpFG1tbXzkIx/h6quv5mMf+xjLly8fUhCCQeTbN998MzfffDPf+MY3mD59ej4o1NfX89WvfnXAE59//vn88pe/RNd1WlpacF2XeDzOzJkz8wFt1apVbNq0iUOHDpHJZDj77LMBf0bGpk2bsG2brVu3snz58oLj4M/IyAXKSy+9lOeeew7btof04SU9VFuKAkYQzbXBzBQcQ9P9147idXPXSNkWuzoauWfHcxxItuXfowI6/jC7tG3zwerZI7aesYiUdksGozmTINBji8JIub4oisJll13GY489xqZNm7j88ssJBAJs2LAh/88DDzxAeXk5a9eu5Uc/+hHl5eWsWbOGDRs2DOlaAwai888/n/PPP58//OEPXH311Zx//vmsXr2ar33ta6xfv37QkxuGwY9+9CNWrlzJkiVLaGxsLCifVVVV0dDQ0Ov4pEmTaGhooK2tjWg0ms/CcseBgvfouk40GqW1tXVIH15Cr306AIrwoFsWAhyVkquutZ47tj/FTVs2cMf2p6hrre/zumgBvPYmtjXv4953XsiblpYaIcp1f2NqeSiC5TpYwj3h5fQDBWuJBHynEqvH/6sj6fqyevVqfvvb31JdXc20adM45ZRT8kHmxRdf5Nprr8VxHJYtW0ZFRQVf+cpX+NSnPjXkEUFF9Yja2tq47rrrAAgGg3zhC18oKhABfP3rX+fLX/4yX/3qV9m7d2+vnyuK0uewpoGO90d3K/PBqKurK/q1o8G2bduO6/VzzFGDGJ1xvG7uA0FFxUVgJlP5Y6prYwdL2NXPuvc7CbY7rXQKm5hisEivZIYeZb+T4EW7MZvNKByxWvnF2y/yLUUn1v26igKuxabJ0/nj+1sBvxcUQsWxHSK6wcRAlKRtorqCEkUb9ncYa9lH1cHXCaTjWOFSGmvOpnPCzGGdqy9G6nc7v/EQrh4Eq9uNRgi05CF2HMU1xsrfvb4Yy2uD0V/fUN29l9XM5ze7tmK6FLjZj5Try5QpU5gyZQqXX345ALfffjvf/e53+dnPfoZhGNx5550YhsHXv/51rr/+ekKhEKWlpfzwhz8c0nWKCkSu69LQ0MDkyZMBaG5u7jNIdGfXrl1YlsX8+fMJh8MsW7aMTZs2oWldaWRjYyNVVVVMnjyZ5uaup+2mpiaqqqqorKwkkUjgui6apuWPg59NNTc3U11djeM4JBKJIU2MHcoYiJFmLNnJe5VGtlej5VVblmkQMAwiga5jZExCusLZr93fq1dR11rPq7u24upgOoKkl6HJPsKK6gXs6XCIaOECbznTdXhh1ulcvqcONA2hGSQdh/umzubN0goAgqrOJTMWEtEDPFP/LhE9iOk5eOh4rs0V8y8YVkbk7dmOeP0VP9MoqyDoWMQOvIIy99QR6b2M5O/W3f103+MgKicO+xpj6e9eT8by2mBsrm9h5VSu5rxRUc0JIWhsbKS5uZmPf/zjAMyZM4f77ruv12svvfRSLr300mFfq6gU4gtf+AKXXXYZ//iP/8i3v/1tLr/8cr70pS8N+J6DBw+ydu1aLMvCsiyeeuopPvvZz7Jnzx727duH67o8+uijXHTRRUybNo1gMJh/2li/fj0XXXQRhmFQW1vLxo0bC44DLF26NJ+Vbdy4kdraWgxjbHuKjUXUWYtQPnotlJRDJgUl5Rw47aMoy6/vOqbqgADP6bNX8cTBHbieR6dt4gmBpqp4wmPTwbc4lGzvVcN2PZc/BQx+NX0u9cBBx2TdjDn5IBTWDEqyASik6Vwx61xURSFhm5QHwlxoVA37f7TxVO5SapeD6/jBRwj/31LaLenBwsqpfHPRx/jX8z/FNxd9bMRK1ps3b+ZTn/oU3/zmN3uNIR9pisqIrrzyShYuXMjLL7+Mpml88YtfZN68eQO+Z+nSpbzxxhtcdtllaJrGsmXLWLlyJZWVlXzta1/DNE2WLl3KihUrAFi3bh1r164lmUyyYMGCfCnwlltu4cYbb+See+5hypQp3HHHHQB84xvf4MYbb2TlypXEYjHWrVt3NN/DSU1P1Vbntm0Fx9wHbveDUD82NM2ZBEnH8s+VLZ2qioIrBK7wsDw3nxGlbIs2M0WJEaStejY/Lp1QsD+o1AhRYgTRFIWQZrC1aR9/d8bSgimqR1UeGUNOBoMp4o6XtFsq9SQAK1asyN+fR5uivVBOP/10Tj/99CGd/Otf/zpf//rXC44tWbKEhx9+uM/zP/jgg72OT5s2rc9UsLy8nHvvvXdI65EMk0Fu3hNDUdrMFFq3Hp0ADEXjjI4W/qp+NxWZNB3hEjZV1eBOmkZ5IMzBVDtxO5N/T2UgQlA3MFSN0uw+oYPJ9pH9LGPEycD988OwZSN4np+heW6fEvljPQ5iMFm9RDIajDM/FMlxoWyif7PuTvbmXddaT8Ix8RDYnovjuXhCIIBF8TZW732bqShYRpASM8Wn973Lgo42Dqfj+SBkKBoqvhAlrBmUGiFSjkWnY474zKexUO7y9mz3g5Dw/N6ccCHdCa573EuE46l0KTlxGEfukJLjRU8bmoyZImOleaiqhu1vP0/UCFBqhIjbGTxAFf5E1KX1uwgFwoRCJYQATwjet9K8owoyrgP4/aCwZqCrKiFNR1NU4lYG0xudmU/H08kgX/Kq3wWuDbnemaL6QSmTOv5mp2OodCk5eZCBSDIo3W/eVtsR6jWNl+Ysoq6kFM9z6bRNKoMRQlqUNiuNEILqSCk1rksgUgrCt+upC0f4Zc1s0lnlZKkRQldUFOCymWcT1nU2HdxBMpsJ5dQ/Pb20Zjk6R6NdGu1yV189FqCr5OV5gOLv1VIUPxApqp+pHW+z0zFSupScXMhANI45lk3l3M37nu1P5Ud9O8l2VEVBAB12hsnhUqo1g5Rj8c1FH8N99y+IRDuObvBEWQUbyyciFIWo6xCLVpJ2bCYES/h4zemcNaEGRVFY0EPx05eX1ot2mrmt9f2qg/oNBMfgu4q17OuSh3frsWCEukpeuuHf3IXwg49m+BmRqh53RZw0YZUcD2QgGqeMdFO5+817jhrEqzT6PE9zJkEk64igqyqu56EoCo7nj+ruvqvbPffjmH/6HfdNruGNWBkAp6STfHbafCKzFhUMsevPQbgvL60M/Xtp9fm9bP45ICBUMuoN+KqDr/c56I62BpiQVf6VlEJnK7jCX5dwQdHg/EuOuyBAmrBKjgcyEI1T+prsmXEdmp75Lf/ZtGfA0lbPDW89b95GZ7zfG/XEUDSfEcX0EG1WCuF5aKqK6fp9nRXTF5C0TfZXVPObBYtpcH0PwA8kO1k5cxFi5hmo2SF2QU0f0EG4e+DLoaP066WV/148D9oa/YzDc31RQKwy/13lXzvCN9hAOg5lFYUHc+t3LP/awTBQCZ3tgICpc8bUzf5YK/UkR8eJILeXgWi80qOpnHZsWh2LqOMU3MyXdM7mz427B7SJ7xnUPM3wHQ/6uFF3txQJ6wa2FyTlmoQ0g/JAmBU1C5gWKecvzQd4cM9rZFwbFYVLZpzBeRNnIhTFl2cHQujZZv1ADsLdA18OB0F1f2q6jma/75LImqWqqr8HynV8MUDuOxulBrwVLiWYCzj5BVtQMRlyUnU94K8rWory0WvH3U1DMnYYbbn9K6+8wk9+8hNCoRC7du3itNNOY926dTzyyCP8/Oc/R1EUzjjjDG6++WZKSkqGfR0p3x6v9JBUx60MAc+jI1yCoigENR1N0fjjoXfyN/nux5842M2UsC8D0n5u1Asrp3L1nPMoD4RJORbVkVK+Ov8ibv/Aav73GUuZFIqx+eDb/Or9LWRcm6ge5PrTlnD+pFNQsptUy4ORfBCCgR2El9XMxxUupusghMB0HTzoX01XNhESHf5/95zWmop3/fcoNeAba87uLQ/PJP0fWiYk45Boh5Jy39ECf8Ow+7Nv4z5wu3TWlgyJYyG3f+211/jOd77D448/Tn19Pb/73e+49957ue+++3jkkUcIh8P8+Mc/PqpryIxonNKzqaw4JrqAV2acln9NQNUwXafgJj+r5TAX7H+X0nSCvdue4k9TTuEjmsYkM0Uo1O2JZoAb9cLKqb36M0nbpDWTZP3bz1Nn+TfemZbJ1ZUziET9klhEDxIL9Pb36yvryfWa+vLSOseN9StUUGqXI9b/u59xiGwPRlXBE75kWohRbcB3TpiJMvfUrlJJIISvkHMgVtHr2nLzqOSoOAZy+7lz51JdXQ34XnMdHR185CMfoaLCL0F/5jOfKZhRNBxkIBqn9Gwqm6EoT0ybzcFcQxzy1jq5f89qOcyyna9hAwlNI5xJsmp3HX+ZPJ2Shv0AhIIRVNcGVwx4o87Vpa22IzQFQjxWPZP3whGSWXeFD3S2c1XDQQK7d5DUdEKnnku4nyF2gzkI9wx8A1n8qLMW4U6YCu2N2d6QDpGKriwlkyqoo49Gfb1oeyToU9gwGr0ryQnKMZDbdzeHVhSF0tJS4vGu6oIQAsdxjuoaMhCNY7rf8BKt9ezctRUtmwHlbuZnVkzltZaDuMJj9d63sIGMqqEqCo6morgOczpa2HjqWSw9tJvpmRR2sITwR67s94acq0tngDYh2KupvBMKkVFVVOHxqeYj/FVrE1q6E8WxiT31P6jBSL8315F2EFY+fEVXppGTICNQPvHlgs90TOxsBntilZtHJUfB8ZLbP/300/zd3/0d5eXl3H///VxwwQVHdT4ZiE4Q+rqZzyur4s+Nu4noATKuTWUmTUrXURTQsv0TW9UozyQ5MHEa/1k+iX89/1Ps2raNxQNkC2LrJjxFo921eWFiNX+aNA0UhahtcfmhXZyWiKObab9mrWootjnoDb6vct9wKVaC3JfyMH98pALRYE+scvOo5Cg4HnL7aDTKV77yFT73uc9h2zZnnHEGt95661GdUwaiE4ieN/M7tj/lq+WCQcoIk4jEKDHTZBQQCBQUDM+lPVSS78nUtdbzaGY/2zdt45N76rIWPV3ZgvsRgdfRRCoU4f6qqbxT5vd/pqYTfGXndtA0Siw/CCk56xrN6GqgDuN/kOE4K/QlQe4ZWGmph2h54RtHOCMZ7IlVbh6VHC2jKbe/4IILCrKdH/zgB/n/vuqqq0bsOjIQnYDkbrifa9xHPBzllRmnsWfCFF6ZcRrLdr5GwHVxNDBcD00IXqg5FVe4zCur4je7tmILh4vqd2OjkHRtKl2HsBH0a8FbHqepvIr/qpjA4WAIgLPam1l1aDeubhDwHHTXQ9F0PwghIFI67Bv8cJwV+v1OepThMNO+31tJadcLRzgjGeyJVW4elUhkIBr39MwWLkdjxtbHQdOxjRAlZpplO1/jiXmwZ8IUHjvVZemh3Uww0xwyAjwxeQa7o2V8vGo2Ozsa0RQNFJXyTJK0HkABOu0MQU3H03Te8Sx+XjOPjPBQheATh/fxgdYjGEKw+dRzMDSNy3Zs8W/omuEHoVDEFwpk3bqH0gsaqrNCf/RZhguXQDoBgeCoZiQDPbEO5Wn2RNi4KJH0hQxE45i+sgXnjefIoBAygpQqKq2mB67LBfvf5Z3ySeysmMSE0y7Ib3INqBqlnsufG3djui7ndLZTu3cHETND2MzQGQxh6wEc4fFUrJxHJ0xGCI+oonF90yFmtTfTECzh6elzSU2bxwerZ2PMPieffSQzafR4C7rwOJzu5PmX1tNePaPfzbU9qajfxafqd1OeSdIeKuGVGadRF4r166zQL32JBiKl4Lr+JNoxfnOXc4IkJzIyEI1j+soWKjJpOowgIXzng0pKiJtpStNJygNhltXM79fJYE7LIT6xdwc20GEEqLBMyjNpDofg/1VWsT07yntapIzPzqmlLBAmAZSrGlcHwl2D8Sqnsue8VoyX1lOV6sRRVFqMIJonuHzvWzyqKTRUzypwUOjPN+6Te+qwUUjrASrSCVbXvcQlikZ7aSXepFnF34T7Ew1MnIZ21Zrh/QKOIcdEWCGRHCdkIBrH9OXD1hEuIWKm8n8O6wZh4ZHMNuV//f5WOqw05YGwf2PLElA1Pnp4D7YCtqbjKSptioKlKPxizpkcjviWOgFFQ1c0GtKdlAXChDSDWCCUHxEO2UzN7OBaTYdgBEvr5prgwgcP7uIP1bPy1x3INy4UCJN0bUKOTYnluxUYqsckz0Vs/n+4kTKwMoNmM+PeVfo4zgmSJUHJaCMtfsYxE0NRLM8tOPZCzakEoMBixrJMHppQRbuVRsGfDdRsJjmc7CBlW6RsiyPpOBWZFI6q+3uMPI+3yibwfxeclw9CKgqWcDmUauex/W/SkI5TFgwXBCHoytQmmGkstfCvmKWqTDDTXX/u5tbdi45mQsEIlcESYraJAN+rTghCKP7m1LbGwlJVPxY56qxFvqVOSbn/vqzFzri5oQ4wJXc0yZcEk+1Ffc8SyXCQGdE4pi9Hgp0Vk4hfuJryd7fmn2Afm1HNntIKXM+jzUqjouAisIVLq5lEIDAUjWSklFAmSQqVrROn8qcqf39Q2LFJ6wYKoCsqYc3AUFUe2V/Hogk1vdaVy9RagmFKLbMgIwp4Hi3BMEdSHZToATRVHdg3LtlO2AiCwM/ghIeL4vvGKYqvzMt5bDFwqWo8u0ofr4xOlgQlxwIZiMYx/TkSzKqcCov+Kv+6v2zZQETVaLRSKICqqihC4AoPD0FY05leUsHLM07jw++9xiPTZrOj3H/SnpZKELEt3i+rRFUUonoQVIXmTApBkro+ZNQ577gXps3hk7vrwPUzoYDnoQuPP1bPwBEeCcdkRc0ZA/vG5W6+quZb5Shg6xE0O2skqhldbxjlUtXxLFEdtzlBcnS45BggA9E4J7eJNSeL/vX7W3vJonOBwfG8fC12fnszHz+yjwlmOq9GiwM/OfVMGrM3nkVtTURCJbxcVomKQjQQwvU84t1Ka32p3nKZ2sGq6TwMfOjQLiaYaVqCYV6YNofOKbOoAUzXYWdHY7+freDmm0mB6UG4BE/o4GZ8L7lR3APUnbGgWjsuGZ0cHS45BshAdAIw0GC5hZVT84FBVRQ8IVjQ1sRV+95B0XTSmkEkk2Luoff4zczTSeshVOFx8ZH9fLSthd/NmIehqJQGwmRcm2S3PoWuqPmREt0DUfdMbe/EqRyZPIMj6TigoKsqMdsiYgQGFCrk6H7zzWUkWuMhKK/yy3M5l+1UHNJJyKRwH7h9WNnCQBnPyVqiGvciD8m4QIoVTgC6y7H7mjmUmyFUFYoihOATR/ahaDqmquEq8NzkGn4+eyFp3aDEsbl677ssTSVJCpel9buZFZtA2jELgpAKVAYj/QaThZVT+eaij3HNqedheg6q4gcMv0+VImVbAwsV+kCdtQjtqjXs+MDn0K67FWX59b74oLPND0LhqG/ZM4yG+qBN+SHMbDqRGPciD8m4QGZEx5Bc+aw+3cKz29uPymG6O33JuHsGiFwJ7+3Ww1TVvUynouKqGhunncr2aBkAM5JxLj+8j4mWQzASIa74CjdHCKJGiLSbzHrU+eW+kB6gw0xjeQ43bdmQLwku6GjOZxZRTWPetNm8X1FFazdZeYedpiwQ6l+o0M9315xJELQ9gq1TWJjNltwHbi8sHw0jWxk04zmJS1TjWeQhGR/IjOgYkSuftVtpAqj58llda/1Rn7unjDsnx+6w0tyx/an8NaxdrzP7if8mlklheA6/nN4VhC5oOcINO99gth4goKgoKISBllAER3iE9AATgiVoKOiqRlAz6DDTdNoZQpqeLwlu3foo5pO/zGcWwUyCle9vZ35HC5XBCIqi4AoP23MJdhcaFPndRfQASeEUfncjka0Mcg6ldnnvyauyRCWRjAgyIzpGdC+fudny2UCuAkPhcjScN56jIpOmLRRmU9V02sonUhmM0G6leWjPa0QOvEvNlsdRXYcdpRX8v9lnkNINVM/jyoPv86HWRhQjiLAtf6+RlQbXZuPUWRxOdVBuhNBUjYgRoDQ7JtzyHMoCIWKBMOA7NHz44C46PZcJuYxCD+I4Fhfsf5cdZ34QIUQ+mDmeN6jFT/fvTgiPxkwKWzholsfv977uv28kspVBznHcVGsSyUmADETHiGLKZ8PB27OdGVsfJ4NChxEkkknz6f3vsikQ4mC0gohm4AqBsu0JPE3j6dIyHp5yCkJRiNkWX3z/TWanE7SESnh8xml8omE/MTvBPjXIkzPmsbN8Eq7n0WKmmFJSxhfmLckHjZu2bMh/poxj0WFniKUTJDWdiGMT1g1KAyFaPYeydJJ2K40QAhSFmBEqOhg3ZxKoQJvlq/UUFDzP43Cyg7rWehaMQEO9mKb8sSpRSScDycmGDEQjQDE3jpyEOtjNVmeozfq+yPU2Qll/uUPJdnTX48OHdvPotFPptDIcScXRMwl+MvM03i715wfNTMb50p4dVJhpDodKCDsW+ydO5a7yCSQdK795VeCPB47qQaJ6sCBg5D6TEF6+/9MSDFNmW7SaSSopIawbTNADNAXCWfm4goJCm5Wi08kQ04ODBuOJoSi7O/0SWU75h6KgKoofxBZ97KizlbGS8YwFmbhEcqyRgegoKfbG0d0FQQiB6Tq4wi26Wd8vPTYc6oqGrSpUmEla0kkaM50IBPfOPZvmkF9Cu6D5MJ89+D4Bz8NSFLTscLwOO03a9WfPi+w/uRt/xrV7BYxlNfP5750vE7cz+WN/rJ7J1ft34roucTNNWHiEgBl/9VmmtB7gSMqfda+Cr6AzU1RHShmIZTXz+Y+3nkVRFE5va+Ijh/cywUwTD5fwwrRTYdHHRiRbGQtN+ZNVJi45uZGB6Cgp9sbRfW9NfSbNxKwTdvcMY6izeoB8byOtqMStDK7wMDyHpkCIlqx9jwCaQ2FU4bHy0B4+fmS/HwiEIGUECCBYP3l6PgjlcIWHqmgogC36zt78s/sowI7yCWwMLOJDh3ZRmk5CxeSuzKLtYP49ruh6Z6bHdXPkMs35Hc18S1Woi8S4oPkwrqJgGgFilskn99Th7dl+4mQL0slAchIiA9HRMoQbR05CvW3bNhYvKhx2Pdim1P5QapdjPvlLEq4NmkHQc8Fz2Tx5OrboUtKpKCywbE5PJUhrGroQuKpKWyTK45On81Z25Hd3clmRJwSaqvTK3p44uIOIHsTyXFzPy2dP22Jl7Fr0IcoDYb656GN+gN3+FIeTHZA9H/iBSwHazFQvq6CemWa1mWLa4b0kdQNLD6IoKpamENOMEckWCsqrAX/ybDGu3iPOSSwTl5y8yEB0tIzQjaO/GUGDNfLVWYt4Yu45nLW3jlg6QVuohOdr5rKzJOpb4ACGqlERiNASKeX/lVaStE2mRMrosNLErTROt6wmh4IfhFzPQ1NUVtQs6LWOnAAjpodos1L5AGO7br7s2D3AaoqCLbz8+TVFRQiBh+Cet59jTumkfBbYM9MMhUrw4q2EPZcEfv+qNBAipOlHnS0UBD0FaD3s/yBWeex7NDWnwZaN4Hld5V5NGzGZeKxlH+4DT0shhGRMIQPRUTJSFijDVdW5nsfb5RN4fdGHEIDjebSZSZxsEAqoGlE9iKooWK4DQlAZLCGgaiRdy2/6C/B6lNh0VSWsBZgSKeu3RJgTK0QMf92dTia7P0jn6jl+JnfH9qfQFA3X83CyQQj8IOd0C0oCCrLA+d0zzUwKUnFU4RF0PSa6EIrF/J9lR5AfDQVBr7PNd/MGSHVC5eSu14zyDdvbsx3eftEfYW768nnSCTj/EgB/4+5RBBBvz3Zq3n8eIhEphJCMKUY1EP34xz/m8ccfB2Dp0qX84z/+Iy+99BK33XYbpmly8cUXc8MNNwCwY8cO1q5dSyKRoLa2lltvvRVd16mvr2fNmjW0tLQwa9Ys1q1bR0lJCfF4nG9961scOHCAyspK7rrrLiZNmjSaH6dPRkptNZiqrmf/aHnNAuaUTqTTNlFQMF0HgaDNTOf7NhMCEVZMP4OXGnbTZqaYGCrhEzXzefHILhoyiXwmoyoKCIGHX8IrR+fa+RcC9GukWtdaT8IxaUp3oqkqZUaI8kAEV7j5IOTt2c6qrZupNNM0BUI8MXk6b5X3DhqaoqKpakEWOD+XaWbSkOooeH3ATEAm4AePkdhU2j3ouQ4oqh+Mcr2rY9SjyQfEUAmU+BuNsU14bxvi7RePWkknXt2MUFQphJCMOUbNWeGll17ihRde4A9/+APr16/nrbfe4tFHH+Wmm27i7rvvZuPGjdTV1fHss88CsGbNGm6++WY2b96MEIL7778fgFtvvZVrrrmGTZs2sXDhQu6++24A7rrrLmpra3n88ce56qqr+P73vz9aH2VQch5o2pd+iHbVmmE9XS6rmY8rXD+g9FDV9XQWsFyHxw5sZ1vTfjzh8cHJs0k5Jq1mKm/BE9I0NE3jsf11AHzylEX83RlLOXvidJZOnYcrXDRVpSIQRlH8bs20knL+/oylXBmZDVBwze5OELn1OJ7HhKB/A281U+iKUhCExNO/osy2Sag6UTPDp/e9y4L23jd0AcR0vy+TywKV2uWQSSFSHX6fKvs6oSgIBUh0jJzvWfehc5ruX0l4XRNsj1WPpj93h7aGrowtN3tJ0/0AMsTze2qPZ08phJCMAUYtEE2aNIkbb7yRQCCAYRjMmTOHvXv3MnPmTKZPn46u66xatYpNmzZx6NAhMpkMZ599NgCrV69m06ZN2LbN1q1bWb58ecFxgGeeeYZVq1YBcOmll/Lcc89h2/ZofZwRo661nkcz+7lpy4YC+52cMWl51rWgPBDO39Rz/aOIblAaCGOoGh1mhuePvI/lOmxvPZRXvGmKQmUwki+HhXQDy3N5sWEXeztbel1LALNjE/n7M5bynXMvyWc8Axmpdv9Z2AgyJVLGpHCMqBHKvz/3dK8HQgRcmwrbpMLKcP3ut3oFo4qAH8wa0nEOpdpJORZvl00kk3VsyOEqKg4KnqJASdmwg35PCux7IjHfwkcI/79tEzJJSCdwf/Zt3AduH73ppP1NYYWRMVwtm4jq9VAoSiGEZAwwaqW5uXPn5v977969bNy4kc997nMF5bOqqioaGhpobGwsOD5p0iQaGhpoa2sjGo2i63rBcaDgPbquE41GaW1tZfLkyaP1kY6aXCaR9ixs21eL7Y43s2L6Ai6deWZeVdeT5kyCikCYkB4gYZvELd/jrdlM8n+2P5n3mTt7Qg2rZpzJ/7y3BcgQ0HRK9ABBTafVTLH54A7OyJ5/oDlGuWsO1LNSgAYrheN5/miHnhtTs+Uur7OVSsvEA1wUgq7LZ/a9y++At7NlulYzmT+nn83p/GbXVr6Z6URRNVQhEN3GkaueO6I3z17l1ZJyyCQg3uJnIpruD+Ub5b5Kf/1GKib7/z1EQUzPjdbUnIbSVO8HVznSQTKGGHWxwnvvvcdXvvIVvv3tb6PrOnv27Cn4uaIovu1LDwY63h+qWnyCV1dXV/RrR4pHM/tJexZpPBTPv+m6wmXj/jrsI63M0Pt2WZhIgKRp0paMk/E8Ut10brkgNFuNMjeh8+6Od2jMtBNAJRDUcV2TRjuOJwT16RTbtm0DYL+TYIvdRJuwUFEIo3LEsvjF2y9yoVFF0PGIWwkMxf9OLeGRFDa+wQ44CHIDwB3Xo8V1qMDIn3+OGsTojBM209mSmv95bUXFUVQ+cWQ/O8on5seWA2golCg6muNhC4cGI8hkxyXqWojsviNNgKeo7CqbQ2f2WiPG7I8Sa9lHzfvPI/QwXiBGMN2O4qSxFANP979r1XWw//Qgu1r7zsC3DbCuWMs+qg6+TiAdxwqX0lhzNp0TZnb9fPoF/s872vyfT/dl/jXvP49QMniqjuo5KMLj4NTF/X4H+c+hqP57mo+gNNXTMvk0Yh31BefvbLWhdYS/y2Ey0Hc3Fhjt9S1evHjwF52AjGog2rZtG1//+te56aabWLlyJVu2bKG5uauc0NjYSFVVFZMnTy443tTURFVVFZWVlSQSCVzXRdO0/HHws6nm5maqq6txHIdEIkF5eXnRa1u4cCHBYHDwF/bDcDafPrTlILYNigdaNmgKIXAR7Ak7XL6o91/CtGNhNx9k/b43UA0Ds9soBfDFBVEjiAgGOfO0hQBse7cTRVXQFJW0axM2dEzXYWIgzOJFi6lrrefVXVvpVFxUoaAoCmkEFYEgmqqyJdNEOBymM5VBU1TCmkHSdgCFimDE93zLBQZVQwiBKgThkhKCp0zhiYM72D61hk/uqSPmub4KT/g9nqSqYqn+eAlVUakIRGizUiAEuqZRHo7lv5enp5zCp/e9SxyDsOdieB6eorC5eian1n6AxSMwQqMn7gNP+6qyXPaRbgNVJeSaUFZOdnGEMqk+bxrbtm0rON5rf1Iq7mdWZRUEHYvYgVdQ5p7aLbtaDKwGIAKU584z99Su81RWo9QuZ16PgYHdxTJi967CzwFgm8Q66in/X//S6/xjgZ7f3VhjrK9vPDNqgejw4cP8/d//PXfeeSdLliwB4KyzzmLPnj3s27ePmpoaHn30Ua644gqmTZtGMBjM/6LXr1/PRRddhGEY1NbWsnHjRlatWpU/Dr4Kb/369Xz1q19l48aN1NbWYhjFjRU4Woa7+XRiKEpLtgzlZm/QCmCoei+Ztic8EpZJ2rWZVTqRlTPO5PnD7+ffD137g1RFoa1bgFo2fT4P79uOIwSu59JhZ3CzJbRcANUUDU8IVFVFwd9k2ulkKNWDtAkLQ4SoDEbosDPE7Qy6olIeiBAxArRZaTQUvOz7dFWlVA/SYabz30uqajrbkh18ZN87eXk2QLlrU5JxaAhGiOoGESNAp5PB8Twcr0vebXkurVNm8QDwkcN7mWhmaAmGeGrKKewum4Tz+lPMbzkybKViv/6APTcoa7q/H6u7+8MgZbH8uVvqfRl2uAQipdB6xD9XINQlOqA41Vp/9kP9WUxhW/6QwO7oAQIdbYN+NxLJsWbUAtF//dd/YZomP/jBD/LHPvvZz/KDH/yAr33ta5imydKlS1mxYgUA69atY+3atSSTSRYsWMB1110HwC233MKNN97IPffcw5QpU7jjjjsA+MY3vsGNN97IypUricVirFu3brQ+Si+Gu/l0XlkV73Y05P+ccy7QUArscyzXodMycbo5I0wKRcl4XaWgsGZQFghjOg6dTgYhBP/z3haW1czn7InT0VWN3+95jSYzhaaqTAhG8mMXMo5DRTCCrqq4noeiZMttnke7nUHFFyeg6USMIAeTbaiKkt8vpKsqjuuiKr7SDvBVfo5V8L3M6WghHggRtTJodAUjXQhKHZtTWo7QNnWOLyM3k6iqv8HV8vwNsZ+efR6/sE3eKpuQL98Zqsai1iaW7n8XSkqH1bcZyB+w1wblSCl0toKi+QKGQfoqsZZ9iNdf8c9tmb76Lt0JmpHfYEwyDsGsEOMoVWv9WUyRSfXZV7LCpUR6n0YiOa6MWiBau3Yta9eu7fNnDz/8cK9jp59+Og8++GCv49OmTeO+++7rdby8vJx777336Bc6DIa7+XRnRyMlepCkYwJdzXkbX6YthCBpW6Qcs8Dr4P14Ew/s/gtp18428w3CmoHp2HTY/miE6lApKAoP7vkLiqLk1XaTwqJgb5Lpgiss4lYGTwgc4aFkL6arvtIu0kNMaSgadrfBe2VGiBY3iZrt4+UCh65qBFSNlG3R6WSIpjrp1A0itoUqvHxmJBSFtKbzV/V7+FFpZX7fU1jV6bDS2J6Lpqg8cXAHRraEqSt+5iaE4ENH9uKpw9sP4+3Zjtj4Uz9I6IYfzLJBQby6ubdgQNP8YBUp82/ug2RfVQdf7woMnguq5gejVHxY2dWg9GcxpWqF+6CyAbRx+uIxVY6TSEA6KwyL4Y50aM4kfGm162GqAsfz0BSFiBbg9PLJtGfHbucQQvBiw26ePLQDAZToAT49+1xsz+PFI7vYn2hFVVSmhEspDUZI2iYCJZ+Z9RcwATrtjG+z000sENODhHWDRLqwDxXSDLzs3qaAqqEoKiVGkJgRJOVY+R7ZEwd3cCQVJ+GYvlNOMEypZaIicLKbRBUEnqJiZvtEuSCkAknHJKjqlAUjBFSNditNh5Xm9PYmPnFkPxPMNC3BMNXpBMng0I1B85mQnfFv1J7jZztU+uWyjua+Nygv/UzRZb9AOg5lFf4fcmo7sptjY5VDyq6Koj+LqYnT/KDao/zY2Y/AQiI5nshANAy6j3QIqFo+IxhspEMugAUUlfJwCeD3WKrDMX8zajeVoOW5bNj7Bm+2+fuMpkbK+OycWsqze2vmlVVxx/anqApHMVSdhJ1BUJiZ9RcwAcoCIVKu7cuv8ZWIcTtD2AhgZjfW5j6bh0csEKQlk0ABqiKlfH7eB/osQ9674zkQAkVVeWrKKVy19x1cRUERAkX4GWCHYRDwPFqCXfuEVEXFER6m5xSUPE9vb+Yz+3biqCpJTafUtgi5Lp5tFl54gMwi37Op3+X3ZpRsoVBR/WwlGQdVLZjGOlynAStcSjBXEivJlvVym2OHmF0Vw0AWU31+jkHUcXIon+R4IAPRMOg+0mEoqrlcALOF54/MVlQMTWFB+ZSCINRmpvjNrlc5kvZn95xdWcOqmWdiqFrB+WbGJtBpZch4XTfl7pnZspr5bN36KB8+uIsKM0VLMMyTk2fQUFaJKzzKA2E8ga9aw8/AHM9DQUFXFFKORUjTUVAwVJ0pkTIsz8V0+36qXlg5lZAWwMTGFYKdlVVsMoJ8fM/bTErFfbdv3cBFQRceT1bP6DJXzfrOCfyNrTE9RMQIsOzIfhxFQRgBjOzPk45DzLGL2g9T0A/yPFCVbK9GyW7nVsCxR2w/TWPN2cQOvOL/IRCCcMz3i9MDvhPEELKrYhjJgX5yKJ/keCED0TDpb/PpYO+5mvP4wztb0BWVqBHirAnTOLWsazPvrngT92f7QSoKK6Yv4IJJp/TaPxXSDP5qylx+tWsLmuL3ZuJWhpRrknIs7tj+FJej8dl9O+n0XBKqTomZ5qp976LMPJ26skpaTd8xOzezSEHB9TwC2bV9Z/HHuGP7UzhCFC3MmBopK8jCDoZL+cmEKcxra+SC/e9Qmk7SFgjyZPWM/IZWoKAnZroOppugzAsywUyTUHV0IfALe4KkESDqaf7G00FuvgXNfN3wS2Wqlp36p/nGooHQyFgFAZ0TZqJ0l1pXTEb5xHWjeiMfqYF+ciif5HghA9ExIieb7rQylAXCXDBlLrNLu92I++kHzYr1LjdF9ABRI0hZcEo+MzuUbMd0HaJGkKgRot1K47zxHJ6mMaGklIZUJ66qIjyXTzTsp658Aq7wCgKAip8dhYWSL+8VK8zIfb7cOgKaju052T6Yylullew86yJaMokCF+6B6LBNWoMRqj2PpKLgCBdd0YgJgVE1De2qNUWcpFszP18qy37qWIWfCfUThIZbphoLk16HhRzKJzlOjJrXnKSLutZ6frtrK7brUhYIU5/pZMO+N9jZ0Qj45bQH97zGE9kgNCVSxlfmf7jPIBQ1gsQCoXyGtLByKt9c9DGmlZRTGSohljUxDWo6FZk0Ha7fE3KEi4KCrWpUmqmsv5t/DgVflaZl9xSl8fLlvYmhaL6vlKOnMCO3r+pIKo7tuTjCJemYWJ6Lofp7rdqsFM3pzkGDUE4dl2PrjNOIahqTjQDTImVMzk6ULbqM1t2/LRj2BQOK5veHBjBNzZepku2FZarR8pkbC/TndSe96CSjjMyIRpi+HBeeOvQuFYESPAQddhoVgaqovHhkF5NC0YJ+0FmV0/jkzEUF/aCdHY282rQvO+vH4GPTTut3SF13OsIlRLIbXXVFwxUeAc+lPVRCxAjQaiXRuj2LCMgPqssJL4oRZjxxcAe26+XVct2zrJgRQlVA2OTVef1hqP5YcjW72VZTVN4un4gy97yCzOTg1MXMm7WoqIylVzNfVSFaOmgp7mQsU43UbC2JZKjIQDSC9OW48Ic9r6GgoihgC4+MbdMpbDzToc1M8h9vP4vluagoLK+ZzweqZhX0g3Z2NPKn+ncp0YM4nkfCSfbp4tCXQu6FmlO5dNd2sE1KjSCd6TiqELw8fR6m66ApClE9iAfErQwim63E0PPn7i7MOJRsxxUeuuq7cOd+3pxJkMnucfJnG3V9J51OBvBT775yIV1R8yVCLztuPLfRN6QZTAxFC0pd3p7tVP3pQdx7nvNHeYejvkt2sh2x+ee4kdJeI76H1cw/CctUIyl8kEiGggxEI0jB6AQUJgRDmK5NfbKDiBHA8/yMyL9P+wIBy3MJqjpXn1rL7D5Kca+3HCCsBzA9B6H0LxaYV1bFpgNvZwOFSokeYGfFJOIXrqb83a2EO5rxyqp4onomb0VLmRgIc/6kmTx7+D2StpkVkKkgBDYeda31BcEIyAfZ3B6fXECcGIrSZqXQUHqt3+y2ebNntpRDRfE36AoHVwgMRSOkGRiaWpB55cplhuX4T+s51wI9a+2USfpBqLK6t+JrqDfTERoBP94Yt/0tybhGBqIRJFceM1SNiGaQdCxSjoWqKnjCo9Mx8wq1HJqiMikU7TMIBVSdTiuDB6QdO+/JpisqyW77aOpa6/lz424ieoCMa2MLl4RjsqLmDGbNPBMW/RUApcCV2X9y/KV5P2nXznvGxfQQtmX2CnQD2Rotq5nP7h1NeJ7oIxR14TtJKIVjyRWFsG7whXlL8tfpTxKfK5d5mgJmpx84Ef4+oBye28vHzWPoT/njrUwl9/9IxjMyEI0gE0NRTNchpOo0pOK02WkczyWg6pw/6RSePbyzIAiFNYNSI0Si5+ZM/LJULBAipAcK3ApUwPFcPCHyWUt+cF4wSBn+JlHTdfJiiO707GF1WBmqw6UF5cCkZfVSxQ2knltYOZUVNWew6cDb2Fl/vL5Kcaqi5r31VAXCWoApkbKCgJOf7vrqZtjyBG5fhqRWussuR1G7rGwUxZdk59AD0HxoWHtjxlOZSu7/kYx3ZCAaQZbXzGf93jc4mGyl0875ySkYqsbWpr0FQajUCBHRA9ieS0UPu5qIHiAW8EdnL6uZX+BWIIRAURSiRmBQK5++JNY9e1im6xC3MpRlXQ5StkW7sFEshzu2P5UPEoPZGl0680xOiU3gnrefQ+Abo1qug676djauEGiqiu26aIrCV+df1Oc+pKIMScE3I020Zv3c9OxUVdc/nsOx/J8XITroN6MYBzfykRRWyMxKcjyQ8u0RImWbTApHuWjKXDKum5/VU6r7UuuEY2U3jUIINR+EPOFxYfUcyP4slpVn58i5FSiKguO5vkmpoqAraoGVz2ASa+h7/HfUCJByTUzXIWWbtJlJPAQVgXC+D1TXWs+ymvm4WesfkfWd66meW1g5lTmlk5gYijI5XEpA0/MD7QxNY3K4lEnhGLNLJ/W7GbjgpporsWl63pAU10F1bV+KHY75GVEgCBVVfuDSsj5utulnSpo+6Jjt8SLV9vZsx33g9t4jyzuaR2SU+Hj5HiQnHjIjGgJ9SbPnV1STsEwyWdubeWVVhDUjn+W0W2kyjv8zDYVVMxfx0v53yDg2FcEIF1bPYV5ZFYoCZUaYoN57plJpIETKNtEUNT+5ttVMUV1SBhTvfddX5hQ1QrhCUB4IsyvehKqqlAiVcPapuqbpCNENP2a66/LtSIwnqmfyl2hpv7ZG3ddSqgdpzcrHY3qwz+DViwHUarlymf2nBwllUhCK+v/k1HNzF8PBdwsHxL26eVDRwUAZxXD6S6PBkEZXQP4z9sxwYmVz8Ifv9eZklKxLxgYyEBVJz7JWp22ycX8dKcdiZrSy4LX+FNMUCdvMb+AMqBpTwmWcO3E6gYY4C884I/96TVEpDYQIaP38OrqPTO/jv4v1vuuvvDYlUsY3F32Mm7ZsIKIHSGfdt2e1HGbZ+9uxFSA2gRIrzeV76lg9wB6cnmupLikDIci4DuWB8OCefIOo1dRZi9jVanNOpdHrxszbL/baH+TB4KKD/oLfMPtLo8FAQaJAWOG6kOjwrYxsG7H55/7as+uvaXoer2AibDdOQsm6ZGwgA1GRdC9rBVWNoGbQkkny9KF3uf60JQWvnV06kafr3833hCJagICq8uEpp/Y67/5EK39pOsD+ZCsT+gkgGdcfZNfpmL5qTlWJ6UE6bZM7tj9VtPHqYJlTLlDluGD/u77hqB4Y0kTR4fjw5ShWrVbs03t/ogMA94Hb/WNmyv8nNytI0/3SX5H9pWNCEZmieP4haGv011xa6asJe0yEFUqm//WfpJJ1yfFHBqI+6KsE15xJUKIHiOoBPCFoM1MIRMGIbiEELzXuzgeh3P6YqnAsX4LrzsFkO08e3EHGcwkPMHL8owfe4/yD7xFyHUxN56Wpc/jTjLmkXYt2K10wrvxLB8uY8fqfoC07CbZiMsqHr0CdtWjQzKmnO/iERAeG5xIw0/4enUh2iFxHc+E4bDdrJJqdgTPUbKFn+YgFF/YqsfU65xCe3nuKDnqVuVKd4OSUiwp4lu/IHQgNufcSa9mH+8DTvdZ+1CKAsonQdsQfPd49WFZU5z+j+/xDXWrCVKdv6CoEtDflx1J4qt7v+sebZF1y4iADUQ/6Upb9ZtdWonqAoB7A9FxSWT+u7oq3nvODpoRLuXpOLeU9B7iR2zsT4OWG3WQ8d0Bn68NP/4qL9r2DABwUAq7LXx14l5Rj8edZZxS8t6bpCJXvbvZvVIri34RaDyM2/xxv+fX5YNRftrKwcipfOliG+8ZGJqbilDg2KAqqmr25JVrBjYGiIR6+Oyubzs71URRoOzLk0lVfvY++Smy9KPLpva8AIJ5/qCtbQMkOr8uRzWMDEf+/+xi3PdDco5r3n4dIpKCU5y7YC2+/OOwSn7dnu7/+RHu3g64vyKg5ves1LYe6SrcF4hWRHwKoaUGonN7ndcaTZF1yYiEDUQ/62ripCJ2IEaAlk/B3/qtageKt5/ygsyqnsWrmovw01J6UBUOUBkIcSnUMKrsurXven9ej+vt8XEDz4GONB3jrtMKm84cOvk/AtbOBQc1aGXhgDlCO6Ya3Zzsztj5OxnYJ5c4hPP8fVet60s6Rn/3t+UaiZhpiFflr5YNA435fUCA8CEbg3E+gLfkkMPwG+WBP796e7X7Aaan3/eUEEG9BHHin6yS5Ka19YaVAD/Y5blupXd53gHt1M0LpPcKcvzzpO38bQf87SsbBsREbf4p77ifgvW19ZrD5z/H0r7Kbdnt4UygqvLcNb88F/mfN9w/78rBQQHgYTqbf9auzFo0bybrkxEIGoh50V5YpKET1AI7n0pCMc8nMhbx4ZBdtZiqveNMVlZ/seJ5Udn7Qspr5LOnhF5dDUaDUCHPI9gUMxYwcNxwHD4GRdS3I951ch8++9gxbZp7OnglTAChLJ1FF9kL5i6p+A7uIhnMv5wJVA5HNrDzP36/j5bIgrSujQHQ9gWdLV/kbqJn2+y85zDS8/DBu3Qv+OpMdEC2H7mLBIhrkAz2956+dyDouuP0Emx6S98IvI/uZ+igTAn2KGLAtPLWPUp6V9n8Hng2e8AOjqvrB+c8b/N+RqvaZweYDdU/X8tzmXeH6r2lr6Hpw6BmEdMP//Wk6jlDQ+lm/3AArOV7IQNSD7sGhJGuZ026lKQuGmVdWle/z+P2gPTxx8G0EENEMPj17ccGMoe6oikpZVhmXm8bal3hgVks9V7Q04m55gmQkhqGAIcgbgeZCjAeUWGk+sfMvPDH3HN6tqKItFKbUsQtfmctmimk4t9SDZRLKT2DNZUIeTJrml4I6WvybZs5KR+SuJfwbW7Z0lb+BmjnxQ7cwKoQ/F6hqOqTi/n8rit/zgP5LbM8/BG0NnOl5uG9NQfnwFWhXrck/3YunfoUbeAg6msBx+r4pDwXPhfe2oV13a8Fh94Hb+8ziyKRQ1R5b81Kd/hI8xw9CuaDd/WXCA0XvO4PN9cK03ENA9nsU2XNpRlfQ7m/ExoRsKdY2MT2NwCBZqNzUKjnWyEDUg+7BoUQLkLDNgk2n4Gctj7z9PG+YfgmtxjK5pno60X6CkKFqlAZCvstAN3qKB85NxFm5byeBQBBCETLxFoLd5NrdcywPiEXKSKUTnL//HRomz0Q/7xK0l9ZDJuW/QojsTU5A8yHcB27vt3lOzWl+0BCeX14Snn/DFNmbXW6DaMVkyCR8s1FF9d0Mcjf7YDhfuhJP/arrxgpdr+n+bzPtCwJScWhv9Eth2U2p3Rvk3p7tvgw5k+z6AloPIzb/P9yzPtrVf1EUaD2cvdFrRxWD/CV60HwQ994buhwbrAwk2igIvjkRh6qhCK9whHk64f/MzlCwIM+lz/3kPTPYXC+spBTazW7nyF4/GPZfY2V8cUKfn0Pky4qN0xcT2/9Kv0IPaRckOR7IQNSD7sEh49qUGiEumDwrnwm1mSl++84LHM4KFhYnOrj6yAGCu3eQ0AzsGQsKzhdUdUqDIVSlbxOL7uIB94HbfZeA3BOq65C19SzABRRFJawbhKPlTMik+OaijwHgxSrzmUNeSBCJ+TfLtgbEw3fj5vo93UYosGVj9uaZ8Z/cc+MchOcryjrboHYFSvUp/o0qHMsHLgR+QKmozgc6N/CQHxQGoqPJ/3c+8Nn+jfv8Swp7JBt/mi3vKb5zAv5/YpmF/ZfOhmxZUumSXvdXlhsKqbj/D0Ag3FXSU9RCEcfEaRwsm8Ocjl1dAT6d9L8bK9P7vLoGTo8sRnh+fDJTuD/7tv/eTMoPCpFY1zrAz1TTSagp98uHfaEo/u8u+31UHXzdP2c/IozR2NQqMyzJYMhA1Ae5wPDikV3s72zlxSO7AH92zv27t/n9ICH4ZFsTfxVvQ9ENhBCEX/9TQSAKZ41L++oX9Ul3SXImRXn25qUAjqKiCQ8PBU8BLddX6lHG6t5sdh+4vUtZZqb9LEYIv2ylqF0jFIJhP7NxHYhW+qWy7v0TVfN/9safoPp6lI9e2yXdVrWBpdv5vsUgGCGonOxnE9mbaj4TyveYhJ9xKarfo3Kz4yAs0/8cjpXNjLzCjb8jiZXuKkkK0RWM0kk48zSqdrwKnlno7FC/2y9nohYGRqdbBpOTWufQ8f8uOJb/uTOpQqEIgBbwP++7W/r/vEL4DzdZqyOjMw5Wt5JlDxGGeOpXI7qpVWZYkmKQgagPchLuikAJId2gw0rz0J7XyLg2AihxHL7QVM+8fP8DhG6gdbbm/1yiB4kGgn2cfQC6S5JT8YLCjZYdHqciUFFRS0rz5bJ+93l0D2y5UQm5G2euV9HezaHbM7teQ/ZmlQuingepOGLjT1Eu+XKXai0U8W+uh3cj1v877oSpKB++ws8AYpXZPTrWwMFICD+rgYKbnnj+oWw5rlAFpggvK6Lw/J+pWQl2TmCQo+hsqL9JSQOsN5/FZUUcqgpvv0ggnc5KpdsQ9bt82yHX6v8akdJsH6l7yQ0/CzXTXfucMsmufl2uYxjMZlo9hhH2oluG42kGBDR/zeEoNB/KZ0vi1c0DZkvFUpABmSn/XKES/4cjuClYZlonDtL0tA9yEm5D1RBAyrVJZ4NQdbiUbzYf4bRER8F7FMfGjVWi4HvDDTkIQd7UE9sEx0YVHgpd4xRyw+vUCVP9G09J+cD7bcomZp+oye4tyhX6lP4DQ0cTipsTPGT/5XXrA1km4ulf+UFC07OWMm3ZXpQCbYf9PUadrRBv8T9Lr2v1yBD7EyrkJM09BQDk1qRkezei6zye27WWYlC1XsspCuH5N9VJ06C0wr+e62LYqWzgdf0s551Xun3vPVAUP8Ao+J9FzUkHRbZP1ARNh7p6Y6pWeJ5UZ7ffa38ofW/KtXwZNwF/oyvRcv8hKBX3r2ebdDePLXZTay/jVDvjn7PbQ9tI2AZJg9YTC5kR9UFOwm15Do2ZTuzsU7ahaHzp9AuJRCeiPP97wELoBopjo7gu5jkfpSwYKZBj98Tbs505b2zAfe3+Xk9xBZLkVv8mrGg6Wu5G47koRhDlw1fknwRzxpz9ChDeftF/b27PjEI3tVvfFN6Xe74ue5NMNMCEKdBx2P9zd3L3xX6zoD6u3fD/t/flUVZVZ76/fc65Y81VVDFLQAiREGKEKMQgTYyggcK0YmtCwlMytFlLzfMPp8S0K64eTMfVtrZD8ux0Z/nat6KGxCEPwXQ7PCMEBQcaJWhQwGIqary37q07nbPfH9/eZ7jzrekWVfu3FlTVHc7ZZ9rf/r7v9/2+j2Gb3P5umLueFYNhzkRruUJKTAPqWxyPbzDq7E90mvV4IbbX4K7DYWTkMkVo3MUQrqOJOhGnn8m469zZRVb0u6aJ8bvAufiYLELNRzZwawtaXqYit5xaJ3mO3Oc8WOMQFSrIB9ne0hA8jZxt6j4aXyxSlBVZKZRA68SCMkR5MCVYi87BKI7H+2GKBzus+9EWrIVf05E+axEGVgKht1+CHu2BWdeM5OcuRs2CpfAVKGIFslpd19XnjZfLHI/5yP90ciMy5MQtIDlIHkeohryBWB+xx3Q/eSaaQSGb4x8CHe8DtU0Azwj6sGSTDTN/Eo/QNqS8T86B5pnYixm/bINlpqm+xhegPAp374PBAqBzTh5Dv/O6HS5jjCZ4TdTfmJn8++bcm6cB8xqGomBApIdyU7KmJ1vNwH18us+hb2cfu/vcZBMs3MZLNgK0mYrCk4xFAJ/fS4gI1NB7iy50FiOGn1pomLx4PigRz6Gsl41s6aVwPXnHmbSHvTds2SAl0DqhoEJzebBm1jmIphMwuQUNDPW+IAK6ji/mES0FSLKnzhcoaoQAuApGfbYIpR2bz8aUmbTilkQBbnlXvYNRoTtm0oo82iOKIk1hKEz6O95P74XrgYZWSvIPi9cskvRygi8XRcNHBZA3rMehgecJp4kxyWsgGX8VgZdphADoGtA6i4yL1KUrhowsBM4auMw3SeScJy7khtyfFduQWnMLz6f7gDFn+5kUsOhC6Cs2gH1pE1DTCCTiSAdqnHCuDN0mB8kDP30M6D5Z+liKwR0OTsTpXpSh3YG+0uHkSvYTjwA9J4HTHfQzHlECrWcolEeUB4ubZ+DimQvxxukj4BZHo6tvEAD4jr6H2ld/A67rQCAMXzwKvPR/YDFW/AFzt7qWKLCKs8kAdU1Ev5X6cZaYbLnlUHmZDAMxsVqGV/UAAOIDQOsM2n9/l7cmpyDyJNh5nlV9OSimYlAMugG7ILQUNB2egk/TldsaaVjSkxE/08kSDEFXMW/OtlznxhILDmlUNB1oaiUDkUmSt8gYEKxzwlJdHUBDizf85mIfupmUh/buxVLxO1u21lufxcT9E++H9dG+gvdyMZKAfd+mEg7LT9NpISTqwzzU/DJ7JeVg1kLy+GXoVob/PrOqvO+XeTwKYwNliArgirmfw/qzPuNR15YIvf0SuK6D+YLQmahtSZcRn3a3upZwxcsLqlD3dlJop7ZBiHWKCnu3RyIT4p6Jzl1AmXbqUTLCqOlGVmgqG6M0iVcC0yTvIztklZ0DAhxDEK6jYzXTo3cIXBTk6gbtx8xQXsVd51MpGKOx6wZ5sZyLOq9+2kdtk/NeMk5Eg1gf3R/1zV5D5C5QLTDZa3OXwAzXk9GQYVtf0A7/mjPOdiSN5Db8QfKygzV56dh2nnPbo3TuDR+RIQIhIJ30qjdk0bqL9krKRsdBoKbBpUYuyhAK1VMVgKKXjw8oQzQE6NEesGANNM0VZikjPi1Xi5rMWWSLaGarUL/zIhBucNhSkuYc7RF5B8PJH4XqgORA0f2j/zQQD4jktR9ophYCiPYBg5Eh5I4qpD1XDOYwyqSXUMjQAkJZQHiNjW200h/oG8IYyzyuWISuR6SbrlE6CQ5GQ9ZcBcHl7tMfJk9GFuNm0tTkTtNo0q2ppxCaDL/Fo3QNNYPGIinSAF1jfzDvZG/WGY5+XqyfFjjBGprUoz2OJ3nsffCO98n41NTTNrpPktfkC4L0p3JJAtrcJTADYVpMsfzPSD6yQdFeSdno7yIjLWn/AN0bFeaIxor0wC2L7gUZ1rY4SP1E/C7fC9WCFSE7TVRMviMeJhgDWMMUaPEIoFVWa5HT6toVBsjRL5O5n2SSqLXRHvonE7QynFPTSBNwMAT4fDTxSs9B0qvdk6ps3SBzKclBIBlzJvqSITD3JD3KHpM/SPRfyS6zE/XFwMkAZdIijKkNISxY5nGlE0BE1AiF6oCBXnBNJ+8kEAJOf1zBKeKk+C3zP7K5nW6QxxMWj6qZcQRS5XWubXCo8q4CVcQH6NpykZsK1UFLJYBdz9K51A0AFoV+wciweZiJOnnSyTh5e+5QZDzi3Iv5FmGl2nTkIRsU65WUg5Fq4leQ9NCd81FdqHrYRiXbuLiNjFStl/8A171Q6KZg3sXEJIIyRBVAEy299c9fNuQGYrLV9dKlWbHw7AciHnEe/GCYfo9202Sh+4l4oAsiw+LVzgp3xnyx0o04kwakJyFDd0y0I+gHEoPORF/bJCaCAg+K4S+fpFCCIl4WuAXUtYhwpGCehWphxSLQi83wlunk4YZCkqgEmo+uj8aAlhlIxwagB0J0fq0Kj19eI5kDHOglpQvdIM/II34KAIyS9GaaQmCaQYuXhim0QPnTH73bjkecB14n5W5bISLWT4sWN3vP7c1I+rU7FCmRxwCUbLKXx5BoVsbx0ktgJJr4cW4BbWcR8cfnByAWY2YaaJwKHhf3nTAw/mQUvE/UtpU0KgqVYNRZcwMDA1i/fj06OjoAADt37kR7ezvWrFmD++67z/7cgQMHcOWVV2Lt2rX44Q9/iEyGbvTjx49j06ZNuPTSS/G9730PsRglViORCL773e/isssuw6ZNm3D69OlRPQ6d6WgMhBDQDWhzl3iYSCPCBHKzjQCHcizd9EAIYKKCf8p0mpQE6w4dB6FfdQv0b/8E+lW3kDEy00JtQK7IuFM3IyeZwRglwHWDJrxgGPD5YTFRed/Y5uwfcFbaQ6oArRC6j44hkyQ6eriWjiEeFaw5jQyym0WWDxUz58oEY3R+WqZReEicJ1v0dKA/lyFXLjTdOa64CP9ZotDZ56drYApKfkaEIwNUPMou3kT3wKG3yjgGjf5JSr8m8oz2/gH73ErDI0Nh8jsFCl5LPSOe4m2xHcatsg2JNncJ2Je/CTROI1vQPAP40jfAZi0ETyXBk4PgiRj44AB4PAIe6wcf6AWP9ID3d4H3dVKoet5nychGusnw954g437WOeQJphM2ScRKpRzDbdeIKYwERtUQvfPOO/ja176Gw4cPAwASiQR+8IMf4OGHH8a2bduwf/9+vPLKKwCAW265BT/60Y+wY8cOcM7x5JNPAgB+/OMf4+tf/zq2b9+OxYsX4+GHHwYA/PM//zOWLVuG559/HldddRX+7u/+btSOw6fpaAqEStKzh4OcB1Ouis00cOoo/cskBf3ahaywiLnrWdIeywfbQ+FCPNTtLQmE68EkTdgfpILJ3A2VnmTd26zYKxGeoO6jUIXuE0bTJRXELZLOqbTVw1CNQzY4p4lLKgYItYKO+SudcKnhE9TrCvdpG08m8kSCABHtpdW7bjjbFHkFaQztUoB0svD2bYq7Rd6jmaGxnv8VOtfucUhWmiwS1jS6Jo1tJRdh2twlngWS+zPa3CVgF3+DPHvLAhqn4cSnvgR21iLwTBo8nQRPJXINyoA0KN1gjVOh/cXV0Nqvh7ZyI7TGNromsV4iVAxGSSk+GScPOZ2ke8ZeoHFoM+cDy9bSdRqMERtxeTu02Qsru2ZZ4JZJ449HyPB1HQM/eRi84yD44f3gH7wJ/t4u8H3/D9abv4e1+//CkkXckxCjGpp78skncdddd+HWW28FAOzbtw9z5szB7NnUqri9vR3bt2/H/PnzkUgkcO655wIArrjiCjzwwAO46qqr8MYbb+Chhx6yX//GN76BW265BS+//DIef5xc8/Xr1+Puu+9GOp2Gz+fLHcgwEBTCpZqYwLydPw3BZBs+0yan0VuojkJx2TkRM02TX6Eq9Td/79C5883PMsdgujyFTIpWhKKtQcYXgL+hhSYaK+NMRtn5AwYAVq5iQDbyMdyKf4EmyGANTfB9p2l/IyFkOpJiqJk05e3QTOe0YQqiLXOgr7kiV3C2vwt2LVjDFKCvC7aShPT+7DFa3tyelaF80UCEvl/bJHo4CQ8mJYyOiylXFLI42n0ufH4qfF2wlDrGmoIwUVNH+w832GE/tupqR8lj7+/BX34C5lsvgp33ZWifWCwOoUAeBdwOd7Eps8Au3mx77ckDB8R9CJR/rxQ5zI8PAvv/QAa8rglY/MUcA8MtE2zqHAoJZlJAOgVkUuAdB+3f5c/W48dgxQ7Ta/J1+ZmM97NDVn1fc+2wj/tMxKgaomwvpbOzE62trfbfbW1tOHXqVM7rra2tOHXqFHp7e1FbWwvDMDyvZ2/LMAzU1taip6cHU6dOHbHxGxrlhJjbCMm2zZoQBh3oJXFPuRodRnguRzl7oM8bVpMTR7TPEadMxAHNoJYBDVNo5acZrjoiwPtQZ63O3e0e+ruAQBhdMxZj9pXX0zjuv94J32i6oxJtmYBh5LYxsPdRhN2WD/lqcAajjmI4G+bE5BEMHUGYGRKOZRqgGajrPgJgqTeH4Q96jy3WL8Yj83aWI4Wja/nH6VaNiEdErsh0amgAr3RP0Ryd+z1GNT61jWRYEwPA8nZg3yu0Pc0Azr0Y2nlfdhkUDvPQO8Abz1NurK4JSMbB/7AVZjwKbcY82jS3/8uLbEMRqpsNIL8nYh05AOx/le79mjpg3hKwlhm2LiP9TFEL9nSSCB4nDjmsy84I8J8fwmKMFjXyPFdgMKYAQInOJiWhG+R9GgEy/u7ffcMoJD7DMaZkBZ7nwWCMVfx6IeR0xyyC/fv3l/1ZibPfeQa+VAZ+MwMOKjhknINHe5EMNkCPHcOBvXsLfr+u+wjaOt7GOYMR9L3zDDpnnQsAaOt4G/7BCFKhenTOOhfRljn49MkjMGxviIGDgTMNjJvglolMfy8AwJccAOJRcE1HJjEIP+fgZkaQiJ3zR79p4BqD5mKRWZx74rNJzY+WUwfx/gu/AQDMkyv1HOYZh2Wajqwc3CYu/+TjmvrE3+5C3DwBLPf1zzJSFZuToRbUuvZXLMCW1gOwYgOY9edX8T6AaMsc1M2+AG0dbyPQ0wkf6Hg5GJDJUC4JHFwzkAw1QrMy0DJJ6GaazgrTocnrzy1Y/V3UeM80ActCyl8Lf2YA4Ba4piEd6QfjHB0zl2HWn/8AFqiDz0zBLowV7chNI0DqR7DAGUM6UAdL9wOWBcY06H2nYfX1ALoflhGEZqbB3noJp7p6EWuYDnAOzjlm/+k/4UsNwnLlEDUzg/QfnsGxBRdBMzN0TFbG/p25XgvGulHXewxcGAo2GMWsU0cQ73ibjt39XTMN5r7ig/1AV8fQlhScgySvin/MYhos3Qeu6bA0UkOhnwb9FL/zPK/Jz/Ps16RKewEwTcOigu9ObIypIZo6dSq6upx8RmdnJ9ra2nJeP336NNra2tDc3IyBgQGYpgld1+3XAfKmurq6MG3aNGQyGQwMDKCxsbHssSxevBiBQGUK2eZbT5JGXGbQWY0CgGUhHDCA5im5bDgB66N94G/vBnQDcSOAOs1E3YevAuAUgmpoQiCTQt3Hu4E6NzMKADjlbTSdEvTT58FYtpY05zSZbLagm0nAFwCzq/ydR5UFamgVGG6gSnwBb8aJIRjww4x04+z3tpfM7ejgtJLnALNMlHq6GRjQ2EqrWnCwpqlCmTle0cq0lFEYMphGK1RfwOlIqxtAOiX2V9jD8DMO1DcgEenH2f2HoK+5AlQ4KsJ0vafABqPOfuSpqmlAON5Pv4frhZI5yAPUDTs/o2sMCDcSHVv3Idg0lX5PJoBACHpjG/Cp87Fg+tmwBo+R7I2pe8NwQpxXr28B+jrBMikEGjSAD9CxZVLEovQHHYo454CVweyjbwAz5wNpQSCJ9ZCBS3MAjqcUSEax8K2tQ74EwcH+yr6g+5xrZvgoPOkLAJ1HKQ/KNCrYlRR2zqkmi5tAoAZs+XrxXb/wUMhLye6mDADv7n8Xn1786SEfW2mMAQlonGJMDdFnP/tZfPTRRzhy5AhmzZqF3/3ud7jyyisxc+ZMBAIB7N27F0uXLsXTTz+Niy66CD6fD8uWLcO2bdvQ3t5uvw4Aq1atwtNPP43rr78e27Ztw7Jly0Y8P5QDSTkN11NXTvmAiyZtxRg/nsK5lEk/+3vo3qtrpg9JKuubvydRU0kflZCTyuAA5ansAk7ToWkznbqIMiYozKLosG22UyX/zIP5PQRNA6I9YLLYDkW8CJsWbtJkUNdIr8uuq/nPAu2jtt5OcJu7niWB07FAqXDVrE/m1qbExMRYNPnvhMfy1sIM9FH4KlhDHWilTJPho+vc0EKTZTLu5Pu4RXk8btE5tjiFzTSNJtiu42Qwps8Dq2uivMaH+2C9v4dCx/Z18HrGGgCcdsk7uftRSQzmU9sYBD54s/A5KAdyYSOvg33emIgWko+M+eeBTZlpGwe+8zlSkdBc0kcAkExCu/qWvLuynv8FFWkbfioC1gX5R9eJHco5kEyATZs7vGNSGBGMqSEKBAK45557cOONNyKZTGLVqlW49NJLAQD33nsv7rzzTsRiMSxatAibN28GANx11124/fbb8cgjj2D69On4p3/6JwDA97//fdx+++1Yt24d6urqcO+99476+O24v25QwljK7TS3ga28sqCGFlu2NrdOKBEXDdNAdNGwqFwX7Cs0TKEJPtbnleGpn0L77DkpXhCTjKSUZlLA7IVE4YVg0b35e6DjICVgiyXr5Sq4nKCH3I7MUUR7HJHWYmGwWAQ47xI72Y13Xiq9r5FCsWOva3a01/p7HOPOQCw4uaK2WWTunwwwgoA/DD1oArWNsA7vp+uYToo25n0iiZ325FoQEXVbQyFRxAH0nSxxtbzvllxzy0meZU36uo8MtfQ64lHg2AdOOYAs5Fz8RbCZ84Vn4Qd/6VdAIppbeBofIEKEiypumSZ0BuDIe+AnPrIJBmhqE0bF591GfXPh41j8ReCPzwFIOUXNDECwlt4307R9hXEBxvMlYiYwkskk9u/fP6TQHFBaINEj1SMf2MEBR7alpgGJRBLBTMzp46MbALgoXtQd6Rj58PacdPS0aurp/XQid3ASU2YR+2n/H4h5VzYYoOsix1QGfEFaqcajTiJdM7wMsLy7YUDLTJqoB3orzt8UD80xl8FgrlW4Zr/tWZnLLdU2AnM+Dbz/BjGfpHFwFTTar9nFwXI0VYBUbw+E7UnfDi2lEkDPCeERa3SNuAVk0rC4BY0Jr8AfpO2YGSBUD/aVb4F3fEATuK47bD7TzEtpLoeVZj15L9WBuXO7nNM9nIyLnAl5bZZlQpMlBc1TnX3PPw/485tljSnv+PpOEbMwGCaqu/z+Besromm/994BLFp0jvsilP5Sacvv/b22EcwY5cjOOIRSVqgQbmZbPnhCcMlBh/UlPYVoD3xyJvUkLhmFgWrqgfMu8fSQsUNw/oCgCxcDI4mbPz6Xy0LL+/EsanYltFN/QLSw1gDozmofglGYvR9pJADyBCSVXNNdq2/m+rw4nuyHOR8d3GMgXB6HJSSNSq23EgOe3NnIgNEqXrbxGCo0w+l7FAiJ3kaZvJOp9fFBuvahGjI0gwNkmJragKVrkXllK/x19V7DoOlArB+MaWCzF9KVe/c18uLqmoDFF0Kb9cncYc1eCNj7LjDjNk+jcyu7xDKQhz9tLhnKgV6hmmGAm8Jr0X2Ux2GMFgXRbuAvrgYO7BbaeNOAc1ZAm7Ega9d0r1jH/+x8tmEKsHw9vX1gl92KAou+AG3mgqzBsqztyZfpBaumgSIhOfcoivydvU0m3pq8+aB8UIZopOEOwcVEIaKk3ta3ANE+aFYK0ANUgyQ/l0kDGrdzJ9a0Tziely/oGDa5PTfjLHtVXlDoU07qLoPAmCgcDdOkJV+Tn3VX2LtDNf6wEFvVnZi/ZWZpuwmvwa3FJQ2FZdE2ZQ7MI0FUHKP3COc5lyW/Is4Pt8AuvxEffHgECz79afJOdB95GNJDdksuebbhOt9yHO7r1NgKOxdoWYLcIcgy774GzFnkfOfQ2+TdSW+6eZrQIqwDQrXQauqoI61hkHcQCNK9N6WWatcAaAs/Tz2OPGNxj9V9vvK87r63zr8UeHUrXVfDJ4qQOdiyNQA4+CtPkadj+MBOd9AxBWscTURwoOck9E9+Hvjk50teDuujfcDLT4ht+oHTR4GTH9Jzte76kt8vhpTJwXyVR1EUSkMZopFG01RvwtsIABCFjCkhp6NrQE0T1UMwRqvOTAoINYB94jPg3AKbsxhszmIAHNbR94DXnqbnUtMBxp2JXJc5GbfhEAZKTggcsJlNkojg/hkIizCJ7kx2xcJQHMNrdzBMlM2aYwzknXFxnsRrDFT/IrXFwBzv1O2ZSWNgb4s53wdznWsA0MCZBrN1FtW3SJ7d0Xfp+rrPqedIkHVus35e9m3on1gM83//mP6OuxY3VgboPQXedwraHCokRaSbvCYr42yXc+DUYaDnOKzBOGAK9Y7+TspNajrY0jVgwZoR782jz1kMy7K821y+AdocYp9ZK533LMuCFgwJDUUR3k0L0d8yQREJXbUQP8MwaQ0Rz6TANeSuwD1x/6xJQb6Wb+KQLy1ZBezZ7kj3m67QkGh0Zlkm9GTcCbXImPVnzgXv+thbpJdOg2WS4NPmAgf+KDwn4cUISi6JV0Is6IeQs0h420dULWhgGwFhIDQ58Qs5IqmVxzSvx+DJBwmvhovrFKolmnMm7XpdXjvLuZayi6h78nZLIsnf/UEimmTfB8Ea4KXHYcy+ALzBRxNf9/Fh9ydiMqQXDFP7d7mokeNnGvjrzwPSEIXrvAuhRJxCVJk0oPtg+UJ0X8YjdN+lEmBf+Y6jlDAKvXmKhbM9Tfte+A3O/nh3jop4RW3FVQvxMxKT1hAhHgVSQ5Pa45ZJD7Zb0kP8ZJkUeNN04OM/EdNHtoh2wd5rvB+exPkLvywvMGQhf0O7oebNs5huhT0OVsDYMdvI2p+xDYbLSLi9DZuKK4yHO6+TTQywvQmTzieHk3PxfI87J4FpZMii3ZR3G4igZBVjSTDKiWUfv+y9k05i+kd/BP/wVTJ+ZmUkjBxwTjT9uUuoI+nHf3Le04TEUk29Z5L1KDqYGdHiAZDdV/3JKBCcQiE7zoFE3DYyY9WbpxCiLXPAFswfnkc2Uu0hFMYUk9YQ8Y//BJ5J2NIg3G1UMikg0kMJ9UyaJk3DLzyQYehICXgm+ZxeJUW+5ROrxIq8HjHhB8KOWrPsxukPAl+8AtrshbCe/Cklj4tRlGU4SsvnfQjvoqbBIVRYrjYDVjrLqHCHkTZazDPNAEIiuT+E9I93W6LAMR/DL50gzyMQQrC3k85VKf09AE7RcZGBdR+nvMd7r5Fn6G4FH64XTMxGZ5hSs/DVrVQ/A+7QnqWXKFs6ZE/Q48CbKEUGKoWRaA+hMPaYvIZo17PgyWh5H5YeUCnoQsLDdFWz52icMVgQKQowoK4F7LyL7epw3n0cePM/RShFJpRDTu1FvezPI3XG0rmeh/Q2hOoBdIMS2qeOiFAeF7kqHXh/L6x3XxNtJuA1EJw72nLZXkq2aCZAE7/P5xhrd1uL0YamOZM/00gv7d3XaGLtPSXeB4bEXpMGo9B3LUvov+kUSmOio24p68c5FbNGegpvm3PHU2lo8bImUwnA588/yaYTYsFgONeUQUgMpfO3b2iYQufKbr9t0H3R5NVvHOk80kgiRzx4nI1PIT8mrSFCIAQYorqdWxRHl+GlVBIAF2w1H+zQkT8M9vm1jiSIu3bD5wc/9menBkM2GgOISisZacQ4oH2F62kymPsZAIJ6+6fXHQ9GY473UttMYan6KfR9KV+SXSApf3fXv6TSwPt7yAOqb7JDMug7SdtunkbjTcaHd05lcevcJd6mbGMBtwdS00AehBSGNTMu72MIYKx0rZMQ2rTDmmU1BhTXQdO9auhuaBrQdcw5DrdBdbEsPVuVhkv3OQsWDQBnRHSR9V6aAf5fj8Ns2EEGadZCoON9Z0FjpoFYCvjMRfa28+aRdvwbzHCDXYg92hN/KUM4XK9KYewxaQ2RdvkN0AwKt1jP/4IePlnr0HNKLGZNICSqt6UkyOxPFdwm3/8HosUaAQARMljyoQ7XQ7au5rqPqNuaTtpvQdGG+eh7VC2uaWR0GMiYDQ7Q3+kkcOKwE55hzOt1SC/I44kIT4lbtEr2+ckIy0JSy6RVsGThDSd+1dRGhbTvvDj0bYwEBnqzGIMcReWKisFNWS/1ufpm8P5ul+RSGUjGqQttvD9/yLemUUg9CS9bGsRAGGg7K/+EL0NsbikqsZjKBGqgf241GeosUgJ8Qco5ZXtEHQftTefkkdwt7VumjQjBoZihGS1ChUJ1MWkNkQfRXqqnkJCUaA6xcmT0YDZPc+VNXMl4mZjXDaBpOiALQ2UhqplxciccSOoBhDNpIBMDO/8rYCEhO3LysKODNdhP3wUTVFyOnFYJ2aKkeeV1uHdlLmub7FCjWO0PM+8FplEY8fVtWYKtVYK7zUHZyGOIyzVEnAHxAZj+EHR3m/KSoUDBnBwsECaO9zvtud1jTMbJg0HuxG17gjLfIxly/iA65q/E2R0H85IS0HsKaJlO96p9XNybIyrW0l6qPWDoBIdShqbahAqF0cEkNkSyol8Dps4BkgPCg9GAUL2zqg7XCQ+DAZ+/FKyxSL8jTaPaDF+AJvdYn2PMAkEKXVgZhAYjtJ+F53tXcZLxkz2BSCNkh2VkgluED800zU9Wdh5DTqCuyTWTpjbW7jEXgie85PKs8oFbNHabgDBOwEDnH6y09FA+b9ASRIuSsIB0Eoa8Z5KDNKE3TCGv5uDuAueF0wIl70KAOW3BZc2SW+Ov42DeidumlwNCiUGI8n5pE/DBn4Hjh+h66YZX4xCge70Y48zNSkvEHTFYptHfjNH91dsJ86mfVhymK2loxgGhQmHkMTT+8gQAq28Ga2gFq28BO/cvKPw10EeTf7yPGGS6IRLdOtgXLocuuk8W3Ka73XcgRJXqTGh9xQccoyJx8HUSJS30/bomUubWdK/XZfgEMcKAfuNDYJffAMxYQJ+Xk0u+1X2ghsaSScI2UtnNwTQdXL4nDWBtIyXKi/Z7Yo4U/5hVIpXjqYisDbeGSOGtxKhyIiskY8CMs6F/+yd0TY++67DusiHbFDAIdh/zbM/5lYuWB37nHujv8k7c0iMJhinkW9PoaecNALP+/Cqca29S6C4RJ4PTNNXbrj5GBgXHPoB5//UwH7uLvDAzQ++5iRNMUOX7u8gjNnyON1OqY6wb/V2OUZRwG5qGKbkkGEXPPuMxiT0iB9onPgNrFffGpUU75Iq2k83YaZoGdolovfDbB8SnJOVZFCa++XtgxYb83xfxcf7sw+LzcPIEwqCZj93lJIkv3kTfjfXRxBCLwJ7MdAMIhoBFF1L4THo2eZLwniZkNfU0AUa7S1CSOYmzysLRMUEZzDT5Od1wJmwpamrr7A2z3icbpum03Hh1q+MpZMNNCTcCnuua9xgyKad+KBCiybeQh5CIQ9/8Y++wHrsLRirmEFsku1JoHLJVV9Oe9uwggkRCtIuQnnDPCVJL/+xqum85yODInJgk52g6LYaGEjYrUQek6NkTE8oQCZRi2pRLWc23HfOpnyLvZMktMiKlvt80lSYBSS5wo+cEeU0y4bzoQkpEB0L0oEZ6iNxgWVTEW0oM1TLBwcCkZlxc5C7KqYsp1rNnBGEz0+y/SsAUObZEDFh2KU2mcpIdaSOUjd5TrrxiFpNOkgHSKSKRFMoTuWFRQzfoBk3Ke3aUTblG93HquG4YjpQTA6AxL/tu7hK6Z0986CyYGGj78SgtZABiYAZraN+xiONZ1zY7xrHCsFkpQ6Po2RMTyhCVgWIJVMD1UPgF4SGbxtrfVZTOa320r+iDxFZeSX1y4lkTlZzc4lGSzQeAjoNgX9rkrGoZAIikeUlDQd4FA3cMz2hP1DKUZ4dbSns4Qwr8WRbR8j/YC7uOqpzasKFA1x0vwM10yx55QAjNarpDIikETXeRFrgdakPfacpnys9kUa7tBdTxQwC4c2al4jk0YMa83Puvv8uhfgMOaw+A3dgv2kvHJI1Onyh7kLJGwXDFYbNyDI2iZ088KENUBgomUF/dSpRo3aCHuucEvV/X5DVWDVOI5ZR0JZEldAP82YdhBkJAywz7octhQn12tfBmslldonYnEacJQNayLFtL40vGKyAPuD4nwzbDZdMVBfMaA5mQ10W753I8hLJ2ozlU954TwJSZdK5GoeCWvDUN6HifwqYeT9J1fplGRihE3k3RolZpDFyyPABocRSPwpPz0X1AuCaXyGCRZ6NxwZC0w4IFwloNU+gcuajfYjC0eKipJ4HVWD8tcNyaeumUIGCkbc+tEoxUdELhzMGkJStUhEIJ1N5TjoGKRx2qb1x0pNQN8D2iWDAYphWwG7qPwmZcrNaF8TJ3PQu+498oNDLQSz/37oBdcKtLyRaLJlMrQ62h0ym7qyt/8XHHMJYTvrIlewRcnTNHD3mo0lJGaaSMEOD1SiyTPMVY/zDZfSzv+WEATcCaRon+fLkhpgEtM4gAUtOQ6+nmG78kimRSgD8Ivu1RINIrJnvdRWARNWvZRAbRz4hn5yhbZuSdxNmytaKhXpaKhsbICAVCFBI2M44R0nRh4IRHnU7lLbgdDmzjKtmlQyFEKIw7KI+oHBRKoAKOgZJV75w7XoSIj3vCDR3vw2I69LpGUajInR4+cvt7tgvKriuZ7g5dafBK2tjgFB6U27EsmqTKARP/edoQuGtxxoCAMNphQDeGSzH3B3Pye97tW05tDQeccyi8i1i/06pa5rCKweLkeUhqdjrhKDLI3JCke8twmJvIUFMvWG5isdTYSrmXlVfm3Z02dwmstVvIq+49Rd/TNSq+DYToQ7rQK0wlvfeZXDAFwrndi4fpyag6ookJZYjKQKEEKpqmOnUXuuEUcuritLri49rcJVQC1HkULBmnsIYMd7i9HMNPE45uOAWR7gnaNkDZE5corJUtxA1/0bxUDixLNC6DM4nqBu1mPBSojiTKIV6UgixaLYTsa2e4vFi4Fhf2vVMsfye2VdNINVFWhrxfy7RrhGCK3zXx96yFQOdRyuPIUFpdM3ikm8ZW05irWJDPSGQrGsjFlnwGdAPQTfLs3U0UzbQnN2R9tI/ynMmEqLGLgO/4d1hrr6vMGKk6ogkJZYgKICdHs+hCkjpxPagAHAMVrnPqKsJ1OaKS9oMM4Xy48wGWSaoEgFM8y7koUs3yEmSS2RTdUN1hE7c3lklR+CfeTzVMZYXndKCmFlY8Ar2xlfYR6Sn9vTMOo+3dSZac5d2VvOa1Dfb9AYDul/5ihsgincNla8H/63GvfA+Yo6hhWSSzs2ApMSd9YlFjpmnhE65Hxl8D47Lrcj2VErI5BUsLJGtvMOqE+4Twqzs3RDT2GN27uk6fScScNheFjryQaoRq8zChoAxRHuStVn/vtbzxbs/D2TydXkwlclac/NWtoiV4ngQ502ilGBCTk6Bj560rkclj2czNVlwQn2UG7WdwgB7amgYgHkNZk28mDfj8SPtC0PtOe1lTEwauENmoGSSey8gzM7Ryr6mn61U/w5nI+7tIbzCrQaEHPj/dk275nnTKMQCAw9p843laRBg+2m46QcYonUTHgtX4ZCGR1BLhLukhSePA/0uMxzKpeDsRd0LU53/F+6z0nqKfdv8tjbwo+XoelFSNUHVEEwbKEAl4Vl7JuKhQr6E3i8Shy6GSyhqOHGUCuXrkFqkdRHtpX+F6mmDyGa1sFhvPXnWbXjZWzwmgnIZwjFF9CbitzEyDH8O8zVhAhpaG0g6iImQbOU6ej6bZE6f73jGf+ikpX+cbl+6jBYWk35sZysskBpyFCtMoXNh9DLYihpUBkmJh4w8CA31o63gb5r/uzi0vKCPcZX20jxZU3cdFH6R6Ebbl9KxoevHcTzZxo4SGXyEDSX2mahVrbgJBGSLkWXkN9Ih2xT4nMTuMODTfs0NM7DJ5LUNpwruRletmRqh0Z5z2D0UnzaxVvTRsoRoK0wyUOV5Np8S5L0j7jg8KIzgMj0EzaPKUnl3O0MsRBB0FjGb9UL59SXo9tyh0O3NB/omz61jh7dQ20k+hmMAu3kSsOYjwLRO5IfdxuUVyYxF6LzkIH9eAuvrc8oISXU3tZ2TAJXI60CuKV2uAUG2OkoMHsigbLo+ec6KkF0IhAznQ54SyFSYEJlrcZUjI0euSxIGBXmoJ0fkx/RvohfnUTyunivZ3UV4AyA11uYVELcuZEDhoTG2zAS1Lv83dHdW9HelxDUS8dR2lYJnkjfWcABJxaic9XBg+oTZu5Q/vVcMIARTO4qIgU55T2X0WKKwJNxTI49YNmw6vX3VL/tW7lMmRNWnORmgyTsRJQinWLxY2OjBlFmwVeACQQriSQi0FU9MJupclO09q0rnLC9wac3ma5tnPiDx38h6MR8papLGVV9Jx2DkkDQiGC7L2AOTXlYtHhZJDn6JvTyAoQwTk1gmF6wUzKE0PsexOKnr3VHzjN0yhBG1dM6D7HFFRIGtC5vbK1Sv9z506Efs72bkjF1GhnFBcNrg4vniEfi+X9l0I0qMakzBYJRDnzTKFNyE9VDdtfYSQSTut593KBPmgu4ITus8xiAyU5I/20DZqG2kSTiVEV1jDGbtsGyJp/zn74/An+un+AjzlBexLm3JEUj0GUz4j0hjJ/bnp4kWgzV0CtnYLMH0eUNsETJ8HtnZLcUWRfAZShp3dIq/CoI4FrI/2wXzqpzD/9bahLUoV8kKF5oDc0EQwXHiFlxgA6lsqqluw6d+6UQZDCrTvQAhIW/TwabrTk6j4njDkcJqLBMHAc3NRZYGhIsp4tZFvnBUZzVLnO+s9y4S561noQuTWg5YZlLhPDDghNqbRAmSgn8JvdY1OqDhU60zK8aigT8v9WAWPgwFAXyf9oek2waZkrlM+I7IeyV16UCZZoFJpnnxMPQzGhLq8C2NE367rPgL+9m7VlG8UoAwRCtQJyRCHRyeMU6fUCm98zwN1/BC4plNcPd6f/wvcolXrwvMdplDSKqOeZ7gGYAS+z8XPiUZyyAe7C2wF523XMzD3/wHsy9+gr7p1CtNJIcXDHFp+Jk33W1ObN2QXrqNz3DSNGHKW6bQA6essT75IqEwUNI7uQ3UvpmqbyDhaGaBlGtjKK0dtIs42XuZTPy2ZzxottHW8rYppRwnKEKHAyqvvNL2ZHboBB7pP0sSAylW5zX+9DUkTCNfWAKl44cQ5Y0BXh50ANnc9C+x6ZoSPfDRwhnhDI4Ghhhyj3eD/9+dkYIJhuta9nYCZ1eJd5vyklpu7c6qoE9OvuiVn8+bPbq5AR48Du56FCRQ0RvY9nkoCVpwm4xnzqsJWq2YbCP9gBGho8r6oimlHBJPWEJnb/hesc1d5CvY8K6/H7nKosNngJhDvJ+Pw3muVueoNU6B1naTfdV9hQ8Q50NVB44iLuiAATvxlEk34ExHpJMnz+IOkE5gNTXNYk4xTSAqgnzJ3mE57lNttg1FKuy4HHHh9G6xpn8itk3MzSuuaPBN/NcJR1WwDkQrVI6CKaUcFk9YQIR4pbjQWLCVDlA1Nkg50ag5WU1+Rq86WrQV7/t+JUpuMFx6fXG33nhBkKLn6VgZoQkCSQyIFVtOSvi+JKkwjAyNDnppOC58d/wZr7RbapDQYQ7lHLCvvfTsetd2q1Qaic9a5qPt4N/2himlHFJPXEPn8QNoo/EB1HKRQSHKQVq+S4iu9H87tvkMeFHDV3SE8U/cVzg9lw7TI6NkhwjESIFUYPWi6Q4M2i4TQMmm63KFaop0zHdBdqt9Ctd1mjNklCEOo0dINoPs45WDcMlaVFLtO8NYM0ZY5YAvmj8pxus+f/u2fjMBozyxMXkMEFI/v9ndR4remgWqJrAxsuipgy/GXo3uVXTCr93VXJiTKNArPcBO5JIqRwnANnDKQZUHSsn0Buv/ixXI5HLZAqm7kqlyD0XvyHs42GJXAMPLW55Rzj5ejVTdRMBreWM75m4SY3HVExeK77mK6GkEXlYrUUrDyvEtKFgICuQWzRrFVcDaMrCJCJiZ8X8CR4ZG06VJoaAWmzqFalIAQzpQFkZXWDRm+LMmirFoWD2TivYwxjhaYRorUttaZKFxunkYMtLEcR/N0sHV/Dbb2uuLXTUothRuI3i09KRvcltXx3K+Gqw4JgN2WgWng0HKLdnWdPK489TkAyi92rVJtz5mOnPM3CTF5PaJ08fiuh53jD5Ko4+CAEK50BE2taZ8o7apnhTeYjP+X8moCYdF0LyLqRBgZD92wW0XbKylTroyLeCWBkN1igl3yP5xOsLLnjDB0KSOIgKF7G54BNN5AmAyz4aecRTziHIdtKEH09HA9FWPKJneG5gipnv8VEuf0tAgHjZ9pRF3v6qBxWcITHE590vL2HFaYHQ7JZIA6P9XwlGynngeyjXeoDoMmRzjeJ7zXLKy4PGcM5vINwB+fzT22cAPVDYmOrHa7+ETMuRc4BwKBAkrwvXCKW8W5DYSoFEDcQ4hFRDuT6dQfKV99jpQUKnaPq9YMw0O+8zfJcEYboueeew6PPPII0uk0rr32WmzatKn8L4frwb7QXjB0kMPOaZoKdsnmnM+X5apnFcxyubI1fGRA5CqXMcoDMA1oaqPJZ88Omohle+emad4+Mu4xTplJMj1uA6f7hGoETWjZE0n2+K2P9iH10q8RsJLUBE0WWPqDwHmXgLkNrzgnAFwN1ECvC+kW/uLjeZWZ9RUbyIhLQ+pO/hbo6ilrSJLxQQTTsazCTQbM+iRN1D0nnHqclhkF61zyXTvzsbvo+4zlVz/PBwaa5Bum4ETD2Tj7490U5rKVohnwqQvy0qP1FRtgAkR8Scbps+F6MkKA7bVTo7rrvOe52VvDk1cJPtbvhPZaZuCkVocZVpQ+M92hYBerzym72FWxyYaGfOdvkuGMNUSnTp3Cfffdh9/85jfw+/245pprcMEFF2D+/PllfV//ynehBYpf+JGKB2fXPmR0P/T0IHkNus/pY1TXJLybjDPBlNh/PkNSyeSeb3uHetJYunRp4Q/l206BbRej2lZKxbXPI2NZRZWFjU2lsD2PZMLxapgmlAyiuYZJ99lK52zZWkR70hUntPUVG4AVG7zXztV8Tno8pe6Hcu6Xzr17MTvPtR1OfU41a3smAnLO3yTEGWuIdu7cieXLl6OxsREAsHbtWmzfvh033HBDdQeWB9kTbirciMA5X3Ya7RXpYzTcfVWbwTQSk6f7s9aXgPRLv0bQSo5KUaXtebgVDwC6Ni0znd/dr7vPcc/eIS9gqnnthrPv8XbPnWnIOX+TEGesIers7ERra6v9d1tbG/btG78ChO7J6dDevcU9jhHc10RDWR7bCOxjstXIDHffE/meGwtM9vN3xhoinid2zypgnOzfv38kh1Mx9u7dW9X9l4Ia39AxnscGjO/xjeexAaM/vtFcYI1nnLGGaOrUqdizZ4/9d2dnJ9ra2sr+/uLFixEokSMaLewdZY9ouFDjGzrG89iA8T2+8Tw2YPyP70zGGVtH9IUvfAG7du1CT08PBgcH8cILL+Ciiy6q9rAUFBQUFCrEGe0R3Xzzzdi8eTPS6TQ2btyIJUsmb4xVQUFB4UzFGWuIAKC9vR3t7e3VHoaCgoKCwjBwxobmFBQUFBQmBpQhUlBQUFCoKpQhUlBQUFCoKs7oHNFQIOuPUqkKFLBHAcnkEMQ1xxBqfEPHeB4bML7HN57HBozN+Px+f0U1kRMBjOerDJ3AiEajeP/996s9DAUFBYW8qGaNY7Uw6QyRZVmIxWLw+XyTbtWhoKAw/qE8IgUFBQUFhTGGIisoKCgoKFQVyhApKCgoKFQVyhApKCgoKFQVyhApKCgoKFQVyhApKCgoKFQVyhApKCgoKFQVyhApKCgoKFQVk07ipxp48MEH8fzzzwMAVq1ahVtvvRU7d+7EP/zDPyCZTOKyyy7DzTffPG7Gdscdd2Dv3r0IhUIAgBtuuAGXXHJJVcZ3//33Y8eOHWCMYePGjbjuuuvGzbkrNL7xdP4A4Cc/+Ql6e3txzz334MCBA7jzzjsxMDCAZcuW4cc//jEMo3rTgHtsDz74ILZu3Yr6+noAwF/91V9h06ZNVRnX5s2b0d3dbZ+bu+++G0ePHsUjjzyCdDqNa6+9tmpjm5DgCqOK1157jV999dU8mUzyVCrFN2/ezJ977jm+atUqfvToUZ5Op/mWLVv4yy+/PC7G9sILL/D169fzU6dOjfl4srF7925+zTXX8HQ6zQcHB/nq1av5gQMHxsW5KzS+Q4cOjZvzxznnO3fu5BdccAG/7bbbOOecr1u3jr/11lucc87vuOMO/vjjj4+bsf31X/81f/PNN6s2HgnLsviFF17I0+m0/drJkyf56tWreW9vL4/FYry9vZ1/8MEHVRzlxIIKzY0yWltbcfvtt8Pv98Pn8+Hss8/G4cOHMWfOHMyePRuGYaC9vR3bt28fF2M7fvw4jh8/jh/96Edob2/HAw88AMuyxnxsAHD++efjscceg2EY6O7uhmmaiEQi4+LcFRpfIBAYN+evr68P9913H66//noAwLFjx5BIJHDuuecCAK644oqqnbvssQHA/v378eijj6K9vR1333131QRQP/zwQzDG8J3vfAcbNmzAf/zHf2Dnzp1Yvnw5GhsbEQ6HsXbt2qqdu4kIZYhGGQsWLLAf/MOHD2Pbtm1gjKG1tdX+TFtbG06dOjUuxrZy5UosX74cf//3f48nn3wSe/bswa9//esxH5uEz+fDAw88gHXr1mHFihXo7OwcF+eu0PhM0xw35+9v/uZvcPPNN9uhruxz19raWrVzlz22WCyGc845B7fddht++9vfIhKJ4OGHH67K2CKRCFasWIGHHnoIv/zlL/GrX/0Kx48fH1f33USDMkRjhA8++ABbtmzBbbfdhrPOOivn/WqKHLrHNm/ePDz00ENoaWlBKBTCN7/5TbzyyitVGxsA3HTTTdi1axdOnDiBw4cP57xfbYFI9/h27do1Ls7fU089henTp2PFihX2azyPrGQ1zl2+sdXU1ODRRx/FnDlzYBgGtmzZUrX77nOf+xz+8R//EeFwGM3Nzdi4cSMeeOCBnM9V+76bSFBkhTHA3r17cdNNN+EHP/gB1q1bh9dffx1dXV32+52dnWhraxsXYzt48CAOHz6MtWvXAqDJq1rJ7EOHDiGVSuGcc85BKBTCmjVrsH37dui6bn+mmucu3/i2bduGxsbGqp+/bdu24fTp07j88svR39+PeDwOxpjnvjt9+nRVzl2+sd1xxx1YunQpNm7cCKC6992ePXuQTqdtQ8k5x8yZM8fNMzshUdUM1STA8ePH+QUXXMB37txpv5ZIJPhFF13EDx8+zDOZDP/Wt77Ft23bNi7GduDAAX7RRRfxvr4+nkql+JYtW/hzzz035mPjnPOXX36ZX3HFFTyZTPJkMsmvu+46/rvf/W5cnLtC4/uXf/mXcXP+JLZu3eohK+zZs4dzzvkPf/hD/uijj1ZzaPbYuru7+fnnn8+PHj3KLcvid9xxB//Zz35WlTG9+OKL/Ktf/SpPJBI8Go3y9vZ2vmfPHr569Wre3d3N4/E437BhA3/nnXeqMr6JCOURjTJ+8YtfIJlM4p577rFfu+aaa3DPPffgxhtvRDKZxKpVq3DppZeOm7F997vfxde+9jVkMhmsWbMG69evH/OxAUQnf+edd/DVr34Vuq5jzZo1WLduHZqbm6t+7gqN74YbbkBTU9O4OH/5cO+99+LOO+9ELBbDokWLsHnz5moPCQDQ3NyMu+++G9/73veQTqdx3nnn4brrrqvKWFavXm1fV8uy8PWvfx1Lly7FzTffjM2bNyOdTmPjxo1YsmRJVcY3EaH6ESkoKCgoVBWKrKCgoKCgUFUoQ6SgoKCgUFUoQ6SgoKCgUFUoQ6SgoKCgUFUoQ6SgoKCgUFUoQ6SgUAS7d+8eFv36/vvvx9NPPz1yA1JQmIBQdUQKCqOI73//+9UegoLCuIcyRAoKJRCPx3HTTTfhyJEjqK+vx913342f//znCAQC+O///m90dXXhsssuQ3NzM1566SWcPn0af/u3f4sVK1bg9ttvx4IFC/Ctb32r2oehoDBuoUJzCgolcOLECVx77bV45plnsH79etx6660AgAMHDuCJJ57A1q1b8ctf/hLhcBi/+tWvsHnzZjz66KNVHrWCwpkDZYgUFEpg4cKFOO+88wAAf/mXf4n9+/cjGo1i9erV8Pl8aG1tRTgcxsqVKwEAZ511Fvr6+qo4YgWFMwvKECkolICmeR8TxhgMw4Df7/e8Xs2W2woKZzKUIVJQKIGDBw/iwIEDAIAnnngCS5cuRSgUqvKoFBQmDpQhUlAogXnz5uHBBx/Ehg0b8OKLL3rUyhUUFIYPpb6toKCgoFBVKI9IQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGqUIZIQUFBQaGq+P/YY0+Dxhui6gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 416.6x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 416.6x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rF_MQI1PZEE0"
      },
      "source": [
        "<font size='2' font>Fumar tiene el mayor impacto en los costos médicos, aunque los costos aumentan con la edad, el índice de masa corporal y los niños. Además, las personas que tienen hijos generalmente fuman menos, lo que también muestran las siguientes gráfico de violín"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxtbIKmfZEE1",
        "outputId": "df602da3-82ae-4ee7-cf51-02bd00e96f17"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
        "ax = sns.violinplot(x = 'children', y = 'charges', data=df,\n",
        "                 orient='v', hue='smoker', palette='inferno')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTxnCHZfNwfQ"
      },
      "source": [
        "df = pd.read_csv(\"insurance.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-nT440hZEE3",
        "outputId": "cde1a947-6178-4f57-ec8a-0d3055b5fab1"
      },
      "source": [
        "##conversión de variables categoricas en etiquetas\n",
        "\n",
        "df[['sex', 'smoker', 'region']] = df[['sex', 'smoker', 'region']].astype('category')\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age            int64\n",
              "sex         category\n",
              "bmi          float64\n",
              "children       int64\n",
              "smoker      category\n",
              "region      category\n",
              "charges      float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r4B7DheIeSe"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\"> Conversión de datos categóricos en números</h1></center>\n",
        "\n",
        "Los algoritmos de aprendizaje automático, en su mayor parte, solo pueden funcionar con números. Por lo tanto, es importante convertir los datos categóricos a un formato numérico.\n",
        "\n",
        "En este sentido, el primer paso es crear un conjunto de datos de todos los valores numéricos. Para hacerlo, elimine las columnas categóricas del conjunto de datos, como se muestra a continuación."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IPRHOiyZEE4",
        "outputId": "6b5601a8-13b0-49ac-a606-8c1aba976c69"
      },
      "source": [
        "##convertir las variables categorias en numérica usando LabelEncoder\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "label = LabelEncoder()\n",
        "label.fit(df.sex.drop_duplicates())\n",
        "df.sex = label.transform(df.sex) # 0:female, 1:male\n",
        "label.fit(df.smoker.drop_duplicates())\n",
        "df.smoker = label.transform(df.smoker) #1 si fuma, 0: no fuma\n",
        "label.fit(df.region.drop_duplicates())\n",
        "df.region = label.transform(df.region)\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex           int32\n",
              "bmi         float64\n",
              "children      int64\n",
              "smoker        int32\n",
              "region        int32\n",
              "charges     float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "b0uEeEe8IeSg",
        "outputId": "c0bb47d4-750d-49f2-cf52-1d2dd2373a78"
      },
      "source": [
        "#Visualicemos nuestro conjunto de datos\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>charges</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.900</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16884.92400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.770</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1725.55230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.000</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4449.46200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.705</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21984.47061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.880</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3866.85520</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex     bmi  children  smoker  region      charges\n",
              "0   19    0  27.900         0       1       3  16884.92400\n",
              "1   18    1  33.770         1       0       2   1725.55230\n",
              "2   28    1  33.000         3       0       2   4449.46200\n",
              "3   33    1  22.705         0       0       1  21984.47061\n",
              "4   32    1  28.880         0       0       1   3866.85520"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aoa9WFq5NwfU"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\"> División de datos en funciones y etiquetas</h1></center>\n",
        "\n",
        "Como primer paso, dividimos los datos en conjuntos de características y etiquetas. Nuestro conjunto de etiquetas consta de valores de la columna \"propina\", mientras que el conjunto de características consta de valores de las columnas restantes. El siguiente script divide los datos en conjuntos de características y etiquetas."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YhCAUVDYNwfV"
      },
      "source": [
        "x = df.drop(['charges'], axis = 1)\n",
        "y = df['charges']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rTJCRsjRIeSi"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\"> Divida los datos en conjuntos de prueba y entrenamiento </h1></center>\n",
        "\n",
        "Después de entrenar un algoritmo de aprendizaje automático, debe evaluarlo para ver qué tan bien funciona con datos invisibles. Por lo tanto, dividimos el conjunto de datos en dos conjuntos, es decir, un conjunto de entrenamiento y un conjunto de prueba. El conjunto de datos se entrena a través del conjunto de entrenamiento y se evalúa en el conjunto de prueba. Para dividir los datos en conjuntos de entrenamiento y prueba, puede usar la función train_test_split () de la biblioteca Sklearn, como se muestra a continuación. El seguimiento El script divide los datos en un conjunto de entrenamiento del 80 por ciento y un conjunto de prueba del 20 por ciento."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qrZOmKbIeSj"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8MH8JYSxIeSk"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\"> Escalado / normalización de datos </h1></center>\n",
        "\n",
        "\n",
        "El paso final (opcional) antes de que los datos se pasen a los algoritmos de aprendizaje automático es escalar los datos. Puede ver que algunas columnas del conjunto de datos contienen valores pequeños, mientras que otras contienen valores muy grandes. Es mejor convertir todos los valores a una escala uniforme. Para hacerlo, puede usar la función `StandardScaler ()` del módulo `sklearn.preprocessing`, como se muestra a continuación:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vx6c5SBWIeSl"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "#scaling the training set\n",
        "x_train = sc.fit_transform(x_train)\n",
        "#scaling the test set\n",
        "x_test = sc.transform (x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-5Ua7_9IeSl"
      },
      "source": [
        "Hemos convertido los datos a un formato que se puede utilizar para entrenar algoritmos de aprendizaje automático para la regresión desde la biblioteca de Sklearn. Los detalles, incluidas las funcionalidades y el uso de todos los algoritmos de aprendizaje automático, están disponibles en este enlace. Puede verificar todos los algoritmos de regresión yendo a ese enlace."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 596
        },
        "id": "8ezhNp0xZEE6",
        "outputId": "2fab22d3-85bc-4af4-b5f7-0801821bc0b6"
      },
      "source": [
        "f, ax = plt.subplots(1, 1, figsize=(10, 10))\n",
        "ax = sns.heatmap(df.corr(), annot=True, cmap='cool')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KpmWJ2B2ZEE9"
      },
      "source": [
        "Sin correlación, excepto con el tabaquismo."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1lJmK9nLZEE9"
      },
      "source": [
        "<center><h1 style=\"font-size:180%; font-family:cursive; background:teal; color:black; border-radius:10px 10px; padding:10px;\">Modelación. Random Forest </h1></center>\n",
        "\n",
        " \n",
        "Un bosque aleatorio es una técnica de aprendizaje automático que se utiliza para resolver problemas de regresión y clasificación. Utiliza el aprendizaje en conjunto, que es una técnica que combina muchos clasificadores para proporcionar soluciones a problemas complejos.\n",
        "\n",
        "Un algoritmo de bosque aleatorio consta de muchos árboles de decisión. El 'bosque' generado por el algoritmo de bosque aleatorio se entrena a través de bagging o de bootstrap. Bagging es un meta-algoritmo de conjunto que mejora la precisión de los algoritmos de aprendizaje automático."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mQ-7nE9LXOuP"
      },
      "source": [
        "El algoritmo (bosque aleatorio) establece el resultado en función de las predicciones de los árboles de decisión. Predice tomando el promedio o la media de la producción de varios árboles. Aumentar el número de árboles aumenta la precisión del resultado.\n",
        "\n",
        "Un bosque aleatorio erradica las limitaciones de un algoritmo de árbol de decisión. Reduce el sobreajuste de los conjuntos de datos y aumenta la precisión. Genera predicciones sin requerir muchas configuraciones en paquetes (como scikit-learn)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "cellView": "form",
        "id": "nbNps2FeVlqw",
        "outputId": "f7672e24-088f-4de9-b53b-a96d5e52ce73"
      },
      "source": [
        "#@title\n",
        "from IPython.display import Image\n",
        "Image(\"/content/drive/MyDrive/Panama_Honduras-Mexico/random_forest.png\",width=650)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 650
            }
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jyKfoyOIeSn"
      },
      "source": [
        "### **¿Por qué utilizar algoritmos de bosque aleatorio?**\n",
        "\n",
        "Los algoritmos de bosque aleatorio son particularmente útiles cuando:\n",
        "\n",
        "1. Tiene muchos datos faltantes o un conjunto de datos desequilibrado.\n",
        "2. Con una gran cantidad de árboles, puede evitar el sobreajuste durante el entrenamiento. El sobreajuste ocurre cuando los modelos de aprendizaje automático funcionan mejor en el conjunto de entrenamiento pero en el conjunto de prueba.\n",
        "3. El algoritmo de bosque aleatorio se puede utilizar cuando tiene datos dimensionales muy altos.\n",
        "4. A través de la validación cruzada, el algoritmo de bosque aleatorio puede devolver mayor acc\n",
        "5. El algoritmo de bosque aleatorio puede resolver tanto la clasificación como la regresión y encuentra su aplicación en una variedad de tareas que van desde la detección de fraude con tarjetas de crédito, la predicción del mercado de valores y la búsqueda de transacciones fraudulentas en línea."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7eO-6EYcIeSo"
      },
      "source": [
        "### **Desventajas de los algoritmos de bosque aleatorio**\n",
        "\n",
        "Hay dos desventajas principales de los algoritmos de bosque aleatorio:\n",
        "\n",
        "1. El uso de una gran cantidad de árboles puede ralentizar el algoritmo.\n",
        "2. El algoritmo de bosque aleatorio es un algoritmo predictivo, que solo puede predecir el futuro y no puede explicar lo que sucedió en el pasado utilizando el conjunto de datos."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcTBxyXzIeSp"
      },
      "source": [
        "### **Implementación de regresión lineal con Sklearn**\n",
        "\n",
        "Para implementar la regresión lineal con `Sklearn`, puede usar la clase LinearRegression del módulo sklearn.linear_model. Para entrenar el algoritmo, los conjuntos de entrenamiento y prueba, es decir, `X_train` y `X_test` en nuestro caso, se pasan al método `fit ()` del objeto de la clase LinearRegression. El conjunto de prueba se pasa al método `predict ()` de la clase para realizar predicciones. El proceso de entrenamiento y realización de predicciones con el algoritmo de regresión lineal es el siguiente:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JT5Ej-BRZEE-"
      },
      "source": [
        "from sklearn.model_selection import train_test_split as holdout\n",
        "from sklearn.ensemble import RandomForestRegressor as rfr\n",
        "\n",
        "#Mandamos llamar al objeto\n",
        "\n",
        "#LLamamos a la función\n",
        "Rfr = rfr(n_estimators = 5000, #El número de árboles en el bosque.\n",
        "          criterion = \"squared_error\", #reducción de varianza como criterio de selección de características\n",
        "          random_state = 2021,  #aleatoriedad utilizadas al construir árboles\n",
        "          max_features='sqrt', #características a considerar al buscar la mejor división\n",
        "          n_jobs = -1) #significa utilizar todos los procesadores\n",
        "\n",
        "#Entrenamos el modelo\n",
        "model_rfr=Rfr.fit(x_train,y_train)\n",
        "\n",
        "#Hacemos las prediciones del modelo\n",
        "y_pred_rfr = model_rfr.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETxjDmMDNwfk",
        "outputId": "5ab283b2-6a68-4531-89c2-a028b2ff1216"
      },
      "source": [
        "print('Feature importance ranking\\n\\n')\n",
        "importances = Rfr.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in Rfr.estimators_],axis=0)\n",
        "indices = np.argsort(importances)[::-1]\n",
        "variables = ['age', 'sex', 'bmi', 'children','smoker', 'region']\n",
        "importance_list = []\n",
        "for f in range(x.shape[1]):\n",
        "    variable = variables[indices[f]]\n",
        "    importance_list.append(variable)\n",
        "    print(\"%d.%s(%f)\" % (f + 1, variable, importances[indices[f]]))\n",
        " \n",
        "# Plot the feature importances of the forest\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.title(\"Variables más importantes\")\n",
        "plt.bar(importance_list, importances[indices],\n",
        "       color=\"y\", yerr=std[indices], align=\"center\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature importance ranking\n",
            "\n",
            "\n",
            "1.smoker(0.604257)\n",
            "2.bmi(0.217532)\n",
            "3.age(0.136483)\n",
            "4.children(0.020916)\n",
            "5.region(0.014764)\n",
            "6.sex(0.006047)\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<BarContainer object of 6 artists>"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GUSt4BCIeSv"
      },
      "source": [
        "### **Calcular el MSE**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rPsTLkaxIeSv",
        "outputId": "61ec7082-daf2-4ba8-eb7f-913fc53c65a0"
      },
      "source": [
        "# The mean squared error\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "print(\"Mean squared error: %.2f\"  % mean_squared_error(y_test, y_pred_rfr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean squared error: 19571388.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tGQFPmPXIeSw"
      },
      "source": [
        "### **Coeficiente de Determinación**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pQC4yTnNIeSw",
        "outputId": "66958ac5-9480-4a1e-bd4e-1c1fa57a6ee7"
      },
      "source": [
        "# The coefficient of determination: 1 is perfect prediction\n",
        "print('Coefficient of determination: %.2f'\n",
        "      % r2_score(y_test, y_pred_rfr))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Coefficient of determination: 0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7IcnnZ4jIeS0"
      },
      "source": [
        "### **Tipos de Metricas**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XZPDh-L8IeS1"
      },
      "source": [
        "Una vez que haya entrenado un modelo y haya hecho predicciones en el conjunto de prueba, el siguiente paso es saber qué tan bien se ha desempeñado su modelo para hacer predicciones en el conjunto de prueba desconocido. Hay varias métricas para comprobarlo. Sin embargo, el error absoluto medio, el error cuadrático medio y el error cuadrático medio medio son tres de las métricas más comunes.\n",
        "\n",
        "**Error absoluto medio(Mean Absolute Error MAE)**\n",
        "\n",
        "El error absoluto medio (MAE) se calcula tomando el promedio del error absoluto obtenido al restar los valores reales de los valores predichos. La ecuación para calcular MAE es:\n",
        "\n",
        "$$MAE=\\frac{\\Sigma_{i=1}^{n} |y_i-\\hat{y}|}{n} $$\n",
        "\n",
        "**Error medio cuadrado (Mean Squared Error MSE)**\n",
        "\n",
        "El error cuadrático medio (MSE) es similar al MAE. Sin embargo, el error para cada registro se eleva al cuadrado en el caso de MSE para castigar los registros de datos con una gran diferencia entre los valores predichos y reales. La ecuación para calcular el error cuadrático medio es la siguiente:\n",
        "\n",
        "$$MSE=\\frac{1}{2} \\Sigma_{i=1}^{n} (y_i-\\hat{y})^2 $$\n",
        "\n",
        "**Root Mean Squared Error**\n",
        "\n",
        "El error cuadrático medio es simplemente la raíz inferior del error cuadrático medio y se puede calcular de la siguiente manera:\n",
        "\n",
        "$$RMSE=\\sqrt{\\frac{1}{n} \\Sigma_{i=1}^{n} (y_i-\\hat{y})^2  } $$\n",
        "\n",
        "Los métodos utilizados para encontrar el valor de estas métricas están disponibles en la clase `sklearn.metrics`. Los valores predichos y reales deben pasarse a estos métodos, como se muestra en el resultado."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fL7TCDAFIeS1",
        "outputId": "7769ba01-c6e1-4029-804d-54e2fc9c5f66"
      },
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "\n",
        "print (\"Mean Absolute Error:\" , metrics.mean_absolute_error(y_test, y_pred_rfr))\n",
        "print (\"Mean Squared Error:\" , metrics.mean_squared_error(y_test, y_pred_rfr))\n",
        "print (\"Root Mean Squared Error:\" , np.sqrt(metrics.mean_squared_error(y_test, y_pred_rfr)))\n",
        "# The coefficient of determination: 1 is perfect prediction\n",
        "print('Coefficient of determination: %.2f'% r2_score(y_test, y_pred_rfr))\n",
        "#print(\"Mean absolute percentage error:\", mean_absolute_percentage_error(y_test, y_pred_lineal) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2657.266513114894\n",
            "Mean Squared Error: 19571388.718973763\n",
            "Root Mean Squared Error: 4423.956229323902\n",
            "Coefficient of determination: 0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "miSfRVqLIeS2",
        "outputId": "652f6eb3-ede2-4405-883b-584279f354bd"
      },
      "source": [
        "# Visualizamos un resumen de la prima\n",
        "df['charges'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/plain": [
              "count     1338.000000\n",
              "mean     13270.422265\n",
              "std      12110.011237\n",
              "min       1121.873900\n",
              "25%       4740.287150\n",
              "50%       9382.033000\n",
              "75%      16639.912515\n",
              "max      63770.428010\n",
              "Name: charges, dtype: float64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GDR2OfzUZEFA"
      },
      "source": [
        "Con un rango de 1121.87 a 63770.42, una media de 13270.42 y una desviación estándar de 12110.01, un RMSE de 4454.01 no está mal, pero tampoco es genial."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBbtHVWfZEFQ",
        "outputId": "fda7f03e-ca88-4aba-ab37-99fdd8fe1437"
      },
      "source": [
        "##Predicting the charges\n",
        "y_test_pred = model_rfr.predict(x_test)\n",
        "##Comparing the actual output values with the predicted values\n",
        "df = pd.DataFrame({'Actual': y_test, 'Predicción': y_test_pred, \"Error absoluto\":y_test-y_test_pred, \n",
        "                   \"Error relativo %\": 100*(y_test-y_test_pred)/y_test })\n",
        "                  \n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Actual</th>\n",
              "      <th>Predicción</th>\n",
              "      <th>Error absoluto</th>\n",
              "      <th>Error relativo %</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>578</th>\n",
              "      <td>9724.53000</td>\n",
              "      <td>11594.518079</td>\n",
              "      <td>-1869.988079</td>\n",
              "      <td>-19.229599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>610</th>\n",
              "      <td>8547.69130</td>\n",
              "      <td>10143.054684</td>\n",
              "      <td>-1595.363384</td>\n",
              "      <td>-18.664261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>569</th>\n",
              "      <td>45702.02235</td>\n",
              "      <td>44359.802151</td>\n",
              "      <td>1342.220199</td>\n",
              "      <td>2.936895</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1034</th>\n",
              "      <td>12950.07120</td>\n",
              "      <td>13198.776411</td>\n",
              "      <td>-248.705211</td>\n",
              "      <td>-1.920493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>9644.25250</td>\n",
              "      <td>10148.317012</td>\n",
              "      <td>-504.064512</td>\n",
              "      <td>-5.226579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1084</th>\n",
              "      <td>15019.76005</td>\n",
              "      <td>16430.252705</td>\n",
              "      <td>-1410.492655</td>\n",
              "      <td>-9.390913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>6664.68595</td>\n",
              "      <td>6639.991449</td>\n",
              "      <td>24.694501</td>\n",
              "      <td>0.370528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1132</th>\n",
              "      <td>20709.02034</td>\n",
              "      <td>11514.046801</td>\n",
              "      <td>9194.973539</td>\n",
              "      <td>44.400814</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>40932.42950</td>\n",
              "      <td>42731.779399</td>\n",
              "      <td>-1799.349899</td>\n",
              "      <td>-4.395903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>963</th>\n",
              "      <td>9500.57305</td>\n",
              "      <td>9503.171354</td>\n",
              "      <td>-2.598304</td>\n",
              "      <td>-0.027349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>268 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Actual    Predicción  Error absoluto  Error relativo %\n",
              "578    9724.53000  11594.518079    -1869.988079        -19.229599\n",
              "610    8547.69130  10143.054684    -1595.363384        -18.664261\n",
              "569   45702.02235  44359.802151     1342.220199          2.936895\n",
              "1034  12950.07120  13198.776411     -248.705211         -1.920493\n",
              "198    9644.25250  10148.317012     -504.064512         -5.226579\n",
              "...           ...           ...             ...               ...\n",
              "1084  15019.76005  16430.252705    -1410.492655         -9.390913\n",
              "726    6664.68595   6639.991449       24.694501          0.370528\n",
              "1132  20709.02034  11514.046801     9194.973539         44.400814\n",
              "725   40932.42950  42731.779399    -1799.349899         -4.395903\n",
              "963    9500.57305   9503.171354       -2.598304         -0.027349\n",
              "\n",
              "[268 rows x 4 columns]"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ar1Yh-wJIeS4"
      },
      "source": [
        "## **Predicción**\n",
        "\n",
        "Queremos predecir el costo de seguro médico de ciertas personas que tenga las siguientes información\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeWo07_dIeS4",
        "outputId": "614616ac-3594-4ff6-885c-6818436aa313"
      },
      "source": [
        "print(\"El costo del seguro médico para un Hombre que: \\n\")\n",
        "##                                             Edad       sex     bmi      No.children    fumar    región\")\n",
        "\n",
        "print(\"Fumador\",            model_rfr.predict([[50,        1,     33,       2,             1,       3]]))\n",
        "\n",
        "                          \n",
        "print(\"No fumador\",         model_rfr.predict([[50,        1,     33,       2,             0,        3] ]))\n",
        "\n",
        "print(\"-------------------------------------------------------------------------- \\n\")\n",
        "print(\"El costo del seguro médico para un Mujer que:\")\n",
        "print(\"Fumador\",            model_rfr.predict([[50,        0,     33,       2,             1,         3]]))\n",
        "                          \n",
        "print(\"No fumador\",         model_rfr.predict([[50,        0,     33,       2,             0,         3] ]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "El costo del seguro médico para un Hombre que: \n",
            "\n",
            "Fumador [57332.8937734]\n",
            "No fumador [16235.80604015]\n",
            "-------------------------------------------------------------------------- \n",
            "\n",
            "El costo del seguro médico para un Mujer que:\n",
            "Fumador [57281.79843154]\n",
            "No fumador [16200.55485422]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQWP2naYZEFR"
      },
      "source": [
        "<font size=2> **Conclusión:** como notamos anteriormente, **fumar** es el factor más importante que afecta los costos médicos, luego es **edad** y **bmi**."
      ]
    }
  ]
}