CODE_Sensitive_Features.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Description**: The dataset is based on the data from the 1994 U.S. Census database and includes only records that meet certain criteria. These criteria include: individuals must be over 16 years old, have an adjusted gross income over 100, a final weight greater than one, and work more than zero hours per week (((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0)))\n",
        "\n",
        "**Predicting Task**: The main goal of this dataset is to predict whether a person’s income is above $50,000 per year.\n",
        "\n",
        "**Target Variables**: Gender and Race"
      ],
      "metadata": {
        "id": "tfk0LEGPb71Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Dataset_1 = \"/content/drive/MyDrive/Fairness and Bias (Sensitive Features)\"\n",
        "\n",
        "Data = \"/content/drive/MyDrive/Fairness and Bias (Sensitive Features)/cleaned_engineered_adult.csv\"\n",
        "df = pd.read_csv(Data)"
      ],
      "metadata": {
        "id": "_8SN78SmbSdk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataset Visualization**"
      ],
      "metadata": {
        "id": "Pi3ZzM8yef2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "IiH5LuypcufX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Settig the style\n",
        "sns.set_style(\"whitegrid\")"
      ],
      "metadata": {
        "id": "hh_1UTBydTLC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar plot of Race\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(y='race', data=df, palette='Set1')\n",
        "plt.title('Distribution of Race')\n",
        "plt.xlabel('Count')\n",
        "plt.ylabel('Race')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "UBwi_rtvb94Y",
        "outputId": "df3f1f55-3f37-4190-cc6e-da3fa14e51d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-20-fa1847cb0ad6>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(y='race', data=df, palette='Set1')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Bar plot of Sex\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x='sex', data=df, palette='Set1')\n",
        "plt.title('Distribution of Sex')\n",
        "plt.xlabel('Sex')\n",
        "plt.ylabel('Count')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "NKJkhHjycqXp",
        "outputId": "99fd5b68-4236-4f65-daa4-4bdbd9fe1777"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-7c8ad891753a>:3: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='sex', data=df, palette='Set1')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Income distribution by race\n",
        "plt.figure(figsize=(6, 4))\n",
        "sns.countplot(x='race', hue='income', data=df, palette='Set3')\n",
        "plt.title('Income Distribution by Race')\n",
        "plt.xlabel('Race')\n",
        "plt.ylabel('Count')\n",
        "plt.legend(title='Income >50K', loc='upper right')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "FiNFxzwHdY4W",
        "outputId": "1a53dd82-7124-4662-e39e-ebfb16a2f509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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7ceTIESiVSowcOfKVV92VhJ2dHfbt24eVK1di//79SEtLg5WVFZo1a4axY8dKWseff/6JP//8E3K5XHz2XJ8+feDr6/vGZ8/VrFkTnp6eiI6OxsGDB6FQKNCoUSOEhoaie/fuYruxY8fi3r172LBhAzIzM9G2bdsShabZs2djyZIlSExMhK2tLb799lutcV2GhoZYuXIlgoKCsGzZMtjY2ODTTz+Fubk5pk+frrW+otTWt29fGBsbY/369QgJCYGpqSm6dOmCL7/8ssB9jUpKyn7mc3R0RJMmTZCQkCD5qrnC1KhRA97e3vjhhx9w+/ZtNGrUCMuXL0doaCgWL14MpVKJgQMHwsrKCjNmzNBa9uOPP4a1tTXWrVuH7777DgYGBmjUqJFWqHR1dcXu3bvx3XffYdu2bcjKyoKNjQ1atGgBX1/fEtdPlY9MeLkflIiIqBB9+vSBhYVFgdPCRJUBxzQREZEkly5dQlxcHPr06aPrUoh0gj1NRERUqKtXr+Ly5cvYtGkTHj9+jKNHj1aKhwwT/RN7moiIqFD//ve/MX36dOTl5WHp0qUMTFRpsaeJiIiISAL2NBERERFJwNBEREREJAHv01RKNBoN8vLyIJfLS/VRDkRERFS2BEGARqOBgYFBoc9TZGgqJXl5ebh06ZKuyyAiIqJicnJygpGR0WvnMzSVkvxk6uTkVG5PHSciIqKSU6vVuHTpUqG9TABDU6nJPyWnUCgYmoiIiCqgNw2v4UBwIiIiIgkYmoiIiIgkYGgiIiIikoBjmoiIiEqRRqNBTk6OrsuglxgaGpbKeGOGJiIiolKSk5ODxMREaDQaXZdC//DOO++gZs2aJbqXIkMTERFRKRAEAf/5z3+gUChQt27dN16+TuVDEARkZWXh4cOHAIBatWoVe10MTURERKUgLy8PWVlZqF27NkxNTXVdDr3ExMQEAPDw4UNUr1692KfqGIOJiIhKgVqtBoBC7yhNupMfZHNzc4u9DoYmIiKiUsTnj+qn0vhcGJqIiIiIJGBoIiIiIpKAoYmIiKiMTZs2DWPGjNF1GXph2rRpUKlUWl9Dhw7VapOWlobJkyejVatWaN26NWbMmIHMzExx/qlTp6BSqZCeni5Oe/DgAby9vTFo0CA8ffq0TGrn1XNERER6ThAEvR0r9eTJEygUCpiZmUlexsPDA8HBweLrfw6enzJlCpKTkxEWFobc3FzMmDEDc+bMwTfffPPK9d2+fRtDhgyBnZ0dli1bBmNj4+LtzBswNBEREZUzf39/qFQqGBkZYe/evTA0NMSAAQPw+eefi23S09MREhKCI0eO4OnTp7CtWxejJ3wO9/feAwD835GjWP/datxJSoK1UomPBw7AJ4P9xeV9PvREr74+uH3rFo4d/Q0W71hgUmAgHFu0QPD8+Th76jRq29bBzHnz0LR5M3G5vy5cwOrlKxD3dxzeeecddOz0AUaP/1y8bP9lBjI5rly5guHDh6Nr167w8fGBm5vbG+9RZWRkBBsbm1fOS0hIwPHjx7F37144OTkBAGbNmoURI0Zg6tSpqFGjhlb7+Ph4DBs2DK6urli8eDEMDMou2vD0HBERkQ7s378fpqam+P777/Hll19i1apV+PPPPwG8eBTL8OHDcf78eXz99dc4fPgwRk/4HJDJkKfR4HLsZcyaGohO3bth8/e7MGTkCKz77jscPPAD8jQa5P33juS7tm2HY4sW2LhzO9q5uyNo1iwEzZqNrh9+iA07tqG2rS2CZs1GrlqNPI0Gt27dxhdjxuG9Tp2wefdOzFu0EDEXLuDrhYvE9Wp9CRq0adMG69evh5GREcaPH48PPvgAS5cuxY0bN16776dPn4abmxu6d++OuXPn4vHjx+K8CxcuwNzcXAxMANC+fXvI5XJcvHhRaz0XLlyAv78/unXrhpCQkDINTAB7moiIiHRCpVJh3LhxAIAGDRpg27ZtiI6ORocOHRAVFYWLFy/i8OHDaNiwIQCgqtJKDEO7t2/Hu23bIGD4MABAvfr1cfPGDewM34qevbzFbbTr0B69P+oHAAgYPgwH9uyFQ/Nm+KBrFwDAoE8/xaiAIXiUmgprpRJbw8LQ9cMe6D/oEwBA3Xr1MPHLL/H58BGYPGMaqlSpUmA/ZDIZ2rZti7Zt22LOnDk4cuQIDhw4gI0bN6J58+bw8fGBl5cXqlWrBuDFqbmuXbvC1tYWSUlJWLp0KYYPH47du3dDoVAgJSUFVlZWWtswMDCAhYUFkpOTtaaPHTsWPXv2xJw5c0r2YUjE0ERERKQDKpVK67WNjQ1SU1MBAHFxcahZs6YYmP7pZmIiPDp21Jrm5OKMPTt2Qq1Wi3e8btykiTjfytr6xTQ7O3GapfWLcPL40WNYK5VIuHoNCdeu4dfIn8U2giBAo9HgP3fvoUGjV9eTz9jYGF5eXvDy8kJiYiImT56MefPm4fnz5wgICAAAeHp6ar0HKpUKXbp0EXufiqJz58749ddfcfbsWbRu3bpIyxYHQxMREZEO/PNUkkwmgyAIAFBqA5lf3kb+QHLFK6YJwoserGdZWejVry8+GjCgwLpq1Kr5xu3l5eXhzz//xA8//ICjR4+ibt26+PLLL9GrV6/XLlO3bl1YWlri1q1bcHNzg1KpxKNHjwqs98mTJwXGQc2fPx9ff/01hg8fjnXr1qFNmzZvrLEkGJqIiIj0jEqlwv3795GYmPjK3qYGDRvi0l9/aU27FPMX6tavV+znqgGAfVMH3LyRCNt6dYu03OXLl/HDDz/gp59+Ql5eHry8vLBt2zatcUmvc//+faSlpYmBqGXLlkhPT0dsbCwcHR0BACdPnoRGo0GLFi20lpXJZFiwYAHkcjlGjBiBtWvXom3btkWqvSg4EJyIiEjPtG3bFq1bt8b48ePx559/IikpCdEn/sSpP6MAAL5+fjh3+gw2r9+A27duIfLQj4j4/nsM8Pd/w5oL98mnnyL24l/4dtFiXLtyBUm3b+P477/j20WLX7vM2bNn4evri6SkJMydOxfHjx/H7NmzXxmYMjMzsXjxYsTExODOnTuIjo7GmDFjUL9+fXh4eAAAGjduDA8PD8yePRsXL17EuXPnsGDBAnh6eha4cg54EZyCgoLQu3dvjBgxAqdOnSrRe1AY9jQRERHpoRUrVmDx4sWYNGkSnj17hjp162Lk5y8GjquaOiBocTA2rl6LLes3wFqpxNBRo7QGgReHnX0TrFi/DutXfYexQ4cDgoDatrbo1K3ra5dp3Lgx/vjjjwKDt19FoVDg6tWrOHDgAJ4+fYrq1aujQ4cOmDBhgta9mkJCQrBgwQJ8+umnkMvl6NatG2bNmvXa9cpkMsydOxdyuRwjR47EmjVr0K5du6LtvAQyIf8EKpWIWq1GTEwMXFxcStQ1SkREFVN2drZ4Oq0sbq746FmmePWcvjCQy2FlUlXXZUhS2Ocj9RjO03NEREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBDoNTWfOnMGoUaPg7u4OlUqFI0eOaM3Pf/rxP782bNggtunUqVOB+evWrdNaT3x8PD755BM4OTmhY8eOWL9+fYFaIiMj0aNHDzg5OcHb2xvHjh0rm50mIiKiCkmnj1HJysqCSqVCv379MG7cuALzT5w4ofX6jz/+wMyZM9G9e3et6ePHj0f//v3F11Wr/u/upBkZGRg6dCjc3NwQFBSEq1evYsaMGTA3N4evry8A4Pz585g8eTImTZqEDz74AIcOHcLYsWMREREBe3v70txlIiKiYlHI5EXq6tAIAjR86Eep0mlo6tixIzp27Pja+flPPM539OhRuLq6om5d7acvV61atUDbfAcPHkRubi4WLlwIIyMjNGnSBHFxcQgLCxNDU3h4ODw8PDBs2DAAwMSJExEVFYVt27Zh/vz5JdlFIiIiAC9CjFwmK/byFsYmRdyeBo+eZelFcDp16hQGDx5cYPqJEye0jt/bt2/Hxo0bkZycDAcHB8yePRstWrQQ53fq1AmDBw9GQEAAAEAQBCxZsgS7d+/G6tWr4erqWqb7UWEe2JuSkoJjx45h0aJFBeatX78eq1evRq1ateDl5YWAgAAYGLzYtZiYGLRu3VrrQYDu7u5Yv349njx5AgsLC8TExIgfwMtt/nm6UAq1Wl3kZYiIqOJTq9UQBEH8+ie5TIYjCfF4nJ1V5rVYGpuiS2MHyGWyUg1NT9PToTAwgKmpqdb0Nz3GNn9+ZGQkzMzMxOlWVlbivMOHDyM4OBjz5s2Ds7MztmzZgqFDhyIyMhLW1tZa6xEEAWq1GrNnz8bvv/+OLVu2wNHRsdA68j8XtVpd4Fgt9dhdYULT/v37UbVqVXTr1k1rur+/P5o1awYLCwtcuHABS5cuRXJyMqZPnw7gRdiytbXVWkapVIrzLCwskJKSIk7LZ21tjZSUlCLXeenSpSIvQ0REbwcDAwM8e/YMmn88WFcul8PExASPs7OQkpWho+qKJy8vD6ejTyLy0CFE/XEca8M3w+4fQ1eys7ML7PPLnj9/DgAwNTXVGkKTnZ0tfr9p0yb4+Pjgww8/BAAEBgbi999/x65duzBkyBAAL4JPTk4O0tLSMH36dMTFxWHDhg1o0KABsrIKD6PPnz9Hbm4u4uPji/YGvKTChKZ9+/bB29sbVapU0Zqe/0YCgIODAwwNDTF37lxMnjxZq3epvDg5ORX6hGQiIno7ZWdn49atWzAxMYGxsbGuyymxhGvXEXnoEH6N/Bl5eXno1K0rlq1dIwYm/4/648F//gPZa045vvvuu+KFV/nH7oEDByI3NxdNmjTBuHHj0KpVKwBATk4O4uLiMGrUKK1erPbt2+Py5cviNJlMhry8PHzxxRe4f/8+du7ciVq1aknaH7lcDkNDQ9jZ2RX4fNRqtaROjwoRms6ePYvExESEhoa+sa2zszPy8vJw584dNGrUCEqlskCPUf7r/N6lV7VJTU0t0PskhUKhYGgiIqqEFAoFZDKZ+FURPUlLwy+HIxH544+4mXAD7dw7YNK0QLR/zwOGhoZabb9evgyCRo13jE1fuS5jY2PxfahevTqCgoLg6OiInJwc7NmzB4MHD8b333+P5s2bIy0tDWq1GkqlUuu9UyqVSExMFKfJZDJ89913qFq1KiIjI2FlZSV53/I/l5IcpytEaNq7dy+aN28OBweHN7aNi4uDXC4Xz3+6uLggNDQUubm54gceFRWFhg0bwsLCQmxz8uRJrXFNUVFRcHFxKfV9ISIi0lf7du1G2Lr1cG7ZEjt/2I8aNWu+tm3N2rVgIJfDyqTqa9vka9SoERo1aiS+btWqFZKSkrB582Z8/fXXRarR3d0dUVFRWLNmDWbMmFGkZUtKp6EpMzMTt2/fFl/fuXMHcXFxsLCwQO3atQG8uGXAzz//jMDAwALLX7hwAX/99RfatWuHqlWr4sKFCwgODkavXr3EQOTt7Y1Vq1Zh5syZGD58OK5du4bw8HBxzBMADB48GP7+/ti0aRM6duyIw4cPIzY2llfOERFRpdKrX18oDBT4+cfDGPyxLzp26oTunj3RsvW7kMu173cg5fTcy/dV/CcnJyecP38eAGBpaQmFQoHU1FStNq8669OuXTv4+flhzJgx0Gg0mDVrVnF2tVh0GppiY2O1LkEMDg4GAPj4+IhXyf30008QBAFeXl4FljcyMsLhw4excuVK5OTkwNbWFgEBAVrjnKpVq4aNGzdi/vz56Nu3LywtLTFmzBjxdgPAi8QbEhKC0NBQLF26FA0aNMCqVat4jyYiIqpUlDY2+HTYMHw6bBgu/fUXfj70I2ZN+RKmVaui64c90N2zJxo2bgxA2um5wsTHx4u3GzAyMkLz5s0RHR2NLl26AAA0Gg2io6Ph5+dXYFl3d3esWbMGo0ePBoByC046DU2urq64cuVKoW18fX21As7Lmjdvju+///6N23FwcMCOHTsKbfPhhx+KI/aJiIgqOydnZzg5O2P8l1Nw/PffEXnwR+zaug0bd2xH4yZ2RTo9t3nzZtja2qJJkyZ4/vw59uzZg5MnT2LTpk1imyFDhiAwMBCOjo5o0aIFtmzZgmfPnqFv376vXGf79u3F4KTRaDBnzpxS2/fXqRBjmoiIiN4Glq/pldHn7VSpUgVdundHl+7dkZKcDBOTot1kEwByc3OxePFiPHjwACYmJrC3t0dYWBjatWsntunZsycePXqE5cuXIzk5GU2bNsWGDRsKvSjLzc0Na9euxahRoyAIAubMmVOmg/BlwpvuSEWSqNVqxMTEwMXFhVfPERFVQtnZ2UhMTETDhg1feWqqpHcEL6ryuCO41J4mfVDY5yP1GM6eJiIionJQ0sD0JPsZ1MLrbyD5T3z2XOljaCIiIqoA1IIGeYXcdZvKXhGel0xERERUeTE0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQB79NERERUAShk8iJ1dfDmlqWPPU1ERETloKRPLbMwNoGVSVXJX9YmpmX62JaPPb3h5tIKKpVK/Fq3bp1Wm/j4eHzyySdwcnJCx44dsX79eq35K1asQO/evbWmnT17Fq1bt8a//vWvEr9npY09TUREROVAJpMhJycWGk1WmW9LLjeFkZEj5DJZkXqbUpKT8Y6lJQwMpMWD4WNG49NPBomvq1b933PoMjIyMHToULi5uSEoKAhXr17FjBkzYG5uDl9f31eu7/fff8eECRMwfPhwjBs3TnLd5YWhiYiIqJxoNFkQhKflsJ3iLXcoYj8O7N2Hbj0/RA8vLzRuYldoe1NTU9jY2Lxy3sGDB5Gbm4uFCxfCyMgITZo0QVxcHMLCwl4Zmg4dOoTp06dj2rRp8PPzK94OlDGeniMiIiIAwCcBn2L8lMm4mZiIoYMG4bNPBmHvzl14/PjxK9tvDdsMV1dX9OnTBxs2bEBeXp44LyYmBq1bt4aRkZE4zd3dHYmJiXjy5InWerZv347p06dj4cKFehuYAPY0ERER0X9VqVIFnbt3Q+fu3fD40SP8GvkzDh86hFXfhqKdewd86OWF9u95wMDAAP0G+qJZ02awrV4DFy5cwNKlS5GcnIzp06cDAFJSUmBra6u1fqVSKc6zsLAAACQkJGD+/Pn417/+hV69epXvDhcRe5qIiIioAEsrK/Qf9Ak27diO4G+/weWLlzBzypdITEgAAAzw80OrNq3h4OCAgQMHIjAwENu2bUNOTk6RtlOzZk00b94cGzduxMOHD8tiV0oNQxMREREVkJWZiZ9+OIgJI0Zh2sRJaNi4MWbOn4cGjRq9sr2zszPy8vJw584dAC96lVJSUrTa5L/O73ECXgweDwsLg4mJCQYPHqzXwYmhiYiIiAAAarUaJ//8E0EzZqJX127Yvnkz3m3bBrsP/oBla1ejh5cXDA0NX7lsXFwc5HI5rK2tAQAuLi44e/YscnNzxTZRUVFo2LCheGoun4WFBTZv3gwzMzP4+/vjwYMHZbeTJcDQRERERACArZvCEDR9JkxNTfHt6u+wY38EBg8bihq1amq1i/3rIr7fvgPXrlxFUlISDh48iODgYPTq1UsMRN7e3jA0NMTMmTNx7do1HD58GOHh4RgyZMgrt21ubo6wsDBYWFhg8ODBehmcOBCciIionMjlpsW+HUBRt1Mc3T17YuBgf1SpUqXQdoZGRjj6718QtnYdcnNzYWtri4CAAK1AVK1aNWzcuBHz589H3759YWlpiTFjxrz2Hk35y2zatAnDhg2Dv78/tm7diho1ahRrX8qCTNC3221WUGq1GjExMXBxcYFCodB1OUREVM6ys7ORmJiIhg0bwtjYuMB8QRAgK8M7dBfcngapz7LK9FEqBnI5rEyqvrmhHijs85F6DGdPExERUTkoaWB6kv0MakF6NxWfPVf6GJqIiIgqALWgQV55nNuj1+JAcCIiIiIJdBqazpw5g1GjRsHd3R0qlQpHjhzRmj9t2jStpyerVCoMHTpUq01aWhomT56MVq1aoXXr1pgxYwYyMzO12rzpKcsAEBkZiR49esDJyQne3t44duxY6e8wERERVVg6DU1ZWVlQqVSYO3fua9t4eHjgxIkT4tfSpUu15k+ZMgXXr19HWFgY1qxZg7Nnz2LOnDni/PynLNeuXRsRERGYOnUqVq5cid27d4ttzp8/j8mTJ+Ojjz7CgQMH0LlzZ4wdOxZXr14t/Z0mIiKiCkmnY5o6duyIjh07FtrGyMjotU9QTkhIwPHjx7F37144OTkBAGbNmoURI0Zg6tSpqFGjhqSnLIeHh8PDwwPDhg0DAEycOBFRUVHYtm0b5s+fX4p7TEREbztelK6fNKUwHkzvB4KfPn0abm5uMDc3R7t27TBx4kRYWloCAC5cuABzc3MxMAFA+/btIZfLcfHiRXTt2vW1T1lev349njx5AgsLC8TExCAgIEBru+7u7gVOFxIREb2OoaEhZDIZkpOTYWNjU+q3F8h5/hxqjX4FMo1chmyZft9mRxAE5OTkIDk5GXK5XCsPFJVehyYPDw907doVtra2SEpKwtKlSzF8+HDs3r0bCoUCKSkpsLKy0lrGwMAAFhYWSE5OBiDtKcspKSlaz8EBAGtr6wLPzJFCrVYXeRkiIno71K5dG3fv3kVGRkaprlcmkyEzN6dUektKk1wux2NDowrRu2Zqaoo6depAEIQCx2qpx269Dk2enp7i9/kDwbt06SL2PumjS5cu6boEIiJ6i1SpUgV2dnb4+dplPM7N0nU5WiyNTdGjoQrXr1/H8+fPdV1OoZ49e4bU1NQSrUOvQ9M/1a1bF5aWlrh16xbc3NygVCrx6NEjrTZ5eXl48uSJOA5KylOWX9UmNTW1QO+TFE5OTrwjOBERlSqFQoFcuQzZ0K8enVy5DMbGxmjatKmuSykRtVotqdOjQoWm+/fvIy0tTQxELVu2RHp6OmJjY+Ho6AgAOHnyJDQaDVq0aAHgxVOWQ0NDkZubKz6Z+Z9PWXZxccHJkye1xjVFRUXBxcWlyDUqFAqGJiIiqlQqy3FPp7ccyMzMRFxcHOLi4gAAd+7cQVxcHO7du4fMzEwsXrwYMTExuHPnDqKjozFmzBjUr18fHh4eAIDGjRvDw8MDs2fPxsWLF3Hu3DksWLAAnp6e4gP+pDxlefDgwTh+/Dg2bdqEhIQErFixArGxsfDz8yv/N4WIiIj0kk4f2Hvq1CkMHjy4wHQfHx/MmzcPY8eOxd9//42nT5+ievXq6NChAyZMmKB12iwtLQ0LFizAb7/9Brlcjm7dumHWrFmoWvV/DxCMj4/H/PnzcenSJVhaWsLPzw8jRozQ2mZkZCRCQ0Nx9+5dNGjQAF9++eUbb4fwMj6wl4iIytKey+eRklW6A8xLSmlqho+bt9J1GSUm9Riu09D0NmFoIiKissTQVHakHsP57DkiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgkYmoiIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSQKeh6cyZMxg1ahTc3d2hUqlw5MgRcV5ubi6+/vpreHt7w8XFBe7u7pg6dSoePHigtY5OnTpBpVJpfa1bt06rTXx8PD755BM4OTmhY8eOWL9+fYFaIiMj0aNHDzg5OcHb2xvHjh0rm50mIiKiCkmnoSkrKwsqlQpz584tMC87Oxt///03Ro8ejYiICKxcuRKJiYkYPXp0gbbjx4/HiRMnxC8/Pz9xXkZGBoYOHYratWsjIiICU6dOxcqVK7F7926xzfnz5zF58mR89NFHOHDgADp37oyxY8fi6tWrZbPjREREVOEY6HLjHTt2RMeOHV85r1q1aggLC9OaNnv2bHz88ce4d+8eateuLU6vWrUqbGxsXrmegwcPIjc3FwsXLoSRkRGaNGmCuLg4hIWFwdfXFwAQHh4ODw8PDBs2DAAwceJEREVFYdu2bZg/f35p7CoRERFVcBVqTFNGRgZkMhnMzc21pq9fvx6urq7o06cPNmzYgLy8PHFeTEwMWrduDSMjI3Gau7s7EhMT8eTJE7GNm5ub1jrd3d0RExNTdjtDREREFYpOe5qK4vnz5wgJCYGnpyfMzMzE6f7+/mjWrBksLCxw4cIFLF26FMnJyZg+fToAICUlBba2tlrrUiqV4jwLCwukpKSI0/JZW1sjJSWlyHWq1eoiL0NERFQYhUKh6xIKVdGPfVLrrxChKTc3FxMmTIAgCAgKCtKaN2TIEPF7BwcHGBoaYu7cuZg8ebJW71J5uXTpUrlvk4iI3l4mJiZo1qyZrsso1JUrV/Ds2TNdl1Hm9D405ebmYuLEibh37x62bNmi1cv0Ks7OzsjLy8OdO3fQqFEjKJXKAj1G+a/ze5de1SY1NbVA75MUTk5Oev8fARERUWlSqVS6LqFE1Gq1pE4PvQ5N+YHp1q1bCA8Ph6Wl5RuXiYuLg1wuh7W1NQDAxcUFoaGhyM3NhaGhIQAgKioKDRs2hIWFhdjm5MmTCAgIENcTFRUFFxeXItesUCgYmoiIqFKpLMc9nQ4Ez8zMRFxcHOLi4gAAd+7cQVxcHO7du4fc3FyMHz8esbGxCAkJgVqtRnJyMpKTk5GTkwMAuHDhAjZv3oz4+HgkJSXh4MGDCA4ORq9evcRA5O3tDUNDQ8ycORPXrl3D4cOHER4ernVab/DgwTh+/Dg2bdqEhIQErFixArGxsVq3LiAiIqLKTSYIgqCrjZ86dQqDBw8uMN3Hxwfjxo1D586dX7lceHg4XF1dcfnyZQQFBeHGjRvIycmBra0tevfujSFDhmiNZ4qPj8f8+fNx6dIlWFpaws/PDyNGjNBaZ2RkJEJDQ3H37l00aNAAX3755Wtvh/AqarUaMTExcHFxqTSJm4iIys+ey+eRkpWh6zK0KE3N8HHzVrouo8SkHsN1GpreJgxNRERUlhiayo7UY3iFuk8TERERka4wNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJUKzQ1LlzZzx+/LjA9PT0dHTu3LnERRERERHpm2KFprt370Kj0RSYnpOTgwcPHpS4KCIiIiJ9Y1CUxkePHhW/P378OKpVqya+1mg0iI6ORp06dUqvOiIiIiI9UaTQNHbsWACATCbDtGnTtFdkYIA6deoUmE5ERET0NihSaIqPjwcAdOrUCXv37oWVlVWZFEVERESkb4oUmvL99ttvpV0HERERkV4rVmgCgOjoaERHRyM1NbXAoPDg4OASF0ZERESkT4oVmlauXIlVq1bB0dERNjY2kMlkpV0XERERkV4pVmjatWsXgoOD0adPn1Iuh4iIiEg/Fes+Tbm5uWjVqlVp10JERESkt4oVmj766CMcOnSotGshIiIi0lvFOj33/PlzfP/994iOjoZKpYKBgfZqpk+fXirFEREREemLYoWmK1euwMHBAQBw9epVrXkcFE5ERERvo2KFpq1bt5bKxs+cOYONGzciNjYWycnJWLVqFbp06SLOFwQBy5cvx549e5Ceno5WrVph3rx5aNCggdgmLS0NCxYswP/93/9BLpejW7dumDlzJqpWrSq2iY+Px/z583Hp0iVYWVnBz88Pw4cP16olMjISy5Ytw927d9GgQQNMmTIFHTt2LJX9JCIiooqvWGOaSktWVhZUKhXmzp37yvnr16/H1q1bMW/ePHz//fcwMTHB0KFD8fz5c7HNlClTcP36dYSFhWHNmjU4e/Ys5syZI87PyMjA0KFDUbt2bURERGDq1KlYuXIldu/eLbY5f/48Jk+ejI8++ggHDhxA586dMXbs2AK9aERERFR5Faunyd/fv9DTcOHh4ZLW07Fjx9f25giCgPDwcIwePVrsfVqyZAnat2+PI0eOwNPTEwkJCTh+/Dj27t0LJycnAMCsWbMwYsQITJ06FTVq1MDBgweRm5uLhQsXwsjICE2aNEFcXBzCwsLg6+sr1uvh4YFhw4YBACZOnIioqChs27YN8+fPl/y+EBER0durWD1NTZs2hYODg/hlZ2eH3Nxc/P3337C3ty+Vwu7cuYPk5GS0b99enFatWjU4OzvjwoULAIALFy7A3NxcDEwA0L59e8jlcly8eBEAEBMTg9atW8PIyEhs4+7ujsTERDx58kRs4+bmprV9d3d3xMTElMq+EBERUcVXrJ6mGTNmvHL6ihUrkJWVVaKC8iUnJwMArK2ttaZbW1sjJSUFAJCSklLgocEGBgawsLAQl09JSYGtra1WG6VSKc6zsLBASkqKOO1V2ykKtVpd5GWIiIgKo1AodF1CoSr6sU9q/cV+9tyr9OrVCx9//DECAwNLc7UVyqVLl3RdAhERvUVMTEzQrFkzXZdRqCtXruDZs2e6LqPMlWpounDhgtZpsJKwsbEBAKSmpqJ69eri9NTUVPF2B0qlEo8ePdJaLi8vD0+ePBGXVyqVBXqM8l/n9y69qk1qamqB3icpnJyc9P4/AiIiotKkUql0XUKJqNVqSZ0exQpN48aN03otCAKSk5MRGxuLMWPGFGeVBdja2sLGxgbR0dFo2rQpgBdXwv31118YOHAgAKBly5ZIT09HbGwsHB0dAQAnT56ERqNBixYtAAAuLi4IDQ1Fbm4uDA0NAQBRUVFo2LAhLCwsxDYnT55EQECAuP2oqCi4uLgUuW6FQsHQRERElUplOe4VayB4tWrVtL4sLCzQtm1brFu3rkCgKkxmZibi4uIQFxcH4MXg77i4ONy7dw8ymQyDBw/G6tWrcfToUVy5cgVTp05F9erVxavpGjduDA8PD8yePRsXL17EuXPnsGDBAnh6eqJGjRoAAG9vbxgaGmLmzJm4du0aDh8+jPDwcAwZMkSsY/DgwTh+/Dg2bdqEhIQErFixArGxsfDz8yvO20NERERvIZkgCIKuNn7q1CkMHjy4wHQfHx8sWrRIvLnl999/j/T0dLz77ruYO3cuGjZsKLbNv7nlb7/9Jt7cctasWa+9uaWlpSX8/PwwYsQIrW1GRkYiNDRUvLnll19+WaSbW6rVasTExMDFxaXSJG4iIio/ey6fR0pWhq7L0KI0NcPHzVvpuowSk3oML1Foio2NRUJCAgCgSZMmej9QrSwxNBERUVliaCo7Uo/hxRrTlJqaii+++AKnT5+Gubk5ACA9PR2urq749ttvC9wGgIiIiKiiK9aYpgULFiAzMxM//fQTTp8+jdOnT+PHH39ERkYGvvrqq9KukYiIiEjnihWajh8/jrlz56Jx48biNDs7O8ydOxd//PFHqRVHREREpC+KFZo0Go14+f7LDAwMoNFoSlwUERERkb4pVmhq164d/vWvf+HBgwfitAcPHiA4OLjAM9yIiIiI3gbFGgg+Z84cjB49Gp07d0bNmjUBAPfv30eTJk3w9ddfl2qBRERERPqgWKGpVq1a2L9/P6KionDjxg0AL2402b59+1ItjoiIiEhfFOn0XHR0NHr27ImMjAzIZDJ06NAB/v7+8Pf3h5OTEzw9PXH27NmyqpWIiIhIZ4oUmrZs2YL+/fvDzMyswLxq1arB19cXYWFhpVYcERERkb4oUmi6cuUKPDw8Xju/Q4cOuHz5comLIiIiItI3RQpNKSkpMDB4/TAoAwMDPHr0qMRFEREREembIoWmGjVq4Nq1a6+df+XKFdjY2JS4KCIiIiJ9U6TQ1LFjRyxbtgzPnz8vMC87OxsrVqzABx98UGrFEREREemLIt1yYPTo0fjll1/QvXt3DBo0CA0bNgQA3LhxAzt27IBarcaoUaPKpFAiIiIiXSpSaFIqldi1axfmzZuHpUuXQhAEAIBMJoO7uzvmzJkDpVJZJoUSERER6VKRb25Zp04drF+/Hk+ePMGtW7cAAPXr14eFhUWpF0dERESkL4p1R3AAsLCwQIsWLUqzFiIiIiK9VawH9hIRERFVNgxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQS6H1o6tSpE1QqVYGvoKAgAIC/v3+BeXPmzNFax7179zBixAg4OzvDzc0NixcvRl5enlabU6dOwcfHB46OjujatSsiIiLKbR+JiIhI/xX72XPlZe/evVCr1eLra9euYciQIejRo4c4rX///hg/frz42sTERPxerVZj5MiRUCqV2LVrFx4+fIjAwEAYGhpi0qRJAICkpCSMHDkSAwYMQEhICKKjozFr1izY2NjAw8OjHPaSiIiI9J3ehyYrKyut1+vWrUO9evXQtm1bcZqxsTFsbGxeufyJEydw/fp1hIWFQalUomnTppgwYQJCQkIwbtw4GBkZYdeuXbC1tcW0adMAAI0bN8a5c+ewefNmhiYiIiICUAFOz70sJycHBw8eRL9+/SCTycTphw4dgqurK7y8vPDNN9/g2bNn4ryYmBjY29tDqVSK09zd3ZGRkYHr16+Lbdzc3LS25e7ujpiYmLLdISIiIqow9L6n6WVHjhzB06dP4ePjI07z8vJC7dq1Ub16dVy5cgUhISFITEzEypUrAQApKSlagQmA+Do5ObnQNhkZGcjOzoaxsbHkGl8+lUhERFQaFAqFrksoVEU/9kmtv0KFpn379uG9995DjRo1xGm+vr7i9yqVCjY2NggICMDt27dRr169cq/x0qVL5b5NIiJ6e5mYmKBZs2a6LqNQV65c0TrL87aqMKHp7t27iIqKwooVKwpt5+zsDAC4desW6tWrB6VSiYsXL2q1SUlJAQBxHJRSqRSnvdzGzMysSL1MAODk5KT3/xEQERGVJpVKpesSSkStVkvq9KgwoSkiIgLW1tZ4//33C20XFxcH4H+ByMXFBWvWrEFqaiqsra0BAFFRUTAzM4OdnZ3Y5o8//tBaT1RUFFxcXIpcp0KhYGgiIqJKpbIc9yrEQHCNRoOIiAj06dMHBgb/y3m3b9/GqlWrEBsbizt37uDo0aMIDAxEmzZt4ODgAODFgG47OztMnToV8fHxOH78OEJDQzFo0CAYGRkBAAYMGICkpCQsWbIECQkJ2L59OyIjIxEQEKCL3SUiIiI9VCF6mqKionDv3j3069dPa7qhoSGio6MRHh6OrKws1KpVC926dcOYMWPENgqFAmvWrMG8efPg6+sLExMT+Pj4aN3XqW7duli7di2Cg4MRHh6OmjVr4quvvuLtBoiIiEgkEwRB0HURbwO1Wo2YmBi4uLhUmm5KIiIqP3sun0dKVoauy9CiNDXDx81b6bqMEpN6DK8Qp+eIiIiIdI2hiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIqJiMTE0hL5fT1aa9VWIWw4QERGR/qmiMIBMJkNOTiw0mixdl1OAXG4KIyPHUlsfQxMRERGViEaTBUF4qusyCtBoSnd9PD1HREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERSaDXoWnFihVQqVRaXz169BDnP3/+HEFBQXB1dUXLli3x+eefIyUlRWsd9+7dw4gRI+Ds7Aw3NzcsXrwYeXl5Wm1OnToFHx8fODo6omvXroiIiCiX/SMiIqKKw0DXBbxJkyZNEBYWJr5WKBTi9wsXLsSxY8cQGhqKatWqYcGCBRg3bhx27doFAFCr1Rg5ciSUSiV27dqFhw8fIjAwEIaGhpg0aRIAICkpCSNHjsSAAQMQEhKC6OhozJo1CzY2NvDw8CjfnSUiIiK9pfehSaFQwMbGpsD0p0+fYt++fQgJCYGbmxuAFyGqZ8+eiImJgYuLC06cOIHr168jLCwMSqUSTZs2xYQJExASEoJx48bByMgIu3btgq2tLaZNmwYAaNy4Mc6dO4fNmzczNBEREZFIr0/PAcCtW7fg7u6Ozp07Y/Lkybh37x4AIDY2Frm5uWjfvr3YtnHjxqhduzZiYmIAADExMbC3t4dSqRTbuLu7IyMjA9evXxfb5Ieul9vkr4OIiIgI0POephYtWiA4OBgNGzZEcnIyVq1ahUGDBuHQoUNISUmBoaEhzM3NtZaxtrZGcnIyACAlJUUrMAEQX7+pTUZGBrKzs2FsbFykmtVqdZHaExERvcnLQ1Oo6N50bJZ67Nbr0NSxY0fxewcHBzg7O+ODDz5AZGRkkcNMebl06ZKuSyAioreIiYkJmjVrpusyKrQrV67g2bNnJV6PXoemfzI3N0eDBg1w+/ZttG/fHrm5uUhPT9fqbUpNTRXHQCmVSly8eFFrHflX173c5p9X3KWkpMDMzKxYwczJyYn/ERAREekRlUpV6Hy1Wi2p06NChabMzEwkJSXBxsYGjo6OMDQ0RHR0NLp37w4AuHHjBu7duwcXFxcAgIuLC9asWYPU1FRYW1sDAKKiomBmZgY7OzuxzR9//KG1naioKHEdRaVQKBiaiIiI9EhpHZf1eiD44sWLcfr0ady5cwfnz5/HuHHjIJfL4eXlhWrVqqFfv35YtGgRTp48idjYWMyYMQMtW7YUA4+7uzvs7OwwdepUxMfH4/jx4wgNDcWgQYNgZGQEABgwYACSkpKwZMkSJCQkYPv27YiMjERAQIDudpyIiIj0jl73NN2/fx+TJk1CWloarKys8O677+L777+HlZUVAGDGjBmQy+UYP348cnJy4O7ujrlz54rLKxQKrFmzBvPmzYOvry9MTEzg4+OD8ePHi23q1q2LtWvXIjg4GOHh4ahZsya++uor3m6AiIiItMgEQRB0XcTbQK1Wi/eH4uk5IiIqbXsun0dKVoauy9BiZ2WDro2bIjv7NAThqa7LKUAmqwZj47ZvbCf1GK7Xp+eIiIiI9AVDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEuh1aFq7di369euHli1bws3NDWPGjMGNGze02vj7+0OlUml9zZkzR6vNvXv3MGLECDg7O8PNzQ2LFy9GXl6eVptTp07Bx8cHjo6O6Nq1KyIiIsp8/4iIiKjiMNB1AYU5ffo0Bg0aBCcnJ6jVaixduhRDhw7FTz/9BFNTU7Fd//79MX78ePG1iYmJ+L1arcbIkSOhVCqxa9cuPHz4EIGBgTA0NMSkSZMAAElJSRg5ciQGDBiAkJAQREdHY9asWbCxsYGHh0f57TARERHpLb0OTRs3btR6vWjRIri5ueHy5cto06aNON3Y2Bg2NjavXMeJEydw/fp1hIWFQalUomnTppgwYQJCQkIwbtw4GBkZYdeuXbC1tcW0adMAAI0bN8a5c+ewefNmhiYiIiICoOen5/7p6dOnAAALCwut6YcOHYKrqyu8vLzwzTff4NmzZ+K8mJgY2NvbQ6lUitPc3d2RkZGB69evi23c3Ny01unu7o6YmJgy2hMiIiKqaPS6p+llGo0GCxcuRKtWrWBvby9O9/LyQu3atVG9enVcuXIFISEhSExMxMqVKwEAKSkpWoEJgPg6OTm50DYZGRnIzs6GsbGx5DrVanWx9o+IiOh1FAqFrkuo0N50bJZ67K4woSkoKAjXrl3Djh07tKb7+vqK36tUKtjY2CAgIAC3b99GvXr1yrtMXLp0qdy3SfQ2MDQ0RPPmzfX64KBWq3H58mXk5ubquhSqRExMTNCsWTNdl1GhXblyRessVHFViNA0f/58/P7779i2bRtq1qxZaFtnZ2cAwK1bt1CvXj0olUpcvHhRq01KSgoAiOOglEqlOO3lNmZmZkXqZQIAJycnvf6jT6TPFAoFjiTE43F2lq5LKcDS2BRdGjugefPmui6FiIpIpVIVOl+tVkvq9NDr0CQIAhYsWIBff/0VW7duRd26dd+4TFxcHID/BSIXFxesWbMGqampsLa2BgBERUXBzMwMdnZ2Yps//vhDaz1RUVFwcXEpcs0KhYKhiagEHmdnISUrQ9dlvBZ/v4kqntL6vdXrgeBBQUE4ePAgvvnmG1StWhXJyclITk5GdnY2AOD27dtYtWoVYmNjcefOHRw9ehSBgYFo06YNHBwcALwY0G1nZ4epU6ciPj4ex48fR2hoKAYNGgQjIyMAwIABA5CUlIQlS5YgISEB27dvR2RkJAICAnS160RERKRn9LqnaefOnQBe3MDyZcHBwejbty8MDQ0RHR2N8PBwZGVloVatWujWrRvGjBkjtlUoFFizZg3mzZsHX19fmJiYwMfHR+u+TnXr1sXatWsRHByM8PBw1KxZE1999RVvN0BEREQivQ5NV65cKXR+rVq1sG3btjeup06dOli/fn2hbVxdXXHgwIGilEdERESViF6fniMiIiLSFwxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAFDExEREZEEDE1EREREEjA0EREREUnA0EREREQkAUMTERERkQQMTUREREQSMDRRpaYRBF2XUCh9r4+IqDIx0HUBRLokl8lwJCEej7OzdF1KAZbGpujS2EHXZRAR0X8xNFGl9zg7CylZGboug4iI9BxPzxERERFJwNBERCSBiaEhBD0eY6bPtRG9LXh67h+2b9+OjRs3Ijk5GQ4ODpg9ezZatGih67KISMeqKAwgk8mQkxMLjUa/xsDJ5aYwMnLUdRlEbz2GppccPnwYwcHBCAoKgrOzM7Zs2YKhQ4fi559/hrW1daltRyMIkMtkpba+0qbv9VUW+T0bMj3+LPS9vrKg0WRBEJ7qugwtGk0ZrFPP/w7oe330dmJoeklYWBj69++Pfv36AQCCgoLw+++/Y9++fRgxYkSpbYdXbJEU+tyzAbB3423Hv1NEBTE0/VdOTg4uX76MkSNHitPkcjnat2+PCxculPr2eMUWSaWPPRtA2fRukH7h3ykibQxN//X48WOo1eoCp+Gsra1x48aNNy6fPwgzJycHCoWi0LYKhQKWVUwg08OBmzVMqyEvL0+vT7kIggBNKR2x9fmzMDeoArVaDUEw0cuAIpebQK1WQ61Wl9o6+XkUT2X7LCrb3ylAfz8Pff69AKT/buTPf9MFFQxNpST/l+Pvv/+W1N7yv19653k6/nr0l66rKFf6+1k8RkzK4/++KDyI60YOgJhSXys/j+KobJ9F5fs7Bejp56HXvxdAUX833hR0GZr+y9LSEgqFAqmpqVrTU1NToVQq37i8gYEBnJycIJfL9fq/HyIiItKW3zNoYFB4LGJo+i8jIyM0b94c0dHR6NKlC4AXiTM6Ohp+fn5vXF4ul8PIyKisyyQiIiIdYWh6yZAhQxAYGAhHR0e0aNECW7ZswbNnz9C3b19dl0ZEREQ6xtD0kp49e+LRo0dYvnw5kpOT0bRpU2zYsEHS6TkiIiJ6u8kE3nufiIiI6I347DkiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJoIGo2mVJ9ZRfS24UXGRNoePnyI69ev67qMcsf7NFVy169fx5o1a5CSkoL69eujd+/eaNWqla7LqrTUavUbH/hM5SMrK0t8DpWZmZmOq6G0tDSkpqZCoVCgdu3afAKDDj148AC9evVCmzZtMHLkSDg5Oem6pHLDnqZK7MaNGxgwYAA0Gg2cnJwQExODf/3rXwgPD9d1aZVSYmIitmzZgocPH+q6lErv+vXr+Pzzz+Hv748PP/wQBw8eBMAeJ125evUqhgwZgokTJ8Lb2xsbNmxg77gO3bx5ExkZGXj69Cm2bduGy5cvi/Pe9t8RhqZKShAE/PDDD3B3d8fSpUsxefJkbN++HV26dEFERATWr1+v6xIrlVu3bmHAgAFYsmQJtm3bhkePHum6pErr+vXrGDRoEOzs7PDZZ5/B09MTM2bMQFxcHB/GrQPXr1+Hv78/2rVrh2+//RZffPEFli9fzn8udEilUuG9995Dz549ce3aNYSFheHatWsA3v7QxNNzlZRMJsPDhw+RkpIiTjMzM4O/vz+qVKmCn376CTVq1ECvXr10WGXlkJWVhbVr16JTp05wdHTEggULkJeXh2HDhsHKykrX5VUqaWlpCA4Ohre3N6ZPnw4A8Pb2xuXLl7Fv3z7MmjULgiAwPJWTR48eYd68eejVqxcCAwMBAI0bN0ZUVBTu37+PtLQ0vPPOO6hVq5aOK6081Go1NBoNbty4gXnz5sHKygpr165FeHg4rl27hurVq2P58uW6LrPMMDRVQvl/9Js1a4abN2/ixo0baNSoEYAXwalfv35ITEzEjh070LVrV5iYmOi44rebXC5H8+bNYWlpiZ49e8LS0hKTJk0CAAancpaXl4f09HT06NEDwIuLJORyOWxtbZGWlgYADEzlSCaTwcPDA927dxenfffddzhx4gRSUlLw+PFj2NnZYfTo0WjdurUOK6085HI5rKys4OTkhKtXr6Jr164wMjJCYGAgcnJy0L9/f12XWKZ4eq4Syv+j37FjRyQmJmLDhg3IzMwE8CJQWVhYYMyYMYiJicGZM2d0WWqlYGxsDB8fH/Ts2RPAiwdHL126FJs2bcL69evx+PFjAC8O4ElJSbos9a2nVCrx9ddfiwfg/HEzNWrUgFyu/ecy/3eGyo6lpSUGDRqEBg0aAAB++uknrFixAkuXLsXmzZsREhKCJ0+e4OTJk7ottBLJP34oFAqcPn0aAPDLL79Ao9GgVq1aOHv2LC5evKjLEssUe5oqsXr16iE0NBTDhw+HsbExxo0bJ/ZqGBgYQKVSoVq1ajqusnIwNTUF8OIgLZfL0bNnTwiCgMmTJ0Mmk+HTTz/Fpk2bcO/ePSxZsoS9f2Uo/wCt0WhgaGgI4MU/E6mpqWKbtWvXwsjICP7+/jAw4J/RsvTylYsuLi7Yt28fmjdvDgBo06YNrK2tERsbq6vyKp38MxXt2rXDnTt3MG/ePBw7dgz79u1DfHw8lixZAkNDQ6hUKlSpUkXX5ZY6/rZXcu3atcOyZcswYcIEJCcn48MPP4RKpcKBAweQmprKsQLlTKFQQBAEaDQaeHp6QiaTYerUqfjtt9+QlJSEvXv3MjCVE7lcrjV+Kb+nadmyZVi9ejUOHDjAwFTO6tSpgzp16gB4EWpzc3NhamoKlUql48oqj/zfB1tbW0yfPh1KpRJr1qxB3bp1UbduXchksrc2MAGATHjbh7qTJJcvX8aiRYtw9+5dKBQKyOVyfPvtt2jWrJmuS6uU8n8t83uZ4uPjER4ezoNDOcsf07RixQokJyejfv36CA0Nxa5du8TeDtKdZcuW4cCBAwgLCxN7CKl85Obm4ocffoCjoyMcHBwqzQUSDE0kysjIQFpaGjIzM2FjY8MByDqmVquxZMkSbNmyBQcOHICDg4OuS6q0Vq9ejWXLlsHMzAxhYWGV6mZ++igyMhJnzpzBTz/9hLCwMP5zpyP5/1RUJpVrb6lQZmZmsLW1hUqlYmDSE3Z2dti/fz8Dk465u7sDAHbt2sXApAfs7Ozw6NEj7Nixg4FJhypbYALY00Sk1ypLl3dFkJWVJQ7YJ93Lzc0VB+oTlReGJiIiIiIJKl/fGhEREVExMDQRERERScDQRERERCQBQxMRERGRBAxNRERERBIwNBERERFJwNBEREREJAGfNklElca0adOwf/9+AICBgQFq1KiBHj16YMKECW/tA0aJqPQwNBFRpeLh4YHg4GDk5eXh8uXLCAwMhEwmw5dffqnr0ohIz/H0HBFVKkZGRrCxsUGtWrXQpUsXtG/fHlFRUQCAx48fY9KkSfDw8ICzszO8vb3x448/ai2v0Wiwfv16dO3aFY6Ojnj//fexevVqcf5//vMfTJgwAa1bt0bbtm0xevRo3Llzp1z3kYjKBnuaiKjSunr1Ki5cuIDatWsDAHJyctC8eXMMHz4cZmZm+P333zF16lTUq1cPLVq0AAB888032LNnD6ZPn453330XDx8+RGJiIoAXz0MbOnQoXFxcsH37dhgYGOC7777DsGHDcPDgQRgZGelsX4mo5PjsOSKqNKZNm4aDBw+iSpUqyMvLQ05ODuRyOUJDQ9G9e/dXLjNy5Eg0atQIgYGByMjIgJubG+bMmYOPP/64QNsffvgBq1evRmRkpPig5ZycHLRp0warVq2Cu7t7me4fEZUt9jQRUaXi6uqKefPm4dmzZ9i8eTMUCoUYmNRqNdasWYOff/4ZDx48QG5uLnJycmBsbAwAuHHjBnJyctCuXbtXrjs+Ph63b99Gq1attKY/f/4ct2/fLtsdI6Iyx9BERJWKiYkJ6tevDwBYuHAhevfujT179uDjjz/Gxo0bER4ejhkzZkClUsHExAQLFy5Ebm4uALzxCrusrCw0b94cISEhBeZZWVmV/s4QUbliaCKiSksul2PkyJFYtGgRvL29cf78eXTu3Bm9e/cG8GLQ982bN9G4cWMAQIMGDWBsbIyTJ0+ibt26BdbXvHlzREZGwtraGmZmZuW6L0RU9nj1HBFVaj169IBcLsf27dtRv359REVF4fz580hISMCcOXOQkpIitq1SpQqGDx+Or7/+GgcOHMDt27cRExODPXv2AAC8vb1haWmJ0aNH4+zZs0hKSsKpU6fw1Vdf4f79+7raRSIqJexpIqJKzcDAAH5+ftiwYQMOHDiApKQkDB06FCYmJujfvz+6dOmCp0+fiu3HjBkDhUKB5cuX4+HDh7CxscGAAQMAvDj1t23bNoSEhGDcuHHIzMxEjRo14Obmxp4norcAr54jIiIikoCn54iIiIgkYGgiIiIikoChiYiIiEgChiYiIiIiCRiaiIiIiCRgaCIiIiKSgKGJiIiISAKGJiIiIiIJGJqIiIiIJGBoIiIiIpKAoYmIiIhIAoYmIiIiIgn+H+JfKphY8fCaAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import classification_report, recall_score, accuracy_score"
      ],
      "metadata": {
        "id": "wQNOXoi3dk4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "s_DtA_Dvd8dq",
        "outputId": "c773f8ed-c580-40bd-c0e2-6e8d84193a9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        age  workclass    fnlwgt     education  education.num marital.status  \\\n",
              "0  1.238510    Private -0.468746       7th-8th      -2.409308       Divorced   \n",
              "1  0.219781    Private  0.706710  Some-college      -0.049553      Separated   \n",
              "2 -0.328766    Private  0.254708       HS-grad      -0.442846       Divorced   \n",
              "3 -0.015311    Private -0.371895          10th      -1.622723      Separated   \n",
              "4  2.805786  State-gov -0.957835     Doctorate       2.310201  Never-married   \n",
              "\n",
              "          occupation    relationship  race  sex  capital.gain  capital.loss  \\\n",
              "0  Machine-op-inspct       Unmarried     4    0     -0.147027      9.448624   \n",
              "1     Prof-specialty       Own-child     4    0     -0.147027      9.448624   \n",
              "2      Other-service       Unmarried     4    0     -0.147027      9.126380   \n",
              "3       Adm-clerical       Unmarried     4    1     -0.147027      9.126380   \n",
              "4     Prof-specialty  Other-relative     4    0     -0.147027      8.910724   \n",
              "\n",
              "   hours.per.week native.country income  \n",
              "0       -0.083814  United-States  <=50K  \n",
              "1       -0.083814  United-States  <=50K  \n",
              "2        0.335593  United-States  <=50K  \n",
              "3       -0.083814  United-States  <=50K  \n",
              "4       -1.761443  United-States   >50K  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b429ea6b-209b-4813-b93a-9a3f0d243d7d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>workclass</th>\n",
              "      <th>fnlwgt</th>\n",
              "      <th>education</th>\n",
              "      <th>education.num</th>\n",
              "      <th>marital.status</th>\n",
              "      <th>occupation</th>\n",
              "      <th>relationship</th>\n",
              "      <th>race</th>\n",
              "      <th>sex</th>\n",
              "      <th>capital.gain</th>\n",
              "      <th>capital.loss</th>\n",
              "      <th>hours.per.week</th>\n",
              "      <th>native.country</th>\n",
              "      <th>income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.238510</td>\n",
              "      <td>Private</td>\n",
              "      <td>-0.468746</td>\n",
              "      <td>7th-8th</td>\n",
              "      <td>-2.409308</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Machine-op-inspct</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.147027</td>\n",
              "      <td>9.448624</td>\n",
              "      <td>-0.083814</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.219781</td>\n",
              "      <td>Private</td>\n",
              "      <td>0.706710</td>\n",
              "      <td>Some-college</td>\n",
              "      <td>-0.049553</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Own-child</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.147027</td>\n",
              "      <td>9.448624</td>\n",
              "      <td>-0.083814</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.328766</td>\n",
              "      <td>Private</td>\n",
              "      <td>0.254708</td>\n",
              "      <td>HS-grad</td>\n",
              "      <td>-0.442846</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>Other-service</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.147027</td>\n",
              "      <td>9.126380</td>\n",
              "      <td>0.335593</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.015311</td>\n",
              "      <td>Private</td>\n",
              "      <td>-0.371895</td>\n",
              "      <td>10th</td>\n",
              "      <td>-1.622723</td>\n",
              "      <td>Separated</td>\n",
              "      <td>Adm-clerical</td>\n",
              "      <td>Unmarried</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.147027</td>\n",
              "      <td>9.126380</td>\n",
              "      <td>-0.083814</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&lt;=50K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.805786</td>\n",
              "      <td>State-gov</td>\n",
              "      <td>-0.957835</td>\n",
              "      <td>Doctorate</td>\n",
              "      <td>2.310201</td>\n",
              "      <td>Never-married</td>\n",
              "      <td>Prof-specialty</td>\n",
              "      <td>Other-relative</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.147027</td>\n",
              "      <td>8.910724</td>\n",
              "      <td>-1.761443</td>\n",
              "      <td>United-States</td>\n",
              "      <td>&gt;50K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "    .colab-df-convert:hover {\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-737eec76-2e47-4934-a642-4e6e4ae3d138 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 29993,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000016670973336,\n        \"min\": -1.6609504747600334,\n        \"max\": 2.8841500347304216,\n        \"num_unique_values\": 59,\n        \"samples\": [\n          1.2385101950873258,\n          2.3356034215160566,\n          -0.563857248331303\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workclass\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Private\",\n          \"State-gov\",\n          \"Local-gov\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fnlwgt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000016670973336,\n        \"min\": -1.6658188185597491,\n        \"max\": 12.243791333388168,\n        \"num_unique_values\": 20118,\n        \"samples\": [\n          -0.6386661998769957,\n          -0.4357434865498011,\n          -0.6211342221283299\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"7th-8th\",\n          \"Some-college\",\n          \"Prof-school\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"education.num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000016670973336,\n        \"min\": -3.589185320466098,\n        \"max\": 2.3102013787078457,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          -2.4093079806313096,\n          -0.0495533009617318,\n          1.9169089320962496\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"marital.status\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Divorced\",\n          \"Separated\",\n          \"Married-spouse-absent\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"Tech-support\",\n          \"Handlers-cleaners\",\n          \"Machine-op-inspct\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relationship\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Unmarried\",\n          \"Own-child\",\n          \"Wife\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"race\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital.gain\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000016670973336,\n        \"min\": -0.1470273398943064,\n        \"max\": 13.369379032760914,\n        \"num_unique_values\": 117,\n        \"samples\": [\n          0.5194733233479549,\n          3.614355839319981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"capital.loss\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000166709733358,\n        \"min\": -0.2187095935166295,\n        \"max\": 9.448624033130985,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          2.602168782669264,\n          9.448624033130985\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hours.per.week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000166709733358,\n        \"min\": -3.355190963629154,\n        \"max\": 4.8651913432791485,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          1.2582889024928523,\n          -0.9226288524011872\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"native.country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"Peru\",\n          \"Poland\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"income\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \">50K\",\n          \"<=50K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preparing the data for modeling\n",
        "# Convert categorical variables to dummy variables\n",
        "df = pd.get_dummies(df, drop_first=True)"
      ],
      "metadata": {
        "id": "9Rq0GZSYeBNn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the features and target variable\n",
        "X = df.drop('income_>50K', axis=1)  # assuming 'income_>50K' is the target variable\n",
        "y = df['income_>50K']"
      ],
      "metadata": {
        "id": "VzhDJpWHeGOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Splitting the data**"
      ],
      "metadata": {
        "id": "Ll5PCmMReUJq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "VqELqbhoePpF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardize the features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "qbWLrgzAeSz2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Selection and Training**\n",
        "We'll explore different models (Logistic Regression, Decision Tree, Random Forest)"
      ],
      "metadata": {
        "id": "W-16uGXTetOV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing Models and metrics\n",
        "\n",
        "from sklearn.metrics import classification_report, roc_curve, roc_auc_score, auc\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.metrics import precision_recall_curve\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "MqSLJLtreocb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create model instances and random state reproducibility\n",
        "\n",
        "logistic_regression = LogisticRegression(max_iter=1000)\n",
        "decision_tree = DecisionTreeClassifier(max_depth=10, random_state=42)\n",
        "random_forest = RandomForestClassifier(random_state=42)"
      ],
      "metadata": {
        "id": "RB_RbuAce2yk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Logistic Regression**"
      ],
      "metadata": {
        "id": "dQuF9Ex8fJ12"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logistic_regression.fit(X_train_scaled, y_train)\n",
        "decision_tree.fit(X_train, y_train)\n",
        "random_forest.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "JUdAKCUYe4vX",
        "outputId": "9ce6ee7d-95e2-482c-b8ae-bfbac8d29d21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the model to make predictions on the scaled data\n",
        "y_pred_logistic_regression = logistic_regression.predict(X_test_scaled)\n",
        "y_pred_decision_tree = decision_tree.predict(X_test)\n",
        "y_pred_random_forest = random_forest.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "g9zXzJ6JfOiS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the models\n",
        "print(\"Logistic Regression Model Evaluation\")\n",
        "print(classification_report(y_test, y_pred_logistic_regression))\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_logistic_regression))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U141bmBcfQ9w",
        "outputId": "948f0425-7e32-437e-db6e-ad3cbcd15715"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Logistic Regression Model Evaluation\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.87      0.93      0.90      4514\n",
            "        True       0.73      0.59      0.65      1485\n",
            "\n",
            "    accuracy                           0.84      5999\n",
            "   macro avg       0.80      0.76      0.78      5999\n",
            "weighted avg       0.84      0.84      0.84      5999\n",
            "\n",
            "Accuracy: 0.8449741623603934\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Vusualizing the outcomes in the form of Confusion Matrix and Precision-Recall Curve**"
      ],
      "metadata": {
        "id": "RcBv2BNFhX_D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to plot confusion matrix\n",
        "def plot_confusion_matrix(y_test, y_pred, model_name):\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "    disp.plot()\n",
        "    plt.title(f'Confusion Matrix for {model_name}')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "xJmiQtPZfUTC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_confusion_matrix(y_test, y_pred_logistic_regression, \"Logistic Regression\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ltkzpbIcf8wr",
        "outputId": "5e84518a-0a8b-4fcc-e887-c7a28a511258"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precision and recall calculation\n",
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_logistic_regression)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize = (5, 4))\n",
        "plt.plot(recall, precision, marker = \".\", label = \"Logistic Regression\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.title(\"Precision-Recall curve\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "S7_n2kq_gDdw",
        "outputId": "bbf6d782-6a0e-49bd-e891-e0d58c5f60d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Decision Tree**"
      ],
      "metadata": {
        "id": "06W9RA1chGrt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the models\n",
        "print(\"Decision Tree Model Evaluation\")\n",
        "print(classification_report(y_test, y_pred_decision_tree))\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_decision_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_sNvs0NgWag",
        "outputId": "c8a4fe52-7a76-4bab-f43e-3fb885461e94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Model Evaluation\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.86      0.95      0.90      4514\n",
            "        True       0.77      0.52      0.62      1485\n",
            "\n",
            "    accuracy                           0.84      5999\n",
            "   macro avg       0.82      0.73      0.76      5999\n",
            "weighted avg       0.84      0.84      0.83      5999\n",
            "\n",
            "Accuracy: 0.8431405234205701\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Confusion Matrix for Logistic Regression\n",
        "plot_confusion_matrix(y_test, y_pred_decision_tree, 'Decision Tree')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fgTxkktlhKyj",
        "outputId": "d20e499a-3591-45d5-8a59-92fcb690701f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precision and recall calculation\n",
        "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_logistic_regression)\n",
        "\n",
        "# Plot\n",
        "plt.figure(figsize = (5, 4))\n",
        "plt.plot(recall, precision, marker = \".\", label = \"Decision Tree\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.title(\"Precision-Recall curve\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "kv34D04Qg_MO",
        "outputId": "c27cb004-7b92-4bc3-cbd8-2c6805da585a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Random Forest**"
      ],
      "metadata": {
        "id": "wSxVO1q8hl4R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the models\n",
        "print(\"Random Forest Model Evaluation\")\n",
        "print(classification_report(y_test, y_pred_random_forest))\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred_random_forest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gF7ov3fRhfL9",
        "outputId": "b4786b02-64eb-4b0a-9fbc-306779adae92"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Model Evaluation\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "       False       0.88      0.92      0.90      4514\n",
            "        True       0.73      0.62      0.67      1485\n",
            "\n",
            "    accuracy                           0.85      5999\n",
            "   macro avg       0.81      0.77      0.79      5999\n",
            "weighted avg       0.84      0.85      0.85      5999\n",
            "\n",
            "Accuracy: 0.8501416902817136\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting Confusion Matrix for Logistic Regression\n",
        "plot_confusion_matrix(y_test, y_pred_random_forest, 'Random Forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "mLRU0Bwehqjp",
        "outputId": "e5549926-2244-4db9-ad63-d9e214ba01e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# precision and recall calculation\n",
        "plt.figure(figsize = (5, 4))\n",
        "plt.plot(recall, precision, marker = \".\", label = \"Random Forest\")\n",
        "plt.xlabel(\"Recall\")\n",
        "plt.ylabel(\"Precision\")\n",
        "plt.title(\"Precision-Recall curve\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "XbNpye6ahuK6",
        "outputId": "5cb902e3-035a-4122-8948-bfd9ae5e7ae7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Model Comparison**"
      ],
      "metadata": {
        "id": "EWNxFPWuh5TV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = [\n",
        "    {\"Model\": \"Logistic Regression\", \"Accuracy\": 0.8449},\n",
        "    {\"Model\": \"Decision Tree\", \"Accuracy\": 0.8431},\n",
        "    {\"Model\": \"Random Forest\", \"Accuracy\": 0.8501},\n",
        "]\n",
        "\n",
        "results_dataset = pd.DataFrame(results)\n",
        "results_dataset = results_dataset.sort_values(by = \"Accuracy\", ascending = False)\n",
        "results_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "k8UG1Ygahx8J",
        "outputId": "064204a0-b0ef-4408-f1f1-d00919877872"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy\n",
              "2        Random Forest    0.8501\n",
              "0  Logistic Regression    0.8449\n",
              "1        Decision Tree    0.8431"
            ],
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              "      <th>2</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.8501</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.8431</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results_dataset",
              "summary": "{\n  \"name\": \"results_dataset\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Random Forest\",\n          \"Logistic Regression\",\n          \"Decision Tree\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0036350149013908224,\n        \"min\": 0.8431,\n        \"max\": 0.8501,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.8501,\n          0.8449,\n          0.8431\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **FAIRNESS AND BIAS**\n",
        "Fairness metrics (“fairness_metrics”) were calculated to assess any bias present in the model's predictions with sensitive attributes like sex and race. This is crucial to ensure that predictions made by models are fair.\n",
        "\n",
        "Demographic Parity (DP): Measures whether all groups have equal chances of receiving a positive prediction. DP zero signifies no bias (perfect parity). Positive or negative indicates bias.\n",
        "\n",
        "Equal Opportunity (EO): Measure the difference in true positive rates for all groups. EO zero means the model is fair in its true positive predictions."
      ],
      "metadata": {
        "id": "ExgoDqLSws5z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Calculating Fairness Metrix**"
      ],
      "metadata": {
        "id": "C8c52CtMiERi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to calculate fairness metrics\n",
        "def fairness_metrics(y_true, y_pred, sensitive_features):\n",
        "    results = {}\n",
        "    for feature_name, sensitive_data in sensitive_features.items():\n",
        "        # Demographic Parity\n",
        "        dp_groups = y_pred[sensitive_data == 1].mean() - y_pred[sensitive_data == 0].mean()\n",
        "        # Equal Opportunity\n",
        "        eo_groups = (y_pred[(y_true == 1) & (sensitive_data == 1)].mean() -\n",
        "                     y_pred[(y_true == 1) & (sensitive_data == 0)].mean())\n",
        "\n",
        "        results[feature_name] = {'Demographic Parity': dp_groups, 'Equal Opportunity': eo_groups}\n",
        "\n",
        "    return results\n",
        "\n",
        "# Sensitive features mapping\n",
        "sensitive_features = {\n",
        "    'sex': X_test['sex'],\n",
        "    'race': X_test['race']\n",
        "}\n",
        "\n",
        "# Calculate and print fairness metrics for each sensitive feature\n",
        "fairness_results = fairness_metrics(y_test, y_pred_logistic_regression, sensitive_features)\n",
        "for feature, metrics in fairness_results.items():\n",
        "    print(f\"Fairness Metrics for {feature}:\")\n",
        "    print(f\"  Demographic Parity Difference: {metrics['Demographic Parity']:.4f}\")\n",
        "    print(f\"  Equal Opportunity Difference: {metrics['Equal Opportunity']:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9j59zCuZh848",
        "outputId": "45ffde6f-2298-457d-d9b2-db3dbb9da526"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fairness Metrics for sex:\n",
            "  Demographic Parity Difference: 0.1824\n",
            "  Equal Opportunity Difference: 0.1354\n",
            "Fairness Metrics for race:\n",
            "  Demographic Parity Difference: 0.1278\n",
            "  Equal Opportunity Difference: -0.2286\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analysis Of Fairness Metrics for Sex and Race:**\n",
        "\n",
        "**Fairness Metrics for sex:**\n",
        "Demographic Parity Difference (DP): 0.1824, Equal Opportunity Difference (EO): 0.1354 Both suggest the model favors males, predicting higher incomes more accurately and frequently. Suggesting a potential bias favoring males\n",
        "\n",
        "**Fairness Metrics for Race:**\n",
        "Demographic Parity Difference (DP): 0.1278, Equal Opportunity Difference (EO): -0.2286 DP Shows a preference for predicting higher incomes in the majority racial group. While the EO indicates that minorities are less likely to be correctly predicted to earn more, highlighting a bias against them."
      ],
      "metadata": {
        "id": "kGsyUNhPwImx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizing the Results**"
      ],
      "metadata": {
        "id": "eRh2B35eiOL1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data for fairness metrics\n",
        "fairness_data = {\n",
        "    'Feature': ['Sex', 'Sex', 'Race', 'Race'],\n",
        "    'Metric': ['Demographic Parity', 'Equal Opportunity', 'Demographic Parity', 'Equal Opportunity'],\n",
        "    'Value': [0.1824, 0.1354, 0.1278, -0.2286]\n",
        "}\n",
        "\n",
        "# Creating a DataFrame\n",
        "df_fairness = pd.DataFrame(fairness_data)\n",
        "\n",
        "# Define a color palette\n",
        "palette = sns.color_palette(\"Set1\")\n",
        "\n",
        "# Plotting\n",
        "plt.figure(figsize=(10, 6))\n",
        "barplot = sns.barplot(x='Feature', y='Value', hue='Metric', data=df_fairness, palette=palette)\n",
        "plt.title('Fairness Metrics Comparison Across Sensitive Features')\n",
        "plt.ylabel('Metric Difference')\n",
        "plt.xlabel('Sensitive Feature')\n",
        "plt.legend(title='Metric', bbox_to_anchor=(1.05, 1), loc=2)\n",
        "plt.grid(True, linestyle='--', alpha=0.6)\n",
        "\n",
        "# Annotate bars with their values\n",
        "for p in barplot.patches:\n",
        "    barplot.annotate(format(p.get_height(), '.4f'),\n",
        "                     (p.get_x() + p.get_width() / 2., p.get_height()),\n",
        "                     ha = 'center', va = 'center',\n",
        "                     xytext = (0, 9),\n",
        "                     textcoords = 'offset points')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "82s0fVQviHgi",
        "outputId": "e4b880bd-31cc-4626-f308-d68896a68d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-56-3c3e57ec18d9>:16: UserWarning: The palette list has more values (9) than needed (2), which may not be intended.\n",
            "  barplot = sns.barplot(x='Feature', y='Value', hue='Metric', data=df_fairness, palette=palette)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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33Xab01XE2NhYmaapXr16OdWLjY1VfHy8cnJynMrj4uLUuHFjx+uIiAh16tRJP/zwg3JzcyW5/lnu06fPRa/WF2V/fv/99/Lw8NCAAQOcyh988EGZpplv+Hfr1q2drmw2aNBA/v7+juPiQr7//nudOHHC6ZaF7t27a+fOnU5Der/88ksZhpFvJICkfMP/W7Roofr16zte5+bm6scff1Tnzp0VGRnpKA8NDVX37t21adMmx+elJI/L3r1765133lGrVq3066+/atq0aerXr59uuukm/frrr456q1atUqVKldSmTRun47BRo0by8/PLdxzWr1/f6XMbHBysOnXqOO3/i7XrUvn7++v666/XypUrnUasrFixQk2bNlVERISkszP62+12denSxaltVapUUa1atYrcxxTW///00086deqUunXr5rR+m82mJk2aOK3/3OPrzJkzSklJcYxk2L59+yXvk4Kc3y8WdX/4+/tLkn744QdlZGSUSGwAgAtjiDjKvdjY2AInucnMzNSMGTO0aNEiJSQkOJ3sFeVetbCwsEJnxg0PD3d6nZds5z2+5e+//5ZpmvrPf/6j//znPwWuIzk5WWFhYXrsscc0ZMgQ3XzzzYqKilLbtm112223qUGDBpLOJvpPP/20Xn31VbVp00ZNmjRRhw4ddPvtt6tq1aoXbUeeq6++WnXr1tXy5ctVuXJlVa1a1ZEwn+/AgQPas2ePrrvuukJjL6qiDGH/+++/ZbPZVK9evULrtGzZUjfffLOmTp2q9957Ty1btlTnzp116623XnAG47wT0rS0tCLFe+TIEcfJ9bnq1q3rWH7uLMV5CUOevMT1/M9IpUqVZLfbdfr0aafh0rVq1cq3rdq1aysjI0MpKSmqWrWqy5/louzzouzPw4cPKzQ01LEP8+T9nQ4fPuxUfn6bpbPHRlEea7R06VLVqFFD3t7eOnDggKSzw5B9fX21bNkyPfXUU5LOflZCQ0MVGBh40XWevx9SUlKUkZGhOnXq5Ktbr1492e12xcfH66qrrirx47Jdu3Zq166dMjIytH37dq1YsUIffvihHnnkEa1cuVIhISE6cOCATp8+XeTjsLD9f+4PABdrlxW6du2qr7/+Wps3b1azZs30999/a/v27U63FOzfv1+maeqmm24qcB1FnWStsP4/7weE++67r8D3nfuZPnHihKZOnaoVK1bk26cldV/z+Z/Nou6PyMhIPfDAA5ozZ46WLVum5s2bq2PHjurRowfDwwHgMiHBxhVr/PjxjpmjmzZtqkqVKskwjIveC5zn3Ksa5yvs6mDeeu12u6SzV/ratWtXYN28K30tWrTQV199pdWrV+vHH3/Up59+qrlz52rcuHGOGZTvv/9+dezYUV9//bV++OEH/ec//9HMmTM1d+5cXX311RdtS57u3btrwYIFqlixorp06ZLvanEeu92uqKioQicQu9h9o+eyatZmwzA0efJkbdmyRd9++63Wrl2rMWPGaM6cOfroo48KvUKdlxjv2rXLpUeJFVVh+7Cw8qJ89s7n6me5KPu8uPvzQi52XBQmNTVV3377rc6cOVNggrF8+XI9+eSTBU5QdyEXOoYv5nIdl76+vmrevLmaN2+uoKAgTZ06Vd9//73uuOMO2e12hYSEFPpc57x7jfMUZY6BorTrUt1www3y9fXVypUr1axZM61cuVI2m0233HKLo47dbpdhGJo1a1aBcV/qrNp5n7nXXnutwB88zt3mE088oc2bN2vgwIFq2LCh/Pz8ZLfbNWjQoCIdr4V9LvNGoBTk/GPUlf0xatQo3XHHHY6/4UsvvaQZM2bo448/dqlvBgAUDwk2rlhffPGFbr/9dsdst9LZ4X+XY6bVvOGnXl5eat269UXrBwYGqlevXurVq5fS0tLUv39/TZkyxemEt2bNmnrwwQf14IMPav/+/br99ts1e/bsQk++C3Lrrbdq8uTJSkpK0r///e9C69WsWVM7d+7Udddd53JSUxw1a9aU3W7Xnj171LBhwwvWbdq0qZo2baonn3xSy5Yt09NPP60VK1YUmhxcc801CggI0Oeff65HHnnkoklIRESE9u3bl6987969juVWyrtae679+/fL19fXkUCV5Gf5QvuzevXqWrdunVJTU52u+OXti+rVq1/y9qWzw77PnDmjF154Id9kaPv27dObb76pTZs2qXnz5qpZs6Z++OEHnThxokhXsc8VHBwsX1/fQv++NpvN6Srw5Tou8+TdKpCUlORY97p169SsWbNL+rHgfEVp16XImzxx1apVGj16tFasWKHmzZsrLCzMUadmzZoyTVM1atQocETBpcrrg0NCQi7YB588eVLr1q3TsGHDnG47KGgIfWF94fkjmPIcOXKkyPG6uj/yZjEfMmSIfv31V919991asGCBnnzyySJvEwBQPNyDjStWQYnUvHnzLnhVwSohISFq2bKlPvroIyUmJuZbfu6ss+c+CkY6e79vzZo1HY90ysjI0JkzZ5zq1KxZUxUrVsz32KeLqVmzpsaMGaPhw4c7zfB7vi5duighIUEff/xxvmWZmZlOM1P7+fkVaQjwhXTu3Fk2m01vvfWW4+p/nrwrSCdPnsx3NSkvGb/QfvD19dWgQYO0Z88eTZw4scArUp999pnjMWvt27fXtm3btHnzZsfy9PR0ffzxx6pevbrTPb1W2Lx5s9N9nvHx8Vq9erXatGnj+AyXxGe5KPvz+uuvV25urj744AOneu+9954Mw9D1119f7O2fa+nSpYqMjNTdd9+tW265xenfwIED5efnp2XLlkmSbrrpJpmmqalTp+Zbz8WuNnp4eKhNmzZavXq106O0jh07puXLl+uaa65x/JBQksflunXrCiz/7rvvJMmRYHXp0kW5ubmOWfvPlZOTU6zj7mLtKoyrx3nXrl2VmJioTz75RDt37lSXLl2clt90003y8PDQ1KlT8/3dTNPMF6er2rVrJ39/f82YMaPAOQby+uDCfnCbO3duvrK82frP/2HL399fQUFB2rhxo1P5f//73yLHW9T9kZqamm8eh6ioKNlsNpe/DwAAxcMVbFyxOnTooM8++0z+/v6qX7++tmzZop9++snlq17F9c9//lP33HOPbr31VvXp00eRkZE6duyYtmzZoqNHj2rp0qWSpG7duqlly5Zq1KiRAgMD9dtvv+mLL75Q//79JZ29knL//ffrlltuUf369eXh4aGvv/5ax44dU7du3VyOq7B7Es912223aeXKlfrnP/+pn3/+Wc2aNVNubq727t2rVatW6Z133nHc99ioUSOtW7dOc+bMUWhoqGrUqFHgPcwXUqtWLT3yyCOaNm2a7rnnHt10003y9vbWb7/9ptDQUA0fPlyLFy/WggUL1LlzZ9WsWVNpaWn6+OOPHZMqXcigQYP0119/afbs2fr555918803q0qVKjp27Ji+/vprbdu2TR9++KEkafDgwfr888/10EMPacCAAQoICNCSJUt06NAhTZkypdCh38UVFRWlgQMHOj2mSzr7CKg8JfFZLsr+7Nixo1q1aqU33nhDhw8fVnR0tH788UetXr1a9913X6GPanJFQkKCfv7553wTqeXx9vZWu3bttGrVKj377LO69tprddttt2nevHk6cOCA2rVrJ7vdrk2bNqlVq1aO46YwTzzxhH766Sfdc889uueee+Th4aGPPvpIWVlZeuaZZxz1SvK4HDJkiGrUqKEbbrhBkZGRysjI0E8//aRvv/1WMTExuuGGGySdvU++b9++mjFjhnbs2KE2bdrIy8tL+/fv16pVqzR27FinYddFcbF2FcbV47x9+/aqWLGiXn31VXl4eOjmm292Wl6zZk098cQTmjRpkg4fPqzOnTurYsWKOnTokL7++mv16dNHAwcOdKlt5/L399cLL7ygESNGqGfPnuratauCg4N15MgRfffdd2rWrJmef/55+fv7q0WLFnrnnXeUnZ2tsLAw/fjjjwU+y7xRo0aSpDfeeENdu3aVl5eXbrjhBvn5+enOO+/UzJkzNXbsWDVu3FgbN24scKREYYq6P9avX68XX3xRt9xyi2rXrq3c3Fx99tlnBe5jAEDJIMHGFWvs2LGy2WxatmyZzpw5o2bNmmnOnDn5nqlbUurXr6+FCxdq6tSpWrx4sU6cOKHg4GBdffXVGjp0qKPegAED9M033+jHH39UVlaWIiIi9MQTTzhOLqtVq6Zu3bpp3bp1Wrp0qTw8PFS3bl29+eabJXZClXc1+b333tNnn32mr776Sr6+vqpRo4YGDBjgNIRx1KhRev755/Xmm28qMzNTd9xxh8sJtnT2ubA1atTQ/Pnz9cYbb8jX11fR0dG67bbbJJ1NNn777TetWLFCx44dU6VKlRQbG6uJEyc6zQhdWHtee+01derUSR9//LFmz56t1NRUBQUFqUWLFnrmmWcUFxcnSapSpYo+/PBD/fvf/9b8+fN15swZRUdHa/r06QU+M/xStWjRQk2bNtVbb72lI0eOqH79+powYYLTpFMl8Vkuyv602Wx6++23NXnyZK1YscLxLO8RI0bowQcfvOS2S2dnl7bb7Y6ksiA33HCDvvjiC33//ffq1KmTJkyYoOjoaH366ad67bXXVKlSJTVu3NjxN7yQq666Sh988IEmTZqkGTNmyDRNxcbG6t///rfT57Ykj8uXXnpJq1ev1sqVK5WYmCjTNBUZGalHHnlEDz30kNMEXy+++KIaN26sDz/8UG+88YY8PDxUvXp19ejRQ82aNbtoe893sXYVxtXjvEKFCurYsaOWLVum1q1bKyQkJF+dwYMHq3bt2nrvvff01ltvSTq7X9u0aaOOHTu63Lbz3XrrrQoNDdXMmTP17rvvKisrS2FhYWrevLnTEyImTZqk8ePH67///a9M01SbNm00a9asfPNnxMbG6vHHH9eHH36otWvXym63a/Xq1fLz89PQoUOVkpKiL774QitXrtT111+vd955p9AJ6gpSlP0RHR2ttm3b6ttvv1VCQoKjn5w1a5aaNm16yfsMAHBxhlmcGXUAACUuOjpa/fr10/PPP1/aoQAAAKAIuAcbAAAAAAALkGADAAAAAGABEmwAAAAAACzAPdgAAAAAAFiAK9gAAAAAAFiABBsAAAAAAAu43XOwP/jgA7377rtKSkpSgwYN9Nxzzyk2NrbAuh9//LGWLFmi3bt3S5IaNWqkp556qtD6BbHb7crJyZHNZpNhGJa0AQAAALgY0zRlt9vl6ekpm43rYoA7cKt7sFesWKERI0Zo3LhxatKkiebOnatVq1Zp1apVCgkJyVd/+PDhatasmZo1ayZvb2+98847+uqrr/T5558rLCysSNvMysrSb7/9ZnVTAAAAgCKJiYmRt7d3aYcBoAjcKsG+8847FRMTo+eff17S2avL7du314ABAzR48OCLvj83N1ctWrTQ888/r9tvv71I28zJydHWrVsVExMjDw+PSwkfsJzdbtehQ4dUo0YNftkGABfRh6Ksy83N1W+//aYmTZrI09PtBp4CVyS3OVKzsrK0fft2Pfzww44ym82m1q1ba/PmzUVaR0ZGhnJychQQEFDk7eYNC/fw8CDBRpljGIZsNps8PDw4OQQAF9GHwl1wmyLgPtwmwT5+/Lhyc3PzDQUPCQnR3r17i7SOiRMnKjQ0VK1bt3Z5+3a73dG5GYYhwzBkmqbOHQCQV263253eW5xySTp/cEFh5TabLV8srpbTJvdsk91ud6pTHtp0KbHTJtpEm2iTK+Xn9qFFbWtZb9OFYqdN7tem898DoOxzmwT7Us2cOVMrVqzQ+++/rwoVKrj8/kOHDjl+3fb391dISIhSUlKUmprqqBMQEKDAwEAlJSUpMzPTUR4SEiJ/f38dPXpU2dnZjvLQ0FD5+vrq8OHDTh1oeHi4PD09dfDgQacYIiMjlZOTo/j4eEeZzWZTZGSkMjMzlZiY6Cj38vJSRESE0tLSlJyc7Cj38fFRWFiYTp48qZMnTzrKaZN7tsk0TaWlpenUqVMKCgoqF20qj38n2kSbaFPZbFNeYmOapg4dOlQu2iSVv7/TldwmEmzA/bjNPdhZWVlq2rSpJk+erM6dOzvKR44cqVOnTuntt98u9L3vvvuu3n77bc2ZM0cxMTEubTc3N1dbtmxRbGysY4h4WfhF81xl/VfaDz74QLNnz9axY8fUoEEDjR071jGT+/lt2r17t6ZMmaI//vhDhw8f1qhRo3Tfffc5xZ6Tk6MpU6Zo2bJlOnbsmEJDQ3XHHXfo0UcflSRlZ2frP//5j77//nsdOnRI/v7+uu666zR8+HCFhobmizErK0t9+/bVzp07tWTJEkVHRxe5rQX9Pdz170SbaBNtok20iTbRprLVptzcXG3btk1NmzblVkXATbjNFWxvb281atRI69atcyTYdrtd69atU//+/Qt936xZszR9+nS9++67LifX57LZbPnuz8rrMAuqW9g6XCkvaN2FlRcWi1XlxW3TihUr9OqrrzrN/P7QQw/lm/k9b5tnzpxRZGSkunTpogkTJsgwjHzbeOedd/Thhx/q1VdfVf369fX7779r9OjRqlSpku69915lZWVpx44dGjJkiBo0aKBTp07p5Zdf1pAhQ7Ro0aJ8MebdOrBz585itbU0/06maSozM1M+Pj7Fir0stqmoMdIm2kSbaNOFYixK+bl9qCttLcttOn/9RS2nTWWzTW5yHUzS2YtS5165B8oTLy+vIv/I5TYJtiQ98MADGjlypBo3bqzY2FjNnTtXGRkZ6tmzpyRpxIgRCgsL0/DhwyWdHRY+efJkTZo0SdWrV1dSUpIkyc/PTxUrViy1dlxJ5syZoz59+qhXr16SpHHjxmnNmjVauHBhgTO/x8bGOq5uT5o0qcB1bt68WZ06dVKHDh0kSTVq1NDnn3+ubdu2SZIqVaqkOXPmOL3nueee05133qkjR44oIiLCUf7dd9/pxx9/1JQpU/T9999fcnsvN9M0lZiYqMjIyEK/oAEABaMPBS6daZo6evSoTpw4UdqhACUqMDBQ1apVu+j3hVsl2F27dlVKSoomT56spKQkNWzYUO+8846qVKkiSYqPj3f6dfDDDz9Udna2HnvsMaf1/OMf/9CwYcMua+xXIitmfi9IXFycPv74Y+3bt0916tTRzp07tWnTJo0aNarQ96SmpsowDFWuXNlRduzYMT333HN66623HFeAAQAAUHR5yXVoaKj8/Pz4sQrljmmaSk9Pd8zpEB4efsH6bpVgS1L//v0LHRI+b948p9fffPPN5QgJhbBi5veCDB48WKmpqerSpYs8PDyUm5urJ598Uj169Ciw/pkzZzRx4kR169ZN/v7+ks4eKKNGjdJdd92lmJiYfJPbAAAA4MJyc3MdyfX553tAeeLr6ytJSkxMVGho6AWHi7tdgg2sXLlSy5Yt06RJk1S/fn3t2LFDEyZMcEx2dq7s7Gw9/vjjMk1T48aNc5TPmzdPaWlpTlfX3ZWXl1dphwAAbos+FCi+vHuu/fz8SjkSoOTlfc6zs7NJsFE6goKC5OHh4fQoDElKTk52DOsvjtdee02DBw9Wt27dJEnR0dE6cuSIZsyY4ZRgZ2dn64knntCRI0c0d+5cx9VrSVq/fr22bNmSb+K7Xr166dZbb9Wrr75a7PguJ5vN5nRPOQCg6OhDAWswLBxXgqJ+zkmwUWKKO/P7xWRmZub7gHt4eDjNtJmXXB84cEDvv/++goKCnOo/++yzeuKJJxyvExMTNXDgQL3xxhtq0qRJsWO73Ezz7HOwK1asyJcbALiIPhQAYDUSbJQoV2d+z8rK0p49exz/T0hI0I4dO+Tn56datWpJkm644QZNnz5dERERjiHic+bMccxUnjex3R9//KEZM2YoNzfXMYN8QECAvL29812xyBvyUbNmTVWrVq3kd4xFTNNUcnIyk4oAQDHQhwK4EkRHR+utt95yXPBCySLBRolydeb3xMRE3X777Y7Xs2fP1uzZs9WyZUvHJHbPPvus/vOf/2jcuHFKTk5WaGio+vbtq6FDh0qSEhISHBPc3XbbbU7xvP/++2rVqlVJNhkAAABwMmrUKC1evFh9+/bViy++6LRs3Lhx+u9//6s77rhDr7zyykXX9fPPP+vee+/Vhg0bnJ6QU5gffvhBAQEBxY4driHBRolzZeb3GjVqaNeuXRdcn7+/v8aOHauxY8cWuLwo67DiPQAAAEBRhYeHa8WKFRozZozjEbFnzpzR8uXLS2Q+iKysLHl7e6tq1aqWrxuFs128CoCyjGd4A0Dx0YcCuFyuvvpqhYeH68svv3SUffnllwoPD1fDhg0dZXa7XTNmzFDHjh0VGxurHj16aNWqVZKkQ4cO6d5775UktWjRQtHR0Ro1apQkacCAAXrxxRf18ssvq1WrVho4cKCks0PEv/76a8f6jx49qqeeekotW7ZU06ZN1bNnT23durXE23+l4Ao24MZsNpvCwsJKOwwAcEv0oQAut169emnRokXq0aOHJGnhwoXq2bOnfvnlF0edGTNmaOnSpRo3bpxq166tDRs26JlnnlFwcLCuueYaTZkyRcOGDdOqVavk7+/v9EPh4sWLdffdd2vBggUFbj8tLU39+/dXWFiYpk2bpqpVq2r79u2y2+0l2/ArCAk24MZM09TJkycVEBDABD0A4CL6UACXW48ePTRp0iQdPnxYkvTrr7/q9ddfdyTYWVlZmjFjhubMmaO4uDhJUmRkpDZt2qSPPvpILVu2dNxPHRISku8e7Nq1a2vEiBGFbn/58uVKSUnRp59+qsDAQElyTCQMa5BgA24s7+SwcuXKnBwCgIvoQwFcbsHBwerQoYMWL14s0zTVoUMHBQcHO5YfOHBAGRkZevDBB53el52d7TSMvDCNGjW64PIdO3bo6quvdiTXsB4JNgAAAABcJr169XLMJP7Pf/7TaVl6erqks8PEz7+Fxdvb+6Lr9vX1veBy5p0oeSTYAAAAAHCZtGvXTtnZ2TIMQ23btnVaVq9ePXl7e+vIkSNq2bJlge/38vKSJOXm5rq87ejoaH3yySc6ceIEV7FLCAk24MYMw2BoIwAUk2EY8vf3pw8FcFl5eHho5cqVjv+fy9/fXw8++KAmTJgg0zR1zTXX6PTp0/r111/l7++vO+64Q9WrV5dhGFqzZo3at2+vChUqqGLFikXadrdu3TR9+nQNHTpUTz31lEJDQ/XHH38oNDTUcc83Lg2P6XIjZjF+pUL5ZhiGAkmwAaBYDMNQSEgIfSiAy87f31/+/v4FLnviiSc0ZMgQzZgxQ127dtWgQYO0Zs0a1ahRQ5IUFhamYcOGadKkSWrdurXGjx9f5O16e3tr9uzZCgkJ0eDBg3Xrrbdq5syZ+RJ9FJ9hmqZZ2kGUZbm5udqyZYuaNm1aJj54e599Tpn79pd2GCgjfOrUVt2Xxss0TU4QAcBFpmkqJSVFwcHB9KEok8raeej5MjMztW/fPtWpU4d7e1HuFfXzzhBxN5O5b7/Sd+0q7TBQxpBgA4DrTNNUamqqgoKC6EMBAJZgiDgAAAAAABYgwQYAAAAAwAIk2EA5wNBGAHCdYRgKCAigDwUAWIZ7sIFygJNDAHCdYRg8BxYAYCmuYAPlgN1uL+0QAMDt2O12JSQk0IcCACxDgg0AAK5YmZmZpR0CAKAcIcEGAAAAAMACJNgAAAAAAFiABBsoB5jkDABcZxiGQkJC6EMBoACLFi1S8+bNL1hn1KhRGjJkyGWKqOhKMy5mEQfKAU4OAcB1hmHI39+/tMMAyiUzN1eGh0eZ3d6oUaO0ePFiSZKnp6cCAgIUHR2tbt26qWfPnrLZuA5ZFGPHjpVpmsV+/7l/By8vL4WHh+u2227TI488Ik/P4qeq58c1YMAANWjQQGPHji32OouKBBsoB+x2O18EAOAiu92uo0ePqlq1avShgMUMDw/tffY5Ze7bX+Lb8qlTW3VfGu/y+9q1a6cJEybIbrfr2LFjWrt2rV5++WV98cUXevvtty8pwSstWVlZ8vb2vmzbq1Sp0iWvI+/vkJWVpe+++04vvviivLy89PDDD7u8rtzcXBmGYUlcxcW3CQAAuGJlZ2eXdghAuZW5b7/Sd+0q8X/FTeK9vb1VtWpVhYWFqVGjRnrkkUc0bdo0ff/9946rqpJ06tQpjR07Vtdee62aNWume++9Vzt37nQsnzJlim677TZ9+umn6tChg+Li4vTCCy8oNzdXs2bNUps2bXTdddfp7bffdtr+kSNH9OijjyouLk7NmjXT448/rmPHjjnVmTZtmq677jrFxcVp7Nixmjhxom677TbH8ryh0G+//bbatm2rW265RZK0ZMkS9ezZU3FxcWrTpo2GDx+u5ORkx/t+/vlnRUdHa82aNbr11lsVExOjPn366M8//8y3n9auXasuXbooLi5OAwcOVGJiYr7t57Hb7Zo1a5ZuvPFGNW7cWB06dMjX7sL+DtWrV9c999yj1q1b65tvvpEkzZkzR7feequaNm2q9u3b64UXXlBaWprjvXnD2FevXq2uXbsqJiZGR44ccYpr1KhR+uWXX/T+++8rOjpa0dHROnjwoG688Ua9++67TrHs2LFD0dHROnDgwAVjvhASbAAAAACQdN1116lBgwb68ssvHWWPP/64kpOTNWvWLC1atEiNGjXSfffdpxMnTjjq/P333/r+++/1zjvvaNKkSfr00081ePBgJSQkaN68eXr66af15ptvauvWrZLOJqJDhgzRyZMnNW/ePM2ZM0cHDx7Uk08+6Vjn0qVLNX36dD399NNatGiRwsPDtWDBgnwxr1u3Tvv27dOcOXM0Y8YMSVJOTo4ef/xxLV26VG+99ZYOHz6sUaNG5Xvva6+9plGjRunTTz9VcHCwHnnkEacfHjMzMzV79my99tprmj9/vuLj4/Xqq68Wuv8mTZqkWbNmaciQIVqxYoUmTpyoKlWqFP0PIKlChQqOGAzD0NixY7V8+XK98sorWr9+vf7973871c/MzNSsWbP00ksvafny5QoJCXFaPnbsWMXFxalPnz764Ycf9MMPPygiIkK9evXSokWLnOouXLhQLVq0UK1atVyK+VzuN+4BAAAAAEpI3bp1tWvXLknSxo0btW3bNq1bt84x9HrkyJH6+uuv9cUXX6hv376SJNM09a9//Uv+/v6qX7++WrVqpX379mnWrFmy2WyqW7euZs2apZ9//llNmjTRunXr9Oeff2r16tUKDw+XdDbZ7datm7Zt26bY2FjNnz9fvXv3Vq9evSRJ//jHP/Tjjz8qPT3dKV4/Pz+99NJLTkPDe/fu7fh/ZGSkxo4dq969eystLU0VK1Z0LPvHP/6hNm3aSJJeeeUVtW/fXl999ZW6du0q6ewon3HjxqlmzZqSpH79+mnatGkF7rfU1FS9//77ev7553XHHXdIkmrWrHnRidLymKapdevW6YcfflD//v0lSffff79jeY0aNfTEE0/on//8p1544QVHeXZ2tl544QU1aNCgwPVWqlRJXl5e8vHxUdWqVR3ld9xxhyZPnuzY39nZ2Vq+fLlGjhxZpHgLQ4INlANMcgYArjMMQ6GhofShAJyYpunoF3bt2qX09HS1atXKqU5mZqb+/vtvx+vq1as7TZpYpUoVeXh4OM3vUKVKFccw7T179qhatWqO5FqS6tevr8qVK2vv3r2KjY3Vvn37dM899zhtNzY2VuvXr3cqi4qKynff9e+//66pU6dq586dOnnypGPCr/j4eNWvX99Rr2nTpo7/BwYGqk6dOtq7d6+jzNfX15FcS1JoaKjTUPNz7d27V1lZWbr22msLXF6YNWvWKC4uTtnZ2TJNU927d9ewYcMkST/99JNmzJihvXv3KjU1Vbm5uTpz5owyMjLk6+sr6ezkaNHR0S5tU5LCwsLUvn17ffrpp4qNjdW3336rrKwsxzD74iLBBsoBTg4BwHWGYThO0AAgz549e1SjRg1JUlpamqpWrap58+blq3fuRFrnT4hmGEaBZXa73fJ4z+/H0tPTNXDgQLVt21YTJ05UUFCQ4uPjNXDgQJfnnSioDYXNGl6hQgXXAv+fVq1a6YUXXpCXl5dCQ0Md2zx06JAefvhh3X333XryyScVEBCgTZs2aezYscrOzna028fHp9jnwnfeeadGjBihMWPGaNGiRerateslfy9wDzZQDpREZw0A5Z3dbtfBgwfpQwE45A3dvummmyRJjRo10rFjx+Th4aFatWo5/QsODi72durVq6ejR48qPj7eUfbXX3/p1KlTqlevniSpTp06+u2335zed/7rguzdu1cnTpzQ008/rebNm6tevXqFXnXesmWL4/8nT57U/v37Vbdu3WK0SKpdu7Z8fHzyXWG/GF9fX9WqVUsRERFOCf327dtlmqZGjRqlpk2bqk6dOk4TrLnCy8urwL6+ffv28vX11YIFC7R27VrHcPxLwRVsAABwxSK5Bq5cWVlZSkpKcnpM14wZM3TDDTfo9ttvlyS1bt1aTZs21dChQ/XMM8+odu3aSkxM1HfffafOnTsrJiamWNtu3bq1oqKi9PTTT2vMmDHKzc3VCy+8oJYtWzrW2b9/fz333HNq3Lix4uLitGLFCu3atUuRkZEXXHdERIS8vLw0b9483X333frzzz8LvW962rRpCgoKUkhIiN544w0FBQWpc+fOxWpThQoV9NBDD+nf//63vLy81KxZM6WkpGj37t268847XV5frVq1lJ2drXnz5qljx47atGmTPvzww2LFVr16dW3dulWHDh2Sn5+fAgMDZbPZ5OHhoZ49e2rSpEmqVauW4uLiirX+c5FgAwAAALCcT53aZXo7a9euVdu2beXp6anKlSurQYMGevbZZ3XHHXc47p02DEMzZ87Um2++qdGjR+v48eOqUqWKmjdv7vLs2OcyDEPTpk3T+PHj1b9/fxmGoXbt2um5555z1OnRo4cOHjyoV199VWfOnFGXLl10xx13XPQqdnBwsF555RW9/vrrmjdvnho1aqSRI0fq0UcfzVd3+PDhevnll7V//341bNhQb7/99iU9R3vIkCHy8PDQ5MmTlZiYqKpVq+quu+4q1roaNGig0aNHa9asWXr99dfVvHlzPfXUU8WahOzBBx/UqFGj1K1bN2VmZmr16tWO2wB69+6t6dOnq2fPnsWK83yGWdggekg6+7DyLVu2qGnTpvLw8CjtcPRHvwFK/9+shoBfdLSu/mCe7Ha70yQaAICLyxsiHhkZSR+KMqmsnYeeLzMzU/v27VOdOnXk4+PjtMzMzZVxGWO+3NsrLQ888ICqVKmS71FVrvr555917733asOGDapcubJF0bmnjRs36v7779eaNWsu+KPJhT7v5+IKNlAOMMkZALjOMAyFh4fThwIl4HInu+Uxuc7IyNCHH36otm3bymaz6fPPP9dPP/2kOXPmlHZo5UJWVpZSUlI0ZcoU3XzzzZc0IuFcJNgAAOCKdf4MuQBQVhiGoe+++07Tp0/XmTNnVKdOHU2ZMkWtW7cu7dDKheXLl2vs2LFq2LChXnvtNcvWy7cKUA6c+7xGAEDRmKbpGCJOHwqgrPHx8dF7771XIutu1aqVdl3ht5327NnTsvuuz8UNRwAAAAAAWIAEGwAAAAAAC5BgAwAAAABgARJsoBzg3kEAcJ1hGNx/DQCwFAk2AAC4YuXk5JR2CACAcoQEGygHTNMs7RAAwO2Ypqn4+Hj6UACAZUiwAQAAAKCUDBgwQC+//HJph1HmjRo1SkOGDCntMC6K52ADAAAAsFSu3ZSH7fLNb+Dq9kaNGqXFixfnK2/btq3effddK0OzRGZmpmbOnKnPP/9chw8fVsWKFdWqVSsNGzZMV111VWmH5zBq1CidOnVK06ZNs3zdY8eOdRpxNGDAADVo0EBjx461fFuXggQbQKn44IMP9O677yopKUkNGjTQc889p9jY2ALr7t69W5MnT9b27dt1+PBhjR49Wvfff79Tnf/+979asGCBDh8+LEm66qqrNGTIELVv395RZ8CAAfrll1+c3te3b1+9+OKL+bZ5/Phx3XbbbUpISNCGDRtUuXLlS2wxgLLIZmMwH1ASPGyGXv1kvQ4mnSrxbUVWrayRd17r8vvatWunCRMmOJV5e3tbFZZlsrKydP/99ys+Pl4jR45UkyZNlJycrBkzZqhPnz6aM2eOmjZtWqox5ubmlviEkZUqVSrR9VuFbxWgHHC3E8QVK1ZowoQJGjp0qBYvXqwGDRpo4MCBSk5OLrB+RkaGatSooeHDh6tq1aoF1qlWrZqefvppLVq0SAsXLtS1116roUOHavfu3U71+vTpox9++MHxb8SIEQWub+zYsYqOjr60hgIo02w2myIjI92uDwXcxcGkU/or/niJ/ytuEu/t7a2qVas6/QsICHAs379/v/r166eYmBh17dpVP/74o6Kjo/X1119Lkn7++WdFR0fr1Kn/3/6OHTsUHR2tQ4cOSTr7g/1TTz2ldu3aqUmTJrr11lu1fPlyl+J87733tGXLFs2YMUNdu3ZV9erVFRsbqylTpqhu3bpOV3bzhlFPnTpV1157rZo1a6bnn39eWVlZjvUNGDBAL774ol588UVdc801atWqld58802nq8MnT57UiBEj1KJFCzVp0kSDBg3S/v37HcsXLVqk5s2ba/Xq1eratatiYmI0ZswYLV68WKtXr1Z0dLSio6P1888/F2k/5a1v7dq16tKli+Li4jRw4EAlJiY63nPuEPFRo0bpl19+0fvvv+/Y1sGDB3XjjTfmG4GQt60DBw64tN+Li28UoBxwtwl65syZoz59+qhXr16qX7++xo0bJx8fHy1cuLDA+rGxsRo5cqS6detW6C/LHTt2VPv27VW7dm3VqVNHTz75pPz8/LRlyxanej4+Pk5fpP7+/vnW9d///lenT5/Wgw8+eMltBVB2maapjIwMt+tDAZQ8u92uYcOGycvLS5988onGjRuniRMnuryerKwsNWrUSDNnztTy5cvVp08fjRgxQtu2bSvyOpYvX642bdqoQYMGTuU2m03333+//vrrL+3cudNRvm7dOu3Zs0fz5s3T66+/rq+++kpvvfWW03sXL14sDw8PffLJJxo7dqzee+89ffLJJ47lo0aN0u+//663335bH330kUzT1ODBg5Wdne2ok5mZqVmzZumll17S8uXL9eyzz6pLly5q166d40JGXFxckduZmZmp2bNn67XXXtP8+fMVHx+vV199tcC6Y8eOVVxcnNOFk4iICPXq1UuLFi1yqrtw4UK1aNFCtWrVKnIsl4IEGygH3OnkMCsrS9u3b1fr1q0dZTabTa1bt9bmzZst2UZubq4+//xzpaen5+vYly1bplatWql79+6aNGmSMjIynJb/9ddfmjZtml599VWuagHlnGmaSkxMdKs+FIB11qxZo7i4OKd/06dPlyT99NNP2rt3r1599VU1aNBALVq00JNPPunyNsLCwjRw4EA1bNhQkZGRGjBggNq1a6eVK1cWeR379+9X3bp1C1xWr149SdK+ffscZd7e3vrXv/6lq666Sh06dNBjjz2m999/X3a73VEnPDxcY8aMUd26ddWjRw/1799f7733nmN733zzjV566SU1b95cDRo00MSJE5WQkOC4ei9J2dnZeuGFF9SsWTPVrVtX/v7+8vHxcRoZ4MqQ++zsbI0bN04xMTFq1KiR+vXrp/Xr1xdYt1KlSvLy8nK6cOLh4aE77rhD+/btc/yAkZ2dreXLl6tXr15FjuNScQ82gMvq+PHjys3NVUhIiFN5SEiI9u7de0nr3rVrl+666y6dOXNGfn5+euutt1S/fn3H8u7duysiIkKhoaHatWuXJk6cqH379mnq1KmSzib/Tz31lJ555hlFRETo4MGDlxQPAAAou1q1aqUXXnjBqSxviPiePXtUrVo1hYWFOZa5cjU2T25urqZPn65Vq1YpISFB2dnZysrKko+Pj0vrceWHwOjoaPn6+jpex8XFKT09XfHx8apevbokqUmTJk73TDdt2lRz5sxRbm6u9uzZI09PTzVp0sSxPCgoSHXq1NGePXscZV5eXpbeTufr66uaNWs6XoeGhhZ6+2BhwsLC1L59e3366aeKjY3Vt99+q6ysLN1yyy2WxXkxJNgAyo06depoyZIlOn36tL744guNHDlS8+fPdyTZffv2ddSNjo5W1apVdf/99+vvv/9WzZo1NWnSJNWrV0+33XZbaTUBAABcJr6+vpc0bDhvpNu5ye+5Q6gl6d1339X777+vMWPGOBLff/3rX/nqXUjt2rULvQiRl/DWqVPH1fAvmY+PT5EmNivKfpIkT0/n1NQwjGKNMLrzzjs1YsQIjRkzRosWLVLXrl2dfnAoaYx/BHBZBQUFycPDI98vksnJyapSpcolrdvb21u1atVS48aNNXz4cDVo0EDvv/9+ofXzfpnNm/Ri/fr1WrVqla6++mpdffXVjpnKr732Wk2ePPmSYgNQNnl5eZV2CADKoHr16uno0aNOk2ydP69LcHCwJCkpKclRdu690JL066+/qlOnTrrtttvUoEEDRUZGOk0WVhTdunXTTz/9lG/ddrtd7733nurXr+90f/auXbuUmZnpFLefn5/Cw8MdZeffA75161bVqlVLHh4eqlevnnJycrR161bH8uPHj2vfvn1OIwML4uXl5TQUXSrafiqOgrYlSe3bt5evr68WLFigtWvXXtbh4RIJNlAuuNO9wt7e3mrUqJHWrVvnKLPb7Vq3bl2xhl5diN1ud5o183w7duyQJMfM5FOmTNFnn32mJUuWaMmSJXrppZcknX2kWL9+/SyNDUDps9lsioiIcKs+FIB1srKylJSU5PQvJSVFktS6dWvVrl1bo0aN0s6dO7Vx40a98cYbTu+vWbOmwsPDNWXKFO3fv19r1qzR7NmznerUqlVLP/30k3799Vft2bNHzz//vI4dO+ZSnPfff79iY2P1yCOPaOXKlTpy5Ii2bdumYcOGae/evXr55ZedriRnZWVp7Nix+uuvv/Tdd99pypQp6t+/v1Nfd+TIEU2YMEF79+7V8uXLNX/+fN17772Szl4x79Spk5577jlt3LhRO3fu1DPPPKOwsDB16tTpgrFWr15du3bt0t69e5WSkqLs7Owi7afiqF69urZu3apDhw4pJSXFkWx7eHioZ8+emjRpkmrVqmX5+eXFMEQcKAdM0yzxZw9a6YEHHtDIkSPVuHFjxcbGau7cucrIyFDPnj0lSSNGjFBYWJiGDx8u6ewXRd4QqKysLCUkJGjHjh3y8/NzDO2aNGmSrr/+eoWHhystLU3Lly/XL7/84nhUw99//61ly5apffv2CgwM1K5duzRhwgS1aNHC8avvuff9SGd/rZXO/orNc7CB8sc0TaWlpalixYpu1YcC7iKy6uX57izudtauXau2bds6ldWpU0erVq2SzWbT1KlTNXbsWPXu3VvVq1fXs88+q0GDBjnqenl5adKkSXrhhRfUo0cPxcTE6IknntDjjz/uqPPoo4/q4MGDGjhwoHx9fdWnTx917txZp0+fLnKcFSpU0Ny5czV9+nS98cYbOnLkiCpWrKhWrVrpo48+UlRUlFP96667TrVq1VK/fv2UlZWl7t27a9iwYU51br/9dmVmZurOO++Uh4eH7r33Xqdb6SZMmKCXX35ZjzzyiLKzs9W8eXPNnDnzoqN++vTpo19++UW9evVSenq63n//fbVq1eqi+6k4HnzwQY0aNUrdunVTZmamVq9erRo1akiSevfurenTpzvOLS8nw2TqzAvKzc3Vli1b1LRpU3l4eJR2OPqj3wCl79pV2mGgjPCLjtbVH8yT3W53uysw8+fP17vvvqukpCQ1bNhQzz77rGPI9oABA1S9enW98sorkqRDhw4V+Itpy5YtNW/ePEnSmDFjtH79eiUmJqpSpUqKjo7WQw89pDZt2kiS4uPj9cwzz2j37t1KT09XeHi4OnfurCFDhhT4qC7p7PMt7733Xm3YsIEEG6Xigw8+cBwnDRo00HPPPafY2NgC6+7evVuTJ0/W9u3bdfjwYY0ePdpxm0OeGTNm6Msvv9TevXvl4+OjuLg4Pf30047ZaQs71iTpzTffVJcuXSSdHVo4adIkbd++XYZhKDY2Vs8880y+R8iUdXa7XQcPHuRZ2Cizytp56PkyMzO1b98+1alTJ9+kXbl2Ux62y/fD1eXaXnR0tN566y117ty5xLdVHKNGjdKpU6c0bdq0QusMGDBADRo00NixYy9jZJfXxo0bdf/992vNmjWXfAtingt93s/FFWwApaJ///7q379/gcvykuY8NWrU0K6L/LD0r3/964LLw8PDNX/+fJdibNWq1UW3C5SUFStWaMKECRo3bpyaNGmiuXPnauDAgVq1alW+WfglKSMjQzVq1NAtt9yiCRMmFLjOX375Rf369VNMTIxyc3P1+uuva+DAgfr8888d9+f98MMPTu/56KOP9O677+r666+XJKWlpemhhx5Sx44d9c9//lO5ubmaMmWKBg4cqDVr1nBPMwBJuqzJdWlsD2VTVlaWUlJSNGXKFN18882WJdeu4OdaAADKoDlz5qhPnz7q1auX6tevr3HjxsnHx0cLFy4ssH5sbKxGjhypbt26Ffrc0XfffVc9e/bUVVddpQYNGuiVV17RkSNHtH37dkln71vLe55o3r+vv/5aXbp0UcWKFSVJe/fu1YkTJ/TYY4+pbt26uuqqqzR06FAdO3ZMR44cKZmdAQBAESxfvlw33HCDTp8+rREjRpRKDFzBBgCgjMnKytL27dv18MMPO8psNptat26tzZs3W7advHsA8577er7ff/9dO3bs0PPPP+8oq1OnjgIDA/Xpp5/q4Ycflt1u16effqp69eo5nq/qTlx9Fi2AK1tZH9mWd3vdhZw/UrA86dmzZ6ncd30urmAD5QD3DgLly/Hjx5Wbm5tvKHhISIjLs88Wxm6361//+peaNWuWb4KcPHmJc7NmzRxl/v7+mjdvnpYuXaomTZooLi5Oa9eu1axZs/I9w7Sss9lsCgsLow8FAFiGbxSgHGCuQgCuGjdunHbv3p3vsTN5MjMztXz5cvXu3Ttf+dixY9WsWTN99NFHWrBggaKiovTwww87PXfVHZimqRMnTtCHApeIYwhXgqJ+zt3rp2YABXK3x3QBuLCgoCB5eHgoOTnZqTw5OdmSCVtefPFFrVmzRvPnz1e1atUKrLNq1SplZmbq9ttvdypftmyZDh8+rI8++shx5XfixIlq2bKlVq9erW7dul1yfJeLaZo6efKkKleuTB8KFEPepIbp6eny9fUt5WiAkpWeni5JF53MkwQbAIAyxtvbW40aNdK6descj4Kx2+1at25dobPvF4Vpmho/fry++uorzZs3T5GRkYXWXbhwoTp27Kjg4GCn8szMTNlsNqeENO+13W4vdmwA3I+Hh4cCAwOVmJgoSfLz8+PHKpQ7pmkqPT1diYmJCgwMvOgj80iwAQAogx544AGNHDlSjRs3VmxsrObOnauMjAzH5C0jRoxQWFiYhg8fLunsxGh79uxx/D8hIUE7duyQn5+fatWqJenssPDly5dr2rRpqlixopKSkiRJlSpVcprs68CBA9qwYYNmzpyZL67WrVvrtdde07hx4zRgwADZ7XbNnDlTHh4eatWqVYnuEwBlT94omLwkGyivAgMDCx31dS4SbKAc4NdioPzp2rWrUlJSNHnyZCUlJalhw4Z65513HEPE4+PjnSbnSkxMdBrOPXv2bM2ePVstW7Z0zBi7YMECSdKAAQOctjVhwgSnWVcXLlyoatWqqW3btvniqlevnqZPn66pU6eqb9++stlsjthCQ0Mta//lYBiG/P396UOBS2AYhsLDwxUaGqrs7OzSDgcoEV5eXhe9cp3HMJmV4IJyc3O1ZcsWNW3atMg7tST90W+A0sv44wFw+VRu3Vr13nxDHjZODpFfrt3kswEAbqysnYcCuDiuYANuzLOSvzxshl79ZL0OJp0q7XBQhkRWrayRd15b2mEAZZppmkpJSVFwcDBXsQEAliDBBsqBg0mn9Ff88dIOAwDcimmaSk1NVVBQEAk2AMASPAcbAAAAAAALkGADAAAAAGABEmwAAHBFMgxDAQEBDA8HAFiGe7ABAMAVyTAMBQYGlnYYAIByhCvYAADgimS325WUlCS73V7aoQAAygkSbABAuWfm5pZ2CCiDbDabQoKCSjsMAEA5whBxAEC5Z3h4aO+zzylz3/7SDgVliE+d2qr70niuYAMALEOCDQC4ImTu26/0XbtKOwwAAFCOMUQcAABc0ZhFHABgFRJsAABwRSPBBgBYhQQbAABc0bgHGwBgFRJsAAAAAAAsQIINAAAAAIAFSLABAAAAALAACTYAALiiMckZAMAqJNgAAOCKRoINALAKCTYAALiiMYs4AMAqJNgAAAAAAFiABBsAAAAAAAuQYAMAAAAAYAESbAAAcEVjkjMAgFVIsAEAAAAAsAAJNgAAuKKZplnaIQAAygkSbAAAAAAALECCDQAAAACABUiwAQAAAACwAAk2AAC4ojGLOADAKiTYAAAAAABYgAQbAABc0ZhFHABgFRJsAAAAAAAsQIINAAAAAIAFSLABAAAAALCA2yXYH3zwgTp27KiYmBjdeeed2rZtW6F1d+/erWHDhqljx46Kjo7We++9d/kCBQAAbsFmc7vTIQBAGeVW3ygrVqzQhAkTNHToUC1evFgNGjTQwIEDlZycXGD9jIwM1ahRQ8OHD1fVqlUvc7QAAMAdMMkZAMAqbpVgz5kzR3369FGvXr1Uv359jRs3Tj4+Plq4cGGB9WNjYzVy5Eh169ZN3t7elzlaAADgDkiwAQBW8SztAIoqKytL27dv18MPP+wos9lsat26tTZv3lzi27fb7TIMQ5JkGIYMw5Bpmk5fynnldrvd6b3FKZfyf+EzhA2Aq+x2u2w2W77+SpJL5aXV712oP3S1TUBhTNN06buY44k2Xa42nf8eAGWf2yTYx48fV25urkJCQpzKQ0JCtHfv3hLf/qFDhxwnaP7+/goJCVFKSopSU1MddQICAhQYGKikpCRlZmY6xejv76+jR48qOzvbUR4aGipfX18dPnzYqQMNDw+Xp6enDh486Cjz9vZWeHh4STYRQDmUmJioatWqKTMzU4mJiY5yLy8vRUREKC0tzek2Gx8fH4WFhenkyZM6efKko7w0+j1JioyMVE5OjuLj4x1lNptNkZGRRW5TXuxAYUzT1KFDh5zKrPjslcfjiTZd3jaRYAPux20S7NJWo0YNeXh4SPr/XxaDg4MVFBTkqJNXfv793nnl1apVK7C8evXqBZZHRkZaFT6AK1RoaKiksyeQBfUpFStWlJ+fX77ygIAAVa5c2fG6tPo9wzDk5eVVYOyutgkojGEYJfrZK4/HE226PG3Kzc1VSkpKvlgBlF1uk2AHBQXJw8Mj34RmycnJqlKlSolv32az5RtimDfkp6C6ha3DlfKC1g0ArsjrXwrrr6wqL+l+z4oYgcLk/YBekJI8btz5eKJNl6dNzA8AuB+3uSnN29tbjRo10rp16xxldrtd69atU1xcXClGBgAA3BlJDADAKm5zBVuSHnjgAY0cOVKNGzdWbGys5s6dq4yMDPXs2VOSNGLECIWFhWn48OGSzk6MtmfPHsf/ExIStGPHDvn5+alWrVql1g4AAFB2mKbJqAcAgCXcKsHu2rWrUlJSNHnyZCUlJalhw4Z65513HEPE4+PjnYbfJCYm6vbbb3e8nj17tmbPnq2WLVtq3rx5lzt8AAAAAEA55lYJtiT1799f/fv3L3DZ+UlzjRo1tGvXrssRFgAAAADgCuc292ADAAAAAFCWkWADAIArWmGzOwMA4Cq+UQAAwBWNWcQBAFYhwQYAAFc0EmwAgFVIsAEAAAAAsAAJNgAAAAAAFiDBBgAAVzTDMEo7BABAOUGCDQAArmgk2AAAq5BgAwCAKxqTnAEArEKCDQAArmgk2AAAq5BgAwAAAABgARJsAAAAAAAsQIINAACuaExyBgCwCgk2AAC4opFgAwCsQoINAACuaHa7vbRDAACUEyTYAAAAAABYgAQbAAAAAAALkGADAAAAAGABEmwAAHBFY5IzAIBVSLABAMAVjQQbAGAVEmwAAHBFYxZxAIBVSLABAAAAALAACTYAAAAAABYgwQYAAAAAwAIk2AAA4IrGJGcAAKuQYAMAgCsaCTYAwCok2AAA4IrGLOIAAKuQYAMAAAAAYAESbAAAAAAALECCDQAAAACABUiwAQDAFY1JzgAAViHBBgAAAADAAiTYAADgimaaZmmHAAAoJ0iwAQAAAACwAAk2AAAAAAAWIMEGAAAAAMACJNgAAOCKxiziAACrkGADAAAAAGABEmwAAHBFYxZxAIBVSLABAAAAALAACTYAAAAAABYgwQYAAAAAwAIk2AAA4Ipms3E6BACwBt8oAADgisYkZwAAq5BgAwCAKxoJNgDAKiTYAAAAAABYgAQbAAAAAAALkGADAAAAAGABEmwAAHBFYxZxAIBV+EYBAABXNCY5AwBYhQQbAABc0UiwAQBWIcEGAAAAAMACJNgAAAAAAFiABBsAAAAAAAuQYAMAgCsas4gDAKzCNwoAALiiMckZAMAqJNgAAOCKRoINALAKCTYAAAAAABYgwQYAAAAAwAIk2AAA4IpmGEZphwAAKCdIsAEAwBWNBBsAYBUSbAAAcEVjkjMAgFVIsAEAwBWNBBsAYBUSbAAAAAAALECCDQAAAACABUiwAQDAFY1JzgAAViHBBgAAVzQSbACAVUiwAQDAFc1ut5d2CACAcoIEGwAAAAAAC5BgAwAAAABgARJsAAAAAAAsQIINAACuaExyBgCwSrES7CVLluiuu+5S27ZtdfjwYUnSe++9p6+//trS4AAAAEoaCTYAwCouJ9j//e9/9corr6h9+/Y6ffq0Y+bNypUra+7cuZYHCAAAUJKYRRwAYBWXE+z58+frpZde0qOPPiqb7f/f3rhxY/3555+WBgcAAAAAgLtwOcE+dOiQGjZsmK/c29tbGRkZlgQFAAAAAIC7cTnBrlGjhnbs2JGvfO3atapXr54lQQEAAAAA4G48XX3DAw88oBdffFFZWVmSpG3btmn58uWaOXOmXnrpJcsDBAAAKElMcgYAsIrLCfadd96pChUq6M0331RGRoaGDx+u0NBQjRkzRt26dSuJGAEAAEoMCTYAwCouJ9iS1KNHD/Xo0UMZGRlKT09XSEiI1XEBAABcFna73WniVgAAisvlBPvgwYPKzc1V7dq15evrK19fX0nS/v375enpqRo1algeJAAAAAAAZZ3LP9eOHj1amzdvzle+detWjR492pKgAAAAAABwNy4n2H/88YeaNWuWr7xp06YFzi4OAAAAAMCVwOUE2zAMpaWl5Ss/ffq0cnNzLQkKAADgcmGSMwCAVVxOsFu0aKEZM2Y4JdO5ubmaOXOmrrnmGkuDAwAAAADAXbg8ydnTTz+tfv366ZZbblHz5s0lSRs3blRqaqrmzp1reYAAAAAlyTRNrmIDACzhcoJdv359LV26VB988IF27twpHx8f3Xbbberfv78CAwNLIESg/JickaYNOTmyS6og6b4KPmrvXaHQ+vMzM/RldpZydfZgvc27gnpW8HEst9vt6tevnzZu3CTJlM3LV1Wvu1N+1eo76mSdTtbR7+YqN/2EJMnTP0ThNzwoT99KjjppB39X0obPZOackQxDvmH1Ve36AZa2HQAAACjvivUc7LCwMD311FNWxwKUa+9npuvnnBy19/TSNZ5eWnAmQzPPZKqeh4dqeOQ/FFdnndHK7Cw19fBUJy9vLcs6o4VZZxRps6mFl7ck6fkff9SmhKOq3fxGpXsFKXnzCiWsna+at42Uh/fZR+jFr54lMydLIc1vk3JzlLxlpY6snqma3YdLknLSTypx3Sfy8AtQSKueykjYq9N//azE9Z8q9Nrel28HAQAAAG6uWAn2qVOntG3bNiUnJ8s0Tadlt99+uxVxAeXOmuxsRdpsGuzrJ0lq4uGh+9NO64MzmRrp55+v/pKsM6osQ8/4VZQkNfPy0oOnT+rjrDNq4eUtu92uXxOOqkOHDvJq0kN/xR9XheAaOvj56zr++zeq0qyb0o7skj0rXVVa9lSl2k0lSblZGTqx/RudOX5EFYIilLL1C0mmatw8VDavCqpYvaGyUg4r7dB2SSTYAAAAQFG5nGB/8803evrpp5Weni5/f3+ne5YMwyDBBgqQabfrjKSm51yp9rTZVNUwdMBuL/A9J0xTcedd2a5j89Ae+9kJBnfa7TIl9ejRQysP/G+dFQNleFXQmWNnC9IP/SFJjuRakgKi2+jE9m+U9vfvqhAUoTMph+Xh4y+b1/8PVfeLbKQzKYeUnZoiL//gS2w9AJRt3H8NALCKywn2q6++ql69eumpp56Sr69vScQElDvx/0uiQ23OE/f7G4ZOFpJg2yUF25xP+gJthnLsees8m2jXqVNHOnDIUcfmWUG5Z9IlSTnppyTDeZs2T6//LTtxdjvZmfKoUNGpjpd/iCQp61QSCTYAAABQRC4/pishIUH33nsvyTUAACgXzr/dDQCA4nI5wW7btq1+++23kogFKLfC/3flOvG8q9WppimfQoYm2iSl2J1P+k7YTcewk3CbhyRp3759TnXsOWfkUeHsfd6efpUl037e8uz/LQs8ux0vH9mzM53qZKcmS5K8K1e9SMsAAAAA5HF5iHj79u3173//W3v27FFUVJQ8PZ1X0alTJ8uCA8oLH5tNFSRtyc3RXf8ry7HblWSaivHwKPA9gYah3bm5TmX77bmq+r9kvYHNJkPSsmXL5Bnb8+w600/KzD6jClVqSZL8alyt1P2bdXr/VlWq3USSdPLPdZKkijUbS5IqBFdX2sHfZc/Jks3z7Ozk6Yf+kGweDA8HAAAAXOBygv3cc89Jkt566618ywzD0I4dOy49KqAc6uDlpS+yszUrM13XeHhpQVamTEn3/O+51k+knlKAYdO4imdnFL/du4Jmn8nUxPQ0df7fY7oyJfX533OzbTabmoVV07fffqvaqV5K9wxW8ublkmFTUOOOkqSKEdGyefspedNnkj1Xpj1HJ7Z/Iw+/AFUIipAkBcfepLSD23X4i7cU3PQWZSTs05nkg6pYM/ay7yMAAADAnbmcYO/cubMk4gDKvXt9/HTcTNP32dlak52tCpIequCjyP/NFJ5mmjL0/8O5O3lXULzdri+zs7Q5N0eeknp5V3A8A1uSXmzTRv/8+4A2bvhKkimbl6/C2vVzPANbksI7PaSj372nYxuXSJI8/YMVfsNAx3LPioEKve5OJW34TIk/LpBkyLdafZ6BDeCKYbO5fMccAAAFKtZzsPOcOXNGFSpUuHhFAJKkx30rFrpsVqWAfGX9fXzV36fwCQVtNps++OAD/WPal/or/niBdbwrhahm9+EXjKtiZGNVjGx8wToAUF6ZpsmjugAAlnD5J9vc3Fy99dZbateuneLi4nTw4EFJ0ptvvqlPPvnE8gABAABKErOIAwCs4nKC/fbbb2vx4sV65pln5OXl5SiPiorSp59+amlwAAAAAAC4C5cT7M8++0zjx49Xjx49nO5Zio6O1t69ey0NDgAAAAAAd+Fygp2QkKCaNWvmKzdNUzk5OZYEdSEffPCBOnbsqJiYGN15553atm3bBeuvXLlSt9xyi2JiYnTrrbfqu+++K/EYAQAAAABXHpcT7Pr162vjxo35yletWqWGDRtaElRhVqxYoQkTJmjo0KFavHixGjRooIEDByo5ObnA+r/++quGDx+u3r17a8mSJerUqZOGDh2qP//8s0TjBAAA7oNZxAEAVnH5G2XIkCEaP368Zs6cKdM09eWXX+rZZ5/V9OnTNXTo0JKI0WHOnDnq06ePevXqpfr162vcuHHy8fHRwoULC6z//vvvq127dho0aJDq1aunJ554QldffbXmz59fonECAAD3wSRnAACruJxgd+7cWdOnT9e6devk6+uryZMna8+ePZo+fbratGlTEjFKkrKysrR9+3a1bt3aUWaz2dS6dWtt3ry5wPds2bJF1113nVNZ27ZttWXLlhKLEwAAuBcSbACAVVx6DnZOTo6mT5+u3r17a86cOSUVU4GOHz+u3NxchYSEOJWHhIQUOrnasWPHVKVKlXz1jx075vL27Xa74xmZhmHIMAyZpun0pZxXbrfbnd5bnHIp/xe+zWaTT53aLseO8ss7IkKSFFm1cilHgrIm7zNht9tls9ny9VeSXCovrX7vQv2hq22i/8T58j4Tpmm69F3M8USbLlebzn8PgLLPpQTb09NT7777rm6//fYSCqfsOnTokOMeLX9/f4WEhCglJUWpqamOOgEBAQoMDFRSUpIyMzMd5SEhIfL399fRo0eVnZ3tKA8NDZWvr68OHz7s1IGGh4fL09PT8YxxSfLw8FD18HDVfWl8STYTbijXbtfIO68t7TBQBuXm2pWQcFQRERHKzMxUYmKiY5mXl5ciIiKUlpbmNI+Fj4+PwsLCdPLkSZ08edJRXhr9niRFRkYqJydH8fHxjjKbzabIyMgit8nPz09VgoPpP1GgXLtdHh4ehS7PS3bOLyvJ8sLuCXe1vKB1F1ZOm/KX5+ba5eFhlGq/R4INuB+XEmxJuvbaa7VhwwbVqFGjJOIpVFBQkDw8PPJNaJacnJzvKnWeKlWq5LtafaH6F1KjRg3HF3Be5xscHKygoCBHnbzyqlWrOr03r7xatWoFllevXr3A8sjISJ234JJ/jS2svCz8SkubXG+T3W7XiRMnHMdHeWjTpcROm5zLDeP/+x0fH5/8fYqkihUrys/PL195QECAKlf+/5ERpdXvGYYhLy+vAmN3pU1GGf475SlPnz13aZPdbpenp6de/WS9DiadEpAnsmplx4/Xpdnv5ebmKiUl5VKbA+AycjnBvv766zVp0iT9+eefatSokXx9fZ2Wd+rUybLgzuXt7a1GjRpp3bp16ty5s6SzX4zr1q1T//79C3xP06ZNtX79et1///2Osp9++klNmzZ1efs2my3fr6nl6Vfai5XTprLZJpvN5vSDUXloU1FjpE20iTbRpgvFWJTyvP8fTDqlv+KPF1gfKM3jifkBAPfjcoI9btw4SSrwHmzDMLRjx45Lj6oQDzzwgEaOHKnGjRsrNjZWc+fOVUZGhnr27ClJGjFihMLCwjR8+HBJ0r333qsBAwZo9uzZat++vVasWKHff/9dL774YonFCFxOpmnq5MmTCggIKPQLGgBQMNM06TsBAJZyOcHeuXNnScRRJF27dlVKSoomT56spKQkNWzYUO+8847jCl58fLzTr4PNmjXTxIkT9eabb+r1119X7dq19dZbbykqKqq0mgBYKi/Brly5MieJAOAiEmwAgNVcTrDPdebMGVWoUMGqWIqkf//+hQ4JnzdvXr6yLl26qEuXLiUdFgAAAADgCufyc7Bzc3P11ltvqV27doqLi3PMfPjmm2/qk08+sTxAAAAAAADcgcsJ9ttvv63FixfrmWeekZeXl6M8KipKn376qaXBAbgwwzDk7+/PEEcAKAb6TgCA1VxOsD/77DONHz9ePXr0cLrfOTo6Wnv37rU0OAAXZhiGQkJCOEkEgGKg7wQAWM3lBDshIUE1a9bMV26apnJyciwJCkDRmKap5ORkHuMBAMVA3wkAsJrLCXb9+vW1cePGfOWrVq1Sw4YNLQkKQNGYpqnU1FROEgGgGOg7AQBWc3kW8SFDhmjUqFFKSEiQaZr68ssvtW/fPi1ZskQzZswoiRgBAAAAACjzXL6C3blzZ02fPl3r1q2Tr6+vJk+erD179mj69Olq06ZNScQIAAAAAECZV6Qr2O+//7769u2rChUq6MiRI7rmmms0Z86cko4NwEUYhqGAgAAm6gGAYqDvBABYrUhXsF955RWlpqZKkjp16qSUlJQSDQpA0RiGocDAQE4SAaAY6DsBAFYr0hXs0NBQffHFF2rfvr1M09TRo0d15syZAutGRERYGiCAwtntdiUlJalq1apOj80DAFyc3W6n7wQAWKpICfajjz6q8ePHa/z48TIMQ717985XxzRNGYahHTt2WB4kgMJlZmaWdggAAAAAVMQEu2/fvurWrZuOHDmiHj16aM6cOQoKCirp2AAAAAAAcBsuTXIWFRWlCRMmKC4uTj4+PiUdGwAAAAAAbsPlSc7GjBmjtLS0Eg0KQNEYhqGQkBAm6gGAYqDvBABYjUnOADdmGIb8/f1LOwwAcEsk2AAAqzHJGeDG7Ha7jh49qmrVqjETLgC4iFnEAQBWY5IzwM1lZ2eXdggAAAAAVMQEW5L8/f0dk5xdc8018vb2Lsm4AAAAAABwK0VOsPPccccdJREHAAAAAABurUgJdsuWLbVq1SoFBwerRYsWF5wU5JdffrEsOAAXZhiGQkNDmagHAIqBvhMAYLUiJdijR492zFQ8evRovpCAMsIwDPn6+pZ2GADgljifAQBYrUgJ9rnDwnv27FliwQBwjd1u1+HDh1W9enVmwgUAFzGLOADAai7fg3369Gn9+OOPOnz4sAzDUGRkpK677jqexQuUErvdXtohAAAAAJCLCfZnn32m8ePHKzU11am8UqVKGjdunLp27WppcAAAAAAAuIsiJ9jbt2/XmDFjdOutt+r+++9X3bp1ZZqm/vrrL82dO1cjRoxQ3bp11aBBg5KMFwAAAACAMqnINx7Nnz9fnTp10iuvvKIGDRrI29tbFSpUUKNGjfTaa6+pY8eOmjt3bknGCuA8hmEoPDyciXoAoBjoOwEAVitygv3rr7/qrrvuKnT5XXfdpV9//dWSoAAUnaeny1MpAAAAACgBRU6wExMTVbt27UKX165dWwkJCVbEBKCITNPUwYMHZZpmaYcCAG6HvhMAYLUiJ9gZGRmqUKFCocu9vb115swZS4ICAAAAAMDduDS2dO3atapUqVKBy06fPm1JQAAAAAAAuCOXEuxRo0ZdcDmThQAAAAAArlRFTrB37txZknEAKAbDMBQZGcmPWwBQDPSdAACrFfkebABlU05OTmmHAAAAAEAk2IBbM01T8fHxzIQLAMVA3wkAsBoJNgAAAAAAFiDBBgAAAADAAiTYgJuz2TiMAQAAgLLA5TPzbdu2aevWrfnKt27dqt9++82SoAAUjc1mU2RkJEk2ABQDfScAwGouf7O8+OKLio+Pz1eekJCgF1980ZKgABSNaZrKyMhgoh4AKAb6TgCA1VxOsPfs2aNGjRrlK2/YsKH++usvS4ICUDSmaSoxMZGTRAAoBvpOAIDVXE6wvb29dezYsXzlSUlJ8vT0tCQoAAAAAADcjcsJdps2bfT666/r9OnTjrJTp07pjTfeUOvWrS0NDgAAAAAAd+HyJeeRI0eqX79+uuGGG9SwYUNJ0s6dOxUSEqLXXnvN8gABXJiXl1dphwAAAABAxUiww8LCtHTpUi1btkw7d+6Uj4+PevXqpW7dunGiD1xmNptNERERpR0GALglZhEHAFitWDdN+/n5qW/fvlbHAsBFpmkqLS1NFStWlGEYpR0OALgV0zTpOwEAlipSgr169Wpdf/318vLy0urVqy9Yt1OnTpYEBuDiTNNUcnKy/Pz8OEkEABeRYAMArFakBHvo0KH68ccfFRISoqFDhxZazzAM7dixw7LgAAAAAABwF0VKsHfu3Fng/wEAAAAAwFkuze6RnZ2t++67T/v37y+hcAC4ysfHp7RDAAAAACAXJznz8vLSrl27SioWAC6y2WwKCwsr7TAAwC0xizgAwGouf7P06NFDn376aUnEAsBFpmnqxIkTMk2ztEMBALdD3wkAsJrLj+nKzc3VggUL9NNPP6lx48by9fV1Wj569GjLggNwYaZp6uTJk6pcuTIz4QKAi5hFHABgNZcT7D///FNXX321JGnfvn2WBwQAAAAAgDtyOcGeN29eScQBAAAAAIBbc/ke7NGjRys1NTVfeXp6OsPDgcvMMAz5+/szxBEAioG+EwBgNZcT7CVLlujMmTP5yjMzM/XZZ59ZEhSAojEMQyEhIZwkAkAx0HcCAKxW5CHiqampMk1TpmkqLS1NFSpUcCzLzc3V999/r+Dg4BIJEkDBTNNUSkqKgoODOVEEABcxyRkAwGpFTrCbN28uwzBkGIZuvvnmfMsNw9CwYcMsDQ7AhZmmqdTUVAUFBXGSCAAuIsEGAFityAn2+++/L9M0dd9992nKlCkKCAhwLPPy8lJERITCwsJKJEgAAAAAAMq6IifYLVu2lCStXr1aERER/OILAAAAAMA5XJ7krHr16tq0aZOefvpp3XXXXUpISJB0dvKzjRs3Wh4ggMIZhqGAgAB+8AKAYqDvBABYzeUE+4svvtDAgQPl4+Oj7du3KysrS9LZSdBmzJhheYAACmcYhgIDAzlJBIBioO8EAFjN5QT77bff1rhx4/TSSy/J0/P/R5g3a9ZMf/zxh6XBAbgwu92uhIQE2e320g4FANwOfScAwGouJ9j79u1T8+bN85VXqlRJp06dsiQoAEWXmZlZ2iEAAAAAUDES7CpVqujvv//OV75p0yZFRkZaEhQAAAAAAO7G5QS7T58+evnll7V161YZhqGEhAQtXbpUr776qu6+++6SiBEAAAAAgDKvyI/pyjN48GDZ7Xbdf//9ysjIUP/+/eXt7a0HH3xQAwYMKIkYARTCMAyFhIQwUQ8AFAN9JwDAai4n2IZh6NFHH9XAgQP1999/Kz09XfXq1VPFihVLIj4AF2AYhvz9/Us7DABwSyTYAACruZxg5/H29lb9+vWtjAWAi+x2u44ePapq1arJZnP5jg8AuKLZ7Xb6TgCApYqcYI8ePbpI9SZMmFDsYAC4Ljs7u7RDAAAAACAXEuzFixcrIiJCV199tUzTLMmYAAAAAABwO0VOsO+++259/vnnOnTokHr27KkePXooMDCwBEMDAAAAAMB9FPnGo3/+85/64YcfNGjQIH377bfq0KGDHn/8ca1du5Yr2kApMQxDoaGhTNQDAMVA3wkAsJpLk5x5e3ure/fu6t69uw4fPqzFixdr3Lhxys3N1fLly5lJHLjMDMOQr69vaYcBAG6JBBsAYLViT52ZN+umaZrKzc21LCAARWe323Xw4EHZ7fbSDgUA3A59JwDAai5dwc7KytKXX36phQsXatOmTerQoYOef/55tWvXjsdcAKWEE0QAAACgbChygv3CCy9oxYoVqlatmnr16qVJkyYpODi4JGMDAAAAAMBtFDnB/vDDDxUREaHIyEht2LBBGzZsKLDe1KlTLQsOAAAAAAB3UeQE+/bbb2cyEKCMMQxD4eHhHJsAUAz0nQAAqxU5wX7llVdKMg4AxeTp6dJUCgAAAABKCDOTAW7MNE0dPHiQZ9EDQDHQdwIArEaCDQAAAACABUiwAQAAAACwAAk2AAAAAAAWIMEG3JhhGIqMjGQmXAAoBvpOAIDVSLABN5eTk1PaIQAAAAAQCTbg1kzTVHx8PDPhAkAx0HcCAKxGgg0AAAAAgAVIsAEAAAAAsAAJNuDmbDYOYwAAAKAs8CztAAAUn81mU2RkZGmHAQBuiR8oAQBW45sFcGOmaSojI4OJegCgGOg7AQBWI8EG3JhpmkpMTOQkEQCKgb4TAGA1EmwAAAAAACxAgg0AAAAAgAVIsAE35+XlVdohAAAAABCziANuzWazKSIiorTDAAC3xCziAACr8c0CuDHTNJWamspEPQBQDPSdAACrkWADbsw0TSUnJ3OSCADFQN8JALAaCTYAAAAAABYgwQYAAAAAwAJuk2CfOHFCw4cPV7NmzdS8eXONGTNGaWlpF3zPRx99pAEDBqhZs2aKjo7WqVOnLlO0wOXj4+NT2iEAAAAAkBsl2E8//bT++usvzZkzR9OnT9fGjRv1/PPPX/A9GRkZateunR555JHLFCVwedlsNoWFhTETLgAUA30nAMBqbvGYrj179mjt2rX69NNPFRMTI0l69tlnNXjwYI0YMUJhYWEFvu/++++XJP3888+XK1TgsjJNUydPnlRAQIAMwyjtcADArZimSd8JALCUWyTYmzdvVuXKlR3JtSS1bt1aNptN27Zt04033ljiMdjtdseXsGEYMgxDpmk6zUCaV263253eW5xyKf/spoWV22y2fLG4Wk6b3LNNdrtdJ06cUKVKleTh4VEu2nQpsdMm2kSbaJMr5Xa7XZ6ebnEqhFJUmsfT+e8BUPa5xbfKsWPHFBwc7FTm6empgIAAJSUlXZYYDh065BhK5u/vr5CQEKWkpCg1NdVRJyAgQIGBgUpKSlJmZqajPCQkRP7+/jp69Kiys7Md5aGhofL19dXhw4edOtDw8HB5enrq4MGDTjFERkYqJydH8fHxjjKbzabIyEhlZmYqMTHRUe7l5aWIiAilpaUpOTnZUe7j46OwsDCdPHlSJ0+edJTTJvdsk2maSktL06lTpxQUFFQu2lQe/060iTbRprLZprxYgAspzeOJBBtwP4ZZig+BnDhxombNmnXBOitWrNBXX32lxYsX64svvnBadt1112nYsGG65557LriOn3/+Wffee682bNigypUruxRjbm6utmzZotjYWHl4eEjiCgFtKjttstvtOnTokCIjI7mCTZtoE22iTS6W513B/se0L/VX/HEBeeqHB2nqkJskle4V7NzcXG3btk1NmzZ1nIcCKNtK9Qr2gw8+qDvuuOOCdSIjI1WlShWlpKQ4lefk5OjkyZOqWrVqSYboYLPZ8k2GktdhFlS3sHW4Ul7QugsrLywWq8ppU9lsk2EYqlSpkiO28tCmosZIm2gTbaJNF4qxKOWFtQM4V2keT6V4HQxAMZVqgh0cHJxv6HdB4uLidOrUKf3+++9q3LixJGn9+vWy2+2KjY0t6TCBMsswDIWEhJR2GADglkiwAQBWc4vnU9SrV0/t2rXTc889p23btmnTpk0aP368unXr5phBPCEhQbfccou2bdvmeF9SUpJ27Nihv//+W5L0559/aseOHTpx4kRpNAOwnGmaSk5O5hduACgG+k4AgNXcYpIz6ez92uPHj9d9990nm82mm266Sc8++6xjeXZ2tvbt26eMjAxH2YcffqipU6c6Xvfr10+SNGHCBPXs2fPyBQ+UENM0lZqaqqCgIK7EAICLTJPHdAEArOU2CXZgYKAmTZpU6PIaNWpo165dTmXDhg3TsGHDSjo0AAAAAADcY4g4AAAAAABlHQk24MYMw1BAQABDHAGgGOg7AQBWc5sh4gDyMwxDgYGBpR0GALglEmwAgNW4gg24MbvdroSEBNnt9tIOBQDcDn0nAMBqJNiAm8vMzCztEAAAAACIBBsAAAAAAEuQYAMAAAAAYAESbMCNGYahkJAQJuoBgGKg7wQAWI1ZxAE3ZhiG/P39SzsMAHBLJNgAAKtxBRtwY3a7XUeOHGEmXAAoBvpOAIDVSLABN5ednV3aIQAAAAAQCTYAAAAAAJYgwQYAAAAAwAIk2IAbMwxDoaGhTNQDAMVA3wkAsBqziANuzDAM+fr6lnYYAOCWSLABAFbjCjbgxux2uw4ePMhMuABQDPSdAACrkWADbo4TRAAAAKBsIMEGAAAAAMACJNgAAAAAAFiABBtwY4ZhKDw8nIl6AKAY6DsBAFYjwQbcnKcnDwMAAAAAygISbMCNmaapgwcPyjTN0g4FANwOfScAwGok2AAAAAAAWIAEGwAAAAAAC5BgAwAAAABgARJswI0ZhqHIyEhmwgWAYqDvBABYjQQbcHM5OTmlHQIAAAAAkWADbs00TcXHxzMTLgAUA30nAMBqJNgAAAAAAFiABBsAAAAAAAuQYANuzmbjMAYAAADKAs/SDgBA8dlsNkVGRpZ2GADglviBEgBgNb5ZADdmmqYyMjKYqAcAioG+EwBgNRJswI2ZpqnExEROEgGgGOg7AQBWI8EGAAAAAMACJNgAAAAAAFiABBtwc15eXqUdAgAAAAAxizjg1mw2myIiIko7DABwS8wiDgCwGt8sgBszTVOpqalM1AMAxUDfCQCwGgk24MZM01RycjIniQBQDPSdAACrkWADAAAAAGABEmwAAAAAACxAgg24OR8fn9IOAQAAAICYRRxwazabTWFhYaUdBgC4JWYRBwBYjW8WwI2ZpqkTJ04wUQ8AFAN9JwDAaiTYgBszTVMnT57kJBEAioG+EwBgNRJsAAAAAAAsQIINAAAAAIAFSLABN2YYhvz9/WUYRmmHAgBuh74TAGA1ZhEH3JhhGAoJCSntMADALZFgAwCsxhVswI2Zpqnk5GQm6gGAYqDvBABYjQQbcGOmaSo1NZWTRAAoBvpOAIDVSLABAAAAALAACTYAAAAAABYgwQbcmGEYCggIYKIeACgG+k4AgNWYRRxwY4ZhKDAwsLTDAAC3RIINALAaV7ABN2a325WQkCC73V7aoQCA26HvBABYjQQbcHOZmZmlHQIAAAAAkWADAAAAAGAJEmwAAAAAACxAgg24McMwFBISwkQ9AFAM9J0AAKsxizjgxgzDkL+/f2mHAQBuiQQbAGA1rmADbsxut+vIkSPMhAsAxUDfCQCwGgk24Oays7NLOwQAAAAAIsEGAAAAAMASJNgAAAAAAFiABBtwY4ZhKDQ0lIl6AKAY6DsBAFZjFnHAjRmGIV9f39IOAwDcEgk2AMBqXMEG3JjdbtfBgweZCRcAioG+EwBgNRJswM1xgggAAACUDSTYAAAAAABYgAQbAAAAAAALkGADbswwDIWHhzNRDwAUA30nAMBqJNiAm/P05GEAAAAAQFlAgg24MdM0dfDgQZmmWdqhAIDboe8EAFiNBBsAAAAAAAuQYAMAAAAAYAESbAAAAAAALECCDbgxwzAUGRnJTLgAUAz0nQAAq5FgA24uJyentEMAAAAAIBJswK2Zpqn4+HhmwgWAYqDvBABYjQQbAAAAAAALkGADAAAAAGABEmzAzdlsHMYAAABAWeBZ2gEAKD6bzabIyMjSDgMA3BI/UAIArMY3C+DGTNNURkYGE/UAQDHQdwIArEaCDbgx0zSVmJjISSIAFAN9JwDAaiTYAAAAAABYgAQbAAAAAAALkGADbs7Ly6u0QwAAAAAgZhEH3JrNZlNERERphwEAbolZxAEAVuObBXBjpmkqNTWViXoAoBjoOwEAViPBBtyYaZpKTk7mJBEAioG+EwBgNRJsAAAAAAAsQIINAAAAAIAFSLABN+fj41PaIQAAAAAQs4gDbs1msyksLKy0wwAAt8Qs4gAAq/HNArgx0zR14sQJJuoBgGKg7wQAWI0EG3Bjpmnq5MmTnCQCQDHQdwIArEaCDQAAAACABdwmwT5x4oSGDx+uZs2aqXnz5hozZozS0tIuWH/8+PG6+eabFRsbqw4dOuill17S6dOnL2PUAAAAAIArhdsk2E8//bT++usvzZkzR9OnT9fGjRv1/PPPF1o/MTFRiYmJGjlypJYvX64JEyZo7dq1Gjt27GWMGihZhmHI399fhmGUdigA4HboOwEAVnOLWcT37NmjtWvX6tNPP1VMTIwk6dlnn9XgwYM1YsSIAmdRjoqK0pQpUxyva9asqSeeeELPPPOMcnJy5OnpFk0HLsgwDIWEhJR2GADglkiwAQBWc4sr2Js3b1blypUdybUktW7dWjabTdu2bSvyelJTU+Xv709yjXLDNE0lJyczUQ8AFAN9JwDAam6RaR47dkzBwcFOZZ6engoICFBSUlKR1pGSkqJp06apb9++xYrBbrc7fuk2DEOGYcg0Tacv57xyu93u9N7ilEv5v/gLK7fZbPlicbWcNrlnm+x2u06fPq3AwEB5eHiUizZdSuy0iTbRJtrkSrndbudHd1xUaR5P578HQNlXqt8qEydO1KxZsy5YZ8WKFZe8ndTUVD388MOqV6+e/vGPfxRrHYcOHZLNdvaCv7+/v0JCQpSSkqLU1FRHnYCAAAUGBiopKUmZmZmO8pCQEPn7++vo0aPKzs52lIeGhsrX11eHDx926kDDw8Pl6empgwcPOsUQGRmpnJwcxcfHO8psNpsiIyOVmZmpxMRER7mXl5ciIiKUlpam5ORkR7mPj4/CwsJ08uRJnTx50lFOm9yzTaZpKi0tTadOnVJQUFC5aFN5/DvRJtpEm8pmm/JiAS6kNI8nEmzA/RhmKY6PSklJ0fHjxy9YJzIyUkuXLtWrr76qDRs2OMpzcnIUGxur//znP7rxxhsLfX9qaqoGDRokHx8fzZgxQxUqVHApxtzcXG3ZskWxsbHy8PCQxBUC2lR22mS323Xo0CFFRkZyBZs20SbaRJtcLM+7gv2PaV/qr/gLn4/gylI/PEhTh9wkqXSvYOfm5mrbtm1q2rSp4zwUQNlWqlewg4OD8w39LkhcXJxOnTql33//XY0bN5YkrV+/Xna7XbGxsYW+LzU1VQMHDpS3t7fefvttl5Prc9lsNscV7Dx5HWZBdQtbhyvlBa27sPLCYrGqnDaVzTYZhqHAwEBHbOWhTUWNkTbRJtpEmy4UY1HKC2sHcK7SPJ5K8ToYgGJyi0nO6tWrp3bt2um5557Ttm3btGnTJo0fP17dunVzzCCekJCgW265xTHpWWpqqh588EGlp6fr5ZdfVmpqqpKSkpSUlKTc3NzSbA5gmbwEm5NEAHAdfScAwGpuM7PHxIkTNX78eN13332y2Wy66aab9OyzzzqWZ2dna9++fcrIyJAkbd++XVu3bpWkfEPIV69erRo1aly+4IESYrfblZSUpKpVqxb6yzgAoGB2u52+EwBgKbdJsAMDAzVp0qRCl9eoUUO7du1yvG7VqpXTa6C8OneCFQAAAAClh59tAQAAAACwAAk2AAAAAAAWIMEG3JhhGAoJCWGiHgAoBvpOAIDV3OYebAD5GYYhf3//0g4DANwSCTYAwGpcwQbcmN1u15EjR2S320s7FABwO/SdAACrkWADbi47O7u0QwAAAAAgEmwAAAAAACzBPdgAAABuyDRNndj+jU7v3SR7dqYqhNRUlWtulVelkELfc2LH90o79IeyTx+T4eEln5BIBcXeJO/KVSRJuWfSdWL7t8pI+Es56Sdlq1BRFSMaKKhxJ9m8fRzrOZNyWCnbvlTW8XhJUoXg6gpqcrMqBFZziu/Urh91au8m5aSfkEcFP1Wu11KBV7cvoT0CAKWPBBtwY4ZhKDQ0lIl6AKAY3L3vPLnzB53a/bOqtLxDXhWDdPz3b3T0+/dV/ZZ/yObhVeB7MpP2q3L9VqoQXF2madfx377S0e/nqsYtw2Tz9FZu5mnlZJxWcJOb5VU5VDlpJ3Rs0zLlZJ5WWOu7JEn27DM6+v378otooJBmt0qmXce3f6OE799XZPfhMmwekqSUzSuUkbBHwU1ulndAmOxZGbJnpV+2/QMApYEh4oAbMwxDvr6+bn+SCAClwZ37TtM0dWr3OgU2vF4VqzeUd2A1VW3ZU7kZp5V+eGeh76t2/b2qVCdO3gGhqhBYTVVb9FRu+kmdOX5EkuQdEKawNnfJL6KBvPyD5RtWV8ExnZR+ZJdMe64kKfv0MdmzMhTUuKO8K1eRd0Cogq6+QbmZqcpJOyFJyjqVpFN7Nii0zT2qWL2BvPyDVCE4Qr7V6pf4vgGA0kSCDbgxu92ugwcPMhMuABSDO/edOWnHlZuZKp+weo4ym7ePKoRU15nkg0Vejz07U5Lk4e17gTpnZPOq4Lgy7VWpimzefjq9d5PM3BzZc7J1et8meVWuKs+KgZKk9CM75eUfpIz4XTr4+es6uPx1JW1YotwzXMEGUL4xRBxwc+58gggAKJ7czFRJkoePv1O5RwV/x7KLMU27kresVIUqNeUdEFbwds6k6fgfa1SpbnNHmc2rgsJveEAJPy7QiR3fSZK8/EMUdv29jiQ8J/W4ctJOKu3gdlVt2VOmaSply0olrvtI4R0ecLm9AOAuSLABAADKuNQDW3Vs0zLH67C2/S55ncm/fq7sk4kK7ziwwOX27EwlrJ0v78pVFdTohv8vz8nWsQ1L5BNSU5WvvVOmadfJXT8qYe18RXR+WDZPL0mmTHuOqrbqKa9KZydQq9Lidh35arqyTh1zTKoGAOUNCTYAAEAZ5xfRQNWDazhe590PnZuZKk/fSo7y3DOp8g4Mv+j6jv26XOlHdin8hoHy9AvIt/zsRGbzZHhWUGibux1XpiUp7e9tyk47ofBOD8kwzt5tWKFVbx1YMkHpR3bKv2aMPHwqSYbNkVxLklelqmdjTD8hkWADKKdIsAE3ZhiGwsPD3XqiHgAoLe7Ud9q8KsjmVcHx2jRNefj4KzNxryoEnU2o7dmZOpN8WJXqtSx0PaZpKnnz50o/vEPhHR6Ul39Qvjr27Ewd/f59GTZPhbW9J9+M5Pbc7P/tu3P2n2Gc/WeakqQKVWpKpl3ZqSny8g+WJGWnJkuS4z5tACiPmOQMcHOenvxOBgBXGsMwVPmq63Tij++Udninsk4kKOnnRfLwrSS/6g0c9eLXzNGp3T87Xif/ulxpB7apaqveMjy9lZNx9rFc9pxsSf9Lrr97X/acbFVpcbvs2Wccdcz/zfnhG1ZP9qxMJf+6XFmnkpR1MlFJG5bIMGzyCa3zvzp15R0UrmMbFuvM8XidSTmi5I1L5RNWz+mqNgCUN5yZA27MNE0dPHhQkZGRbnUlBgDKAtM03brvDGjQVmZulpI3LZU9K1MVqtRUtesHOF1xzkk9rtwzaY7Xp/dskCQdXTPHaV1VWtyhSnXi/pcMH5IkHVrxplOdGt2elFfFIHlXrqqwtvfo+B9rFL96lmQY8g6sprDrBziGqxuGTWFt+yn51xWK//Zd2Ty95VvtKgU3ubkkdgUAlBkk2AAAAG7IMAwFNe6koMadCq0T2f0pp9d1+rx4wXX6hta5aB1J8q1W/6LPtPb0raywNndddF0AUJ4wRBwAAAAAAAuQYAMAAAAAYAESbMCNGYbB/dcAUEz0nQAAq5FgA24uJyentEMAAAAAIBJswK2Zpqn4+HiZ/3vuKACg6Og7AQBWI8EGAAAAAMACJNgAAAAAAFiA52ADbs5m43cyAP/X3t3HVF3+fxx/ncP9jZkKqSFZ6YJ1nKao3FqmEk4jVw2zOypvhkBJNxokZbWRWtGwtHkDLW+iWq7SdJJFLadBJiOLtajWzQ5YadyYcucBzuf7h+v84gep0ScOR5+PjY1zPtfnc97XZ+zaeZ3rOhf4N8JDL3J3Cehn+JsA0FsEbMCDWa1WhYeHu7sMAPBIVqtVnU5D2Skx7i4F/VCn05CXlZ3mAfwzTH0BHswwDLW2trJRDwD0gmEYanecYgxFjwjXAHqDgA14MMMwdOzYMd4cAkAvGIaho0ePMoYCAExDwAYAAAAAwAQEbAAAAAAATEDABjycj4+Pu0sAAI/FGAoAMBO7iAMezGq16tJLL3V3GQDgkRhDAQBmYwYb8GCGYaipqYkNegCgFxhDAQBmI2ADHswwDNXX1/PmEAB6gTEUAGA2AjYAAAAAACYgYAMAAAAAYAICNuDh/P393V0CAHgsxlAAgJnYRRzwYFarVUOHDnV3GQDgkRhDAQBmYwYb8GCGYej48eNs0AMAvcAYCgAwGwEb8GCGYeiPP/7gzSEA9AJjKADAbARsAAAAAABMQMAGAAAAAMAEBGzAg1ksFgUHB8tisbi7FADwOIyhAACzsYs44MEsFouGDBni7jIAwCMxhgIAzMYMNuDBDMNQfX09G/QAQC8whgIAzEbABjyYYRhqamrizSEA9AJjKADAbARsAAAAAABMwHewz+LPT7U7OzvdXAnQndPplNPpVGdnJzMwAPAPMYaiv/vz/Sd/n4DnIGCfhdPplCRVVVW5uRLg7zU0NLi7BADwWIyh6O/+fD8KoP+zGHwkdkZOp1MdHR2yWq38Gw8AAAD0GcMw5HQ65e3tLauVb3YCnoCADQAAAACACfgoDAAAAAAAExCwAQAAAAAwAQEbAAAAAAATELABAAAAADABARsAAAAAABMQsAEAAAAAMAEBGwAAAAAAE3i7uwAAPWtoaNCLL76offv2qa6uTgMHDlRkZKQyMjIUFRXl7vIAoF/IycnRu+++K0ny9vbW0KFDNXPmTGVlZcnPz8/N1QEALjQEbKCfeuCBB9Te3q7Vq1crPDxc9fX1Ki8v1/Hjx91dGgD0K1OmTNGqVavU0dGhr7/+WtnZ2bJYLFq2bJm7SwMAXGBYIg70QydOnFBFRYWWLl2qmJgYhYWFaezYsUpLS9P06dNdbXJzcxUTE6MJEyYoNTVV1dXVkk7PfsfHx2vDhg2ua1ZWVmrMmDEqLy93S58A4L/i6+ur0NBQDR8+XDNmzFBcXJzKysokSY2NjXr44Yc1ZcoUjRs3TsnJydq9e3eX851OpwoLC5WYmKgxY8Zo6tSpWr9+vev4r7/+qqysLE2cOFGTJ09Wenq6amtr+7SPAADPQMAG+qHAwEAFBgaqtLRUDoejxzZZWVmqr69XYWGh3nnnHdlsNt1zzz06fvy4Bg8erJUrV2rdunWqqqpSU1OTHn30Ud15552KjY3t494AQN/57rvv9MUXX8jHx0eS5HA4ZLPZtGnTJu3evVtz587Vo48+qq+++sp1zgsvvKDCwkJlZGRoz549ys/PV0hIiCSpvb1dCxYsUFBQkIqLi/XGG28oMDBQCxcu/NvxGQBw4bIYhmG4uwgA3e3du1dPPPGE2tradPXVV2vy5MmaNWuWIiMjVVFRobS0NJWXl8vX19d1TmJiohYuXKjbbrtNkvT000+rrKxMY8aM0Xfffae33367S3sA8HQ5OTl677335Ofnp46ODjkcDlmtVq1Zs0ZJSUk9npOWlqYrr7xS2dnZampqUmxsrFasWKGUlJRubXfu3Kn169erpKREFotF0unQPmnSJL388stKSEj4T/sHAPAsfAcb6KeSkpI0depUVVRU6PDhw9q/f7+KioqUl5en1tZWtbS0KDo6uss5bW1tstvtrsfZ2dm68cYbtXfvXsI1gPNWdHS0nnrqKbW2tmrz5s3y8vJyhevOzk5t2LBB77//vo4ePar29nY5HA75+/tLkn788Uc5HA7FxMT0eO3q6mrZ7XZNmDChy/OnTp3qMt4CACARsIF+zc/PT/Hx8YqPj1dmZqZyc3O1du1a3X777QoNDdW2bdu6nTNgwADX73a7XceOHZPT6dSRI0cUERHRl+UDQJ8ICAjQyJEjJUkrV67UnDlztH37dqWkpOiVV17R1q1btXz5ckVERCggIEArV65Ue3u7JJ11p/GWlhbZbDbl5+d3OzZ48GDzOwMA8GgEbMCDjB49WqWlpbLZbKqrq5OXl5dGjBjRY1uHw6Fly5Zp1qxZuuKKK/T4449r165dGjJkSB9XDQB9x2q1Ki0tTatXr1ZycrIqKys1ffp0zZkzR9LpDc1+/vlnjRo1SpJ0+eWXy9/fX5999pnCw8O7Xc9ms6mkpERDhgxRcHBwn/YFAOB52OQM6IcaGxuVmpqqnTt3qrq6WjU1NSopKVFRUZGmT5+uuLg4XXPNNcrMzNSBAwdUW1uryspKFRQUqKqqSpJUUFCgkydP6vHHH9eiRYt0+eWXa/ny5W7uGQD892bOnCmr1ari4mKNHDlSZWVlqqys1A8//KAVK1aorq7O1dbPz0+LFi3S888/rx07dshut+vw4cPavn27JCk5OVmDBg1Senq6KioqVFNTo4MHDyovL0+//fabu7oIAOinmMEG+qGgoCCNGzdOW7Zskd1uV0dHh4YNG6aUlBQtXrxYFotFmzZt0po1a/TYY4+psbFRISEhmjhxokJCQnTw4EFt3bpVW7Zscc24PPfcc5ozZ45ef/113XHHHW7uIQD8d7y9vXXXXXepqKhIO3bsUE1NjRYsWKCAgADNnTtXM2bM0MmTJ13tMzIy5OXlpZdeeknHjh1TaGio5s2bJ+n08vPXXntN+fn5uv/++9Xc3KyhQ4cqNjaWGW0AQDfsIg4AAAAAgAlYIg4AAAAAgAkI2AAAAAAAmICADQAAAACACQjYAAAAAACYgIANAAAAAIAJCNgAAAAAAJiAgA0AAAAAgAkI2AAAAAAAmICADQAXqJycHGVkZJyxzcGDBxUREaETJ070UVUAAACey2IYhuHuIgDgQtPQ0KAXX3xR+/btU11dnQYOHKjIyEhlZGQoKiqqT2o4efKkDMPQRRddJEm6++67FRkZqdzcXFcbh8OhP/74QyEhIbJYLP9JHbW1tZo+fXq355OTk5Wfn2/Ka0ybNk2pqam69957TbkeAABAT7zdXQAAXIgeeOABtbe3a/Xq1QoPD1d9fb3Ky8t1/PjxPqthwIABZ23j6+ur0NDQPqhG2rx5s0aPHu167O/v3yev+084HA75+vq6uwwAANBPsUQcAPrYiRMnVFFRoaVLlyomJkZhYWEaO3as0tLSuszknjhxQrm5uYqJidGECROUmpqq6upq1/G1a9dqzpw52rFjh6ZNm6aoqCg99NBDampqcrV5//33lZycrLFjxyo6Olr33nuvWlpaJHVdIp6Tk6PPP/9cW7duVUREhCIiIlRbW9tliXhTU5PGjh2rffv2denPhx9+qPHjx6u1tVWS9OuvvyorK0sTJ07U5MmTlZ6ertra2rPel4svvlihoaGunz8/ADjbfbDb7UpPT1dcXJzGjx+vW2+9VWVlZa7jd999t44cOaJVq1a5+vbX+/dXmzdv1rRp01yP/7xH69evV0JCgmbOnPmv+ggAAM5vBGwA6GOBgYEKDAxUaWmpHA7H37bLyspSfX29CgsL9c4778hms+mee+7pMsttt9v10UcfacOGDdq4caMOHTqkwsJCSdKxY8f0yCOP6NZbb9WePXu0detWJSYmqqdvBuXm5mr8+PGaO3euDhw4oAMHDmj48OFd2gQHB2vq1KnavXt3l+d37dqlGTNmKCAgQO3t7VqwYIGCgoJUXFysN954Q4GBgVq4cOEZ+3omZ7sPLS0tuu6667R582a9++67mjJlihYvXqxffvlF0ukgPWzYMC1ZssTVt3+ivLxcP/30k1599VVt3LjxP+kjAAA4P7BEHAD6mLe3t1avXq0nnnhCb775pq6++mpNnjxZs2bNUmRkpCSpoqJCX331lcrLy11LkrOzs1VaWqq9e/fqtttukyQZhqFVq1YpODhYknTTTTepvLxcDz30kH7//Xd1dHQoMTFRYWFhkuSavf3/BgwYIB8fH/n7+59xSfhNN92kZcuWqbW1VQEBAWpqatInn3yidevWSZL27Nkjp9OpZ555xvWd7VWrVmnSpEn6/PPPlZCQ8LfXnjdvnqzW//vct7i4WC0tLWe9D5GRka77JkkPPvigSktL9fHHH+uuu+7SxRdfLC8vLwUFBfVquXtgYKDy8vJcr79z585e9xEAAJzfCNgA4AZJSUmaOnWqKioqdPjwYe3fv19FRUXKy8vTLbfcom+//VYtLS2Kjo7ucl5bW5vsdrvrcVhYmCtcS9Ill1yi+vp6SVJkZKRiY2OVnJyshIQEJSQkKCkpSQMHDux13ddee618fHz08ccfa/bs2dq7d6+Cg4MVFxcnSaqurpbdbteECRO6nHfq1KkudfekoKBAo0aNcj0ePny4tm/fftb70NzcrHXr1umTTz7R77//rs7OTrW1tblmsP+tq666qsv3rv9NHwEAwPmNgA0AbuLn56f4+HjFx8crMzNTubm5Wrt2rW655RY1NzcrNDRU27Zt63beXzcn8/buPoz/uQTcy8tLr776qiorK/Xpp59q27ZtKigo0FtvvaXw8PBe1ezr66ukpCTt2rVLs2fP1u7duzVr1ixXHS0tLbLZbD3u/j148OAzXnv48OEaOXJkl+fO5T48++yzKisrU3Z2ti677DL5+/tryZIlam9vP+PrWSyWbsvlOzo6urULCAjo8vjf9BEAAJzfCNgA0E+MHj1apaWlkiSbzaa6ujp5eXlpxIgRvb6mxWJRVFSUoqKilJmZqeuvv16lpaW67777urX18fGR0+k86zWTk5M1f/58ff/99/rss8/04IMPuo7ZbDaVlJRoyJAhXWbWe+tc7sMXX3yhm2++WYmJiZJOh/IjR450adNT3wYPHqy6ujoZhuFa6v3NN9+cU01m9hEAAJw/2OQMAPpYY2OjUlNTtXPnTlVXV6umpkYlJSUqKipy7SIeFxena665RpmZmTpw4IBqa2tVWVmpgoICVVVVndPrfPnll9qwYYOqqqr0yy+/6IMPPlBDQ4OuvPLKHtuHhYXpyy+/VG1trRoaGv42bE+aNEkhISFaunSpRowYoXHjxrmOJScna9CgQUpPT1dFRYVqamp08OBB5eXl6bfffvuHd+rc7sPIkSP14Ycf6ptvvlF1dbUeeeSRbrWHhYXp0KFDOnr0qBoaGiRJ0dHRamhoUGFhoex2u4qLi7V///6z1mR2HwEAwPmDGWwA6GNBQUEaN26ctmzZIrvdro6ODg0bNkwpKSlavHixpNMzz5s2bdKaNWv02GOPqbGxUSEhIZo4caJCQkLO6XWCg4N16NAhbdmyRU1NTbr00kuVk5Oj6667rsf28+fPV05OjmbPnq22tjZ99NFHPbazWCyaPXu2ioqKlJmZ2eVYQECAXnvtNeXn5+v+++9Xc3Ozhg4dqtjY2F7N9p7LfcjJydHy5cs1b948DRo0SIsWLVJzc3OX6yxZskQrVqzQjBkz5HA49O2332rUqFF68skntXHjRq1fv1433HCD5s+fr7feeuuMNZndRwAAcP6wGD39vxYAAAAAAPCPsEQcAAAAAAATELABAAAAADABARsAAAAAABMQsAEAAAAAMAEBGwAAAAAAExCwAQAAAAAwAQEbAAAAAAATELABAAAAADABARsAAAAAABMQsAEAAAAAMAEBGwAAAAAAExCwAQAAAAAwwf8A393wxAksMTIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analysis Of Fairness Metrics Across Three Models:**\n",
        "\n",
        "**Logistic regression** shows potential gender bias with a Demographic Parity Difference (DP) of 0.1824 and an Equal Opportunity Difference (EO) of 0.134, and racial bias where the model is less accurate for minorities (EO of -0.2286).\n",
        "\n",
        "**Decision tree** indicates a slight reduction in gender and racial biases compared to logistic regression where gender DP and EO are at 0.1555 and 0.1293, respectively, and racial DP and EO at 0.1017 and -0.1571.\n",
        "\n",
        "**Random forest** exhibits the highest gender bias among the models DP of 0.1948 but has a somewhat reduced EO of 0.1069. Racial bias is also noted with DP and EO at 0.1117 and -0.2048, respectively."
      ],
      "metadata": {
        "id": "umKhBlw2xDBz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "6ENXUFamw7PR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to calculate fairness metrics\n",
        "def fairness_metrics(y_true, y_pred, sensitive_features):\n",
        "    results = {}\n",
        "    for feature_name, sensitive_data in sensitive_features.items():\n",
        "\n",
        "        p_group = y_pred[sensitive_data == 1].mean()\n",
        "        p_nongroup = y_pred[sensitive_data == 0].mean()\n",
        "\n",
        "\n",
        "        demographic_parity_difference = p_group - p_nongroup\n",
        "\n",
        "\n",
        "        true_positive_group = y_pred[(y_true == 1) & (sensitive_data == 1)].mean()\n",
        "        true_positive_nongroup = y_pred[(y_true == 1) & (sensitive_data == 0)].mean()\n",
        "        equal_opportunity_difference = true_positive_group - true_positive_nongroup\n",
        "\n",
        "        results[feature_name] = {\n",
        "            'Demographic Parity': demographic_parity_difference,\n",
        "            'Equal Opportunity': equal_opportunity_difference\n",
        "        }\n",
        "\n",
        "    return results\n",
        "\n",
        "sensitive_features = {\n",
        "    'sex': X_test['sex'],\n",
        "    'race': X_test['race']\n",
        "}\n",
        "\n",
        "\n",
        "fairness_results_logistic = fairness_metrics(y_test, y_pred_logistic_regression, sensitive_features)\n",
        "fairness_results_decision_tree = fairness_metrics(y_test, y_pred_decision_tree, sensitive_features)\n",
        "fairness_results_random_forest = fairness_metrics(y_test, y_pred_random_forest, sensitive_features)\n",
        "\n",
        "# Printing results for each sensitive feature across models\n",
        "for model_name, fairness_results in [('Logistic Regression', fairness_results_logistic),\n",
        "                                     ('Decision Tree', fairness_results_decision_tree),\n",
        "                                     ('Random Forest', fairness_results_random_forest)]:\n",
        "    print(f\"Fairness Metrics for {model_name}:\")\n",
        "    for feature, metrics in fairness_results.items():\n",
        "        print(f\"  {feature} - Demographic Parity Difference: {metrics['Demographic Parity']:.4f}\")\n",
        "        print(f\"  {feature} - Equal Opportunity Difference: {metrics['Equal Opportunity']:.4f}\")\n",
        "        print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHd1z3t5iR_0",
        "outputId": "e4b38ee1-1dee-422f-df2b-e216239e42b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fairness Metrics for Logistic Regression:\n",
            "  sex - Demographic Parity Difference: 0.1824\n",
            "  sex - Equal Opportunity Difference: 0.1354\n",
            "\n",
            "  race - Demographic Parity Difference: 0.1278\n",
            "  race - Equal Opportunity Difference: -0.2286\n",
            "\n",
            "Fairness Metrics for Decision Tree:\n",
            "  sex - Demographic Parity Difference: 0.1555\n",
            "  sex - Equal Opportunity Difference: 0.1293\n",
            "\n",
            "  race - Demographic Parity Difference: 0.1017\n",
            "  race - Equal Opportunity Difference: -0.1571\n",
            "\n",
            "Fairness Metrics for Random Forest:\n",
            "  sex - Demographic Parity Difference: 0.1948\n",
            "  sex - Equal Opportunity Difference: 0.1069\n",
            "\n",
            "  race - Demographic Parity Difference: 0.1117\n",
            "  race - Equal Opportunity Difference: -0.2048\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to calculate fairness metrics\n",
        "def fairness_metrics_and_visualization(y_true, y_pred_dict, sensitive_features):\n",
        "    results = {}\n",
        "    for model_name, y_pred in y_pred_dict.items():\n",
        "        model_results = {}\n",
        "        for feature_name, sensitive_data in sensitive_features.items():\n",
        "\n",
        "            p_group = y_pred[sensitive_data == 1].mean()\n",
        "            p_nongroup = y_pred[sensitive_data == 0].mean()\n",
        "\n",
        "\n",
        "            demographic_parity_difference = p_group - p_nongroup\n",
        "\n",
        "\n",
        "            true_positive_group = y_pred[(y_true == 1) & (sensitive_data == 1)].mean()\n",
        "            true_positive_nongroup = y_pred[(y_true == 1) & (sensitive_data == 0)].mean()\n",
        "            equal_opportunity_difference = true_positive_group - true_positive_nongroup\n",
        "\n",
        "            model_results[feature_name] = {\n",
        "                'Demographic Parity': demographic_parity_difference,\n",
        "                'Equal Opportunity': equal_opportunity_difference\n",
        "            }\n",
        "        results[model_name] = model_results\n",
        "\n",
        "    # Visualization\n",
        "    for feature_name in sensitive_features.keys():\n",
        "        fig, ax = plt.subplots(figsize=(10, 6))\n",
        "        metrics_df = pd.DataFrame({\n",
        "            model_name: {metric_name: results[model_name][feature_name][metric_name]\n",
        "                         for metric_name in ['Demographic Parity', 'Equal Opportunity']}\n",
        "            for model_name in results\n",
        "        })\n",
        "\n",
        "        metrics_df.plot(kind='bar', ax=ax)\n",
        "        ax.set_title(f'Fairness Metrics for {feature_name}')\n",
        "        ax.set_ylabel('Metric Value')\n",
        "        ax.set_xlabel('Metric Type')\n",
        "        ax.axhline(0, color='black', linewidth=0.8)\n",
        "        plt.xticks(rotation=0)\n",
        "        plt.show()\n",
        "\n",
        "    return results\n",
        "\n",
        "\n",
        "y_pred_dict = {\n",
        "    'Logistic Regression': y_pred_logistic_regression,\n",
        "    'Decision Tree': y_pred_decision_tree,\n",
        "    'Random Forest': y_pred_random_forest\n",
        "}\n",
        "\n",
        "fairness_results = fairness_metrics_and_visualization(y_test, y_pred_dict, sensitive_features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KyUPjRTxiVSY",
        "outputId": "aefa0d47-ce4e-4964-a0b2-69cd34c9eac3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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2bZlMJn322WcyGo168sknJV0PwSNGjND06dP1xx9/qE2bNipZsqROnjypTZs2qXfv3goPD1dqaqomTJig5s2bW74f3njjDe3cuVOvvfaa/u///o/uhwDuC4QvACjixo4dK4PBoM8//1xXr15V48aNFRMTo+eff94m269fv75Wr16tWbNmKTY2Vn/99ZcqVKggb29vDRkyxNKuT58++vbbb/Xjjz8qIyNDVatW1YgRIxQeHi7p+vNVHTt2VFxcnNatWyej0ai6detqxowZll/mC1r2XaglS5bos88+0zfffCMXFxdVr15dffr0seoqFxERofHjx2vGjBm6cuWKunfvnu/wJV1/H1j16tW1YsUKvf/++3JxcZGnp6e6du0q6fqAG3v37tWGDRuUkpKi0qVLy8/PT9OmTbN088yvyZMnq3r16oqNjdWmTZvk6uqqgQMH6qWXXrqj9UnXuxsGBQXpu+++U3JysmU/Fi5cKH9/f0u7AQMGqHbt2lqyZInlnWJVqlRRy5YtFRISIkmaMGGCMjIyFBUVZen+Wb58eU2cOFGDBw/Whx9+qBdeeOGOawUAW3Ew5+VpawAAAADAXeEePQAAAADYAOELAAAAAGyA8AUAAAAANkD4AgAAAAAbIHwBAAAAgA0QvgAAAADABnjP1x3KysrStWvXZDAYLO8cAQAAAPDgMZvNysrKUrFixW750nfC1x26du2a9u7da+8yAAAAANwjfH195eTkdNP5hK87lJ1ofX19ZTQa7VwNYB8mk0l79+7l5wAAHmBcC4D//Rzc6q6XRPi6Y9ldDY1GIycaPPD4OQAAcC0AdNvHkRhwAwAAAABsgPAFAAAAADZA+AIAAAAAG7gnnvn66KOP9OGHH+rs2bPy8vLSG2+8IT8/v1zbrly5UmvXrtXvv/8uSfLx8dHLL79s1d5sNmvmzJn69NNPlZaWpsaNG2vChAmqXbu2pc1ff/2lSZMm6bvvvpPBYFDbtm01duxYlSxZslD3FQAA4EFgNpt17do1mUwme5cC3DWj0ahixYrd9Sum7B6+NmzYoKioKEVGRqpRo0ZaunSpwsPDtXHjRlWsWDFH+507d6pjx45q3LixnJyctGjRIj333HP64osvVLlyZUnSwoULtXz5ck2ZMkXVq1fXBx98oPDwcG3YsEHFixeXJL3yyis6e/asYmJilJmZqddff13jx4/X9OnTbbr/AAAARU1GRoZOnz6ty5cv27sUoMCUKFFC7u7utxxK/nYczGazuQBryrdevXrJ19dX48ePl3T95cWtWrVSnz59NGDAgNsubzKZ1LRpU40fP17dunWT2WxWcHCw+vfvr/DwcEnS33//rcDAQE2ZMkUdO3ZUYmKiOnTooFWrVsnX11eS9MMPP2jAgAHasmWLJcTdbru7d++Wv78/I/vggcXPAQDgn9eCrKws/f777zIajXJzc5OTk9Nd3y0A7MlsNisjI0Nnz56VyWTSQw89lGNI+bz+TmTXO18ZGRnav3+/Bg4caJlmMBgUGBio+Pj4PK0jPT1d165dU9myZSVJJ0+e1NmzZxUYGGhpU7p0aTVq1Ejx8fHq2LGj4uPjVaZMGUvwkqTAwEAZDAbt2bNHTzzxRJ73gVvpeJBlf//zcwAAD65/XguuXr0qk8mk6tWrq0SJEvYsDSgwzs7OKlasmI4dO6YrV65YetNly+vvQnYNX+fPn5fJZMrRvbBixYpKSkrK0zqmTZumSpUqWcLW2bNnLev45zpTUlIkSSkpKapQoYLV/GLFiqls2bKW5fNq7969+WoPFEX8HAAAbrwWFCtWTFevXrVjNUDBu3r1qjIzM3XgwIE7Xofdn/m6GwsWLNCGDRu0bNmyHOnTVnibOx5k2W9z5+cAAB5c/7wWXLlyRceOHZOLi4ucnZ3tXR5QYAwGgxwdHVW/fv0c39vZPwe3Y9fwVb58eRmNRqWmplpNT01Nlaur6y2X/fDDD7VgwQLFxMTIy8vLMt3Nzc2yjkqVKlmtM7udq6urzp07Z7W+a9eu6cKFC5bl84q3uQP8HAAA/nctMBqNcnBwsHyAoiL7e/pufu+x63u+nJyc5OPjo7i4OMu0rKwsxcXFKSAg4KbLLVy4UHPmzNGiRYusntuSpOrVq8vNzc1qnRcvXtRvv/1mWWdAQIDS0tK0b98+S5sdO3YoKyvrpkPcAwAA4M6Zsmw7xputt5cXISEhWrJkyR0vv2bNGjVp0qTgCipC7vbY2ordux32799fY8aMUcOGDeXn56elS5cqPT1dPXr0kCSNHj1alStX1qhRoyRd72o4c+ZMTZ8+XdWqVbM8o1WiRAmVLFlSDg4O6tu3r+bOnatatWpZhpqvVKmS2rRpI0mqV6+egoOD9cYbbygyMlKZmZmaNGmSOnbsmKeRDgEAAJA/RoODhv8nXofPXCz0bdWvVEof/Ovmf8jPTUREhNLS0jRnzpxCqkpatWqVXFxc8tQ2JCREffv2Vb9+/SzTOnTooFatWt3x9tesWaPXXntN0vW7OK6urmrSpIlGjx6tqlWr3vF67wX5Obb2ZPfw1aFDB507d04zZ87U2bNn1aBBAy1atMjS7fD06dNWQzn+5z//UWZmpoYNG2a1npdeeklDhw6VJL3wwgtKT0/X+PHjlZaWpocffliLFi2yei5s2rRpmjRpkp599lnLS5bHjRtngz0GAAB4MB0+c1H7T6XZuwy7+eeAb/nl7Ox818/RlSpVShs3bpTZbNbJkycVGRmp4cOH69NPP72r9d5OZmamHB0dC239d3tsbcWu3Q6zhYWF6bvvvtO+ffv06aefqlGjRpZ52S9Lzvbtt98qISEhxyc7eEnXk/zw4cP1448/au/evVqyZInq1Kljtc1y5cpp+vTpio+P165duxQVFaWSJUsW/s4CAADgvvPTTz+pZ8+eatiwoYKCgjRt2jRdu3bNMv/ixYsaNWqU/P39FRQUpCVLlqhPnz566623LG1u7BpnNpsVHR2t1q1bW9Y5efJkSVKfPn30xx9/KCoqSp6envL09JSUe7fDb7/9VqGhofL19VXz5s01ZMiQW+6Hg4OD3NzcVKlSJTVu3Fg9e/bUnj17dPHi/+5Ibtq0Sd27d5evr68ef/xxzZo1y2pfExMT9e9//1u+vr7q0KGDtm/fLk9PT23atEnS9Vc/eXp6asOGDQoLC5Ovr68+//xzSdKnn36q9u3by9fXV+3atdNHH31kWW9GRoYmTpyooKAg+fr66rHHHtP8+fNve7z+eWwl6dSpU3rxxRcVEBCgxo0ba/jw4ZaRzyUpOjpaXbt21dq1axUSEqKHH35YI0eOtDoOhcHud74AAACAe1lycrIGDBig7t2765133tGRI0c0btw4FS9e3HIDYMqUKYqPj9fcuXNVsWJFzZw5U/v377caGO5GX331lZYsWaL33ntPDz30kFJSUnTw4EFJ/wsGvXv3Vu/evW9a1/fff6+XXnpJgwYN0tSpU5WZmaktW7bkeb9SU1P1zTffyGg0Wnqa/fLLLxozZozGjRunJk2a6Pjx43rjjTckXe9pZjKZNGTIEFWtWlWffvqpLl68qHfeeSfX9U+bNk0RERFq0KCBihcvrnXr1umDDz7Q+PHj1aBBAx04cEBvvPGGSpQooe7du2v58uX69ttvNWPGDLm7u+v06dP6888/b3u8/ikrK0uDBw9WiRIltHz5cplMJkVGRmrkyJFavny5pd3x48e1efNmzZs3T2lpaRoxYoQWLlyokSNH5vkY5hfhCwAAALiF//u//1OVKlU0fvx4OTg4qF69ekpOTta0adM0ZMgQXb58WWvXrtW0adPUokULSVJUVJSCg4Nvus7Tp0/L1dVVgYGBcnR0VNWqVS0Dv5UrV05Go1ElS5a85Ujc8+bNU4cOHawex7lZ2Mv2999/KyAgQGazWenp6ZKu32nLfiH2rFmzLEFTkmrUqKHhw4fr3Xff1UsvvaQff/xRJ06c0PLlyy21jRw5Uv3798+xrWeffVZt27a1fB0dHa2IiAjLtBo1aujw4cP65JNP1L17d50+fVq1atXSww8/LAcHB1WrVi1Px+uf4uLidOjQIW3evFnu7u6SpKlTp6pjx47as2ePZTmz2ayoqCiVKlVKktSlSxfFxcURvgAAAAB7SUxMVEBAgNXQ+Q8//LAuX76sP//8U2lpacrMzLQKA6VLl87x2MuN2rVrp6VLl6pNmzYKDg5Wq1at9Nhjj6lYsbz/en7gwAH16tUrX/tSsmRJxcbG6tq1a/rhhx/0+eefW4WNgwcP6tdff9W8efMs00wmk65evar09HQdOXJEVapUsQqFNwtBDRs2tPz78uXLOn78uMaOHWu5kyZdf91T6dKlJUndu3fXc889p3bt2ik4OFitW7dWUFCQpPwdr8TERFWpUsUSvCSpfv36KlOmjJKSkiz1VqtWzRK8JKlSpUo5XoFV0AhfAAAAgI25u7tr48aN2r59u7Zv367IyEh9+OGHWr58eZ4HpriTwTcMBoNq1aol6foI4MePH9eECRP07rvvSroekoYOHWp1xyrbjYPX5UX23bTs9UrSpEmTrMZ3yK5Jknx8fLR582b98MMP2r59u0aMGKHAwEDNnDmzQI7XP+UW3Mzmwn1FwT0x4AYAAABwr6pXr57i4+OtfjHftWuXSpYsqSpVqqh69epydHTU3r17LfP//vtvHT169JbrdXZ2VkhIiMaNG6dly5YpPj5ehw4dkiQ5OjoqKyvrlst7eHhYvdv2TgwYMEBffvml9u/fL0ny9vbWkSNHVKtWrRwfg8GgOnXq6M8//7QavOLG/b4ZV1dXVapUSSdOnMix3ho1aljalSpVSh06dNDkyZP1/vvv66uvvtJff/0l6dbH60b16tXTn3/+qdOnT1umHT58WGlpaapXr96dHqoCwZ0vAPc1U5ZJRsOdvWW+KHjQ9x/A/aV+pVK3b2TH7fz99986cOCA1bRy5crp6aef1tKlSzVp0iQ988wzOnLkiKKjo9W/f38ZDAaVKlVK3bp109SpU1W2bFlVrFhR0dHRcnBwsOqqeKM1a9bIZDKpUaNGcnFx0bp16+Ts7Gx531a1atX0888/q2PHjnJ0dMx1KPWXXnpJ/fr1U82aNdWxY0ddu3ZNW7Zs0YABA/K8z+7u7mrTpo1mzpyp+fPna8iQIRo0aJCqVq2qJ598UgaDQQcPHtShQ4c0cuRItWzZUjVq1NCYMWP06quv6tKlS5oxY0aetjVs2DBNnjxZpUuXVnBwsDIyMrRv3z6lpaWpf//+iomJkZubmxo0aCCDwaCNGzfKzc1NZcqUue3xulFgYKA8PDz0yiuv6PXXX5fJZNKECRPUrFkz+fr65vnYFAbCF4D7mtFgVMQPEUq6kGTvUmyubtm6mvLolNs3BIB7gCnLnO8XH9/t9oyG3IPPzfz000/q1q2b1bSePXvqrbfe0oIFCzR16lStXLlS5cqVU8+ePfXiiy9a2kVEROjNN9/UoEGDVKpUKT3//PM6ffr0TbvqlSlTRgsWLNCUKVOUlZUlDw8PzZs3T+XLl5d0PaiMHz9ebdq0UUZGhhISEnKso3nz5vrggw80Z84cLViwQKVKlVLTpk3ztc+S1K9fPz311FPas2ePgoODNW/ePM2ePVsLFy5UsWLFVLduXcuzZUajUbNnz9a4cePUs2dP1ahRQ6NHj9agQYNu2y2xV69ecnZ21ocffqipU6eqRIkS8vDw0LPPPivp+vNoixYt0rFjx2QwGOTr66sFCxbIYDDc9njdyMHBQXPmzNGkSZMUFhYmBwcHBQcHWz1rZi8O5sLu2FhEmUwm7d69W/7+/jIa+aszHkz3ys9B789768C5A7dvWMQ0qNBAKzuvtHcZAB5w/7wWXLlyRUeOHFGdOnXu+oXA97PLly/r0Ucf1ZgxY/I9KMb9ZteuXXr66af1zTffqGbNmvYup9Dc6ns7r78TcecLAAAAuEv//e9/LSPp/f3335o9e7Yk6fHHH7dzZQXvm2++UYkSJVSrVi0dP35cb731lho3blykg1dBIXwBAAAABWDx4sU6cuSIHB0d5ePjo48++ijXZ7Xud5cuXdK0adN06tQplS9fXoGBgRozZoy9y7ovEL4AAACAu+Tt7a01a9bYuwyb6NatW45n45A3DDUPAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAKX5apaG8vH0JCQrRkyZICb4t7H+/5AgAAQOEzGKXVz0sphwp/W64eUuiifC0SERGh2NhYSVKxYsVUtmxZeXp6qmPHjurRo4cMhoK7Z7Fq1Sq5uLgUeNs7ceN+56ZatWr69ttvC237DxrCFwAAAGwj5ZB0+jd7V3FTwcHBioqKUlZWllJSUrR161a99dZb+uqrrzR37lwVK1YwvzpXqFChUNreibFjx2rUqFGWr4OCghQVFaXg4GBJktFotGqfkZEhJyenQq2pKKPbIQAAACDJyclJbm5uqly5snx8fDRo0CDNmTNHP/zwg9XdobS0NI0dO1aPPPKIGjdurL59++rgwYNW6/r2228VGhoqX19fNW/eXEOGDLHMu7ErodlsVnR0tFq3bq2GDRsqKChIkydPzrWtJJ06dUovvviiAgIC1LhxYw0fPlwpKSmW+dHR0eratavWrl2rkJAQPfzwwxo5cqQuXryY6z6XLl1abm5ulo8klSlTxvJ1z549NXv2bI0ePVqNGzfW+PHjJUm//PKLnn76afn5+alVq1aaPHmyLl++bFlvRkaG3nnnHQUHB8vf31+9evXSzp078/k/UvQQvgAAAICbaNGihby8vPT1119bpg0fPlypqalauHCh1qxZIx8fHz377LP666+/JEnff/+9XnrpJbVq1Upr167V0qVL5efnl+v6v/rqKy1ZskSRkZH6+uuvNWfOHHl4eOTaNisrS4MHD9aFCxe0fPlyxcTE6MSJExo5cqRVu+PHj2vz5s2aN2+e5s+fr59//lkLFy6842OwePFieXl5ae3atRo8eLCOHz+uF154QW3bttW6dev0/vvva9euXZo0aZJlmYkTJyo+Pl7vv/++1q1bp3bt2un555/X0aNH77iOooBuhwAAAMAt1K1bVwkJCZKu3/HZs2eP4uLiLN3vxowZo02bNumrr77SU089pXnz5qlDhw4aNmyYZR1eXl65rvv06dNydXVVYGCgHB0dVbVq1ZsGtbi4OB06dEibN2+Wu7u7JGnq1Knq2LGj9uzZY1nObDYrKipKpUqVkiR16dJFcXFxOUJaXj3yyCN67rnnLF+PHTtWnTt3Vr9+/SRJtWvX1tixY9WnTx9NmDBBqampWrNmjb777jtVrlxZkhQeHq6tW7dqzZo1evnll++ojqKA8AUAAADcgtlsloODgyQpISFBly9fVvPmza3aXLlyRcePH5ckHThwQL169crTutu1a6elS5eqTZs2Cg4OVqtWrfTYY4/l+nxZYmKiqlSpYgleklS/fn2VKVNGSUlJlvBVrVo1S/CSpEqVKik1NTV/O32Dhg0bWn198OBBJSQk6PPPP7dMM5vNysrK0smTJ3XixAmZTCa1a9fOarmMjAyVK1fujusoCghfAAAAwC0kJiaqevXqkqRLly7Jzc1Ny5cvz9GudOnSkiRnZ+c8r9vd3V0bN27U9u3btX37dkVGRurDDz/U8uXL5ejoeEf15hbczGbzHa1LUo7RFi9fvqx//etf6tOnT4627u7uSkhIkNFo1OrVq3MM2FGiRIk7rqMoIHwBAAAAN5Hd1S+7i52Pj49SUlJkNBotgeyfPDw8FBcXp9DQ0Dxtw9nZWSEhIQoJCdHTTz+t9u3b69ChQ/Lx8bFqV69ePf355586ffq05e7X4cOHlZaWpnr16t35TuaTt7e3Dh8+rFq1auU6v0GDBjKZTDp37pyaNGlis7ruB4QvAAAA2IZr7gNJ3CvbycjI0NmzZ62Gmp8/f74ee+wxdevWTZIUGBgof39/DRkyRK+++qpq166tM2fOaMuWLWrTpo18fX310ksvqV+/fqpZs6Y6duyoa9euacuWLRowYECOba5Zs0Ymk0mNGjWSi4uL1q1bJ2dnZ1WtWjVH28DAQHl4eOiVV17R66+/LpPJpAkTJqhZs2by9fW9o32+Ey+88IKeeuopTZw4Ub169ZKLi4sOHz6s7du3a/z48apTp446d+6s0aNHKyIiQg0aNND58+cVFxcnT09PtW7d2ma13msIXwAAACh8WaZ8v/j4rrdnMN6+3Q22bt2qoKAgFStWTGXKlJGXl5fGjRun7t27W16y7ODgoAULFmjGjBl67bXXdP78ebm6uqpJkyZydXWVJDVv3lwffPCB5syZowULFqhUqVJq2rRprtssU6aMFixYoClTpigrK0seHh6aN2+eypcvn6Otg4OD5syZo0mTJiksLEwODg4KDg7WG2+8kc+Dc3e8vLy0fPlyzZgxQ08//bQkqUaNGurQoYOlTVRUlObOnaspU6bozJkzKleunPz9/R/o4CVJDua76QD6ADOZTNq9e7f8/f1z9GUFHhT3ys9B789768C5A3bbvr00qNBAKzuvtHcZAB5w/7wWXLlyRUeOHFGdOnXy9ewTcK+71fd2Xn8n4j1fAAAAAGADhC8AAAAAsAHCFwAAAADYAOELAAAAAGyA8IX7minrwR4v5kHffwAAgPsJQ83jvmY0OGj4f+J1+MxFe5dic/UrldIH/wqwdxkAAADII8IX7nuHz1zU/lNp9i4DAAAAuCW6HQIAAACADRC+AAAAAMAGCF8AAAAodKYsU5He3t3y9PTUpk2b7F0GChnPfAEAAKDQGQ1GRfwQoaQLSYW+rbpl62rKo1PytUxERIRiY2MlScWKFVPlypXVrl07DR8+XMWLFy+MMu8JN+73jb7++mvVqlXLDhVdryktLU1z5syxy/YLE+ELAAAANpF0IUkHzh2wdxk3FRwcrKioKF27dk379+/XmDFj5ODgoFdffdXepRWq7P2+UYUKFe5oXRkZGXJyciqIsookwhcAAAAgycnJSW5ubpIkd3d3BQYGavv27Zb558+f16RJk/Tzzz8rLS1NNWvW1MCBA9WpUydLmz59+sjT01NOTk5atWqVHB0d9a9//UtDhw61tDl69KjGjh2rPXv2qEaNGho7dmyOWhISEvTWW29p9+7dcnFxUdu2bRUREaGSJUtK+t/dIT8/Py1btkwZGRnq16+fBg0apOnTp2v16tVydnbW8OHDFRoamuf9/qeffvpJU6dO1cGDB1WuXDl169ZNI0aMULFixSz7+9BDD8loNGrdunXy8PDQ8uXLdejQIU2dOlW7du2Si4uLWrZsqddee80S6jZu3KjZs2fr2LFjcnFxUYMGDTRnzhx9+OGHljtxnp6ekqRly5apefPmt/7Pu0/wzBcAAADwD4cOHVJ8fLwcHR0t0zIyMuTj46MFCxZo/fr16t27t0aPHq09e/ZYLRsbG6sSJUpo5cqVevXVVzV79mz9+OOPkqSsrCwNHTpUjo6O+vTTTxUZGalp06ZZLX/58mWFh4erbNmyWrVqlWbMmKHt27dr0qRJVu127NihM2fOaMWKFYqIiFB0dLQGDhyosmXLauXKlfrXv/6lN998U3/++ecdHYPk5GQNGDBAvr6++uyzzzRhwgStWrVKc+fOzbG/jo6O+vjjjxUZGam0tDQ9++yz8vb21qpVq7Ro0SKlpqZqxIgRkqQzZ85o1KhRCg0N1YYNG7Rs2TI98cQTMpvNeu6559S+fXsFBwdr27Zt2rZtmwICis57TbnzBQAAAEj6/vvvFRAQoGvXrikjI0MGg0FvvPGGZX7lypUVHh5u+bpPnz7atm2bvvzyS/n5+Vmme3p66qWXXpIk1a5dWytWrFBcXJxatmyp7du3KykpSYsWLVLlypUlSSNHjtQLL7xgWX79+vXKyMjQO++8oxIlSkiSxo8fr0GDBumVV16Rq6urJKlcuXIaN26cDAaD6tatq0WLFunKlSsaNGiQJGngwIFauHChdu3apY4dO952v7MFBwdr5syZ+r//+z9VqVJF48ePl4ODg+rVq6fk5GRNmzZNQ4YMkcFgsOzj6NGjLcvPmTNH3t7eevnlly3T3n77bbVq1UpHjhzR5cuXde3aNT3xxBOqVq2a5Zhlc3Z2VkZGxk3vxt3PCF8AAACApObNm2vChAlKT0/XkiVLZDQa9eSTT1rmm0wmzZs3Txs3blRycrIyMzOVkZEhZ2dnq/XcGCQkyc3NTampqZKkxMREValSxRK8JOW4s5OYmChPT09L8JKkxo0bKysrS0eOHLGEr/r161sCkCS5urrqoYcesnxtNBpVrlw5y7Zvt9/ZXFxcLHUEBATIwcHBMu/hhx/W5cuX9eeff6pq1aqSJB8fH6v1HTx4UDt37sz1jtXx48cVFBSkFi1aqHPnzgoKClJQUJCefPJJlS1b9pZ1FgWELwAAAEDXQ0f2CH9vv/22unbtqk8//VS9evWSJH344YdatmyZXn/9dXl6esrFxUVvv/22MjMzrdaT/TxUNgcHB5nN5gKvN7ft5DYtKyvrluu5cb/vRHZYy3b58mU99thjeuWVV3K0dXNzk9FoVExMjH799Vf9+OOPWr58ud5//32tXLlSNWrUuOM67gd2f+bro48+UkhIiHx9fdWrV68cfWZv9Pvvv2vo0KEKCQmRp6enlixZkqNN9rx/fiIjIy1tsh+EvPEzfvz4wtg9AAAA3IcMBoMGDhyoDz74QFeuXJEk/frrr3r88cfVtWtXeXl5qUaNGjp69Gi+1luvXj39+eefOnPmjGXa7t27c7RJSEjQ5cuXLdN+/fVXGQwG1alT5473Kb/q1aun+Ph4q+C4a9culSxZUlWqVLnpcj4+Pvr9999VrVo11apVy+qTfTfPwcFBDz/8sIYNG6a1a9fK0dHR8p4zR0fH2wbG+5Vd73xt2LBBUVFRioyMVKNGjbR06VKFh4dr48aNqlixYo726enpql69utq1a5djOMxsq1atksn0v5fq/f777+rfv7/atWtn1a53794aNmyY5et/JnYAAAAUrLpl695X22nXrp2mTp2qjz76SOHh4apVq5a++uor/frrrypbtqxiYmKUkpKievXq5XmdgYGBql27tiIiIjR69GhdvHhR77//vlWbzp07a+bMmYqIiNBLL72kc+fOadKkSeratauly6EtPP3001q6dKkmTZqkZ555RkeOHFF0dLT69+9v1d0xt+VWrlypl19+Wc8//7zKlSunY8eOacOGDZo8ebL27dtneQauYsWK+u2333Tu3DnVrXv9/61atWratm2bkpKSVK5cOZUuXdpq4JP7mV3DV0xMjHr37m0Z/jIyMlLff/+9Vq9erQEDBuRo7+fnZ3mYcfr06bmu85/vJFiwYIFq1qypZs2aWU13dnYukIf4bgx6sD2j0WjvEuzOnt+D2du2Zw18D3AeAmBf/7wWmEwmmc1myydbljkr3y8+vqu6skwyOOS9k1d2rTfWbDQa9cwzz2jRokX617/+pUGDBunEiRMKDw+Xi4uLevfurccff1wXL17Msfw/uxlmHw8HBwdFR0dr3Lhx6tmzp6pVq6axY8fqhRdesLRxdnbWokWL9Pbbb6tnz55ydna2DDV/q+3kddrt9jtbpUqVNH/+fL377rtauXKlypUrp549e2rQoEG3rKNSpUr6v//7P02fPl3h4eHKyMhQ1apVFRwcLAcHB5UsWVI///yzli5dqosXL6pq1aoaM2aMHn30UZnNZvXq1Us7d+5UaGioLl++rKVLl94TQ81n//+YTKYc1968XosdzIXRATUPMjIy5O/vr5kzZ6pNmzaW6WPGjFFaWlqOISz/KSQkRH379lW/fv1uuY3g4GD179/fMuqLdL3b4e+//y6z2Sw3Nzc99thjGjx4cL7ufplMphy3iGFbLi4u8vb2VseZW7X/VJq9y7E5n6pl9MWwYP33v/9Venq6vcuxi+zvgd6f976nX9pZWBpUaKCVnVc+0N8DAO5NxYoVU40aNVS8eHHLNAcHB6uBGwrbP8MfcLeuXr2qEydO6Nq1azdt4+/vf8s/DNvtztf58+dlMplydC+sWLGikpKSCmQbmzZt0t9//63u3btbTe/UqZOqVq2qSpUqKSEhQdOmTdORI0c0a9asfG/D19eXv7zDrv45opItmUwm7d27l58DO7Pn9wAA/PNacOXKFcuLc/85CiBwPzMYDHJ0dFT9+vVzfG9n/xzcTpEe7XD16tV69NFHrYbylKSnnnrK8m9PT0+5ubmpX79+On78uGrWrJmvbRiNRn7phF3dC99//BzYF8cewL0g+1pgNBotd7lseacLKGzZ39N383uP3UY7LF++vIxGY473DqSmphbIg4R//PGHtm/frp49e962baNGjSRJx44du+vtAgAAAEBu7Ba+nJyc5OPjo7i4OMu0rKwsxcXF5fpCtvxas2aNKlasqNatW9+27YED158VKYpv0QYAAABwb7Brt8P+/ftrzJgxatiwofz8/LR06VKlp6erR48ekqTRo0ercuXKGjVqlKTrA2gkJiZa/p2cnKwDBw6oRIkSVi+Gy8rK0po1a9StW7ccL5o7fvy4Pv/8c7Vq1UrlypVTQkKCoqKi1LRpU3l5edlozwEAAIo2BrtAUVMQ39N2DV8dOnTQuXPnNHPmTJ09e1YNGjTQokWLLN0OT58+bfUOgTNnzqhbt26WrxcvXqzFixerWbNmWr58uWX69u3bderUKcsQ9jdydHRUXFycli1bpsuXL8vd3V1t27bV4MGDC29HAQAAHhDZ72O6fPky71FFkZL90uu7eeeY3QfcCAsLU1hYWK7zbgxUklS9enUlJCTcdp1BQUE3befu7q4VK1bkv1AAAADcltFoVLly5XTmzBlJUokSJRh4A/c1s9msy5cv68yZMypXrtxdDXRl9/AFAACAoqVKlSqSZAlgQFFQrlw5y/f2nSJ8AQAAoEA5ODjI3d1dlSpVUmZmpr3LAe6ao6NjgbzahfAFAACAQsF7IAFrdhtqHgAAAAAeJIQvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwDuiouLi71LAAAAuC8Us3cBAO6MW6niUpZJMhjtVoPRaJS3t7fdtg8AAHA/IXwB96kyLsWuB6/Vz0sph+xdjn3UbyM9Pt7eVQAAAOQJ4Qu436Uckk7/Zu8q7MPVw94VAAAA5BnPfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbMDu4eujjz5SSEiIfH191atXL+3Zs+embX///XcNHTpUISEh8vT01JIlS3K0iY6Olqenp9WnXbt2Vm2uXr2qyMhINW/eXAEBARo6dKhSUlIKetcAAAAAwMKu4WvDhg2KiorSkCFDFBsbKy8vL4WHhys1NTXX9unp6apevbpGjRolNze3m673oYce0rZt2yyf//u//7Oa//bbb+u7777TjBkztHz5cp05c0YvvfRSge4bAAAAANzIruErJiZGvXv3VmhoqOrXr6/IyEg5Oztr9erVubb38/PTmDFj1LFjRzk5Od10vUajUW5ubpZPhQoVLPP+/vtvrV69WhEREWrRooUaNmyot99+W/Hx8dq9e3dB7yIAAAAASJKK2WvDGRkZ2r9/vwYOHGiZZjAYFBgYqPj4+Lta97FjxxQUFKTixYvL399fo0aNUtWqVSVJ+/btU2ZmpgIDAy3t69Wrp6pVq2r37t3y9/fP17ZMJtNd1Yq7YzQa7V0CYHechwDYU/Y5iHMRHmR5/f63W/g6f/68TCaTKlasaDW9YsWKSkpKuuP1+vn5KSoqSnXq1NHZs2c1e/ZsPfPMM/r8889VqlQppaSkyNHRUWXKlMmx3bNnz+Z7e3v37r3jWnF3XFxc5O3tbe8yALtLSEhQenq6vcsA8IDjdyLg9uwWvgpLq1atLP/28vJSo0aN9Nhjj+nLL79Ur169Cnx7vr6+3H0BYFeenp72LgHAA8xkMmnv3r38ToQHWvbPwe3YLXyVL19eRqMxx+AaqampcnV1LbDtlClTRrVr19bx48clSa6ursrMzFRaWprV3a/U1NRbDuJxM0ajkRMNALviHATgXsDvRMDt2W3ADScnJ/n4+CguLs4yLSsrS3FxcQoICCiw7Vy6dEknTpywBKuGDRvK0dHRartJSUk6depUvp/3AgAAAIC8smu3w/79+2vMmDFq2LCh/Pz8tHTpUqWnp6tHjx6SpNGjR6ty5coaNWqUpOuDdCQmJlr+nZycrAMHDqhEiRKqVauWJOmdd97RY489pqpVq+rMmTOKjo6WwWBQp06dJEmlS5dWaGiopkyZorJly6pUqVKaPHmyAgICCF8AAAAACo1dw1eHDh107tw5zZw5U2fPnlWDBg20aNEiS7fD06dPy2D43825M2fOqFu3bpavFy9erMWLF6tZs2Zavny5JOnPP//Uyy+/rL/++ksVKlTQww8/rJUrV1oNN//666/LYDBo2LBhysjIUFBQkN58803b7DQAAACAB5LdB9wICwtTWFhYrvOyA1W26tWrKyEh4Zbre//992+7zeLFi+vNN98kcAEAAACwGbu+ZBkAAAAAHhSELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAC4r5myzPYuwe44BsD9oZi9CwAAALgbRoODhv8nXofPXLR3KXZRv1IpffCvAHuXASAPCF8AAOC+d/jMRe0/lWbvMgDgluh2CAAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAOCuuLi42LsE4L5A+AIAALiPuZUqLmWZ7LZ9o9Eob29vGY1Gu9Vgz/0H8qOYvQsAAADAnSvjUkwyGKXVz0sph+xdju25ekihi+xdBZAnhC8AAICiIOWQdPo3e1cB4BbodggAAAAANkD4AgAAAAAbsHv4+uijjxQSEiJfX1/16tVLe/bsuWnb33//XUOHDlVISIg8PT21ZMmSHG3mz5+v0NBQBQQEqEWLFho8eLCSkpKs2vTp00eenp5Wn/Hjxxf0rgEAAACAhV3D14YNGxQVFaUhQ4YoNjZWXl5eCg8PV2pqaq7t09PTVb16dY0aNUpubm65tvnpp5/0zDPPaOXKlYqJidG1a9cUHh6uy5cvW7Xr3bu3tm3bZvmMHj26wPcPAAAAALLZdcCNmJgY9e7dW6GhoZKkyMhIff/991q9erUGDBiQo72fn5/8/PwkSdOnT891nR9++KHV11OmTFGLFi20f/9+NW3a1DLd2dn5pgEOAAAAAAqa3cJXRkaG9u/fr4EDB1qmGQwGBQYGKj4+vsC28/fff0uSypYtazX9888/17p16+Tm5qbHHntMgwcPvqMXBJpMvFfCnuz6ThHgHsF5CA86rgWQOBfCvvL6/We38HX+/HmZTCZVrFjRanrFihVzPKN1p7KysvT222+rcePG8vDwsEzv1KmTqlatqkqVKikhIUHTpk3TkSNHNGvWrHxvY+/evQVSK/LPxcVF3t7e9i4DsLuEhASlp6fbuwzALrgWIBvnQtwPivR7viIjI/X777/r//7v/6ymP/XUU5Z/e3p6ys3NTf369dPx48dVs2bNfG3D19eXv7gBsCtPT097lwAAdse5EPZkMpnydFPGbuGrfPnyMhqNOQbXSE1Nlaur612vf+LEifr++++1YsUKValS5ZZtGzVqJEk6duxYvsOX0WgkfAGwK85BAMC5EPcHu4126OTkJB8fH8XFxVmmZWVlKS4uTgEBAXe8XrPZrIkTJ+qbb77R0qVLVaNGjdsuc+DAAUliAA4AAAAAhcau3Q779++vMWPGqGHDhvLz89PSpUuVnp6uHj16SJJGjx6typUra9SoUZKuD9KRmJho+XdycrIOHDigEiVKqFatWpKudzVcv3695syZo5IlS+rs2bOSpNKlS8vZ2VnHjx/X559/rlatWqlcuXJKSEhQVFSUmjZtKi8vLzscBQAAAAAPAruGrw4dOujcuXOaOXOmzp49qwYNGmjRokWWboenT5+WwfC/m3NnzpxRt27dLF8vXrxYixcvVrNmzbR8+XJJ0scffyzp+ouUbxQVFaUePXrI0dFRcXFxWrZsmS5fvix3d3e1bdtWgwcPLuS9BQAAAPAgs/uAG2FhYQoLC8t1Xnagyla9enUlJCTccn23m+/u7q4VK1bkr0gAAAAAuEt2e+YLAAAAAB4khC8AAAAAsAHCFwAAAADYwF2Fr6tXrxZUHQAAAABQpOU7fGVlZWn27NkKDg5WQECATpw4IUmaMWOGPv300wIvEAAAAACKgnyHrzlz5ig2NlavvvqqHB0dLdM9PDy0atWqAi0OAAAAAIqKfIevzz77TJMmTVKXLl2s3sHl6emppKSkAi0OAAAAAIqKfIev5ORk1axZM8d0s9msa9euFUhRAAAAAFDU5Dt81a9fX7/88kuO6Rs3blSDBg0KpCgAAAAAKGqK5XeBwYMHKyIiQsnJyTKbzfr666915MgRrV27VvPnzy+MGgEAAADgvpfvO19t2rTRvHnzFBcXJxcXF82cOVOJiYmaN2+eWrZsWRg1AgAAAMB9L993viSpSZMmiomJKehaAAAAAKDIuquXLAMAAAAA8ibfd768vLzk4OBw0/kHDhy4q4IAAAAAoCjKd/iaNWuW1dfXrl3TgQMHFBsbq6FDhxZYYQAAAABQlOQ7fLVp0ybHtHbt2ql+/frasGGDevXqVSCFAQAAAEBRUmDPfPn7+2vHjh0FtToAAAAAKFIKJHxduXJFy5YtU6VKlQpidQAAAABQ5OS722HTpk2tBtwwm826dOmSnJ2d9e677xZocQAAAABQVOQ7fL322mtW4cvBwUEVKlRQo0aNVLZs2QItDgAAAACKinyHrx49ehRGHQAAAABQpOUpfB08eDDPK/Ty8rrjYgAAAACgqMpT+OrWrZscHBxkNptv2c7BwYGXLAMAAABALvIUvjZv3lzYdQAAAABAkZan8FWtWrXCrgMAAAAAirR8D7iR7fDhwzp16pQyMzOtpj/++ON3XRQAAAAAFDX5Dl8nTpzQkCFDdOjQIavnwLKHn+eZLwAAAADIyZDfBd566y1Vr15d27dvl7Ozs7744gutWLFCDRs21PLlywujRgAAAAC47+U7fMXHx2vYsGGqUKGCDAaDHBwc1KRJE7388suaPHlyYdQIAAAAAPe9fIevrKwslSxZUpJUvnx5nTlzRtL1QTmOHDlSsNUBAAAAQBGR72e+HnroISUkJKhGjRpq1KiRFi1aJEdHR61cuVI1atQojBoBAAAA4L6X7ztfL774orKysiRJw4YN08mTJ/XMM89oy5YtGjt2bIEXCAAAAABFQZ7vfPXo0UO9evVS586dVapUKUlSrVq1tHHjRv31118qW7asZcRDAAAAAIC1PN/58vLy0rvvvqugoCCNHj1aO3futMwrV64cwQsAAAAAbiHP4evtt9/Wjz/+qPHjx+v06dPq16+fnnjiCc2bN0/JycmFWSMAAACQu1KVZMoy2bsKu+MY3B/yNeCGi4uLevTooR49euj48eNavXq1PvnkE0VHR6tly5bq2bOn2rZtW1i1AgAAANacy8loMCrihwglXUiydzV2UbdsXU15dIq9y0Ae5Hu0w2w1a9bUyJEjNWLECH311Vd68803tXXrVh04cKAg6wMAAABuK+lCkg6c4/dQ3NvuOHxJ0s6dO7VmzRp9/fXXMhqN6tWrV0HVBQAAAABFSr7D159//qk1a9YoNjZWJ06cUJMmTfTmm2+qXbt2cnZ2LowaAQAAAOC+l+fwtWHDBq1evVo7duxQhQoV1L17d4WGhqpWrVqFWR8AAAAAFAl5Dl+jR49Wq1atNGvWLLVq1UoGQ77fzwwAAAAAD6w8h68tW7aoYsWKhVkLAAAAABRZeb59RfACAAAAgDtH30EAAAAAsAHCFwAAAADYAOELAAAAAGwg3+Frz549+u2333JM/+2337R37958F/DRRx8pJCREvr6+6tWrl/bs2XPTtr///ruGDh2qkJAQeXp6asmSJXe0zqtXryoyMlLNmzdXQECAhg4dqpSUlHzXDgAAAAB5le/wNXHiRJ0+fTrH9OTkZE2cODFf69qwYYOioqI0ZMgQxcbGysvLS+Hh4UpNTc21fXp6uqpXr65Ro0bJzc3tjtf59ttv67vvvtOMGTO0fPlynTlzRi+99FK+agcAAACA/Mh3+EpMTJSPj0+O6Q0aNNDhw4fzta6YmBj17t1boaGhql+/viIjI+Xs7KzVq1fn2t7Pz09jxoxRx44d5eTkdEfr/Pvvv7V69WpFRESoRYsWatiwod5++23Fx8dr9+7d+aofAAAAAPIqz+/5yubk5KSUlBTVqFHDavrZs2dVrFjeV5eRkaH9+/dr4MCBlmkGg0GBgYGKj4/Pb1l5Xue+ffuUmZmpwMBAS5t69eqpatWq2r17t/z9/fO1TZPJdEe1omAYjUZ7lwDYHechPOi4FgDXcT2wn7we+3yHr5YtW+q9997TnDlzVLp0aUlSWlqa3n//fatAczvnz5+XyWTK8f6wihUrKikpKb9l5XmdKSkpcnR0VJkyZXK0OXv2bL63eSfPuaFguLi4yNvb295lAHaXkJCg9PR0e5cB2AXXAuB/uB7c+/IdvsaMGaNnnnlGjz32mBo0aCBJOnjwoCpWrKipU6cWeIH3Ol9fX/7iBsCuPD097V0CAOAewPXAfkwmU55uyuQ7fFWuXFnr1q3T559/roMHD8rZ2VmhoaHq2LGjHB0d87ye8uXLy2g05hhcIzU1Va6urvktK8/rdHV1VWZmptLS0qzufqWmpt50EI9bMRqNhC8AdsU5CAAgcT24H+Q7fElSiRIl9NRTT93Vhp2cnOTj46O4uDi1adNGkpSVlaW4uDiFhYUV2jobNmwoR0dHxcXF6cknn5QkJSUl6dSpU/l+3gsAAAAA8ipP4Wvz5s169NFH5ejoqM2bN9+y7eOPP57njffv319jxoxRw4YN5efnp6VLlyo9PV09evSQJI0ePVqVK1fWqFGjJF0fUCMxMdHy7+TkZB04cEAlSpRQrVq18rTO0qVLKzQ0VFOmTFHZsmVVqlQpTZ48WQEBAYQvAAAAAIUmT+FryJAh+vHHH1WxYkUNGTLkpu0cHBx04MCBPG+8Q4cOOnfunGbOnKmzZ8+qQYMGWrRokaWL4OnTp2Uw/G80/DNnzqhbt26WrxcvXqzFixerWbNmWr58eZ7WKUmvv/66DAaDhg0bpoyMDAUFBenNN9/Mc90AAAAAkF95Cl8HDx7M9d8FISws7KbdDLMDVbbq1asrISHhrtYpScWLF9ebb75J4AIAAABgM/l6yXJmZqaeffZZHT16tJDKAQAAAICiKV/hy9HRMU93ngAAAAAA1vIVviSpS5cuWrVqVWHUAgAAAABFVr6HmjeZTPr444+1fft2NWzYUC4uLlbzX3vttQIrDgAAAACKinyHr0OHDsnb21uSdOTIkQIvCAAAAACKonyHr3+OQAgAAAAAuL18P/P12muv6eLFizmmX758mS6HAAAAAHAT+Q5fa9eu1dWrV3NMv3Llij777LMCKQoAAAAAipo8dzu8ePGizGazzGazLl26pOLFi1vmmUwm/fDDD6pQoUKhFAkAAAAA97s8h68mTZrIwcFBDg4OevLJJ3PMd3Bw0NChQwu0OAAAAAAoKvIcvpYtWyaz2axnn31W0dHRKlu2rGWeo6OjqlatqsqVKxdKkQAAAABwv8tz+GrWrJkkafPmzapataocHBwKrSgAAAAAKGryPeBGtWrVtGvXLr3yyiv617/+peTkZEnXB+L45ZdfCrxAAAAAACgK8h2+vvrqK4WHh8vZ2Vn79+9XRkaGpOsDcsyfP7/ACwQAAACAoiDf4Wvu3LmKjIzU5MmTVazY/3otNm7cWP/9738LtDgAAAAAKCryHb6OHDmiJk2a5JheunRppaWlFUhRAAAAAFDU5Dt8ubq66vjx4zmm79q1SzVq1CiQogAAAACgqMl3+Ordu7feeust/fbbb3JwcFBycrLWrVund955R//+978Lo0YAAAAAuO/leaj5bAMGDFBWVpb69eun9PR0hYWFycnJSc8995z69OlTGDUCAAAAwH0v3+HLwcFBL774osLDw3X8+HFdvnxZ9erVU8mSJQujPgAAAAAoEvIdvrI5OTmpfv36BVkLAAAAABRZeQ5fr732Wp7aRUVF3XExAAAAAFBU5Tl8xcbGqmrVqvL29pbZbC7MmgAAAACgyMlz+Pr3v/+tL774QidPnlSPHj3UpUsXlStXrhBLAwAAAICiI89Dzb/55pvatm2bnn/+eX333Xdq3bq1hg8frq1bt3InDAAAAABuI18Dbjg5OalTp07q1KmT/vjjD8XGxioyMlImk0nr169nxEMAAAAAuIl8v2TZsqDh+qJms1kmk6nACgIAAACAoihfd74yMjL09ddfa/Xq1dq1a5dat26t8ePHKzg42BLGAAAAAAA55Tl8TZgwQRs2bFCVKlUUGhqq6dOnq0KFCoVZGwAAAAAUGXkOX//5z39UtWpV1ahRQz///LN+/vnnXNvNmjWrwIoDAAAAgKIiz+GrW7ducnBwKMxaAAAAAKDIynP4mjJlSmHWAQAAAABFGqNkAAAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2MA9Eb4++ugjhYSEyNfXV7169dKePXtu2f7LL79Uu3bt5Ovrq86dO2vLli1W8z09PXP9LFq0yNImJCQkx/wFCxYUyv4BAAAAQDF7F7BhwwZFRUUpMjJSjRo10tKlSxUeHq6NGzeqYsWKOdr/+uuvGjVqlF5++WU99thj+vzzzzVkyBCtWbNGHh4ekqRt27ZZLfPDDz9o7NixevLJJ62mDxs2TL1797Z8XbJkyULYQwAAAAC4B+58xcTEqHfv3goNDVX9+vUVGRkpZ2dnrV69Otf2y5YtU3BwsJ5//nnVq1dPI0aMkLe3t1asWGFp4+bmZvXZvHmzmjdvrho1alitq2TJklbtSpQoUaj7CgAAAODBZdc7XxkZGdq/f78GDhxomWYwGBQYGKj4+Phcl9m9e7f69etnNS0oKEibNm3KtX1KSoq2bNmiKVOm5Ji3cOFCzZ07V+7u7urUqZP69eunYsXyd0hMJlO+2qNgGY1Ge5cA2B3nITzouBYA13E9sJ+8Hnu7hq/z58/LZDLl6F5YsWJFJSUl5bpMSkqKXF1dc7RPSUnJtX1sbKxKliyptm3bWk3v06ePvL29VbZsWcXHx+u9997T2bNn9dprr+VrH/bu3Zuv9ig4Li4u8vb2tncZgN0lJCQoPT3d3mUAdsG1APgfrgf3Prs/81XYVq9erc6dO6t48eJW0/v372/5t5eXlxwdHfXmm29q1KhRcnJyyvP6fX19+YsbALvy9PS0dwkAgHsA1wP7MZlMebopY9fwVb58eRmNRqWmplpNT01NzXF3K5urq2uOu1w3a//LL7/oyJEjmjFjxm1radSoka5du6aTJ0+qbt26ed4Ho9FI+AJgV5yDAAAS14P7gV0H3HBycpKPj4/i4uIs07KyshQXF6eAgIBcl/H399eOHTuspm3fvl3+/v452q5atUo+Pj7y8vK6bS0HDhyQwWDIdYRFAAAAALhbdh/tsH///lq5cqViY2OVmJioCRMmKD09XT169JAkjR49WtOnT7e079u3r7Zu3arFixcrMTFR0dHR2rdvn8LCwqzWe/HiRW3cuFG9evXKsc34+HgtWbJEBw8e1IkTJ7Ru3TpFRUWpS5cuKlu2bOHuMAAAAIAHkt2f+erQoYPOnTunmTNn6uzZs2rQoIEWLVpk6UZ4+vRpGQz/y4iNGzfWtGnTNGPGDL333nuqXbu2Zs+ebXnHV7YvvvhCZrNZnTp1yrFNJycnbdiwQbNmzVJGRoaqV6+ufv36WT0HBgAAAAAFye7hS5LCwsJy3LnKtnz58hzT2rdvr/bt299ynU899ZSeeuqpXOf5+Pho5cqV+S8UAAAAAO6Q3bsdAgAAAMCDgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA/dE+Proo48UEhIiX19f9erVS3v27Lll+y+//FLt2rWTr6+vOnfurC1btljNj4iIkKenp9UnPDzcqs1ff/2lUaNGqXHjxmrSpIlef/11Xbp0qcD3DQAAAACkeyB8bdiwQVFRURoyZIhiY2Pl5eWl8PBwpaam5tr+119/1ahRo9SzZ0+tXbtWjz/+uIYMGaJDhw5ZtQsODta2bdssn/fee89q/iuvvKLDhw8rJiZG8+bN0y+//KLx48cX2n4CAAAAeLDZPXzFxMSod+/eCg0NVf369RUZGSlnZ2etXr061/bLli1TcHCwnn/+edWrV08jRoyQt7e3VqxYYdXOyclJbm5ulk/ZsmUt8xITE7V161ZNnjxZjRo1UpMmTTRu3Dh98cUXSk5OLtT9BQAAAPBgKmbPjWdkZGj//v0aOHCgZZrBYFBgYKDi4+NzXWb37t3q16+f1bSgoCBt2rTJatpPP/2kFi1aqEyZMnrkkUc0YsQIlS9fXpIUHx+vMmXKyNfX19I+MDBQBoNBe/bs0RNPPJHnfTCZTHlui4JnNBrtXQJgd5yH8KDjWgBcx/XAfvJ67O0avs6fPy+TyaSKFStaTa9YsaKSkpJyXSYlJUWurq452qekpFi+Dg4O1hNPPKHq1avrxIkTeu+99/TCCy/ok08+kdFoVEpKiipUqGC1jmLFiqls2bI6e/ZsvvZh7969+WqPguPi4iJvb297lwHYXUJCgtLT0+1dBmAXXAuA/+F6cO+za/gqLB07drT8O3vAjTZt2ljuhhUkX19f/uIGwK48PT3tXQIA4B7A9cB+TCZTnm7K2DV8lS9fXkajMcfgGqmpqTnubmVzdXW1ust1u/aSVKNGDZUvX17Hjh1TixYt5OrqqnPnzlm1uXbtmi5cuCA3N7d87YPRaCR8AbArzkEAAInrwf3ArgNuODk5ycfHR3FxcZZpWVlZiouLU0BAQK7L+Pv7a8eOHVbTtm/fLn9//5tu588//9Rff/1lCVYBAQFKS0vTvn37LG127NihrKws+fn53cUeAQAAAEDu7D7aYf/+/bVy5UrFxsYqMTFREyZMUHp6unr06CFJGj16tKZPn25p37dvX23dulWLFy9WYmKioqOjtW/fPoWFhUmSLl26pHfeeUe7d+/WyZMnFRcXp8GDB6tWrVoKDg6WJNWrV0/BwcF64403tGfPHu3atUuTJk1Sx44dVblyZdsfBAAAAABFnt2f+erQoYPOnTunmTNn6uzZs2rQoIEWLVpk6UZ4+vRpGQz/y4iNGzfWtGnTNGPGDL333nuqXbu2Zs+eLQ8PD0nXb7ceOnRIa9eu1d9//61KlSqpZcuWGj58uJycnCzrmTZtmiZNmqRnn31WBoNBbdu21bhx42y78wAAAAAeGHYPX5IUFhZmuXP1T8uXL88xrX379mrfvn2u7Z2dnfXhhx/edpvlypWzuqMGAAAAAIXJ7t0OAQAAAOBBQPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAOELwAAAACwAcIXAAAAANgA4QsAAAAAbIDwBQAAAAA2QPgCAAAAABsgfAEAAACADRC+AAAAAMAGCF8AAAAAYAP3RPj66KOPFBISIl9fX/Xq1Ut79uy5Zfsvv/xS7dq1k6+vrzp37qwtW7ZY5mVmZurdd99V586d5e/vr6CgII0ePVrJyclW6wgJCZGnp6fVZ8GCBYWyfwAAAABg9/C1YcMGRUVFaciQIYqNjZWXl5fCw8OVmpqaa/tff/1Vo0aNUs+ePbV27Vo9/vjjGjJkiA4dOiRJunLliv773//qxRdf1Jo1azRr1iwdOXJEL774Yo51DRs2TNu2bbN8wsLCCnVfAQAAADy47B6+YmJi1Lt3b4WGhqp+/fqKjIyUs7OzVq9enWv7ZcuWKTg4WM8//7zq1aunESNGyNvbWytWrJAklS5dWjExMerQoYPq1q0rf39/vfHGG9q/f79OnTplta6SJUvKzc3N8ilRokSh7y8AAACAB1Mxe248IyND+/fv18CBAy3TDAaDAgMDFR8fn+syu3fvVr9+/aymBQUFadOmTTfdzsWLF+Xg4KAyZcpYTV+4cKHmzp0rd3d3derUSf369VOxYvk7JCaTKV/tUbCMRqO9SwDsjvMQHnRcC4DruB7YT16PvV3D1/nz52UymVSxYkWr6RUrVlRSUlKuy6SkpMjV1TVH+5SUlFzbX716VdOmTVPHjh1VqlQpy/Q+ffrI29tbZcuWVXx8vN577z2dPXtWr732Wr72Ye/evflqj4Lj4uIib29ve5cB2F1CQoLS09PtXQZgF1wLgP/henDvs2v4KmyZmZkaPny4zGazIiMjreb179/f8m8vLy85OjrqzTff1KhRo+Tk5JTnbfj6+vIXNwB25enpae8SAAD3AK4H9mMymfJ0U8au4at8+fIyGo05BtdITU3NcXcrm6ura467XLm1z8zM1IgRI3Tq1CktXbrU6q5Xbho1aqRr167p5MmTqlu3bp73wWg0Er4A2BXnIACAxPXgfmDXATecnJzk4+OjuLg4y7SsrCzFxcUpICAg12X8/f21Y8cOq2nbt2+Xv7+/5evs4HXs2DEtWbJE5cuXv20tBw4ckMFgyNEFEgAAAAAKgt27Hfbv319jxoxRw4YN5efnp6VLlyo9PV09evSQJI0ePVqVK1fWqFGjJEl9+/ZVnz59tHjxYrVq1UobNmzQvn37NHHiREnXg9ewYcP03//+V/Pnz5fJZNLZs2clSWXLlpWTk5Pi4+P122+/6ZFHHlHJkiUVHx+vqKgodenSRWXLlrXPgQAAAABQpNk9fHXo0EHnzp3TzJkzdfbsWTVo0ECLFi2ydCM8ffq0DIb/3aBr3Lixpk2bphkzZui9995T7dq1NXv2bHl4eEiSkpOT9e2330qSunbtarWtZcuWqXnz5nJyctKGDRs0a9YsZWRkqHr16urXr5/Vc2AAAAAAUJDsHr4kKSws7KYvOF6+fHmOae3bt1f79u1zbV+9enUlJCTccns+Pj5auXJl/gsFAAAAgDtk95csAwAAAMCDgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAGyB8AQAAAIANEL4AAAAAwAYIXwAAAABgA/dE+Proo48UEhIiX19f9erVS3v27Lll+y+//FLt2rWTr6+vOnfurC1btljNN5vN+uCDDxQUFCQ/Pz/169dPR48etWrz119/adSoUWrcuLGaNGmi119/XZcuXSroXQMAAAAASfdA+NqwYYOioqI0ZMgQxcbGysvLS+Hh4UpNTc21/a+//qpRo0apZ8+eWrt2rR5//HENGTJEhw4dsrRZuHChli9frgkTJmjlypVycXFReHi4rl69amnzyiuv6PDhw4qJidG8efP0yy+/aPz48YW+vwAAAAAeTHYPXzExMerdu7dCQ0NVv359RUZGytnZWatXr861/bJlyxQcHKznn39e9erV04gRI+Tt7a0VK1ZIun7Xa9myZXrxxRfVpk0beXl5aerUqTpz5ow2bdokSUpMTNTWrVs1efJkNWrUSE2aNNG4ceP0xRdfKDk52Wb7DgAAAODBUcyeG8/IyND+/fs1cOBAyzSDwaDAwEDFx8fnuszu3bvVr18/q2lBQUGWYHXy5EmdPXtWgYGBlvmlS5dWo0aNFB8fr44dOyo+Pl5lypSRr6+vpU1gYKAMBoP27NmjJ5544ra1m81mSdK5c+dkMNg9wz6wDAaD6paRDJl2/Va2C3fnLJ0/f14q5SFVfPD2X5LkVFU6f161HWvLwcXB3tXYXC3HWjp//ryysrLsXQpgVw/ytUDievCgXwskrgf3guxjn50RbsauP6Hnz5+XyWRSxYoVraZXrFhRSUlJuS6TkpIiV1fXHO1TUlIkSWfPnrVMu1mblJQUVahQwWp+sWLFVLZsWcvyt5N9gI8fP56n9ig8/XxLSCph7zLsIikpSfJ60d5l2FdSkp5ye0pys3ch9nGzcyXwoHmQrwUS14MH/VogcT24V9wuAD+Afx4pGMWKFZOvr68MBoMcHB7Mv7IAAAAAuH7HKysrS8WK3Tpe2TV8lS9fXkajMcfgGqmpqTnubmVzdXW13MHKrb2bm5tlWqVKlazaeHl5WdZx7tw5q3Vcu3ZNFy5csCx/OwaDQU5OTnlqCwAAAAB2fVjJyclJPj4+iouLs0zLyspSXFycAgICcl3G399fO3bssJq2fft2+fv7S5KqV68uNzc3q3VevHhRv/32m2WdAQEBSktL0759+yxtduzYoaysLPn5+RXU7gEAAACAhd1Hiujfv79Wrlyp2NhYJSYmasKECUpPT1ePHj0kSaNHj9b06dMt7fv27autW7dq8eLFSkxMVHR0tPbt26ewsDBJkoODg/r27au5c+dq8+bNSkhI0OjRo1WpUiW1adNGklSvXj0FBwfrjTfe0J49e7Rr1y5NmjRJHTt2VOXKlW1/EAAAAAAUeXZ/5qtDhw46d+6cZs6cqbNnz6pBgwZatGiRpRvh6dOnrUYTbNy4saZNm6YZM2bovffeU+3atTV79mx5eHhY2rzwwgtKT0/X+PHjlZaWpocffliLFi1S8eLFLW2mTZumSZMm6dlnn5XBYFDbtm01btw42+04AAAAgAeKg/l24yECAAAAAO6a3bsdAgAAAMCDgPAFAAAAADZA+AIAAAAAGyB8AShwa9asUZMmTW7ZJiIiQoMHD7ZRRXl3r9YFAPnVp08fvfXWW/Yu457HeR+2ZPfRDoH7TUREhGJjYyVJxYoVU9myZeXp6amOHTuqR48eVqNz4ubGjh2ruxnv58b/B0dHR7m7u6tr164aNGjQbd8un5+6+vTpIy8vL40dO/aO1wngwXHjuelGQUFB+vDDD+1Q0a1duXJFCxYs0BdffKE//vhDJUuWVPPmzTV06FA99NBD9i7PIiIiQmlpaZozZ06Br5vzPmyJ8AXcgeDgYEVFRSkrK0spKSnaunWr3nrrLX311VeaO3fuXf3yby8ZGRlycnKy2fZKly591+vI/n/IyMjQli1bNHHiRDk6OmrgwIH5XpfJZJKDg0OB1AXgwZZ9brqRLc+veZWRkaF+/frp9OnTGjNmjBo1aqTU1FTNnz9fvXv3VkxMjPz9/e1aY/a5uTBx3oct8Sd64A44OTnJzc1NlStXlo+PjwYNGqQ5c+bohx9+sPqLZ1pamsaOHatHHnlEjRs3Vt++fXXw4EHL/OjoaHXt2lWrVq1S69atFRAQoAkTJshkMmnhwoVq2bKlWrRooblz51pt/9SpU3rxxRcVEBCgxo0ba/jw4UpJSbFqM2fOHLVo0UIBAQEaO3aspk2bpq5du1rmZ3ezmDt3roKCgtSuXTtJ0tq1a9WjRw8FBASoZcuWGjVqlFJTUy3L7dy5U56envr+++/VuXNn+fr6qnfv3jp06FCO47R161a1b99eAQEBCg8P15kzZ3JsP1tWVpYWLlyoJ554Qg0bNlTr1q1z7PfN/h+qVaump59+WoGBgfr2228lSTExMercubP8/f3VqlUrTZgwQZcuXbIsm901cvPmzerQoYN8fX116tQpq7oiIiL0008/admyZfL09JSnp6dOnDihJ554IsdfsA8cOCBPT08dO3bsljUDKPqyz003fsqWLWuZf/ToUT3zzDPy9fVVhw4d9OOPP8rT01ObNm2S9L/zbFpammWZ7HPMyZMnJUnnz5/Xyy+/rODgYDVq1EidO3fW+vXr81XnkiVLtHv3bs2fP18dOnRQtWrV5Ofnp+joaNWtW9fqjlD2uXHWrFmWa9r48eOVkZFhWV+fPn00ceJETZw4UQ8//LCaN2+uGTNmWN1VunDhgkaPHq2mTZuqUaNGev7553X06FHL/NzOza+//rpiY2O1efNmy7l4586deTpO2evL6/WI8z4KG+ELKCAtWrSQl5eXvv76a8u04cOHKzU1VQsXLtSaNWvk4+OjZ599Vn/99ZelzfHjx/XDDz9o0aJFmj59ulatWqUBAwYoOTlZy5cv1yuvvKIZM2bot99+k3Q9pAwePFgXLlzQ8uXLFRMToxMnTmjkyJGWda5bt07z5s3TK6+8ojVr1sjd3V0ff/xxjprj4uJ05MgRxcTEaP78+ZKka9euafjw4Vq3bp1mz56tP/74QxERETmWnTp1qiIiIrRq1SpVqFBBgwYNUmZmpmX+lStXtHjxYk2dOlUrVqzQ6dOn9c4779z0+E2fPl0LFy7U4MGDtWHDBk2bNs3ysvW8Kl68uKUGBwcHjR07VuvXr9eUKVO0Y8cOvfvuu1btr1y5ooULF2ry5Mlav369KlasaDV/7NixCggIUO/evbVt2zZt27ZNVatWVWhoqNasWWPVdvXq1WratKlq1aqVr5oBPFiysrI0dOhQOTo66tNPP1VkZKSmTZuW7/VkZGTIx8dHCxYs0Pr169W7d2+NHj1ae/bsyfM61q9fr5YtW8rLy8tqusFgUL9+/XT48GGrPxjGxcUpMTFRy5cv13vvvadvvvlGs2fPtlo2NjZWRqNRn376qcaOHaslS5bo008/tcyPiIjQvn37NHfuXH3yyScym80aMGBAjuvHjefmcePGqX379goODraciwMCAvK8n/m5HnHeR2EjfAEFqG7duvrjjz8kSb/88ov27NmjmTNnytfXV7Vr19aYMWNUpkwZffXVV5ZlzGaz3n77bdWvX18hISFq3ry5jhw5otdff11169ZVaGio6tSpo507d0q6fvE7dOiQpk+froYNG6pRo0aaOnWqfvrpJ8tFd8WKFerZs6dl2ZdeekkeHh456i1RooQmT56shx56yNK3v2fPnmrVqpVq1Kghf39/jR07Vj/88IPVXSNJeumll9SyZUt5enpqypQpSk1N1TfffGOZn5mZqcjISPn6+srHx0fPPPOMduzYketxu3jxopYtW6ZXX31V3bt3V82aNdWkSRP16tUrT8fdbDZr+/bt2rZtm5o3by5J6tevnx555BFVr15dLVq00IgRI/Tll19aLZeZmakJEyaocePGqlu3rlxcXKzmly5dWo6OjnJ2drb89dpoNKp79+46cuSI5XhnZmZq/fr1Cg0NzVO9AIq277//XgEBAVafefPmSZK2b9+upKQkvfPOO/Ly8lLTpk2t/niWV5UrV1Z4eLgaNGigGjVqqE+fPgoODs5xnruVo0ePqm7durnOq1evniTpyJEjlmlOTk56++239dBDD6l169YaNmyYli1bpqysLEsbd3d3y/WrS5cuCgsL05IlSyzb+/bbbzV58mQ1adJEXl5emjZtmpKTky13/aSc5+ZSpUrJ2dnZ6o5ifrpx5ud6xHkfhe3+ezAFuIeZzWZL3/SEhARdvnzZEgayXblyRcePH7d8Xa1aNZUqVcrytaurq4xGo9XAHa6urpauf4mJiapSpYrc3d0t8+vXr68yZcooKSlJfn5+OnLkiJ5++mmr7fr5+eW42Hh4eOS4gO3bt0+zZs3SwYMHdeHCBUt3kdOnT6t+/fqWdjc+B1CuXDnVqVNHSUlJlmkuLi6qWbOm5etKlSpZdV+8UVJSkjIyMvTII4/kOv9msn/ByczMlNlsVqdOnTR06FBJ13/BmT9/vpKSknTx4kWZTCZdvXpV6enplpDl6OgoT0/PfG1Tuv5LT6tWrbRq1Sr5+fnpu+++U0ZGhqXrJoAHW/PmzTVhwgSradndDrPP4ZUrV7bMy89dnGwmk0nz5s3Txo0blZycrMzMTGVkZMjZ2Tlf68nPwEeenp5Wf6QKCAjQ5cuXdfr0aVWrVk2S1KhRI6tntPz9/RUTEyOTyaTExEQVK1ZMjRo1sswvX7686tSpo8TERMu0Oz0330x+rkc3w3kfBYXwBRSgxMREVa9eXZJ06dIlubm5afny5Tna3fhw7z8H53BwcMh12o1/WSwo/7zTc/nyZYWHhysoKEjTpk1T+fLldfr0aYWHh1t1CcmL3PbhZhf54sWL56/w/y/7FxxHR0dVqlTJss2TJ09q4MCB+ve//62RI0eqbNmy2rVrl8aOHavMzEzLfjs7O9/xg9y9evXS6NGj9frrr2vNmjXq0KFDjuMJ4MHk4uJyV13Rsv/4duM585/n4A8//FDLli3T66+/bglFb7/9dr7O1bVr17b6o9mNssNQnTp18lv+XcvruTkvx0nK3/XoVjjvoyDQ7RAoINndAdu2bStJ8vHxUUpKioxGo2rVqmX1qVChwh1vp169evrzzz91+vRpy7TDhw8rLS3N0k2kTp062rt3r9Vy//w6N0lJSfrrr7/0yiuvqEmTJqpXr95N/zq4e/duy78vXLhwy+4rt1O7dm05OzvftBvIzWT/glO1alWri+v+/ftlNpsVEREhf39/1alTx+rh6vxwdHTMNfi2atVKLi4u+vjjj7V161a6ngDIk+xz+I3npBvPp5Is14izZ89apt347JUk/frrr3r88cfVtWtXeXl5qUaNGlYDV+RFx44dtX379hzrzsrK0pIlS1S/fn2r58ESEhJ05coVq7pLlChh1RPjn8+c/fbbb6pVq5aMRqPq1auna9euWZ5hlq4PHHLkyBGrnhW5ye1cnJfjdCc476MwEb6AO5CRkaGzZ88qOTlZ+/fv17x58zR48GA99thj6tatmyQpMDBQ/v7+GjJkiLZt26aTJ0/q119/1fvvv5+nIHQzgYGB8vDw0CuvvKL9+/drz549Gj16tJo1ayZfX19JUlhYmFatWqXY2FgdPXpUc+bMUUJCwm3/kli1alU5Ojpq+fLlOnHihDZv3nzTd6rMmTPHEjgjIiJUvnx5tWnT5o72qXjx4nrhhRf07rvvau3atTp+/Lh2795t9ZB2ftSqVUuZmZmW/Vi7dq3+85//3NG6qlWrpt9++00nT57UuXPnLBdko9GoHj16aPr06apVq9YddRsCUDRlXyNu/Jw7d07S9XN47dq1FRERoYMHD+qXX37R+++/b7V8zZo15e7urujoaB09elTff/+9Fi9ebNWmVq1a2r59u3799VclJiZq/PjxOUa9vZ1+/frJz89PgwYN0pdffqlTp05pz549Gjp0qJKSkvTWW29ZXTcyMjI0duxYHT58WFu2bFF0dLTCwsKsusmfOnVKUVFRSkpK0vr167VixQr17dtX0vU/tD3++ON644039Msvv+jgwYN69dVXVblyZT3++OO3rLVatWpKSEhQUlKSzp07p8zMzDwdpzvBeR+FifAF3IGtW7cqKChIISEhev7557Vz506NGzdOc+bMkdFolHS9W8OCBQvUtGlTvfbaa2rXrp1efvll/fHHH/kexe9GDg4OmjNnjsqUKaOwsDD169dPNWrUsLp4d+nSRQMGDNA777yj7t276+TJk+revfttu/dVqFBBU6ZM0caNG9WhQwctXLhQY8aMybXtqFGj9NZbb6lHjx5KSUnR3Llz7+o9NoMHD1b//v01c+ZMdejQQSNHjrT8spJfXl5eeu2117Rw4UJ16tRJn3/+uV5++eU7Wtdzzz0no9Gojh07qkWLFjp16pRlXs+ePZWZmakePXrc0boBFE3Z14gbP9nP4RoMBs2aNUtXrlxRz549NXbs2BwDbjg6Omr69OlKSkpSly5dtHDhQo0YMcKqzYsvvihvb2+Fh4erT58+cnV1zfcfwIoXL66lS5eqa9euev/999W2bVu98MILMhqN+uSTT3K846tFixaqVauWnnnmGY0YMUIhISGW52yzdevWTVeuXFGvXr00ceJE9e3bV0899ZRlflRUlOUVLU899ZTMZrMWLFggR0fHW9bau3dv1alTR6GhoWrRooV+/fXXPB2nO8F5H4XJwXwnnV4B3Hf69+8vV1fXHMOt59fOnTvVt29f/fzzzypTpkwBVXd/+uWXX9SvXz99//33dxWoAcDT01OzZ8++4x4EhS0iIkJpaWk37Q0hXX/Pl5eXl8aOHWvDymyL8z7uFgNuAEVQenq6/vOf/ygoKEgGg0FffPGFtm/frpiYGHuXViRkZGTo3Llzio6O1pNPPskFGACKOM77KCh0OwSKIAcHB23ZskVhYWEKDQ3Vd999p+joaAUGBtq7tCJh/fr1euyxx/T3339r9OjR9i4HAFDIOO+joNDtEAAAAABsgDtfAAAAAGADhC8AAAAAsAHCFwAAAADYAOELAAAAAGyA8AUAAAAANkD4AgDgNjw9PbVp0yZ7lwEAuM8RvgAA97yIiAh5enpq/PjxOeZFRkbK09NTEREReV7fzp075enpqbS0tDy137Ztmx599NE8rz/bmjVr5OnpecvPyZMn871eAMD9qZi9CwAAIC/c3d21YcMGvf7663J2dpYkXb16VevXr1fVqlULZZsZGRlycnKSm5vbHS3foUMHBQcHW74eOnSoHnroIQ0bNswyrUKFCnddJwDg/sCdLwDAfcHb21vu7u76+uuvLdO+/vprubu7q0GDBlZts7KyNH/+fIWEhMjPz09dunTRxo0bJUknT55U3759JUlNmza1umvWp08fTZw4UW+99ZaaN2+u8PBwSTm7Hf755596+eWX1axZM/n7+6tHjx767bffctTs7OwsNzc3y8fR0dEy7ccff1SnTp1kMpmslhk8eLBeffVVSVJ0dLS6du2q//znP2rVqpUaNWqk4cOH6++//7Za5tNPP1X79u3l6+urdu3a6aOPPrqjYwwAKFyELwDAfSM0NFRr1qyxfL169Wr16NEjR7v58+dr7dq1ioyM1BdffKF+/frp1Vdf1U8//SR3d3dFR0dLkjZu3Kht27Zp7NixlmVjY2Pl6Oiojz/+WJGRkTnWfenSJYWFhSk5OVlz5szRZ599pueff15ZWVn52pd27drJZDJp8+bNlmmpqanasmWLQkNDLdOOHz+uL7/8UvPmzdOiRYt04MABTZgwwTJ/3bp1+uCDDzRy5Eht2LBBL7/8smbOnKnY2Nh81QMAKHx0OwQA3De6dOmi6dOn648//pAk/frrr3rvvff0008/WdpkZGRo/vz5iomJUUBAgCSpRo0a2rVrlz755BM1a9ZMZcuWlSRVrFhRZcqUsdpG7dq1NXr06JvWsH79ep07d06rVq1SuXLlJEm1atXK9744OzurU6dOWrNmjdq3by/pepByd3dX8+bNLe2uXr2qqVOnqnLlypKkcePGaeDAgYqIiJCbm5uio6MVERGhtm3bWvb18OHD+uSTT9S9e/d81wUAKDyELwDAfaNChQpq3bq1YmNjZTab1bp16xzPTB07dkzp6el67rnnrKZnZmbm6J6YGx8fn1vOP3DggLy9vS3B62707t1bPXv2VHJysipXrqw1a9aoe/fucnBwsLRxd3e3BC9JCggIUFZWlo4cOaKSJUvq+PHjGjt2rN544w1Lm2vXrql06dJ3XR8AoGARvgAA95XQ0FBNnDhRkvTmm2/mmH/58mVJ17se3hhaJMnJyem263dxcbnl/OzBPgqCt7e3vLy8tHbtWrVs2VKHDx/OtRvlzWTv66RJk9SoUSOreQYDTxYAwL2G8AUAuK8EBwcrMzNTDg4OCgoKyjG/Xr16cnJy0qlTp9SsWbNc1+Ho6ChJOQa7yAtPT099+umn+uuvvwrk7lfPnj21dOlSJScnKzAwUO7u7lbzT58+bbkzJkm7d++WwWBQnTp15OrqqkqVKunEiRPq0qXLXdcCAChc/FkMAHBfMRqN+vLLL7VhwwYZjcYc80uVKqXnnntOUVFRio2N1fHjx7V//34tX77cMghFtWrV5ODgoO+//17nzp3TpUuX8rz9jh07ytXVVUOGDNGuXbt04sQJffXVV4qPj7+j/encubOSk5O1cuVKq4E2shUvXlwRERE6ePCgfvnlF02ePFnt27e3DH8/bNgwLViwQMuWLdORI0eUkJCg1atXKyYm5o7qAQAUHu58AQDuO6VKlbrl/BEjRqhChQqaP3++Tp48qdKlS8vb21uDBg2SJFWuXFlDhw7V9OnT9dprr6lbt26aMmVKnrbt5OSkxYsX65133tGAAQNkMplUr169XLtA5kXp0qXVtm1bbdmyRW3atMkxv2bNmnriiSf0wgsv6MKFC2rdurXVtnr16iVnZ2d9+OGHmjp1qkqUKCEPDw89++yzd1QPAKDwOJjNZrO9iwAA4EH27LPP6qGHHtK4ceOspkdHR2vTpk367LPP7FQZAKAg0e0QAAA7uXDhgr755hv99NNPevrpp+1dDgCgkNHtEAAAO+nevbsuXLigV155RXXr1rV3OQCAQka3QwAAAACwAbodAgAAAIANEL4AAAAAwAYIXwAAAABgA4QvAAAAALABwhcAAAAA2ADhCwAAAABsgPAFAAAAADZA+AIAAAAAG/h/frFkLQnYucsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "j1yNqrlbihMI"
      }
    }
  ]
}