Share_Price_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Share Price Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMhq6+rU1uuv55vGH0F2Fp4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mukul-mschauhan/Data-Science-Projects/blob/master/Share_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vEHoppqcHGde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "3f636cd9-8b4b-415b-cb64-088dfe81bf86"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, StackingRegressor\n",
        "from sklearn.linear_model import Ridge, Lasso, LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from statsmodels.tools.eval_measures import rmse\n",
        "\n",
        "#from pmdarima import auto_arima"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k--QS6QgHI1I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c589270f-3702-4751-a1e9-8f0311b0a879"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moNIH2p1HNtY"
      },
      "source": [
        "price = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/Jubilant.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n-uC2eLEHfmw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "90cccfb0-b3ae-445b-d98c-f8499d3fcc59"
      },
      "source": [
        "price.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>08/02/10</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>120.449997</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>120.300003</td>\n",
              "      <td>118.110657</td>\n",
              "      <td>113926600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>09/02/10</td>\n",
              "      <td>115.275002</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>110.800003</td>\n",
              "      <td>122.099998</td>\n",
              "      <td>119.877899</td>\n",
              "      <td>49861112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/02/10</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>126.199997</td>\n",
              "      <td>115.800003</td>\n",
              "      <td>117.500000</td>\n",
              "      <td>115.361618</td>\n",
              "      <td>22791756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11/02/10</td>\n",
              "      <td>117.500000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>112.550003</td>\n",
              "      <td>115.474998</td>\n",
              "      <td>113.373466</td>\n",
              "      <td>11715914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15/02/10</td>\n",
              "      <td>116.800003</td>\n",
              "      <td>118.400002</td>\n",
              "      <td>112.750000</td>\n",
              "      <td>114.574997</td>\n",
              "      <td>112.489853</td>\n",
              "      <td>6380560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Date        Open        High  ...       Close   Adj Close     Volume\n",
              "0  08/02/10   80.000000  120.449997  ...  120.300003  118.110657  113926600\n",
              "1  09/02/10  115.275002  125.000000  ...  122.099998  119.877899   49861112\n",
              "2  10/02/10  126.000000  126.199997  ...  117.500000  115.361618   22791756\n",
              "3  11/02/10  117.500000  120.000000  ...  115.474998  113.373466   11715914\n",
              "4  15/02/10  116.800003  118.400002  ...  114.574997  112.489853    6380560\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7Z1jPREHg5x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "outputId": "c7b232fa-d310-44e6-8bf2-fb2736e5de88"
      },
      "source": [
        "plt.figure(figsize = [17, 7])\n",
        "plt.plot(price.Open)\n",
        "plt.plot(price.Close)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3O4/H+u6Mx+3Yiqd0xgEAAABgNEi2qsxrVnN4Yr8fibRtzZ60bBvY96W76uteuF9+rLrbW2aaWknSXrOmSJe9IJ19U85173PPZo5XbO9hL/RRijAOAAAAAKOA++N5kqSqmXv3+xlHnfYXTzQP7Att8Gx5qlZbqw/p9dYDZ1RIU/aR9jo1M/agjldoyoLM+cFzp3T36Kg1tuYBAAAAAMBolGxV2G2TJFXOO7rfj5lkS/Yk0dLzjd3xUpKkarVqq0yvt4bnpGuKVcpVWGG58pyYFIpI39yultXP6ON7HTew7x/h6IwDAAAAwEjXtEWS9KXk5xWbNKffjznJ1uzJQDvj6TAeMZ5SiXhmeF1kT0nSy/58SdJT3oGZKe2S5DrBsR9Kj4Vjqtj7JDlO74F+tCGMAwAAAMAIl6wPVkJPlE/X+/bu/3Tv6DnXZ479AYbxhta2zLGbSmSOX9rryuCnH0yXjyua85zrBKum+87YWj19Z4RxAAAAABjhtr79siTpo6edJGMG0GGedkDm0IsPLIzXNHSa1u5mw/hHz7lAVx3wL/3FO05xG9Gv3LNynvPSIdxECOMAAAAAgJFs2zJtt9WaPbv/U9Q7LNn/W5KkxcveUTzl9fs5Pz1NXZIcP6nNdqLaP3KLjDEyRnrbztbeiVv1pt0j5znHWEmSGyodcK2jCWEcAAAAAEa4UNs2bbETNK402vfNO6nb50Ld6r5fhzir1dLW1vcDab6bG8aX+nMV2e9MScrpzu8/syrnufJksN1ZXXjqgGsdTQjjAAAAAFCMGjZIrbX9ujXSvkM1dpyqyyID/poJ5VGttdNVapLyBzBV3e80NT3iJ4NV0kNBxOxYi+3CI3fTbZcclfOcY11J0pbQ9AHXOpoQxgEAAACgGP3iAOnXh0uSmt/6h5768Tn66wsrMpevuvcNPfzmFslzVdayQTvsOEVCA494VaUReelo6Lpuv5/r3Bkvse3ynGxXvjQSkiTtO6NK40q7/wPBay3VA651NGGfcQAAAAAoNr4f/Gyr1T23/17va3lQJ7Q9qXv/4UlH/UW+b/XKkud05+LZeufTMVWYuN6we+r8QXzVrPGl8tNh3BtAGLde5zAel+9kQ/cXT95LjmN0zqGzenz+ax8+ZhDVjh50xgEAAACgyCRe/lPm+KNvX6lUMilJOta+qvrWpFpfu1ePxr6mU53FaqvbLEk66ISzuv2svpRFw/roEXMlSV6nbndfOofxMsXld+qMV8TCuvr0fRQLh7o+OOdYyYno2PmTB1XvaEFnHAAAAACKTMvrD6jzxl/hlnclSdNMvRa9864O2fiqKiUtMBu1Zn1IB0mqGD/4BdEcJwjNrtePzvg7T0iV03NWUw8Zq1R/V0f/5EOS9QdT5qhCGAcAAACAYrLhBbnl03KGwqkmJWxEMZNSy/YNaksEnfLPhh/SiqWzlTQhVVaNH/RXOqEgGvZrmvqfPhz8nHtJzrAbKunflxkjmW465mMMYRwAAAAAisWml6WbT9XOPe6o164NzgzNt+vVXrdJ8fZgC7JK067DzdvaZqtVXT7wbc06mHQYd70BTFP3c+91Q2WD/v6xiHfGAQAAAKBYtO7odrhM7drgBIuh+Q2bFW1cm3O9zlZqfNngw3jI6eiMe/1+xrjJnHM/3M9p6pBEGAcAAACA4mF6jmgrExMlSR/c/HOFa1fmXKu3lYPaY7yDk15oze9rmrq1mcNYsi7nkh+mMz4QhHEAAAAAKBa3f7TL0HOhwyRJKQWBudq0apap6XJfRWzwbyGHOqap+32EcTeeOazbvjnnEmF8YAjjAAAAAFCk2k+4RguPO12SFJOrx2b/R+baGj+7yFu1aZUxZtDf44SCrrrf19ZmqfbM4RTVy7XZSPmvDe3dPYEeEMYBAAAAdOvBP9+q3/3wy4UuY0wzx31RJSXBu9hn7Dte7/vU9zLX7vWO19PvuU1Lj/qp7Nk37dL3OKGg697nO+OptszhfGezaisXZM4jpeW7VMNYQxgHAAAA0K0PvfUFfS5+k75x3xvyfdv3AxhysbAjzT9FkjT7uI/LcYz0yYclScef/G86/qQPar/TLtF+Bx2+S9/TMU3d72Oauk225Zw3zjk1c/zlDyzcpRrGGrY2AwAAANCrTUv+pr/Pm6wzDpxe6FJGvZSJ6KbUaWqw5dpn3wN0pjHSpPnStxuzN809Vvr6Fh0ZHbp3tEPpzrjvdR/GX1y+VuPKy7SHaVHnNdtLJ83NHJdXVA5ZPWMBYRwAAABAV352uvKt0R/p7sQnCljM2LD5pgs106Y0rqpKH/n8zzS5ItbzzUMYxCUpFA4ittfDaupH3nWwNvqTFf/YjTlhvHr67tmSSiuGtKbRjmnqAAAAALqKN+40wDT1fJu58W+SpDbP0ZTKkl1akG2gOt4Z722a+mxnh5ItuduZVYyfnDmOlNIZHwjCOAAAAICu2nJDl9taX6BCxp5/WzD8W4Q56XfGrdf7Am6p1obM8Q3uGTKxbACPldEZHwjCOAAAAIAsLyX5vtZs3JAznGraWqCCxp7JpnnYv7MjjEfc3r871emPMmF5UjQbwGOx0vwUN0rxzjgAAACArO9N0n3ecTrcrJQcKR6qVInXLBsf/oA4Zs07adi/0jhBNDz+9f8nffhzuRc7LeqW6DRN3ZGfE8YjoeGbVj8a9NkZN8bcbIzZbox5q9PYt40xm40xr6X/+UCna1cbY1YbY1YaY07tNH5aemy1Meaqof9VAAAAAOyKtduCoPWR0DOa7eyQJC075FuSJN9NFKyusaIpPFEPm+OlA84Z9u92nFDPF1OtmcOqFXdljtfa6VI4u5zbcL7jPhr0Z5r6LZJO62b859bag9P/PCxJxph9JZ0vab/0M78xxoSMMSFJv5Z0uqR9JX0sfS8AAACAInHBz//WZawhHCzQZQnjeedYV+2mMFO9TaiXMJ7MhvGptkab7CR9NPEt/dF7/zBUNnr1OU3dWvu0MWZuPz/vTEl3WmsTktYaY1ZLOiJ9bbW1do0kGWPuTN+7bMAVAwAAAMiLc0JPZY6fmHSBwvVrdcge06QXJLnJwhU2RjjWk+8U5k3ijnfGu5VsyzlttmU688xz9K1Z1ZIk/8zfypu4lyL5LHAU2pX/pK8wxlwkaYmkK6219ZJmKvg/1Q6b0mOStHGn8SO7+1BjzKWSLpWk3XbbbRfKAwAAANCbLY3tWlvTqmP2nKRkw1ZdGfmzJOn+g2/UWWedG9y09c3gp0dnPN9C1pU1BQrjvf0RwG2XJG3wJ2s3Z4eaVKZzDp2lkkjQTXcOuYCVwQdhsP/OfitpT0kHS9oi6adDVZC19gZr7WHW2sMmT57c9wMAAAAABuVDv3xGF9z4oiQptfEVSdJfvWOyQVySQrHgJ9PU886xnmyBOuMm1Es0TM+KWG2DPmtMScXCxO9dNah/g9babdZaz1rrS7pR2anomyXN7nTrrPRYT+MAAAAACuTW5JW6KnyHXM9Xoq1RkhQ58Wu5N6UX6DJMU8+7kFxZpzCTvUNeL//5pmdFJCpmSZIi8lisbQgMKowbY6Z3Ov2wpI6V1h+QdL4xJmaM2V3SfEkvSVosab4xZndjTFTBIm8PDL5sAAAAALtqP2e9/j38oFoTntx0GA+Xjcu9Kd0ZNz6d8bzyfTmysr2tap5HjvWyJ+ufC2ZCbH1Lqn0ns3ifu9t71FAyUw0n/rAgNY42fc6BMMbcIem9kiYZYzZJukbSe40xB0uyktZJ+pwkWWuXGmPuVrAwmyvpcmuD/1SNMVdIekRSSNLN1tqlQ/7bAAAAABiw5kRKofYGSVJ05zAeTofx3jqnRcbWr9ebf/mJJp71A82cUNH3A8XATwU/C9QZd6YfkD35w+nS4ZdIi38vSfLPv1MhSa2l01R91TIdW5AKR5/+rKb+sW6Gb+rl/u9L+n434w9LenhA1QEAAADIu5aEq4r2ZnnWKFpWmXsxE8ZHTme8+f8u0oG1r+mNXy3RpKueVCwaK3RJffM6wnhh3hmXMfqj935dFFoUnG/LbnzlpRIKSdn1AzAkeOseAAAAGIs8N3PY0JaS196oFpWqqjSae186gDn+yOmMh5s3SZIO9Jfpz48+1cfdw2/lw7/UdT+6WknXzw76wX8ehXpnXJLcnM3JbPbIjQcH4Z3+dwO7hDAOAAAAjEWdOt1PLN8mG29Ss8o0rnSnMBgKy5fT+wJfxcbP/qFhy4ZVBSykG9ZqwUvf1Bfaf6Oa+rrseLpmU6jOuCS3h23V3EQ6jNMZH1KEcQAAAGCMeWTpVi3bsD1z3rRjg5RoUrMtVVVJ185sc3iiKpLb5fm2y7ViFHdKM8du/SZZW0R1t2T/vfutncJ4xzT1UAHDuDotHtfp31kyEewzTmd8aBHGAQAAgDHi9nvu1j0vrdXn/vSyPv37f2XGy5vWyEk2q0WlqijpGgZT43bTVH+blm9pGs5yBy3mtuj+0CmSpHMT9+mW59YVtqDO3PbMYbK9JTve0c0v4DT1dq/zSu7ZMJ5KBp1xQ2d8SBXuzy4AAAAAhs/25bpg6We16I1D9dNIqfY2G7PX4g0Kq0XtTrlCTtf9o8MT99Ccmse0pLZV+88c1+V6UfFSKveblayYIrVIezhbtceig7V299XafcbkQlcnpeKdDluz4+nV1E0BO+OpzvGwU2c81TFNPUwYH0p0xgEAAIBRzvOtWretlSS9P/Syzg49o/2c9ZnrRzQ/rhltK5Rwyrt9vmzaPE0z9dq4rXZY6t0l7fWSJK9kQs7w86+8WohqunI7hfHOnXE3eCffFrD7nOo8Tb1jqzVJM5cE+4obpqkPKcI4AAAAMMp998Gl+vad/+zx+qmhJZKkth7CeHTSnpKk1q1Fthhad9qCPxjYsgnSx+7MDCfqNvb0xPByswvnefHOYbzw3eeczviOlV2uO4TxIUUYBwAAAEar1hop3qjXX3xcRznLu1x+ccK/5ZxvTlV0/zmzDpUkVW9fPOQlDrV4Y7BAmlM+SVpwunT2TZIk2/huIcvK6vTO+KvvbMqOe8XQGe8UxlNtXa6Hu3mFAYPHO+MAAADAaHXtnlJJte6PNuQMb7KTtHLKGTr44muln0zJjL/rVnX/OeN3V3uoQhXN78haK2OKN5Td/+QLOl9S9aRpwcBep0mSTLy+cEV14iXbM5PB31q3JXuhozMeKRn2mjIldJ6m3g2HMD6k6IwDAAAAo1m8ocvQEn8vnXT5dZpYkduFDcnv/jOMUXvZTE30dujGf63RjU+vyUelvduxUvrZvlLz1l5vm1/zmOKK6tRjjwgGouXy5CicbByGIvsWj2c74xXKHtuOhd3ChQvjSdt7r7Yi1ntYx8AQxgEAAIDRyHN7vGSV7XA2XPiI7qr6lJ6pPE3vOfuynj+ucqZmmlo9/487dMPDzw9pqf3y/K+kps3Syr/3fI+12jO5UkvKT5ApSa/6bozioUqZeKOeenvH8NTaCzeenf49s6TT++PpMG6K5Z3xbhT9SvojDNPUAQAAgNGoreeVzzuH8er5R+m8Lx/V9+eVTdQUs0R/iF6rpf4cSRcOQZED0LHwWQ+d45Tn6401m3SobdSOkt1zriUjlTrRfUVX/OE2Hfxfl2tcWeH28radVlMv85ozx16yXWFJpoinqU+pLFxtoxGdcQAAAGAUsi09T+eOhgYeA0KlVZpkmiRJs00BOsyp9JTuHjrHP3lkhS6/6UlJkh+tzLnmRqo009TqL7Fr1JLsecbAcLCp4I8KbSpRmdcsm97P20t2dMYLOE29t17tMV8YvkLGCMI4AAAAMAo11/S8evgp+00b8OeFy7JTlNsUk+/bQdU1aH10xj/62qf0Qsl/BCex3DAejWa35GpLFDiM+8H3N5pxqlKL2lOepKAzLklOATvjPU1Tv9c7Tjrle8NczehHGAcAAABGg18cKP32uMxp/Y7NXe+ZEOwXHp22z4A/PlpenTlutSWZEDkUOrrDveqY3m27/955yRXZk2jufullfnY/79Zk73UnXE8/eHi5GttSfdc0CL4XfG5dySztYd7V6u1BbX76nfFChnG3hwXcHA3zH17GCMI4AAAAMBo0rJe2vSlJiqc83f74kpzL/qk/lD73tHTRA9KxXxzwx3cO4+jeYVMAACAASURBVO2KqXWIpnuv3LRdR1/9Jz3d1+Jq6c54TWNL7/dJckpyO+OR918TfIR1+uyMv/jIndrjuat06HcfVm1Lotd7ByXdGW+fulC7O9u0cn0wg8Emgt/LREqH/jv7KdXDO+OGMJ4XhHEAAABgFPF9q1Ur39LVkTskSVuO/Kb8c26Vc/TnpViFtMcJkjPwLaqcsgmZ4zbF1JoYms541b0X6IWS/9Bjy7f1ep/nB993z6NPqqmp63ZtnU2cMCF3YN9/0/ZDviAjq9aEKzeV7HGa/fGLL9N54Se1uuQiXff4qv7/Iv3kp1e5t9VzJUleU/Buf6J2g3bYcZpYXdnTo3nX0zvjdMbzgzAOAAAAjCLbmuMqWfzrzLl75OVy9j9r1z94xsLsZ9qQWofo3evp9YslSWVO75/Xngr2QP+8f6cWXXuhNr32uNas39DtvfvMmdFlLFQ6TiFjNfG1Xyn8/cn6359+p+uDydbc804rnw8Vmw7jpjL93n5rMCPAr1+njXay9ppaMeTf2V9uD2H8sN3Y0iwfCOMAAADASNdpT/HWhKdw3erMeVXpEG3jNW5m5rDEJAcfxuvXS6/d3mW40vQefFOenzk+O/SMZt3/Eb1+y5ckSU8v25hb6uRZXZ4PT5gtSVq46peSpKOb/5FzveWdF6T/yQ3xZV5TrzUNiu/KtY78skmSJNNaI0mKtNdomx2vqQXcPqynaeozxrGlWT4QxgEAAIARrmZHduX05vakJre+LUlaOen9qirpZbuqAXq06iOSpFIlB//O+I0nSvd/XmrN3Qc97Pb+LnjnvdE7THSDqe1vLnkqZzxaMb7LvZUz9s4592xuFGp75e4uz5y7YuDv1vfF91x5CilVNlWStOLt5frGfW8o3tqoVpXKcbr+nsNl52nqf3VOSh8xTT0fCOMAAADACOeteCRzXP/uO6rwm3X/rK9owRV/ljFDF+7+MuUK/dk7XlWmdfDvjLelQ/i2t3KG3bbGXh/r7h3vhIKu/1k1v8+MbbVdg7gkORP3zDn3dopCtr2+yzO7+xvUMtRbofmuXDmypZNUayv15fCfddXrp2iWqVGr7X4P9eGS2mk1dTdUFhxYv5u7sasI4wAAAMAIU9uS0M3PrM1sCWZbtkuSWmyJWta/LEmaMO+IIf/eb31oX5VUTdJ4tQx6mrpvgqnQq95eKnXa0sxP9NUZ7+r9oVdkfU9GQVj8YflXdWnJT7r/gFj2Xeym0ARVm9ZMwF+zo0V+YzdbwUl6ZdX6XusaKJvujDsho+22WpWmXZUm2GO8TYWdDn7ff7w359wNp7eI68/WcxgwwjgAAAAwwnzjL2/pu39bpjc2Bd1kL94sKdiCKlT3TnDT5L17enzQpo8r1ckL91GZSSje3jaoz4g7QSjetnldzgJpkb6mqfcQCFPL/yHfWj3n76evfeUbeuDqc3r8jOQhn1TTlCNUO/UYTTYNerexXbXNcf3459cqtWNNt8+sXLGi2/FBs0FnPOw4mlOe+weNMg39gnEDMWNi7kJtfpQwnk+EcQAAAGCEKW16RwvMBtW3JSVl96iOypXXVq+4jai8oiov3x2pnChJ8lrrBvkJQbBr2L5JSmYDveMle3/MS3U7vPjtDQq57Uo4JX1OyY+e+b+qumyRSibtpklq1Ip3G1W/Zomuj/5Cuzk7tNwPFnn7ceo86eP3SZKS9Rt7+8iBS3fGQ46R2WmLuWmRwf2BY8iEcqfJe5nOONPU84EwDgAAAIwwP9/xWT0Su0rbmoJOakcYjxhPpr1eTSof0oXbOguVB2Hcb6vtsVvd6/M2CNVO2w55nbcS6yFsdzB+99f/uuQdhb24XKf/U7zLJ85UxHiqr9mSmVUgSfd579GLpz2os/7jJ9KkvSRJJW1b+v25/eKn5KbDuJOeXn+dG2w9d+LCfYb2uwYqFM05TYbSYZwF3PKCMA4AAACMUJvrg3eNlcqG2tJUnZps2dBtabazsgmSpPPevFS/fOTNAT8e9hPBxyghN94pjPcQtjt0DuMr0h1sSZqkRoX9gYXxkvHBFmZe01b5bQ2Z8a12go448j3aa1qVVDldvhxVxLf2+3P7xffkyVHYMTLpkHuPd4KaP/R7RU/97tB+10A5ufHwjaoTpL0/KJ32gwIVNLoRxgEAAICRpFM3+t36IMyaTh3maapTk8o0Ll9hvDQI41WmTSteyq7ibq3VDx5erjU7enn323MVSneDS01Cqebs9mZOH2Hc8V0ttnsr/pmndFryR/pI4tuSpMmmUSbVJi9c1u9fITJuenDQsk22LbuK+nVf/mR2qnsorKbwBE1Jbuizaz8gvivXhuQYo4bdTpEkNdoKVR76UanjHe0iYcMl0vm3SRP2KHQpoxJhHAAAABgpPFcrnrwjcxqqWS5JcpJNmbH9nXVKlE5TSSTU5fEhke6MS5LT6R3jTfXt+t3Ta3TJrUt6ftZLZA5LlVD75mxn3fi9vzPu2JS2mCmKzTpIkrSubH/VxGZrttmuatOq8tLSfv8KpnKaJOnlt5aprjZYib515ntkJs7Lua85OlUnes/prWtP7fdn96mjMx4y2n7Mt3RU/Jc6Zr89+35umN3inlLoEkY9wjgAAAAwQrjP/1Z7P/X5zPleLYslSbFkvZLKviO+ftIJ+SuiNBvGo+FOC6ZZq3K1K+H2stiX2zmMJ+XWZFcwd/p8Z9yVZ6Iyxug3Fy7UXy8/VhMSm3Vy6FVJ0rTKAbwjXxl0xg8xq/TK22vlW6MdZ90h7bQAXHT8LEnS/vGX5T/7S0lSwg32V9+09Fm9tPTt/n+npK0vP6hJmx6Vq6Azvt+sifrS2e/Vj845cECfMxyudz8kt5u93TF0COMAAADACLHqzRclSRv9yZKksNsixZs0MbFJ7zrTM/eFq2fmr4hI9t3sCVEvc1z56vVaWvIZjfdru3sq0DmMm4SWrFijJhtMLze2933LQzYl6wSB+wMHTNfsCWXaULIgc33Bud8b8O9wQfif2t+sU5PKVBLtOq1//PjqzLGz6Ju69pEVWvDNf2jF1ibNuucDmnP3+/v9ldZaTXvw48GK9woF74wbo/MO3y1/rxTsAl8O67blGWEcAAAAKHKbXrhXryxbqXB7rZb6c/SVmX9S3ClXxG1T4sGvSMrdo7pqwpRhqWt8JBugS95+UJI0zdvW8wPpfcVbbIlmmRpNSG7RDhvsbW28lJ5dXSOvh26sY12ZnVb7Xva+m3Rq4odac9ESORWTBvU7vC/0mhptuWLhrtEoMn3/nPO3nrpPl4fu19rtwXvxU02D/H52jx94/KnMsStHIaf3bdgKzcrIksbzijAOAAAAFLN4o2b949MK3Xm+Sv1m1doq/eqChUqGyxXzWlW7db0kqX7O6ZlHTjh4r/zWdOZvgp+p7L7YvhN0dzu2LutWeqG5BhPsgX5saKnaFLx3vmfrK3r9li/psbc2qrYl0eXRsJ+UCeeG8Q8csZ/u/fZntcce8wf+O1y5MnPYqPJu37E3R/57zvmt0R/pq5G7lerU4e+u1u6c+cyZmeOQ/KIP454cDWLnOgwAYRwAAAAoUr7ratkPgve/D3LWqL2pTq2mXJMrY0qFK1Rq2+SmUlrlz9QeF/ws8y50rGpqfgubH0zPdhPZVdw9JwjKEfW8ENtr9/9CklQTynbuD3DWKaWwjgst1WXhB/TAnTfq0P9+TI8u7bSlmO8rqpQU6bpIW0VskPupV05TWyjoyrc6Fd12xhWOyv9y1/fCS7ZkF6n7yV2PDvir55l3FS7yMO7LyCeN5xVhHAAAAChS8Vfv1r5mbeZ8qqlXvR8E0kiqWR8IvaRkwxatsjMViUSkK1dI36rLea87L9Kh+Ivx67Xo1dWSsmG83G/r/pnad3Tw1nskSVtsdkr5Q1M/n3Pbr6PX6bbI93XXbTfo7hfeCQbT09u7C+O7wjFB2Gyv3F1OD+HYqZoqfXl5ztgpiy/JHK9b87a2N8d3fqxXT/sHqiSap9Xuh0gQxgtdxehGGAcAAACK1Op1QRBP2GAKeJVpU5OCvahLI0F4nOe8q5r0e9eSJGcYQl4kux927fqlkqTn1zdLkspt9/uMJ7dlp4XXhrJh/IxPXa2IchdvOza0VDdFf6ppD10sa62UapckOdGhDeMRPwjRy8sO7/3Gyuk9Xqo2LTrvdy/0+zsvTX5Jy4/5qapKim/Rts4s09TzjjAOAAAAFCNrdeDSH0uS7jro5sxwkw2CcOTs32XGoup9W7Ah52RjhJve6iuV3lqt0rZ2+8jat5dmjhce0GlhtJLsHxIaDs59R/v40Juqb0vJpt9ND8XKlQ9tTh+fa4z0nq9I59/e5dLvoj+XW7umm4e6t9chx+sLpx8y0BKHzbZQ8IcH2+l/Ij8I4wAAAEAR8pq2ZI7n7JXdh/qV0qOCg+o5mbF/+gcPW10Z5/5JkmTcICgf6QRTuUv87jvjzy0J9kRff8btKq8MAvj93jE59zg7rV4uST+6+5+qa2iUJFVVVA5B4V3FnX503E/6L2nvM4Itv3ZydGhlNw/kSpmI7rfH60tnnziYEofNdyb9WN9IfVotKqMznmeEcQAAAKAIpTa/kTnec+YUJT77jO468Z+69rLzg8GqGZnrJ33kkp0fz79xwV7mJtkqtTdoqmmQJFWqLZha3knTssf0qfAjkqSpB5+mULqz3vGW9mPeIfqVe6aipRVdvua8dd/Qltrgs8dXV3e5PhTaTf877o2fea7L2I/D1+vPd/y+54d8TxGbUmvZ7KJfRb3GTNZt3smSxAJueUYYBwAAAIpQqnmHJOm1PT+vWePLFJt5gM47YaFmTygLbui0mNm+06uGv8BoEJyN2ybFGzPDVaZN9W250+brnvilJKl2r3NVEgkpEg7eazfpadCXpL6qn7jnKTr7sGDa+iful/Y6TZIUk6ttdfWSpEnV45QP/eqMp42fvY/cSNc/GsxefmOXsYTr6c6XNmjdmqBzbmL56ewPJdf3M8dE8fwijAMAAABFyI0H0703zD23z3tLC7EydyT4o0Ao1SYls1PTq9Smlnh2QTbft4rWLNUToWM18fzgPfeKWLB4WUcY/9HZB2i/GVVyxs+Wrtog7XmidMFd2la+QLV+hU569uOSpEkThjaM2+rdJEnnHrvPgJ4z592m2im5U+yPdFaorXFHzth19z+to/52ssr/dKoSNqLN00/etYKHgddpCXUa4/lFGAcAAACKkBcPFkILlfbcTV0050v6buoThVmZOxpM7Q67rbLblkmSWm1M+zjr1RpPZG7bvGmdZmiHWivmZhZ+K90tWMDsES9Yxfy8w3fTQ194T5ev8CKVOj70ZuY8PHHPIf0Vwhc/IH3kRh21YPaAngvNe68mXvZ31e7/6Zzx0p/Pl+JNmfOvvnWW5jrbNNk06TbvJL3n8IVDUnc+eZ0SOFk8vwjjAAAAQJGpb02qpSWY+h0pKevxvvddfI0++/+u1eTK2HCVlpXujJ+x/QaZ+4J31peUHK2ppkGppq2Z22Kv3SJJ2m2fwzJjzpQF2it+q/7mH93rV3jR7B8iXjngW1JVz1uMDcq4mdKBfc886MnEk/4z59zIqn31093em5q2UEftMXHQ3zVcXK9zZ5w4nk+EcQAAAKCIPLJ0q47/3l/16msvq91GVRqL9nhvyDGaPm5o997ut3DwB4CYyb4fXj5+qiSpPZ7MjKUSwV7erfM+lPN4UhHtP7P3d939dBhP2ZDGHVeARer6Ei7JHP7KPVOSVPrnC3XzDy7vcuvqeNf3zIsR09SHT7jQBQAAAADIWrV2nd4sCYJnja1SaaQA74P3h+m6Kvhu06ZKW6V4OoBLkh9vUp2tUMVOU+lXfO+0PlcW98qmSJI22CnafXIBFqnrSzg7I+En7rm6IvxXSdIn4ndI3i/ky8hJT/Y+94RDC1LiQHUO41e8b14BKxn96IwDAAAAReSUTb/MHIfkq6RYw3g3nCkLJEnxRLYzrkSTmm2ZKmK5fcCSSEiRUO9xxFRMliStNLvLKcYtwTp1xi977zx5NqjRkyO1bJcjq2tSF2vTh+7S4YcfVagqB6TjnfHHrzxhREyrH8kI4wAAAEARmdi4NHM83rRktzIrcrUap0g06BQnO3XGt++oUYtKu4Tx/phx9HlaG5kn+75vDlmdQyqUfYXgvMNny4aC3z+lsNatf0eSdNhBB2nWoacVpLzB6OiMR/v4Qwl2HdPUAQAAgCLS5pSpcz9yXGkBVkofBN9K4UhQq+tm3yO37XVqVpn2HMTvEZu8u3b/xsvafciqHGLp1eFX+TO1e3WpnLJxUktclaZdDX/+tORIfsXUAhc5MB1hvK9XCLDr+HMHAAAAUESiqWY94RwtHfQx6eP3FbqcAQmHg06x56b3GW+r0yHOajVUzBtR0+0H4gezr9fZyWsUDjlyTv9xZny2k95zvHRkTfV202E8TBjPO8I4AAAAkA/JNiUe/rrUvG1Aj5W6TYqHq6QPXy/NOylPxQ298vnHZzrjvpd+Z7y1RiH52jH+4AJWll9Xf+Zjev0H6e3R9jtLdbNOzrkeLh0Zq6h3+MV5B+uI3SdoQnnPq/hjaBDGAQAAgDyoe/YWxV76te7+0Wf7/9D25aryG9QWHVnd1Kffd6/Kzr1BTigI47ZjmnqqNfgZKS9QZcPDdFpZ3p12UM61cMnICuPHzpukuz93tMK8M553/BsGAAAA8iBRt0GSNMk0Kp7y+vfQtmDxthXjjstXWXnhTjlAipbJhDo64+lp6sm24Gd0ZCxCNxRCMw/JOY/Gxs7vjoEhjAMAAAB5YN2EJGmCaVJda7KPu9MSTZIkr2xavsoaUn46ToTSC5nJCdaH9r2gM26TQWfciY7uznhn5RNm5JyXRFkzG90jjAMAAAB54KfD+EQ19zuM23izJClSPi5vdQ2lr+1xrw6P/0alHYuzhdJh3E3pTy+sV0NjoyTJGUOd8ZLx03PPI0QudI8/0wAAAAB5YN0ggI83zVrX1r8wvqOmRhOt0bwZU/JZ2pD5+tnHaf7cjTp87vhgwAmmqW+qbdLv7n9LjVOX6QpJTmzsdMZVNinnNL04OdAFf6YBAAAA8sCmgs54hYmroam5X8+0NtepRaU6aLfx+SxtyIwvj+rS4/fMLmCWnqa+sPVfeiF2ubyWOklSaIQtYrZLwlHpypWZ07Lo6NzSDbuOzjgAAACQD14ic9jesE3SHn0/E29Ss8pUWTJC/2t6egG3U72nJCPNTq6W7xhVjx9Zq8PvssppSn3hDS3dsF0HT68qdDUoUiP0/8oBAACA4tYxTV2Skk07+vWMSbao2ZZq1kgN405uF/goZ5nW2ynab87UAhVUOJEJc3TwhDmFLgNFjGnqAAAAQB4YLxvG3ebt/XrGSTapRaUqH6krcKffGe8ww9RpnZ2mKZUlBSoIKF6EcQAAACAfvITetRMkSan0nuO9Wbd5i9yWWrWbcjmOyXd1+RGKdBlqd8oUGqm/D5BHhHEAAAAgD4yf0gY7Ta6JytSt6fP+uTfurT28tYqHRvDK46Fol6HkSP59gDwijAMAAAB5EPJTSpqoWmJTNcXWKOX5Pd/suZnDFls6DNXlSWnXVeDd0NjZYxwYCMI4AAAAkAeOn1TKRORGylWmuNqSXs83J5oyh1vdEdxJNl2no29qH6HvvwN5RhgHAAAA8sDxU/JMRH64TGVKqC3p9nxzvCFzeJ//3vwXl08T50lT9pVXvbskqayiusAFAcWJMA4AAADkQchPyjUR2UiZykzvnfH7H7xfknRJ8kr96NKzhqvE/LhiiXTZ8wqVVEqSLjrp4AIXBBQnwjgAAACQB2GbkmsiUrQi6Iwnug/j1nN11trvSJJOPXSBDp3T9b3rEaVjqvqJX5cm7aWS/T5U2HqAIsULHAAAAEAehGxKvhORiZaqzCRU18M09fj21epYss2ZvGD4Csy3BacH/wDoFp1xAAAAIA9CfkquiSoUCmmWqVG8rUk3Pb1Kh3/7b/J9m7kvvu3t7HFshHfFAfQbnXEAAAAgD8I2Jd9EFAsF57G1T2jCS/dpcegZvbRujY6IP6OnH/iD6icu1JmSjo3/r/60x8SC1gxg+PTZGTfG3GyM2W6MeavT2ARjzCJjzKr0z/HpcWOMuc4Ys9oY84YxZmGnZy5O37/KGHNxfn4dAAAAoAhYq4iCaerOe6+SJL22bKU+HHpGknTjoy+rddEPdHz8n5q28SFJ0g8+caL2mFxRqIoBDLP+TFO/RdJpO41dJelxa+18SY+nzyXpdEnz0/9cKum3UhDeJV0j6UhJR0i6piPAAwAAAKOOlwp+hKIqm7KHUjYkp3lT5vKO9ctVU1srSTrSWaEWW6JxVeMKUiqAwugzjFtrn5ZUt9PwmZJuTR/fKumsTuN/tIEXJFUbY6ZLOlXSImttnbW2XtIidQ34AAAAwOjgJSRJ1olIjqN6p1qfCz+UuXx/7Fua42zPnG+31ZozsWzYywRQOINdwG2qtXZL+nirpKnp45mSNna6b1N6rKfxLowxlxpjlhhjluzYsWOQ5QEAAAAFlO6M+05UkpRwyru9bXvZPElSRJ6qy6LDUxuAorDLq6lba60k2+eN/f+8G6y1h1lrD5s8efJQfSwAAAAwfNygM94RxifYeknSF5JX5Nz27gdvlySN232hAIwtg11NfZsxZrq1dkt6GnrHHJvNkmZ3um9WemyzpPfuNP7kIL8bAAAAKG4d09RDEUlSzLZLkp7z98vc8ti+P9BJ++wlXfGyqipoQgFjzWA74w9I6lgR/WJJf+00flF6VfWjJDWmp7M/IukUY8z49MJtp6THAABAsbM9T4Crb03qZ4+uVML1hrEgoHhZa3X1fW/qfx54PTgPxSRJd+99nW5wz1CNgkXa/LJJOvncy2SMkSbNk0pYvA0Ya/qztdkdkp6XtMAYs8kY8xlJP5T0fmPMKkknp88l6WFJayStlnSjpMskyVpbJ+l7khan//luegwAABSx+JsPKP7fs9W8eUW31//x6ENa+uRd+r8XNgxzZUBxevOpe/X24kV6YcV6SVJZeZUkafvEw/U/7oWSpF/tc7ucy18sWI0AikOf09SttR/r4dJJ3dxrJV3ew+fcLOnmAVUHAAAKyn3kv1ThNeuOh/6usy7cQ03xlOZMzC5E9bHXP6mPRaX/2nq6pN0LVyhQJA588jO6Nyat9GdJkiqmzpEkRULZHtgxRx8jlbPLLzDW7fICbgAAYPQKJRokSamGzfr69XfoQ9f+Tb7fddr6rNrnhrs0oPgkWzOHC5xgT/FJM/aUJBkTjP/7CXtq4W4EcQCEcQAA0JNkm2KpRklSVcs6/bb5C3o89lUt29IUXLdWcVMqSRrfuLRQVQLFozEI4G9WHCdJStiIZs2YIUly0mnc9rIGA4CxhTAOAAC6ldy6VE5699LznUWSpMmmUY+//KYk6cmbv6GS9ArRplNHEBiz2oPty1ZOO0PLp31Iny75qWZUl0mS0o1x+YRxAGmEcQAA0MWWxna98Py/JEnrd/twzrXoa/8n67k6duPvtMg7VA2hiQq7hHEg3lwrSTJVM7XPv/+fbrv6YjlOEMOznfGClQegyBDGAQBAF3dcd7WOX/4dNdtSOWf+Sus/eKdqP3iztpUv0AHum7rr9hsUkautk4+VF61U1Gtn+i3GvHdee1qSVFo1scu1jnfGu1lyAcAY1edq6gAAYOz5svcHSdKj/mH6yIRymYmnS5KWvfGEjmq5XeF3rpYkmaqp8lJlKlO72pKeymP8VwuMXfutul6SdNwB83q8x4o0DiBAZxwAAGRZq42/PTtz+q8JZ8t0tPQkte95msLGz5x/+PD5spEynRh6XW1vPTSspQLFqqp6UpcxpqkD2BlhHACAMSz59uOqfeflzLm7cbFmb3tMkvT11Gf0lU+el3P/woMXZo5XVx2p8n1OVij9Tmz1A58ahoqB4pU0Ud0T/bAU6jpDpONvWrzOAaADYRwAgDEsevtHNPFP78vsHf72m4sz1xz5mjW+LOd+Uzk9c/zy3l+VnJBC6ZQRMd4wVAwUyOt36dk/fF3/u+jt7q8n2xS1SSVj1d1eLomEJEnRMP/1G0CA/28AAMBYFW/KHDa2pyQ3obYXb5EkPeQdoYmHn9P1GSf7Xx0iE3aTJJlQJDN223cuyE+tQIH5f/+ajl3/a7lP/kjbmuKZ8ZVbm/XN+17TyrXrJEmmdEK3z5+9cJYuP3FPffHkvYajXAAjAKusAAAwRnk1qxVKH9e2JlW+6Tkd5ryta1IX6zvfv05n9PG8jZRLkkw4G8YvtLw3jtFl1Wv/0i3/XKoro9M0IV6vT4Yf0Tfuf13XX3SkJCl+24X67+anpDeC+8PT9+v2c6JhR189de/hKhvACEAYBwBgjKlZ86pq7vmSNniTdErHWEtC1RuXaZKkw8/4TK/PN59zl5564QWdcWAwZd0Jx/JbMFBA8+//oL4vabMzQ5I00TQrUvu2pCMlN6GDmp/K3NtoyzRuzyMKUyiAEYdp6gAAjCGJ1c+o8tb3a+/2V3VKclFm/Kk31ym5baUabLmmTJvV62dU7n+aPnjJtzPvwDac8L2c6ymXd8cx+sz039Vif4H+P3v3HR5F0Qdw/LvXL733EFrovQuChSIo2EBFUfTFrrx2X7Fj7wUb2BUFQcUCIk2K0nsvoYZAes9dcv32/WPDHUcSSEISgpnP8/CwOzu7N0e4y/52Zn4D0Ny8VSnM2OpTZ5O7LcmxoQ3dNEEQzlMiGBcEQRCEJqRoz1L0koPd7iQANruTAbBunc2xlC2kyjEkhPmd7hIV6MKTuM3+uGffVFxYdw0WhHOsVPL3bO9yNwfgUdeXbE1JxZG6DoC1xosAyJZDSAr3r3ANQRCEyohgXBAEQRCaELfLjlNWMc79AlfbXmS0fTKl6hCe51P6qvZxVI4mOshQo2satGpWuLvzuOMuAIoLK6bizAAAIABJREFUsuuj6YLQ4PLMNlwnrUQmI3m2v/z5N5YvmUuWHIomsjUARQR6lvoTBEE4ExGMC4IgCEIT4rLbcKLm49sGsk1uTUSAgbyw7p7jpbK+xsGEn04Zrm6SlR71+b/P5rGZa+uu0YJwjlzx3goC5DL2G7oAynJ/rnG/AvCR43mGqTdzwB1Pu5bKSJOhiWINcUEQqk8kcBMEQRCEJsTltGFHQ7BRy/wHLiTIoMW+ciPkLleOq401vqZWreKL8b1IKrLDYphonsLGfQtwuzegEr2EwnnsKuuvqLQyS4Ou4njHu1H7d0QdE+dTJ4dQAnuOhbXvkDz8/nPUUkEQzkciGBcEQRCExi5nH47vr8My+jv+XrUKueM1DOkQjZ+u5r/G3U4HDjQYdWpaRQYAYO54CWx9HYCr/vterZo4pEM0tsNRnv3eqv2kFVpoFl6z+eeCcK6V2Z2ez9Zg9VbMsoFdAf25d1R/pYLL6VO/78WjIDAankxr6KYKgnCeE8PUBUEQBKGRk1e/j7YkjaCvL2LUgacZ8Wtnnp21ulbXcjntONDgf1IgH9C8h2c7KCKm1u3UGwN99jOKSgFwW024XG5P+ZZ1y9mxZV2tX0cQ6su87Rl0eG4R+7NNACSoClip6sXka3p6K6m9n51FYTeTcMmdDd1MQRD+JUQwLgiCIAiNnMPh8NnXSi5Mh2oXzLodNhyy0jPuodHBlR/BbfPPppmg880iXZJ1BKzFqF5P4LNX7sPhcoO9jB4Lr6b579ec3WsJQj1YnpIDwPZjRcguB5FyPmGxLYkM1Fda39B+GKjE7bQgCLUjvj0EQRAEoZGz2R0VylprC2p3MZcdOxpP0jWPHrdA8wtrd80TTgnGL1p8Oan7tgBwt2sWuzNKKD68EYAgqQxZFsmuhMYlQi6gpZSBzenGlPIPOsmFOaJblfV79B/agK0TBOHfRgTjgiAIgtDIOexWz/Zzjlspkv25xLECt7vmwazssuOUNGjV9XALoPWdH66XHGRtWwKASpLZnV5I8ZHNABTKAZRYnBUuIQjn0sP7x7NM/xgb504ja/8mAORmF1SsOGER3PYngf4iJ4IgCLUngnFBEARBaMxy9xOWtsizmxSfQHbCZbTjKG9MeYc1B/Nqdj2nA5dUT/lb9d4549mJIwCIzVrqKbP9/T7N1r8AQCkGSu0iGBcaF6NLmSs+RfcJIVs/wSwbCI+sJI9Cs37QfEADt04QhH8bEYwLgiAIQiPmztrps68yBmGMSCJQsvBk8UtM/forn+NHt/7FxndGU5C6q/ILuuy4JG39NFbyLmNW1uMeAJKs+zxlEyzfeLb9sVImgnGhEXHnHvTZj5KK8MNGYrh/FWcIgiCcHRGMC4IgCEIjZistAuCQHA+AWpIJjm7mOZ5EBm8vSvFkf9Ytm0xv01+s+/45nCdlMD9Bcjtwq+opGAecty3gwNBvaJEY7yk75I6tUM8PK6U2V721QxBqwmxzMn/+LwBMc46k8IovANghtyAioPLkbYIgCGdLBOOCIAiC0Ig5yooBWBp3NwDa+C4Exbf3HB+lXstja/sw8aM5AOjshQAk2Q9yrNBS4Xr1HYxrmvcnecA1YAjxlB3WtPKpY9WGoJecZBeV1Fs7BKEm5v74FV0PfwbA/KDrCe19HTsHTiVzxNfnuGWCIPybiWBcEARBEBoxZ1kRblkitu9osh/JZuyQ/kgxnTzH+6qUYeBXyssA8LMrWdajpUKyiq0Vrqdy25HrMRj3MHqD8UKDt5f8XsfD5PV7CoB3Zs7nzRl/MH97ev23RxBO46ZDj9NMlQvAfy/vBUDnwTcx4oKqM6kLgiCcLRGMC4IgCEIj5iwrwYyBAIOO6CADkiT5JEo7wYAdHBaMchkW9ERIJeQVFftWcrsJd+Zi1QTVf8PV3oC/KKg9+9yJHHFHs0HbE3tERwAW6SfxvwPjWP/LB/XfHkGohhLZSIi/8Vw3QxCEJkIE44IgCILQiMmWIswY8defkgE9tqvPbiAWnPlHAEgP7AKAOTfN95yiVELkIg77N2xvn19IFFnjlnGJ/T2MRn/i2vdlpbqv53hv9zYclcxvB2DF6xx5+2J+XX+ggVorNCW7Zz3LmCff8SkL89edo9YIgtDUiGBcEARBEBoxR85+jsoxNA8/ZT3jO5bBs/me3b6qvRzYtx0AU4yyLrKt4LjvOVZljrZdH1Z/Da5EaHgUPZJCAXhwcDIGnZaBzyzCccsfHAntTwfpKIt2Z1U80V4GK16jhXkrafNe9T0my/zw51I2HM6veJ4gVIcs03HfB/ysf9FTdESOrfhZEwRBqCciGBcEQRCERiy87AjZ+hZEBRl8D6g1yp9yzVXZ7F67GABrXB8A5OJT5mLbzcrfuorD3OuDTaUM901s1pwgg5bU16/gul6JykFJQttqIKEdBtNKlYnup5vZudt3GTdy93o2B6p2YLF7s6/LR9dw44ZrWfPVY9idVfSqC8Lp2Et9d9FwsOODaNTi9lgQhIYhvm0EQRAEobFy2jHKZZRqQ6usYrvlD8oCkgAYY/sFtyyhKk/wpjFnKJfJOUDun68il/eMqwwNs26ya8w3bOv9Jl3aJldZJ6TtQACGqTcT8+PltJw0D7dbBkAuK/DU66E6yLG96zz79kLlQcON6mUs25ddH80X/uVka5HP/s5rljP6htvOTWMEQWiSRDAuCIIgCI2VTQmeHbqqE67pWw3E7/Z5nn2VJBMcGk6ZKgA/qxKkFsy8g8gNb7B3xSyljr4BErgBfh2G0+2Ku09fKbEPtl73AhAplTBD+yo5JhsANpMSjM9s9RYA6Xs3eE6zmk5kjS/iw5m/4CoP4AWhuhylhT773Tp3PkctEQShqRLBuCAIgiA0VseU4NOlPUPwHJwAkd61x6MDDZgNMQQ5cnG7ZQJNhwHokPU7ABpDQP20tzYkCf3I1ynsdg8AF6j3kF5UBngDbv9m3XChouBYiuc020mB1NfaN1l7SMwdF6rmcstYHS6fMrvJ+39IHvM1apXU0M0SBKGJE8G4IAiCINSlDZ9T9tdrjH/xEx6dtYmZM7/m6ienVJ0t/HRm3QiAST7DUksqNdyzCmfryzD1vI9Qfx12vxhiyKOg1IbRVeJTPduqrnlb6llQdHPP9qFsZW673awE4/rgaIqMSUSbdlJUZuftHxawePVGbLKG9NhhRElFHEo7XtllBQGAB2Ztpd2zC33K7OUPdBb1n4XU6dpz0SxBEJo4EYwLgiAIQl368zH8Vr3OdPeTJOz8hJv2P8Rv+uc8Q69rY2dGyZkrqTVobv6RwFGvASAHxhIjFZCTl1uharO4mFq3pb6ou93o2S5M+QcAe0kOZbKesOBAnK0v40LVLr5/9Q4eSxnLzZqlaHFB7wkA2PYtPiftFs4PYbu/pa+012c6g6NMCcbVfsHnqlmCIDRxIhgXBEEQhHrysHaOZzujyFLj84v0cQAMuuLmGp+rCUkgUirhqzl/+JR/qh7LtT2b1fh69c4YAg9sBUBbfBQAZ85+UuUYEsOMRA4YD8BEze+eU1SSTEzXYRRqY2iTuxBZFvPGhcq9pP2G2fqXKLY4PGWuUiWBm9ov5By1ShCEpk4E44IgCIJQh2ySgVmaK9mo7+dTnpVfXOU5stvN6v1Z7DpeRG5xGcs/e5wvZ8xA7yjhR9UIbh3Qssbt0LcbjFNW8aLpeZ9ydVAsktRI58bqlR7KCXlv4sg7jKZgPwflOKIDDahiOpLX53Gf6qs0F6BWq8mP6kdH+SDptXjgITQBbu8UkVlfvsP87cqUBrdFCcZ1/lWvViAIglCfRDAuCIIgCHXFaUMvW8EYSrPOA30OFedUPaf5+MyJDJjZlvjPO/LTlEe5JOMzrt//KHp3KXZN7dYED2s7kIOtxuMn+Q6PD+44rFbXaxAGb6K6Ix+MJEHKwxGajKo8sVZQcJjn+Cz/m+nysDLywC+qBZFSMXuP5TVse4VGp7jMwV97svl163Fvwja72XP8voLX2fnjS1jsLhK2vQdASKDfuWiqIAiCCMYFQRAEoc6U97TJhhAiW3YBIFdWensv33RblaclHpwBQKhk5j73TAACJQsqZJza2q8J3vaKByuUNerlm9Raz2YblbKOuCqqnadMm9AdgPH2Jxj7+McE+Sv/NuHxysiB9KOHGqqlQiP19fId7Jn5P97/cRF/bd5L8fRxpPz4jE+dSdpZXPH8V579jnFizrggCOeG5lw3QBAEQRAam6IyOyark8SwGvaYWZSEUG5DKKq2I/gj6AZMEd258fAkwlx5HMwsoHWst3d3zaE8YnUWElQGtG5r5W1x6mv9PqRw7/D2aTEvsDatjKmhZ8jM3siYAlp4tqWkC3ir2xLGJfvOedeHKfty8bEGbZvQ+HQ6Poshmt94QPMblCdPryzUXqZ/DIBFnd/hsoZrniAIgg8RjAuC0Pi5nPzy8ZPktB3HPZd1q7xO5nbIOwCdxzRs25oCp53co7twRXQgJthwrlvTIJZOewS58CiBT84hxE9X/ROtSs84hlBQaxn5yGfgdmOd8hmG4sPsOZTqDcbNufT/rrXn1OmuYdzcQYsqZT7mu9YT8FlfAI6Xnd0yZNu6Ps8vO/KYNGEiE1QqdJrza1DcmGGDfPYfv7pPxUpBCQDoyjIboklCIxZv3lWj+rE9RtZTSwRBEM7s/PqNLAhCk+TaM49rCz5Dt/I15Jy9mF9OYunKf3wrfToI5tyObc20c9PI811xOvx2Hzgq9s7al71G5HeX8OS02U0jW7XTxuiS7xij/ofvFq6s2aml+QCo/L2936hUcOmzAPhlrAO3m5yFb3L0w8t9zw1qhuq6r+F/RwiIa0dZm2sAcNVmffKTdL36YSY/+wp+Os15EYi/32sp+bIyT36xcQR+fgFnPik4HoCynCP12TThPBBuTatQtq/bU5iGvOnZz7vem5G/ZVxEg7RLEAShMo3/t7IgCE1eQZ7S2xUn5ZP5+wsEOIvYtHCG53jRwfWebf3iJ6p3UbdL+SMoljwH22bg3vtHhUPmtO0AfG15gCWL5jV0yxpewWHP5u5N/3D/N6spszurdaqtRAnGNadkZ9YHRQIwZM+TlH5/E1HrXiHJtt+nTkRiG9DowU8J5PUByuBaf6ny4evVJUmSJwHa+eChkb2g7QgAsvo9V72TtEZ2uFtwoX01Kw9UXFddaBqKLQ4MLhO71B18ytte9T8C/b0PdcLbXwTAP67OBOjFIFFBEM4dEYwLgtDomTMPADBcvZG49AUAxEr5HM0vBSDke9/s0CevI4vNxKYfXiJ1xbdYVk319OyWTBvGvhe6szezpAHeQeMna5V5xPM2nhQgut0se2MMhWl7PEXha16kxOo49fRGYfPRAuxOby/ynowSpvx1oOa9+QXe3tVpuvf5OPVythwtqrK66Z9PyMtMBcBckAVAQGi0Tx3JP9Kz7X94QaXXCYlr7bOvHvAAxzRJ9L381pq0/l8h/LoPOTx+M7cMal/tcxa5etNelcbOlIN13h65JIPUtb/icjeBkSHnsQkvfoify8R+fQd4OluZvtBptLKUn6MMgEPNrkOSJGyPHKTbEwvPcYsFQWjqRDAuCEKNOVPXsGH26zjPcvhstV8v90CFsvGaJcz57WewmXzKU9wJ7DspwDYvmEyvlLdpvuIBjH9NYvazV5OTn09QzibaSUe5YUrlgVFT49IoWal3HD6OsyidH1+dwD+bt3GpZQmtVJnsUbXmePI4OkqpbD6cc45bW1Hm2h9J+Ko7r8/b5imb+u03/Ln0L75ftBr2zK32tWxFykgMu8o7P76wpIo1ws25BC57kpKpSgooe+5BCuQA4mOifOuFt/LZNclGFrR5iT9c3rXI+3TvUeGcxGd2MGpA92q3/V9Da6Bly9Y1Wg9d30H5GbQ8+lOdNsXhcuN+rxPNF90met0bC5sZ0xsd+HvRrzicLr794Xvy9vzNHP0LaCQ3Dk0AaA3wyG4YU5413aU8RGyVGAcoo1WCAqoxBUIQBKEeiWBcEIQa03wzgj57X2PW+vqfn5m++U+SC1eS6vb2NG7s8iIA96c9gjXfd+3mKKmIPLPds1+S63t8rGYFX7/7lGf/ds3C2j9UkGXlz7+AU6vclAZKFlzzHuF6+xzWzP/Wc3xP6GCi2g/CIDl4e8b8ag/bbihhqycTLRVx/Mg+pWD/Ij60Pcsi/SRuWXcF/HgLczYdxeWWOZ6Tz5KdxykstVe4jvuXu3Gt/QSAvy7xDslfu3Jp5S9cvn5xS1UW8t4/UOft46gcQ1L4KcuRnbRkF8CGgEsZcsNE0gZ/jL3vf0EfjCHQd2i7UDMPjBtDjiqKAHPdfi+1fWYBalmZ0mIqO7spA0IdydlLoCWdsNWTObx4Krem3M++39/2HFarKxl63mM89LwNLnyk4dopCIJwBiIYFwSh1iynBMJ17fAPjxE/70YA/nT3xd37Tqx+cXS/8n72upuhl5zMX+Dt8SyT9YRKZg5l5nvK7DYLAHPDb0fudjMAT2hnAZDu146RqrVsPeqtXxPyq/HYZtxYq3MbG5dGGaYegAWXwwZAiNP772LtcD26RKXndr72cb74+gvlQCN5GOHQBgEQYleGiRelVEy8NmReP2ZtSCXq42SGzunIw6+85VvBaUe1YxZ+xQcplv0IimkJk9JwyGpaFq+r/IXLg3EAafY44kq2s8PYl2CjtkJVR+vhnu1ifSxatYr7Lm6NbsTL8GTFpFNCzZkMcehL08k12Wp/kbyDFC99F7fLDbLMaNUKzyGnXQTjjUL5kPPOqlTabngagCTrXs/hSAornqMPgFFTwBjSIE0UBEGoDhGMC4JQY26UpZbiijbV6+u0TPkcUIb09hv/EqrL38Lw2G40Gg2tbvkQgNHHXvXUz5WVhFcRK59hyco15JpsOHIO8pe7F1f+912kqz/G5eeduxtyyX9ppcpky7rlNW+cLCM5StEfXPCvmEd6ImO3P1Yc5T/fezRKz/A3yR9xw8U9ILINjmGvATAs/SOO/PU57teToLR2DzPqkk2t9ERHOJVg3G5Rpi9s13QmGyUhWrBUhn/2JnSS0sv5je5NrI6TkviZszybO/Q96ZwQDIZgSvyb09x5mFKbMhpg2T9/893KlPIX9gbjJxjaXlJpG7Vjp7Oj9+sA5IT1qLSOcHb8o5oTK+VzKNeMLMt8uPQA+7NNZz7xJNavRhG88gWWbNiGfHQ1b2k/8xxz2i113WTmrEth1vqjdX7df7WyvApFiZJ3CkFWxzsbsjWCIAi1JoJxQRBqzKJTehauOPQimcXVvDl1WMFeVqvXeyb2U3q0aQ6SpCwTBeia96tQr7DjeABu0iwnesl9LH93PMmqdA64Yz11VLfMoVQXQfo1v+LfbohSeGwDrpIsNs2dyutP38387RlnbpTVm9DrcG7FgOx843YqQ7YDJAulTt9fDW3adUGrVsq0/e/DdvUXtFMdo8Wqx1DZisn49jZIq6LnuIHYZaUnOtyegd3pxm01kyGHYb95LlE3fOCpd/XWO3zOKzhpqHrRX+8AsDFhPAMnzfX0blvC2tFOdYzjhRZwWLh02ZVELr5fed2yinPJW3SsZB1sAI2e9sPv5v3+67jx2utr/2aFKmlDYomkiDyTFdPx3XRfcSt3fr6iRtcwlCmf/6IjW1m2zTcZnMtWt8G4XHSM0Qv7sGvue3V63X87l7nqufuvdJrP6Ev6NmBrBEEQak8E44Ig1Jhd5efZ3ngkj9SNC9iw7NfTnzTtQngtvvovUp5s5z3HaJ666bKKx7UGn901Nx+i23VPwa3K0lxdVEe4XlYy5fYcMcFTT4rtiv9Th4jveikERGNTGdGZ0rC8241eWyYxSTuL2X/8eeb2lWR6NnenZTN1eQqjPqjZmtSNias8GB+lXkfqMd/pB7qwBJ99fceRPvtxOf/AV5X8jBpIWdZBYvPXAnC7ah4PfPwzss1MmWzAX6dBouokYKkrf/BsOzN3AxA69HHPQx8AVUwnEqQ87p4yG8zZgJLZ326zUWZWgvGNIcpSXHlyEG2bxVX5elq1ioeGtSfYr+IwduHs6UPj0EtOSvMzCPjmUi5U76ajfUetrtUj9XMGb3vQp8zlqNth6uad8wHoq1KGWMuyjMUullw8E3up70Ow/KHeB24PXdELjVrc3gqCcH4Q31aCINSY1lnq2TaUZdJ8/lj6/HMbu46ftPyTw8rxnyex9MURvP703ZB/AGQ3O49VMpfvFN8s38VLn30PQEh4FNFBhsorjv8dudft7Lp6MRe0CgeVGloMJOvS9z1V1l+zmj4DLq38fEnCpAnjP5pFBODt8TKUZVZe/2SWAs9m/t6VjFkxlLtzX/bpaT2fyE7vHNv+au9SZnfbH6ZNbJBv5fJl0ABSokZ4th0NlF3/VOb5z/jsX5s3DdluphQDgQYNhCb5HN/qbo1dVobiuzd/izNPSfilLstlgas3MTG+wbR/p8txyRKTNdNx753vKV/y0USs5cF4RuIV5MghfOm6vNL54kLD8AtVfnZJB6ejcin/pzVS9aeRyG5vIJxs31vheK2DcUsRmLIrFJeV593IK59is3DO19w3+TWKyxrn8oGNhd3i/R30h6sfxvbKKKcl7V7C31jF7wtBEIRGSATjgiDUmN5l5ogcA4DfMW9v8D+btysbbhcp39xLwq6pDHavYVJ5wjSAFRs2n/baBX9P47a/B/Bs9kMAyIbTJNtpeTHSyHfp1K2vzxJIMYP+Q+7YP1k58Dt6d+542tezlSfVSjV28pTFU/Gm+VSy1bt82oCD7xApFTNSvY6DR+s3qV19OTFM/QSnrML64D4+fXUyQYaqg8tWY9/wbGdXd8pCHTOX9yS+Z/wvoMx7d1rKe8b1GojpDHd4s6F/EnA/i0fvIj2wKxeyDc1H3cgrKSPQmk6WOoYAvW8m5qCkrrzLzVys3o5qsTcT/xWmH9m/c4NSp81ArtR9Sdexk+v53QqnowpUVl3wL/QG0jHa0qqq+zgybSzSi2GnreOuZTBunzkO3mnDlkO+U2DMecr3hR82ZFlmxK6H+Vr3Fn5vRDPl/dcabPnI843DWopJNvLTkNU0m/ANfmHxMLmYoWMfONdNEwRBqBERjAuCUDNOO1rZTp5GCcb1x1Z5Drl3z0V2OXGkLKJt+i8A/N3jA5/TjUUpnu0taYXYnL5DMsOWP+Gzn5jcpVbNjGw3gIGDr0SlOv06xfHXvwtPZdL88X88Zc2lLOQzZAl3lM8VzlTH0U51zFNeeGhjrdp7rslOO/lyIO81nwbAW8YHMITGVn3Cg9vh4d1owpI40PNZAPKy0xuiqRUYyzJZ5erIA4+/RGar6xig3k2UaTel6PHXKz3gRHsfynTr3IWRXeKISWjhKdu8cyca2UGJIeHUyyNJEuMffKXS1x5U8BM2WUtkWCjrnh7C8E6n+TcT6l+g8r0UbTnkKepg2+abqK8KLbIWVCjb2PweeCoDbla+z9y1zKauK/+eXL74d2+hy0lIplIeKJWxaJd3RI5WcjGk4Ae2H69iffsmzmkrxYqOxLhYurQQnzlBEM5fIhgXBIE5a/fRadJP1RtmbFMyExdrowBob1qDSTZiUQcx0f4F6b8+gzU3FYC3Ax5l4MjxOO/zZl33K9wPQOH+NbT6sgPbX7yAZTvKMwnLMg5Jy2znxZjbXQfAkIsurps3eTo6P2WIe7lbNUt4avpfpw3IbWZluP3RSCVztrU8gZgpu37XXj+QbTqr7O2Wfz5k95RrK6wTLrvsOFEz4KLLsD5dwJOTXjj9hUKbQ7ASuPpFNgcg9/ihquvXMbPNibk8u7nOUUyxFIRaJeFudxUABsmBxRiNXlP+cz1paH1slNJ7qo7v5imLKFHWJ7cFNqv09aLDgslroVz7ZvuTTO29iKnOUQDoJQfhAbo6fHdCrQUo30tRFHBcjmBV4HCuVq9h9bfPgvv0AblV0nu23eP/QL7rb3rf9gbo/EFTfsxZu2C82JgIQGD+DnYcL8Jsc2JN30m4S0lEdrl6A0dnP+apn9NqNK2lDFbvz6r0ek2dy1aGRdYp01AEQRDOYyIYFwSB1otvZYP+ftIyfYdnb9y5j/f/3Orbe21TemrMOuWmN0CyslhzEdLF5T3ae37HVqIsO9N+yK2oVBKaqGScT2ZRpIshsGQ/ZpsTx665BEtl9FGlsHd9eY+UpRCt7MAW1paA66bBo/uRdN5kcQ1p8MFXTjv/21Gk9AJrIpMB2CG3BMBZeKzKc87WzrULWfrhPdz7Xe2XlDMue4aOhUuZvTrFpzzk0FzCMKHTqDBo1VWcXbnYxNYAHDq4r9btqhGbiYDXwnnplecA0DtNWNUBAMT3HuWpVmrw7TErlQ2sd7ejfZwyP5cBD1F40csAaIuUBwm6oJgqXzbomnd4xzGGrgNHcfeIvlx5x/MAmAOaExtsrPI8oQHpg8jRKzkC8uQgYntdDcDg4x+z7vdpVT9gs5kxyDYO6trBuDmoWg5EivM+rEGjzEO2WS2Y07aTn5nG/B2Z1V82zaU8OOph38TxT6/jqdkbMHx1MeB9kHe3RslH8FvXaUT1uQ695KBo27wzjtJpimR7KRb0p51CIwiCcD4QwbggNHVOG13lffhJNvYu987tXr/iD3rP6cuY9WP4c2cmuBys/O4ltuxTlvopM0R56oYMvAvDwIkcDOqH2aXBYc6nRPYj2N/fU0ejN+IIbU0SmaTll+Ew5WGRld5Efe4upVKB0qvsDm4Gag2Uz/9saDZNAC2kLByHV1VZR977B2bZQJC/EoRlEIVZE4qmtBrJ32rDnEvnRTdwj+YPrjv4BMWWGiZ4kmXkQu9axo4sb5I2nDYMshWt5EJzhmH9lVGFKr3JWnM1loSrCwWHAbiLX0CWMThNWNWBnsOl181mlW4AXa962Oe0ecPX8Ij+JdpGl9eVJHRRbZS2lyj/NoEhVc+X+wvdAAAgAElEQVQZ1gVFcv/kz3j0sg6oVBLxzZPhmk8JuGdJXb474WxIEqWtlWz/JbI/zTp6l7iStk5Xlqc72bqpsG4ahYeVB1zrw6+B5CEVr6tXkhh2TZtOwFeDCP+0MxfO6cGH3/9crWbpnUqOiV6q/Vyu3oA6fYPn2BF/36k4kUkdofVQSvXRPFf6CuYf767WazQlsqMMCzqCRLJEQRDOcyIYF4SmrijNszny0GQyVs1g7+uDiFulJKpKkPLAYUPe+AUDD71N4EIlsdr6wgDPee279QfAHphIFAXYirMplAMIOWX5JlVQDJFSMdklVqSS4xyQlaXO7rB/B5ODyZ15JwBSVLv6e7/VkB93Ca1UmcT8ci2rduyvWMFpJ9SSxjzNMJIvugFTi+EMvOcjygwxRLjyqjU/tUZkGf54yLM7VL2ZfRk1m0ua++skpCnem37JlEnBt+PYN/Nx5NK8s2ufMRSbZEAqTmPjrFfZ9VJfbFkpZz6vFqwOFxM+nAuACxU4LGhwYtN6M777dxzOhU/9SftWvlnUx17QitVPD/PJI6APigBAVx6Mh4RFnPb1DVq1bx6CrmM9Q6OFxiGpg7LOe6RUhDbcmxcgFJNnaoPHwkmw8AlCZytTENz+VfwsQ5Rh5ievNBAslXFl0XfsOHkVico47RjcpRTL3lE+zQxlnu2Wl3s/2+lyOK1atQG1BnuXmwAI3Dv79NdvgiSHBSt6AvVimLogCOc3EYwLQlNnN/vsxv11H+2t20l0HsWMcvPoMOXiPLgcgGRVOplyGBcOG+0558TSY+qgGMIkM2EZf3OQRFpG+vtcWx8cQzjFpGQVo8vbTYo7EXOL4Z7jkWXKUOHIxLZ1/z6r479b4IGtSEZvBvcZs2dUGCYq5+5DhRtnVCckYyiBt84mPK45Dv9YYqV8ckpsp1757OSmwL4/fIpWb9pE80nzWbbvzJnfKckgcsc0n6Lso/sIO/IH7fZ/xpS3lKXB3nTcQKf44Jq3T5JIV8UxQbOQ3vveoJNrHylLv635daqhqMxBC0mZR9tGlc72KUpuAasm6HSnVUkToATfQRYlq3VYWHgdtFI4l1TNBwJQoIsHSWLrNX+z3d0SA3YsJz0oM6/6tMK5Lr8qgnGt7zSEze5kFrr7MFS9mSNrfmXBVy9WyMNwglyiTGlZFD/RUzayUFm68fu2H6LvdCX8ZyHmG+dSMuZHYoKV79OQ5Auq+Y4V781dR6/J82p0zvlK7TBjUxnPmKBTEAShsRPBuCA0YRa7i0VblQB4umZMheOpemUIr7s0F/XBvzzlB9zxjOiiJO9aoB2KuvyGKKo8KA+SysgN64GfzrfXwi8sFp3kYvffc4iQStgqJxMwfhabrt9IulEJwO+wP8qIrhUzWjeI8FYQ1hK1v3eo8lTt+/y+ZrdPtbzV3+CQ1QS0P2U4a3AiCVIu//t5m888e5fLxYzZ37P+UM17oK0l+Wybeptn/+MkZQ119c7ZTFAv4Ot/DpzxGq5jSob32e5L2T1ECQKe0c7wHH9Io2SK3iq3rnH7TtBdcKfPvulI7ee1n47NbmOwaotnv2upMpVgd/mc/RrzU37Wke5cSmQjsaH+ZzhBaPT8w+GOpQx4VJl2071rN2La9cMo2bHYvZ9L9Y6ZFU49UFr13P+UnpMB+NPVh81DZnPZBCVnwFV7HmJE2jtMX5Na6XmWPGX0UUBUC5ik5JRIVikBekLni5RKSRcQ0PYi2nfu5TlPaqkkhyyRqveg6eEtl/GJ+6Vq1T3fGR1FmNWnWfZSEAThPCGCcUFowlK+uY+E9S8CcEjXxlNu0UcC4EpUhp+H521Ghfcm9pAch79ODc8XMeKpnzzlIW0Herbjulacd6lKHoZd5ceH8msADB12BUgSvTq0If6JDRx7IJNXJv3PZ83wc0HjF+qzn7F1ATjLe7tlGUPKXJbJPbm0t+9cz/CWXfCXbBw7sp+Z65UbcNmUjfqlMMbtvZ/3vpru+0JuN9unXMerH1fsoQPAWoLh3ZZ0k5U1kz9wXk1Q24uwqIN4UPMLz2m/o0PJysrPPUlZvnLjr7r0aTpeOApbePtK6zVv0abS8upIGHQbuXIwOXIIx6RYQuxZ5JvreIQAoEmZ6zNU+ISJN11TuwvqAnCgPDQ6JkcRGyQSsf0rJPQCo/dzrNIZMWCn7KRg3Fr+PXfCDOdgrrig6qUU2456mHd7LqFwxFTuGtQKKb4nTrwPHGVr5cPVTwTjUkg8GILY3fxWAOaF3caF7ROrfg8aHVsCL6GQwKrrnOBSckj0UaU0ioRvbrfMnzsz62eddFnG31lEmUYE44IgnP9EMC4ITVRx9lG6Zcyio0qZK5unU3qj3Wo9xjsXwBXv0nXEHQCEpipZft9wjOUe+0P4j3hBCZhP/CkntbwIHtqFacwsBl18WcUXjWyDe9jLnt3Etj19DieG+XmGvJ9LOj/fm9/78l6Gl6P4fs1BKMkg0JHLfmNXgk9JHmRIULIvrzY8SGj+VgCK1n3nOR4jFQCw+7vH+PanObgKj9K1cDFP5f6v0nnmO9Yu9tn/M+J2xvVrgcHlzeAcYU079bQKLAUZuGSJwHAlu7h75BTPsczIAQDscyfyzPiRZ7xWlXR+yI/t58itW5BaX0qilMum1PzaX68qJcoQ9UXD/0Ye8Sb74kdzYMJe2sfV8sZcksiMUB46zXANIcgo5qD+G6n1/hixUWbzJj4sU3nzXuTLgdz00hz6t46s7HSPR0b1YVz/8hEkWiMZ/t78Fv7WnErPsRcon1FdmJLosOOtU+DRFEY9MAWN+vS3YSqdEa37zA+15Je9yS5tznoIgKvh4KKpWJ6P5Pv5S1nw3ZsE/Tia6WsO1+lruFxu5n3+HFocWHWhZz5BEAShkRN3HYLQFBWlkbt0CifPDnZr/eC/W1Dp/CEwBiKSwaks7dVLpSQxC+w0nImDLj39vOKQRAJDqu7tMfT5DyWrP2GfNYwu4dXo8TkH9OXB+BZ3a1r42QktD3g/nfcPI4ObEQKUBidXPDGuu2fz6i3/4euQTvxntXfYaFtjCXt276Djoc/pyOcUH0rw/AwW/LOWawZf6HM5eeePAOQHtUMe/TUzwluhUkm41HrULmW943DnmbO3O4szySOYqGAlB4AxvrNSLqvg5jmMe2MKKe5mbDrLZEhRgQaiAg1Yii7CeGAG+9bM47JOdZsJ2m1TchwEhcciJd9Nu75nf/1mE+fxxrwtRBoCzvmoDKF+qPX+aCQ3dvtJga2lkHw5kHDJxFxXf/5Ti5+9Or4H7FdWgzA4CiqtIxcfp1AOIDio/NMuScp3bDVo9H7osVFmd1aY9uPhciLJJ/X4O1w1Xp7wbFkLjtN67SSQoN/6ibRWZYAa9q77DPnCN+rsc5WxZw2jMj4AIDChY51cUxAE4VwSPeOC0NSUFcD7nWm9/0scsveGzeQ2KHOmT75J1Oh8Tg0Lj6hdgq+TqVQEPbSePk8uavAbxurSGrw9Zu5b/2B6kDIfeqX+YTZsUeYrJydXMtRbpab4Rm+itSuXXgrAPkM37JKBQGcB8b96h1MHW497tpetWuU7vNRpp23RSn5mCOGPrCciqQPhAXoA8pqN8FSLcWVjsp5+mTPJnE2OHEJk+fno/HCNnYXzgW3EBht55r/38eGdlYxkqCVj56uwSgYezPgffHKBkg2+jsg2M1ZZi9Ggr7NrAjwxqgcPD639MH2hcdPqlQdRdos3YaXaksdedzOG2d7gZefNtbpueGwzz7bT4rvmuPvEEO3idDLlcCICav5/VqczEC6ZmDx9YdWVTN4lBVPd0T5J6hpK6tzXPNutVd72XG3+gV3pJXX2OvYjqz3b3QffUGfXFQRBOFdEMC4ITU2Od76t/aTBMduyK88E/Hbkq55tm9qv0jo1ptaAqhF//ei8SbzCY5MYf+fjnv3AA7/ikiW6dqq8Vya47UAO91V6w8Ml5eZcd91n2DT+dHfvJNjpm8Rt3uClADzn/pitR3O9B45vwOAuY4exT4XXyBj0OhfaprBYfRGJUi57MpSbXfuKt/lj+lusOZQH9jL2/vkxh9JzcJVkkCOHEhnoDQbU7UZgCFeW/mofG8QFreowi7jWQJGhfHREzh4Ks46evn4NyDYzpRgwNtIHOULjpDUon2m71bukmNGSRb4mii8eG8/fT1Sytng1GPy90yNODvSPrP6Z7BdbUfzZSOJy/iZLiiAxrObfn5GlyqikyccmVPlQy56fCkCuFI5WcmJ1NPwwdW3ubnaSTDrKcPk97iRy+jxBpFTCsWzvd57T4TirOe369LUALO35MUkRAWeoLQiC0Pg14rthQRDqQ8ncSZ5tf8nGi45beMbxH966oVel9SdO+I9nu9h17udzNwiN8j5lyodWBkZjunMDABeo9rBTbklCRNVzlFskd/Ldb9kGh9qfjlIqJbKR7tZpFCaP5uDwGYwa2IutcTcSKZVwcMd6zzmO1HUA5EX0rnD97i1ieP6WEfTv2494KQ/Xj7dhnXUbuhUvMfLwy7z9xXdkLXid9hue4si064m2ppIthzboSIRQp3fJtaKpw/hh/hIA1v/9JzOXrKXo+F6Wzp+F/aT5ra78I+x5czBLNyrDfmeuTyMtv8z3wvZSSmURjAs1oykPxr/5ew+T5+4Gp51AZz5mfQzNwv1ICK3lg0aX3bPptpaQc+wAc95/EP2ad4iVCgjOUBIsWo0xnlUnasIREAeAn2Rj+aYdvgetxex+dyQrFs4BIMPQmkDKKs0/UV9cphwOfXQNrUq3kmFozZHh3zI35BYi7vsTv3DlgVxZgTICyHFoJZpXIvhh5lfM+2sZjlokdwso3Msf7v5cdPlNdfo+BEEQzhUxZ1wQ/qV2HC+idVQARq2aNxamcEPvRFpE+OMoLfSp95VrBP46Nc93iK70Ogaj9ybVKWkrrfOvU0nPTWCcdwjzr1zCC6cJBqXWl1J4xwb8tn6OPeliAiUJp0YJBjbJHdj4yg1o1DdyIv1Q96sfhE9+oCwzBRgFQNGxPbjlEK7sW3nm86EdoqHlRNL2rqB/4T+wz3vsF/1kUPLHMUStbOS3adghnXqHd2hqC1U2h9a9QlanZvRdfiOdZD3+q20MBt4yhTBh5CWEB+jJmv8qHco20WH+AGbn/8oLK4pJiAxl6aMXe64lOUopxUC4TjxLFqpP0irfY09pZvLGWjdcFIIKGbtf5d971db9FkjfDLvmINvMuH+awOiSHRWqOcJqNwVCO+pdXnvHyJPaH0j74w2OtfycxHDlu0Q+toGOJSvpyErcskRpSBsCyjZgsZ9+2kpdylo2lVZ5ywDIj+rHZf0ugH7K+ujuUiUYX7tpM2OGXkTe2u+JBW468AgcgG8MW7k1cCPmPYsIGPMJkvYMD3ttZkLsWRQHDjtj4jtBEITzhfg2E4R/oTK7kys/WsWo579ixFPTmPn3Dlp8FMesz15H67LwC5d66m57bii7XxyO9jQ3N7bxC1kcP5E7B7ZoiOafe1pleau2rU9K0iZJlLUbwyHi6TRy4hkvEZrQFv2otwnsomQol8qH5ecGdah4IxnWEptkIDq/vGfc7UJ7bDUpcjP6tDjN8HFDEM0eWOTZ3e1O8jlcIHl77+8fd/0Z21ynrv0cObYbrrtXkx45kCHqrWQvUbK4+0veJFqP77uBHxauUJJQpa3xlN+w7ho+175DscV3+oRkN1OGoUIme0E4rfJg/DL1Jp7SzMBmVpKtufRnmQPDEATXfg5AN/NK9DZvErfHHXcB4JYlbK2G1+ryoWHhPPnc21j8E7hVms+CX772HLMUe6e1rHe3JzomHpUk4yituznaZ+LO2kW2HML83l9z7c33+xxTJfTCIuvoXfo3JqsDe84Bn+NHUw9i/3MSgSlzWPzjJ2fsKXfnKkP2VVHtTltPEAThfHJWwbgkSamSJO2UJGmbJEmbysvCJElaIknSgfK/Q8vLJUmSPpAk6aAkSTskSepRF29AEISKnDt+IdUwjqX6x1mon0SypAwT7HX8W4yuEmzaEFKv/Jkv2n9VraBG3/ICht35CoGGJhIAxXaFUR8QcN1Un2K/sV/S8vndXNen5g8lQs0HAbBFVbKOsUZPWnBvWtn24HC5KTi6kxB7FgejhhPmr6tY/2SSxIx+c3nAPpEr7N4kSsW6aEJHvUTORa+z4dJZqBq6J6nL9Uh3/406thOxfccA0PX4DJ8qRf4tAdi/5W/SN/xKnPM42X5tPccHqXfSQlfsc47WXohJCkCvEcPUhRo4qdd1iHorH84rf/BjqIO1qlXK/8WeqgM4rd5A+NH/vcSBe4/zULvljBjQs6qzz0wfgPG+fwAINB/xFJvzlO/1/apWhN31G36Bylgbc0nl652frU2pBdidbiwnrdWuLTjAblpx2fCrMehO+f2gDyA79mLGalbw/TuPkFSy2edwQtpvnocXlx14geHPfMG2Y1W3PffwdgBCkjrX0TsSBEE49+ri7uwSWZa7ybJ8YsLpJGCpLMvJwNLyfYARQHL5n7uAqRWuJAjnMXdxJht+mcKRXNOZK9czzd7ffPaf1ipBkBoXWtmBUx9C8x5DueOG0WIpp8pIEvS8FYwVb9Rr+++1J0LpGTNFV0zIBqAJiSWUElJzzez94UkAjM2rdwPft0dP5rr7c1W3OE/ZZ51nIfUYT9Ql99Jn0IjTnF3/VJ2uwS4pDxXWu9uRP/QDtsVeh/7+1bhkiTs0fxK/SFnTfs9lP5A3/FPyW1wJQB+Db/I3vaOYUvVZ9mYKTU/5aJcTmuUpc7lVlXzGa0PWKcnEIiUlGH/ZMY7oID3J0YF8cGP3s3+Q6R9OqeRPsN2bi8GWn4ZZNmC+9S/aJkbjHxQGwI7DaWf3WpXIyjzO5599wPvP38NLLzxO/pFtHH7rImJsR1BFJFc5bLzs4hfJk4O41zEdlyzxTNyXXGx7h1RdG/paV/vUXap/nP9+8kuVbSg5tgu7rKb5KTk5BEEQzmf10VVyFfBt+fa3wNUnlU+XFeuAEEmSYuvh9QXhnCj55nr67HiOz6ZPP9dNwWaM8tnvoVJ6ZUOkUgBkY2iFc4T6tardU3SwfkVCdGSlx/3DYgnDRMa+9Qywr2G3O4mRgy+p1rVbRwWQ+voVTBnbnfRoZQqCX0BQnbX9rBmCKX7kOBPbLCf+4eWED7iVbnd/gdHPj2Nxw+miUnr7CuUAurVOJKLfWMLHTsWNhDF/LyUnLd3m5yzBqhHBuFBDWt8Ebd3tyhKFar+6+S6UHvUmbfhWPZoRd71S5w86izSRJNgPc3zLItwuN9EHZnNQjiO+PPmcf6DyYOG6lEdwu+tuOUEA3boP+FT3Hv/TzuZV7Zdkfn0rLUu3AdCmTeV5LQA6tGtPXvJYABa5e/PYzVcyf/JtaFoMoJMqFYCFod5kbINUO6u8lrZgP0fkWJpFNKLvNkEQhLN0tsG4DCyWJGmzJEl3lZdFy7KcWb6dBZzIjhIPHDvp3OPlZT4kSbpLkqRNkiRtys3NPfWwIDRabrsS6Mabtp3bhjhtOJ1Kht9f/UZ7ijeHjyRUKl96xy/iXLSsSbtjUBteuaEvV3aNq/R4aGQcaknGtlF5lrmu3zQCjTVflzjmjln8NHgldw5seVbtrWuRgXo+uqlHhazVze+cSXpIb37WXcXh27YRemJYvj4AW2ASg+W1TH55MpN/WsembVvRy1aoowBKaELKe8ZtspK3NlmVrhQHVf5wrMb0gdiu/Yb0kF5cf+/z9Eyq+/+jFk0wXVy7SZh7PasW/4gOO2lytGf9cnW0EhQnSHmkpe4HV90lcrO7fR8sdFKlUiCFYGl9BXGX3nPac1u2bAXAYWMXQvx0+Os1GJMHeY4XxFzo2Y6Uqh6mbrBkk0EEfjoxRUUQhH+Psw3GL5RluQfKEPT7JUkadPJBWVlMskaPZ2VZ/kyW5V6yLPeKjKyjX5KC0AAkpxWASFdunfdK1ITrl7uJ3KcMS48d85an/OSkN7rAOlxTWqgWnUbFNd0Tquwt07ZUvj6Hlc4jxZ1ARFxSpfXORK3Vc93ALug050l+TpWK+If+YsxT0+nZwvc7X9fiAtqrjvGu5iMm776MXr9dDEBAi8qH+gtClTRKMH5qxu6kuJg6ewl9l2uIf2gpxojafXbPxKb19ghr1yjJEI/3e8G7ZFpIIoeGKgnemk/vQ/7Muypco7ZcduX3254LPwTAKavQTzqE8eaZPvPxK6PrM4H0Aa8w9v4XPGWh7by3i6OvutqzHY4yzN+ZtgF5xvWQf8hzzOAoxKwOEVOrBEH4VzmruzVZltPL/84BfgX6ANknhp+X/51TXj0dSDzp9ITyMkFoVI4d2c+q3UfOXPEUBofyRD+WPPJKbWeoXX/Ue371bBu1ao6okjjmjkQK8yYd0weKnvFGJ7oDlvLh1wvdfWhevnxRU6Ye+gK7okZRKAd4yiyyjjb9rzyHrRLOS+U947oW/dnY9jEA/nF1pnPC+TPlwarxBuMXqPew1d2aey/3fTDVqrc3a3vooV+pK7LNRKYchpw8jIz44Wwb9iP++mqujqvRET90IhHB3s+xKiCCtEHvsebyRegN/pjuUZK7xWjM5B3fj+aroUgHFuH+oCcLtx0Ft4sQRw6lmrqZ4y8IgtBY1HqdcUmS/AGVLMum8u1hwIvAXOBW4PXyv38vP2UuMFGSpFlAX6D4pOHsgtBoJH7bmyJ3c3hx++kryjLr3r6Gnyy9CYmI5ll3+TB1KY+cEhtRgWdYM7UeOFPX+nyo/XRqjo9dwhM/72BRmwgoz5fTr4tYGqYxcusCwFnMTncLbo8KOPMJ/3aB0XS673sc2ftgal8ANo/bw4WRgee4YcJ5xxAEty+BqA700vljyR/PwJAYJM35s0KEMtjQK10Op/upvcQ6PwqunkHYb+NQIVOYl01oxFmupQ5gN1Mm6/HzDyTuztlUPtmmZppdOoFm5duBMa05GtCNYFMJpl8e4cTjYpUks2/zCrpnZhMNqDVnWF1CEAThPHM2PePRwCpJkrYDG4D5siwvRAnCh0qSdAAYUr4P8CdwGDgIfA7cdxavLQj1w6Wsady5PLHMaZXm0a90Oe+43+TZnEcB2K1uR5yUT0pmCb9va/iBH+pvfLNmh/jpGNgmmjVPDSUwviOpgT34O+4OYmMrpGsQGgE5vA0AurBEAqrb69QEaCPbsDfhen7o9h0XthHTl4RaSuwD+gAkScIYkYSkqXlOhnMp+aKbMIW0wz1GySux2l15VvGwbiM923/M+cazvW5vKrmm2o3aUtnNmDHiX4/ztW26MNpzhPiC9Ux3DsX5cAoA4UfmYtusTL3SdLu+3l5fEAThXKj13Z4sy4eBrpWU5wODKymXgftr+3qC0CAsBZ5Nu9ONTqOixFTCVyv2cu+IXr5rG5syfE59xzGG/wzqgvGf54j6fSzBUin3r3mBd+8aiUal8s7rq0culQ6NW7nZkpGIDDzpZlOjo/mjy2le760Qasvv+s9Y+uMHPDJy9JkrNyUqFe3v+JyqczYLwr9fcLdR0G0UABfNeJeYpKo/Ea86buQp7Q+UWpS53s4jq+g3+wqecN/PGy++WrMXtpmIy1tNCYlnv0TbaZj94gkssACg6nMHmuAY8gLacov5L3DC+/q7eeiyofX2+oIgCOfCeZLhRxAaSGmeZ9NidylFU4fw0OZh/L7VN/h2FB732c+J6EtQJ6VneqB6F11UR/g4ZzxzJo/hvx/MBsBVWkDB8f0g10+CN5vKyAa5A3S8BmnConp5DaH+qAKjGHz7y7SOFkv3CIJQtcUv3caMO/tVefw7lxK0+juUB8ym1K0A3MdP2J3umr1YeRK1rOBuGOuxZ9xiUIbT22QNF/YfCID2ui88x0MH1l1COkEQhMZCjIMUhJOV5ng2yxxOgs2FxJYpQ+UcTgcl+1ezZN0WrrjxPuyFWWiBe4I/4Y7WZh4ddAuaIANOVGjw3uzcpFlGUn42MJbiqZcRZt4PwD+Df2fQwIvrru1OO/7OInbrR9Dnuml1d11BEAShUfEZpVWJZtGRlBbpuaX0G1572kbPkDKGAWGSiZQsU/US15mywT8CuSQdCTiccDUX10Xjq7Ah4FKOOTeR3+l27o9QElgGJ3VBfuwAeSYrt8Y2O8MVBEEQzj8iGBeEk1gLMzAAZtlAqc2F48g8TgzKWzRvFuN0bzAa+H3XVfQ35QNw1aA+9OqZ7LnGyYH4Ca7yQSgnAnGAtOVfQh0G4+aCDAIAu1HMqRUEQWjKfr73AvxfV6YsPan9AZT8ogRKFtIys88YjNsOr0Y//XI2tLyf9pm/EQgERNZvMHzU6scU5128new7A1IKiCJS5LMUBOFfSgxTF4STlOYpQ89N+FFmd6Jd8Ijn2HTdG55tKe8ADlMedllNUFDlS63MTXrKs21Dh83p8jku28tIfvpPXHW0JnnmlzcCkGoVdy2CIAhNWWVzu3e7y9c/n3s/Uz/9wPegy8Guryby1XdfU2pzYt6/EoA+hz8m0KIkI+3frWO9tvmhIW0Y1CaSyzrWQfZ3QRCE84QIxoWmKWcflFRcWc9WqNx0yIB+7Xue8pKgNj71SnNTcZUVUkwAwX6VL7XSZdREVo9cxh5jT6KlAnJNNpyScoOUp4mhjeo4uBzkmKyVni8f+Iusnx+vsJxNpWwmkm17ADhmM565viAIgtAkHExUEkJulZSEb1eoN3Bv5rN8/9sfnjp5Sz+gU9p3TDj0EL/8vRGHKcfnGvfbHyAhzL9e29k8wp/pE/rUa5I4QRCExkYE40KT40zbCJ/0JW3qtRWOuYuVAD1OKqDt7vcB+DDoUYIe3gDPFUBzJamMtSAdLIUUygGE+J1y43D7ErhtPs0j/BnQqychsS2JlQrILTKhkR1853cLRLShr2ofn2incMFry9h2rKhCW6QZo4nZ9Rm/bT5yxvd0bPGHAOTJQdw7dkyN/uqfSfQAABgXSURBVD0EQRCEfx95wCNYApvRauybpCSModdtb/scv3nbOBb8NhMA1845nvJb1gwnZveXnv15rn48/+TTDdNoQRCEJkYE40LT4nJgn6EM525m2cPsdYdw56SQs+Z7AFSlWRVO6dLzApAkUKnhZuWGxViSSmLWEooIIOTUnvHEPtD8wv+3d+fxVVXnwsd/KwNJyAAJBARkxjCIAwgoCuJIAXvrWCu1Dq2t1be22tpabW31o7fVtlet3ltt7YtttVZeX4eq2OKA1tJaZRJBQBQtCgEZDBDmkJx1/zjHAEJAIDknwu/7T/Zee5+9n8Pn+azwZK+9Vv1udmlnysMaVq9MXjvRopiydh0BODV7Ok+1+AG/m7ygwZCXL35nl19p0aM/pPP05BD6Z/v+hOP6dNzl+ZKk/V849QYKrp5NKGxL76+Oo0/3znDNv+HCJ3m94koARs+8nJmTxtN+7Rweyxq5wzXeSnSiZNjXaFecn+7wJemA4ARuOrAsn0fLzSvqd78wcSAA7YB/lp1E73Vbly97PdGDHlf9hRFlHbZ+PiePD1t05LyaZFE+ICwgZzdLvbRsk5z0pmbpHABiixKyRn2PLVl55M78A4dlLaRs7VvAoJ1+PrHsLaDhtVU7z/6f+u0Rw0bsMhZJ0gGsZRn0GEGfjoOZ9Xjk8Pl3ceTkrwOwvv/5VK3NY9XiN9ky8BKW53Xl2BGjqMj2uY0kNRV7WB1YNiRnQH+23SU7HDpufF/aJlbwTF2yKC47/RaKty3EUz4s2vr+eG6oI4Swy1sWtUtOmpO17A0AEgVl0LKM3IHn15/z2crbeWrb4egbquo3i1fOSG7UrE8uNdOAn5TeTKeDu+4yFkmS8vJbcvjYm5lXPBSAmYkenDHmNMoufICe171Kn9GXc/xJY8ixEJekJmUvq/3a+pmPsfCGCibPT74LntiwCoDKDieT+NEqNh1+4XbnP103hA1j7oLrFtP5qFE7vWZNy60Fek3c9VNxgNCqEwDFq99MNhSUJX92HsIdpT/kkYO+w1FZb1P5/K/YePcJfPj+XKqXbF0C7aBN7/L6otWs+80ouK2Cd1esqz/23l+3TjJ3xdcu220skiR9pLBTPwDmJbomJ0776JUsSVJaWIxrv9bir1fTLSzjj5OmAbCpOjlEPa+knKzsLPKPOLP+3LfzD2PI957izKGHQl5xg9eMhVvX8Z4z5NbdB1GSfIe7w8ZkgZ1V1DbZHgLfvvIazvnqtSTI4rKNv6Vg+Wv880+3sOWB5CRsSwp6c0r2DI4Y15WiD2cB8N3bf8vE6W/D8jfp+uqNANxd+7kGZ3WXJGlnSvqcBMATieMyHIkkHZh8Z1z7tUROPmyGgi3JJ+Ib1yynJVDUKlVQ9zwJrpoNrbtwyCe8Zm6rg4DkU/GuJ1y8+w/kFbMxq4iuiUXJzxe12f54Th7rCztTvP49AD636Un4aOR7r5Nh9vztTn8s70beeuK3vNn3c/QB7qo9g2Hnff8TRi9JUlLrIz/LQ+te4ZY+XTIdiiQdkHwyrv1abXZyBtjCmuQ72DXvT2dhoj09O5ZtPan1nv0npKz/KbxUdzg/qx1L6ceXNWvAuvz2AGyJ2RQUl+5wfFOrXju03bDlIkq7HLrT61VkVdJn/j0AtBtzPQP79/uk4UuSVG/ssL50b9u0a4hLknbOYlz7pXWL5zDuhguo2pIHQNna5NPlwg/nMJMKerdveBj67pR3ruCiLdcyrm7Mbidv+0hti9YArKKYNjtZIiaruN12+5fUXM2Pb76Tgh5Dt2t/qe5wqmNL5icOrm8b2POgPf0KkiRJkjLMYeraL+U+9HkuCZXMXd8VsmBInMXq6rWUbFnB2oKT9mmG2BACD371aKrW13ziz8QWRQBUxWLaFu34bnc8YizMf4iray7jP4YfxR0nnkl2VoA2PZly2kSKX7mdvh8+y7pWFUwY/iADurSGta+w9oMFVOzDHxYkSZIkZYbFuPZL2ZuSw9I7hORSZj2zlrD0ram0JlLbqts+X/+4Xm336PyPivFVsZgeRXk7HC+uGMahm8axngJuG33adseGDB4Ka4+Avz/LqAE9yD46Nay+w0iKK0bu3ReQJEmSlFEOU9d+Z+4LD5JTtxGA0pBcBqxjqKLvhDNZF/MpPfI/0h9UXurJOEW0KdzxyXheTjbrKWj480MuhV6nkn3M15sqQkmSJElp5JNx7VfWVK+l39//z3Zt7yfK6ZKVXNLs/diekwf2TntcWaml0lbF4gaHyB/VtZRB3Xac3A2AonbwpUeaKjxJkiRJaeaTce1XEpNuAmBmokd924S2l9RvP148luL8TzYDemPKzk/OVFtNwzPWPnr5sVw3um+6QpIkSZKUQT4Z134le/kcAN6JHZnY6ksMLq/j8rHfZt7jdfz13Rq+/53MrMednZscgt6mRW1G7i9JkiSpebEYV1qtXvY+v31sIueeez5d2zT+uqZrW3amBOhx/l2c3adnfXvfc64nk8+cS4Z9lXdnPc2xp12RwSgkSZIkNRcW42oadVtg81poWQZA7fK3mPzCU/RaMoHvVc/guj/lccs3L2rceyYSbFg0iwWJjpS0ad+4195HucXl9Pj+PzIdhiRJkqRmwnfGtfdqN/P+L4Yz7je3bdf82uO/hJvbsv5nfZi+YAkAmx66iBPfvInO1TMA+M7K65m3ZE2jhbJ5zQfMeeRmDqmZywbyKG2544zlkiRJktRcWIxr71Uvocv6WVyy9CZenvwcTLoZ1lQy4PUbACgMm5k1cyoAdXV19R9b2+YIykM1M96YnWzYUAV1+/YudeLOozh07u0A/POQayjbyfJhkiRJktRcOExde29zdf3msZPOSW5M/q/tTsmpfIXKD46G9etoBUw+8VGG92wN//dkRk69lMg5hJfvZGNswYJL5nFYl7Z7Hkf1UgoSyfXEq2IR55559t5+I0mSJElKC5+Ma+9taniY+fDNdwBwwaq76fTr3nSqq+SewssYPuIUKEsuO1a+pZLw8p0AFIQaXplw3x6H8OCr77Fi5tMArIv53F50NW2K8vb4OpIkSZKUThbj2mt1G1cDMP6wcSSytxbA/ygew6SbLiSxTXqtjQWMOf87yZ2C0u2u81Ld4azOak358n/u8n7PzvmA8VPeZ+H81/nx7yfwzop1/PDxN3j62WdYF/OZfPYMrr/qykb6dpIkSZLUdBymrr22bGklHYFQchDrr36PMTc/xKLYjoU/Hg1Z2UCi/tzHE8dzYcfUDOchkMhpSVbtBgD+ccStVLx/Le1WLWdR1QY6l7Xc/ka1m0nMHE+rJ+9mdl1/uuU+wk3AnXeeydCsfpySPYP5dOO4inbk52an5btLkiRJ0r7wyfgB6tU/38Mff/o1FlVtqG/bVFXJxn/+hheevJ+FK9bt+gIxUj3lQapjS4YP6Edhfj6LYnsgpArxrTbEPNqNvna7tqxv/Ivq037DVe1/x1dOHUhNYSeOzZ7Lb277wQ63WvD/riNrwrc4OutNrs59pL79ypzHeajFTzg4rGRh9y9Qkp+75/8QkiRJkpQBFuMHohg5eua1fKnmYSa9/CoAtR8uJP+ufhQ8dw0nzfgmf3zg3vrTV61ew1V3PsB9E14gkYiQSFD1iwH02TybyYn+dGxbRlZW2OE2dxz2BEM2/YpfHv0So44duP3B0m6UDD6PX15+Fh1aFbCkd3LN8f/M/R2/f35G/WnrV6+g19vj6vd/V/sZbthyEXMPuQyAlUW9WZtTyoATz2q0fx5JkiRJamoOUz8QbVpdvxlfuYfJix5h+LI/bndK9+opxBgJIVD5wKX8ctVE1k/N43Ymcs7aB+m24d8AvNNtbIO32ZDfjuWspyhv92l29PGjWDz1UA5eP4fyl77PjIqHOeLg1ix98df0Al7OPYYJZRdz3ZfPYWNNHe1K8oGf0RYgRorDjn8MkCRJkqTmymL8QLRmcf3ml3OegWXJ7XsKL+fSq29h1R1DOaf6Oe741Z1cPvZs+n84EUiuG/7daSfWf3Z1LOTSCy9q8DZd2hQCsL5m92uIZ2UFOn3jafh5N07LnsL4ey/mzZDDF7OfpzK2ZeD3nubYFsl0Lf74cHQLcUmSJEmfMg5TPwDF1YsAeKxuWH3bv0Y8yOXfu5XsrEBWSUfyQi3fWXkD0/77AgBmJbqzctQ9LMs+iD+3OI1ll82j9Y8Wbjdh2lkDO3HJsO71++cOOpjLRvTka8N7fKK4QstSVnX/LADn5fyNL2Y/D8D47j8lv4V/N5IkSZK0/7DCOQBtXPEeLYGJdYM5K/sfbCaXQwafWn+8eNC5UDkJgOG8BsCmkb+g7TGnwjFf5IwGrnv7uUdut5+Xk821o/vsUWylZ90Ot00AoCq/C0sTrbjgrNP36BqSJEmS1NxZjB9A5r08gTff/TedFv+VI2IOfUZ8gadKzuGzg3rRNnfrOuG5A84j0XkItXcPpaquJRsvm8KQDuXpCbK4PRtyy2i5pYrZZz7PiN7t03NfSZIkSUoji/EDSN9nz6dvantB7MgFx/agvDhvp+dmte1BiytncFB+K8grSl+QQN63pjDlzXc4vqJdWu8rSZIkSeliMX6gqNt+ErW3up3PmAYK8XqtOjVhQA3LLi5nyOA0PYmXJEmSpAxwArdM2bh6u1nNm9qq+ZMBWBFLuKDdo4z5yvVpu7ckSZIkaXsW45ly7wi449D03GvRFEofTk67dvOWC/j5+cel576SJEmSpJ2yGM+UVQsBWFa1pslvlVhTWb99+ufOpEOrgia/pyRJkiSpYRbjGXb/X19q8ntsrK4C4OzNN3DS0YOa/H6SJEmSpF2zGM+wjcveIcbYtPdY/QEAF59zBiGEJr2XJEmSJGn3nE09E6qX1G/+uPpG7n/iEDrULiGrz2mc3L/xZzDfuGIhq2MhHduWNvq1JUmSJEl7zmI8XWprWDXtYe5fdRh9q15g5DaHLpx5PgBLZ9/C01V/YvSwY8jK2rsn2DW1CVrkJAc8rHj3dSbPnMeQpTOZG7tzVKeSff0WkiRJkqRGYDGeLgv/TunEb3AlsDi2hQCHb7qXR7o/TcXSJwDoEKo47YVRLHy+Pb/v+lNOP/VkBnT5hE+zY2Tqn/+bVq/9mgJqWBjbMzz7Dc5KHX6x8AKOzclukq8mSZIkSdozvjOeJrXVy+q3Dw4r2RxzqKaIws9czzP5o/n1gD/zfvkJAHTLWsaNiy7h1Xu/SfWmLZ/o+nXzJzL49R9RkVUJ2TlUZG1dw3xZbE2PkZc36veRJEmSJO09n4ynyfqqpbQCXis5kQHVLzI/dgagU7cKOl07PnXWibw5bRJ9JiSfZ1+W8xSPvvgiZ48eueMFE3XMeexW8t+eQEldFeW1yUnaXjjpCU4aPoKJc5bRriSPdhv/TcuOfTmu2OXMJEmSJKm5sBhvYpW3n8Bz63sytM16WsQWrBl5JzV1U2hTNpjppR12OL/PoJOhz4LkOuTjTiG8MwlSb5jXVK9k+j/+Qr8TzmPj1Ac49I2fb/fZ5+NgBg8+DkJgVP+DUq1O2iZJkiRJzY3FeFNav5JO1a9xMa/BcpgWK+jXpT0tSj7PLudMLyqHonI+yOvOoSue5nc/30TnTW9xSuJlhgJM+SatgMrYhkWjfk+fvoexZtl79ChoQ6uC3LR8NUmSJEnS3rMYb0J1la+x7ZRp/7/XrfysJP8Tfz576OX0/ts19N7w+/q2zTGHvFALwLtH/YjhQ48HoHVrn4BLkiRJ0qeFxXgjWbtpC0vXbKKifXF9W9Xbr1IOnLH5Jl6PPfhu5657dM3y4V+Bv10DwLT2n6ff6VdTcFAFEQhZ2QxvxPglSZIkSeljMd4Yajfzt7u/xTMry/n2iI7kDziXTu3aUvP+dN5JdGBm7AVAj7aFe3bd7FzWXjiJGe+tYMSJo5sgcEmSJElSJoQYY6ZjaNCgQYPitGnTMh3G7i2aCuNOqd/dErN5vf2ZDFr+CE/E4fS/YjxLVm9kWK+2hBAyGKgkSZIkKV1CCNNjjIN2dsx1xhtD58FcmftjZiW6A5Ab6hi0/BE2x1wKT7qanuVFDD+k3EJckiRJkgQ4TL3RTKrpT9ngB6n4TG/+9af/ZNaKBOdfdh2nuL63JEmSJOljLMYbQV0ism5zLSX5ueS3yOHEi2/kxEwHJUmSJElqthym3gjWbUouNVac7982JEmSJEm7ZzHeCKo3bQGgpCA3w5FIkiRJkj4NLMYbQX0xnm8xLkmSJEnaPcdVN4KubQp56GvHUNG+KNOhSJIkSZI+BSzGG0FRXg5De7bJdBiSJEmSpE8Jh6lLkiRJkpRmFuOSJEmSJKWZxbgkSZIkSWlmMS5JkiRJUppZjEuSJEmSlGYW45IkSZIkpZnFuCRJkiRJaZb2YjyEMCqEMD+EsCCEcG267y9JkiRJUqaltRgPIWQDvwJGA/2AsSGEfumMQZIkSZKkTEv3k/EhwIIY47sxxhpgPHB6mmOQJEmSJCmj0l2MdwIWbbO/ONVWL4RwaQhhWghh2ooVK9IanCRJkiRJ6dDsJnCLMd4bYxwUYxxUXl6e6XAkSZIkSWp06S7GK4HO2+wfnGqTJEmSJOmAke5ifCpwSAihewihBXAe8GSaY5AkSZIkKaNy0nmzGGNtCOEK4BkgG7gvxjgnnTFIkiRJkpRpIcaY6RgaFEJYAbyX6Tj2QFtgZaaDkLZhTqq5MSfV3JiTam7MSTU35uS+6Rpj3OlkaM26GP+0CSFMizEOynQc0kfMSTU35qSaG3NSzY05qebGnGw6zW42dUmSJEmS9ncW45IkSZIkpZnFeOO6N9MBSB9jTqq5MSfV3JiTam7MSTU35mQT8Z1xSZIkSZLSzCfjkiRJkiSlmcW4JEmSJElpZjHeCEIIo0II80MIC0II12Y6Hh04QggLQwizQwgzQwjTUm1lIYTnQghvp36WptpDCOGuVJ7OCiEMzGz02h+EEO4LISwPIbyxTdse52AI4aLU+W+HEC7KxHfR/qGBnLwxhFCZ6itnhhDGbHPsulROzg8hfGabdn+3q1GEEDqHEF4MIcwNIcwJIVyZarevVEbsIiftK9PMd8b3UQghG3gLOBVYDEwFxsYY52Y0MB0QQggLgUExxpXbtP0cqIox3prqFEtjjN9PdajfBMYARwN3xhiPzkTc2n+EEI4H1gH3xxj7p9r2KAdDCGXANGAQEIHpwFExxlUZ+Er6lGsgJ28E1sUY/+tj5/YDHgKGAB2B54GK1GF/t6tRhBA6AB1ijDNCCMUk+7gzgIuxr1QG7CInz8W+Mq18Mr7vhgALYozvxhhrgPHA6RmOSQe204E/pLb/QLJz/aj9/pj0CtA61RlLey3G+Heg6mPNe5qDnwGeizFWpf5T+Rwwqumj1/6ogZxsyOnA+Bjj5hjjv4EFJH+v+7tdjSbGuDTGOCO1vRaYB3TCvlIZsoucbIh9ZROxGN93nYBF2+wvZtfJLDWmCDwbQpgeQrg01dY+xrg0tf0B0D61ba4qXfY0B81NpcMVqSG/9300HBhzUmkWQugGDABexb5SzcDHchLsK9PKYlz6dBsWYxwIjAa+kRqeWS8m30PxXRRljDmoZuIeoCdwJLAUuC2z4ehAFEIoAh4FrooxVm97zL5SmbCTnLSvTDOL8X1XCXTeZv/gVJvU5GKMlamfy4HHSQ4XWvbR8PPUz+Wp081Vpcue5qC5qSYVY1wWY6yLMSaA35LsK8GcVJqEEHJJFj0PxhgfSzXbVypjdpaT9pXpZzG+76YCh4QQuocQWgDnAU9mOCYdAEIIhalJNwghFAIjgTdI5t9HM6xeBDyR2n4SuDA1S+sxwJpthsdJjWlPc/AZYGQIoTQ1JG5kqk1qFB+bH+NMkn0lJHPyvBBCXgihO3AIMAV/t6sRhRACMA6YF2O8fZtD9pXKiIZy0r4y/XIyHcCnXYyxNoRwBcnOMBu4L8Y4J8Nh6cDQHng82Z+SA/wpxjgxhDAVeDiEcAnwHsmZMQH+QnJm1gXABuDL6Q9Z+5sQwkPACUDbEMJi4AbgVvYgB2OMVSGEm0n+Uge4Kcb4SSfgkrbTQE6eEEI4kuQw4IXA1wFijHNCCA8Dc4Fa4BsxxrrUdfzdrsZyHHABMDuEMDPV9gPsK5U5DeXkWPvK9HJpM0mSJEmS0sxh6pIkSZIkpZnFuCRJkiRJaWYxLkmSJElSmlmMS5IkSZKUZhbjkiRJkiSlmcW4JEmSJElpZjEuSZIkSVKa/S9/JuAZvOKVMgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1224x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZu5ZZNUHmP_"
      },
      "source": [
        "# Split the Data in Train and Test and Check the Model Performance\n",
        "tgt = price.Close\n",
        "x = price.drop([\"Date\", \"Close\", \"Adj Close\", \"Volume\"], axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVk9FCXPHqF6"
      },
      "source": [
        "\n",
        "xtrain,xtest,ytrain,ytest = train_test_split(x, tgt, test_size = 0.10, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNb4sulnHsFr"
      },
      "source": [
        "# Stacking Models\n",
        "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor, AdaBoostRegressor, BaggingRegressor, StackingRegressor\n",
        "from sklearn.linear_model import Ridge, Lasso, LinearRegression\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "\n",
        "def base_models():\n",
        "    models = dict()\n",
        "    models['lr'] = LinearRegression()\n",
        "    models[\"Ridge\"] = Ridge()\n",
        "    models[\"Lasso\"] = Lasso()\n",
        "    models[\"Tree\"] = DecisionTreeRegressor()\n",
        "    models[\"Random Forest\"] = RandomForestRegressor()\n",
        "    models[\"AdaBoost\"] = AdaBoostRegressor()\n",
        "    models[\"Bagging\"] = BaggingRegressor()\n",
        "    models[\"GBM\"] = GradientBoostingRegressor()\n",
        "    return models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TokNfEShHvwD"
      },
      "source": [
        "# Now we will apply K Fold Cross Validation. We will now create a evaluate function with Repeated Stratified K Fold\n",
        "# And Capture the Cross Val Score\n",
        "from sklearn.model_selection import cross_val_score, RepeatedStratifiedKFold, StratifiedKFold, KFold, RepeatedKFold\n",
        "\n",
        "# Function to evaluate the list of models\n",
        "def eval_models(model):\n",
        "    cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "    scores = -cross_val_score(model, x, tgt, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')\n",
        "    return scores"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNAXpU_ZHyC_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 158
        },
        "outputId": "f01dea77-1ca3-4cf9-85e2-576db94dee9e"
      },
      "source": [
        "# get the models to evaluate\n",
        "models = base_models()\n",
        "# evaluate the models and store results\n",
        "results, names = list(), list() \n",
        "\n",
        "for name, model in models.items():\n",
        "  scores = eval_models(model)\n",
        "  results.append(scores)\n",
        "  names.append(name)\n",
        "  print('>%s %.3f (%.3f)' % (name, scores.mean(), scores.std()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">lr 5.194 (0.423)\n",
            ">Ridge 5.194 (0.423)\n",
            ">Lasso 7.502 (0.529)\n",
            ">Tree 8.008 (0.648)\n",
            ">Random Forest 6.508 (0.512)\n",
            ">AdaBoost 20.650 (1.304)\n",
            ">Bagging 6.764 (0.582)\n",
            ">GBM 7.976 (0.539)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l5-LCCJ8H0EK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "0f1eea3d-eb72-4649-f003-e686ea593e28"
      },
      "source": [
        "# plot model performance for comparison\n",
        "plt.boxplot(results, labels=names, showmeans=True)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"Model Performance\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4q5gxhZJH7fT"
      },
      "source": [
        "# get a stacking ensemble of models\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "def get_stacking():\n",
        "\t# define the base models\n",
        "  level0 = list()\n",
        "  level0.append(('Tree', DecisionTreeRegressor()))\n",
        "  level0.append(('RF', RandomForestRegressor()))\n",
        "  level0.append(('XGB', XGBRegressor()))\n",
        "  level0.append(('Bagging', BaggingRegressor()))\n",
        "\t# define meta learner model\n",
        "  level1 = LGBMRegressor()\n",
        "\t# define the stacking ensemble\n",
        "  model = StackingRegressor(estimators=level0, final_estimator=level1, cv=5)\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CROCiRLyH_Zm"
      },
      "source": [
        "def base_models():\n",
        "  models = dict()\n",
        "  models[\"Tree\"] = DecisionTreeRegressor()\n",
        "  models[\"Random Forest\"] = RandomForestRegressor()\n",
        "  models[\"Bagging\"] = BaggingRegressor()\n",
        "  models[\"XGB\"] = XGBRegressor()\n",
        "  models[\"Stacked Model\"] = get_stacking()\n",
        "  return models"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-GgpU3mIB7Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "76a34d6b-6035-4ad8-f759-8c4544f7e14a"
      },
      "source": [
        "# Function to evaluate the list of models\n",
        "def eval_models(model):\n",
        "  cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)\n",
        "  scores = -cross_val_score(model, x, tgt, scoring='neg_mean_absolute_error', cv=cv, n_jobs=-1, error_score='raise')\n",
        "  return scores\n",
        "\n",
        "\n",
        "# get the models to evaluate\n",
        "models = base_models()\n",
        "# evaluate the models and store results\n",
        "results, names = list(), list() \n",
        "\n",
        "for name, model in models.items():\n",
        "  scores = eval_models(model)\n",
        "  results.append(scores)\n",
        "  names.append(name)\n",
        "  print('>%s %.3f (%.3f)' % (name, scores.mean(), scores.std()))\n",
        "\n",
        "\n",
        "# plot model performance for comparison\n",
        "plt.boxplot(results, labels=names, showmeans=True)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.ylabel(\"MSE\")\n",
        "plt.title(\"Model Performance\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ">Tree 7.981 (0.679)\n",
            ">Random Forest 6.494 (0.535)\n",
            ">Bagging 6.687 (0.585)\n",
            ">XGB 8.154 (0.600)\n",
            ">Stacked Model 28.116 (3.094)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ekGzZuqIEW3"
      },
      "source": [
        "# Lets apply on Train and Test Split\n",
        "level0 = list()\n",
        "level0.append(('Tree', DecisionTreeRegressor()))\n",
        "level0.append(('RF', RandomForestRegressor()))\n",
        "level0.append(('GBM', GradientBoostingRegressor()))\n",
        "level0.append(('Bagging', BaggingRegressor()))\n",
        "level0.append((\"XGB\", XGBRegressor()))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HYO3HhkJIntq"
      },
      "source": [
        "level1 = LGBMRegressor()\n",
        "model = StackingRegressor(estimators=level0, final_estimator=level1, cv=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_9GdHnkQIqjs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "1ddc3e38-aa15-4453-8b51-160b57325dcf"
      },
      "source": [
        "# Making Predictions on Train & Test\n",
        "stacked_pred = model.fit(xtrain, ytrain).predict(xtest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[06:49:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[06:49:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmxbkWjjIuZ5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2e87c7e5-742b-4a73-9f58-400a33db810d"
      },
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error\n",
        "from statsmodels.tools.eval_measures import rmse\n",
        "# Checking Model Metrics\n",
        "print(r2_score(ytest, stacked_pred))\n",
        "print(np.sqrt(mean_squared_error(ytest, stacked_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9991996811128692\n",
            "12.137576072283768\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz3PHufiKNM8"
      },
      "source": [
        "xgb = XGBRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uLiKvM-KRPl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "77df71c2-1d03-49b7-8581-490d10450bc2"
      },
      "source": [
        "stacked_pred = xgb.fit(xtrain, ytrain).predict(xtest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[06:53:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cjKIcOfKU7o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e5bbf1ad-ec93-423b-87c3-8289a70ce234"
      },
      "source": [
        "print(r2_score(ytest, stacked_pred))\n",
        "print(np.sqrt(mean_squared_error(ytest, stacked_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9989655321612422\n",
            "13.799359479679797\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q4U8P9sKaMk"
      },
      "source": [
        "lgbm=LGBMRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVsnCsrLKg3g",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "cc7eba23-fecc-480a-c222-b8b264144343"
      },
      "source": [
        "stacked_pred = lgbm.fit(xtrain, ytrain).predict(xtest)\n",
        "print(r2_score(ytest, stacked_pred))\n",
        "print(np.sqrt(mean_squared_error(ytest, stacked_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9993517824583638\n",
            "10.923475061500046\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUGk-DADKlm8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f9bf9c11-d211-42ca-8d74-0153b82b14ff"
      },
      "source": [
        "rf = RandomForestRegressor()\n",
        "stacked_pred = rf.fit(xtrain, ytrain).predict(xtest)\n",
        "print(r2_score(ytest, stacked_pred))\n",
        "print(np.sqrt(mean_squared_error(ytest, stacked_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.999442300644988\n",
            "10.132124156880547\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bPFrrahNcHy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "3dcad318-9d23-405c-c43d-dceb50108413"
      },
      "source": [
        "price.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>08/02/10</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>120.449997</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>120.300003</td>\n",
              "      <td>118.110657</td>\n",
              "      <td>113926600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>09/02/10</td>\n",
              "      <td>115.275002</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>110.800003</td>\n",
              "      <td>122.099998</td>\n",
              "      <td>119.877899</td>\n",
              "      <td>49861112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10/02/10</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>126.199997</td>\n",
              "      <td>115.800003</td>\n",
              "      <td>117.500000</td>\n",
              "      <td>115.361618</td>\n",
              "      <td>22791756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11/02/10</td>\n",
              "      <td>117.500000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>112.550003</td>\n",
              "      <td>115.474998</td>\n",
              "      <td>113.373466</td>\n",
              "      <td>11715914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15/02/10</td>\n",
              "      <td>116.800003</td>\n",
              "      <td>118.400002</td>\n",
              "      <td>112.750000</td>\n",
              "      <td>114.574997</td>\n",
              "      <td>112.489853</td>\n",
              "      <td>6380560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Date        Open        High  ...       Close   Adj Close     Volume\n",
              "0  08/02/10   80.000000  120.449997  ...  120.300003  118.110657  113926600\n",
              "1  09/02/10  115.275002  125.000000  ...  122.099998  119.877899   49861112\n",
              "2  10/02/10  126.000000  126.199997  ...  117.500000  115.361618   22791756\n",
              "3  11/02/10  117.500000  120.000000  ...  115.474998  113.373466   11715914\n",
              "4  15/02/10  116.800003  118.400002  ...  114.574997  112.489853    6380560\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCppEo6hQevZ"
      },
      "source": [
        "# DLF\n",
        "dlf = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/DLF.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j68ga1QdReog",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5fd7b05f-e610-48e0-9b44-a29cecb6a878"
      },
      "source": [
        "dlf.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>05/07/07</td>\n",
              "      <td>526.599976</td>\n",
              "      <td>583.950012</td>\n",
              "      <td>526.599976</td>\n",
              "      <td>569.799988</td>\n",
              "      <td>500.565369</td>\n",
              "      <td>78025942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>06/07/07</td>\n",
              "      <td>568.000000</td>\n",
              "      <td>586.450012</td>\n",
              "      <td>568.000000</td>\n",
              "      <td>573.599976</td>\n",
              "      <td>503.903503</td>\n",
              "      <td>13724278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>09/07/07</td>\n",
              "      <td>574.099976</td>\n",
              "      <td>581.000000</td>\n",
              "      <td>571.500000</td>\n",
              "      <td>574.700012</td>\n",
              "      <td>504.870117</td>\n",
              "      <td>4829804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10/07/07</td>\n",
              "      <td>575.000000</td>\n",
              "      <td>580.000000</td>\n",
              "      <td>568.599976</td>\n",
              "      <td>571.750000</td>\n",
              "      <td>502.278412</td>\n",
              "      <td>5321889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11/07/07</td>\n",
              "      <td>570.000000</td>\n",
              "      <td>594.400024</td>\n",
              "      <td>567.000000</td>\n",
              "      <td>590.750000</td>\n",
              "      <td>518.969910</td>\n",
              "      <td>8859610</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Date        Open        High  ...       Close   Adj Close    Volume\n",
              "0  05/07/07  526.599976  583.950012  ...  569.799988  500.565369  78025942\n",
              "1  06/07/07  568.000000  586.450012  ...  573.599976  503.903503  13724278\n",
              "2  09/07/07  574.099976  581.000000  ...  574.700012  504.870117   4829804\n",
              "3  10/07/07  575.000000  580.000000  ...  571.750000  502.278412   5321889\n",
              "4  11/07/07  570.000000  594.400024  ...  590.750000  518.969910   8859610\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jHvOxXEyRgWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "93cce706-af83-41ef-8fd6-d587157369d0"
      },
      "source": [
        "plt.figure(figsize=[10, 5])\n",
        "plt.plot(dlf.Open)\n",
        "plt.plot(dlf.Close)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fu-aJMpNRm7-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "outputId": "2071c1b0-90cf-4317-f614-0d33777fb1df"
      },
      "source": [
        "plt.figure(figsize=[10, 5])\n",
        "plt.plot(dlf.Open)\n",
        "plt.plot(dlf.High)\n",
        "plt.plot(dlf.Low)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9CFfsSttWBqXOIWoODj5gK6fvEhERkRFOgasPsWiE9q0MXDEbA8AxDo6FqLoURURERiUFrj7EYhHaBjjYvbNlq7NLEQM+DDHlLRERkVFJgasPsWiULQS36topZVMAOHHSiThARC1cIiIio9Kg3lIcDZatb+K/Be5bitcecS1rW9f2+9qaghrmzpgLwG/5sVq4RERERikFrj4sjG1mbq77VuKh9Ydu9X3cMVzdCLVALAq5xVt9bxEREcluClx9CPhjGbmPD0O0u8H3v6l1/714S0aeIyIiItlHY7j6ECUzgcuhmxaucFtG7i0iIiLZTYGrDx2xcEbu43TTwhXbvCIj9xYREZHspsDVh45YJCP3cTBpLVzRkFq4RERERgMFrj6ErRuT7jnugUHdx2dJ65yMRjMT5kRERCS7KXD1IeK1S+UHBj4XV+cEqOC2cEW6dilGM9NdKSIiItlNgasPIa+FK9ff/8Blunkb0QdEuxTHIhFOqa3hV+Vlg6miiIiIZDkFrj5EvPUQg77+B67klq1ODoZY50zzmz5yz4uGWZAT5J7iokHXU0RERLKXAlcfOrsUBxK4cv25APidxDRnPgzxEVtX7QVLniPS0ZSpaoqIiEgW08SnfYh4Q90DTqDf11y030XUFdYxvX56vKzr4tUr3n6GyrlXw4QGAGwshnGUf0VERLZH+g3fh87Aldxa1ZeSnBK+tc+38Dm+eJk7LUQicc1Zuo5w0n4oHMpAbUVERCQbKXD1IWJj+G33A+EHouug+ZhxUt5aDIc6BnV/ERERyV4KXJ2Wvw4tG9KKI7iBa7CMcVJauMAQTtqNKHCJiIhstxS4ADqa4PqjWHHdaWmHwtiMBK4tBZNTWrgiJkAkKYCFwwpcIiIi2ysFLoCOZgDqN89OO5QX3UQgEy1c/jyacN9etECubePXFWPix9XCJSIisv1S4AIItXRf3rqRqImRZwe/BI/f8bM6GOOxgnz2nNjAFrOW5/Pz4scjrVsG/QwRERHJTgpcAM/9tvvy1o2EjSHA4Ju4HG+A/IVVFQBscFamHI82rxn0M0RERCQ7KXABkZVvdV/e0UKHMeTEBh+4SvNTJ07Nj25OPf7KZaC1FUVERLZLClzAf2LT+f2YUu7Iq04pj3S08G5ODh+bwa91OCY/N2U/x6aO2fJvnAdzbhr0c0RERCT7aKZ5IGYtt5UUQwmcmVS+uXkD6/0+oIcxXgPgmNRs6zOp48JCxsCWFYN+joiIiGQftXABhTndzPK+4UMiD87I2DOeX/F8yv4n/tSsGzKGxk3rMvY8ERERyR4KXEBjftIi0tYbr7XmfbfVCRiXO2nQz9jckTpmq9lbN3GnvH0BuKashA/X6U1FERGR7ZECFxCOtQMwJhqFcJtbGMiPr3V4SPXpg37Gt/f5dsp+W2eYK54IwINFhcxy1g/6OSIiIpJ9FLiADi9w+a2FcCsAG+/9Tnytw9xAbo/X9ldlXmXK/kaf+6kfVzIhXtZi2wb9HBEREck+ClxAWeMcAAIW1qx311Nsjq7mjLoaAGzMN+hn+JzUe2zy9uuLxsXL/FF1KYqIiGyPFLgiHbR4bxAGrCXqLfPzQtIs8G3djKkfqJ5auMYWJqaiiJGBB4mIiEjW0bQQoRZavAHsfizlT30Hmr5KNOmUM/eZNujHHFh7YMr+Ou8txaqCxHqKjm2DWAwc5WAREZHtyaB+sxtjSo0x9xpjPjDGzDfGHGiMGWOMecoYs8j7t8w71xhjrjbGLDbGvGuMmZqZD2Fwwm2N8damCIacdXPhoe8QMYlzdhgzdsieXxxMtKS1OwYWPz1kzxIREZHhMdimlKuAx621OwN7AfOBi4BnrLWTgWe8fYBjgcnef+cBfxvkszMi3NbEKq+1qXMaCCA+YH6oFeQmlvxZ4/MRvf+b2+S5IiIisu1sdeAyxpQAhwLXA1hrQ9bazcAJwM3eaTcDJ3rbJwC3WNerQKkxZuiajvop3NrIJ353AHtHUsjq3K7qstxPpuX7E4Hrw2CALRQN6fNERERk2xtMC9dEYB1wozHmLWPMP40xBUC1tfYT75zVQGdiqQOWJ12/witLYYw5zxgz2xgze926oZ95PdS6hZVeC9cGv491PoeP/X7yvAWrbzz6xiF9vt/n58wJP3Wf7/OzNpj2KREREZERbjCByw9MBf5mrd0Hd8HBi5JPsNZawA7kptbamdbaadbaaZWVlX1fMEgfNy2nLWmQ+uEN9Rw3rpYmx8GJOTSUjOvl6sz46fQz+fGnfkzEwPrNWk9RRERkezOYwLUCWGGtfc3bvxc3gK3p7Cr0/l3rHV8JJKeXeq9sWNkV/+22fLXfR05k8BOeJvv8Dp/v8djYQrd3NT9nFYRaM/pcERERGV5bHbistauB5caYnbyiI4B5wINA56rPM4AHvO0HgXO8txUPALYkdT0OD2vJWfp4t4dW+v2UB8d0e2xr/eaQ38S3dyzZMeXYLmN2AeAfpcW0bF7T7fWxaJRYNNrtMREREcleg52H69vA7caYILAE+DJuiLvbGHMusAw4zTv3UeCzwGKg1Tt3eEVDKW8mJpuXE2Syr2pIHluVX8Vtn72NLaHEzPLV+e5QtybHYeXajUypmph2nXPpGN7J25+9fvzkkNRLREREhsagApe19m2gu1lBj+jmXAucP5jnZZw/h4vH/Ai4hTN2uIA7l1wdPxQxhrUbKzL+yPtPuJ/y3HIKg4UUBgvj5T7HR357CeXRT7DhbtZUbHEXtt6r7bX0YyIiIpLVRv1M8x/l3gJAXf4krHUwJhY/duJeh2T8eTuW7tjjMRML0OY4REPdBK5Hf5TxuoiIiMi2oTVkPJ+etENa2e51E7ZtJWJB2owh0tGSfqxdC1uLiIiMVKM+cAUi9RQxhfEl4zGxnJRjFQWFPVw1NCZVjqHVMcS6aeFqio76L5WIiMiINep/i1tiBHCD1dWHXZ9yrKZ42wauMflFtBmn28D1yvp8zh5bzU8rysEOaGozERERGWYKXETwGXco26d32CPlWK4vs/Nw9aUgWMhav6/bLkXHGN7NzeGhogLaW5u2ab1ERERkcBS4iOEYX7fH8gP527QuYwtqCRvD5tDGtGOBpMH8kTdvhzvP3pZVExERkUEY9W8pWqLxFq6ugr5gt+VDpaLAnfcrFG1PO7bJScw+X/iMt4KStdDDPGIiIiKSPUZ9Cxem5xaubS0v6LaohSPpgev+/G4m5Y90DHWVREREJANGfeByW7iyI3Dl5xQAEO6mhavIdvOlCnUzfYSIiIhknVEfuOghcF2wzwXbvCa5wTwAotFQ2rHiaDcD5UMaPC8iIjISjPrAlUst1QU1aeVf2/Nr27wuAW/M2E7rHko7VhRJn/g0+uzvh7xOg9bRBK3pLwGIiIiMJqM+cL3xlfuZeXx2LJtTGHDn/WpyDNz31cSBmYdREEu8pdg5C5fv3X9tw9ptpav2hj+kL8QtIiIymoz6wJVNqvLdtxTX+n0w957EgVVvkZc02WkUCJMIXlmtdf1w10BERGTYKXBlkc5pKMKkT/UQSSpa7/MxdWIDN5YUbauqiYiIyCAocGURv+POBxbpZm6t5BB2ep075uzPY8q2TcVERERkUBS4sojfm4A12s1cpisCiclZN/qyYxoLERER6R8FriwSb+HC8EFsXLx8USDAvGAPs953aGoIERGRbDfql/bJJp3zgV09phTDZnb2yr9QP7bHa0Id7QRzNJZLREQkmylwdXHpwZdSX1g/LM/2OYmuwqvGlPLVPtZKPLi1jfb2doLF26J2WyGSPoGriIjIaKTA1cWJk04c7irEhUPtBAI9L6Dd7DhEwlm8nmKoebhrICIikhUUuLJY+/plzHvybz2OtGt2DJFsbkXqaBzuGoiIiGQFDZrPUqXRKCsfv4K9lt3U4zlNjkM0nM2BK2lAvx0R07SKiIgMCQWuLHNEwxGA+4XZefldacd/ceAv4ttNWd6laNsbeSo/j5V+H8Siw10dERGRYaPAlWWu/PSVHBfcnZYeBssfPeHo+Hab4xAKtW+rqg3Yuk1r+H51Jd+oroJYZLirIyIiMmwUuLJQkeOnw3EIe/s5SQtXF/gLACj3lQDQEsreebhWbVkJuEsR2Vi4j7NFRES2XwpcWchWTgOg1XGIAB1O4svkc3zMnTGX40oOAdw3GbNVyBs03+RzCIfVwiUiIqOXAlcWKsgvB2CDz+GiyvJ4+R4Ve8S3A75cAELRLApckQ54/CfQtgm2rCDavDJ+KBTK3rFmIiIiQ03TQmSh4qA7c/z1JcU8Ueh2IX5v75/w5T3PjJ8T8Lvzc5n172/7Cvbk7nNg4eMQDcMb11EYDIK30HYom9+mFBERGWJq4cpCJTluyHqwqDBeVhQsxCQNpA/6cgConj9z21auJ7GYG7YA3rgOgHBSfUMhjeESEZHRS4ErCxUE09dGLAjmp+wHvRauMGb4plx4/o+w8El3O9yCBV7KyyUK/G91JVeOKYmfGlYLl4iIjGLqUsxCRcGCtLLCLmW51h2EHjZANARO3tBVaPV7UFABRTWp5c9e5v578RboaObFvFzOr6niuxs38VJ+an1CHRrDJSIio5dauLJQXUl6C1dhlxauaMlkwO22Cw/V5KehVnj/fvj7wfCnnXo91XY0scHnLr595ZiytOPtHdk7fYWIiMhQUwtXFqrML08rKwoWpu7nu6EsbAwdHR0E8tMuGbw/7dTzeojR1GkeQq2N+Ol5+Z6Wts2ZrJmIiMiIMugWLmOMzxjzljHmYW9/ojHmNWPMYmPMXcaYoFee4+0v9o5PGOyzt1eFwUJ2bU8dZF7YJVGNKysGoM0YQh1DNDVE17C1+OnEdriFF/JyWRLwQyxKuLWRLY6vx1u1tytwiYjI6JWJLsXvAPOT9n8P/NlaOwnYBJzrlZ8LbPLK/+ydJz0ojqXu53cJXAEnAMD5NVXYlW9ukzq1L5ud2Am1cn5NFSfU19L20euE2jbz+/L0rsRd8nd0rw1t2SZ1FBERyUaDClzGmHrgOOCf3r4BDgfu9U65GTjR2z7B28c7foQxPSwYKAS69M51DVx+J9EbHFr60pDVwwLv5gRZEAjw2PKkHuhQS3wz75ZjsA+fm34x8Kkx+wLQ0d0YLmthzbxMVldERCQrDbaF60rgQqCzPaYc2Gyt7RzgswKo87brgOUA3vEt3vnSjRxSs2hni1anSNJi0Hd+MHQD0p/Nz+Ps2hpOqR9LLNSaOBBuSTmv0df9t9KE4h0A6Ag1px2LvXMX/O1AWt9/NHMVFhERyUJbHbiMMZ8D1lpr52SwPhhjzjPGzDbGzF63bl0mbz2itJuSXo+PKx4X346Fh2581IeBRNDLafw4vh1qSx3ftTSQGgh/feCf+b8D/o/CPLebsWHxddgHL6Bp8wZev/ZcQoueY+mCtwFY8OwdQ1X9dDcfDxeXYFs3bbtniojIqDeYFq6DgeONMR8Bd+J2JV4FlBpjOvue6oHOBfVWAuMAvOMlwIauN7XWzrTWTrPWTqusrBxE9Ua2JXn79nq8OFgc3841rb2cOThjoolJVZ32RABuaUkElqvKSvhOderX6tBxn+K0nU4jv8BtxGw3BvPmzbx7z6/Zb929BG8/gfc3vsEeExso3vIohLfRmpBLn2ej42D+MAHaNa5MRES2ja0OXNban1hr6621E4AzgFnW2rOBZ4FTvNNmAA942w96+3jHZ1lre55HYJRzfP2f56E9mN5dlyk2qWfz1YJEPm5Omubhn6XprXEluW4gLMp1p69o94brjd/0SvycdyMfAvBKXh6blr2XuUr34vbiQqaPr2e538e6jz/YJs8UEREZiolPfwx83xizGHeM1vVe+fVAuVf+feCiIXj2dsPvzqYBQFE3S/0kay5oG7J6hJPGkq0JJJ7T07xaJ47/Mt/e59vxdR9Lct3g2OF4+23z+f2YUjY7DnkxN2//vryMtZu3QWvT6rlcV+KGw6/XVLHyrSeH/pkiIiJkKHBZa5+z1n7O215ird3PWjvJWnuqtbbDK2/39id5x5dk4tnbq4DjBq6xHMVLZ/T+FqLjG7qFoUNJL5JGTKJ7sb29+4H6UysP4Lw9z4vvF+W4geu5vDzCwO0lRdxWUswh42Qs+IAAACAASURBVOuZn5MIlV9+9wLOm3lwhmufatmi94l4H87yQIDQohuH9HkiIiKdtLRPloritibtUF5BX7NnRBm6haHDPQSutlD3gSs/mJuyn+PLAeDJwgKmTmzg9dzE8dfyEtslMcNHztC2ctnNy9jiS0zOWmoaCc1/fEifKSIiAgpcWcsat9Uqxyns40wIM3QtXC1OInCFncRsrG09rI2Y589J2S8IpC66/UZeaiADeO2s12iIlWJ7WRpo0CIhPpp3ZUrRxwE/Cx66AjZ8OHTPFRERQYEra1UWusGkrCDQx5kAIegYmoHzLSbxLRIxUXeyUiDkzcPl2NTB/fmB1MCVPEFrdypieeQH8jE4xIZwGtzmRS+yNOi2bl30qZ8D8L3qSjpib8M1U4fuwSIiIihwZa1JVW7L1o6VPbdwvXj6i+TFcogaoG3jkNQjuYWrxQc0rYa1H9DhBa6gKU45P+jv33roxVE3mB3lzhSCMYZYbxcMUkfj2vibkmfsfEq8/LyaKi4uH0N769BNHisiIqLAlaUcr2Wpt5kzSnNL8UWDRIDYLSelLi6dIa1O4lukyXHgsR/BX/encPWLAOxUPjHlfJ/peQHrZEW57nXtVZ8GwMEMZYcibavf4a9lpQD4HB83HXMT4I5Ru6+4kObNaVPCiYiIZIwCV5YK+NyuxL665CIxh4gxOBsXw20nZ7QOb+UEebog0WXY5DjYRU8B0OG1Fu1ctisAPtx61pf0vFrT1YddG9/eoaoagOm77wGAMc6QBq4X16XOubVvderEsq3NQzdbv4iISP/6f2Sb+/qeXycai3LylN5DVFFODo2xocnN59TWpOxHjMFE3BnhG72Wr9N3PoWG0jGcMuWUtAW2u9q9cpf49vf3/S5luSX8T/0BAD2O4WraspH8P08iuvPxBM+4aas/lo2RzdDLcLjGpvVbfW8REZG+qIUrSxUECvjRp34Un1ahJ+tYyxt5uWx0hvBLad1uwuRlflb5/TgWdigbyzm7ndNn2AIYkzsmvj2pbBK//p9fxz8+g9PtGK53H52JjyjBD/4DL/xxqz+EttbVANz4me7n3trSoi5FEREZOgpc24nV/v6NndoaedRyaMG++JLGky0JBqiKFuBz+v/c3s41pvvAFXZymRsM8mBhAcy6DCIDmHNs7Qfwhx1hzfvcXO42b02tSbyReM/n7+GoMe5kq02tauESEZGho8C1nejoY3LUwWgzywkU1NKS1Iq2yXHIoaCXqwbGMU7Kuo2dCiKbOKuuhp9VumPDwm2Nvd6nfeGzbL77W2AtHa/fwNqOTWx5+YaU53TaeczOHFAxDYDmDo3hEhGRoaPAtZ0I9xS4Ni6BWb+Oz5+1Nb4w+QvkBMtodRz2mNjAV2uq2OJzaGrtvbuz028P+S2/PPCXvZ7T2cJlYzFYOx9e/TsAy1sTg92vKS2heUsv01+smUfuv06kdN6ttM+9n/lzb+SIhjpu/PhtiqNRDrI7pl0ypqwegI7NH8Hr10F06CaRFRGR0UuD5ke4ncp2YcGm+T0Hrvu+CivnwB6nQOVOA7r3pFCUxUEf5+15Hre++2C8/LW8XIqjUWy090W1O31uh8/Ft3cv351PN3w67RzH+IhZCIc68N/6BZymVVBSz3uxVfFzZpaV8IXmzZT18Bz7j0NYEvDzdk4OJ//7S8wvcucwW+lfSqOvgKpgVdo1NeVu4Kpb/RAsvZvGthDF08/v18clIiLSXwpcI9xP9/s5M544m0iPZ3hBrG3TgO9dEYkSMkXUFdYxrix1gtNGn4+xgdIB3/OOz93RbbmTU0Ksw+C/YgqrYi3MLcjn2LvOxpaXQW4i2LUsexV23q/bezydG+D71ZUA5FjLbyrcQfofB9xv8zElk9OuKQi63aJNPsMz+XnE3pvPUdMH/GGJiIj0Sl2KI1x+IAi4UzYA0Lw2pfswFHCDkn3mVxBqGdC9w8bg875FmkPpSwcdtdOUralyt/yFlcQAJ9TIt6orubCqgkMa6vgokDqXg3/2JbBpWbf36AxbAD+pqohvz8txuz532OHAtGuq8t1Wr5klJXy3upLXjdZVFBGRzFPgGuGCPjdwxbsUL58ML10VP76w0X0z0Cx7Cfvsbwd074gBn9dClutPX3S6MGfgLVw9KSyvi7+luNHn1nmzz8fsvFwmthRzWN1RAPy5rBSu2pPoqnfS7rFDHy8wfm5KeuDqXFz7o6Ab7JbE9LaiiIhkngLXCNc5E/2Pqipo9kJX7Pk/xI+/ZWOcX11JGFi4cu2A7h3F4rfut8hZO5+Vdjw30PM6jwO1svVDrDHsMbGBmkg05djulftx7p7nAPCcN/P9nPcXpN2jOBZhYih1PrApBW4rXH1s335NYbE8p22r6i8iItIbBa4RLnnpn0cL3dYaJ5zoOvxX0XJeyM9jcTDA6raBzdUVNgafN41CwBfg5mNu5oQJn48fzwtmblqI8rzEkkDzc4LUhBPfmjmFO7JX5V7kWrdr8OqyEsLh9qSKtsPsG2k1MfLIJdfn1uuOox/iwPEHAXD4rqlL+SQ7uO7g+HapDUNHMxvuPJ9Vr9yVkY9NREREgWuE6+wSA2hyurypGGqlLuTOL3VLSTEhXy9r23QjYsBPIqRNrZ7KiZO/EN/Pyy3u7rKtcvFBF/Oz/X8W31/TmlgGqL5id4wxnFR3NADXlZawvsVr4YpG4NfV8PB3afI55Dr53H/ifTx44oPsXjOB4ye5AfHknY/r8dkXTrswvh0y0PyPz/DPtQ/z0QsXZOzjExGR0U2Ba4QrDvYQeqyFxy4kL+aOjHq4sIB3/CsGdO+wIT5ovlNJXkl8Oz+DLVyOcTh24rHx/cnV49ipxG2VOnsfd73Fg3Y+On68ebM7cL5j8QvMDQYJ4S6uneMrpK6wjoklEwGYUjaFuTPmskPJDj0+uyI/McD+w2CQLY0fcFtJMV+vSZ9GQrZCy3pic25h3T+OxzatGe7aiIgMC00LMcKZpPm3bi0u5twtTQBEwx343rqVnMpEV127bR3QvSOYtMBVmDRuKz+QPpB+MIqCiekfztxnf45sOJKmUFN8wH5DUX38+PzQMri4hA1+H2eNq0vUOTLwcWX5/tRxX8ck3Q9rYQhn8d/uRcPwxx1xgDJgxRXTGffLD/q6SkRku6MWru3IBr+PJmPY4DiE2t3B361Jy/E4kX7Moh7pcP+1lrABv0kd91WQ1KpVEMxs4EpedufkySdTmlvKuOJx8bLkcV4PFLpdpR/7U/9mKK7ca8DP9Znux7aVRqO0N2lR660RmfcwoXu+Ruz2UwF4OyfIPhMbmB+I9nGliMj2SYFrO/CV3b8S3/58fS2Hja8n3NHG2zlBXszPix8ztrvloZOsXwSXVcG794CNETYGv0n9FinwJweuvK53GLSnT3maWafOSmm561SSU8IbZ78BwE4dIWaMreLu4tTZ7r9/2BfSrutLd88CaDOGjauW9nxhpIOmvx9F2yM/6/mcUcp/99kE378bZ8mzPJ+Xy4yx1QC8UNzzFL0iI5618M5dsGXlcNdEspC6FLcD39v3e9zwnrtA8wa/21oTbtnA0i6ThkZsH60La95z/513PzSvdgOXTW39SZ5aoWgIAld1QXWvx3P9uezV3sE7uYl1HOtDfh796pt80vIJtYW1g3q+z/j40q5foXX1J9yx4WHa5lwHO3+q23Mb37iD5Rvfomj9m4w7+hcYf//WlhwN/q+8gvuL86mKRFib1AoZMoZYuAMnoM+VbH9aF79I4D/n8XHRVHb8wbPDXR3JMmrh2k7lzP4HxbHUFq0IfbRwdfpwFjz5c8KYtC7FZGV5mRs0PxCNkdTB7OMjTRhjBh22AN4+522+O+0CasrdQfbOR/f1uPD3Ox+/xhl1YzluXC3NmzQYvJNd+CT3F7vj4tZ26fJd6/PR0tTLAuQiI9Tm568lfMfxTJ3YwIVFq+DFK4a7SiPHh7PgH4fCxl56FLYDClzbidMnnJGyv6ZwF5bG6lPKov0NXGF3cH3YgM/03Aia4w8OrJIZ4hTvlrK/snhSxp8xvtqdMPWHlRVEn/plt6FredPH8e2WzesyXod+i4TYcMs5NL7/5PDVoVPzOtrvOK3Hw6v9ftpWL+oxxIqMVKEXrmJR0P2ZuDAnCM9cAqGBvag0KoXb2Xz7yVwYWcHGv0xNjCPeDilwbScOrts/ZX9TWwumS8CK9RG41jYlvtGjgDUGn5Pe9XNI3SFbX9EMmL7zPin7O9dOzfgzJoxxw+rCnCC+l6+iZV36+o1NocQyQE2r52e8Dv3V8dGrlC95gPfv/9Ow1aFTy5JXeMYbN1jklKQdX+P3UXn354m+e29ibc+OZpj/0LaspkjGLQq08eWxiSERfy4rIfbMJcNYo5Ghdc6/OGR8PY8VFjB9fD2Rj14e7ioNGQWu7UReIHVqg46OJiyJVoRgrO/AtXJDY3y7c23GklD6W3p/OeIvvPXFtwZT3UE5btLhAOxQvCO5vjwu/p/BD1q/fPrl3HHcHfH9irzE3Fx7TGyg8dGfwsUl0J74HDVHE9tFz12QuVabaAQeOJ/Yg99xp1XowzOvPMBXa6r4MNLHYpLbwJpnfx9fOPyAqvQXGCLG0GwMvv98FX5TS+N/Z9J+3zfgrv9HdN2ibV1dkcywlltLUv84vaG0hOfnPz9MFRo5Xlz8dMp+5PaThqkmQ0+BazsR6DJgu6OjhZhJBCwffXcp+qOJ5XI6f82vL0sfMO4YJ2VJoW1tStkU3v7i2/znxH/z/OnPpcy2v7WOnnA0u1fsHt8vDhYzvmhifN+//DG2OA4r5r/qFkTDzMpNBKw1fh+N7z3e6zPsxqVwcQntL/2t9/OWv4p96zba3rqZtW891mfdF4bm8VpeLm9UDNEbgJEOmhf9l/bGvhf2fiia6GYeVzgh5Viht+TSIePrWe73My8YwP/Mj1nw8ctcWFnOm/PmZrTaIttKbO69lEfdl5JO3jGx7uyLTj+HcYxir7cuw7GwM+4faqv8A1uCbiRR4NpO+Lss21O79imeLU0EAh+GGL23wPijiYWbn/YWiSanLHOVzCCf48MxDvldWvYyxRjDT/e/KL5/ZVkp/zO+nua3b3QLGlfycTDxOf9aTRWvvJL4S23zf68jPD8pLL3xT9Zduy8v5OWS+1TivmnmPQB3nMUfxpRywIRxzFnyTu8V3fQRtLkrCBSaIZjjKhaFy6oovP04Vlx1VO/nrp3PluAn8d0Td0usX3nlYVfy3Z2/BUDUGD47rpbT68bym/JSflnu57HCApatfzvz9c+UeQ/CHWcOdy0kC7UtmMXG+8/jqYJ8xrTtyLf3PY+gNxQj5GunuVEvifRmU7SZ4qjD/lQCcEL94F9+ylYKXNuJoC91AHtuxyreS2qB8dm+W7icqDuGa7XPxy+8Ger9QxRoRoIDag/gmOojAHiwyJ3B/rVNCwHYtMZdy7HWqQGgzXF4v302dDQR3biM0qd/SOCuM9gw+z6IRYk+8gMuqRjD+TVVvJGbA609/BC++xw6Qo3cVuIu2bSmvY/5fK7ai5jXtZkTbRrUx9utVYmu40nRJT2fZy389QAidFAWjfLISY8wsXQ8X9v9G/zzMzdyxPgjqB0zIe2yl/LyWO53g2tLa98taMPm7i/Cgkf1Nqqk2LBsPm/efybfrKmizXH40r7nUp5XzqtnvUJZ1IcxTRReMbHvG41WW1aw2YQojgWJxpKGRHSO79zOKHBtJ/y+1C6+NX4fE71+wVuPvbVfLVyBSAshiE9SCRDwjd75khzjcHjdkSlll5eHYPNyFrz5LwCOrjo+fmyLWQ+/rce5ek8+Wz+Wgxrq4dGvEnn6Uo4ZV8sL3mDyZ/PzaJz3VLfPfC4vj09NSMyuX7HmgV7ruDTg59U8d8Z/S1PPQW4rtV5/JAc11HNhZTm9tZ+F13/IuTVV/KeokCbHoaG4AYAL9v0m+4+dBkDdmPS/XNf7fYS8Rdc7wo1px7PFFWXl7DGxgZULvQB6ywnw+nXDWykZXuF2ym88gF9UjGF+jvsH78QKN1wFfAFy8dHiOESBlo+zuPV2GMTWfEDkkjH8+7r9WOu3FJkCkt9NXPOXI3u8diRT4NpOBLoErnW+XCaGoCLiZ++qvfFhsL3+yoTyVU/z46oKVgUS9wp26aocbcqKUuf8qg1H4MrdaV3xBAATxu4ZP7bF185bOUHezMlheSBAk8/hsPH1LH3vCVYnzUd1a0kxsxe8mv6wjUv5dk1lStHKgB+aVsNLV3f7uvTx9bXxH/ZbfD5WPnTZVn+s3XkjN4cmn8NjhQX8rjy9e3nVU9ew+epDafrbp3jdC36RHn6slOSkvrX49d2/m7LfEWnOUK0z78ZSd/xZ20rv67bkOXj0h4Q+mTd8lZJhFfnTrlxaXpYy19xeNeOSzsjhqYJ8jh5XS+4N0wldsSdcXIJdk/ieaVy1mC0bh3FKmW1t1dvQtolXbzqGfSbU8cvKcpYFAuQ7BWysTrxt/kF4CbZ5+/u8KHBtJ5JngAfY5MslbCL4beeX2MHpI3AtDC1PjN3ydO2qHG3GFKYGoFUBPyfV1fCdard85+opXHvEtQA8U5DPObU1fKk2dbb8V1iedt+WT+5MLWhaA1fvnXbeAl8hax66mIdf/i3LXvl36sGLUwPMUwX5vLAms/P+fLc68fG/kJfn/sBMUvvSz4ltnsv08e40Gp+pPpX7T/xPt/dKDlx7VuzJV/Y8k2/s8QPqC91fUuFoFs5Z1LIeLinD772B6pv/D2KP/zR++J07Lx6mislwe9rXlra0WFle4nt8jXX/AFnj97P3xAaudzZxxLhaXn3hdveEptXkzdyXwqsnsfbJAUySunoubQ/+sF9vMGeVWAxmToffT+DrNamftxZfHbUVid89TxXk88YLj27rGg45Ba7tRNcFmJ8sMIRMBL/3P73pR+B6Kye9+9Dfy8Sno0F5QWl8e8aELwKwOJgIoTtXVnNo/aEcXfu5tGsPq3YnAL18TOIeF+x+KQBLuiy7FNu41B3blWRCNJ8tgRg3bn6Hn1RV8PR7NyYONq3m5dz0xcNn+XsZZ9WNjg+eZMuce90da1n/h33cNymf+BXgTuMAsEMkyGq/j/DM6e4PzkVPgbWcVlsTD1sAlx7+I3Ys3bHbZ/kdP3NnzGXujLncftzt5Afy+ebUL3H35+8CIBhePaC6bwtt7z0MNhb/PKwPWGa9ewNXlZWwx8QG2kLPj7xffKNVNOy+BDJIkdbNbLr72/yoqqLX82L+1Baav5aVstbvZ8Gqx7DhdvjTThw3rpa9JzZwzcJr+jetTCRE7O//w/vzbuHDt54bxEex7TW9NJNXc3N4JTf998xHdhXhWKIF/4GiQnJW9P2G9kiz1YHLGDPOGPOsMWaeMeZ9Y8x3vPIxxpinjDGLvH/LvHJjjLnaGLPYGPOuMSbzs1WOYl0D19u5QRodh0jUHTdkjQ8/YVjTQxdINEKLk1jEuZQ9gJ4Xdh4tinMK49sH7zg95diUsik4jvu/0Bl7nJx27a+mXwC4E8h2OmtP902/f5YmtU41r6X9pmP4ijd27sicw5k7Yy4FMUOHsSw17lxopS3vxS9pWvYOXx+b2t0JUBVtpmn9in5/fOvvPYOnn72ARXf9FNo2UdC2lOkNdVy66EYa1yxjWluYhnA+5O1CzBgurhhD06wr4PZTaH38l/HuTIDfHHQl+Vuxvmae371mha+ZxjUfDfj6obRhzXKak75+vy0v43vVlfGv3/k1Vax6/8Xhqp70l7VE/7IfHXd+adC3mnfH93ht2b3x/UdOeoRZp87isS/0LyC8Emwk9rvxvJSXyyded+T9RYVEN6+ADR/2eu3H93ybb1RX8uWx1Xzw2u9Yfv8ImVi1fQuxZ3/C18ZWc573c+6EmvO4dvqtABxeeyLH7XBcyiXzm17c7v6YGUwLVwT4gbV2V+AA4HxjzK7ARcAz1trJwDPePsCxwGTvv/OA3icjkgFJ7lLcxY5jUijkBq6Q+9dExOQSA8IfvdL9DSLttHrh4c7P3kfnt8bojlsQcAIct8NxXHbwZUwsGx8vf/P/vcm9n0/80N23el++sdc3uOXYW+Jlyd0LnTrnDMuPxdhy3fHw7/OIXT6ZU+tq4ueM9eav2tCRT6tj+CDH/aG83u9zW5eAWYsTv+RPqP0WX9/z6wBM6/iITX85nPX3/sAdL7JiTvya7hwzro6LK8v595rbiVw+hf0mjGOjz8eDRYUsfvcZmh1Lgc1hqre6wDu5ObS+8hu+X1XB+jnXxu+zU+mefH7yEX18NrvXOafbI4UFzJubXRNFzgmt5sCklxg+8ae3+K6YMxPC7Wnlkj06VrzNT/xbuHv1s7Rt/CT9hLbN7ooH/XBb+/Px1q0jze40FDdQmV9JfVF9t+dfftANXDg1EYxezs/jgPoK/rcm9Q8m56rd4ZqpbLn+JFj+erf3urDxv7zsvXzjb3mfcW9fkbkJl2MxePRHsPaDzNwvScd9/8vptYmfcbuPmcaPps/g0Al78/IZr/PrI7/C3lV7M3fGXP79efdFoV9XjKHthaszXpfhtNWBy1r7ibX2TW+7CZgP1AEnADd7p90MnOhtnwDcYl2vAqXGmLFbXXNJkdzClevLp90YmhyDibmBa3OshScLC9jw5I+6v0E0xJ3eeITdKqckHRjtkQt+d8jvOGHSCVTmVTKheALfnfpdAr5ASuufMYZv7v1N9qnah11LDqAmdwIA1bkNaff7VLuPVsdh9bqXib57F9+sruTjpC7G3RrcgfjtTiWLgkE2+tyv7Wqfj5bnr3S3mz6Kn79r1c6ctpPbffmLynLeKWjirhX38JlxtZh/Hs6iR6/s/gNLCgm3lRSzz/jU/x3Xzb2WLT6HApPDzw45F581LAsEOLKhjqcK8vmJN3XI93f7E/eecHt/PpV9Knz9h33+lb8tvRla1ec5uaufof3mL2RVvSXV/Jf+ymOFBfyhvIwP3/NefFgxBxY8DheXEPr9eKK/revfvZKGFETydujz/CN3nMrxU47g6AlHx8vanfRfvU8U5LPv+HE8vvl13rj9c7BuYcpx27Ke93MT1/2wupKfVJYT3dLH1DH9EPnoZfhVGbw+E/66f/oJrRtTVtkYqA3r17gvAAGzTp3FHZ+/kZJcd+qbopy8eE8BwOQxic/pO69sX+O4MjKGyxgzAdgHeA2ottZ2/gmxGugcQVwHKaOHV3hlkgHJLVy5wQJaHYdmx6G2eAwA1uf2j/+jqLrb6224rdtyjBYZ7uRzfDx00kOcu8e5vZ53xwn/4LFT3YHjt33uev7vgP9LOb4k4AaVH1RWsvfEBl7KT+2Gqy53W9JOn3Zs6nXBAAXPXwKbPiKw/jkAKnNrOXWPgwg6iV8CP62q4O9lJXzi93NqbQ0bPngi5T62aTVcXEL0t/XkddP6deoUN7w9H9zEJ34/NcFi/I6fHH9hynnv5uaQG4Mv7dvHhKj98O293UlRFwYDfPz8LX2cve3Eon3P3n92bQ0PbnmH2DVTtSh3NoqEWLzykfju0jVz3Nasfx5Oy51nsMfEBvad2MBptTXpXVhLnneX2upkLflJX+OG2p16fOwPp/2Q06achs/xUZJTwuXTLyc3aX3RTxV/lrkz5vKnQ/4CwI+qKgg5hssqxvCVsdXYa5NW+WhcRePfj2VKR+ryXQ8XFrD4rUG2CluL/6ZjscC3qyp4pMuLU7F37oI/TIRrtn4U0ApvqMH42DlU5lf2cTaUOe4b0ZdUpi8tN5INOnAZYwqB+4DvWmtTIrC11kIfkz+l3+88Y8xsY8zsdeu2v9dCh0ry4PbCUrdbKGYMk8pTv7k7TPdvHXa0d31DzP2yqX1r4JKXPqopqIm3PnUKOW4I3ujr/n+/XSu9N/4mHZRS/k5ODk/k57HyX99kld9PQSzGrNOfIOAEyPWnD6AH+CAnyIvBBfCrClj2MjR+wuzbL+aJ/DwW+t0JWz9XdHTKNRft92Ow8FCR2/15tDcW7Yf7fS/t/mV5dRkZ53faTqcD8MvKcp5b9nRGBjdngk36ZTulbAq7lu/Ka2e9xm7lu3HhtB/Hj11aMYYvj62i9cOXhqOa0pN1C7CXVXFJRXm8qH7xP7CPX8RKv48nk8LFwpwgzZdVYjcsgViMjplH0XrrCYQvLU9MydLRxPqk/2+/PPWzPT56xm4z+L8DU//Yumj/78e3Z57wawAOadiv2+u/V1VBy61n0zrrj3DFLhQ1LWRhTpDJTaldkR+v6X0N0vCaD4g09fK7NNTM96oq2HNiA88V5HNRl5cBnP+cx7xggKWhTe7zHruCtZdOomPR8/3+A+PdZrel+OdHfaZf5+M1+K8IZsfPgUwZVOAyxgRww9bt1trOd9bXdHYVev+u9cpXAsmTlNR7ZSmstTOttdOstdMqK/tOwuJyTOJLWVOa+OFSlJs6d1IB3Y81aWnvMn7Bur9ER/ug+aHgM+5Pk6ZuAtdpE79NftANTxOKJ8TLf3/wb4kaww+rK5nd9jZ3FRfRkRSyuwtc+da9frnfRzgWhhuPxV6xM5sa7+eH1ZWcVud2IR62y3Fcc7g7HmuP0oPdqUCSvuyHjXdfFjh1yqk8cMIDHDX+KF44/QVOnnwy937h7q3/RCQpzimOb38YWEXz3f9LdPGzGbn3YBRG3MDlGId7P38vdx53J/mBfO783J2cMPn4lHPfzM1lwX0XDEc1pQcL5z/O3hNSx1adU1vDi2sWc8y4uviKGjU57qS8B04Yx+K/7we/KuP70aXsP2EcUyc2sHiO10rcvpm2pJ+1yYvc98fJU77AzcfczE3H3BT/o6zzpZFOlXnu771nCvKZtXoWb79xOet8Dl/0Bpvn1O6V0mre3rGp5wdGQgT+tj8fXHOKu99NQAptWZM2HZBtSaz68J/CAk6vG8vx9bV88Owd/GvR1ZxWG2T9XSex/snL+AINVQAAIABJREFU+/6g1y2kMbgRx8LUsbv0fT5wYO2BAEzpUOAC3LcOgeuB+dba5ElEHgRmeNszgAeSys/x3lY8ANiS1PUog5TcpViSm/ifp6BL8+09JQEi3izpKZ5Lfdvl5wf/gECknjP2mJ5+rgxKTpfZ+0/KP5I/Tf8zX9r1K/zfoefFy4O+IBftdxHHTDiGqd5s7QA/935JHFTd81/XAI+edhs7dFTwbEE+Uyc2sMfEBvac2MAPqlO/J6ZPPpDDxh3KX4/4KzOPdX+AGtzvp59NvSrl3B1Kd+CKw66gLLeMiw+6mOJgMZngGAfHG4f476JCHln5KL7bTuzjqgx5+Rp4baa7veFDWDE7fijmzX4/5+w5GGNS/gApDhZTGkjtol/pfEKkpZdfgLJNPbvuXWLe16w2nPjj89Fw6vioe09K/OFwZm01y/2++MoQAC9+8CAA7WsX0eIYDvXtx7vnvLtVdZpaPZV9q/dNKavKd1ut7jjuDp459RlOHe++9fzTqgq+XlPF4Q31vOtNp/DHo37IaTudxoXTLgQgFOrm+y3U4naFtm/mirJSFgUXYW87BS4pZenDf0wJXvOXp34cdeEI5o/e1C4dzVxfmvh/3Hn5F9xeUsQGv49jxtXxtw/6/oMrOvPTzMsJMj7s7/e8jpccdAk1sXwsMex29KbiYFq4Dga+CBxujHnb+++zwO+Ao4wxi4AjvX2AR4ElwGLgOuCbg3i2dJE8aP69DYnpA0qLUpdTiRqD/8FvYGMxbNL4HbPMHQdwcqG7pMLRk/fhzXMfo6YoOxevHmmO3/F4/nDoHwC48OAZKcdMwU58ZsKR/OBT6V12Z+9yNn+c/kfKc8sp6DKG6qxdz07Zv+HoG/jW3t/itbNe45GTHqE8vwR/zuRe6/X1XX4ebx07pP4QCoPuM5459Sn+ffy/OWOPwwf2gQ7CG2cn3sy6rGIMl5SX0fLnTxFZv4TIliH82+zJn8NjP6Ltnv91x6n884j4L6RItJlgzKQtndXpxbOeZu6Mubz5xTcBuKaslPnPbOUYtGjYnWhVMqY9qeW+uCIxP9wjhQXx7Uv2v5ySnJJ4gMq1ls+Oc4cXF/ndOfQ2tq/Ctm1mwX1nEDOGCfUHZbT1f5+qfQAozy3HGMNJu6VPM9Op3vuZvlvFbgBsSPp539HWTKSjDX5TC5eWw+WTubG0mF9UlnP/6pfZY+L/b++8w6Oq0gb+OzOTyaRM6iQkJCEJRWpAEBARkCYdKcKCivoB1tW1ra6w6i7ggoJ8uhZU1rbYAEVQ1w9XBQXpCkhHFBFUWiiBCCSkzPn+uDczE2ZSISSQ9/c88+TOOWfuPfPm3rnvPW+rx47tT6OfbuZxgj+9/X0AukZ0pqkjg71BNqbGRJH72yY49gunfL7nS9HFfT53BR/j5Pt3wKoZ8MPn/pPdMp9tKo81IQ5O06Lc8nDYHKTZEvndqsg++Eu5PxeQwz/C/NtrRCTx2UQpLtdaK611S631peZrodb6iNa6h9a6kda6p9b6qDlea63v0lo30FpnaK3XlnUMofz4mhQ7myH8ADHh/kvepxWcmpjIrsnGU5b+5AHyzIsqLCjcb7xw9kzuNJm+6YYTfK/0rkRZvIpQ0+Syl9mDrEF8MGhesbaMhOJFcdsltOP2VrcTGhTqqWV4f4/RAffXOOIy3uz7Jne3HxGwPy40jkbRpStr5xq71c7CoQs9cRrzIpy8aM3E8kJrbM808f9AeR3Us3bD7hUljl/rCOaD8DD0tjlsswex2hFM7tHfQGuOWPJw6rKfyoMshpl4X5CNrO1T0BOjK+5A//ZQeKoBFOSVPVYoFyHHdnq2p3efQFKeNxK3dWh7Xu/9OkObGD6MSinST1s5bvU+vL7S+2UAcguOkv1UGqPM1AY3txt6Tuc5qeMkZvacSWK4Mb+MuAy/MSMbjmHFdV4fwbTINAA2O72Fnq1PpmB7IoFXIiPoXC+JeU6vYllkPn0o3sX+nIOwaAIAJ/cZpvvLE7oyvJWh6L0dGUHQq5058cUT5ClFS9tlpOXjZ3pcG+Jg60/zeX/lZL768CZvh6nc5K2YweMuw2f10V7+D5SlEWGP5KjFSlb22flyu5c9DZvmcHrD3LPaz7lAMs1fhAy/ZDho40fDFeqfCypXWQhTuTQoNLKSq7WvcYuZRNMRHOU3Xjj3xIZ7f9T7NvYv6ROIpPAkJnb0mn4jgsNKGW3QOKahZ/vToZ/yP81uBaCRK9HzVF2TSHGmkOj2/qi/GRlBq/R6fpFTnP4dJkZxaHEZeXryTnH4hdYceXMAhcufDThkdGIdJsTFcnlaCiOSErk1sQ57dn0POVn8ZrMQT/lWeUOtxsPKXQnx/CMmksxdFShYXFgAP38NQG4gJ+hyREteUGht/A+rkhOHCMo7CMCKEStJjUjlYLC3uHvn9MG0S2hX7CMhbu91GWwNobmrOSFuRb4+SKdUrwtyfNi5XfkPDQqlY1LxIJn/XvtfXr3aW13ixubXFTPhF/l+LQm3U3j6FLgL2Rtk4fmoSJ6LieKY1VosWMCXRaGhcPp3cl7tz8RYQyHqeEkneqZ5V7QvTa/H2r1fcNxqpWFCC3Isgb/z2MQ6THLFck+dOHJzTpL3/ecwuQ4nf1zG4rCGbA+2c4mjN51TKxblmBycSJ5FcXj/t2UPzvwevg+cQuLHggiejInmy3Xl2E8VIwrXRYhSCrRhAolyOP36T5+xFP5YrIs9RXmgzKSbQtXyTPdpPH7l4ywbscyvqHNpDG00lEkdJ/HPbiXk1jqDuNA4Fg9fzMabNpLsTKZ7qrH62TSmfM6r1YHd6h8sMy7eRV6e15fDnX2AtyOcnFgxFYDcL6fy+44lxT5zYs937PtsOt3qJdM1NZlTX07wP1gJSWGPHN1D/tFfOWSzEmEPfNM6kyc7e11Z34tw8tOuAAXKwfATO/pz8bbs3/hTvIuM9Hps3WokJ9YnDxs5ov49GB6P5ejPFVDgaiq52RSsnEHhxChW/LMhRz4aX2Wmnvxje/k5KIgwtxunWTFiROPhnv5hLTr5fSYiyOtfOfNqIzd3jkXzgdO78v/5tQFMZ1VAUngS7RO9vl5JEcWvC4fVGyiTu2sFev9GHnfF8K/okn9P/tXzdQCCtIbN77E0az1HbFbq4qJ+bF2iHdG809db5/VPCcYxh7TozRUN+wDwUOtJrB21lo8GfeS3f8fUutjnDGe3zcaWhS+y+7Sh8I7vFHilvTQizaVux6ZXyx784uUw57qAXUsPfcY7kU4+o2Jlz6oCUbguUm5t/Bj2gjSSI/xvFh/5+C/kbXiPDyO8qwdN69b1Gy+ce9Ij0xnccDBRjoqvKA5pNIQe9cqf1T0+NN5jcm4d35r3BrzHqGajKnzc88Uph6EEjWtdPALq6O6N8OFdkJPFtu8/YWpsNBPjnOAuZPG6f7LhwxHkT6mHO9NwiC544xpOb/KuanVMSzGy7/tmFM89FnAO2Qe/4/CBnWRZrUSHlC8/c7SjeLRZ8Nq/B05v8Xwbo1C5j8kx79BOlpirePnr/wonMtny6TT+HBfLhgMr+d4exIr5L5RrHjUOrSnYMJf9z/ciZ2o9Zq9+gkvT63FHQjz/3v0uu2YM9ozj1NHS91UBcvduYYEznBzsHn+rh9o9yJfDv2TzzZuJdviv2PwebCgYLksCbeL9V2Tm9J/rMfudD5RSPNxuHPe1/kuxwKiiviLC5g5Dv9KNDT6ltm5r8Sdj43RdNt+8mW9u+IbmcUbesCmuGBaHhngy5l8ZN8TzuZbxzYutpAO0im/BY1c+yCdDPuGmlkMItgZTP6o+U658sti4f8RGsyLEwcCUuiwv3MSRvAMoDS3rVNw9wV33SgD2J3YscUzeq31hglfB9HOwP/4bRwqMa7z+6S1UN6JwXaTc03EQ68b+B3uAUiTP+RRTtn94KxafH/5mcal+44WLi6axTYv5/NU0hjY2aqoNadqZdnFdPO3Rs3vAhrdhzUzUMiOHUZbVip5/G+PiXfwxIR5r3nE2fjmXgsM/szKsgEfOMKnckhDPqhe8ZhOd443wWjpiKc90NkyUOZkLyNxppAKIiyo7mzhAgfamkAB4PSqC7F1nmDEK83krwsmsCCdHtn/tad6w3VuqaXdQEExvxMFdb/J5eBhjE+swPCmRLdYlF2RiVff+zRz7+A5+ObmB9mkpTIv1KjpvRTqJP76avNf6w8QomJbOr7NuI3PJyxU+ji7IQxcpuAV57Ft8HwA27b0hWy3WUhNvWiIM14r+6bd4FJpom9F2Xcr9NHc1q/C8zpZRzW5gbMsbA/b1iDVSk6x0OPhndBSnzYztjZytubP1GDq4BjLlqkcAwwTpm6/xPp9o5dFXFvflHNpoKC/3eM3zXimF3WonNaL4/aFTckfSw1uQZDFkNDfC6SlZtDDSzRF9iJiCIOy28kUn+uI0U8XYD6yEd0f6Dzh5GPtvKwF4xBXDffEuDm0snuT52PKXeDfSsPJ8ERoKG6vXj6vm/uoKVUqGmSbgCzP0+Sp7O+YMmEPdcFnhEqqXO1vdyZrr1xAaFMrr/WYwNtG4GWSbN5NthwvINZUajWLNzv94PtsqvR5hPz1L1ktteDjexWYzlP7Rdk8AsCbEwV2x3iS/WVlGQsY+9k7EOGJoGGsEIvwtLpYdZnbypNjyPZ3HhhjK3cjGxs1haWgI9nf6eM2W7kLyHncxLTaa6bHRrP/OLHbsdrN9p9f/ZLIrhqkxUUyJLb4C8250CJm7q/8pvaIcOvAT3VKTuSWxeAqNNGc6hUpxfx0XS4+so3tKXUYnxDP3+Gd8sGEy2ZvLVwyagtOwZxULn+nE509fDYUF6F/XMN18sJzRq3zmd4CoUMO1omWyV/bPX/1PWkZ35K6OJUcOVhfJrjQAbk+M5w0zfcO/e7/FB0NmYbPYeKX/FAY29j60BFmD/PaxYuQqUiL9K5BcmdyeUU1HMbnT5BKPH+2I5uNrZ9M51N9FIdNmY2uwnWjlX96sPEQ4DEuMzjkIP3wKe9dBfo4nGvLkzhUMTEokI70eHzvDWRwWyhdLbmf/8rc9+/jgoDdoYrc9iMIFt1GdBI51Fi5IHuvwGI1jSi41ATAwrhf/OeT1QXjAfMpJCmtI89jmVTo/QSgPFmUhNMhr5o6Nqgf7oU9yEgNPnOSqI1ksDzf67di59Ywb+T114jx12wBCc6MZ0WwAYcEWxi9/mAKl0HknUWtfZ98RQ+GKDTOiz9Ii02hsS2JHwV5PdFXTOuW7YaRHprNo2CLiQuN493sj19198S6mZP5GTEI99JGdzI7w+lSuP7WZqwG9dCrHbAcB70rM25GB85tt+24h8en+EWw1mbU7vy72Pjo4lk51uzC580RavtmS1SEhrA4xHvwO2WysDTF8kwoXPcDdGX399leMo7vgudZoYFx6PSCHiKfq8oPdzurYaJw58XRILl9QCsC9be7lwKkDxZzpW8Vn8M41M8u9j/PJ74X+tT7b1GlVYsoK3xUugBe6v0BEcMmR6Q+3f7jEvmLzcDaGE/4lhvbbbPR3Vi7aOSLYuMb3BNkgB3ilO4WpnbHuWQaj5rP652/ZbS+uQD4ZG0PY8gkMTm9Kzo9f48xaDuZ1DNA9OY2v3O5itRvPJ7LCdRHxh8Z/oFVcq1LH1IsIbB6JcEodcaFmEhdjmDHyLIoPIsKJPr6E90zFpX7+MZyFxR3ffZWta1L/h5cHGUlNBzToRxdLUyIKC8mfUpfDi/7Gia2G2SQx3luw/e0RH3q2rRqax3vzN5VFnbA6WJSFSVc8DsCK0BCOZh+BvFOoGe2Z7rNq5c47jvvkUQqWPsmrUZE4Ci0kh/ub9D8a9BEvdn8RgJ37lhl1AM8Gt5uste/71w2sInZlbQRgypXTmNZlGl+PXMKULpNQShEXEl/i52bG2Nix+v9K7Ac4OKOtJ6FvEbcl1vHI+ZbL7q7QXBvHNGb+NfMD+nfVRPrWL16W68ZL7io1P5hv34YbN3BVyrlJbH3aJ/jk+ibXM6qRV+7JMZcE+kiZFEVh+l4z7FnGT0E2I4XKD4Gd6ZdEaA6/1pNfVk72PDQtHGKYGo8GuXFXo1VeFK5awgOXPcCtGbcSHRFYsdL2wEWtBaG6SYwonktuocNb0PaExcLvVguN8/xXZ5eOWMrkrn+mdYI3h9dhdxTZViuXpdejW2qyZ3Uso553FcRhcxBdaChtMYXBfpUBykOvdG9B70MnMtm+YBzDzBxOAGFuRaH7N3Km12eVuaITbE/g02s/YWrnqQxu4I2mqx9Vn1Z1jAep/IKNHJuawqmsA+WbSGFBcb+vwgL2TWvD6q/uYcP7kyr8vcqNu5DszZ9y8qlm7CrcQ6hbM6BBH08uuiLeGzgXlz0Rq4+xpVtKN2Ksxo1y5op/lXiI3IM/8UpUyZUOXrn6dUa3PU/VCqqJDokdeKbrM4RaDfPp6JaBI/XOZEyLMX5O+GeDzWbUmmwfNYLxl48nyO7NDda0XuWKXjeI9fpfLg4NISO9HlNioxmcXJfbEuLYbjf8wuwWw/S4bMQyki3JLA4LpVtqMsOSvPe6lIi6PHL5I3xwzQfYSqhhez4Qk2ItYXQLIyz3813exHnPdn2We5fcC0DPJuc3yaUglBdXaPFIzm9DDAXI6XawzLQ8RkY147tr32bz4c28s+1dnuzyhKdWnS9dWrRn25ZVfu0ZdRoWe9+ibjeWHfyCy+sOq9Scw4LCiHfHkWk5RG7uCZ4/+iU7Qo0bRKPI5hw9toe5EZq5PibGBUPeAaBf/X70q9+PPPdJOiR2APDkX3oxOooXo6P4ZNYfSL3va0pFa3g8lrzLbsE+8H8BOLDxI3onFgIuHDnz+fLw7ThdyaXvp6JoDZNimB/h5K0YJ5m2UELd7oArL64QF5/94RNO5J+g69xuXBbbnee6P8MnP33C+OXjibVvDHyMwnyOvNGfuYn+aW/ua/1nmrouoUPddgE+ePHRM7UnV6Vcxd7f9xIXVnaKmc03bz7nc2ibnMp/98K1GUYai3y3d/X0qrTSrS4lEemIpKFKYqfe63HwL1rZXhUSwirTDP31H5YSFmz8JtzR/j4eXf2gZx+hlmie6vYPAEY2CeB4f54RhauWkeKTJqJ7anfaxLdhfeZ6Lokpv9lEEM4nkfbiN5Gd5pPt6FZ/5LnNRu6r5wb9GZvFRuv41qUmdL2j9Whe3mKkinixyzxcTis7juzxcyZ+ofd0juYerXBxYl+uS7yKZw/OQx/ayrJQQ9nomzaA8Zf/hcEfDvEzDcaFFT/W1C5TS9z31vwfSdUaSjEfnd40n0KlsK5/FQZMh8M/cPCzO8Bcacu1WJj7Wj9uebhyNQF9Kdy/haxZ1+DKPYK2O5kWE1XMD+1UKT4zdqudGGsM629c5ylR1q9+P8YvH09MYYC0Gtn72DX7egYlGv+zIWmj2XbsWx5sdz8d6rY/6+9yIRJkCfJknq8Ohl0yjMTwRK5INItORxtmxJk9ZwZ88Ckvcc527MzeW2J/rKWJR9kC6N3gKt77sS2/nviZwfVH8ED7Oyt97KpAFK5aRpTDuHkV2cdn9Z1VndMRhDIJC/LmjVPuELQlB4AbMkbw3OansWgHYfays+6DkRbg8cuf5dNdi+icbgSYNHU19BtnUZazUrYAgu3G8ptz+wuQWIcu0X2YdpURLTm961OM+WwMzaMup0FMCtc2KNsU9KdmD/H8tqcAeDjeRdKEDJr8ZRnBYdFGJGT+SQg2V3wK88n9aCyd0ozs6Ct+Xs/h2b0YlWxEIccGpXAk/1eeTdD0eW0kyWPn+B+wIA/KEc5/YPEMflo7iTsS44Ewup485ckpVkRLV9mrHL43ZouyYNWa/AAK5dLZf+L+4MOA0Texy/3ntK6hUHGsFitdkr3RkIMbDibDlUHDaP9rqyKkuuqxyij5SJ3QRA6e8tZUre9swbxBxeuWOmwO3hnwBjUVUbhqGUWJNse2GFvNMxGE8uF7M20QncrO498DRjmUN3q9RbSj/Jn6AQY36c7gJlVflDvYbjzUPG2mJ+jTxOu/1C6hXYVNO40TGsA27/tR9RVfPl0f17h9sPAh1Hdv4W54NWrQDFTeCfone1O82N7qwaA0b2ma9we/xf2f3c3G7C2MK1jPq1n70Ps2YnPnEtRiMFlfTMf2zfME3/0N9qgEAuIuZO/TXXg0PJO1CV7n9yJl687m4xjYuAvLflvGgAYDKvRdAQqV4uuQEO4pWsnTGrJ2syxvHflmBY1J7WeIslUDUUqdtbIFEOP0OuMvGv45Ww9vZfLqafRL78eo5oHrwNZklK7BifTatm2r166VGtfnmgJ3AVZllR8q4YLhH8tnsGj3EuYNfYkJqybQpk4bxrQYU93TKpX/fPMaf93uzQG14roVxWrhVZRCdyFzvp9Lr7Sr6f6+V2H8v6wYDuZuZ4wZAND0dB7T7W3or7w5uyYdOuIpXjy+zTSuz+jLjqM7GPYfw0dtXsElfHt8DXttVkZkn2BRWCjPxkTxtLULV4+aEXA+m17qwZ3B+8m2Bna+3nTTprP6jcmYZaS/2PDzL/xoDyI9P5+DVhsPxLvYbXfwSr9/18h6oMK5Iys3iy5zjZWzqvA9qwqUUuu01m0D9ckKVy3kbGzqglAdPNrpLh7tdBcAL/S4MErcOBzFzWpno2yBYba5odn1gKHMtHyzJQCv2n5hgU8usu3Bdu7K+w58chQVKVujG43nejO3VYMor9/mjZbvyTHD7319r7IPLCAn88+ExJvpZNyFMMmIILwhvR5gKFs31n+IMZf1JzI4gg2HNlDgLjhnD3SXpvvnQesT2kmUrVpAUXqODNeFlXuuJCQthCAIQhUQEuT1f4rWTUoZWXGUUszoYaw8LfAprPxQa6MG3pkJIYu4t4PXDGOz2Pj2BqP0UM4ZTu0hylC6JsTFsmudT7mU09lstdu5OdFrQkwOaseDV47CFRpLkDWIdgntuKLuFWfx7cqmVXqfKt2/UHNYO2rtReNrLAqXIAhCFZBj8a7wfDzi3DvyOu3FUyI80X4GN7Uc6nlv1ZpZvb03qoHp1/rlXnLYHIxuNrpYmw0bq0Z5U04s+e1zyNwOv34Lp39nZFIC6x0OT/+C4S9VSebuIoUS4KVubxQzKQ1uWf7i7cKFTbA1mCBL4AeICw2xLQmCIFQBnRr0hdV/x+FOISqk5PIplSXDlcHoFqPpnNSZS+Mv9dyUnuv8Evcsu5NCpTxBMgATrvxrwP3c3eZuVu7+loyo9vyt532Gj6fFysrrVtJxdkf0yXV8/UZXkgoKSLl5sedzf233OEMb961UYtjy0CW5C4uHLyb7dLbHAbtFTFuy844Rbg8t49OCUPMQhUsQBKEKCLGFsGDAf8uVjLIy2Cw2HrjsAb/2zmkdYBlYsHlWwWIdLuzWwCke7FY784bP9rwvyknmtDsJcStmRnvnv2DhRAiG/uF9ua5Z1Wdxjw+NJz7Ua76cPbDmhvwLQlmIwiUIglBFNIxNOu/HtFlszOw5k4SwBFwhLsa2GMs1Da+p1L7axrZjWdY3nvf/yt8IwWFEONPO0WwFofYgaSEEQRCEgBzOOcyuY7vYt3cdj2190dM+veOr9G50eTXOTBBqJpIWQhAEQagwrhAXrhAX37rdsNVoS1IZ9GwQ8H4iCEIpiMIlCIIglEpMmFE8OMWZwsKh71bzbAThwkTSQgiCIAilEhdqKFy9UntV80wE4cJFVrgEQRCEUomwR7BsxDK/3F+CIJQfUbgEQRCEMvHN6SUIQsURk6IgCIIgCEIVIwqXIAiCIAhCFSMKlyAIgiAIQhUjCpcgCIIgCEIVIwqXIAiCIAhCFSMKlyAIgiAIQhUjCpcgCIIgCEIVIwqXIAiCIAhCFSMKlyAIgiAIQhUjCpcgCIIgCEIVo7TW1T2HElFKHQL2nIdDuYDD5+E4Fxsit8ohcqs8IrvKIXKrHCK3ylGb5ZaqtY4L1FGjFa7zhVJqrda6bXXP40JD5FY5RG6VR2RXOURulUPkVjlEboERk6IgCIIgCEIVIwqXIAiCIAhCFSMKl8G/qnsCFygit8ohcqs8IrvKIXKrHCK3yiFyC4D4cAmCIAiCIFQxssIlCIIgCIJQxdRqhUsp1UcptUMptVMpNa6651PTUErtVkptVkptUEqtNdtilFJfKKV+NP9Gm+1KKfWcKctNSqk21Tv784tS6nWlVKZSaotPW4VlpZS62Rz/o1Lq5ur4LueTEuQ2QSm11zzvNiil+vn0jTfltkMp1dunvVZdy0qpFKXUV0qpbUqprUqpe812OedKoRS5yTlXBkoph1LqG6XURlN2E832dKXUGlMOc5VSdrM92Hy/0+xP89lXQJle9Gita+ULsAI/AfUBO7ARaFbd86pJL2A34DqjbRowztweB0w1t/sBnwIK6ACsqe75n2dZdQHaAFsqKysgBthl/o02t6Or+7tVg9wmAA8GGNvMvE6DgXTz+rXWxmsZSATamNtO4AdTPnLOVU5ucs6VLTsFhJvbQcAa81x6Dxhptr8M3Glu/xF42dweCcwtTabV/f3Ox6s2r3C1B3ZqrXdprfOAOcCgap7ThcAgYJa5PQsY7NP+pjZYDUQppRKrY4LVgdb6a+DoGc0VlVVv4Aut9VGtdRbwBdCn6mdffZQgt5IYBMzRWp/WWv8M7MS4jmvdtay13q+1Xm9u/w5sB5KQc65USpFbScg5Z2KeOyfMt0HmSwPdgXlm+5nnXNG5OA/ooZRSlCzTi57arHAlAb/6vP+N0i+82ogGPldKrVNK3Wa21dFa7ze3DwB1zG2Rpz8VlZXI0Mvdpunr9SKzGCK3gJimmtYYKw5yzpWTM+QGcs6ViVLKqpTaAGRiKOc/Ace01gXmEF85eGRk9h8HYqmlsoParXAJZdNJa90G6AvcpZTq4tupjfVhCXMtByKrCvES0AC4FNgP/G/1TqfmopQKBz57GSwgAAACGklEQVQA7tNaZ/v2yTlXMgHkJudcOdBaF2qtLwWSMValmlTzlC4oarPCtRdI8XmfbLYJJlrrvebfTGABxgV2sMhUaP7NNIeLPP2pqKxEhoDW+qD5w+4GXsFrbhC5+aCUCsJQGt7RWs83m+WcK4NAcpNzrmJorY8BXwFXYJinbWaXrxw8MjL7I4Ej1GLZ1WaF61ugkRlhYcdw6vu4mudUY1BKhSmlnEXbQC9gC4aMiiKZbgY+Mrc/Bm4yo6E6AMd9TBu1lYrK6jOgl1Iq2jRp9DLbahVn+P4NwTjvwJDbSDP6KR1oBHxDLbyWTV+Y14DtWuunfbrknCuFkuQm51zZKKXilFJR5nYIcDWGD9xXwDBz2JnnXNG5OAz40lx1LUmmFz/V7bVfnS+MyJ0fMOzQj1T3fGrSCyP6ZqP52lokHwwb/GLgR2AREGO2K2CGKcvNQNvq/g7nWV6zMUwR+Rg+CWMrIytgDIYT6U5gdHV/r2qS21umXDZh/Dgn+ox/xJTbDqCvT3utupaBThjmwk3ABvPVT865SstNzrmyZdcS+M6U0Rbgb2Z7fQyFaSfwPhBstjvM9zvN/vplyfRif0mmeUEQBEEQhCqmNpsUBUEQBEEQzguicAmCIAiCIFQxonAJgiAIgiBUMaJwCYIgCIIgVDGicAmCIAiCIFQxonAJgiAIgiBUMaJwCYIgCIIgVDGicAmCIAiCIFQx/w8ksbjIWdaoagAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BQ0OElloR4OI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "bdf082f4-cc3e-40d5-8302-a059ce13e267"
      },
      "source": [
        "sns.heatmap(dlf.loc[:, [\"Open\", \"High\", \"Low\", \"Close\"]].corr(), annot = True, cmap = \"YlGnBu\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc7ea15c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eMwm25LS83t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b8f939c0-c9df-46ec-92e1-3163ae1becc9"
      },
      "source": [
        "dlf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3242, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PreZYfRUTXAM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "76072b19-bb8a-4b1a-859c-c3fb9bb7aa7d"
      },
      "source": [
        "dlf.Close.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    3242.000000\n",
              "mean      250.064374\n",
              "std       169.890604\n",
              "min        80.800003\n",
              "25%       155.899994\n",
              "50%       199.125000\n",
              "75%       259.337502\n",
              "max      1208.150024\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONyefkJoTZpr"
      },
      "source": [
        "x = dlf.drop([\"Date\", \"Close\", \"Adj Close\", \"Volume\"], axis = 1)\n",
        "tgt= dlf.Close"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u9X5WX1RTg0S"
      },
      "source": [
        "xtrain, xtest, ytrain, ytest = train_test_split(x, tgt, test_size = 0.10, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ib2bOn87UkGe"
      },
      "source": [
        "rf1 = RandomForestRegressor()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1OTBRa73Urlj"
      },
      "source": [
        "pred = rf1.fit(xtrain,ytrain).predict(xtest) # training & testing"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jMe4gtFGUyS8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7aa1341e-1ed2-4fef-8c78-cb0c05654224"
      },
      "source": [
        "print(\"R2Score: \", r2_score(ytest, pred))\n",
        "print(\"RMSE: \", rmse(ytest, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2Score:  0.9990511607057045\n",
            "RMSE:  4.7843856327981005\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MfV0_-W6bibV"
      },
      "source": [
        "names = [\"Open\", \"High\", \"Low\"]\n",
        "price = [156, 158.35, 154.40]\n",
        "\n",
        "test = pd.DataFrame(data = [price], columns = names)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbz1TQk-bn9W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1351663e-9297-4078-b643-51d214cc31a8"
      },
      "source": [
        "# Predicting the CLose Price for DLF\n",
        "rf1.predict(test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([156.13149994])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_4GXLhqW0YX"
      },
      "source": [
        "trial = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/trial2.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQFcke76ZyM5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "882559ab-973a-4442-efd7-1f4c62c2c645"
      },
      "source": [
        "trial"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>149.90</td>\n",
              "      <td>156.90</td>\n",
              "      <td>149.15</td>\n",
              "      <td>154.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>151.20</td>\n",
              "      <td>153.25</td>\n",
              "      <td>147.50</td>\n",
              "      <td>150.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>149.00</td>\n",
              "      <td>151.35</td>\n",
              "      <td>145.05</td>\n",
              "      <td>150.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>152.75</td>\n",
              "      <td>153.90</td>\n",
              "      <td>149.25</td>\n",
              "      <td>150.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>139.00</td>\n",
              "      <td>141.00</td>\n",
              "      <td>136.60</td>\n",
              "      <td>138.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>125.85</td>\n",
              "      <td>134.20</td>\n",
              "      <td>123.80</td>\n",
              "      <td>126.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Open    High     Low   Close\n",
              "0  149.90  156.90  149.15  154.30\n",
              "1  151.20  153.25  147.50  150.20\n",
              "2  149.00  151.35  145.05  150.10\n",
              "3  152.75  153.90  149.25  150.25\n",
              "4  139.00  141.00  136.60  138.60\n",
              "5  125.85  134.20  123.80  126.15"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IggJScIQW_j7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "c7dc11e7-cbd3-4042-81c5-afc8a95383a3"
      },
      "source": [
        "# Predicted\n",
        "rf1.predict(trial.drop(\"Close\",axis = 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([154.47900234, 149.89749835, 149.04600396, 151.30050138,\n",
              "       138.80900444, 127.45750165])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YbtNOGybfR0t"
      },
      "source": [
        "# xgb.predict(trial.drop(\"Close\",axis = 1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SrcMCTehYpet"
      },
      "source": [
        "#nifty = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/Bank Nifty.csv\")\n",
        "sbi = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/SBIndia.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcFpwqpflIC-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cb5c2bd6-21b0-4eb2-b24a-ff0f44668eca"
      },
      "source": [
        "nifty.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2899</th>\n",
              "      <td>07/09/20</td>\n",
              "      <td>23036.25000</td>\n",
              "      <td>23109.09961</td>\n",
              "      <td>22716.25000</td>\n",
              "      <td>22945.05078</td>\n",
              "      <td>22945.05078</td>\n",
              "      <td>245300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2900</th>\n",
              "      <td>08/09/20</td>\n",
              "      <td>22971.19922</td>\n",
              "      <td>23064.15039</td>\n",
              "      <td>22641.40039</td>\n",
              "      <td>22744.40039</td>\n",
              "      <td>22744.40039</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2901</th>\n",
              "      <td>09/09/20</td>\n",
              "      <td>22486.69922</td>\n",
              "      <td>22554.59961</td>\n",
              "      <td>22080.40039</td>\n",
              "      <td>22267.00000</td>\n",
              "      <td>22267.00000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2902</th>\n",
              "      <td>10/09/20</td>\n",
              "      <td>22466.55078</td>\n",
              "      <td>22678.65039</td>\n",
              "      <td>22225.25000</td>\n",
              "      <td>22466.19922</td>\n",
              "      <td>22466.19922</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2903</th>\n",
              "      <td>11/09/20</td>\n",
              "      <td>22509.94922</td>\n",
              "      <td>22668.75000</td>\n",
              "      <td>22205.94922</td>\n",
              "      <td>22479.94922</td>\n",
              "      <td>22479.94922</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date         Open         High  ...        Close    Adj Close  Volume\n",
              "2899  07/09/20  23036.25000  23109.09961  ...  22945.05078  22945.05078  245300\n",
              "2900  08/09/20  22971.19922  23064.15039  ...  22744.40039  22744.40039       0\n",
              "2901  09/09/20  22486.69922  22554.59961  ...  22267.00000  22267.00000       0\n",
              "2902  10/09/20  22466.55078  22678.65039  ...  22466.19922  22466.19922       0\n",
              "2903  11/09/20  22509.94922  22668.75000  ...  22479.94922  22479.94922       0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MHUicC3_lMDR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f96a3356-03a1-41fd-a449-a54cda19666a"
      },
      "source": [
        "# Split the Data in Train and Test\n",
        "x = nifty.drop([\"Date\", \"Close\", \"Adj Close\", \"Volume\"], axis = 1)\n",
        "tgt= nifty.Close\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, tgt, test_size = 0.10, random_state = 0)\n",
        "\n",
        "rfn = RandomForestRegressor()\n",
        "pred = rfn.fit(xtrain,ytrain).predict(xtest) # training & testing\n",
        "print(\"R2Score: \", r2_score(ytest, pred))\n",
        "print(\"RMSE: \", rmse(ytest, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2Score:  0.9997724993936693\n",
            "RMSE:  119.30145593141356\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxMqOjkGlkrj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "140a8c8a-23a2-40f4-b4a2-ef6fa547590b"
      },
      "source": [
        "# Predicting the Test Set\n",
        "names = [\"Open\", \"High\", \"Low\"]\n",
        "price = [22683.35, 22757.30, 22370.55]\n",
        "\n",
        "test = pd.DataFrame(data = [price], columns = names)\n",
        "rfn.predict(test) # Close Price"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([22660.7649025])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ou4Whb28mdIr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "e7fad477-fa63-48cb-d1f4-c21ea2cbe3d9"
      },
      "source": [
        "z_nifty = (22330 - nifty.High.mean())/nifty.High.std()\n",
        "print(z_nifty)\n",
        "print(\"The Prob is: \", 1 - stats.norm.cdf(z_nifty))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8509175756700955\n",
            "The Prob is:  0.19740757043484758\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IULEFBEidXTs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1bf625cd-42db-4659-af05-a80781c387cc"
      },
      "source": [
        "# Preview of SBI\n",
        "\n",
        "# Tick by Tick Data \n",
        "# Candlestick Data\n",
        "# Volumes, Price, High & Low \n",
        "# Is Volume Live - yes, it is available...on an hourly basis\n",
        "# Deviation from Moving Avg - 2x of Moving Avg (40 Hours)\n",
        "# \n",
        "\n",
        "sbi.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6096</th>\n",
              "      <td>07/09/20</td>\n",
              "      <td>207.500000</td>\n",
              "      <td>209.649994</td>\n",
              "      <td>205.399994</td>\n",
              "      <td>207.899994</td>\n",
              "      <td>207.899994</td>\n",
              "      <td>35687359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6097</th>\n",
              "      <td>08/09/20</td>\n",
              "      <td>207.899994</td>\n",
              "      <td>208.500000</td>\n",
              "      <td>202.899994</td>\n",
              "      <td>204.050003</td>\n",
              "      <td>204.050003</td>\n",
              "      <td>34146789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6098</th>\n",
              "      <td>09/09/20</td>\n",
              "      <td>201.100006</td>\n",
              "      <td>201.399994</td>\n",
              "      <td>192.500000</td>\n",
              "      <td>194.850006</td>\n",
              "      <td>194.850006</td>\n",
              "      <td>72715826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6099</th>\n",
              "      <td>10/09/20</td>\n",
              "      <td>197.699997</td>\n",
              "      <td>201.449997</td>\n",
              "      <td>195.199997</td>\n",
              "      <td>198.149994</td>\n",
              "      <td>198.149994</td>\n",
              "      <td>67789617</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6100</th>\n",
              "      <td>11/09/20</td>\n",
              "      <td>198.550003</td>\n",
              "      <td>203.850006</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>202.699997</td>\n",
              "      <td>202.699997</td>\n",
              "      <td>48487971</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date        Open        High  ...       Close   Adj Close    Volume\n",
              "6096  07/09/20  207.500000  209.649994  ...  207.899994  207.899994  35687359\n",
              "6097  08/09/20  207.899994  208.500000  ...  204.050003  204.050003  34146789\n",
              "6098  09/09/20  201.100006  201.399994  ...  194.850006  194.850006  72715826\n",
              "6099  10/09/20  197.699997  201.449997  ...  198.149994  198.149994  67789617\n",
              "6100  11/09/20  198.550003  203.850006  ...  202.699997  202.699997  48487971\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qYCvIhIkdgzq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "423e89d1-9bfc-47ae-de48-6e6bdaa4a85a"
      },
      "source": [
        "plt.plot(sbi.Open)\n",
        "plt.plot(sbi.Close)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f39f024c550>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 173
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjcBvJA4cX8C"
      },
      "source": [
        "# Split the Data in Train and Test\n",
        "from sklearn.model_selection import train_test_split\n",
        "x = sbi.drop([\"Date\", \"Close\", \"Adj Close\", \"Volume\"], axis = 1)\n",
        "tgt= sbi.Close\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, tgt, test_size = 0.10, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PD41iWG7cTUI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "b55487c9-3ef6-4cfa-fb3a-e02c2a3853c7"
      },
      "source": [
        "rfsbi = RandomForestRegressor()\n",
        "pred = rfsbi.fit(xtrain,ytrain).predict(xtest) # training & testing\n",
        "print(\"R2Score: \", r2_score(ytest, pred))\n",
        "print(\"RMSE: \", rmse(ytest, pred))\n",
        "\n",
        "# S.Rate is > 60% & Avg Win is 2*Avg Loss : Metric of ML.\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2Score:  0.9997411121305015\n",
            "RMSE:  1.669874171061494\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wnrChBHRcsET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3dbd090f-1359-462b-f950-f024f99c0d6c"
      },
      "source": [
        "# Predicting the Test Set\n",
        "names = [\"Open\", \"High\", \"Low\"]\n",
        "price = [199.85, 200.05, 192]\n",
        "\n",
        "test = pd.DataFrame(data = [price], columns = names)\n",
        "rfsbi.predict(test) # Close Price: 199 | 198.65"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([194.92651132])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkrVnVMG-x3s",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "4512c56f-0754-411d-e2be-4f2630bed3f6"
      },
      "source": [
        "sbi.Close.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    6101.000000\n",
              "mean      141.256497\n",
              "std       105.745924\n",
              "min        13.346100\n",
              "25%        26.692200\n",
              "50%       143.962006\n",
              "75%       236.639999\n",
              "max       372.399994\n",
              "Name: Close, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RYN1ZR4A_5Ct",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "1d5c1025-5893-4e00-c80e-2f00787892bf"
      },
      "source": [
        "# Covariance between Nifty CLose and SBI Close\n",
        "np.cov(nifty.tail(2900)[\"Close\"], sbi.tail(2900)[\"Close\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[6.05619123e+07, 2.19399931e+05],\n",
              "       [2.19399931e+05, 2.90725458e+03]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 216
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsHWQw83ebty",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "7acb09f5-84dc-413c-fe3b-9cb191112aea"
      },
      "source": [
        "# Prob of SBI Price going above 206.20\n",
        "zsc_sbi = (206.20 - sbi.Close.mean())/sbi.Close.std()\n",
        "print(zsc_sbi)\n",
        "print(\"The Prob is: \", 1 - stats.norm.cdf(zsc_sbi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.6141466264361388\n",
            "The Prob is:  0.2695592196965809\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ubxl5IxYgLs4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "fcb1bd01-4a56-423a-8692-a79a83bb6644"
      },
      "source": [
        "zsc_sbi1 = (206.20 - sbi.High.mean())/sbi.High.std()\n",
        "print(\"The Prob is: \", stats.norm.cdf(zsc_sbi1) - stats.norm.cdf(zsc_sbi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The Prob is:  0.1480995391628125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EAa4UEgn4UXg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "026c641c-9851-4094-c697-29ebccaae1e5"
      },
      "source": [
        "# Low \n",
        "zsc_sbi = (200 - sbi.Low.mean())/sbi.Low.std()\n",
        "print(zsc_sbi)\n",
        "print(\"The Prob is: \", 1 - stats.norm.cdf(zsc_sbi))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.5823334944015618\n",
            "The Prob is:  0.2801710343037056\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "msmFPuR4dk8f"
      },
      "source": [
        "daawat = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/Daawat.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_apxrgJ2iZo5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "29901e61-06bb-40de-9f60-d587e979820f"
      },
      "source": [
        "daawat.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2006-12-19</td>\n",
              "      <td>5.300</td>\n",
              "      <td>5.785</td>\n",
              "      <td>5.015</td>\n",
              "      <td>5.390</td>\n",
              "      <td>0.599912</td>\n",
              "      <td>63443000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2006-12-20</td>\n",
              "      <td>5.365</td>\n",
              "      <td>5.530</td>\n",
              "      <td>5.175</td>\n",
              "      <td>5.205</td>\n",
              "      <td>0.579321</td>\n",
              "      <td>12663000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2006-12-21</td>\n",
              "      <td>5.225</td>\n",
              "      <td>5.255</td>\n",
              "      <td>5.000</td>\n",
              "      <td>5.030</td>\n",
              "      <td>0.559844</td>\n",
              "      <td>5408000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2006-12-22</td>\n",
              "      <td>5.075</td>\n",
              "      <td>5.090</td>\n",
              "      <td>4.830</td>\n",
              "      <td>4.885</td>\n",
              "      <td>0.543705</td>\n",
              "      <td>3637000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2006-12-26</td>\n",
              "      <td>4.800</td>\n",
              "      <td>4.955</td>\n",
              "      <td>4.590</td>\n",
              "      <td>4.645</td>\n",
              "      <td>0.516993</td>\n",
              "      <td>5168000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date   Open   High    Low  Close  Adj Close      Volume\n",
              "0  2006-12-19  5.300  5.785  5.015  5.390   0.599912  63443000.0\n",
              "1  2006-12-20  5.365  5.530  5.175  5.205   0.579321  12663000.0\n",
              "2  2006-12-21  5.225  5.255  5.000  5.030   0.559844   5408000.0\n",
              "3  2006-12-22  5.075  5.090  4.830  4.885   0.543705   3637000.0\n",
              "4  2006-12-26  4.800  4.955  4.590  4.645   0.516993   5168000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBLzyPI9ibMQ"
      },
      "source": [
        "daawat = daawat.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s5M5NpJe7mi3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "e83ab923-284f-437e-8546-6b73eb4fdb15"
      },
      "source": [
        "plt.plot(daawat.Open)\n",
        "plt.plot(daawat.Close)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f3a000fdc88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TMH3hzI1ieSm"
      },
      "source": [
        "# Train & test\n",
        "from sklearn.model_selection import train_test_split\n",
        "newx = daawat.drop([\"Date\", \"Close\", \"Adj Close\", \"Volume\"], axis = 1)\n",
        "tgt= daawat.Close\n",
        "\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(newx, tgt, test_size = 0.10, random_state = 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fE3YySBtixkF"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rfdaawat = RandomForestRegressor()\n",
        "pred = rfdaawat.fit(xtrain,ytrain).predict(xtest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rcKOh9s2jFy_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f19b0820-d12e-4923-9f8c-81c711fd0786"
      },
      "source": [
        "from sklearn.metrics import r2_score\n",
        "from statsmodels.tools.eval_measures import rmse\n",
        "print(\"R2Score: \", r2_score(ytest, pred))\n",
        "print(\"RMSE: \", rmse(ytest, pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R2Score:  0.9997176772734467\n",
            "RMSE:  0.4000622027333959\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFH_FZtKjKHl"
      },
      "source": [
        "trial = pd.read_csv(\"/gdrive/My Drive/Colab Notebooks/trial3.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SOBcgSc6kCVH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "b6cb16fa-4f49-4eed-fc65-80ce823e77e4"
      },
      "source": [
        "trial"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>56.20</td>\n",
              "      <td>57.80</td>\n",
              "      <td>54.00</td>\n",
              "      <td>57.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>56.70</td>\n",
              "      <td>56.80</td>\n",
              "      <td>55.70</td>\n",
              "      <td>56.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35.95</td>\n",
              "      <td>37.00</td>\n",
              "      <td>34.25</td>\n",
              "      <td>34.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>21.00</td>\n",
              "      <td>21.45</td>\n",
              "      <td>20.70</td>\n",
              "      <td>21.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>28.50</td>\n",
              "      <td>29.10</td>\n",
              "      <td>28.35</td>\n",
              "      <td>28.55</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Open   High    Low  Close\n",
              "0  56.20  57.80  54.00  57.20\n",
              "1  56.70  56.80  55.70  56.25\n",
              "2  35.95  37.00  34.25  34.70\n",
              "3  21.00  21.45  20.70  21.05\n",
              "4  28.50  29.10  28.35  28.55"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xi_5fnpSj7Ns",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "631af577-01c1-413e-8cba-55fcb39692a0"
      },
      "source": [
        "# Predicted\n",
        "rf2.predict(trial.drop(\"Close\",axis = 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([56.99050064, 55.65100018, 34.89025112, 21.09249935, 28.53004942])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XArFAxuQj979",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b4df60a1-59a0-48e0-f241-599d6dcf8b90"
      },
      "source": [
        "names = [\"Open\", \"High\", \"Low\"]\n",
        "price = [53.55, 55.20, 53.30]\n",
        "\n",
        "test = pd.DataFrame(data = [price], columns = names)\n",
        "rfdaawat.predict(test) # Close Price: 52.37"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([53.73549964])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9ngBgYW9bNh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "418096b3-83e0-45c0-e629-6de8a005e2fc"
      },
      "source": [
        "print(\"Mean: \", daawat.High.mean())\n",
        "print(\"SD: \", daawat.High.std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean:  20.581711727137765\n",
            "SD:  22.97103317749702\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QL0D-BdoAd12"
      },
      "source": [
        "# What is the probability that price would be in range of 53 to 55\n",
        "zscore1 = (51.28 - daawat.High.mean())/daawat.High.std()\n",
        "zscore2 = (54.70 - daawat.High.mean())/daawat.High.std()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B7xotqEeBdRN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b0148163-daf5-499c-89bb-f8965c3c033f"
      },
      "source": [
        "100*(1-stats.norm.cdf(zscore2))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.8735570874213"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ufa-OZ_tA-TF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "ab5c7cae-775b-448c-ff65-f41e1e422023"
      },
      "source": [
        "1-  stats.norm.cdf(zscore2) # This shows the probability of z score = 1.49"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.07654791038301256"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tv-x_2rPAwc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1dd141c7-f3cf-4943-8117-65967bce90f3"
      },
      "source": [
        "(stats.norm.cdf(zscore2) - stats.norm.cdf(zscore1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.014162798240189622"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yVkNQ6L_JDx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "7b3c09d5-cd66-4cf6-9d73-935e2305ee3d"
      },
      "source": [
        "daawat.High.plot(kind = \"box\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39f04c5ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgS03xfZ_w5L",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "a49d7657-9b5f-42e6-d85c-14457980bdde"
      },
      "source": [
        "sns.distplot(daawat.High)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f39f06f3550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZrXoRSS0F/TS6B0foHowl/UDsuM018aDfq6AXkRRT0E+jKcnz0E9WGY1QVRzRAVkRSTkF/TTOBn2Kum4g3qpXi15EUk1BP42zFxxJUYseoL6mhIYzPQzHxlL2GiIiCvppNHb0EwmHqIpGUvYam2uKGRl1nSErIimloJ/G+Dz0qRhDP66+RiNvRCT1FPTTaOpI3Rj6cesqi4iEQzpxSkRSSkE/jcYkX3BkKuGcEBevKGbfKQW9iKSOgn4KA8OjtPUNp3TEzbhLa0vZ3djF2Jin/LVE5MKkoJ/C0bb4PPFrK4pS/lpXrymjZzDGwTM6ICsiqaGgn8LR1njQr6tMfdBvqSsDYNex9pS/lohcmBT0UzhytkWf3GvFTmVNeSGV0TyeO9qR8tcSkQuTgn4Kx1r7qYzmUZyf3Hnop2JmXL22jF3HFPQikhrhdBeQiY609VG3CP3zAF/bcZyQGcfb+/mXnxz6lTeXO65Zsyg1iEh2S6hFb2Y3mtl+M2sws/umWB8xs68H63eYWV2w/DfM7Dkz+0Xw/XXJLT81jrb2UbcI/fPjxg/6HmvrX7TXFJELx6xBb2Y5wIPATUA9cLuZ1U/a7G6gw903Ag8AHw2WtwJvcffLgLuALyer8FTpG4pxpmdoUQ7EjltZmk84FG/Vi4gkWyIt+q1Ag7sfdvdh4GHglknb3AJ8Mbj9CHC9mZm7/9zdTwbL9wAFZpa6yWOSYHxo5WJ13UD8xKnasgKOBa8tIpJMiQR9LXBiwv3GYNmU27h7DOgCKiZt81vA8+4+NPkFzOweM9tlZrtaWloSrT0ljrbGW9V1lakfcTPR2vIiTnYOMjKqmSxFJLkW5WCsmb2MeHfODVOtd/eHgIcAtmzZktZTRBfzZKmJ6ioKeeKgc7y9nw1V0ZS8xtd2HJ92nQ78imSvRFr0TcDqCfdXBcum3MbMwkAp0BbcXwX8B/AOdz+00IJT7WhrH1XFEaKRxR2QtK6qiJyQsf+UrjglIsmVSNDvBDaZ2TozywO2AdsnbbOd+MFWgFuBx93dzWwZ8F3gPnd/KllFp9LRtj7WLXJrHiASzmFDVRH7mrtx17w3IpI8swZ90Od+L/AYsA/4hrvvMbOPmNnNwWafBSrMrAF4PzA+BPNeYCPwITN7IfiqTvpPkURHWvsXvX9+3CUrSmjrG6al97zDGCIi85ZQ/4S7Pwo8OmnZhybcHgRum+JxfwX81QJrXDQ9gyO09g4t6hj6iS5ZUcz2F+Gl5h6qi/PTUoOIZB9NgTDB+AlL6ei6AVhWmEdNaT4vaX56EUkiBf0EZ8fQp6lFD/Hum2Nt/fQPxdJWg4hkF811M0HDmV7Mkn+y1EzDGifbXFPMj/afYf9pjb4RkeRQi36CvSe7WVdZREFeTtpqWLmsgJL8MLsbu9JWg4hkFwX9BHubu6mvKUlrDSEzrl5bzoHTPZzQ3DcikgTqugl0DYzQ2DGQEWeIbl1Xzk8OnOErzxzj/jduTslrjLnT1jtMz9AIFUURvvrMMczsvO0yYX+IyMIo6AMvNcdHuqS7RQ9QWpBLfU0JD+88wR++/qKkdiUdb+vjxwdaONLax1Ds3Lw6eeEQV65exmsvrqa0IPUXXBGRxaOgD+zNoKAHuHZDBb988gjffvEkv/2K1bM/YBbH2/r5ws+OcOB0L4V5OVyxehmrygoozs+lvW+Yxo5+dh5tZ9exDq7bWMnrNy8nJ3R+C19Elh4FfWDvyW4qo3lUFWfGLMrrKoq4eHkxX/jZUW7bsmrKbpVEPbbnFB/45ouMjI7xhpet4Nr15UTCkz8lVHD9Jcv5wb7T/ORACyc7B9j2CnXbiGQDHYwN7G3uZnNNyYICNZnMjLtfvY69zd1887nGeT3HyOgYf/3dvbz7y8+xrrKI//Xrm3jNRVVThHxcWVEet21ZzduurOVwSx//9OMGTnYOLOTHEJEMoKAHhmNjHDzdS/3KzOi2GXfrVavYWlfOX393H2d6Buf02FNdg9z+0DP865NH+N1r1/LN97ySsqK8hB67pa6cu69bR+9QjLd/ZgetmntHZElT1w1wqKWX4dGxjOmfHxcKGX/7W5dx08ef5C++vZcH77gqocc9ebCF9z38AoMjo3zy9iu5+fKVc37tusoi7nplHZ//2RHe8o8/5V3XrT/voLBG5IgsDWrRE++fh8w5EDvRhqoof3D9Rr67u5mvPHNsxm1Hx5yP/+AA7/jcs1RG89h+73XzCvlxdZVF3HnNWs50D/HVHccYHdP0ySJLkYIe2NfcTSQcWtQLgs/Fu1+zgddcVMWffeuX/O339jE2ReA+e6Sdt/7TU3z8Bwd56xW1fOu9r2Jj9cKvVHXR8mLedlUth1v7+M8XmjRXvsgSpK4b4gdiL1lRTDgnM9/3cnNCfPauLXz423v4l58cZsfhdl53STX1NSXsP93DM4fbePJgKytK8vnEtiu4+fKVST2ofOWaMlp6hvjxgRaqiyNct6kqac8tIql3wQf9UGyUnx/v5LYtq9JdyozCOSH+8pZL2VxTwlefOc4DPzjAeON6Q1UR7/+Ni/i9V5/fj54sr69fTkvvEN/75SkqoxEuycBuLhGZ2gUf9M8f62RgZJTrNlamu5RZmRl3XrMWw+gfjnGme4jqkgiFefFf43/8vCllB0hDZtx29Wr+tf8wD+86wbt/bX1KXkdEku+CD/qfNrSQEzKu3VCR7lLmpDAvTF3l+b++uUyJPFd54RBvv3Ytn/5xA19++hjveGVdxpxgJiLTy8xO6UX004OtXLF6GSX5mt8lEaUFufzutXX0Dce458u7GBwZTXdJIjKLCzroO/uH2d3UtSS6bTJJbVkBt129mp8f7+SDj+zWSByRDHdBB/3PDrXhDq/epKCfq0trS/mTN1zM9hdP8sD3D6S7HBGZwQXdR//kwVaikTCXr16W7lKWpN9/7QaOtvbxyccbGHXnAzdcnDFzBYnIORd00P+0oYVr11eQm6Hj5zOdmfHR33o54ZwQD/7oEP3Do/zZm+o1vbFIhrlgg/5oax8n2gd413UaJrgQoZDxN2+9lILcHD731BFeau7hgd+5ghWl+Ul9nZlGE2XrnDvT/czZ+vNK6lywQf/wzhOELH4ikCyMmfF/3ryZS1YU8+fb93DTJ57g/jdu5q1X1urT0iymC3N3x4mfvyCyUBdk0A+OjPLwzuPcUL+C2mUF6S4nK5gZv/2K1VxdV8b7v/4CH3xkN5/84UHedd06Xl+/nFVlhWe3TbSl6u4MxcZwj9/Otv7/0TGnpWeI5q4BTnUN0tw1yKnuQfqGYsTGHAOKImFK8sOsKM1ndXlhxs7HJJntggz6/3yhic7+Ed75qrp0l5J1NlRF+dZ7X8UP953hHx8/yIe/vZcPf3sv66uKuKi6mNqyAo639xMCnPibbv/wKAMjo2x/sYmugRi9QyP0DMboHYwHHoABkdwQFUURKqJ5rCorZH1lUdK7iFKlo2+YQy297GvuZm9zD3ubuzlwqoeB4DyEkEF1cT7rKosoyQ8Tzgnh7vQOxegaGGH/qR6eP94JwHd2N3PTpSu47erVrKkonOllRYALMOjdnc8/dZRLVhRzzbrydJeTlcyM19cv5/rN1Rxq6ePH+8/w9KE2Glp6+fGBMwyOnLsoeU7IKMzLoSA3h6K8MLXLCijJLyaaHyYaCRPND2MYzx5pZ2AkRlvvMMfb+9nd2AXEW7wHTvdw8+UruXptWcpb/RM/jbg7/cOj9AzG2FJXxpmeIVp6hmjvG6Ktd5i2vmHa+oZo7Bigs3/k7ONKC3LZXFPM7VvX0DUwQk1pPtXFkRkn1XN32vuGOXCml7beIR78UQOf+lEDv7apirdfu5bXXVKtg+AyrQsu6J890s5Lp3r4u7ddlnVdAZnGzNhYHWVjdZR3vTp+0Nvd+fIzx85OyBYO2dnfw0wHGUsLfvXM5a6BEQ4HLeSv7zzBl54+xvrKIm7fuoa3XVVLRTS5UzO4OwdO9/JUQytNnQM0dw3Q2js85Rz9uTlGUST+RlWUF+ZNl9WwrrKIdZVFbK4poaY0/+zPnOiUFWZGRTTCK6MR7rhmDae6Bvm3Z4/z8M7j/N6XdrGyNJ87rlnDb79iNdXFS+NTjiwey7SzGrds2eK7du1KyXOPjI6x7aFnONTSy9P3XT+vmR5TOZfMUjNTMC/mfhoaGWXPyW52Hm3nWHs/OWbUryzhvpsu4dr1FfNu6Z7sHOCnDa081dDKUw1tZy+pWFqQy4qSfKpLIpTk51JSkEtxJExxfpji/Fzywr/aMk/lfhodc/Y1d/PskXYaWnoJh4wbL13B269dyzXrytWYuYCY2XPuvmWqdRdUi/5j3z/Ac8c6+MS2K1I2ne+FJFPe9CK5OVy1toyr1pZxunuQnUfb+fnxTu78zA6qiiO86bIafv2Saq5eW0Y0Mv2ffFf/CE8fbuXzTx3lUEsvrb3DQLx7aGNVEa+5qJINVVGWFSZ27d3FkBMyLq0t5dLaUlp7hugaHOGbu07wnd3NbKqOcvvWNbz58pq0tvLHjzW09g7TMzjC4MgYQ7HRs99DZuTmhHj6UBuFeTkUBW+aE0ccaUjpwlwwLfof7z/DOz+/k9u3ruFv33bZvJ8nU8JNZjYyOkZFNI/vvNjM4/vPMBwbIydkbKqOsnJZActL4sE3HBvjTM8gR1r7aOocwB3ycuJXG9tYHWVDVZTlJZEl1TIejo3xi6ZOnjncTlPnAAZsqI7yjleu5dcvrmZ1eXIP4LrHRw8dbevnaFsfx9r6ONrWT1PHAC09Q7T2DjEUG5v9iSYIh4zyojwqo/GD729+eQ2ba0q4aHkx+blqpE1lphb9BRH0/73nFH/yyG5qSvP51ntftaA/FAX90jHeCuwfjvHcsQ52HG7npVPdnOwc5EzPIGZGbije972+qoiNVVFeuaGCfc09WXNg83T3IC82dvKLxi7a+uKfUNZWFHL12jKuXL2MjdXFbKyOUhnNm/bNzN3pHojR3D1Ac9cgzZ2DHG/v58mDLbT3DdPWO8zw6LkgDxmUFeZRVph39qB6cfA9PzeH3JwQuTlGOBQinGO4x7ughkZH6R8apW84RnvvMK19w7T2DtHed+5YSE7I2FBVRH1NCfUrS6ivKWVzTXHSj8ksRQvuujGzG4FPADnAZ9z97yatjwBfAq4G2oDfcfejwbr7gbuBUeAP3P2xef4cc3ame5CPff8AD+88wctWlvDpO69Wa+ACVJgX5tWbqnh1gpdAPHC6N8UVLZ7lJfncUL+CG+pXcO36ch5/6QzPHmnniQMt/PvzTWe3C4eMymiEkoIw4VCInJCdHfra1jf0KyOlIP6pp7Qgl4poHusriyiPRqgoyqOiKI9lhXlJfaMcc+e6jZXB0NRu9p7sZseRdr71wsmz26woyWdzTTEbqqLULCtgZWk+NcsKqCnNZ1lhLpHwhf1/P2uL3sxygAPAbwCNwE7gdnffO2Gb3wde7u7vMbNtwFvd/XfMrB74N2ArsBL4AXCRu087ifl8W/TDsTGOtPZxpLWXQy19/ORACzuPtgPwP1+zgT98/UXnHSSbD7Xos0OmHEhOF3ena2CElp4hWnqHWF1eSFvvED2DMUZGndGxMfJzczjVNRg/aasgl9KCXEryw/HvBblpP2u3fyhGc/cgNaX57D3ZzZ6T3Rxr7zvvTQnib0zR/DBFkfgw3t6hGOGQnX1TC+dY/HsoxMUriomEQ+SFQ+TlBN8n3p5q2UzbjN/PCRGa9Abo7ox5/M1szB3D5p1TC23RbwUa3P1w8GQPA7cAeydscwvw4eD2I8CnLP458BbgYXcfAo6YWUPwfE/P5weZye7GTm7953NPu6k6yvuu38RbLl/Jhqposl9OlrgLIcxnYmYsK4y3vjctLwZgddnSOvmqMBI++7+9pa6cLXXluDsDw6N0DozQFXwNjpw78DsUG2MoNkZBbg6xMWd4dIzRESc2NkZs1Bkdcw6c7iE2NsbomBMbjU9FkSy5OfGgHwu6qyZ788tr+NQdVyXxFeMSCfpa4MSE+43ANdNt4+4xM+sCKoLlz0x6bO3kFzCze4B7gru9ZrY/oepncIz4x4c/mt/DK4HWhdaQRqo/fZZy7aD60+pBqHzwznnXv3a6FdLPK1wAAAXaSURBVBkxvNLdHwIeSncd48xs13QfgZYC1Z8+S7l2UP3plqr6E+kMagJWT7i/Klg25TZmFgZKiR+UTeSxIiKSQokE/U5gk5mtM7M8YBuwfdI224G7gtu3Ao97/CjvdmCbmUXMbB2wCXg2OaWLiEgiZu26Cfrc7wUeIz688nPuvsfMPgLscvftwGeBLwcHW9uJvxkQbPcN4gduY8B7Zxpxk0EyphtpnlR/+izl2kH1p1tK6s+4E6ZERCS5dPkfEZEsp6AXEclyCvoJzOxGM9tvZg1mdl+665mNma02sx+Z2V4z22Nm7wuWl5vZ983sYPC9LN21zsTMcszs52b2neD+OjPbEfwevh4MAshIZrbMzB4xs5fMbJ+ZvXIp7X8z+6Pgb+eXZvZvZpafyfvfzD5nZmfM7JcTlk25vy3uk8HPsdvMkn8m0hxMU/v/Df52dpvZf5jZsgnr7g9q329mb1jIayvoA8FUDw8CNwH1wO3BFA6ZLAb8sbvXA9cC7w1qvg/4obtvAn4Y3M9k7wP2Tbj/UeABd98IdBCfKylTfQL4L3e/BLic+M+xJPa/mdUCfwBscfdLiQ+22EZm7/8vADdOWjbd/r6J+Ei/TcRPyPz0ItU4nS9wfu3fBy5195cTn2rmfoDg/3gb8LLgMf8UZNS8KOjPOTvVg7sPA+NTPWQsd2929+eD2z3EQ6aWeN1fDDb7IvCb6alwdma2CngT8JngvgGvIz6VBmRw/WZWCvwa8VFnuPuwu3eyhPY/8ZF3BcH5L4VAMxm8/939CeIj+yaabn/fAnzJ454BlplZzeJUer6panf3/3b3WHD3GeLnGsGE6WPc/QgwPn3MvCjoz5lqqofzpmvIVGZWB1wJ7ACWu3tzsOoUsDxNZSXi48AHgfGZqCqAzgl//Jn8e1gHtACfD7qePmNmRSyR/e/uTcA/AMeJB3wX8BxLZ/+Pm25/L7X/6f8BfC+4ndTaFfRZwMyiwP8D/tDduyeuC05cy8gxtGb2ZuCMuz+X7lrmKQxcBXza3a8E+pjUTZPh+7+MeMtxHfHZZYs4v2thScnk/T0TM/tT4l2xX03F8yvoz1mS0zWYWS7xkP+qu/97sPj0+EfU4PuZdNU3i1cBN5vZUeJdZa8j3ue9LOhKgMz+PTQCje6+I7j/CPHgXyr7//XAEXdvcfcR4N+J/06Wyv4fN93+XhL/02b2TuDNwJ1+7sSmpNauoD8nkakeMkrQn/1ZYJ+7f2zCqolTUtwF/Odi15YId7/f3Ve5ex3x/f24u98J/Ij4VBqQ2fWfAk6Y2cXBouuJnwW+JPY/8S6ba82sMPhbGq9/Sez/Cabb39uBdwSjb64FuiZ08WQEi1/U6YPAze7eP2FVcqePcXd9BV/AG4kf+T4E/Gm660mg3uuIf0zdDbwQfL2ReD/3D4GDxGdrLk93rQn8LK8FvhPcXh/8UTcA3wQi6a5vhrqvAHYFv4NvAWVLaf8DfwG8BPwS+DIQyeT9T/xCRs3ACPFPVHdPt78BIz6S7hDwC+KjizKt9gbiffHj/7//PGH7Pw1q3w/ctJDX1hQIIiJZTl03IiJZTkEvIpLlFPQiIllOQS8ikuUU9CIiWU5BLxc8M+uddP+dZvap4PZ7zOwdszz+7PYimWjWSwmKXMjc/Z/TXYPIQqlFLzIDM/uwmX0guP2KYN7wF4J5xH85YdOVZvZfwZzof5+mckWmpBa9SHya3hcm3C9n6ukvPg/8nrs/bWZ/N2ndFcRnDx0C9pvZP7r7ifOeQSQN1KIXgQF3v2L8C/jQ5A2CK/8Uu/vTwaKvTdrkh+7e5e6DxOeLWZvakkUSp6AXSY6hCbdH0adlySAKepEEePzKUT1mdk2waFs66xGZCwW9SOLuBv416M8vIn5FJpGMp9krRRJkZlF37w1u3wfUuPv70lyWyKzUjyiSuDeZ2f3E/2+OAe9MbzkiiVGLXkQky6mPXkQkyynoRUSynIJeRCTLKehFRLKcgl5EJMv9f2qVbUE5KAQhAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_pO2tJU47as_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "outputId": "bb8883ab-9487-40aa-b750-ff718a7b0245"
      },
      "source": [
        "pip install nsetools"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting nsetools\n",
            "  Downloading https://files.pythonhosted.org/packages/f5/b3/6aac4226a927d5f23f154c06de7c100361e9fa07e932cd76aaa8c45a2af4/nsetools-1.0.11-py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nsetools) (1.15.0)\n",
            "Collecting dateutils\n",
            "  Downloading https://files.pythonhosted.org/packages/5e/bf/e68c863e72fbf19c84841cdc0d2719ce1741c2ae004f133af9cb3fba3adc/dateutils-0.6.11-py2.py3-none-any.whl\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from dateutils->nsetools) (2.8.1)\n",
            "Requirement already satisfied: pytz in /usr/local/lib/python3.6/dist-packages (from dateutils->nsetools) (2018.9)\n",
            "Collecting argparse\n",
            "  Downloading https://files.pythonhosted.org/packages/f2/94/3af39d34be01a24a6e65433d19e107099374224905f1e0cc6bbe1fd22a2f/argparse-1.4.0-py2.py3-none-any.whl\n",
            "Installing collected packages: argparse, dateutils, nsetools\n",
            "Successfully installed argparse-1.4.0 dateutils-0.6.11 nsetools-1.0.11\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "argparse"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dab7-RzM7iek"
      },
      "source": [
        "import nsetools as nse"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtsAGgIVuche"
      },
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ny5kLg-Y0T6O",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7e3e9bea-66b9-42a2-e63d-7dc9bb65bc42"
      },
      "source": [
        "stockcode = \"DAAWAT\"\n",
        "print(stockcode)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DAAWAT\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SwQTAsWl0pIQ"
      },
      "source": [
        "https://in.finance.yahoo.com/quote/DAAWAT.NS?p=DAAWAT.NS"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2jyepStM0cBo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "0fa53a52-293c-4fac-c529-6800ad4bdd2c"
      },
      "source": [
        "stock_url  = 'https://in.finance.yahoo.com/quote/'+stockcode+'.NS?p='+stockcode+'.NS'\n",
        "print(stock_url)\n",
        "#headers = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36'}\n",
        "response = requests.get(stock_url)\n",
        "print(response)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "https://in.finance.yahoo.com/quote/DAAWAT.NS?p=DAAWAT.NS\n",
            "<Response [200]>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qk3L_fm09FG"
      },
      "source": [
        "soup = BeautifulSoup(response.text, 'html.parser')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_e37bcd3tLC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e437ac93-7308-48f3-c5b1-8998bbee7daf"
      },
      "source": [
        "# Search Span - Data Lies in span\n",
        "float(soup.find_all(\"span\", {\"class\":\"Trsdu(0.3s) Fw(b) Fz(36px) Mb(-4px) D(ib)\"})[0].text)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54.75"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EYaMvOuo6W4G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "001522e8-1283-40a1-c69c-d3b80d5e8eeb"
      },
      "source": [
        "import time\n",
        "time.time()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1600243223.8517792"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iWPBc48c7cpo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "22566beb-07eb-4c2d-a7aa-c5d776275946"
      },
      "source": [
        "# Web Scraping to Pull the Stock Price of Daawat.\n",
        "\n",
        "import requests\n",
        "from bs4 import BeautifulSoup\n",
        "import time\n",
        "\n",
        "stockcode = \"GLOBUSSPR\"\n",
        "print(stockcode)\n",
        "\n",
        "stock_url  = 'https://in.finance.yahoo.com/quote/'+stockcode+'.NS?p='+stockcode+'.NS'\n",
        "print(stock_url)\n",
        "#headers = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.117 Safari/537.36'}\n",
        "response = requests.get(stock_url)\n",
        "#print(response)\n",
        "\n",
        "soup = BeautifulSoup(response.text, 'html.parser')\n",
        "\n",
        "\n",
        "start_time = time.time()\n",
        "seconds = 30\n",
        "\n",
        "while True:\n",
        "    current_time = time.time()\n",
        "    elapsed_time = current_time - start_time\n",
        "\n",
        "    if elapsed_time > seconds:\n",
        "        stock_url  = 'https://in.finance.yahoo.com/quote/'+stockcode+'.NS?p='+stockcode+'.NS'\n",
        "        response = requests.get(stock_url)\n",
        "        soup = BeautifulSoup(response.text, 'html.parser')\n",
        "        print(soup.find_all(\"span\", {\"class\":\"Trsdu(0.3s) Fw(b) Fz(36px) Mb(-4px) D(ib)\"})[0].text)\n",
        "        break\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GLOBUSSPR\n",
            "https://in.finance.yahoo.com/quote/GLOBUSSPR.NS?p=GLOBUSSPR.NS\n",
            "280.15\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7buAYjgX9LKq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}