{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8362389e-64ca-4520-b06c-94b084afcd4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0089aa17-ae94-498f-8673-6d4f7af64421",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"yahoo_data.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d867065e-5022-433d-b3ca-373e154a90bb",
   "metadata": {},
   "source": [
    "# Pre-Processing of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "292e4241-e114-435b-a841-9244e2addf86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Apr 28, 2023</td>\n",
       "      <td>33797.43</td>\n",
       "      <td>34104.56</td>\n",
       "      <td>33728.40</td>\n",
       "      <td>34098.16</td>\n",
       "      <td>34098.16</td>\n",
       "      <td>354310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apr 27, 2023</td>\n",
       "      <td>33381.66</td>\n",
       "      <td>33859.75</td>\n",
       "      <td>33374.65</td>\n",
       "      <td>33826.16</td>\n",
       "      <td>33826.16</td>\n",
       "      <td>343240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apr 26, 2023</td>\n",
       "      <td>33596.34</td>\n",
       "      <td>33645.83</td>\n",
       "      <td>33235.85</td>\n",
       "      <td>33301.87</td>\n",
       "      <td>33301.87</td>\n",
       "      <td>321170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr 25, 2023</td>\n",
       "      <td>33828.34</td>\n",
       "      <td>33875.49</td>\n",
       "      <td>33525.39</td>\n",
       "      <td>33530.83</td>\n",
       "      <td>33530.83</td>\n",
       "      <td>297880000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apr 24, 2023</td>\n",
       "      <td>33805.04</td>\n",
       "      <td>33891.15</td>\n",
       "      <td>33726.09</td>\n",
       "      <td>33875.40</td>\n",
       "      <td>33875.40</td>\n",
       "      <td>252020000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date      Open      High       Low    Close*  Adj Close**  \\\n",
       "0  Apr 28, 2023  33797.43  34104.56  33728.40  34098.16     34098.16   \n",
       "1  Apr 27, 2023  33381.66  33859.75  33374.65  33826.16     33826.16   \n",
       "2  Apr 26, 2023  33596.34  33645.83  33235.85  33301.87     33301.87   \n",
       "3  Apr 25, 2023  33828.34  33875.49  33525.39  33530.83     33530.83   \n",
       "4  Apr 24, 2023  33805.04  33891.15  33726.09  33875.40     33875.40   \n",
       "\n",
       "      Volume  \n",
       "0  354310000  \n",
       "1  343240000  \n",
       "2  321170000  \n",
       "3  297880000  \n",
       "4  252020000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d8dec39-9caf-4cdd-9a51-376e9170c80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       33797.43\n",
      "1       33381.66\n",
      "2       33596.34\n",
      "3       33828.34\n",
      "4       33805.04\n",
      "          ...   \n",
      "1253    24317.66\n",
      "1254    23865.22\n",
      "1255    23836.23\n",
      "1256    24097.63\n",
      "1257    24117.29\n",
      "Name: Open, Length: 1258, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'Open'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ae70f8fd-6315-4a81-a50c-cdc0ef83f417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       34104.56\n",
      "1       33859.75\n",
      "2       33645.83\n",
      "3       33875.49\n",
      "4       33891.15\n",
      "          ...   \n",
      "1253    24479.45\n",
      "1254    24333.35\n",
      "1255    23996.15\n",
      "1256    24185.52\n",
      "1257    24117.29\n",
      "Name: High, Length: 1258, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'High'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ded159e-4478-4216-ae7e-407653c0a0f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       33728.40\n",
      "1       33374.65\n",
      "2       33235.85\n",
      "3       33525.39\n",
      "4       33726.09\n",
      "          ...   \n",
      "1253    24263.42\n",
      "1254    23778.87\n",
      "1255    23531.31\n",
      "1256    23886.30\n",
      "1257    23808.19\n",
      "Name: Low, Length: 1258, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'Low'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "04518170-e4d8-4650-ac69-153b89f4470d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       34098.16\n",
      "1       33826.16\n",
      "2       33301.87\n",
      "3       33530.83\n",
      "4       33875.40\n",
      "          ...   \n",
      "1253    24357.32\n",
      "1254    24262.51\n",
      "1255    23930.15\n",
      "1256    23924.98\n",
      "1257    24099.05\n",
      "Name: Close*, Length: 1258, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'Close*'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d10af993-e28e-421c-9cb9-5596939269d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       34098.16\n",
      "1       33826.16\n",
      "2       33301.87\n",
      "3       33530.83\n",
      "4       33875.40\n",
      "          ...   \n",
      "1253    24357.32\n",
      "1254    24262.51\n",
      "1255    23930.15\n",
      "1256    23924.98\n",
      "1257    24099.05\n",
      "Name: Adj Close**, Length: 1258, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'Adj Close**'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a525039a-c3ec-440f-baf2-91932785ce3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       354310000\n",
      "1       343240000\n",
      "2       321170000\n",
      "3       297880000\n",
      "4       252020000\n",
      "          ...    \n",
      "1253    307670000\n",
      "1254    329480000\n",
      "1255    389240000\n",
      "1256    385350000\n",
      "1257    380070000\n",
      "Name: Volume, Length: 1258, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "column_name = 'Volume'\n",
    "\n",
    "column_values = df[column_name]\n",
    "print(column_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "58f5404d-c5ac-44d9-8a33-6eae19859bb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1258, 7)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e5e543d1-abc6-466d-896b-f225f653b018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close*', 'Adj Close**', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f883ebad-c9b9-4458-bf7f-7349629c4d70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1258 entries, 0 to 1257\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Date         1258 non-null   object \n",
      " 1   Open         1258 non-null   float64\n",
      " 2   High         1258 non-null   float64\n",
      " 3   Low          1258 non-null   float64\n",
      " 4   Close*       1258 non-null   float64\n",
      " 5   Adj Close**  1258 non-null   float64\n",
      " 6   Volume       1258 non-null   int64  \n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 68.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3b895bb-7060-4ea2-94c8-ec18dfaca803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date           0\n",
       "Open           0\n",
       "High           0\n",
       "Low            0\n",
       "Close*         0\n",
       "Adj Close**    0\n",
       "Volume         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73a35df7-263d-488c-b493-3c69b8ada223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ff473267-b3b3-4def-8dc9-32cda6dcd0f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Date'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "19efd64f-6661-4a36-a464-68da9a6f4282",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1258 entries, 0 to 1257\n",
      "Data columns (total 6 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Open         1258 non-null   float64\n",
      " 1   High         1258 non-null   float64\n",
      " 2   Low          1258 non-null   float64\n",
      " 3   Close*       1258 non-null   float64\n",
      " 4   Adj Close**  1258 non-null   float64\n",
      " 5   Volume       1258 non-null   int64  \n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 59.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ba7309d-e6e9-4852-bb33-2313b478e1b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_excel(\"yahoo_data.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1439c4-c3bc-4e57-a70b-e4b7de714dfa",
   "metadata": {},
   "source": [
    "# Visualization of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16de549b-2f61-4c79-b015-0b0bfdccae90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7223bb58-3698-482a-a128-659ba77d659e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QUODRrqKJpCwRERE6cOBAifnFg7QVj6J+IerWrasaNWp4fT89e/bUqlWrtGLFilJfF2OMli9frvDwcLVr185jWVnPefHzXRxPSkqK+vXrV+r+y7t9zvlo3br1OZ+Hir6v6tevrw0bNuj06dNlFvP16tWTzWbTxx9/7P7idqYz57Vq1UqLFi2SMUZffvml5syZo2eeeUZBQUGWuCMAAP9EXi/Jynm9V69e5eb1cyl+bg8cOFBisLz9+/d7xHSuvFaZHFmeq666yuOOC6WpTG4vKipSdnZ2mT8sFffxP//5j5o0aVLu9po0aaJZs2ZJkr799lu99dZbSk1NVWFh4TnHgcCliSPy8Ik9e/Zo9OjRcjgcGjp0aIXWqVmzpjp06KB//OMfkuQ+Ha6yR1rPZceOHfriiy885i1YsEChoaG69tprJck9aumXX37p0e7sUUqL46tobN27d9eaNWtKjK7+xhtvKDg42Cu3tQkJCVGHDh20ZMkSj7hOnz6tefPmqVGjRmrRokWlt3v//ferQYMGSklJUU5OTonlkydP1jfffKMxY8YoICDAY9nChQs9TsfcvXu3MjIy3F8Gr7jiCkVHR+uLL75Q+/btS51CQ0MrHfPFkpiYqF9//bXUQfmKFd+nd9++faX2r1WrViXWsdlsat26tV566SXVqVOnyk8RBYCykNdLZ+W8PmTIEDmdTo0ZM0b79u0rtU3x4HmlKb70bN68eR7zN27cqK+//rrU29+VldfOJ0dWtcTEREnSzJkzy2zTq1cv1apVS//73//K/P5SmhYtWugvf/mLWrVqRW5HmTgijyq3fft29zVBOTk5+vjjjzV79mzVrFlTS5cu9Rgp9GyvvPKK1qxZo969e6tx48b69ddf9frrr0uSevToIUkKDQ1VkyZN9Pbbb6t79+4KDw9XvXr1yr2lSnlcLpf69u2r1NRUNWzYUPPmzVN6eromTZqk4OBgSb+dInXFFVdo9OjROnXqlOrWraulS5dqw4YNJbbXqlUrLVmyRDNnzlS7du1Uo0aNMv9xjx8/Xu+88466du2qp59+WuHh4Zo/f75WrlypyZMny+FwnFefzpaWlqaePXuqa9euGj16tAIDAzVjxgxt375dCxcurNSRjmJ16tTRkiVL1KdPH7Vr106PP/64Wrdurfz8fC1evFjz58/XHXfcoccff7zEujk5Obr11lv1wAMPKC8vT+PHj1ft2rWVkpLibvPPf/5TiYmJ6tWrlwYPHqzf/e53+uWXX/T1119ry5Yt+ve//31Bz0lVuvPOOzV79mw9+OCD2rlzp7p27arTp0/r008/1VVXXaUBAwaoc+fO+vOf/6w//elP2rRpk/7whz8oJCREBw4c0IYNG9SqVSs99NBDeueddzRjxgzdcsstuvzyy2WM0ZIlS3T48GH17NnT110FcAkgr18aed3hcOjtt99Wnz591LZtWw0fPlydOnVSYGCgdu3apXnz5umLL74o90y5P//5z5o2bZpq1KihxMRE/fDDD3rqqacUFRWlRx99VJIqlNcqmiMvpi5duuiee+7Rs88+q4MHD6pPnz6y2+36/PPPFRwcrBEjRqhp06Z65plnNG7cOH3//fe68cYbVbduXR08eFCfffaZQkJCNGHCBH355ZcaPny4/vjHPyo6OlqBgYFas2aNvvzyS860Q9l8MMAeLhHFI4QWT4GBgaZBgwYmLi7OTJw40eTk5JRY5+wRTzMzM82tt95qmjRpYux2u4mIiDBxcXFm+fLlHuutXr3atG3b1tjtdiPJDBo0yGN7P/300zn3Zcxvo9v27t3b/Oc//zEtW7Y0gYGBpmnTpmbKlCkl1v/2229NQkKCCQsLM/Xr1zcjRowwK1euLDG67S+//GJuv/12U6dOHWOz2Tz2qVJG5d22bZtJSkoyDofDBAYGmtatW5vZs2d7tCke3fbf//63x/zi0WjPbl+ajz/+2HTr1s2EhISYoKAg07FjR7NixYpSt1eR0W2L7dmzxzz88MPm8ssvN4GBgcbhcJg//OEPZt68eR4j05/ZjzfffNM88sgjpn79+sZut5suXbqYTZs2ldj2F198Yfr3728aNGhgAgICjNPpNN26dTOvvPKKu01ZoyqXNvJwacp7z5wpLi7OtGzZssxlZ45ab4wxJ06cME8//bSJjo42gYGBJiIiwnTr1s1kZGR4tHv99ddNhw4d3K9L8+bNzb333ut+Pr755htz5513mubNm5ugoCDjcDjM9ddfb+bMmVNuvABwocjrv7nU8np2drYZO3asadmypQkODjZ2u938/ve/N0OHDjXbtm1ztyvt+S8qKjKTJk0yLVq0MAEBAaZevXrm7rvvNnv37nW3qUxeO1eOLEtF7rhgzG8j04eEhJS57Oy7FRQVFZmXXnrJxMTEuL/zdOrUqcTzvmzZMtO1a1cTFhZm7Ha7adKkibn99tvN6tWrjTHGHDx40AwePNhceeWVJiQkxFx22WXmmmuuMS+99JI5depUuTHj0mUz5hzDiwJAFfnoo4/UtWtX/fvf/9btt9/u63AAAAAAS+AaeQAAAAAALIRCHgAAAAAAC+HUegAAAAAALIQj8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhdTydQDn4/Tp09q/f79CQ0Nls9l8HQ4AAOdkjNGRI0fkcrlUowa/o5+N3A4AsBpf5nZLFvL79+9XVFSUr8MAAKDS9u7dq0aNGvk6jGqH3A4AsCpf5HZLFvKhoaGSfnvCwsLCfBwNAADnlp+fr6ioKHcOgydyOwDAanyZ2y1ZyBefchcWFkayBwBYCqeNl47cDgCwKl/kdi7SAwAAAADAQijkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAup5esAAKtISvLu9las8O72AACA7yQt9O4XhRV38kUBQNk4Ig8AAAAAgIVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhVDIAwAAAABgIRTyAAAAAABYCIU8AAAAAAAWQiEPAAAAAICFUMgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVUupBfv369kpKS5HK5ZLPZtGzZsjLbDh06VDabTVOnTvWYX1BQoBEjRqhevXoKCQlR37599eOPP1Y2FAAAcIHI6wAAWE+lC/ljx46pdevWmj59erntli1bpk8//VQul6vEsuTkZC1dulSLFi3Shg0bdPToUfXp00dFRUWVDQcAAFwA8joAANZTq7IrJCYmKjExsdw2+/bt0/Dhw/XBBx+od+/eHsvy8vI0a9Ysvfnmm+rRo4ckad68eYqKitLq1avVq1evyoYEAADOE3kdAADrqXQhfy6nT5/WPffco8cff1wtW7YssXzz5s06efKkEhIS3PNcLpdiYmKUkZFRasIvKChQQUGB+3F+fr63wwYAAKWoirwukdtRPSQtTPJ1CABwXrw+2N2kSZNUq1YtPfLII6Uuz87OVmBgoOrWresxPzIyUtnZ2aWuk5aWJofD4Z6ioqK8HTYAAChFVeR1idwOAMCF8Gohv3nzZr388suaM2eObDZbpdY1xpS5TkpKivLy8tzT3r17vREuAAAoR1XldYncDgDAhfBqIf/xxx8rJydHjRs3Vq1atVSrVi3t3r1bjz32mJo2bSpJcjqdKiwsVG5urse6OTk5ioyMLHW7drtdYWFhHhMAAKhaVZXXJXI7AAAXwquF/D333KMvv/xSW7dudU8ul0uPP/64PvjgA0lSu3btFBAQoPT0dPd6Bw4c0Pbt2xUbG+vNcAAAwAUgrwMAUD1VerC7o0eP6rvvvnM/zsrK0tatWxUeHq7GjRsrIiLCo31AQICcTqeuuOIKSZLD4dCQIUP02GOPKSIiQuHh4Ro9erRatWrlHu0WAABcHOR1AACsp9KF/KZNm9S1a1f341GjRkmSBg0apDlz5lRoGy+99JJq1aql/v3768SJE+revbvmzJmjmjVrVjYcAABwAcjrAABYj80YY3wdRGXl5+fL4XAoLy+Pa+pw0SRV4zvUrFjh6wgAnAu5q3w8P/CF6nz7uRV3ktyB6s6Xucvrt58DAAAAAABVh0IeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAup9H3kAQAAAAD+xZu3Y+T2iVWPI/IAAAAAAFgIhTwAAAAAABbCqfUAAAAAYDHePBUe1sMReQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEAp5AAAAAAAshFHrAeASkOTlgW1XrPDu9gAAAFBxHJEHAAAAAMBCKOQBAAAAALCQShfy69evV1JSklwul2w2m5YtW+ZedvLkSY0dO1atWrVSSEiIXC6X7r33Xu3fv99jGwUFBRoxYoTq1aunkJAQ9e3bVz/++OMFdwYAAFQOeR0AAOupdCF/7NgxtW7dWtOnTy+x7Pjx49qyZYueeuopbdmyRUuWLNG3336rvn37erRLTk7W0qVLtWjRIm3YsEFHjx5Vnz59VFRUdP49AQAAlUZeBwDAeio92F1iYqISExNLXeZwOJSenu4xb9q0abr++uu1Z88eNW7cWHl5eZo1a5befPNN9ejRQ5I0b948RUVFafXq1erVq9d5dAMAAJwP8joAANZT5aPW5+XlyWazqU6dOpKkzZs36+TJk0pISHC3cblciomJUUZGRqkJv6CgQAUFBe7H+fn5VR02AKAcjIJ/6fJGXpfI7QAAXIgqHezu119/1RNPPKGBAwcqLCxMkpSdna3AwEDVrVvXo21kZKSys7NL3U5aWpocDod7ioqKqsqwAQBAKbyV1yVyOwAAF6LKCvmTJ09qwIABOn36tGbMmHHO9sYY2Wy2UpelpKQoLy/PPe3du9fb4QIAgHJ4M69L5HYAAC5ElRTyJ0+eVP/+/ZWVlaX09HT3r/aS5HQ6VVhYqNzcXI91cnJyFBkZWer27Ha7wsLCPCYAAHBxeDuvS+R2AAAuhNcL+eJkv2vXLq1evVoREREey9u1a6eAgACPwXMOHDig7du3KzY21tvhAACAC0BeBwCg+qn0YHdHjx7Vd999536clZWlrVu3Kjw8XC6XS7fffru2bNmid955R0VFRe7r48LDwxUYGCiHw6EhQ4boscceU0REhMLDwzV69Gi1atXKPdotAAC4OMjrAABYT6UL+U2bNqlr167ux6NGjZIkDRo0SKmpqVq+fLkkqU2bNh7rrV27VvHx8ZKkl156SbVq1VL//v114sQJde/eXXPmzFHNmjXPsxsAAOB8kNcBALCeShfy8fHxMsaUuby8ZcVq166tadOmadq0aZXdPQAA8CLyOgAA1lOlt58DAAAAAADeRSEPAAAAAICFUMgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhVDIAwAAAABgIRTyAAAAAABYCIU8AAAAAAAWQiEPAAAAAICFUMgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVUupBfv369kpKS5HK5ZLPZtGzZMo/lxhilpqbK5XIpKChI8fHx2rFjh0ebgoICjRgxQvXq1VNISIj69u2rH3/88YI6AgAAKo+8DgCA9VS6kD927Jhat26t6dOnl7p88uTJmjJliqZPn66NGzfK6XSqZ8+eOnLkiLtNcnKyli5dqkWLFmnDhg06evSo+vTpo6KiovPvCQAAqDTyOgAA1lOrsiskJiYqMTGx1GXGGE2dOlXjxo1Tv379JElz585VZGSkFixYoKFDhyovL0+zZs3Sm2++qR49ekiS5s2bp6ioKK1evVq9evW6gO4AAIDKIK8DAGA9Xr1GPisrS9nZ2UpISHDPs9vtiouLU0ZGhiRp8+bNOnnypEcbl8ulmJgYdxsAAOB75HUAAKqnSh+RL092drYkKTIy0mN+ZGSkdu/e7W4TGBiounXrlmhTvP7ZCgoKVFBQ4H6cn5/vzbABAEApqiqvS+R2AAAuRJWMWm+z2TweG2NKzDtbeW3S0tLkcDjcU1RUlNdiBQAA5fN2XpfI7QAAXAivFvJOp1OSSvwCn5OT4/413+l0qrCwULm5uWW2OVtKSory8vLc0969e70ZNgAAKEVV5XWJ3A4AwIXw6qn1zZo1k9PpVHp6utq2bStJKiws1Lp16zRp0iRJUrt27RQQEKD09HT1799fknTgwAFt375dkydPLnW7drtddrvdm6HiEpCU5OsIAMDaqiqvS+R2AAAuRKUL+aNHj+q7775zP87KytLWrVsVHh6uxo0bKzk5WRMnTlR0dLSio6M1ceJEBQcHa+DAgZIkh8OhIUOG6LHHHlNERITCw8M1evRotWrVyj3aLQAAuDjI6wAAWE+lC/lNmzapa9eu7sejRo2SJA0aNEhz5szRmDFjdOLECQ0bNky5ubnq0KGDVq1apdDQUPc6L730kmrVqqX+/fvrxIkT6t69u+bMmaOaNWt6oUsAAKCiyOsAAFiPzRhjfB1EZeXn58vhcCgvL09hYWG+DgfV1KV0av2KFb6OANVddf88XArvYXJX+Xh+4AtJC6vvP8cVd14C/xhxQXj/+p4vc1eVjFoPAAAAAACqBoU8AAAAAAAW4tVR6wEAAABcOG+eNn2pnOYMXEo4Ig8AAAAAgIVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhVDIAwAAAABgIRTyAAAAAABYCIU8AAAAAAAWQiEPAAAAAICFUMgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZSy9cBAABKl5Tk6wgAAABQHXFEHgAAAAAAC6GQBwAAAADAQrxeyJ86dUp/+ctf1KxZMwUFBenyyy/XM888o9OnT7vbGGOUmpoql8uloKAgxcfHa8eOHd4OBQAAXCDyOgAA1Y/XC/lJkybplVde0fTp0/X1119r8uTJev755zVt2jR3m8mTJ2vKlCmaPn26Nm7cKKfTqZ49e+rIkSPeDgcAAFwA8joAANWP1wv5zMxM3Xzzzerdu7eaNm2q22+/XQkJCdq0aZOk3361nzp1qsaNG6d+/fopJiZGc+fO1fHjx7VgwQJvhwMAAC4AeR0AgOrH64X8DTfcoA8//FDffvutJOmLL77Qhg0bdNNNN0mSsrKylJ2drYSEBPc6drtdcXFxysjI8HY4AADgApDXAQCofrx++7mxY8cqLy9PV155pWrWrKmioiL97W9/05133ilJys7OliRFRkZ6rBcZGandu3eXus2CggIVFBS4H+fn53s7bAAAUIqqyOsSuR0AgAvh9SPyixcv1rx587RgwQJt2bJFc+fO1QsvvKC5c+d6tLPZbB6PjTEl5hVLS0uTw+FwT1FRUd4OGwAAlKIq8rpEbgcA4EJ4/Yj8448/rieeeEIDBgyQJLVq1Uq7d+9WWlqaBg0aJKfTKem3X/AbNmzoXi8nJ6fEr/nFUlJSNGrUKPfj/Px8Ej4AABdBVeR1idyO85O0MMnXIQDnjfcvvMnrR+SPHz+uGjU8N1uzZk33bWqaNWsmp9Op9PR09/LCwkKtW7dOsbGxpW7TbrcrLCzMYwIAAFWvKvK6RG4HAOBCeP2IfFJSkv72t7+pcePGatmypT7//HNNmTJF9913n6TfTr1LTk7WxIkTFR0drejoaE2cOFHBwcEaOHCgt8MBLglJXv6Bd8UK724PgHWR1wEAqH68XshPmzZNTz31lIYNG6acnBy5XC4NHTpUTz/9tLvNmDFjdOLECQ0bNky5ubnq0KGDVq1apdDQUG+HAwCwAG/+GMUPUd5FXgcAoPqxGWOMr4OorPz8fDkcDuXl5XEqHsrk7aPUlxIKoeqB9/D5qa7vX3JX+Xh+UBFcY3x+VtxZTf8xXmIupffvpfKe82Xu8vo18gAAAAAAoOpQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhVDIAwAAAABgIRTyAAAAAABYCIU8AAAAAAAWUsvXAQAAAACoOkkLk7y6vRV3rvDq9uB/eM9VPY7IAwAAAABgIRyRB1BCkhd/RF3BD6gAAACAV3FEHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACykVlVsdN++fRo7dqzee+89nThxQi1atNCsWbPUrl07SZIxRhMmTNCrr76q3NxcdejQQf/4xz/UsmXLqggHAABcAPI6gEtV0sIkX4cAlMrrR+Rzc3PVuXNnBQQE6L333tNXX32lF198UXXq1HG3mTx5sqZMmaLp06dr48aNcjqd6tmzp44cOeLtcAAAwAUgrwMAUP14/Yj8pEmTFBUVpdmzZ7vnNW3a1P23MUZTp07VuHHj1K9fP0nS3LlzFRkZqQULFmjo0KHeDgkAAJwn8joAANWP14/IL1++XO3bt9cf//hHNWjQQG3bttW//vUv9/KsrCxlZ2crISHBPc9utysuLk4ZGRmlbrOgoED5+fkeEwAAqHpVkdclcjsAABfC64X8999/r5kzZyo6OloffPCBHnzwQT3yyCN64403JEnZ2dmSpMjISI/1IiMj3cvOlpaWJofD4Z6ioqK8HTYAAChFVeR1idwOAMCF8Hohf/r0aV177bWaOHGi2rZtq6FDh+qBBx7QzJkzPdrZbDaPx8aYEvOKpaSkKC8vzz3t3bvX22EDAIBSVEVel8jtAABcCK8X8g0bNtTVV1/tMe+qq67Snj17JElOp1OSSvxKn5OTU+LX/GJ2u11hYWEeEwAAqHpVkdclcjsAABfC64Pdde7cWTt37vSY9+2336pJkyaSpGbNmsnpdCo9PV1t27aVJBUWFmrdunWaNGmSt8MBgIsmiTvUwA+R1wEAqH68Xsg/+uijio2N1cSJE9W/f3999tlnevXVV/Xqq69K+u3Uu+TkZE2cOFHR0dGKjo7WxIkTFRwcrIEDB3o7HAAAcAHI6wAAVD9eL+Svu+46LV26VCkpKXrmmWfUrFkzTZ06VXfddZe7zZgxY3TixAkNGzZMubm56tChg1atWqXQ0FBvhwMAAC4AeR0AgOrHZowxvg6isvLz8+VwOJSXl8c1dSgTpzlXDytW+DqC8vE+8T/V9T1H7iofzw8qImkh/7SrgxV3VtN/tFWA91z1UF3fc77MXV4/Ig8AAADAf3m7uK2uRRpQnXl91HoAAAAAAFB1KOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAthsDsAAAAAfoFR5nGp4Ig8AAAAAAAWQiEPAAAAAICFcGo9AEtJ4ow5AAAAXOIo5AEAAAD4DNe1A5XHqfUAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVwjTyqFQYyAwAAAIDycUQeAAAAAAALoZAHAAAAAMBCOLUeQJXicgkAAADAuzgiDwAAAACAhVR5IZ+Wliabzabk5GT3PGOMUlNT5XK5FBQUpPj4eO3YsaOqQwEAABeIvA4AgO9VaSG/ceNGvfrqq7rmmms85k+ePFlTpkzR9OnTtXHjRjmdTvXs2VNHjhypynAAAMAFIK8DAFA9VFkhf/ToUd11113617/+pbp167rnG2M0depUjRs3Tv369VNMTIzmzp2r48ePa8GCBVUVDgAAuADkdQAAqo8qK+Qffvhh9e7dWz169PCYn5WVpezsbCUkJLjn2e12xcXFKSMjo9RtFRQUKD8/32MCAAAXjzfzukRuBwDgQlTJqPWLFi3Sli1btHHjxhLLsrOzJUmRkZEe8yMjI7V79+5St5eWlqYJEyZ4P1AAAHBO3s7rErkdAIAL4fUj8nv37tXIkSM1b9481a5du8x2NpvN47ExpsS8YikpKcrLy3NPe/fu9WrMAACgdFWR1yVyOwAAF8LrR+Q3b96snJwctWvXzj2vqKhI69ev1/Tp07Vz505Jv/2C37BhQ3ebnJycEr/mF7Pb7bLb7d4OFQAAnENV5HWJ3A4AwIXw+hH57t27a9u2bdq6dat7at++ve666y5t3bpVl19+uZxOp9LT093rFBYWat26dYqNjfV2OAAA4AKQ1wEAqH68fkQ+NDRUMTExHvNCQkIUERHhnp+cnKyJEycqOjpa0dHRmjhxooKDgzVw4EBvhwMAAC4AeR0AgOqnSga7O5cxY8boxIkTGjZsmHJzc9WhQwetWrVKoaGhvggHAABcAPI6AAAXl80YY3wdRGXl5+fL4XAoLy9PYWFhvg4HXpSU5OsIAFjdihW+jqB05K7y8fz4r6SFJHcAF2bFndUzufsyd1XZfeQBAAAAAID3UcgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIX45PZzAAAAAABUhDfvflFdR8CvLAp5AIBf8fZtLKvr7ewAAMCli1PrAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAuhkAcAAAAAwEIo5AEAAAAAsBAKeQAAAAAALIRCHgAAAAAAC/F6IZ+WlqbrrrtOoaGhatCggW655Rbt3LnTo40xRqmpqXK5XAoKClJ8fLx27Njh7VAAAMAFIq8DAFD9eL2QX7dunR5++GF98sknSk9P16lTp5SQkKBjx46520yePFlTpkzR9OnTtXHjRjmdTvXs2VNHjhzxdjgAAOACkNcBAKh+bMYYU5U7+Omnn9SgQQOtW7dOf/jDH2SMkcvlUnJyssaOHStJKigoUGRkpCZNmqShQ4eec5v5+flyOBzKy8tTWFhYVYaPiywpydcRAICnFSu8sx1/yV1Vkdcl/3l+UFLSQpI7gOpjxZ1eSuzybe6q8mvk8/LyJEnh4eGSpKysLGVnZyshIcHdxm63Ky4uThkZGVUdDgAAuADkdQAAfK9WVW7cGKNRo0bphhtuUExMjCQpOztbkhQZGenRNjIyUrt37y51OwUFBSooKHA/zs/Pr6KIAQBAWbyV1yVyOwAAF6JKj8gPHz5cX375pRYuXFhimc1m83hsjCkxr1haWpocDod7ioqKqpJ4AQBA2byV1yVyOwAAF6LKCvkRI0Zo+fLlWrt2rRo1auSe73Q6Jf2/X/CL5eTklPg1v1hKSory8vLc0969e6sqbAAAUApv5nWJ3A4AwIXweiFvjNHw4cO1ZMkSrVmzRs2aNfNY3qxZMzmdTqWnp7vnFRYWat26dYqNjS11m3a7XWFhYR4TAACoelWR1yVyOwAAF8Lr18g//PDDWrBggd5++22Fhoa6f6F3OBwKCgqSzWZTcnKyJk6cqOjoaEVHR2vixIkKDg7WwIEDvR0OAAC4AOR1AACqH68X8jNnzpQkxcfHe8yfPXu2Bg8eLEkaM2aMTpw4oWHDhik3N1cdOnTQqlWrFBoa6u1wAADABSCvAwBQ/VT5feSrAvea9V/cRx5AdcN95C8Onh//xX3kAVQn3EceAAAAAABcdBTyAAAAAABYCIU8AAAAAAAWQiEPAAAAAICFUMgDAAAAAGAhXr/9HC4tjDIPAAAAABcXR+QBAAAAALAQjsgDAADAjfu+A0D1xxF5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEUestgHu1AwAAAACKUcgDAABcZN68xduKO1d4bVsAAGvg1HoAAAAAACyEQh4AAAAAAAvh1HoAAAAL8+Zp+gAAa/BpIT9jxgw9//zzOnDggFq2bKmpU6eqS5cuPomFAeUAALgw1SmvexvFMgCgOvHZqfWLFy9WcnKyxo0bp88//1xdunRRYmKi9uzZ46uQAADAeSKvAwBw8diMMcYXO+7QoYOuvfZazZw50z3vqquu0i233KK0tLRy183Pz5fD4VBeXp7CwsK8Eg9H5AEApVnhpQHBqyJ3VScXktelKsrtHEUHAJzFm3f68GVu98kR+cLCQm3evFkJCQke8xMSEpSRkeGLkAAAwHkirwMAcHH55Br5n3/+WUVFRYqMjPSYHxkZqezs7BLtCwoKVFBQ4H6cl5cn6bdfQLzl5EmvbQoA4Ee8lWqKc5aPToSrUpXN69JFyu3HSe4AAE/ezDO+zO0+HezOZrN5PDbGlJgnSWlpaZowYUKJ+VFRUVUWGwAAkuRweHd7R44ckcPbG60mKprXJXI7AMA3HPd7Pwf7Irf7pJCvV6+eatasWeJX+pycnBK/5ktSSkqKRo0a5X58+vRp/fLLL4qIiCjzC4Kv5efnKyoqSnv37vXLayH9uX/+3DfJv/tH36zLn/tX3Lc9e/bIZrPJ5XL5OiSvq2xel6yZ2yvCX9/L/tgvf+yTRL+shn5ZR2l9MsboyJEjPsntPinkAwMD1a5dO6Wnp+vWW291z09PT9fNN99cor3dbpfdbveYV6dOnaoO0yvCwsL85s1bGn/unz/3TfLv/tE36/Ln/jkcDr/tW2XzumTt3F4R/vpe9sd++WOfJPplNfTLOs7uk6/OsvPZqfWjRo3SPffco/bt26tTp0569dVXtWfPHj344IO+CgkAAJwn8joAABePzwr5O+64Q4cOHdIzzzyjAwcOKCYmRu+++66aNGniq5AAAMB5Iq8DAHDx+HSwu2HDhmnYsGG+DKHK2O12jR8/vsRpg/7Cn/vnz32T/Lt/9M26/Ll//ty3s/lzXq8of329/bFf/tgniX5ZDf2yjurWJ5vxx/vgAAAAAADgp2r4OgAAAAAAAFBxFPIAAAAAAFgIhTwAAAAAABZCIf//S0tL03XXXafQ0FA1aNBAt9xyi3bu3OnRxhij1NRUuVwuBQUFKT4+Xjt27PBoU1BQoBEjRqhevXoKCQlR37599eOPP3q0yc3N1T333COHwyGHw6F77rlHhw8f9mizZ88eJSUlKSQkRPXq1dMjjzyiwsLCKuvfyZMnNXbsWLVq1UohISFyuVy69957tX//fo/txMfHy2azeUwDBgzwaf8q8toNHjy4RNwdO3b0aFMdX7uK9O3sfhVPzz//vLtNdXzdJGnmzJm65ppr3Pfj7NSpk9577z33cit/5srrm5U/bxXpn2Tdz1xF+mblzxxK8sf8768531/zvb/men/N8f6a3/0xr/t9PjcwxhjTq1cvM3v2bLN9+3azdetW07t3b9O4cWNz9OhRd5vnnnvOhIaGmv/+979m27Zt5o477jANGzY0+fn57jYPPvig+d3vfmfS09PNli1bTNeuXU3r1q3NqVOn3G1uvPFGExMTYzIyMkxGRoaJiYkxffr0cS8/deqUiYmJMV27djVbtmwx6enpxuVymeHDh1dZ/w4fPmx69OhhFi9ebL755huTmZlpOnToYNq1a+exnbi4OPPAAw+YAwcOuKfDhw97tLnY/avIazdo0CBz4403esR96NAhj+1Ux9euIn07s08HDhwwr7/+urHZbOZ///ufu011fN2MMWb58uVm5cqVZufOnWbnzp3mySefNAEBAWb79u3GGGt/5srrm5U/bxXpnzHW/cxVpG9W/syhJH/M//6a8/013/trrvfXHO+v+d0f87q/53MK+TLk5OQYSWbdunXGGGNOnz5tnE6nee6559xtfv31V+NwOMwrr7xijPktMQYEBJhFixa52+zbt8/UqFHDvP/++8YYY7766isjyXzyySfuNpmZmUaS+eabb4wxxrz77rumRo0aZt++fe42CxcuNHa73eTl5VVJ/0rz2WefGUlm9+7d7nlxcXFm5MiRZa5THfpXWt8GDRpkbr755jLXscprV5HX7eabbzbdunXzmGeF161Y3bp1zWuvveZ3n7kz+1Yaq37eznRm//zlM1da385m9c8cPPlj/vfXnO+v+d6fc72/5nh/ze/+mNf9KZ9zan0Z8vLyJEnh4eGSpKysLGVnZyshIcHdxm63Ky4uThkZGZKkzZs36+TJkx5tXC6XYmJi3G0yMzPlcDjUoUMHd5uOHTvK4XB4tImJiZHL5XK36dWrlwoKCrR58+Yq6V9ZbWw2m+rUqeMxf/78+apXr55atmyp0aNH68iRI+5l1aF/ZfXto48+UoMGDdSiRQs98MADysnJcS+zymt3rtft4MGDWrlypYYMGVJiWXV/3YqKirRo0SIdO3ZMnTp18qvP3Nl9K41VP2/l9c8fPnPneu2s/JlD6fwx//trzvfXfO+Pud5fc7y/5nd/zOv+mM9rnfeafswYo1GjRumGG25QTEyMJCk7O1uSFBkZ6dE2MjJSu3fvdrcJDAxU3bp1S7QpXj87O1sNGjQosc8GDRp4tDl7P3Xr1lVgYKC7jbf7d7Zff/1VTzzxhAYOHKiwsDD3/LvuukvNmjWT0+nU9u3blZKSoi+++ELp6enVon9l9S0xMVF//OMf1aRJE2VlZempp55St27dtHnzZtntdku8dhV53ebOnavQ0FD169fPY351ft22bdumTp066ddff9Vll12mpUuX6uqrr3b/87PyZ66svp3Nqp+38vpn9c9cRV87K37mUDZ/zP/+mvP9Nd/7W6731xzvr/ndH/O6P+dzCvlSDB8+XF9++aU2bNhQYpnNZvN4bIwpMe9sZ7cprf35tDlf5fVP+m2gjgEDBuj06dOaMWOGx7IHHnjA/XdMTIyio6PVvn17bdmyRddee22FY6+q/pXVtzvuuMMj7vbt26tJkyZauXJliQ/shcZ9sft2ptdff1133XWXateu7TG/Or9uV1xxhbZu3arDhw/rv//9rwYNGqR169aVuU8rfebK6tuZCcTKn7fy+mf1z1xFXjvJmp85lM0f87+/5nx/zff+luv9Ncf7a373x7zuz/mcU+vPMmLECC1fvlxr165Vo0aN3POdTqcklfjVJCcnx/0Li9PpVGFhoXJzc8ttc/DgwRL7/emnnzzanL2f3NxcnTx5ssSvOd7qX7GTJ0+qf//+ysrKUnp6usevh6W59tprFRAQoF27drlj91X/ztW3MzVs2FBNmjTxiLs6v3YV6dvHH3+snTt36v777z/n9qrT6xYYGKjf//73at++vdLS0tS6dWu9/PLLfvGZK6tvxaz8eatI/85ktc9cRfpm1c8cSueP+d9fc76/5nt/zPX+muP9Nb/7Y17363x+3lfX+5nTp0+bhx9+2LhcLvPtt9+WutzpdJpJkya55xUUFJQ6KMfixYvdbfbv31/qIA+ffvqpu80nn3xS6oAI+/fvd7dZtGjRBQ2IcK7+GWNMYWGhueWWW0zLli1NTk5Ohba7bds2jwFZfNG/ivTtbD///LOx2+1m7ty5xpjq+9pVpm+DBg0qMSpqWarD61aWbt26mUGDBln+M1de34yx7uetPGf272xW+cxVpm/+8pm71Plj/vfXnO+v+f5SyvX+muP9Nb/7Y173p3xOIf//e+ihh4zD4TAfffSRx+0Fjh8/7m7z3HPPGYfDYZYsWWK2bdtm7rzzzlJvk9GoUSOzevVqs2XLFtOtW7dSb7twzTXXmMzMTJOZmWlatWpV6i0KunfvbrZs2WJWr15tGjVqdEG3KDhX/06ePGn69u1rGjVqZLZu3erRpqCgwBhjzHfffWcmTJhgNm7caLKysszKlSvNlVdeadq2bevT/p2rb0eOHDGPPfaYycjIMFlZWWbt2rWmU6dO5ne/+121f+0q8r40xpi8vDwTHBxsZs6cWWIb1fV1M8aYlJQUs379epOVlWW+/PJL8+STT5oaNWqYVatWGWOs/Zkrr29W/rxVpH9W/sydq2/FrPqZQ0n+mP/9Nef7a77311zvrzneX/O7P+Z1f8/nFPL/P0mlTrNnz3a3OX36tBk/frxxOp3GbrebP/zhD2bbtm0e2zlx4oQZPny4CQ8PN0FBQaZPnz5mz549Hm0OHTpk7rrrLhMaGmpCQ0PNXXfdZXJzcz3a7N692/Tu3dsEBQWZ8PBwM3z4cPPrr79WWf+ysrLKbLN27VpjjDF79uwxf/jDH0x4eLgJDAw0zZs3N4888kiJe0he7P6dq2/Hjx83CQkJpn79+iYgIMA0btzYDBo0qMTrUh1fu4q8L40x5p///KcJCgoqcV9LY6rv62aMMffdd59p0qSJCQwMNPXr1zfdu3f3+Odq5c9ceX2z8uetIv2z8mfuXH0rZtXPHEryx/zvrznfX/O9v+Z6f83x/prf/TGv+3s+txljTMkT7gEAAAAAQHXEYHcAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhVDIAwAAAABgIRTyAAAAAABYCIU8AAAAAAAWQiEPAAAAAICFUMgDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAC7InDlzZLPZ3FPt2rXldDrVtWtXpaWlKScnp8Q6qampstlsldrP8ePHlZqaqo8++qhS65W2r6ZNm6pPnz6V2s65LFiwQFOnTi11mc1mU2pqqlf3520ffvih2rdvr5CQENlsNi1btqzUdj/88INsNpteeOGFixvgORTHVTzVqFFDERERuummm5SZmVmhbRS/l3/44YeqDRa4QBTyuChI8L8hwf/mhRdeKJEkBw8erKZNm55XXMWv388//3xe6w8ePNjj/Wm323XFFVdo/Pjx+vXXXyu0jaZNm2rw4MHntX8A8BezZ89WZmam0tPT9Y9//ENt2rTRpEmTdNVVV2n16tUebe+///4KF1fFjh8/rgkTJlQ6z5/Pvs5HeXk+MzNT999/f5XHcL6MMerfv78CAgK0fPlyZWZmKi4uztdhnZcRI0YoMzNTH3/8sdLS0vTFF1+oa9eu+vzzz8+5bu/evZWZmamGDRtehEiB81fL1wHg0jJ79mxdeeWVOnnypHJycrRhwwZNmjRJL7zwghYvXqwePXq4295///268cYbK7X94gQvSfHx8RVe73z2dT4WLFig7du3Kzk5ucSyzMxMNWrUqMpjOF/FCb5FixZavny5QkJCdMUVV3ht+0899ZRGjhzpte1VVlBQkNasWSNJys3N1cKFC/XMM8/om2++0eLFi8+5/tKlSxUWFlbVYQJAtRYTE6P27du7H99222169NFHdcMNN6hfv37atWuXIiMjJUmNGjWq8rx3/PhxBQcHX5R9nUvHjh19uv9z2b9/v3755Rfdeuut6t69u6/DuSCNGzd2P9+dO3fW73//e3Xv3l0zZszQv/71r1LXOXHihGrXrq369eurfv36FzNc4LxwRB4XVUxMjDp27KguXbrotttu00svvaQvv/xSISEh6tevnw4ePOhu26hRoypPesePH79o+zqXjh07+vxLRnnOTvAdO3ZU3bp1vbb95s2bq23btl7bXmXVqFFDHTt2VMeOHZWYmKg33nhDXbp00VtvvaV9+/aVud6JEyckSW3btlXz5s0vVrgAYBmNGzfWiy++qCNHjuif//yne35pZ8OtWbNG8fHxioiIUFBQkBo3bqzbbrtNx48f1w8//OAusCZMmOA+i6r4bKji7W3ZskW333676tat6/6/XN5ZfkuXLtU111yj2rVr6/LLL9ff//53j+VlnWr90UcfyWazuc8OiI+P18qVK7V7926Ps7yKlXbm3fbt23XzzTerbt26ql27ttq0aaO5c+eWup+FCxdq3LhxcrlcCgsLU48ePbRz586yn/gzbNiwQd27d1doaKiCg4MVGxurlStXupenpqa6v4OMHTtWNpvtvM+SO9OePXt09913q0GDBrLb7brqqqv04osv6vTp0+421113nXr37u2xXqtWrWSz2bRx40b3vCVLlshms2nbtm2VjqP4O97u3bsl/b/XdNWqVbrvvvtUv359BQcHq6CgoMzX+/3331f37t3lcDgUHBysq666SmlpaR5tNm3apL59+yo8PFy1a9dW27Zt9dZbb3m0OX78uEaPHq1mzZqpdu3aCg8PV/v27bVw4cJK9wuXNgp5+BwJ/jeXYoI/U2mn1h8+fFhDhgxReHi4LrvsMvXu3Vvff/99mZchHDx4UHfeeaccDociIyN13333KS8v77xjOjvxF19usWTJErVt21a1a9d2nwFS2qn1hw8f1mOPPabLL79cdrtdDRo00E033aRvvvnG3aawsFDPPvusrrzyStntdtWvX19/+tOf9NNPP3lsq7z3PgBUdzfddJNq1qyp9evXl9nmhx9+UO/evRUYGKjXX39d77//vp577jmFhISosLBQDRs21Pvvvy9JGjJkiDIzM5WZmamnnnrKYzv9+vXT73//e/373//WK6+8Um5cW7duVXJysh599FEtXbpUsbGxGjly5Hld+z1jxgx17txZTqfTHVt5p/Pv3LlTsbGx2rFjh/7+979ryZIluvrqqzV48GBNnjy5RPsnn3xSu3fv1muvvaZXX31Vu3btUlJSkoqKisqNa926derWrZvy8vI0a9YsLVy4UKGhoUpKSnKfcXb//fdryZIlkv7faelLly6t9HNwpp9++kmxsbFatWqV/vrXv2r58uXq0aOHRo8ereHDh7vb9ejRQ+vXr9fJkycl/ZbLt2/frqCgIKWnp7vbrV69WpGRkWrVqlWlY/nuu+8kqcSR9vvuu08BAQF688039Z///EcBAQGlrj9r1izddNNNOn36tF555RWtWLFCjzzyiH788Ud3m7Vr16pz5846fPiwXnnlFb399ttq06aN7rjjDs2ZM8fdbtSoUZo5c6YeeeQRvf/++3rzzTf1xz/+UYcOHap0v3Bp49R6VAuVSfBdunTR66+/rjp16mjfvn16//33PRL8jTfeqCFDhrivQzv7n3a/fv00YMAAPfjggzp27Fi5cRUn+NTUVDmdTs2fP18jR45UYWGhRo8eXak+zpgxQ3/+85/1v//9r0LJsTjBN2jQQH//+98VERGhefPmafDgwTp48KDGjBnj0f7JJ59U586d9dprryk/P19jx45VUlKSvv76a9WsWbPM/axbt049e/bUNddco1mzZslut2vGjBlKSkrSwoULdccdd+j+++9X69at1a9fP40YMUIDBw6U3W4/Zx9Onz6tU6dOlTq/IusmJSVp06ZNSk1N1bXXXqvMzMxyL4G47bbbdMcdd2jIkCHatm2bUlJSJEmvv/76OfdXmtIS/5YtW/T111/rL3/5i5o1a6aQkJBS1z1y5IhuuOEG/fDDDxo7dqw6dOigo0ePav369Tpw4ICuvPJKnT59WjfffLM+/vhjjRkzRrGxsdq9e7fGjx+v+Ph4bdq0SUFBQed87wcHB59X/wDgYgkJCVG9evW0f//+Mtts3rxZv/76q55//nm1bt3aPX/gwIHuv9u1ayep/DPpBg0a5P6R9Vz279+vzz//3L2/xMRE5eTk6K9//auGDRtWqf+vV199terUqSO73V6hs/xSU1NVWFiotWvXKioqStJv34cOHz6sCRMmaOjQoXI4HB7bnzdvnvtxzZo11b9/f23cuLHc/T3xxBOqW7euPvroI1122WWSpD59+qhNmzYaPXq0+vfvr0aNGrnz9ZmnpV+IKVOmaN++ffr00091/fXXS5J69eqloqIivfLKK0pOTlaLFi3Uo0cPPffcc/rkk0/UpUsXrV69WqGhobrnnnu0evVqPfnkk5J+K+Qresp/8fePoqIi7dixQw8++KAk6a677vJo1717d4+DSKU5evSoRo0apc6dO2vNmjXugzBnxzJs2DC1bNlSa9asUa1atdz9/fnnn/Xkk0/q3nvvVY0aNfR///d/SkhI0KOPPupe9+wzEoCKoJBHtUCCL8kfEvzYsWM1duzYCrU92/vvv68NGzZo5syZ7gTcs2dPBQYGugv0sw0ZMkSPP/64pN9+4f/uu+/0+uuva9asWRUaOLG4j4cPH9aCBQu0bNkyXXfddYqOjna3ycnJ0VdffaUWLVqUu62pU6dqx44dSk9P9xj7oV+/fu6/33rrLb3//vv673//6zG/devWuu666zRnzhw99NBDFXrvA0B1Z4wpd3mbNm0UGBioP//5zxo2bJi6dOmiyy+/vNL7ue222yrctmXLlh7/V6Xf/remp6dry5YtuuGGGyq9/4pas2aNunfv7s7xxQYPHqz33nuvxI/Xffv29Wh3zTXXSPrtrLGy8vKxY8f06aef6qGHHnLneOm37wj33HOPxo4dq507d+rKK6/0Vrfc1qxZo6uvvtpdxBcbPHiwZs6cqTVr1qhFixbq3LmzateurdWrV6tLly5KT09XfHy8brzxRs2aNUvHjx/XoUOHtGvXrjLz/9nO/v4RGRmpf/7zn7rppps82lXkvZKRkaH8/HwNGzaszO8S3333nb755hv3mRxnHsS46aab9M4772jnzp266qqrdP3112v+/Pl64okndOONN6pDhw4KCgqqUL+AM3FqPaqNyiT4uXPn6vvvvz+v/Xgjwefn52vLli3ntf+KKi/BHz9+vMTpeuUl+LIUJ/jbb7+91AT/448/Vvj0/NKMHDlSGzduLDFVZFC7devWSZL69+/vMf/OO+8sc53SnoNff/211LsinO3YsWMKCAhQQECA6tevr+TkZCUmJpY4e+Kaa645ZxEvSe+99577SENZ3nnnHdWpU0dJSUk6deqUe2rTpo2cTqf7sgxvvfcBwFeOHTumQ4cOyeVyldmmefPmWr16tRo0aKCHH35YzZs3V/PmzfXyyy9Xal+VGW3c6XSWOa+qT3U+dOhQqbEWP0dn7z8iIsLjcfGZccVjtZQmNzdXxphK7cdbKtq/2rVrq3Pnzu67Gnz44Yfq2bOn4uPjVVRUpI8//th9in15OfVMxd8/Nm/erP/97386cOCA/vznP5doV5H3SvGlbuWNY1Q8xtPo0aPd3yWKp2HDhkmS+846f//73zV27FgtW7ZMXbt2VXh4uG655Rbt2rWrQn0DinFEHtVCcYIv77qn4gQ/efJkPfzwwzp27Jguv/xyPfLII5Ua7ZwE//9UdYJv1KiRx+jFxSpy26BDhw6pVq1aCg8P95hfPNpxac7nOSgWFBTkvrTDbrerSZMmpY5CX9H3z08//aTGjRuX2+bgwYM6fPiwAgMDS11enPS99d4HAF9ZuXKlioqKznlHmS5duqhLly4qKirSpk2bNG3aNCUnJysyMlIDBgyo0L4qc+va7OzsMucV55TatWtLkgoKCjzane8tT4tFRETowIEDJeYXn51Yr169C9q+JNWtW1c1atSo8v2UpjL96969u55++ml99tln+vHHH9WzZ0+FhobquuuuU3p6uvbv368WLVqUOLhRlrK+f5ytIu+V4svrzrwe/mzFfUlJSfE4w+5MxXf6CQkJ0YQJEzRhwgQdPHhQ7733np544gklJSV5jKEDnAtH5FEtVCbBr1ixQnl5efrkk0/UqVMnJScna9GiRRXeFwn+//Flgj+XiIgInTp1Sr/88ovH/NJeE2+oUaOG2rdvr/bt26tVq1Zl3kquou+f+vXrl5v0pd+e24iIiFLPWti4caNmzJjhbuuN9z4A+MKePXs0evRoORwODR06tELr1KxZUx06dNA//vEPSXKfBVeZH2grYseOHfriiy885i1YsEChoaG69tprJck9EOuXX37p0W758uUltme32yscW/fu3bVmzZoSlxW+8cYbCg4O9sp16iEhIerQoYOWLFniEdfp06c1b948NWrUqEJnmZ2P7t2766uvvipxBuMbb7whm82mrl27uuf16NFDp06d0lNPPaVGjRq5T/Xv0aOHVq9erTVr1lT4aLy3xcbGyuFw6JVXXinz7NErrrhC0dHR+uKLL9zfJc6eQkNDS6wXGRmpwYMH684779TOnTsZwBaVwhF5+NyFJPgrr7xS8+fP15YtWzRgwIAqS/Bnnl5fXoI/877q3kjwS5cu1f79+z1ORayqBP/CCy+4r9G6GAn+XOLi4jR58mQtXrxYDz30kHu+VQrXxMREPf3001qzZo26detWaps+ffpo0aJFKioqUocOHSq03bLe+wBQHWzfvt19mVBOTo4+/vhjzZ49WzVr1tTSpUvLvT/3K6+8ojVr1qh3795q3Lixfv31V/dgpcVFXGhoqJo0aaK3335b3bt3V3h4uOrVq3fed1JxuVzq27evUlNT1bBhQ82bN0/p6emaNGmSexyc6667TldccYVGjx6tU6dOqW7dulq6dKk2bNhQYnutWrXSkiVLNHPmTLVr1879I3Fpxo8fr3feeUddu3bV008/rfDwcM2fP18rV67U5MmTPcbBuRBpaWnq2bOnunbtqtGjRyswMFAzZszQ9u3btXDhwkod4Djbtm3b9J///KfE/Ouuu06PPvqo3njjDfXu3VvPPPOMmjRpopUrV2rGjBl66KGHPL5ftGvXTnXr1tWqVav0pz/9yT2/R48e+utf/+r+2xcuu+wyvfjii7r//vvVo0cPPfDAA4qMjNR3332nL774QtOnT5ck/fOf/1RiYqJ69eqlwYMH63e/+51++eUXff3119qyZYv+/e9/S5I6dOigPn366JprrlHdunX19ddf680331SnTp0YvBaVQiGPi4oEf2kl+Atx4403qnPnznrssceUn5+vdu3aKTMzU2+88Yak346gV2fJyclavHixbr75Zj3xxBO6/vrrdeLECa1bt059+vRR165dNWDAAM2fP1833XSTRo4cqeuvv14BAQH68ccftXbtWt1888269dZbK/TeB4DqoLgICwwMVJ06dXTVVVdp7Nixuv/++8vN8dJv44GsWrVK48ePV3Z2ti677DLFxMRo+fLlSkhIcLebNWuWHn/8cfXt21cFBQUaNGiQx+29KqNNmzb605/+pPHjx2vXrl1yuVyaMmWKx4jiNWvW1IoVKzR8+HA9+OCDstvtGjBggKZPn15itPGRI0dqx44devLJJ5WXlydjTLlHcTMyMvTkk0/q4Ycf1okTJ3TVVVdp9uzZJW5neiHi4uK0Zs0ajR8/XoMHD9bp06fVunVrLV++XH369Lmgbb/xxhvuvHym4j5kZGQoJSVFKSkpys/P1+WXX67Jkydr1KhRHu1r1Kih+Ph4LV261COvderUSSEhITpx4oTHEfyLbciQIXK5XJo0aZLuv/9+GWPUtGlTDRo0yN2ma9eu+uyzz/S3v/1NycnJys3NVUREhK6++mqP8X66deum5cuX66WXXtLx48f1u9/9Tvfee6/GjRvni67BygxwEcyePdtIck+BgYGmQYMGJi4uzkycONHk5OSUWGf8+PHmzLdoZmamufXWW02TJk2M3W43ERERJi4uzixfvtxjvdWrV5u2bdsau91uJJlBgwZ5bO+nn346576MMaZJkyamd+/e5j//+Y9p2bKlCQwMNE2bNjVTpkwpsf63335rEhISTFhYmKlfv74ZMWKEWblypZFk1q5d6273yy+/mNtvv93UqVPH2Gw2j31KMuPHj/fY7rZt20xSUpJxOBwmMDDQtG7d2syePdujzdq1a40k8+9//9tjflZWlpFUon1pPv74Y9OtWzcTEhJigoKCTMeOHc2KFStK3d7zzz9/zu2dq+3zzz9vJJmsrCz3vEGDBpkmTZp4tPvll1/Mn/70J1OnTh0THBxsevbsaT755BMjybz88svudmW9tsXvuzP3U5pBgwaZkJCQc/ar+D1R1rLi91qx3NxcM3LkSNO4cWMTEBBgGjRoYHr37m2++eYbd5uTJ0+aF154wbRu3drUrl3bXHbZZebKK680Q4cONbt27TLGVPy9DwAAgEuDzZhzDBUOANXIggULdNddd+n//u//FBsb6+twAAAAgIuOQh5AtbVw4ULt27dPrVq1Uo0aNfTJJ5/o+eefV9u2bd23pwMAAAAuNVwjD6DaCg0N1aJFi/Tss8/q2LFjatiwoQYPHqxnn33W16EBAAAAPsMReQAAAAAALKR6D/sMAAAAAAA8UMgDAAAAAGAhFPIAAAAAAFiIJQe7O336tPbv36/Q0FDZbDZfhwMAwDkZY3TkyBG5XC7VqMHv6GcjtwMArMaXud2Shfz+/fsVFRXl6zAAAKi0vXv3qlGjRr4Oo9ohtwMArMoXud2ShXxoaKik356wsLAwH0cDAMC55efnKyoqyp3D4IncDgCwGl/mdksW8sWn3IWFhZHsAQCWwmnjpSO3AwCsyhe5nYv0AAAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAuhkAcAAAAAwEJq+ToAwDKSkry7vRUrvLs9AADgQ17+niC+JwAoG0fkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBALiErV+/XklJSXK5XLLZbFq2bFmZbYcOHSqbzaapU6d6zC8oKNCIESNUr149hYSEqG/fvvrxxx+rNnAAAC5hFPIAAFzCjh07ptatW2v69Onltlu2bJk+/fRTuVyuEsuSk5O1dOlSLVq0SBs2bNDRo0fVp08fFRUVVVXYAABc0ipdyJ/rl/vBgwfLZrN5TB07dvRowy/3AABUD4mJiXr22WfVr1+/Mtvs27dPw4cP1/z58xUQEOCxLC8vT7NmzdKLL76oHj16qG3btpo3b562bdum1atXV3X4AABckipdyFfkl/sbb7xRBw4ccE/vvvuux3J+uQcAwBpOnz6te+65R48//rhatmxZYvnmzZt18uRJJSQkuOe5XC7FxMQoIyOjzO0WFBQoPz/fYwIAABVTq7IrJCYmKjExsdw2drtdTqez1GXFv9y/+eab6tGjhyRp3rx5ioqK0urVq9WrV6/KhgQAAKrIpEmTVKtWLT3yyCOlLs/OzlZgYKDq1q3rMT8yMlLZ2dllbjctLU0TJkzwaqwAAFwqquQa+Y8++kgNGjRQixYt9MADDygnJ8e97Hx+uedXewAALr7Nmzfr5Zdf1pw5c2Sz2Sq1rjGm3HVSUlKUl5fnnvbu3Xuh4QIAcMnweiGfmJio+fPna82aNXrxxRe1ceNGdevWTQUFBZLO75f7tLQ0ORwO9xQVFeXtsAEAwFk+/vhj5eTkqHHjxqpVq5Zq1aql3bt367HHHlPTpk0lSU6nU4WFhcrNzfVYNycnR5GRkWVu2263KywszGMCAAAV4/VC/o477lDv3r0VExOjpKQkvffee/r222+1cuXKctcr75d7frUHAODiu+eee/Tll19q69at7snlcunxxx/XBx98IElq166dAgIClJ6e7l7vwIED2r59u2JjY30VOgAAfq3S18hXVsOGDdWkSRPt2rVLkucv92celc/JySkz4dvtdtnt9qoOFQCAS87Ro0f13XffuR9nZWVp69atCg8PV+PGjRUREeHRPiAgQE6nU1dccYUkyeFwaMiQIXrssccUERGh8PBwjR49Wq1atXKPhQMAALyryu8jf+jQIe3du1cNGzaUxC/3AABUJ5s2bVLbtm3Vtm1bSdKoUaPUtm1bPf300xXexksvvaRbbrlF/fv3V+fOnRUcHKwVK1aoZs2aVRU2AACXtEofkS/vl/vw8HClpqbqtttuU8OGDfXDDz/oySefVL169XTrrbdK4pd7AACqk/j4eBljKtz+hx9+KDGvdu3amjZtmqZNm+bFyICLIcnXAQAXwNvv3xVe3h6qUqUL+U2bNqlr167ux6NGjZIkDRo0SDNnztS2bdv0xhtv6PDhw2rYsKG6du2qxYsXKzQ01L3OSy+9pFq1aql///46ceKEunfvrjlz5vDLPQAAAAAA51DpQv5cv9wXD35THn65BwAAAADg/FT5NfIAAAAAAMB7KOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAuhkAcAAAAAwEIo5AEAAAAAsBAKeQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAuhkAcAAAAAwEJq+ToA4JKVlOS9ba1Y4b1tAbikrF+/Xs8//7w2b96sAwcOaOnSpbrlllskSSdPntRf/vIXvfvuu/r+++/lcDjUo0cPPffcc3K5XO5tFBQUaPTo0Vq4cKFOnDih7t27a8aMGWrUqJGPegUAgH/jiDwAAJewY8eOqXXr1po+fXqJZcePH9eWLVv01FNPacuWLVqyZIm+/fZb9e3b16NdcnKyli5dqkWLFmnDhg06evSo+vTpo6KioovVDQAALikckQcA4BKWmJioxMTEUpc5HA6lp6d7zJs2bZquv/567dmzR40bN1ZeXp5mzZqlN998Uz169JAkzZs3T1FRUVq9erV69epV5X0AAOBSwxF5AABQYXl5ebLZbKpTp44kafPmzTp58qQSEhLcbVwul2JiYpSRkeGjKAEA8G8ckQcAABXy66+/6oknntDAgQMVFhYmScrOzlZgYKDq1q3r0TYyMlLZ2dllbqugoEAFBQXux/n5+VUTNAAAfogj8gAA4JxOnjypAQMG6PTp05oxY8Y52xtjZLPZylyelpYmh8PhnqKiorwZLgAAfo0j8gBwKfDmXRKqAndeqNZOnjyp/v37KysrS2vWrHEfjZckp9OpwsJC5ebmehyVz8nJUWxsbJnbTElJ0ahRo9yP8/PzKeYBAKigSh+RX79+vZKSkuRyuWSz2bRs2TL3spMnT2rs2LFq1aqVQkJC5HK5dO+992r//v0e24iPj5fNZvOYBgwYcMGdAQAA3lVcxO/atUurV69WRESEx/J27dopICDAY1C8AwcOaPv27eUW8na7XWFhYR4TAAComEofkS++Tc2f/vQn3XbbbR7LzrxNTevWrZWbm6vk5GT17dtXmzZt8mj7wAMP6JlnnnE/DgoKOs8uAACA83X06FF999137sdZWVnaunWrwsPD5XK5dPvtt2vLli165513VFRU5L7uPTw8XIGBgXI4HBoyZIgee+wxRUREKDw8XKNHj1arVq3co9gDAKzAm2fvcaZdVat0IX+ht6kpFhwcLKfTWdndAwAAL9q0aZO6du3qflx8uvugQYOUmpqq5cuXS5LatGnjsd7atWsVHx8vSXrppZdUq1Yt9e/fXydOnFD37t01Z84c1axZ86L0AQCAS02VXyN/9m1qis2fP1/z5s1TZGSkEhMTNX78eIWGhpa6DUa2BQCgasTHx8sYU+by8pYVq127tqZNm6Zp06Z5MzQAAFCGKi3kS7tNjSTdddddatasmZxOp7Zv366UlBR98cUXJY7mF0tLS9OECROqMlQAAAAAACyhygr58m5T88ADD7j/jomJUXR0tNq3b68tW7bo2muvLbEtRrYFAAAAAOA3VVLIl3ebmtJce+21CggI0K5du0ot5O12u+x2e1WECgAAAACApXi9kD/zNjVr164tcZua0uzYsUMnT55Uw4YNvR0OAAAAAAB+pdKF/IXepuZ///uf5s+fr5tuukn16tXTV199pccee0xt27ZV586dvdczAAAAAAD8UKUL+Qu9TU1gYKA+/PBDvfzyyzp69KiioqLUu3dvjR8/ntvUAAAAAABwDpUu5C/0NjVRUVFat25dZXcLAAAAAAAk1fB1AAAAAAAAoOIo5AEAAAAAsBAKeQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyklq8DAAAAAHC2JC9ua4UXtwWgOuCIPAAAl7D169crKSlJLpdLNptNy5Yt81hujFFqaqpcLpeCgoIUHx+vHTt2eLQpKCjQiBEjVK9ePYWEhKhv37768ccfL2IvAAC4tFDIAwBwCTt27Jhat26t6dOnl7p88uTJmjJliqZPn66NGzfK6XSqZ8+eOnLkiLtNcnKyli5dqkWLFmnDhg06evSo+vTpo6KioovVDQAALimcWg8AwCUsMTFRiYmJpS4zxmjq1KkaN26c+vXrJ0maO3euIiMjtWDBAg0dOlR5eXmaNWuW3nzzTfXo0UOSNG/ePEVFRWn16tXq1avXResLAACXCo7IAwCAUmVlZSk7O1sJCQnueXa7XXFxccrIyJAkbd68WSdPnvRo43K5FBMT424DAAC8iyPyAACgVNnZ2ZKkyMhIj/mRkZHavXu3u01gYKDq1q1bok3x+qUpKChQQUGB+3F+fr63wgYAwO9xRB4AAJTLZrN5PDbGlJh3tnO1SUtLk8PhcE9RUVFeiRUAgEsBhTwAACiV0+mUpBJH1nNyctxH6Z1OpwoLC5Wbm1tmm9KkpKQoLy/PPe3du9fL0QMA4L8o5AEAQKmaNWsmp9Op9PR097zCwkKtW7dOsbGxkqR27dopICDAo82BAwe0fft2d5vS2O12hYWFeUwAAKBiuEYeAIBL2NGjR/Xdd9+5H2dlZWnr1q0KDw9X48aNlZycrIkTJyo6OlrR0dGaOHGigoODNXDgQEmSw+HQkCFD9NhjjykiIkLh4eEaPXq0WrVq5R7FHgAAeBeFPAAAl7BNmzapa9eu7sejRo2SJA0aNEhz5szRmDFjdOLECQ0bNky5ubnq0KGDVq1apdDQUPc6L730kmrVqqX+/fvrxIkT6t69u+bMmaOaNWte9P4AAHApsBljjK+DqKz8/Hw5HA7l5eVxKh4unqQkX0dQthUrfB0Bqrvq/P6VLon3MLmrfDw/8I1q/r/Ra/z/f+ylqTq/fy+N95wvcxfXyAMAAAAAYCGVLuTXr1+vpKQkuVwu2Ww2LVu2zGO5MUapqalyuVwKCgpSfHy8duzY4dGmoKBAI0aMUL169RQSEqK+ffvqxx9/vKCOAAAAAABwKah0IX/s2DG1bt1a06dPL3X55MmTNWXKFE2fPl0bN26U0+lUz549deTIEXeb5ORkLV26VIsWLdKGDRt09OhR9enTR0VFReffEwAAAAAALgGVHuwuMTFRiYmJpS4zxmjq1KkaN26c+vXrJ0maO3euIiMjtWDBAg0dOlR5eXmaNWuW3nzzTfdotvPmzVNUVJRWr16tXr16XUB3AAAAAADwb169Rj4rK0vZ2dlKSEhwz7Pb7YqLi1NGRoYkafPmzTp58qRHG5fLpZiYGHcbAAAAAABQOq/efi47O1uSFBkZ6TE/MjJSu3fvdrcJDAxU3bp1S7QpXv9sBQUFKigocD/Oz8/3ZtgAAAAAAFhGlYxab7PZPB4bY0rMO1t5bdLS0uRwONxTVFSU12IFAAAAAMBKvHpE3ul0SvrtqHvDhg3d83NyctxH6Z1OpwoLC5Wbm+txVD4nJ0exsbGlbjclJUWjRo1yP87Pz6eYx7lV9/tmAwAAAMB58OoR+WbNmsnpdCo9Pd09r7CwUOvWrXMX6e3atVNAQIBHmwMHDmj79u1lFvJ2u11hYWEeEwAAAAAAl6JKH5E/evSovvvuO/fjrKwsbd26VeHh4WrcuLGSk5M1ceJERUdHKzo6WhMnTlRwcLAGDhwoSXI4HBoyZIgee+wxRUREKDw8XKNHj1arVq3co9gDAAAAAIDSVbqQ37Rpk7p27ep+XHzK+6BBgzRnzhyNGTNGJ06c0LBhw5Sbm6sOHTpo1apVCg0Nda/z0ksvqVatWurfv79OnDih7t27a86cOapZs6YXugQAAAAAgP+yGWOMr4OorPz8fDkcDuXl5XGaPcp2KV0jv2KFryNAdVfdPw+XwHuY3FU+nh/4RjX/3+g1/v8/9tJUnd+/l8Z7zpe5q0pGrQcAAAAAAFWDQh4AAAAAAAuhkAcAAAAAwEIo5AEAAAAAsBAKeQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwkFq+DgAAAFRfp06dUmpqqubPn6/s7Gw1bNhQgwcP1l/+8hfVqPHb8QBjjCZMmKBXX31Vubm56tChg/7xj3+oZcuWPo4eAKqTJF8HAD/CEXkAAFCmSZMm6ZVXXtH06dP19ddfa/LkyXr++ec1bdo0d5vJkydrypQpmj59ujZu3Cin06mePXvqyJEjPowcAAD/RSEPAADKlJmZqZtvvlm9e/dW06ZNdfvttyshIUGbNm2S9NvR+KlTp2rcuHHq16+fYmJiNHfuXB0/flwLFizwcfQAAPgnCnkAAFCmG264QR9++KG+/fZbSdIXX3yhDRs26KabbpIkZWVlKTs7WwkJCe517Ha74uLilJGRUeZ2CwoKlJ+f7zEBAICK4Rp5AKiukriWDr43duxY5eXl6corr1TNmjVVVFSkv/3tb7rzzjslSdnZ2ZKkyMhIj/UiIyO1e/fuMreblpamCRMmVF3gAAD4MY7IAwCAMi1evFjz5s3TggULtGXLFs2dO1cvvPCC5s6d69HOZrN5PDbGlJh3ppSUFOXl5bmnvXv3Vkn8AAD4I47IAwCAMj3++ON64oknNGDAAElSq1attHv3bqWlpWnQoEFyOp2S5B7RvlhOTk6Jo/RnstvtstvtVRs8AAB+iiPyAACgTMePH3ffZq5YzZo1dfr0aUlSs2bN5HQ6lZ6e7l5eWFiodevWKTY29qLGCgDApYIj8gAAoExJSUn629/+psaNG6tly5b6/PPPNWXKFN13332SfjulPjk5WRMnTlR0dLSio6M1ceJEBQcHa+DAgT6OHgAA/0QhDwAAyjRt2jQ99dRTGjZsmHJycuRyuTR06FA9/fTT7jZjxozRiRMnNGzYMOXm5qpDhw5atWqVQkNDfRg5AAD+i0IeAACUKTQ0VFOnTtXUqVPLbGOz2ZSamqrU1NSLFhcAAJcyrpEHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEa+QBAAAAv5bk5e2t8PL24H94z1U1jsgDAAAAAGAhXi/kmzZtKpvNVmJ6+OGHJUmDBw8usaxjx47eDgMAAAAAAL/k9VPrN27cqKKiIvfj7du3q2fPnvrjH//onnfjjTdq9uzZ7seBgYHeDgMAAAAAAL/k9UK+fv36Ho+fe+45NW/eXHFxce55drtdTqfT27sGAACAX/P2dbcAYE1Veo18YWGh5s2bp/vuu082m809/6OPPlKDBg3UokULPfDAA8rJyanKMAAAAAAA8BtVOmr9smXLdPjwYQ0ePNg9LzExUX/84x/VpEkTZWVl6amnnlK3bt20efNm2e32UrdTUFCggoIC9+P8/PyqDBsAAAAAgGqrSgv5WbNmKTExUS6Xyz3vjjvucP8dExOj9u3bq0mTJlq5cqX69etX6nbS0tI0YcKEqgwVsLYkL59quIJbfOAi8+Z7mPcvAADwc1V2av3u3bu1evVq3X///eW2a9iwoZo0aaJdu3aV2SYlJUV5eXnuae/evd4OFwAAAAAAS6iyI/KzZ89WgwYN1Lt373LbHTp0SHv37lXDhg3LbGO328s87R4AAAAAgEtJlRyRP336tGbPnq1BgwapVq3/91vB0aNHNXr0aGVmZuqHH37QRx99pKSkJNWrV0+33nprVYQCAAAAAIBfqZIj8qtXr9aePXt03333ecyvWbOmtm3bpjfeeEOHDx9Ww4YN1bVrVy1evFihoaFVEQoAAAAAAH6lSgr5hIQEGWNKzA8KCtIHH3xQFbsEAAAAAOCSUKX3kQcAAAAAAN5FIQ8AAAAAgIVU6X3kAVgU9/QGAACQ5MXvRIAXcUQeAACUa9++fbr77rsVERGh4OBgtWnTRps3b3YvN8YoNTVVLpdLQUFBio+P144dO3wYMQAA/o1CHgAAlCk3N1edO3dWQECA3nvvPX311Vd68cUXVadOHXebyZMna8qUKZo+fbo2btwop9Opnj176siRI74LHAAAP8ap9QAAoEyTJk1SVFSUZs+e7Z7XtGlT99/GGE2dOlXjxo1Tv379JElz585VZGSkFixYoKFDh17skAFUOW+fbs5leEBlcUQeAACUafny5Wrfvr3++Mc/qkGDBmrbtq3+9a9/uZdnZWUpOztbCQkJ7nl2u11xcXHKyMjwRcgAAPg9CnkAAFCm77//XjNnzlR0dLQ++OADPfjgg3rkkUf0xhtvSJKys7MlSZGRkR7rRUZGupeVpqCgQPn5+R4TAACoGE6tBwAAZTp9+rTat2+viRMnSpLatm2rHTt2aObMmbr33nvd7Ww2m8d6xpgS886UlpamCRMmVE3QAAD4OY7IAwCAMjVs2FBXX321x7yrrrpKe/bskSQ5nU5JKnH0PScnp8RR+jOlpKQoLy/PPe3du9fLkQMA4L8o5AEAQJk6d+6snTt3esz79ttv1aRJE0lSs2bN5HQ6lZ6e7l5eWFiodevWKTY2tszt2u12hYWFeUwAAKBiOLUeAACU6dFHH1VsbKwmTpyo/v3767PPPtOrr76qV199VdJvp9QnJydr4sSJio6OVnR0tCZOnKjg4GANHDjQx9EDAOCfKOQBAECZrrvuOi1dulQpKSl65pln1KxZM02dOlV33XWXu82YMWN04sQJDRs2TLm5uerQoYNWrVql0NBQH0YOAID/opAHAADl6tOnj/r06VPmcpvNptTUVKWmpl68oAAAuIRxjTwAAAAAABZCIQ8AAAAAgIVwaj2AS1tSkve2tWKF97YFAADOgxfzOlCNcUQeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyEQh4AAAAAAAuhkAcAAAAAwEIo5AEAAAAAsBCvF/Kpqamy2Wwek9PpdC83xig1NVUul0tBQUGKj4/Xjh07vB0GAAAAAAB+qVZVbLRly5ZavXq1+3HNmjXdf0+ePFlTpkzRnDlz1KJFCz377LPq2bOndu7cqdDQ0KoIBwAAAEC1leTrAADLqZJT62vVqiWn0+me6tevL+m3o/FTp07VuHHj1K9fP8XExGju3Lk6fvy4FixYUBWhAAAAAADgV6qkkN+1a5dcLpeaNWumAQMG6Pvvv5ckZWVlKTs7WwkJCe62drtdcXFxysjIqIpQAAAAAADwK14/tb5Dhw5644031KJFCx08eFDPPvusYmNjtWPHDmVnZ0uSIiMjPdaJjIzU7t27y9xmQUGBCgoK3I/z8/O9HTYAAAAAAJbg9UI+MTHR/XerVq3UqVMnNW/eXHPnzlXHjh0lSTabzWMdY0yJeWdKS0vThAkTvB0qAHhXEtf4AQAAoOpV+e3nQkJC1KpVK+3atcs9en3xkfliOTk5JY7SnyklJUV5eXnuae/evVUaMwAAAAAA1VWVF/IFBQX6+uuv1bBhQzVr1kxOp1Pp6enu5YWFhVq3bp1iY2PL3IbdbldYWJjHBAAAAADApcjrp9aPHj1aSUlJaty4sXJycvTss88qPz9fgwYNks1mU3JysiZOnKjo6GhFR0dr4sSJCg4O1sCBA70dCgB/xOnrAAAAuMR5vZD/8ccfdeedd+rnn39W/fr11bFjR33yySdq0qSJJGnMmDE6ceKEhg0bptzcXHXo0EGrVq3iHvL4DUUaAAAAAJTL66fWL1q0SPv371dhYaH27dun//73v7r66qvdy202m1JTU3XgwAH9+uuvWrdunWJiYrwdBgAAqAJpaWnuM+yKGWOUmpoql8uloKAgxcfHa8eOHb4LEgAAP1fl18gDAAD/sHHjRr366qu65pprPOZPnjxZU6ZM0fTp07Vx40Y5nU717NlTR44c8VGkAAD4Nwp5AABwTkePHtVdd92lf/3rX6pbt657vjFGU6dO1bhx49SvXz/FxMRo7ty5On78uBYsWODDiAEA8F8U8gAA4Jwefvhh9e7dWz169PCYn5WVpezsbCUkJLjn2e12xcXFKSMjo8ztFRQUKD8/32MCAAAV4/XB7gAAgH9ZtGiRtmzZoo0bN5ZYlp2dLUmKjIz0mB8ZGandu3eXuc20tDRNmDDBu4ECAHCJoJAHULW4EwFgaXv37tXIkSO1atUq1a5du8x2NpvN47ExpsS8M6WkpGjUqFHux/n5+YqKirrwgAEAuARQyAMAgDJt3rxZOTk5ateunXteUVGR1q9fr+nTp2vnzp2Sfjsy37BhQ3ebnJycEkfpz2S322W326sucAAA/BjXyAMAgDJ1795d27Zt09atW91T+/btddddd2nr1q26/PLL5XQ6lZ6e7l6nsLBQ69atU2xsrA8jBwDAf3FEHgAAlCk0NFQxMTEe80JCQhQREeGen5ycrIkTJyo6OlrR0dGaOHGigoODNXDgQF+EDACA36OQBwAAF2TMmDE6ceKEhg0bptzcXHXo0EGrVq1SaGior0MDAMAvUcgDAIBK+eijjzwe22w2paamKjU11SfxAABwqeEaeQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEAp5AAAAAAAshEIeAAAAAAALoZAHAAAAAMBCKOQBAAAAALAQCnkAAAAAACyklq8DAAAAgD9L8nUAAOB3KOQBAAAAANWYN38QXOHFbfkOp9YDAAAAAGAhFPIAAAAAAFgIhTwAAAAAABZCIQ8AAAAAgIV4vZBPS0vTddddp9DQUDVo0EC33HKLdu7c6dFm8ODBstlsHlPHjh29HQoAAAAAAH7H66PWr1u3Tg8//LCuu+46nTp1SuPGjVNCQoK++uorhYSEuNvdeOONmj17tvtxYGCgt0MBAFyKkrx8q6sV/jG6LQAA8B9eL+Tff/99j8ezZ89WgwYNtHnzZv3hD39wz7fb7XI6nd7ePQAAAAAAfq3Kr5HPy8uTJIWHh3vM/+ijj9SgQQO1aNFCDzzwgHJycsrcRkFBgfLz8z0mAAAAAAAuRVVayBtjNGrUKN1www2KiYlxz09MTNT8+fO1Zs0avfjii9q4caO6deumgoKCUreTlpYmh8PhnqKioqoybAAA8P+ryNg3xhilpqbK5XIpKChI8fHx2rFjh48iBgDA/1VpIT98+HB9+eWXWrhwocf8O+64Q71791ZMTIySkpL03nvv6dtvv9XKlStL3U5KSory8vLc0969e6sybAAA8P8rHvvmk08+UXp6uk6dOqWEhAQdO3bM3Wby5MmaMmWKpk+fro0bN8rpdKpnz546cuSIDyMHAMB/ef0a+WIjRozQ8uXLtX79ejVq1Kjctg0bNlSTJk20a9euUpfb7XbZ7faqCBMAAJTjXGPfGGM0depUjRs3Tv369ZMkzZ07V5GRkVqwYIGGDh3qi7ABAPBrXj8ib4zR8OHDtWTJEq1Zs0bNmjU75zqHDh3S3r171bBhQ2+HAwAAvOjssW+ysrKUnZ2thIQEdxu73a64uDhlZGT4JEYAAPyd14/IP/zww1qwYIHefvtthYaGKjs7W5LkcDgUFBSko0ePKjU1VbfddpsaNmyoH374QU8++aTq1aunW2+91dvhAAAALylt7JviPB8ZGenRNjIyUrt37y5zWwUFBR5j4zCQLQAAFef1I/IzZ85UXl6e4uPj1bBhQ/e0ePFiSVLNmjW1bds23XzzzWrRooUGDRqkFi1aKDMzU6Ghod4OBwAAeElZY99Iks1m83hsjCkx70wMZAsAwPnz+hF5Y0y5y4OCgvTBBx94e7cAAKAKlTX2jdPplPTbkfkzL5HLyckpcZT+TCkpKRo1apT7cX5+PsU8AAAVVOX3kQcAANZ1rrFvmjVrJqfTqfT0dPe8wsJCrVu3TrGxsWVu1263KywszGMCAAAVU2Wj1gMAAOs719g3NptNycnJmjhxoqKjoxUdHa2JEycqODhYAwcO9HH0AAD4Jwp5AABQppkzZ0qS4uPjPebPnj1bgwcPliSNGTNGJ06c0LBhw5Sbm6sOHTpo1apVjH0DAEAVoZAHAABlOtfYN9JvA92lpqYqNTW16gMCAABcIw8AAAAAgJVQyAMAAAAAYCEU8gAAAAAAWAiFPAAAAAAAFkIhDwAAAACAhTBqPS5MUpKvIwAAAACASwpH5AEAAAAAsBAKeQAAAAAALIRCHgAAAAAAC6GQBwAAAADAQijkAQAAAACwEEatBwAAsDRv30FmhZe3BwDwNo7IAwAAAABgIRTyAAAAAABYCKfWAwAA4AzePlUfAOBtHJEHAAAAAMBCOCJ/KUril3YAAAAAsCoKeSug8AYAwM+Q2wEA549T6wEAAAAAsBCOyFcVjqIDAAAAAKqATwv5GTNm6Pnnn9eBAwfUsmVLTZ06VV26dPFlSAAA4DxVv7zOj+oAAP/ks1PrFy9erOTkZI0bN06ff/65unTposTERO3Zs8dXIQEAgPNEXgcA4OLxWSE/ZcoUDRkyRPfff7+uuuoqTZ06VVFRUZo5c6avQgIAAOeJvA4AwMXjk1PrCwsLtXnzZj3xxBMe8xMSEpSRkVGifUFBgQoKCtyP8/LyJEn5+fneC6p/f+9tCwDgP7yUa4pzljHGK9urTiqb16WLlNt10ovbAgD4B+/lGV/mdp8U8j///LOKiooUGRnpMT8yMlLZ2dkl2qelpWnChAkl5kdFRVVZjAAASJIcDq9u7siRI3J4eZu+Vtm8LpHbAQC+4v0c7Ivc7tPB7mw2m8djY0yJeZKUkpKiUaNGuR+fPn1av/zyiyIiIkpt70v5+fmKiorS3r17FRYW5utwvIq+WRN9syb6Zk3l9c0YoyNHjsjlcvkouqpX0bwunTu3++v7xB/7RZ+sgT5Zgz/2SfLPfhX36auvvvJJbvdJIV+vXj3VrFmzxK/0OTk5JX7NlyS73S673e4xr06dOlUZ4gULCwvzmzfp2eibNdE3a6Jv1lRW3/ztSHyxyuZ1qeK53V/fJ/7YL/pkDfTJGvyxT5J/9ut3v/udatS4+EPP+WSwu8DAQLVr107p6eke89PT0xUbG+uLkAAAwHkirwMAcHH57NT6UaNG6Z577lH79u3VqVMnvfrqq9qzZ48efPBBX4UEAADOE3kdAICLx2eF/B133KFDhw7pmWee0YEDBxQTE6N3331XTZo08VVIXmG32zV+/PgSpwv6A/pmTfTNmuibNflz387F23ndX59Lf+wXfbIG+mQN/tgnyT/75es+2Yw/3gcHAAAAAAA/5ZNr5AEAAAAAwPmhkAcAAAAAwEIo5AEAAAAAsBAKeQAAAAAALOSSL+TT0tJ03XXXKTQ0VA0aNNAtt9yinTt3erQxxig1NVUul0tBQUGKj4/Xjh07PNoUFBRoxIgRqlevnkJCQtS3b1/9+OOPHm1yc3N1zz33yOFwyOFw6J577tHhw4c92uzZs0dJSUkKCQlRvXr19Mgjj6iwsLBK+nby5EmNHTtWrVq1UkhIiFwul+69917t37/fYzvx8fGy2Wwe04ABA6p13yRp8ODBJeLu2LGjRxsrvm6SSvSreHr++efdbarj6zZz5kxdc801CgsLU1hYmDp16qT33nvPvdyqn7Vz9c3Kn7Vz9U2y7metIn2z6mfNV9avX6+kpCS5XC7ZbDYtW7bMY7nVnk9//Y7gj98P/PF7gT9+H/DH7wH+mP/9Ne/7Xc43l7hevXqZ2bNnm+3bt5utW7ea3r17m8aNG5ujR4+62zz33HMmNDTU/Pe//zXbtm0zd9xxh2nYsKHJz893t3nwwQfN7373O5Oenm62bNliunbtalq3bm1OnTrlbnPjjTeamJgYk5GRYTIyMkxMTIzp06ePe/mpU6dMTEyM6dq1q9myZYtJT083LpfLDB8+vEr6dvjwYdOjRw+zePFi880335jMzEzToUMH065dO4/txMXFmQceeMAcOHDAPR0+fNijTXXrmzHGDBo0yNx4440ecR86dMhjO1Z83YwxHn06cOCAef31143NZjP/+9//3G2q4+u2fPlys3LlSrNz506zc+dO8+STT5qAgACzfft2Y4x1P2vn6puVP2vn6psx1v2sVaRvVv2s+cq7775rxo0bZ/773/8aSWbp0qUey632fPrrdwR//H7gj98L/PH7gD9+D/DH/O+ved/fcv4lX8ifLScnx0gy69atM8YYc/r0aeN0Os1zzz3nbvPrr78ah8NhXnnlFWPMbwkvICDALFq0yN1m3759pkaNGub99983xhjz1VdfGUnmk08+cbfJzMw0ksw333xjjPntC1CNGjXMvn373G0WLlxo7Ha7ycvL83rfSvPZZ58ZSWb37t3ueXFxcWbkyJFlrlNd+zZo0CBz8803l7mOP71uN998s+nWrZvHPCu8bsYYU7duXfPaa6/51Wft7L6VxqqftWJn9s1fPmul9e1sVv6sXWylFfJns9rz6a/fEfzx+4E/fi/w1+8D/vg9wB/zv7/mfSvn/Ev+1Pqz5eXlSZLCw8MlSVlZWcrOzlZCQoK7jd1uV1xcnDIyMiRJmzdv1smTJz3auFwuxcTEuNtkZmbK4XCoQ4cO7jYdO3aUw+HwaBMTEyOXy+Vu06tXLxUUFGjz5s1e71tZbWw2m+rUqeMxf/78+apXr55atmyp0aNH68iRI+5l1blvH330kRo0aKAWLVrogQceUE5OjnuZv7xuBw8e1MqVKzVkyJASy6rz61ZUVKRFixbp2LFj6tSpk1991s7uW2ms+lkrq2/+8Fk71+tm1c9adWXF59NfvyP44/cDf/xe4G/fB/zxe4A/5n9/zfv+kPNr/X/t3U1oE1sUB/BToRMjxtJA21GDQVQUrYolm4J0kYIgiojgZxYB0UUxumg3VhBxpW4EN4obpTtdqCB0YwvxC6uISW3c+AFDuyi1KlEKNW0g/7d4L0MnkzR5fT6de/v/gaCTY2bOXE/vuWm9U3PkIgBAuru7ZefOndLa2ioiIhMTEyIi0tLS4ohtaWmR0dFRO8YwDGlsbHTFFP/+xMSENDc3u87Z3NzsiCk9T2NjoxiGYcf8ytxK5XI5OXv2rBw7dkxWrFhhH4/FYrJ27VoxTVPevXsnvb298vbtWxkYGPB0brt375aDBw9KOBwWy7Lk/PnzEo1G5c2bN+Lz+bQZt76+PgkEAnLgwAHHca+OWyaTkfb2dsnlcrJ8+XJ58OCBbN682f7ipnKtVcqtlIq1Nl9uqtdareOmWq15nWr3U9ceQcf+QMe+QKd+QMc+QMf5X9d5X6c5nwv5ORKJhIyMjMjz589dr9XV1Tn+DMB1rFRpTLn4hcQsxHy5ify9GceRI0ekUCjI9evXHa+dPHnS/n1ra6ts2LBBIpGIpFIpaWtrq/m6f3duhw8fdlx3JBKRcDgs/f39rqL8r9f9p8ZNROTWrVsSi8Vk6dKljuNeHbeNGzfK8PCwfP/+Xe7duyfxeFyePHlS8Xwq1Vql3OZOEKrW2ny5qV5rtYybiHq15nWq3U9dewQd+wMd+wKd+gEd+wAd539d532d5nz+aP0/Tp8+LQ8fPpRkMimhUMg+bpqmiIjr05HJyUn7kxTTNGV2dlay2ey8MZ8/f3ad98uXL46Y0vNks1nJ5/OuT21+RW5F+XxeDh06JJZlycDAgOMTwnLa2tqkvr5ePn78aF+3V3Oba+XKlRIOhx3XrfK4iYg8e/ZM3r9/LydOnKj6fl4ZN8MwZP369RKJROTSpUuyfft2uXbtmha1Vim3IpVrrVpuc6lWa7XkpmKteZlq91PXHkHH/kDHvkC3fkDHPkDH+V/XeV+rOb/m/02vqUKhgFOnTmHVqlX48OFD2ddN08SVK1fsYzMzM2U33rh7964dMz4+XnZDh1evXtkxL1++LLvxwfj4uB1z586dBW/oUC03AJidncX+/fuxZcsWTE5O1vS+mUzGsdmKV3Mr9fXrV/h8PvT19QFQe9yK4vG4a+fTSrwwbuVEo1HE43Gla61aboC6tVbJ3NxKqVJr/yY3HWrtd5N5NrtT5X7q2iPo2B/o2Bcsln5Axz5Ax/lf13lf5Tl/0S/ku7q60NDQgMePHzseIzA9PW3HXL58GQ0NDbh//z4ymQyOHj1a9lEYoVAIg4ODSKVSiEajZR+xsG3bNgwNDWFoaAhbt24t+yiCzs5OpFIpDA4OIhQKLfgRC9Vyy+fz2LdvH0KhEIaHhx0xMzMzAIBPnz7h4sWLeP36NSzLQn9/PzZt2oQdO3Z4OrepqSn09PTgxYsXsCwLyWQS7e3tWL16tfLjVvTjxw8sW7YMN27ccL2HV8ett7cXT58+hWVZGBkZwblz57BkyRI8evQIgLq1Vi03lWutWm4q11q13IpUrLU/ZWpqCul0Gul0GiKCq1evIp1OO3ZnVul+6toj6Ngf6NgX6NgP6NgH6Dj/6zrv6zbnL/qFvIiU/XX79m07plAo4MKFCzBNEz6fDx0dHchkMo73+fnzJxKJBILBIPx+P/bu3YuxsTFHzLdv3xCLxRAIBBAIBBCLxZDNZh0xo6Oj2LNnD/x+P4LBIBKJBHK53P+Sm2VZFWOSySQAYGxsDB0dHQgGgzAMA+vWrcOZM2dcz4r0Wm7T09PYtWsXmpqaUF9fjzVr1iAej7vGRMVxK7p58yb8fr/r2ZWAd8ft+PHjCIfDMAwDTU1N6OzsdHzxVLXWquWmcq1Vy03lWquWW5GKtfanJJPJsv/O5363Q6X7qWuPoGN/oGNfoGM/oGMfoOP8r+u8r9ucXwcAQkRERERERERK4GZ3RERERERERArhQp6IiIiIiIhIIVzIExERERERESmEC3kiIiIiIiIihXAhT0RERERERKQQLuSJiIiIiIiIFMKFPBEREREREZFCuJAnIiIiIiIiUggX8kREREREREQK4UKeiIiIiIiISCFcyBMREREREREphAt5IiIiIiIiIoX8BWYzghDvemWoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(10, 6))\n",
    "\n",
    "axs[0, 0].hist(df1['Open'], bins=20, color='blue', alpha=0.7)\n",
    "axs[0, 0].set_title('Distribution of Open Prices')\n",
    "\n",
    "axs[0, 1].hist(df1['Close*'], bins=20, color='green', alpha=0.7)\n",
    "axs[0, 1].set_title('Distribution of Close Prices')\n",
    "\n",
    "axs[1, 0].hist(df1['High'], bins=20, color='red', alpha=0.7)\n",
    "axs[1, 0].set_title('Distribution of High Prices')\n",
    "\n",
    "axs[1, 1].hist(df1['Low'], bins=20, color='yellow', alpha=0.7)\n",
    "axs[1, 1].set_title('Distribution of Low Prices')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b6916543-11f7-4c20-91b2-26bf2bccbed9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(df1['Date'], df['Close*'], marker='o', linestyle='-')\n",
    "plt.title('Stock Close Price Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Price')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "74d6dc0a-12c5-4c03-adc7-b428e764030f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x2muvmb3Ot9K/f38BQLz33ntmbYcPHxbDhw8Xzs7OwtXVVTz11FPizJkzZp9obPzk3IULF8y20dSn6jIzM4VWqxWOjo7ivvvuE88995zIy8szey3+6M9SVVWV+Pvf/y78/f2FUqkUarVa9O3bV7z44ouipKRECCFEcnKyGDlypLj//vuFUqkUHh4eYtSoUeKnn36S/RoS3S2FEEJYJrIRUWsRHR2N//u//0NVVZWlD4WIqFXjNU5EREREMjE4EREREcnEU3VEREREMnHGiYiIiEgmBiciIiIimRiciIiIiGTiDTDvoWvXruHcuXNwdnaW9TUGREREZHlCCFy+fBne3t7o0OHWc0oMTvfQuXPn4OPjY+nDICIiortQVFR02zv4MzjdQ87OzgCuv/AuLi4WPpqWYzQakZKSAp1Od9NvNaf2g+NtXTje1sVax7uyshI+Pj7S7/FbsWhwWr16NVavXo1Tp04BAPr06YM33ngDI0eOBHDzb+1+77338NJLLwG4/oWR6enpJu2TJ09GUlKS9LysrAyxsbHYvn07AGDcuHFYuXIlOnXqJNWcOXMGs2bNwq5du+Dg4IDIyEgsW7YMSqVSdn8aj9fFxcXqgpOjoyNcXFys6h+ateJ4WxeOt3Wx9vGWc5mNRYNTly5d8M4776B79+4AgA0bNmD8+PHYt28f+vTpg+LiYpP67777DtOmTcMTTzxhsjwmJgZvvvmm9NzBwcGkPTIyEmfPnoXBYAAATJ8+HXq9Ht988w0AoKGhAaNHj8Z9992HjIwMXLx4EVFRURBCYOXKlfe830RERNQ2WTQ4jR071uR5QkICVq9ejezsbPTp0wcajcakfdu2bRg6dCgeeOABk+WOjo5mtY2OHDkCg8GA7OxshIaGAgA++eQTaLVaFBYWwt/fHykpKTh8+DCKiorg7e0NAFi+fDmio6ORkJBgVbNHREREdHOt5hqnhoYGbN68GdXV1dBqtWbtpaWl2LFjBzZs2GDWlpiYiI0bN8LT0xMjR47EggULpPOUWVlZUKvVUmgCgIEDB0KtViMzMxP+/v7IyspCYGCgFJoAICIiArW1tcjNzcXQoUObPOba2lrU1tZKzysrKwFcn+o0Go1390K0QY19taY+WzOOt3XheFsXax3vO+mvxYPTwYMHodVqcfXqVXTs2BFbt25FQECAWd2GDRvg7OyMiRMnmiyfOnUq/Pz8oNFoUFBQgPj4eOzfvx+pqakAgJKSEnh4eJhtz8PDAyUlJVKNp6enSburqyuUSqVU05QlS5Zg0aJFZstTUlLg6Oh4+863M42vOVkHjrd14XhbF2sb75qaGtm1Fg9O/v7+yM/PR3l5ObZs2YKoqCikp6ebhadPP/0UU6dOhb29vcnymJgY6e+BgYHo0aMHBgwYgLy8PAQFBQFo+mIvIYTJcjk1N4qPj8e8efOk541X5et0Oqs6vWc0GpGamorhw4db5cWE1objbV043tbFWse78YyRHBYPTkqlUro4fMCAAdi7dy8++OADrFmzRqr56aefUFhYiE2bNt12e0FBQbCzs8Px48cRFBQEjUaD0tJSs7oLFy5Is0wajQY5OTkm7WVlZTAajWYzUb+nUqmgUqnMltvZ2VnVD1wja+23teJ4WxeOt3WxtvG+k762uq9cEUKYXDcEAGvXrkVwcDAeeuih265/6NAhGI1GeHl5AQC0Wi0qKiqwZ88eqSYnJwcVFRUYNGiQVFNQUGDyKb6UlBSoVCoEBwffi24RERFRO2DRGadXX30VI0eOhI+PDy5fvoykpCSkpaVJtw0Ark+fbd68GcuXLzdb/8SJE0hMTMSoUaPg7u6Ow4cPY/78+ejfvz/CwsIAAL1798aIESMQExMjzWJNnz4dY8aMgb+/PwBAp9MhICAAer0eS5cuxaVLlxAXF4eYmBirOuVGREREt2bR4FRaWgq9Xo/i4mKo1Wr069cPBoMBw4cPl2qSkpIghMDTTz9ttr5SqcQPP/yADz74AFVVVfDx8cHo0aOxYMEC2NjYSHWJiYmIjY2FTqcDcP0GmKtWrZLabWxssGPHDsycORNhYWEmN8AkIiIiamTR4LR27drb1kyfPh3Tp09vss3Hx8fsruFNcXNzw8aNG29Z07VrVyQnJ992W0RERGS9Wt01TkREREStlcU/VUdEllFTU4OjR4/e8XqXL19Geno6OnXqJOsLMW/Uq1cvq7zPGRG1DwxORFbq6NGjf+hTo++///5drZebmyvdY42IqK1hcCKyUr169UJubu4dr1dQUICoqChs2LABgYGBd7VfIqK2isGJyEo5Ojre1cxPfX09gOsBiDNHRK0TT8U3HwYnkvAfGhFR+8BT8c2HwYkk/IdG1H7xP0bWhafimw+DE0n4D42o/eJ/jKwLT8U3HwYnkvAfGlH7xf8YEd0bDE5ERFaA/zEiujd453AiIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhkYnAiIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpLJosFp9erV6NevH1xcXODi4gKtVovvvvtOao+OjoZCoTB5DBw40GQbtbW1mDNnDtzd3eHk5IRx48bh7NmzJjVlZWXQ6/VQq9VQq9XQ6/UoLy83qTlz5gzGjh0LJycnuLu7IzY2FnV1dc3WdyIiImp7LBqcunTpgnfeeQc///wzfv75Zzz22GMYP348Dh06JNWMGDECxcXF0uPbb7812cbcuXOxdetWJCUlISMjA1VVVRgzZgwaGhqkmsjISOTn58NgMMBgMCA/Px96vV5qb2howOjRo1FdXY2MjAwkJSVhy5YtmD9/fvO/CERERNRm2Fpy52PHjjV5npCQgNWrVyM7Oxt9+vQBAKhUKmg0mibXr6iowNq1a/H555/j8ccfBwBs3LgRPj4+2LlzJyIiInDkyBEYDAZkZ2cjNDQUAPDJJ59Aq9WisLAQ/v7+SElJweHDh1FUVARvb28AwPLlyxEdHY2EhAS4uLg010tAREREbYhFg9PvNTQ0YPPmzaiuroZWq5WWp6WlwcPDA506dcKQIUOQkJAADw8PAEBubi6MRiN0Op1U7+3tjcDAQGRmZiIiIgJZWVlQq9VSaAKAgQMHQq1WIzMzE/7+/sjKykJgYKAUmgAgIiICtbW1yM3NxdChQ5s85traWtTW1krPKysrAQBGoxFGo/HevDBtQGNfra3f1orjbV043tbFWsf7Tvpq8eB08OBBaLVaXL16FR07dsTWrVsREBAAABg5ciSeeuop+Pr64uTJk3j99dfx2GOPITc3FyqVCiUlJVAqlXB1dTXZpqenJ0pKSgAAJSUlUtD6PQ8PD5MaT09Pk3ZXV1colUqppilLlizBokWLzJanpKTA0dHxzl6INuzEiRMAgJycHPz2228WPhpqbhxv68Lxti7WOt41NTWyay0enPz9/ZGfn4/y8nJs2bIFUVFRSE9PR0BAACZPnizVBQYGYsCAAfD19cWOHTswceLEm25TCAGFQiE9//3f/0jNjeLj4zFv3jzpeWVlJXx8fKDT6azq9N6ePXsAAKGhoQgJCbHw0VBz43hbF463dbHW8W48YySHxYOTUqlE9+7dAQADBgzA3r178cEHH2DNmjVmtV5eXvD19cXx48cBABqNBnV1dSgrKzOZdTp//jwGDRok1ZSWlppt68KFC9Isk0ajQU5Ojkl7WVkZjEaj2UzU76lUKqhUKrPldnZ2sLOzu13X243Gvlpbv60Vx9u6cLyti7WO9530tdXdx0kIYXLd0O9dvHgRRUVF8PLyAgAEBwfDzs4OqampUk1xcTEKCgqk4KTValFRUSGlaOD6FGRFRYVJTUFBAYqLi6WalJQUqFQqBAcH3/M+EhERUdtk0RmnV199FSNHjoSPjw8uX76MpKQkpKWlwWAwoKqqCgsXLsQTTzwBLy8vnDp1Cq+++irc3d3x5z//GQCgVqsxbdo0zJ8/H507d4abmxvi4uLQt29f6VN2vXv3xogRIxATEyPNYk2fPh1jxoyBv78/AECn0yEgIAB6vR5Lly7FpUuXEBcXh5iYGKs65UZERES3ZtHgVFpaCr1ej+LiYqjVavTr1w8GgwHDhw/HlStXcPDgQXz22WcoLy+Hl5cXhg4dik2bNsHZ2Vnaxvvvvw9bW1tMmjQJV65cwbBhw7B+/XrY2NhINYmJiYiNjZU+fTdu3DisWrVKarexscGOHTswc+ZMhIWFwcHBAZGRkVi2bFnLvRhERETU6lk0OK1du/ambQ4ODvj+++9vuw17e3usXLkSK1euvGmNm5sbNm7ceMvtdO3aFcnJybfdHxEREVmvVneNExEREVFrxeBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTBb9rjoiujeOHz+Oy5cvt8i+jh49Kv1pa9sybyHOzs7o0aNHi+yLiOhWGJyI2rjjx4+jZ8+eLb7fqKioFt3fsWPHGJ6IyOIYnIjauMaZpo0bN6J3797Nvr+qqip8/fXXmDBhAjp27Njs+zty5AieeeaZFptRIyK6FQandoqnbqxP7969ERQU1Oz7MRqNKCsrg1arhZ2dXbPvj8ja8f28dWFwaod46oaIqH3g+3nrw+DUDvHUDRFR+8D389aHwakd46kbIqL2ge/nrQeDExFRG8NrXogsh8GJiKgN4TUvRJbF4ERE1Ibwmhciy2JwIiJqg3jNC5Fl8LvqiIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpLJosFp9erV6NevH1xcXODi4gKtVovvvvsOwPWPwL788svo27cvnJyc4O3tjWeffRbnzp0z2UZ4eDgUCoXJY8qUKSY1ZWVl0Ov1UKvVUKvV0Ov1KC8vN6k5c+YMxo4dCycnJ7i7uyM2NhZ1dXXN2n8iIiJqWyx6H6cuXbrgnXfeQffu3QEAGzZswPjx47Fv3z506dIFeXl5eP311/HQQw+hrKwMc+fOxbhx4/Dzzz+bbCcmJgZvvvmm9NzBwcGkPTIyEmfPnoXBYAAATJ8+HXq9Ht988w0AoKGhAaNHj8Z9992HjIwMXLx4EVFRURBCYOXKlc35EhAREVEbYtHgNHbsWJPnCQkJWL16NbKzszFt2jSkpqaatK9cuRIhISE4c+YMunbtKi13dHSERqNpch9HjhyBwWBAdnY2QkNDAQCffPIJtFotCgsL4e/vj5SUFBw+fBhFRUXw9vYGACxfvhzR0dFISEiAi4vLvew2ERERtVGt5hqnhoYGJCUlobq6GlqttsmaiooKKBQKdOrUyWR5YmIi3N3d0adPH8TFxZncqj8rKwtqtVoKTQAwcOBAqNVqZGZmSjWBgYFSaAKAiIgI1NbWIjc39x72koiIiNoyi3/lysGDB6HVanH16lV07NgRW7duRUBAgFnd1atX8corryAyMtJkBmjq1Knw8/ODRqNBQUEB4uPjsX//fmm2qqSkBB4eHmbb8/DwQElJiVTj6elp0u7q6gqlUinVNKW2tha1tbXS88rKSgDXr88yGo138CrcW/X19dKfLXEcjftoqT63dP9aO463deF4WxeOd8u4k31bPDj5+/sjPz8f5eXl2LJlC6KiopCenm4SnoxGI6ZMmYJr167ho48+Mlk/JiZG+ntgYCB69OiBAQMGIC8vT/oeJ4VCYbZfIYTJcjk1N1qyZAkWLVpktjwlJQWOjo636HXzOnHiBAAgIyMDxcXFLbbfG0+tNhdL9a+14nhbF463deF4t4yamhrZtRYPTkqlUro4fMCAAdi7dy8++OADrFmzBsD10DRp0iScPHkSu3btuu31RkFBQbCzs8Px48cRFBQEjUaD0tJSs7oLFy5Is0wajQY5OTkm7WVlZTAajWYzUb8XHx+PefPmSc8rKyvh4+MDnU5n0eui9u3bBwAYPHgw+vfv3+z7MxqNSE1NxfDhw1vkS0Bbun+tHcfbunC8rQvHu2U0njGSw+LB6UZCCOn0V2NoOn78OHbv3o3OnTvfdv1Dhw7BaDTCy8sLAKDValFRUYE9e/YgJCQEAJCTk4OKigoMGjRIqklISEBxcbG0XkpKClQqFYKDg2+6L5VKBZVKZbbczs7Oot8ibmtrK/3ZksfRUv22VP9aK463deF4WxeOd8u4k31bNDi9+uqrGDlyJHx8fHD58mUkJSUhLS0NBoMB9fX1ePLJJ5GXl4fk5GQ0NDRI1xu5ublBqVTixIkTSExMxKhRo+Du7o7Dhw9j/vz56N+/P8LCwgAAvXv3xogRIxATEyPNYk2fPh1jxoyBv78/AECn0yEgIAB6vR5Lly7FpUuXEBcXh5iYGH6ijoiIiCQWDU6lpaXQ6/UoLi6GWq1Gv379YDAYMHz4cJw6dQrbt28HADz88MMm6+3evRvh4eFQKpX44Ycf8MEHH6Cqqgo+Pj4YPXo0FixYABsbG6k+MTERsbGx0Ol0AIBx48Zh1apVUruNjQ127NiBmTNnIiwsDA4ODoiMjMSyZcua/0UgIiKiNsOiwWnt2rU3bevWrRuEELdc38fHB+np6bfdj5ubGzZu3HjLmq5duyI5Ofm22yIiIiLr1Wru40RERETU2jE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyWTQ4rV69Gv369YOLiwtcXFyg1Wrx3XffSe1CCCxcuBDe3t5wcHBAeHg4Dh06ZLKN2tpazJkzB+7u7nBycsK4ceNw9uxZk5qysjLo9Xqo1Wqo1Wro9XqUl5eb1Jw5cwZjx46Fk5MT3N3dERsbi7q6umbrOxEREbU9Fg1OXbp0wTvvvIOff/4ZP//8Mx577DGMHz9eCkfvvfceVqxYgVWrVmHv3r3QaDQYPnw4Ll++LG1j7ty52Lp1K5KSkpCRkYGqqiqMGTMGDQ0NUk1kZCTy8/NhMBhgMBiQn58PvV4vtTc0NGD06NGorq5GRkYGkpKSsGXLFsyfP7/lXgwiIiJq9WwtufOxY8eaPE9ISMDq1auRnZ2NgIAA/OMf/8Brr72GiRMnAgA2bNgAT09PfPHFF5gxYwYqKiqwdu1afP7553j88ccBABs3boSPjw927tyJiIgIHDlyBAaDAdnZ2QgNDQUAfPLJJ9BqtSgsLIS/vz9SUlJw+PBhFBUVwdvbGwCwfPlyREdHIyEhAS4uLi34qhAREVFrZdHg9HsNDQ3YvHkzqqurodVqcfLkSZSUlECn00k1KpUKQ4YMQWZmJmbMmIHc3FwYjUaTGm9vbwQGBiIzMxMRERHIysqCWq2WQhMADBw4EGq1GpmZmfD390dWVhYCAwOl0AQAERERqK2tRW5uLoYOHdrkMdfW1qK2tlZ6XllZCQAwGo0wGo337LW5U/X19dKfLXEcjftoqT63dP9aO463deF4WxeOd8u4k31bPDgdPHgQWq0WV69eRceOHbF161YEBAQgMzMTAODp6WlS7+npidOnTwMASkpKoFQq4erqalZTUlIi1Xh4eJjt18PDw6Tmxv24urpCqVRKNU1ZsmQJFi1aZLY8JSUFjo6Ot+t6szlx4gQAICMjA8XFxS2239TU1BbZj6X611pxvK0Lx9u6cLxbRk1Njexaiwcnf39/5Ofno7y8HFu2bEFUVBTS09OldoVCYVIvhDBbdqMba5qqv5uaG8XHx2PevHnS88rKSvj4+ECn01n09N6+ffsAAIMHD0b//v2bfX9GoxGpqakYPnw47Ozsmn1/Ld2/1o7jbV043taF490yGs8YyWHx4KRUKtG9e3cAwIABA7B371588MEHePnllwFcnw3y8vKS6s+fPy/NDmk0GtTV1aGsrMxk1un8+fMYNGiQVFNaWmq23wsXLphsJycnx6S9rKwMRqPRbCbq91QqFVQqldlyOzu7FvmBuxlbW1vpz5Y8jpbqt6X611pxvK0Lx9u6cLxbxp3su9Xdx0kIgdraWvj5+UGj0ZhMF9bV1SE9PV0KRcHBwbCzszOpKS4uRkFBgVSj1WpRUVGBPXv2SDU5OTmoqKgwqSkoKDCZJkxJSYFKpUJwcHCz9peIiIjaDovOOL366qsYOXIkfHx8cPnyZSQlJSEtLQ0GgwEKhQJz587F22+/jR49eqBHjx54++234ejoiMjISACAWq3GtGnTMH/+fHTu3Blubm6Ii4tD3759pU/Z9e7dGyNGjEBMTAzWrFkDAJg+fTrGjBkDf39/AIBOp0NAQAD0ej2WLl2KS5cuIS4uDjExMfxEHREREUksGpxKS0uh1+tRXFwMtVqNfv36wWAwYPjw4QCAv/3tb7hy5QpmzpyJsrIyhIaGIiUlBc7OztI23n//fdja2mLSpEm4cuUKhg0bhvXr18PGxkaqSUxMRGxsrPTpu3HjxmHVqlVSu42NDXbs2IGZM2ciLCwMDg4OiIyMxLJly1rolSAiIqK2wKLBae3atbdsVygUWLhwIRYuXHjTGnt7e6xcuRIrV668aY2bmxs2btx4y3117doVycnJt6whIiIi69bqrnEiIiIiaq0YnIiIiIhkYnAiIiIiksni93Eioj9O01EBh/JjwLkW+L9QfT3UNaeA4v2AbfO/hTiUH4Om461vektE1FIYnIjagRnBSvT+cQbwY/Pvyw5AOAAUNv++AKA3rvePiKg1YHBqpzgDYV3W5NZh8hvr0btXr2bfl7G+Hv/+978RFhYGuxYY7yNHj2LN8kiMa/Y9EbVOfD9vXRic2inOQFiXkiqBK516At4PN//OjEZUOP4KeD0EtMBXJFwpuYaSKtHs+yFqrfh+3rowOLVTnIEgImof+H7eujA4tVOcgSBqv3jqxrrw/bx1YXAiImpjeOqGyHIYnIiI2hieuiGyHAYnIqI2hqduiCyHdw4nIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKS6Q8Fp19++QXff/89rly5AgAQgp+EICIiovbrroLTxYsX8fjjj6Nnz54YNWoUiouLAQDPPfcc5s+ff08PkIiIiKi1uKvg9OKLL8LW1hZnzpyBo6OjtHzy5MkwGAz37OCIiIiIWpO7ugFmSkoKvv/+e3Tp0sVkeY8ePXD69Ol7cmBERERErc1dzThVV1ebzDQ1+u2336BSqf7wQRERERG1RncVnB599FF89tln0nOFQoFr165h6dKlGDp06D07OCIiIqLW5K5O1S1duhTh4eH4+eefUVdXh7/97W84dOgQLl26hH//+9/3+hiJiIiIWoW7mnEKCAjAgQMHEBISguHDh6O6uhoTJ07Evn378OCDD97rYyQiIiJqFe5qxgkANBoNFi1adC+PhYiIiKhVu6sZp3Xr1mHz5s1myzdv3owNGzb84YMiIiIiao3uKji98847cHd3N1vu4eGBt99++w8fFBEREVFrdFfB6fTp0/Dz8zNb7uvrizNnzsjezpIlS/DII4/A2dkZHh4emDBhAgoLC01qFApFk4+lS5dKNeHh4WbtU6ZMMdlOWVkZ9Ho91Go11Go19Ho9ysvLTWrOnDmDsWPHwsnJCe7u7oiNjUVdXZ3s/hAREVH7dlfBycPDAwcOHDBbvn//fnTu3Fn2dtLT0zFr1ixkZ2cjNTUV9fX10Ol0qK6ulmqKi4tNHp9++ikUCgWeeOIJk23FxMSY1K1Zs8akPTIyEvn5+TAYDDAYDMjPz4der5faGxoaMHr0aFRXVyMjIwNJSUnYsmULv0KGiIiIJHd1cfiUKVMQGxsLZ2dnPProowCuh6C//vWvZjM9t3Lj17OsW7cOHh4eyM3Nlbar0WhMarZt24ahQ4figQceMFnu6OhoVtvoyJEjMBgMyM7ORmhoKADgk08+gVarRWFhIfz9/ZGSkoLDhw+jqKgI3t7eAIDly5cjOjoaCQkJcHFxkd0vIiIiap/uasZp8eLFCA0NxbBhw+Dg4AAHBwfodDo89thjf+gap4qKCgCAm5tbk+2lpaXYsWMHpk2bZtaWmJgId3d39OnTB3Fxcbh8+bLUlpWVBbVaLYUmABg4cCDUajUyMzOlmsDAQCk0AUBERARqa2uRm5t7130iIiKi9uOuZpyUSiU2bdqEt956C/v374eDgwP69u0LX1/fuz4QIQTmzZuHwYMHIzAwsMmaDRs2wNnZGRMnTjRZPnXqVPj5+UGj0aCgoADx8fHYv38/UlNTAQAlJSXw8PAw256HhwdKSkqkGk9PT5N2V1dXKJVKqeZGtbW1qK2tlZ5XVlYCAIxGI4xGo8ye33v19fXSny1xHI37aKk+t3T/WjuOt3XheFsXjnfLuJN93/V9nACgZ8+e6Nmz5x/ZhGT27Nk4cOAAMjIyblrz6aefYurUqbC3tzdZHhMTI/09MDAQPXr0wIABA5CXl4egoCAA1y8yv5EQwmS5nJrfW7JkSZP3skpJSWnyu/xayokTJwAAGRkZKC4ubrH9NgbV5map/rVWHG/rwvG2LhzvllFTUyO7VnZwmjdvHt566y04OTlh3rx5t6xdsWKF7AMAgDlz5mD79u348ccf0aVLlyZrfvrpJxQWFmLTpk233V5QUBDs7Oxw/PhxBAUFQaPRoLS01KzuwoUL0iyTRqNBTk6OSXtZWRmMRqPZTFSj+Ph4k9eisrISPj4+0Ol0Fr0mat++fQCAwYMHo3///s2+P6PRiNTUVAwfPhx2dnbNvr+W7l9rx/G2Lhxv68LxbhmNZ4zkkB2c9u3bJ01l5eXl3XQW5mbLmyKEwJw5c7B161akpaU1eYuDRmvXrkVwcDAeeuih22730KFDMBqN8PLyAgBotVpUVFRgz549CAkJAQDk5OSgoqICgwYNkmoSEhJQXFwsrZeSkgKVSoXg4OAm96NSqaBSqcyW29nZtcgP3M3Y2tpKf7bkcbRUvy3Vv9aK421dON7WhePdMu5k37KD0+7du6W/p6Wl3dEB3cysWbPwxRdfYNu2bXB2dpauJVKr1XBwcJDqKisrsXnzZixfvtxsGydOnEBiYiJGjRoFd3d3HD58GPPnz0f//v0RFhYGAOjduzdGjBiBmJgY6TYF06dPx5gxY+Dv7w8A0Ol0CAgIgF6vx9KlS3Hp0iXExcUhJiaGn6gjIiIiAHfxqbr6+nrY2tqioKDgD+989erVqKioQHh4OLy8vKTHjafjkpKSIITA008/bbYNpVKJH374AREREfD390dsbCx0Oh127twJGxsbqS4xMRF9+/aFTqeDTqdDv3798Pnnn0vtNjY22LFjB+zt7REWFoZJkyZhwoQJWLZs2R/uJxEREbUPd3xxuK2tLXx9fdHQ0PCHdy6EkFU3ffp0TJ8+vck2Hx8fpKen33Ybbm5u2Lhx4y1runbtiuTkZFnHRERERNbnru7j9Pe//x3x8fG4dOnSvT4eIiIiolbrrm5H8M9//hO//PILvL294evrCycnJ5P2vLy8e3JwRERERK3JXQWnCRMmQKFQyD7VRkRERNQe3FFwqqmpwUsvvYSvv/4aRqMRw4YNw8qVK+Hu7t5cx0dERETUatzRNU4LFizA+vXrMXr0aDz99NPYuXMnXnjhheY6NiIiIqJW5Y5mnL766iusXbsWU6ZMAXD9O+LCwsLQ0NBg8tF/IiIiovbojmacioqK8Kc//Ul6HhISAltbW5w7d+6eHxgRERFRa3NHwamhoQFKpdJkma2trfTtxkRERETt2R2dqhNCIDo62uT72a5evYrnn3/e5JYEX3311b07QiIiIqJW4o6CU1RUlNmyZ5555p4dDBEREVFrdkfBad26dc11HERERESt3l195QoRERGRNWJwIiIiIpKJwYmIiIhIprv6rjoiaj1qamoAtNyXa1dVVSE9PR2urq7o2LFjs+/vyJEjzb4PIiK5GJyI2rijR48CAGJiYlp0v++//36L7s/Z2blF90dE1BQGp3aIMxDWZcKECQCAXr16wdHRsdn3V1BQgKioKGzYsAGBgYHNvj/gemjq0aNHi+yLqDXh+3nrw+DUDnEGwrq4u7vjueeea7H9NX5TQK9evRAUFNRi+yWyRnw/b30YnNohzkAQtV+cgbAufD9vfRic2iHOQBC1X5yBsC58P299GJyIiNoQzkAQWRaDExFRG8IZCCLL4g0wiYiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIJosGpyVLluCRRx6Bs7MzPDw8MGHCBBQWFprUREdHQ6FQmDwGDhxoUlNbW4s5c+bA3d0dTk5OGDduHM6ePWtSU1ZWBr1eD7VaDbVaDb1ej/LycpOaM2fOYOzYsXBycoK7uztiY2NRV1fXLH0nIiKitseiwSk9PR2zZs1CdnY2UlNTUV9fD51Oh+rqapO6ESNGoLi4WHp8++23Ju1z587F1q1bkZSUhIyMDFRVVWHMmDFoaGiQaiIjI5Gfnw+DwQCDwYD8/Hzo9XqpvaGhAaNHj0Z1dTUyMjKQlJSELVu2YP78+c37IhAREVGbYdE7hxsMBpPn69atg4eHB3Jzc/Hoo49Ky1UqFTQaTZPbqKiowNq1a/H555/j8ccfBwBs3LgRPj4+2LlzJyIiInDkyBEYDAZkZ2cjNDQUAPDJJ59Aq9WisLAQ/v7+SElJweHDh1FUVARvb28AwPLlyxEdHY2EhAS4uLg0x0tAREREbUir+sqViooKAICbm5vJ8rS0NHh4eKBTp04YMmQIEhIS4OHhAQDIzc2F0WiETqeT6r29vREYGIjMzExEREQgKysLarVaCk0AMHDgQKjVamRmZsLf3x9ZWVkIDAyUQhMAREREoLa2Frm5uRg6dKjZ8dbW1qK2tlZ6XllZCQAwGo0wGo334BVpGxr7am39tlYcb+vC8bYu1jred9LXVhOchBCYN28eBg8ebPJFkiNHjsRTTz0FX19fnDx5Eq+//joee+wx5ObmQqVSoaSkBEqlEq6uribb8/T0RElJCQCgpKREClq/5+HhYVLj6elp0u7q6gqlUinV3GjJkiVYtGiR2fKUlJQW+fLN1uLEiRMAgJycHPz2228WPhpqbhxv68Lxti7WOt41NTWya1tNcJo9ezYOHDiAjIwMk+WTJ0+W/h4YGIgBAwbA19cXO3bswMSJE2+6PSEEFAqF9Pz3f/8jNb8XHx+PefPmSc8rKyvh4+MDnU5nVaf29uzZAwAIDQ1FSEiIhY+GmhvH27pwvK2LtY534xkjOVpFcJozZw62b9+OH3/8EV26dLllrZeXF3x9fXH8+HEAgEajQV1dHcrKykxmnc6fP49BgwZJNaWlpWbbunDhgjTLpNFokJOTY9JeVlYGo9FoNhPVSKVSQaVSmS23s7ODnZ3dLfvRnjT21dr6ba043taF421drHW876SvFv1UnRACs2fPxldffYVdu3bBz8/vtutcvHgRRUVF8PLyAgAEBwfDzs4OqampUk1xcTEKCgqk4KTValFRUSElaeD6NGRFRYVJTUFBAYqLi6WalJQUqFQqBAcH35P+EhERUdtm0RmnWbNm4YsvvsC2bdvg7OwsXUukVqvh4OCAqqoqLFy4EE888QS8vLxw6tQpvPrqq3B3d8ef//xnqXbatGmYP38+OnfuDDc3N8TFxaFv377Sp+x69+6NESNGICYmBmvWrAEATJ8+HWPGjIG/vz8AQKfTISAgAHq9HkuXLsWlS5cQFxeHmJgYqzrtRkRERDdn0Rmn1atXo6KiAuHh4fDy8pIemzZtAgDY2Njg4MGDGD9+PHr27ImoqCj07NkTWVlZcHZ2lrbz/vvvY8KECZg0aRLCwsLg6OiIb775BjY2NlJNYmIi+vbtC51OB51Oh379+uHzzz+X2m1sbLBjxw7Y29sjLCwMkyZNwoQJE7Bs2bKWe0GIiIioVbPojJMQ4pbtDg4O+P7772+7HXt7e6xcuRIrV668aY2bmxs2btx4y+107doVycnJt90fERERWSd+Vx0RERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJJNFg9OSJUvwyCOPwNnZGR4eHpgwYQIKCwuldqPRiJdffhl9+/aFk5MTvL298eyzz+LcuXMm2wkPD4dCoTB5TJkyxaSmrKwMer0earUaarUaer0e5eXlJjVnzpzB2LFj4eTkBHd3d8TGxqKurq7Z+k9ERERti0WDU3p6OmbNmoXs7Gykpqaivr4eOp0O1dXVAICamhrk5eXh9ddfR15eHr766iscO3YM48aNM9tWTEwMiouLpceaNWtM2iMjI5Gfnw+DwQCDwYD8/Hzo9XqpvaGhAaNHj0Z1dTUyMjKQlJSELVu2YP78+c37IhAREVGbYWvJnRsMBpPn69atg4eHB3Jzc/Hoo49CrVYjNTXVpGblypUICQnBmTNn0LVrV2m5o6MjNBpNk/s5cuQIDAYDsrOzERoaCgD45JNPoNVqUVhYCH9/f6SkpODw4cMoKiqCt7c3AGD58uWIjo5GQkICXFxc7mXXiYiIqA1qVdc4VVRUAADc3NxuWaNQKNCpUyeT5YmJiXB3d0efPn0QFxeHy5cvS21ZWVlQq9VSaAKAgQMHQq1WIzMzU6oJDAyUQhMAREREoLa2Frm5ufeie0RERNTGWXTG6feEEJg3bx4GDx6MwMDAJmuuXr2KV155BZGRkSYzQFOnToWfnx80Gg0KCgoQHx+P/fv3S7NVJSUl8PDwMNueh4cHSkpKpBpPT0+TdldXVyiVSqnmRrW1taitrZWeV1ZWArh+bZbRaLyD3rdtjX21tn5bK463deF4WxdrHe876WurCU6zZ8/GgQMHkJGR0WS70WjElClTcO3aNXz00UcmbTExMdLfAwMD0aNHDwwYMAB5eXkICgoCACgUCrNtCiFMlsup+b0lS5Zg0aJFZstTUlLg6OjY5Drt0YkTJwAAOTk5+O233yx8NNTcON7WheNtXax1vGtqamTXtorgNGfOHGzfvh0//vgjunTpYtZuNBoxadIknDx5Ert27brt9UZBQUGws7PD8ePHERQUBI1Gg9LSUrO6CxcuSLNMGo0GOTk5Ju1lZWUwGo1mM1GN4uPjMW/ePOl5ZWUlfHx8oNPprOqaqD179gAAQkNDERISYuGjoebG8bYuHG/rYq3j3XjGSA6LBichBObMmYOtW7ciLS0Nfn5+ZjWNoen48ePYvXs3OnfufNvtHjp0CEajEV5eXgAArVaLiooK7NmzR/pByMnJQUVFBQYNGiTVJCQkoLi4WFovJSUFKpUKwcHBTe5HpVJBpVKZLbezs4OdnZ28F6EdaOyrtfXbWnG8rQvH27pY63jfSV8tGpxmzZqFL774Atu2bYOzs7N0LZFarYaDgwPq6+vx5JNPIi8vD8nJyWhoaJBq3NzcoFQqceLECSQmJmLUqFFwd3fH4cOHMX/+fPTv3x9hYWEAgN69e2PEiBGIiYmRblMwffp0jBkzBv7+/gAAnU6HgIAA6PV6LF26FJcuXUJcXBxiYmKsavaIiIiIbs6in6pbvXo1KioqEB4eDi8vL+mxadMmAMDZs2exfft2nD17Fg8//LBJTeOn4ZRKJX744QdERETA398fsbGx0Ol02LlzJ2xsbKR9JSYmom/fvtDpdNDpdOjXrx8+//xzqd3GxgY7duyAvb09wsLCMGnSJEyYMAHLli1r2ReFiIiIWi2Ln6q7lW7dut22xsfHB+np6bfdl5ubGzZu3HjLmq5duyI5Ofm22yIiIiLr1Kru40RERETUmjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyMTgRERERycTgRERERCQTgxMRERGRTAxORERERDIxOBERERHJxOBEREREJBODExEREZFMDE5EREREMjE4EREREcnE4EREREQkE4MTERERkUwMTkREREQyWTQ4LVmyBI888gicnZ3h4eGBCRMmoLCw0KRGCIGFCxfC29sbDg4OCA8Px6FDh0xqamtrMWfOHLi7u8PJyQnjxo3D2bNnTWrKysqg1+uhVquhVquh1+tRXl5uUnPmzBmMHTsWTk5OcHd3R2xsLOrq6pql70RERNT2WDQ4paenY9asWcjOzkZqairq6+uh0+lQXV0t1bz33ntYsWIFVq1ahb1790Kj0WD48OG4fPmyVDN37lxs3boVSUlJyMjIQFVVFcaMGYOGhgapJjIyEvn5+TAYDDAYDMjPz4der5faGxoaMHr0aFRXVyMjIwNJSUnYsmUL5s+f3zIvBhEREbV+ohU5f/68ACDS09OFEEJcu3ZNaDQa8c4770g1V69eFWq1Wnz88cdCCCHKy8uFnZ2dSEpKkmp+/fVX0aFDB2EwGIQQQhw+fFgAENnZ2VJNVlaWACCOHj0qhBDi22+/FR06dBC//vqrVPPll18KlUolKioqZB1/RUWFACC7vr3IyckRAEROTo6lD4VaAMfbunC8rYu1jved/P62tVhia0JFRQUAwM3NDQBw8uRJlJSUQKfTSTUqlQpDhgxBZmYmZsyYgdzcXBiNRpMab29vBAYGIjMzExEREcjKyoJarUZoaKhUM3DgQKjVamRmZsLf3x9ZWVkIDAyEt7e3VBMREYHa2lrk5uZi6NChZsdbW1uL2tpa6XllZSUAwGg0wmg03qNXpfVr7Ku19dtacbytC8fbuljreN9JX1tNcBJCYN68eRg8eDACAwMBACUlJQAAT09Pk1pPT0+cPn1aqlEqlXB1dTWraVy/pKQEHh4eZvv08PAwqblxP66urlAqlVLNjZYsWYJFixaZLU9JSYGjo+Nt+9xenDhxAgCQk5OD3377zcJHQ82N421dON7WxVrHu6amRnZtqwlOs2fPxoEDB5CRkWHWplAoTJ4LIcyW3ejGmqbq76bm9+Lj4zFv3jzpeWVlJXx8fKDT6eDi4nLL42tP9uzZAwAIDQ1FSEiIhY+GmhvH27pwvK2LtY534xkjOVpFcJozZw62b9+OH3/8EV26dJGWazQaANdng7y8vKTl58+fl2aHNBoN6urqUFZWZjLrdP78eQwaNEiqKS0tNdvvhQsXTLaTk5Nj0l5WVgaj0Wg2E9VIpVJBpVKZLbezs4OdnZ2svrcHjX21tn5bK463deF4WxdrHe876atFP1UnhMDs2bPx1VdfYdeuXfDz8zNp9/Pzg0ajQWpqqrSsrq4O6enpUigKDg6GnZ2dSU1xcTEKCgqkGq1Wi4qKCilJA9enISsqKkxqCgoKUFxcLNWkpKRApVIhODj43neeiIiI2hyLzjjNmjULX3zxBbZt2wZnZ2fpWiK1Wg0HBwcoFArMnTsXb7/9Nnr06IEePXrg7bffhqOjIyIjI6XaadOmYf78+ejcuTPc3NwQFxeHvn374vHHHwcA9O7dGyNGjEBMTAzWrFkDAJg+fTrGjBkDf39/AIBOp0NAQAD0ej2WLl2KS5cuIS4uDjExMVZ12o2IiIhuzqLBafXq1QCA8PBwk+Xr1q1DdHQ0AOBvf/sbrly5gpkzZ6KsrAyhoaFISUmBs7OzVP/+++/D1tYWkyZNwpUrVzBs2DCsX78eNjY2Uk1iYiJiY2OlT9+NGzcOq1atktptbGywY8cOzJw5E2FhYXBwcEBkZCSWLVvWTL0nIiKitsaiwUkIcdsahUKBhQsXYuHChTetsbe3x8qVK7Fy5cqb1ri5uWHjxo233FfXrl2RnJx822MiIiIi68TvqiMiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpKJwYmIiIhIplbxlStE1PJqampw9OjRO16vcZ2jR4/C1vbO30J69eplVV+CTUTtC4MTkZU6evToH/o6oaioqLtaLzc3F0FBQXe9XyIiS2JwIglnIKxLr169kJube8frXb58Gdu2bcP48eNN7uB/J/slImqrGJxIwhkI6+Lo6HhXr7vRaER5eTkGDRpkVd+eTkQEMDjR73AGgoiI6NYYnEjCGQgiIqJb4+0IiIiIiGRicCIiIiKSiafqiIiI2hl+Srr5MDgRERG1M/yUdPNhcCIiImpn+Cnp5sPgRERE1M7wU9LNhxeHExEREcnE4EREREQkE0/VERFZAX7KiujeYHAiIrIC/JQV0b3B4EREZAX4KSuie4PBiYjICvBTVkT3Bi8OJyIiIpKJwYmIiIhIJgYnIiIiIpkYnIiIiIhksmhw+vHHHzF27Fh4e3tDoVDg66+/NmlXKBRNPpYuXSrVhIeHm7VPmTLFZDtlZWXQ6/VQq9VQq9XQ6/UoLy83qTlz5gzGjh0LJycnuLu7IzY2FnV1dc3VdSIiImqDLBqcqqur8dBDD2HVqlVNthcXF5s8Pv30UygUCjzxxBMmdTExMSZ1a9asMWmPjIxEfn4+DAYDDAYD8vPzodfrpfaGhgaMHj0a1dXVyMjIQFJSErZs2YL58+ff+04TERFRm2XR2xGMHDkSI0eOvGm7RqMxeb5t2zYMHToUDzzwgMlyR0dHs9pGR44cgcFgQHZ2NkJDQwEAn3zyCbRaLQoLC+Hv74+UlBQcPnwYRUVF8Pb2BgAsX74c0dHRSEhIgIuLyx/pJhEREbUTbeYap9LSUuzYsQPTpk0za0tMTIS7uzv69OmDuLg4XL58WWrLysqCWq2WQhMADBw4EGq1GpmZmVJNYGCgFJoAICIiArW1tXd1wzgiIiJqn9rMDTA3bNgAZ2dnTJw40WT51KlT4efnB41Gg4KCAsTHx2P//v1ITU0FAJSUlMDDw8Nsex4eHigpKZFqPD09TdpdXV2hVCqlmqbU1taitrZWel5ZWQng+g3jjEbj3XW0DWrsqzX12ZpxvK0Lx9u6WOt430l/20xw+vTTTzF16lTY29ubLI+JiZH+HhgYiB49emDAgAHIy8uT7pKrUCjMtieEMFkup+ZGS5YswaJFi8yWp6SkWOWXWjaGVbIOHG/rwvG2LtY23jU1NbJr20Rw+umnn1BYWIhNmzbdtjYoKAh2dnY4fvw4goKCoNFoUFpaalZ34cIFaZZJo9EgJyfHpL2srAxGo9FsJur34uPjMW/ePOl5ZWUlfHx8oNPprOq6KKPRiNTUVAwfPpxfyWAFON7WheNtXax1vBvPGMnRJoLT2rVrERwcjIceeui2tYcOHYLRaISXlxcAQKvVoqKiAnv27EFISAgAICcnBxUVFRg0aJBUk5CQgOLiYmm9lJQUqFSqW36buEqlgkqlkp4LIQAAV65csaofOKPRiJqaGly5cgX19fWWPhxqZhxv68Lxti7WOt5XrlwB8N/f47ckLOjy5cti3759Yt++fQKAWLFihdi3b584ffq0VFNRUSEcHR3F6tWrzdb/5ZdfxKJFi8TevXvFyZMnxY4dO0SvXr1E//79RX19vVQ3YsQI0a9fP5GVlSWysrJE3759xZgxY6T2+vp6ERgYKIYNGyby8vLEzp07RZcuXcTs2bPvqD9FRUUCAB988MEHH3zw0QYfRUVFt/1drxBCTrxqHmlpaRg6dKjZ8qioKKxfvx4A8D//8z+YO3cuiouLoVarTeqKiorwzDPPoKCgAFVVVfDx8cHo0aOxYMECuLm5SXWXLl1CbGwstm/fDgAYN24cVq1ahU6dOkk1Z86cwcyZM7Fr1y44ODggMjISy5YtM5lRup1r167h3LlzcHZ2vuW1Ue1N4ynKoqIiqzpFaa043taF421drHW8hRC4fPkyvL290aHDrW84YNHgRO1DZWUl1Go1KioqrOofmrXieFsXjrd14XjfXpu5jxMRERGRpTE4EREREcnE4ER/mEqlwoIFC+7oejBquzje1oXjbV043rfHa5yIiIiIZOKMExEREZFMDE5EREREMjE4EREREcnE4EREN7V+/XqTG8XKER0djQkTJjTL8RDRnVMoFPj6668tfRjtBoMTAbh+F/Zp06bB29sbSqUSvr6++Otf/4qLFy9a+tComdws4KSlpUGhUKC8vByTJ0/GsWPHWv7gqFkx3LYvJSUlmDNnDh544AGoVCr4+Phg7Nix+OGHHyx2TKdOnUJ0dLTF9t+cGJwI//nPfzBgwAAcO3YMX375JX755Rd8/PHH+OGHH6DVanHp0iVLHyJZiIODAzw8PCx9GER0E6dOnUJwcDB27dqF9957DwcPHoTBYMDQoUMxa9asFj+exMREnDhxQnouhMCHH37Yrn6PMDgRZs2aBaVSiZSUFAwZMgRdu3bFyJEjsXPnTvz666947bXXAADdunXDW2+9hcjISHTs2BHe3t5YuXKlybYqKiowffp0eHh4wMXFBY899hj2798vtS9cuBAPP/wwPv/8c3Tr1g1qtRpTpkzB5cuXW7TPJE9Tp+oWL14MDw8PODs747nnnsMrr7yChx9+2GzdZcuWwcvLC507d8asWbNgNBpb5qDpD0lPT0dISAhUKhW8vLzwyiuvoL6+HgDwzTffoFOnTrh27RoAID8/HwqFAi+99JK0/owZM/D0009b5Nit0cyZM6FQKLBnzx48+eST6NmzJ/r06YN58+YhOzu7yXUOHjyIxx57DA4ODujcuTOmT5+OqqoqqT0tLQ0hISFwcnJCp06dEBYWhtOnT0vt33zzDYKDg2Fvb48HHngAixYtkn5G/Pz8EBUVhY8//hhnz57FiBEjUFJSAgcHh+Z9IVoQg5OVu3TpEr7//nvMnDnT7Adbo9Fg6tSp2LRpExpv97V06VL069cPeXl5iI+Px4svvojU1FQA1/9nMXr0aJSUlODbb79Fbm4ugoKCMGzYMJP/bZw4cQJff/01kpOTkZycjPT0dLzzzjst12m6a4mJiUhISMC7776L3NxcdO3aFatXrzar2717N06cOIHdu3djw4YNWL9+vfTF3dR6/frrrxg1ahQeeeQR7N+/H6tXr8batWuxePFiAMCjjz6Ky5cvY9++fQCuhyx3d3ekp6dL20hLS8OQIUMscvzW5tKlSzAYDJg1axacnJzM2pu6PrGmpgYjRoyAq6sr9u7di82bN2Pnzp2YPXs2AKC+vh4TJkzAkCFDcODAAWRlZWH69OnSF9d///33eOaZZxAbG4vDhw9jzZo1WL9+PRISEgAAgwYNwu7du5GVlYW0tDTMnTsXb731VrsKThBk1bKzswUAsXXr1ibbV6xYIQCI0tJS4evrK0aMGGHSPnnyZDFy5EghhBA//PCDcHFxEVevXjWpefDBB8WaNWuEEEIsWLBAODo6isrKSqn9pZdeEqGhofewVyRHVFSUsLGxEU5OTiYPe3t7AUCUlZWJdevWCbVaLa0TGhoqZs2aZbKdsLAw8dBDD5ls19fXV9TX10vLnnrqKTF58uTm7hLJFBUVJcaPH2+2/NVXXxX+/v7i2rVr0rIPP/xQdOzYUTQ0NAghhAgKChLLli0TQggxYcIEkZCQIJRKpaisrBTFxcUCgDhy5EiL9MPa5eTkCADiq6++umXd79/j/+d//ke4urqKqqoqqX3Hjh2iQ4cOoqSkRFy8eFEAEGlpaU1u609/+pN4++23TZZ9/vnnwsvLSzqmP/3pT+Lll18Ww4YNEzqdTrz++uviypUrf6CnrQtnnOiWxP+faWr834ZWqzVp12q1OHLkCAAgNzcXVVVV6Ny5Mzp27Cg9Tp48aXLOu1u3bnB2dpaee3l54fz5883dFWrC0KFDkZ+fb/L43//935vWFxYWIiQkxGTZjc8BoE+fPrCxsZGec4zbhiNHjkCr1Ur/3gEgLCwMVVVVOHv2LAAgPDwcaWlpEELgp59+wvjx4xEYGIiMjAzs3r0bnp6e6NWrl6W6YFVufH+W48iRI3jooYdMZqjCwsJw7do1FBYWws3NDdHR0YiIiMDYsWPxwQcfoLi4WKrNzc3Fm2++afIeHxMTg+LiYtTU1ODYsWNYt24dnn/+eXTp0gUGgwGenp6oqam5dx23MFtLHwBZVvfu3aFQKHD48OEmP2Vz9OhRuLq6wt3d/abbaPxHe+3aNXh5eSEtLc2s5vdTxnZ2dmbrN14zQS3LyckJ3bt3N1nW+AvyZm58kxZNfGsTx7htEkLcdHwbl4eHh2Pt2rXYv38/OnTogICAAAwZMgTp6ekoKyvjaboW1KNHDygUChw5ckT2pySbGuNGjcvXrVuH2NhYGAwGbNq0CX//+9+RmpqKgQMH4tq1a1i0aBEmTpxotr69vT2eeeYZANcvWm/cpiUuUm9OnHGycp07d8bw4cPx0Ucf4cqVKyZtJSUlSExMxOTJk6V/UDdebJidnS397zIoKAglJSWwtbVF9+7dTR63Cl7Udvj7+2PPnj0my37++WcLHQ3dawEBAcjMzDQJw5mZmXB2dsb9998P4L/XOf3jH//AkCFDoFAoMGTIEKSlpfH6phbm5uaGiIgIfPjhh6iurjZrLy8vN1sWEBCA/Px8k/p///vf6NChA3r27Ckt69+/P+Lj45GZmYnAwEB88cUXAK6/zxcWFpq9x3fv3h0dOvw3UnTr1q3dXtfI4ERYtWoVamtrERERgR9//BFFRUUwGAwYPnw47r//fumiP+D6P7D33nsPx44dw4cffojNmzfjr3/9KwDg8ccfh1arxYQJE/D999/j1KlTyMzMxN///nf+cm0n5syZg7Vr12LDhg04fvw4Fi9ejAMHDtzRqQJqHSoqKsxO006fPh1FRUWYM2cOjh49im3btmHBggWYN2+e9EtRrVbj4YcfxsaNGxEeHg7gepjKy8vDsWPHpGXUMj766CM0NDQgJCQEW7ZswfHjx3HkyBH885//NLu0AgCmTp0Ke3t7REVFoaCgALt378acOXOg1+vh6emJkydPIj4+HllZWTh9+jRSUlJw7Ngx9O7dGwDwxhtv4LPPPsPChQtx6NAhHDlyRJqVshY8VUfo0aMHfv75ZyxcuBCTJ0/GxYsXodFoMGHCBCxYsABubm5S7fz585Gbm4tFixbB2dkZy5cvR0REBIDrU7LffvstXnvtNfzlL3/BhQsXoNFo8Oijj8LT09NS3aN7aOrUqfjPf/6DuLg4XL16FZMmTUJ0dLTZLBS1fmlpaejfv7/JsqioKHz77bd46aWX8NBDD8HNzQ3Tpk0z+6U4dOhQ5OXlSSHJ1dUVAQEBOHfunPQLllqGn58f8vLykJCQgPnz56O4uBj33XcfgoODm/zEq6OjI77//nv89a9/xSOPPAJHR0c88cQTWLFihdR+9OhRbNiwARcvXoSXlxdmz56NGTNmAAAiIiKQnJyMN998E++99x7s7OzQq1cvPPfccy3ab0tSiKYuUCBqQrdu3TB37lzMnTvX0odCrcjw4cOh0Wjw+eefW/pQiIiaHWeciEi2mpoafPzxx4iIiICNjQ2+/PJL7Ny5U7qXFxFRe8fgRESyNZ6OXbx4MWpra+Hv748tW7bg8ccft/ShERG1CJ6qIyIiIpKJn6ojIiIikonBiYiIiEgmBiciIiIimRiciIiIiGRicCIiIiKSicGJiIiISCYGJyIiIiKZGJyIiIiIZGJwIiIiIpLp/wGvWv4KFqcf0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.boxplot([df1['Open'], df['High'], df['Low'], df['Close*']], labels=['Open', 'High', 'Low', 'Close*'])\n",
    "\n",
    "plt.title('Boxplot of Price Variables')\n",
    "plt.ylabel('Price')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b6becc5-4c6c-4f83-a4f3-fa09ec1c2a90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0hg4d6p5us9ny/cO4ZcuW815AfyGNGjVS8+bNNW7cOL399tvq06fPBZ+/dKnkPXbh3GB4tnbt2qlMmTL65Zdf1Lhx4wJf5/PEE08oNDRU/fr109GjR/PNN8Z4dVG/zWZTYGCgR2hKS0sr8K46K0JDQ9W4cWMtXrxY2dnZ7unHjx/Pd/ddp06dZIzRgQMHCuyPunXrFlhvvXr19Morr+jqq6++5KfVUXww4oRSZ9u2be5rFNLT0/Xll19q5syZCggI0KJFizzuVjrXtGnTtGLFCt1xxx2qWrWqTp06pRkzZkiS+8GZYWFhio2N1QcffKC2bduqQoUKioyM9PrW+ZiYGHXu3FnJycmKjo7WnDlzlJKSonHjxikkJETSmdMq119/vYYMGaLTp0+rfPnyWrRokdasWZNvfXXr1tXChQs1depUNWrUSFdddVWh/xgOHz5cH3/8sVq3bq3nnntOFSpU0Ntvv60lS5Zo/PjxBd5u7o0xY8YoISFBrVu31pAhQxQYGKgpU6Zo27ZtmjdvXpGf3n62ffv2af369crNzdXRo0fdD8Dcu3evXn75ZSUmJrrbdurUSc8//7yGDx+u+Ph47dy5UyNHjlT16tXdd955Y+DAgerRo4dsNpv69evn9XouVsuWLXX//fdr1KhROnjwoDp16iSHw6HNmzcrJCREAwYMULVq1TRy5EgNGzZMu3fvVvv27VW+fHkdPHhQ33zzjUJDQ89711716tU1f/589ejRQ/Xr13c/AFOSduzYoRkzZsgYo65duxap9k6dOmnhwoXq16+f7r77bv322296/vnnFR0drV27dnnVHyNHjtQdd9yhdu3aaeDAgcrJydGLL76osmXLuu+wlKQWLVroH//4hx588EFt2LBBt912m0JDQ5Wamqo1a9aobt26+uc//6mPP/5YU6ZM0Z133qkaNWrIGKOFCxfqr7/+UkJCglc1ogTy11XpgK/l3aWU9woMDDSVKlUy8fHxZvTo0SY9PT3fMufe6bZu3TrTtWtXExsbaxwOh4mIiDDx8fHmww8/9Fhu+fLlpkGDBsbhcBhJpnfv3h7r++OPPy64LWPO3FV3xx13mPfee8/UqVPHBAYGmmrVqpkJEybkW/6nn34yiYmJply5cqZixYpmwIABZsmSJfnu9Przzz/N3Xffba6++mpjs9k8tqkC7gbcunWrSUpKMuHh4SYwMNDUq1fPzJw506NN3h1l7777rsf0vDvbzm1fkC+//NK0adPGhIaGmuDgYNO0aVPz0UcfFbi+otxVl/cKCAgw5cuXN40aNTKDBg0y27dvz7dMVlaWGTJkiLnmmmtMUFCQadiwoVm8eHG+u9OMsXZX3dnrdTgcpn379hesO4+Vu0CNOXPnXGhoaKHzzq07JyfHvPLKKyYuLs4EBgaa8PBw06xZs3x9vXjxYtO6dWtTrlw543A4TGxsrLn77rvN8uXLLdX/yy+/mH79+pnrrrvOOBwOExwcbG688UYzePBgj7s/4+PjTZ06dSzVPnbsWFOtWjXjcDjMDTfcYN54440Cf24kmcceeyzfOmNjY90/i3kWLVpk6tatawIDA03VqlXN2LFjzeOPP27Kly+fb/kZM2aYJk2auI/Ra6+91jzwwANmw4YNxhhjfvzxR3PPPfeYa6+91gQHB5vw8HBzyy23mFmzZlnqM5QONmMucJsRAOC8PvroI3Xu3FlLlixRx44d/V0OzsPlcql+/fq65pprtGzZMn+XgxKI4AQAXtqxY4f27t2rgQMHKjQ0VJs2bbqo047wvb59+yohIUHR0dFKS0vTtGnTtHr1ai1btszjeysBq7jGCQC81K9fP3311Vdq2LChZs+eTWgqhjIyMjRkyBD98ccfstvtatiwoT755BNCE7zGiBMAAIBFPI4AAADAIoITAACARQQnAAAAi7g4XGeeYPz7778rLCyMizsBALjCGGOUkZGhmJgYXXXV+ceUCE46831Z5347PAAAuLL89ttvqly58nnbEJx05is0pDMdZuUbyEsCl8ulZcuWKTExUXa73d/lXHHof/+i//2L/vcf+t47x44dU5UqVdx54HwITvrfN4+XK1euVAWnkJAQlStXjh8eP6D//Yv+9y/633/o+4tj5XIdLg4HAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEV8yS+AUiEp6X9/t9ul3r2lHj0kl8va8h99dGnqAlC6MOIEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsMivwWnMmDG6+eabFRYWpkqVKunOO+/Uzp07PdoYY5ScnKyYmBgFBwerVatW2r59u0ebrKwsDRgwQJGRkQoNDVXnzp21f//+y7krAADgCuDX4LR69Wo99thjWr9+vVJSUnT69GklJiYqMzPT3Wb8+PGaMGGCJk+erG+//VZOp1MJCQnKyMhwtxk0aJAWLVqk+fPna82aNTp+/Lg6deqknJwcf+wWAAAopcr4c+NLly71eD9z5kxVqlRJGzdu1G233SZjjCZOnKhhw4apW7dukqTZs2crKipKc+fO1SOPPKKjR49q+vTpeuutt3T77bdLkubMmaMqVapo+fLlateu3WXfLwAAUDoVq2ucjh49KkmqUKGCJGnPnj1KS0tTYmKiu43D4VB8fLzWrl0rSdq4caNcLpdHm5iYGMXFxbnbAAAA+IJfR5zOZozR4MGDdeuttyouLk6SlJaWJkmKioryaBsVFaW9e/e62wQGBqp8+fL52uQtf66srCxlZWW53x87dkyS5HK55HK5fLNDfpa3H6Vlf0oa+v/ys9vP/rvL408r+Kh8h+Pff+h77xSlv4pNcOrfv7+2bNmiNWvW5Jtns9k83htj8k071/najBkzRiNGjMg3fdmyZQoJCSlC1cVfSkqKv0u4otH/l0/v3vmn9eplvf8/+cSHxUASx78/0fdFc+LECctti0VwGjBggD788EN98cUXqly5snu60+mUdGZUKTo62j09PT3dPQrldDqVnZ2tI0eOeIw6paenq3nz5gVu7+mnn9bgwYPd748dO6YqVaooMTFR5cqV8+m++YvL5VJKSooSEhJkP/u/4rgs6P/Lr0eP//3dbnepV68UzZ2bIJfLWv8vWHCJCrsCcfz7D33vnbwzT1b4NTgZYzRgwAAtWrRIq1atUvXq1T3mV69eXU6nUykpKWrQoIEkKTs7W6tXr9a4ceMkSY0aNZLdbldKSoq6d+8uSUpNTdW2bds0fvz4ArfrcDjkcDjyTbfb7aXuQCuN+1SS0P+XT0Ej7S6X3XJw4mPyPY5//6Hvi6YofeXX4PTYY49p7ty5+uCDDxQWFua+Jik8PFzBwcGy2WwaNGiQRo8erZo1a6pmzZoaPXq0QkJC1KtXL3fbvn376sknn1RERIQqVKigIUOGqG7duu677AAAAHzBr8Fp6tSpkqRWrVp5TJ85c6b69OkjSRo6dKhOnjypfv366ciRI2rSpImWLVumsLAwd/tXXnlFZcqUUffu3XXy5Em1bdtWs2bNUkBAwOXaFQAAcAXw+6m6C7HZbEpOTlZycnKhbYKCgjRp0iRNmjTJh9UBAAB4KlbPcQIAACjOCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCojL8LAIA8SUn+rgAAzo8RJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwqIy/CwCA4iAp6eKW/+gj39QBoHhjxAkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCK/BqcvvvhCSUlJiomJkc1m0+LFiz3m9+nTRzabzePVtGlTjzZZWVkaMGCAIiMjFRoaqs6dO2v//v2XcS8AAMCVwq/BKTMzU/Xq1dPkyZMLbdO+fXulpqa6X5988onH/EGDBmnRokWaP3++1qxZo+PHj6tTp07Kycm51OUDAIArTBl/brxDhw7q0KHDeds4HA45nc4C5x09elTTp0/XW2+9pdtvv12SNGfOHFWpUkXLly9Xu3btfF4zAAC4cvk1OFmxatUqVapUSVdffbXi4+P1wgsvqFKlSpKkjRs3yuVyKTEx0d0+JiZGcXFxWrt2baHBKSsrS1lZWe73x44dkyS5XC65XK5LuDeXT95+lJb9KWnof+/Y7b5aj8vjz8uBj/p/OP79h773TlH6q1gHpw4dOuhvf/ubYmNjtWfPHj377LNq06aNNm7cKIfDobS0NAUGBqp8+fIey0VFRSktLa3Q9Y4ZM0YjRozIN33ZsmUKCQnx+X74U0pKir9LuKLR/0XTu7dv19er1+Xr/3OuIoA4/v2Jvi+aEydOWG5brINTjx493H+Pi4tT48aNFRsbqyVLlqhbt26FLmeMkc1mK3T+008/rcGDB7vfHzt2TFWqVFFiYqLKlSvnm+L9zOVyKSUlRQkJCbL76r/xsIz+985ZP/IXxW53qVevFM2dmyCX6/L0/4IFl2UzJQLHv//Q997JO/NkRbEOTueKjo5WbGysdu3aJUlyOp3Kzs7WkSNHPEad0tPT1bx580LX43A45HA48k232+2l7kArjftUktD/RePrswsul/2yBSc+5vw4/v2Hvi+aovRViXqO0+HDh/Xbb78pOjpaktSoUSPZ7XaPIcnU1FRt27btvMEJAADAG16NOO3Zs0fVq1e/6I0fP35cP//8s8d6v/vuO1WoUEEVKlRQcnKy7rrrLkVHR+vXX3/VM888o8jISHXt2lWSFB4err59++rJJ59URESEKlSooCFDhqhu3bruu+wAAAB8xasRp+uuu06tW7fWnDlzdOrUKa83vmHDBjVo0EANGjSQJA0ePFgNGjTQc889p4CAAG3dulVdunRRrVq11Lt3b9WqVUvr1q1TWFiYex2vvPKK7rzzTnXv3l0tWrRQSEiIPvroIwUEBHhdFwAAQEG8GnH6/vvvNWPGDD355JPq37+/evToob59++qWW24p0npatWolY0yh8z/77LMLriMoKEiTJk3SpEmTirRtAACAovJqxCkuLk4TJkzQgQMHNHPmTKWlpenWW29VnTp1NGHCBP3xxx++rhMAAMDvLuri8DJlyqhr16565513NG7cOP3yyy8aMmSIKleurAceeECpqam+qhMAAMDvLio4bdiwQf369VN0dLQmTJigIUOG6JdfftGKFSt04MABdenSxVd1AgAA+J1X1zhNmDBBM2fO1M6dO9WxY0e9+eab6tixo6666kwOq169ul5//XXVrl3bp8UCAAD4k1fBaerUqXrooYf04IMPFvoFvFWrVtX06dMvqjgAAIDixKvglPfk7vMJDAxUb19/8RQAAIAfeXWN08yZM/Xuu+/mm/7uu+9q9uzZF10UAABAceRVcBo7dqwiIyPzTa9UqZJGjx590UUBAAAUR14Fp7179xb4lSuxsbHat2/fRRcFAABQHHkVnCpVqqQtW7bkm/79998rIiLioosCAAAojrwKTj179tTjjz+ulStXKicnRzk5OVqxYoUGDhyonj17+rpGAACAYsGru+pGjRqlvXv3qm3btipT5swqcnNz9cADD3CNEwAAKLW8Ck6BgYFasGCBnn/+eX3//fcKDg5W3bp1FRsb6+v6AAAAig2vglOeWrVqqVatWr6qBQAAoFjzKjjl5ORo1qxZ+vzzz5Wenq7c3FyP+StWrPBJcQAAAMWJV8Fp4MCBmjVrlu644w7FxcXJZrP5ui4AAIBix6vgNH/+fL3zzjvq2LGjr+sBAAAotrx6HEFgYKCuu+46X9cCAABQrHkVnJ588km9+uqrMsb4uh4AAIBiy6tTdWvWrNHKlSv16aefqk6dOrLb7R7zFy5c6JPiAAAAihOvgtPVV1+trl27+roWAACAYs2r4DRz5kxf1wEAAFDseXWNkySdPn1ay5cv1+uvv66MjAxJ0u+//67jx4/7rDgAAIDixKsRp71796p9+/bat2+fsrKylJCQoLCwMI0fP16nTp3StGnTfF0nAACA33k14jRw4EA1btxYR44cUXBwsHt6165d9fnnn/usOAAAgOLE67vqvvrqKwUGBnpMj42N1YEDB3xSGACUJElJ3i/70Ue+qwPApeXViFNubq5ycnLyTd+/f7/CwsIuuigAAIDiyKvglJCQoIkTJ7rf22w2HT9+XMOHD+drWAAAQKnl1am6V155Ra1bt9aNN96oU6dOqVevXtq1a5ciIyM1b948X9cIAABQLHgVnGJiYvTdd99p3rx52rRpk3Jzc9W3b1/de++9HheLAwAAlCZeBSdJCg4O1kMPPaSHHnrIl/UAAAAUW14FpzfffPO88x944AGvigEAACjOvApOAwcO9Hjvcrl04sQJBQYGKiQkhOAEAABKJa/uqjty5IjH6/jx49q5c6duvfVWLg4HAAClltffVXeumjVrauzYsflGowAAAEoLnwUnSQoICNDvv//uy1UCAAAUG15d4/Thhx96vDfGKDU1VZMnT1aLFi18UhgAAEBx41VwuvPOOz3e22w2VaxYUW3atNHLL7/si7oAAACKHa+CU25urq/rAAAAKPa8fgAmAJwrKcnfFQDApeVVcBo8eLDlthMmTPBmEwAAAMWOV8Fp8+bN2rRpk06fPq3rr79ekvTTTz8pICBADRs2dLez2Wy+qRIAAKAY8Co4JSUlKSwsTLNnz1b58uUlnXko5oMPPqiWLVvqySef9GmRAAAAxYFXz3F6+eWXNWbMGHdokqTy5ctr1KhR3FUHAABKLa+C07Fjx3Tw4MF809PT05WRkXHRRQEAABRHXgWnrl276sEHH9R7772n/fv3a//+/XrvvffUt29fdevWzdc1AgAAFAteXeM0bdo0DRkyRPfdd59cLteZFZUpo759++rFF1/0aYEAAADFhVfBKSQkRFOmTNGLL76oX375RcYYXXfddQoNDfV1fQAAAMXGRX3Jb2pqqlJTU1WrVi2FhobKGOOrugAAAIodr4LT4cOH1bZtW9WqVUsdO3ZUamqqJOnhhx/mUQQAAKDU8io4PfHEE7Lb7dq3b59CQkLc03v06KGlS5f6rDgAAIDixKtrnJYtW6bPPvtMlStX9phes2ZN7d271yeFAQAAFDdejThlZmZ6jDTlOXTokBwOh+X1fPHFF0pKSlJMTIxsNpsWL17sMd8Yo+TkZMXExCg4OFitWrXS9u3bPdpkZWVpwIABioyMVGhoqDp37qz9+/d7s1sAAADn5VVwuu222/Tmm2+639tsNuXm5urFF19U69atLa8nMzNT9erV0+TJkwucP378eE2YMEGTJ0/Wt99+K6fTqYSEBI+HbA4aNEiLFi3S/PnztWbNGh0/flydOnVSTk6ON7sGAABQKK9O1b344otq1aqVNmzYoOzsbA0dOlTbt2/Xn3/+qa+++sryejp06KAOHToUOM8Yo4kTJ2rYsGHuh2rOnj1bUVFRmjt3rh555BEdPXpU06dP11tvvaXbb79dkjRnzhxVqVJFy5cvV7t27bzZPQAAgAJ5NeJ04403asuWLbrllluUkJCgzMxMdevWTZs3b9a1117rk8L27NmjtLQ0JSYmuqc5HA7Fx8dr7dq1kqSNGzfK5XJ5tImJiVFcXJy7DQAAgK8UecQpL6i8/vrrGjFixKWoSZKUlpYmSYqKivKYHhUV5b4APS0tTYGBgR5fNpzXJm/5gmRlZSkrK8v9/tixY5LO7Fvek9BLurz9KC37U9Jcqf1vt/u7gjPsdpfHn8VdaTtMrtTjvzig771TlP4qcnCy2+3atm2bbDZbURf1yrnbMcZccNsXajNmzJgCQ9+yZcsKvOi9JEtJSfF3CVe0K63/e/f2dwWeevUqGf3/ySf+ruDSuNKO/+KEvi+aEydOWG7r1TVODzzwgKZPn66xY8d6s7glTqdT0plRpejoaPf09PR09yiU0+lUdna2jhw54jHqlJ6erubNmxe67qefflqDBw92vz927JiqVKmixMRElStXzte74hcul0spKSlKSEiQvbgMA1xBrtT+79HD3xWcYbe71KtXiubOTZDLVfz7f8ECf1fgW1fq8V8c0PfeyTvzZIVXwSk7O1v//e9/lZKSosaNG+f7jroJEyZ4s1oP1atXl9PpVEpKiho0aODe7urVqzVu3DhJUqNGjWS325WSkqLu3btLOvM1MNu2bdP48eMLXbfD4SjwsQl2u73UHWilcZ9Kkiut/4vb2QGXy14iglNpPUSutOO/OKHvi6YofVWk4LR7925Vq1ZN27ZtU8OGDSVJP/30k0ebopzCO378uH7++Wf3+z179ui7775ThQoVVLVqVQ0aNEijR49WzZo1VbNmTY0ePVohISHq1auXJCk8PFx9+/bVk08+qYiICFWoUEFDhgxR3bp13XfZAQAA+EqRglPNmjWVmpqqlStXSjrzFSuvvfZavgu4rdqwYYPHc5/yTp/17t1bs2bN0tChQ3Xy5En169dPR44cUZMmTbRs2TKFhYW5l3nllVdUpkwZde/eXSdPnlTbtm01a9YsBQQEeFUTAABAYYoUnIwxHu8//fRTZWZmer3xVq1a5Vvn2Ww2m5KTk5WcnFxom6CgIE2aNEmTJk3yug4AAAArvHqOU57zhR4AAIDSpkjByWaz5buG6XI9lgAAAMDfinyqrk+fPu470k6dOqVHH3003111Cxcu9F2FAAAAxUSRglPvc55ud9999/m0GAAAgOKsSMFp5syZl6oOAACAYu+iLg4HAAC4khCcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEVFeo4TAMD3kpIubvmPPvJNHQAujBEnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARQQnAAAAiwhOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIvK+LsAAMVLUpK/KwCA4ovgBJQyBB8AuHQ4VQcAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWFevglJycLJvN5vFyOp3u+cYYJScnKyYmRsHBwWrVqpW2b9/ux4oBAEBpVqyDkyTVqVNHqamp7tfWrVvd88aPH68JEyZo8uTJ+vbbb+V0OpWQkKCMjAw/VgwAAEqrYh+cypQpI6fT6X5VrFhR0pnRpokTJ2rYsGHq1q2b4uLiNHv2bJ04cUJz5871c9UAAKA0KvYPwNy1a5diYmLkcDjUpEkTjR49WjVq1NCePXuUlpamxMREd1uHw6H4+HitXbtWjzzySKHrzMrKUlZWlvv9sWPHJEkul0sul+vS7cxllLcfpWV/Shp/9r/dftk3WezY7S6PP0u74vZjzu8f/6HvvVOU/rIZY8wlrOWifPrppzpx4oRq1aqlgwcPatSoUfrxxx+1fft27dy5Uy1atNCBAwcUExPjXuYf//iH9u7dq88++6zQ9SYnJ2vEiBH5ps+dO1chISGXZF8AAEDxdOLECfXq1UtHjx5VuXLlztu2WAenc2VmZuraa6/V0KFD1bRpU7Vo0UK///67oqOj3W3+/ve/67ffftPSpUsLXU9BI05VqlTRoUOHLthhJYXL5VJKSooSEhJkZwjisvNn//focVk3VyzZ7S716pWiuXMT5HKV/uN/wQJ/V+CJ3z/+Q99759ixY4qMjLQUnIr9qbqzhYaGqm7dutq1a5fuvPNOSVJaWppHcEpPT1dUVNR51+NwOORwOPJNt9vtpe5AK437VJL4o/8Zof8fl8t+RQSn4vojzu8f/6Hvi6YofVXsLw4/W1ZWln744QdFR0erevXqcjqdSklJcc/Pzs7W6tWr1bx5cz9WCQAASqtiPeI0ZMgQJSUlqWrVqkpPT9eoUaN07Ngx9e7dWzabTYMGDdLo0aNVs2ZN1axZU6NHj1ZISIh69erl79IBAEApVKyD0/79+3XPPffo0KFDqlixopo2bar169crNjZWkjR06FCdPHlS/fr105EjR9SkSRMtW7ZMYWFhfq4cAACURsU6OM2fP/+88202m5KTk5WcnHx5CgIAAFe0EnWNEwAAgD8RnAAAACwiOAEAAFhEcAIAALCI4AQAAGARwQkAAMAighMAAIBFBCcAAACLCE4AAAAWEZwAAAAsKtZfuQJciZKS/F0BAKAwBCcAKOEuJmx/9JHv6gCuBJyqAwAAsIjgBAAAYBGn6gDgCnax19Rxqg9XGkacAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEU8jgAowMXeor1woW/qAAAUL4w4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCIuDgcugR49pN69z/zpcvm7GgCArzDiBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABYRnAAAACwiOAEAAFjEc5wAAF4r6Aux7XZrzzH76KNLVxdwqTDiBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgAAABZxVx1KrYLu9gEA4GIw4gQAAGARwQkAAMAighMAAIBFXOMEAPCLi70OkSePwx8YcQIAALCI4AQAAGARwQkAAMAighMAAIBFXBwOACiRuLgc/kBwwnnxiwkAgP/hVB0AAIBFpWbEacqUKXrxxReVmpqqOnXqaOLEiWrZsqW/y/K7Hj0kl8t/27+YEStGqwBcSvx+gjdKRXBasGCBBg0apClTpqhFixZ6/fXX1aFDB+3YsUNVq1b1d3mc7vISX9ILAPmd73ej3S717n3+/zRfqf+m+EqpCE4TJkxQ37599fDDD0uSJk6cqM8++0xTp07VmDFj/FzdxfMmQOT98AAAcDZ/jrSVhoGEEh+csrOztXHjRj311FMe0xMTE7V27Vo/VQUAQOEYUS+5SnxwOnTokHJychQVFeUxPSoqSmlpaQUuk5WVpaysLPf7o0ePSpL+/PNPufx5QZBPuXTixAlJhyXZ/V3MFYj+9y/6379Kf/8fPuzvCgpzafve3/t9qbafkZEhSTLGXLBtiQ9OeWw2m8d7Y0y+aXnGjBmjESNG5JtevXr1S1Kbvyxa5O8Krmz0v3/R//5V2vs/MtLfFRTuUva9v/f7Um8/IyND4eHh521T4oNTZGSkAgIC8o0upaen5xuFyvP0009r8ODB7ve5ubn6888/FRERUWjYKmmOHTumKlWq6LffflO5cuX8Xc4Vh/73L/rfv+h//6HvvWOMUUZGhmJiYi7YtsQHp8DAQDVq1EgpKSnq2rWre3pKSoq6dOlS4DIOh0MOh8Nj2tVXX30py/SbcuXK8cPjR/S/f9H//kX/+w99X3QXGmnKU+KDkyQNHjxY999/vxo3bqxmzZrpP//5j/bt26dHH33U36UBAIBSpFQEpx49eujw4cMaOXKkUlNTFRcXp08++USxsbH+Lg0AAJQipSI4SVK/fv3Ur18/f5dRbDgcDg0fPjzfKUlcHvS/f9H//kX/+w99f+nZjJV77wAAAMCX/AIAAFhFcAIAALCI4AQAAGARwamEeeGFF9S8eXOFhIQU+uypffv2KSkpSaGhoYqMjNTjjz+u7OxsjzZbt25VfHy8goODdc0112jkyJH5HjW/evVqNWrUSEFBQapRo4amTZt2qXarxKpWrZpsNpvH69zvTfTV5wFrpkyZourVqysoKEiNGjXSl19+6e+SSrzk5OR8x7nT6XTPN8YoOTlZMTExCg4OVqtWrbR9+3aPdWRlZWnAgAGKjIxUaGioOnfurP3791/uXSkRvvjiCyUlJSkmJkY2m02LFy/2mO+r/j5y5Ijuv/9+hYeHKzw8XPfff7/++uuvS7x3pYBBifLcc8+ZCRMmmMGDB5vw8PB880+fPm3i4uJM69atzaZNm0xKSoqJiYkx/fv3d7c5evSoiYqKMj179jRbt24177//vgkLCzMvvfSSu83u3btNSEiIGThwoNmxY4d54403jN1uN++9997l2M0SIzY21owcOdKkpqa6XxkZGe75vvo8YM38+fON3W43b7zxhtmxY4cZOHCgCQ0NNXv37vV3aSXa8OHDTZ06dTyO8/T0dPf8sWPHmrCwMPP++++brVu3mh49epjo6Ghz7Ngxd5tHH33UXHPNNSYlJcVs2rTJtG7d2tSrV8+cPn3aH7tUrH3yySdm2LBh5v333zeSzKJFizzm+6q/27dvb+Li4szatWvN2rVrTVxcnOnUqdPl2s0Si+BUQs2cObPA4PTJJ5+Yq666yhw4cMA9bd68ecbhcJijR48aY4yZMmWKCQ8PN6dOnXK3GTNmjImJiTG5ubnGGGOGDh1qateu7bHuRx55xDRt2vQS7E3JFRsba1555ZVC5/vq84A1t9xyi3n00Uc9ptWuXds89dRTfqqodBg+fLipV69egfNyc3ON0+k0Y8eOdU87deqUCQ8PN9OmTTPGGPPXX38Zu91u5s+f725z4MABc9VVV5mlS5de0tpLunODk6/6e8eOHUaSWb9+vbvNunXrjCTz448/XuK9Ktk4VVfKrFu3TnFxcR7ft9OuXTtlZWVp48aN7jbx8fEez/lo166dfv/9d/3666/uNomJiR7rbteunTZs2CCXy3Xpd6QEGTdunCIiIlS/fn298MILHqfhfPV54MKys7O1cePGfMdtYmKi1q5d66eqSo9du3YpJiZG1atXV8+ePbV7925J0p49e5SWlubR7w6HQ/Hx8e5+37hxo1wul0ebmJgYxcXF8dkUka/6e926dQoPD1eTJk3cbZo2barw8HA+kwsgOJUyaWlp+b7cuHz58goMDHR/EXJBbfLeX6jN6dOndejQoUtVfokzcOBAzZ8/XytXrlT//v01ceJEjwex+urzwIUdOnRIOTk5BfYl/XhxmjRpojfffFOfffaZ3njjDaWlpal58+Y6fPiwu2/P1+9paWkKDAxU+fLlC20Da3zV32lpaapUqVK+9VeqVInP5AIITsVAQRdenvvasGGD5fXZbLZ804wxHtPPbWP+/wuRi9qmNCrK5/HEE08oPj5eN910kx5++GFNmzZN06dP1+HDh93r89XnAWsK6kv68eJ06NBBd911l+rWravbb79dS5YskSTNnj3b3cabfuez8Z4v+tvK7ybkV2q+cqUk69+/v3r27HneNtWqVbO0LqfTqa+//tpj2pEjR+Ryudz/Q3E6nfn+R5Geni5JF2xTpkwZRUREWKqlpLqYz6Np06aSpJ9//lkRERE++zxwYZGRkQoICCiwL+lH3woNDVXdunW1a9cu3XnnnZLOjGBER0e725zd706nU9nZ2Tpy5IjHKEh6erqaN29+WWsv6fLuZrzY/nY6nTp48GC+9f/xxx/8vFwAI07FQGRkpGrXrn3eV1BQkKV1NWvWTNu2bVNqaqp72rJly+RwONSoUSN3my+++MLjWpxly5YpJibGHQiaNWumlJQUj3UvW7ZMjRs3lt1uv8g9Lt4u5vPYvHmzJLl/ofnq88CFBQYGqlGjRvmO25SUFP5x9rGsrCz98MMPio6OVvXq1eV0Oj36PTs7W6tXr3b3e6NGjWS32z3apKamatu2bXw2ReSr/m7WrJmOHj2qb775xt3m66+/1tGjR/lMLsRPF6XDS3v37jWbN282I0aMMGXLljWbN282mzdvdt8Cn3f7e9u2bc2mTZvM8uXLTeXKlT1uf//rr79MVFSUueeee8zWrVvNwoULTbly5Qp8HMETTzxhduzYYaZPn87jCM6xdu1aM2HCBLN582aze/dus2DBAhMTE2M6d+7sbuOrzwPW5D2OYPr06WbHjh1m0KBBJjQ01Pz666/+Lq1Ee/LJJ82qVavM7t27zfr1602nTp1MWFiYu1/Hjh1rwsPDzcKFC83WrVvNPffcU+Dt8ZUrVzbLly83mzZtMm3atOFxBIXIyMhw/26X5P49k/dYDV/1d/v27c1NN91k1q1bZ9atW2fq1q3L4wgsIDiVML179zaS8r1WrlzpbrN3715zxx13mODgYFOhQgXTv39/j1vdjTFmy5YtpmXLlsbhcBin02mSk5Pz3fq+atUq06BBAxMYGGiqVatmpk6dejl2scTYuHGjadKkiQkPDzdBQUHm+uuvN8OHDzeZmZke7Xz1ecCaf//73yY2NtYEBgaahg0bmtWrV/u7pBIv7zlBdrvdxMTEmG7dupnt27e75+fm5prhw4cbp9NpHA6Hue2228zWrVs91nHy5EnTv39/U6FCBRMcHGw6depk9u3bd7l3pURYuXJlgb/ne/fubYzxXX8fPnzY3HvvvSYsLMyEhYWZe++91xw5cuQy7WXJZTOGxxMDAABYwTVOAAAAFhGcAAAALCI4AQAAWERwAgAAsIjgBAAAYBHBCQAAwCKCEwAAgEUEJwAAAIsITgCKjVatWmnQoEH+LkN9+vRxf3ktAJyN4ATA5/r06SObzSabzSa73a4aNWpoyJAhyszMPO9yCxcu1PPPP39JazPG6D//+Y+aNGmismXL6uqrr1bjxo01ceJEnThx4pJuG0DJV8bfBQAondq3b6+ZM2fK5XLpyy+/1MMPP6zMzExNnTo1X1uXyyW73a4KFSpc8rruv/9+LVy4UP/61780efJkVaxYUd9//70mTpyoatWqMdIE4LwYcQJwSTgcDjmdTlWpUkW9evXSvffeq8WLF0uSkpOTVb9+fc2YMUM1atSQw+GQMSbfqbqsrCwNHTpUVapUkcPhUM2aNTV9+nT3/B07dqhjx44qW7asoqKidP/99+vQoUOF1vTOO+/o7bff1rx58/TMM8/o5ptvVrVq1dSlSxetWLFCrVu39mj/0ksvKTo6WhEREXrsscfkcrnc8+bMmaPGjRsrLCxMTqdTvXr1Unp6unv+qlWrZLPZ9Pnnn6tx48YKCQlR8+bNtXPnTo9tjBo1SpUqVVJYWJgefvhhPfXUU6pfv75Hm5kzZ+qGG25QUFCQateurSlTplj9GAD4GMEJwGURHBzsETx+/vlnvfPOO3r//ff13XffFbjMAw88oPnz5+u1117TDz/8oGnTpqls2bKSpNTUVMXHx6t+/frasGGDli5dqoMHD6p79+6F1vD222/r+uuvV5cuXfLNs9lsCg8Pd79fuXKlfvnlF61cuVKzZ8/WrFmzNGvWLPf87OxsPf/88/r++++1ePFi7dmzR3369Mm33mHDhunll1/Whg0bVKZMGT300EMe9bzwwgsaN26cNm7cqKpVq+YbkXvjjTc0bNgwvfDCC/rhhx80evRoPfvss5o9e3ah+wngEjIA4GO9e/c2Xbp0cb//+uuvTUREhOnevbsxxpjhw4cbu91u0tPTPZaLj483AwcONMYYs3PnTiPJpKSkFLiNZ5991iQmJnpM++2334wks3PnzgKXueGGG0znzp0t1R8bG2tOnz7tnva3v/3N9OjRo9BlvvnmGyPJZGRkGGOMWblypZFkli9f7m6zZMkSI8mcPHnSGGNMkyZNzGOPPeaxnhYtWph69eq531epUsXMnTvXo83zzz9vmjVrdsH9AOB7jDgBuCQ+/vhjlS1bVkFBQWrWrJluu+02TZo0yT0/NjZWFStWLHT57777TgEBAYqPjy9w/saNG7Vy5UqVLVvW/apdu7Yk6ZdffilwGWOMbDabpfrr1KmjgIAA9/vo6GiPU3GbN29Wly5dFBsbq7CwMLVq1UqStG/fPo/13HTTTR7rkORez86dO3XLLbd4tD/7/R9//KHffvtNffv29djPUaNGFbqPAC4tLg4HcEm0bt1aU6dOld1uV0xMjOx2u8f80NDQ8y4fHBx83vm5ublKSkrSuHHj8s3LCyjnqlWrln744YcLVH7GufXabDbl5uZKkjIzM5WYmKjExETNmTNHFStW1L59+9SuXTtlZ2cXup680Ja3nrOn5THGuP+e1+6NN95QkyZNPNqdHeoAXD6MOAG4JEJDQ3XdddcpNjY2Xwixom7dusrNzdXq1asLnN+wYUNt375d1apV03XXXefxKiyU9erVSz/99JM++OCDfPOMMTp69Kil2n788UcdOnRIY8eOVcuWLVW7dm2P0Sirrr/+en3zzTce0zZs2OD+e1RUlK655hrt3r073z5Wr169yNsDcPEITgCKpWrVqql379566KGH3Bdfr1q1Su+8844k6bHHHtOff/6pe+65R9988412796tZcuW6aGHHlJOTk6B6+zevbt69Oihe+65R2PGjNGGDRu0d+9effzxx7r99tu1cuVKS7VVrVpVgYGBmjRpknbv3q0PP/zQq+dPDRgwQNOnT9fs2bO1a9cujRo1Slu2bPEYhUpOTtaYMWP06quv6qefftLWrVs1c+ZMTZgwocjbA3DxCE4Aiq2pU6fq7rvvVr9+/VS7dm39/e9/dz9EMyYmRl999ZVycnLUrl07xcXFaeDAgQoPD9dVVxX8q81ms2nu3LmaMGGCFi1apPj4eN10001KTk5Wly5d1K5dO0t1VaxYUbNmzdK7776rG2+8UWPHjtVLL71U5P2799579fTTT2vIkCFq2LCh+868oKAgd5uHH35Y//3vfzVr1izVrVtX8fHxmjVrFiNOgJ/YzNkn1AEAfpWQkCCn06m33nrL36UAKAAXhwOAn5w4cULTpk1Tu3btFBAQoHnz5mn58uVKSUnxd2kACsGIEwD4ycmTJ5WUlKRNmzYpKytL119/vf71r3+pW7du/i4NQCEITgAAABZxcTgAAIBFBCcAAACLCE4AAAAWEZwAAAAsIjgBAABYRHACAACwiOAEAABgEcEJAADAIoITAACARf8flIfSrcPtmIwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "price_change = df1['Close*'] - df['Open']\n",
    "plt.hist(price_change, bins=30, color='b', alpha=0.7)\n",
    "\n",
    "plt.title('Distribution of Daily Price Changes')\n",
    "plt.xlabel('Price Change')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7dccc57d-a0d2-4bc0-8e71-2d65d96de88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "\n",
    "plt.scatter(df1['High'], df1['Low'], color='b', alpha=0.5)\n",
    "plt.plot([df1['High'].min(), df1['High'].max()], [df1['Low'].min(), df1['Low'].max()], color='k', linestyle='--', linewidth=2)\n",
    "\n",
    "plt.title('High vs Low Prices')\n",
    "plt.xlabel('High Price')\n",
    "plt.ylabel('Low Price')\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5b7470-f606-46a2-98c3-d45e02aa1342",
   "metadata": {},
   "source": [
    "# Training and Testing of the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d468b314-6c6a-43a4-a275-962e2fa1771e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8a35a46b-e3f0-4442-9a86-3289607b138d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Close*'])\n",
    "y = df['Close*']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c106f716-c509-4447-a1ca-6fc38dc1131b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7e831d7-2419-48db-8bd7-6021b377661c",
   "metadata": {},
   "source": [
    "## Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7f81577f-5993-4d52-873e-09e90734ba2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "25bf1fa3-c60a-462d-bf87-9fb887e59320",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0f198f58-7f80-45d8-a9ec-2a5214864ac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1.1466036275084254e-21\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred,)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a415a2bf-1f30-4e4b-8525-76b405d632ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.350249800771948e-11"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae= mean_absolute_error(y_test, y_pred,)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1d3e83df-ca4c-42d3-ac18-eac281ae05ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bfe0b10a-e895-4067-9d30-f7e018db58aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14104668926127162"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = r2_score(y_test, y_pred)\n",
    "r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de364a4a-0270-40fe-b02c-7a7f440e701b",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ee4972bc-dc96-4a07-8ad1-4cc2ca0b2fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7a872c7a-a03d-4936-b1ba-ea31b7435657",
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5e2fc94d-9987-40ca-a029-7cc4b85f9362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5da294b-ce86-4cf9-bc89-585b5211daa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "57b42b27-030a-43a0-86a6-31018c66b1b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1988.0575059523783\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred,)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "be9face5-f697-4401-a7e8-7db472de23d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.022738095237933"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae= mean_absolute_error(y_test, y_pred,)\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81334cf4-51b4-4d27-aed2-ee15bec0faf6",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "250e6171-68a8-4c73-b6fa-cbb6ffcdee0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "68bedc3e-a068-422d-aded-dd831a3fbcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestRegressor(n_estimators=100, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5c61dca1-9c0e-47d9-9af6-4ec2bb2f47d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor(random_state=42)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "a998ca1c-790b-475d-8091-5dec565155fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "01184417-e901-42d1-9e6a-97e2ec67abee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 1165.301400972801\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred,)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "148a6892-9817-4cb9-9fd5-0bbf58d1849d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.295050396823706"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae= mean_absolute_error(y_test, y_pred,)\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85ce3117-b185-4312-88c5-0c8c17d7f06b",
   "metadata": {},
   "source": [
    "## K Nearest Neighbour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "971bcd74-fd99-44be-9235-29eb537421a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "0a007845-bbd4-44e5-af23-81d544bf5934",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = KNeighborsRegressor(n_neighbors=5)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "e73e1e69-4446-4c30-b01b-6c77d6631529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "71bba8b1-2ac7-416f-8ede-63b3f0e64552",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "52886279-a441-42f1-8161-91a444957ba5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 18506478.89529476\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred,)\n",
    "print('Mean Squared Error:', mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7ea1f358-e381-4a1d-a922-82e980177877",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3659.069825396825"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae= mean_absolute_error(y_test, y_pred,)\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63e56623-6d3e-47f1-9495-2a81c767581c",
   "metadata": {},
   "source": [
    "## Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "4bccea65-928d-4d7a-9be2-6f32545b23d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "256e2d6d-7fde-4645-af65-2c504d4e5b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = SVR(kernel='rbf', C=1.0, gamma='scale') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b22dd15d-9a29-4084-a674-6cb8df634911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "50afb13e-4d40-4761-b411-7a7df517e8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "4a5860e0-e0fc-4cde-9d55-6e5dd92ac9a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 17301949.851431612\n"
     ]
    }
   ],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0bf83c93-a354-4950-a5c3-b4ef8518f6c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3712.0671148005667"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae= mean_absolute_error(y_test, y_pred,)\n",
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d9704999-0942-43fd-9a53-bd56f58b55f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Saving the model\n",
    "model = model  # Your trained model object\n",
    "filename = 'model.pkl'\n",
    "with open(filename, 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a928d498-07b6-4747-87bb-4194430ed346",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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