Tisco.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:26px;\"> Analysis of Course Utilization</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "notebookRunGroups": {
     "groupValue": "2"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Name          Division                        Group  \\\n",
      "0        Person_1  Operations - TSK          Steel & Mills - TSK   \n",
      "1        Person_1  Operations - TSK          Steel & Mills - TSK   \n",
      "2        Person_1  Operations - TSK          Steel & Mills - TSK   \n",
      "3        Person_1  Operations - TSK          Steel & Mills - TSK   \n",
      "4        Person_1  Operations - TSK          Steel & Mills - TSK   \n",
      "...           ...               ...                          ...   \n",
      "6630  Person_1408       TQM and E&P  Projects & Construction TSK   \n",
      "6631  Person_1409       TQM and E&P  Projects & Construction TSK   \n",
      "6632  Person_1409       TQM and E&P  Projects & Construction TSK   \n",
      "6633  Person_1409       TQM and E&P  Projects & Construction TSK   \n",
      "6634  Person_1409       TQM and E&P  Projects & Construction TSK   \n",
      "\n",
      "                        Department                            Program Name  \\\n",
      "0     Steel Melting Shop & LCP TSK   Six Sigma Black Belt Learning Program   \n",
      "1     Steel Melting Shop & LCP TSK   Six Sigma Black Belt Learning Program   \n",
      "2     Steel Melting Shop & LCP TSK   Six Sigma Black Belt Learning Program   \n",
      "3     Steel Melting Shop & LCP TSK   Six Sigma Black Belt Learning Program   \n",
      "4     Steel Melting Shop & LCP TSK   Six Sigma Black Belt Learning Program   \n",
      "...                            ...                                     ...   \n",
      "6630      Cold Rolling Mills - POG               School of Analytics-Basic   \n",
      "6631      Cold Rolling Mills - POG  Operations Management Learning Program   \n",
      "6632      Cold Rolling Mills - POG  Operations Management Learning Program   \n",
      "6633      Cold Rolling Mills - POG               School of Analytics-Basic   \n",
      "6634      Cold Rolling Mills - POG               School of Analytics-Basic   \n",
      "\n",
      "                                            Course Name  Duration(in hrs)  \\\n",
      "0               The Define Phase for the 6 σ Black Belt               5.8   \n",
      "1              The Analyze Phase for the 6 σ Black Belt               7.5   \n",
      "2              The Measure Phase for the 6 σ Black Belt              10.8   \n",
      "3                           DFSS for the 6 σ Black Belt               5.0   \n",
      "4                Team Management for the 6 σ Black Belt               2.5   \n",
      "...                                                 ...               ...   \n",
      "6630           Data – What It Is What We Can Do With It               5.8   \n",
      "6631                            Supply Chain Excellence               6.8   \n",
      "6632                            Supply Chain Operations               7.6   \n",
      "6633           Data – What It Is What We Can Do With It               5.8   \n",
      "6634  Basic Data Descriptors Statistical Distributio...               8.9   \n",
      "\n",
      "     Completed           Enrollment Time           Completion Time  \\\n",
      "0          NaN                       NaN                       NaN   \n",
      "1          NaN                       NaN                       NaN   \n",
      "2          NaN                       NaN                       NaN   \n",
      "3          NaN                       NaN                       NaN   \n",
      "4          NaN                       NaN                       NaN   \n",
      "...        ...                       ...                       ...   \n",
      "6630       Yes  2023-02-15T11:48:06.000Z  2023-03-17T06:11:14.000Z   \n",
      "6631        No  2023-04-02T14:10:21.000Z                       NaN   \n",
      "6632       NaN                       NaN                       NaN   \n",
      "6633       Yes  2023-02-12T12:04:42.000Z  2023-04-02T14:01:56.000Z   \n",
      "6634       NaN                       NaN                       NaN   \n",
      "\n",
      "      Learning Hours Spent  Course Grade  \\\n",
      "0                      NaN           NaN   \n",
      "1                      NaN           NaN   \n",
      "2                      NaN           NaN   \n",
      "3                      NaN           NaN   \n",
      "4                      NaN           NaN   \n",
      "...                    ...           ...   \n",
      "6630                  7.31         90.00   \n",
      "6631                  2.09         33.23   \n",
      "6632                   NaN           NaN   \n",
      "6633                  6.34         90.00   \n",
      "6634                   NaN           NaN   \n",
      "\n",
      "                                         Skills Learned  \n",
      "0     Trigonometric Integral; Lean Six Sigma; Six Si...  \n",
      "1     Six Sigma; Training; Culture; Analysis; Leader...  \n",
      "2     Process Analysis; Process; Measurement; Genera...  \n",
      "3     Trigonometric Integral; Six Sigma; Leadership ...  \n",
      "4     Project; Conflict Management; Conflict Resolut...  \n",
      "...                                                 ...  \n",
      "6630  Data Visualization; Data Analysis; Analysis; P...  \n",
      "6631  Supply Chain; Finance; Chaining; Leadership an...  \n",
      "6632  Supply Chain; Trigonometric Integral; Lean Six...  \n",
      "6633  Data Visualization; Data Analysis; Analysis; P...  \n",
      "6634  General Statistics; Statistical Analysis; Prob...  \n",
      "\n",
      "[6635 rows x 13 columns]\n"
     ]
    }
   ],
   "source": [
    "coursera = pd.read_csv(\"Coursera MASKED data.csv\")\n",
    "print(coursera)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6635, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coursera.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                       0\n",
       "Division                   0\n",
       "Group                      0\n",
       "Department                 0\n",
       "Program Name               0\n",
       "Course Name                0\n",
       "Duration(in hrs)           0\n",
       "Completed               4897\n",
       "Enrollment Time         4897\n",
       "Completion Time         5960\n",
       "Learning Hours Spent    4897\n",
       "Course Grade            4897\n",
       "Skills Learned             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coursera.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6635 entries, 0 to 6634\n",
      "Data columns (total 13 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Name                  6635 non-null   object \n",
      " 1   Division              6635 non-null   object \n",
      " 2   Group                 6635 non-null   object \n",
      " 3   Department            6635 non-null   object \n",
      " 4   Program Name          6635 non-null   object \n",
      " 5   Course Name           6635 non-null   object \n",
      " 6   Duration(in hrs)      6635 non-null   float64\n",
      " 7   Completed             1738 non-null   object \n",
      " 8   Enrollment Time       1738 non-null   object \n",
      " 9   Completion Time       675 non-null    object \n",
      " 10  Learning Hours Spent  1738 non-null   float64\n",
      " 11  Course Grade          1738 non-null   float64\n",
      " 12  Skills Learned        6635 non-null   object \n",
      "dtypes: float64(3), object(10)\n",
      "memory usage: 674.0+ KB\n"
     ]
    }
   ],
   "source": [
    "coursera.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Duration(in hrs)</th>\n",
       "      <th>Learning Hours Spent</th>\n",
       "      <th>Course Grade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6635.000000</td>\n",
       "      <td>1738.000000</td>\n",
       "      <td>1738.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.788832</td>\n",
       "      <td>2.968953</td>\n",
       "      <td>41.338423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.425742</td>\n",
       "      <td>3.892452</td>\n",
       "      <td>41.900973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.590000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.900000</td>\n",
       "      <td>4.377500</td>\n",
       "      <td>87.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>40.940000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Duration(in hrs)  Learning Hours Spent  Course Grade\n",
       "count       6635.000000           1738.000000   1738.000000\n",
       "mean           7.788832              2.968953     41.338423\n",
       "std            3.425742              3.892452     41.900973\n",
       "min            1.600000              0.000000      0.000000\n",
       "25%            5.800000              0.210000      0.000000\n",
       "50%            6.600000              1.590000     22.500000\n",
       "75%            8.900000              4.377500     87.890000\n",
       "max           49.000000             40.940000    100.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coursera.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:22px;\">Visualizing the distribution of numeric features using histograms and box.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Duration(in hrs)           Axes(0.125,0.11;0.227941x0.77)\n",
       "Learning Hours Spent    Axes(0.398529,0.11;0.227941x0.77)\n",
       "Course Grade            Axes(0.672059,0.11;0.227941x0.77)\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coursera.plot(kind = 'box', subplots = True, sharex = False, figsize = [12, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: ylabel='Frequency'>, <Axes: ylabel='Frequency'>,\n",
       "       <Axes: ylabel='Frequency'>], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "coursera.plot(kind = 'hist', subplots = True, sharex = False, figsize = [10, 10], bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 8000x5000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4HnvsMbz22mv4559/0LZtWyQkJODHH3/E5MmTpf+hh4SEYMiQIYiPj8fVq1eladSnTp0CUL6Bme7u7ujatSsWLVoEg8GABx54AAkJCUhPT6/0YxIeHg6tVosuXbrAz88PJ06cwAcffIB+/fpZjPe53ZgxY/DRRx8hOjoaKSkpaNCgAb799lv88ccfiI+PL3XZijL1wtx+jxmT4cOHY8aMGRg0aBAmTpyIvLw8LF++HE2bNrU62Plu2dnZ4ZNPPkGfPn3QsmVLjBw5Eg888AAuXLiAHTt2wN3dHT///HOF19u4cWO89tprWLBgAR599FEMHjwYarUa+/fvh7+/P+Li4u465kaNGmHhwoWYOXMm/vnnHwwcOBBubm5IT0/Hhg0bMGbMGLz88sto3rw5GjVqhJdffhkXLlyAu7s7vvvuO4uxMEDJnw8PDw88+eSTWLp0KRQKBRo1aoSNGzdajN8qTbdu3fDCCy8gLi4Ohw8fRnh4OBwcHHD69GmsX78e77//PoYOHYo1a9bgww8/xKBBg9CoUSPcuHEDH3/8Mdzd3dG3b9+7Pl5UA9lg5hORBdM06pKmTnbr1q3MadQLFy4UHTt2FBqNRjg5OYnmzZuL119/XRQWFkptioqKxIQJE0SdOnWEQqEwmzJ648YNMWXKFOHv7y8cHBxEkyZNxNtvv20x3TU3N1fExMQILy8v4erqKgYOHChSU1MFALNpzaZpsJcvX7bYn3///VcMGjRIaDQa4eHhIZ588klx8eLFEqdi37mOqKgo4eLiUuZx+uijj0TXrl2Ft7e3UKvVolGjRmLatGkiKyvL6nG+XUZGhhg5cqTw8fERKpVKtG7d2my6rMndTqO+3enTp4VSqbSYRi2EEAkJCaJVq1ZCpVKJZs2aiS+//LLEadQxMTFmZaZpvm+//bZZubUp20IIcejQITF48GDpeAUGBophw4aJbdu2SW1Ke12txSWEEJ999plo3769UKvVwtPTU3Tr1k0kJiaWcJTK3s7tvvvuO/HII48IFxcX4eLiIpo3by5iYmJEamqq1Ob48eMiLCxMuLq6Ch8fHzF69Gjx559/WkyBLu3zcfnyZTFkyBDh7OwsPD09xQsvvCCOHj1qdRq1tfemycqVK0VISIhwcnISbm5uonXr1mL69OnSdPqDBw+Kp59+WtSvX1+o1Wrh6+sr+vfvLw4cOFDqcaDaRyHEPfZjEhEOHz6M9u3b48svv5TuOkpERFWHY2CIKuj226+bxMfHw87OTroDLhERVS2OgSGqoEWLFiElJQWPPfYY7O3tsXnzZmzevBljxoyxuN8IERFVDV5CIqqgxMRExMbG4vjx48jJyUH9+vUxYsQIvPbaa5X+S8RERGQdExgiIiKSHY6BISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSG7tm8efOgUCjuy7a++eYbeHl5IScnRypr0KABoqOjK20b0dHRcHV1rbT1lWTFihWoX78+CgoKqnxbRFWlsj9/ROXFBKaaWL16NRQKhfRwdHSEv78/IiIisGTJEty4ccOm8eXl5WHevHnYuXOnzWIoLi7G3LlzMWHChPuSYFS16OhoFBYW4qOPPrJ1KGRjps//gQMHbB2KrCgUCowfP95qnZyPaU5ODubOnYtWrVrBxcUF3t7eaNeuHSZNmoSLFy/aOjwAwPHjxzFv3jz8888/NovB3mZbJqvmz5+PoKAgGAwG6HQ67Ny5E5MnT8bixYvx008/oU2bNjaJKy8vD7GxsQCA7t27m9XNmjULr7zySpXH8PPPPyM1NRVjxowxK09NTYWdnfxycUdHR0RFRWHx4sWYMGHCfevFIqpMcv38VVcGgwFdu3bFyZMnERUVhQkTJiAnJwfHjh3DunXrMGjQIPj7+9s6TBw/fhyxsbHo3r07GjRoYJMYmMBUM3369EGHDh2k5zNnzsT27dvRv39/PPHEEzhx4gScnJzueTtFRUUwGo1QqVT3vC57e3vY21f9W2nVqlXo0qULHnjgAbNytVpd5dsur4oe12HDhmHRokXYsWMHevToUcXREZXubs4L1enzVx0YjUYUFhbC0dHxrpb/4YcfcOjQIaxduxbPPPOMWV1+fj4KCwsrI8wagWmzDPTo0QOzZ8/G2bNn8eWXX0rl3bt3t+gNAW5dmrg9I/7nn3+gUCjwzjvvID4+Ho0aNYJarcbx48dRWFiIOXPmICQkBB4eHnBxccGjjz6KHTt2mC1fp04dAEBsbKx0mWvevHkArI+BKSoqwoIFC6RtNWjQAK+++qrFeI8GDRqgf//++P3339GxY0c4OjqiYcOG+Pzzz83a5efnY8uWLQgLC7PY3zuvwZu6jv/44w9MnToVderUgYuLCwYNGoTLly+Xeqxvd+HCBQwcOBCurq6oU6cOXn75ZRQXF5fruALA0qVL0bJlSzg7O8PT0xMdOnTAunXrzLYREhICLy8v/Pjjj+WOi2qvCxcu4LnnnoOfnx/UajVatmyJzz77zKxNeT7TQOnvX9NnOi0tDdHR0dBoNPDw8MDIkSORl5dntp57+fwZjUbMmzcP/v7+cHZ2xmOPPYbjx49X6bia7du349FHH4WLiws0Gg0GDBiAEydOmLW58xxqYu1cZ7qMtXbtWrRs2RJqtRpbtmwBAHz11VcICQmBm5sb3N3d0bp1a7z//vulxvf3338DALp06WJR5+joCHd3d7M4XV1dcebMGURERMDFxQX+/v6YP38+hBBmyxqNRsTHx6Nly5ZwdHSEn58fXnjhBVy/ft2sXXnOyatXr8aTTz4JAHjsscek74T7PcSAPTAyMWLECLz66qtISEjA6NGj72odq1atQn5+PsaMGQO1Wg0vLy9kZ2fjk08+wdNPP43Ro0fjxo0b+PTTTxEREYF9+/ahXbt2qFOnDpYvX46xY8di0KBBGDx4MACUejnr+eefx5o1azB06FC89NJLSE5ORlxcHE6cOIENGzaYtU1LS8PQoUMxatQoREVF4bPPPkN0dDRCQkLQsmVLAEBKSgoKCwvx4IMPlnt/J0yYAE9PT8ydOxf//PMP4uPjMX78eHz99ddlLltcXIyIiAh06tQJ77zzDrZu3Yp3330XjRo1wtixY8s8rh9//DEmTpyIoUOHYtKkScjPz8eRI0eQnJxs8b+qBx98EH/88Ue594tqp4yMDHTu3Fn6wqxTpw42b96MUaNGITs7G5MnTwaAcn2mb2ft/WsybNgwBAUFIS4uDgcPHsQnn3wCX19fvPXWW2XGW57P38yZM7Fo0SI8/vjjiIiIwJ9//omIiAjk5+eX+7jk5+fjypUrFuW3D/Q32bp1K/r06YOGDRti3rx5uHnzJpYuXYouXbrg4MGDd30pZPv27fjmm28wfvx4+Pj4oEGDBkhMTMTTTz+Nnj17SsfrxIkT+OOPPzBp0qQS1xUYGAgA+PzzzzFr1qwyLy0XFxejd+/e6Ny5MxYtWoQtW7Zg7ty5KCoqwvz586V2L7zwAlavXo2RI0di4sSJSE9PxwcffIBDhw7hjz/+gIODg9S2rHNy165dMXHiRCxZsgSvvvoqWrRoAQDSv/eNoGph1apVAoDYv39/iW08PDxE+/btpefdunUT3bp1s2gXFRUlAgMDpefp6ekCgHB3dxeZmZlmbYuKikRBQYFZ2fXr14Wfn5947rnnpLLLly8LAGLu3LkW25s7d664/a10+PBhAUA8//zzZu1efvllAUBs375dKgsMDBQAxO7du6WyzMxMoVarxUsvvSSVffLJJwKA+Ouvvyy2HxgYKKKioqTnpmMZFhYmjEajVD5lyhShVCqFXq+3WMftoqKiBAAxf/58s/L27duLkJAQ6Xlpx3XAgAGiZcuWpW7HZMyYMcLJyalcbalmKs/nf9SoUaJu3briypUrZuXDhw8XHh4eIi8vTwhR/s90ae9f02f69vZCCDFo0CDh7e1tVna3nz+dTifs7e3FwIEDzdY3b948AcBsnSUBUObj9mParl074evrK65evSqV/fnnn8LOzk48++yzUtmd59A7j8udMdjZ2Yljx46ZlU+aNEm4u7uLoqKiMvfjdnl5eaJZs2YCgAgMDBTR0dHi008/FRkZGRZtTeeqCRMmSGVGo1H069dPqFQqcfnyZSGEEL/99psAINauXWu2/JYtWyzKy3tOXr9+vQAgduzYUaH9q0y8hCQjrq6u9zQbaciQIdKlIBOlUild7zYajbh27RqKiorQoUMHHDx48K62s2nTJgDA1KlTzcpfeuklAMAvv/xiVh4cHIxHH31Uel6nTh00a9YMZ86ckcquXr0KAPD09Cx3HGPGjDH738ujjz6K4uJinD17tlzLv/jii2bPH330UbOYTKwdV41Gg3///Rf79+8vczuenp64efOmRdc8kYkQAt999x0ef/xxCCFw5coV6REREYGsrCzp81rRz7S196+Jtc/A1atXkZ2dXWbMZX3+tm3bhqKiIowbN85suQkTJpS57tsNGDAAiYmJFo9p06aZtbt06RIOHz6M6Ohos16mNm3aoFevXtJ5625069YNwcHBZmUajQa5ublITEys0LqcnJyQnJwsxb969WqMGjUKdevWxYQJE6zeduH2mVimHrrCwkJs3boVALB+/Xp4eHigV69eZu+dkJAQuLq6WlxeLM85uTrgJSQZycnJga+v710vHxQUZLV8zZo1ePfdd3Hy5EkYDIYy25fl7NmzsLOzQ+PGjc3KtVotNBqNRQJRv359i3V4enpaXJsFYHFdtzR3rteU/Fhb750cHR0tTuolxWTtOM2YMQNbt25Fx44d0bhxY4SHh+OZZ56xel3btE+chUQluXz5MvR6PVauXImVK1dabZOZmSn9XZHPdGmf89I+Q7ePxajosgCk88Cd5wkvL68K/UelXr16VsfG/fvvv2bPTdtr1qyZRdsWLVrg119/RW5uLlxcXMq9bRNrx3DcuHH45ptv0KdPHzzwwAMIDw/HsGHD0Lt37zLX5+HhgUWLFmHRokU4e/Ystm3bhnfeeQcffPABPDw8sHDhQqmtnZ0dGjZsaLZ806ZNAUCa4nz69GlkZWWV+P1x+3sHqNg52ZaYwMjEv//+i6ysLLMPu0KhsPqFfvtA09tZm7305ZdfIjo6GgMHDsS0adPg6+sLpVKJuLg4aTDZ3SrvF7JSqbRafvu+eXt7A7h18qtXr16lrbeiy1pj7bi2aNECqamp2LhxI7Zs2YLvvvsOH374IebMmSNNRze5fv06nJ2dK2V2GdVMRqMRAPCf//wHUVFRVtuYxqRV9DNd2vuuKj5DFflPiK2UdO6qyLnV19cXhw8fxq+//orNmzdj8+bNWLVqFZ599lmsWbOm3LEEBgbiueeew6BBg9CwYUOsXbvWLIEpD6PRCF9fX6xdu9ZqvbWeeWuq22vHBEYmvvjiCwBARESEVObp6Wm1S6+8l0gA4Ntvv0XDhg3x/fffm31o586da9auIr0DgYGBMBqNOH36tNmgroyMDOj1emmQWkU0b94cAJCeno7WrVtXeHlbcHFxwVNPPYWnnnoKhYWFGDx4MF5//XXMnDnTbIplenr6/R/8RrJSp04duLm5obi42Gpvw+3K+5m2NdN5IC0tzawH4+rVq1XyP33T9lJTUy3qTp48CR8fH6n3xdPTE3q93qJdRc6tAKBSqfD444/j8ccfh9FoxLhx4/DRRx9h9uzZFj1PZfH09ESjRo1w9OhRs3Kj0YgzZ85IvS4AcOrUKQCQBiU3atQIW7duRZcuXSrtP0rVoceYY2BkYPv27ViwYAGCgoIQGRkplTdq1AgnT540m5r4559/VmhGiynTvj2zTk5ORlJSklk7Z2dnALD6ob5T3759AQDx8fFm5YsXLwYA9OvXr9zxmYSEhEClUsnmrpqmMTsmKpUKwcHBEEKYdekDwMGDB/Hwww/fz/BIZpRKJYYMGYLvvvvO4gsMgNk5oLyfaVvr2bMn7O3tsXz5crPyDz74oEq2V7duXbRr1w5r1qwxO48dPXoUCQkJ0nkLuHVuzcrKwpEjR6SyS5cuWcygLM2d5wA7Ozupl6y0nw/5888/rc6qOnv2LI4fP271Etjtx0wIgQ8++AAODg7o2bMngFuzyYqLi7FgwQKLZYuKisp1Xr+TKdm7m2UrC3tgqpnNmzfj5MmTKCoqQkZGBrZv347ExEQEBgbip59+Mvuf+3PPPYfFixcjIiICo0aNQmZmJlasWIGWLVuWa5AdAPTv3x/ff/89Bg0ahH79+iE9PR0rVqxAcHCw2TREJycnBAcH4+uvv0bTpk3h5eWFVq1aoVWrVhbrbNu2LaKiorBy5Uro9Xp069YN+/btw5o1azBw4EA89thjFT4ujo6OCA8Px9atW82mBlZX4eHh0Gq16NKlC/z8/HDixAl88MEH6NevH9zc3KR2KSkpuHbtGgYMGGDDaKm6+Oyzz6R7iNxu0qRJePPNN7Fjxw506tQJo0ePRnBwMK5du4aDBw9i69atuHbtGoDyf6Ztzc/PD5MmTcK7776LJ554Ar1798aff/6JzZs3w8fHp0r+h//222+jT58+CA0NxahRo6Rp1B4eHtJ9rQBg+PDhmDFjBgYNGoSJEyciLy8Py5cvR9OmTcs9ueH555/HtWvX0KNHD9SrVw9nz57F0qVL0a5du1J7XBMTEzF37lw88cQT6Ny5s3Sfl88++wwFBQVmcQK3zo1btmxBVFQUOnXqhM2bN+OXX37Bq6++Kl0a6tatG1544QXExcXh8OHDCA8Ph4ODA06fPo3169fj/fffx9ChQyt0LNu1awelUom33noLWVlZUKvV6NGjxz2N06wwW0x9IkumqYemh0qlElqtVvTq1Uu8//77Ijs72+pyX375pWjYsKFQqVSiXbt24tdffy1xGvXbb79tsbzRaBRvvPGGCAwMFGq1WrRv315s3LjR6jTCPXv2iJCQEKFSqcymVFubWmgwGERsbKwICgoSDg4OIiAgQMycOVPk5+ebtQsMDBT9+vWziMvaFPHvv/9eKBQKce7cOYt1WJvGeeeU1B07dpRr2l9UVJRwcXGxKL9zP0s7rh999JHo2rWr8Pb2Fmq1WjRq1EhMmzZNZGVlmbWbMWOGqF+/vtl0U6p97vz83/k4f/68EEKIjIwMERMTIwICAoSDg4PQarWiZ8+eYuXKldK6yvuZLu39a3qvm6bh3hlnenq6VHYvn7+ioiIxe/ZsodVqhZOTk+jRo4c4ceKE8Pb2Fi+++GKZxw2AiImJsVpXUhxbt24VXbp0EU5OTsLd3V08/vjj4vjx4xbLJyQkiFatWgmVSiWaNWsmvvzyyxKnUVuL4dtvvxXh4eHC19dXqFQqUb9+ffHCCy+IS5culbpPZ86cEXPmzBGdO3cWvr6+wt7eXtSpU0f069fP7BYUQvzvXPX333+L8PBw4ezsLPz8/MTcuXNFcXGxxbpXrlwpQkJChJOTk3BzcxOtW7cW06dPFxcvXpTaVOSc/PHHH4uGDRsKpVJpkynVCiGq2agcohIUFxcjODgYw4YNs9oVKjcFBQVo0KABXnnllVJvbEVUm+j1enh6emLhwoV47bXXbB1OtRYdHY1vv/22WvWs3U8cA0OyoVQqMX/+fCxbtqxGfGBXrVoFBwcHi3ttENUWN2/etCgzjZ2z9jMpRLdjDwwREdnE6tWrsXr1avTt2xeurq74/fff8d///hfh4eH49ddfbR1etVfbe2A4iJeIiGyiTZs2sLe3x6JFi5CdnS0N7K3ofU6odmIPDBEREckOx8AQERGR7DCBISIiItmpsWNgjEYjLl68CDc3t2pxy2OimkIIgRs3bsDf3x92drXz/0A8vxBVnfKeY2psAnPx4kUEBATYOgyiGuv8+fPl/mHNmobnF6KqV9Y5psYmMKbbtZ8/fx5OTk5ISEiQbp9Md89gMPBYVgI5H8fs7GwEBASY/SRCbXP7+cXd3b3EdnJ+nQHGb2u1Nf7ynmNqbAJj6tZ1d3eHk5MTnJ2d4e7uLss3QXViMBh4LCtBTTiOtfnSye3nl7ISGDm/zozftmp7/GWdY2rnBWwiIiKSNSYwREREJDtMYIiIiEh2auwYmPtFp9NBr9eXWK/RaKDVau9fQERU7Zw6dQpKpdJqHc8RRHeHCcw90Ol06PvEIOhv5JbYRuPmgk0/beAJiqgWysjIAAAMjYxGQUGB1TY8RxDdHSYw90Cv10N/Ixd1uj8LZ++6FvV5Vy/h8s7PodfreXIiqoWysrIAAD6PPA17jZ9FPc8RRHePCUwlcPauC1e/QFuHQUTVlLOXH1R1eI4gqkwVGsQbFxeHhx56CG5ubvD19cXAgQORmppq1iY/Px8xMTHw9vaGq6srhgwZInWjmpw7dw79+vWDs7MzfH19MW3aNBQVFZm12blzJx588EGo1Wo0btwYq1evvrs9JCIiohqnQgnMrl27EBMTg7179yIxMREGgwHh4eHIzf3fGJApU6bg559/xvr167Fr1y5cvHgRgwcPluqLi4vRr18/FBYWYs+ePVizZg1Wr16NOXPmSG3S09PRr18/PPbYYzh8+DAmT56M559/Hr/++msl7DIRERHJXYUuIW3ZssXs+erVq+Hr64uUlBR07doVWVlZ+PTTT7Fu3Tr06NEDALBq1Sq0aNECe/fuRefOnZGQkIDjx49j69at8PPzQ7t27bBgwQLMmDED8+bNg0qlwooVKxAUFIR3330XANCiRQv8/vvveO+99xAREVFJu05ERERydU9jYEwD1Ly8vAAAKSkpMBgMCAsLk9o0b94c9evXR1JSEjp37oykpCS0bt0afn7/G9AWERGBsWPH4tixY2jfvj2SkpLM1mFqM3ny5BJjKSgoMBvln52dDeDWrYzt7e2lvytTcXEx1Go1VHaAg8JoUa+yA9RqNYqLiyt927Zi2o+asj+2IufjKMeYiajmuesExmg0YvLkyejSpQtatWoF4Na0YpVKBY1GY9bWz88POp1OanN78mKqN9WV1iY7Oxs3b96Ek5OTRTxxcXGIjY21KE9ISICzszMAIDEx8S72tHRvxs76/7+uW1YGuAIhs5CWloa0tLRK37YtVcWxrI3keBzz8vJsHQIR0d0nMDExMTh69Ch+//33yoznrs2cORNTp06Vnpt+zTI8PBxOTk5ITExEr169KvUHsU6dOoWhkdGo/8RkuPgGWNTnZp7HuZ/i8e3a1WjatGmlbdeWDAZDlRzL2kbOx9HUu0lEZEt3lcCMHz8eGzduxO7du1GvXj2pXKvVorCwEHq93qwXJiMjQ7rHgVarxb59+8zWZ5qldHubO2cuZWRkSL8sbY1arYZarbYod3BwkL4gbv+7MiiVShQUFKDQCKiE5XjoQuOtS1tKpVJ2X1JlqexjWVvJ8TjKLV4iqpkqNAtJCIHx48djw4YN2L59O4KCgszqQ0JC4ODggG3btkllqampOHfuHEJDQwEAoaGh+Ouvv5CZmSm1SUxMhLu7O4KDg6U2t6/D1Ma0DiIiIqrdKpTAxMTE4Msvv8S6devg5uYGnU4HnU6HmzdvAgA8PDwwatQoTJ06FTt27EBKSgpGjhyJ0NBQdO7cGQAQHh6O4OBgjBgxAn/++Sd+/fVXzJo1CzExMVIPyosvvogzZ85g+vTpOHnyJD788EN88803mDJlSiXvPhHJSXFxMWbPno2goCA4OTmhUaNGWLBgAYQQUhshBObMmYO6devCyckJYWFhOH36tNl6rl27hsjISLi7u0Oj0WDUqFHIycm537tDRPegQgnM8uXLkZWVhe7du6Nu3brS4+uvv5bavPfee+jfvz+GDBmCrl27QqvV4vvvv5fqlUolNm7cCKVSidDQUPznP//Bs88+i/nz50ttgoKC8MsvvyAxMRFt27bFu+++i08++YRTqIlqubfeegvLly/HBx98gBMnTuCtt97CokWLsHTpUqnNokWLsGTJEqxYsQLJyclwcXFBREQE8vPzpTaRkZE4duwYEhMTpcvhY8aMscUuEdFdqtAYmNv/l1MSR0dHLFu2DMuWLSuxTWBgIDZt2lTqerp3745Dhw5VJDwiquH27NmDAQMGoF+/fgCABg0a4L///a80rk4Igfj4eMyaNQsDBgwAAHz++efw8/PDDz/8gOHDh+PEiRPYsmUL9u/fjw4dOgAAli5dir59++Kdd96Bv7+/bXaOiCqEv4VERLLx8MMPY+XKlTh16hSaNm2KP//8E7///jsWL14M4NZdvHU6ndl9pDw8PNCpUyckJSVh+PDhSEpKgkajkZIXAAgLC4OdnR2Sk5MxaNAgi+2Wdp+p0u6LYzTeuj+Ug0zvFSXn+xUBjN/W7jb+8rZnAkNEsvHKK68gOzsbzZs3h1KpRHFxMV5//XVERkYC+N+9pKzdR+r2+0z5+vqa1dvb28PLy0tqc6fy3GeqNKPbu0LO94qS4/2Kbsf4baui8Zf3XlNMYIhINr755husXbsW69atQ8uWLaXfSvP390dUVFSVbbe0+0y5u7uXuNzJkydx5swZfHwoByof+d0rSs73KwIYv63dbfzlvdcUExgiko1p06bhlVdewfDhwwEArVu3xtmzZxEXF4eoqCjpXlIZGRmoW7eutFxGRgbatWsH4NZ9pm6/jQMAFBUV4dq1a9LydyrPfaassbO7NU/CYAQUMr5XlBzvV3Q7xm9bFY2/vG0rNAuJiMiW8vLypKTARKlUSmNNgoKCoNVqze4jlZ2djeTkZLN7Uen1eqSkpEhttm/fDqPRiE6dOt2HvSCiysAeGCKSjccffxyvv/466tevj5YtW+LQoUNYvHgxnnvuOQCAQqHA5MmTsXDhQjRp0gRBQUGYPXs2/P39MXDgQAC3ft2+d+/eGD16NFasWAGDwYDx48dj+PDhnIFEJCNMYIhINpYuXYrZs2dj3LhxyMzMhL+/P1544QXMmTNHajN9+nTk5uZizJgx0Ov1eOSRR7BlyxY4OjpKbdauXYvx48ejZ8+esLOzw5AhQ7BkyRJb7BIR3SUmMEQkG25uboiPj0d8fHyJbRQKBebPn292c8w7eXl5Yd26dVUQIRHdLxwDQ0RERLLDHpgqVlRkwJkzZ0qs12g0Jc58ICIiIuuYwFShghw9MnU6vDDxpRKnhWncXLDppw1MYoiIiCqACUwVKsrPg7BTwqfbCGjqBlrU5129hMs7P4der2cCQ0REVAFMYO4DJy8tXP0sExgiIiK6OxzES0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkOxVOYHbv3o3HH38c/v7+UCgU+OGHH8zqo6OjoVAozB69e/c2a3Pt2jVERkbC3d0dGo0Go0aNQk5OjlmbI0eO4NFHH4WjoyMCAgKwaNGiiu8dERER1UgVTmByc3PRtm1bLFu2rMQ2vXv3xqVLl6THf//7X7P6yMhIHDt2DImJidi4cSN2796NMWPGSPXZ2dkIDw9HYGAgUlJS8Pbbb2PevHlYuXJlRcMlIiKiGsi+ogv06dMHffr0KbWNWq2GVqu1WnfixAls2bIF+/fvR4cOHQAAS5cuRd++ffHOO+/A398fa9euRWFhIT777DOoVCq0bNkShw8fxuLFi80SHSIiIqqdKpzAlMfOnTvh6+sLT09P9OjRAwsXLoS3tzcAICkpCRqNRkpeACAsLAx2dnZITk7GoEGDkJSUhK5du0KlUkltIiIi8NZbb+H69evw9PS02GZBQQEKCgqk59nZ2QAAg8EAe3t76e/KVFxcDLVaDZUd4KAwWtSr7O3g5OgIlVJhvd7uVrJXXFxc6bFVFVOccom3upLzcZRjzERU81R6AtO7d28MHjwYQUFB+Pvvv/Hqq6+iT58+SEpKglKphE6ng6+vr3kQ9vbw8vKCTqcDAOh0OgQFBZm18fPzk+qsJTBxcXGIjY21KE9ISICzszMAIDExsVL28XZvxs76/7+uW1YGNAZ6LSul3hUImYW0tDSkpaVVemxVqSqOZW0kx+OYl5dn6xCIiCo/gRk+fLj0d+vWrdGmTRs0atQIO3fuRM+ePSt7c5KZM2di6tSp0vPs7GwEBAQgPDwcTk5OSExMRK9eveDg4FBp2zx16hSGRkaj/hOT4eIbYFGfeXI/jq5/D+1GLoBXvUYW9bmZ53Hup3h8u3Y1mjZtWmlxVSWDwVAlx7K2kfNxNPVu2sqFCxcwY8YMbN68GXl5eWjcuDFWrVol9eoKITB37lx8/PHH0Ov16NKlC5YvX44mTZpI67h27RomTJiAn3/+GXZ2dhgyZAjef/99uLq62mq3iKiCquQS0u0aNmwIHx8fpKWloWfPntBqtcjMzDRrU1RUhGvXrknjZrRaLTIyMszamJ6XNLZGrVZDrVZblDs4OEhfELf/XRmUSiUKCgpQaARUwnI8dGGRETfz81FYLGCwVm+8delLqVTK7kusso9lbSXH42jLeK9fv44uXbrgsccew+bNm1GnTh2cPn3arFd20aJFWLJkCdasWYOgoCDMnj0bEREROH78OBwdHQHcmkhw6dIlJCYmwmAwYOTIkRgzZgzWrVtnq10jogqq8gTm33//xdWrV1G3bl0AQGhoKPR6PVJSUhASEgIA2L59O4xGIzp16iS1ee2112AwGKSTZWJiIpo1a2b18hER1Q5vvfUWAgICsGrVKqns9svNQgjEx8dj1qxZGDBgAADg888/h5+fH3744QcMHz68XBMJiKj6q3ACk5OTYzZeIz09HYcPH4aXlxe8vLwQGxuLIUOGQKvV4u+//8b06dPRuHFjREREAABatGiB3r17Y/To0VixYgUMBgPGjx+P4cOHSyeOZ555BrGxsRg1ahRmzJiBo0eP4v3338d7771XSbtNRHL0008/ISIiAk8++SR27dqFBx54AOPGjcPo0aMB3Dof6XQ6hIWFSct4eHigU6dOSEpKwvDhw8s1keBOpU0SKG1Qs9F4a/C+Q0kD/av5QH45DzYHGL+t3W385W1f4QTmwIEDeOyxx6TnpnEnUVFRWL58OY4cOYI1a9ZAr9fD398f4eHhWLBggdnlnbVr12L8+PHo2bOndP15yZIlUr2HhwcSEhIQExODkJAQ+Pj4YM6cOZxCTVTLnTlzBsuXL8fUqVPx6quvYv/+/Zg4cSJUKhWioqKkiQCmQf8mfn5+ZpMEyppIcKfyTBIozej2rpDzQH45Dja/HeO3rYrGX96JAhVOYLp37w4hRIn1v/76a5nr8PLyKvNac5s2bfDbb79VNDwiqsGMRiM6dOiAN954AwDQvn17HD16FCtWrEBUVFSVbbe0SQLu7u4lLnfy5EmcOXMGHx/KgcrHcqB/dR/IL+fB5gDjt7W7jb+8EwWqfAwMEVFlqVu3LoKDg83KWrRoge+++w7A/wb5Z2RkSOPuTM/btWsntSlrIsGdyjNJwBo7u1uD9w1GQCHjgfxyHGx+O8ZvWxWNv7xt+WOORCQbXbp0QWpqqlnZqVOnEBgYCODWgF6tVott27ZJ9dnZ2UhOTkZoaCgA84kEJndOJCCi6o89MEQkG1OmTMHDDz+MN954A8OGDcO+ffuwcuVK6XfSFAoFJk+ejIULF6JJkybSNGp/f38MHDgQQPkmEhBR9ccEhohk46GHHsKGDRswc+ZMzJ8/H0FBQYiPj0dkZKTUZvr06cjNzcWYMWOg1+vxyCOPYMuWLdI9YICyJxIQUfXHBIaIZKV///7o379/ifUKhQLz58/H/PnzS2xTnokERFS9cQwMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHYqnMDs3r0bjz/+OPz9/aFQKPDDDz+Y1QshMGfOHNStWxdOTk4ICwvD6dOnzdpcu3YNkZGRcHd3h0ajwahRo5CTk2PW5siRI3j00Ufh6OiIgIAALFq0qOJ7R0RERDVShROY3NxctG3bFsuWLbNav2jRIixZsgQrVqxAcnIyXFxcEBERgfz8fKlNZGQkjh07hsTERGzcuBG7d+/GmDFjpPrs7GyEh4cjMDAQKSkpePvttzFv3jysXLnyLnaRiIiIahr7ii7Qp08f9OnTx2qdEALx8fGYNWsWBgwYAAD4/PPP4efnhx9++AHDhw/HiRMnsGXLFuzfvx8dOnQAACxduhR9+/bFO++8A39/f6xduxaFhYX47LPPoFKp0LJlSxw+fBiLFy82S3TuB51OB71eb7XuzJkzKC4uvq/xEBER0V0kMKVJT0+HTqdDWFiYVObh4YFOnTohKSkJw4cPR1JSEjQajZS8AEBYWBjs7OyQnJyMQYMGISkpCV27doVKpZLaRERE4K233sL169fh6elpse2CggIUFBRIz7OzswEABoMB9vb20t8VkZGRgaHDI5GVk2e13lCQjxvZN6AwFsFBYbSoV9nbwcnRESqlwnq9HaBWq1FcXFzh2GzFFKdc4q2u5Hwc5RgzEdU8lZrA6HQ6AICfn59ZuZ+fn1Sn0+ng6+trHoS9Pby8vMzaBAUFWazDVGctgYmLi0NsbKxFeUJCApydnQEAiYmJFd6nmdOmlrPldcuigMZAr2Wl1LsCIbOQlpaGtLS0CsdmS3dzLMmSHI9jXp71hN4W3nzzTcycOROTJk1CfHw8ACA/Px8vvfQSvvrqKxQUFCAiIgIffvih2Xnp3LlzGDt2LHbs2AFXV1dERUUhLi5O+s8OEVV/NebTOnPmTEyd+r9kIzs7GwEBAQgPD4eTkxMSExPRq1cvODg4lHudp06dwtDIaNR/YjJcfAMs6jNP7sfR9e+h3cgF8KrXqML1uZnnce6neHy7djWaNm1a7rhsyWAw3NWxJHNyPo6m3k1b279/Pz766CO0adPGrHzKlCn45ZdfsH79enh4eGD8+PEYPHgw/vjjDwBAcXEx+vXrB61Wiz179uDSpUt49tln4eDggDfeeMMWu0JEd6FSExitVgvg1qWXunXrSuUZGRlo166d1CYzM9NsuaKiIly7dk1aXqvVIiMjw6yN6bmpzZ3UajXUarVFuYODg/QFcfvf5aFUKlFQUIBCI6ASluOdC4uMuJmfj8JiAcPd1BtvXfpSKpWy+xKr6LEk6+R4HKtDvDk5OYiMjMTHH3+MhQsXSuVZWVn49NNPsW7dOvTo0QMAsGrVKrRo0QJ79+5F586dkZCQgOPHj2Pr1q3w8/NDu3btsGDBAsyYMQPz5s0zu3RNRNVXpSYwQUFB0Gq12LZtm5SwZGdnIzk5GWPHjgUAhIaGQq/XIyUlBSEhIQCA7du3w2g0olOnTlKb1157DQaDQTpZJiYmolmzZlYvHxFR7RITE4N+/fohLCzMLIFJSUmBwWAwG4fXvHlz1K9fH0lJSejcuTOSkpLQunVrs0tKERERGDt2LI4dO4b27dtbbK+0MXaljQkyGm+NfXOwgyzHwcl5rBbA+G3tbuMvb/sKJzA5OTlm4zXS09Nx+PBheHl5oX79+pg8eTIWLlyIJk2aICgoCLNnz4a/vz8GDhwIAGjRogV69+6N0aNHY8WKFTAYDBg/fjyGDx8Of39/AMAzzzyD2NhYjBo1CjNmzMDRo0fx/vvv47333qtouERUw3z11Vc4ePAg9u/fb1Gn0+mgUqmg0WjMyu8ch2dtnJ6pzpryjLErzej2rpDzODg5jtW6HeO3rYrGX95xdhVOYA4cOIDHHntMem4adxIVFYXVq1dj+vTpyM3NxZgxY6DX6/HII49gy5YtcHR0lJZZu3Ytxo8fj549e8LOzg5DhgzBkiVLpHoPDw8kJCQgJiYGISEh8PHxwZw5c+77FGoiql7Onz+PSZMmITEx0eycUtVKG2Pn7u5e4nInT57EmTNn8PGhHKh8LMfRVfdxcHIeqwUwflu72/jLO86uwglM9+7dIYQosV6hUGD+/PmYP39+iW28vLywbt26UrfTpk0b/PbbbxUNj4hqsJSUFGRmZuLBBx+UyoqLi7F792588MEH+PXXX1FYWAi9Xm/WC5ORkWE2xm7fvn1m662MMXbW2NndGvtmMAIKGY+Dk+NYrdsxftuqaPzlbcvfQiIi2ejZsyf++usvHD58WHp06NABkZGR0t8ODg7Ytm2btExqairOnTuH0NBQALfG2P31119mkwkSExPh7u6O4ODg+75PRHR3asw0aiKq+dzc3NCqVSuzMhcXF3h7e0vlo0aNwtSpU+Hl5QV3d3dMmDABoaGh6Ny5MwAgPDwcwcHBGDFiBBYtWgSdTodZs2YhJibGai8LEVVPTGCIqEZ57733pLF1t9/IzkSpVGLjxo0YO3YsQkND4eLigqioqFIvexNR9cMEhohkbefOnWbPHR0dsWzZshJ/cBYAAgMDsWnTpiqOjIiqEsfAEBERkewwgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdpjAEBERkewwgSEiIiLZYQJDREREssMEhoiIiGSHP+ZIRGRDRUUGnDlzpsR6jUYDrVZ7HyMikgcmMERENlKQo0emTocXJr4EBwcHq200bi7Y9NMGJjFEd2ACQ0RkI0X5eRB2Svh0GwFN3UCL+ryrl3B55+fQ6/VMYIjuwATGxth9TEROXlq4+lkmMERUMiYwNsTuYyIiorvDBMaG2H1MRER0d5jAVAPsPiYiIqoY3geGiIiIZIcJDBEREckOExgiIiKSnUpPYObNmweFQmH2aN68uVSfn5+PmJgYeHt7w9XVFUOGDEFGRobZOs6dO4d+/frB2dkZvr6+mDZtGoqKiio7VCIiIpKpKhnE27JlS2zduvV/G7H/32amTJmCX375BevXr4eHhwfGjx+PwYMH448//gAAFBcXo1+/ftBqtdizZw8uXbqEZ599Fg4ODnjjjTeqIlwiIiKSmSq5hGRvbw+tVis9fHx8AABZWVn49NNPsXjxYvTo0QMhISFYtWoV9uzZg7179wIAEhIScPz4cXz55Zdo164d+vTpgwULFmDZsmUoLCysinCJSCbi4uLw0EMPwc3NDb6+vhg4cCBSU1PN2rCXl6h2qJIemNOnT8Pf3x+Ojo4IDQ1FXFwc6tevj5SUFBgMBoSFhUltmzdvjvr16yMpKQmdO3dGUlISWrduDT8/P6lNREQExo4di2PHjqF9+/ZWt1lQUICCggLpeXZ2NgDAYDBIPUAGg6FC+1FcXAy1Wg2VHeCgMFrUq+zt4OToCJVSUTX1doBarUZxcXGFY68qpjiqSzxyJefjaMuYd+3ahZiYGDz00EMoKirCq6++ivDwcBw/fhwuLi4A2MtLVFtUegLTqVMnrF69Gs2aNcOlS5cQGxuLRx99FEePHoVOp4NKpYJGozFbxs/PDzqdDgCg0+nMkhdTvamuJHFxcYiNjbUoT0hIgLOzMwAgMTGxwvvzZuys///rumVlQGOg17IqrHcFQmYhLS0NaWlpFQu8it3NsSRLcjyOeXl5Ntv2li1bzJ6vXr0avr6+SElJQdeuXaVe3nXr1qFHjx4AgFWrVqFFixbYu3cvOnfuLPXybt26FX5+fmjXrh0WLFiAGTNmYN68eVCpVLbYNSKqoEpPYPr06SP93aZNG3Tq1AmBgYH45ptv4OTkVNmbk8ycORNTp06VnmdnZyMgIADh4eFwcnJCYmIievXqVeIt+605deoUhkZGo/4Tk+HiG2BRn3lyP46ufw/tRi6AV71GlV6fm3ke536Kx7drV6Np06bljrsqGQyGuzqWZE7Ox9HUu1kdZGVlAQC8vLwAoMp6eUvr4S2tR8povNWz6nC3vbg27oWVc08hwPht7W7jL2/7Kr8Tr0ajQdOmTZGWloZevXqhsLAQer3erBcmIyNDulW+VqvFvn37zNZhun5d2u301Wo11Gq1RbmDg4P0BXH73+WhVCpRUFCAQiOgEpbDhQqLjLiZn4/CYgFDVdQbb504lUpltfuSq+ixJOvkeByrS7xGoxGTJ09Gly5d0KpVKwCosl7e8vTwlmZ0e1fIuRdWjj2Ft2P8tlXR+Mvby1vlCUxOTg7+/vtvjBgxAiEhIXBwcMC2bdswZMgQAEBqairOnTuH0NBQAEBoaChef/11ZGZmwtfXF8CtnXd3d0dwcHBVh0tEMhETE4OjR4/i999/r/JtldbD6+7uXuJyJ0+exJkzZ/DxoRyofCrei3st/Sj+/GIBfOr4wb6ExNHD1RnffrXWIimrDHLuKQQYv63dbfzl7eWt9ATm5ZdfxuOPP47AwEBcvHgRc+fOhVKpxNNPPw0PDw+MGjUKU6dOhZeXF9zd3TFhwgSEhoaic+fOAIDw8HAEBwdjxIgRWLRoEXQ6HWbNmoWYmBirPSxEVPuMHz8eGzduxO7du1GvXj2pXKvVVkkvb3l6eK2xs7vVs2owAoq76IXNy81FXoEBrp2HlfiDr5k7P0dOTo7ZcahscuwpvB3jt62Kxl/etpWewPz77794+umncfXqVdSpUwePPPII9u7dizp16gAA3nvvPdjZ2WHIkCEoKChAREQEPvzwQ2l5pVKJjRs3YuzYsQgNDYWLiwuioqIwf/78yg6ViGRGCIEJEyZgw4YN2LlzJ4KCgszqa2ovL3/wlchSpScwX331Van1jo6OWLZsGZYtW1Zim8DAQGzatKmyQyMimYuJicG6devw448/ws3NTRqz4uHhAScnJ/byEtUiVT4GhoiosixfvhwA0L17d7PyVatWITo6GgB7eYlqCyYwRCQbQogy27CXl6h24K9RExERkeywB4aISMaKigw4c+aM1TqNRlPq/bOI5IwJDBGRTBXk6JGp0+GFiS9ZnXqqcXPBpp82MImhGokJDBGRTBXl50HYKeHTbYTFfWLyrl7C5Z2fQ6/XM4GhGokJDBGRzPE+MVQbMYGROZ1OB71eX2I9r4ETEVFNxARGxnQ6Hfo+MQj6G7kltuE1cCIiqomYwKDkXowzZ86guLj4/gdUTnq9HvobuajT/Vk4e9e1qOc1cCIiqqlqfQJTWi9GYUE+rl7TI8BQZIPIbiltiqQpwXL2rsvr30REVKvU+gSmtF6Mq2l/4vLmz2A02qYXpqwpktUhwSIiIrKFWp/AmFjrxci9ctFG0dxS2hRJwPYJFhERka0wgZGBkqZI2jrBIiIishUmMERENVRpY+gA3maB5I0JTC3Ge8gQ1VxljaEDeJsFkjcmMLUU7yFDVLOVNYaOt1kguWMCU0vxHjJEtQN/ZoBqKiYwtRzvIUNUe5U1RsbV1fU+RkNUMUxgiIhqofKMkfH19sTMaVPvc2RE5cMEhoioFirPGJmspK9tEBlR+TCBqeFK6iKu7r/zRET3R2ljZLLvcyxEFcEEpgYrrYuYP0NARERyxgSmBiuti5g/Q0BERHLGBKYWsNZFzJ8hIKKyFBcZAACnTp2CUqm0qOfNLsmWqnUCs2zZMrz99tvQ6XRo27Ytli5dio4dO9o6LCKqIXiOKVlBjh6XMzMAAEMjo1FQUGDRxsXRAR8uiUedOnWsroMJDlWlapvAfP3115g6dSpWrFiBTp06IT4+HhEREUhNTYWvr6+tw6sVrA0ANg38PXXqFLy9vXlyItniOaZ0Rfl5EIpbvS71n5iMQqN5vf58Kk58vxTPjBxT4jTsshKcwsJCqFQqq3VMfqgs1TaBWbx4MUaPHo2RI0cCAFasWIFffvkFn332GV555RUbR1fzlTQAWK1W483YWRgaGQ0nlT1/aoBki+eY8nPxDYBK2JmV5V65WOo07LISnKIiA65ezoSPr9b65Sn+lAmVoVomMIWFhUhJScHMmTOlMjs7O4SFhSEpKcnqMgUFBWZdnFlZWQCAa9euwdHREXl5ebh69arFBykrKwv29vbIzzwLO0OeWV3R9UtwVKlQeOUc8uwsB7vKub6sZfMv/Q21oxM0bXrA2fN//3tSKe2Ql5cHt+aP4tqfW/Hbb7+hfv36FssDgEKhgBDCat291lfluqu6XqFQoLi4GHl5eThw4ADs7OzKvez9qPfy8oKXl1eJ9Tdu3ACAUtdR3VX0HFPa+cVgMJS4nezsbOTl5SE/8xyKCvIs6m35GS9PvUqlQl5eHnIvZKLIaH15pbHQ4twJAIr8HKvnEJMbF9KRpU+Ee6vuFvWF2deRfXTHPZ9fbPU5q6x1G41Gq/Hb+vxV3mWtxV/W+QWowDlGVEMXLlwQAMSePXvMyqdNmyY6duxodZm5c+cKAHzwwcd9epw/f/5+nA6qREXPMTy/8MHH/X+UdY6plj0wd2PmzJmYOvV/t7w2Go24du0avL29cePGDQQEBOD8+fNwd3e3YZTyl52dzWNZCeR8HIUQuHHjBvz9/W0dyn1T2vlFoVCUuJycX2eA8dtabY2/vOeYapnA+Pj4QKlUIiMjw6w8IyOjxOuharUaarXarEyj0QCAdIJxd3eX5ZugOuKxrBxyPY4eHh62DuGeVPQcU9r5pTzk+jqbMH7bqo3xl+ccY1dmCxtQqVQICQnBtm3bpDKj0Yht27YhNDTUhpERUU3AcwyR/FXLHhgAmDp1KqKiotChQwd07NgR8fHxyM3NlWYMEBHdC55jiOSt2iYwTz31FC5fvow5c+ZAp9OhXbt22LJlC/z8/Cq8LrVajblz51p0AVPF8VhWDh5H26vMc0xJ5P46M37bYvylUwgh47mQREREVCtVyzEwRERERKVhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSnVqRwCxbtgwNGjSAo6MjOnXqhH379tk6pGotLi4ODz30ENzc3ODr64uBAwciNTXVrE1+fj5iYmLg7e0NV1dXDBkyxOKupmTuzTffhEKhwOTJk6UyHseaS07nnd27d+Pxxx+Hv78/FAoFfvjhB7N6IQTmzJmDunXrwsnJCWFhYTh9+rRtgr2D3M9Xy5cvR5s2baS71YaGhmLz5s1SfXWO/U73+xxX4xOYr7/+GlOnTsXcuXNx8OBBtG3bFhEREcjMzLR1aNXWrl27EBMTg7179yIxMREGgwHh4eHIzc2V2kyZMgU///wz1q9fj127duHixYsYPHiwDaOu3vbv34+PPvoIbdq0MSvncayZ5Hbeyc3NRdu2bbFs2TKr9YsWLcKSJUuwYsUKJCcnw8XFBREREcjPz7/PkVqS+/mqXr16ePPNN5GSkoIDBw6gR48eGDBgAI4dOwagesd+O5uc4yrr112rq44dO4qYmBjpeXFxsfD39xdxcXE2jEpeMjMzBQCxa9cuIYQQer1eODg4iPXr10ttTpw4IQCIpKQkW4VZbd24cUM0adJEJCYmim7duolJkyYJIXgcazI5n3cAiA0bNkjPjUaj0Gq14u2335bK9Hq9UKvV4r///a8NIixdTThfeXp6ik8++UQ2sdvqHFeje2AKCwuRkpKCsLAwqczOzg5hYWFISkqyYWTykpWVBQDw8vICAKSkpMBgMJgd1+bNm6N+/fo8rlbExMSgX79+ZscL4HGsqWraeSc9PR06nc5sfzw8PNCpU6dquT9yPl8VFxfjq6++Qm5uLkJDQ2UTu63OcdX2pwQqw5UrV1BcXGxxa3A/Pz+cPHnSRlHJi9FoxOTJk9GlSxe0atUKAKDT6aBSqSx+jdfPzw86nc4GUVZfX331FQ4ePIj9+/db1PE41kw17bxjei9a25/q9j6V6/nqr7/+QmhoKPLz8+Hq6ooNGzYgODgYhw8frvax2/IcV6MTGLp3MTExOHr0KH7//XdbhyI758+fx6RJk5CYmAhHR0dbh0NU48n1fNWsWTMcPnwYWVlZ+PbbbxEVFYVdu3bZOqwy2focV6MvIfn4+ECpVFqMeM7IyIBWq7VRVPIxfvx4bNy4ETt27EC9evWkcq1Wi8LCQuj1erP2PK7mUlJSkJmZiQcffBD29vawt7fHrl27sGTJEtjb28PPz4/HsQaqaecdU8zVfX/kfL5SqVRo3LgxQkJCEBcXh7Zt2+L999+v9rHb+hxXoxMYlUqFkJAQbNu2TSozGo3Ytm0bQkNDbRhZ9SaEwPjx47FhwwZs374dQUFBZvUhISFwcHAwO66pqak4d+4cj+ttevbsib/++guHDx+WHh06dEBkZKT0N49jzVPTzjtBQUHQarVm+5OdnY3k5ORqsT818XxlNBpRUFBQ7WO3+TnunocBV3NfffWVUKvVYvXq1eL48eNizJgxQqPRCJ1OZ+vQqq2xY8cKDw8PsXPnTnHp0iXpkZeXJ7V58cUXRf369cX27dvFgQMHRGhoqAgNDbVh1PJw+wh9IXgcayq5nXdu3LghDh06JA4dOiQAiMWLF4tDhw6Js2fPCiGEePPNN4VGoxE//vijOHLkiBgwYIAICgoSN2/etHHk8j9fvfLKK2LXrl0iPT1dHDlyRLzyyitCoVCIhIQEIUT1jt2a+3mOq/EJjBBCLF26VNSvX1+oVCrRsWNHsXfvXluHVK0BsPpYtWqV1ObmzZti3LhxwtPTUzg7O4tBgwaJS5cu2S5ombjzw83jWHPJ6byzY8cOq5/5qKgoIcStqdSzZ88Wfn5+Qq1Wi549e4rU1FTbBv3/5H6+eu6550RgYKBQqVSiTp06omfPnlLyIkT1jt2a+3mOUwghxL334xARERHdPzV6DAwRERHVTExgiIiISHaYwBAREZHsMIEhIiIi2WECQ3QXVq9eDYVCgX/++cfWoRAR1UpMYGTo77//xgsvvICGDRvC0dER7u7u6NKlC95//33cvHnT1uFVqezsbLz++uvo0KEDPDw8oFarERgYiKeeegq//PKLrcMjIqL7hL+FJDO//PILnnzySajVajz77LNo1aoVCgsL8fvvv2PatGk4duwYVq5caeswq0RaWhoiIiJw9uxZDBo0CM8++yxcXV1x/vx5bNq0Cf3798fnn3+OESNG2DpUIiKqYkxgZCQ9PR3Dhw9HYGAgtm/fjrp160p1MTExSEtLu++9ELm5uXBxcany7RQVFWHQoEHIyMjArl270KVLF7P6uXPnIiEhAcXFxaWu537FS0REVYuXkGRk0aJFyMnJwaeffmqWvJg0btwYkyZNkp4XFRVhwYIFaNSoEdRqNRo0aIBXX30VBQUFZsspFArMmzfPYn0NGjRAdHS09Nw07mPXrl0YN24cfH19pR9Nu3HjBiZPnowGDRpArVbD19cXvXr1wsGDB83WmZycjN69e8PDwwPOzs7o1q0b/vjjjzL3ff369Th69Chmz55tkbyYhIeHo0+fPuWK9+zZsxg3bhyaNWsGJycneHt748knn7Q6puXYsWPo0aMHnJycUK9ePSxcuBBGo9FqDJs3b8ajjz4KFxcXuLm5oV+/fjh27FiZ+0dERBXDHhgZ+fnnn9GwYUM8/PDD5Wr//PPPY82aNRg6dCheeuklJCcnIy4uDidOnMCGDRvuOo5x48ahTp06mDNnDnJzcwEAL774Ir799luMHz8ewcHBuHr1Kn7//XecOHECDz74IABg+/bt6NOnD0JCQjB37lzY2dlh1apV6NGjB3777Td07Nix1H0HgP/85z+VEu/+/fuxZ88eDB8+HPXq1cM///yD5cuXo3v37jh+/DicnZ0BADqdDo899hiKiorwyiuvwMXFBStXroSTk5PFdr744gtERUUhIiICb731FvLy8rB8+XI88sgjOHToEBo0aFDh2ImIqASV8oMEVOWysrIEADFgwIBytT98+LAAIJ5//nmz8pdfflkAENu3b5fKAIi5c+darCMwMFD6LRQhhFi1apUAIB555BFRVFRk1tbDw0PExMSUGI/RaBRNmjQRERERwmg0SuV5eXkiKChI9OrVq9T9ad++vdBoNBblOTk54vLly9IjKyurXPHe/kNvJklJSQKA+Pzzz6WyyZMnCwAiOTlZKsvMzBQeHh4CgEhPTxdC3PoxPI1GI0aPHm22Tp1OJzw8PCzKiYjo3vASkkxkZ2cDANzc3MrVftOmTQCAqVOnmpW/9NJLAHBPY2VGjx4NpVJpVqbRaJCcnIyLFy9aXebw4cM4ffo0nnnmGVy9ehVXrlzBlStXkJubi549e2L37t0lXpYBbu2/q6urRflrr72GOnXqSI9nnnmmXPHe3oNiMBhw9epVNG7cGBqNxuyy16ZNm9C5c2ez3qE6deogMjLSbH2JiYnQ6/V4+umnpX27cuUKlEolOnXqhB07dpS4b0REVHG8hCQT7u7uAG6NNSmPs2fPws7ODo0bNzYr12q10Gg0OHv27F3HEhQUZFG2aNEiREVFISAgACEhIejbty+effZZNGzYEABw+vRpAEBUVFSJ683KyoKnp6fVOjc3N1y9etWifNy4cejfvz+Aki8vWYv35s2biIuLw6pVq3DhwgWI237TNCsrS/r77Nmz6NSpk8XyzZo1M3tu2r8ePXpYjcH0+hERUeVgAiMT7u7u8Pf3x9GjRyu0nEKhuOttljSjx9r4j2HDhuHRRx/Fhg0bkJCQgLfffhtvvfUWvv/+e/Tp00fqXXn77bfRrl07q+u11sNi0rx5cxw+fBgXLlzAAw88IJU3bdoUTZs2BQA4OjqWO94JEyZg1apVmDx5MkJDQ+Hh4QGFQoHhw4eX2hNUEtMyX3zxBbRarUW9vT0/akRElYlnVRnp378/Vq5ciaSkJISGhpbaNjAwEEajEadPn0aLFi2k8oyMDOj1egQGBkplnp6e0Ov1ZssXFhbi0qVLFYqvbt26GDduHMaNG4fMzEw8+OCDeP3119GnTx80atQIwK1ELCwsrELrBW7t+1dffYW1a9di+vTpFV7+Tt9++y2ioqLw7rvvSmX5+fkWxyEwMFDqXbldamqq2XPT/vn6+t7V/hERUcVwDIyMTJ8+HS4uLnj++eeRkZFhUf/333/j/fffBwD07dsXABAfH2/WZvHixQCAfv36SWWNGjXC7t27zdqtXLmyzHuqmBQXF5tddgFufZH7+/tLU7ZDQkLQqFEjvPPOO8jJybFYx+XLl0vdxrBhwxAcHIwFCxZg7969VtvcfhmoLEql0qL90qVLLfa5b9++2Lt3L/bt22cW69q1a83aRUREwN3dHW+88QYMBoPF9sraPyIiqhj2wMhIo0aNsG7dOjz11FNo0aKF2Z149+zZg/Xr10v3bWnbti2ioqKwcuVK6PV6dOvWDfv27cOaNWswcOBAPPbYY9J6n3/+ebz44osYMmQIevXqhT///BO//vorfHx8yhXXjRs3UK9ePQwdOhRt27aFq6srtm7div3790s9HHZ2dvjkk0/Qp08ftGzZEiNHjsQDDzyACxcuYMeOHXB3d5emSlvj4OCADRs2ICIiAo888ggGDx4s3W/lwoUL+Omnn3Du3DmzxKw0/fv3xxdffAEPDw8EBwcjKSkJW7duhbe3t1m76dOn44svvkDv3r0xadIkaRp1YGAgjhw5IrVzd3fH8uXLMWLECDz44IMYPnw46tSpg3PnzuGXX35Bly5d8MEHH5QrNiIiKgcbz4Kiu3Dq1CkxevRo0aBBA6FSqYSbm5vo0qWLWLp0qcjPz5faGQwGERsbK4KCgoSDg4MICAgQM2fONGsjhBDFxcVixowZwsfHRzg7O4uIiAiRlpZW4jTq/fv3my1fUFAgpk2bJtq2bSvc3NyEi4uLaNu2rfjwww8tYj906JAYPHiw8Pb2Fmq1WgQGBophw4aJbdu2lWvf9Xq9mD9/vmjfvr1wdXUVKpVKBAQEiKFDh4qff/7ZrG1J8QohxPXr18XIkSOFj4+PcHV1FREREeLkyZMW+yyEEEeOHBHdunUTjo6O4oEHHhALFiwQn376qdk0apMdO3aIiIgI4eHhIRwdHUWjRo1EdHS0OHDgQLn2j4iIykchRAX63YmIiIiqAY6BISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDs19kZ2RqMRFy9ehJub2z39HhARmRNC4MaNG/D394edHf8PRES2UWMTmIsXLyIgIMDWYRDVWOfPn0e9evVsHQYR1VI1NoFxc3MDcOsk6+7uXmI7g8GAhIQEhIeHw8HB4X6FV6m4D9VDbdmH7OxsBAQESJ8xIiJbqLEJjOmykbu7e5kJjLOzM9zd3WX9pcN9sL3atg+8NEtEtsQL2ERERCQ7TGCIiIhIdpjAEBERkezU2DEwFXXq1CkolUqLco1GA61Wa4OIiIiIqCS1PoHJyMgAAAyNjEZBQYFFvcbNBZt+2sAkhoiIqBqp9QlMVlYWAMDnkadhr/Ezq8u7egmXd34OvV7PBIaIiKgaqfUJjImzlx9UdQJtHQYRERGVAwfxEhERkewwgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdpjAEBERkewwgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdpjAEBERkewwgSEiIiLZYQJDREREslMlCcyFCxfwn//8B97e3nByckLr1q1x4MABqV4IgTlz5qBu3bpwcnJCWFgYTp8+bbaOa9euITIyEu7u7tBoNBg1ahRycnKqIlwiIiKSmUpPYK5fv44uXbrAwcEBmzdvxvHjx/Huu+/C09NTarNo0SIsWbIEK1asQHJyMlxcXBAREYH8/HypTWRkJI4dO4bExERs3LgRu3fvxpgxYyo7XCIiIpIh+8pe4VtvvYWAgACsWrVKKgsKCpL+FkIgPj4es2bNwoABAwAAn3/+Ofz8/PDDDz9g+PDhOHHiBLZs2YL9+/ejQ4cOAIClS5eib9++eOedd+Dv71/ZYRMREZGMVHoC89NPPyEiIgJPPvkkdu3ahQceeADjxo3D6NGjAQDp6enQ6XQICwuTlvHw8ECnTp2QlJSE4cOHIykpCRqNRkpeACAsLAx2dnZITk7GoEGDLLZbUFCAgoIC6Xl2djYAwGAwwGAwlBiv0WgEADjYAQ4Ko1mdyg5Qq9UoLi4udR22ZoqtOsdYFu5D9VCefZDz/hFRzVHpCcyZM2ewfPlyTJ06Fa+++ir279+PiRMnQqVSISoqCjqdDgDg5+dntpyfn59Up9Pp4Ovrax6ovT28vLykNneKi4tDbGysRXlCQgKcnZ3LjHt0e1cA180LA1yBkFlIS0tDWlpameuwtcTERFuHcM+4D9VDafuQl5d3HyMhIrKu0hMYo9GIDh064I033gAAtG/fHkePHsWKFSsQFRVV2ZuTzJw5E1OnTpWeZ2dnIyAgAOHh4XB3dy9xuZMnT+LMmTP4+FAOVD4BZnW5medx7qd4fLt2NZo2bVplsd8rg8GAxMRE9OrVCw4ODrYO565wH6qH8uyDqXeTiMiWKj2BqVu3LoKDg83KWrRoge+++w4AoNVqAQAZGRmoW7eu1CYjIwPt2rWT2mRmZpqto6ioCNeuXZOWv5NarYZarbYod3BwKPXLxM7u1jhmgxFQCPMxzYXGW5emlEqlLL6QytpXOeA+VA+l7YPc942IaoZKn4XUpUsXpKammpWdOnUKgYGBAG4N6NVqtdi2bZtUn52djeTkZISGhgIAQkNDodfrkZKSIrXZvn07jEYjOnXqVNkhExERkcxUeg/MlClT8PDDD+ONN97AsGHDsG/fPqxcuRIrV64EACgUCkyePBkLFy5EkyZNEBQUhNmzZ8Pf3x8DBw4EcKvHpnfv3hg9ejRWrFgBg8GA8ePHY/jw4ZyBRERERJWfwDz00EPYsGEDZs6cifnz5yMoKAjx8fGIjIyU2kyfPh25ubkYM2YM9Ho9HnnkEWzZsgWOjo5Sm7Vr12L8+PHo2bMn7OzsMGTIECxZsqSywyUiIiIZqvQEBgD69++P/v37l1ivUCgwf/58zJ8/v8Q2Xl5eWLduXVWER0RERDLH30IiIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsMIEhIiIi2WECQ0RERLLDBIaIiIhkhwkMERERyQ4TGCIiIpIdJjBEREQkO0xgiIiISHaYwBAREZHsVHkC8+abb0KhUGDy5MlSWX5+PmJiYuDt7Q1XV1cMGTIEGRkZZsudO3cO/fr1g7OzM3x9fTFt2jQUFRVVdbhEREQkA1WawOzfvx8fffQR2rRpY1Y+ZcoU/Pzzz1i/fj127dqFixcvYvDgwVJ9cXEx+vXrh8LCQuzZswdr1qzB6tWrMWfOnKoMl4iIiGSiyhKYnJwcREZG4uOPP4anp6dUnpWVhU8//RSLFy9Gjx49EBISglWrVmHPnj3Yu3cvACAhIQHHjx/Hl19+iXbt2qFPnz5YsGABli1bhsLCwqoKmYiIiGTCvqpWHBMTg379+iEsLAwLFy6UylNSUmAwGBAWFiaVNW/eHPXr10dSUhI6d+6MpKQktG7dGn5+flKbiIgIjB07FseOHUP79u0ttldQUICCggLpeXZ2NgDAYDDAYDCUGKfRaAQAONgBDgqjWZ3KDlCr1SguLi51HbZmiq06x1gW7kP1UJ59kPP+EVHNUSUJzFdffYWDBw9i//79FnU6nQ4qlQoajcas3M/PDzqdTmpze/JiqjfVWRMXF4fY2FiL8oSEBDg7O5cZ8+j2rgCumxcGuAIhs5CWloa0tLQy12FriYmJtg7hnnEfqofS9iEvL+8+RkJEZF2lJzDnz5/HpEmTkJiYCEdHx8pefYlmzpyJqVOnSs+zs7MREBCA8PBwuLu7l7jcyZMncebMGXx8KAcqnwCzutzM8zj3Uzy+XbsaTZs2rbLY75XBYEBiYiJ69eoFBwcHW4dzV7gP1UN59sHUu0lEZEuVnsCkpKQgMzMTDz74oFRWXFyM3bt344MPPsCvv/6KwsJC6PV6s16YjIwMaLVaAIBWq8W+ffvM1muapWRqcye1Wg21Wm1R7uDgUOqXiZ3drWFABiOgEOZDggqNty5NKZVKWXwhlbWvcsB9qB5K2we57xsR1QyVPoi3Z8+e+Ouvv3D48GHp0aFDB0RGRkp/Ozg4YNu2bdIyqampOHfuHEJDQwEAoaGh+Ouvv5CZmSm1SUxMhLu7O4KDgys7ZCIiIpKZSu+BcXNzQ6tWrczKXFxc4O3tLZWPGjUKU6dOhZeXF9zd3TFhwgSEhoaic+fOAIDw8HAEBwdjxIgRWLRoEXQ6HWbNmoWYmBirvSxERERUu1TZLKTSvPfee7Czs8OQIUNQUFCAiIgIfPjhh1K9UqnExo0bMXbsWISGhsLFxQVRUVGYP3++LcIlIiKiaua+JDA7d+40e+7o6Ihly5Zh2bJlJS4TGBiITZs2VXFkREREJEf8LSQiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESywwSGiIiIZKfSE5i4uDg89NBDcHNzg6+vLwYOHIjU1FSzNvn5+YiJiYG3tzdcXV0xZMgQZGRkmLU5d+4c+vXrB2dnZ/j6+mLatGkoKiqq7HCJiIhIhio9gdm1axdiYmKwd+9eJCYmwmAwIDw8HLm5uVKbKVOm4Oeff8b69euxa9cuXLx4EYMHD5bqi4uL0a9fPxQWFmLPnj1Ys2YNVq9ejTlz5lR2uERERCRD9pW9wi1btpg9X716NXx9fZGSkoKuXbsiKysLn376KdatW4cePXoAAFatWoUWLVpg79696Ny5MxISEnD8+HFs3boVfn5+aNeuHRYsWIAZM2Zg3rx5UKlUlR02ERERyUilJzB3ysrKAgB4eXkBAFJSUmAwGBAWFia1ad68OerXr4+kpCR07twZSUlJaN26Nfz8/KQ2ERERGDt2LI4dO4b27dtbbKegoAAFBQXS8+zsbACAwWCAwWAoMT6j0QgAcLADHBRGszqVHaBWq1FcXFzqOmzNFFt1jrEs3IfqoTz7IOf9I6Kao0oTGKPRiMmTJ6NLly5o1aoVAECn00GlUkGj0Zi19fPzg06nk9rcnryY6k111sTFxSE2NtaiPCEhAc7OzmXGOrq9K4Dr5oUBrkDILKSlpSEtLa3MddhaYmKirUO4Z9yH6qG0fcjLy7uPkRARWVelCUxMTAyOHj2K33//vSo3AwCYOXMmpk6dKj3Pzs5GQEAAwsPD4e7uXuJyJ0+exJkzZ/DxoRyofALM6nIzz+PcT/H4du1qNG3atMpiv1cGgwGJiYno1asXHBwcbB3OXeE+VA/l2QdT7yYRkS1VWQIzfvx4bNy4Ebt370a9evWkcq1Wi8LCQuj1erNemIyMDGi1WqnNvn37zNZnmqVkanMntVoNtVptUe7g4FDql4md3a1xzAYjoBDmY5oLjbcuTSmVSll8IZW1r3LAfageStsHue8bEdUMlT4LSQiB8ePHY8OGDdi+fTuCgoLM6kNCQuDg4IBt27ZJZampqTh37hxCQ0MBAKGhofjrr7+QmZkptUlMTIS7uzuCg4MrO2QiIiKSmUrvgYmJicG6devw448/ws3NTRqz4uHhAScnJ3h4eGDUqFGYOnUqvLy84O7ujgkTJiA0NBSdO3cGAISHhyM4OBgjRozAokWLoNPpMGvWLMTExFjtZSEiIqLapdITmOXLlwMAunfvbla+atUqREdHAwDee+892NnZYciQISgoKEBERAQ+/PBDqa1SqcTGjRsxduxYhIaGwsXFBVFRUZg/f35lh0tEREQyVOkJjBCizDaOjo5YtmwZli1bVmKbwMBAbNq0qTJDIyIiohqiyu8DQ0T3l06ng16vL7Feo9GUOBieiEgumMAQ1SA6nQ59nxgE/Y3cEtto3Fyw6acNTGKISNaYwBDVIHq9HvobuajT/Vk4e9e1qM+7egmXd34OvV7PBIaIZI0JDFEN5OxdF65+gbYOg4ioyjCBIapmqnoMS1GRAWfOnLFaV1xcfNfrJSK6n5jAEFUjVT2GpSBHj0ydDi9MfMnqHXXVajXejJ2FjIwMsztoExFVN0xgiKqRqh7DUpSfB2GnhE+3EdDUtbzEVKS/9ZMdWVlZTGCIqFpjAkNUDZU2hqW0S0Bnzpwp12UgJy+t1fUXVvqPixARVQ0mMEQyUtYloMKCfFy9pkeAocgG0RER3T9MYIhkpKxLQFfT/sTlzZ/BaORgXCKq2ZjAEMlQSZeAcq9ctEE0RET3H694ExERkewwgSEiIiLZYQJDREREssMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7TGCIiIhIdpjAEBERkezwTrxE95lOp4Ner7daV94fYyQiqu2YwBDdRzqdDn2fGAT9jVyr9fwxRiKi8mECQ3Qf6fV66G/kok73Z+HsXdeinj/GSERUPkxgqrnSLjcAgEajgbe39/0LiCqFs3dd/hgjEdE9YAJTjZV1uQEANG4u+On79fcxKiIiIttjAlONlXW5Ie/qJVze+TmysrJsEB0REZHtMIGRgZIuNxAREdVWTGCqWFljWAoLC6FSqazWcUotERGRdUxg7lFpCcrly5cxbuIU5OYXWq0vKjLg6uVM+PhqoVQqLerLM6W2qMiAf/75BwBw6tQpi/VoNBpotdry7QxVmpLeF0xKiYgqBxOYe1Dee3oEPzkV7n4BFvVX0/5ExubP4PVoJDR1LS8RlTWltiBHj0ydDpOnv4Z33ojF0MhoFBQUmLXRuLlg008bqiSJuZfeJaDmJlcZGRl4YvCTVt8XvM8LEVHlYAJzD8p7Tw+1xrfUKbNOXtq7mlJblJ8HYaeE9yPDAQD1n5iMQuP/6k2DfPV6faUnCmUlb2X1LgFVm1zZUlZWVonvC97nhYioclTrBGbZsmV4++23odPp0LZtWyxduhQdO3a8rzEUFRlw5swZq3WmywG2vqeHo6cfAMDFNwAqUXk/b1XWLe+v6rOh7TmyxOSttN6lqkyuKkN57r9TVtzW3he8zwsRUeWotgnM119/jalTp2LFihXo1KkT4uPjERERgdTUVPj6+t6XGEyXaF6Y+BIcHBws6uVwOaC0BKy0Szxljd+R9t29zl31Lt0Zm2lcyO3jeEqL714vP93L2CUAcHF0wIdL4lGnTh2pzLQP//zzD8e5EBFVsWqbwCxevBijR4/GyJEjAQArVqzAL7/8gs8++wyvvPLKfYnBdInGp9uIuxqjYmulJWDlHUBc2vide9n3O2NTq9V4M3aWNI6nrPisJRC3Ky3BudexS/rzqTjx/VI8M3KM2XE17cOkl19BZublap3YEhHJXbVMYAoLC5GSkoKZM2dKZXZ2dggLC0NSUpLVZQoKCswGsJpu7nbt2jUYDIYSt5WdnY28vDzkZ55DUUGeWV3R9UtwVKmgNBbCzpBnsay9KIKjSoXCK+eQZ2f5RW5avsrrr55HXp4jci9koui2MTD5l/6G2tEJmjY94Oxp/kV/40I6svSJcG/V3aLOVH8jJRGKovwq2fc7Y1Mp7ZCXlwefjo+jsNhYany5ly/i/J4fET16HJRWesYAwFVtj9cXxMLLy8ui7ty5c8jOvQmPVo9B5e5Z4X1X5OdYPa6mfXBr3BGqK5uRn5mOPJj34ty398Td1mddQV5eA2RnZ+Pq1asW9QBw48YNAIAQwmo9EdH9oBDV8Cx08eJFPPDAA9izZw9CQ0Ol8unTp2PXrl1ITk62WGbevHmIjY29n2ES1Wrnz59HvXr1bB0GEdVS1bIH5m7MnDkTU6dOlZ4bjUZcu3YN3t7eUCgUJS6XnZ2NgIAAnD9/Hu7u7vcj1ErHfageass+CCFw48YN+Pv73+foiIj+p1omMD4+PlAqlcjIyDArz8jIKHFcg1qthlqtNivTaDTl3qa7u7tsv3RMuA/VQ23YBw8Pj/sYDRGRpcqbc1uJVCoVQkJCsG3bNqnMaDRi27ZtZpeUiIiIqHaqlj0wADB16lRERUWhQ4cO6NixI+Lj45GbmyvNSiIiIqLaq9omME899RQuX76MOXPmQKfToV27dtiyZQv8/PwqdTtqtRpz5861uPwkJ9yH6oH7QER0/1TLWUhEREREpamWY2CIiIiISsMEhoiIiGSHCQwRERHJDhMYIiIikh0mMERERCQ7tTqBWbZsGRo0aABHR0d06tQJ+/bts3VIJYqLi8NDDz0ENzc3+Pr6YuDAgUhNTTVr0717dygUCrPHiy++aKOILc2bN88ivubNm0v1+fn5iImJgbe3N1xdXTFkyBCLuzHbWoMGDSz2QaFQICYmBkD1fA12796Nxx9/HP7+/lAoFPjhhx/M6oUQmDNnDurWrQsnJyeEhYXh9OnTZm2uXbuGyMhIuLu7Q6PRYNSoUcjJybmPe0FEZK7WJjBff/01pk6dirlz5+LgwYNo27YtIiIikJmZaevQrNq1axdiYmKwd+9eJCYmwmAwIDw8HLm5uWbtRo8ejUuXLkmPRYsW2Shi61q2bGkW3++//y7VTZkyBT///DPWr1+PXbt24eLFixg8eLANo7W0f/9+s/gTExMBAE8++aTUprq9Brm5uWjbti2WLVtmtX7RokVYsmQJVqxYgeTkZLi4uCAiIgL5+flSm8jISBw7dgyJiYnYuHEjdu/ejTFjxtyvXSAisiRqqY4dO4qYmBjpeXFxsfD39xdxcXE2jKr8MjMzBQCxa9cuqaxbt25i0qRJtguqDHPnzhVt27a1WqfX64WDg4NYv369VHbixAkBQCQlJd2nCCtu0qRJolGjRsJoNAohqv9rAEBs2LBBem40GoVWqxVvv/22VKbX64VarRb//e9/hRBCHD9+XAAQ+/fvl9ps3rxZKBQKceHChfsWOxHR7WplD0xhYSFSUlIQFhYmldnZ2SEsLAxJSUk2jKz8srKyAABeXl5m5WvXroWPjw9atWqFmTNnIi8vzxbhlej06dPw9/dHw4YNERkZiXPnzgEAUlJSYDAYzF6T5s2bo379+tX2NSksLMSXX36J5557zuwXz6v7a3C79PR06HQ6s+Pu4eGBTp06Scc9KSkJGo0GHTp0kNqEhYXBzs4OycnJ9z1mIiKgGv+UQFW6cuUKiouLLX6WwM/PDydPnrRRVOVnNBoxefJkdOnSBa1atZLKn3nmGQQGBsLf3x9HjhzBjBkzkJqaiu+//96G0f5Pp06dsHr1ajRr1gyXLl1CbGwsHn30URw9ehQ6nQ4qlcriF8T9/Pyg0+lsE3AZfvjhB+j1ekRHR0tl1f01uJPp2Fr7LJjqdDodfH19zert7e3h5eVVbV8bIqr5amUCI3cxMTE4evSo2fgRAGZjElq3bo26deuiZ8+e+Pvvv9GoUaP7HaaFPn36SH+3adMGnTp1QmBgIL755hs4OTnZMLK78+mnn6JPnz7w9/eXyqr7a0BEVFPUyktIPj4+UCqVFjNcMjIyoNVqbRRV+YwfPx4bN27Ejh07UK9evVLbdurUCQCQlpZ2P0KrMI1Gg6ZNmyItLQ1arRaFhYXQ6/Vmbarra3L27Fls3boVzz//fKntqvtrYDq2pX0WtFqtxeD2oqIiXLt2rVq+NkRUO9TKBEalUiEkJATbtm2TyoxGI7Zt24bQ0FAbRlYyIQTGjx+PDRs2YPv27QgKCipzmcOHDwMA6tatW8XR3Z2cnBz8/fffqFu3LkJCQuDg4GD2mqSmpuLcuXPV8jVZtWoVfH190a9fv1LbVffXICgoCFqt1uy4Z2dnIzk5WTruoaGh0Ov1SElJkdps374dRqNRStCIiO47W48itpWvvvpKqNVqsXr1anH8+HExZswYodFohE6ns3VoVo0dO1Z4eHiInTt3ikuXLkmPvLw8IYQQaWlpYv78+eLAgQMiPT1d/Pjjj6Jhw4aia9euNo78f1566SWxc+dOkZ6eLv744w8RFhYmfHx8RGZmphBCiBdffFHUr19fbN++XRw4cECEhoaK0NBQG0dtqbi4WNSvX1/MmDHDrLy6vgY3btwQhw4dEocOHRIAxOLFi8WhQ4fE2bNnhRBCvPnmm0Kj0Ygff/xRHDlyRAwYMEAEBQWJmzdvSuvo3bu3aN++vUhOTha///67aNKkiXj66adttUtERKLWJjBCCLF06VJRv359oVKpRMeOHcXevXttHVKJAFh9rFq1SgghxLlz50TXrl2Fl5eXUKvVonHjxmLatGkiKyvLtoHf5qmnnhJ169YVKpVKPPDAA+Kpp54SaWlpUv3NmzfFuHHjhKenp3B2dhaDBg0Sly5dsmHE1v36668CgEhNTTUrr66vwY4dO6y+d6KiooQQt6ZSz549W/j5+Qm1Wi169uxpsW9Xr14VTz/9tHB1dRXu7u5i5MiR4saNGzbYGyKiWxRCCGGTrh8iIiKiu1Qrx8AQERGRvDGBISIiItlhAkNERESywwSGiIiIZIcJDBEREckOExgiIiKSHSYwREREJDtMYIiIiEh2mMAQERGR7DCBISIiItlhAkNERESy83/gfHK7oeOjEQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_columns = ['Duration(in hrs)', 'Learning Hours Spent', 'Course Grade']\n",
    "\n",
    "plt.figure(figsize = (80, 50))\n",
    "coursera[numeric_columns].hist(bins = 25, edgecolor = 'black', alpha = 0.8)\n",
    "plt.suptitle('Histograms of Numeric Features', y = 1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:20px;\">Data Exploration:\n",
    "Calculated basic summary for the numeric columns (mean, median, min, max, standard deviation) </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Basic Statistics for 'Duration(in hrs)':\n",
      "Mean: 7.79\n",
      "Median: 6.60\n",
      "Standard Deviation: 3.43\n",
      "min value: 1.60\n",
      "max value: 49.00\n"
     ]
    }
   ],
   "source": [
    "column_to_analyze = \"Duration(in hrs)\"\n",
    "mean_value = coursera[column_to_analyze].mean()\n",
    "median_value = coursera[column_to_analyze].median()\n",
    "std_deviation = coursera[column_to_analyze].std()\n",
    "\n",
    "print(f\"\\nBasic Statistics for '{column_to_analyze}':\")\n",
    "print(f\"Mean: {mean_value:.2f}\")\n",
    "print(f\"Median: {median_value:.2f}\")\n",
    "print(f\"Standard Deviation: {std_deviation:.2f}\")\n",
    "\n",
    "min_value = coursera[column_to_analyze].min()\n",
    "max_value = coursera[column_to_analyze].max()\n",
    "\n",
    "print(f\"min value: {min_value:.2f}\")\n",
    "print(f\"max value: {max_value:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Basic Statistics for 'Learning Hours Spent':\n",
      "Mean: 2.97\n",
      "Median: 1.59\n",
      "Standard Deviation: 3.89\n",
      "min value: 0.00\n",
      "max value: 40.94\n"
     ]
    }
   ],
   "source": [
    "column_to_analyze = \"Learning Hours Spent\"\n",
    "mean_value = coursera[column_to_analyze].mean()\n",
    "median_value = coursera[column_to_analyze].median()\n",
    "std_deviation = coursera[column_to_analyze].std()\n",
    "\n",
    "print(f\"\\nBasic Statistics for '{column_to_analyze}':\")\n",
    "print(f\"Mean: {mean_value:.2f}\")\n",
    "print(f\"Median: {median_value:.2f}\")\n",
    "print(f\"Standard Deviation: {std_deviation:.2f}\")\n",
    "\n",
    "min_value = coursera[column_to_analyze].min()\n",
    "max_value = coursera[column_to_analyze].max()\n",
    "\n",
    "print(f\"min value: {min_value:.2f}\")\n",
    "print(f\"max value: {max_value:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Basic Statistics for 'Course Grade':\n",
      "Mean: 41.34\n",
      "Median: 22.50\n",
      "Standard Deviation: 41.90\n",
      "min value: 0.00\n",
      "max value: 100.00\n"
     ]
    }
   ],
   "source": [
    "column_to_analyze = \"Course Grade\"\n",
    "mean_value = coursera[column_to_analyze].mean()\n",
    "median_value = coursera[column_to_analyze].median()\n",
    "std_deviation = coursera[column_to_analyze].std()\n",
    "\n",
    "print(f\"\\nBasic Statistics for '{column_to_analyze}':\")\n",
    "print(f\"Mean: {mean_value:.2f}\")\n",
    "print(f\"Median: {median_value:.2f}\")\n",
    "print(f\"Standard Deviation: {std_deviation:.2f}\")\n",
    "\n",
    "min_value = coursera[column_to_analyze].min()\n",
    "max_value = coursera[column_to_analyze].max()\n",
    "\n",
    "print(f\"min value: {min_value:.2f}\")\n",
    "print(f\"max value: {max_value:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style=\"font-size:20px;\">Frequency plotting of Course Completion </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize = (6, 6))\n",
    "ax = sns.countplot(data = coursera, x = 'Completed')\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)\n",
    "\n",
    "plt.suptitle('Frequency Distribution of Completion')\n",
    "plt.xlabel('Completion Status')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}