Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explorotary Data Analysis (EDA)\n",
    "The main objecitves of this notebook are \n",
    "- Performing a descriptive analysis of the dataset\n",
    "- investigating relationships between features and the target variable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"./Dataset/train.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df_c = train_df.copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>Student ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Home Region</th>\n",
       "      <th>Home City</th>\n",
       "      <th>Program ID</th>\n",
       "      <th>Program Main Category Code</th>\n",
       "      <th>Program Sub Category Code</th>\n",
       "      <th>Technology Type</th>\n",
       "      <th>Program Skill Level</th>\n",
       "      <th>...</th>\n",
       "      <th>Completed Degree</th>\n",
       "      <th>Level of Education</th>\n",
       "      <th>Education Speaciality</th>\n",
       "      <th>College</th>\n",
       "      <th>University Degree Score</th>\n",
       "      <th>University Degree Score System</th>\n",
       "      <th>Employment Status</th>\n",
       "      <th>Job Type</th>\n",
       "      <th>Still Working</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4f14c50d-162e-4a15-9cf0-ec129c33bcf0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>453686d8-4023-4506-b2df-fac8b059ac26</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>PCRF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>نعم</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>هندسة حاسب الالي</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.0</td>\n",
       "      <td>غير موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0599d409-876b-41a5-af05-749ef0e77d32</td>\n",
       "      <td>21.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة عسير</td>\n",
       "      <td>خميس مشيط</td>\n",
       "      <td>cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60</td>\n",
       "      <td>APMR</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>نعم</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>الإذاعة والتلفزيون والفيلم</td>\n",
       "      <td>الفنون والعلوم الإنسانية</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>طالب</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38a11c0e-4afc-4261-9c64-e94cc0a272fb</td>\n",
       "      <td>24.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>e006900d-05a9-4c2b-a36f-0ffb9fce44cd</td>\n",
       "      <td>APMR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>نعم</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>Information Technology</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>موظف</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1693e85b-f80e-40ce-846f-395ddcece6d3</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>2ec15f6b-233b-428a-b9f5-e40bc8d14cf9</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>TOSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>نعم</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>حوسبة تطبيقية - (مسار شبكات الحاسب)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.55</td>\n",
       "      <td>5.0</td>\n",
       "      <td>خريج</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98a0e8d0-5f80-4634-afd8-322aa0902863</td>\n",
       "      <td>23.0</td>\n",
       "      <td>ذكر</td>\n",
       "      <td>منطقة الرياض</td>\n",
       "      <td>الرياض</td>\n",
       "      <td>d32da0e9-1aed-48c3-992d-a22f9ccc741e</td>\n",
       "      <td>CAUF</td>\n",
       "      <td>SWPS</td>\n",
       "      <td>تقليدية</td>\n",
       "      <td>متوسط</td>\n",
       "      <td>...</td>\n",
       "      <td>لا</td>\n",
       "      <td>البكالوريوس</td>\n",
       "      <td>نظم المعلومات الحاسوبية</td>\n",
       "      <td>تكنولوجيا الاتصالات والمعلومات</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Student ID   Age Gender   Home Region  Home City  \\\n",
       "0  4f14c50d-162e-4a15-9cf0-ec129c33bcf0  37.0    ذكر  منطقة الرياض     الرياض   \n",
       "1  0599d409-876b-41a5-af05-749ef0e77d32  21.0    ذكر    منطقة عسير  خميس مشيط   \n",
       "2  38a11c0e-4afc-4261-9c64-e94cc0a272fb  24.0    ذكر  منطقة الرياض     الرياض   \n",
       "3  1693e85b-f80e-40ce-846f-395ddcece6d3  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "4  98a0e8d0-5f80-4634-afd8-322aa0902863  23.0    ذكر  منطقة الرياض     الرياض   \n",
       "\n",
       "                             Program ID Program Main Category Code  \\\n",
       "0  453686d8-4023-4506-b2df-fac8b059ac26                       PCRF   \n",
       "1  cc8e4e42-65d5-4fa1-82f9-6c6c2d508b60                       APMR   \n",
       "2  e006900d-05a9-4c2b-a36f-0ffb9fce44cd                       APMR   \n",
       "3  2ec15f6b-233b-428a-b9f5-e40bc8d14cf9                       TOSL   \n",
       "4  d32da0e9-1aed-48c3-992d-a22f9ccc741e                       CAUF   \n",
       "\n",
       "  Program Sub Category Code Technology Type Program Skill Level  ...  \\\n",
       "0                      PCRF             NaN                 NaN  ...   \n",
       "1                      SWPS             NaN               متوسط  ...   \n",
       "2                       NaN             NaN               متوسط  ...   \n",
       "3                      TOSL             NaN                 NaN  ...   \n",
       "4                      SWPS         تقليدية               متوسط  ...   \n",
       "\n",
       "  Completed Degree Level of Education                Education Speaciality  \\\n",
       "0              نعم        البكالوريوس                     هندسة حاسب الالي   \n",
       "1              نعم        البكالوريوس           الإذاعة والتلفزيون والفيلم   \n",
       "2              نعم        البكالوريوس               Information Technology   \n",
       "3              نعم        البكالوريوس  حوسبة تطبيقية - (مسار شبكات الحاسب)   \n",
       "4               لا        البكالوريوس              نظم المعلومات الحاسوبية   \n",
       "\n",
       "                          College University Degree Score  \\\n",
       "0                             NaN                    2.44   \n",
       "1        الفنون والعلوم الإنسانية                    5.00   \n",
       "2                             NaN                    3.50   \n",
       "3                             NaN                    3.55   \n",
       "4  تكنولوجيا الاتصالات والمعلومات                    4.00   \n",
       "\n",
       "  University Degree Score System Employment Status Job Type  Still Working  Y  \n",
       "0                            4.0          غير موظف      NaN            NaN  0  \n",
       "1                            5.0              طالب      NaN            NaN  0  \n",
       "2                            5.0              موظف      NaN            NaN  0  \n",
       "3                            5.0              خريج      NaN            NaN  0  \n",
       "4                            5.0               NaN      NaN            NaN  0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6548 entries, 0 to 6547\n",
      "Data columns (total 24 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Student ID                      6548 non-null   object \n",
      " 1   Age                             6456 non-null   float64\n",
      " 2   Gender                          6548 non-null   object \n",
      " 3   Home Region                     6546 non-null   object \n",
      " 4   Home City                       6546 non-null   object \n",
      " 5   Program ID                      6548 non-null   object \n",
      " 6   Program Main Category Code      6548 non-null   object \n",
      " 7   Program Sub Category Code       5613 non-null   object \n",
      " 8   Technology Type                 3566 non-null   object \n",
      " 9   Program Skill Level             4902 non-null   object \n",
      " 10  Program Presentation Method     6548 non-null   object \n",
      " 11  Program Start Date              6548 non-null   object \n",
      " 12  Program End Date                6548 non-null   object \n",
      " 13  Program Days                    6548 non-null   int64  \n",
      " 14  Completed Degree                6548 non-null   object \n",
      " 15  Level of Education              6522 non-null   object \n",
      " 16  Education Speaciality           6271 non-null   object \n",
      " 17  College                         2658 non-null   object \n",
      " 18  University Degree Score         6467 non-null   float64\n",
      " 19  University Degree Score System  6467 non-null   float64\n",
      " 20  Employment Status               5982 non-null   object \n",
      " 21  Job Type                        1981 non-null   object \n",
      " 22  Still Working                   1981 non-null   object \n",
      " 23  Y                               6548 non-null   int64  \n",
      "dtypes: float64(3), int64(2), object(19)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df_c.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Student ID', 'Age', 'Gender', 'Home Region', 'Home City', 'Program ID',\n",
       "       'Program Main Category Code', 'Program Sub Category Code',\n",
       "       'Technology Type', 'Program Skill Level', 'Program Presentation Method',\n",
       "       'Program Start Date', 'Program End Date', 'Program Days',\n",
       "       'Completed Degree', 'Level of Education', 'Education Speaciality',\n",
       "       'College', 'University Degree Score', 'University Degree Score System',\n",
       "       'Employment Status', 'Job Type', 'Still Working', 'Y'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Analysis of different featrues with relation in completion of the program\n",
    "In this section I explore the relations of different features with the completion of the program \n",
    "1. **Group Data by Gender and Completion Status**\n",
    "2. **Group Data by Program Days and Completion Status**\n",
    "3. **Group Data by College and Completion Status**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Does gender play role in the completion of the program?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **Group Data by Gender and Completion Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_y_sum  = train_df_c.groupby(['Gender','Y']).size().unstack()\n",
    "gender_y_sum.plot(kind='bar', stacked=True)\n",
    "plt.title('Distribution of Genders completeing the program')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(title='Target Variable (Y)', labels=['Completed', 'Didnt complete'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>Y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>أنثى</th>\n",
       "      <td>3065</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ذكر</th>\n",
       "      <td>2444</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Y          0    1\n",
       "Gender           \n",
       "أنثى    3065  599\n",
       "ذكر     2444  440"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_y_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **What about the length of the program does it play a role in the program completion?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. **Group Data by Program Days and Completion Status**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABN4AAAIjCAYAAADcLdbbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAACFaElEQVR4nOzdZ3gUZf/28XMT0khIQktCICQBFAjSQYgISJGAEUFQRJGiFAugFKUoKoi3UpQi3QJYEBVvFW+RJk1FOoQmIGAoCgEUklATSK7nhU/2z6YHMmwC389x7KE7c801v5nMzO6eTLEZY4wAAAAAAAAA5CsXZxcAAAAAAAAA3IwI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAWRo5cqRsNtsNmdc999yje+65x/5+9erVstls+uqrr27I/Hv06KGwsLAbMq9rde7cOfXq1UtBQUGy2WwaMGCAs0vCLcRms2nkyJHXPP0999yjO+64I/8KuskU1GPQjfwcuFZXrlzRkCFDFBISIhcXF7Vv3z7PfcydO1c2m02bN2/O/wIBALc0gjcAuEWk/ahIe3l6eio4OFhRUVF69913dfbs2XyZz7FjxzRy5EjFxMTkS3/5qSDXlhtvvvmm5s6dq2eeeUaffPKJunbtmmXbsLAwh793QECAGjdurG+++eYGVlw43Kh941ZQ2Pexwmr69OmaO3fuNU9/4cIFjRw5UqtXr863mm6k2bNna/z48XrooYf00UcfaeDAgVm2vd51BQBAXhVxdgEAgBvr9ddfV3h4uC5fvqy4uDitXr1aAwYM0IQJE/Tdd9+pRo0a9rYjRozQsGHD8tT/sWPHNGrUKIWFhalWrVq5nm7ZsmV5ms+1yK62999/X6mpqZbXcD1Wrlyphg0b6rXXXstV+1q1amnw4MGS/l32WbNmqUOHDpoxY4aefvppK0stlPKybyBz17r/4/pMnz5dpUqVUo8ePa5p+gsXLmjUqFGS5HDmsXRtnwM32sqVK1W2bFlNnDgxx7bXu64AAMgrgjcAuMW0adNG9erVs78fPny4Vq5cqfvvv18PPPCA9uzZIy8vL0lSkSJFVKSItR8VFy5cUNGiReXu7m7pfHLi5ubm1PnnxsmTJxUREZHr9mXLltXjjz9uf9+tWzdVqlRJEydOzDJ4u3LlilJTU2/I3yM1NVXJycny9PS0fF65kZd9A7hV3IjPget18uRJ+fv7O7sMyxljdOnSpRt2HEr7fAYAXB8uNQUAqHnz5nrllVd0+PBhffrpp/bhmd3bZ/ny5br77rvl7+8vHx8fVa5cWS+99JKkf+/LVr9+fUnSE088Yb90L+2ynrR7PG3ZskVNmjRR0aJF7dOmv8dbmpSUFL300ksKCgqSt7e3HnjgAR09etShTVhYWKZnL1zdZ061ZXZ/pfPnz2vw4MEKCQmRh4eHKleurLffflvGGId2NptN/fr107fffqs77rhDHh4eqlatmpYsWZL5Ck/n5MmT6tmzpwIDA+Xp6amaNWvqo48+so9Pu99dbGysFi1aZK/90KFDueo/TVBQkKpWrarY2FhJ0qFDh2Sz2fT2229r0qRJqlixojw8PPTbb79J+vcsksaNG8vb21v+/v5q166d9uzZk6Hf1atXq169evL09FTFihU1a9asTLedtPU0b948VatWTR4eHvZ19Pbbb+uuu+5SyZIl5eXlpbp162Z6f7+0PhYsWKCIiAh5eXkpMjJSO3fulCTNmjVLlSpVkqenp+655548r6P0sto3duzYoR49eqhChQry9PRUUFCQnnzySf3zzz/2NqtWrZLNZsv08t7PPvtMNptN69atkyTFxcXpiSeeULly5eTh4aEyZcqoXbt2Odafmzqk/9uXDxw4oB49esjf319+fn564okndOHCBYe2SUlJGjhwoEqXLq1ixYrpgQce0J9//pnjusppH0vz22+/qVmzZipatKjKli2rcePGZegrKSlJr732mipVqiQPDw+FhIRoyJAhSkpKyrEOSdqwYYPuu+8+FS9eXN7e3qpRo4YmT57s0CY323faevv999/1+OOPy8/PT6VLl9Yrr7wiY4yOHj2qdu3aydfXV0FBQXrnnXcyrBObzaYvvvgix+NYZlJTUzVp0iRVq1ZNnp6eCgwM1FNPPaUzZ87Y24SFhWn37t1as2aNfZ1ffSyNj4/XgAED7MexSpUqaezYsfYzfA8dOqTSpUtLkkaNGmXvI+1+fpnty1euXNHo0aPtx4ywsDC99NJLGf4+YWFhuv/++/XLL7/ozjvvlKenpypUqKCPP/44x2WXcj4Gpx3DVq1apd27d9trz+qS2ZzWlfTvtjdo0CCVLl1a3t7eevDBB3Xq1KkMfS1evNi+/RQrVkzR0dHavXt3jsuUdmn7Tz/9pKeeekolS5aUr6+vunXr5vB3vXr9LV26VPXq1ZOXl5dmzZolSfrjjz/08MMPq0SJEipatKgaNmyoRYsWZZjf4cOH9cADD8jb21sBAQEaOHCgli5dmmE9Zff5vHDhQkVHRys4OFgeHh6qWLGiRo8erZSUFId5pfWxY8cONW3aVEWLFlWlSpXsx/I1a9aoQYMG8vLyUuXKlfXjjz/muL4A4KZgAAC3hDlz5hhJZtOmTZmOP3r0qJFkHnroIfuw1157zVz9UbFr1y7j7u5u6tWrZyZPnmxmzpxpXnjhBdOkSRNjjDFxcXHm9ddfN5JMnz59zCeffGI++eQTc/DgQWOMMU2bNjVBQUGmdOnSpn///mbWrFnm22+/tY9r2rSpfV6rVq0ykkz16tVNjRo1zIQJE8ywYcOMp6enuf32282FCxfsbUNDQ0337t0zLNPVfeZUW/fu3U1oaKh92tTUVNO8eXNjs9lMr169zNSpU03btm2NJDNgwACH+UgyNWvWNGXKlDGjR482kyZNMhUqVDBFixY1f//9d7Z/lwsXLpiqVasaNzc3M3DgQPPuu++axo0bG0lm0qRJ9to/+eQTU6pUKVOrVi177efOncuy39DQUBMdHe0wLDk52QQGBpqgoCBjjDGxsbFGkomIiDAVKlQwY8aMMRMnTjSHDx82y5cvN0WKFDG33367GTdunBk1apQpVaqUKV68uImNjbX3uXXrVuPh4WHCwsLMmDFjzH/+8x8THBxsatasadJ/zZBkqlatakqXLm1GjRplpk2bZrZt22aMMaZcuXLm2WefNVOnTjUTJkwwd955p5Fkvv/++wx91KhRw4SEhJgxY8aYMWPGGD8/P1O+fHkzdepUExERYd555x0zYsQI4+7ubpo1a5bt+jfm2vaNt99+2zRu3Ni8/vrr5r333jPPP/+88fLyMnfeeadJTU01xvy7DYWEhJiOHTtm6PO+++4zFStWtL+/6667jJ+fnxkxYoT54IMPzJtvvmmaNWtm1qxZk23tuanDmP/bl2vXrm06dOhgpk+fbnr16mUkmSFDhjj0+fjjjxtJ5rHHHjNTp041HTp0MDVq1DCSzGuvvZZlLbnZ/4ODg01ISIh5/vnnzfTp003z5s2NJPPDDz/Y+0lJSTGtWrUyRYsWNQMGDDCzZs0y/fr1M0WKFDHt2rXLdn0YY8yyZcuMu7u7CQ0NNa+99pqZMWOGee6550zLli3tbXK7faett1q1aplHH33UTJ8+3URHRxtJZsKECaZy5crmmWeeMdOnTzeNGjUykhz+Znk5jqU/BhljTK9evUyRIkVM7969zcyZM83QoUONt7e3qV+/vklOTjbGGPPNN9+YcuXKmSpVqtjX+bJly4wxxpw/f97UqFHDlCxZ0rz00ktm5syZplu3bsZms5nnn3/eGGPMuXPnzIwZM4wk8+CDD9r72L59u8M6uFr37t3t+8S0adNMt27djCTTvn17h3ahoaGmcuXKJjAw0Lz00ktm6tSppk6dOsZms5ldu3Zl+3fMzTH43Llz5pNPPjFVqlQx5cqVs9ceFxeXaZ/Zrau040Dt2rVN8+bNzZQpU8zgwYONq6ur6dSpk0M/H3/8sbHZbKZ169ZmypQpZuzYsSYsLMz4+/s7bD+ZSZtP9erVTePGjc27775r+vbta1xcXEyTJk0c9tvQ0FBTqVIlU7x4cTNs2DAzc+ZMs2rVKhMXF2cCAwNNsWLFzMsvv2wmTJhgatasaVxcXMzXX39tn/7cuXOmQoUKxsvLywwbNsxMmjTJ3Hnnnfbj86pVq+xts/t8bt++venUqZMZP368mTFjhnn44YeNJPPCCy84LNvV+/iLL75opkyZYiIiIoyrq6v5/PPPTVBQkBk5cqSZNGmSKVu2rPHz8zOJiYnZri8AuBkQvAHALSKncMEYY/z8/Ezt2rXt79P/4Jo4caKRZE6dOpVlH5s2bTKSzJw5czKMa9q0qZFkZs6cmem4zIK3smXLOnwx//LLL40kM3nyZPuw3ARvOdWW/kfvt99+aySZN954w6HdQw89ZGw2mzlw4IB9mCTj7u7uMGz79u1GkpkyZUqGeV1t0qRJRpL59NNP7cOSk5NNZGSk8fHxcVj2zMK0rISGhppWrVqZU6dOmVOnTpnt27ebzp07G0mmf//+xpj/C958fX3NyZMnHaavVauWCQgIMP/884/DMrm4uJhu3brZh7Vt29YULVrU/PXXX/Zh+/fvN0WKFMk0eHNxcTG7d+/OUO/VAUTaOrjjjjtM8+bNM/Th4eHh8ON21qxZRpIJCgpyWF/Dhw83knL9Qzgv+0b6eo0xZv78+UaS+emnnxxq8PDwMPHx8fZhJ0+eNEWKFLGHWGfOnDGSzPjx47OtMzO5rSNtX37yyScd2j744IOmZMmS9vcxMTFGknn22Wcd2j322GM5Bm/G5G7///jjj+3DkpKSTFBQkEM4+cknnxgXFxfz888/O0w/c+ZMI8msXbs2y/lfuXLFhIeHm9DQUHPmzBmHcVcHGrndvtPWW58+fRzmUa5cOWOz2cyYMWPsw8+cOWO8vLwcjkV5OY6lPwb9/PPPRpKZN2+ew3IsWbIkw/Bq1ao5HOvSjB492nh7e5vff//dYfiwYcOMq6urOXLkiDHGmFOnTmX5903/OZC2jfTq1cuh3QsvvGAkmZUrV9qHhYaGZtgWT548aTw8PMzgwYMzzOtqeTkGN23a1FSrVi3b/tJkta7SjgMtW7Z02FYGDhxoXF1d7fvw2bNnjb+/v+ndu7fD9HFxccbPzy/D8KzmU7duXXt4aowx48aNM5LMwoUL7cPS1t+SJUsc+hgwYICR5LCPnD171oSHh5uwsDCTkpJijDHmnXfeMZLsAZoxxly8eNFUqVIl0+Atq8/nzI4zTz31lClatKi5dOlShj4+++wz+7C9e/faj/3r16+3D1+6dGmWxwoAuNlwqSkAwM7HxyfbJzim3UNn4cKF1/wgAg8PDz3xxBO5bt+tWzcVK1bM/v6hhx5SmTJl9MMPP1zT/HPrhx9+kKurq5577jmH4YMHD5YxRosXL3YY3rJlS1WsWNH+vkaNGvL19dUff/yR43yCgoL06KOP2oe5ubnpueee07lz57RmzZprXoZly5apdOnSKl26tGrWrKkFCxaoa9euGjt2rEO7jh072i81k6Tjx48rJiZGPXr0UIkSJRyW6d5777Wv+5SUFP34449q3769goOD7e0qVaqkNm3aZFpT06ZNM71P3dX3LDpz5owSEhLUuHFjbd26NUPbFi1aOFwW3KBBA/tyXL2tpA3P6W+QG+n3javrvXTpkv7++281bNhQkhxq7tatm5KSkhwum/3iiy905coV+/33vLy85O7urtWrV2e41Cwnua0jTfp7+zVu3Fj//POPEhMTJcn+t02/3Q8YMCBPdWXFx8fH4b6D7u7uuvPOOx3+RgsWLFDVqlVVpUoV/f333/ZX8+bNJf17CW9Wtm3bptjYWA0YMCDDPb/SLpfM7fZ9tV69etn/39XVVfXq1ZMxRj179rQP9/f3V+XKlTPd3q7lOLZgwQL5+fnp3nvvdVgPdevWlY+PT7br4eo+GjdurOLFizv00bJlS6WkpOinn37KsY/00moeNGiQw/C0B7mkv9wxIiJCjRs3tr8vXbp0lusp/XzycgzOL3369HG4tLZx48ZKSUnR4cOHJf17u4X4+Hg9+uijDuvU1dVVDRo0yNXfJW0+V99b9JlnnlGRIkUybBPh4eGKiopyGPbDDz/ozjvv1N13320f5uPjoz59+ujQoUP22wUsWbJEZcuW1QMPPGBv5+npqd69e2daU1afz1cfZ86ePau///5bjRs31oULF7R3716Htj4+PurcubP9feXKleXv76+qVavaj8lS/h6fAaCgK9h3SgUA3FDnzp1TQEBAluMfeeQRffDBB+rVq5eGDRumFi1aqEOHDnrooYfk4pK7f8spW7Zsnm7cf9tttzm8t9lsqlSp0nXfuysnhw8fVnBwsMOPZUmqWrWqffzVypcvn6GP4sWL5xikHD58WLfddluG9ZfVfPKiQYMGeuONN2Sz2VS0aFFVrVo10xuQh4eHZ6hJ+vcHU3pVq1bV0qVLdf78eSUmJurixYuqVKlShnaZDctsXmm+//57vfHGG4qJiXG4T1T6e0tJGde1n5+fJCkkJCTT4XkNszKTft84ffq0Ro0apc8//1wnT550aJuQkGD//ypVqqh+/fqaN2+ePaSZN2+eGjZsaF9HHh4eGjt2rAYPHqzAwEA1bNhQ999/v7p166agoKBs68ptHWnSr7vixYtL+ncd+fr66vDhw3JxcXEIkaXMt4VrUa5cuQx/0+LFi2vHjh329/v379eePXscwuCrpV/Oqx08eFCSdMcdd2TZJrfbt7e3t314Ztucp6enSpUqlWF4+vvrSdd2HNu/f78SEhKyPCZntx6u7mPHjh3XtC6zkraNpN/Hg4KC5O/vn6/Hxrwcg/NLdvuI9O86lWQPgtPz9fXN1XzSbxM+Pj4qU6ZMhm0is2Pm4cOHHUKsNFevmzvuuEOHDx9WxYoVM+xzWR2fs/p83r17t0aMGKGVK1faQ/o06Y8zme3jfn5+lh6fAaCgI3gDAEiS/vzzTyUkJGT5hVz691+9f/rpJ61atUqLFi3SkiVL9MUXX6h58+ZatmyZXF1dc5yPFU9jyyyckf49Iys3NeWHrOZj0j2I4UYqVaqUWrZsmWO7G/mkzszm9fPPP+uBBx5QkyZNNH36dJUpU0Zubm6aM2eOPvvsswzts1rXVv0NMts3OnXqpF9//VUvvviiatWqJR8fH6Wmpqp169YZzgbt1q2bnn/+ef35559KSkrS+vXrNXXqVIc2AwYMUNu2bfXtt99q6dKleuWVV/TWW29p5cqVql27dpa15aUOyfnbaW7mn5qaqurVq2vChAmZtk3/A/5GyKxuq9dlamqqAgICNG/evEzHZxWmpe/j3nvv1ZAhQzIdf/vtt19zfVkdd9Nz9jaXVznVm7ZfffLJJ5kG4/n9BFhnH5/j4+PVtGlT+fr66vXXX1fFihXl6emprVu3aujQoRmOMzf6+AwAhQHBGwBA0r8/IiRluKQlPRcXF7Vo0UItWrTQhAkT9Oabb+rll1/WqlWr1LJly1z/GMuttLML0hhjdODAAdWoUcM+rHjx4oqPj88w7eHDh1WhQgX7+7zUFhoaqh9//FFnz551OOMi7bKa0NDQXPeV03x27Nih1NRUh7Pe8ns+ea1Jkvbt25dh3N69e1WqVCl5e3vL09NTnp6eOnDgQIZ2mQ3Lyn//+195enpq6dKl8vDwsA+fM2fONVSf/9LvG2fOnNGKFSs0atQovfrqq/Z26bfVNJ07d9agQYM0f/58Xbx4UW5ubnrkkUcytKtYsaIGDx6swYMHa//+/apVq5beeecdh6epXi2vdeRGaGioUlNTdfDgQYczwjLbFjKTH/t/xYoVtX37drVo0SLP/aWdqbdr164sQ+fcbt/5KTfHsfQqVqyoH3/8UY0aNcoxfMlqPVWsWFHnzp3LMYDP67ExNTVV+/fvt59hJUknTpxQfHx8vh4brTgGX+82mraNBQQE5OofNrKyf/9+NWvWzP7+3LlzOn78uO67774cpw0NDc1y+00bn/bf3377TcYYh+XOy/F59erV+ueff/T111+rSZMm9uFpT8cGAOSMe7wBALRy5UqNHj1a4eHh6tKlS5btTp8+nWFYrVq1JMl+eWDaD9bMgrBr8fHHHzvcW+urr77S8ePHHe4hVrFiRa1fv17Jycn2Yd9//72OHj3q0FdearvvvvuUkpKS4cykiRMnymazZXkPs7y67777FBcXpy+++MI+7MqVK5oyZYp8fHzUtGnTfJlPXpQpU0a1atXSRx995LCudu3apWXLltl/GLq6uqply5b69ttvdezYMXu7AwcO5On+S66urrLZbEpJSbEPO3TokL799tvrXpbrldm+kXbmRvozNSZNmpRpH6VKlVKbNm306aefat68eWrdurXDJYoXLlzQpUuXHKapWLGiihUr5nDZbXp5rSM30rbrd99995r6zI/9v1OnTvrrr7/0/vvvZxh38eJFnT9/Pstp69Spo/DwcE2aNClDDWnrKbfbd37KzXEsvU6dOiklJUWjR4/OMO7KlSsOtXt7e2e6zjt16qR169Zp6dKlGcbFx8frypUrkqSiRYvah+Ukbf2k3ybSzlCMjo7OsY/csOoYnNW6yq2oqCj5+vrqzTff1OXLlzOMP3XqVK76ee+99xymnzFjhq5cuZKr5brvvvu0ceNGrVu3zj7s/Pnzeu+99xQWFma/j2ZUVJT++usvfffdd/Z2ly5dynTfykpmx5nk5GRNnz49130AwK2OM94A4BazePFi7d27V1euXNGJEye0cuVKLV++XKGhofruu+/k6emZ5bSvv/66fvrpJ0VHRys0NFQnT57U9OnTVa5cOftNnitWrCh/f3/NnDlTxYoVk7e3txo0aJDlvb1yUqJECd1999164okndOLECU2aNEmVKlVyuDl0r1699NVXX6l169bq1KmTDh48qE8//TTDfaryUlvbtm3VrFkzvfzyyzp06JBq1qypZcuWaeHChRowYECGvq9Vnz59NGvWLPXo0UNbtmxRWFiYvvrqK61du1aTJk3KcH+jG2X8+PFq06aNIiMj1bNnT128eFFTpkyRn5+fRo4caW83cuRILVu2TI0aNdIzzzxj/6F8xx13KCYmJlfzio6O1oQJE9S6dWs99thjOnnypKZNm6ZKlSo53PvLarndN3x9fdWkSRONGzdOly9fVtmyZbVs2bJszwDp1q2bHnroIUnKEKT8/vvvatGihTp16qSIiAgVKVJE33zzjU6cOOFwk/L0rqWOnNSqVUuPPvqopk+froSEBN11111asWJFrs+QyY/9v2vXrvryyy/19NNPa9WqVWrUqJFSUlK0d+9effnll1q6dKnq1auX6bQuLi6aMWOG2rZtq1q1aumJJ55QmTJltHfvXu3evdseQOV2+84vuTmOpde0aVM99dRTeuuttxQTE6NWrVrJzc1N+/fv14IFCzR58mT7NlW3bl3NmDFDb7zxhipVqqSAgAA1b95cL774or777jvdf//96tGjh+rWravz589r586d+uqrr3To0CGVKlVKXl5eioiI0BdffKHbb79dJUqU0B133JHpvfJq1qyp7t2767333rNfhrhx40Z99NFHat++vcNZXNfDqmNwVusqt3x9fTVjxgx17dpVderUUefOnVW6dGkdOXJEixYtUqNGjTKEhZlJTk627/f79u3T9OnTdffddzs8CCErw4YN0/z589WmTRs999xzKlGihD766CPFxsbqv//9r/3s6aeeekpTp07Vo48+queff15lypTRvHnz7Mey3Jz9d9ddd6l48eLq3r27nnvuOdlsNn3yySdcIgoAeXGDn6IKAHCSOXPmGEn2l7u7uwkKCjL33nuvmTx5sklMTMwwzWuvvWau/qhYsWKFadeunQkODjbu7u4mODjYPProo+b33393mG7hwoUmIiLCFClSxEgyc+bMMcYY07RpU1OtWrVM62vatKlp2rSp/f2qVauMJDN//nwzfPhwExAQYLy8vEx0dLQ5fPhwhunfeecdU7ZsWePh4WEaNWpkNm/enKHP7Grr3r27CQ0NdWh79uxZM3DgQBMcHGzc3NzMbbfdZsaPH29SU1Md2kkyffv2zVBTaGio6d69e6bLe7UTJ06YJ554wpQqVcq4u7ub6tWr2+tK3190dHSO/eW2bWxsrJFkxo8fn+n4H3/80TRq1Mh4eXkZX19f07ZtW/Pbb79laLdixQpTu3Zt4+7ubipWrGg++OADM3jwYOPp6enQLqv1ZIwxH374obntttuMh4eHqVKlipkzZ06G7S+rPrJajrRtaMGCBdmuh2vZN/7880/z4IMPGn9/f+Pn52cefvhhc+zYMSPJvPbaaxnaJyUlmeLFixs/Pz9z8eJFh3F///236du3r6lSpYrx9vY2fn5+pkGDBubLL7/Mtu681JG2Lk+dOpXpssfGxtqHXbx40Tz33HOmZMmSxtvb27Rt29YcPXo0y2VLL6/7f2b7XnJyshk7dqypVq2a8fDwMMWLFzd169Y1o0aNMgkJCTnW8Msvv5h7773XFCtWzHh7e5saNWqYKVOmOLTJzfad1Xrr3r278fb2zjDf9MuYl+NYZuvBGGPee+89U7duXePl5WWKFStmqlevboYMGWKOHTtmbxMXF2eio6NNsWLFjCSH497Zs2fN8OHDTaVKlYy7u7spVaqUueuuu8zbb79tkpOT7e1+/fVXU7duXePu7u7wt85sP7x8+bIZNWqUCQ8PN25ubiYkJMQMHz7cXLp0yaFdVsehzI7NmcntMTi7z5b0slpXafvCpk2bHNqn/Q1XrVqVYXhUVJTx8/Mznp6epmLFiqZHjx5m8+bN2c4/bT5r1qwxffr0McWLFzc+Pj6mS5cu5p9//nFom91x/ODBg+ahhx4y/v7+xtPT09x5553m+++/z9Dujz/+MNHR0cbLy8uULl3aDB482Pz3v/81ksz69evt7bJbh2vXrjUNGzY0Xl5eJjg42AwZMsQsXbo0w3rJqo+sliO7zwQAuJnYjOGfKwAAQP5q3769du/efV33G7uZXLlyRcHBwWrbtq0+/PBDZ5eDG2T16tVq1qyZFixYYD87Dbe2uXPn6oknntCmTZuyPHPTapMmTdLAgQP1559/qmzZsk6pAQBuJdzjDQAAXJeLFy86vN+/f79++OEH3XPPPc4pqAD69ttvderUKXXr1s3ZpQC4haQ/Pl+6dEmzZs3SbbfdRugGADcI93gDAADXpUKFCurRo4cqVKigw4cPa8aMGXJ3d9eQIUOcXZrTbdiwQTt27NDo0aNVu3ZtpzwsA8Ctq0OHDipfvrxq1aqlhIQEffrpp9q7d6/mzZvn7NIA4JZB8AYAAK5L69atNX/+fMXFxcnDw0ORkZF68803ddtttzm7NKebMWOGPv30U9WqVUtz5851djkAbjFRUVH64IMPNG/ePKWkpCgiIkKff/65HnnkEWeXBgC3DO7xBgAAAAAAAFiAe7wBAAAAAAAAFiB4AwAAAAAAACzAPd5yITU1VceOHVOxYsVks9mcXQ4AAAAAAACcyBijs2fPKjg4WC4uWZ/XRvCWC8eOHVNISIizywAAAAAAAEABcvToUZUrVy7L8QRvuVCsWDFJ/65MX19fJ1cDAAAAAAAAZ0pMTFRISIg9M8oKwVsupF1e6uvrS/AGAAAAAAAAScrxlmROfbjCjBkzVKNGDXugFRkZqcWLF9vHX7p0SX379lXJkiXl4+Ojjh076sSJEw59HDlyRNHR0SpatKgCAgL04osv6sqVKw5tVq9erTp16sjDw0OVKlXS3Llzb8TiAQAAAAAA4Bbm1OCtXLlyGjNmjLZs2aLNmzerefPmateunXbv3i1JGjhwoP73v/9pwYIFWrNmjY4dO6YOHTrYp09JSVF0dLSSk5P166+/6qOPPtLcuXP16quv2tvExsYqOjpazZo1U0xMjAYMGKBevXpp6dKlN3x5AQAAAAAAcOuwGWOMs4u4WokSJTR+/Hg99NBDKl26tD777DM99NBDkqS9e/eqatWqWrdunRo2bKjFixfr/vvv17FjxxQYGChJmjlzpoYOHapTp07J3d1dQ4cO1aJFi7Rr1y77PDp37qz4+HgtWbIkVzUlJibKz89PCQkJXGoKAAAAAABwi8ttVlRg7vGWkpKiBQsW6Pz584qMjNSWLVt0+fJltWzZ0t6mSpUqKl++vD14W7dunapXr24P3SQpKipKzzzzjHbv3q3atWtr3bp1Dn2ktRkwYECWtSQlJSkpKcn+PjExMf8WFAAAAAAAXDNjjK5cuaKUlBRnl4KbmKurq4oUKZLjPdxy4vTgbefOnYqMjNSlS5fk4+Ojb775RhEREYqJiZG7u7v8/f0d2gcGBiouLk6SFBcX5xC6pY1PG5ddm8TERF28eFFeXl4Zanrrrbc0atSo/FpEAAAAAACQD5KTk3X8+HFduHDB2aXgFlC0aFGVKVNG7u7u19yH04O3ypUrKyYmRgkJCfrqq6/UvXt3rVmzxqk1DR8+XIMGDbK/T3tELAAAAAAAcI7U1FTFxsbK1dVVwcHBcnd3v+6zkYDMGGOUnJysU6dOKTY2VrfddptcXK7tMQlOD97c3d1VqVIlSVLdunW1adMmTZ48WY888oiSk5MVHx/vcNbbiRMnFBQUJEkKCgrSxo0bHfpLe+rp1W3SPwn1xIkT8vX1zfRsN0ny8PCQh4dHviwfAAAAAAC4fsnJyUpNTVVISIiKFi3q7HJwk/Py8pKbm5sOHz6s5ORkeXp6XlM/Tn2qaWZSU1OVlJSkunXrys3NTStWrLCP27dvn44cOaLIyEhJUmRkpHbu3KmTJ0/a2yxfvly+vr6KiIiwt7m6j7Q2aX0AAAAAAIDC41rPPALyKj+2Naee8TZ8+HC1adNG5cuX19mzZ/XZZ59p9erVWrp0qfz8/NSzZ08NGjRIJUqUkK+vr/r376/IyEg1bNhQktSqVStFRESoa9euGjdunOLi4jRixAj17dvXfsba008/ralTp2rIkCF68skntXLlSn355ZdatGiRMxcdAAAAAAAANzmnBm8nT55Ut27ddPz4cfn5+alGjRpaunSp7r33XknSxIkT5eLioo4dOyopKUlRUVGaPn26fXpXV1d9//33euaZZxQZGSlvb291795dr7/+ur1NeHi4Fi1apIEDB2ry5MkqV66cPvjgA0VFRd3w5QUAAAAAAMCtw2aMMc4uoqBLTEyUn5+fEhIS5Ovr6+xyAAAAAAC45Vy6dEmxsbEKDw+/5vttIXNz587VgAEDFB8fn+tpwsLCNGDAAA0YMCDLNjabTd98843at29/XfW98sorOnHihN57771ctV+yZImGDRumrVu3Xtflotltc7nNirgwGgAAAAAA3PRsNlu2r5EjRzq1tm+//TbL8SdOnJCbm5s+//zzTMf37NlTderUueb5P/LII/r999+veXorxcXFafLkyXr55ZdljFHLli0zvYpx+vTp8vf3159//qnWrVvLzc1N8+bNc0LFjgjeAAAAAADATe/48eP216RJk+Tr6+sw7IUXXshTf8nJyRZVmlFgYKCio6M1e/bsDOPOnz+vL7/8Uj179rymvi9fviwvLy8FBARcb5mW+OCDD3TXXXcpNDRUNptNc+bM0YYNGzRr1ix7m9jYWA0ZMkRTpkxRuXLlJEk9evTQu+++66yy7QjeAAAAAADATS8oKMj+8vPzk81ms78/f/68unTposDAQPn4+Kh+/fr68ccfHaYPCwvT6NGj1a1bN/n6+qpPnz6SpPfff18hISEqWrSoHnzwQU2YMEH+/v4O0y5cuFB16tSRp6enKlSooFGjRunKlSv2fiXpwQcflM1ms79Pr2fPnlqxYoWOHDniMHzBggW6cuWKunTpoiVLlujuu++Wv7+/SpYsqfvvv18HDx60tz106JBsNpu++OILNW3aVJ6enpo3b57mzp3rUPPBgwfVrl27bNeHJJ09e1aPPvqovL29VbZsWU2bNi3bv8HRo0fVqVMn+fv7q0SJEmrXrp0OHTqU7TSff/652rZta38fEhKiyZMn64UXXlBsbKyMMerZs6datWqlrl272tu1bdtWmzdvdlh+ZyB4AwAAAAAAt7Rz587pvvvu04oVK7Rt2za1bt1abdu2zRByvf3226pZs6a2bdumV155RWvXrtXTTz+t559/XjExMbr33nv1n//8x2Gan3/+Wd26ddPzzz+v3377TbNmzdLcuXPt7TZt2iRJmjNnjo4fP25/n959992nwMBAzZ0712H4nDlz1KFDB/n7++v8+fMaNGiQNm/erBUrVsjFxUUPPvigUlNTHaYZNmyYnn/+ee3ZsyfTyzZzuz7Gjx9vXx9pfS5fvjzT+i9fvqyoqCgVK1ZMP//8s9auXSsfHx+1bt06y7MHT58+rd9++0316tVzGN69e3e1aNFCTz75pKZOnapdu3Y5nAEnSeXLl1dgYKB+/vnnTPu+YQxylJCQYCSZhIQEZ5cCAAAAAMAt6eLFi+a3334zFy9evO6+5syZY/z8/LJtU61aNTNlyhT7+9DQUNO+fXuHNo888oiJjo52GNalSxeHvlu0aGHefPNNhzaffPKJKVOmjP29JPPNN9/kWPewYcNMeHi4SU1NNcYYc+DAAWOz2cyPP/6YaftTp04ZSWbnzp3GGGNiY2ONJDNp0iSHdte6Plq3bu3Q5pFHHjFt2rTJdLk++eQTU7lyZXvtxhiTlJRkvLy8zNKlSzOd57Zt24wkc+TIkQzjTpw4YUqVKmVcXFyyXHe1a9c2I0eOzHa5spPdNpfbrIgz3gAAAAAAwC3t3LlzeuGFF1S1alX5+/vLx8dHe/bsyXCGV/ozr/bt26c777zTYVj699u3b9frr78uHx8f+6t37946fvy4Lly4kKc6n3zyScXGxmrVqlWS/j3bLSwsTM2bN5ck7d+/X48++qgqVKggX19f+2WrOS1HerldH5GRkRne79mzJ9M+t2/frgMHDqhYsWL29VCiRAldunQpy8tBL168KEmZPsU2ICBATz31lKpWrZrlU1O9vLzyvI7zWxGnzh0AAAAAAMDJXnjhBS1fvlxvv/22KlWqJC8vLz300EMZLoH09vbOc9/nzp3TqFGj1KFDhwzjMguUsnPbbbepcePGmjNnju655x59/PHH6t27t2w2m6R/72sWGhqq999/X8HBwUpNTdUdd9yR5+XI7frIi3Pnzqlu3bqZPmm0dOnSmU5TqlQpSdKZM2cybVOkSBEVKZJ1tHX69Oks+75RCN4AAAAAAMAtbe3aterRo4cefPBBSf+GRDnd9F+SKleunOGebOnf16lTR/v27VOlSpWy7MfNzU0pKSm5qrVnz5565pln9MADD+ivv/5Sjx49JEn//POP9u3bp/fff1+NGzeWJP3yyy+56jO93K6P9evXZ3hftWrVTPusU6eOvvjiCwUEBMjX1zdXdVSsWFG+vr767bffdPvtt+dpGdLOpKtdu3aepstvBG+4eYz0s7DvBOv6BgAAAAA41W233aavv/5abdu2lc1m0yuvvJLhgQSZ6d+/v5o0aaIJEyaobdu2WrlypRYvXmw/A02SXn31Vd1///0qX768HnroIbm4uGj79u3atWuX3njjDUn/Ptl0xYoVatSokTw8PFS8ePEs5/nwww/rueee01NPPaVWrVopJCREklS8eHGVLFlS7733nsqUKaMjR45o2LBhlq6PtWvXaty4cWrfvr2WL1+uBQsWaNGiRZn22aVLF40fP17t2rXT66+/rnLlyunw4cP6+uuvNWTIEJUrVy7DNC4uLmrZsqV++eWXLC8nzcr69evl4eGR4XLYG417vAEAAAAAgFvahAkTVLx4cd11111q27atoqKiVKdOnRyna9SokWbOnKkJEyaoZs2aWrJkiQYOHOhwCWlUVJS+//57LVu2TPXr11fDhg01ceJEhYaG2tu88847Wr58uUJCQnI8Q6to0aLq3Lmzzpw5oyeffNI+3MXFRZ9//rm2bNmiO+64QwMHDtT48eOvYW3kfn0MHjxYmzdvVu3atfXGG29owoQJmT4lNa3un376SeXLl1eHDh1UtWpV9ezZU5cuXcr2DLhevXrp888/z1UQerX58+erS5cuKlq0aJ6my282Y4xxagWFQGJiovz8/JSQkJDr0yHhBJzxBgAAAAA3rUuXLik2Nlbh4eF5vjfajdS7d2/t3btXP//8s7NLuSkYY9SgQQMNHDhQjz76aK6m+fvvv1W5cmVt3rxZ4eHh1zzv7La53GZFnPEGAAAAAABwjd5++237EzunTJmijz76SN27d3d2WTcNm82m9957T1euXMn1NIcOHdL06dOvK3TLL9zjDQAAAAAA4Bpt3LhR48aN09mzZ1WhQgW9++676tWrl7PLuqnUqlVLtWrVynX7evXqqV69etYVlAcEbwAAAAAAANfoyy+/dHYJKMC41BQAAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWKOLsAgAAAAAAAK5H2LBFN2xeh8ZE37B53QiHDh1SeHi4tm3bplq1at3w+d9zzz2qVauWJk2adMPnfSNwxhsAAAAAAIDF4uLi1L9/f1WoUEEeHh4KCQlR27ZttWLFCmeXlmf33HOPBgwY4OwyCgXOeAMAAAAAALDQoUOH1KhRI/n7+2v8+PGqXr26Ll++rKVLl6pv377au3evs0uERTjjDQAAAAAAwELPPvusbDabNm7cqI4dO+r2229XtWrVNGjQIK1fv16SdOTIEbVr104+Pj7y9fVVp06ddOLECXsfI0eOVK1atTR79myVL19ePj4+evbZZ5WSkqJx48YpKChIAQEB+s9//uMwb5vNphkzZqhNmzby8vJShQoV9NVXX2Vb765du9SmTRv5+PgoMDBQXbt21d9//y1J6tGjh9asWaPJkyfLZrPJZrPp0KFDOU4nSefPn1e3bt3k4+OjMmXK6J133smP1VugEbwBAAAAAABY5PTp01qyZIn69u0rb2/vDOP9/f2Vmpqqdu3a6fTp01qzZo2WL1+uP/74Q4888ohD24MHD2rx4sVasmSJ5s+frw8//FDR0dH6888/tWbNGo0dO1YjRozQhg0bHKZ75ZVX1LFjR23fvl1dunRR586dtWfPnkzrjY+PV/PmzVW7dm1t3rxZS5Ys0YkTJ9SpUydJ0uTJkxUZGanevXvr+PHjOn78uEJCQnKcTpJefPFFrVmzRgsXLtSyZcu0evVqbd269XpXcYHGpaYAAAAAAAAWOXDggIwxqlKlSpZtVqxYoZ07dyo2NlYhISGSpI8//ljVqlXTpk2bVL9+fUlSamqqZs+erWLFiikiIkLNmjXTvn379MMPP8jFxUWVK1fW2LFjtWrVKjVo0MDe/8MPP6xevXpJkkaPHq3ly5drypQpmj59eoZapk6dqtq1a+vNN9+0D5s9e7ZCQkL0+++/6/bbb5e7u7uKFi2qoKCgXE8XHBysDz/8UJ9++qlatGghSfroo49Urly5a1mthQbBGwAAAAAAgEWMMTm22bNnj0JCQuyhmyRFRETI399fe/bssQdvYWFhKlasmL1NYGCgXF1d5eLi4jDs5MmTDv1HRkZmeB8TE5NpLdu3b9eqVavk4+OTYdzBgwd1++23X9N0Fy9eVHJyskMgWKJECVWuXDnT/m4WBG8AAAAAAAAWue2222Sz2fLlAQpubm4O7202W6bDUlNTr3ke586dU9u2bTV27NgM48qUKXPN0x04cOCaayrMuMcbAAAAAACARUqUKKGoqChNmzZN58+fzzA+Pj5eVatW1dGjR3X06FH78N9++03x8fGKiIi47hrSHuBw9fuqVatm2rZOnTravXu3wsLCVKlSJYdX2j3q3N3dlZKSkqfpKlasKDc3N4f7z505c0a///77dS9fQUbwBgAAAAAAYKFp06YpJSVFd955p/773/9q//792rNnj959911FRkaqZcuWql69urp06aKtW7dq48aN6tatm5o2bap69epd9/wXLFig2bNn6/fff9drr72mjRs3ql+/fpm27du3r06fPq1HH31UmzZt0sGDB7V06VI98cQT9rAtLCxMGzZs0KFDh/T3338rNTU1x+l8fHzUs2dPvfjii1q5cqV27dqlHj16OFwmezPiUlMAAAAAAFCoHRoT7ewSslWhQgVt3bpV//nPfzR48GAdP35cpUuXVt26dTVjxgzZbDYtXLhQ/fv3V5MmTeTi4qLWrVtrypQp+TL/UaNG6fPPP9ezzz6rMmXKaP78+VmeSRccHKy1a9dq6NChatWqlZKSkhQaGqrWrVvbQ7IXXnhB3bt3V0REhC5evKjY2FiFhYXlON348ePtl6QWK1ZMgwcPVkJCQr4sY0FlM7m5y98tLjExUX5+fkpISJCvr6+zy0FWRvpZ2PfNfSAAAAAAgILu0qVLio2NVXh4uDw9PZ1dTqFhs9n0zTffqH379s4updDJbpvLbVZ0c5/PBwAAAAAAADgJwRsAAAAAAABgAe7xBgAAAAAAcJPiDmPOxRlvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsEARZxcAAAAAAABwXUb63cB5JVjWtc1m0zfffKP27dtnOv7QoUMKDw/Xtm3bVKtWLcvqKEgK+zJzxhsAAAAAAIBFevToIZvNJpvNJjc3NwUGBuree+/V7NmzlZqa6tD2+PHjatOmTb7Of+TIkYUysLoe99xzjwYMGODsMiQRvAEAAAAAAFiqdevWOn78uA4dOqTFixerWbNmev7553X//ffrypUr9nZBQUHy8PBwYqXIbwRvAAAAAAAAFvLw8FBQUJDKli2rOnXq6KWXXtLChQu1ePFizZ07197OZrPp22+/tb/fuHGjateuLU9PT9WrV0/btm1z6Hf16tWy2WxasWKF6tWrp6JFi+quu+7Svn37JElz587VqFGjtH37dvtZd1fPL73Zs2erWrVq8vDwUJkyZdSvXz/7uCNHjqhdu3by8fGRr6+vOnXqpBMnTtjHp51ZN3v2bJUvX14+Pj569tlnlZKSonHjxikoKEgBAQH6z3/+4zBPm82mGTNmqE2bNvLy8lKFChX01VdfZbs+d+3apTZt2sjHx0eBgYHq2rWr/v77b0n/nmG4Zs0aTZ482b7Mhw4dynE6qxC8AQAAAAAA3GDNmzdXzZo19fXXX2c6/ty5c7r//vsVERGhLVu2aOTIkXrhhRcybfvyyy/rnXfe0ebNm1WkSBE9+eSTkqRHHnlEgwcPVrVq1XT8+HEdP35cjzzySKZ9zJgxQ3379lWfPn20c+dOfffdd6pUqZIkKTU1Ve3atdPp06e1Zs0aLV++XH/88UeGvg4ePKjFixdryZIlmj9/vj788ENFR0frzz//1Jo1azR27FiNGDFCGzZscJjulVdeUceOHbV9+3Z16dJFnTt31p49ezKtMz4+Xs2bN1ft2rW1efNmLVmyRCdOnFCnTp0kSZMnT1ZkZKR69+5tX+aQkJAcp7MKD1cAAAAAAABwgipVqmjHjh2Zjvvss8+UmpqqDz/8UJ6enqpWrZr+/PNPPfPMMxna/uc//1HTpk0lScOGDVN0dLQuXbokLy8v+fj4qEiRIgoKCsq2ljfeeEODBw/W888/bx9Wv359SdKKFSu0c+dOxcbGKiQkRJL08ccfq1q1atq0aZO9XWpqqmbPnq1ixYopIiJCzZo10759+/TDDz/IxcVFlStX1tixY7Vq1So1aNDAPp+HH35YvXr1kiSNHj1ay5cv15QpUzR9+vQMdU6dOlW1a9fWm2++aR82e/ZshYSE6Pfff9ftt98ud3d3FS1a1GGZczOdFQjeAAAAAAAAnMAYI5vNlum4PXv2qEaNGvL09LQPi4yMzLRtjRo17P9fpkwZSdLJkydVvnz5XNVx8uRJHTt2TC1atMiylpCQEHvoJkkRERHy9/fXnj177MFbWFiYihUrZm8TGBgoV1dXubi4OAw7efKkQ//plysyMlIxMTGZ1rJ9+3atWrVKPj4+GcYdPHgwywDtWqe7XgRvAAAAAAAATrBnzx6Fh4dfdz9ubm72/08L8tI/MTU7Xl5e111D+jrSaslsWF5qS+/cuXNq27atxo4dm2FcWuiYn9NdL+7xBgAAAAAAcIOtXLlSO3fuVMeOHTMdX7VqVe3YsUOXLl2yD1u/fn2e5+Pu7q6UlJRs2xQrVkxhYWFasWJFlrUcPXpUR48etQ/77bffFB8fr4iIiDzXlF765Vq/fr2qVq2aads6depo9+7dCgsLU6VKlRxe3t7ekjJf5txMZwWCNwAAAAAAAAslJSUpLi5Of/31l7Zu3ao333xT7dq10/33369u3bplOs1jjz0mm82m3r1767ffftMPP/ygt99+O8/zDgsLU2xsrGJiYvT3338rKSkp03YjR47UO++8o3fffVf79+/X1q1bNWXKFElSy5YtVb16dXXp0kVbt27Vxo0b1a1bNzVt2lT16tXLc03pLViwQLNnz9bvv/+u1157TRs3bnR4ourV+vbtq9OnT+vRRx/Vpk2bdPDgQS1dulRPPPGEPWwLCwvThg0bdOjQIf39999KTU3N1XRW4FJTAAAAAABQuI1McHYF2VqyZInKlCmjIkWKqHjx4qpZs6beffddde/e3eH+Z1fz8fHR//73Pz399NOqXbu2IiIiNHbs2CzPkMtKx44d9fXXX6tZs2aKj4/XnDlz1KNHjwztunfvrkuXLmnixIl64YUXVKpUKT300EOS/r08dOHCherfv7+aNGkiFxcXtW7d2h7MXa9Ro0bp888/17PPPqsyZcpo/vz5WZ5JFxwcrLVr12ro0KFq1aqVkpKSFBoaqtatW9vX5QsvvKDu3bsrIiJCFy9eVGxsrMLCwnKczgo2Y4yxrPebRGJiovz8/JSQkCBfX19nl4OsjPSzsO+CfRAHAAAAgJvdpUuXFBsbq/DwcIcHDqBws9ls+uabb9S+fXtnl5JBdttcbrMiLjUFAAAAAAAALEDwBgAAAAAAAFiAe7wBAAAAAADAKW72O6BxxhsAAAAAAABgAYI3AAAAAABQaNzsZ0ih4MiPbY3gDQAAAAAAFHhubm6SpAsXLji5Etwq0ra1tG3vWnCPNwAAAAAAUOC5urrK399fJ0+elCQVLVpUNpvNyVXhZmSM0YULF3Ty5En5+/vL1dX1mvsieAMAAAAAAIVCUFCQJNnDN8BK/v7+9m3uWhG8AQAAAACAQsFms6lMmTIKCAjQ5cuXnV0ObmJubm7XdaZbGoI3AAAAAABQqLi6uuZLKAJYjYcrAAAAAAAAABYgeAMAAAAAAAAs4NTg7a233lL9+vVVrFgxBQQEqH379tq3b59Dm3vuuUc2m83h9fTTTzu0OXLkiKKjo1W0aFEFBAToxRdf1JUrVxzarF69WnXq1JGHh4cqVaqkuXPnWr14AAAAAAAAuIU5NXhbs2aN+vbtq/Xr12v58uW6fPmyWrVqpfPnzzu06927t44fP25/jRs3zj4uJSVF0dHRSk5O1q+//qqPPvpIc+fO1auvvmpvExsbq+joaDVr1kwxMTEaMGCAevXqpaVLl96wZQUAAAAAAMCtxWaMMc4uIs2pU6cUEBCgNWvWqEmTJpL+PeOtVq1amjRpUqbTLF68WPfff7+OHTumwMBASdLMmTM1dOhQnTp1Su7u7ho6dKgWLVqkXbt22afr3Lmz4uPjtWTJkhzrSkxMlJ+fnxISEuTr63v9CwprjPSzsO8E6/oGAAAAAACFSm6zogJ1j7eEhH/DjRIlSjgMnzdvnkqVKqU77rhDw4cP14ULF+zj1q1bp+rVq9tDN0mKiopSYmKidu/ebW/TsmVLhz6joqK0bt26TOtISkpSYmKiwwsAAAAAAADIiyLOLiBNamqqBgwYoEaNGumOO+6wD3/ssccUGhqq4OBg7dixQ0OHDtW+ffv09ddfS5Li4uIcQjdJ9vdxcXHZtklMTNTFixfl5eXlMO6tt97SqFGj8n0ZAQAAAAAAcOsoMMFb3759tWvXLv3yyy8Ow/v06WP//+rVq6tMmTJq0aKFDh48qIoVK1pSy/DhwzVo0CD7+8TERIWEhFgyLwAAAAAAANycCsSlpv369dP333+vVatWqVy5ctm2bdCggSTpwIEDkqSgoCCdOHHCoU3a+6CgoGzb+Pr6ZjjbTZI8PDzk6+vr8AIAAAAAAADywqnBmzFG/fr10zfffKOVK1cqPDw8x2liYmIkSWXKlJEkRUZGaufOnTp58qS9zfLly+Xr66uIiAh7mxUrVjj0s3z5ckVGRubTkgAAAAAAAACOnBq89e3bV59++qk+++wzFStWTHFxcYqLi9PFixclSQcPHtTo0aO1ZcsWHTp0SN999526deumJk2aqEaNGpKkVq1aKSIiQl27dtX27du1dOlSjRgxQn379pWHh4ck6emnn9Yff/yhIUOGaO/evZo+fbq+/PJLDRw40GnLDgAAAAAAgJubzRhjnDZzmy3T4XPmzFGPHj109OhRPf7449q1a5fOnz+vkJAQPfjggxoxYoTD5Z+HDx/WM888o9WrV8vb21vdu3fXmDFjVKTI/93CbvXq1Ro4cKB+++03lStXTq+88op69OiRqzpz+4hYONlIPwv7TrCubwAAAAAAUKjkNityavBWWBC8FRIEbwAAAAAA4AbIbVZUIB6uAAAAAAAAANxsCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWKCIswsA8kvYpc8s6/uQZT0DAAAAAICbFWe8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWMCpwdtbb72l+vXrq1ixYgoICFD79u21b98+hzaXLl1S3759VbJkSfn4+Khjx446ceKEQ5sjR44oOjpaRYsWVUBAgF588UVduXLFoc3q1atVp04deXh4qFKlSpo7d67ViwcAAAAAAIBbmFODtzVr1qhv375av369li9frsuXL6tVq1Y6f/68vc3AgQP1v//9TwsWLNCaNWt07NgxdejQwT4+JSVF0dHRSk5O1q+//qqPPvpIc+fO1auvvmpvExsbq+joaDVr1kwxMTEaMGCAevXqpaVLl97Q5QUAAAAAAMCtw2aMMc4uIs2pU6cUEBCgNWvWqEmTJkpISFDp0qX12Wef6aGHHpIk7d27V1WrVtW6devUsGFDLV68WPfff7+OHTumwMBASdLMmTM1dOhQnTp1Su7u7ho6dKgWLVqkXbt22efVuXNnxcfHa8mSJRnqSEpKUlJSkv19YmKiQkJClJCQIF9fX4vXAq5V2LBFlvV9aEy0ZX0DAAAAAIDCJTExUX5+fjlmRQXqHm8JCQmSpBIlSkiStmzZosuXL6tly5b2NlWqVFH58uW1bt06SdK6detUvXp1e+gmSVFRUUpMTNTu3bvtba7uI61NWh/pvfXWW/Lz87O/QkJC8m8hAQAAAAAAcEsoMMFbamqqBgwYoEaNGumOO+6QJMXFxcnd3V3+/v4ObQMDAxUXF2dvc3XoljY+bVx2bRITE3Xx4sUMtQwfPlwJCQn219GjR/NlGQEAAAAAAHDrKOLsAtL07dtXu3bt0i+//OLsUuTh4SEPDw9nlwEAAAAAAIBCrECc8davXz99//33WrVqlcqVK2cfHhQUpOTkZMXHxzu0P3HihIKCguxt0j/lNO19Tm18fX3l5eWV34sDAAAAAAAAODd4M8aoX79++uabb7Ry5UqFh4c7jK9bt67c3Ny0YsUK+7B9+/bpyJEjioyMlCRFRkZq586dOnnypL3N8uXL5evrq4iICHubq/tIa5PWBwAAAAAAAJDfnHqpad++ffXZZ59p4cKFKlasmP2ebH5+fvLy8pKfn5969uypQYMGqUSJEvL19VX//v0VGRmphg0bSpJatWqliIgIde3aVePGjVNcXJxGjBihvn372i8XffrppzV16lQNGTJETz75pFauXKkvv/xSixZZ9xRMAAAAAAAA3NqcesbbjBkzlJCQoHvuuUdlypSxv7744gt7m4kTJ+r+++9Xx44d1aRJEwUFBenrr7+2j3d1ddX3338vV1dXRUZG6vHHH1e3bt30+uuv29uEh4dr0aJFWr58uWrWrKl33nlHH3zwgaKiom7o8gIAAAAAAODWYTPGGGcXUdAlJibKz89PCQkJ8vX1dXY5yELYMOvOYDw0JtqyvgEAAAAAQOGS26yoQDxcAQAAAAAAALjZELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAgRvAAAAAAAAgAUI3gAAAAAAAAALELwBAAAAAAAAFiB4AwAAAAAAACxA8AYAAAAAAABYgOANAAAAAAAAsADBGwAAAAAAAGABgjcAAAAAAADAAtcUvFWoUEH//PNPhuHx8fGqUKHCdRcFAAAAAAAAFHbXFLwdOnRIKSkpGYYnJSXpr7/+uu6iAAAAAAAAgMKuSF4af/fdd/b/X7p0qfz8/OzvU1JStGLFCoWFheVbcQAAAAAAAEBhlafgrX379pIkm82m7t27O4xzc3NTWFiY3nnnnXwrDgAAAAAAACis8hS8paamSpLCw8O1adMmlSpVypKiAAAAAAAAgMIuT8FbmtjY2PyuAwAAAAAAALipXFPwJkkrVqzQihUrdPLkSfuZcGlmz5593YUBAAAAAAAAhdk1BW+jRo3S66+/rnr16qlMmTKy2Wz5XRcAAAAAAABQqF1T8DZz5kzNnTtXXbt2ze96AAAAAAAAgJuCy7VMlJycrLvuuiu/awEAAAAAAABuGtcUvPXq1UufffZZftcCAAAAAAAA3DSu6VLTS5cu6b333tOPP/6oGjVqyM3NzWH8hAkT8qU4ALDMSD8L+06wrm8AAAAAQKFxTcHbjh07VKtWLUnSrl27HMbxoAUAAAAAAADgGoO3VatW5XcdAAAAAAAAwE3lmu7xBgAAAAAAACB713TGW7NmzbK9pHTlypXXXBAAAAAAAABwM7im4C3t/m5pLl++rJiYGO3atUvdu3fPj7oAAAAAAACAQu2aLjWdOHGiw2vq1Kn65ZdfNGDAgAxPOM3OTz/9pLZt2yo4OFg2m03ffvutw/gePXrIZrM5vFq3bu3Q5vTp0+rSpYt8fX3l7++vnj176ty5cw5tduzYocaNG8vT01MhISEaN27ctSw2AAAAAAAAkGv5eo+3xx9/XLNnz851+/Pnz6tmzZqaNm1alm1at26t48eP21/z5893GN+lSxft3r1by5cv1/fff6+ffvpJffr0sY9PTExUq1atFBoaqi1btmj8+PEaOXKk3nvvvbwvIAAAAAAAAJBL13SpaVbWrVsnT0/PXLdv06aN2rRpk20bDw8PBQUFZTpuz549WrJkiTZt2qR69epJkqZMmaL77rtPb7/9toKDgzVv3jwlJydr9uzZcnd3V7Vq1RQTE6MJEyY4BHQAAAAAAABAfrqm4K1Dhw4O740xOn78uDZv3qxXXnklXwpLs3r1agUEBKh48eJq3ry53njjDZUsWVLSv0Gfv7+/PXSTpJYtW8rFxUUbNmzQgw8+qHXr1qlJkyZyd3e3t4mKitLYsWN15swZFS9ePMM8k5KSlJSUZH+fmJiYr8sEAAAAAACAm981BW9+fn4O711cXFS5cmW9/vrratWqVb4UJv17mWmHDh0UHh6ugwcP6qWXXlKbNm20bt06ubq6Ki4uTgEBAQ7TFClSRCVKlFBcXJwkKS4uTuHh4Q5tAgMD7eMyC97eeustjRo1Kt+WAwAAAAAAALeeawre5syZk991ZKpz5872/69evbpq1KihihUravXq1WrRooVl8x0+fLgGDRpkf5+YmKiQkBDL5gcAAAAAAICbz3Xd423Lli3as2ePJKlatWqqXbt2vhSVlQoVKqhUqVI6cOCAWrRooaCgIJ08edKhzZUrV3T69Gn7feGCgoJ04sQJhzZp77O6d5yHh4c8PDwsWAIAAAAAAADcKq4peDt58qQ6d+6s1atXy9/fX5IUHx+vZs2a6fPPP1fp0qXzs0a7P//8U//884/KlCkjSYqMjFR8fLy2bNmiunXrSpJWrlyp1NRUNWjQwN7m5Zdf1uXLl+Xm5iZJWr58uSpXrpzpZaYAAAAAAABAfnC5lon69++vs2fPavfu3Tp9+rROnz6tXbt2KTExUc8991yu+zl37pxiYmIUExMjSYqNjVVMTIyOHDmic+fO6cUXX9T69et16NAhrVixQu3atVOlSpUUFRUlSapatapat26t3r17a+PGjVq7dq369eunzp07Kzg4WJL02GOPyd3dXT179tTu3bv1xRdfaPLkyQ6XkgIAAAAAAAD57ZrOeFuyZIl+/PFHVa1a1T4sIiJC06ZNy9PDFTZv3qxmzZrZ36eFYd27d9eMGTO0Y8cOffTRR4qPj1dwcLBatWql0aNHO1wGOm/ePPXr108tWrSQi4uLOnbsqHfffdc+3s/PT8uWLVPfvn1Vt25dlSpVSq+++qr69OlzLYsOAAAAAAAA5Mo1BW+pqan2yzav5ubmptTU1Fz3c88998gYk+X4pUuX5thHiRIl9Nlnn2XbpkaNGvr5559zXRcAAAAAAABwva7pUtPmzZvr+eef17Fjx+zD/vrrLw0cONDSp40CAAAAAAAAhcU1BW9Tp05VYmKiwsLCVLFiRVWsWFHh4eFKTEzUlClT8rtGAAAAAAAAoNC5pktNQ0JCtHXrVv3444/au3evpH8fdNCyZct8LQ4AAAAAAAAorPJ0xtvKlSsVERGhxMRE2Ww23Xvvverfv7/69++v+vXrq1q1atxLDQAAAAAAAFAeg7dJkyapd+/e8vX1zTDOz89PTz31lCZMmJBvxQEAAAAAAACFVZ6Ct+3bt6t169ZZjm/VqpW2bNly3UUBAAAAAAAAhV2egrcTJ07Izc0ty/FFihTRqVOnrrsoAAAAAAAAoLDLU/BWtmxZ7dq1K8vxO3bsUJkyZa67KAAAAAAAAKCwy1Pwdt999+mVV17RpUuXMoy7ePGiXnvtNd1///35VhwAAAAAAABQWBXJS+MRI0bo66+/1u23365+/fqpcuXKkqS9e/dq2rRpSklJ0csvv2xJoQAAAAAAAEBhkqfgLTAwUL/++queeeYZDR8+XMYYSZLNZlNUVJSmTZumwMBASwoFAAAAAAAACpM8BW+SFBoaqh9++EFnzpzRgQMHZIzRbbfdpuLFi1tRHwAAAAAAAFAo5Tl4S1O8eHHVr18/P2sBAAAAAAAAbhp5ergCAAAAAAAAgNwheAMAAAAAAAAscM2XmgJAYRZ26TPL+j5kWc8AAAAAgMKEM94AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACzg1ePvpp5/Utm1bBQcHy2az6dtvv3UYb4zRq6++qjJlysjLy0stW7bU/v37HdqcPn1aXbp0ka+vr/z9/dWzZ0+dO3fOoc2OHTvUuHFjeXp6KiQkROPGjbN60QAAAAAAAHCLc2rwdv78edWsWVPTpk3LdPy4ceP07rvvaubMmdqwYYO8vb0VFRWlS5cu2dt06dJFu3fv1vLly/X999/rp59+Up8+fezjExMT1apVK4WGhmrLli0aP368Ro4cqffee8/y5QMAAAAAAMCtq4gzZ96mTRu1adMm03HGGE2aNEkjRoxQu3btJEkff/yxAgMD9e2336pz587as2ePlixZok2bNqlevXqSpClTpui+++7T22+/reDgYM2bN0/JycmaPXu23N3dVa1aNcXExGjChAkOAR0AAAAAAACQnwrsPd5iY2MVFxenli1b2of5+fmpQYMGWrdunSRp3bp18vf3t4duktSyZUu5uLhow4YN9jZNmjSRu7u7vU1UVJT27dunM2fOZDrvpKQkJSYmOrwAAAAAAACAvCiwwVtcXJwkKTAw0GF4YGCgfVxcXJwCAgIcxhcpUkQlSpRwaJNZH1fPI7233npLfn5+9ldISMj1LxAAAAAAAABuKQU2eHOm4cOHKyEhwf46evSos0sCAAAAAABAIVNgg7egoCBJ0okTJxyGnzhxwj4uKChIJ0+edBh/5coVnT592qFNZn1cPY/0PDw85Ovr6/ACAAAAAAAA8qLABm/h4eEKCgrSihUr7MMSExO1YcMGRUZGSpIiIyMVHx+vLVu22NusXLlSqampatCggb3NTz/9pMuXL9vbLF++XJUrV1bx4sVv0NIAAAAAAADgVuPU4O3cuXOKiYlRTEyMpH8fqBATE6MjR47IZrNpwIABeuONN/Tdd99p586d6tatm4KDg9W+fXtJUtWqVdW6dWv17t1bGzdu1Nq1a9WvXz917txZwcHBkqTHHntM7u7u6tmzp3bv3q0vvvhCkydP1qBBg5y01AAAAAAAALgVFHHmzDdv3qxmzZrZ36eFYd27d9fcuXM1ZMgQnT9/Xn369FF8fLzuvvtuLVmyRJ6envZp5s2bp379+qlFixZycXFRx44d9e6779rH+/n5admyZerbt6/q1q2rUqVK6dVXX1WfPn1u3IICAAAAAADglmMzxhhnF1HQJSYmys/PTwkJCdzvrQALG7bIsr4PjYm2rG84B9sLAAAAAOBa5TYrKrD3eAMAAAAAAAAKM4I3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALAAwRsAAAAAAABgAYI3AAAAAAAAwAIEbwAAAAAAAIAFCN4AAAAAAAAACxC8AQAAAAAAABYgeAMAAAAAAAAsQPAGAAAAAAAAWIDgDQAAAAAAALBAEWcXAAAAAKAQGelnYd8J1vUNAIATFOgz3kaOHCmbzebwqlKlin38pUuX1LdvX5UsWVI+Pj7q2LGjTpw44dDHkSNHFB0draJFiyogIEAvvviirly5cqMXBQAAAAAAALeYAn/GW7Vq1fTjjz/a3xcp8n8lDxw4UIsWLdKCBQvk5+enfv36qUOHDlq7dq0kKSUlRdHR0QoKCtKvv/6q48ePq1u3bnJzc9Obb755w5cFAAAAAAAAt44CH7wVKVJEQUFBGYYnJCToww8/1GeffabmzZtLkubMmaOqVatq/fr1atiwoZYtW6bffvtNP/74owIDA1WrVi2NHj1aQ4cO1ciRI+Xu7n6jFwcAAAAAAAC3iAIfvO3fv1/BwcHy9PRUZGSk3nrrLZUvX15btmzR5cuX1bJlS3vbKlWqqHz58lq3bp0aNmyodevWqXr16goMDLS3iYqK0jPPPKPdu3erdu3amc4zKSlJSUlJ9veJiYnWLSAAAABQiIRd+syyvg9Z1jMAAM5RoO/x1qBBA82dO1dLlizRjBkzFBsbq8aNG+vs2bOKi4uTu7u7/P39HaYJDAxUXFycJCkuLs4hdEsbnzYuK2+99Zb8/Pzsr5CQkPxdMAAAAAAAANz0CvQZb23atLH/f40aNdSgQQOFhobqyy+/lJeXl2XzHT58uAYNGmR/n5iYSPgGAAAAAACAPCnQZ7yl5+/vr9tvv10HDhxQUFCQkpOTFR8f79DmxIkT9nvCBQUFZXjKadr7zO4bl8bDw0O+vr4OLwAAAAAAACAvClXwdu7cOR08eFBlypRR3bp15ebmphUrVtjH79u3T0eOHFFkZKQkKTIyUjt37tTJkyftbZYvXy5fX19FRETc8PoBAAAAAABw6yjQl5q+8MILatu2rUJDQ3Xs2DG99tprcnV11aOPPio/Pz/17NlTgwYNUokSJeTr66v+/fsrMjJSDRs2lCS1atVKERER6tq1q8aNG6e4uDiNGDFCffv2lYeHh5OXDgAAAAAAADezAh28/fnnn3r00Uf1zz//qHTp0rr77ru1fv16lS5dWpI0ceJEubi4qGPHjkpKSlJUVJSmT59un97V1VXff/+9nnnmGUVGRsrb21vdu3fX66+/7qxFAgAAAAAAwC2iQAdvn3/+ebbjPT09NW3aNE2bNi3LNqGhofrhhx/yuzQAAAAAAAAgW4XqHm8AAAAAAABAYUHwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWIHgDAAAAAAAALEDwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYIEizi4AAJAHI/0s7DvBur4BAAAA4BbEGW8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWIHgDAAAAAAAALEDwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABYo4uwAAQO6FXfrMsr4PWdYzkEcj/SzsO8G6vgEAAIB0CN4AAECBQsAMAACAmwWXmgIAAAAAAAAWIHgDAAAAAAAALEDwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWIHgDAAAAAAAALEDwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWIHgDAAAAAAAALEDwBgAAAAAAAFiA4A0AAAAAAACwAMEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjeAAAAAAAAAAsQvAEAAAAAAAAWIHgDAAAAAAAALFDE2QUAAADcNEb6Wdh3gnV9AwAAwBIEbwAAAPkk7NJnlvV9yLKeAQAAYBWCN2TEv9YDAAAAAABcN+7xBgAAAAAAAFiAM94AAABudZztDgAAYAmCN2TA/WkAAAAAAACuH5eaAgAAAAAAABbgjDcAAIBbXGE+2z1s2CLL+j40JtqyvgEANwlu14AccMYbAAAAAAAAYAGCNwAAAAAAAMACXGoKAAAA4ObH5WAAACcgeAMAAABw0yvM9zIEABReXGoKAAAAAAAAWIAz3qzCqezIA57IBgAAAADAzYfgzSKcyo5bAgEzAAAAbkL8wzhyi9/+yMktFbxNmzZN48ePV1xcnGrWrKkpU6bozjvvdHZZQKHFhwwAANeIf7wCAODaFLLP0FsmePviiy80aNAgzZw5Uw0aNNCkSZMUFRWlffv2KSAgwNnlAcBNr7D+y3FhrVsq3LUDNzv+8Qq3Aj6HnKCQBRKO/Rfi2gupwrqPFrbP0FsmeJswYYJ69+6tJ554QpI0c+ZMLVq0SLNnz9awYcOcXB0AAAAAANensAUSVyvMtQPZuSWCt+TkZG3ZskXDhw+3D3NxcVHLli21bt26DO2TkpKUlJRkf5+Q8G86npiYmOt5piZduI6Ks5eXOq5FYa29sNYtFd7aC2vdUuGtvbDWLRXe2gtr3VLhrb2w1i0V3toLa91S4a29sNYtFd7aC2vdknTHa0st63vXqCjL+i7M67yw1l5Y65YKb+2FtW6p8NZeUOpOa2uMybadzeTU4iZw7NgxlS1bVr/++qsiIyPtw4cMGaI1a9Zow4YNDu1HjhypUaNG3egyAQAAAAAAUIgcPXpU5cqVy3L8LXHGW14NHz5cgwYNsr9PTU3V6dOnVbJkSdlstnyfX2JiokJCQnT06FH5+vrme/9WKax1S4W39sJat1R4ay+sdUuFt/bCWrdUeGsvrHVLhbf2wlq3VHhrL6x1S4W39sJat1R4ay+sdUuFt/bCWrdUeGsvrHVLhbf2wlq3ZG3txhidPXtWwcHB2ba7JYK3UqVKydXVVSdOnHAYfuLECQUFBWVo7+HhIQ8PD4dh/v7+VpYoSfL19S10G7FUeOuWCm/thbVuqfDWXljrlgpv7YW1bqnw1l5Y65YKb+2FtW6p8NZeWOuWCm/thbVuqfDWXljrlgpv7YW1bqnw1l5Y65YKb+2FtW7Jutr9/HJ+KIhLvs+1AHJ3d1fdunW1YsUK+7DU1FStWLHC4dJTAAAAAAAAIL/cEme8SdKgQYPUvXt31atXT3feeacmTZqk8+fP259yCgAAAAAAAOSnWyZ4e+SRR3Tq1Cm9+uqriouLU61atbRkyRIFBgY6uzR5eHjotddey3B5a0FXWOuWCm/thbVuqfDWXljrlgpv7YW1bqnw1l5Y65YKb+2FtW6p8NZeWOuWCm/thbVuqfDWXljrlgpv7YW1bqnw1l5Y65YKb+2FtW6pYNR+SzzVFAAAAAAAALjRbol7vAEAAAAAAAA3GsEbAAAAAAAAYAGCNwAAAAAAAMACBG8AAAAAAACABQjenGTGjBmqUaOGfH195evrq8jISC1evNjZZeXZmDFjZLPZNGDAAGeXkqORI0fKZrM5vKpUqeLssnLt7NmzGjBggEJDQ+Xl5aW77rpLmzZtcnZZDn766Se1bdtWwcHBstls+vbbbx3GG2P06quvqkyZMvLy8lLLli21f/9+5xSbTna1X758WUOHDlX16tXl7e2t4OBgdevWTceOHXNewf9fTuv8xIkT6tGjh4KDg1W0aFG1bt26QKzzt956S/Xr11exYsUUEBCg9u3ba9++fQ5tLl26pL59+6pkyZLy8fFRx44ddeLECSdV/H9yU/t7772ne+65R76+vrLZbIqPj3dOsVfJTd333HNPhuPk008/7aSK/09uapekdevWqXnz5vL29pavr6+aNGmiixcvOqHif+X0Wf/UU0+pYsWK8vLyUunSpdWuXTvt3bvXafVmJ7PP+4K6j14tu+8pxhi1adMm02NnQZC+9kOHDmXYP9NeCxYscFqduf1+VRDXd061Hzx4UA8++KBKly4tX19fderUqcBs43/99Zcef/xxlSxZUl5eXqpevbo2b95sH//111+rVatWKlmypGw2m2JiYpxXbDo51S5Je/bs0QMPPCA/Pz95e3urfv36OnLkiJMqllJSUvTKK68oPDxcXl5eqlixokaPHq2rn1NYkL/n5uZ3REFY5zl9rx05cqSqVKkib29vFS9eXC1bttSGDRsc2oSFhWXYr8eMGePUurM6do8fP97e5oEHHlD58uXl6empMmXKqGvXrpb/1sjt9ysp+2P4pk2b1KJFC/n7+6t48eKKiorS9u3bnVp3TsfvQ4cOqWfPng779Guvvabk5GRLaiZ4c5Jy5cppzJgx2rJlizZv3qzmzZurXbt22r17t7NLy7VNmzZp1qxZqlGjhrNLybVq1arp+PHj9tcvv/zi7JJyrVevXlq+fLk++eQT7dy5U61atVLLli31119/Obs0u/Pnz6tmzZqaNm1apuPHjRund999VzNnztSGDRvk7e2tqKgoXbp06QZXmlF2tV+4cEFbt27VK6+8oq1bt+rrr7/Wvn379MADDzihUkfZ1W2MUfv27fXHH39o4cKF2rZtm0JDQ9WyZUudP3/eCdX+nzVr1qhv375av369li9frsuXL6tVq1YOdQ0cOFD/+9//tGDBAq1Zs0bHjh1Thw4dnFj1v3JT+4ULF9S6dWu99NJLTqzUUW7qlqTevXs7HCfHjRvnpIr/T25qX7dunVq3bq1WrVpp48aN2rRpk/r16ycXF+d91cnps75u3bqaM2eO9uzZo6VLl8oYo1atWiklJcVpNWcmq8/7grqPpsnpe8qkSZNks9lucFW5k1ntISEhDvvm8ePHNWrUKPn4+KhNmzZOrDZ3368K6vrOqvbz58+rVatWstlsWrlypdauXavk5GS1bdtWqampTq35zJkzatSokdzc3LR48WL99ttveuedd1S8eHF7m/Pnz+vuu+/W2LFjnVhpRrmp/eDBg7r77rtVpUoVrV69Wjt27NArr7wiT09Pp9U9duxYzZgxQ1OnTtWePXs0duxYjRs3TlOmTLG3Kcjfc3P6HVFQ1nlOvyVuv/12TZ06VTt37tQvv/yisLAwtWrVSqdOnXJo9/rrrzvs1/3793dq3emP3bNnz5bNZlPHjh3tbZo1a6Yvv/xS+/bt03//+18dPHhQDz30kKV15/a7oZT1MfzcuXNq3bq1ypcvrw0bNuiXX35RsWLFFBUVpcuXLzul7twcv/fu3avU1FTNmjVLu3fv1sSJEzVz5kzrvrsbFBjFixc3H3zwgbPLyJWzZ8+a2267zSxfvtw0bdrUPP/8884uKUevvfaaqVmzprPLuCYXLlwwrq6u5vvvv3cYXqdOHfPyyy87qarsSTLffPON/X1qaqoJCgoy48ePtw+Lj483Hh4eZv78+U6oMGvpa8/Mxo0bjSRz+PDhG1NULqSve9++fUaS2bVrl31YSkqKKV26tHn//fedUGHWTp48aSSZNWvWGGP+3Tbc3NzMggUL7G327NljJJl169Y5q8xMpa/9aqtWrTKSzJkzZ258YTnIrO7CcjzPrPYGDRqYESNGOLGq3Mnus3779u1Gkjlw4MANriprWX3eF/R9NKfvKdu2bTNly5Y1x48fz9Ux/0bKy3esWrVqmSeffPLGFZeJ3Hy/KqjrO7valy5dalxcXExCQoJ9WHx8vLHZbGb58uU3qMLMDR061Nx99925ahsbG2skmW3btllbVC7lpvZHHnnEPP744zeootyJjo7OsK916NDBdOnSxRhTsL/n5uZ3REFc57k5ViQkJBhJ5scff7QPCw0NNRMnTrS2uGzkpu527dqZ5s2bZ9tm4cKFxmazmeTk5HysLntZfafN7hi+adMmI8kcOXLEPmzHjh1Gktm/f79T6r7W4/e4ceNMeHi4JTVyxlsBkJKSos8//1znz59XZGSks8vJlb59+yo6OlotW7Z0dil5sn//fgUHB6tChQrq0qWLU09Zz4srV64oJSUlw786eXl5FZqz9mJjYxUXF+ewzfj5+alBgwZat26dEyu7NgkJCbLZbPL393d2KVlKSkqSJIftxsXFRR4eHgVuu0lISJAklShRQpK0ZcsWXb582WF7qVKlisqXL1/gtpf0tRcWWdU9b948lSpVSnfccYeGDx+uCxcuOKO8bKWv/eTJk9qwYYMCAgJ01113KTAwUE2bNi1Q23lOn/Xnz5/XnDlzFB4erpCQECdUmLmsPu8L+j6a3feUCxcu6LHHHtO0adMUFBTkhOqyl9vvWFu2bFFMTIx69ux5gyrLWnbfrwr6+s6q9qSkJNlsNnl4eNjbenp6ysXFxenHlu+++0716tXTww8/rICAANWuXVvvv/++U2vKrZxqT01N1aJFi3T77bcrKipKAQEBatCggdMvT77rrru0YsUK/f7775Kk7du365dffrGfbVqQv+fm9DuioK7znCQnJ+u9996Tn5+fatas6TBuzJgxKlmypGrXrq3x48frypUrTqoyoxMnTmjRokXZHrtPnz6tefPm6a677pKbm9sNqy2z74Y5HcMrV66skiVL6sMPP1RycrIuXryoDz/8UFWrVlVYWJhT6r7W43dCQoJ13+ctifOQKzt27DDe3t7G1dXV+Pn5mUWLFjm7pFyZP3++ueOOO8zFixeNMYXnDIkffvjBfPnll2b79u1myZIlJjIy0pQvX94kJiY6u7RciYyMNE2bNjV//fWXuXLlivnkk0+Mi4uLuf32251dWqaU7l9D1q5daySZY8eOObR7+OGHTadOnW5wddlLX3t6Fy9eNHXq1DGPPfbYjSsqF9LXnZycbMqXL28efvhhc/r0aZOUlGTGjBljJJlWrVo5r9B0UlJSTHR0tGnUqJF92Lx584y7u3uGtvXr1zdDhgy5keVlK7Par1ZQz3jLqu5Zs2aZJUuWmB07dphPP/3UlC1b1jz44INOqjJzmdW+bt06I8mUKFHCzJ4922zdutUMGDDAuLu7m99//92J1eb8WT9t2jTj7e1tJJnKlSsXqLPdsvu8L8j7aE7fU/r06WN69uxpf5/TMf9Gyst3rGeeecZUrVr1BlaXuZy+XxXk9Z1d7SdPnjS+vr7m+eefN+fPnzfnzp0z/fr1M5JMnz59nFq3h4eH8fDwMMOHDzdbt241s2bNMp6enmbu3LkZ2ha0M95yqj3tjJqiRYuaCRMmmG3btpm33nrL2Gw2s3r1aqfVnZKSYoYOHWpsNpspUqSIsdls5s0337SPL+jfc7P7HVFQ13lWx4r//e9/xtvb29hsNhMcHGw2btzoMP6dd94xq1atMtu3bzczZsww/v7+ZuDAgTeo6pyPcWPHjjXFixe3H+evNmTIEFO0aFEjyTRs2ND8/fffFlbqKKvvhrk5hu/cudNUrFjRuLi4GBcXF1O5cmVz6NChG1F2pnVfy/F7//79xtfX17z33nuW1Enw5kRJSUlm//79ZvPmzWbYsGGmVKlSZvfu3c4uK1tHjhwxAQEBZvv27fZhhSV4S+/MmTPG19e30Fzee+DAAdOkSRMjybi6upr69eubLl26mCpVqji7tEzdrMFbcnKyadu2raldu7bD6csFQWZ1b9682dSsWdO+3URFRZk2bdqY1q1bO6fITDz99NMmNDTUHD161D6sIP+ov1pmtV+toAZvOdWdZsWKFQXu0sfMak87vgwfPtyhbfXq1c2wYcNudIkOcvqsj4+PN7///rtZs2aNadu2ralTp06mX8ZvtJw+7wvqPppT3QsXLjSVKlUyZ8+etY8vKEFQXr5jXbhwwfj5+Zm33377BlaYO1d/vyrI6zsz6b8bLl261FSoUMHYbDbj6upqHn/8cVOnTh3z9NNPO7VONzc3ExkZ6TCsf//+pmHDhhnaFrTgLafa//rrLyPJPProow5t2rZtazp37nzD6kxv/vz5ply5cmb+/Plmx44d5uOPPzYlSpSwB4YF/Xtudr8jCuo6z+pYce7cObN//36zbt068+STT5qwsDBz4sSJLPv58MMPTZEiRcylS5csrPb/5HSMq1y5sunXr1+m406dOmX27dtnli1bZho1amTuu+8+k5qaalGljjL7fpWbY/iFCxfMnXfeabp162Y2btxo1q1bZzp27GiqVatmLly44JS6jcnb8fvPP/80FStWdAgY8xvBWwHSokULp/8LWk6++eYb+wE77SXJvkFfuXLF2SXmSb169Zz+oyyvzp07Z/9Q79Spk7nvvvucXFHm0h+UDx48mOkXvyZNmpjnnnvuxhaXg6w+MJOTk0379u1NjRo1bui/QOVWdh/08fHx5uTJk8YYY+68807z7LPP3sDKsta3b19Trlw588cffzgMTwt80gdW5cuXNxMmTLiBFWYtq9qvVhCDt9zUnebcuXNGklmyZMkNqCxnWdX+xx9/GEnmk08+cRjeqVOnAndmanaf9UlJSaZo0aLms88+u8FVZZTT5/2PP/5YIPfRnOru16+f/f+vHu/i4mKaNm3qtLpzU/vV37E+/vhj4+bmZj+uFzRp36+ef/75Aru+s5LZd8NTp07Zt/XAwEAzbtw4J1T2f8qXL5/hB+L06dNNcHBwhrYFLXjLqfakpCRTpEgRM3r0aIc2Q4YMMXfdddcNqzO9cuXKmalTpzoMGz16tKlcubIxpvB8z83sd0RBXee5DekrVarkcPZhert27TKSzN69e/OxuqxlV/dPP/1kJJmYmJgc+zl69KiRZH799dd8rjCjrL5f5eYY/sEHH5iAgACTkpJiny7t+4zV9zfMzXfanI7ff/31l7nttttM165dHZYhvxW53ktVkX9SU1Pt92QqqFq0aKGdO3c6DHviiSdUpUoVDR06VK6urk6qLO/OnTungwcPqmvXrs4uJU+8vb3l7e2tM2fOaOnSpQXiiYO5ER4erqCgIK1YsUK1atWSJCUmJmrDhg165plnnFtcLly+fFmdOnXS/v37tWrVKpUsWdLZJeWJn5+fpH/vZbN582aNHj3aqfUYY9S/f3998803Wr16tcLDwx3G161bV25ublqxYoX9iU/79u3TkSNHnH4vzJxqL6iupe6YmBhJUpkyZSyuLns51R4WFqbg4OAMj5L//fffnf60x/Sy+6w3//6DaIH4LpDT531ISEiB3EdzqrtUqVJ66qmnHMZXr15dEydOVNu2bW9kqRnk5TvWhx9+qAceeEClS5e+0WXm6OrvV506dVKvXr0cxheU9Z2ZrL4blipVSpK0cuVKnTx50ulPNW/UqFGmx7vQ0FAnVZR7OdXu7u6u+vXrF7jlu3DhQoanZLu6utqfkFhYvudm9juioK7z3MrpN3RMTIxcXFwUEBBwA6vK3Icffqi6detmuCddZtK2LSu/E+T0/WrYsGE5HsPT9o2rn3ia9t6qJ0Dn5Tttdsfvv/76S82aNbM/ZT79Pp7fRcMJhg0bZtasWWNiY2PNjh07zLBhw4zNZjPLli1zdml5VlguNR08eLBZvXq1iY2NNWvXrjUtW7Y0pUqVKrD/WpzekiVLzOLFi80ff/xhli1bZmrWrGkaNGhwQ590k5OzZ8+abdu2mW3bthlJ9vtEpD35c8yYMcbf398sXLjQ7Nixw7Rr186Eh4cXiMuqsqs9OTnZPPDAA6ZcuXImJibGHD9+3P5KSkoqsHUbY8yXX35pVq1aZQ4ePGi+/fZbExoaajp06ODUmo35995Efn5+ZvXq1Q7r8+pT0p9++mlTvnx5s3LlSrN582YTGRmZ4fIUZ8hN7cePHzfbtm0z77//vpFkfvrpJ7Nt2zbzzz//FNi6Dxw4YF5//XWzefNmExsbaxYuXGgqVKhgmjRp4rSa0+RmnU+cONH4+vqaBQsWmP3795sRI0YYT09Pp14mm91n/cGDB82bb75pNm/ebA4fPmzWrl1r2rZta0qUKJHtJTPOlP7zvqDuo+nl9D1FBfjSx8xq379/v7HZbGbx4sXOKSqdvH6/KkjrO6faZ8+ebdatW2cOHDhgPvnkE1OiRAkzaNAgJ1f975PVixQpYv7zn/+Y/fv3m3nz5pmiRYuaTz/91N7mn3/+Mdu2/b/27j6mqvqPA/ibp3uv9/J0Q7hcQWBwCW5B9GRqNguwkRs0KCZDQ8yYUwOyWhHLglFp6LQl5BNNQAVtFTMjhgna1pAcT6YkY8wkRpE6FAgkQ/n8/vDn/XV9Qvx1vFTv18bGzjn3ez/f4zx8z3vn+z0t8tVXXwkA2b17t7S0tEhPT48NK7+12isqKsTJyUm2bt0qHR0dUlBQIA4ODvLtt9/arO7U1FTx8fGRyspKOXnypFRUVMjkyZOtptZP5HHuWPcRE+Wc32xcOzg4KNnZ2VJfXy+dnZ3S2Ngozz//vKjVamltbRURkUOHDskHH3wgR44ckRMnTsjOnTvF09NTFi5caLO6r+jv7xetViubNm265vPfffedFBQUSEtLi3R2dkptba08+uijEhQUpOgU2VsZX13t6mt4W1ubqNVqWbZsmRw/flxaW1vlueeeEzc3t2umXt/Juse6fnd3d4vJZJLo6Gjp7u62akcJDN5sZPHixeLv7y8qlUo8PT0lOjr6bxm6ifx9grekpCQxGo2iUqnEx8dHkpKSJtS6RWP55JNPJDAwUFQqlXh7e8uLL74ofX19ti7LypVpdVf/pKamisjlV62/9dZbYjAYRK1WS3R0tLS3t9u26P+6We1Xpmlc7+fgwYMTtm4RkQ8//FB8fX3FyclJ/Pz8ZOXKlTYPC0XkhuezuLjYcszw8LAsX75c9Hq9aLVaSUhIsPkNg8it1Z6TkzPmMROt7q6uLpk9e7bcddddolarxWQyyWuvvTYh1jK8lXMuIrJ69Wrx9fUVrVYrM2fOtOlNmsjN/9b//PPPMnfuXPHy8hInJyfx9fWV+fPn37GpMLfj6r/3E/X/6NX+acFbdna2TJ06VdEpMeMx3vHVRDrfY9WelZUlBoNBnJycJDg4WNatW3fH1lsay5dffilhYWGiVqslNDT0mgXBi4uLr3vdzMnJsU3BfzJW7SKX1+UymUyi0WgkIiJC9uzZY4NK/2dgYEBeeukl8fPzE41GI4GBgfLmm29ajakm8jj3Vu4jJsI5v9m4dnh4WBISEmTKlCmiUqnEaDTK008/bfVyhaamJpk+fbq4ubmJRqMRs9ksq1atUnx9t7HG4yKXX2A1adKk696/HT16VCIjIy1jsICAAFm6dKl0d3crWvetjq+u/szV1/Ara9K5ubmJXq+XqKgoqa+vt2ndY12/b3SNVOrZNLv/Fk5ERERERERERER/IQUnsRIREREREREREf17MXgjIiIiIiIiIiJSAIM3IiIiIiIiIiIiBTB4IyIiIiIiIiIiUgCDNyIiIiIiIiIiIgUweCMiIiIiIiIiIlIAgzciIiIiIiIiIiIFMHgjIiIiIiIiIiJSAIM3IiIiIiIiIiIiBTB4IyIiIrrDFi1aBDs7O9jZ2UGlUsFkMiEvLw8XL160dWmKKykpsfTdwcEBer0e06dPR15eHvr7+21dHhEREdFfisEbERERkQ089dRT6OnpQUdHB1599VXk5uZi7dq11z32jz/+UKwOJdu+EVdXV/T09KC7uxuHDh3CkiVLsH37dtx///345Zdf7ng9REREREph8EZERERkA2q1Gt7e3vD398eyZcswZ84c7N27F8DlJ+Li4+Px3nvvYcqUKQgJCQEAHDt2DFFRUZg0aRI8PDywZMkSDA4OWtq8ePEiMjMz4e7uDg8PD2RlZSE1NRXx8fGWY5544gmkp6djxYoVmDx5MmJiYgAA69evR3h4OHQ6HaZOnYrly5dbtV1SUgJ3d3dUVlYiJCQEWq0WiYmJOH/+PEpLSxEQEAC9Xo/MzExcunTppn23s7ODt7c3jEYjzGYzXnjhBRw6dAiDg4N4/fXXLcdVV1fjscces/QnNjYWJ06csOyPiopCenq6VdtnzpyBSqVCbW0tAGDjxo0IDg6GRqOBwWBAYmLieP6ZiIiIiP4vDN6IiIiIJoBJkyZZPX1WW1uL9vZ27N+/H5WVlRgaGkJMTAz0ej0aGhrw6aefoqamxip4ys/PR1lZGYqLi1FXV4eBgQHs2bPnmu8qLS2FSqVCXV0dNm/eDACwt7fHhg0b8MMPP6C0tBQHDhywCsEA4Pz589iwYQN2796N6upqfPPNN0hISEBVVRWqqqqwY8cObNmyBZ999tm4++/l5YUFCxZg7969luBuaGgIr7zyChobG1FbWwt7e3skJCRgdHQUAJCWloby8nJcuHDB0s7OnTvh4+ODqKgoNDY2IjMzE3l5eWhvb0d1dTVmz5497tqIiIiIbpejrQsgIiIi+jcTEdTW1mLfvn3IyMiwbNfpdPj444+hUqkAAEVFRfj999+xfft26HQ6AEBhYSHi4uKQn58Pg8GAgoICZGdnIyEhwbK/qqrqmu8MDg7GmjVrrLatWLHC8ntAQADeffddLF26FBs3brRsHxkZwaZNmxAUFAQASExMxI4dO3Dq1Ck4OzvjnnvuQWRkJA4ePIikpKRxn4vQ0FD89ttv6O3thZeXF5599lmr/du2bYOnpyeOHz+OsLAwPPPMM0hPT8cXX3yBefPmAbj8ZN6VNfS6urqg0+kQGxsLFxcX+Pv744EHHhh3XURERES3i0+8EREREdlAZWUlnJ2dodFoMHfuXCQlJSE3N9eyPzw83BK6AUBbWxsiIiIsoRsAzJo1C6Ojo2hvb0d/fz9OnTqFRx55xLLfwcEBDz300DXffb1tNTU1iI6Oho+PD1xcXJCSkoLe3l6cP3/ecoxWq7WEbgBgMBgQEBAAZ2dnq22nT58e/wnB5RASuDwVFQA6OjqQnJyMwMBAuLq6IiAgAADQ1dUFANBoNEhJScG2bdsAAM3NzWhtbcWiRYsAAE8++ST8/f0RGBiIlJQUlJWVWfWHiIiISGkM3oiIiIhsIDIyEkeOHEFHRweGh4dRWlpqFar9+fe/2tVtd3Z2IjY2Fvfddx8+//xzNDU14aOPPgJg/fIFJycnq8/Z2dldd9uVqaDj1dbWBldXV3h4eAAA4uLicPbsWRQVFeHw4cM4fPjwNTWlpaVh//796O7uRnFxMaKiouDv7w8AcHFxQXNzM3bt2gWj0Yi3334bERER6Ovru636iIiIiMaLwRsRERGRDeh0OphMJvj5+cHRcezVP8xmM77//nsMDQ1ZttXV1cHe3h4hISFwc3ODwWBAQ0ODZf+lS5fQ3Nw8ZttNTU0YHR3FunXrMGPGDNx99913/O2ip0+fRnl5OeLj42Fvb4/e3l60t7dj5cqViI6Ohtlsxrlz5675XHh4OB5++GEUFRWhvLwcixcvttrv6OiIOXPmYM2aNTh69Cg6Oztx4MCBO9UtIiIi+pfjGm9EREREfwMLFixATk4OUlNTkZubizNnziAjIwMpKSkwGAwAgIyMDKxevRomkwmhoaEoKCjAuXPnLFM3b8RkMmFkZAQFBQWIi4uzeumCEkQEv/76K0QEfX19qK+vx6pVq+Dm5ob3338fAKDX6+Hh4YGtW7fCaDSiq6sLb7zxxnXbS0tLQ3p6OnQ6nWV9O+DydN4ff/wRs2fPhl6vR1VVFUZHRy1viSUiIiJSGp94IyIiIvob0Gq12LdvH86ePYtp06YhMTER0dHRKCwstByTlZWF5ORkLFy4EDNnzoSzszNiYmKg0Whu2nZERATWr1+P/Px8hIWFoaysDKtXr1asLwMDAzAajfDx8cHMmTOxZcsWpKamoqWlBUajEcDlt6zu3r0bTU1NCAsLw8svv4y1a9det73k5GQ4OjoiOTnZqq/u7u6oqKhAVFQUzGYzNm/ejF27duHee+9VrG9EREREf2YnV1axJSIiIqJ/lNHRUZjNZsybNw/vvPOOrctRTGdnJ4KCgtDQ0IAHH3zQ1uUQERERWXCqKREREdE/xE8//YSvv/4ajz/+OC5cuIDCwkKcPHkS8+fPt3VpihgZGUFvby9WrlyJGTNmMHQjIiKiCYdTTYmIiIj+Iezt7VFSUoJp06Zh1qxZOHbsGGpqamA2m21dmiLq6upgNBrR0NCg6Jp0RERERLeLU02JiIiIiIiIiIgUwCfeiIiIiIiIiIiIFMDgjYiIiIiIiIiISAEM3oiIiIiIiIiIiBTA4I2IiIiIiIiIiEgBDN6IiIiIiIiIiIgUwOCNiIiIiIiIiIhIAQzeiIiIiIiIiIiIFMDgjYiIiIiIiIiISAH/ASHrQ3sKP1TqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Program_Days_y_sum  = train_df_c.groupby(['Program Days','Y']).size().unstack()\n",
    "Program_Days_y_sum.plot(kind='bar', stacked=True, figsize=(15, 6))\n",
    "plt.title('Distribution of Program Days and the completetion of the program')\n",
    "plt.xlabel('Program Days')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(title='Target Variable (Y)', labels=['Completed', 'Didnt complete'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Program Days\n",
       "5      2902\n",
       "12     1566\n",
       "54      531\n",
       "19      374\n",
       "26      311\n",
       "33      125\n",
       "4       113\n",
       "40       99\n",
       "173      80\n",
       "44       77\n",
       "59       60\n",
       "3        37\n",
       "9        35\n",
       "10       32\n",
       "80       28\n",
       "292      25\n",
       "47       22\n",
       "66       21\n",
       "25       19\n",
       "21       18\n",
       "248      18\n",
       "96       17\n",
       "20       15\n",
       "135      15\n",
       "61        8\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_c['Program Days'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** There might be high variance in the <font color='red'>Program Days</font> feature\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Are some college students tend to complete the program more than others?**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. **Group Data by College and Completion Status**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "College_y_sum  = train_df_c.groupby(['College','Y']).size().unstack()\n",
    "College_y_sum.plot(kind='bar', stacked=True, figsize=(20, 6))\n",
    "plt.title('Distribution of Colleges and the completetion of the program')\n",
    "plt.xlabel('College')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend(title='Target Variable (Y)', labels=['Completed', 'Didnt complete'])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In-depth Analysis (Answers for the questions above): \n",
    "**Objective**: To analyze the *completion rates* of different features to answer the questions above, taking into account the lack of data.\n",
    "1. Completion rates for each College\n",
    "2. Completion rates for each Genders\n",
    "3. completetion rates for Program Days\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Completion rates for each College\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completetion rates for each College\n",
      "العلوم الطبيعية والرياضيات والإحصاء    0.903226\n",
      "تكنولوجيا الاتصالات والمعلومات         0.872837\n",
      "الأعمال والإدارة والقانون              0.823529\n",
      "الفنون والعلوم الإنسانية               0.815789\n",
      "العلوم الاجتماعية والصحافة والإعلام    0.800000\n",
      "الهندسة والتصنيع والبناء               0.790698\n",
      "التعليم                                0.789474\n",
      "الصحة والرفاة                          0.750000\n",
      "البرامج والمؤهلات العامة               0.500000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "total_per_college = College_y_sum.sum(axis=1)\n",
    "print(f\"Completetion rates for each {(College_y_sum[0]/total_per_college).sort_values(ascending=False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insights**\n",
    "\n",
    "* STEM majors have more completion rates than others\n",
    "* Mangment majors complete the programs more than Engineering majors\n",
    "* The majors of Education and Health show low completion rates close to 78% and 75%, respectively. I assume this because they have a more stable jobs\n",
    "and don't need to complete the programs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>College</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>الأعمال والإدارة والقانون</th>\n",
       "      <td>126</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>البرامج والمؤهلات العامة</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>التعليم</th>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>الصحة والرفاة</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>العلوم الاجتماعية والصحافة والإعلام</th>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>العلوم الطبيعية والرياضيات والإحصاء</th>\n",
       "      <td>56</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>الفنون والعلوم الإنسانية</th>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>الهندسة والتصنيع والبناء</th>\n",
       "      <td>34</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>تكنولوجيا الاتصالات والمعلومات</th>\n",
       "      <td>2018</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Y                                       0    1\n",
       "College                                       \n",
       "الأعمال والإدارة والقانون             126   27\n",
       "البرامج والمؤهلات العامة                1    1\n",
       "التعليم                                15    4\n",
       "الصحة والرفاة                           3    1\n",
       "العلوم الاجتماعية والصحافة والإعلام    20    5\n",
       "العلوم الطبيعية والرياضيات والإحصاء    56    6\n",
       "الفنون والعلوم الإنسانية               31    7\n",
       "الهندسة والتصنيع والبناء               34    9\n",
       "تكنولوجيا الاتصالات والمعلومات       2018  294"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "College_y_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Completion rates for each Genders\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completetion rates for each Gender\n",
      "ذكر     0.847434\n",
      "أنثى    0.836517\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "total_per_gender = gender_y_sum.sum(axis=1)\n",
    "print(f\"Completetion rates for each {(gender_y_sum[0]/total_per_gender).sort_values(ascending=False)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>Y</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>أنثى</th>\n",
       "      <td>3065</td>\n",
       "      <td>599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ذكر</th>\n",
       "      <td>2444</td>\n",
       "      <td>440</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Y          0    1\n",
       "Gender           \n",
       "أنثى    3065  599\n",
       "ذكر     2444  440"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_y_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insights**\n",
    "* The rates of completion for males 84% and females 83% are similar, with that I can conclude that there's no relation between the Gender and the completion rate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Completion rates for Program Days\n",
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completetion rates for each Program Days\n",
      "25     1.000000\n",
      "20     1.000000\n",
      "80     1.000000\n",
      "61     1.000000\n",
      "9      1.000000\n",
      "248    1.000000\n",
      "44     0.974026\n",
      "26     0.961415\n",
      "54     0.956685\n",
      "47     0.954545\n",
      "21     0.944444\n",
      "33     0.936000\n",
      "59     0.900000\n",
      "12     0.867178\n",
      "19     0.823529\n",
      "10     0.812500\n",
      "3      0.810811\n",
      "66     0.809524\n",
      "40     0.808081\n",
      "292    0.800000\n",
      "5      0.798070\n",
      "4      0.796460\n",
      "135    0.733333\n",
      "96     0.529412\n",
      "173    0.375000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "total_per_days = Program_Days_y_sum.sum(axis=1)\n",
    "print(f\"Completetion rates for each {(Program_Days_y_sum[0]/total_per_days).sort_values(ascending=False)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "* I notied that Program Days might be high in variance so let's do some math\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The formula for Variance is:\n",
    "\n",
    "$$ \\sigma^2 = \\frac{\\sum\\limits_{i=1}^{N} (x_i - \\mu)^2}{N-1} $$\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The variance is: 1031.1844302309537 and the mean is 19.691661576053757\n"
     ]
    }
   ],
   "source": [
    "variance = train_df_c['Program Days'].var()\n",
    "mean = train_df_c['Program Days'].mean()\n",
    "print(f\"The variance is: {variance} and the mean is {mean}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Insights**\n",
    "* *Variance* of 1031 is high which means the data is very spread out since the mean is only 19.6\n",
    "* This high variance could affect the model with high noise and over fit it, feature scaling might be important for this feature"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}