Task 2 Prodigy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28e2b112",
   "metadata": {},
   "source": [
    "# Divyansh Singh"
   ]
  },
  {
   "cell_type": "raw",
   "id": "29913320",
   "metadata": {},
   "source": [
    "Dataset- https://www.kaggle.com/c/titanic/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c584aafe",
   "metadata": {},
   "source": [
    "# <font color='green' size='6'>Exploring Titanic Dataset: Data Cleaning, Feature Engineering, and In-Depth Analysis</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d1a98f2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "def load_data(folder_path):\n",
    "\n",
    "    train_data = pd.read_csv(folder_path + 'train.csv')\n",
    "\n",
    "    test_data = pd.read_csv(folder_path + 'test.csv')\n",
    "\n",
    "    gender_submission_data = pd.read_csv(folder_path + 'gender_submission.csv')\n",
    "    \n",
    "    return train_data, test_data, gender_submission_data\n",
    "\n",
    "def display_data(data, name):\n",
    "    print(f\"{name} Data:\")\n",
    "    print(data.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58ffc586",
   "metadata": {},
   "outputs": [],
   "source": [
    "folder_path = 'data/'\n",
    "\n",
    "train_data, test_data, gender_submission_data = load_data(folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb225887",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Data:\n",
      "   PassengerId  Survived  Pclass  \\\n",
      "0            1         0       3   \n",
      "1            2         1       1   \n",
      "2            3         1       3   \n",
      "3            4         1       1   \n",
      "4            5         0       3   \n",
      "\n",
      "                                                Name     Sex   Age  SibSp  \\\n",
      "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
      "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
      "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
      "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
      "4                           Allen, Mr. William Henry    male  35.0      0   \n",
      "\n",
      "   Parch            Ticket     Fare Cabin Embarked  \n",
      "0      0         A/5 21171   7.2500   NaN        S  \n",
      "1      0          PC 17599  71.2833   C85        C  \n",
      "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
      "3      0            113803  53.1000  C123        S  \n",
      "4      0            373450   8.0500   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "display_data(train_data, \"Train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eeb317a8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Data:\n",
      "   PassengerId  Pclass                                          Name     Sex  \\\n",
      "0          892       3                              Kelly, Mr. James    male   \n",
      "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
      "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
      "3          895       3                              Wirz, Mr. Albert    male   \n",
      "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
      "\n",
      "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
      "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
      "1  47.0      1      0   363272   7.0000   NaN        S  \n",
      "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
      "3  27.0      0      0   315154   8.6625   NaN        S  \n",
      "4  22.0      1      1  3101298  12.2875   NaN        S  \n"
     ]
    }
   ],
   "source": [
    "display_data(test_data, \"Test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e691fe14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender Submission Data:\n",
      "   PassengerId  Survived\n",
      "0          892         0\n",
      "1          893         1\n",
      "2          894         0\n",
      "3          895         0\n",
      "4          896         1\n"
     ]
    }
   ],
   "source": [
    "display_data(gender_submission_data, \"Gender Submission\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d19d8261",
   "metadata": {},
   "source": [
    "## Exploring Column Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d89de8b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "train_columns = train_data.columns.tolist()\n",
    "print(train_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a223314",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Titanic Dataset Column Summary\n",
    "**PassengerId**: Unique identifier for each passenger.  \n",
    "**Survived**: Binary value indicating whether the passenger survived (1) or not (0).  \n",
    "**Pclass**: Ticket class indicating the socio-economic status of the passenger (1st, 2nd, or 3rd class).  \n",
    "**Name**: Full name of the passenger.  \n",
    "**Sex**: Gender of the passenger (male or female).  \n",
    "**Age**: Age of the passenger in years.  \n",
    "**SibSp**: Number of siblings/spouses aboard the Titanic.  \n",
    "**Parch**: Number of parents/children aboard the Titanic.  \n",
    "**Ticket**: Ticket number.  \n",
    "**Fare**: Passenger fare.  \n",
    "**Cabin**: Cabin number where the passenger stayed.  \n",
    "**Embarked**: Port of embarkation (C = Cherbourg, Q = Queenstown, S = Southampton).  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2308ec72",
   "metadata": {},
   "source": [
    "## Exploring Column Value Counts\n",
    "In order to better understand the distribution of values within each column of the 'train_data' dataset, the following code snippet calculates and displays the value counts for each column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9fca525",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value Counts for Column 'PassengerId':\n",
      "891    1\n",
      "293    1\n",
      "304    1\n",
      "303    1\n",
      "302    1\n",
      "      ..\n",
      "591    1\n",
      "590    1\n",
      "589    1\n",
      "588    1\n",
      "1      1\n",
      "Name: PassengerId, Length: 891, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Survived':\n",
      "0    549\n",
      "1    342\n",
      "Name: Survived, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Pclass':\n",
      "3    491\n",
      "1    216\n",
      "2    184\n",
      "Name: Pclass, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Name':\n",
      "Kimball, Mr. Edwin Nelson Jr         1\n",
      "Byles, Rev. Thomas Roussel Davids    1\n",
      "Fahlstrom, Mr. Arne Jonas            1\n",
      "Baxter, Mr. Quigg Edmond             1\n",
      "Zimmerman, Mr. Leo                   1\n",
      "                                    ..\n",
      "Abbing, Mr. Anthony                  1\n",
      "Duane, Mr. Frank                     1\n",
      "Pavlovic, Mr. Stefo                  1\n",
      "Smith, Mr. Thomas                    1\n",
      "Asplund, Miss. Lillian Gertrud       1\n",
      "Name: Name, Length: 891, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Sex':\n",
      "male      577\n",
      "female    314\n",
      "Name: Sex, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Age':\n",
      "24.00    30\n",
      "22.00    27\n",
      "18.00    26\n",
      "19.00    25\n",
      "30.00    25\n",
      "         ..\n",
      "55.50     1\n",
      "70.50     1\n",
      "66.00     1\n",
      "23.50     1\n",
      "0.42      1\n",
      "Name: Age, Length: 88, dtype: int64\n",
      "\n",
      "Value Counts for Column 'SibSp':\n",
      "0    608\n",
      "1    209\n",
      "2     28\n",
      "4     18\n",
      "3     16\n",
      "8      7\n",
      "5      5\n",
      "Name: SibSp, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Parch':\n",
      "0    678\n",
      "1    118\n",
      "2     80\n",
      "5      5\n",
      "3      5\n",
      "4      4\n",
      "6      1\n",
      "Name: Parch, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Ticket':\n",
      "1601                7\n",
      "347082              7\n",
      "CA. 2343            7\n",
      "347088              6\n",
      "CA 2144             6\n",
      "                   ..\n",
      "2693                1\n",
      "2624                1\n",
      "SOTON/OQ 3101316    1\n",
      "237671              1\n",
      "C.A. 6212           1\n",
      "Name: Ticket, Length: 681, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Fare':\n",
      "8.0500     43\n",
      "13.0000    42\n",
      "7.8958     38\n",
      "7.7500     34\n",
      "26.0000    31\n",
      "           ..\n",
      "8.4583      1\n",
      "9.8375      1\n",
      "8.3625      1\n",
      "14.1083     1\n",
      "17.4000     1\n",
      "Name: Fare, Length: 248, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Cabin':\n",
      "G6             4\n",
      "B96 B98        4\n",
      "C23 C25 C27    4\n",
      "E101           3\n",
      "F2             3\n",
      "              ..\n",
      "D30            1\n",
      "D37            1\n",
      "A20            1\n",
      "C86            1\n",
      "C103           1\n",
      "Name: Cabin, Length: 147, dtype: int64\n",
      "\n",
      "Value Counts for Column 'Embarked':\n",
      "S    644\n",
      "C    168\n",
      "Q     77\n",
      "Name: Embarked, dtype: int64\n",
      "\n"
     ]
    }
   ],
   "source": [
    "column_value_counts = {}\n",
    "\n",
    "for column in train_data.columns:\n",
    "    column_value_counts[column] = train_data[column].value_counts()\n",
    "\n",
    "for column, values in column_value_counts.items():\n",
    "    print(f\"Value Counts for Column '{column}':\\n{values}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddc5d520",
   "metadata": {},
   "source": [
    "Here are the value counts for each column in the train_data DataFrame:\n",
    "\n",
    "**PassengerId**: Each PassengerId appears only once, indicating a unique identifier for each passenger.  \n",
    "**Survived**: 549 passengers did not survive (0), while 342 passengers survived (1).  \n",
    "**Pclass**: 491 passengers were in Pclass 3, 216 in Pclass 1, and 184 in Pclass 2.  \n",
    "**Name**: Each passenger's name is unique, with no duplicate names.  \n",
    "**Sex**: 577 passengers are male and 314 are female.  \n",
    "**Age**: The age distribution varies, with some ages repeated multiple times.  \n",
    "**SibSp**: 608 passengers had no siblings/spouses onboard, 209 had 1, and others had more.  \n",
    "**Parch**: 678 passengers had no parents/children onboard, 118 had 1, and others had more.  \n",
    "**Ticket**: There are 681 unique ticket values, some of which repeat.  \n",
    "**Fare**: Fare values vary, with some repeating.  \n",
    "**Cabin**: There are 147 unique cabin values, with some repeating.  \n",
    "**Embarked**: 644 passengers embarked from S (Southampton), 168 from C (Cherbourg), and 77 from Q (Queenstown).  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f4a35b",
   "metadata": {},
   "source": [
    "## Dataset Information and Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e919f95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    },
    {
     "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display general information about the dataset\n",
    "train_data.info()\n",
    "\n",
    "# Display summary statistics of the numerical columns\n",
    "train_data.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a924bf7",
   "metadata": {},
   "source": [
    "Based on the provided information and summary statistics:\n",
    "\n",
    "The dataset contains 891 entries.   \n",
    "The columns 'Age', 'Cabin', and 'Embarked' have missing values.   \n",
    "The average age of passengers is around 29.7 years, with a minimum age of 0.42 and a maximum age of 80.  \n",
    "The survival rate ('Survived') indicates that about 38% of passengers survived.  \n",
    "The majority of passengers were in Class 3 ('Pclass'), followed by Class 1 and Class 2.  \n",
    "The average fare paid by passengers is approximately 32.20 units, with fares ranging from 0 to 512.33 units.  \n",
    "Most passengers did not travel with parents or children ('Parch') and did not have siblings or spouses ('SibSp') onboard.  \n",
    "The 'Sex' column indicates that there were more male passengers (577) compared to female passengers (314).  \n",
    "The 'Cabin' column has a high number of missing values, with only 204 out of 891 entries having cabin information.  \n",
    "Most passengers embarked from Southampton ('S'), followed by Cherbourg ('C') and Queenstown ('Q').  \n",
    "\n",
    "These findings provide an initial understanding of the dataset, its variables, and the general characteristics of the passengers on the Titanic. Further exploratory data analysis can help uncover more insights and patterns in the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "295d57d7",
   "metadata": {},
   "source": [
    "## Visualizing Missing Values Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18878988",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def visualize_missing_data(data):\n",
    "    \"\"\"\n",
    "    Visualize the percentage of missing values in each column of the dataset.\n",
    "    \n",
    "    Parameters:\n",
    "        data (pd.DataFrame): The dataset to be visualized.\n",
    "    \"\"\"\n",
    "    # Calculate the percentage of missing values for each column\n",
    "    missing_percentage = (data.isnull().sum() / len(data)) * 100\n",
    "    \n",
    "    # Create a bar chart to visualize the percentage of missing values\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(x=missing_percentage.index, y=missing_percentage.values, color='skyblue', edgecolor='black')\n",
    "    plt.xlabel('Columns')\n",
    "    plt.ylabel('Percentage of Missing Values')\n",
    "    plt.title('Percentage of Missing Values in Each Column')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Visualize missing data using the function\n",
    "visualize_missing_data(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0349e3db",
   "metadata": {},
   "source": [
    "## Visualizing Duplicate Rows Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03c71529",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def visualize_duplicates(data):\n",
    "    \"\"\"\n",
    "    Visualize the distribution of duplicate rows in the dataset.\n",
    "    \n",
    "    Parameters:\n",
    "        data (pd.DataFrame): The dataset to be visualized.\n",
    "    \"\"\"\n",
    "    # Check for duplicate rows\n",
    "    duplicates = data.duplicated().sum()\n",
    "    \n",
    "    # Create a bar chart to visualize the distribution of duplicates\n",
    "    plt.figure(figsize=(6, 4))\n",
    "    sns.barplot(x=['Non-Duplicate', 'Duplicate'], y=[len(data) - duplicates, duplicates], color='skyblue', edgecolor='black')\n",
    "    plt.xlabel('Duplicate Status')\n",
    "    plt.ylabel('Number of Rows')\n",
    "    plt.title('Distribution of Duplicate Rows in the Dataset')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Visualize duplicate rows using the function\n",
    "visualize_duplicates(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74d8cf58",
   "metadata": {},
   "source": [
    "## Count of Data Types in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f2801e29",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "object     5\n",
      "int64      5\n",
      "float64    2\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv('data/train.csv')\n",
    "\n",
    "# Group columns by data type and count\n",
    "data_type_counts = data.dtypes.value_counts()\n",
    "\n",
    "# Print the count of each data type\n",
    "print(data_type_counts)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98f66290",
   "metadata": {},
   "source": [
    "## Count of Unique Values in Different Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5238be87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integer Columns:\n",
      "PassengerId    891\n",
      "Survived         2\n",
      "Pclass           3\n",
      "SibSp            7\n",
      "Parch            7\n",
      "dtype: int64\n",
      "\n",
      "Float Columns:\n",
      "Age      88\n",
      "Fare    248\n",
      "dtype: int64\n",
      "\n",
      "Object Columns:\n",
      "Name        891\n",
      "Sex           2\n",
      "Ticket      681\n",
      "Cabin       147\n",
      "Embarked      3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Group columns by data type\n",
    "data_types = train_data.dtypes\n",
    "\n",
    "# Separate columns by data type\n",
    "int_columns = data_types[data_types == 'int64'].index\n",
    "float_columns = data_types[data_types == 'float64'].index\n",
    "object_columns = data_types[data_types == 'object'].index\n",
    "\n",
    "# Print columns and their counts for each data type\n",
    "print(\"Integer Columns:\")\n",
    "print(train_data[int_columns].nunique())\n",
    "\n",
    "print(\"\\nFloat Columns:\")\n",
    "print(train_data[float_columns].nunique())\n",
    "\n",
    "print(\"\\nObject Columns:\")\n",
    "print(train_data[object_columns].nunique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7449d844",
   "metadata": {},
   "source": [
    "# EXPLORATORY DATA ANALYSIS(EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1454fb96",
   "metadata": {},
   "source": [
    "## UNIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "539bca64",
   "metadata": {},
   "source": [
    "performing univariate analysis on the Titanic dataset by visualizing the distribution of its features. We'll separate the categorical and continuous variables for analysis.\n",
    "\n",
    "Categorical Variables:  \n",
    "\n",
    "1. Survived  \n",
    "2. Pclass  \n",
    "3. Sex  \n",
    "4. SibSp  \n",
    "5. Parch  \n",
    "6. Embarked \n",
    "\n",
    "Continuous Variables:  \n",
    "\n",
    "1. Age  \n",
    "2. Fare  \n",
    "\n",
    "Let's create visualizations for each of these variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95ea159",
   "metadata": {},
   "source": [
    "## Visualizing Feature Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a12ba39b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "class TitanicDataVisualizer:\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "        self.categorical_vars = ['Survived', 'Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']\n",
    "        self.continuous_vars = ['Age', 'Fare']\n",
    "        sns.set_palette(\"pastel\")\n",
    "\n",
    "    def plot_categorical_distribution(self):\n",
    "        fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(18, 12))\n",
    "        for ax, cat_var in zip(axes.flatten(), self.categorical_vars):\n",
    "            sns.countplot(data=self.data, x=cat_var, ax=ax)\n",
    "            ax.set_title(f'Distribution of {cat_var}')\n",
    "            ax.set_ylabel('Count')\n",
    "            ax.set_xlabel(cat_var)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    def plot_continuous_distribution(self):\n",
    "        fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
    "        for ax, cont_var in zip(axes.flatten(), self.continuous_vars):\n",
    "            sns.histplot(data=self.data, x=cont_var, bins=20, kde=True, ax=ax)\n",
    "            ax.set_title(f'Distribution of {cont_var}')\n",
    "            ax.set_ylabel('Count')\n",
    "            ax.set_xlabel(cont_var)\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "# Create an instance of the TitanicDataVisualizer class\n",
    "data_visualizer = TitanicDataVisualizer(train_data)\n",
    "\n",
    "# Plot categorical distribution\n",
    "data_visualizer.plot_categorical_distribution()\n",
    "\n",
    "# Plot continuous distribution\n",
    "data_visualizer.plot_continuous_distribution()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e47bb182",
   "metadata": {},
   "source": [
    "## BIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29413a92",
   "metadata": {},
   "source": [
    "## Visualize the proportions of survivors for each feature's values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91c1e6f1",
   "metadata": {},
   "source": [
    "To visualize the proportions of survivors for each feature's values, we can create stacked bar plots for categorical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f27f7ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "class SurvivalProportionVisualizer:\n",
    "    def __init__(self, data, categorical_vars):\n",
    "        self.data = data\n",
    "        self.categorical_vars = categorical_vars\n",
    "        sns.set_palette(\"pastel\")\n",
    "\n",
    "    def plot_survival_proportions(self):\n",
    "        num_plots = len(self.categorical_vars)\n",
    "        ncols = 3\n",
    "        nrows = (num_plots + ncols - 1) // ncols\n",
    "\n",
    "        fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(18, 12))\n",
    "        for ax, cat_var in zip(axes.flatten(), self.categorical_vars):\n",
    "            sns.countplot(data=self.data, x=cat_var, hue='Survived', ax=ax)\n",
    "            ax.set_title(f'Survival Proportion by {cat_var}')\n",
    "            ax.set_ylabel('Count')\n",
    "            ax.set_xlabel(cat_var)\n",
    "            ax.legend(title='Survived', labels=['No', 'Yes'])\n",
    "\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "# List of categorical variables\n",
    "categorical_vars = ['Pclass', 'Sex', 'SibSp', 'Parch', 'Embarked']\n",
    "\n",
    "# Create an instance of the SurvivalProportionVisualizer class\n",
    "survival_visualizer = SurvivalProportionVisualizer(train_data, categorical_vars)\n",
    "\n",
    "# Plot survival proportions\n",
    "survival_visualizer.plot_survival_proportions()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8f502b",
   "metadata": {},
   "source": [
    "## Survival Proportion by Categorical Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "84f4976d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by Pclass:\n",
      "+----------+----------------+------------+---------+\n",
      "|   Pclass |   Not Survived |   Survived |   Total |\n",
      "+==========+================+============+=========+\n",
      "|        1 |       0.37037  |   0.62963  |       1 |\n",
      "+----------+----------------+------------+---------+\n",
      "|        2 |       0.527174 |   0.472826 |       1 |\n",
      "+----------+----------------+------------+---------+\n",
      "|        3 |       0.757637 |   0.242363 |       1 |\n",
      "+----------+----------------+------------+---------+\n",
      "Survival Proportions by Sex:\n",
      "+--------+----------------+------------+---------+\n",
      "| Sex    |   Not Survived |   Survived |   Total |\n",
      "+========+================+============+=========+\n",
      "| female |       0.257962 |   0.742038 |       1 |\n",
      "+--------+----------------+------------+---------+\n",
      "| male   |       0.811092 |   0.188908 |       1 |\n",
      "+--------+----------------+------------+---------+\n",
      "Survival Proportions by SibSp:\n",
      "+---------+----------------+------------+---------+\n",
      "|   SibSp |   Not Survived |   Survived |   Total |\n",
      "+=========+================+============+=========+\n",
      "|       0 |       0.654605 |   0.345395 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       1 |       0.464115 |   0.535885 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       2 |       0.535714 |   0.464286 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       3 |       0.75     |   0.25     |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       4 |       0.833333 |   0.166667 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       5 |       1        | nan        |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       8 |       1        | nan        |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "Survival Proportions by Parch:\n",
      "+---------+----------------+------------+---------+\n",
      "|   Parch |   Not Survived |   Survived |   Total |\n",
      "+=========+================+============+=========+\n",
      "|       0 |       0.656342 |   0.343658 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       1 |       0.449153 |   0.550847 |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       2 |       0.5      |   0.5      |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       3 |       0.4      |   0.6      |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       4 |       1        | nan        |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       5 |       0.8      |   0.2      |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "|       6 |       1        | nan        |       1 |\n",
      "+---------+----------------+------------+---------+\n",
      "Survival Proportions by Embarked:\n",
      "+------------+----------------+------------+---------+\n",
      "| Embarked   |   Not Survived |   Survived |   Total |\n",
      "+============+================+============+=========+\n",
      "| C          |       0.446429 |   0.553571 |       1 |\n",
      "+------------+----------------+------------+---------+\n",
      "| Q          |       0.61039  |   0.38961  |       1 |\n",
      "+------------+----------------+------------+---------+\n",
      "| S          |       0.663043 |   0.336957 |       1 |\n",
      "+------------+----------------+------------+---------+\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from tabulate import tabulate\n",
    "\n",
    "def calculate_survival_proportions(data, group_by_column):\n",
    "    survival_proportions = data.groupby(group_by_column)['Survived'].value_counts(normalize=True).unstack()\n",
    "    survival_proportions.rename(columns={0: 'Not Survived', 1: 'Survived'}, inplace=True)\n",
    "    survival_proportions['Total'] = survival_proportions.sum(axis=1)\n",
    "    return survival_proportions\n",
    "\n",
    "def display_survival_proportions_table(survival_proportions, group_by_name):\n",
    "    print(f\"Survival Proportions by {group_by_name}:\")\n",
    "    print(tabulate(survival_proportions, headers='keys', tablefmt='grid'))\n",
    "\n",
    "# Grouping by Pclass and calculating survival proportions\n",
    "pclass_survival = calculate_survival_proportions(train_data, 'Pclass')\n",
    "display_survival_proportions_table(pclass_survival, 'Pclass')\n",
    "\n",
    "# Grouping by Sex and calculating survival proportions\n",
    "sex_survival = calculate_survival_proportions(train_data, 'Sex')\n",
    "display_survival_proportions_table(sex_survival, 'Sex')\n",
    "\n",
    "# Grouping by SibSp and calculating survival proportions\n",
    "sibsp_survival = calculate_survival_proportions(train_data, 'SibSp')\n",
    "display_survival_proportions_table(sibsp_survival, 'SibSp')\n",
    "\n",
    "# Grouping by Parch and calculating survival proportions\n",
    "parch_survival = calculate_survival_proportions(train_data, 'Parch')\n",
    "display_survival_proportions_table(parch_survival, 'Parch')\n",
    "\n",
    "# Grouping by Embarked and calculating survival proportions\n",
    "embarked_survival = calculate_survival_proportions(train_data, 'Embarked')\n",
    "display_survival_proportions_table(embarked_survival, 'Embarked')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31961c0d",
   "metadata": {},
   "source": [
    "Analyzing survivors based on age can provide insights into how different age groups were affected during the Titanic disaster. Let's visualize the survival proportions for different age groups. We'll first need to categorize the passengers into age groups and then calculate survival proportions within each group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34f239d",
   "metadata": {},
   "source": [
    "## Survival Proportions by Age Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "addeec13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by Age Group:\n",
      "Survived  Not Survived  Survived\n",
      "AgeGroup                        \n",
      "0-18          0.460177  0.539823\n",
      "19-30         0.649446  0.350554\n",
      "31-50         0.582031  0.417969\n",
      "51+           0.635135  0.364865\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def analyze_age_groups(data):\n",
    "    # Categorize passengers into age groups\n",
    "    bins = [0, 18, 30, 50, 100]\n",
    "    labels = ['0-18', '19-30', '31-50', '51+']\n",
    "    data['AgeGroup'] = pd.cut(data['Age'], bins=bins, labels=labels, right=False)\n",
    "\n",
    "    # Calculate survival proportions for each age group\n",
    "    agegroup_survival = data.groupby('AgeGroup')['Survived'].value_counts(normalize=True).unstack()\n",
    "    agegroup_survival.rename(columns={0: 'Not Survived', 1: 'Survived'}, inplace=True)\n",
    "\n",
    "    # Plot the survival proportions for different age groups\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(data=agegroup_survival, x=agegroup_survival.index, y='Survived')\n",
    "    plt.xlabel('Age Group')\n",
    "    plt.ylabel('Survival Proportion')\n",
    "    plt.title('Survival Proportions by Age Group')\n",
    "    plt.xticks(rotation=45)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Display survival proportions in numbers format\n",
    "    print(\"Survival Proportions by Age Group:\")\n",
    "    print(agegroup_survival)\n",
    "\n",
    "# Call the function with train_data\n",
    "analyze_age_groups(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d154f7",
   "metadata": {},
   "source": [
    "Analyzing survival based on gender is a crucial aspect of understanding the Titanic disaster, as the \"women and children first\" policy was followed during the evacuation. To analyze survival by gender, we'll calculate the survival proportions for male and female passengers and visualize them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b58add",
   "metadata": {},
   "source": [
    "## Survival Proportions by Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a432dc25",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by Gender:\n",
      "Survived  Not Survived  Survived\n",
      "Sex                             \n",
      "female        0.257962  0.742038\n",
      "male          0.811092  0.188908\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def analyze_gender_survival(data):\n",
    "    # Calculate survival proportions by gender\n",
    "    gender_survival = data.groupby('Sex')['Survived'].value_counts(normalize=True).unstack()\n",
    "    gender_survival.rename(columns={0: 'Not Survived', 1: 'Survived'}, inplace=True)\n",
    "\n",
    "    # Plot the survival proportions by gender\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.barplot(data=gender_survival, x=gender_survival.index, y='Survived')\n",
    "    plt.xlabel('Gender')\n",
    "    plt.ylabel('Survival Proportion')\n",
    "    plt.title('Survival Proportions by Gender')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Display survival proportions by gender in numbers format\n",
    "    print(\"Survival Proportions by Gender:\")\n",
    "    print(gender_survival)\n",
    "\n",
    "# Call the function with train_data\n",
    "analyze_gender_survival(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00ae5094",
   "metadata": {},
   "source": [
    "## Survival Proportions by Pclass, SibSp, and Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee1741a5",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by Pclass:\n",
      "Survived  Not Survived  Survived\n",
      "Pclass                          \n",
      "1             0.370370  0.629630\n",
      "2             0.527174  0.472826\n",
      "3             0.757637  0.242363\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by SibSp:\n",
      "Survived  Not Survived  Survived\n",
      "SibSp                           \n",
      "0             0.654605  0.345395\n",
      "1             0.464115  0.535885\n",
      "2             0.535714  0.464286\n",
      "3             0.750000  0.250000\n",
      "4             0.833333  0.166667\n",
      "5             1.000000       NaN\n",
      "8             1.000000       NaN\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Survival Proportions by Parch:\n",
      "Survived  Not Survived  Survived\n",
      "Parch                           \n",
      "0             0.656342  0.343658\n",
      "1             0.449153  0.550847\n",
      "2             0.500000  0.500000\n",
      "3             0.400000  0.600000\n",
      "4             1.000000       NaN\n",
      "5             0.800000  0.200000\n",
      "6             1.000000       NaN\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def analyze_group_survival(data, group_column, group_name):\n",
    "    # Calculate survival proportions by the specified group column\n",
    "    group_survival = data.groupby(group_column)['Survived'].value_counts(normalize=True).unstack()\n",
    "    group_survival.rename(columns={0: 'Not Survived', 1: 'Survived'}, inplace=True)\n",
    "\n",
    "    # Create subplots to display survival proportions\n",
    "    fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    sns.barplot(data=group_survival, x=group_survival.index, y='Survived', ax=ax)\n",
    "    ax.set_xlabel(group_column)\n",
    "    ax.set_ylabel('Survival Proportion')\n",
    "    ax.set_title(f'Survival Proportions by {group_name}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Display survival proportions in numbers format\n",
    "    print(f\"Survival Proportions by {group_name}:\")\n",
    "    print(group_survival)\n",
    "\n",
    "# Call the function for Pclass, SibSp, and Parch\n",
    "analyze_group_survival(train_data, 'Pclass', 'Pclass')\n",
    "analyze_group_survival(train_data, 'SibSp', 'SibSp')\n",
    "analyze_group_survival(train_data, 'Parch', 'Parch')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce4892ac",
   "metadata": {},
   "source": [
    "## MULTIVARIATE ANALYSIS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7b62a05",
   "metadata": {},
   "source": [
    "## Violin Plot: Survival by Age and Class\n",
    "1. Survival by Age and Class:\n",
    "We can use a violin plot to visualize the distribution of ages and classes for both survivors and non-survivors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "290b438f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_violin_survival(data, x_column, y_column, hue_column):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.violinplot(x=x_column, y=y_column, hue=hue_column, data=data, split=True)\n",
    "    plt.title(f'Survival by {y_column} and {x_column}')\n",
    "    plt.xlabel(x_column)\n",
    "    plt.ylabel(y_column)\n",
    "    plt.show()\n",
    "\n",
    "# Call the function to plot survival by Age and Class\n",
    "plot_violin_survival(train_data, 'Pclass', 'Age', 'Survived')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "142d2c89",
   "metadata": {},
   "source": [
    "## Bar Plot: Survival by Gender and Class\n",
    "Let's visualize the survival rates based on both gender and class using a bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b15471bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_survival_by_gender_class(data):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    sns.barplot(x='Sex', y='Survived', hue='Pclass', data=data)\n",
    "    plt.title('Survival by Gender and Class')\n",
    "    plt.xlabel('Gender')\n",
    "    plt.ylabel('Survival Rate')\n",
    "    plt.show()\n",
    "\n",
    "# Call the function to plot survival by Gender and Class\n",
    "plot_survival_by_gender_class(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "675d0edd",
   "metadata": {},
   "source": [
    "## Categorical Plot: Survival Rate by Embarked Port, Gender, and Class\n",
    "We can create a multi-level categorical plot to analyze the survival rate based on the embarked port, class, and gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "eb49cbcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1058.88x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_survival_by_embarked_port(data):\n",
    "    g = sns.catplot(x='Embarked', y='Survived', hue='Pclass', col='Sex', data=data, kind='bar')\n",
    "    g.set_titles(\"Survival Rate by Embarked Port for {col_name}\")\n",
    "    plt.show()\n",
    "\n",
    "# Call the function to plot survival by Embarked Port for each gender and class\n",
    "plot_survival_by_embarked_port(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad163f",
   "metadata": {},
   "source": [
    "## Value Counts of Titles\" and \"Distribution of Titles\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d526340b",
   "metadata": {},
   "source": [
    "- **Mr:** Used to address adult men, regardless of marital status.\n",
    "- **Mrs:** Used to address married women.\n",
    "- **Miss:** Used to address unmarried women.\n",
    "- **Rev:** Used for clergy members, especially in Christian denominations.\n",
    "- **Ms:** A more neutral title that can be used for women regardless of marital status.\n",
    "- **Dr.:** Used for individuals who hold a doctorate degree, such as medical doctors or PhD holders.\n",
    "- **Lady:** A title of nobility used to address women of high rank or aristocratic status.\n",
    "- **Master:** A title used for young boys, typically under the age of 18.\n",
    "- **Don:** An honorific title used in Spanish and Italian cultures, often used to show respect for a male individual, particularly one with authority or expertise.\n",
    "- **Mme (Madame):** A French title used to address a married woman. Similar to \"Mrs.\" in English.\n",
    "- **Ms.:** A neutral title that can be used for women regardless of marital status. It does not indicate whether a woman is married or not.\n",
    "- **Major:** A military rank or title, usually above Captain and below Lieutenant Colonel.\n",
    "- **Sir:** A polite and respectful form of address for a man. It can be used as a title of honor in various contexts.\n",
    "- **Mlle (Mademoiselle):** A French title used to address an unmarried woman. Similar to \"Miss\" in English.\n",
    "- **Col (Colonel):** A military rank, typically above Lieutenant Colonel and below Brigadier General.\n",
    "- **Capt (Captain):** A military rank, typically above Lieutenant and below Major.\n",
    "- **The Countess:** A title of nobility for a woman who holds the rank of Count or is married to a Count.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a74cc29f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "class TitlesAnalyzer:\n",
    "    def __init__(self, data_path):\n",
    "        self.data = pd.read_csv(data_path)\n",
    "        self.extract_titles()\n",
    "        \n",
    "    def extract_titles(self):\n",
    "        titles = [name.split(',')[1].split('.')[0].strip() for name in self.data['Name']]\n",
    "        self.data['title'] = pd.Series(titles)\n",
    "    \n",
    "    def plot_value_counts_table(self, value_counts):\n",
    "        fig, ax1 = plt.subplots(1, 1, figsize=(6, 5))\n",
    "        cell_text = [[title, count] for title, count in value_counts.items()]\n",
    "        ax1.table(cellText=cell_text, colLabels=['Title', 'Count'], cellLoc='center', loc='center')\n",
    "        ax1.axis('off')\n",
    "        ax1.set_title('Value Counts')\n",
    "    \n",
    "    def plot_count_plot(self, column):\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        sns.countplot(data=self.data, x=column)\n",
    "        plt.title('Count Plot')\n",
    "        plt.xticks(rotation=40, ha='right')\n",
    "    \n",
    "    def replace_titles(self):\n",
    "        self.data['title'] = self.data['title'].replace(\n",
    "            ['Lady', 'the Countess','Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'],\n",
    "            'Rare'\n",
    "        )\n",
    "        self.data['title'] = self.data['title'].replace(['Miss','Ms','Mme','Mlle','Mrs'],'Miss/Ms/Mme/Mlle/Mrs')\n",
    "    \n",
    "    def plot_title_distribution(self):\n",
    "        sns.set_theme(style=\"darkgrid\")\n",
    "        custom_palette = [\"#F08080\", \"#90EE90\", \"#ADD8E6\", \"#F8B1D0\"]\n",
    "        self.data['title'].value_counts().plot(kind='bar', color=custom_palette, width=0.3)\n",
    "        plt.title('Distribution of Title Feature')\n",
    "        plt.xticks(rotation=0)\n",
    "        plt.xlabel('')\n",
    "        plt.ylabel('Count')\n",
    "    \n",
    "    def analyze_titles(self):\n",
    "        # Calculate value counts and plot as a table\n",
    "        value_counts = self.data['title'].value_counts()\n",
    "        self.plot_value_counts_table(value_counts)\n",
    "\n",
    "        # Replace titles according to specified values\n",
    "        self.replace_titles()\n",
    "\n",
    "        # Plot the distribution of title feature\n",
    "        self.plot_title_distribution()\n",
    "\n",
    "        plt.show()\n",
    "    \n",
    "    \n",
    "# Create an instance of the TitanicAnalyzer class\n",
    "analyzer = TitlesAnalyzer('data/train.csv')\n",
    "\n",
    "# Analyze and visualize the data\n",
    "analyzer.analyze_titles()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56614db8",
   "metadata": {},
   "source": [
    "# DATA CLEANING AND FEATURE ENGINEERING"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8e3c67",
   "metadata": {},
   "source": [
    "### Missing Values:  Imputation\n",
    "We can handle missing values by either imputing them with appropriate values or removing the rows/columns containing missing data. Here's a general approach for each column:\n",
    "\n",
    "1. **Age**: We can impute missing age values with the median age.\n",
    "2. **Cabin**: Since a significant portion of cabin data is missing, we might consider dropping this column.\n",
    "3. **Embarked**: We can impute the missing embarked port with the most common port."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "02c6331a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_data(data):\n",
    "    # Impute Age with median\n",
    "    data['Age'].fillna(data['Age'].median(), inplace=True)\n",
    "    \n",
    "    # Drop Cabin column\n",
    "    data.drop('Cabin', axis=1, inplace=True)\n",
    "    \n",
    "    # Impute Embarked with mode\n",
    "    data['Embarked'].fillna(data['Embarked'].mode()[0], inplace=True)\n",
    "    \n",
    "    return data\n",
    "\n",
    "# Call the function to preprocess the train_data\n",
    "train_data = preprocess_data(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d88ff8e",
   "metadata": {},
   "source": [
    "## check for and remove any duplicate rows from the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "505578bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate rows: 0\n"
     ]
    }
   ],
   "source": [
    "# Check for duplicates\n",
    "duplicate_rows = train_data.duplicated()\n",
    "print(\"Number of duplicate rows:\", duplicate_rows.sum())\n",
    "\n",
    "# Remove duplicates\n",
    "train_data = train_data[~duplicate_rows]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afdabc8c",
   "metadata": {},
   "source": [
    "## Detecting and handling outliers\n",
    "We can use box plots to identify potential outliers in numeric columns like Age and Fare.\n",
    "\n",
    "### Box Plots for Age and Fare\n",
    "To detect outliers in the dataset, we can use box plots to visualize the distribution of numerical features and identify any data points that fall far from the median. Outliers can significantly impact the analysis and modeling, so it's important to identify and handle them appropriately. Let's use box plots to identify potential outliers in the 'Age' and 'Fare' columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0e331880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_boxplot(data, column):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.boxplot(x=data[column])\n",
    "    plt.title(f'Box Plot of {column}')\n",
    "    plt.show()\n",
    "\n",
    "# Call the function to plot box plots for Age and Fare\n",
    "plot_boxplot(train_data, 'Age')\n",
    "plot_boxplot(train_data, 'Fare')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3359584",
   "metadata": {},
   "source": [
    "### Applying winsorization to the 'Fare' column\n",
    "Winsorization: Winsorization involves capping extreme values at a certain percentile. This can help mitigate the impact of extreme values without removing them entirely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "1045f838",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats.mstats import winsorize\n",
    "\n",
    "def apply_winsorization(data, column, lower_limit, upper_limit):\n",
    "    data[column] = winsorize(data[column], limits=[lower_limit, upper_limit])\n",
    "\n",
    "# Apply winsorization to the Fare column\n",
    "apply_winsorization(train_data, 'Fare', 0.05, 0.05)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37b9fbd2",
   "metadata": {},
   "source": [
    "### Analyzing survival proportions for each age group after binning the 'Age' column.\n",
    "Binning or Categorization: Instead of treating extreme values as outliers, you can categorize or bin them into predefined ranges. This can help you analyze the data more effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8dab738f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AgeGroup\n",
      "0-18     0.503597\n",
      "19-30    0.331096\n",
      "31-50    0.423237\n",
      "51+      0.343750\n",
      "Name: Survived, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def create_age_group_column(data, age_column, bin_edges, bin_labels):\n",
    "    data['AgeGroup'] = pd.cut(data[age_column], bins=bin_edges, labels=bin_labels)\n",
    "\n",
    "# Define bin edges and labels for age groups\n",
    "age_bin_edges = [0, 18, 30, 50, 100]\n",
    "age_bin_labels = ['0-18', '19-30', '31-50', '51+']\n",
    "\n",
    "# Create the 'AgeGroup' column and calculate survival proportions\n",
    "create_age_group_column(train_data, 'Age', age_bin_edges, age_bin_labels)\n",
    "age_group_survival = train_data.groupby('AgeGroup')['Survived'].mean()\n",
    "\n",
    "print(age_group_survival)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba208050",
   "metadata": {},
   "source": [
    "## Binning or Categorization:  Visualization of the binning process for the 'Age' and 'Fare' columns using bar plots\n",
    "\n",
    "Bin the 'Age' column into categories (e.g., child, young adult, adult, senior).\n",
    "Bin the 'Fare' column into categories based on ranges."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "234fc804",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def bin_age_and_fare(data):\n",
    "    age_bins = [0, 12, 18, 35, 60, 100]\n",
    "    age_labels = ['Child', 'Teen', 'Young Adult', 'Adult', 'Senior']\n",
    "    data['Age_Category'] = pd.cut(data['Age'], bins=age_bins, labels=age_labels)\n",
    "\n",
    "    fare_bins = [-1, 10, 30, 100, 1000]\n",
    "    fare_labels = ['Low', 'Mid', 'High', 'Very High']\n",
    "    data['Fare_Category'] = pd.cut(data['Fare'], bins=fare_bins, labels=fare_labels)\n",
    "    return data\n",
    "\n",
    "def visualize_age_fare_categories(data):\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15, 6))\n",
    "    sns.set_palette(\"pastel\")\n",
    "\n",
    "    sns.countplot(data=data, x='Age_Category', ax=axes[0])\n",
    "    axes[0].set_title('Distribution of Age Categories')\n",
    "    axes[0].set_xlabel('Age Category')\n",
    "    axes[0].set_ylabel('Count')\n",
    "\n",
    "    sns.countplot(data=data, x='Fare_Category', ax=axes[1])\n",
    "    axes[1].set_title('Distribution of Fare Categories')\n",
    "    axes[1].set_xlabel('Fare Category')\n",
    "    axes[1].set_ylabel('Count')\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Bin Age and Fare columns\n",
    "train_data = bin_age_and_fare(train_data)\n",
    "\n",
    "# Visualize Age and Fare categories\n",
    "visualize_age_fare_categories(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bce82df",
   "metadata": {},
   "source": [
    "## Visualizing Feature Correlations with Heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dd731b8",
   "metadata": {},
   "source": [
    "Analyzing correlations between variables can provide insights into relationships and dependencies within the dataset. Let's analyze the correlations between numerical columns in the Titanic dataset using a correlation matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01baa6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Io0wiIiIiIhIhjTCICIiIiI5ypN8gNp/gUYYREREREQkQ0oYREREREQkQ5qSJCIiIiI5ihY9W5ZGGEREREREJEMaYRARERGRHEWLni1LIwwiIiIiIpIhJQwiIiIiIpIhTUkSERERkRxFi54tSyMMIiIiIiKSIY0wiIiIiEiOYrDWCIMlaYRBREREREQypBEGEREREclRrDTCYFEaYRARERERkQwpYRARERERkQxpSpKIiIiI5CgGK01JsiSNMIiIiIiISIY0wiAiIiIiOYrBWtfELUm9KSIiIiIiGVLCICIiIiIiGdKUJBERERHJUfQcBsvSCIOIiIiIiGRIIwwiIiIikqPotqqWpREGERERERHJkEYYRERERCRH0RoGy1LCYCEOfl40PLSK/R37cm3r3qwOJ1uJjY1m8eyJHNq7hbjYaIqXrsQrvQbjU6DofdvduBbBwp/Gc/LIHpKSEilXuTav9BpM7rwepjrJyUmsWDCNXVvWEBV5gwKFS/Jij4GUKFPZVCck8AJD+3dMs30v38KMmrLMYvuZleJio1k2ZzxH920mLjaaoqUq07nnELwLFLtvu5vXI1g6exynju4mOSmRMpXq0LnnEHLl9TTVuRoexPK5EzlzYh9JSYmUKl+Ddl3fxdOnsKlOaNAFPhvQLs32PX0KM3zSb5bazSxx5MAulsybTtDlC7i656Zpq/Y816k7BkPGf4y2b17LqiU/Ex4WTN78nrRp35VnWrxgVmfzuhX8sfJXU51mz3ak5XMvZbjdA7v/YsKXQ/h41FTKVqhm0X3MDuJio1kx9xuO7ttEfFwMRUpWomPPD/H2+/djeNnPY/E/tovkpERKV6xLx54fkCvP3WN4+/pFLPzh8zRt6zXrzMv/Gwaknks2rprN7k3LuXk9gvxeBWne/g2q1W1l2R19QrL6vHuvi+dO8uUHPfhy6gryefgAcCU8mA/6tM0wjnpNnuP1/iMffsefgGMHd7Js3jSCA87j6p6bxi070qZjj/ueE3ZuWcPqpbOJCAsibz5PWrXvRqPm7czqnD9znEU/T+LiuVPYOzhRp9GzdOz6Nra2dqY6ARf/ZvGcSZw/cxwbW1vKVa7Ni93fwT1XXlOdK2HBLPp5EqeO7089r1eoQedu/fHyLWTxvpD/DiUMFuBY0Ieaq3/ENpdbVoeSLc385iMunD1Bp+4DcHR0ZuWimYwd1ofPJy3BxdU93TbJyUlM/Lw/8XExdO3zEcnJSSydO5nxI99m+PgF2NjYArDgh7Hs2LyKTt3eIW9+b9avnMeEz/oybNx808nx8oUzAAz+fKbZidfOzuEx7/mTM2vCB1z8+zjtXxuIg6MzaxbP4NuRvfl0wnKc79PH343qS3xcDK/0/iT1S8C8b5n8+Zt8PG4R1ja2xMVGM+mzN7Gytubl3p9gY2vL2iUzmTCsF59OWGbaduCF0wC8O+IHbO3sTZ9x789PozOnjjJ+1PvUrt+MTl3e5MypIyz+ZTpGo5EXXuyZbps92zcyY+JIWj73EhWr1uHA7r/4ccqX2NnZU69x6pfPP9csZfb0MbTt2I3ylWtyzv8E82dNIj4ujhde7JFmm5G3bjJr6lePc1ez3Oxvh3Dp7+O80HUgDo4urF0yjckje/HJhN9wdsn4GJ725VvEx8Xw0htDSU5OYuW8iXz3RR8+HLMY69vnicCLp/HyK8arb5p/AXW950vW2sVTWb/8R1p1epOipSpzZM+fzJ44GCsrK6rUbvH4dvwxyerz7h0BF87w7RcDSE5OMit3z52Pj7+anSaGTWsXsW/Heho0bWeRfrC0s6eP8O2Xg6hZrzkdurzFmVOHWTZvKkZjCs917pVum307/uSHb4fTvO3LlK9al0N7tjD7uy+ws7OnTqNnAQgPCWTciL4UL12Jt94fTUjgRZbOm0psTBQ9+w4F4Ob1K4z5tA958nvR653hJMTHsXjOZMZ/9g6fjvkZGxsbYmOjGTuiL9bW1nR780NsbexYufgHvhr6P76YtCjD//ci/0YJw6MwGPDr1p4yXw/J6kiyrb9PH+Hoge0MGDqJitXqA1CybFU+eLMtm/9YzHOd30i33f6dfxJw8QyffbsY34KpVxgLFinFsAGd2bd9PXUat+HalVC2bljOy73ep8mzLwJQrnIdPu7bjj9W/EyPvqlXDgMu+JPPw4fS5as/gT1+8s77H+H4wW28/fEUyldtAEDxMlUZ1rc1W9ct5NlO/0u33cFdGwi86M/QCUvxKVAcAL/CpfhiUEf271xHrYZtObBzHRGhl/l0wjLTaIVvwRJ8+nZrDu5aT4MWnQEIvOhPXg8fSpav8QT2+MlZ/usPFCpSkrcGpX7RrFStDslJSaxaOodnX3gFO/u0SefiedOpUbcJXd8YCEDFqrWJirrF0vnfU69xK4xGI6uWzqFW/Wa83L0vAOUr1SA0+DLrVy9KN2GYPX0M1jY593R94cxhThzcypsffke5qg0BKFamKiP6tmLbul9p1bFPuu0O7VpP0CV/Pv5mOd63j2HfwqUZ/V57Du78gxoNnwMg6JI/hYqXp0jJShnGsGvzCqrXb03rzm8BULpiHQIvnmbbul+fuoQhO5x3kxIT2bjmV1bMn5buhQNbWzuKlapoVnbx75Ps27GeDl36UaJsFYv1hyX99uv3FCxSkv8NTB2xqlC1LslJSaxZ+jMtn++S7jlh2bypVK/TlFd6vZfapkodoiJvsWLBDFPCsGb5zzg4OvPOR99gY2tLper1sbN34Jfvx/Bc517k8/Dm0N6tREXe5NMxP+Ph7QeAk7Mr4z97h79PH6F0+Wrs276B8JAAvpi8CN/bo0l+hYozuM/z7Nv5J8+0TDvanlMZNCXJorTo+RG4VSxF+SkjCJy7gsM9lDSk58ThXdg7OFKucm1Tmat7bkqWq8axA9szbHf80C68fAub/mgB+BQoirdfEY4d3AHAyaN7SU5OolrtJqY6trZ2VKrWgKP3bDvgoj8FipSy5G5lKycP78TewZEyleqYylzd81C8bHWOH8y4j08d3omnT2FTsgDgXaAYXr5FOHG7XZXazXh/1ByzqU13rtomJSaYygIv+uNXOGf1cWJiAqeOHaR6ncZm5TXqNSEuNgb/k4fTtIkICyY06DI1/tGmZt0mhIcGEhJ0GYAhIybySo9+ZnVsbGxISkxMs83d2zZw/PBeXu7eL817OcWpwzuxs3ekdKW6pjJXt9Rj+OShbRm3O7IDD5/CpmQBwNuvGJ6+RTlxu11KSgrBl8/iV7j0fWNISkzAwdHZrMzZNRfRkTcysUdZKzucd48e3M7KhTNp0+l1OnV7519jNhqN/DJjNN5+RWjxXJeH2t8nJTExAf/jB8z2HaB63abExcVwJp1zwpWwYEKDL1OtzjNm5TXqNiU8NJDQoEsAHD+0m0rV62Nja2u2XWNKCscP7QLunnMdnO4epy5uuQCIiryZ2qZOUz75apYpWQCwtr1zzk57fhF5UEoYHkHs5RC2lG7OqcFfkRwTl9XhZEshgRfI7+mLtbX51VEPrwKEBV++bztPn4Jpyj28ChAafMlUx97BCffc+czreBfg5vUrxMXGAKlTkmKjIxn1QXf6vFibgT2bs2TOJJKScsbJMzToPHk9/NL0cX6vAoSHXMq4XeB5PHzSzmnN71WQ8Nt97OTsRtFSqVdlkxITCbzoz5wpn+LilpuqdVua2gRe9Cc2JoqxH7/GO6/U4MM3mrDil4kkP8V9HB4aRFJSIt7/OA69bl/ZCwkKSNMmOPBiah1f8zaet9uEBl/GYDDgW6AI+Ty8MRqNREXeZPP639i+eS3NWptf/bt5/Sqzp4/ltd4DyZXH/DjPSUKDzpPPM71juOB9j+GwoAt4eBdOU37vsR8ecpGE+FgunDnKZwPaMuCVKnw+4Dn2/LXSrE2Ttt3Yu3UVJw9vJzYmin3bfufU4R2mUYqnSXY47xYpXo6vZ/xO285vYGVt/a8x79n2Bxf+PsErvQY/UP2sEHH7nPDPPvL0LgBg6qN7BQdeSK3zj3Otxz3nhIT4OK5GhOD1j+26uefG0cmZ0Nv/z2rUb06uPPn5ZeYYbly7QkRYEItmf0uu3PkoW7EmAE4urhQvnTpyk5SYyOULZ/jh2+G4uOWiZr1mj9oFTxWDlVWWvXKinDvG/QQkXr9J4vWbWR1GthYTHYmDo0uacgdHJ2Jjo+7b7s5J+J/t4mKiAYiNjsTJOb1tp159iY2NJj4+lls3rmIwGFLn2+bz5tTRvaxdPptrV8P438BRmd21bCM2OhJHJ+c05Q6OzsTdr49jIsnvnfbLgb2jU7rtpo7ux+mjuzFYWfFK76GmLwy3bly93cdWtOs6gNz5vPE/tocNK37i+pUwer47+hH2LuvERKf2wT/71sHRCYDY28fhvaKjIlPbOD5Ym7OnjvLZh6lTxooUL02Lti+avf/jd6MpUboC9Z9pzcljBzK7K9lebEzG54m4mIyP4djoSPJ7pXMMOzgTF5va10EX/QG4fiWYDt0GY21tw96tK/nlu09ISkygXrNOADRs9SrnTh1k2pdvmbZT+5n2NHs+/bUq2VlWn3cdHJ3MFkk/iHW/zaV46crZeupoTPTtf98ZnBPi0jknZNzmdn/FRJvONQ5O6ffrne2658rLa30+YMY3n7BvxwYAnF3cGPL59HT/n0z4YgAnj+zFYGVF9zc/SpPkiTyMB04YunXr9sAbnTNnTqaCkadbSkoKRmOKWZnRaCTdG0cYjVhZZXwVyWhMIb2GRsDqdvaekmIE0qljNAJgZTDg6OjMeyOm4eVbiDz5vAAoVb4aNra2LJ8/lbadev3rXUOyk4z6OKN+uG8fpxjTv6uHEQzptGvdqQ8tO/TiyN7NzJ/xGVG3rtOq4xup826HzcDDp7Cpj0uWq46NrR2rFkyhVafeePs9PX18hzHldj9ncOeT9B4KdOfYS9uv6Zfn9/Thky+ncf1qBEvnz2TYoB589s1PuOfOy9aNq/E/eYSvJs9/tB3JZtI9hlMy+Pdu5F/PE+nfmcZoOk+UKFeDNz/8jhLla5pudFCmcj0ib15jzaLvqNu0I0lJiUwc3p3IG1d4qfenePoW4fzpQ6xb9j32Dk506vlh5nf4McuO592HdfbUYS6fP02/D8c/dNsn6U4/G9LZfyDdK8umc8I/2pj6y8pwz3bT+0yj6Vyz668/+H7ip9So14wGTV8gISGOP1bM5ZsR/fhw1Ey8/QqbtX3hxd607fQ6B/dsYfbUUUTevE7bzq8/8P6K3OuBEwZfX1/Tz/Hx8axZs4YyZcpQuXJlbGxsOHbsGMeOHaNz586PJVDJ/lYtmsnKhTPNyqrVacatm9fS1I2Li8Uxnaspdzg5u6Z7tSY+NgbH21dSnJxdTFcRzerEpQ6JOzq5YGfvQNlKtdLUqVi9AcvnTyXg4tmnKmFYs3gGaxZPNyurUrs5ken0cXxczH372DGjPs6gXfGyVQEoVb4mMVG3WLv0e5q364GdvQOlK9ZOU7981QasWjCFoIv+T2XC4OTiCqQdFbgz5cIpnT66c5UvbZtYs/fvyJ03P7nz5gegWMlyvP9mJ7ZsWEmDpm345YcJdHn9Hdxy5SY5OYmU2wlMSkoKKcnJ2Xbaxr/5Y8l01i6ZZlZWuXZzItOZehQfF5PuVdc7HJ1d0x0NSz2GU///ueXKZ1pIfa9yVRvif2w3kTevcub4HoIvnaHv0JmUrpi6FqhE2Ro4OrmyeNaX1G3aAZ+CJR9qP5+U7HjefVgHdv2Jk4sbFarVe+i2T5KT8+1zQmz654T09v1uG/Pj9N7+ymi7d+rd2e5vC2ea7qJ0R7nKtfmkXyeWzZtK3w/GmLUtWS514XiZCtWJibrFqsU/8myHbmmmquVUetKzZT3wUTN69N0DdOjQofTo0YMPPzS/6jJx4kTOnTtnuejkqdKwRUcqVTf/w3xwz2ZOHN5FSkqK6QoVQHhoAN5+RTLclpdPIS5f8E9THh4aQJES5VLr+BYmNiaKyJvXcXXPfbdOSAD5PHyws3cgNOgip4/tp1bDVmYn84T41DUnrrcXjD0t6jfvSIVq5n18ZN9mTh3ZmaaPI0ID8PTNuI89fQqZbod6r4jQyxQqUR6Ai2ePcTUimGr3rFcAKFS8HHu3/k7UrRvExUThf3wvNRq0NuvjxIR4AFzccvM08vDyxcrKmrAQ87UKoSGBAPgWTNu33rdvKRkWEkjhYncXgYfdaVOgCLEx0Rzcu41iJcvh5XN3+oentx/OLq5cvRLG8cN7iYmO5PvJo/h+svm0ua8+7Uc+D28m/rDCIvv5pNVr1ony1RqZlR3dt5HT6R7Dl/G6T7Lp4V2YwIvpHcMBFCqeegyfPbmPaxEh1Gr0vFmdxIR4rKyscXR241pECABFS5vfmad42dTpMSGB57NtwpAdz7sP68j+bVSp2dh029bsysPLDysra8L/cU64c47wKZC2b+/cZjY8JJBCRe8uvA+/fU7wKVAUewdHcuf1MJXdcevmdWJjok0Xta6Gh1C1VmOzOvb2DhQpXpaggPNA6rMcroSHULN+c7N6RYqXZeeWNUTduqGpSZIpmVqZsXr1al5++eU05e3atWPbtozvaCE5W+48+SlcvKzZq1zl2sTFRnPi9l0eACJvXufMiQNmd/D4p3KV6xASeIHg2ydBgOCA84QEXqBc5dQrgHdGDvbv+tNUJzExgSMHtpm2ff1qOHNnfMn+nX9yr3071uPg6EyhYmUefcefoFx5PChUvJzZq0ylOsTFRnPq8E5Tvcib1/j75H7K3HPXmX8qU6kuoUEXCAm4m+SHBJwjNOiCqd2xA1v5aeJHpi9Ud5w8vBO3XPlwdc/DjWth/Pr9KA7t2mBW58DOdTg4OlOwaFlL7PoTZ2dnT+lyldm/a4tp+gDAvh2bcHJ2pViJtPvl5VMADy9f9u7cZFa+d+cmvHwLks/DGytra36YPIrVy+aa1Tl39iRRkbcoWLgEVWo04LNvZpu9er79AQA93/6A94aOewx7/GS45/GgYLFyZq/SFeumHsNHdpjqRd5KPYZLV8z4GC5dqS5hQecJCbznGA48R1jQeVO7M8f2MG/qUCJC7y72TUlJ4fDu9RQuURFbWztTYn3u1EGz7Z/3PwRAXg9fsqvseN59GFGRNwkPCcjwoW/Zia2dPSXLVeHA7s1m54T9Ozfi5OxK0dtJ1b08vQuQ39OXfTs3mpXv27kRL5/UcwJAucq1OLJ/G4n33H1u/86NWFlZU6ZCauLq7VeYs6cOm312YkI8l877mx6Id2T/dmaMH8rViFCzzzt2aBfuufPi6p7nEXvh6WFlbciyV06UqXEpNzc3Tp48SeHChc3K9+/fT968edNvJP9JpcpVo1T56syc+Amduw3AxdWd3xbOwMnZlcYtO5nqBQecJzExwXQFpkb9FqxeOosJn/ej02upt+RbMncSvoWKU71u6p0e8nn4UPeZ5/h11jckxMfj5VOQ9St/ITY6ipbtupl9/sKfxpMQH4eXb2GOHtjGxtW/0rn7uzi7PP0P2ytRtholylXnp0kf0b7rQJxd3Vm9aDqOTq40bHF3imBIwDmSEhMoUDQ1SapWryXrlv3Ad6P68kLXAQD89su3+BQsQdU6qVenGrZ8kR1/LmXq6H607tQHewcndm9ZyYmD2+g5YDRWVlaUKFudEuWqs/Tnb4iPj8XLtwjHD25jy5r5tH9tIE5PcR+/8OLrfDWsH5O//phGzZ/jzKmjrF7+Cy9174udvQMxMVEEX76Ah7cfbrevtrZ76XVmfvs5Lq7uVK3ZgIN7t7Fn+5/0G5I6UmBv70Dbjq+xYuEsXFzdKVe5JqFBl1m24HsKFilBw2ZtsbOzx9XN/AFLcbenMHj7FqJA4eLkJMXLVqdEuRrMmfQhL3QdhLNrLtYsnoqTsyv1m99zDAfePoaLpB7DVeu2Yv3y75n25Vs8/+q7AKycPxHvgiWoUif12Qn1W7zEjj8XM+PrfrTu/DZ29o5sXbeAkIC/6T98FgAVqjemcImKzJn8Ea1ffBtPnyJc/Pso65bOpHy1xhQuXuHJdsgjyurz7sMIuvQ3wH1HPrKT5zr3Ytzwt5k69kMaNH2ev08f5Y8Vc+ncrT929g7ExkQRHHCB/F53zwnPv/gGP04eyRxXd6rUbMihvVvZt2OD2dSi1u27s2fbesZ/9g4tn+9CWPAllvwylcYtO5A3f+rasPavvsnk0e8zdeyHNGz2AomJCaxfOZ/rV8NNz4V4plUntqxfzoTPB/DCS72xd3Bkx+bfOXpgB/8b+IXZiJPIwzAY701VH9DUqVOZNWsW3bt3p3z58hiNRg4cOMC8efMYPHgwXbo8+j2UV9s+Xfd0z9OwJnU2zmVX09e4tnVvVodzX20SU4ect59MO1/ycYiOusXCn77h0J7UK7XFS1fi5dffw8u3sKnOmKG9uRIezJiZq01l166EsuCHsZw4sgdrGxvKVa7Nyz3fI1ee/KY6iYkJLJ07iT1b/yAuLoZCxcrwYveBFC1Z3lQnJjqSlQtncGjPFm5cv4KHlx/N2r5KoxYdHsv+1i+beveLjcee3K12Y6JuseTnsRzZm3rlq1ipynTqMRjPe/p4wrBeXI0I5otpa01l166EsnjW15w+uhtrGxvKVKxDp56Dcc99t4/DQy7z27xJ/H3qAHGx0RQoUoZWHd8wPSQOUu+csnrRdI7s28TN61fI7+nHM226Ur/543lIUNMKqdMe9vnfeCzbv9e+XVtYNv97QoIukTtvfpq37kTr9qnnuJPHDvDlJ2/zvwGf0rBpW1ObjX8sY83yeVy7Ek5+Lx+e79Sd+s+0Nr2fkpLCpj+W8+fapYSFBOLi4kb1Oo3p3PXNdO92cu9nfTxqKmUrVHu8Ow3UKJWL9UcS/r2ihcRE3WTZnLEc3bcJY4qRoqUr06H7EDx97n6R/HZET65FBDPyu3WmsutXQlky+yv8j+7C2tqG0pXq0qH7kH8cw5dYNf9bzp0+QFxsDIWKlaPNy/0pXuZuP8bGRPH7r5M4vOdPYqJuks/DjxqNnqNJ2+6PZapMi0qpT51/XOfhrD7v3mv7ppX8NHkEX8/43XQl/I59O9YzfdyHfDF5qcWThvplndl5KtKi2wQ4sHszKxbMIDToErnzetDk2c60atcVgNPH9vP1p2/Sq/9w6je9e0vezeuW8seKX7h2JQwPT1/adOxB3WfamG33zIlDLPz5Wy5fOIOrWy7qNG5Nh1ffNFtzcOzgTlYu+oFL5/1xcHSiSPGydOzal4JF7k6ZCwsJYMncKZw5cYjY2GgKFS1F206vU6l6fYv2Q90yrhbdnqUdaZV27dKTUumPrVn22Y9LphIGgBkzZvDLL78QEREBgLe3N7179+bVV1+1SGBPW8LwNHnSCcN/TVYkDP81TzJh+K960gnDf83jThjk8SUMkkoJQ8ZyYsKQ6aXyffr0oU+fPly/fh2A3LmfzoWNIiIiIiKSsUe+t5YSBRERERHJTnLqE5ezygMnDKVLl87gATlpnTp1KtMBiYiIiIhI9vHACcOXX375wAmDiIiIiEhW0YPbLOuBE4YOHe7eUWbv3r1UqVIFW9vs/ZAVERERERF5NJma4PXOO+9w5swZS8ciIiIiIiLZTKYWPefNm5fISN2qTERERESyn5z6xOWskqmEoX79+vTp04dGjRpRqFAh7O3tzd7v16+fRYITEREREZGslamEYcOGDeTNm5fjx49z/Phxs/cMBoMSBhERERHJMlr0bFmZShg2bdpk6ThERERERCQbeqQHt+3bt49z587Rtm1bQkNDKVSokO6cJCIiIiJZSg9us6xMJQxRUVH06tWLI0eOYDAYqFevHuPGjePixYvMnj0bLy8vS8cpIiIiIiJZIFPp1/jx4zEYDGzYsAEHBwcAhgwZgpOTE2PGjLFogCIiIiIiknUylTBs3ryZIUOGUKBAAVNZ0aJFGT58OLt27bJYcCIiIiIiD8tgZciyV06UqYTh2rVr5M+fP025i4sLsbGxjxyUiIiIiIhkD5lKGCpUqMCaNWvSlM+ZM4eyZcs+clAiIiIiIpmlEQbLytSi50GDBtGzZ08OHTpEUlIS06ZN4++//+bkyZP8+OOPlo5RRERERESySKZGGKpWrcrChQtxdXWlUKFCHD58GG9vb+bNm0etWrUsHaOIiIiISI6XkpLCpEmTaNCgAZUqVeL111/n0qVLGdaPiIhg0KBB1KpVi1q1ajFgwABCQ0MtHlemn8NQunRpxo4da8lYREREREQe2dM6NWjq1Kn8+uuvjB49Gk9PT8aOHUvv3r35/fffsbOzS1N/4MCBJCcn89NPPwEwcuRI3n77bZYtW2bRuDKVMHz00UfplhsMBmxtbfHy8qJVq1YUKVLkkYITEREREfkvSEhIYNasWQwePJhGjRoBMGHCBBo0aMCGDRto06aNWf1bt26xb98+pk2bZlpD/L///Y+3336b69evkzt3bovFlqkpSYmJiaxYsYIdO3Zw69Ytbt26xe7du1m+fDn+/v789ttvtGvXjgMHDlgsUBERERGRB2GwssqyV2adPn2a6OhoateubSpzc3OjbNmy7Nu3L019e3t7nJycWLFiBVFRUURFRfHbb79RuHBh3N3dMx1HejI1wuDg4EDLli0ZM2aMaXgkKSmJoUOH4ujoyPDhwxk3bhwTJ05k7ty5Fg1YRERERCS7atq06X3f37hxY7rld9YeeHt7m5V7eHgQEhKSpr69vT2jRo3is88+o3r16hgMBvLnz88vv/yC1SMkLunJ1Nb++OMP+vXrZzaXysbGxjTHCqBTp06cOHHCMlGKiIiIiDwgK2tDlr0y686zzP65VsHe3p74+Pg09Y1GI/7+/lSpUoV58+bx888/4+vrS9++fYmKisp0HOnJ1AiDjY0NV65coXjx4mbl4eHhGAypHZWcnIyNTabXVIuIiIiIPHUyGkH4Nw4ODkDqWoY7PwPEx8fj6OiYpv7q1auZP38+mzdvxsXFBYDp06fzzDPPsHTpUrp3756pONKTqRGGli1bMmzYMHbu3El0dDRRUVFs376dESNG0LRpU2JiYpg2bRoVKlSwWKAiIiIiIjnVnalI4eHhZuXh4eF4eXmlqX/gwAGKFCliShYA3N3dKVKkCBcvXrRobJlKGD766CNKly7N66+/TvXq1alRowa9e/emXLlyfPLJJ+zcuZN9+/YxePBgiwYrIiIiIvJvnsYnPZcuXRoXFxf27NljKrt16xYnT56kevXqaep7e3tz6dIls+lKsbGxBAYGUqhQoUzHkZ5ML3qeNGkSAQEBnDp1Cmtra0qVKoWfnx8ADRs25K+//rJooCIiIiIiOZWdnR1du3Zl3Lhx5MmTB19fX8aOHYuXlxfNmzcnOTmZa9eu4erqioODA+3atePHH3/k3XffZcCAAQBMnDgROzs7OnToYNHYHmmRgaOjIxUqVMBoNAIQHBwMgI+Pz6NHJiIiIiKSCY9ye9Os9M4775juPBoXF0eNGjX48ccfsbOzIzAwkKZNmzJ69Gg6dOiAh4cH8+fPZ+zYsXTv3h0rKyuqV6/OggULcHNzs2hcmUoYDh8+zAcffMDly5fNyo1GIwaDgVOnTlkkOBERERGR/wpra2sGDx6c7rR+Pz8//P39zcqKFSvG9OnTH3tcmUoYvvjiC9zd3ZkyZQqurq6WjklERERERLKJTCUM/v7+LFq0iDJlylg6HhERERGRR/Ioi48lrUxN8PL29iYxMdHSsYiIiIiISDaTqYTh7bff5ssvv8Tf31+Jg4iIiIhkK0/jbVWzs0xNSZo0aRLh4eG0a9cu3fe16FlEREREJGfIVMLQv39/S8chIiIiIiLZUKYShvbt21s6DhERERERi3han8OQXWW6N//66y+6detG/fr1CQoKYtKkSaxYscKCoYmIiIiISFbL1AjDjh076NevH23atOHw4cOkpKSQnJzMxx9/THJyMh07dnzkwNok+v97JXkk9cs6Z3UIOVrTCg5ZHUKOV6NUrqwOIUdrUckuq0PI8XQefrzqltGzov6rcuri46ySqRGGyZMn89577/HVV19hbW0NwMCBA3nvvff46aefLBqgiIiIiIhknUw/uG3MmDFpylu0aMGkSZMeOSiA7SejLbIdSevOFa3VtqWyOJKc6c7o2MhfdMvhx2V4V1sA/jickMWR5FytKtux8VhcVoeRY90ZgTz2d1gWR5JzVSjuya0D67I6jBzLrVrLrA7hvrSGwbIy1Zuurq6EhaU9yZ09exZ3d/dHDkpERERERLKHTCUMzz33HKNGjeLEiRMYDAaio6P566+/+Pzzz2ndurWlYxQRERERkSySqSlJ7777LqGhoabFze3bt8doNNK4cWMGDhxo0QBFRERERB6KQYueLSlTCYOtrS3ffPMNAwYM4OTJk6SkpFCyZEmKFy9u6fhERERERCQLZSphAIiLi8PLy4uCBQty7tw5tmzZwq1bt6hataol4xMREREReSi6raplZWoNw759+2jYsCEHDhwgIiKCF198kWnTptG1a1fWrl1r6RhFRERERCSLZCphGD9+PE2bNqVChQqsWbMGZ2dntm/fzieffMKMGTMsHaOIiIiIiGSRTCUMJ0+e5O2338bFxYXt27fTuHFjHBwcaNy4MefPn7d0jCIiIiIiD8xgZZVlr5woU3vl6OhIQkICCQkJ7N+/nzp16gBw5coVXF31GHYRERERkZwiU4uea9WqxdixY00PaWvQoAGnTp3iiy++oFatWhYNUERERETkYWjRs2VlaoRh+PDh2Nra4u/vz9ixY3FxceG3337DxsaGjz76yNIxioiIiIhIFsnUCEOePHmYPHmyWdl7772Hra2tRYISEREREcmsnLqWIKtkujcPHjzItWvXAFixYgX9+vVjxowZGI1GiwUnIiIiIiJZK1MJw6+//kqXLl3w9/fnzJkzfPTRRyQmJvLTTz/x3XffWTpGERERERHJIplKGH7++WeGDh1KnTp1WLt2LSVKlGDWrFmMGTOGZcuWWTpGEREREZEHZrAyZNkrJ8pUwhAYGEiTJk0A2LFjBw0bNgSgePHiXLlyxXLRiYiIiIhIlsrUoue8efMSHh6Ora0tx48fZ+DAgQCcPn2afPnyWTRAEREREZGHkVOv9GeVTCUMbdq04f3338fR0REvLy9q1qzJmjVr+Pzzz+nUqZOlYxQRERERkSySqYThvffew8vLi4CAALp06YK1tTVXr17lxRdfpH///paOUUREREREskimEgYrKytee+01s7J//i4iIiIikiX0HAaLylTCALBp0yb8/f1JTk42lSUkJHDkyBF+/vlniwQnIiIiIiJZK1MJw4QJE5gxYwYeHh5ERETg6enJlStXSE5Opk2bNpaOUURERETkgRkMWvRsSZkar/ntt9/49NNP2bp1K56ensyfP5/t27dTtWpVChQoYOkYRUREREQki2QqYbhy5QqNGjUCoHTp0hw9epRcuXIxcOBA1qxZY9EARUREREQehsHKKsteOVGm9srd3Z3o6GgAChUqxN9//w2Aj48PYWFhlotORERERESyVKYShjp16jBmzBhCQkIoX748a9eu5dq1a6xbt448efJYOkYREREREckimUoYBg8ezNWrV1m3bh0tW7bE3t6eevXqMWbMGLp3727pGEVEREREHpjBypBlr5zooe6SFBYWxoYNG7Czs2P69Onkzp0bOzs75s+fz7Zt2/D09KRixYqPK1YREREREXnCHjhh2L9/P7179yY2NhYAZ2dnvv32W+rXr4+DgwPNmzd/bEGKiIiIiDywHLr4OKs8cG9OmjSJ2rVrs3XrVnbs2EH9+vX56quvHmdsIiIiIiKSxR54hOHUqVMsWLAADw8PAD7++GMaN25MVFQULi4ujy1AERERERHJOg+cMERHR5MrVy7T756entja2nLz5k0lDCIiIiKSbeTUxcdZ5YGnJKWkpKR5zLa1tTUpKSkWD0pERERERLKHh7pLkoiIiIhIdmcwaNGzJT1UwjBr1iwcHR1NvyclJTFnzhzc3d3N6vXr188y0YmIiIiISJZ64ITBx8eHtWvXmpXlz5+fjRs3mpUZDIanKmGIjY1m8eyJHNq7hbjYaIqXrsQrvQbjU6DofdvduBbBwp/Gc/LIHpKSEilXuTav9BpM7rwepjrJyUmsWDCNXVvWEBV5gwKFS/Jij4GUKFPZVCck8AJD+3dMs30v38KMmrLMYvv5tHPw86LhoVXs79iXa1v3ZnU42Z6dDTSvakXpAlbY2UJAhJE/9idz5eb92xX0MNCkshVeuQ0kJMHJSylsOpJCQmL69d2c4K22Nuw+ncJfR3Pu9MS42Gh+++Ubju3bRHxcDIVLVqJjjw/x8it233Y3r0ewYs5Y/I/tIikpkdIV69KhxwfkyuNpqmM0Gtn8+8/s+HMxN66GkiefDw2ffZUGLV8x1QkLOs+Xg15Is30Pn8J8MmGV5XY0C8XFRrNszniO7ttMXGw0RUtVpnPPIXgX+Pc+Xjp7HKeO7iY5KZEylerQuecQcuW928dXw4NYPnciZ07sIykpkVLla9Cu67t4+hQ21QkNusBnA9ql2b6nT2GGT/rNUruZJQ7t38OCuT8QGHARN7dctGj9Au07d0kzzfheWzetZ9niXwgPDSZffk+e7/gKzVq2Nauzd9c2liz4meCgAHLlzkPDJi1o37krtra2pjoXzp1l/pyZnDtzmhSjkaLFS9K1Rx+KFi/12PY3O9h55CTTF63mfFAoud1c6NC0Hj2eb55hn8fFJzBz6Vo27D7I9VtRlCzoyxsdW1G3Utl060fFxPLqh1/Tu+OzPNeo1uPclaeX1jBY1AMnDJs2bXqccWSZmd98xIWzJ+jUfQCOjs6sXDSTscP68PmkJbi4uqfbJjk5iYmf9yc+LoaufT4iOTmJpXMnM37k2wwfvwAbm9ST5YIfxrJj8yo6dXuHvPm9Wb9yHhM+68uwcfPx8i0EwOULZwAY/PlMbG3tTJ9hZ+fwmPf86eFY0Ieaq3/ENpdbVofy1OhY3xrffAY2HEwmPhEaVbSmezMbvluVRFxC+m28ckPXJtacDzWyaGsyro7QrIo1+dwM/LIpOd02z9exxsEu55+Uf540hMt/H+f5LgNxcHThjyXTmPJZLz4a/xvOLhmfJ2aMfov4uBg69xpKcnISq+ZPZNqoPgz5ejHWt88TK+eN5681v/Dsi30pVLwCJw9tY8msL7G2tqFus84ABF30B6DfsFlm5wnbHHSemDXhAy7+fZz2rw3EwdGZNYtn8O3I3nw6YTnO9zkXfzeqL/FxMbzS+5PUizTzvmXy52/y8bhFWNvYEhcbzaTP3sTK2pqXe3+Cja0ta5fMZMKwXnw6YZlp24EXTgPw7ogfsLWzN33GvT8/jU6fPMbXn39E3QZNeOW1Nzh98igL5nyPMSWFji93S7fNzm2bmTx+FK2f70SVarXYu3sb0yeNwc7OjobPtADgyKF9jB01lLoNmtClZx8uXzzP/J+/59bNG7zx1kAAQkOCGPZBf4oUL8lbAz7AYGXFqmULGTqkH2Mn/YivX8En1g9P0pEz53lv3Pc0r1OFN19swxH/80xbtBqj0cjr7Vqm22bkjHnsOnKKfi8/RwEvD1Zv28OgsTOZNrQ/VUqbJ803o6J5b9z3hFy59iR2RwT4j69h+Pv0EY4e2M6AoZOoWK0+ACXLVuWDN9uy+Y/FPNf5jXTb7d/5JwEXz/DZt4vxLZj6D7lgkVIMG9CZfdvXU6dxG65dCWXrhuW83Ot9mjz7IgDlKtfh477t+GPFz/ToOwyAgAv+5PPwoXT56k9gj58yBgN+3dpT5ushWR3JU8Uvn4GSflbM25TE38FGAC6HJzGgnQ01Slqx7Xj6IwG1y1gTEw+LtiZz914GybSra0Net2Su3jKvX72kFfnccn6ycOHMYU4e3Mr/PvyOclUaAlCsTFVG9mvF9nW/0rJjn3TbHd69nqBL/nw4bjneBYoD4Fe4NF+9356Du/6gRoPnuBoeyObf59Cx50c0aPkyACXL1+LG1VBOHdlpShgCL50mT35fSpSr8QT2+Mk773+E4we38fbHUyhftQEAxctUZVjf1mxdt5BnO/0v3XYHd20g8KI/QycsxcfUx6X4YlBH9u9cR62GbTmwcx0RoZf5dMIy02iFb8ESfPp2aw7uWk+DFrf7+KI/eT18KFk+Z/Xx4gWzKVy0OO+8PxSAKtVrkZSUxPIl82jb/iXs7dMmRL/O/YHa9RrT83/9AahcrSZRkbdYNO8nU8KwecNa8uX35J33h2JtbU2lKjW4eeM6v69YTI/e/bGxsWH1b0uws7fn4xFf4+CQOp25QsWqvNWzM2tXLTUlFjnND0v/oGQhXz57OzUhq1upLEnJyfy88k9ebf0MDnZ2ZvUDwyL4c/chPujZmU7NU4//GuVKcMT/Aks2bDNLGP7af5Rxc5YSGxf/5HZIhIe4S1JOdOLwLuwdHClXubapzNU9NyXLVePYge0Ztjt+aBdevoVNyQKAT4GiePsV4djBHQCcPLqX5OQkqtVuYqpja2tHpWoNOHrPtgMu+lOgSM4ems0st4qlKD9lBIFzV3C4h5KGB1XMx0BCopFzIUZTWUw8XAw3UsI34y/4Gw8nM39zEvfe+Cz59s/W/zhT5HKBZlWsWLU7/ZGHnOT0kZ3Y2TtSumJdU5mLWx6Kl63OycPb7tNuBx4+hU3JAoCXXzE8fYty8lBqu6P7NmFja0edJh3M2vZ4dxy93ptg+j3ooj++hXPueeLk4Z3YOzhSplIdU5mre2ofHz+Y8bn41OGdePoUNiULAN4FiuHlW4QTt9tVqd2M90fNMZvadGd0Jynx7nBb4EV//HJYHycmJnDi6GFq1WloVl6nfmPiYmM5deJImjbhYSEEBwVQq655m9r1GhMaEkRwUIBp2/YODlhbW5vquLq5k5SUSGxsDAB+BQrxfIeXTckCgL2DA3nyeRAaEmyx/cxOEhITOXDqLM/UrGRW3qRmZWLi4jl8+lyaNh55cvHzF+/Tqt7dC4dWVlbYWFuRkJRkKouMjmHIhB+pVqYEkz58+/HtRA5hsLLKsldO9MAjDCtWrHjgjbZr1y4ToTx5IYEXyO/pi7W1eTd4eBVgz9a1GbRKbefpk3Yo1cOrAKHBl0x17B2ccM+dz7yOdwFuXr9CXGwMDo5OXL5wBh+/Ioz6oDuXL/jj5OxKvWeeo92rb5mmNv1XxV4OYUvp5sQFhZGnYc2sDuepkc/NwPUoMBrNy69HGvErnPGJLDIm9QVga5M6UtG0sjWXwlIIv2Fet10da05eMk9KcqqwoPPk9fRLc57I51WQA9tX36fdBTy8C6cpz+dVgIiQ1PNE0EV/PLwL8fep/ayaP5Hgy2dxz52fZi/0on6Ll0xtgi6exsuvGBOGdiHw4mkcnVyp2fgF2rzYz/Tl92kWGnSevB5p+zi/VwH2bVuTcbvA83j4FEpTnt+rIOG3z8VOzm4ULZX65S0pMZHQoPMsmzMeF7fcVK17d3pI4EV/vPyKMvbj1wi4cBonZ1dqN36e517u+9T2cVhIMElJiXj7FjAr9/L2AyAkKIDKVc3PrYEBqf3m4+tn3sbHF4DgoAB8fAvwbNsOfDHsfX5buoBmLdsSFHiZ1b8toWr12ri6pk4fbdmmXZqYgoMCCLh0noqVq1lkH7OboPCrJCYlU9DLw6y8gGd+AC6HRFC7Yhmz9+xsbSlbNPU7RUpKCuHXbjBvzWYCw64wuEcnUz0HezsWjv2Ywj6eBEdcfcx7ImLugROGDz/80Ox3g8GA0WjE0dERGxsbIiMjsba2Jnfu3E9NwhATHYmDY9qHzjk4OhEbG3Xfdp7eBdKUOzg6ERcTDUBsdCROzult2zn1/dho4uNjuXXjKgaDIXWdQz5vTh3dy9rls7l2NYz/DRyV2V3LERKv3yTx+r+s0pU0HOwgPjHtF/n4RLB/wO89QzrbYGNtICbOyPqD5lOYapexIreLgQVbkjJonbPEZnSecHAiLibj80RsdCT5vNJeWLB3cCYuNvU8EXXrGjeuhTN38ke06vQWnr5FOLhzLYt//AKA+i1e4taNK0TevIrByornXx1I7nxenDm+hz9/m8WNK6F0e+drC+1p1omNjsTRyTlNuYOjM3H3OxfHRJLfO50+dnRKt93U0f04fXQ3BisrXuk91HRB59aNq7fPxVa06zqA3Pm88T+2hw0rfuL6lTB6vjv6EfYu60RHp/aB0z/61tEp9Yp/TExMmjYxUVG36/yjjaMTALG3/8aVq1iFFzq+wtxZ05g7axoARYqVYMCQYRnGEx8Xx5TxX2JnZ0/r59Pe7CMniIyOBcDZ0Xx9kZNj6tSv6Ni4+7afvXID0xalXoh4oXFtqpUtYXrP1saGwj6eGTWVf9CD2yzrgROG06dPm35es2YN33//PaNHj6Z06dIAXLhwgY8++og2bdpYPkoLSElJwWg0/+JjNBpJ94YFRiNWVtbpvHHn7RTSa2gkdRgx9fOMQDp1bl/2tTIYcHR05r0R0/DyLUSefF4AlCpfDRtbW5bPn0rbTr3+9W5NIv88FA2G1GMxTT0g5QEGBKwMsGBLMlYGqFXaip4trJm3KZmLYUbyukGTSlYs2pq6mDqnSe88kWJM+9BKuPPvPePzREbtMBqxun1/8OSkRKIjr/P6oAlUqtUMSF3DcP1KKOuWzaB+i5dwcHLh7U9m4uFThNy3zxPFy9bAxsaO1Qsn06JDH7z8np7zREbn4ozOl/c9F6cYM+hjMKTTrnWnPrTs0Isjezczf8ZnRN26TquOb+Dg6Mw7w2bg4VPYdC4uWa46NrZ2rFowhVadeuP9FPXxHXf+3qT/hy7179A/pdz+f5OmX29v68697WdOGcfmP9fS8eVuVKhUjfCwEBbNm8WoYYMZPmoC9g7mX5hjYqL5+rOPOXf2NIOHjiJf/pz5xddo6qf03/+3L7ENq1agcqlinDp/me+XrSXs6g0mf6TpR5L1MrXoedy4cUyYMMGULAAUKVKETz75hLfeeovXXnvNYgFayqpFM1m5cKZZWbU6zbh1M+1dBuLiYnF0SntF8Q4nZ1fTSMK94mNjcLw9quDk7GK6imhWJy71io6jkwt29g6UrZT2dmgVqzdg+fypBFw8q4RB7qtRRSsaVzT/YnTiUgp501mMbGfLA33JTzHC+dtTjc6HJvN2Wxvql7fiUngy7epYc+L2VKR7/yAauJ2oPOUzlNYtnc4fS6aZlVWu1Zzw21OI7hUfF4PDfc4Tjk6u6V7ljo+PwcHJFQB7R2cMBgNlqzQwq1OmUj1OH9nBrRtXcMuVj1IV66TZTtmqDVm9cDLBl/yfqoRhzeIZrFk83aysSu3mRKZzLo6Pi7nvudgxo3NxBu2Kl60KQKnyNYmJusXapd/TvF0P7OwdKF2xdpr65as2YNWCKQRd9H8qEwbn23+PYv/RR7ExqVfB0xsFd3ZOPTZj/tkm7k4bZ65eieDPdb/T4cWuvPLanZuDVKF4idIM6tuDTRtW8+xzd0cQrkSE8eWIDwgJCmTQhyOpXrMuOZWLc+rozT9HEmJiUxcpuzje/85mxQv6AFC1THFcnR35fOYCjvifp1Kpp+/4k5wlUwnDjRs30r2zQkpKCnFx9x9uyyoNW3SkUnXzRVwH92zmxOFdpKSkmEYGAMJDA/D2K5Lhtrx8CnH5gn+a8vDQAIqUKJdax7cwsTFRRN68jqt77rt1QgLI5+GDnb0DoUEXOX1sP7UatjL745YQn9qHrm65MrWv8t9x4GwKZwLNv6WXLmCguHfahCG3q4GImxl/oy/pZyAuAS6H362TkgJhN4x4uBtwcwK//Fb45YfKxczXQjSqaE2jitZMXJ7IzbTf354adZt2olzVRmZlx/Zt5NTRnWnOE1dCL+Ppm/EfcQ+fwgRdPJ2m/EpoAAWLlQcgv1chjEYjyUmJZrfvTE5One5lZ+dAWPAFzp7YR/V6rc0SlMSE1POEs2uuh9/RLFS/eUcqVDM/Fx/Zt5lTR9L2cURoAJ6+GZ+LPX0KmW6Heq+I0MsUKpHaxxfPHuNqRDDV6prfzrJQ8XLs3fo7UbduEBcThf/xvdRo0NrsXJyYcPtLnltunkae3j5YWVkTGhJkVh4aEgiAX8HCadr4+KVOtw0NDqJosZJ32wSnbqNAwcJciQjDaDRSqmwFs7YFCxfF1c2dgMsXTWUXL5xj1LD3SYiP55PPxlK+YhVL7Fq25eeRD2srKwJDr5iVB4RFAFDEzytNm6Dwq+w/cYZW9apjb3d33mjZoqnrc8KuXn+MEedgetKzRWWqN2vVqsVnn31GYGCgqezcuXOMHDmSxo0bWyo2i8qdJz+Fi5c1e5WrXJu42GhOHNplqhd58zpnThwwu3PSP5WrXIeQwAsEB5w3lQUHnCck8ALlKqdeCbwzcrB/15+mOomJCRw5sM207etXw5k740v27/yTe+3bsR4HR2cKFTNfGCXyT1GxEHLNaPY6F2LE3s5AcZ+7SYOTPRT2MJhGDtJTt4wVbWtam40c2NtCgXwGQq8biYyFmWuS0rwgNXGZuSaJyNjHtqtPhHseDwoWK2f2KlWxLvGx0Zw+ssNUL+rWNf4+ud/szkn/VLpiXcKCzhMaePeuKKGB5wgLOk/pSqnt7owsHNxpfpOF4we24FOwJA5OLty8Fs7iHz7n8J4NZnUO7VyHvaMzBYqm/2Cn7CpXHg8KFS9n9ipTqQ5xsdGcOrzTVC/yZmofl6mUcR+XqVSX0KALhATc7eOQgHOEBl0wtTt2YCs/TfyIaxEhZm1PHt6JW658uLrn4ca1MH79fhSHdpn38YGd63BwdKbgU9bHd9jZ2VO2fEX27Nx6d3oSsGv7FpydXSheMu3fGG8fPzy9fNi9Y4tZ+e4dW/DxLUB+Dy+8fPywsrLm1ImjZnWCAi8TeesmHp7eQOrIwudDB2EwGPhi3NQcnywA2NvZUqV0MTbvO2LW55v2HsbVyZFyxdIu0g+OuMoX3y9g8z7zu1btOnIKgBKFfB9v0CIPIFMjDCNGjKBXr140b94cN7fUuyHcunWLihUr8umnn1o0wMepVLlqlCpfnZkTP6FztwG4uLrz28IZODm70rjl3TsTBAecJzExgUJFU6dg1ajfgtVLZzHh8350eu0dAJbMnYRvoeJUr5s6Dzmfhw91n3mOX2d9Q0J8PF4+BVm/8hdio6No2a6b2ecv/Gk8CfFxePkW5uiBbWxc/Sudu7+Ls4seVCYP73K4kQuhKXSoZ82GQ8nExqeOAMQlwP6zd+eO53MHGysIvX3x6q9jKXRtYs2LDa3ZfyYFe1uoV84aWxvYcjT12Qwh19JPOCJjjRm+97QrXrY6xcvWYO7kD3m+yyCcXHPxx+KpODq7Uq95Z1O90MBzJCUm4Fck9UtY1bqt2LDie6aPfovnXn0XgFXzJ+JdoASVa6fey75EuRqUr9aYZT+PIT4+Fu8Cxdm3dRUX/A/xxuBJZp+/Ys5YEuJi8PAtwsmD29j6xzye7/IeThk8OO5pUqJsNUqUq85Pkz6ifdeBOLu6s3rRdBydXGnY4m4fhwSk9nGBoql9XK1eS9Yt+4HvRvXlha4DAPjtl2/xKViCqnWaA9Cw5Yvs+HMpU0f3o3WnPtg7OLF7y0pOHNxGzwGjsbKyokTZ6pQoV52lP39DfHwsXr5FOH5wG1vWzKf9awNxeorPxR1f7sZnnwzim9HDadKiNf6njrNy2a907fkm9vb2xMREE3j5Ip7evri75wKg08vd+W7iaFxd3aleqx779+xg57bNDPpwBADu7rlo80InVi5dAEClytWJCA9j8YKfyJffk2YtnwNg1oxJ3Lxxnf/1e4/YmGjOnD5hisvRyZkC6Yxw5ASvt29J3y+/46Nvf+K5xrU5euYCc3/fRP+Xn8fBzo6omFguBIXi55mP3G6uVC1TnOplSzB29hIiY2Ip5O3BgZNnmbNqI+2b1KWIb9pRCfl3WvRsWQajMXOzjpOTk9m5cydnz54FoEyZMtSuXfu+j5p/GNtPPpl5DdFRt1j40zcc2rMFo9FI8dKVePn19/DyLWyqM2Zob66EBzNm5t1bKF67EsqCH8Zy4sgerG1sKFe5Ni/3fI9cefKb6iQmJrB07iT2bP2DuLgYChUrw4vdB1K0ZHlTnZjoSFYunMGhPVu4cf0KHl5+NGv7Ko1amN+X3ZLql029+8Vq26fnnuN5Gtakzsa57Gr6Gte27s3qcO6rTWLqdLWRv2TdqmAHO2hRzZrSfgYMBgiIMLLugPnD17o3tyaXs4FvV9y921ERLwONKljhlduAEbgQamTT4WSu3Er7GXcM72rLlqPJ/HU0/QfCPQ7Du6YO2/9xOIPHVltYTNRNls8Zy7H9mzCmGClSqjLtuw/B0+fudJnJI3tyLSKY4VPWmcquXwll2eyv8D+2C2trG0pVrEv77kNwz33PeSIhnj+WTGP/9t+JunUdL99itOzUh4o1mprqxMZEsnbxVI7t28ytGxHk8yxAo9Zdqdv07oUNS2tV2Y6Nx57cFNOYqFss+XksR/Zuxmg0UqxUZTr1GIznPefiCcN6cTUimC+m3R2RuXYllMWzvub00d1Y29hQpmIdOvUcbNbH4SGX+W3eJP4+dYC42GgKFClDq45vmB4SB6l3alq9aDpH9m3i5vUr5Pf045k2Xanf/PHczadphdS57Mf+Dnss27/Xnp1bWThvFsGBAeTJm49WbdvzfIfUBwUeP3qIER8NoO+7H/FM82dNbdav/Y2Vy37lakQEnl7etH+xK42a3J3WZTQaWf3bYtavXUl4aAi58+SlUpUavNK9N+7uuUhMTKRrxxYkJ6f/rJayFSrz2VeTHut+Vyjuya0D6/694mOwed8RZi5Zy6WQMPLnzkXnFg3o2ib1uUwHTp7lzS8mM6xPF55rlDobISomlh+Wr2Pz3sNEXL+JT/68dGhaj5dbNTKbpndHcMRVXhgw0mwbT5pbtfSfWp1d3Br/bpZ9ttugiVn22Y9LphMGgODgYM6dO0eNGjWIjo4mb968FgvsSSUM/0VPY8LwNMkOCUNO96QThv+iJ50w/Nc8yYThvyorE4b/gmyfMEwclGWf7fbu+Cz77MclU1OSEhIS+OCDD1i7di1WVlasW7eOr7/+msjISKZMmYKrq6ul4xQRERERkSyQqUXP06ZN4/Tp0/z888+muyV169aNoKAgxo4da9EARUREREQk62QqYVi9ejWffvoptWrdnTdXs2ZNPv/8czZt2mSx4EREREREHpbBYMiy16NISUlh0qRJNGjQgEqVKvH6669z6VLaZwHdkZiYyDfffEODBg2oXLkyXbt25dSpU48UQ3oylTCEhYVRsGDBNOXe3t7cunWf1ZEiIiIiIpKuqVOn8uuvv/LFF1+wcOFCDAYDvXv3JiEh/TV7I0aMYMmSJXz++ecsXbqUXLly0bt3byIjIy0aV6YShmLFirFz58405b///jvFixd/5KBERERERDLNyirrXpmUkJDArFmz6N+/P40aNaJ06dJMmDCBsLAwNmzYkKZ+QEAAS5YsYfTo0TRu3JhixYrx5ZdfYmdnx/Hjxx+l99LI1KLn/v378+6773LmzBmSk5NZvnw558+fZ/369UyYMMGiAYqIiIiI5HSnT58mOjqa2rXvPjzYzc2NsmXLsm/fPtq0aWNWf/v27bi5udGwYUOz+o9jeUCmEoZnnnmGyZMnM2PGDKytrfnxxx8pUaIEEyZMoGXL7H2bLRERERGRx6Vp06b3fX/jxo3ploeGhgKpU/zv5eHhQUhISJr6Fy9epECBAqxfv56ZM2cSFhZG2bJl+fDDDylWrFgmo09fphKGgIAAGjZsaJbRiIiIiIhkB0/jk55jY2MBsLOzMyu3t7fn5s2baepHRUVx+fJlpk6dypAhQ3Bzc2PatGm8+uqrrFmzxqLPR8tUwtC8eXOqVatGhw4dePbZZ3FycrJYQCIiIiIiT6uMRhD+jYND6gMdExISTD8DxMfH4+jomKa+ra0tkZGRTJgwwTSiMGHCBBo1asTy5ct54403MhVHejK1MmPevHkUL16cMWPGUK9ePQYPHpzuImgRERERkSfOYJV1r0y6MxUpPDzcrDw8PBwvL6809b28vLCxsTGbfuTg4ECBAgUIDAzMdBzpydReVatWjZEjR7J9+3bGjh1LfHw8b731Fo0bN9aiZxERERGRh1S6dGlcXFzYs2ePqezWrVucPHmS6tWrp6lfvXp1kpKSOHbsmKksLi6OgIAAChUqZNHYMp8GkToU0qxZM4YPH07//v2JjIzkhx9+sFRsIiIiIiIPz8qQda9MsrOzo2vXrowbN46NGzdy+vRpBg4ciJeXF82bNyc5OZmIiAji4uKA1IShbt26fPDBB+zfv5+///6bIUOGYG1tzQsvvGCpngQyuYYBICYmhvXr17Nq1Sr27NmDr68vvXr1on379paMT0RERETkP+Gdd94hKSmJoUOHEhcXR40aNfjxxx+xs7MjMDCQpk2bMnr0aDp06ADA5MmTGTduHP369SMuLo6qVasyZ84c8uTJY9G4MpUwDBw4kC1btmAwGGjZsiWzZ89Od6hEREREREQejLW1NYMHD2bw4MFp3vPz88Pf39+szMXFhREjRjBixIjHGlemEoYrV64wbNgwWrVqle6qbRERERGRrGJ4hMXHklamEoa5c+daOg4REREREcmGHjhhaNq0KUuWLCF37tw0adIEgyHjRR2Zvf+siIiIiMgjewof3JadPXDC0L59e9NDJNq3b3/fhEFERERERHKGB04Y+vXrZ/q5Xbt2FChQ4LEEJCIiIiIi2Uem1jA0b96catWq0aFDB1q1aoWzs7Ol4xIRERERyRSDlRY9W1KmenPevHkUL16cMWPGUL9+fQYPHszOnTstHZuIiIiIiGSxTCUM1apVY+TIkWzfvp2xY8cSHx/PW2+9RePGjZkwYYKlYxQREREReXAGQ9a9cqBHGq+xtbWlWbNmDB8+nP79+xMZGckPP/xgqdhERERERCSLZWoNA0BMTAzr169n1apV7NmzB19fX3r16kX79u0tGZ+IiIiIiGShTCUMAwcOZMuWLRgMBlq2bMns2bOpXr26pWMTEREREXl4WvRsUZlKGMLDwxk2bBitWrXC0dHR0jGJiIiIiEg2kamEITY2ljJlyihZEBEREZHsJ4cuPs4qmRqvCQoK0rMXRERERET+AzI1wtC7d28+/vhjevXqRcGCBXFwcDB738fHxyLBiYiIiIg8LD24zbIylTCMGzcOgH379mG4Z8jHaDRiMBg4deqUZaITEREREZEslamEYc6cOZaOQ0REREREsqFMJQw1a9a0dBwiIiIiIpZh0JQkS8pUwvDRRx/d9/3Ro0dnKhgREREREcleMpUwBAYGmv2elJREQEAA0dHRtG7d2iKBiYiIiIhkipVuq2pJmUoY5s6dm6bMaDQyfPhwcufO/chBiYiIiIhI9mCxCV4Gg4HXX3+dJUuWWGqTIiIiIiKSxTI1wpCRK1euEBMTY8lNioiIiIg8FIMWPVuUwWg0Gh+20ZQpU9KURUZGsnr1aipXrpzu+yIiIiIiT0Lcgq+z7LMdXvkgyz77ccnUCMOyZcvSlNna2tKgQQMGDRr0yEGJiIiIiGSaFj1bVKYShk2bNpl+vnbtGvv27SNfvnxUq1bNYoFtPBZnsW2JuaYVHAAY+UtiFkeSMw3vagvAattSWRxJztUm0R+A4XN0DD8uI7vZcuzvsKwOI8eqUNwTgF2nbmVxJDlXnTJu1H/ur6wOI8favqpRVocgT9BDJQzfffcdc+bMYdGiRRQqVIhDhw7Ru3dvoqOjAahduzbTpk3DwcHhsQQrIiIiIvKvtIbBoh64NxcuXMiMGTN46aWXyJs3L5D6ADcnJyd+//13tmzZQnR0NDNmzHhswYqIiIiIyJP1wAnD4sWL+fDDDxk0aBAuLi4cPXqUixcv0q1bN4oVK4anpydvvfUWa9aseZzxioiIiIjIE/TAU5LOnTtH3bp1Tb/v3r0bg8FAo0Z357AVL16c4OBgy0YoIiIiIvIwDFr0bEkPNcHLcE/nHzhwgDx58lCiRAlTWXR0NI6OjpaLTkREREREstQDJwylSpVi3759ANy6dYs9e/ZQv359szpr166lZMmSlo1QRERERORhWFll3SsHeuApSV26dGHYsGH4+/tz6NAhEhISeO211wAIDw9n1apV/Pjjj4waNeqxBSsiIiIiIk/WAycMzz33HPHx8SxYsAArKysmTpxI+fLlAZg5cya//vorvXv35oUXXnhswYqIiIiIyJP1UM9h6NSpE506dUpT3rt3b/r27Uvu3LktFpiIiIiISKboOQwWlaknPf+Tp6enJTYjIiIiIiLZjEUSBhERERGRbMNKt1W1JI3XiIiIiIhIhjTCICIiIiI5i9YwWJR6U0REREREMqSEQUREREREMqQpSSIiIiKSsxi06NmSNMIgIiIiIiIZ0giDiIiIiOQsVrombknqTRERERERyZASBhERERERyZCmJImIiIhIzqJFzxalEQYREREREcmQRhhEREREJGfRk54tSr0pIiIiIiIZ0giDiIiIiOQsuq2qRak3RUREREQkQ0oYREREREQkQ5qSJCIiIiI5i26ralEaYRARERERkQxphEFEREREchbdVtWi1JsiIiIiIpIhJQwiIiIiIpIhTUkSERERkZxFi54tSiMMIiIiIiKSIY0wiIiIiEjOoic9W9R/PmGIi41m2ZzxHN23mbjYaIqWqkznnkPwLlDsvu1uXo9g6exxnDq6m+SkRMpUqkPnnkPIldfTVOdqeBDL507kzIl9JCUlUqp8Ddp1fRdPn8KmOqFBF/hsQLs02/f0KczwSb9ZajezHTsbaF7VitIFrLCzhYAII3/sT+bKzfu3K+hhoEllK7xyG0hIgpOXUth0JIWExPTruznBW21t2H06hb+Oplh+R3IQBz8vGh5axf6Ofbm2dW9Wh5Pt2dlAi2pWlC5ohb1N6jG8dl8yEf9yDBfyMNC0ihVeeQwkJMKJSylsOpxC/H2O4b7P27DrVApbjuTMY/jQ/j0smPsDgQEXcXPLRYvWL9C+cxcM95lSsHXTepYt/oXw0GDy5ffk+Y6v0KxlW7M6e3dtY8mCnwkOCiBX7jw0bNKC9p27Ymtra6pz8vhh5s/5gYvnz+Lg4Eid+s/wSrc3cHJyfmz7+6QdPbiTZfOmExxwHlf33DzTsgNtOva4b//u3LKG35fOJiIsmLz5PHm2/Ws0at4u3bqxMVEMHfAq7V7uTYOmz5m9F3DxbxbNmcT5MyewsbWlfOXavNi9P+658lpyF58ajo7W9O1ZlAa18+HkaM3xU7f49vu/uRgQk2GbxT/UwtvTId33gsNiefENna9zkpSUFKZMmcLixYu5desW1apVY/jw4RQqVOhf265atYr333+fjRs34ufnZ9G4/vMJw6wJH3Dx7+O0f20gDo7OrFk8g29H9ubTCctxdnVPt01ychLfjepLfFwMr/T+hOTkJFbM+5bJn7/Jx+MWYW1jS1xsNJM+exMra2te7v0JNra2rF0ykwnDevHphGWmbQdeOA3AuyN+wNbO3vQZ9/6cE3Wsb41vPgMbDiYTnwiNKlrTvZkN361KIi4h/TZeuaFrE2vOhxpZtDUZV0doVsWafG4GftmUnG6b5+tY42CneYz/xrGgDzVX/4htLresDuWp0amhNb557x7DjSta072FDd/9lkRsRsdwHnitmTXnQ4ws3JKMqxM0q2pNPncDc/9M/xhuVzdnH8OnTx7j688/om6DJrzy2hucPnmUBXO+x5iSQseXu6XbZue2zUweP4rWz3eiSrVa7N29jemTxmBnZ0fDZ1oAcOTQPsaOGkrdBk3o0rMPly+eZ/7P33Pr5g3eeGsgAOfPneGLT9+nQuXqvP/x51y/epV5s2cQFHiJYV+Mf2J98DidPX2Eb798j5r1mtOhy5ucPXWEpfOmkWI08nzn19Nts3fHn3z/7Qiat32ZClXrcHDPX/z03Shs7eyp2+hZs7pRkTf59sv3uBoRkmY7N65f4etP3yRvfi/eeGcY8fHxLJ4zmfGfDeDTMbOxsfnvfQUZMbgMZUu4MnX2eaJjknn9lUJ8O6oSXd/eR2RUUrptPv7yBHa25ueAcqXdeOeN4vy2Nm2/SyrjU7qGYerUqfz666+MHj0aT09Pxo4dS+/evfn999+xs7PLsF1QUBAjR458bHH99/613uO8/xGOH9zG2x9PoXzVBgAUL1OVYX1bs3XdQp7t9L902x3ctYHAi/4MnbAUnwLFAfArXIovBnVk/8511GrYlgM71xEReplPJywzjVb4FizBp2+35uCu9TRo0RmAwIv+5PXwoWT5Gk9gj7MHv3wGSvpZMW9TEn8HGwG4HJ7EgHY21Chpxbbj6V9FrV3Gmph4WLQ1mRRTlWTa1bUhr1syV2+Z169e0op8bk/nCeOJMRjw69aeMl8PyepInip++QyU8rPil41JnA1KPYYvhSXxbgcbapSyYuux9I/humWsiY6HhX8lk3zPMdy+XvrHcI1SVuRzz9nH8OIFsylctDjvvD8UgCrVa5GUlMTyJfNo2/4l7O3TXjz5de4P1K7XmJ7/6w9A5Wo1iYq8xaJ5P5kShs0b1pIvvyfvvD8Ua2trKlWpwc0b1/l9xWJ69O6PjY0Nvy9fhJt7Lt7/+HOzUYfvJo4mKPAyvn4Fn0APPF6//foDBYuUpM/AzwCoWLUuyUlJrFn6M62efxU7+7RXrpfNm0b1Ok15tdcgACpUqUN05E1WLJhpljAc3PMX834YR1xcbLqffXjvVqIibzJszGw8vFOvdjo5uzD+swH8ffoIpctXs/TuZmvlSrlRr0Ze3h9xjN0HrgFw9MRNFv1Qiw6tffh50eV02509H2X2u5OjNSMGl2XHvqvMWxrw2OOWJychIYFZs2YxePBgGjVqBMCECRNo0KABGzZsoE2bNum2S0lJYfDgwZQrV47du3c/ltj+0xO8Th7eib2DI2Uq1TGVubrnoXjZ6hw/uD3DdqcO78TTp7ApWQDwLlAML98inLjdrkrtZrw/ao7Z1CZrm9Q/SEmJdy8/Bl70x69wKYvt09OgmI+BhEQj50KMprKYeLgYbqSEb8ZfjjYeTmb+5qR7kgVMX7qs/3Ek53KBZlWsWLU7/au2ksqtYinKTxlB4NwVHO6hpOFBFfc1EJ9o5Fyw+TF8Kez+x/Cfh5KZvzHpnmTh7jFs849jOLdL6rS9lbty7jGcmJjAiaOHqVWnoVl5nfqNiYuN5dSJI2nahIeFEBwUQK265m1q12tMaEgQwUEBpm3bOzhgbW1tquPq5k5SUiKxsanTP7r0+B8fDv/aLFmwsbW53T6DOWJPkcTEBE4fP0C12s+YlVev24S4uBjOnDycpk1EWDChwZepVuefbZoSHhpIaNAlAKKjIpn89RBKl6/G+8MnZfD5qX3ocM/0Lle3XEDqyMR/Ta2quYmJTWbfoWumshu3Ejl8/Aa1q+d54O30fKUQudxtGT/t7OMIU7LQ6dOniY6Opnbt2qYyNzc3ypYty759+zJsN336dBITE+nTp89ji+0/PcIQGnSevB5+WFubd0N+rwLs27Ym43aB5/HwSTuXLL9XQcKDU0+mTs5uFC1VCYCkxERCg86zbM54XNxyU7VuS1ObwIv+ePkVZezHrxFw4TROzq7Ubvw8z73c15Rg5DT53AxcjwKj0bz8eqQRv8IZ57CRMakvAFub1Ku8TStbcykshfAb5nXb1bHm5CXzpETSir0cwpbSzYkLCiNPw5pZHc5TI7976jGc8o/D61qkkQpFMj6Gb8WkviB1DYRffgNNq1hzMSyFsBt36xmAdvWsOXHRaBqFy4nCQoJJSkrE27eAWbnX7avRIUEBVK5qflwGBqSeY318zefnevn4AhAcFICPbwGebduBL4a9z29LF9CsZVuCAi+z+rclVK1eG1fX1Kl3efN5kDefBwCxsTGcPX2S+T9/T5lyFSlc5P7r2J4GEaFBJCUl4uVjPlLi6Z3a36HBlylfpbbZeyGBFwDu28bLtxD29g58OXkh3r6FiQgLTvfza9ZvxuplP/PLzDG82us9EhPjWTh7Erly56Nsxf/e+aZQASeCQ2PNLhgABIXE0ryxZ/qN/sHLw56ObX35ZfFlwiLiH0OUOUgWPum5adOm931/48aN6ZaHhoYC4O3tbVbu4eFBSEj608+OHj3KrFmzWLJkCWFhYZmI9sFkOmEIDg7Gzc0NFxcXdu/ezfr166latSpt27b998bZRGx0JI7pLGxzcHQmLjYqnRapYmIiye+ddqja3tEp3XZTR/fj9NHdGKyseKX3UNxz5wPg1o2r3LpxFYPBinZdB5A7nzf+x/awYcVPXL8SRs93Rz/C3mVfDnYQn5j2S1B8Itg/YI40pLMNNtYGYuKMrD9ofvatXcaK3C4GFmxJfz6o3JV4/SaJ1/97V/oelSWO4Q9eSj2Go+OMrNuf/jE8f1POPoajo1PPl/9cYOzo5AhATEzahaAxUVG36/yjjaMTALEx0QCUq1iFFzq+wtxZ05g7axoARYqVYMCQYWm2aTQa6fnycyQlJeLq5k73N/o+ym5lGzHRkUDavnL4R1+Zt0m/f//ZxsbWFm/fwvf9fPdceenW5wOmf/MJe3f8CYCzixsffD4NJ2eXh9ybp5+Lsw0xsWlHDGNik3F2tE6nRVqdn/cjMTGFxSuDLB2eZAOxsanT+/65VsHe3p6bN9P+rY6JieH999/n/fffp3DhwtkvYdiwYQMDBw5k+vTpFCpUiDfeeIMCBQqwbNkybt68SZcuXSwd5yNLSUnBaDT/o2w0Gkm9lkeaciurjP/xGlOM6d9dwgiGdNq17tSHlh16cWTvZubP+IyoW9dp1fENHBydeWfYDDx8CpMnnxcAJctVx8bWjlULptCqU2+8/Yo+3I5mQ//sKoMB0rtmaiDtFdv0WBlgwZZkrAxQq7QVPVtYM29TMhfDjOR1gyaVrFi0NTnDu86IPAwD6RzDkO5BbCDtyFl6rAwwf1MyVlapycHrraz55c97juEqVizckvOPYeOdzspgcaJVOuUpt8/jac7Bt7dluH1VceaUcWz+cy0dX+5GhUrVCA8LYdG8WYwaNpjhoyZg73B37n5ycjIfDh9NSnIyq1cu4dMh/flk5BjKV6r6qLuYpe70ryGdv3MABqv79O8/2pi29RC3qtz11x/MnDiMGvWa0bDp8yQkxLN2xVzGjejPh6Nm4ONX+IG39bQxGFL/nd/LymBI9/xgMEDKA/zxs7Ozom1zL37fEEpkdM6+mGARWTjCkNEIwr9xuH1eSkhIMP0MEB8fj6OjY5r6X3zxBYULF+bll1/OXKAPIVMJw9SpU+nVqxd169bl+++/x8fHh9WrV7N27VqmTJmSLROGNYtnsGbxdLOyKrWbE3nzWpq68XExODplfPXD0dmVuHSuzGTUrnjZ1D86pcrXJCbqFmuXfk/zdj2ws3egdMXaaeqXr9qAVQumEHTR/6lPGBpVtKJxRfMk6sSlFPKmsxjZzpYH+oKUYoTzt6canQ9N5u22NtQvb8Wl8GTa1bHmxO2pSPd+n7jzpe9BvsyJ3KtRJSueqfSPY/hixsdw3AMew3emy50PSabv8zY0rJB6DLevlzqd7nyI0ewLh4HULyAPklQ/LZxvX2X+55Xu2JjUq2zpXYV2dnYFIOafbeLutHHm6pUI/lz3Ox1e7Morr71xu0YVipcozaC+Pdi0YTXPPtfR1NbGxsY09alC5eoMfLsbyxb98tQnDHf6LzbWvK/ibq/hcErn75XT7f79Z5v4O/37ELebXbHwe0qUrsTb739pKitXuRYf9+vMsnnT6PfB1w+8radNz5cL8fqrhc3KNm+PoECutF/6HB2siYr597VKNavkxtnJhvVbHt9VZMlad6YihYeHU7Dg3Zks4eHhlC5dOk39pUuXYmdnR5UqVYDUix8Abdu25fnnn+ezzz6zWGyZShjOnTvHlClTsLKyYvv27TRq1AgrKyuqVKlCUFD2HCar37wjFaqZL5I7sm8zp47sJCUlBat7rppEhAbg6Vskw215+hQy3Q71XhGhlylUojwAF88e42pEMNXuWa8AUKh4OfZu/Z2oWzeIi4nC//heajRobZZoJCakzkt0ccv98DuazRw4m8KZQPNvOKULGCjunfbLVm5XAxE3M/42VNLPQFwCXA6/WyclBcJuGPFwN+DmBH75rfDLD5WLmV9ZaFTRmkYVrZm4PJGbaXM9kQwdOJP+MVzMJ/Ua7L3v5HE1cOU+x3Cp28fwpXuO4eQUCLtuJH8uA+5OUCC/FQXSOYYbV7KmcSVrJixN5EYOOYY9vX2wsrImNMT870ZoSCAAfgULp2nj43dnLn0QRYuVvNsmOHUbBQoW5kpEGEajkVJlK5i1LVi4KK5u7gRcvgjAvt3bcXZxoWz5yqY6tra2FCpczFTnaZbfyw8rK2vCbvfnHWEhqQvDfQqk/Tvn7VvIVKdQ0VLptHnwi1hXw0OoVquxWZm9vQNFipclKOD8A2/nafTbuhB27LtqVtawdj5qVs2d5uKVr7cjl+7zHIY76tbIS3BoLP5/ZzxlWp5upUuXxsXFhT179pgShlu3bnHy5Em6du2apv769evNfj9y5AiDBw9m5syZFCtm2XVYmUoY3NzciIyMJCoqisOHD/P666n3cr58+TK5cuWyZHwWkyuPB7nyeJiVJSTE8cfS7zl1eCflqtYHIPLmNf4+uZ+WHXpnuK0yleqyf/taQgLOme6CFBJwjtCgC7S6fSvWYwe2sm7ZjxQpUZE8+e8uXjl5eCduufLh6p6H0MBz/Pr9KGxsbKnbtL2pzoGd63BwdKZg0bIW2/+sEhULUbHmX6BsbaBhBQPFfQymBZ1O9lDYw5DhLVUB6paxwsnewLTVSaaTrb0tFMhn4FK4kchYmLkm7TDt/1rbcOBsCgfOphCZ/t3/RDIUGQuR/zyGraFRxdSk4d5juJCngW0Z3FIVoG6528fwqiTTSIG9beri58u3j+EZq9Mew33a2LD/TM47hu3s7ClbviJ7dm7l+Q4vm6YZ7dq+BWdnF4qXLJOmjbePH55ePuzesYW6De7eyWf3ji34+BYgv4cXdvYOWFlZc+rEUapWvzuKGxR4mchbN/HwTD0nr1z2K5G3bvLNdz+Zbn4RHR3FmdMnKFOu4uPc9SfCzs6eUuWqcGD3Zp5t19XUv/t3bsLJ2ZWiJcqlaePpXYD8nr7s37mRmvWamcr379yIl09B8nl4p2mTEW+/wpw5dRij8e403oSEeC6dP/2v6x+edlevJXD1mvkDWRzsren+UiFqVc1juq1qLjdbKpfPxZxFl/51m2VLunL01K1/rSepnsbnMNjZ2dG1a1fGjRtHnjx58PX1ZezYsXh5edG8eXOSk5O5du0arq6uODg4pHmY251F0z4+PuTNa9mHI2YqYWjUqBHDhg3DxcUFFxcX6tWrx86dOxkxYgSNGze2aICPU4my1ShRrjo/TfqI9l0H4uzqzupF03F0cqXh7eckQGoykJSYQIGiqX+8qtVrybplP/DdqL680HUAAL/98i0+BUtQtU5zABq2fJEdfy5l6uh+tO7UB3sHJ3ZvWcmJg9voOWA0VlZWlChbnRLlqrP052+Ij4/Fy7cIxw9uY8ua+bR/bSBOLjnzIVqXw41cCE2hQz1rNhxKJjY+dQQgLgH2n737ZSufe+qtJkOvp/7+17EUujax5sWG1uw/k4K9LdQrZ42tDWw5mvpshpBr6V/djYw1ZvieyMO6dPsY7tjAmg0HkomJh2cqpR7D+87cPYbzu4O1NYTenvn419EUXmtqzYuNrNl3+xhuUN4aOxvYfDj12QzBVzM+hjN672nW8eVufPbJIL4ZPZwmLVrjf+o4K5f9Steeb2Jvb09MTDSBly/i6e2Lu3suADq93J3vJo7G1dWd6rXqsX/PDnZu28ygD0cA4O6eizYvdGLl0gUAVKpcnYjwMBYv+Il8+T1p1jL1acSdX+nBF8PeZ9yXw2jZuh0xMdGsWDyP+Lg4XuqS/kPNnjbPdX6dscP78t3Yj2jY9DnOnj7K2hVz6dytP3b2DsTGRBEUcAEPLz/c3FNHtZ9/sRc/Tv4MF1d3KtdsyOG9W9m740+zqUUPosOrfZg0enDqZzd7nqTERNatnM/1qxH0Gfj549jdbO3IiZscPHqDYe+VZurs89y6lcjrrxYmKjrJ7AFshQs4YWtrZfb8BSur1PI/t4ZnRejyBL3zzjskJSUxdOhQ4uLiqFGjBj/++CN2dnYEBgbStGlTRo8eTYcOHZ5oXAaj8eFndcfFxTFx4kQCAgLo3bs3lStXZvLkyVy6dImRI0fi7PzgcxwzsvFY3CNv40HERN1iyc9jObJ3M0ajkWKlKtOpx2A877n6MWFYL65GBPPFtLWmsmtXQlk862tOH92NtY0NZSrWoVPPwbjnzm+qEx5ymd/mTeLvUweIi42mQJEytOr4hukhcZB6p6bVi6ZzZN8mbl6/Qn5PP55p05X6ze/Or7W0phVSF9KM/CXrVlQ62EGLataU9jNgMEBAhJF1B8wfXNW9uTW5nA18u+LuFdciXgYaVbDCK7cBI3Ah1Mimw8lcuc9Fl+FdbdlyNJm/jmZ85deShndNvU3Oatun6/kaeRrWpM7Guexq+hrXtu7N6nDuq02iPwDD52TtMdyqujWlC6Qew5fDjfyx3/wY7tHCmlwuBiYuMz+GG1e6ewxfDDXy56FkrtznZlUju9my+UgyW448mWP4zmce+/vJzJXes3MrC+fNIjgwgDx589GqbXue75C6iO/40UOM+GgAfd/9iGea331o2Pq1v7Fy2a9cjYjA08ub9i92pVGTu1NAjUYjq39bzPq1KwkPDSF3nrxUqlKDV7r3NiUeAEcPH2Dx/J+4eP5vDFZWlK9YhVe79U53OpQlVSieehvNXU/givGB3ZtZvmAmoUGXyJ03P02e7cyz7VKnN5w6doCvP32TXv2H0aDpc6Y2m9ct448Vv3D1Shgenr606diDes+0Tnf7EWHBDO7zQpptABw9uJOVi37k0nl/HBydKFK8LJ26vk3BIiXT3ZYl1SnjRv3n/nrsn/MwXJ1t6PdGMRrWzovBYODYqZtM+uEcAUF3hw4nf1kJLw8HOr+xx1SWy92W33+py9jvzvDbH9nj6c7bVzXK6hDuK2broiz7bKeGL2bZZz8umUoYnoQnlTD8F2WHhCEne1oThqdJdkgYcronmTD8Fz3JhOG/KjsmDDmJEoaM5cSEIdP3nFq1apVprtTUqVNp27Ytw4YNIz5eDxIRERERkSxkMGTdKwfKVMIwdepUPvnkE4KDgzl06BCTJk2iSpUq7Nmzh3Hjxlk6RhERERERySKZShiWLl3K119/TdWqVVm/fj2VK1fm888/Z9SoUfzxxx+WjlFERERERLJIpu6SFB4ebnpIxM6dO2nePPXOQN7e3ty6pfmYIiIiIpKFHuKp5PLvMtWbXl5eXLhwgcuXL+Pv70+9evUA2L9/P15eXhYNUEREREREsk6mRhhefvllBgwYgL29PaVKlaJKlSrMmzePsWPH0r9/f0vHKCIiIiLywJ7GB7dlZ5lKGHr16kWRIkUICAjg+eefB8DV1ZVPPvmEzp07/0trERERERF5WmQqYQBo0qSJ2e93EgcREREREck5MpUwJCQksHDhQvz9/UlOTjYrP3bsGOvXr7dYgCIiIiIiD8WgRc+WlKmE4csvv2TZsmWUK1eOI0eOUKVKFS5dusTVq1fp0aOHhUMUEREREZGskqmE4c8//+Srr76idevWtGjRgs8//5wCBQowcOBAEhMTLR2jiIiIiMgDM2qEwaIy1Zs3btygcuXKAJQsWZKTJ09ia2tLnz592Lx5syXjExERERGRLJSphCFfvnxcvXoVgIIFC3LmzBkAcufOzZUrVywXnYiIiIiIZKlMJQyNGjVi+PDh+Pv7U7VqVVatWsWxY8eYN2+eHtwmIiIiIlnLYMi6Vw6UqYTh/fffx8vLi/3799O0aVNKlChB586dmTt3Lu+8846lYxQRERERkSySqUXPrq6uTJ061fT7zJkzOXnyJPny5cPDw8NiwYmIiIiIPCwterasB04YgoOD7/t+rly5SEpKIjg4GB8fn0cOTEREREREst4DJwxNmjTB8C/zsoxGIwaDgVOnTj1yYCIiIiIimZJD1xJklQdOGObMmfM44xARERERkWzogROGmjVrmv1+7tw5oqOjqVixIgCzZs2icePGFC1a1LIRioiIiIhIlsnUipBt27bRvn17Nm3aZCpbs2YNHTt2ZP/+/RYLTkRERETkoRmssu6VA2VqryZMmMAbb7zBu+++aypbsmQJ3bp1Y9y4cZaKTUREREREslimEobz58/Tvn37NOWdOnXC39//kYMSEREREckso8GQZa+cKFMJQ548eTh58mSa8rNnz+Lm5vbIQYmIiIiISPaQqQe3tW/fnpEjR3Lr1i0qVqyIwWDg2LFjTJw4Md2RBxEREREReTplKmF4++23uX79Op999hlJSUkYjUZsbGx47bXXeOeddywdo4iIiIjIg8uhi4+zykMlDGFhYWzYsAF7e3v+97//8d5773HhwgVsbGwoXLgwDg4OjytOERERERHJAg+cMOzfv5/evXsTGxsLgLOzM99++y3169d/bMGJiIiIiDwsIzlz8XFWeeDxmkmTJlG7dm22bt3Kjh07qF+/Pl999dXjjE1ERERERLLYA48wnDp1igULFuDh4QHAxx9/TOPGjYmKisLFxeWxBSgiIiIi8jCMWsNgUQ/cm9HR0eTKlcv0u6enJ7a2tty8efNxxCUiIiIiItnAAycMKSkpGP7xMApra2tSUlIsHpSIiIiIiGQPmbqtqoiIiIhItqUpSRb1UAnDrFmzcHR0NP2elJTEnDlzcHd3N6vXr18/y0QnIiIiIiJZ6oETBh8fH9auXWtWlj9/fjZu3GhWZjAYlDCIiIiISJYxGnRbVUt64IRh06ZNjzMOERERERHJhjTBS0REREREMmQwGo3GrA5CRERERMRSrh3dlmWfnadigyz77MdFIwwiIiIiIpKhbHtb1X3+N7I6hByrRqlcAPxxOCFrA8mhWlW2A2D4nMQsjiTnGtnNFoDVtqWyOJKcq02iP9+t/fd6kjl9n03979ytWRtHTvZaQzh77lJWh5FjlShWKKtDuD8terYojTCIiIiIiEiGsu0Ig4iIiIhIZhj14DaLUm+KiIiIiEiGlDCIiIiIiEiGNCVJRERERHIUI1r0bEkaYRARERERkQxphEFEREREchQterYs9aaIiIiIiGRICYOIiIiIiGRIU5JEREREJGfRk54tSiMMIiIiIiKSIY0wiIiIiEiOYtQ1cYtSb4qIiIiISIY0wiAiIiIiOYpRaxgsSiMMIiIiIiKSISUMIiIiIiKSIU1JEhEREZEcRU96tiz1poiIiIiIZEgjDCIiIiKSoxjRomdL0giDiIiIiEg2kJKSwqRJk2jQoAGVKlXi9ddf59KlSxnWP3v2LP/73/+oVasWderU4Z133iE4ONjicSlhEBERERHJBqZOncqvv/7KF198wcKFCzEYDPTu3ZuEhIQ0da9fv07Pnj1xdnbml19+4fvvv+f69eu88cYbxMfHWzQuJQwiIiIikqMYDVZZ9sqshIQEZs2aRf/+/WnUqBGlS5dmwoQJhIWFsWHDhjT1//zzT2JjY/nqq68oUaIE5cuXZ+zYsZw7d46DBw8+SveloYRBRERERCSLnT59mujoaGrXrm0qc3Nzo2zZsuzbty9N/Tp16vDdd99hb2+f5r2bN29aNDYtehYRERGRHCUrn/TctGnT+76/cePGdMtDQ0MB8Pb2Niv38PAgJCQkTX0/Pz/8/PzMymbMmIG9vT01atR4mJD/lUYYRERERESyWGxsLAB2dnZm5fb29g+0JmHOnDnMnz+fQYMGkTdvXovGphEGEREREclRsvK2qhmNIPwbBwcHIHUtw52fAeLj43F0dMywndFo5Ntvv2XatGn06dOHHj16ZOrz70cjDCIiIiIiWezOVKTw8HCz8vDwcLy8vNJtk5iYyODBg5k+fTpDhgxh0KBBjyU2JQwiIiIiIlmsdOnSuLi4sGfPHlPZrVu3OHnyJNWrV0+3zZAhQ/jjjz/45ptv6NWr12OLTVOSRERERCRHeZTbm2YVOzs7unbtyrhx48iTJw++vr6MHTsWLy8vmjdvTnJyMteuXcPV1RUHBweWLVvGmjVrGDJkCDVr1iQiIsK0rTt1LOXp600RERERkRzonXfeoVOnTgwdOpRXXnkFa2trfvzxR+zs7AgJCaF+/fqsWbMGgN9//x2AMWPGUL9+fbPXnTqWohEGEREREclRsnLR86OwtrZm8ODBDB48OM17fn5++Pv7m36fNWvWE4tLIwwiIiIiIpIhJQwiIiIiIpIhTUkSERERkRzlaVz0nJ2pN0VEREREJEP/6RGGIwd2sWTedIIuX8DVPTdNW7XnuU7dMRgyXiizffNaVi35mfCwYPLm96RN+6480+IFszqb163gj5W/muo0e7YjLZ97KcPtHtj9FxO+HMLHo6ZStkI1i+5jdhAXG81vv3zDsX2biI+LoXDJSnTs8SFefsXu2+7m9QhWzBmL/7FdJCUlUrpiXTr0+IBceTxNdYxGI5t//5kdfy7mxtVQ8uTzoeGzr9Kg5SumOmFB5/ly0Atptu/hU5hPJqyy3I5mI3Y20KKaFaULWmFvAwERRtbuSybi5v3bFfIw0LSKFV55DCQkwolLKWw6nEJ8Yvr13Zyg7/M27DqVwpYjKZbfkRzEwc+LhodWsb9jX65t3ZvV4WR7CXFRbF85hvPHNpIQH4134So07PAJeb2K37ddROApdq4eT9jlY2A0kr9AOeq1fQ+PAuVMdW5dDWT7qrEEnt1DSnIifiVqU++598ntUeRx71a2Eh8XxcbFY/A/vJGEuGj8ilWhxcufkN/n/n18r8VT+2Hv4MLzr39lKls560OO7lqeYZt+X20iV17fR4o9uzuwfx9z58wmIOAybm7uPNu6DZ1ffPm+3y/u+PvsGd4bNICZP/yEp6f5w7r++GMNK1csJzQ0hPz5PWjd5jmef6HdA233v+ZpXfScXf1nE4Yzp44yftT71K7fjE5d3uTMqSMs/mU6RqORF17smW6bPds3MmPiSFo+9xIVq9bhwO6/+HHKl9jZ2VOvcSsA/lyzlNnTx9C2YzfKV67JOf8TzJ81ifi4OF54sUeabUbeusmsqV+lKc9Jfp40hMt/H+f5LgNxcHThjyXTmPJZLz4a/xvOLu7ptklOTmLG6LeIj4uhc6+hJCcnsWr+RKaN6sOQrxdjbWMLwMp54/lrzS88+2JfChWvwMlD21gy60usrW2o26wzAEEXU+8o0G/YLGxt7UyfYWtnufsTZzedGlrjm9fAhoPJxCdC44rWdG9hw3e/JRGbkH4brzzwWjNrzocYWbglGVcnaFbVmnzuBub+mZxum3Z1rXGw00n53zgW9KHm6h+xzeWW1aE8Nf6Y8x5hl49S77nB2Dm4sHfdFJZ/152uH67GwTlXum1uXLnMksld8PArR7NXRmEwWHFw8yyWTHqVV95fTm7PoiTERbF82utYWVnzTOcRWNvYsW/9VJZOeY2uH/ye4bZzouUz3yP44lGadhyMvaMLW1dN4ZdvuvPmZ6tx/Jd+SElJZv2vX+J/aAMV67Q3e69+27ep2uhls7K4mJssnT6AQqVq4p7b29K7kq2cOnmCzz8bToMGjXitWw9OnjjO3DmzMRqNvPTyq/dte/78OUaM+JTk5LTn3DWrVzH1u8l06vwSlatU5Yz/aX78YQbx8XG8+NIr6WxNxHL+swnD/9m767Cosj4O4F9qaAHpVAQBMRAFExO7u3XNVddaV1117bXXXLtf106stbsDExMTpUMR6Zr7/jE67jgzijgIjN/P88zz6Ln33Hvucbxzf/fUnm1rUMzZDQN/mwIA8KpYFdlZWTiwewMat+wMka78w+TOzSvgW60uuvUdDgAoV6EKkpLeYfeW1aheuxEEQcCB3RtQ2a8eOv00CABQxssXURGvcOzgDoUBw/oVf0FLW33/GV48vo0HN8/h5zFLUdq7JgDApVQFTBncCBeObkPDtv0V5rt95RjCXwZjzNw9sHWUvO1yKO6BWSNb4+blI/Ct0RyvY8Jw+t8NaNtrLGo0lPw4uZWpjLevo/DwziVpwBD28hGKWtqjZGnf73DF+c/BQgPuDprYdDILT8IFAMDL6Cz82kYbvu6aOHdXcUtAtVJaSE4Htp/NRrZ0l2y0rq4N8yLZeP1Odn9fd01YmDBY+CwNDTj0aI1Ss3/P75IUKpEvbiHkwRm0+HkVinvWAgDYufjgnz/9EXRxCyo1+EVhvjtnN0BbRw8tfl4JHV0DAIBDySr435Q6uHN+E2q3m4gntw8jIe4luo45KG2tsLB1w/qp/nhy+wjKVu+k8NjqJuzZLTy9ewadhq6Ca1lJHTuW9MGSsf64fnoLajRTXMcAEB32CEe2TEXky3vQVvDipaiVE4paOcmk7Vw6CHqGJmjVdy40NNW7N/SWLZvgXMIFI0aNBgBU9PFFVnY2du3cjlat20JXV1cuT2ZmJg4c2IfNG/+BSCSS2y4IAnbu2I4aNWqhZy/Jar7ly3sjPDwMB/bvY8CgAMcwqFauajMtLQ179+7FvHnz8PbtW1y7dg1v3rxRddnyTGZmBh7evQmfqrVl0n2r10VaagqCH9yWyxMbHYGo8Ffw/SRPpWp1ERMVhsjwVwCA3ycvROeeg2X20dbWRlamfJ+OK+eP497ta+j002C5beri0Z1LEOnqw6NcNWmaUZGicPX0wYPb5z+T7yKs7IpLgwUAsHFwgbV9CTy4JckXFHgK2joiVK3bRiZvz1/nos+IBdK/h4cEw764u6ouqcBztddAeqaAZxGCNC0lHXgZLaCkvfIH/BO3srHlZNZ/ggVI/6z9yZ3CzAioX0ET+y8rbnkgiSLl3FFmyWSEbdyL2z0ZNOTUy0cXoCMygJN7dWmagVFR2Lv6IuTBOaX5zKxLoELd3tJgAQB0RPowMrVBQpzkHu1ariHaD9sm07VJ832LZXaWkuY3NfTs/gXo6BqghOfHOjY0Lopibr54dk95HQPA/rWjIQhi9Bq7HYbG5l8815M7pxF8+wQadBwLPQP1bmXLzMzA3aAgVKtWXSa9ul8NpKam4v79uwrzXQ+8hq2bN6FDx87o2auvwn3+nDoDvXrLbtPW1kamgucLIlX76lfbcXFx6NSpE+Li4pCRkYEOHTpg3bp1uHv3LjZs2AAXl8/3Sy8IYqLCkZWVCVs72TcgNrYOAIDI8FCU9a4isy0iLESyj71sHuv3eaIiXsHW3gn2jpI+sIIgIDnpHQIvn8GF04fRpHVXmXwJ8a+xfsUcdO83HKZFLVR2bQVNdPhzmFs7QEtL9qtmYeOEGxcOfibfC1jZFpdLt7BxRGzkSwCSQMDKthiePryOA1sWIuLVE5iYWaJeyz7wa9BRmic85BFsHFywYHxXhIU8gr6BMSrVbommHQZLuzapE0sTDcQnAWJBNv1NooCyzsrfEbxLkXwAyRgIB0sN+HtrISRajOi3H/fTANCquhbuhwh4GiEoOhS9l/oqEmc86iMtPBpFa1bK7+IUGvHRz1DE3AGan9w3TCycEHVD+bijcn7y3T3iY17gTeQTOLlVBQDoGhSBrbM3AEmA8Cb6OS7smw09QzOU9G6swqso2F5HPoOZhXwdm1k54d7Vz4/tatFnNqwdPHJ0HkEsxoldf8HJrRJKVWyU6/IWFlGRUcjKyoS9vYNMup2tHQAgPDwcFSr4yOUr6eaOdes3wNi4CE4cPya3XUNDA45OkucPQRCQlJSISxcv4tTJE2jTtn0eXAmRrK8OGGbNmgVXV1ccOHAA1apJ3hrPnj0bv/32G2bPno1Vq1apvJCqlpKcBADQNzCUSdfTl7yVSk1JlsuTnJQoyaOfszxPHgbhzzE/AwCcXT3QoFkHme1rl85ESY+y8KvTBA/u3sjtpRR4qcmJ0NM3kkvX0zNAWkrSZ/NZ2DjJpevqGSItVVLXSe/e4O2bGGxcPBaN2g2Etb0zbl46jJ1rpwEA/Bp0xLu3cUhMeA0NTU206DIcZhY2eHzvKk7sW4e3cVHoMXS2iq604NATAemZ8g/y6ZmAbg7jo9EdtaGtpYHkNAFHr8t2YapSShNmRhrYcipLFcVVa5nxCciM/8JIc5KTnpoIkZ78fUOka4iMNPn7szKZGak4vnkMtHR04VWzh9z2/av6I/TxJWhoaKJOhykwLGL5TeUuTNJSEiFScG8W6RoiPfXzdZzTYAEAHgedwuuo52jYecJXl7EwSnr/fGFgYCCTrm/w4VkhRWE+C4ucvzh8+OABfh8l6Rrt6loSzVvIT+pBHPSsal8dMFy5cgWrVq2Cvr6+NM3ExASjRo1Cjx7yN+SCSBC/fwBSMquAhqZ8uiAI77N8uk1xuqW1HcbNWI7417HYvWUVJv7WE3/O+x9MzMxx7uRBBD+4g1mLt3zbhRQwYrEYgiD7cCkWxApnbxAAaGpqKT+WknwQBGi+75eYnZWJ5MR49P5tAbwq1wMgGcMQHxeFowEr4degI/QMjPDLuFWwsnOGmYVktglXT19oa4twcPtiNGjTHzYOJXJ5xflPA/JfYw3gw9dSLl3IQYOApgaw5VQ2NDUlwUHvRlrYdCIbIdECzIsAdb01sf1MttKZk4i+hqDgviF85r6R0/7v6WlJ+HfNQMSE3kPTPktgrGCgbaWGg+BTvz+e3z2BU9snIDXpDXzrD8jVdRRkSutYyQOVpgrHGFw/tRnWjqVQwrPal3dWA8KHpl1lzxcqmM3I2sYGM2fPxeu4OGzevAHDhw3G/IVLYGZm9s3HJlLmqwOG5ORkmWDhv7KyCscbRwMjYwDyrQJpqZLI38BA/q2LgaGRkjypMts/MDO3hJm55G2Vi1tpjBzQDmeO70cN/6bYtGYBuvYeiiKmZsjOzoL4fQAjFoshzs6GppbyB+mC7OjuFTiya7lMWvnK9RHzvgvRf6WnpUBPQT1/oG9gjLRU+RaI9PQU6BlI/v109Q2hoaEBT+8aMvuU8qqOR3cu4t3bOBQxtYB7uapyx/GsUBMHty9GxMvgQh0w1PLSRB0v2e/L/RAxzIvI/yiJdIC0HDzkiwXgWaTkR+95ZDYGtdBGzbKaeBmTjdbVtfDgpYDnkQL+G1drQBJofNoNiuhLrh5dimtHl8ikuXo1xNvY13L7ZqYnQ1fP+IvHTIyPxP5VP+NtbAga91wI59J1FO5n7yLpGuJYsgrSU94h8NhyVKzbV66bTmF37t+lOH9Ato5LVWyI19HydZyRngxd/S/XcU6kJMUjJPgq6rYZoZLjFQaGRpJeCCmfPCt8aFkwNDSUy/O1zM3NYW4uGTvi7uGBn/v2wrGjh784A9OPRuBUsyr11XdFX19fbN68GePHj5emZWZmYunSpahQoYJKC5dXrGzsoamphejIUJn0qMgwAIC9k/xc3Lb2xQAA0ZFhKO7ycQBt9Ic8js5ITUnGzWvn4eJWGjZ2jtJ9rG0dYGhkjNdx0bh3+xpSkhOxevF0rF48XeYcsyYMhoWVLRau2auS6/zeqvm3Q+kKtWTS7gaexMOgSxCLxTJvreKiXsHaXvmDupVdcYSHPJJLj4sKhZNLGQCApU0xCIKA7KxM6Ig+zjqRnS0JXEUiPURHvMCT+4Hwqd5EJkDJzEgDABgam379hRYgNx6L8ThM9indw1EDLnaSd4f/3VLUWANxCcqf6N0dNJCWAbyM+bhPthiIjhdgaaoBEwPA0VITjpZAeRfZN5C1vbRQ20sLC3Zn4m3Oe4wQoUy1DnAuXVsm7fndE3j56AIEsVimRSEh7hXMrD8f4MdFPMK+FX2RlZmOlv3XwKFkZZntUS+D8O5NGNy8m8ikWzuVxaPr+5CaHK92XZMq1OyAkuVqy6QF3zqBZ/fl6zg+5hUsbFXzEuXZvfMQxNko5aP+Yxc+sLW1g6amJiIjImTSIyIlf3d0Kpar46akpODq1ctwd/eAnd3HNSxsbe1gZGSE2NjY3BeaKAe+ut1x9OjR+Pfff9GiRQtkZmZi8uTJaNCgAS5fvoyRI0fmRRlVTiTShUfp8rh++Yy0qxEABF48BQNDY7iU9JTLY2PnCCsbe1y7dEom/dqlU7Cxd4KFlS00tbSwZvF0HAzYKLPPsycPkJT4Dk7FS8Lbtwb+nLde5tPrF8nUa71+GY0R4+fmwRV/HyZFreDkUlrm416uGtJTk/HozkXpfknv3uDpg+syMyd9yqNcNUSHP0dU2DNpWlTYM0SHP4eHlyTfh5aFm5cOy+S9d+MM7JzcoGdghIQ3Mdi5ZipuXz0us8+tS0ehq28IxxLy/9aFSWIqEPFakPk8ixCgJ5IEDR8Y6ALFrDVkZk76VLXSmmhWRUum5UBXRzL4OTpeQGIqsPJgltwHAK4/FmPlwSwkpubZpZKaMjKxhrVTWZmPk4cfMtOT8fLRx5nUUpLeIPxpIJzc/ZQeKzE+EnuW9QY0NNBu2Fa5YAEAQu6fxtGNI5EYL/tA9/LReRgUsYSB0Zdn/SlsjE2tYVe8rMynRGk/ZKQl49n9j3WcnPgGLx8HooSn8jr+GhEvgmBsZqP2i7T9l0gkQpkyZXHp0kWZ54uLF87D0MgIbm65m7FPU1MTixbOx+5dO2TSHz8ORmJiIkqUKLwt5VQ4fHULg4uLC/bt24etW7fC1tYWYrEYjRs3RpcuXeDg4PDlAxQQLTv0xqyJg7F49h+oVb85Hj8MwsE9m9Dxp0EQ6eohJSUJEa9ewMrWAUVMJP0CW3XsjVV/T4WRsQkqVKqBm9fO4+qFExj8u6SlQFdXD83adsfe7etgZGyC0uUrISr8FQK2roaTc0nUrNcMIpEujIvILlaWliZpqrS1LwbH4jlfYbMwcPX0gaunLzYuHoMWXX+DgbEpjuxcBn1DY1Sv/3Fmh6iwZ8jKzICDcykAQIVqjXB872qsmDkQzbv8CgA4sGUhbB1LonyVBgCAkqV9UaZibQT88xfS01Nh6+iKwHMH8CL4FvqOWiRz/r0b5iAjLQVW9s54cPM8zh3ZjBZdR8BAycJxhdnLGAEvosRoW0MLx29kIyUdqOOlhbQMIPDxx37MliaAlhYQ9X5G5LNBYnT310KHWloIfCyGrg5Qo4wWRNrA6duStRkiXisOOBJTBaXbiL6WvYsv7F0r4eimUajefBT0DU1x9chiiPSNZdZJeB31FNlZGbBykAT+ZwOmITXpNeq0n4KMtCREhtyW7ivSM4K5jSvKVu+Me5d3YP+q/qjU8BfoiAzwMHAvQh6cRcPu6r9GwAfF3HxRzL0S9q4ZBf92kjo+d2Ax9PSNZRZdi42Q1LGN09e/XIkJD4alrXr9puVEx05dMH7cGMyaOQ316zfEw4cPELB7J3r26gNdXV2kpCTj1atXsLW1hYmJaY6Oqaenh3btO2Lb1s0wNi6C8t4VEBEehi2bN8K5RAnUq98wby+qEBIEdklSpVx11LS2tsavv/6q4qJ8X6W9fDB0zCwEbFmNBdN/h5m5JTr3HCKd/jTkWTBmjPsFPw+bgJr+zQAANf2bITMzA4f2bMa5EwdgaWOHAcMnoYpfPelxW3fqCxNTc5w4vBtHDmyHkVERVParh/bdBkAkkl+s5UfQZ8QC7NkwB/s2z4MgFuDsXh49h8+VeVjfuXYa3sRGYNKSowAAbR0Rfhm3GgHrZ2H7qinQ0tKGe7lqaP3T7zJTtPb8dS6O7FqOMwc3IOldPGzsXdB7xAKUqVgbgGRgdd9Rf+PwzmU4fXAj3r2NhYW1Izr0m4hq/u2+az18T9vOZKORjxYaVNSChgbwKkbAjnPZSPvPNPNNK2vB1EgDCwMkrQTPIwVsOJGN2l6a6FBTCwKAkCgBuy9kI+6d4vMQ5ZWmvZfg/N5ZuLj/LwiCGLbOFdD4p4XQM/h43zizcwrevQlHr0mnkJ2VgZD7ZwAAp3dOkjuevUsltB2yEYYmVmg3dAsu/TsPZ3ZNRUZ6EqwcPGUWiftRtBu4BMd3zMLJXZI6dnCpgLb9F0Lf8GMdH948BQmvwzFk1qnPHEmx5HevYeXw46yB84FXeW+MHTcBWzZtxLSpU2BuYY5effqhTRvJb87Tp0/xx5hR+HX4SNSr3yDHx+3cpRvMzMxw8N8D2L9vD4yMjeH3fjVpRYu9EamShiDkZN6Uj7p3765wlL+GhgZ0dHRgY2ODli1bwtf321bVDQx++035STlfd1MAwJHbP84iRd9To/KSG/ekDZxGKK9M6SGZH/agzo/3MPK9NM0MxtLDX96PcmfQ+yUfNn5+jTT6Bt1rAk+eyU+6QapR0iV34zG+l/z8ty/odZMbX932WqpUKQQGBiIpKQkeHh7w8PBARkYGrl69Cj09PURGRqJXr144efJkXpSXiIiIiIi+o6/ukhQVFYWuXbvKzJIEAHPnzkVERASWLFmC9evXY8WKFfD391dZQYmIiIiI6Pv76haG8+fPo2vXrnLp7dq1w+nTpwEA/v7+ePr06beXjoiIiIjoKwnQyLePOvrqgMHIyAjPnj2TS3/69Kl0Qbfk5GTo6el9e+mIiIiIiChffXWXpDZt2mDixImIj4+Hl5cXxGIx7ty5g7///hstW7ZEfHw8/vrrr28e9ExERERElBvq+qY/v3x1wDBs2DBkZGRg+vTpSE9PhyAI0NPTQ48ePfDzzz/jypUrSE1NxbRp0/KivERERERE9B19dcCgqamJ0aNHY9iwYXj27Bm0tLSQmpqKgIAA1KxZEzdv3kS9evW+fCAiIiIiojzAFgbVytXCbYBk3YXg4GBs27YNd+/ehaamJurXr6/KshERERERUT776oDh+fPn2LZtG/bt24eEhARoaGigbdu2GDBgABwcHPKijERERERElE9yFDBkZWXh2LFj2LZtGwIDA6Gjo4NatWqhcePG+P3339GzZ08GC0RERERUILBLkmrlKGCoXbs2kpKSUKVKFcycORP16tWDkZERAGDUqFF5WkAiIiIiIso/OQoYEhMTYW5uDhsbGxgaGkJHRyevy0VERERElCuCwBYGVcpRwHDx4kUcOnQIu3fvxrZt22BgYIC6deuicePG0NDgPwgRERERkbrK0UrPRkZG6NChA7Zv346DBw+iY8eOuHLlCgYNGoTs7GysX78eISEheVxUIiIiIiL63nIUMPyXi4sLRo8ejbNnz2Lp0qXw9/fH3r170aRJE/Tt2zcvykhERERElGMCNPLto45yvQ6DlpYW/P394e/vjzdv3mDfvn0ICAhQZdmIiIiIiCiffXULgyJFixZFr169cODAAVUcjoiIiIgo19jCoFoqCRiIiIiIiEg95bpLEhERERFRQaSub/rzC1sYiIiIiIhIKQYMRERERESkFLskEREREZFa4UrPqsUWBiIiIiIiUootDERERESkVsQc9KxSbGEgIiIiIiKlGDAQEREREZFS7JJERERERGqF6zCoFlsYiIiIiIhIKbYwEBEREZFa4bSqqsUWBiIiIiIiUootDERERESkVjiGQbXYwkBEREREREoxYCAiIiIiIqXYJYmIiIiI1AoHPasWWxiIiIiIiEgptjAQERERkVrhoGfVYgsDEREREREppSEIgpDfhSAiIiIiUpXA4Lf5dm5fd9N8O3deYZckIiIiIlIrHPSsWgU2YDh2JyO/i6C2GniJAAAn76blc0nUk39ZPQDA3afR+VwS9VXW1RoAsPRwPhdEjQ1qDBzUcc/vYqitppnBAIC0kxvyuSTqS8+/B+LuXc7vYqgtizJV87sI9B0V2ICBiIiIiCg3xPldADXDQc9ERERERKQUWxiIiIiISK1wDINqsYWBiIiIiIiUYsBARERERFQAiMViLFq0CDVq1ICXlxd69+6Nly9fKt0/Pj4eI0aMgK+vL3x9fTFhwgSkpKSovFwMGIiIiIhIrQjQyLfPt1i2bBm2bduGadOmYfv27dDQ0EC/fv2QkaF49tChQ4ciNDQU69evx6JFi3Dx4kVMmTLlm8qgCAMGIiIiIqJ8lpGRgXXr1mHIkCGoVasWPDw8sGDBAkRHR+P48eNy+9+6dQvXrl3DzJkzUbp0aVStWhV//vkn9u3bh+ho1U7tzoCBiIiIiNSKIGjk2ye3Hj16hOTkZFSpUkWaVqRIEXh6eiIwMFBu/+vXr8PS0hIuLi7StEqVKkFDQwM3btzIdTkU4SxJREREREQq4u/v/9ntJ0+eVJgeFRUFALC1tZVJt7KyQmRkpNz+0dHRcvuKRCKYmpoq3P9bsIWBiIiIiCifpaamApA89P+Xrq4u0tPTFe7/6b6f2/9bsIWBiIiIiNTKtw4+/hbKWhC+RE9PD4BkLMOHPwNAeno69PX1Fe6vaDB0eno6DAwMclUGZdjCQERERESUzz50L4qJiZFJj4mJgY2Njdz+NjY2cvtmZGTg7du3sLa2VmnZGDAQERERkVoRC/n3yS0PDw8YGRnh6tWr0rR3797hwYMH8PHxkdvf19cXUVFRMus0fMhboUKF3BdEAXZJIiIiIiLKZyKRCN26dcPcuXNRtGhR2NvbY86cObCxsUH9+vWRnZ2NN2/ewNjYGHp6evDy8kKFChUwfPhwTJ48GSkpKZg0aRJatWrFFgYiIiIios8prAu3DR06FO3atcP48ePRuXNnaGlpYe3atRCJRIiMjISfnx8OHToEANDQ0MCSJUvg4OCAn376Cb/++itq1qyJyZMnq6AGZbGFgYiIiIioANDS0sKoUaMwatQouW0ODg4IDg6WSTM3N8eiRYvyvFxsYSAiIiIiIqXYwkBEREREauVbVlwmeWxhICIiIiIipdjCQERERERqRfiG6U1JHlsYiIiIiIhIKQYMRERERESkFLskEREREZFaEX/jeggkiy0MRERERESkFFsYiIiIiEitcFpV1WILAxERERERKcUWBiIiIiJSK5xWVbXYwkBEREREREoxYCAiIiIiIqXYJYmIiIiI1IrAaVVVii0MRERERESkFFsYiIiIiEitiDnoWaXYwkBEREREREoxYCAiIiIiIqXYJYmIiIiI1ApXelatHz5gSEtNxt6N8xAUeArpaSlwdvNC215jYOvg8tl8CfGxCPhnDoLvXkZ2ViY8ylVD216jYVrUWrrPhWM7sH3NVLm81eu1R6efJwIAsrOzcPLAelw5tQcJ8bGwtHFC/dZ9UbFaI9VeaD5KS01GwIb5CAo8jbTUZJRwL4/2vX6HreOX63j3+rl4GHQF2VmZKOVVFe17/Q5T8491/DomHHs2LsTj+4HIysqEexlftOr2K6ztikv3iQp/gT+HtZI7vrVdcUxatE9Vl5lvbl2/iq0b1yAsNARFipiiQZOWaN2+KzQ0lN8sz506hoCdmxATFQELS2u0aNsZ9Ro2k9nn2uXz2LX1H0SEh8LUrChq1m2A1u27QUdHR7rPg3u3sWXDGoQ8fwI9PX1U9auDzj36wsDAMM+ut6DISEvChf1/4fndk8hIT4ZtcW/UbDMO5jaun80XG/YQlw7OR/Sru4AgwNKxNKo3GwErx9LSfd69DsOFA3MQ9uQqxNmZcChZBdWbj4SZlXNeX1ahpudgg5q3DuB620F4c+5afhenQLt4/xmWHDiD55FxMDM2QHu/CujdsNpn7xsfZGWL0WPOeujr6mDt8O4K90lKTUf76asxoGkNtKzqpeLSF3xXbgVh1dYAhIRGwLSIMVo1rIPurZsqrd/MzCxsPXAEh09fQMzrN7A0L4oGNaqie+um0NH5+KjWvM9QvHn7Ti7//jULYW5mmleXQ8SAYf3fv+Pl03to2W049PSNcHjXciye0gfjFuyDoZGJwjzZ2VlYPmMg0tNS0LHveGRnZ2H/5oVYOq0/xvy1E1rakgeqsJBHsHFwQZcBU2TyG5uaS/98eOcyHNuzFo3aDUAJ9/K4c/UE1i8cBU1NTXhXaZB3F/4drVswGiFP76F19+HQ0zfEoZ0r8feUfpiwYA8MjZXX8dLpg5CeloLO/cYhOzsLezf/jcVTB+CPuTugpa2DtNRkLPpzADS1tNCp3zho6+jg8K5VWDCxDyYsCJAeO+zFIwDAr5PXQEekKz3Hf/9cWD16cBezp45FtRp10bl7Xzx6EIStG1ZDEIvRtlMPhXkunT+NxfOno0mLdvCuWBnXrpzHikV/QSQSoWYdyXfuzq1AzJk+HtVq1EXXXv3xKuQ5tvyzGu8S3qLvwOEAgOfPHmPahJEoW94HI/+YivjXr7F5/UqEh73ExGnzv1sd5JcjG0Yg+lUQqjcfBZGeEa4dXYI9S39CtzEHoWdoqjDP27hX2LW4K6wcSqNe5+nQ0NDEzdPrsGtRF3QeuQdm1iWQkZaEPct7Q1NTC3XaT4aWtgiBx5Zh95Lu6Db6X6XH/tHpO9mh0sG10DEtkt9FKfBuPwvD0BU70LCiJwY3r41bz0Kx+MAZiAUB/Rr7fTH/umOXcP9VJHxKOincnpCciqErdiDiTYKKS1443H30BKNn/Q3/apXwc+c2CHr4BKu27IYgFuOndi0U5vn7f1tw+MwF9GzXAqVcSyD4eQjW7diL6Ng4jB3UBwDw5m0C3rx9hyE9O6OMu+wLNxNjozy/rsKGKz2r1g8dMLx4fBv3b57DgDFLUbpCTQCAS6kKmDyoEc4f3YZGbfsrzHfr8jGEvwzGH/P2wNZR8jbRvrgHZo5ojZuXjsC3ZnMAQPjLYBRzLQNnN+VvVy6f3gsfvyZo0n4gAMCjXFWEhTzC+aPb1CJgeB58B/dunscvfyxBmQo1AACupSpg4qAmOHd0Oxq3+1lhvpuXjyMsJBjjF+yG3fs6dijujmm/tcX1S0dRuWYz3Lh0FLFRrzBhQYC0tcLeqSQm/NIENy8fQ40G7QEAYSHBMLeyg1sZ3+9wxd/Xzq3rUbyEK4aOHA8A8PapjKysLOzZtRnNWneErq58ULRt4xpUqV4bvX4eAgAoX7ESkhLfYcfm/0kDhtPHD8PC0hpDR46HlpYWvLx9kfA2Hv/u3Yme/YZAW1sb/+7ZgSImphj5x1SZVoelC2ciPOwV7B0UP0yog8gXtxDy4Axa/LwKxT1rAQDsXHzwz5/+CLq4BZUa/KIw352zG6Cto4cWP6+Ejq4BAMChZBX8b0od3Dm/CbXbTcST24eREPcSXccclLZWWNi6Yf1Ufzy5fQRlq3f6PhdZWGhowKFHa5Sa/Xt+l6TQWHHoPNwdrDGjZ0sAQPXSLsjMFmPdscvo7l8ZeiIdpXmDw6Kx9shFWBRR3Ip4+k4wZu88hpT0zDwpe2Gwbsc+lCzuhInDJM8QVbzLISs7Gxv3HEKn5o2gqyuS2f9dYhL2HjuNgd3ao2urJgAAn3KeAIBlG3dgQLf2MDMpgscvXgIAaleuCBsri+94RUQ/+KDnh7cvQaSrDw+vatI04yJF4erpgwe3zivPd+cirOyKS4MFALB1cIG1fQncf59PLBYj4tUTOBT3+GwZsjIzoKcve+M1NDZFcuLbXFxRwfPg9iXo6umjlFdVaZqxiaSO7928oDTfw9uXYG1XXBosAICtowts7J1x/30+7yr1MHL6BpmuTR9ad7IyM6RpYSHBcCjurrJrKigyMzNwP+g2KletKZNe1a820lJT8fD+Hbk8MdGRiAgPReVqsnmqVK+NqMhwRISHSo+tq6cHLS0t6T7GRUyQlZWJ1NQUAEDXnj9jzKTZMsGC9vum88xM9X5YePnoAnREBnByry5NMzAqCntXX4Q8OKc0n5l1CVSo21saLACAjkgfRqY2SIh7BQBwLdcQ7Ydtk+napPn+e52dlQGSVaScO8osmYywjXtxuyeDhi/JyMzC9Scv4V9e9repvrcHUtIzcPNpqNK8mdnZmLBhPzrX8UVxa3O57e9S0vDbqt3wKVkMywf/mIFtRmYmbt1/hFqVK8qk16nqg9S0NNx++FguT1JKKlo1qAM/X2+ZdCc7GwBARHQsAODJi1cwNjRgsED54ocOGKLCn8PC2gFaWrINLZY2ToiJfKk0X3T4C1jZFpdLt7RxlOaLiQxBRnoqXjwOwp/DmmFYZ29MHdYcV8/ul8lTt1kPXDt3AA9uX0BqShICz/+Lh7cvSlspCruo8Ocwt1JUx46freOosOewsisml25p44SYCEk+A8MiKOEuab3JysxEWEgwNiyZAKMiZqhQraE0T1hIMFJTkjDnj+4Y2tkXY/rWxd5NC5GdVbgfaqMjI5CVlQlbe0eZdBtbBwBAZLj8D39YqKTu7OwdZPPY2QOANGBo3KwNoiLCsG/3ViQnJeLxo/s4uG8XKvhUgbGxpMuHuYUVijtLgrXU1BQE3bqOLf+sRqnS5aTp6io++hmKmDtA85PvtYmFE97GvlCar5xfF1Ss21f2WDEv8CbyCcxtSwIAdA2KwNZZ8uCQnZWB2PBHOL55DPQMzVDSu7GKr6TwS30ViTMe9fFw1Cxkp6Tld3EKvLC4t8jMykYxq6Iy6U5WZgCAlzGvleZdefA8MrPE+KVpTYXb9UU62DOxP6b91AKmRgYK91F3EdGxyMzKguP7h/0P7G0kY+9CI6Lk8thZW2Lkzz1QzN5WJv3M1RvQ1taSHutJyCsYGxpi7F+L0aD7QNTr2h8T5y9DXPzbvLmYQk4MjXz7qKNcd0k6fPgw/vnnHzx+/BhaWlrw9PREv3794Of35f6PBUVqSiL09OX7/enpGyAtJUl5vuREWNrId7fQ1TNEWmoyACA8JBgAEB8XgTY9RkFLSxvXzu3HpqXjkJWZger12gEAajbqgmcPb2L5jIHS41Sp0xr1WvT6pmsrKFKTE6GvYACsnr4h0lKV13FKSiIsbRXUsb6BwnzLZg7Go6Ar0NDUROd+42FiJnkD8+7ta7x7+xoaGppo1W0YzCxsEXz3Ko7v/R/i46LR69eZ33B1+Ss5WVIPnw4w1jfQBwCkpKTI5UlJSnq/zyd59CU/7qkpku9v6XLeaNm2MzauW46N65YDAJxdSmLY7xPljikIAnp1ao6srEwYFzHBT30HfctlFQrpqYkQ6cnfO0S6hshIS87xcTIzUnF88xho6ejCq6b8mJP9q/oj9PElaGhook6HKTAsYvlN5VZHmfEJyIz/MfvK50ZiqiSoMtKX7a5o8L77YnKa4laseyER+OfEFaz7rTtEOoofHXS0tRS2PPxIkpIl911DfX2ZdAN9PQBAcmpqjo5z+nIgjp69hPZN66OIkeR+/eTFK8S8eYPm9WuhY7MGeBkWiTXbAzB4wkz8b+6f0Ncr/OPyqODKVcCwa9cuTJw4EY0aNUKTJk0gFotx8+ZN9O/fH3///Tfq1aun6nJ+M7FYDEEQy6QJYjGgYMYCQQA0NbXk0j9uFyuZ6UCApqak0aZkaV8MGLMUJctUgkgkuVGUKl8diQlvcGjHUlTzb4usrEwsnPQTEt/GoWO/CbC2d8bzR7dwNGA1dPUM0K7XmNxfcD5QWMeCACiItgVB+HwdiwXFdSwAGgryNWnXHw3b9MGda6exZeWfSHoXj0Zt+0JP3xBDJ66ElV1xFLWQvKVxK+0DbR0RDmxdgkbt+sHWocTXXWgBIXwY0aVk1g1NBeni9/8+cnX7/lgaGpLv76olc3H6xGG07dQDZb0qIiY6Ejs2r8P0iaMwafoC6OrpSbNmZ2djzKSZEGdn4+D+XZjw+xCMm/IXynhV+NZLLBAEhd9rxfcAAYCGZs4abtPTkvDvmoGICb2Hpn2WwNjMVm6fSg0Hwad+fzy/ewKntk9AatIb+NYfkKvrIAIA8Yf/60q2K/pep2dmYcKGA+hatxLKFrfPw9IVftL6/Yr78qdOXw7ElIUrUd7THQO7tZemjxvcFyIdHbiVkLS+l/d0h7OTPQaOm44jZy6idaO6KrgC9cFBz6qVq4Bh9erV+P3339GzZ09pWs+ePbFmzRosWrSoQAYMR3atwOFdy2XSylepj0QF3WLS01KgZ6B8xgF9Q2OFb7nT01Kgb2AMAChiaiEdSP1fpSvURPDdK0hMeI3H964i4uVjDBq/Ch7lJH38S3r6Qt/AGDvXzUA1/zawc3L7quvMT4d2rsShnStk0ryr1Ediwhu5fSV19YU6TpF/U6ssn6un5OHUvUwlpCS9w+Hdq1G/VU+IdPXgUa6K3P5lKtTAga1LEB4SXGgDBkNDST2kflJPqSmSN1gGhvL1ZGgo+X6mfJon7UMeQ7yOi8WJo/+iTYdu6Nz9Q/cZb7iW9MBvg3ri1PGDaNy8rTSvtrY2yleoBAAoW94Hw3/pgYAdm9QmYLh6dCmuHV0ik+bq1RBvY+W7bmSmJ0NXz/iLx0yMj8T+VT/jbWwIGvdcCOfSdRTuZ+/iAwBwLFkF6SnvEHhsOSrW7SvXFYoop4zfv+lOSkuXSU9JT3+/Xf4t9ZL9khmUfm7sh6xsSfD84WEsK1sMLU2NHE3H+iMwMpC01n7akpDyoWXH4PNdtbbtP4KlG7fDu7QHZo0eBtF/xoiVcZefsrmcR0kYGejjyctX31p0os/K1a9OVFQUateuLZdev359LF68+FvLlCeq12uHMhVryaQFBZ7EozuXIBaLpS0DABAb9Qo2n3mItLItjrCQR3LpsVGhKOZaBgDw5EEg3sRGonIt2SnUMjPSoampBX3DIngTGwkAKOEhO9DJ1VPykBAZ9rxQBQx+9duibEXZIOlO4Gk8VFjHobC2Vz6nvLVdMel0qP8VG/UKxUpK6jjkyV28jo1Axf+MVwCAYq6lce3cv0h69xZpKUkIvncNvjWayAQamRmSH0ejImZff6EFhLWtHTQ1tRAVGS6THhUZBgBwcCoul8fOQTLeISoiHCVcPn63oiIkx3B0Ko642GgIggB3z7IyeZ2Kl4BxEROEvgoBAAReuQBDIyN4likv3UdHRwfFirtI91EHZap1gHPp2jJpz++ewMtHFyCIxTItCglxr2Bm/fkANC7iEfat6IuszHS07L8GDiUry2yPehmEd2/C4ObdRCbd2qksHl3fh9TkeHZNolxztDSDlqYGQmPjZdJfxUj+XsJGfkDtiVuPEPEmAVWHz5HbVnHITPzZvdkPudaCIvY2ltDS1ERYZLRMeniU5O/FHe0U5hMEAQvWbsLuwydRt1olTBjaTyZYSExOxtkrN1DazQXOjvYy+TKzsmFq/OUXFT8aLtymWrka9Fy1alUcOnRILv3ChQvw9vZWkCP/mRS1gpNLaZmPR7lqSEtNxsM7F6X7Jb57g6cPrsOjXDWlx/Lwqobo8OeIDHsmTYsMe4bo8OfSfI/vXsXmZeMRG/Ux6heLxbh95RiKlywHHR2R9IH52cObMsd/HnwLAGBuVbiafk2LWqGYa2mZTymvqpI6vn1Jul9igqSOS3kpr+NSXtUQFf4CkaH/qePQZ4gKfyHNd/fGOfxv4Vhp4PXBg9uXUMTUAsYmRfH2TTS2rZ6OW5ePy+xz49JR6OkbwqmEpyouPV+IRLrwLFMOVy+d+9g9CcDlC2dgaGgEV7dScnls7RxgbWOHKxfPyKRfuXgGdvaOsLSygY2dAzQ1tfDwfpDMPuFhr5D4LgFW1pKuM/sDtmHVknnIzs6S7pOcnITHj+6r1aBnIxNrWDuVlfk4efghMz0ZLx99nE0tJekNwp8Gwsld+TiuxPhI7FnWG9DQQLthW+WCBQAIuX8aRzeORGJ8hEz6y0fnYVDEEgZGP3Yfcfo2ujraqODqhJO3g2XuG8dvPYKxvh7KFJd/oF00sAO2jO4l8ynlaINSjjbYMroXapUt+T0voUDTFYng5emOs1dvyNTv6cvXYWxoAE9XxS8UVmzehd2HT6Jjs4b487eBMsECAOhoa2Pe6g3YtOegTPr5azeRnpEB79Kfn5GR6FvlqoXB29sby5Ytw/379+Hr6wsdHR3cvXsX//77L1q3bo0lSz423w8ePFhlhVU1V08flCztiw2LxqBlt99gaGyKQzuXwcDQGH71P/YbjAx7hqzMDDg6Sx7AKlRrhGN7VmP5jIFo0eVXAMD+LQth61QS3lUl89j7NeiIiyd2YuXswWjS/heIdPVx7uhWRIY+xZBJ6wAAZX1qo3jJctiweCyadPgF1nbOCHkahKO7V6FMxdoo7ir7hrcwKulZESVL++B/i8aidbfhMDQ2wcEdK6BvYIyaDf5Tx6Hv67iEpI4rVm+IowFrsHT6ILTsNgwAsG/T37BzKokKVesDAGo27ICLJ3Zj2czBaNKuP3T1DHDlzH7cv3kevYbNhKamJkp6+qBkaR/s/mce0tNTYWPvjHs3z+PMoS1o3X04DIwK9yJPbTv1wJ/jfsO8mZNQt0ETBD+8h/0B29Ct1wDo6uoiJSUZYa9CYG1rDxMTUwBAu04/YenCmTA2NoFP5eq4fvUiLp0/jd/GTAYAmJiYomnLdti/eysAwKu8D2JjorFz6/9gYWmNeg0lM3i179wT0yaOxNwZE9GwSSukpCRj787NSE9LQ8euvfOjOr4bexdf2LtWwtFNo1C9+SjoG5ri6pHFEOkby6yT8DrqKbKzMmDlIAlMzwZMQ2rSa9RpPwUZaUmIDLkt3VekZwRzG1eUrd4Z9y7vwP5V/VGp4S/QERngYeBehDw4i4bd5+Z4jASRMv0a+6H/os0YtSYArap54fbzMPxz4jJ+bVUXeiIdJKWm43lUHBwsTFHU2BAl7a3kjmGoJ1lLoHQxxW/Mf2Q92zXHsClzMGHeUjStWxN3g59gy77DGNitPXR1RUhOScWLsHDYW1tJ11fYvPcQPFycUbe6L+4/eSZzPGcHexga6KNLyyZYv2s/zEyKoIp3OTx9GYp1O/aiWkUv+HqVVlIaItXQEISvHxZSt27OBtZoaGjg5MmTX10oADh25/vMN56SlICADXMQFHgKglhACY/yaPPT77C2+9hd5u/JvfAmNgJTlh6VpsXHRWHX+lkIDroMLS1teHhVQ5uffoeJ2ceuAjGRL3Fgy9949ugG0lJTUMylNJp2GgLXUh/nZ05NScK/2xbh9tUTSElKgIWVA3xrNUfdZj9BW1v54jnfooGX5EZ/8u73mYIwJekddv0zB3eunYYgCHBxL492PUfB2r64dJ8FE/vgdWwEpi0/LE17ExeFnetm41HQFWhpa6NUuapo12vUJ3X8Cvs2L8LThzeQlpoMR+dSaNS2r3SROEAyU9PBHStwJ/AUEuLjYGntgDpNu8Gv/sd++KrkX1bSR/ju0+gv7KkaVy+dw/bN6xARFoqi5hZo1Kw1WrSRPLTeC7qFyWOHYdCvY1Gn/scpOY8d3of9AdvwOjYW1ja2aN2hG2rV/di1SxAEHNy3E8cO70dMVCTMiprDy9sXnX/qJw08ACDo9g3s3PI/hDx/Cg1NTZQp540uPfop7A6lSmVdJVMULj38hR3zUFpKAs7vnYXnd09AEMSwda6Amq3GynRJ2r24O969CUevSaeQnZWB5b97QyzOUng8e5dKaDtkIwDgbexLXPp3HsKfXUdGehKsHDzhW3+gdJG472FQY+CgTuFav6RozUqoenIjLvt3x5tz1/K7OJ/VNFMyk17ayQ35cv6Ttx9h+b/nERLzGlYmxuhYqyJ+qicZ7xX4+CX6Ltz02a5GfRZIvqtrh3dXuD389Vs0mbA0X7sr6fn3QNy9y/ly7rNXb2Dttj14FREFy6JmaNO4Ljq3kNyDb957iCGTZuOPQX3QtG4NrN4agPW79is91uIpo1GhTCmIxWLsOXoKe46eRnh0DEyMjFC/RhX07dhabjG478GiTNUv75SP9gZm59u5W/kqn9SlsMpVwPA9fK+A4Uf0vQOGH833Dhh+RAUhYFB3hTFgKEzyO2D4EeRnwPAjYMCgnDoGDN881cabN29w/fp1WFhYoEIF9ZgVhYiIiIgKr4L5Orzw+qrOsEuXLkXlypXx8qVkKtKbN2+iQYMGGDp0KLp06YJevXohLY1vrYmIiIiI1EWOA4bt27dj5cqV6NixI8zNJbN0/PHHHzAwMMDBgwdx9uxZJCcnY+XKlXlWWCIiIiIi+r5yHDDs3LkTY8aMwW+//QYjIyMEBQUhJCQEPXr0gIuLC6ytrTFw4ECF060SEREREX0vAjTy7aOOchwwPHv2DNWqfZw3/8qVK9DQ0ECtWh9n7XB1dUVERISi7EREREREVAh91aDn/y79fuPGDRQtWhQlS35csCU5ORn6+vqqKx0RERER0VcSc9CzSuW4hcHd3R2BgYEAgHfv3uHq1avw85Nd0fTw4cNwc3NTbQmJiIiIiCjf5LiFoWvXrpg4cSKCg4Nx69YtZGRkoHt3yYItMTExOHDgANauXYvp06fnWWGJiIiIiL6E06qqVo4DhubNmyM9PR1bt26FpqYmFi5ciDJlygAAVq1ahW3btqFfv35o2bJlnhWWiIiIiIi+r68aw9CuXTu0a9dOLr1fv34YNGgQzMzMVFYwIiIiIiLKf9+00nNoaCgeP34MkUgEV1dXVZWJiIiIiCjX2CVJtXIVMKSmpmLkyJE4deoUhPf/IpqammjVqhUmTZoEXV1dlRaSiIiIiIjyR45nSfqvmTNn4tmzZ1i7di2uX7+Oa9euYcmSJbhy5Qpmz56t6jISEREREeWYWNDIt486ylULw6FDh7BixQr4+PhI0+rWrQtdXV389ttvmDhxosoKSERERERE+SdXLQw6OjowMjKSSy9atCjEYvE3F4qIiIiIiAqGXAUMPXr0wIwZM/DmzRtpWkpKChYsWIAuXbqorHBERERERF9LEPLvo45y3CWpbt260ND42C8rPDwcderUQfHixaGlpYUXL14gPT0dCQkJeVJQIiIiIiL6/nIcMLRu3VomYCAiIiIiKojU9U1/fslxwDBkyJC8LAcRERERERVAOQ4YlixZgj59+kBfXx9LlixRup+GhgYGDRqkksIREREREX0tMVsYVCrHAUNAQAC6du0KfX19BAQEKN2PAQMRERERkfrIccBw6tQphX9+8+YNAgMDYWFhgYoVK6q2dERERERElK++auG2pUuXYsOGDdixYweKFSuGW7duoV+/fkhOTgYAVKlSBcuXL4eenl6eFJaIiIiI6EsENV1xOb/keB2G7du3Y+XKlejYsSPMzc0BAGPHjoWBgQH+/fdfnDlzBsnJyVi5cmWeFZaIiIiIiL6vHAcMO3fuxJgxY/Dbb7/ByMgIQUFBCAkJQY8ePeDi4gJra2sMHDgQhw4dysvyEhERERF9FhduU60cBwzPnj1DtWrVpH+/cuUKNDQ0UKtWLWmaq6srIiIiVFtCIiIiIiLKNzkOGADILNx248YNFC1aFCVLlpSmJScnQ19fX3WlIyIiIiKifJXjgMHd3R2BgYEAgHfv3uHq1avw8/OT2efw4cNwc3NTbQmJiIiIiL6CWMi/jzrK8SxJXbt2xcSJExEcHIxbt24hIyMD3bt3BwDExMTgwIEDWLt2LaZPn55nhSUiIiIi+lGlp6dj1qxZOHLkCNLS0lCjRg1MmjRJOiGRIjdv3sSCBQvw4MEDGBgYoGbNmhg1ahRMTU1zfN4ctzA0b94c48aNw82bN6GhoYGFCxeiTJkyAIBVq1ZhwYIF6NevH1q2bJnjkxMRERERqZq6DnqePHkyLl68iMWLF+Off/5BaGgohg0bpnT/Fy9eoE+fPvDw8MDOnTuxYMECBAUFYejQoV913q9ah6Fdu3Zo166dXHq/fv0waNAgmJmZfdXJiYiIiIjoy6Kjo7F3716sXLkSPj4+AID58+ejUaNGuH37NsqXLy+XZ+/evbCyssIff/wBDQ0NlChRApMmTULXrl0RGhoKR0fHHJ37qwIGZaytrVVxGCIiIiKib6aO05veuHEDAFC5cmVpmrOzM6ytrREYGKgwYGjRogXq1KkjM3HRB2/fvv2+AQMREREREQH+/v6f3X7y5MlcHTc6OhpmZmbQ1dWVSbeyskJkZKTCPC4uLnJpq1evhqWlJTw8PHJ8bgYMRERERET5LCws7LPBxrBhwyASieTSdXV1kZ6enqNzzJo1C2fPnsWiRYugo6OT47IxYCAiIiIitZKf05vmtgXB2toahw4dUrr97NmzyMjIkEtPT0//4jpomZmZmDhxIvbs2YNJkyahQYMGX1U2BgxERERERPlMR0dHYReiD4KDg/H27VtkZGTItDTExMTAxsZGab6kpCQMHjwY169fx7x589C0adOvLttXrfRMRERERFTQqeO0qhUrVoRYLJYOfgaA58+fIzo6Wjpr0qcyMjLQv39/3L17F2vWrMlVsAAAGoKgjuPIiYiIiOhHtfpE/p27X728O/aIESNw+/ZtzJgxA/r6+pg0aRKMjIywceNGAJIAISEhASYmJhCJRFi8eDGWLl2KefPmoVKlSjLH+rBPTjBgICIiIiK1oq4BQ0pKCmbMmIGjR48CAGrWrInx48dL10K7evUqevTogQ0bNqBy5cpo2LAhQkJCFB7rwz45UWADhgsPkvO7CGrLz9MQAHD3aXQ+l0Q9lXWVrEty+eG7fC6J+qpaqggAYOO5fC6IGuteE0g7uSG/i6G29Px7AAAO6rjnc0nUV9PMYLy+dym/i6G2zMtUy+8ifNbKY/l37v5fN564UOAYBiIiIiIiUoqzJBERERGRWimY/WcKL7YwEBERERGRUmxhICIiIiK1whYG1WILAxERERERKcWAgYiIiIiIlGKXJCIiIiJSK2J2SVIptjAQEREREZFSbGEgIiIiIrWSv+sSa+TjufMGWxiIiIiIiEgpBgxERERERKQUuyQRERERkVrhOgyqxRYGIiIiIiJSii0MRERERKRWxOL8LoF6YQsDEREREREpxRYGIiIiIlIrHMOgWmxhICIiIiIipRgwEBERERGRUuySRERERERqRcwuSSrFFgYiIiIiIlKKLQxEREREpFY46Fm12MJARERERERKMWAgIiIiIiKl2CWJiIiIiNSKkK+jnjXy8dx5gy0MRERERESkFFsYiIiIiEitcFpV1WILAxERERERKcWAgYiIiIiIlGKXJCIiIiJSK1yHQbXYwkBEREREREqxhYGIiIiI1IqYo55Vii0MRERERESkFFsYiIiIiEitcAyDarGFgYiIiIiIlGLAQERERERESv1wXZJSU5Oxc/1C3Lp2BmmpyXD18ELnPqNg51jis/nevonF9v/Nx4M7V5GVlYnS5augc59RMDO3ku6TnZ2FvVuX4/KZQ0hKfAvH4m7o0HM4SpYqr/CYIc8eYMbonpixbC8srOwAAHExERjdv5nSclSv2xy9h0z5+gvPJ7euX8XWjWsQFhqCIkVM0aBJS7Ru3xUaGhpK85w7dQwBOzchJioCFpbWaNG2M+o1lK2Ta5fPY9fWfxARHgpTs6KoWbcBWrfvBh0dHek+L549wZYNq/Ds8SOIBQElXN3QrWd/lHB1z7PrzQ9BNy8hYPMKRIQ+h7GJGeo0bIOmbXt+to4vnTmEf3evR2x0BMwtrNG4dXfUqt9K4b6pKUkYP6wLWnXqhxr+zWW2hYY8xY4Ni/D88X1o6+igTPkq6PDTEJiYmqvyEguk9LQknNz5F4Jvn0RGWjIcXLzRoNM4WNq55vgYO5cNhq6eEVr0niVN279uDIIu71GaZ/CsUzA1t/+mshd0F+8/w5IDZ/A8Mg5mxgZo71cBvRtW++x3+oOsbDF6zFkPfV0drB3eXeE+SanpaD99NQY0rYGWVb1UXHr1pudgg5q3DuB620F4c+5afhenwLpy6y5WbQ3Ai9AImBYxRuuGtdG9dVOl3+HMzCxsPXAEh05fRMzrN7AyL4oGNaqge+um0NH5+KgWEhaJZRt34Ob9R9DW0kJ5T3cM+akj7G2sFB73R8YuSar1wwUMq+aNxYsn99Hup2HQ1zfE/h2rMGdif0xdtAtGxiYK82RnZ2Hh1CFIT0tBt/5jkZ2dhd0bF2P+lF8waf5WaGtLHlK3rpmDi6cPoF2PoTC3tMWx/Zux4M9BmDh3C2zsi8kcM/TFY/w9bRiys7Nk0k3MLPDHrPVyZTh1eAcCLx5DDf9WKqmH7+HRg7uYPXUsqtWoi87d++LRgyBs3bAagliMtp16KMxz6fxpLJ4/HU1atIN3xcq4duU8Viz6CyKRCDXrNAAA3LkViDnTx6Najbro2qs/XoU8x5Z/VuNdwlv0HTgcABAVGY6Jo4fA2dUNA4eNhoamJg4EbMf43wdjzqK1sHdw+m71kJeePLqDv2eMQKXq9dGm6wA8eXgHuzcvh1gQ0KJ9b4V5rl08gdV/T0b9Zp1QtkJV3Lx6Fv9bOh06Il1Uq9VYZt+kxAT8PWMEXsdGyh3nbXwcZk8YAHNLG/QdOhHp6enYuWEx5v85DBP+Wg9tbfW+vexZNQIRIUHwbzsKuvpGOHdgCTbN+wkD/jwIfUPTz+YVi7NxbNsMBN86jnJVW8ts82v2CyrU6iSTlpaSgN0rhqGYeyWYmNmq+lIKlNvPwjB0xQ40rOiJwc1r49azUCw+cAZiQUC/xn5fzL/u2CXcfxUJn5KK/48nJKdi6IodiHiToOKSqz99JztUOrgWOqZF8rsoBdrdR0/w+6y/4V+tEn7u3AZ3Hj7Gyi0BEIsF9GzXXGGehf/bgsNnLqJnuxbwdHVG8POXWLtjL6JiX+OPQZJ7eXTcawwYNx1O9jaY8mt/pGdkYtXWAPw6dR42zZ8KXV3R97xM+sGo9y/6J54+uoOgGxcwbPwilKso+eFx86yA0QOa4fSRnWjevq/CfNcvnUBoyGP8+fdO2Du5AACcnN0xcVh7BF44hqq1m+JNXBTOHd+DTn1Gom7jDgCA0uWr4o9BrXBk7z/oOWgiACArMxMnD23D3i3LoSPSlTuXjo4ILu7lZNJCnj5A4MVjaNN1MEp6equsPvLazq3rUbyEK4aOHA8A8PapjKysLOzZtRnNWneErq789W/buAZVqtdGr5+HAADKV6yEpMR32LH5f9KA4fTxw7CwtMbQkeOhpaUFL29fJLyNx797d6JnvyHQ1tbGwX27INLVxR+TZ0NPTx8AULZcBQzs1R6HD+yWBhaF3b5ta+Dk7Ib+w/8EAJSrUA3ZWVk4tPsfNGrRBSJdPbk8AZuXw6eqP7r0+Q0AUNa7KpITE7B36yqZgOHm1bPYvGYu0tJSFZ779rVzSEpMwMS/1sPK1gEAYGBohPl/DsPTR3fgUaaiqi+3wAh7dgtP755Bp6Gr4Fq2FgDAsaQPloz1x/XTW1Cj2S9K80aHPcKRLVMR+fIetEXy/z5FrZxQ1Er2YXfn0kHQMzRBq75zoaGp3j1JVxw6D3cHa8zo2RIAUL20CzKzxVh37DK6+1eGnkhHad7gsGisPXIRFkUMFW4/fScYs3ceQ0p6Zp6UXW1paMChR2uUmv17fpekUFi7Yx9KFnfCpGE/AwCqeJdFVnY2Nu05hM7NG8o92L9LTMLeY2fwS7f26NpKcg/2KecJAFi6cQcGdmsHM5MiWLN9Lwz09bBo0ijovf/9tLWywOhZi/DwWQjKe7p9x6ss+MRsYlAp9f7l+cT925ehq6eP0uWrSNOMTczgVroi7t64oDTfvVuXYWNfXBosAICdYwnYOjjj7s2LAIAHQdeQnZ2FilXqSvfR0RHBq2INBP3n2EE3L2D/9lVo2q432vUY+sUyC4KATStnwtbBGQ2ad/2q681PmZkZuB90G5Wr1pRJr+pXG2mpqXh4/45cnpjoSESEh6JyNdk8VarXRlRkOCLCQ6XH1tXTg5aWlnQf4yImyMrKRGpqCgDAwbEYWrTpJA0WAEBXTw9FLawQFRmhsuvMT5mZGXh07wYqVqkjk+5TrS7S0lLw+MFtuTyx0RGIiniFilU/zeOPmKgwRIW/BAAkJyVi8ezf4VGmIkZOWqTk/JKHLj2Djw9nxkVMAUhaJtTZs/sXoKNrgBKe1aVphsZFUczNF8/unfts3v1rR0MQxOg1djsMjb/cdevJndMIvn0CDTqOhZ6Ber/ZzcjMwvUnL+Ff3kMmvb63B1LSM3DzaajSvJnZ2ZiwYT861/FFcWv5en2XkobfVu2GT8liWD64k4IjkDJFyrmjzJLJCNu4F7d7Mmj4nIzMTNy6H4xalWVfmNSp6oOUtDTcfvhYLk9SSipaNagNP9/yMumOdtYAgIjoWAiCgLNXbqC5f01psAAApVydsX/NAgYLlOd+qIAhMuwFLK3toaUl27BiZeOI6IhXn81nbSffvG1l44ioiJfSfXT1DGBiZiG7j60jEuLjkPb+QdbZtTRmr/wXzdr3heZ/HniVuXr+CF48vY/OfUblaP+CIjoyAllZmbC1d5RJt3n/JjoyXP6HPyxUUpd29g6yeewk/bU/BAyNm7VBVEQY9u3eiuSkRDx+dB8H9+1CBZ8qMDaWPFA1bNoKLdt2ljlORHgoQl8+h1MxZxVcYf6LjQpHVlYmbD75blrbSuo8SsF3OjLsBQB8MY+urh5mLN6OfsMmw8jYVOH5K/nVg2lRS2xa9RfevolDbHQ4tq9fBFMzC3iWq/RN11bQvY58BjMLB2h+ci8xs3LC6+gXn83bos9s9By9FdYOHp/dDwAEsRgndv0FJ7dKKFWx0TeVuTAIi3uLzKxsFLMqKpPuZGUGAHgZ81pp3pUHzyMzS4xfmtZUuF1fpIM9E/tj2k8tYGpkoLpC/wBSX0XijEd9PBw1C9kpafldnAItIjoWmVlZcHr/sP+Bg43k76ERUXJ57KwtMernHihmL9vd8OzVG9DW1oKjnQ0iY+KQlJIKG0sLzF29EY1+Gozanfph1IyFiIpV/v+CSFV+qC5JKcmJ0NM3kkvX0zdAamrSZ/N9eKD6NF9aSjIAIDU5EQaGio4tefuampoMPX0DmUHSOXF030a4epSHRxmfr8qX35KTJfVpYCDbNUDfQPLGPyUlRS5PSlLS+30+yaMv+XFPfV/Xpct5o2Xbzti4bjk2rlsOAHB2KYlhv09UWp70tDQsmT8DIpEumrRom5tLKnBSkhMByNeX3if1JZtHcR1/mkdbRwe29sU/e34TU3P06D8aK+aNw7WLJwAAhkZFMHrqcoX/F9RJWkoiRAruJSJdQ6Snytf7f+UkUPjgcdApvI56joadJ3x1GQujxFTJw6iRvmx3RYP3b1ST0zIU5rsXEoF/TlzBut+6Q6Sj+GdNR1tLYcsDfVlmfAIy49W71VBVEpMlv22G+voy6Qb6ku6Hyak5C7hOX76OI2cvo0PT+ihiZIiwyGgAwPJNO1HK1RlThg9AfMI7rNi8C4MnzcbG+VOhryffzfdHJojzuwTqJVcBw5s3b/DXX3/h3r17SEtLg/BJP7GTJ0+qpHDfQiwWQ/jk2yIIAhROUCAI0NRU/vZeEMRQlFEAoPm+P7FkCXIF+7yvG80czO7xqScPb+PV80cYPGb+V+fNb9LvhJLrVlQf4vf/XnKzSLw/loaGpK5XLZmL0ycOo22nHijrVREx0ZHYsXkdpk8chUnTF0BXT7ZfeEpKMmb/+QeePXmEUeOnw8JS9s1PYfWhjjUUfO8AQEPzM3X8SR7psb6if/zls0ewauFE+Favh5r+LZCRkY7Dezdi7uQhGDN9Jewciuf4WAWZoPBeIlZa75oqHGNw/dRmWDuWQgnPaio7ZkEmln6nFVM0w0x6ZhYmbDiArnUroWxx9Z49igo+6b1UyW9fTmb6OnU5EFMWroK3pzsGdmsHAMjMkkyQYmZaBDN/Hyy9zzjYWuPnsdNw9NwltGpQR+kxib5VrgKGCRMm4Pbt22jSpAlMTBTPLJTfDuxYhf3bV8mkVaxaD+8S3sjtm5aWCn0D5W9EDQyNpS0J/5WemgL9929SDQyNkKbgzWJ6muRtw+eOr8yNyydgYFQEZStW//LOBYzh+3r59C13aopkAK2iN9CGhsYAJA/4MnnSPuQxxOu4WJw4+i/adOiGzt0/DFL3hmtJD/w2qCdOHT+Ixs0/tiDExUZjxuTRiAwPw29jpsCnkvo8eH2ow9RPvncfur8ZKPjOGbyv40/zpH+oYwPFg0UV2bt9NUp6eOGXkTOkaaXLV8Yfg9sjYPNyDB49O8fHKsjO/bsU5w8skUkrVbEhXkfLdwPISE+Grr6xSs6bkhSPkOCrqNtmhEqOVxgYv38Lm5SWLpOekp7+frv8G9Ql+yUzKP3c2A9Z2ZLA7sP7iqxsMbQ0NXL0kEakCkYGktba5FTZySJSPrSeGejL5fmvrfuPYunG7fAu7YHZo4dC9H6q8A8tFFW9y8q8lCjj5gJjQwM8eaF8fM+P6tOX2fRtchUwXLp0CatWrYKvr6+qy6MyNRu0hZePbF/Wm1dP4/7tyxCLxTL/4WKiQmHroLxfu41dMbx6ESyXHhMVCueSpSX72BdHakoSEhPiYWxi9nGfyFBYWNkpnK3mS+5cPw/vSrWl07YWJta2dtDU1EJUZLhMelRkGADAwam4XB47hw/96MNRwuXjAK6oCMkxHJ2KIy42GoIgwN2zrExep+IlYFzEBKGvQqRpIS+eYfrEkchIT8e4P+egTLnCM8NUTljaOEBTUwvR7+v0g+hIyQ+HnaP8d9r2/fS+0ZGhKFbCXUGez69H8l+vYyJRsXJtmTRdXT04u3oiPPR5jo9T0FWo2QEly9WWSQu+dQLP7l+AIBbLtMrEx7yChW3O6/Bznt07D0GcjVI+6j924QNHSzNoaWogNDZeJv1VjOTvJWws5PKcuPUIEW8SUHX4HLltFYfMxJ/dm3GtBfpu7G2soKWpibDIGJn0sChJlyJnRzuF+QRBwIK1m7Hr8En4V/PFhKH9pMECANhbW0FTUwMZmVlyebOysyH6zOxhRKqQq7ZzfX19WFpaqrosKmVW1BLFXT1lPqXLV0FaajLu37os3S8xIR6P79+QmTnpU6XLV0Vk2AtE/OchKCL0OSLDXqB0+aoAAE+vygCA65dPSPfJzMzAnRvnP3tsZZISExATGap00beCTiTShWeZcrh66ZxMlH/5whkYGhrB1a2UXB5bOwdY29jhysUzMulXLp6Bnb0jLK1sYGMneUh+eD9IZp/wsFdIfJcAK2vJoLG42GhMHf8bNDQ0MG3uMrULFgBJHbuX9saNK6dl6vj6pVMwMDRGiffB7H9Z2zrC0toe1y/Jdhu8fukkbOycYGGV8zn+bR2K4/HD2zLnzshIx8vnj2BppfhHsTAyNrWGXfGyMp8Spf2QkZaMZ/fPS/dLTnyDl48DUcLzy2sF5ETEiyAYm9mo/SJt/6Wro40Krk44eTtY5nt1/NYjGOvroUxx+e/VooEdsGV0L5lPKUcblHK0wZbRvVCrbMnveQn0g9MV6cDL0w1nrt6Q+Q6fvnwdxoYG8HRV/EJhxeZd2HX4JDo2a4A/fxsoEywAkhYGr1JuOHv1BjIyP04LfD3oAVLT0jlLkgJicf591FGuWhhatWqFNWvWYOrUqYWqqde9dEW4l/HBqoXj0L7HMBgZm2Df9pUwMDRG7YbtpPtFhD5HZmYGipWQDE709WuAg7vXYcHUwWjXXTIV6q6Ni2BfzBU+1eoBACys7FCtTnNsWzcPGenpsLFzwrH9m5CanISGrRQvUvY54S+fAsBnWz4KuradeuDPcb9h3sxJqNugCYIf3sP+gG3o1msAdHV1kZKSjLBXIbC2tYeJiSkAoF2nn7B04UwYG5vAp3J1XL96EZfOn8ZvYyYDAExMTNG0ZTvs370VAOBV3gexMdHYufV/sLC0Rr2GkkVx1q1chIS38fh58AikpiTj8aP70nLpGxjCUUELR2HUvH1vzJk0CEvnjEVN/+Z48igIh/duRPseQyDS1UNqShLCQ1/AysYBRd63fLXo0AdrF/8JI2MTlK9UE7evncO1iydkuhblRJsu/bFo5ijJueu1QFZmJo7u34L417HoP3xqXlxugVHMzRfF3Cth75pR8G83CvqGpjh3YDH09I1lFl2LjXiK7KwM2Dh5fvU5YsKDYWmb81Wj1UW/xn7ov2gzRq0JQKtqXrj9PAz/nLiMX1vVhZ5IB0mp6XgeFQcHC1MUNTZESXv5iSQM9STz3Jcupj6BKxUePds1x7ApczF+3jI0q1sDd4OfYsu+I/ilW3vo6oqQnJKKF2ERsLe2hJlJETx+8Qqb9h6Gh0tx+FevhPtPZFtonR3sYGigjwFd22HwxNkYMX0BurRohDdv32HZpp0oXbIE/HzU76UYFSw5Dhh69Pj40JuVlYWbN2/i7NmzKFasmNwgvw0bNqiuhCo2aPRcbP/fPOz8ZyEEQYCrhxcGjpwNQ6OP85tvWjkTcTER+GvVQQCS9RRGTF6GrWvm4J9l06ClrY3S5augU68RMlO09hg4DoZGxjiyZz3S0lJQzKUUfpu0DNa2X7+q8LsESf/o/5arsCnrVREj/5iK7ZvX4a+p41DU3ALdew9EizaSB6rnTx9j8thhGPTrWNSpL1mspk79xsjMysD+gG04dfwQrG1sMWTEOFSr8XF9ix59foG5hSWOHd6PAwHbYVbUHF7evuj8Uz8YGRsjMzMTN65dAgCsWjJPrlyeZcvjz1mK1xYobDzL+WLw6NnYs3UVFs0cBTNzS3T4aSgat+oGAAh5FozZEwagz5CJqOEvCaZq+DdHVlYmjuzdhHMnD8DK2h79hk1BJb/6X3Vu70q1MHzCQuzfsRaLZ/0OPX0DOLt6YuKc9XByVv+3Xe0GLsHxHbNwctdfEAQxHFwqoG3/hdA3/Diu6/DmKUh4HY4hs0599fGT372GlYP7l3dUM5Xdi2Nev7ZY/u95/LpyF6xMjDG8tT9+qidpqX0YGoW+CzexqxEVWD5lPTFj1CCs2bYXY2YvhmVRMwzq0QFdWki6FwY/f4nBk2Zj3KA+aFrXD2evSFojHj0Lwc9jp8kdb8mU0ahQxgNl3V2xZMrvWLklAH/MWQo9XRFqVvLG4J86QUvrh5oln/KBhpDDUSFjx47N8UFnzpyZ6wJ9cOHB56cmpNzz85QMbL37NDqfS6KeyrpKZmG6/PBdPpdEfVUtJQmkN35+jTT6Bt1rAmknC+7Ln8JOz1/yEu6gzo8XFH4vTTOD8frepfwuhtoyL1OwJxGZ+I/iaZi/hz9/En15p0Imxy0MioKAjIwMiESSSomMjIStbc77PxMRERERUcGXqzas169fo2vXrliy5ONUg61atULPnj2RkMDFXYiIiIgo/4iF/Puoo1wFDNOnT0dWVhZatmwpTfvf//6H1NRU/PXXXyorHBERERER5a9cBQwXL17ElClT4OLiIk3z9PTEhAkTcOrU1w/uIyIiIiKigilX06pmZ2dDrGCiWW1tbaSnpyvIQURERET0fQjq2jcon+SqhaFy5cqYN28eEhMTpWlJSUlYtGhRgV79mYiIiIiIvk6uWhjGjBmDLl26oGbNmnB2liwsFhISAlNTU6xdu1alBSQiIiIi+ho5WzSAcipXAYOjoyMOHz6MgwcP4vHjx9DW1kbnzp3RvHlz6OnpqbqMRERERESUT3IVMAwcOBAjR45Ex44dVV0eIiIiIqJvIuYYBpXK1RiG69evQ1dXV9VlISIiIiKiAiZXAUPr1q0xd+5cPHnyBBkZ+bf0NhERERER5a1cdUk6ceIEIiIicPToUYXbHz58+E2FIiIiIiLKLYGjnlUqVwHDkCFDVF0OIiIiIiIqgHIVMLRu3VrV5SAiIiIiUglBfn1htZCeno5Zs2bhyJEjSEtLQ40aNTBp0iSYm5vnKP/y5cuxcOFCBAcHf9V5cxUwAMCpU6cQHByM7OxsaVpGRgbu3LmDf/75J7eHJSIiIiIiBSZPnowbN25g8eLFEIlEmDRpEoYNG4ZNmzZ9MW9QUBCWLFmSq/PmKmBYsGABVq5cCSsrK8TGxsLa2hpxcXHIzs5G06ZNc1UQIiIiIiJSLDo6Gnv37sXKlSvh4+MDAJg/fz4aNWqE27dvo3z58krzpqSkYNSoUfDx8cGVK1e++ty5miVp3759mDBhAs6dOwdra2ts2bIFFy5cQIUKFeDo6JibQxIRERERqYRYEPLtk1du3LgBAKhcubI0zdnZGdbW1ggMDPxs3unTp8PNzQ0tW7bM1blz1cIQFxeHWrVqAQA8PDwQFBSERo0aYfjw4Rg3bhyGDRuWq8IQERERERVm/v7+n91+8uTJXB03OjoaZmZmcmuhWVlZITIyUmm+48eP4+zZszhw4ABOnz6dq3PnKmAwMTFBcnIyAKBYsWJ4+vQpAMDOzg7R0dG5KggRERERkSoUxmlVw8LCPhtsDBs2DCKRSC5dV1cX6enpCvNER0djwoQJ+Ouvv2BmZpbrsuUqYKhatSr++usvTJs2DWXKlMGKFSvQpUsXHD16FEWLFs11YYiIiIiICrPctiBYW1vj0KFDSrefPXtW4YLJ6enp0NfXl0sXBAFjxoxB48aNUbNmzVyV6YNcBQyjRo1C//79cfToUXTp0gX/+9//UL16dQDAmDFjvqlARERERETfQiwufC0MOjo6cHFxUbo9ODgYb9++RUZGhkxLQ0xMDGxsbOT2j4iIwKVLl3Dz5k3s3bsXAJCVlQUA8Pb2Rv/+/TFgwIAcle2rAobo6GgcP34cIpEIK1asgJmZGUQiEbZs2YLz58/D2toa5cqV+5pDEhERERHRF1SsWBFisRg3btxA1apVAQDPnz9HdHS0dNak/7K2tsaxY8dk0o4dO4a5c+di7969MDExyfG5cxwwXL9+Hf369UNqaioAwNDQEH///Tf8/Pygp6eH+vXr5/ikRERERESUc9bW1mjatCnGjx+PGTNmQF9fH5MmTUKlSpWkU6pmZGQgISEBJiYmEIlEKFasmMwxPizw9mn6l+R4WtVFixahSpUqOHfuHC5evAg/Pz/MmjXrq05GRERERJTXBCH/Pnlp6tSpqFq1KgYPHow+ffqgRIkSWLRokXT7rVu34Ofnh1u3bqn0vDluYXj48CG2bt0KKysrAMAff/yB2rVrIykpCUZGRiotFBERERERyTIwMMC0adMwbdo0hdsrV66M4OBgpfnbtGmDNm3afPV5cxwwJCcnw9TUVPp3a2tr6OjoICEhgQEDERERERUYQiEc9FyQ5bhLklgshoaGhkyalpYWxGKxygtFREREREQFQ44DBiIiIiIi+vF81bSq69atk1kYIisrCxs2bJCblmnw4MGqKR0RERER0VcSF8KVnguyHAcMdnZ2OHz4sEyapaWl3Gp2GhoaDBiIiIiIiNREjgOGU6dO5WU5iIiIiIhUgoOeVYtjGIiIiIiISKmvGsNARERERFTQsYVBtdjCQERERERESjFgICIiIiIipdgliYiIiIjUCnskqRZbGIiIiIiISCkNQeDKFkRERESkPgbMjs+3c68YbZZv584rbGEgIiIiIiKlCuwYhksPE/O7CGqrWiljAMC7G0fzuSTqqUjFhgAAv+Zn87kk6uvCgVoAgCfPXuZzSdRXSZdiiLt3Ob+LobYsylQFALy+dymfS6K+zMtUw0Ed9/wuhtpqmhmc30Wg76jABgxERERERLnBHveqxS5JRERERESkFFsYiIiIiEitiDmvqkqxhYGIiIiIiJRiCwMRERERqRWOYVAttjAQEREREZFSDBiIiIiIiEgpdkkiIiIiIrUicNCzSrGFgYiIiIiIlGILAxERERGpFbYwqBZbGIiIiIiISCkGDEREREREpBS7JBERERGRWhFzHQaVYgsDEREREREpxRYGIiIiIlIrHPSsWmxhICIiIiIipRgwEBERERGRUuySRERERERqReCgZ5ViCwMRERERESnFFgYiIiIiUitiDnpWKbYwEBERERGRUmxhICIiIiK1wmlVVYstDEREREREpBQDBiIiIiIiUopdkoiIiIhIrXBaVdViCwMRERERESnFFgYiIiIiUiuCWJzfRVArbGEgIiIiIiKlGDAQEREREZFS7JJERERERGqFKz2rFlsYiIiIiIhIKbYwEBEREZFa4bSqqsUWBiIiIiIiUootDERERESkVgSOYVAptjAQEREREZFSP1QLw92blxCweTkiQp/D2MQMtRu2RdO2PaGhoaE0z6Uzh3Bw93rERofD3MIajVr3QK36rWT2ef74Hnb8swghzx5CV88AVWs1Rttuv0BHRyTdJzTkKXZuWITnj+9BW0cHpctXQYefhsLE1Fy6T1x0BHb8swgP711HdlYmSpX1RfseQ2BjX0zldZGfLt15gBU7DuJ5eBTMihihjX919GxRX+m/Q1p6BlbtPozjV24i/l0S3Jzs0bdtI1Tz8lS4f1JKKrqMmY1+bRujea3KeXkphYq+vhYG9SqBGlUsYKCvhXsP3+Hv1U8REpqiNM/ONZVha62ncFtEdCo69L2WV8Ut0G5cD8TGDesRGvoKRYqYoHGTpmjfodNn7yUfPH3yGCN+G4ZVa/4Ha2sbmW1HjhzC/r17EBUVCUtLKzRp2hwtWrbK0XHVyZVbQVi1NQAhoREwLWKMVg3roHvrpkrrITMzC1sPHMHh0xcQ8/oNLM2LokGNqujeuil0dD7+zDXvMxRv3r6Ty79/zUKYm5nm1eUUSFdu3cWqrQF48b6OWzesnaM6PnT6ImJev4GVeVE0qFFFro5DwiKxbOMO3Lz/CNpaWijv6Y4hP3WEvY3V97q0Qk3PwQY1bx3A9baD8Obcj3l/pYLphwkYnjy6g79n/IZK1eujTdeBePzwNgI2L4MgiNG8fR+FeQIvnsCavyehfrNOKFOhGm5dPYP1S6dBJNJF1VqNAQAxkWGYO3kQXD28MHDkTESGhWD35mVITUlCr0HjAQAJ8XH4a0J/FLW0QZ+hk5CRnoadGxZj/p9DMeGvf6CtrY3U1GTMmTwIWlpa6DFgDHS0Rdi/cw1mjf8Z0xbtgJGxyXerq7x05/FzjJi7GvWremNAh6a4E/wcy3cchCAI6N2qocI8U1ZuxuU7DzG4U3M42ljh4Pmr+G3OKiwfPwTeHi4y+yYkJWPE3NWIjHvzPS6nUJk8qhQ8Sxpj2frnSE7JRu/OxfD3dC90+yUQiUlZCvP8MeM+RDqyDxClPYpgaF9X7Dsc+T2KXeA8fHAfU/+chBo1aqF7j554cP8eNm5YD0EQ0LFTl8/mff78GSZPnoDs7Gy5bYcOHsCypYvRrn1HlPeugMfBj7B2zUqkp6ehQ8fOeXU5Bc7dR08wetbf8K9WCT93boOgh0+wastuCGIxfmrXQmGev/+3BYfPXEDPdi1QyrUEgp+HYN2OvYiOjcPYQZL7+5u3CXjz9h2G9OyMMu6y9w0TY6M8v66C5O6jJ/j9P3V85+FjrNwSALFYQM92zRXmWfi/LTh85iJ6tmsBT1dnBD9/ibU79iIq9jX+GNQbABAd9xoDxk2Hk70NpvzaH+kZmVi1NQC/Tp2HTfOnQldXpPDYJKHvZIdKB9dCx7RIfhdFLbBLkmr9MAHDvm2r4eTshp+HTwUAlK1QDdlZWTi0+x80bNEVIl35t6gBm5fBp6o/OvcZIcnjXRVJie+wd+tKacBwaM8/0NM3xNCx86CtowMvHz+IdPWwafVfaN6+DyysbHHr2jkkJSZgwl//wMrWAQBgYGiM+X8OxdNHd+BRpiICLxxHTGQopi3eAXvHEgAAh2KuGNW/BQIvnUCdhm2/RzXluTW7j8CtmD3+/KUHAKCalyeysrPxz/4T6NKkDvREsj8oYdGxOHHlFkb3ao929WsAAHxLl8Sd4BfYdfy8TMBw9noQ5m7YjdS09O93QYVEafciqO5rjpGT7+LKDUkwFXQ/ATvWVEabJnb4Z8crhfmePE+S+buBvhYmj/LExcDX2Lw7NM/LXRBt2bIJziVcMGLUaABARR9fZGVnY9fO7WjVui10dXXl8mRmZuLAgX3YvPEfiETyD02CIGDnju2oUaMWevaSPOCWL++N8PAwHNi/74cKGNbt2IeSxZ0wcVh/AEAV73LIys7Gxj2H0Kl5I7mHzneJSdh77DQGdmuPrq2aAAB8yklaH5dt3IEB3drDzKQIHr94CQCoXbkibKwsvuMVFTxr39fxpGE/AwCqeJdFVnY2Nu05hM7NGyqp4zP4pVt7dG0l+e37UMdLN+7AwG7tYGZSBGu274WBvh4WTRoFvff/D2ytLDB61iI8fBaC8p5u3/EqCxENDTj0aI1Ss3/P75IQKfVDjGHIzMxA8L0bqFilrky6TzV/pKWl4PGD23J54qIjEBXxChWr1pFJ963mj5ioMESFS3587t26Ai8fP2jr6MgcVxCLce/WZQBAVmYGAEDPwFC6j1ERUwBAUmKCJE9Vf4ybtU4aLACA1vtjZmVm5uayC5yMzEzcePgEdSp5yaTXrVQeKWnpuP3omVweq6Km+GfaSDSq7iNN09TUhLaWJjKyPr4VT0xOwe8L1qJiqZJYNOaXvLuIQqpyBTOkpGYj8NbHlpe37zJx+95bVPEpmuPj9OpcDKYmOpi//EleFLPAy8zMwN2gIFSrVl0mvbpfDaSmpuL+/bsK810PvIatmzehQ8fO6Nmrr8J9/pw6A716y27T1tZGppr8/8+JjMxM3Lr/CLUqV5RJr1PVB6lpabj98LFcnqSUVLRqUAd+vt4y6U52ku5eEdGxAIAnL17B2NDghw8WJHUcrLCOUz5bx7Xh51teJt3RzhqApI4FQcDZKzfQ3L+mNFgAgFKuzti/ZgGDhc8oUs4dZZZMRtjGvbjdk0GDqogFcb591NEPETDERoUjKysT1nZOMunWto4AgKiIl3J5IsJeSPaxkx0/8KGFICriFTLS0/A6NhI2nxy3iIkZ9A0MERUheWvr61cfpkUtsWnVX3j7Jg6x0eHYsf5vmJpZwLNcJQCAgZExXD3KAZAECK9ePMaavyfBqIgpKlWv961VUCCEx7xGZlY2nD7py+pobQkAeBUZK5dHpKMDzxJOMDLQh1gsRlTcG8zbsBth0XFo6//xoU1PV4Ttc/7A5IHdYGpsKHecH10xRwNERKUi+5P7WHhkKhztDXJ0DBsrXbRtZo+tAaGIjv0xW3GiIqOQlZUJe3sHmXQ7WzsAQHh4uMJ8Jd3csW79BnTs1AVaWlpy2zU0NODo5AQra2sIgoDExHc4euQwTp08gabNFHcRUUcR0bHIzMqCo53s2A57G8mDaWhElFweO2tLjPy5B4rZ28qkn7l6A9raWtJjPQl5BWNDQ4z9azEadB+Iel37Y+L8ZYiLf5s3F1NAfahjp/cP+x84fKGORymo47P/qePImDgkpaTCxtICc1dvRKOfBqN2p34YNWMhomJf590FqYHUV5E441EfD0fNQnZKWn4Xh0ihXHdJiomJwY4dO/D8+XOMGzcO165dg5ubG1xcXL6c+TtLSU4EAOgbyD5I6ulLHpTSUpK/Io/k76kpyUhJlnTX0DOQ7/+qp28oPa6JqTm69x+NlfPGIfDicQCAoVER/D51BQwM5fMumDYMD+5cg4amJn4aMBYmZurxRiwxORUAYKgv2/3LQF/yNio59fM3yvX7j2P5joMAgJa1q6CiZ0npNh1tbRT/5AeQPjIy1EZKqny/+ZTUbBjqyz/AKtK+hQMyM8XYuV/xQ/GPIOn9/3kDA9kgS//931NTFA8gt7DI+f/hhw8e4PdRwwEArq4l0bxFy9wUtVBKSpbUn6G+vky6wft7RnJqao6Oc/pyII6evYT2TeujiJHknv3kxSvEvHmD5vVroWOzBngZFok12wMweMJM/G/un9DXk+9Kpo4Sv1jHOXtgPX35Oo6cvYwO7+s4LDIaALB8006UcnXGlOEDEJ/wDis278LgSbOxcf7UH6aOv1ZmfAIy4xPyuxhEn5WrgOHly5fo0KEDjIyMEB0djeHDh+Pw4cP4448/sHbtWlSoUEHV5fwmwvvmIQ0onv1BQ1O+oeXDCoGf5vmQrqmp8Z/jKjqnAA1NyZbLZ49g9cIJ8K1eDzX8WyIjIw1H9m7EvMmDMWb6Ktg6FJfJ27JDPzRr1xs3r57B+mXTkZgQj2bte+f4egsqaZ0qmfDlQ30pU7NCWZR3d8HD56+wOuAwol+/xeKx7H70KQ0N4NOq1NTQgKJFLzU0AHEOBoaJRJpoVt8G/x6PQmKy4gHSPwLpIDolX2JVzGZkbWODmbPn4nVcHDZv3oDhwwZj/sIlMDMz++ZjF3Ri6T1CcT1q5qB+T18OxJSFK1He0x0Du7WXpo8b3BciHR24lZC0Gpf3dIezkz0GjpuOI2cuonWjusoOqVaEL9RxTr7Dpy4HYsrCVfD2dMfAbu0AAJnvu4iamRbBzN8HQ/P976qDrTV+HjsNR89dQqsGdZQek0jVOOhZtXIVMMyaNQv16tXDtGnTpMHBggULMGbMGMyfPx+bNm1SaSG/lYGhMQAgNVW2JSEtVfKmRV9BC8HHPLKDPtPTPuZRdtwP+3047r7tq6SzKH1QunwVjBvcDgGbl2HQ6L9k8rqVlvTFLVXWBylJ73Bg51o0btMDWlqFe4y6kaHkjdanb7BSUiXdW4z0FU/f+YGrk6TbR4VSrjA21MfUVVtxJ/g5vNxLfDbfj6ZXp2Lo3aW4TNrpC7FwNNWX21dfTwtJKfItD5+q5G0GQwNtHDsTrapiFkqG799Wp3zSKvmhZcHQ8Nu7w5mbm8PcXDLdsruHB37u2wvHjh7+4gxM6sDofUvNpy0JKe/vGUYGn+8+t23/ESzduB3epT0wa/QwiP4ztqyMu6vc/uU8SsLIQB9PXioe9K+OvlzH8veJ/9q6/6i0jmePHiqt4w8tFFW9y0qDBQAo4+YCY0MDPHnxY06SQKQucvUEeuvWLWzatEnmTYSWlhYGDBiADh06qKxwqmJl4wBNTS3ERMresKLf/93O0Vkuz4e1D2Iiw1CshIc0PSYy7H2eEtDV04eZuZU07YN3CfFITUmG3fsBzK9jIlGhcm2ZfXR19eDs6onw0OcAJGs5xMVEopJffZn9nF09cenMISS9e1vouyY5WFlAS1MTYVFxMumh7wclOjvYyOUJj3mN6/cfo1F1H+iKPv74e75/Sxj9Oj4PS1w47TsaiYuBsn2Ga1axQKUKZtDQgExLg72tPl5+Zh2GD6r5miMiKhXBT5O+uK86s7W1g6amJiIjImTSIyIlf3d0yt2aKSkpKbh69TLc3T1gZ2cvcz4jIyPExsqP71FH9jaWkntEpGxgGh4l+XtxRzuF+QRBwIK1m7D78EnUrVYJE4b2kwkWEpOTcfbKDZR2c4Gzo71MvsysbJgaG+fB1RRM9jZW7+s4RiY97H0dO3+2jjdj1+GT8K/mK1fH9tZW0NTUQEamfAtkVnY2RP+5fxN9D2xhUK1cDXrOzs6GWCw/CjwpKUnhgL78piPShVtpb9y4clraHAsA1y+dhIGhMUqULC2Xx9rWEZbW9gi8dFImPfDSSdjYOcHCSjL4q3T5yrhz/Twy38+E9OG4mppaKFVWMrOPrUNxPHl4W+bcmRnpePk8GBZWkpvznesXsHL+eLyOlR1wdvfWZZiYmcPYJOcz2RRUuiIdeHu44HTgHZm6OHXtNowN9FHaRf5hKyL2Naat3orTgXdk0i/feQgAKFnMXi7Pj+71mwwEP02S+Vy7FQ9DA21UrvDxe2RaRAfly5ji2q0vr1nh6WaMoIfyC179aEQiEcqUKYtLly7KfIcvXjgPQyMjuLm55+q4mpqaWLRwPnbv2iGT/vhxMBITE1GixI/RiqYrEsHL0x1nr96Qqd/Tl6/D2NAAnq6K62HF5l3YffgkOjZriD9/GyjzIAtIxjjNW70Bm/YclEk/f+0m0jMy4F3aAz8KXZEOvDzdcCYXdbzr8El0bNZAYR0b6OvBq5Qbzl69gYz/zOx1PegBUtPSOUsSUSGXqxYGPz8/LF++HHPnzpWmxcfHY86cOahSpYrKCqdKzdv3wdxJv2DZnDGo4d8CTx8F4cjejWjfYwhEunpITUlCROgLWNo4oIiJpK9wiw59sXbxFGwwNoF3pZq4de0cAi8el+la1KT1T7h6/hjm/zkUDVt0RXTES+zatAy1G7aBuaXkjXnrLgOweOZILJszBjXrtURmZgaO7d+C+Ncx0nUh6jRqhzPH9mDB1GFo2bEfdPX0cfH0vwi6cRE/D58m08RbmPVu3RCDZizF2L//h+a1qyDo8Qts/PcUhnRqAT2RCEkpqXgRHgUHawuYFTFGhVKu8PEsiTnrdyExJRXFbK1w48ETbDhwEq3rVoOzvXyrBMm7cz8BN4PeYuIIDyxb/xzv3mWid5fiSErOklmArbijAXR0NGXWX9DUlKSfOBej6NA/nI6dumD8uDGYNXMa6tdviIcPHyBg90707NUHurq6SElJxqtXr2BrawsTE9McHVNPTw/t2nfEtq2bYWxcBOW9KyAiPAxbNm+Ec4kSqFdf8aKG6qhnu+YYNmUOJsxbiqZ1a+Ju8BNs2XcYA7u1h66uCMkpqXgRFg57ayvp+gqb9x6Ch4sz6lb3xf0nstMzOzvYw9BAH11aNsH6XfthZlIEVbzL4enLUKzbsRfVKnrB10v+pZE6k9TxXIyftwzN6tbA3eCn2LLvCH6RqeMI2Ftbvq/jV9i09zA8XIrDv3ol3H/yXOZ4zg52MDTQx4Cu7TB44myMmL4AXVo0wpu377Bs006ULlkCfj7eSkpDlDcERQP3KNc0hFzUaHR0NHr06IG3b99K336Fh4fD1NQUmzZtgr39t7/1vfQw8ZuP8akbV05j79aViAp/CTNzK9Rt3B6NWnUDADy6ex2zJwxAnyGT4Of/cRrD00d348jeTXgTFw0ra3s0bdsT1eo0lTnu4/u3sP2fv/HqxWMYFzFF1dpN0KbLAJkxB3dvXsL+HWvw8nkw9PQN4OzqibbdBsHJ+eNbl+jIUOzauASP799CamoyipVwR7N2veHl46fSeqhWStL8/u7GUZUeN6dOB97Bql2H8TIyGpZmpmjfoAa6NZUMOLzx4AkGTFuMif27onmtygAkc4Cv2XMUp6/dRmx8AuwszdHGvzo6NaqlMJCKiH2NlsOmyBzjeypSUfJw59f87Hc/9+cYG2pjcF8X1KxiDg0NDdx9mIBFa54hNPxjX+bFM7xgY6WH9n2vStNMTXTw76ZqmLP0MfYdKRirO184UAsA8OSZ/JTI38OlSxewZdNGhIWFwdzCHE2btUCbNpLBn0FBd/DHmFH4dfhI1KvfQC7viePHsHDBXKz93wZYW38MeMViMY4cPoiD/x5AZGQEjIyNUa2aH7r36KmSsRFfq6RLMcTdu/zdzwtIputcu20PXkVEwbKoGdo0rovOLSQLht289xBDJs3GH4P6oGndGli9NQDrd+1XeqzFU0ajQplSEIvF2HP0FPYcPY3w6BiYGBmhfo0q6Nuxdb6sQGxRpioA4PW9S9/93ICkjtds2ytTx11aNAIA3Lz3CIMnzca4QX3QtK4fVm/dg/99po6XTBmNCmUkrTR3Hz3Byi0BuP/kOfR0RahZyRuDf+oEY8OcTd+sSuZlquGgTu5a/fJL0ZqVUPXkRlz27443567ld3E+q2lmcH4X4bNaDsy/8u1bnnffu/T0dMyaNQtHjhxBWloaatSogUmTJknHvimSlJSEOXPm4OjRo8jMzISvry/GjRsHR0fHHJ83VwGDIAhIS0vDv//+i4cPH0IsFqNkyZJo2bIljIzkBxDnRl4EDCSR3wGDuiuoAYM6ye+A4UeQnwHDjyC/A4YfQWEMGAoTBgzK5WXAMHbsWNy4cQMzZsyASCTCpEmTYGho+NkJh3r16oXw8HBMnz4dxsbGmDJlChITE7F///4c92DJVZektm3bYsaMGWjfvv2XdyYiIiIi+o4UjbUt7KKjo7F3716sXLkSPj6ScbLz589Ho0aNcPv2bZQvX14uz9WrV3H58mXs27cP7u6SQGbq1Kno168fQkJCcjxGLlcBQ3h4uNzCRUREREREPzp/f//Pbj958uRntytz48YNAEDlyh+7Wzs7O8Pa2hqBgYEKA4bz58/Dzc1NGiwAgKurK06fPv1V585VwNCvXz+MGzcOffr0gZOTE/T0ZOfPt7NTPC0bEREREVFeU8dpVaOjo2FmZgZdXdlV062srBAZqXiMYUhICIoVK4YtW7Zg8+bNePfuHSpWrIixY8fC2to6x+fOVcDwYXakwMBAmbUYBEGAhoYGHj58mJvDEhEREREVarltQQgLC/ts68SwYcMgEslP0qCrq4v09HSFeZKSknD//n3Ex8djypQpACTP8T169MD+/fvlgg9lchUwbNiwITfZiIiIiIhIAWtraxw6dEjp9rNnzyIjI0MuPT09Hfr6ildp19HRQXp6OpYuXQoTExMAwJIlS1CjRg2cOnUKjRs3zlHZchUwVKpUKTfZiIiIiIjynCAUvkHPOjo6cHFxUbo9ODgYb9++RUZGhkxLQ0xMDGxsFK9LZWNjA2tra2mwAAAWFhYwNTVFWFhYjsuWq4AhIyMD27dvR3BwMLKzs2XS7969i2PHjuXmsEREREREpEDFihUhFotx48YNVK0qmZr5+fPniI6Ols6a9CkfHx8EBAQgJiYGVlZWACQBRnx8PIoVK5bjc+dq+eAZM2Zgzpw5ePbsGfbt24dXr17h/PnzOHTo0BdHhhMRERER5SVBLOTbJ69YW1ujadOmGD9+PK5evYqgoCCMGDEClSpVks6QlJGRgdjYWGnXpcaNG6N48eIYNmwY7t69iwcPHuC3336Ds7MzateuneNz5ypgOHHiBGbNmoWtW7fCwcEBU6dOxenTp+Hv74/MzMzcHJKIiIiIiD5j6tSpqFq1KgYPHow+ffqgRIkSWLRokXT7rVu34Ofnh1u3bgEARCIR1q9fDzs7O/Ts2RPdunWDmZkZ1q9fr3AAtTK56pL09u1baSTj5uaGBw8eoESJEujfvz9+/fVXjB8/PjeHJSIiIiL6Zuo4rSoAGBgYYNq0aZg2bZrC7ZUrV0ZwsOwq15aWlpg3b943nTdXLQwWFhZ4/fo1AMDJyQmPHz8GAJiZmSEuLu6bCkRERERERAVHrgKGWrVqYdKkSQgODkaFChVw4MAB3L17F5s3b1Y6SpuIiIiIiAqfXAUMI0eOhI2NDa5fvw5/f3+ULFkS7du3x8aNGzF06FBVl5GIiIiIKMfEgjjfPuoox2MYZs6ciWHDhsHAwADGxsZYtmyZdNuqVavw4MEDWFhYSKdsIiIiIiKiwi/HLQwbNmxAamqqTFqfPn0QExMDAPD09GSwQERERET5Th2nVc1POQ4YBEG+Am7evIn09HSVFoiIiIiIiAqOXI1hICIiIiKiH0Ou1mEgIiIiIiqoBLF6Dj7OL1/VwqChoZFX5SAiIiIiogLoq1oYpk2bBl1dXenfMzMzMWfOHBgaGsrsN3PmTNWUjoiIiIjoK6nr4OP8kuOAwdfXF7GxsTJp3t7eiI+PR3x8vMoLRkRERERE+S/HAcPGjRvzshxERERERCohqOkCavmFsyQREREREZFSDBiIiIiIiEgpTqtKRERERGpFzEHPKsUWBiIiIiIiUootDERERESkVrhwm2qxhYGIiIiIiJRiwEBEREREREqxSxIRERERqRWu9KxabGEgIiIiIiKl2MJARERERGqFKz2rFlsYiIiIiIhIKbYwEBEREZFa4RgG1WILAxERERERKcWAgYiIiIiIlGKXJCIiIiJSK1zpWbU0BEFgJy8iIiIiUht+zc/m27kvHKiVb+fOKwwYiIiIiIhIKY5hICIiIiIipRgwEBERERGRUgwYiIiIiIhIKQYMRERERESkFAMGIiIiIiJSigEDEREREREpxYCBiIiIiIiUYsBARERERERKMWAgIiIiIiKlGDAQEREREZFSDBiIiIiIiEgpBgxERERERKQUAwYiIiIiIlKqwAcMdevWhbu7u/RTqlQp+Pj4oHv37rh+/Xp+Fy/fBQQEwN3d/bP7jBkzBt27d1f5uQ8cOICOHTvC29sb3t7eaNu2LbZt26by8/zX4sWLUbdu3Tw9h7u7OwICAvL0HN/iW/9PXL16Fe7u7ggLC/sOpf0xJCUlwcvLC9WqVUNGRkZ+F6fQ+dK9pG7duli8eDGAnN8D8uP+VFDl1+9oXv32FHTdu3eXqe//fqZPn57fxSPKFe38LkBO9O7dG7179wYACIKAt2/fYv78+ejbty+OHDkCGxubfC7hj2fXrl2YNm0a/vjjD/j6+kIQBFy+fBnTp09HXFwcBg8enCfn7d27N7p27Zonxy5M+H+iYDl48CDMzc0RFxeH48ePo2nTpvldpEIjJ/eSXbt2QVdXV6XH/NHwnvF9NW7cGOPGjZNL19fXz4fSEH27QhEwGBgYwNLSUvp3KysrTJkyBTVr1sSxY8fQo0ePfCzdj2nLli1o164dOnToIE0rUaIEoqKisGHDhjz7QTY0NIShoWGeHLsw4f+JgmX37t3w8/NDdHQ0tm3bxoDhK+TkXlK0aFGVH/NHw3vG96WnpydT30SFXYHvkqSMtrYk1hGJRIiKisLIkSNRrVo1lC5dGrVq1cKCBQsgFosBANnZ2ZgzZw5q1aqFMmXKoFGjRti6dav0WK9fv8bQoUNRuXJllCtXDp06QJxhFAAAFAJJREFUdcK1a9ek2zMyMjBnzhzUqFED3t7e6NChAy5cuCDdHhAQgLp162LPnj2oX78+ypQpg7Zt2+LWrVvSfVJTUzFp0iRUrlwZFSpUwLhx4zBixAiMGTNGus/NmzfRtWtXlCtXDrVr18aUKVOQlJQk3V63bl3MmDEDTZo0QeXKlXHlyhW5ehEEAcuWLUPNmjVRvnx5jBs3Dunp6SqocVmampq4efMmEhISZNL79euH7du3S8v7oRvBB927d5de84euMatXr0blypXRunVrdOnSBb/++qtMnhs3bsDd3R0vX76U6Y7QvXv3z+4LAKdPn0abNm1Qrlw51K9fHwsXLpTpMhIVFYWBAwfC29sbtWvXxsGDB7+5bvLLf/9PZGVlSevKy8sLbdq0wblz5xTme/fuHSZNmoRatWqhdOnSqF69OiZNmoS0tDTpPmvXrkW9evVQpkwZ1K1bF0uXLoUgCAAk3+1x48ahevXqKFu2LFq1aoVjx47l/QUXEM+ePcOdO3dQvXp1NGrUCNeuXcOzZ8+k27Ozs7FgwQL4+fnBy8sLQ4YMwfTp02W6ajx79gz9+vWDt7c3/Pz8MGLECMTGxubH5Xx3ub2XLFu2DFWqVEHFihUxceJEpKSkfNUxPxx3xYoV6N+/v/QesXPnTlVfYoH1Nb+jH37npk+fDh8fHwwYMAAAEBoaikGDBqFixYqoXLkyhg8fjri4OOk5MjMzMXv2bFStWhXly5fHL7/8IrP9R/Sle66i38bs7GxER0dj+PDh8PHxQeXKlTFgwACEhITk78XQD6NQBgzR0dH4888/YWBggJo1a6J///548+YN1q5diyNHjqBv375YsWIFTp06BUDytunIkSNYsGABjh49im7dumHy5MnSvpuTJ09GWloaNm3ahAMHDsDZ2Rm//PKL9Ado7NixOH/+PObMmYM9e/agcePGGDBgAM6cOSMtU0xMDLZt24Y5c+Zg+/bt0NTUxOjRo6UPVaNHj8bFixexYMECbNu2DUlJSTIPp48ePULPnj1RvXp17N+/H3PnzsX9+/fRu3dv6TEAYOvWrRg/fjzWrFmDChUqyNXNqlWrsGbNGvz+++8ICAiAkZERDh06pPJ/g379+uHhw4eoWbMmfv75Z6xatQpBQUEwNjaGs7PzVx3rzJkz2L59O2bMmIF27drh9OnTMoHS/v37UaFCBRQrVkwmX+vWrT+777lz5zBs2DC0b98e//77LyZNmoTDhw9j1KhRAICsrCz07dsX8fHx2LRpExYsWIDVq1d/Q63kn0//T8yYMQObN2/GyJEjceDAAdSqVQu//PILnj59Kpd39OjRCAoKwqJFi3D06FGMHTsWAQEB0gerU6dOYcWKFZgyZQqOHTuGkSNHYvny5di/fz8A4O+//0ZwcDBWrVqFQ4cOoWbNmhg+fPgPM0Zi165d0nqvV68eRCKRzAuJuXPnYvv27Zg4cSICAgJgZWWFjRs3SrdHR0ejS5cucHR0xK5du7BixQokJSWhU6dOMg/B6io395Lw8HBcvnwZ69atw/Lly3H16lWMGDEiV8dcunQpypYti71796Jr166YOHFintwzC5qv/R0FJPUeHR2NPXv2YMSIEUhMTESXLl2QkpKC9evXY/369QgPD8eQIUOkeW7duoWEhARs3rwZK1euxO3bt/HXX3/lxyUXGF+6537w39/G9PR0dO/eHdnZ2di0aRM2btwIMzMzdOjQAdHR0fl0JfRDEQq4OnXqCKVLlxbKly8vlC9fXihTpozg5uYmNG7cWDhz5oyQmpoqrF27VggLC5PJ5+fnJyxZskQQBEGYNm2a0KxZMyEmJka6/eLFi0JcXJwgCILQokULYeTIkUJaWpogCIKQmJgoXLx4UUhLSxNCQkIENzc3ISgoSOb4v//+u9CtWzdBEARh9+7dgpubm/DgwQPp9uPHjwtubm5CdHS08OrVK8HNzU04d+6cdHtaWprg5+cnjB49WhAEQRg5cqTw888/y5zjQ74rV65I62LQoEEy+3w4tyAIglgsFqpXry4sWLBAZp+WLVtKy6pKd+7cEUaOHClUqVJFcHNzE9zc3IQGDRoI169fl5Z30aJFMnm6desmveYrV64Ibm5uwokTJ6Tbk5OThfLlywsBAQGCIAhCenq6UKlSJWHHjh2CIAjCokWLhDp16uRo386dOwtTpkyROf/ly5cFNzc3ITQ0VDh37pzg5uYmvHz5Urr9wYMHgpubm7B7926V1ZOqfen/RGJiolC6dGlh69atMvnmz58v3L59W1rvoaGhgiAIwsaNG4WHDx/K7NuxY0dh7NixgiAIwv/+9z+hevXqQkhIiHR7YGCgEB4eLgiCIAwcOFD46aefhHfv3gmCIAhZWVnC2bNnpX9XZ5mZmUL16tWF4cOHS9MGDRok+Pj4CCkpKUJKSopQrlw5uX+L1q1bS/9PLliwQGjWrJnM9g/5CvL3UJW+5l6yaNEioUyZMkJsbKw0/4ULFwQ3NzeZ7+iXjvnhuP3795cpy6+//ip06NAhLy/3u1PF7+iH35r/3iu2bdsmeHl5CfHx8dK0x48fC3PmzBHS0tKE0aNHC9WrVxeysrKk26dOnSo0btw4by84n3Xr1k3w9PSU1veHT69evQRB+PI9V9Fv444dOwQfHx8hIyNDmpadna3wd5YoLxSKMQydOnWSNt9ramrC1NQUxv9v596Doir7OIB/l8sqCq1DMETM4pYC4ZguoQHSDYiATdAlrAmsHAaSDBkkoJiRIGFETIZbE1HApEEXUgIZTFm3XIhoVJKLLYhsC6xAhWZABrS5+/7Bu+fluKBrKgjv7zOzM7vnts85u8/vnOec3/NYWDDzN2/ejKNHj2L//v3o6elBR0cHfvvtN+ZRalhYGI4fP44nnngCK1euhKenJwICAnDvvfcCAKKjo5GQkACJRII1a9bgscceg0gkwoIFCyCXywFAL79TrVbjnnvuYU1btmwZ815XPrVazWzDxcWFmb9gwQI8/PDDzGe5XI6enh7WMjoKhQJubm4AoHeXfbLLly9jcHCQtV0AEAqFrBSJ22XVqlV49913odVq0dnZCZlMhgMHDiAyMhISicTg7QgEAub9okWL4O/vj+rqaojFYtTV1WFsbAwBAQF6691oWblcjtbWVnz11VfMOtr/Pq1RKBTo6uoCj8eDvb09M9/Z2XlOdEq7Xp1oa2uDWq2GUChkrbNjxw4AE4+7JwsNDcU333yDqqoq9Pb2orOzEyqVivldgoKCcOjQITzzzDNwcnKCp6cnfH19cf/99wOYuJsbFRUFDw8PuLi4wNPTE88++yyrjs5XMpkMg4ODEIlEzDSRSASJRIKamho89NBDGBsb0/stXF1d0dHRAWDif6pQKPTq/vj4+B2pt3ejm40lAoEAVlZWzOfVq1cDAM6fP8/EyBttUxf/dbFVRygUsp4ezxe3eh7VmRyvz507B4FAgCVLljDTHBwcEB8fz3y2t7eHsbEx85nH47HSHecrb29v1nEAJvo1ADeOuTqTP8vlcvz555949NFHWcv8P8UJMrvmRIOBx+NNe6E8OjqKsLAwjI6OIiAgABs2bEBycjJrJB2BQIDa2lqcPHkSDQ0NkEql+OCDD5CRkQGxWAxfX1/U19ejvr4e33//PYqKipCbm4vy8nLmArOsrEyvs62RETuji8vl6pVPq9UywfLawDuZRqNBYGAgkxc62eQOf7qAcz3aSSlMwP/yVG+XX375BR999BFeffVV2NjYgMPhMEPG+fj4QCQS4dSpU1OWRa1W623v2tFPgoOD8corr2BwcBCHDx/G008/DXNz8ynLcr1lNRoNIiIiIBaL9daztrZGV1eXXvmA23+87oTr1QlTU1ODt6PVahEVFYVz584hMDAQfn5+iIuLQ3JyMrOMpaUlqqqqcObMGTQ0NOC7775DSUkJtm/fjujoaLi4uEAmk6GhoQGNjY04ePAg8vPzUVRUBA8Pj1ve17uZbvjdmJgYvXmff/450tPTAejXg8k0Gg3c3d2RkpKiN2++N7puJpZMNvkCFJjoJwJM/PcN3aa/vz8A/fqu1Wr1Yvt8cKvnUZ3J5yATExNwOJzrfu+1v9X/i8WLF095vA2JuTqTz40ajQYPPPAACgoK9JZbtGjR7S08IVOY81Gxvr4eP/30Ez755BPExMRAJBLB3Nwcly5dYk7SBw4cQG1tLTw9PZGYmIjq6mp4eHjgyJEj+Pvvv5GRkQGVSgWRSIT09HRIJBIYGRnhxIkTcHBwADDRR2Hp0qXMq6KiAocOHTKojE5OTuBwOGhubmamTX7yAEzcldHdHdO9rl69ioyMDAwMDBj0PZaWlrC1tUVTUxNr+tmzZw1a31BcLhdffPEFk8M+me5i3crKCqamphgZGWHmaTQag/La165dCzs7O1RWVuLEiRNTXvAbsqyDgwN+/vln1jH99ddfsXfvXly5cgUrVqzA8PAwzp8/z6yjVCpZZZ6Lli5dClNTU7S1tbGmh4SEoKioiDVNLpdDJpMhLy8P8fHxCAoKgr29PXp7e5n6U1VVhc8++wyurq6IiYlBeXk5Nm3axOR55+XloampCT4+Pti5cyeOHTsGPp+PY8eOzcwOz5Lff/8dMpkMwcHBqKysZL1CQkLQ1taGv/76CwsXLmTVfQBobW1l3js4OEChUMDW1pb5n/J4POzevRudnZ0zvFczy9BYcq3u7m5W36WmpiZwOBwsX778prd5bT358ccfsWLFin+3Q3OUIefRqSxfvhzd3d2smCmXy+Hm5oa+vr6ZKPqcY0jMnYqjoyP6+/thYWHBxAk7OztkZWVN2agm5Hab8w0G3djRhw8fRl9fH06fPo1t27ZBrVYzo+FcunQJu3btglQqRV9fH+rq6iCXy+Hi4gIul4uWlhYkJyejubkZFy5cQEVFBa5cuQIXFxc4ODjAy8sLKSkpkEqlUKlUKC4uRmFhIfh8vkFl5PP5CAgIQFpaGhobG6FQKJCcnIyBgQHm7kx4eDja29vx9ttvo6urCy0tLYiPj4dSqdR7THk9kZGRKCsrw5dffgmlUomcnBzWxcntYGlpiYiICOTk5CA7Oxvt7e1QqVT49ttvER0dDTc3N6xZswaPPPIIjhw5glOnTkGpVCI1NdXgi/GNGzeioKAAS5Yswbp16/7VspGRkaitrUV+fj6USiUaGxuRlJSE4eFhWFtbw83NDatXr0ZiYiKam5vR1taGt956a87fXTQzM8PmzZuRm5sLqVSK3t5eZGdno6urC15eXqxlraysYGJigq+//hoqlQptbW2IjY3F4OAgU3/Gx8eRmZmJyspKXLhwAadPn8bJkyeZFJqenh6kpKSgsbERfX19OHr0KPr7+6dMr5tPqqqqmI7zjo6OrFdUVBSMjY1RUVGBl156CXl5eTh+/DiUSiX27dvHakCEhoZiZGQEcXFxaG9vR0dHB9544w20trYyNyzmK0NjybXGx8cRGxsLuVyOhoYGpKWlYePGjbCzs7vpbdbU1KC0tBTd3d0oKiqCRCJBRETETB6GWWfIeXQqgYGB4PF4SEhIQEdHB86ePYvU1FQ4OjrCzs5upoo/pxgSc6cSFBQEHo+H6OhoNDc3Q6FQICkpCTKZbN7HCXJ3uPtzL25g1apVSEpKwscff4ycnBzY2NhAJBLB1tYWLS0tACb6KPzzzz9IS0vDxYsXYW1tjdDQUGzduhXAxCgvGRkZeO211zAyMoIHH3wQWVlZzEklOzsb2dnZSElJwdDQEPh8PtLS0vDcc88ZXM60tDSkp6dj+/bt0Gq1WL9+PYRCIZM+IhQKmVSo4OBgmJmZwd3dHW+++eaUqU7TCQsLg0ajQUFBAS5evIjHH38cISEhUCqVBm/DELGxsRAIBCgvL0dZWRnGxsZga2sLkUjEHNcdO3ZgaGgIkZGRMDMzw6ZNmyASia57F0VHLBbjvffeQ1hY2A0v4Kdb1t/fH9nZ2SgsLERhYSF4PB68vLyYUZKMjIxQWFiI9PR0hIeHY+HChdi6deu8GN0nLi4OJiYmSE1NxfDwMJycnPDhhx9i2bJlrCENbWxssGfPHuTn56OsrAzW1tZ46qmnsGXLFkilUmi1Wjz//PMYGhrC+++/j4GBAfB4PPj5+TH5ue+88w4yMzORkJCAP/74A3Z2doiPj8eGDRtma/dnREVFBdatW8fqu6TD5/Ph6+uLmpoa1NXVQa1WY+fOnRgdHYWXlxd8fHyY4Y75fD5KS0uRlZWF0NBQGBsbQygUYv/+/Uye/XxmSCy51sqVK+Hs7IyXX34ZHA4HIpGINUT1zWxTNwxwZmYmBAIBcnJy8OSTT97Rfb7bGHIenYqZmRmKi4uxZ88evPjii+ByufD29kZiYuIMln5uMSTmTsXCwgKlpaXYu3cvIiIicPXqVTg7O6O4uJgaDGRGcLSGXL2RWzI+Po76+nq4u7uzcvH9/PwQFBSE119/fRZLRwi5kyQSCVxdXVl9kcLDw3Hfffdh9+7ds1gy4u3tDbFYzBoGlBBCiL45/4RhLuByudi1axfWrl2Lbdu2wdjYGAcPHkR/fz/T8Y4QMj8VFxfj008/RWJiIszNzSGVSvHDDz+gpKRktotGCCGEGGRuJ2vPERwOB4WFhbh8+TJeeOEFiMVinDlzBiUlJVOmMxBC5o99+/Zh8eLF2LJlC9avX4/q6mrk5ubC3d19totGCCGEGIRSkgghhBBCCCHToicMhBBCCCGEkGlRg4EQQgghhBAyLWowEEIIIYQQQqZFDQZCCCGEEELItKjBQAghhBBCCJkWNRgIIYQQQggh06IGAyGEEEIIIWRa1GAghBBCCCGETOs/DH940yxXWAsAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_correlation_heatmap(data):\n",
    "    # Calculate the correlation matrix\n",
    "    correlation_matrix = data.corr()\n",
    "\n",
    "    # Create a heatmap to visualize the correlations\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5)\n",
    "    plt.title('Correlation Heatmap')\n",
    "    plt.show()\n",
    "\n",
    "# Call the function with your train_data\n",
    "plot_correlation_heatmap(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6edf3dac",
   "metadata": {},
   "source": [
    "Interpreting the heatmap:\n",
    "\n",
    "Positive correlation values (closer to 1) indicate that when one variable increases, the other tends to increase as well.\n",
    "Negative correlation values (closer to -1) indicate that when one variable increases, the other tends to decrease.\n",
    "Correlation values closer to 0 suggest little to no linear relationship between the variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "349f366c",
   "metadata": {},
   "source": [
    "## Feature Transformation:\n",
    "\n",
    "1. Create a new 'FamilySize' feature by adding 'SibSp' and 'Parch' columns.  \n",
    "2. Convert 'Sex' column into numerical values (e.g., 0 for male, 1 for female).\n",
    "3. Extract 'Title' from names\n",
    "4. Replace uncommon titles. \n",
    "\n",
    "The functions make the data more suitable for analysis and modeling by structuring and transforming relevant information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "85786ea2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def create_family_size(data):\n",
    "    data['FamilySize'] = data['SibSp'] + data['Parch'] + 1\n",
    "    return data\n",
    "\n",
    "def convert_sex_to_numerical(data):\n",
    "    data['Sex'] = data['Sex'].map({'male': 0, 'female': 1})\n",
    "    return data\n",
    "\n",
    "def extract_titles(data):\n",
    "    data['Title'] = data['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "    return data\n",
    "\n",
    "def replace_uncommon_titles(data):\n",
    "    title_mapping = {'Mlle': 'Miss', 'Ms': 'Miss', 'Mme': 'Mrs'}\n",
    "    data['Title'] = data['Title'].replace(title_mapping)\n",
    "    data['Title'] = data['Title'].fillna('Other')\n",
    "    return data\n",
    "\n",
    "# Apply the functions\n",
    "train_data = create_family_size(train_data)\n",
    "train_data = convert_sex_to_numerical(train_data)\n",
    "train_data = extract_titles(train_data)\n",
    "train_data = replace_uncommon_titles(train_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e68396",
   "metadata": {},
   "source": [
    "## Dropping Unnecessary Columns:\n",
    "\n",
    "Drop 'PassengerId', 'Name' and 'Ticket' columns as they may not provide significant predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "90e29656",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop 'PassengerId', 'Name' and 'Ticket' columns\n",
    "columns_to_drop = ['PassengerId', 'Name', 'Ticket']\n",
    "train_data.drop(columns_to_drop, axis=1, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3d975518",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <td>Mr</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked AgeGroup  \\\n",
       "0         0       3    0  22.0      1      0   7.2500        S    19-30   \n",
       "1         1       1    1  38.0      1      0  71.2833        C    31-50   \n",
       "2         1       3    1  26.0      0      0   7.9250        S    19-30   \n",
       "3         1       1    1  35.0      1      0  53.1000        S    31-50   \n",
       "4         0       3    0  35.0      0      0   8.0500        S    31-50   \n",
       "\n",
       "  Age_Category Fare_Category  FamilySize Title  \n",
       "0  Young Adult           Low           2    Mr  \n",
       "1        Adult          High           2   Mrs  \n",
       "2  Young Adult           Low           1  Miss  \n",
       "3  Young Adult          High           2   Mrs  \n",
       "4  Young Adult           Low           1    Mr  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd0a28f",
   "metadata": {},
   "source": [
    "## Performing One-Hot Encoding for Categorical Variables\n",
    "One-hot encoding is a technique used to convert categorical variables into a binary matrix format, making them suitable for machine learning algorithms. In the context of your Titanic dataset, you can perform one-hot encoding for the categorical variables 'Embarked', 'Title', and 'CabinType'. Here's how you can do it using Python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ab7132b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform one-hot encoding for categorical variables\n",
    "categorical_columns = ['Pclass', 'Sex', 'Embarked', 'Title', 'Age_Category', 'Fare_Category']\n",
    "encoded_train_data = pd.get_dummies(train_data, columns=categorical_columns, drop_first=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "68d2c5c7",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked AgeGroup  \\\n",
       "0         0       3    0  22.0      1      0   7.2500        S    19-30   \n",
       "1         1       1    1  38.0      1      0  71.2833        C    31-50   \n",
       "2         1       3    1  26.0      0      0   7.9250        S    19-30   \n",
       "3         1       1    1  35.0      1      0  53.1000        S    31-50   \n",
       "4         0       3    0  35.0      0      0   8.0500        S    31-50   \n",
       "\n",
       "  Age_Category Fare_Category  FamilySize Title  \n",
       "0  Young Adult           Low           2    Mr  \n",
       "1        Adult          High           2   Mrs  \n",
       "2  Young Adult           Low           1  Miss  \n",
       "3  Young Adult          High           2   Mrs  \n",
       "4  Young Adult           Low           1    Mr  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01fd30b2",
   "metadata": {},
   "source": [
    "![thanks](images/thankyouimage.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d86255b3",
   "metadata": {},
   "outputs": [],
   "source": []
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