Project_13_3_Feature Engineering Assignment - 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "64a132b4",
   "metadata": {},
   "source": [
    "## Q1: What are missing values in a dataset? Why is it essential to handle missing values? Name some algorithms that are not affected by missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c60809",
   "metadata": {},
   "source": [
    "Missing values in a dataset refer to the absence of data or information for certain variables or data points. When collecting data, missing values can occur due to various reasons, such as data entry errors, survey non-responses, or technical issues during data collection.\n",
    "\n",
    "### The Types of Missing Data Are:\n",
    "\n",
    "**1.Missing Completely at Random(MCAR)**:\n",
    "Missing completely at random (MCAR) is a type of missing data mechanism in which the probability of a value being missing is unrelated to both the observed data and the missing data. In other words, if the data is MCAR, the missing values are randomly distributed throughout the dataset, and there is no systematic reason for why they are missing.\n",
    "\n",
    "For example, in a survey about the prevalence of a certain disease, the missing data might be MCAR if the survey participants with missing values for certain questions were selected randomly and their missing responses are not related to their disease status or any other variables measured in the survey.\n",
    "\n",
    "**2. Missing at Random (MAR)**:\n",
    "Missing at Random (MAR) is a type of missing data mechanism in which the probability of a value being missing depends only on the observed data, but not on the missing data itself. In other words, if the data is MAR, the missing values are systematically related to the observed data, but not to the missing data. Here are a few examples of missing at random:\n",
    "\n",
    "Income data: Suppose you are collecting income data from a group of people, but some participants choose not to report their income. If the decision to report or not report income is related to the participant's age or gender, but not to their income level, then the data is missing at random.\n",
    "\n",
    "Medical data: Suppose you are collecting medical data on patients, including their blood pressure, but some patients do not report their blood pressure. If the patients who do not report their blood pressure are more likely to be younger or have healthier lifestyles, but the missingness is not related to their actual blood pressure values, then the data is missing at random.\n",
    "\n",
    "**3. Missing data not at random (MNAR)**: \n",
    "It is a type of missing data mechanism where the probability of missing values depends on the value of the missing data itself. In other words, if the data is MNAR, the missingness is not random and is dependent on unobserved or unmeasured factors that are associated with the missing values.\n",
    "\n",
    "For example, suppose you are collecting data on the income and job satisfaction of employees in a company. If employees who are less satisfied with their jobs are more likely to refuse to report their income, then the data is not missing at random. In this case, the missingness is dependent on Missing data not at random (MNAR)\n",
    "It is a type of missing data mechanism where the probability of missing values depends on the value of the missing data itself. In other words, if the data is MNAR, the missingness is not random and is dependent on unobserved or unmeasured factors that are associated with the missing values.\n",
    "\n",
    "For example, suppose you are collecting data on the income and job satisfaction of employees in a company. If employees who are less satisfied with their jobs are more likely to refuse to report their income, then the data is not missing at random. In this case, the missingness is dependent on job satisfaction, which is not directly observed or measured.\n",
    "\n",
    "\n",
    "### Handling missing values is essential for several reasons:\n",
    "\n",
    "**Data Quality**: Missing values can lead to biased or inaccurate analysis, affecting the quality and reliability of the results.\n",
    "\n",
    "**Model Performance**: Many machine learning algorithms cannot handle missing values directly, leading to errors or biased model performance if not properly dealt with.\n",
    "\n",
    "**Statistical Analysis**: Missing values can distort statistical analysis, leading to incorrect conclusions and interpretations.\n",
    "\n",
    "\n",
    "### Some common methods for handling missing values include:\n",
    "\n",
    "**Dropping Rows**: Removing rows with missing values can be done when the amount of missing data is relatively small and doesn't significantly impact the overall dataset.\n",
    "\n",
    "**Mean/Median/Mode Imputation**: Filling missing values with the mean, median, or mode of the corresponding feature can be a simple way to impute missing data.\n",
    "\n",
    "**Interpolation**: Interpolating missing values based on surrounding values or time series patterns.\n",
    "\n",
    "**Advanced Imputation Methods**: Techniques like Multiple Imputation, K-nearest neighbors imputation, or matrix factorization can be used for more advanced imputation of missing data.\n",
    "\n",
    "\n",
    "\n",
    "### Algorithms Not Affected by Missing Values:\n",
    "Some machine learning algorithms are not directly affected by missing values and can handle them effectively:\n",
    "\n",
    "**Decision Trees**: Decision trees can handle missing values by considering alternate paths when encountering missing values during the tree construction process.\n",
    "\n",
    "**Random Forest**: As an ensemble of decision trees, Random Forest can naturally handle missing values by averaging predictions from multiple trees.\n",
    "\n",
    "**k-Nearest Neighbors (k-NN)**: k-NN imputation can be used to fill missing values by considering the k nearest neighbors of the data point with missing values.\n",
    "\n",
    "**Gradient Boosting Machines (GBM)**: Similar to decision trees, GBM can handle missing values naturally during the boosting process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defc5cc2",
   "metadata": {},
   "source": [
    "## Q2: List down techniques used to handle missing data. Give an example of each with python code."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7131b95a",
   "metadata": {},
   "source": [
    "### Dropping Rows / Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3979515c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset(\"titanic\")\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0e80f950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "869f9ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b426524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(182, 15)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2301396f",
   "metadata": {},
   "source": [
    "Here there is a heaby loss of data if we drop rows the number of rows reduced from 891 to 182 there is a significant loss of data here so this data is not feasable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "86aa39a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 11)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping by coloumns if you want to make it permanent change make sure to put inplace = True\n",
    "df.dropna(axis = 1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f7951e",
   "metadata": {},
   "source": [
    "### Mean / Median / Mode Imputation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f793e823",
   "metadata": {},
   "source": [
    "#### Mean Imputation(if no outlier present)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b0501d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>age_filled_bymean</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>29.699118</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age_filled_bymean   age\n",
       "0            22.000000  22.0\n",
       "1            38.000000  38.0\n",
       "2            26.000000  26.0\n",
       "3            35.000000  35.0\n",
       "4            35.000000  35.0\n",
       "..                 ...   ...\n",
       "886          27.000000  27.0\n",
       "887          19.000000  19.0\n",
       "888          29.699118   NaN\n",
       "889          26.000000  26.0\n",
       "890          32.000000  32.0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean Imputation Creating a new coloumn age_filled with filled mean\n",
    "df['age_filled_bymean'] = df['age'].fillna(df['age'].mean())\n",
    "df[['age_filled_bymean','age']]\n",
    "#We observe that the null values have been filled with mean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "12916181",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3478786d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_filled_bymean'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "286dad59",
   "metadata": {},
   "source": [
    "Here we observe that 177 null values have been filled with means of the value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a658927e",
   "metadata": {},
   "source": [
    "#### Median Imputation(If outliers are present)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f4a4c28",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_filled_bymedian</th>\n",
       "      <th>age</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
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       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age_filled_bymedian   age\n",
       "0                   22.0  22.0\n",
       "1                   38.0  38.0\n",
       "2                   26.0  26.0\n",
       "3                   35.0  35.0\n",
       "4                   35.0  35.0\n",
       "..                   ...   ...\n",
       "886                 27.0  27.0\n",
       "887                 19.0  19.0\n",
       "888                 28.0   NaN\n",
       "889                 26.0  26.0\n",
       "890                 32.0  32.0\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['age_filled_bymedian'] = df['age'].fillna(df['age'].median())\n",
    "df[['age_filled_bymedian','age']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "98423f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#here also we see there all the missing values are filled\n",
    "df['age_filled_bymedian'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb874747",
   "metadata": {},
   "source": [
    "#### Mode Imputation(Categorical Values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9afa1f1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived                 0\n",
       "pclass                   0\n",
       "sex                      0\n",
       "age                    177\n",
       "sibsp                    0\n",
       "parch                    0\n",
       "fare                     0\n",
       "embarked                 2\n",
       "class                    0\n",
       "who                      0\n",
       "adult_male               0\n",
       "deck                   688\n",
       "embark_town              2\n",
       "alive                    0\n",
       "alone                    0\n",
       "age_filled_bymean        0\n",
       "age_filled_bymedian      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd98bef3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived                  int64\n",
       "pclass                    int64\n",
       "sex                      object\n",
       "age                     float64\n",
       "sibsp                     int64\n",
       "parch                     int64\n",
       "fare                    float64\n",
       "embarked                 object\n",
       "class                  category\n",
       "who                      object\n",
       "adult_male                 bool\n",
       "deck                   category\n",
       "embark_town              object\n",
       "alive                    object\n",
       "alone                      bool\n",
       "age_filled_bymean       float64\n",
       "age_filled_bymedian     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c50401d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "      <th>age_filled_bymean</th>\n",
       "      <th>age_filled_bymedian</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "      <td>38.0</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  age_filled_bymean  \\\n",
       "0    man        True  NaN  Southampton    no  False               22.0   \n",
       "1  woman       False    C    Cherbourg   yes  False               38.0   \n",
       "2  woman       False  NaN  Southampton   yes   True               26.0   \n",
       "\n",
       "   age_filled_bymedian  \n",
       "0                 22.0  \n",
       "1                 38.0  \n",
       "2                 26.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "220fac3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#So we will fill the missing values of the embarked and embark_town coloumn since they are categorial\n",
    "df['embarked'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e75ff989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['embark_town'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c109c706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('S', 'Southampton')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "value_1 = df[df['embarked'].notna()]['embarked'].mode()[0]\n",
    "value_2 = df[df['embark_town'].notna()]['embark_town'].mode()[0]\n",
    "\n",
    "value_1 , value_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d4fbc27f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['emarked_bymode'] = df['embarked'].fillna(value_1)\n",
    "df['emark_town_bymode'] = df['embark_town'].fillna(value_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "912a87f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived                 0\n",
       "pclass                   0\n",
       "sex                      0\n",
       "age                    177\n",
       "sibsp                    0\n",
       "parch                    0\n",
       "fare                     0\n",
       "embarked                 2\n",
       "class                    0\n",
       "who                      0\n",
       "adult_male               0\n",
       "deck                   688\n",
       "embark_town              2\n",
       "alive                    0\n",
       "alone                    0\n",
       "age_filled_bymean        0\n",
       "age_filled_bymedian      0\n",
       "emarked_bymode           0\n",
       "emark_town_bymode        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5bdc6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived               0\n",
       "pclass                 0\n",
       "sex                    0\n",
       "sibsp                  0\n",
       "parch                  0\n",
       "fare                   0\n",
       "class                  0\n",
       "who                    0\n",
       "adult_male             0\n",
       "alive                  0\n",
       "alone                  0\n",
       "age_filled_bymean      0\n",
       "age_filled_bymedian    0\n",
       "emarked_bymode         0\n",
       "emark_town_bymode      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.drop(['age','embarked','deck','embark_town'],axis=1)\n",
    "df_new.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ca90a4",
   "metadata": {},
   "source": [
    "## Q3: Explain the imbalanced data. What will happen if imbalanced data is not handled?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea3bb0bd",
   "metadata": {},
   "source": [
    "Imbalanced data refers to a situation in a classification problem where the distribution of classes is not equal, resulting in one or more classes having significantly fewer samples compared to the others ie. there is an unequal proportion of data points among the different classes.\n",
    "\n",
    "For example, consider a binary classification problem to detect fraudulent transactions. If only a tiny fraction of transactions are fraudulent, and the vast majority are non-fraudulent, the dataset is imbalanced.\n",
    "\n",
    "\n",
    "#### If imbalanced data is not handled properly, several issues may arise:\n",
    "\n",
    "**Biased Model**: When the data is imbalanced, the model can become biased towards the majority class. The classifier may perform well on the majority class but poorly on the minority class. The model may classify almost all examples as the majority class, leading to low sensitivity or recall for the minority class.\n",
    "\n",
    "**Misleading Accuracy**: Accuracy is not an appropriate metric to evaluate model performance on imbalanced data. A model that predicts the majority class for all instances may have high accuracy due to the large number of majority class samples but fail to detect the minority class.\n",
    "\n",
    "**False Positives and Negatives**: A biased model may produce a large number of false positives or false negatives for the minority class. For example, in medical diagnosis, a model may falsely predict that a rare disease is not present in most cases.\n",
    "\n",
    "**Reduced Generalization**: The model's performance on new, unseen data may suffer due to its inability to generalize well across imbalanced classes. It may struggle to make accurate predictions on real-world scenarios.\n",
    "\n",
    "#### To address imbalanced data, various techniques can be employed:\n",
    "\n",
    "**Resampling**: It involves either oversampling the minority class to create more instances or undersampling the majority class to reduce its dominance. Both techniques aim to balance the class distribution.\n",
    "\n",
    "**Synthetic Data Generation**: Techniques like SMOTE (Synthetic Minority Over-sampling Technique) can be used to generate synthetic samples for the minority class.\n",
    "\n",
    "**Cost-Sensitive Learning**: Assigning different misclassification costs to different classes during model training to encourage the model to focus on correctly classifying the minority class.\n",
    "\n",
    "**Ensemble Methods**: Ensemble techniques like Random Forest or Gradient Boosting are often robust to imbalanced data and can improve model performance.\n",
    "\n",
    "**Anomaly Detection Techniques**: In cases where the minority class represents anomalies or rare events, anomaly detection methods can be used to handle imbalanced data effectively."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84d01ef8",
   "metadata": {},
   "source": [
    "## Q4: What are Up-sampling and Down-sampling? Explain with an example when up-sampling and down- sampling are required."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8869ad",
   "metadata": {},
   "source": [
    "**Upsampling**: Upsampling involves increasing the number of instances in the minority class to balance the class distribution. This is typically achieved by replicating existing instances of the minority class or by generating synthetic samples using techniques like SMOTE (Synthetic Minority Over-sampling Technique).\n",
    "\n",
    "**Downsampling**: Downsampling involves reducing the number of instances in the majority class to balance the class distribution. This is typically achieved by randomly removing instances from the majority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c040f12a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X,y = make_classification(n_samples=1000,\n",
    "                          n_redundant=0,\n",
    "                         n_features=2,\n",
    "                         n_clusters_per_class=1,\n",
    "                         weights=[0.80],\n",
    "                         random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6a2c0663",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df1 =pd.DataFrame(X,columns=['f1','f2'])\n",
    "df2 =pd.DataFrame(y,columns=['t'])\n",
    "\n",
    "df = pd.concat([df1,df2],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f74097cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.267359</td>\n",
       "      <td>0.779033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.075436</td>\n",
       "      <td>-1.051162</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.610115</td>\n",
       "      <td>-0.909802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.023284</td>\n",
       "      <td>-0.428945</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.812921</td>\n",
       "      <td>-1.316206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1        f2  t\n",
       "0 -1.267359  0.779033  1\n",
       "1 -1.075436 -1.051162  0\n",
       "2 -0.610115 -0.909802  0\n",
       "3 -2.023284 -0.428945  1\n",
       "4 -0.812921 -1.316206  0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eed99f2a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    800\n",
       "1    200\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['t'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ba61843",
   "metadata": {},
   "outputs": [],
   "source": [
    "## By upsampling\n",
    "\n",
    "majority_class = df[df['t'] == 0]\n",
    "minority_class = df[df['t'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08238956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((800, 3), (200, 3))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "majority_class.shape , minority_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4246b413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14508a7d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['f1'],df['f2'],c=df['t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58c5f47d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "df_minority_unsampled = resample(minority_class,\n",
    "                                replace = True,\n",
    "                                n_samples=len(majority_class),\n",
    "                                random_state=340)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ef3e5a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 3)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minority_unsampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d2521f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([majority_class,df_minority_unsampled]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a0505fba",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x144fa8130>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['f1'],df['f2'],c=df['t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "24d7108b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.530120</td>\n",
       "      <td>-0.939897</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.555676</td>\n",
       "      <td>0.741501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.106253</td>\n",
       "      <td>-0.772606</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.040782</td>\n",
       "      <td>2.049532</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.196920</td>\n",
       "      <td>0.063501</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1        f2  t\n",
       "0 -1.530120 -0.939897  1\n",
       "1 -1.555676  0.741501  0\n",
       "2 -2.106253 -0.772606  1\n",
       "3 -2.040782  2.049532  0\n",
       "4 -0.196920  0.063501  0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "\n",
    "X,y = make_classification(n_samples=1000,\n",
    "                          n_redundant=0,\n",
    "                         n_features=2,\n",
    "                         n_clusters_per_class=1,\n",
    "                         weights=[0.80],\n",
    "                         random_state=229)\n",
    "\n",
    "\n",
    "df1 =pd.DataFrame(X,columns=['f1','f2'])\n",
    "df2 =pd.DataFrame(y,columns=['t'])\n",
    "\n",
    "df = pd.concat([df1,df2],axis = 1)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "86dcbf67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    800\n",
       "1    200\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['t'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3c4ab72b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((800, 3), (200, 3))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "majority_class = df[df['t'] == 0]\n",
    "minority_class = df[df['t'] == 1]\n",
    "\n",
    "majority_class.shape , minority_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "35ddcfe4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14524dde0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['f1'],df['f2'],c=df['t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d914e3a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "df_majority_unsampled = resample(majority_class,\n",
    "                                replace = False,\n",
    "                                n_samples=len(minority_class),\n",
    "                                random_state=340)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "94dcc15c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((200, 3), (200, 3))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_majority_unsampled.shape , minority_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ebda3273",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df_majority_unsampled,minority_class],axis = 0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4fed9e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1452dd750>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['f1'],df['f2'],c=df['t'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eddfb82",
   "metadata": {},
   "source": [
    "## Q5: What is data Augmentation? Explain SMOTE."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af7529d7",
   "metadata": {},
   "source": [
    "Data augmentation is a technique used to artificially increase the size and diversity of a dataset by creating new variations of the existing data. In various machine learning tasks, especially in computer vision and natural language processing, data augmentation is employed to improve model generalization and robustness. By introducing variations in the data, the model becomes more capable of handling different scenarios and variations that may be encountered in real-world applications.\n",
    "\n",
    "SMOTE (Synthetic Minority Over-sampling Technique) is a specific data augmentation technique designed to address imbalanced datasets in classification problems, where one class has significantly fewer instances than the other class(es). SMOTE creates synthetic samples for the minority class by interpolating between existing minority class instances.\n",
    "\n",
    "How SMOTE works:\n",
    "\n",
    "For each minority class sample, SMOTE selects k nearest neighbors from the same class based on some distance metric (e.g., Euclidean distance) in the feature space.\n",
    "\n",
    "A random neighbor is selected among the k nearest neighbors, and a synthetic sample is generated by linearly interpolating between the selected minority class sample and the random neighbor. The amount of interpolation is controlled by a random value between 0 and 1.\n",
    "\n",
    "The process is repeated until the desired number of synthetic samples is generated to balance the class distribution or reach a specific level of desired balance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "acd3e000",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification\n",
    "\n",
    "X,y = make_classification(n_samples=1000,\n",
    "                          n_redundant=0,\n",
    "                         n_features=2,\n",
    "                         n_clusters_per_class=1,\n",
    "                         weights=[0.80],\n",
    "                         random_state=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3b9caed2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df1 =pd.DataFrame(X,columns=['f1','f2'])\n",
    "df2 =pd.DataFrame(y,columns=['t'])\n",
    "\n",
    "df = pd.concat([df1,df2],axis = 1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "70ad475e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>f2</th>\n",
       "      <th>t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.267359</td>\n",
       "      <td>0.779033</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.075436</td>\n",
       "      <td>-1.051162</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.610115</td>\n",
       "      <td>-0.909802</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.023284</td>\n",
       "      <td>-0.428945</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.812921</td>\n",
       "      <td>-1.316206</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         f1        f2  t\n",
       "0 -1.267359  0.779033  1\n",
       "1 -1.075436 -1.051162  0\n",
       "2 -0.610115 -0.909802  0\n",
       "3 -2.023284 -0.428945  1\n",
       "4 -0.812921 -1.316206  0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fd0e44b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    800\n",
       "1    200\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['t'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7fcc0373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((800, 3), (200, 3))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "majority_class = df[df['t'] == 0]\n",
    "minority_class = df[df['t'] == 1]\n",
    "\n",
    "majority_class.shape , minority_class.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "51dafc6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14535dcf0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['f1'],df['f2'],c = df['t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5702b33a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: imblearn in /Users/rachit/anaconda3/lib/python3.10/site-packages (0.0)\r\n",
      "Requirement already satisfied: imbalanced-learn in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imblearn) (0.10.1)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (2.2.0)\r\n",
      "Requirement already satisfied: scipy>=1.3.2 in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.10.0)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.23.5)\r\n",
      "Requirement already satisfied: scikit-learn>=1.0.2 in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.2.1)\r\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/rachit/anaconda3/lib/python3.10/site-packages (from imbalanced-learn->imblearn) (1.1.1)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install imblearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7ad5ee4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "oversample = SMOTE()\n",
    "\n",
    "X,y = oversample.fit_resample(df[['f1','f2']],df['t'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e3bc9ada",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1600, 2), (1600,))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape , y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "cbe9905a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 800)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y[y==0]) , len(y[y==1]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "91367305",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 =pd.DataFrame(X,columns=['f1','f2'])\n",
    "df2 =pd.DataFrame(y,columns=['t'])\n",
    "\n",
    "over_sampled_df = pd.concat([df1,df2],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "92fad660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    800\n",
       "0    800\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_sampled_df['t'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7496ef4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x146ccab90>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vTOIYIg4ODl2K8gwhkTcEYw39ZJgq2YcCbmLfeGORxMJbc1Pq82mByjsPPa8Yc8s+JmkPkDJ7gHeP2H11J5QHXANjn3ati5F/OUbJCxjFT6JyjkmriKA0vI2UHwPB6TQZCPYSpOY2pOKk1I2R/CtQuRdqz0cTWuGVvKuS6MFoluq35iUzItjzif+dFCQ0M3FX1iII/01CtVr/5CTmlTkcQ8TBwaFr8Y7UhgaxZMqVDsiLEx8SC2UUowrvQ8flZ1gGPZS6R6QlyrMNqugh/RQNNN+OXZA9FpV3WWr95V8BahXItPC/h1Rf3ylDiVWGVF1Gk9eg9Vm9JVL/XEp9KmWgck9G9ZqOKn4BVfQUquc0jKIHUO4Nk+jBBiNiYKpkDCwh8VKtdPBqwq4akhjP0MHfnYhjiDg4OHQpSrlQRY9G9DBa3pIihoNvNGQf1/7+fSNRPd6B7CNB9SFzBkn62xXKO0LHvhQ+rONB8m9E9foCI//SlOvHKLM3qse72rBLfSYJzmdyqRBoeAmxFmWwzxg0vE78AFvRcSntCJVUyoPyDEF5h6HMXvqYa21wb0Hc90vlgm8X/X/3pkl4+tzgGUr8762N8uyceNJmX8CboFEY1clqtY4h4uDg0OUo94aoHpNQuWeBOVA/MXqGogofQhXc0S5vSKv+Xeti5F+N0XsaqvfPqPxxzU+lgL4VpmigqMKkmom1DKl9CLvqUuyq67D9X2DbzUaMUi6UbxdUzlhU9qFp6UcoswSj6BHo+R3kXQquIQmuyNHbCa6NiPn6s0+MLGCZ9Cgp8H+Qwf6iI+G/EjeylyTpKUgOlXc5+r2K/p1VeVc0Zf4oZaJyz43fYc4JqNzTiG34mmD0hCRk/pWRA1kHEs/7iMoH3x4J+8okTtaMg4PDfxaxa3XKb3g24IP6R0g6KDbnLIy8WNkHkf7rnkRqbov02bJfD+ScjMo9BaUSPaGmhwS+QmofiAiMCeAF13qQfRwqax8ttmbXIrV3Q/2rQGRRNvujck6DrIPBXoFUXxWRNs/EkqFTk428CzLQV2zsqiuS8IooVO+ZWsQtQ0jwB6T62kjKdQSjDyrvoqgibPp7ckdkniba6BDIOgaVfzlKmUj9RP0ZoGjOKBIweqCKnklaYVbsCqTscF0CoNX7oo0TVfQQyjsi1ZfcBid918HBwSFFxK5Clm+TXGPVA3p+gWHE0TVpeFdLtcfvSPflHQZ5F2JEXPwdgb7VS3wtFrs+UqPGA+babYW3rEURZVUP4t5K18+pujySKZIaKv8GVHbqKdCpkFijxgTPcIzixzI/tgiE/4woqxaBe4u4221iV0DDO4i1RHvFsvbRejgt24QXIg2v6M9AeVHekeAbnXJZArErkdqHdWaY1KLF/3ZG5Z7RJKCWLo4h4uDg4JAiIjayfHuQygQtXdDjGwxXXpy+ROtNWP+mNonssfqpOcX4kK5Cah9Cau8l9XgZLRiXTjZMMohYSNl+cYoHKlTx81qX5T+ISAjsKq2rkuEaS6ms347Eu4ODQ4choT8Q/ySQapS5NmTtlzAGwg7+CTW36CdJsy/kXYLh2bjj5iiCBKdDeC54hkdK0cd5Pit+Lq4RAmivQqpGCED9kwgKlX9J6td2AVI/kdSMkEixuvyrO9wIAR2DQdETSMWJEP6HRiVS/eNCFdz8nzVCAF1tOgkRtI7GMUQcHBwyjkgDUnkRBD6iUZZcsKDmNsi/EpV9VJtrbLsWVuwD0qI+hz0Pyg/AViVQ8jJGO8qrS3gWhP/V0tuebVrFZNh1z0LNnTTFRQDN2h5RjJHcSzE8WycxqD/leTZR/ySSMxZl9mx/H52FvTy19uZAVN45KN+eHTOfKCizD5S8DYFpSOBjkIAWR8s66D9VWK474xgiDg4OGUcqL4XAx5HfWrrEwzqIzyhptRiJCCzfFaiI0WEZlI5Gen6S9AItob91rZnQT80HVT7kngbZY5Hah6DunmhXtj1klEDO/zByxiY1Nuaa6DTJQHLtVx7f/wHkxFLw7EYYxWAvjdcAPCNRuafqCr3mul1SrE4pE3wjUb72pDY7dDRO+q6Dg0NGkfAcCEwitsteIbX3t9JukPq3iWmENOFH6h5Ncg5zkfIjYGW1SalGam5Fqm6KYYTEwC6HmluwK87UAZ0J0GmSB9G+W6yZRJxKNyHrYOK/RhuVcxTKMxjlGtg1FXMduj2OIeLg4JBZ/JOJf2sRLTNtLWg+VP9Icn3Xv5JUM6m9N6INESNt0/9McuM196j/CXyMVF2Y1BUq7zwwUt9KAiviUen+qJxjwehFdF0KQwtxeYZ19rQcVjEcQ8TBwSGjaHnoJG4t0sKzYFcm2XsDIvGDI8Wui4hlZbZkvSZSFCz0hx5LBAlMx666GrvyPOyaexFrMQDKKET1mAhZR5PSrVZlgXevDph75lFGMarkJXCvHDdjgO8ArUmRphidw+qPEyPi4NANEAlC6A8gCK4NUEZBwmu6C2ItRepfhtDPgEvHDSQqYocbzH7Nvxo9wSpNPJjKTryw2RV0jBHSiIn4PwCzL1JxCoR+pDkbA6RuPORdjMo5EWUUoAquwc67XMfMBD7WlVcl9vaOyrsm46mUHYky+6JKntdBwaGZaEny7VaNYFuHboFjiDg4dCEiNtQ9itQ92SIuwI349kflX9LtDRJpeAepuhi9CNskV/3VBN9+rdM38y+AipMSX5p1ROI2RiHaA5HB0vWtUCC1SOXZEeMLVjZ8pOZWraSZtY+ekuGGrFGQNQqRAFL3LNQ90VoIzFwHlXd+p2aUZBLlWk8rtjo4pIgjaObg0IVoCeqJUc6Y4BqIKn5ZBz52QyQ0Eyk7lOgLfmP668ppsCaYfVDFr6LMklZX2Mt31+m6MTGafzzb69os3qFRW9oVZ2rPQ0d5RlxDIPx9nAYKXOujSt6JG6Ap1lKwFupsHtf6KKUQCQMhwOcEdzqssqSyfjubdw4OXYQEf45hhABYWoCp/oVOnVMqSN1TxPaARIwPc93mNioLso9ClbzWxgiR8BzwbBenv8Y+w0AQgtORihMic2iLyjtXy5THusWZ6yUYKwFxjRBoCsi1l8Rtpcw+uoKrewMIz9RZOcs2Q5YNRlbshNQ+jKSjSeLgsArgGCIODl2ENLxK/IqmgjS8lFqfYmsxsc5wdAamkKiYGOaaqF7f61L3vb7DyL+qjYiUBH9ESvcH/2u01fBo+STV8pweV2puxg5Mbzuyaz1U8YQoWwVZusJv8cvg3iruy8sEYlcl187/qS5E1tKLYy9Dau9Byo+LBAA7OKyeODEiDg5dRZvql9HaxH+ibkTCs5DaR8D/PhACVYRkH6m3L4wO2taURAGpAgRRRh4QXRJdJIRUnokW/opmPFUTU+W0kYrTkB7vo1z9Wh1W7k2h5B0I/QLWbFDZ4BmOMnJ0j8XP6QyYuolg/aPTfcWP3moKJXhtSVJ+FFJwS8y4D5EwUv8i1NwQ4zXaEPoJqboRCsY1BeqKtRhCf4Jyg3urbrt95+CQDI4h4uDQVRjFaI9IHGNEJTYiJPgDUj4GvXhG+pIKqHtYZ3eUvIQyCtOf78q4N9PVV2MGhRrgTlDJM/AJ2ImyZRJ5d/xI9RWo4rbaIEop8AwG2s5DKRf49kL5WqfKigjinwJV8aq2JonUI5XnQPFzbWqaiISQ8mMjWTcJ8L+C+F9BzI1AFUH4qxYnvYhnW/DugvIORbnWSX/eDg6diLM14+DQRSjfvsT3iJiQdWDcPkTCeqEjGKUvG6x5SM0dac1T7Gqk7kns0oOxV+yBXXE6EpgG2ceSKDNFZcfPchH/p2nNrYngV0h4ftrdSOg3pHSPzBghuke0kuyDrY7agenIsiHJGSEtsf5cyQgBCEDwc6i5DindE7v8JMQuj3q5g0N3pEMNkZtvvpltttmGvLw8evXqxQEHHMBff/3VkUM6OKw6eHcC95ZEjxMxQeWhco6P30dgWqTwWCyDwIKGNxG7pl1TlPBcpHQUUnMLhGeCNVdvZ1ScBHUvgKexdkfLwE9d5E4V3IRKpBDasg5MuoTTu7doWfhjWiu+JoMqStDAguCXiF0NgB34BirG0LrQXgYJfoGUHYdIe+rcODh0Ph1qiEydOpUzzjiDr7/+msmTJxMOh9ljjz2oq6vryGEdHFYJlDJRRY+Dt+ViHvmTNNdBlbygK4fGI/wn8QNeAYJgxUuLjY6IjVScHHvrJPw1BD+jdQyHCZ5dddpx1kEJ+m/fvGLSoqpue5DaR1vEiKSAZwcSfwY6sFbsOqi6qF3zSx4brL+R2gfbbYA6rJrYts3if5ey8O/FhEOJYri6Dx0aI/LBBx+0+v2pp56iV69e/PDDD+y0004dObSDwyqBMvJQReOR8DwIfA6EdOyFe+vkNCSUl8QxFKArwaZI8EvtAUmItP6/slCeLZqPiGixNrHAKG5WRhU/yc09CVQ2rBSDkQwS+luLyfk/BNrxgGT0gMD7ybVteAMJ/pqgWm0GqXsYqXsE8Y5E5Z6Ncm/cOeM6dDoiwlsPfsDEO95m+Xz94FDQI4/9zxzFEZcegNvj7uIZxqdTg1WrqnQqW3FxcdTzgUCAQKDZnVhdXd0p83Jw6GhERGc/BKYAIZRrE/DtjlIeAJRrALgGpN6xdyTU3Bq/jbkmuAamPufA16nPBxsCn2Av3QDI1kaVXa6zUgCMNSDnOMg+HlSuNiDiyJ0nTfZYlMpK6RIJTEMqTkUbQ6kKn7mAMNhlKVxjg9XZW9MCgan6tWYfg3KtD94dE3vaHFYZRIT7z3ycdx76qNXxqtIanhs3kT++/pvr374U05XYa9dVdJohIiKcf/75DB8+nE033TRqm5tvvpnrrruus6bk4NApiFWGVJ4WiYfQ8RNCGGqKofD+NtkUqaBc6yLe3SDwKbG2FFTOae0rPBZKJNqViHoIfdP6kL0EqbkNgt9rufZMGCG+w1C5Z6Z0iR38GypOof3Kq41u724rTN2CyGusfzoyWwPx7YcquC5l482h+/Hbl3+1MUIaEVv47oOf+PSFL9j9uJ07eWbJ02lZM2eeeSa//PILL774Ysw2l112GVVVVU0/CxakGDTm4NDNEAkjFWMixcBALwqRRcyuRMrHIuF/0xpDFdwWUSUF/Wxh0GjwkHMmZB260pwspP5V7NL9sZdujL1sC+zKi5oqygKIXdtizplGdNpu9TXpd5V3I0bhDSkZWnbN3VA+mo4tjNedscH/NlJxVucI3zl0KO89OhnTFfv7bxiKdx6Obqh0FzrFI3LWWWfx9ttvM23aNPr16xezndfrxetNL+DMwaFbEfgsElAaDRsII3VPogpubPcQysiFoqch9D3S8B5IFZhrobIORrnWatW2Kd03MJmmIFMJg/8dxP824t0FlTMGscrImKhX9FmDvTiNy/Oh8D6MGLVmWiIShsBniH8ShGfF+Tw6Cx/Q1bLtNgSnaa9XGh45h65n3m8LscKxA6xtW1j416JOnFHqdKghIiKcddZZvPHGG0yZMoV11nGEdhz+W4j/A+KLllngfw/SMESgUbhrm8TbPPUv6FL0enYtTkRuZIFPkMAnYPZPaz6Jae+TeB5kHwm5Z2EYiR9axC5HysdC+HcSisd1CiZkH6ErBNfeS9du7ZhIw9tpbQ06dD25RTmRYomxv0vZ+dmdOKPU6VBD5IwzzuCFF17grbfeIi8vj6VLdbR4QUEBWVnO3qTDfwC7loSLX6Q2TEdXWhURpP7Z5Bpb3fUJqgbqH4XAJKR4QsKgS6k4q4W+SDcwQlQWKud4lLkmdvYYqHskMr9cXaTP/w7N3hJF0xYb2WD2QkvhG2D2A2ND8D9J+40ZWwcSO6zSjDh8GDM+ib2NapgGuxw1vBNnlDpKOnCTMNaN9amnnuKEE05IeH0qZYQdHLojdvUtUP8MsRdBBWZ/jJ4fxzifGUQCiLUcSnft0HE6Fx/0+AjDFd0YkdBMpOzgTp5TNCKeGLMfqvCBuGm0YtdFgpotcA1CmT3j9mzXPAx1d7V/XtnHYORf0c7rHboD/voAJ29+Acvnr2izRWOYBtl5WTw28056rFkSo4eOIZX1u8O3Zhwc/ouIhCH4DRg9SfQkrrKPztCYNuJ/C2ofASsSf2EUA1lgzyFloa5ujx/KD0N6TkZFEzMLTKNztmMMWr+3kdgb14bgHYlSPp3G7BmWMKhWGTngHZb0yCr3FFAKqb0fLfPf6izxvSUWKqs7GGoO6eDL9nLHp9dw1f63MvvneTpNV4EVsuixZjHj3rqk042QVHGK3jk4ZBhpeFtLoics5hYpCpd9VPpjShApO1LLsLcknYDQVQF7KVL7CCrv7DanREK0lp5PEt+B4H+D+EaMF1VwC7g3Bf9HiNQBblAmSrnBsy3KvVnqY6eIUgpyT9HfocBksCsQcnTAsjVfC7VJLVGN0KyjUO6NOnyODh1Pr7V68vCPt/PLtN+Z8fFMLMtmk6Ebss2oLTDN7qsf0kiHbs2ki7M147CqIQ1vIlUXJ26o8iH7SFTu6RnRcrArLwf/q2n3s8ri2hBV9DjK7N10SPyTkcozUuvHuxdG0X1IYDpS94RWl8XWwbtmPzD66sU76wCUUZDZ19ABiFWKVF8PgQ9pMkZUASrnRMg5uX36Mg4OSZDK+u0YIg4OGUIkiCwfruXMo6L0Ylb4BMrVt0lVNe1x7Vpk+TZ0fTBmGqieICvS7MQLPSdjRAJYRcLIihERz1SCbSmVB9knYuSd3uqwiA3YKLVqO4/FWgHhv3VJAPfmGfvuOTjEotvEiDg4/KcITI9jhAAIWAtQ1GR2IQjNYJU2QkBvrWQdDIFPtd6H/7129BKAFSOxi99AudfXxkPhg0jFcSBBmt+jyHaNazMouB2MIgyzMPq8lEFL3UfbqoHQryhlg2sjlNm9994bUWZPSBD46uDQVTiGiINDBhC7NoXU2BWQ0RpUq7YRAgZ4R2jDwbcHyrcHdu36UHtPO/qyoHw/BBPx7gnZx0HxRGh4ARre0ZLy5po6QDj7SB1IGgM7vBgCX4PhgtCf0PAKiK5/pd3ICvGNRuVfhTIK2zFXBwcHcAwRB4e0sa0VUDqqaZFKSKafTF3RazetGijIOqhVbAfo+jgifqh7uJ39WroqblNlXDd4d0LlX4ky14x5lR1eARUngpWM+qpoRdrQr1Dymla4jddaBMJ/gLUMzBJwbdbh2jEODqsCjiHi4JAuZUcmaYQoMNfOuOGgzB6IZwQEp2S0347BoDmTxQLvnqj8a9u0Ukqh8s5Hsg5DGl4F/8eRCr7tDWkLadXYFdOg+FmUZ2sARBqQ0J9gLYTAD+B/IfWurTlI3VOovLNiNpHAl0j1DWDNanE0G8kaDb79IDgdpAHlGgi+0TqN18HhP4ITrOrgkAZ24EeoOCKJlgpQqKJHUd6dMj4PsSuR0v3AXprEPLrgT969I+RfDf43tWqrUYTK2g/l3iRqc+09mAnhuTqQ1LsDUv861Fyb/lxULvT8BGrHQ/0EmivppkMORp8ZUc9IYDpScSL6fY/13jfGooS1+mrBrSjfXhmYl4ND1+AEqzqsFjQuRhJeFIn2H4JhdjODtOHl5NqpPFThnR1ihAA6RqHHB1rYqv5FoL51A6OHVur0bA1ZByHVN0LgIzrGKFGAB8w1wb015F2BYUZqXbjPiXuliCANr0Htfa2NKpULOSeDsRbY89ObntRC2eFgzU2vn1bUIXZVm5ReEUGqryO+EQI6qyeS2SMNSOU5iHswSAjM/qjsQ8Az3Em3dVgtcQwRh26JBL5Cqq6NKII2Y5tro/IvR3lHdMm82iB1ybXLOh7l3blDp6KMbFT+JZB/CQBiLQNrqfY+rFSFl4JbkMoqCH5NRtVHVT5kH47KOQVlpGY02v6PofJC2hhRoI2H2rsg+3+6cB9Jvu+xyKgREiE8FzyDWx8L/dLOsSQi9Q6E/0QCH4BrE8S9ja45k30khrN947Ca4BgiDt0OCXyDVIwhqvaDNRepOBnyrkblHNPpc2uDa1DEs5CArM53syuzN6wUBNp0zsiBomcg+BXifwfsSkDp7BB7YWoDuTaGgjtRZjGofJRKXcnRrn0Cam9N3LD+cSh+F8r3JzNbKhkkmsx8wq2yZIgYieHf9A9A7e3Yvr1RBXc5Aa8OqzyOIeLQrRARpOYGEm0ZSM314BsZMwNCJBgJcJynYwx8e6DMXhmfr8o+GKm7L/58jX4Y7vUzPna6KKXAOxTlHdp0TG+H/aq3w6xFOt3VKoXAe1ECck3IPhOVd3pai6EdXpqcEaJnCNUXQslkKDsIqGj3uJlGQvPaSqYbHaUzIuB/D7HrUMWPdtAYDg6dgxOs6tCtkNBfSNm+SbRUkHMaRt65bfvwf4hUXRFZOE20Z0VB9jGovEszrpJp1zwIdffGOOuGkncw3OtmdMyuwLZCEPhA66C4+qK8O2Yku8MuPRbC36R2kXtbCM8HaY/HYeUidZmiCKNP69chYiMrRoK9pAPGi9DjQwzXOh3Xv4NDO0hl/XYinxy6F/ayJBsKhP9pezQwHak8G6QmcsTSbbGh/jmk+uYMTbQZlXs6Kn8cqOLWJ9xb6TL1q4ERAmCYbozsfTHyxmJk7ZW5FNPw96lfE/q2fUaIKoCsw2hXMbyEVCDh1oG0Shmo/Ms6YKwW1N7Xsf07OHQwztaMQ/fC6JFsQ4hSLE5q7kYvMtGeeAUaJiC5/0NF6pFkAqUUZB8BWYdC6FeQBnCt00akyyEWnaEMmwu5Z6CyDwUJIP4PEsjxtxNrAawUGKx8e0HBXUj1Ffq7kfExO9Db4uDQCTgeEYfuhWsQmAOTaGijfLu3OiLWIgj/QkK3u/+Ddk+vaSy7FgnPRqyypmNKmSjPYJR3e8cISYnYMusZoeAZjD4/YuSeiDLyUWZPVMkLYK6V+NpUiZEppLJGo3p9DzlngtliG8VcT4vcNXdAyt4aw6kh47Bq43hEHLoVSinIvwypOClOKwPMAeDdpfVhuyqJEQyt99DO+Ul4IVJ7N/gn0Zi1IZ7tUbnnojxbtW4rAuFZYJeDuUbbFFoHje8Q8D/fQZ0XYmTt0PawUQyeYdCwgMxpqRSAK7pAG4BSbl3cL+/sSFVfmnRBxCoDqUewoPz4yBZlknEsuae1e8YiYZ0xpbJRRna7+3FwSAfHEHHodijvTlD4CFJ1KUiUrAjXQFTREyi1UuU4cw0SByKGUWb/ds1LwvORskMjQbAtthOC3yLlR0PRoyjvjrptYCpSc7suvd54vWtrVMEVKLeWeNdx4n7AEzflVexaCP8JGIjKQ1kLQPnAszUqWspoN0bsGmh4HWl4G6RKx9XYKzpwxCrEWqGrzzbOIfQ7Un5c8rWBkiX/8qQFx1Zup6v4lmjd2x5vQf3LWtjNWor+jsTAvTWGe+OUpyp2BVL7MDRM1BotKMQ7Usc7uTdPub9VCREh6A/i9roxDGdToDvgZM04dFtEbCQwVXsf7FKtMOnbNa7CpF1xFgQ+JmbcgcpG9fyy1dOf2HWRxfH1yDh9UVmHQ9ZolPK06PtUCEyN0bcCowTVc5ouZV95FtGftBXkXYWSCqT+BbDL9DHcgK3TPVWuTjvG1k/udhUQitJVHirnZMg5udO1JMQuh/BsUF7E3AilBHAhdY9Cw6s69RcFnm1ROSegvCOQ8AKk/JiItkbn3XZU/jhUtpbhFwlGslhKMzgHA/Iux8g5LkP9tcauex5qbqKNbopnR1TRw20N8gSIXY6UHRb5jFp+l010GYJHmgzq1Ynayjpevesd3n1kMlUrqnF73exy5DAOu/gA1toodiFEh/aRyvrtGCIOqxXaa3FIJGum5U02UmMl52zIPq5JKl6sFdqbYc1r7KG5rXsLVNFTKCMHsZYhK3Yi4eJV8ADUXBMxMDqJrBMwCi7vlKHs0D9QdUlEWGvl9yJOHZucsyHwod6qSjo41YNefNNJtVWQfTzKsz0YJTqrpfqCNPqL4N4JXL3AvSnKt3+HF6mzbVuXEwj/pr1IOcdhmMkGdq/UV9WV0PAaMQ1qVYDq9UUrI3xVp7qshnOHX8miWUuxrebvk+kycHnc3Pbx1Wy8/QZdOMPVD8cQcfhPI+H5SM0tEPiEmAuj6gF5l4L/jYjMebSbsgFZh2AU3IAEv0fKj0owsglZB0Ru8p2MZxgq9xyUZ4sO6V4Lzd0F9Y90SP+xybTmRw7QkEafhdDjPQzXqhkgKnYtsnx7IBi3nSq4B5W1d+dMqhO448TxTH52aisjpBHDMChZs4jnZj+IaaauCmxZFh8/N40375/EnJnzcHvdDDtgWw45f1/W2/K/q+/i6Ig4/KdRrrUg7xK0qzkGUqoVOoPTif2EbkPDG4hdASqZJ14bJM5+fkcSnI6UH6HTUjsAqXs6TSOkvVtHmRYeq2tnn27tWek9fZU1QgCwFpLICAEXEkWjZ1WltrKOTyZ8HtUIAe1tWrGgjO8//Dnlvi3L4qYj7+GOseP59+e5WGEbf12AKS9P58ztLmX6m9+mO/3/BI4h4rB6Un4cmalFEtLaIK4NIZkgV/c2GRizvdhI5YWI3ToIU8JzkcDXSHhWu3oVCULtnWnOrZ2OV88wohsxppbuz7udDruN5ZyDyr9J13Pp9QVG/hUpx2N0O1QymTE2KopGz6rKon+WEA7GvxeYLoPZP8+L2yYa7z/6MdNe+xoAsZu/41bYxrJsbjrqHqrLa2Jd7hDBMUQcVjvs0O8ZltRWWiEz97wE7RpLvafu3s0cQWT57thlY7DrJ2GXHYqU7oFUHIeU7o1duh8SmJ5Sj+KfSuKn6A7AKIbC8ZA9Bh0v0gLXBpB3GdReQ4fItef8T2eQZB+iNUCMosyP0RWY/SM6PfE8VDb4dku5axFBQn8jgS/bbfR2BB5fYuPRtiWpdivz+n3vx34nBUKBMJOfmZpyv/81HEPEYfWjYVIGO/NAYzqjb8/EWzR194P3wAyO3x4qIDQdqs+B0Eru5vBfSMWJSGBK8t2FZmR0dsmici/AMLIg+2jw7qXl2ckDYyD4DobqcWluhUVZQowBUDQBI++i1bKqrVIKlXsmsT1UBnj3RLlSK0sggelI2b5I2Wik4oSI0XsgEvwu7Tmny1ob96Nn//jFB8UWthu9dUr9Bv1BFv61mHhRlkrBX993H6Osu+IYIg6rFWKtgIZXMtSbAVmHohrVMoPfgtTFv8Quh6xRoDqq6mq6aK+NVF3TJKqVEKM4cZuM4gVjTaT6WuylG0LprhCI6I5QA/a/UHsDWl8jDW+Id5RWOTX6gHckqugxVM+PMLxdub3W8aisfVB5V6A9dwZaTioiKeXdCVWQbCVksMMLsMtPRCrGtNLMASD8B1J+PBJIsaBhhjFNkyMvOyjmecM0GHbgtvRbf42U+jVMg0S2qjIULo8j15UI5x1yWK2QynPbIVQVIzPDXBeVf0nz73ZyJeeVVEKPd5Gyg8FeHGO8xjThjqgCmwjRW1fBb8AbRXV0JZRvV6T2tg6ai0nbYOEA2Is6aLwIKhdVeNtqlaKaCirnePDtDQ1vItY8rUmTtTfKvVlS14uEkYrTIBhv28EGBKm+Fnq836UeptGn7E7pwjJeuOl1TJeBbQuGYWCFLTbfeWMufvrMlPt0uV1ssctm/Dzlt5iBsFbYZrtRW0U959CMY4g4rDZI6E8ItccVHMMYsGYhwe9R3uH6d7Nvct2ZfbVSZs9PkYa3oP5prZCpssE3Wi8C4tfKmeHZWvNEQmjp+r5glkB4DtghrRshHaQ8akUzkqK9nrVArQmSyDhYWUek0eCKF6jaGQXvopB32X/WCGlEmT0h938p5TOJCFL3EtTeTFzF1+YrwPoXwjObtzi7AKUUY244kt2PH8GHT37KkjnLyC3MZZejhrPZjoPabSQdfvH+zPhkZtRzhmnQs18JQw9YvT1smcDREXFYbZC6Z5Cam0g+Q8NNVMXSlpj9MXp+ovsXQUr3AGt+jDGUVn/tMTkjT392w4dQdVba/cRCFT6klWrjIP4PkeobI2qo8fBBzhjI2hclQcCllWMbXiEz2UuZQkHu+Ri5p3T1RFY57NA8KDsESKamU2tU4X26CnEKSHihFho08sC1KSAQmKaNeykFY01U9sHg3qZLvS3vPPQh95/1BEopbMtGGQqxhZ79S7jt42tS3vJZXUhl/XY8Ig6rEckaINl60Wx4F+wEKXvWAsRahjJ7RwryXYtUnEjbbRUFKFT+dZm7KdZ1oHiYyoNGT08MxP8hUnk2yb2vfpRvD5RrveZD7o2Qhi40QnLPg8BXOnZBecA7FHIvbLci6X8ZsZZD2b4k5wWJgko+60jC/yLV10Pwy+aDRh9QWWDNoXk7z0T8b4B3byi8vctSq/c9bU+G7LUF7z/2Cf/+NAdvtpcd9h3CzoftgDdr1aoF1VU4hojD6oNnaxIvmj5Ur+koIwe7/qXk+rXLwOwNgPIOg6Kntecl/GdzG9cGqLzLUUnEXCSDiETEpzoGlXtu3IJ5Ipb2hKSg/yGB71DuFtVnfaOh5uZIZksXOF4922KkUZm2JbqS8p+6Ro3RS3/eq2FWTSyk9lHab4T0BM+Q5MYJz9V1cKS+9YlWHjmr9b+BSUhtf1ReBqT728ka6/TmxJsSKS87xMLJmnFYbVDuzSL70LF0PAzIPqK5LoiZpMvUaP0ErbzbY/R4G1Xynq5FU/IuquTtzBkhwe+Q0t1BKjPSX9QxrATu9eD3SWzHrEyg1W/KyEUV3Ib2FnWBtkr5MUjDuyldImIhwZ+1FkYkhqY5NXV/nfpcti9Sth8S+LojZt3tEBFoSNJoj0otUntPG6G9qGPV3BkxQlKJHRKofw6x6xM3deiWOIaIw2qFKrxXP7G2CsGL/N+zLSrv/ObDyTwtm+ujzF7Rx3Kvj/IOQ7kz93QswZ+R8hM61BsCQP19SP3E2OftdgTImoPbHFK+PVHFL4B3J5o+B6NY63V0OBZSdWHSWhZSPxFZMQIpP1RrYawYqbUwKsbCypLn4b+QiuOwa+5NPg06mTnYtUhgGuL/BLEyKcqXDn7SErSTBqh7HCk7FLErYzezKyEwmXYFMEs9hH5p7wwduhhna8ZhlUbEDw3v6AwUezngjvzbcitAdLBb4QMo5Ws6qry7Ia5NIpVko6Gg4KYOnH1bpPZu9I2449N6pfoq8I7Q2RMrY7Sjnkp4BrB9m8PKsxXK8wgiAb1No/Ig/LtOb+7wLRuF1D6KKo6fuSB1TyA1K+tnSJzvRoS6B3VdlsJ7UKr9t1ORkP7s656neQtEId6RqPzro39GnYaXuJWVk8ICaz5ScxeqYFyMJitI73vfnYKiHVLB8Yg4rLKIXaGfsqqv0Oqf1sJIMFuUJ6rw71B1ZatDSikofhW8u9JGZVMVa4VNT9un/IzM3VqMXX0j9rJtsJcOwl6xC3b13ZEAvc7SFrFjVwr2DNEBgqkkdza8SrwkPKW8KKMApQwI/UbnxI1YEJym6+XEQOxyvSXQXgKToe6xSF/1emst8HVSWxEQycaqvBDqnqB1HIZAYKouZminnqmSOYTMPLNakSKStdFPG4Vp9G2Ca1Aa1zt0JY5HxGGVRaoub6HmmGhRsyHwARKe1SqzwzBMpHA8EpoJ/kmAF3w7otxbdVgwooT+QMqPab0Xbi2E+oc7ZLy4cwn/E9XUUMqE/CsiWTNJYi9Aqq5CFd4Qf8zA59ob02kISFBnzkSj4W3S0zMRnTpuV0PDiy0CLQ3EXA8820HoG7BKweyDyj4Usg5qLiwX+gECscoSWGAtgvrnIfeMNOaYBlJFwjT3JqIJ1LUkoL/rxkZtziizJ+LZDoLfkZoxboJvb63d47BK4nhEHFZJ7MBPEPiEVJ+qpXQ09rIh2FVX6TRB/wdI6Z5QfgjUPwH1D0HdoxGtkMyjn37PjhGQl8xrUZB1NJiboHVQ0vkTNqDFVlWbkXx7ogrvA3on36X/ldhPvBGk9iFS8rQ04QHyUr/M6Bm3RpAOSk3zmUzK9fenVbaHDdbf0PCcNpilXMueV1+HlB3e5OWQhteJH8xrI/Uvpze/tPCR1OdlDiSp73Ccyr66sKSR3Hig25pro/KvTNzUodviGCIOqxwiAhVj23m1rSXgG15BSvfRRoHVUkskIppUdggS7gBjJPh1ZLz2PoG7UXnnYvR8A6PPbxh9/oSCR9vZl43yxq+yqnx7gje17Smpfyv2ObsWQt/Tvm2ZIKkHTRqo7GPierd0Zd3OUniNvO7wP0h1JFbCWpJ4/PYED2cIZWSDZxgJM59yTyO+J0Pp2j7mWrFbeLZCFT0WCTiH5iXKDVnHQvZpYPQGPGCuhcq7EFUycfWpjvwfxdmacVjlkMBnQPyn7iR6oXkxXHlRtEBqkZo7UEX3pTnOSoRmkth9DbHq36i881BGQeuWWSOweTiiwpqsC90A1/qRbJbYSHgeBD5Kss8IrQy7lTsMxD6XFKlcb4B7Cy1eFw/fPlB7Txpzag8W+N9DrEsjgcEJvhNdvNCq3NOR8i+JHrRqgntrlG9fxP8+BKYQ3SARVO7pCbc8lXcY9JwCwekQngtGLnh3QTXGkOSfl96Lceh2OB4Rh1WP+hc7YRALAh/FTTdsF8pNUt4A1/qtfzd6oPLHoXJOjNrcyNoF1ftXKHwE3CPAGAjkxul/A1TREzoWJB7+9xLPtU3f68c+ZxSC0Ql7+aoYlXsWqvjpVplSUZu61oKsI4m+HWCgjYSOiBeyITQTlbU/8Q1TA7IO6YDxk0d5hujU+KZtFRdNHhLPtqii8SilUAV3gGf7ldrorRaVe1HktSYxnjJR3p1QOcehsg5qNkIcVkscj4jDqsfKqosdhg3WsjSj+VfCu5NWG42H0QNV8gZYcyE8X9facG+ZMD1UKYXyjUTMfkjod51BZJdDeIF2/9uloAxwDYSc/7URaouGNsRSSd1UqOzYi41SJmQfjdQ+QIdlBxm9UD2n6eycJFH5VyLKB/XPotNAI6/Z6IMquBUxSqD8qJVE5mJUbU4JBZ4dwLOj9gC06c8EoxiVfXya46SP8u0JnuHgfxcJzwLlQ/l2R7UoZqeMXCh6CkI/au+IXYNyDdDBuckKCDr853AMEYdVD/fm7ayy2w5W2gZpD2JXaeXH+le0MYAHvYUSY3FXBTp2xTNcu+QbpkPdk7q1uQ7Kuy14dmzjzZDwHKTqUp3KHHMyQOgnqDwV8e4Chfe1qkIrdrnWczCKUGYvlNkvtWiO7BMTV7XNOQkCn8efZzrknJeSEQKglAuVfymSezL4p4DUaoPNswNKGdos6fmJTj/1vw12DWBE0sXba4y4wbOlnmvRgzpmpOENWnlH3FuiCm7rNhkhysiB7MPj+oeUUuDZGuXZutPm5bBq41TfdVjlsK1aWJFMXZl0MXQwaBqItRwpPwKsxURfsBqfqhu9Div/GwOzP6rwYZR7/cg4S5DSAyKplskujApyTsLIuwgJz0Jqbo/s70fG9WwP2f+DylNISiwqayxGwaVJjWxX3gz+p5KcZyp4oOhRlHsLHWTZgUjDu0jV+YkbRkVB1hEYBde17tMqheBXICFwb4pyb5D+RB0cugCn+q7Dao1h5mLnnAl193fwSOlXzpTqqyNZETGMEFUEZn8I/9R4xUr/xsBapLVIeryPMkuQusd0NlBKT+cC9ROwyYe6u9qOGfxG15zJOgIano/djXtnKHwYw4webyJiQeBTLSlvLdQxIqFvU5hnKgSh4gQEhbg2hpwzUL4RaamexsS3G1Tnau9JyhiQ1zboUpk9IGvf9OfWgSyfv4LX73mPjyd8Tl1VPX3W6cW+p+7BPifv5lSbdWgXjkfEYZXErjgjoiPSUSqkCnyjMQrbr7gp1mJkxUgSGhWekRCcRuoppAYq9yzIOR1ZvmUHxc4oMNaA3PO04dekr6LAtTkU3oHhals3RiQI/o+Q+tcjWzB1ZCamoiXJZB8BqgQKrtdGX/BrUArl2gi8wxNvIyVAGt5Gqi6kXRLo3lEYRfemNX5nM+unOVw48loaav3YVuSzVKBQbDBkILd/cjVZubF1Qhz+O6Syfndo1sy0adPYd9996du3L0op3nzzzY4czuE/gliLIfAxHS2FrnLSDBBMVsY8+CXt07GwkYa3gUAHBvAK2It1zEiPyaiSd1DFL6F6TsfoMbGNESLBGdgVZyLLButti9AXaCNEzzezJPmeSRlUng4VR0LdvVB7D1J5KrJ8+7SFwlTWfqjCh8BcN6XrRNBKv9aytMbvTGzbZtwhd7Y2QkAL14rwz4+zeerKdKr0OvxX6VBDpK6ujsGDB/PAAw905DAO/zVCP9Ox8SEKVXBrq2yA9nXjTrJhGmJaUgt44yqHZoSqs6H+OZ3269lKbyE0TiE8C7t6HPbyoUj54RHdkc4QCEszpVZqkeqrkPr0Fk/l2xXV431UyXuo4gmRVOD42FZEmC/wRVpjp0PFskrm/7mIuqq6xI2BHyb/wpLZy1obIS2wLZtJT3xCQ50/6nkHh1h0aIzIqFGjGDVqVNLtA4EAgUCzYFF1dXJFoxz+a2TAfnYP1QqNwcm6TLny6d+9u6JyjkKZa6bVvYggZKH/xBIFera3aqgB5roopZCsQ3Q9ko4yAKQeqbkBJbWQe3rz4YbXdc0foPOK9TWNnpleam6HrANRqv3xDUopiAQO49oIu/5F4up2KZj/j4e1h6SqFAv//jyX2b/Mw5vlYavdNie3MDUj9Ncv/uDpq1/m5ym6srDpMtn5sB0Yc8OR9Fm7V8zr/v7uX0yXgRWO/Tn76wIs+nsJ6225Tkpzcvhv062CVW+++Wauu+66xA0d/tu4h5B0fMDKmBtCwW0YnsZKnSuXfk8fu+4dqLkK6Gi9ExuVrZ++Vc5JiP89rRuStEHgJTWlUpDa+xAJgWsDvR1UfVlK13dLpAYCU8G3R0a6U0Yev//Yn40GL8CMcYdVwCsP9Obi5zdOut95fyzkjjEP8ue3s5qOub1u9j9zL066+WhMVwJxOuDbSTO4ev9baBkZaIUtpr7yJd9/+DP3f30TfQf2iXqt6TZJJqLQdCeeRyP++gAVyyrJKcgmv7gddYQcVgu6lbLqZZddRlVVVdPPggULunpK/ylELKThDezSg7GXbo69bDvsqmuR8JyunlorlFkCvgNIzTWvAA+q+OkWRkj6iNjYdY9jL98Ze+km2Es3hJoLSM4ISXNrwbsb+PbUPZm9UcUvgXubGEPlgCoGCsC1CSp/HGSf0I5Bbah7EKrO6UIjxACjb2a7tMsy2t2Ee/oTCiqsGM6uVx/uycxvi7QmThIsm7eC83a8ir9/mN3qeCgQ4rW73uXOkx5K2Ec4FOb2MQ9iW9Jme8UK29RW1vHQ+U/HvH6bvbaIuS3TSEnfItYalNibWLq4nLv+9xAH9RjDcQPP5OAeY7l0z+v5/au/El7rsPrRrTwiXq8Xr9dJ/+oKRMJaRCvwMU3ZDeKHhpeRhleh6HGUd/tE3XQiJsm75rVEtyq8N6PCUCINSOlosNprMKexteDeC1V4VytRM+VaC1XyHHZguq5po3qAeyNQrkihMBMVmKyzecJzwDWI5LaOuprGjJSIF8yzA9j1Oog2UxjRvQDtpaq8FxccuB7n37mAgZs2x0zU1xq88kAvXryvJwMH901Yd6WRl255g/rq+qiGgIgw+dmpHHL+vqy7edsMpka+fX8GlcurYp63LZtv3v2RsiUVlKzRtrbNwMFrs8XITfll2u8xDZLDLtwfM0YadyMrFpZx1vaXUbG8CrvFNs+MT3/lp89+Zdxbl7LtqC3j9uGwetGtDBGHLqT+uUg6LLR27VuAIJVnQK8vUHFKeHcWdt1z4H8liZY+ULng203XrHCtl9F5SMWZaRghaRL6AFk2GXFtDa5+4N1ZB8fWPgjh35rbqcJWsuTdNlc/LgJGP/Buh/LtD57ttIZKplBF4B2euf6AkUcM4/HL5nL6Hhuw3mYN9F8vQEOdwYzPcwk0mChDMfLgIuwVe+sigSoHskajsk/QtW9aYFkWk5+dGjc2w3SZfPTMFE69M3am18K/F2OYRlyvhoiw5N+lUQ0RgCtfPo9L97yBWTPmNPXVGDey/xl7ceA5eyd4Z+CRi56lciUjBLQhpJTituPv58WFj+D2JBvs7bCq4xgiDjqwsu4ZYi9Ttt5Hb3gPsru2+JbYNVCTbFyHH4zeqJyxKNfamZ2HVQbBzzPaZ+pYEP5W//hfj96kVW2UVRi7FJV3ha5lAohvl4zJ/Kv8q1BJZzglx14n7sKrd71DVWkNs2ZmM2tms8qrYRoUFNvsedBEsLShj1RC/YtIw+tQ9AzKM7ipfaA+SKAhflCriFCxrDJum5yCbMRObIpm58dWpC3okc8D39zMV+98z2cvfkFNRS19B67BqJN2ZcMhAxP2XVVazRevfR3TqBIRqkpr+PqdH9jx4O7kgXXoSDo0RqS2tpaffvqJn376CYA5c+bw008/MX/+/PgXOnQuUpGEm9uFhH7ulOnEQgLTkbLDgBQyDaz5SNnB2FZpZifTYcqgDtHx6xo5EVTWYUCaxoMqRhXeh8oanV4/UcgvzuPOKdfRZx2dhWK6zaYgzt5rubnt1b/JLw7T2vi3QPxI5emING+X+XK8+HLib1krBT36Fsdts8N+Q1BmnK0gBX3X68M6mzV7ZKywhb8+QEvdS9NlMvzA7bjqlQu4bfI1nPvwyUkZIQBL5yyP69lp7H/+n4uS6s9h9aBDPSLff/89I0eObPr9/PN1XYbjjz+ep59+uiOHdkiJJL8GHSGTnSRS9zxSM47UAzxFe3NKd0OKX0C5k89SiN9tZ+hkOLSmeXFWRh5S9ARUjKFd2VM5F6JyT0q5OF4q9N9wTZ76816+//Bnfpmqt8s223EDtt7qNAzVEOMqG+wVEPi0KYvHMAz2PGEk7zzyUZvtjEassM3ux4+IO5/iPkXsd9qevPXAB0QV1BY4YdwRKKX49Ys/ePHWN/lu0gzEFnoN6MkBZ47igLP2SmrLJBQMsWxeKW6Pi15r9WiKhfHl+hJea9s22XldvwXs0Hk4Eu8OANilB0L4D+KlfqrCh1G+XTpvUhHs4A9QnlgkKjEeVK/pqExU1A3PQ0p3z8CcHJLDQPX8HGX2bHXUDi+F2jv1wi0BHRMk5Qn6Uqien6HMDGfeJIGEZyOleyVo5YKcEzHyLmg6UrqojNO2voSa8pq2HgUF+/xvN859+JSE41thiwfOfoJ3H5mMYRgYhsIK27i8Lk6783j2PW1PPntpOjcffS/KUK3iSZShGDxiE256//KYxkigIcCEG17jnYc+orZSC6Wtuf4aHHHpgex5wggAxg46h0X/LImZCqyU4vk5D+LL8dFQ66eodwEeX3pS/A6dTyrrt2OIOAAg/klI5Tkxzppg9kP1+KBN6fmOxq59AmozqPXh2QGj+JmMdGUvHw323xnpywFi12sxwTcKo/CuhD2ICFJ2AIT/JrqnxATPjhjFj6Y31XYi4flI6W4JWpmQcypGXuu/x8X/LuWu/z3cJEQG2sNwyHmjOebqQxJmq7Rk6dzlTHn5S2rKauizbm9GHjGM3MIcqkqrOaLfKYSD0TOplKH43y3HcOiF+7U5FwyEuHSP6/lt+p/YLWNRIh/rkZcdyNgbj2LKy9O58ch7YvY/ZI/BBBqC/DL1dwC82V72GjOSY685lIIezjqwquAYIg7tQmrHI7X30CwWFrmDGH1Rxc+2iebvaOzqG6E+M0ZDMwrV+/eMGFR2aAmU7ZyBOa3KRL4jqjBS7yZ1pVAAzIEgdWAvp9krF9mGc62HKp6AMgqT6krCs5CyIyPy9y2NEROMnqiSV1BmZtN1k0XE1p40ayHxcphU8SsozxZRzy38ezFzfl2AN8vDZjsNIisn8XZHsky88x0eu+S5uEGtPfuXMGHuQ21Sj998YBLjz3kyrujZoz/fwTqbDeCtBz/g4QuewQpbmKaBLYIdttlsx0H8Ov1PlGrtjTFMg94DenLfVzdS2DN9j6ZDx5PK+u1kzTg0oXJPB+9uSMPLEPoLVDbKtwdk7dPpabsS+KIDjBAAQcqOQLwjI+Xof9axL1kHYnhS0y4w3GtguzaF8K8dMM9VAHNtVPZx4OoPnmFI6d5gzU2hg2wwe4NrfXBvBuYGOvXY/4ZWiDV6o7KPgKxDUUbyMubKtR70eBOpexwaXo9I+OdB9uGo7BMzqiWTKkoZkHMyUn1VjBYmuDcF9+AY56HfBn3pt0HHbCvN/mWuLhkQx0hasaCMhlp/mziOdx76MG56uOkyeP+xTzjjvrHsf8ZejDxiGJ9M+Jwls5eRV5TL9vtuzQUjrtFerZUMIduyWTZvBc9c/TLnPHRyOi/RoRviGCIOrVDuDVDuWDfJzsGuexlqrknhCqVLvUuSmTHhn/VPSxpewjb6QPEEDFf/5IcuehZWDKHz66xkEFUIeCKxFSmIm1lzEXwY3p2R0MwUjRCAerDm6J/ARy2Ou8C3H0bhLSn214wy10TlX4PkXY320niSFg/rcLIOA2s+1D1Gs/cxIiLoWhdVOL7VXGf9NId3H/qIv77/F4/PzdD9t2WvsSM7ZJvC60v8PikFLk/bpWPxrKVxhWqssM2Cv5qzYfJL8jjw7GbdkQ+e+oyG2tgF82zL5qNnpnDyHcdl1Avk0PV0K4l3Bwe78uJInZZUFnYTih4HM03BMnsplO6D2BVJX2KYuVD8JtCdovwLU2suVSDLaZfCas0ViLUU8X+UuG3ShMH/upbMD81K3DwOSimU8iZthNi2TcXyqqZAy45AKYWRdxGq5C3IPkIrxXp318q/JW+2Csh95fa3OG2ri/ngqU+ZNWMOv3/1N09cPoETNjybv3/4N+Nz22G/IVjh2FlIhmmw9e6D8XjbBqv6EhgHylCgFJUroqu7LvhzEa4EdWqC/hClCzMrx+/Q9TiGiEO3wfZ/Dv43U7zKgIL7MDwbQ9ELGZiFX+uOlB2FXXYydv2L2Hb8BdrwbITq9SXkXQ+kVgm1Y6hMsX06YWKC1L0I0s7YkLiEoGx/bLu2A/puTTgU5uXb3uLoAadxWJ+TOLD4BM7Y9lK+eOObDhtTuQdh5F+DUfwMRtH9KN+oVsJq306awWOXPA/QKlNGbKG+uoHL9rqRhrrYHoT2MGSvLRiwSX9MV/SlwbZtjrj0wKjnRh4xDCPGdaDn/cNHP3NYn/9x1X63MOfX1npSWbm+1kGuMchKIgXYYdXCMUQcug+1d7TjIhvqHkKsZRiuQsgam/48rIUQ+h5CU6D6Glg+GLvu6ejaCxGUkYNyrwckepL2Qv4dkHU0NMXdGKRdAK8rCXwB7q06qPMQ1CTOlkmHcCjM1fvfyhOXT6B0UXPq76wfZ3PdwXfwyu1vdej4sZh4x9sYZgyDwLKpLqvhsxe+yOiYpmlyywdXNMWgmC6d4qsMhek2ufipMxk8YpM21/nrA6y92VoYhqE9H3EQEb6dNIOztr+cWTOaC2oOP2i7uPLzylBsMGQgPdbsuhgfh47ByZpxyAgiNvjfReqfg9AfoDzg3Q2VMwblTq7arb10c6A9T3gmmANQPd5GKQ927UNQO55US9wnQuWeg8o9I+Z5qXscqbmDxNtKPnBvDLlnoqyFSPBbQIFdDcGpmZxy52Csg+r5LrJ8u0imSoZRJRi9v8p8vxHefWQy957+aGzHkIKn/ryPfuuv0WFzWBnbttnLc0Tc7BVlKHY+dAeuePG8jI9vWRbfvj+DL9/6jqA/yDqbrsWeY0ZS1LuwVTsRYeIdb/P8Da/SUNP6bzdRXRvDNFhvy3V48NvmWKBrD7qdr97+HtuOft0N71zKdvts3f4X5tBppLJ+Ox4Rh7QRsZGqi5GqC3XVV4J6QfK/g5QdjPgnJ9lTe70CFlizIRKnYOSehur9CxQ9De7hafTbGqm9D7GWxmmR7J+TH0I/QsVYpO5JVPaxGIV3YhQ/hip8AFjFXM/KrbcUCh+hQ24pEkuFNDO89eAkVJzviGEYvP9ost/hzCAixM2DjZDMVkZ7ME2THfYdwgWPn8Zlz5/DEZce2MYIAZhww2s8dsnzbYwQpYjrQQTt1fn7+39bbdFc8txZbDdaGxqmy8DlNlFK4fG5ufDJ0x0jZDXFyZpxSJ+G18D/duSXlSv3glSei/SchmGWIBIE/4dIRAlTuQfp9EyzD3i2gGB7n3wNxD+pqW6IUgrlHYqYayKle7T3lbWl4Q3IPS36Oc9QUs6eseYi5YcjuRej3Bsigek61VQyu/ffoShdT8XwboNd8i6UHQZk0DNixq+hki4L/lwUd9G0LZu5v3VulWXTNFl/64HM+nF2bGNDYJMdNuzUebWkqrSaCTe8GvWcSPLm/8K/l7DOplqjKCvHx7g3L2b2L/OYNvEr6qrr6bdBX3Y9ekdyC1vHX9m2zS9Tf2fBX4vJzsti2723JK8oN52X5NBFOIaIQ9pI/TPEVsUECEHpXtg5J0H9S2AvojFdUQKf6m2U/Osh7zIoa6vYmBw2SHWbo8o1APKvRaqvSTDHZFCItTjmDVa5N0Lc2+n4klTrn9TeltbMuhRXs6GgCCOZNEIAssek1FzsWghMA6lG8EDwVwhOBrtGx+W4NgTP5iBhUG42HxZgxlQXsZZOw1BJ1UjJNAefN5qbj7436jllKLw+D3tEZNO7gmkTv4pbwC7ZXf+cgrbVftfdfADrbj4g5jW/fvEHt53wIEtmL2s65va6OPjc0ZxwwxEpqcw6dD2OIeKQFiLhiJx2ooZVuiZI083ebvWvVF+BKn4Oyb0Mam9ux0xMrc4ZDaMYXak11I5+V+6rMP75/Kuh7AigJv2xVhGUb1TzL6FfMtu5uRYq++iYp0WC0DARqXshomNioNOQYxiCUg+hL/UPAAa3vGjz989ZXHP8OpQvb5uWatvC8AO3S/OFpM7II4bxx9d/8+b9k1rFWxguA9M0uOb1i9p4CTqTimVVGC4DK9T+ApD5PfLYbMeNUrrmnx9nc/Hu4wivNG4oEOal297EXx/gjHszELTu0Gk4MSIOaZJqxkespyQDqX0cI3cMlLwP7q3RYk/JYqGyD2s7WnBGpIZOtBRcBaonmBuR3GuwUb59Y5/1fwVl+/NfMkJwbQzeES0PZLDzbMCDlI/Brr4HCbfeHhEJIhUnIdXXgTULbWgGSM0bpRf3dTdp4JZX/sXlbv2Eb7oM+q7Xh+EHpW+IiAg/fvwLD1/wDA+c9QSTn51KoCF2QLVSitPvGcON713O1rtvTkHPfHr0K2G/0/bk0V/uYsgesdVXO4MeaxbH1RxJhmOvPjSpar4tefrql7DCdvRAXtFS80vnLk9rXg6di5M145A2dvkYCH5Nu8qxt8KI1IFpto/FWg4Nr2jBrPBc2mbVRLZbcv6HkXdR27lVnKLd9PHmln8zVI8D4gVFKvCNxii8M+pZsZYhK3YhI16XLqXRIEvituAegip6AGU0b82ItRhZMaJDZgZAzuk6e0kp7Jr7oG48mVS1ffeZYsZftTagK9WuNWhNbp50Bb3W6hn/wgSsWFjGlaNvZvYv8zBdJkpBOGSRW5TDNa9eyOCdipH6VyA0A3ChvDtC1oEZqRTdUdRW1nFY3/8R8kf/zhumwZa7bsZG267HS7e8gW0LpmlghW1Ml8Hx1x3O4ZcckJLibXV5DQf3HBv362mYBieMO4IjL4uud+LQOTi1Zhw6FZVzEhKcnoGebLTnornktzJ76TTX3DMRCSC1D0H9BL3VA2D2R+WcDFmHtulNJAiBKcRfVF0Q+hlV8jxScXYkfmVlDMg6HJV/RcxepP5lOsUIMTcH6xdixrsYvSH7KP1+lP8PrN/atomHaxDknqONvsAksJbr7SjXRqBydByO2R/lG4Fyb97mcmX2RdKOxYlD3XjE6AHZh0P982TSCBFg9PHl9B+0Fl9+vBfb7r0lW+8xGMNIz3EcCoa4eLfrmuIZWnoR6qrquXzv6xn/0V+stb4fsFmx2M3Ut/+kuuJ5+mx4NCOPPoycgu4glNea3MIcTrzxKB6+oG1NKMNQuDwuTrrlaNbbYh32P2Mvprz8JeVLK+mxZjEjjxhGfkleymPWlNcm/GoZhqIqhnqrQ/fEMUQc0kZ5h0H+NUj1ONq/ACkw10EpT+wWyovKOxfJPR2sJbpYndE39hOVBJOYj4DUo9ybQc9PIPg1EvwD7AVgFKLMvuAd2Up2OyoNnyYYJxOYYC+J/F/07yhabTsZJWCsCSv2JnWFVSD8O1SeAq6NUAW3aMMEUqzTkkNGs2ZWpvZOxDMcpDKj3Ta+wsHb/sTgvS7HiFH9NlW+eP1bFv69JOo5sXXV2dcfLeasWxby6LV9eeupHgAYhmCF3+P+cz5g+EHbceZ9Y1ul0PrrA3z+6tfM/2Mhvlwfww/ajgGD+mVkzsly8Hmj8eV4efrql6lc3rz4D9xiHc55+GTW22IdAIp6F7aqK5MMDXV+3ntkMu8+MpnlC0rJK8xhxOFDE+qTWJZNz/492veCHLoEZ2vGIWPY9W9C9cXtvFqh8q9BZR+VsfmICLJiONgr4rQyULnnoXJPSWsse9mOIMsSN+xwMumNaFyaDfDsgMo5URudCbBLD4FwhoNWV6ZgPFSd3nH9u4dglGSiZACMO+xOpr/+bUyRLoDsPItRR5fx2iM9QaIbfcpQHHX5QRx/3eF8+dZ33D7mQeqq6jHdpjZoLJvhB2/HxU+f2elF4cKhML9+8Se1lXX0HdgnbsZLMtRV1XHByGuZ/fM8XQk48pU2TKPJEIlljJgugxcXPkpRr+67rfVfwNmaceh0xK6EmKXN4xG56Xp3g6zDUx9XBLBRqm1gq1IKso9Bau8ltgvfgKyDUx637Rzq0+ojc2TyuaKxLwuCXyHBLyDvSlTOcbGvCHwO4ZkZnEMM7FIw19WVeztiGyj0I+L/DOUbmXZXDTUNcY0QAH+9wZuPxzZCQHtPJtzwGpXLq3j/8U+a0mNbZq18+eZ33HLMfVz3RnsfCNqHy+1ii5GbZqy/Ry56jjkz57dJAbYtGxG9+RfLM3LCuCMcI2QVw8maccgIUj+Bdkmqu9ZD5V+HKrwvqjERdSwJYlffiL1sC2TZhsiyQdjLhmDXPKil5luSMwbcm9P2q65/V/nXosw03bhSAbK6Z8pExOlqbkTCsSviSgfXhWnC7InKPZUOi0XBRipPQeqeTLunARv3T1B/RRAbrCSLH0964tPGy9pgWzZfvvUd//48N9VpdhtqK+v4+LmpMT0eYgu2LQwcvHar40V9CjnnoZNjFuVz6L44HhGHdiMSgIZ3kYY3IPRD6h3k346RvX/KY0rp/lrSvdWJaqi7Fwl+DcXPNGXeKOWD4meQ2keh/gVtNAC4t0LlnqazExxSwEDqX0Tla++X/g68gdS/BOEFJE5dTiErJ14vni1BFUF4HtQ9iI6XsYi6NaV6gATQWVFCKtldUnMreHdGuWJo1CTB3v/bjdfufjdOC6WVSJUko+oeNz4C9NbE1Fe+bLNQt0RE+P2rv5k28Svqq+tZc4O+7HnCiKgy7p3N3N8WEAokqHhtKHY9ekeueuV8Fv69mKy8LAZttz6myxEyWxVxDBGHdmEHvoXKU9tf5MxYA8K/Iv4cHQyarDek5s62RkhLQt8g9a+gco5oOqRUFirvHCT3TLArQHlRRuoR+zFRRSQvmOaJtOu2oVkJsJpEy8SuRcqPj2zFJBubYpB2mrd7h6aUYZV3DuIbhTS8DOF/tTy+bw8weqOo14JornVbXW7Xvw7VlyY5mIHUv4zKv7zNmfl/LuLXL/5EKRg8YhP6DuzTps2KhWV8/fb3iUcxBNvOTE0kpRT11bFT0euq67n2oNv56dNfMUwDsQVBePKKFzjl9uM4+LzRGZlHe0nGmBAB022yxrq9WWPd3p0wK4eOxDFEHFLCti0oPyz9OAB7BdRP0PLwZj8oehTlWi/uJSIhLRGfiLqHoIUh0ohSJqS7DRNtXsEvSMoIybsRI+dQpP4lpPpqGmXuW8yQVcJAUV4ApOZmCDemByc773S1ZnJRha23f5R7A5Q7+fgkI/sg7NAMaHg5ZhsRWPivl0CDQY8BvxIoW4bb66ZkjSLKl1Zy63H3M+OT1n8DW+22OfudvidFvQsYuMXaPHbJ87w9/sPkpM4VeLw2wYAi3SKN4ZBFVhxJ+huPvIdfpujPraV3RUR4+IJn8NcHOPqK9OKm0mHgFmuTV5yrU3VjICJdLujmkDmcrBmHpLGtKijdpQPiIUwwilA9JsUVcJLwQqR0l6R6VL3/SjHltH1I3XNIzQ0ktRDnXY+RowNyxf8ZUjceQj/rcyoPso8Acx2obvv03X1QqLxLIetAZPkwUtdO8YJ7M119OFUNEO8uqIJbUIlk9pPEDnwNFSe0mcfkiUW8cHdvFs/1trlmwCb9qS6robq0Om6dFU+Wh2BDMKX57H2cyaTnw+gwp9iZM9n5WdRXN0RXFm3BoO3W59xHTmmVwfLvz3M5dcu2wn+txlCKp/++L6qHp5FFs5bwzkMf8cvU31GGYuvdN2f0qXvQK0NpsxNufI2nr34pulSOabDtqC25/u1kvVoOXUEq67djiDgkhR36B8oOoONEuxQq7xJUTuwaEVq9NJmYDgOjz5+Zm1qs+dRP0PLiyWKsgdFraus+7HJdadfo0aShohVDH0iy00YvSmOMhEdXAQ5OSX5eSWOAykP1/BhCvyEVJ6Tcg21ux4qKo+iVdx5KCW1XGoVlbMNnbw3irxkBfHk92WLEYLbafSimK/MOXLvqSl09OuKpefn+Xjx58xqReUUxBjrAaaUMhWka2LaNbcXu3DANTJfB1RMvZPy5T7Fs3oq48SKGaeDN8nD/Nzc36Ys8d91Enrt+YkIjZq8xI7ngiejp0Z++8Dm3Hq+/n031byJzu+bVC9lun63j9p0MlmVx98mP8OFTn2G6tBprY5bMRtutx82TruzSOjsOiXEMEYeUEREITEHqn4PQTFAe8O6GyjkWjBJkxW5pekKySZji6t4coyR6WfHGOcqKHcFOUEfCvQ1GyYTUp5gCIgFk+dDU35OiFzC8QxI2s4N/QPWNEP6OmCufsRaUvIgKfgPWYl3cz7c7Uvsw1D+R2rxaknVUZHFufKJXgA2qCFX8JMq9CRL4Bqk4NqVuLQvefaY346/sw5CR1VzywELyi0JoI8oGFGWVe3H6iAoql2t9DNDpqf02WIOb3r8i4/EAEp5FeNm+KCxKl7o5bttBSJwU2kzTuLgqUyEtjZBGg6eF4bP17psz5saj2HDIQCpXVPH0lS/x4bNTCMcJ7DRMg6H7DeGa17QX5LFLnmfiHW8lDIot6JnPq8vafodm/zKPU7e6KLoho8DtcfHUn/fRe0B6kvjQHFD7wROfsHj2Mgp75rPr0Tux3T5bOUGpqwCOjsh/FBGB0Ayk4TWwluoUR9/+4Nk+7jaFiCA1N0L9szQ9WQu6xkvDRPCNTsMIyUYVPYBUXQp2AkPErot7WimF5J6fONAwN77rOR1EGqD+FaTu8fa9JxVHYfsOQRWMQ6nYf36GZxD0eF5/pvXPIXWPtjDAfJB9MCr3ApSRC1mtgwslycDftphg9kXlXw15Z+tsmOCPoFwoz1Dw7YsyIiXb3ZsCWcSvz7NS7yZ8+4m+/vvP8jlii0Fsu2s1o09cn21GDWfpks3439Y3Ewpor1tLfYwls5dx0a7X8fhvd+PLbrtlEo3qshref/wTPn3xc+qrGlhr0JqMPnUPth+9dZNseyDYn5tOXo9L7vuHj14qRimSylzJBD37lZCdn8WCvxa19YREfvX4PNzx6TX0WbtXq4yWwp4FnPvIKfRdrw+PXzohZhyKbdlMf+s7qstryC/OY+1N+yf1+kLB6J7PN+9/H8NQWDEKzoUCYe4Y+yC3fHQVppmesaCUYpOhG7LJ0A3T6seh++MYIqsJImGk6iLwv0ezm95EGl4Hz05Q9IBOZY2Gf1LECIHWwYSRdEj/W2nMzEB5hyOuDSBYRuxgRRNcGyTuLfsg7PBvUP9c9AZ512J4t2jvZOOis0SO1TLo6fjn/a8i4d8R7zCUb8+oNVsaUUpBznGQfTSE/wFCWgrfyI3dv3dXqHs0xUmZoHJRheN16rMqhpwTUTknRp+XkYNkHwX1T5Lse7FisYsfpjRnK1khxVcfFPDVB8t5/LcdeWf8h4RDYewoi5wVtlk2bwVTXprOXmMTxwkt+GsRF4y4hsoV1U1P7ysWlvHdBz8x4vChXPr82ZimyXcf/MRXk3wctdXGFPUMkUB3LGMoQ3HbJ9dw0ibnxd2OCfqDzPttIYO2i/63UbWiGtNlEA7FDgIWW6hYVkV+cR47HbI9d/3vYcLB+Omx624WXRn1+49+jhsbA/DTZ79x63EPcMHjp/LR01N455GPWDZnBblFOex2zE7sf+ZeFPcpituHw38LR9BsNUFq7wb/+5HfrNb/Br+I1IGJcW3908T+KkTbx08BQxs/Wro9XsaEhco+Mrku86+Ckre0gWX01IXefPtAj2kYOZmTiF8ZqbktfSOkkfDvUPckUnYIdvkJiB3fu6KUiXJvhHJvFt8IAZR7MC0LB8ZvnK3fv5wTUT3eQbmTf/pUeeeBN3nl0VvPWCvqtofpMpj02MdMfeXLuIucMhSfv/Z1wnFs2+bKfW+hqrSm1RZCYzzD1Fe+5LW7tK5HTZl+3+trTBbN9pFuxkoy6O2SbZgzc16rAnjRMF0mi/9dGvN8UZ/ChLoiKCjsqV3j3iwvJ9+WeEttv9P3ino8ntHUks9e/IJTt7qY+858nLkz51Nf08Dy+aW8dOubnDz4Qub/Ga24pMN/FccQWQ0QuxbqniP2AmlrN7tV2vZaER0TksEqpq3wHaD/9e4Kvv2iNIjc+LOOBs92SXdruAdhFD+O0Ws6Rq/PMQrvxnDFjvJPBQnPRepfRxpeR8IL9DG7JhI3kUm/faOh+A1SeVbGelXKANcWybXt8YF+//IuRJmpvX9KeVCF4yHn/LjtRLQ3ZOY30bVbrLDNwn+W4K+Pn2UitlBfk3gr6IePfmbxrKWxlTkFXrvnPSzLos86vRL2l0kM0yCnIJsxNx7Jvacm9lqJLXGDMkccMSzhN3KzHQdR0KN5j/7As/dm1Im7xu7z8KHsfNgOUc9tvtMgTFcSy4aChX8t1jUlW0zQtmxqymsZd8gdyaU1J8mS2ct4/NLnuWzUDVx78O1MfnYqQX9yWUtLZi/j0Yue5dQtL+SULXQg8MK/F2dsbg6JcQyR1YHQDMCfoJEFwS9jnEv0FNjep0R3a9d+1iHgGQGqReCSuRYqfxwq/+pOSbeNh1il2OUnIqV7INWXIlWXIqW7Yi8dglTdRMdlDOnPxi49Anv5jtjLd8WuuhbbSi39sxXeoST+3NyQZiqsUoYuGOiKU2dE4Naz1op5unFxHrDxmnGl0E2XwTqbxu6nkV+m/ZEwmLF8SQVL5yxn8MhN6NGvJGGf7UJBTgsjwjANhh2wLQ9+ewt/f/cvVaWJY4xsy2beHwv5/PVvonpPevQtZsRhQ+P28de3s3jjvveoq2qOwTr/sVO58qXzGLjF2k3H1li3N2fcN5ZLnz+7KYZmZQ44e++EWzNAXHvdtmzm/b6QmZ//kbifJHjz/kkcv/5ZTLzzHb7/8Ge+fOs7bjvhAcZsdE5cbxLAF298w9hB5/DaPe/x78/zmP3LPN568ANO3OQ8Pn3h84zMzyExjiGyOiBJLpBR2imlwLMDOq4k5oXg2ijFSWVB8Ssos0R7GEpHQ8VxEPy8WY3VHARFz6Kyj+hSI0TExvZ/gpTuDcEvorSohsBrHT+R8I9gLwN7ATS8ACs2xa5JNo23NSo7Ub0NUwefxoobSmUspVDFz4FneJSzObz9woH8/l1sfRjbstn5sKHsd/pecdNKrbDNGuv1YcanMwmHYsc4NNQ0JN6uiMzbNE3OffjkDtmR2X701ry86BGem/0gD/14G68seYyrJ17AGuv25ofJP2OYyXkWPpnwOeMOuYNjB57BrBlz+P2rv/jmvR+Y97v21tVW1cX9+wn6Q4w/92kO63syk59rTh/f+bChPPzj7bxZ+QyvlT7JM//czwFnjoobZLrx9htw2l0nJP0exMIwDX7/6u+0+/l20gwePOdJRKTpM2/8DpUuKufSPW+Iuf21+N+l3HDE3YTDVqvvS2Nl31uPf4C5vy1Ie44OiXGCVTNEY7G1xhonnYp7EEkJHLijP7WqnBORYCzr39AejKJnUQ0vIHXPNNdrUfk6o8ZaHvHKBHS8QfYJqOyDUcqF2BVI+VFaWh1oFSdi/Q0VxyIlbzdnY7QTkTDYZaB8cUXRdFvRgZ9Sj2BB1WVgzU1r/A6j7j5s5cXI/V9KlylzDSTnVK0y24ZIYGruGZmZIzp4VRU/qUXnAh+BXQWerVGeHdlmvyU8ce3FiB1sE4hquAzW3WwA2+2zFfN/X4g3y0MgjhDYoxfqoOqCnvkcf93h7HvqHq3Oz5k5jw+fmZLQ7V/St4jea+sU0+323oqbJl3JVaNvSu5pPwFKKdYdPIDr39LZXX3Wbrv9Y1l2cuk50pw9VLqwnNOHXNLqtW0wZCCzfpyd1DZHsCHIbSc8QGGvArbZc4um4zn5qf3tHXTuPiyatYS3x3+Y0nWtEMlICu7Lt70Zswqvbdksmb2ML9/+nh0Parvt+85DH2mjJcZbp5T2tpz78Mlpz9MhPo5HJA1EBPFPwi47DFk2SFeBLTsG8X/WqfNQ5hqRoMFYf9imLvIWIxBReYei8q5EGzMt+1B6wSp+HMMsROWejur1BarH+1oFtdfXGAXXYhSPx+j9FUbvHzF6TsLIObw5NbX+JbDLiR6oaoE1H1mxM3b9xHa9drHrsWvuRpbvgKzYEVm+DXbZEUhgWvT2De8gpbsjZaOR8sOg/Eiw5rVr7E6j9u527aer3HO1CqpayTBzD0GVvIJy9c/QBFuM6eqHkTMWI+88DO9OKKXot0Ffbv3oKgojpdlNt4kRiTPYdOhG3PLhldRV1XPRbuMIBpLz7lWtqOa+0x9j4h1vNx2zbZtrD76DQH3iKtAHnzu61ZP/NnsM5oTrkwuWbqTv2gG23rmaDbesi4iz6Sf93KIcrnjx3LjXbrz9BimnCYtIm+/BPz/8GzXLKBaGUjw3rn1/ay0ZddKuTTovbVAk9HDatjBkj9jZYskQ9Af5Zervcb1fpsvk2/d/jHru+49+inutFbb5/qOf0pqjQ3I4HpF2orU3bo2kLxo0mdWh75HKbyH3PFTuaZ02H5V/PVJ+uBa2ahV4GpFPL7gt/vU5x4F3KFL/IgR/0oXhfLtC1kEooznVTik3JKgJ0xJpeIuEgbBSBdVXYAe/xCi8O/m+7fpIOu1vrccI/YRUnAT5N6CyD2tuX/dsRI595ZtkRwpHZEKKM4z430NlpVaMTKf+joXsYyD4I0g9uAaiXNFTMzuSTYZuyIR5D/HVOz/wzw//4va62XbvrdhwiK5qO/GOt6kuq0mo+LkyT1w+gb1O3IW8olx++uw3Fs+KHxMA4PG5GX36Hnzz3g8s+mcp2QXZDN1vCIdeuC9zZs7j0xe+wHAZ2GFba9eI4PKYhIPamF57owbOvGkRm23fHHOxbKGbCXf3x1d8KIddtB+91oov6LX7cTvz5OUvEGgIphW0meqlti388dXfVCyrbHel3coVVVw5+uaYn5XLZbLhtuvx2/S/op43TIPNd9qYdWKkCCdLoowj0PfpWNt4STmkUvw+OrQPxxBpL8EvI0YItF5oI/uUtXeDdzjKvVmnTEeZPaHkdS1+Vf+KLiqnirTwVfbx+nyiPlzrNZV3zxhSlXxb/3vYDbtjZO2dXPv6J9oaIdD0u1RfC95ddZyKXY7U3NI4qeTnlC7ePSHvIkBBxUlgzWnf+OF/2z0FpTzg3b7d12cKl9vFjgdtF9VNPuWVL9t107fCNvee+ihXvnw+f307C2WohP0E/SGOXus0asprMQyFbQv3uk0OOnc0Fz19JiMOH8bLt77JsnkrKOiZT9/1+vBFJG14wAZ+7n57Fl5f6+9crzVDnH/nbFR+Fio78d9abmEOV792IVfvfyti2xnZEkqFhlo/Re0UqX3rgQ+oWFYV05sQDln89mWzEdJozDW+1wM27scVL53bvsFb4MvxscbA3iyZvSzmn5TYwoZDoj84Dd55Yxb+tSjme2+6DLYYGScI2yFjOIZIO5H652gWDouGidS9gCq8udPmpIxCyD0LlZu5VNC0MdeOxIckeaOtvQ+SMEREbKT+hQT92tDwOuT+DxreIv3Kr6lggGcYRtF9AEh4PmLX0W4jyJU4WyTTiAgS/B7qnwKrTHvCck/HcK0ZOR8EuxRUViuvWXtoSCItNxZTX/2KUxeVJZdWGqGmQgdMN25rhEMWr9zxFr9//Rezf55PfbVWAS5dVM7c3xYgAkoJ596xAF+WjbHSrkTjToRU3wK+/RJqvcyaMYd5vy3goHP2YcnsZcz4dCa1FfGVhZOhcdGPhzfbS0nf9n9eHz71WeJg4BZTEBHyS/IYtP0G7Hr0jgw/aFvcHne7x29EKcVBZ+/Dg+c+GeM8eLLc7H7cTlHP73fGXrzz8Ecx+7csmwPOGpX2PB0S4xgi7SX0K4kEugj/0lmz6bao7CORqh+Sv8Can1w7qdXBqfFHR8L/6M0RayHacIyvKJk5bPBoL4RIGKkYC9JWxyU5XFqqvxOR4M9IxcnNgckA4Rngn4htbAG+zbWuiujFU9xbo3LPRHmHxe5TghCYAtYiMAp0LSNDp3Kvu/kAFv+7NKFnwOOzycq2qakysS29+isUHzz5GUP334bHLnm+5YgU9wpjuoSyZe6m9pFTUSYI82b+Ss81QtRkuShfphdLK2QxeGgt5981nz5rxY9hEfFDw/uonMOini9dXM4Nh93Fb1/+hWEoQGHbegso3V28k+84jk8nfM6sGXNitjFMgz1PGIE3KzmZ/GhUlVanfE11WQ3/u/VoBmyc2bikfU/bg5+m/Mr0N75t5Q1rDIS98qXzySmIrsMyYFA/LnzidO44cbyWrY9890yXgWXZnP3ASay/1boZna9DdBxDpN0kY9G3/499tcG3NzS8GdEwScYrkmQepfKS+M6toDEbRxUkaNsB1N6OXf8MSCC1LaqVyTm1KRtLRECqEWsFSC3KKES51s7MfCNI6A+k/AhiGtr2T1D/U+tjoRna2Cq4DZXV1mgS/4dI1VUgleiYKhvwQO6pSPbp7DMmm6kTY38/Nhhcz5HnLGP7PaoxDKitNpg0oYSX7+9FXY2HhX8vZt3NB7DFyE346bNf2fXgCg4/azkDNtCBqxUrXLz1ZA8mju9JONTWc7LGgABjLlvC8L2rMCN3xZlf5/D0LX2wRXHTi/+STEKcFYavX32T4cce0kaLo6HOz4Ujr2XpnGX6bbSbVYvTFffacJv1OPT8fTn0/H2578zHeSdKRosyFH3W6cXx1x2e1lglfYv1dkgKmC6Dz16czgnXH5HW2G37NbnqlfOZ/MxU3nxgEvN+W4Db52b4gdtx8HmjGTh47bjX73H8CAZusTZv3j+JHyb/jIiwxchNOeCsvZvilxw6HscQaQdiV+vAv7gYKN/unTKfbo29XBsjygWBqSQ0BlzJSYwr5UU8O2tdkpieqTDKu6dun7U3Utc+TY60SFQpOC4Ksk/ByDtbL1QNryG1D2mdkQgCiGsQKu8ClFe7oEUsCExDAp+A+FHujSJBx8WR84KEftQlAUK/gV0NKgu8O6Kyj0Bq7iL1baxIXE7VleAd2eTpAJDAVKTybJo/+0aDI4jU3gf+KWw++Bf2ObYf7z1XEmnXbJBuvXM1456do+3KyNqem29z0P9WsMMeVVx40EZk5Wo9lCtfPp9PHj2UA8YuwG7xEop6hjnuwqVsul0tVx+3Lla4uf++6wS4991/yMmzmowQgI2H1HHbq/+y4F8vytBF+xJhmPDT1CV89OqtXPb82a2exj+d8DmLZi3JuD2sFPzv1mMArTkSzQgBHS+xwZCBTHn5S7bcdVP6b7hmu8bb5+TdeeLyCanF9ChFdXltu8ZLhGma7DV2l6RqEEVj4OC1ueDxzksscGiLY4i0A6k4o7XLOhoqG7IO7ZwJdUPErkGqLofAR6R0581LUFm3BSr3VKR8GtE9I6bWTYlsjyjXeohvP/C/k3g+Rg9Uzlgk6xhU4FOwFiD2UqifkPzraA89poD/DbCWgGtdyDoWw3BjW6VQfSsEYhQfDP+BVPwPCu8F95ZIxYkQ/pvGVGzxvws1d0PBjYjRByrPj75NFP5NVxUmDUVXgtDwNuTohVFnl90e/5LwLygFPfoGWdkIcXtsLn1wPoZBm7gM0wVrDAhy7AWL6DdES5LnFyzhgLF6a2Ll9oYJW+9cyx6HlTPphWY11dPGLWpjhDT2b9uw9oaJ04EbsS2Y9k4BVWU/cu6OV3HPFzc06XRMfm4qCoVk2BI59upDGTxiE6ywxSMXPhO37ZSXpjPlpekAbLv3llzy7FnkF0eX3o/F6FN358OnP2PRP0uSEo4DEMtmjU6W03dYdXAMkRSxQ3Mg9E3ihgX3oMweHT+hbohIECkfA+Ffabvox9lOyTkNw7tt0uMoz1ZQeC9SeTFa4r5x5QmDe3NU0UOt9AxUwU2I1EHgk7adeXaE3HNQKgtc66CUSy+HjYGzYiGhPyD0Ex1Sl8dcE2Wu0UpkTAKfY9feHxkzEaK3PoyekcwcaO3VsJGqS5raRscmfRl7E7H+bTYlrH8jRlF8bBvefqrt38vQvarIL47tnTFdsMfhFbj7rQOANLxEvCBysWHfMaVNhkiPNYJsM7Im5rZLDKXz6H0LTBzfi6oyvW077/eF3Hz0vdRU1LFk9jKqS6szWl8FtLDb/meNoqHOz9NXvkTFsuS3AL//8Gcu2f167vvqxpSCR3Pys7l72jgeOOsJpr36dXIqtoZit2OjB406ODiGSKrUP55UM5XWU+Uqjv+jOIG6kSdeo2+z2qp7EORdhuFJXeBI+faEXsPA/w4S/gvwoXy7gXvrtqJK1oJIrEpjjEILgtOhLhdVdG/0cZQJRY8hVRdHDBkV+cmEUaJ0inWL+UrD60jVZaSkPS5VYMVbiJJZBNNdKEV7AxuxK5O6qqHOoGJ528VwwIZ+QkFwxykm7PbaKHsxmBtAeBbxtpUME/qt2+zhWGNAMKnYj0SEQvDSfb2ZcFdzTqzYwjfvRRfTag/RUpOrVlRzzNqnIwj+2uQ9N6CVR2fNmMP0N75lxOGxg4yjUdAjnytePI/T7q5g1oy51FXV8fAFz1C5oho7SsDxiTcf027dEofVH8cQSZXw3KSaifg7oaB490QaXifqYt8SIwejV2YUaJWRC9lHJny/pfZhkGCMedkQmISETkG5N44xTh6q6CEkPFfHYEgA6p/Wmi3tIvIeeXfXomON87SrkKqriUSApNBfJo2j9mKBt3mvXow1krpKxXidgQYjOa9EY80clUui756/obnDuprEgR8i2pOy8lZPI8EgjNl+EKVL41hLaaAMRX5JHlUromerNNQmKngZG8NQTH5uasqGSCPFfYrYdpROBd50+CAeueAZPn/9myYvSZ91enHcNYex+3E7t3uODqs/jiGSKkaS2y3uwR07j26I2LXg/wCCvxN/MRSw2pvKmjoiIcT/USQ+JN68lPZ4ZB+tC8LF0IJQrrXBtbbeZHKtjVSeSeqeBB+4N0BlHxMpPtdilWt4m/ZtkXQTFcia25DiZylbXMvV+9/LiZfksvkOtW1iMFqSlSusu3EDs//wgTSblF9+UMDYy+OppSrCsjZL/oae/RvI8u2lg3RjEA7D1LcKm36f87uPxXM9rLFWbM+IbUfMO7v1Vo3WFoHHxvXtMCMEtGcllhGSLrYtVKawnROPnv1KuPLl86lcUcWif5aSletj7U37x6zk6+DQiPMNSRHl2y2JRgUYHVDHoztj172ILN8Wqb4cKE/QWoHZpzOmpfUwVoyAqvNI7CnQxfCk+hpkxfCY9Wpaony7owrvBZVCKXlzA4w+v2CUvIrKOqC1EQKINZv41ZBj0aLUQFcS+olw9dNctNs4/v1lHo9c25dQUGGtJOFiRz4OwYNSBoeftbyVEQKwYJaP6e/nt7m2GeHWU21O2vQCDu45lnvOnosl/Yj2/lkWhIOKNx5rVj4VUTx9a5+YRogITJpQzLn7rce/v7WuVFyx3MWd5/fj7ScTK6l2VwyXwRoD2ymxGoPCngVsMnRD1t18gGOErMSSOcuY9upXTH/zW6rLa7p6Ot2GTvmWjB8/nnXWWQefz8fWW2/N55/HqvS6CuDbE1QCr0gKmR+rA3b921BzDcmLhQkqOz0tg6RGCc9HKo5LQvis1VWRfxqQitOQUPR6GU2tQ78jDR9E9DEAEj0Zu6DosfhNVDbtMiiyjyU5fZtEpLupKITKn2bhX4uxwzZz/sjivP3WZ+Y3rYWlFs32cu2YtXn+vn1B5TNi/0qOu0h7PwxDmvq67ey1+HGazuzQabcuRBThMDx0zZpMe6cQgHAwzAfPfMnZe6/JiqU6VTkcVoQi4Vo1lSaXHTmQxXNb6/tMfauI+y5Zk4BfYdtoo8nShtKbT/Tgwcv78deMHM7cc0NO220Drh27NhceNJCjh2zMRy+lYIB2Q+ywzagTd43bJhQMJVXXxSE2pYvLuXzvGzluvTO5/rC7uPag2zm878k8cNYTSRd6XJ1Rkukw7pV4+eWXOfbYYxk/fjzDhg3jkUce4fHHH+f3339nrbXiy1ZXV1dTUFBAVVUV+fn5cdt2JhL6Cyk7FqhsezL7FIz8Czp7Sl2GiI0s3wYkWeveBNcGqJKXUcqXuHka2NXjoP5F2i/tboJvf4zCW5DwLAh+ByjwDNHpwIHpWn0Ue6UxYmQGGb3AsycE3tM6NEYPyBmDyj6yuVoxIKGZSNnBqc0zZywq9wLwv4dUXYh+xmiek6BYtqCI3v20typ2cVQF+bdD6AdoeJHW8RaRi8y1Igq48W8do9fZnFCg9UC9+wfo0z9EdYXJnD98TX3e8/kVbLzlLII1X/LeEzN57xkfC/71Inbz9YOH2Zx5Rx/WWDeHCTd/zwcT8qlYEcPwUsLWI6rZfvdarJDw54wcpr9fQCgY+9krO89i5/0q6dM/SHWFydS3Cyld0nFbLp2BMiA7L5v6moY2ga5KKYYftC1XvXJBm8Bu27b56OkpvH7ve8yZOR+lYPCITTnsov3YZq8tO/MlrPJUl9dw+pBLKF1Y1kY5WBmK7fbeinFvXZKwYvGqRirrd4cbIttttx1bbbUVDz30UNOxQYMGccABB3Dzza3rsAQCAQKB5sjv6upq+vfv3+0MEdABhTS8jvg/BGkA18aonCNR7vRKW69q2MHfoPzAJFub4NsHlX91K8GrjsJetk16iqagvROuzdqmbLu3h/DvEQMs2p+QAa7NwewJRpEO4Kw8H4gihGesCT3exzCymudePja+Gq25MbgGaKMoa58msTIACX6H1D4KwWmAIKo3rz1cyLO3+dhiWBWXPDifnLzW/QoK5RmJyjsX5d5Ip5kGPkLqnofwTMAD3l1QOccj9c8krN1jhRX7DNgMkcQ3V8NU7Hjw9lz+wrkYhkFDbQOTnviU1+99j2XzVoDooEplGFhhi6LehVQsq4zZ37obN3DUucsYOqoK09QZOR+8WMyL9zan1nYEbp8bwzAI1KeWvdLR3PHptXz64hdMfnYKoYD2WmbnZ3HAmaM49ppDcblbB++ICHeMHc9Hz0xplaljmAa2ZXPqncdz8HmpVYL+L/PcuIk8P25iU12jaNz28dVsuUvnFEjtLLqNIRIMBsnOzmbixIkceGDzYnXOOefw008/MXXq1Fbtr732Wq677ro2/XRHQ+S/ioiF1D8P9S/rRVgUSOLS6wCY66Gy9oGswztcY0XERpYNIjMxE9F0KZIoDKKyUb2+BjzIiuG6QFwsXJtg9Hij6Vexa5HKcyPGhBkZLwxkoQpuRiVVGDAIEuKtB6cy/tynmzQslBK23bWabXerJhxSfDmpgNFnXcmIw4cn7BNA/J8hlafEaWHy96/rce7e2SlVle01oCcHn7MP+5+1F8vmruDkwRcS8gfj3sBXZtPtarn5pdkYpuBqsb5aYShd6ubc0etTHiVNOB0KeuZzyh3HcddJD2FZdrcoHd9Y6faYqw5pknSvrazj35/nYpoG6221Lr7s6CUopk78ihsOvyt25wqe+O0e1tqofcqs/zWOGnAaKxbE/ts3XAa7HDmcS57pRsVKM0AqhkiHZs2UlpZiWRa9e7cOhurduzdLl7ZdvC677DLOP//8pt8bPSIO3QPbWgqlo0HaGcFvzUJq74e6J6H4aZS7A58A/G+RrhFi23oLQ6loT/5J9C31YC1GwgvjGyEA4d+w/Z9h+EYCOiVZFT+uY1D8H4HUo1wDtUcpQWXXRpTygPIw6clPW6l5iii++biAbz4uAPSTrjt3StKGCN6dwLURhP8huoGmyO59FlY4QSzMSiyft4KHzn+a37/+i8JeBYSDoZSMEMMQLh0/D9MlbeTYTReU9Alx8jWLueWMASnNKx6my2DInlvw+KUTMmKEuL2uJq9FshiGYp3NBmBZFnN/1fL/6289kMMu2o+dDtmhqV1uYQ6Dd94kYX9vPjCpyfsRdTzT4N2HP+L0e8akNM//KpXLK+Oet8M2KxamEse2+tEp6bsr732JSNT9MK/Xi9frFIrrjojYULp/+42QJmyQOi1J3nNKh8WJSN2zafehVHOKZvs78UVXco1G5blI7++0AdF4uXvjqLomImEITNE1Y1Aoz1Dw7NBUHK8l5Usq49pNtmVTujhRplMzWtztSaTilMi2TeNtJKy9QIV3s1afERx24TJeuePtpPttZOorX+H2uFLypgAMGVlDzzViL+IuF+y4byXjr1qT6vLM3PqssM36W67DJ88nzrDactfNmPHJzJjnDdNg9Cl78Mb97ydl5zZumwzYuD+3fXw1+SV5+OsDKEVa1XVn/zQ3rlqqHbb558fZ7e7/v0Zhz4K4hobpMuixZnHM8/8FOjRrpkePHpim2cb7sXz58jZeEofujfgnJa6vkzQ22OW66FoHIGLr+I2Urmn+v23rn4BfpSTx3RoFrg3AWIPka7c0gD96wbKWSOh3ZMWuSOXpUPc01D2FVIxBSvdBwvPbtO+xZnFcY8owDXr2T22rTJk9UCWvooonQPbxkH0EKv9GVM/pKO8IAE669RjOe/RUeg9IfRsuFEzNKwCw9kb+OGm+GpcL+q6duRiOTXccxFNXvZhU27U36Rf3vGEaHHzeaPqsnbgmi2EaDNpufc579FTu/+Ym8kt0VpEv25uWEQLg8sY30rSh0zqI17Ztgv5gxiXsVwdGnbgrhhn7RmKFbfY4fmQnzqj70aGGiMfjYeutt2by5Mmtjk+ePJmhQ4d25NAOmab+uQx3aCLBb9scFasMqX0Uu/Ic7MoLkYZ3daxDSiiS1+EwmfHVJvw5o1mWfMUiN4+N68tfM7Kw2p21KKic07Tnr4XSaHwM7MC3fPXO97x297t88NRnVJe1zkYSaxlSfhzYjWXYwzSlTVtzkfJjtbBcC0aduGvcB2zbstlrTOo3QqUUyrMNRv4lGPlXo7IPRRnZrc7vfdKuPDd7PHd8eg0DEizE6eJvUEnJtQcaMnfb+/3LvwjUJ/f9fOO+SSgjtkWYleujx5rFbLnrZnEXLtNlMPzAbbl3+o3sfdKuaRseKzP8gG0xXbHHF2CH/bYBYM6v87nl2PvYJ+so9sk+miP6nczz179KQ21DRue0KrP/mXvRs19J1PdUGYpt996SLXfZtAtm1n3o8K2Z888/n2OPPZYhQ4awww478OijjzJ//nxOPfXUjh7aIUOI/yMIzeiAjgWxFoGEwOwL/o+RqotojjtQiP9tqOmrY0pcayfVrVIK8e4EgakkSt1V+VfxzO2/8cfXLnzZFm6PUFOpg0MrVrgYPLSth6EV5jqRInMumjNchKDrXN64J8ikJ86kYnklL80wycqJPxfbhk+em8Yd5/zZFGx4r9vk4PNGM+bGIzFNE6l/AaSW6Nk0FthLqF32Em8+VsAv035HKcVmOw5irY3WZOHfbaulGqbBJkM3ZNgByRcbTBWlFINHbMrjM+/mr+9mceORd7Nk9vKMj/P1hwWcNm5xzPO2rY3MuX9mbjsw2eqzjcSLIakpr+X7j35m/zP2YtLjsbfzrLDN/meOSmncVDjo3H346NmpKCVtPByGaZBfksdux+7Ez1N+47JRN2BbdtM2WvmSSp677hW+eP1r7pw6rqny8H+Z/JI87v78eu48cTw/TG6uweVym+x14i6cdtcJq13qbqp0uCFy+OGHU1ZWxrhx41iyZAmbbrop77//PgMGZC5gzKHjkNDvSOU5ZF6x04LAx4i/MVMkh+bU1pXGspch5cdDz49QKrmnP5VzIhKIV8tGQf44Gqz9qKvUnhl/vYm/RXbttHcKGXV0GZtuV9cm+BEM8GwHhY+jQt9Htq5qwVybyprdOWe7B1g277Omhefq49bitld1VdxY9xzDsPn2Y/36GoM0wyGLl29/i3DI4tQ7jwf/u8RTiBWB2d+M5/lx6zX18dNnv+L2utl0+IbM/PzPpjmZLpPdj9uJ0+8di+lKTck1FAyx6J+lKAVrrr9GmxTQWPRZpxdrrNu7QwyR5Ys8fPJaEbscVBHl89Ly7M/f3TuplOKuQCnFF69/zfCDtuekW4/h8UuebxU02vj/4649jM13il4PKRMM2Lg/175+EdcfdifBhhAoPTfbsinsVcAtH16Jx+fm+sPvIhyy2hhXti3M+XUBz1z9shPQGqFnvxJu+fAqFv6zhH9+mI3LbbL5zhtT0MPJBoVO0BFJh+4qaPZfwq68EPzv0X5RsMyhCm5HZe2fdHupn4hUXxX5rcXirQqg6Dlqa9bkvJ2uYv6fi2LaWd4sm5OuWsLex1TicjUGIHgh+3BU3oVRg20v3fN6Znz2a5sqpEees4QTLom+AFuWonyZi+O3HxRRD22NYRpMmPcQxebeIPEDS//9zcfpu2/Y6phSCrfPzV1Tr2PFgjIMw2DjoRtQ2LMgbl8ADbUNfPrCF/z5zT8oQ1FX3cCPH/9CbUUdAIW9Cjj43H049KL9MKNYACLCzM//4KkrX+LXL/5IOF46eHw2F983nx1HVxGOCFYqQ2/WPX1rH15+oEVsWhIZ2F2FL9fH8AO3paaijp8+/RVE2HT4Rhx83mi22WtL6msamP3LPAxDMXCLtTO+PQNQU1HL5Gem8se3/+jsoD22YKdDtsfj8yRO8Y28holLH4+ZJuywetNtdETSxTFEug4taPUxUnkWXVvNtREDfHtiFN6b0lViLULqX4bQr6C8KO9I8I1GGdncdNQ9TJ34VVz3umEa5BXl8MjP11FcvBBQ4N40Zgrtwn+WMGbDs2PNhpOvWczBp5QiYqCUHldEUVVucPEhA5n3V1bUK5WhOPXO4zngmAkQ+pFYn0k4DNPeLuTWM9t6HA3T4IhLDmDMDUfGfL0r8+MnM7nu4Nupr2nANE1syyLWHSO/Rx5uj4vCXgXscfwI9hq7C6WLyhl36J3M+21B0mNmgoGb1jPigEryCi2WL/Ty9cdrMftXf5PnxwpbFPyfvfMOj6ro4vA7dze9UULvvffee+8gUgTkExAELChIU0ABEURBQGkiqDSVqvTee2/SOwFCCelld+/9/lgSCNmaRoB5nycP5O7cmTN7N3vPnTnndzL5plgxuWRBQMv3G/HxzPfjDkVFRDNv+CLW/7KV6EhzbIqnrwet+zelx5iOuLi6EHT/CQfXHiMqPJo8JXJSpm6JZKv5cv/GAw6uPcbuFQc5tfOs3a2pX878QJ7iUoLhTUQ6IpIkoWkqWvDQp1ocaQi3+ijpZyVLV0H3n9A5Z1+7X6RZ82Zi3JrhDn+Zblu8mwndptlsU7JyGMPmZiZT1kAQnjwOqsL7VfcTGmR9e0PRCSo3K8+wXwrgoY602f9n7Qpw5qBlRyl/6TzMPjHZ/kSAWxfu0LfcEIwxxkTrYyg6BVVVk33lITa12hGGLBhAvc412LPiECd3nAVNo1Tt4tR6qyo9CgzgUUByZYOlDLE3c0OMgc8bfs25/RcTfG6FEFRpWR7/HBlY98tWVKNqjpXSNLIVyMKwPz6ieNXC8c6JiYph51/72b50DyGPw8hVJDst+jSkRI2iCWIWoiKimdp3NtsW74k75sit4/fLM8iWX2ZIvok4c/+WpRElCYlY5JwTouQm5T9KCuiLJVtvl+1oJcTSeVg7p57obGU7xHLmkDdXrg1EybQexX85Lun7ERZsVvssWCqCTybfZM728/y08SLdPrtHhiwGVJPGgTVHeTvPMq5fKoKWoDCd+fd/FmTkzAvF5Z7H6ETxspU/rkM1JowBcAbVlLxOSNP36jHsj49wcbOvjioUwahln9G4R130Lnr8MvsgBISHRhL8MISo8Cia9bZd8O1lo9MrbPrNrEC9bfEezuw5b/Fzq2kaB/49yprZm+O2BGMdhfvXH/B5g6+4/tyq1KO7QfQr/zmTes7gyKaTXDh0me1L9jCo9iim9pttdh6f63tc5ylsX7IHTdPifmwhBOQskp2s+eynIksk0hGRxEPTNLSIBThVgVW9CbiCviT2q88mHuH5drL15Whwps7BIMxYytQtgWIj9RFA76qnZM2icb/7+ftSvXUl3h7wgJ82XqJRxyDyFImmYClzzZRf95ynZGVzSm5MtEr/Bm4c2F7bXDQvztAcbFhWi1mjcmHt2un05gwZR9m17IDTomIpiXd6L7p92RFFpzh0/YpVLUyt9lU5f+gSXXL1ZWjDsayZvZkdS/cyc9AC3srci8jQqDgNjtTA2ewITYNHT8Xm1s7ZYjP913xCwkOqScVgMLJo/LKnfWqMaf8dAZfvmn9/6mjGXut1c7eyYurauPP/O3iJg2uOOqVyq2nwzsgOb3w2iMQxpCMiiY8WBKZbOP8YGwXGs5DuR0g3JxkNMqfSCt9xCF22ZOu1WJWCuHvZDqITQlC+oXMy9OmzpKNht9pWV0aEImjWqz6+GeLf/AZ8V5heI82pp/rnHvZ1OnMA5te/X8PL17yaYTIKvuoWTGDEcg7sm8hXfVvQrmB25o4yYjJav24mo0rr/k0cnkt0lLP6LSmBeT45CmejzYCm9Cn9Kd90nUpkeJTNs2Krmt44d4tP6462uP2iqRrLp6whT4mcSVPPdQJnd8JVkxoX3xFw5V6iV6dUo8ruZQeIjozm/KHLnD94yaaT+ff3/2J6KqKzffEehxw/oQjz517A/8Z1oWG32omyVfLmkSoS75JXiaR8I2sQNg3FfzWazxdooeNwLjVBAHqzpojpBuYU2ZoI7z4I1+TVufDwNlcf/XPSaos3B0WnULtjNTI7qTgK8OGM3jy4+ZDj287EpVzG/luxSVlzGu4LZEy/Gi36WQDr8+h04OGt0qjjY1bNywQ83XZoO5lrp27GaY7EHtdUDUUnUE2xaboKJqNK/6n/o2DZfA7PI1/J3Fw4dMmpJ+HkRcPNXaVBjybkKpKd2Z/9/vxLVhHCrDDarFd9Zg5agCHKYHOU07tSNpMnqexecQBffx9CH4fZb2wDk1ElIiSSY5tP2awlA/D4bhC3L94lT7GchDwOdciBqt6mEnmK5aRpr/pkyyfjQiSOIx0RSXxEOtAVANNVErW5b/wPNXI9ilcP0BdAC//laTn75/tyAcXHLPP+fGVZJSMi3U8I13JomgHQWaydkly8+3Un7l69z86/98fdrGO/oEvUKMKnc2xVmLWOu6cbEzZ+wZENJ9j42w4e3XlMplwZafK/+lRoVNpyBkPMAYtOyPOUqREW54gAXDtlFlt73lHQVO2pLylw93ZDEYJyDUrR/pMWTmtPtBnQlG8PXHTqnMQRa3/CuJfoKIV1c7Y415tmju3x8vNk59/7k8XCl0l0RDTLp6xJcj9uHq54p/dCNakObZmoT+OJHJGc90rnyai/P0u27BzJm4V0RCQAaGowRK1HMwWCS2kwXUl8Z8HD0dzqINxqINxqmLNwNBWhBqCpkUAEqMHmirTGWyCM5kq8bg0QwrwvEftvSqJ30TNy6SBa9W/Chl+3cf/GAzJkTU+j7rWp2LSsRU0MR9HpdFRpUYEqLSoki62C+EJoNgNtNfPrH3z/Ls37NEz0mPW61GD/v4ef3cxf8EuFYk5dSVrenYa7l0pUuLUbWCJW6ASsmb2JJu/Vc1r5NC2SHHmNil6hcc96uLi6ULRqIUx2gpa9/DzJUci8Fdrkf/VYPGGF9b51Ci16N5ROiCTRSEfkDUfTNAifixb2I+aaJbqn/yokXj8kAqL+Bc9OAAihIISCZrwPIWPBeP5ZU10ehM8IhLvtWieaZrYtuYPfhBCUqVPCofLoKYXRYGTPurIcWB9IdJQgf/FImnZ5TMaszyq4aWAzG+ZFdHod/x24mCRHRFEUhi/6mJI1i7Hix7XcvWKub1O0SiEqNysHmlmP48imE1w4nHjH1eyEJON11eDBrUdcPXkDVw9XYiLTQqzLy0PRKaTz96XryPYAVGhUmuwFsnDv+gOLjppQBK36NcbV3Rx4nr1AVroMa8eSCSsTtDUXTMzI2587LjQokbyI1BF5w1FDp0D4zGTuVQcebVH8JsQd0WIOm2XaMWFxy0fJC/rcoM+PcKuJJtKD4T+I+gcMJ4irYKtkA+9BKJ5tk9nm1EfTNO5cussXrb7lzqW7KDoNTTWvfAgBH0+6TZMuj1FNEBMt6FapuE2tkefR6XU0/V89Ppltf3spMiyS1TM2sGb2Zh7ceoinnyeNutfhrU9bkjl3pjhbI0IiUPQ6PLziq8maTCZ2/rWfSe9OT1NZNuky+1KwXD6ObDqZZhVUU5pYfZEPp/eKu5ZgLlb3Wd3RhAdHxDkjsfFFpesWp/PQdpzYdgZjjJEilQpQo30VNszbxuJvVvD4rjnwV6dXqP1WNT6Y0pP0WdK9jOlJ0jBS0EwCYK5aG70H1EBQMoNbTYR4ll6rxpyBx+1TYGQ9eLRH8Rv31A4N7VErMF4i2e4Inu+h+A5Lnr5SmeCHISz7/l/WzNkcJ5OeEHOsxzdLrlKqSjije+bj2E4fMuf2p+fXnfnn5w1cPHLFZiDpqGWDqdW+ik1bwp6E82mdUVw/eytBRoaruwtT94yjUPn8ducUE22gU7Y+hD2xNh8zzgiRJRcePu5EhtrOsnkdefvzNrT7sBn+OTJafP3R3SD++WkDWxbuIjw4guwFslCvS022/LGLq6dumDNlBJgMJnwz+jBmxRCKVyvMlZPXiYmMIWeR7A6VCJC8mUhHRIIWuQot5BvQnjw7KNIhfIchPMzOhxrYANSUkd4WflMRHs3Nthj+Q3uUAku3GdejuBRI/n5TkEd3g/i4+kge3H5kN35B0WmUre3LkPkfcuuyHk9fTwqVz4eiKOxecZCv37KskBq7XP7bxel20y5/6DOTDfO3W00LFUIw79wUchXJYbOfa6dv8H6ZwTbbALh7uREVHm23XXKi6AQCgek1iBdxFJ1eR/fRHXlnZAeHzzHEGOhbZjB3rtxLUCdJUQQubi7MOv4dOQtnT25zJa8hUln1DUeLXIMW/Hl8JwRAe4IWPAwtcjWa4WIKOSE68/aJe6Nnh0z3UmAcIHSC/TYv8ORBMLtXHGTn3/u5f+NBChhlmxkfzuPBHftOCIBqEhzfEYpf1pKUq1+KIhULxAUE1mpfhZ5jOwPmJXJ4KpYlIH0WP77d8IV5q8f0CE2LtNh/2JNwtizcZVObQtM0+pYbQnSkbedBOBCoqOgUXN1TPgj5RVST9kY5IWDeLnN2u2TPikPcuhCQwAkBc2aW0WBMluwdieRFZLDqa4ammdDs3KC10IngY7teSeIQoKRHZJgXP+tFyZACYwFGx1NLoyKi+enjX9n8285nGQMCqrWsyKC5/UifOeWXmB8GPGbvqkNOiVJpGhgNJlxcXYiKiGbLH7vYuGA7QfeekCVvJnpN6Mr96w+4fvYW7l5u1GxXhfpdSuKmzUcL/Bu0cEBBc6uH8O5vzk56ys3/bmOINlof/CmGKAOLv1nJ/8Z2xmQysXvZAf6ZuZGb/93B08eDup2q0/z9RnaLyKkmFXdvd0IeJU0PIynExkG8yjgyBxdXPbU62N6We5Fdy/bH06R5EZNRZcef++IV4ZNIkgPpiKRRNM1grn4btRHUCHApiPB4G6HPa/vEmMOg2nnSVx8+VU9NRvRFER5vg0fbhJVpXUqRMjXXjajBI8ypwLpcCM+3EPqCCVqZTCa+bPWtuVro81+yGhxcd4xPa3/JjEPf4uXrmcz2xeeGhTgMmwizhoO7pxtBgcEMrjeam+fvIDAXM3tw6yGndp6jZM2ifLN+JB5e7mjqY7RHHcEUgDkwGEBFi94B0Tsh/VyEWw3AXFnXUTb/toMeozsyrvMU9qw4GHfDCn4Qwp+TVvPPzI006laHVT+tt3iZdXqF/GXykq9kbjb9tsPx9yAZEYogZ6Fs3LoQ8FLGTw7SZ01H+4+a4+7tzqxPF6CaLNd96fZlR3zSWy58aI3I0Ei74nVRdhRtJZLEILdm0iCa6R7aw1ZoTz6GqA0QswPC56M9bIIaOh3NcA7NeNPyyeojxwaJOQok0zK58EBkWILw6pbQCcGcvotIgRgf9SFEroToLRDxG9rD5qghExN8MR9ce4wT289Y/JJVTSq3L95l8TfWdRKSC0cKtT2PQNDuw+YIIZj07nRuX7wL2jOZ8Nj5nNt/kTmDzaqjWsh3aMY7PHNCYvsymVfLnnxKdEQY47tO4bdRfzpsS0RoJMt+WMPelQfjjQ3m9zAyNIrdKw5Qr3NNIOF2UebcmRi28CPqd63l1HuQnAjgYUCQ/XotL4EOn7R0yK7eE96h87B2tB3YjPFrR5Apd3zlXw8fd96f1J0uw9s5bUPuYjnjrpslhDBL7UskyY1cEUljaJqKFtTnqcQ5PNPyeHpjCZ+OFj7d3FZfFOE9KL4Ghy6rYwPF7CbxOiEv4PkOQrGjceHeBCL/Sp7x4mGK/2/EPNBlA68ecS1WTV9nt5e/Jq0ma55MtPrA8VoszlK0SiG803nZzSwBQEClpmVpPaAJty/d5cjGk1abqiaVjQu28974VnhGr7aq0CqEBloQv3zWix1Lnbv26TL7snLaWqsZL6pJ5VFAELU6VKFJz7qsnbOFWxfu4JPBm1I1i3Hl5HV6F/8k1TNm4iHMT/0vY1wvX0+iwqMspjd3H/UWPcZ0IvDWQ/asOohmsvwmZcmbiUY96sT9XqFRGf64MoOTO85y92ogPhm8qdS0LO6etmsoWaN5n4asnGb9b0UD2vRvmqi+JRJbyBWRtEbMfjBe4MUnWosYL6A96YsWuerZMZdyoLNegfUZyeSEuNVHeH9it5nw7E5qfdy08Nlo2rP379Kxaw6dN23AL5zceTalzMLVzYW3hziWPeTh7c77k3ugd9Fzetc5u+0N0Ub2r1qFELZjPowG8PKK3ZrQ8ElvxNvPfpxIyKMwi4XjXmTK+7P5d+YmmvWqz5yT3/PumE78NfkfDq0//nKdEIirvZOa+GTwZvSywcw8NonKzcvHE+TLXiALIxZ/Qo8xZuG/AdPeI3NO/wQFExWdwNPXg69Wfp5A0O/hncec2X2eE9tPc3TjCc7uPY+q2v7b1jSNY1tPM+X9WYzt9APzRizmzuW75C2Rix6jzRWuXxxHKIJy9UvRtFf9RL8XEok1ZPpuGkMN+QYiFmJWN3UUD0TmfXGrElr0LrSg9zE/w9i7vAJEJtAXMFfP1awHGybAbyrCvanD9WC0yNVowUOf/payWQwi4yqES3GiIqJp5d3NoXMUvUKVZuX5evVQ+42d5O61++xdeYiIkEjOHbjI0U0nbYbNKDqFAmXz8vPhiayYtpaZnyywO0b2fNHM33veZhuTCeZ/k5WoSB3t339A9rxmobirZ9356+fMbF+ZjqSqnMbW7anTqQand50l6H7wKx8gmhia9WrAhz/1wsX12Zbco7tB3L16Hy9fD/KWzB13w48Mj2L74j3s+Hsfty8E8CQwBEO0AVcPVxp1r0Onz9uQLX/8QnLLp6xhzpDf48RZhKJgMpooVrUQ49YMT1DhGSA8OJwv20zk9K7/0OkVVFVDCIFqUukx+m26jXqLrYt2s/Tbldw4dxsAv0y+tBnQlE5D2+Lq5Pai5M1F6oikMlrMSbSIhWA4CcLVXDPFswvC0W2S51BDvoKIP3HOEQHh+w3C861nNkXvQQsZ97R4nQPn+69H6AugGa+a5xKxHLC2jK2HjFtRXJzfLzb3vwRiDgAaKDlBlxMUf9DnBF0+84pOyHCI3swzqfmn/yoZQQ3CniMjMvyFcC3Lw4DHdMnpePE6V3cX1kYsdnpe1oiJNjC172w2/7ETIcxl0k0GEx7e7kSFR9utavrtxi/4oc8sAm8+dGA0jV92XSBH/mhsZdPu2+hDtUahaBDXTjWBooOl0zMzf4KMA0gKQoCbhxtLA+bYDIC+duYmh9cf59HdIDb/vpPQx2EIYQ5Ejs2M6TOpO28Pbg2YVzKiI2Nw83Bl9/IDjH37B4v9KjqFUrWKMnnbVwleG9nyG45sPGk1ffyzXz6g6Xv10TSNx/eeYIwx4p8jg109GonkRaQjkopoYTPRwqZgrtESux2gAG6IDHOdLl+vRSxHCxnupBV68OqN4vNp/L40DS1k9NPYDDs37qcrCM+jhi+FsBmgBT4bx7UppJuEoqRseJGmaWA4jBbxF5iug0iP8GiNJrzhiT3HQg9eH4D6CFX1ZkDt3Vw962rnnKdnuupZH7UkyfbH8s07P7Ljz72JWhEQwhxXcuHwFYeLt9VtG8Twny0HMptMcP6YJyUqRdjs46PmhbhwImUziF5XYoM9R/09mOptKgHm1PHbFwLQ6RVyF8tJeEgE33T9kaObTtpMl41lyIKBXD52lQ3ztxEZGoWbpytuHq6EPg636cjOODiBIpWeZZFdO3OT90t/Zn2gp1lav12aLgvYSZKMM/dvGaxqA00NgqhtoAWDLje41Ymnj6FF73zqhED8mA4ViEYL6guZdiIUJ5woj+YQ+g1oYTie7qpaHEMIAW510CKX2jnfzTy/F1C8OoNX57g959T8chJCgGvlhI6cpqLpcj5NT7V0cxaAEcJ/AhQUNGZuNrFtRXq+H5QTo8H6HBSdQvFqhZNtDjf+u832JXsSfb6m4ZQTArBjVXrSZzLS58sAEKA9PVXvAoe2+uLlbcRkBJ2Vv3yjAVr0eMiFEwk/D2kbjSw5DehdNQJvu2CISb3PqoubHkO0EZ1eoXqbynT6vA1FKhUkKiKaBV8uZe3cLUSFmdNe02Xxw8VVHxdvY88JAZjSZyaqqsV9DqIjYoiOsF3IT6fXsW/14XiOyME1R1F0ivXPkwb3rgVy59Jdu0q6EklyIh0RC2iaanYwwucRrxKtkgF8v0G4mwO2tPB5xF8JeR4VtAhzeqnXuw6PLYQHpJtmdmIwWek7gcXgZiWa3a2Ouc6M+hDLN24deHSwmHYbS1p6OhJCgXQ/oj3uAVoUCd+f2C92lefnW69dED7pjXzxjvW6KapJpf3HLZLN1l1/7Y+Ll0gsiSljv3JuJnasSkejTo/JnieGsBAdO1al4/JpT/48fcaqEwJmhyV/8ZeQWZIEGnR4TJePA8lV0Kz+Gh6isPaPjCz8IQvRkSm/pRB7fXMUzk7vb98he4GsxEQbGNZkLP/tvxjP2XhyP9jp/o0GR74D4iMExEQZ4h0zRBsdql794nkSSUqTdu4waQgtbDKEz+ZZnMbTm4EahPakP1r0fvOSaMwR7DkKWsxBp8cXbjUQGVeCexsQHtgOHhRmR0Kf0/KrQo9INw1wxew0PY9irnb7wpZOWke4lEJkXAXub+GoLy0EVKoXSvu+gQlfe6rf0PGzVnHL6clBaFCYQ1/8llB0ChUalU702EEPXPhrRhamDsnFL2Ozc/m0easlOsL2n7yqQmTEqxMP0HPYYz6ffosc+Z9J0Hv5qnTo94CJf1/F1T3lpd1jncU7FwP4rN4YwoPD2TBvG2f3XXBoxSMlMBpM5C+TJ96x/GXyPFMVtoKbhyvZC2Sx2UYiSW6kI/ICmukBhP9q7VVAe247xpEvmcR9EQmXQijpvkXJchKR5bzZmRCxUfB6zJdOgMdbCN8xtvtyLY/wXwnubYGnGgNKJoT3QESGP53bOkojCH0e0GXE2aDe90fd57O5XajasgLpMvvik96bik3K8s26Ebz/XY9EOw6WyF4gq90aJ7FOUGxsQWzqZrGqhRj55yCy5M2U1CSWeOxakw6Tnbds95q0X1FVURRy5I+my0e3nv4e/3WdDoqUiaB1T0eCfG3jaKCmyajy6M5jNi7YwZrZm5LzsjmFUATe6b2o07FavONVW1YgQ7b0VoXTFJ1C43fr4uHtkRpmSiRxyGDVF9DC/0ALHYc9B0L4bzMHlcYcxnogqED4DEV4vZc8tmlRELUJzXjdvJXi3hShc64SpvlyGxDCseDNtIqmhqIFVgNs75VbQviMRDixXZZYQh6H0in7+xhjLN/5dXqFJj3r0ahHHdb/uo171wJJnyUdDbvVplKzsuh0Ojb8uo3ve89MNpsy54hh9vYLuHmo6F64v5qMEPxYT69aRYkI05GzcHZup1U5dAG9RgbQ/v0H6K0siqkq3L/lSs9qxWx2VaVFeW5dCCDofjBevh4UrlSQIpUK4O7pxuH1xzmyybqYnCWKVCrAjXO3U73KMJg/U0IIvv5nGJWalE3w+pk9/zG08ThMRmO8LUNFp5C7aA6m7B6Ldzo74oQSiQPIYNWkoD3BvIVh+7FRi96K8HzPxtaLMG+reLRPNtOEcAeP1kl60jI/8b8cJ8RkMnFkwwn2rDhIVEQ0eUvkpsl79fDPnoiieNE7SYwTAgpazFE0w39m8TgEuNVAeHZHuBRNRH/W8c3gQ/+p/2Na/7lxaZmx6PQK6bOko8dXnciYLT0la1q+WTZ9rz6P7gbx26g/EcKx4EZbBN5xZWTX/IyZfw2/jCaMT8MB9C7w8K4LI9/JT0So2UNJs04IgAY580cncKaeR1EgW54YhNDQNMt/Ne5eboxcOggPL/cEr92+dJeZny5w2rSwJxF4+XmmiiMihMAngxchj8LQ6RVqdqhK58/bUrBcPovtS9Ysxs9HJ/LXpNVsX7oXQ7SB9Fn8aNWvCe0HtUjxeksSiSXkisgLaJEr0IKH2W+oL4ri/w9q6DQIn0H8oFUdoEekn4Nwq2a9jzeIx/eCGNZkHNdO30SnV8zprE+3QT6c0ZuWfRs51Z8WsRQtZFQSLHpeTexpXRS/bxDJ4DiaTCZCHoXh7umKh7cHu5bt57fRf3LzvzuAeam/bqfq9J7YzWEnLPDmAzbO38Hqn9YT/DA0yTa6uKnUbvmEEpXDUU2C47u92b/JD9X07Ib9ovP0snH3cic6MjouFXrItJvUbRtkdUUEICpCoU3BUlZf7zXhHToPbWvxtTHtJ7F31WHnjBRQsGw+StUqxuqfNiQq2NjZ8dCg3w89afthU3S2PLMX0DQNQ4xRipRJUgSpI5IENDUcLbASjsQeiEy7ELqsaDFH0MJjBc1cwL0RwqOL1QDSNw1VVRlQaRjXTt+wmkHyzboRVGpazuE+teiDaEHdk8vEpygI/zUWK/g6QmRYJEu/XcWa2ZsJeWR2Fso3LEXXER0oXac4dy7fIzI0kix5M1lUvbRG2JNwNv22g32rDxMaFM7tC3cwGkwpf5NLQzToVot9qw8TGfqs+muVRsF8/dt1q+domsLFM6WZOaogV0/fSJDyKoRAKIKeYzuTPrMfURHR5CuVm9K1ixMaFMZbmXslWhFW76pHUUSqXqfvto6mbL2SqTKWRGIPuTWTBITiheZaA2J22m+shZnPca2IcK2Ywpa9mmiaxoltp7l83Hq9F0WnsGTCSqccEVwrmRVYTXdIPrl4DS1iEcJ3tNNnRoZF8mmd0Vw9eSNerY8T289yfOsZhi/6mHqda8Qdj4qI5t61QFzc9GQvkPWZ1HdYpPm4uys5Cmbl6qkbfN7wa0Ifh8WtTig6gWrS0LvoMBpM6F105C2Vm5CHoQ4qsL56FC5fgINrjsU7dnirLxdPepC/RGSCVRHVBIYYjR8+jubmJctaLJqmoZk0fh1hVtKNXQHyz5mR4AchSZKlj40L8k7vRVhQuE05/+RA0Sn8/f0/0hGRvJLIFRELOLY944rIfNB+1dk3FC16J1r4fIg5hGpSOXPIk+Wz/TmwyXpGxqonvzm1R63FHEZ73BPzllgyOSMiF0qWrU6f9uvIxfw5abXVp19XD1f+vDMHIWDBl3+y/tdtREeYYwhyFMpGh0EtuXb6JpsWbCc60vzknjV/FkIehhAZFmX1pjh6xRCqt67I3lWH+fqtyU7b/aqgd9FhMqkJ3gffDEZG/XKdUlXDMRrN2TSKovLkoZ6xffJw5qB1fZzUQKdXGLJgIBN7TE/xejt6Fx3ropYka+aXRJJY5IpIUnFvBiFjzYJkFh9jdODR9o11QjTjDTCeB1zBtVICMbSYxzPQx0zDZBLodBqKDkpUCqN0tTAWTcnC799ZrsFjiI4vpKRp6tNqxNdA8QG3ugjlmSMjXCtBxqVooT9CzG6S5ZFTu41mvILQF7D4sqqqnNh+ltsXAvDwcadK8/J4+XmyZvZmm0vwhigD6+dtZcOv27h1ISDeTenO5bsWA1rvXb1v19z5XyyhZtvKrJm10bZqpiVi71dp9lHkGdZEvUIe6xncvgBFykZSpVEolZoU5+8fr7Bvg69NFd3UwmRSCbz5MFWK/j1/7TVNkw6J5JVBOiIWEMID/L5Fe/Ix5m/r57/cdaDLivAe9JKse3loxttoISOfZpvE4o7m1RPh/RFC6AkN3I2XOg0Ane75LBHzv+8Mus+xXd6cOehFxbqhtPrfQ4qVj8DFDTy1dqhBxRGendDQQfBwUO/ybF3bFc3rfwjvTxDCHJQnXEohMvyCpj5GC/0BIpfjmBqtlTlqGoFnevHf+fHUaFc5XiDfqV3nmNRzBvevP4gteIreVU/T9+oT+jjMZr9CJ1g4dhkRIRZUS7VnYzvLzXO3uXc9kFsXApyORdC76K2mFr9aCC6c8OTCCU9Wzosm9HG6l21QHDqdQkxkTJIVdu2hKIJC5fPz56TVrJm1icCbD3D3dqd+l1p0HNyKHAVlIUNJ2kVuzdhAiz6IFjYDDLEpuu7g2d5801USkXL6CqOZAtEetQP1MQlv9ALc24N7U0yP3o/ngLyI0QB7N/gSFOhC216PUNWEYlRxfSZwAp/i2RPFd0RCG8PmoIX9YPkcJ/mgUWEe3c/Ml399Rpm6Jbh49Aqf1PwCo8GU5kradx7ejoP/HuXaGcvF7iQvl4HTe7Ft8R7OHbhgc/VJKObgWdWo4pXOEyGEOb7EQbLkzZRg9UWnV3B1d2XSllEUrVwoKdOQSJxCZs0kM5r6GNRw0GUya3m8gaghEyDid2ytNsR+kuytCAc/1uGXIfGrFiAQmXYidPG3eDTjTbSHDZPQ7zO++zgX25ZnROeq5+fDE5k79A+b5dPh5aW7Fq1cEHdvd05sO5PqY6cZBHj5eRIRHJmmUo4VvYLqwErI89tq7l5utP+kBV2Gt+P07vMEPwghc25/gh+GMr7zFECLW12JPS9fqdzcOHfb4udT0SlkyJaehdd+ciq9VyJJCs7cv1/+JuorgFAyIPS53lgnRNM0iPwbe1seQth3QgA8vFTUpPghCIham/CoPvdTGfukY4g2l2c3xhiY9dkCDq07bnfr42XdAM8fvvxqOCEpGbKgQURI2nJCwPGihc+3iwqPZsk3K/m2+3TKNSiJb0Yftvyxi93L99OyXyNqd6yGn78Pnr4elK5TnBGLPybgyj2rY6kmlYe3H3Fkw4nkmJJEkuzIGBGJA8TEpSonFZMRXN2SerNQ0NTHFu9rBtcvCb51Ef9M5xLduyFGcGyXWedDU+HoplNxr/lmMNK402NKVwtDCDi135uNSzMQ8vgl/imlrXuvdVLYzrS0Zebu5UZ0ZEyibdI0jb0rD/G/Ih9z71ogOr3O7GRpGkIRfDijNy3eN4sAXj5xLYFGyovo9DouHr1KlRYVEmWPRJKSyBURiQO4gkh6hpCmEScpnjRMCCVh8N3Fo1d4J98nvFPGhX0bE1e4TVXh3wUZCX2S0LEoVyuUPw6do9eIu1RuEEql+qG8N/wufxw6R/naSVc7lbweVG5WjpgoQ7I4Rveum6tFm4xmYTRVNW/LTO03h8MbjgPg4mrfCdY0DRepoCpJo0hHRGIXIQR4dMAsXZ84nj7MsXpRW3ApS9I+enrwaBnvSFBgMEMbjY3LXvnqf3m4dNqdB3cTfklrz61gqyqYTM8cpB2r0vHLuISFBDPniOGrBddwdTOnI8duQyk68wrPmAXXyJwjMbVvXhIyszMejlbYdYTcxXIkn5qqFV8mVgQQIFfRHGTO7W+zG9WkUqW5E4KBEkkqIrdmJA4hvHqjRf4LWggWs2aEL2jBNvvY9Hcx2nzyFUK/C+3Jh4m3xWcwl44/YuW0ZwGk6TL7ERYc/twXt+CjZoUBsxPRpMtjard+greviTvX3DCa0lOm2XdE3F/N4XUbefJAz5Zl6bl82rKgWst3H6J3MTshL6LoQK9ptOr5kHnjnauG/NJIO7sYaQKT0X7QkqM6LbXeqsayH9Ykh1lWUU0qp3f/R3hIBF6+nnQe1o5p/edabKvoFMrWL0m+UnlS1CaJJLFIR0TiEEKXFTL+iRY8FAzHn3vFFTy7mINEH7fH2h3OaPSg8Qe/o3f1AJqgeQ18WizQEgrgAfoCYDz13OEsCO9P2Px3Ria/NxxFJ+KyB2JruzyPqpof++/dcuO3Sdn4bdKz7RxFEfwdWATf3GO5/7gc80cvsTn/Ko1C4rRQLKHTQ9XGIa+OIyJxCqGApjq2yuHh5UaxaoX578DFFHf4YqIMePlCy76NuH89kD8nrY7TLIl1nApXyM/IJZ+krCESSRKQ6bsSp9EMF8zKqsINXKvFqZ2q0fshqB/wgmiXyIzIuNic1fJ8PzEn0cJ/h5h9T1daDIA3eHZAePVE6HKgGW+C6SYIb3ApxZ1L9+lV/BPUZNh/r9G2MmNWDAFgcL3RnNxpPcD1173/kSOf7a2Xuzdc6VmtWJLtkry6CEXQql9jytUvxVcOSO67e7sTHRGdMJ7Egdo06TL7sfTO7HgpuTfO3WLd3K0EXL2Hdzov6naqQaWmZVEsi/VIJCmGlHhPYczS43vRotaDFg66fAiPt96YarvCpQi4FElwXHGrhpblKFrkeojZY65E7NYU4VYdIRJ+EQrXMgjX7+N+tyRLLfS54TkH5t+Zm0ARkAyOyNHNJzGZTOh0OtoPamnTETl3xIssOWPQW4n3Mxrg3BHbdXKEIugwqCUnd5zlyvFryeJMSVKOWPVcZ9BUjWtnbpKjYDaHdGWiwqIsHlcUBZ1ewRBjtOiQCEXQ+oMmCXRB8hTPxQdTejpntETykpFuspNoahDao7fRgnpB5EqI2gDhs9EeNkALs7xH+yYhhB7FsxVKuokofuNQ3GtadEIsn2s/gvL07v8cEohyhKjwaB7efgxAlRblyVM8J4pi2YZ/F2S06oQA6F1gy7JcKDorcxXw7YYv6PtdD7oMby+dECcoUTOh05vSmJ2IxJwIHt7uPLzzCJ0+aV+vHt4eCCES9CMUQfFqhXn789ZJ6l8iSSukqCMyfvx4qlevjqenJ+nSpUvJoVINLWggGM8+/c2E+XHF/K8W9h1aZMoGqb3pKEn8cn+Raf3nEBNtQKfTMWHDF+QqmsNiuwvHvZj3jVnJ1fRceRbj0/8vmJSdtoOG4Z3Oyxy7+9SfUXQKik7h8wUDKd+wNAC12lehTN3iyTqP15F8pfMw7I8PMUSlfj2cRO9Ya1CrfVXSZ0mXpMwZ1aQS8iiU3t92o2rLinEOsn/ODLw3visTN32Jm4dbovuXSNISKbo1ExMTQ8eOHalWrRrz5s1LyaFSBc1wCgyHbbQQaOGzwL2FrHyZDERHRrPjz31sX7KHkEdh5CycjVxFsnPx8GWrT6uKIvBK70XoI8cE2A5tOMGMD+fx6Zx+ZMqZkdknJ3No3XH2rDzI/esP8E7vRfXWlTi49ih/zTjA5dOetO/zgNLVzP2fPuDF8jmZOb7blyL7VvD7lRlsXbibQ+uPYYg2ULRyIVq835DMuTMB5uq9Mz6cx8kdiRdce5F8xSIpU90ssHbmkBeXTtneInIUv8y+lK9fih1/7XspYmFVW5ZnyYSV3L4YkOpjJwZFJ8iU05+6nWsQ+jiMucMWJqk/nYuO0MdhjFkxBJPRhNFgxNXdVX63SF47UiVYdcGCBXzyySc8efLEqfPSWrCqGvojhM/CrtR5pp0Inax2mRQe3Q1iSP0x3LoQgFAEmqrFZQMInUAzWf7YCkUw8+gkshfMwv+KfMyjgCC7Yyk6hSW3ZpEha3qrbe7ffEi3fB/YDSCceXQSBcvl4/KJa+xYupdz+y8SHRlDplwZqdqyIvdvBLLw62V2bXKEdP4GRsy8QZka4XGS+YoOzh/3YPz7eQm845qk/otWLsiFI1fSlGJpiuBAYKhNFECFHIWyMWH9SLLlzwLA3KEL+eu71YnvVifoMrw9Pb/unATjJJKXwysbrBodHU10dHTc7yEhIS/RGktE45ASlPYKCVulUcZ2/J6AK/eAZ9Ldsam6mkkzS16ralysRWxsxtDfBlKgTF4Apuway0fVR/Ak0PbnSDWpHFp/gqb/q2e1ze0Ldxy6WR3fdoZZg3/j5Paz8Y5fPHKFvSsPOVSLxxHcPFS+W36F7PnMfy/P65sULBXJ96su80HDwoQFJ+JPXIDeRc/5Q5eTx9g0TObc/rh6uBJw6W6i4nb0rnqa925ApablqNSsbLzg0V4TuuLu5cbCscsStU2jmjTyl8ptv6FE8oqTphyRCRMm8NVXX71sM6wi9MXQsLdfLdC0iDdSuFLTNM7tv8j+fw4THRlDgTJ5qdu5Bu6ezu1lXzhyhbP7Lths45XOk9b9mnBs6ylUk0rp2sVp2a8x7t7ubF20m5ioGAqWy8egOf0Y3XaS3TGjwqI4uvkkm37bQeCtR2TMlo5GPerGpT46qry5avo6Ht55ZPX15Fp/rNcuiFwFoy06Nno9+Gcz0LTrY5bNzOx85xoYY1I/LuNlkLdELmq0q8KUvrMSdX7f77rT9sPmFl9TFIXuozpStEohRjQbn6j+j209Te2O1RN1rkTyquD01syYMWPsOguHDx+mYsWKcb87ujVjaUUkV65caWZrRtNi0AJrPlUQtfa2CVAyIPzXIpQMqWneSyX4YQij233H2b3n0el1CAFGgwlPXw9GLPrYqWJbS79dyfwvl9p9ipx9YjL5S5vVImOiDfz8yXw2zNsWTyUzX6ncXDt90+6YuYrm4Nb5O3EiULH/lmtQiq9XDwXg7ay9ibSSbpnaTFp2mVJVwi0qvYJZuv7GRXf61U/9jJNXibqdqvPp3H68na0PURHRTm3R5CycjV//+9GhmI0PKnzO5ePXnLbP1cOVf4J/T1YJeokkNUjRrZmBAwfSubPtPcu8efM62y0Abm5uuLml3UhwIVwh3RS0oN5YjxPRQH0MEX+Bd7/UNO+loaoqI1tM4NLxq0B8uezI0ChGt/uOH/eNp0jFAg71ZzKpDu2AxToqmqbxTdep7Ft9OEE8w/Wztxwa89b5O/H6jP33xLYzdMvXH/8cGchWIAtXT96weL6iU8iYPT2PAoKSr86IDdJlNFp1QgAUBfwypOCqRlLjKtIAQhHkLZkbD28PBv86gPFdpiAUYfn6PT9fARmzpeebdSMdDhx1c3JVMJaYyBgiw6LM2VgSyWuK046Iv78//v62Cyy9zgi3Gmi6fGCytX+uoUX+jXjOEdG0GNAiQPggRMI7iKZFgSkQhCdCl/rvb0xUDJt/38naOZu5d/0Bfv6+NOpRh5b9GuGbwcfmuce2nObCYcvvh6ZpaGgs/XYlo5cNdsiW4tUK29UK8fT1IFdRs5z6fwcvsXflIcvjJzHQUtM0gh+EEPwgJJ6eQ2wAbewNqkT1Iig6hQe3rG/LJCcB193IWSDaquy8yWRWek0pBJAptz+BNx+m2BipQZ4SudA0jTodq5Euky8Lxy3jxLYzAHj4uNOkZz2y5c/C1kW7uXv1Pr4ZfWj8bl1a9m2Eb0bbfxfPkylnBodr1TyPm4crHt7uTp0jkbxqpGiMyM2bN3n8+DE3b97EZDJx4sQJAAoWLIi3t3dKDp3COPCkabqFZgoENQgtbCZEbwRMILzRPN5GePdFKOnNAmlh0yFiObHS6JpLWYT3QIRb7RSdRSyRYZEMbTyW/w5eQmBWgwx9HMaCUUv5d9Ympuz6mqx5rcca7Fl+AJ1eZ7VwmGpU2bf6MCajyaEl5rL1SpKraHbuXLpn8YtbUQQt+zaO01HY8vtOm+MnF6bnnCNN1eLiMzTAN5MP7qmo67B+cQaqNbEehKvTwbqFGVNsfE9fT979uhPf9fwpxcZIaTRV46v235G9YFY+nduPMnVLUKZuCUKDwogMiyJ9Fj9cXM0qdu0/bhF3nqqqnNlznmNbTnP/RiBZ8mSiZM1ilG9YyqqUeuN367Ljz31O2afTKzR+t67clpG89qSooNmoUaMoV64co0ePJiwsjHLlylGuXDmOHDmSksOmPPoCOLJ3oIVNQXvU4ZkTYj4IEb+hPeqAaryC9qgTRCwhXn0Wwym0oD5okStTwvoEzPl8IRcOXwEtvpCTpmo8vhfE+M5TbJ4fGR5ltyCYalKJiTY4ZI8QgjErPscnvXc8pVLxVNSpVO3ivPvV23HHnzwITpXtkBfRtGfBp3tXHGLb4t2pNvahLb7s2+CLpbddNcHJfV5sX2k9HTmpVGtVkQZda+GTMQ08UCQxMvzu1fsMe+qIA/ik9yZzLv84J+R5Dq0/zjt5P+CzuqNZNG4ZW/7YxaJxyxnedBz/K/oxN85Z3gqs0LiMWdDOQVsVnYKXnxedh7dL9LwkkleFFHVEFixYYF6af+Gnbt26KTlsiiM8u+DQBnnkasyrJy8+qZvAdBeCBoDploXXVUBDCx6FpqZsCnN4cDgb52+3eiNXjSrnD13m0rGrVvvIVSQH9r5hM2RL51T2TO6iOZh7+nu6jmhP1nyZ8UnvReEK+fnslw+YsGEkru7Pth38c2RE0b38PKXULB+paYJx7+flz+mZCQ959mccFSFYNc+fL7rlx2RMuffEN6MPX7b51mHhuBTFzvuu0yu4urtYld/XVA1VVfl1xGKb/RzeeIKRLb+JKwvwInev3uezuqMJuv8k3nFDjIGFXy8z/w1ZsDV7way4ecbfRitRvQjT9o8nc643dxtc8uYgq+8mAk3T0O6XxqwrkpIIhM+XCK9uKTbCyZ1nGVxvjG0rhKD/1P/R9sNmFl9/cPsR3fJ+YFWHQSiCd7/qxDsjOyTVXItcOXmdfuWGpEjfrwKu7ioFSkQiBFw9505URMov5Ss6xfx3kIpiZ3FxOU7g6u5CvS418c+RgcXjV9iVbl9yaxb+ORJuaWmaRu+Sg7j53x2b5yuK4J0v3qLHGPOKndFg5ItW33JsyymLtjfqUYfPFwwkMiySUzvPER0ZQ96SucltpdSARPKq4Mz9Wxa9SwRCCHCtie1VAGHndUfQo5mcT/lzBp21Im0vYLWYG5ApZ0Y+mPo/4Nn2Sdx5ikKh8vlp/0kLS6cmCwXK5KV5nwZJf7tfUWKiFP476sW5I16p4oSAeastNZ2QUrWKJmq8H3Z9zeB5/RFCOFSnaM3sLRaPXzlx3a4TAqCqGlsX7Yr7fcvC3RzddNKq7Zt/38l/By/h4e1BlRYVqP1WNemESN44pCOSSIRXT2yvCSfHl7QKImXT9gqWz4+Hj+2ofE3TKFu/pM02bQc246uVn1OofP64Y97pvGj3UTPe+qwVB/49anX//OqpG/zQZyZd8/Sjc86+TOj2Y9x+vaN89HMfarWr6tQ5klcEARGhUQmcXEeI3cLzz5HBoarN63/ZgsmUMOj50V37pQJiCQ+OiPv/vzM32rRbp1dYO3uTw31LJK8jaUpZ9VVCuFVB8xoA4T8RV2wiAbacEQG4AAYb7UwI96ZJM9QO7p5utBnQjD8nrrK4bK3oFMo3Ku3QU1r1NpWo3qYSQYHBRIdHsWbOFlZNW8fyqWvj2pSoUYTB8/qTs7A59Xbrot1MfHc6iiLislJ2/rWPbYv38MGUnvGyFWxhiDZydMtJh9pKXi1K1y7OtVM3nF4R8c+RgdzFzJ/bOm9XZ/rAeXYzqx7fe8Klo1cpWrlQvOMZszkW+CuEIEehZ3Wmbl8MsGm3yahyw4GVFonkdUauiCQBxedjSDcbSEzapgae3Z/+39ITkwJudREuKV8u/t2v3qZ620rmUZ9uwcQ+xeUtkYvhf3zkVH/pM/ux6JsV/DVpFdGR8evu/HfgEh/X+ILAmw+4fekuk3rOQFO1eKmxsf+fOWgB5w5cdGjM3csPEBESab+hk2TMnj4uTTcxT+SSpNOoRx28EiHo1Wlo27jaL97pvKja0jF134jQhOq5BcrmJU/xnHa3/zRNo9UHTeJ+d0QDxMsveaolSySvKtIRSSJC6ImXemsV3dMfBRAI70EovkMRflNAxH5Z6Z+2AdzqmF9LBfQuekb9/RnfrBtBjbaVKVguHxUalmbYHx8x/eAEp4SbAK6ducmGedssZpGoJpXw4HCWTlzNmpkbbX6x6/QKq6avc2jMu1fuo3NJ/viIRwFBcfN47avQplGMBpM5e8QBPzBWdO6tT1vRZkD81cRWHzS234EwS7eDWS8kKDCY8ODwuIBtm6cKqNikLPU614g7ljl3JrtD5ispC9tJ3mzk1kwS0aL3YX4b7YiceXQAzYDQ5wKPDgid+ctOeDQHtzoQtRbNeMWsrOreBOFSNMVtfx5FUcwVRJuWS3Jfm3/bgU6vxFvleB6TUWXTbzvIWTibzX17k1Hl5I6zVl9/Hu/0XonWEtG76uMVeUuMAqYkZdC76vmx3xxzoKkVP1BRBG5e7pSsUYSchbPTrHcDizf3cg1KkTlPJgJvPrDYl6JTqNikDOky+7Fo/HJWz1hP0P1gAIpVLUSX4e35Zt1Ipg2Yy72rgfHOdfdyo8OglrzzRYd4AmR3r963O0dbRRIlkjcB6YgkGcfUPIV3f4Quu+XXFC/wfPu1Sfp4fO+JXU2N6Ihoh8J5Ha3lUatDVWZ9usChtvEHgH7fv0udt6txYtsZzu6/wLZFewh5FOp8X5JkR30a02HJYRWKwDd9DP8bqdG40wN0yn+gy43w9EHTspprQz2HoigM/W0gw5qMxWRU4zmbik6gd9UTERpF1zz9CH4QEs9ZuXDoMqPaTGTAtPf4/dIMzu2/yL1rgRgNRgqVz0+uotktCqCFPQm3O8cnD1JWK0giSevIrZkkIlzLY3c1RMkESpZUsSctkCFrOovl6Z/HzcOVio3L2kwL1ukVKjYpa/G1R3eDOL7tNOcOXMRkNJEpZ0YKPpexY4/Yceu+XZ2W/Rrh5uHKih/Xsnr6eumEpAHcvcxxV9a0aRDQqGtuFp8IoFnnC+jEHXNVbONZtJARaI97mes3vUDp2sX5ce94KjcvFxfzY14B0zDGGDmz+z+CA0MSrJjE2jHzk/k8uPWQEtWL0OCdWjTpWY/8pfNYdELAfpCrTq+QObcULZO82UhHJKm4NTA7GlbfSoHwfNdiobvXlUbv1rW6LQOg6BUa96xHm/6NUXSKVadFVTXafdQ83rEHtx8xpv13dMnVl88bfs3H1UfSJXc/Vs1YT4s+DR2yz8PHnWJVCjF80ccMX/QxOp2OX4Yt4sLhK6mqjiqxTlS4HbFATaPLgG3odaHEz1h7+n/DYbTQHy2eWqh8fsauHsbKR/MZOKNXgorLNhGCdb9std/uKc37NESxEeRsMqo0/V99h/uTSF5HpCOSRIRwQaSfBcKTuEBTIO6tdWsAXu+9DNNeGvlK5qZpr/oWHQxFp+Dt50XnoW3InDsTo5cNRueij7cyougUFEUw5NcBFCiTN+7443tBfFh1OPvXHIkXOBp07wk/ffQrAVfv2y+3LqBi47IMnN6L+l1qoigK4SERbPh12ysdF/KmZfSUrhZO9jxhWN8aVSFyKZpmPZDcy8+LDfO2OfXeaarKjXO3HW7fZkATshXIalFMTQhBnY7VKFkzdePBJJK0hnREkgHhUgrhv9bscCjZQfiBS1mE3/eIdNOfZta8WXwy6306DW2Hm0f8ffpiVQvx495xcdkEVVtWYMGFabw9uDUFy+Ujf5k8tBnQlF/OTqFRjzrxzl3yzUqC7gdbDXD967vVvPPFUxl5a/cWDfauOsSH1UZwbMspAG6cvUVMlGMF+dIaru6ujFjyCYUrFHjZpqQqxStFYDLZy6UNB+MVqy8HPwzh8vFrNrKhNF7coxGKErdt5Ahefl5M2T2W6q0rxXN43Dzd6Di4NcMWfuRwHJRE8roia81IUpSI0EhObD9DTGQM+UrlJk/xXInqx2Q00Tb9uzaX7IUiqNmuCmXqlmDpxJVWi5OB+WlU76rHzdOVyNAou0JX9shVJDu3LgQkqQ9Hia25ki1/ZkYvH0KBMnlRVZUm+k6pMn5aoGP/QHqNvI8QtlexRMbVCJdiFl97GPCYLjn7xm8vNBq8FUTbXg8pUDISk0FwaKsPy2Zl5twRs5bJmBVDqNG2stM2P7zziCsnrqNz0VOiemE8vD2c7kMieVVw5v795j2qS1IVTx8PqreulOR+wp6E240b0FSN3csPsHv5Abv9aZqGIdqAITp5VkJSwwnxzehNpyFtMZlUilQqQNn6JVEU86Lm0aerO8mNh7c7A6e/x3f/+zlF+k8MQhFERJVFiPW2GyoZQF/Q6svps/iRPotfXIquEBqDp96iYccgVBMoCihuGlUah1CtaQg/fJabi2dKOyyM9iL+OTJaLKgnkbzpSEdE8krg5sRy+OtK1ZYVefvzNhZfu3HGch2fpJCneE5mHPqW9fMcD85Mafz8fajephJbluyhfmsvipYPR2/lW0x49kQIy9ksADqdjjYDmvHbmD/RVI367YNo2NFcU0Z5LtxLrwdNg0GTbxEiZsTTCXkRTX0MURvQTA8Ruizg3gyhyNVcicQWMkZE8kpwbp9jUu+vM5t/38nswb8TGR4/LdUQY+DIpuSvs3Pj3G32rT5M0L0nyd63UwjIXjArc0//wMLrP3NgzVEMUQbGv5+HgGtmBzW2Tp0xNpPevQ149bHbdcchrSlbryRCQNveD7FQ785sggCdTpDex3KBOk3T0MJ+QgusiRbyFYTPQgsZhRZYHS18vrMzlkjeKOSKiOSV4PqZmwghLBbme1PQNI3lU9dwevd/TN4+BndPN0wmE2M6TObo5pQp+Del72xMMXZ0clIYRVEYNLsveUvkYtey/XFbKY8DXejfuDB1Wj2hXvsgfNObuHPNjR2rsjNq9XhcHEiZd3VzYfza4az/ZRsFSn6EzuYpKhjOWH4pYh5a2PPpwrHvWQxa6AQQHgjPzo5MVyJ545COiOSVwN3T7Y12QmLRVI0Lhy+z/Id/eeeLt9i36jCH1h5LsfGiwhKKgqU2H0zpSdl6JQG4cPgKOhcdJoN56cIQrbBlWQa2LMsQ75y5wxbz/qRu6F3sf8W5uLrQun8T1HuugK04JAEi4RahpkWihf1kcwwtZBxa1B6Ea0nweAuhkyJmEkks0hGRpCkCbz1kw7xtXD93C3cvN2q2rULB8vnYsmj3yzYtTbFw7DLaftSctXM2v/a1cZ7PUHFx1VutOfM8q6at4+6Ve4xZOSSuAq9d3OpB9Gasa5NoCLe6CQ9H7zGnCtskBmI2ocVsgbDp4DcR4dHSMbskktcc6YhI0gwrflzLrE9/i7fysfm3nehddJhS+UYrBCj6Z0/eAC5ueowGU5qowms0mFj49TLuXL73+johAio0LE2mnM8yTSo2Lcui8cvtnqppGgfWHGXnX/up36WmY8N59UKLthwDAjpzFo5H84Qvqc6UBFABFS14MOhyIlzLOnGuRPJ6IoNVJWmCXcv2M3PQAovbLy/j5q9pMOrvz5iyeyyf/zaQcf8OY87J7/H08bBZHyc1WffLFnwyeFsXb3vF0ekU+v3QM96xEtWLUKRSQXQWlEpfRFEE//y80eHxhGsZhN93mJ/PYvt/+q+SAZHhN4SwoP2hT1jp14HR0MJ/ScR5EsnrR9r4RpW8kty5fJd5wxfx9dvfM+X9WRzfdjrRcRw/ffRrou3IkC29zZTKxKDTK2xbtJuSNYrSqHsdqrSoQM7C2Zm8bUxckTKdXnHIKdHpdSkiwR4REknlpuUQifREFJ2CdzrPZLYqIUKIRKmHthnYjLwl4gvgCSH4evXn5CxsuZL186iqxu0Ld5waU3i0QmTagfD+0Fyewa0xwvdbRKatCGuaJC4VQJcb575OTRC9XcY9SSRIZVVJItA0jd/H/MXCsctQdAqapqEoCiajiVK1izH2n2F4+Tp+g7OkcOkMhSsWYNya4Zzdex7VpDJnyB/cv/Eg0f3FonfVsy5yMapJJTIsCg9vd3R6HaqqcnTzKS4evkJ4SAR5S+Ti0d0g5n+xxOLKTawSakpQuVk5rp+7ReDNhw7FTrxIyZpFObP3fKLOdRRFEbi4uxATZXDqfVh8cyaZcloO6jQajPQtN4Sbduq+ZMufhd8vz3DK3sSgxRxGe/wusVsvjiEQWf5DCPk8KHn9kMqqkhRl/bxtLBy7DHhWsdSkmmMpzu69wLfdpjH2n2EO93c9CWJcik4hc66MpM/sR812VQCY/F7yqIAaDUam9Z/L5t93Eh0Zg6u7Cw271abLiPbodAp7Vh7k8vFrce2LVyvMtTM3iQyNn2mSkttKh9YfN8dQJGIIRRGc2XM++Y16AVXVGLNiCJePX+fk9jNomsaVU9d5cj/E5nln916gbifLjojeRU/bgc2YNmCu1bkrOoX6XR2LD0kqwrUSZFiMFvo9GA46cgboC0knRCJBbs1InERVVZvBgqpJ5cCao9z4z/EKpb4ZvBNvj0ml6Xvxy6inz5LO5jlCCLIVyGJ7W0WY9SvWzdtKdGQMADFRBjYu2E6fUp8ytMlYrpy8Hu+Uc/svJnBCnKFwxfyJOu/B7UeJOk9NDgfJwR2XdJn86Dy0LRM2fMHX/wyz64To9AqHN56w2aZht1pkyZ3JYmVbRafg5etBqw+aOGZgMiBcy6Jk/AORaRekm4vtr1cN4dkjtUyTSNI00hGROMWt83cItLPtoSgKB9ccdbjP/GXy4ObpvIS7UAQVGpWmYtOy8Y4361UfWyEJmqbR7Yu3bHeumVcyXqz0azKq5po3WjKudDxVDh29bHDy9JcYExRh8YZuFwfeAl9/H/KWfBbrYYiOsd+tht1ChB7eHkzePobcRXMA5lgcnYs5Vsg3ow8Vm5TljzF/8e+sTUSERto3NJkQuqwo7nUQft9g9tSej196+sF0awoeHVLNJokkLSMdEYlTGKIdUNkUEOmEEJbeRU/nYe1stvH08YgX8Kl31dOybyO+Xj00gU6EhoatyKeSNYtSonoRCpXPZ3PMVAuf0iDg8j1GtpyQOuNZMkHV0Ewa9brUSPbA2sIVCsQJi105eZ3xnac6ZE/RSoXstsuaNzNzTn7Pd1tH0+nzNrR4vyFZ8mTiSWAwu5YdYMP87UwbMJdO2ftwwAnnODkQHu0RGRaCWx3inBFdAYTv14h0UxAOKL9KJG8CMkZE4hSu7nrzQ52Ne7RqUlk0bhkXj16hy/D2lKxR1G6/XUe0486lALb8sSvBa9kKZOHHveMxGYxmZU29juLVCxMdEcP9Gw/IlMsf96crKkH3n/DbqD9tjuWTwZv3yw4mJtL+k3lqcv1s8heucwZN02j7YTO2L92brP0qOkHAlXtM6DaN8wcvOXSOq7sLjXrUdqitEIKy9UpSqlYx3i/zGQ/vmLeqnl9RiY6IZkz775h+4BsKlU/cFlhiEK6VEK6V0DRzEKsQ8itXInkRuSIicZjLJ67zfpkhDi3Haxoc2XiST+uMYtey/XbbK4rC5wsGMnn7aKq3qUT+0rmp1Kwswxd9zLyzU0if2Q//HBmp0bYyJqOJzxt+Tdfc/Xiv2Ce8lbkX0wf+QsjjULb8sctu7MP+f468NCckQ9Z01l98yflrbp6uFCqfnzb9myafNokAY4yJT2p+wcUjVxw+rc3AZnj5eTk11L5/jnDzvzuYjAmzVsyLWxp/TV7tVJ/JhRCKdEIkEitIR0TiEIYYA583/Mruvv3zqCYVTdOY+O4MwoNtS2CrqsryKWuY8M409q0+zNVTN7lw6Ap3Lt6Np0Hx76xNjGn/HVdP3Yg7Fh0RzZrZmxlYeTiXj19LM4JjscTaU79rTRbfmkXN9lVQdGlPhSxTTn9cXF34YGpPugxrh6u7S5L7FJgLFQY/DHVKATZP8ZxOj7V31UGb195kVNmz4pDU7pBI0hhp6xtbkmbZt+owoY/DnD9Rg5ioGLYu2mO9iabxQ59ZzB78O48CguKOhzwK5Y+v/2Z0++8wmUwEBQbz08dm4bMXA0VVk8rdq/fZtmSPU85SSuOXyZfyDUvz1arPGfr7h+h0Om6ev4Nqsn0zdPdyPng3qdy5fJdHdx+j0+l4b3xX/ro7ly+WDkq0LUIRePi4c/XUDadl6AtVcH77JCo8GlW1PY4xxmi3jUQiSV2kIyJxiGNbTiX6XJ1ex7XTN6y+fmL7GTbO327xNU3TOLT2GJ83/Jqp789yzMmwcY9PjMJnUuj25VtMWD+S6q0roSjmPzd3BzKEMmRLT8UmZVLavHhoqsYHFYay+fedAHj5eVHn7eqky+zn0PnPy64LReDu6cb4tSMIC3LcgVV0CiWqFyFfSedl0/MWzxX3HltEQI6CWR0vgieRSFIF6YhILBITFUNM1LM4iiQVVtPAzcPV6strZ2+2Wzvk1M5z7PvnSJLiKIQiUr0uy+Lxy7l9MSDesVrtq9jMTFF0CrU7VHUsQymZCbr3hEk9Z/DnpGexFP45Mti0VwhBvlK5ad67IQXK5KFo5YL0/Lozv12aTskaRcmQNb1DYys6Bd+M3lRvU4mpfWczrf9c9qw86PAKV/M+DbCVLiUQtBnYzKG+nOHutfvsXn6A/f8eIeyJvSq8EonkRaTEuyQOTdPYvnQvy77/l0vHrgJQuEJ+Og5uTXhIJFP7zk5039/v+IrStYvHO3bz/G3mDl3IwTXHUnTfXqfXYTKa8PX3ASDkoTPVUpOOm4crEzZ8QalaxQB48iCY94p+TFhwRIItJqEI3DxcmXd2CjM//Y19qw/Z3cZJEQTUfbs6xaoWRgjBz5/Mt9n2k5nv0+L9RhZfnj34d5b98K/N4bzSeVKleXkOrjtG+JOIuNpBJqOJLHkzMWH9SHIVyWHX7FUz1vPTR7+iKCJe0LJQzJk149cOx8U16bEvAA/vPOKHPrPMwmtPh3Jxd6F1v8b0+vadZBtHInkVceb+LR0RCWB2QmYP/p3lU9bEq40S+4XeYVBL1v+6lYhg54ShFL1CkQoF+HHf+HjbIrtXHGBsxx9SJXCw4+DWFKtamJBHIUztO8due6GAp68nEcGRyWKfEOCTwYclt2fj6uaCpmnM/HQBq6atT9C/p68H49eOoGSNouz8ez/jOv2Q5PETi1AEaKB305M+kx8PAx4nWBlTdAq5iuZgxsEJcVtOF45c4cKhy+j0CuUalubrtyZz5cR1q+PkL5OH0csG836ZwRiiYhJkPSk6hfRZ/Pj1vx/x9LFQ/fYFDq49yuIJKzm37wJg3uZqO7AZHT5tiatb8jgHIY9C6V9xKA/uPEogeieEoGb7ynz512epvhUokaQVZK0ZidOc2H6G5VPWAPEDQWNvCsunrKH/j//jl6ELiYkyJDjfN6M34cGRqKqKUAQCgcloolC5/Hz9z9B4X8ghj0IZ12lKqmUv5C6agyvHr/Hnd46lbmoqhAdHmG1OBhM1zTznFVPX0nloW34ZupCVP65L0E4oAld3V7LmzQSYM0c8vN2dEodLTmI/B4YoAw9uP6J49SKc3fdcgTwBlZqWZcj8Abh7unHn8l3Gd5nKpaNXEcLmLkk8gu4Fs2r6OgwxBoup16pJ5dHdILYu3OWQZHuVFhWo0qIC4SERGKIN+Gb0sR07kghWTlvHg9uPLG5ZaprG7uUHObv3PCVrFkvWcSWS1xHpiKRRNE1j17IDrP5pPVdP3sDV3YVaHarS7qPmDpVAd5Z/ft6ITq9Y1GAAcyDi2b0X+OXMFFbPWM/2v/YRGRpFlrz+tPuwOc16NeDJgxA2LdjBjXO3cPd0o2aHqpSrXzLBU+Hf3/+TtJgTJ/ll2EJCg8KdG1NLfmXV30b/SdHKBflr8j+Wh1Q1Qh6HsmTCSio0KsPXb3+PyZA2MoCEIvDwcuP3yzM4s/s8mqZRsmZRshfICpiF5AbV+pLgp9tezrx1QfefsH3pvgQrC/HGB3YtP+BU7RhnKkA7y/pfttr8POn0ChsX7JCOiETiAHJrJg2iqirf/e8ntvyxC0WnxH3h6fQKil7HuH+HU75BqWQds1u+/ty3U0MmR8GsLLg4Pclj9Ss3JEHBuJRE0YmUjbOwozQb10wICpbLy5WTttNZXT1cMRmMVp3Cl4UQ8G/YQtw8Emb9/DpyMX9OWp1oB9PT14OIENvbfkUqF2TGgZcng/88TVw62Z1rxSZlmLD+i1SySCJJWzhz/5ZZM2mQDfO2xUmdP/9lZzKqGGOMjGn/XbIX8XLztJ7VEourjcwXZ0gtzzdW3OqlBHtaQNM0rpy8YbMgH0BMZMI4ibSApmFxWw5g0287Eu2EeKfzIl+p3DbFyHR6hQJl8iaq/5TAL5PtL1adXiFDNseyhSSSNx3piKRBlk9dY/VmpakakWGRbF20O1F9R0dGs2vZflbNWM/uFQeJiTbfWGq1r2rzRqAoglodqtrs22Q0xROL0jTNonhUhUalE2X7i+hd9eQtmStBaqkQgIBMuTImyzh2ccJncOhmLRJX2TdvyVzxqtwmNy7uLmxZuMuiSm5IYsTuMG/5tO7fhLYDm9l8b0xGlZZ9LWflvAya/q+eXRXXxj3qpp5BEskrjIwRSWNERURz8787NtsoisK5/Rdo1a+xU32vmb2ZuUMXEhFiDsTUNA3vdF58MKUnLfs1YsW0tUSHR1vMWvDwdqfF+w0T9KmqKpt+28nKH9dy9dQNFEVQpHJBXNxdOLfvAsYYExmypqNxz3r0/LoTOr2OriPbs3zKmiTHiRhjjDy++wRN1XBx1ePp54kQgix5/GnWqwHps6ZndNuJSRojJbC15aLTK/hk8OZJYIjdfhSdoHj1Inyx9FN0egU/f19+GbqQW+cDUkRd1hht4OdP5vPriMWM+vszKjUtF/daphwZCbhyz+G+YrOxKjQszTtfvoXeRce+1YfZ/ufTgntPP4KxGVzvfNEhVYvV2aPdxy3Y/PtOgu4/SXA9hSKo3Lw8pesUt3K2RCJ5HrkiksZQHCzB7qw65LpftvLjB3OICIkAngVihj0J57v//cSpnef4duOXeKUzFxpTdErcE593Oi++3fhFAmGq2FiW73v9zLUzN58e0/jvwCVO7TiHMcZ8M3x87wlLv11Jh0zvcftiAN5+Xgxf+JFVcTFffx/SZ0vn0LxCHpmDIw0xRsKCwogMi6L/j+/R4v1GlGtQEndvd8ffJCcRQiSqZkz+MnkspnUqOgVXd1fqdKzuUL0cTYUeo98mY7b0pMvkZ65C26BUop0Qj6fvlbWxNQ3QzFLqX7aeyI1zz6oFt3i/oU3RM4Am/6tH9oJZ8cvkS7Gqhfn8t4GMWzMcVzcXFEVh6B8fMmDqe2TLlyXunPyl8zBi8Sf0/LpzouaUUqTP7MfUPeMSBKPqXXS0eL8ho/6WqbsSiaPIYNU0yMc1RnL+4CWbcQLFqhTi63+Gki6TffltQ4yBTtnft1krJmP29Cy6MZOYKAM7lu7l1K5zCCEoXbs4dTvXsChLvm3JHia886Njk3qKp68Hv12aTrpMflw9fYMZH/7Kuf0XMBlNeHp7ULNdFYpVL0zYk3DmDVvkVN9gvolmyJqOhdd/RqfTMf+LJSz+ZoXT/ThC5ebl8cvo/bS+jeOrO5Wblyd7gSysmbUJ4wtZMfnL5KHNgGbM+HAexhiD1ewTVw9XPl8wkDodq8U7rqoqvUt+SsDlu04Hu/Yc25ni1QqzZMJKjm89bX8ezcoxfu0IACLDIvmw2khunb+TYKVLCKjbuSbDF37k0M1Z0zRCH4eh0ytOV+B9Gdw8f4dLR6+id9VTtl4J/PzfnO8qicQaUtDsFWff6sOMbjfJZhuhE+QokJUZByfY/bI+uO4YX7S0n20wefsYytQp4bCdH1UfyYVDth0mS3Qf3ZEeo9+Od+zxvSAm9fyJo5tOOtWXNb5a9TnVW1fi/OHLfFhleLL0+TytBzRh4LRe3Ll8j/4VPic6MsahrSahCGq0rczoZYP5Zdgi/py0Km6bDJ5tWdR+qyr7/jmCalLj+o3dpqjftRafzH4fNw9XIkMjcXF3jSfUdffqfQbXH0PgrYdx1W/tGwYurnrG/jOMCo3KMLTJWI5ttl1fSAjB+pglcatzoUFh/PzJfLYv2Ru3KuPp60G7j5rTfVTHOLXUtITRYOTE9rM8CQwmU86MlKpdLNk1RySSNxEpaPaKU71NJd4b35VfRy622kYzady5fI/fx/yFm6cbIQ9DyZI3M43erYN/9gzx2joqaR78wH5cwvNcPXk9UdkdG37dFs8RCQ+JYFDtUdy/Huh0X5ZQFMGq6esxRBkIfpy8cu46vcL73/Wg3UfNEUKQs1A2vt/xFd90ncrti3ftnq9pGhUbl+G/g5f4c9KquGOxxL6fu5YdYPiij7ly/BqH1h/HaDRRulYxWg9oSvaCWVk2+V/+mbmRJ4HBCEVQpUV5ugxvT/GqhcmWPwu/nPmBrYv2sPOvfdy6EMCjgMd2DDNvb3311vf8eWc2EcERDs3lyMaTVGleHgCf9N4M/e1D+n3/LldP3UDvoqdwxfwW030dISI0kn2rDxN0Pxj/HBmo1rqiQwUDHWXzHzuZM+QPngQGxx3LnNufD2f0pmrLCsk2jkQisU2KrYhcv36dsWPHsm3bNu7du0f27Nnp1q0bI0eOxNXVsTTQN3VFJJZRbSdy4N8jdsWhdHoFhPlpWdM0en7dma4j2se9fnLHWQbXH2N3vOkHvqFo5UIO29c2/buEO3DDehF3Tzf+DVsY9/uyH/5lzpA/bD65x8nOO6HZoWkaelc9xhj7xeNiVyIy5/Yn8OZDq+28/Dz5M2BOgpurpmmc2nWOPSsPsmraestj6BR80nvzx9UZTBvwC9ttbOno9ArV21Rm1N+fxTseGRbJ4HpjuHz8WjwnMDauY/SywVRvUyneOd/2mMb2JXsdztj5+Of3ObLpJHtXHrTbvP/U/9Huo+b2+3UCTdNYOW0dv45cQnREdJyWjoePO/0mv0vzPgmDpp1l44LtTH7v5wTHzTtHgrH/DotzsCQSifOkCR2R8+fPo6oqs2fP5uzZs0yZMoVZs2YxYsSIlBrytSMyLMohhUqTUcVkMKGaVDRVY/4XS1j3y9a410vVLkbm3P5W9+eFIshZJDtFKhV0yr7qbSrZrZpricx5/OP9vnH+djQb3oWiUyhbvyS/XZrO8ge/kjFHBqttY4l1ahxxQgBqv12NSZu/JOi5p2NLhAdHsHt5whu0EIIydUowYOp7DJrdF6GIZ0GfT9OJvdN58e2mL/Dw9uDs3gs2YzhMRpXzhy4nOL54/Aoun0i4EmW+9irfdp9GZFh8jRlXN1e72iWxKIrgwuHL1O9Sw6H2Xn7Jr176z88bmTloAdER0cCzlOfI0Cim9J3N5t93Jql/Q4yB2YN/t/ha7N/bzEELUq0EgUTyppNijkjTpk2ZP38+jRs3Jn/+/LRu3ZrBgwezYkXKBA6+jmTIms6h7AlLLBz7NyaTeZ9eURQ++rkPCBJqbigCRRF89FNvp6P8OwxqiblT52x7MQ0z6P4Tm6scqkklODCE7AWy4u7lTnR4tHMD2kEogpGLB5EhW3oMVgS7YtG76Lhx7rbNNs37NGTBhWm8NaglpesUp2KjMgz48T1+vzydgmXzcXDdMe5du2/XLlf3+AXajAYja2ZvtrqyoWkQGR7F9iV74x2v2qqCw4Grqknj6OaT+Gb0wTu97dgjnYuOqq2SdwsjJiqG+V8ssdnml+GL4j7bieHIxpM2A7c1TePOpbtcPHo10WNIJBLHSdWorODgYDJksP40Gx0dTUhISLyfN5kG79ROtNbGg1uP4lU8rdK8PBPWjyR3sZzx2uUvlZuJm0dRrr7zkvEFyuTly78+xdXNxVzoThEOPXnvWXkwnjKsf86MNs9TdErcKsruZQcIe5JQUCspFCibF8ChYEpV1eLSXG2RvUBW+kzqzvfbv2LChi9oO7AZ187cYnizcXzRcoJDK10vKok+vvfE7tz1eh3XTt+Md6xKi/LkLpbD4dWrh3ceM6ThV9R+q5rNdh0/bYVvBh+H+nSUo5tP2d3ue3w3iDN7zid6jKB7Txxq9/huUKLHkEgkjpNqjsiVK1eYPn06/fr1s9pmwoQJ+Pn5xf3kypVyKpGvAhWblKFsvZJ29RmsEfXCykGFRmWYe+p7Zp+YzIQNXzD39A/MOj7ZqUyZF6nRtjJLbs2mz7fdqNWhKrXeqmb3hhcdGcOOpc+e2pv3tr3nr5pUmr3XAIDDG48nepXIGu0/agHA9qV77bQ021KjXWWnx/h31iYG1frSqaygB7fjx6r88/NGu+doWkIpfp1Ox7cbvyRHoWwOjaupGmiwcf42/jeuCy5PV2Z0eiXO4ewwqCU9xyW/tkesLoz9dolTcgVzqnpytpNIJEnD6ayZMWPG8NVXX9lsc/jwYSpWrBj3e0BAAE2bNqVjx4707t3b6nnDhw/n008/jfs9JCTkjXZGFEWh87C2nNp1zmYMhSWEIshZOOGNRwhB/tJ5yF86T3KZiW9GHzoObg3ArQt32PX3fpvt9Xoddy49yzBp3LMu6+Zu5tqZWwk1KBRB2XolqdLSHDhojDEmy979s1TYmjToVguT0cTqnzbYPS9n4eyYDCa+7z2To5tOoqoaZeoWp+2HzSlWxXKg743/bjNtwFzAuaq0/x24RHRkNG4ebmz+Yyd/Tlxl9xyT0USNtpUSHM+UMyNzTn7PwbXHWDR+ORePXLHbl6ZqmIwm/r47lx1/7iPw5kN8M/pQ5+1q+OdIGfn8rHkzO9guU6LHqNC4DH7+PnGVgl9ECHPMVFpScpVIXmecdkQGDhxI5862n4Ty5s0b9/+AgADq1atHtWrVmDNnjs3z3NzccHNLvvS8Vx1DjIEJ3aY5d/fC/ORatVXFBEqoqUFEaJTdNqqq4flciXZ3Tze+2zaGGR/OY8ef++KcERc3PU3fq0/fyT3itCqKVCzIrmUHkmxnvpK5afdRcxr3rIuiKATeeehQ+vLtSwH0LTcYnU6Ji7vY+dc+ti3ew4Af36Pth80SnLNm5qZ47Z3BaDDh6q6xZMIKhLD9URCKoESNohSrWtji6zq9juptKlG9TSVunLtF75KfWmwXi6ZpXD11Ay8/L1q8nzp1XkrVLkaWvJkIvPHQosMpFEGe4jkpWC5fosfQu+jpP/V/5r+tF/sX5pin/lP/J5VRJZJUwmlHxN/fH39/f/sNgTt37lCvXj0qVKjA/PnzpVCQk+xdechpbQ9Fr+CXyY/+U/9ns53RYEQIkWwiUw9uP+LoppNsX7rHblvVpFK7Y/wCej7pvRm+8GP6Tu7BxSNXEYqgeLXC+KT3jteuTqfq/DJiUaKKwsWSt1RuZp+YHO/Y8/E0Nnk67PNORez/f/r4V4pULphgZeTsPtsZMtbIkjcTnj4eBN58yK3zAXbbZ8yenq9WDHHoBpq7WE50LjpMButBn0JREgTMpjSKovDJrL6MbPENaPGL/ylPM5E+/rlPkp2E+l1roegUZg/+nYd3nmmsZCuQhYHTe1GxcZkk9S+RSBwnxQTNAgICqFu3Lrlz52by5Mk8ePAg7rWsWbOm1LCvFRePXEHvoksgA/4isdsMbh6uNH63Ll2/6JBA1AzMT7jbl+5l2Q//culpRkDx6kXo+FkrararkigboyKi+fGDOWxdtNth5yB9Fj9yFclh8bUMWdNbFZOKDI9i3Ns/JLlY3q3zd4iKiI4Tx1o4dhm/jf4zSX2CeSVq1fR1hDysyYYF23l4+zH+OdITGWZ/lehFhBC0+9AsmmaItp3JEzt2g6618M3oWPCoEIJqLSuw798jqFacJNWkUq1VRYuvWSM0KIxNC3Zwdv8FFEVQrn4p6netiYe3h8N9VGxchombvmTOkD+4dOxZ5kqRKoXoN7kHxasVccoma9TtVINab1XlzO7zPAkMxj9nRopXKyxXQiSSVCbFHJFNmzZx+fJlLl++TM6c8TM1ZH6+Y+hd9Q7tyvxydgpevp74ZvTGxdXyE6ymacz67DdWTF0bL/j1/IGLfNVhMt1HdaTHmLctnmsNTdP4qsNkjm0+6dQKhX9O+zogllg0dhkXDl9xSNDMFiaDidO7zlGpaTkObzieLE4ImFdGdi8/wLbFe+JEuGL/dQoBlZqVo83ApgBkzpMJT1/PuIKF1sa2FdNwcsdZlk9dw8ntZ9E0jTL1SlCxcVn2rj5ssb1Or5Apl79TgbmHN57g6w6TiY6MiUvp3vn3fuaNXMyE9V9QpGIBh/sqW68kPx+ZyK0Ld3h87wn+OTKQo6BjwbbOoNPpKFM38cHaEokk6aTYXknPnj3RNM3ij8QxKjcrZ7OSqhCQq2gOchXOTsZs6a06IQDHtpxixdS1QPzl7lhhrD++/pv/Dl5yyr5Tu85xZOMJp2TedXqF/KWcD5SNiTaYNTRU2zf1olYCRhP091QvZPmUNcmahWOINguoxTofjjohQjHHJuQunpOPZvTm61Wfo3cxPye4urnQsm8jq3YqisDP35fqFoJUwaxcO7j+GA6tO0ZEaCSRYVEcXn+cGR/Oo37nmuhd9XF6MrEZT5lzZ2LS5lE2P1PPc/vSXUa3nUh0ZIz571zV4rJvwp+EM6zxWIIf2t9mjIk2cOO/29y+GICqquQqkoMydUqkiBMikUjSBrLWTBolKiKa7AWzUrhCfq6cvG4xxkDToMvwdg4tJa/+aQM6vfWASZ1e4d+ZG61mflhi68LdNvu0hMmo0rhnPYfbx/Lg1kO7+hI6Fx25imTnvAMOVb5SuQE4tfu/JG/1JAWdXmHk0k+p1b4KmqZZvZbdRr3FqZ1nuXDkSjxHUqdX0Ol1jFr2mUWn4eLRK3EqopbiWrYu3s0360dw7dRNLh27ioubC1VbVqB6m0pxjpAjrJ6+3qzuauFBQzVphIdEsOHX7XT6vI3F82OiYlg4dhn/ztwUp5WSKVdG3h7ShjYDmsrtEonkNUY6ImmMS8eusmjccvb/cxhV1XD3csPT15PQx2Fxy/yxN//uozrSqHsdx/o9etWupPgFB1I6nyf4YUiigjBHtZlIj9Fv0+7j5g7fYFzc7D+Za6pKppwZKVyxAJeOXbW4XaToFMrWK0H2AuY4pZd6exPmTJbi1cxZLrbeCw8vdyZvH8M/P23kn583cu96IG6ebtTtVJ2Og1uT5wWhulj+ccAB3bZ4D0N/+zBJU9mz6pDNz4KmauxbfciiI2KIMTCy5QRO7jgb75o9uPWInz76ldsXAhg4vVeS7JNIJGkX6YikIY5vO82I5t+YS78//UKOCo8mJsqAXyZfSlQvjCHaSO6iOWjWp6HVm48lHMl+cPNwrBhhLJlz+Tu9IgLmei0zP11ATFQMnYe1c+icTDkzkrtYDm6dD7C6vaeaNJZOXEWBMnlQhMD0QjCJolPw8/dh0Jxnonolaxbl2JbTjhv/tOheXBE+zJlKmkl1NssaNLMuyrq5W+g+qqPd5m4ebnQc3JqOg1ujqqpDWWhn9p6364Ce3XvBKbMt4UhAbYwV+fzNv+/ixLYzVs9b/dMGGnSr7dRqnUQieXWQ+bRpBJPRxIRu0zAZTQm2ClSTSmhQGJ6+nnyzbiT9fujplBMCULN9VZuxEEIR1GjrnGJok/fqJWpFJJbfv/qL0CDHFDKFELwzsoPdGCPVpHLp2DVMFrZb0mX2pevI9oQ8Co3r54mT6dH+OTIyZP4A3h7cmoLl8pG/TB7aDmjGd1tHJ2p5RVU1jmw84fR5jqbCO5KerXNJegp34YoFbH6+dHrFalHFf2dutKkerNMrrJ2zOck2SiSStIlcEUkjHFhz1GYNDNWosmPpXvpP/V8CbQ1HaN2/Cat/2oAhKiZBcKmiE3h4ezhdXj0xdjyPIcbI7mUHHB63ftda3Lv+gPlfLkFRnM9GeXz3CT99NB+A/KXz8NanLbl68oZD5wpFMHLJJ9RsXyVOXK33t/HbVGtZkYPrjjltl62A5KRStUUFbl+8a9UmRadQtUXSC9e1GdCUw+uPW33dZFRp9UFji6/duXTXZtaVyahy6/ydJNsokUjSJnJFJI1w/ewtu0+vRoOJgCv2q7ZaIkueTHyzbgTu3u4gzDeg2CdYr3ReTNz0Jekz+znV565lBxwuL28JnU7H4+ecr+jIaNbP28pn9UbTu+QgvmzzLfv/PRIvU6briPYsuDCNjp+1SlIJ+utnb/F971kOZ8y8N64LdTpWj3NCLDFobj+yF8jiVG0gRadQsmYxh9s7S8sPGptrxFi4UEKYVxta92+S5HEqNytHu4+aA+Ysnlhi398+E7slKOIXi6evbY0RoQi809muBCyRSF5d5IpIGsHDy91uaiqAu6dzcRzPU7p2cZbcms3Whbs4vec/hBCUqVuSel1q4OFlv6Lsi4QFhaHobatz2sJkMsUVFgu6/4TP6o3h1vk7cfEXty4EcODfo1RrXZFRf38Wl8WRo2A2ek14h78n/5OoceFpWq0A4cB+yv/GdXYoliV9Zj9mHPqWdXO2sO6XrTy+G4Snr0c85c4EaBot+1leKUgOsuXLwlcrP2dMh8kYog1xKw9CEbi46hm9fAjZ8mdJ8jhCCD6Y0pPi1QqzfMoaLhy+jBCC0nWK0/Gz1lRuVs7quQ261mLZlDVWV200VaNu5xpJtlEikaRNhJaGhT1CQkLw8/MjODgYX1/fl21OinL36n16FBpoXaxLQPb8WVhwcXqaSWXcMH873/f+OdECY67uLvx1dy5efl4Mrj+a03vOW1T5FELQaWhben3TNd7x5p5dMVgJgEwuPH08+DtwHq5uLkSGRbJu7lbW/bKFh3ceky6zH03/V5+W/RrZ3KZaNG45C0YtjRfYq9MrqCaNwb/2p/G7dVN0DmB29Nb9spWTO84CGmXqlKRZ7/opVo9IVVWEEA59VgNvPeT9Mp8RGRqVwBlR9Ao5CmRl1vHvcHVPvBMukUhSF2fu39IRSUNM6PYjO5butSoQ9vlvAx1O100NIsMieTtbH6LCoy2+rugUszYGWJxTv+/fpcOgllw9dYO+ZQfbHMvT14O/7s7FzeNZUcSvO37PvtW200YdwVoxOUWn0P7jFvSd3IOQR6F8Vnc0N87dNldCftpeKILMuf2ZunuszYq0x7edZuW0dZzZfR5FJ6jYtCztPmrhlNro68zlE9cY3XYSgTcfPt2i1DAZVYpUKsCYlZ9bLFkgkUjSLtIReUWJjoxmQrdp7F15KC5eJPby9J7wDh0Ht36Z5llk8x87mfTuDIQQ8TJadHoF7/TefDLrff746m+unnoWFOqXyZeeX3emZV9zRdcVP65l1me/2ZWJ/3HvOIpXK8LDgMc8vhvEw4AgxrSblKQCeIhn+h3x+nkujiZ7gay4urtw5cR1i2Pp9Aqlahfnuy2jE2+HBJPJxOH1Jzh/8BI6vY7yjUrL2i8SySuKdERecS4du8qOpXsJexJOtvxZaNijTpp+Itz/7xEWfLk0ztlQdAo121Wmz6TuZM2bGU3TuHz8Gnev3sc7nRel6xSPp9q5YupaZg2274h8Nq8/2xbv5vjWZ7ofuYvl4M7Fu6hPZcWdQQiBzkWHMcZopyEObT/NOzeV3EUtF/OTSCSSNwnpiEhSHU3TCLhyj/DgCLLkyYSfv+PX68KRKwysPMxmm1hl1Rd1VhSdgqIIchXJzrUztxJnfDIx+Nf+NHlBvl7TNPasPMSKqWv47+Alc0XahqXp+GkrytYr+ZIslUgkkpTFmfu3zJqRJAtCiEQXJitSsQBFKhXg8vFrFuM9hAAPH3fCHocliDUxq9Dy0p0QSCgepmkac4b8zrIf1sTJ85uAoxtPcGjtMQb8+B5tP2zmVGCnRCKRvG5IHRFJmuDLvz4jY/YM8TQ4Yv9fsFw+Qh6GWq/y68SanncGL9y93Ow3dBKhiATl5A9vOMGyH9YA8avwxjpbP338K+/k/YAm+k609HqHb7tP48rJ68lum0QikaRlpCMiSRNkyZOJ2Scm0+fbbuQtkYv0WdNRrEohhswfkKxBuu0/bmk1yyexKDqFOm9XJ1PO+Fkzq6avsyuYFnjzIWCuw7Ljz70MrDyMwxusK5RKJBLJ64aMEXlNuXj0CldP3sDFzYUKjUuTLpNzqqlpiWNbTzO00ddJ60TA/8Z2oV7nGvQoODBpXT0VXIvdbilRowjfrBuJp098hdAOmd4j5FGoc30Lgbu3O38GzEmUyFxieXD7EWFBYfjnzJhk6X6JRCKRMSJvKPeuB3Ji+xn+nLSa2xcC4o7r9Dqa92lAvx964upmvwpvShAVEc3BNUd5dDeIjNnSU6VlBdw9HdsiKVWrKL7+PoQ8dO6m/jw/7PyKUjWLo2ka2QtmJeDKvUQJsRUqn49bFwKIiTLg6eNB/a41ef+77vH0TWLRuzr/56VpGpGhkWxfspfmvRs4b6CTHNt6mgVfLOG/g5cAcypyrbeq0uubd8iaN3OKjy+RSCTSEXkFCbz5gLVztnBmz3kUvUKBsnm5eOgyp/ect9jeZDSxZvZmngQGM+pv28JhSSU8JILNv+9kz4qDRIVHkb9MXtJn9mPVjPVEhESiKAJV1fD09aDPxO5xWiK2cHF1oefXnZnWf671RlZSbBWdQonqRShVs7i5mRB0Gd6e73v97PTcdK46Lh+/HqeXEh4SwT8/b+Tmf3cYv3Z4AuXP6q0rsn7eVqcF13QuOi4fuwqkrCOye8VBxnb8Pl7VYJNRZfeyAxzfcobpB78hW76ky79LJBKJLaQj8oqxfeleJvaYhqY9C4A8se2M3fM0VWP38oNcOHzZajn2pHLz/B2G1B/D4/tPng4KF49ejafvERtwGhESyY8fzEHvoqPpe/Xt9t2qX2OiI6L59YslGKIN6PQ6VKOKzkVHg3dqsXXRLlSTliC1193LjY9+7hOvryY963Lv2n0WjVturuJrp8ZPrN6IyWiKJ9oWO6+TO8/y+5i/6P1tt3jntf2oORt+3WZVudUWLim8chUTFcMPvWeaVWJfmL7JqBIaFMacIX8welnKOq4SiUQiY0TsEBNt4MqJ66gmlbwlc+Hlm/iKr0nlysnr9K841Oky87Ho9Dpa9m3EwOm9ktkyMBqM9Cz8EQ9uP3LKPr9Mviy9PTuewJktwp6Es2vZAU7uOMP5Q5cJuHwPgCx5/XH38uDGuVugPRNV+9+4LuQsnN1iXzfP32Hd3C3cuXQXVVUxGkwc23zKLE2vqgidgmpUyVYgC3ftVD22JEEPZrG3cZ1+wBBjRFO1BAq01pi4eRTlG5Ry6D1JDDv+3Mv4LlNttlF0Cn8GzHml44skEsnLQcaIJAMmk4nF41ewYupawp6EA+Di7kLTnvXoPbFbgsDE1GDltHUkRWpCVVWCAoOTz6Dn2Lf6MPdvPHD6vOAHIZzYfpaKjcs41N47nRfhwRFsW7wnXkZK4I2HaBrUaF+Fnl93wj97Brul43MXzUG/79+Nd+zutfus/2Urty4E4OHtTq0OVbl8/BqLxi2zucUSERLJrQsBFCybL97xaq0qsvD6TDbM28bZfefR6XX4ZfJl/S9bLfaj6BXylchFufopK3Z259I9dHrzKo81VJPK/esPpCMikUhSFOmIWEDTNCa9O4PtS/bEW1I3RBlYO3cLF49e4fsdX1kMUExJDq49lqQCb4qikClHykjFH9ty2u6NzRrOZJbcPH+HOUN+B+Jrc8Rep70rDlK7Q1XyFs/ltB0A2fJl4b3x8av8Xj9zy6GtFZ2VVN30mf3oMrxdvGN5iuVk9uDfEYpANalxGTg5C2Vj3JrhKS5u5uXnaXdLKradRCKRpCRSR8QCp3aeY9viPRZvPqpJ5eKRK2z4dXuq25WYm/yL5zdxIB4jMZidgsTt8mXO7e9w27WzN6PTW//YKopg9Yz1ibLDGuUblrK73ZQusx+5nKgz02FQS367PJ1On7eheptK1O1UnVHLBjPn5Pc2q/gmFzXbV7b5uhCCfKVyk6NQ4tRyJRKJxFHkiogF1s/bik6v2Fx9WDtnM20GNE1Fq6BEjSIcXn88casiAlr0aUi+krmT3zCgeLXCrJ9nebvBqkmKIFv+LJSoXsThcy4dv2pz/qqqxRXfu3DkCofWHsMQY6BguXxUb1PJ4ViU5ylSqSDFqhbm4pHLlscW8NanrZzu29LqS2rhnyMjLfs1Zs3MTRZjVjRNo+fXnaXsvEQiSXGkI2KBgCv3bd7sNA3uXQtMRYvMtB3YjAP/HnX6PE9fD976tBXvfNEhBawyU7dzDWYP+Z2I4AjrUuzPIRSBogg+/rmPUzc7d083uwGfelc9n9Udzald51B0CkIRmAwm0mfxY9Tfn1GyZjGHx4tl9PLBDKk/hlsXAuIEzWKd1Ubd69BxcCuiI6O5dNTsKOUrlRvfjD5Oj5Oa9J/SE01VWTt7C0KYg1ONRhNuHm589FNvqrep9LJNlEgkbwAyawbz099/By9x8cgVXFz17Px7Pye3n7W5h545tz+Lrs9MMZusseDLpSwavzwupgCI9//Ym6TeRUf1tpVp1qsBpWoVTZV4ltO7/2N403EYYowJbPNJ70VoUHhc20Ll89F38rsJ6rPYY83szfzYf47VXSCdXsE7nXmsF7dThCJwdXPh56OTyO3ENkos0ZHR7PhzH9sW7yH0cRg5C2ejeZ+GlKxZlEXjlrNy2jrCgyMA0LvoqP9OLT74oafdoNmUwmQyERMZg7uXu01nL/DmA3b+fYCwoDCy5c9Cnber4eGd+sHYEonk9cGZ+/cb74jcPH+H8Z2ncPXUDYdTKxWdQtcR7Xn3q04pYpM9Dm84zspp6zi79wKKTlChSVnafdiMsCcRBFy+h5efJ9VaV0wg1X35xDUO/HsUQ7SBAmXzJnqrwhZ3r95n1fT17Fq2n+iIaPKVzkPr/k2p1aEKty4E8CggiIzZ0pEnkcGkkWGR9CzyMU8CgxM6GkKg6ITN1SydXqHxu3X5dO4HiRr/RTRNY2KP6WxbvDtBTJGiU8hbIhdT945LVbn2Wxfu8OfEVWxbsgdDtBHvdF60eL8hbw9pk+ZXaSQSyeuBdEQc5OGdR/QtO4SwJwmfnq2h6BT8/H2YfWIy6bOkS3abUoLQoDDGdvqB41tOx9uqSJfZjy//+pTStYu/bBNtYjQY2bf6MBcOm1eschbNzrzhi3h4+zE6vWIWd1NVdDodqslkN8PFzdONNWELk8W207v/49M6o6y+LoTg/e+689anrZJlPHtcOHyZwfXHYIg2xHPIFJ1C5tz+TNs3/pX53EokklcXqSPiIMunrHXICRGKQGAOhMxbIhdf/vXpK/NlrqoqI1tO4MKhy+bfTSo8Tb4JeRjC8Kbj+OnwRPKWSNwKRUpzbv8Fvuowmcf3nqBz0YGmYTKqFK5YgK4jOnD+0CUiw6I4se0MYU/CHUqzjY6IRlVVFCXpSWMbft1mM7BZQ2PNnM2p4oioqsr4LlOJiTIk+EyrJpXAWw+ZOWgBIxZ/kuK2SCQSiaO80Y7Ipt932HRCFJ1C5eblKN+gNCajieLVClOsauFXKpPg6OZT/Lf/osXXVFXDZDTx13er+XxB0irSWkPTzFkswQ9DyZzbn5xOpIMGXLnH0MZjiYkyAGAyPEtfvnj0CsEPQph/4UdmDJxHeHBEPCl5W2TJkylZnBAwC6DZzGLS4MHNh8kylj1ObD/L3avWFWBVo8quZfvp/+P/pEiZRCJJM7zRjkjYc8GTllBNKsYYI+0+ap5KFiU/u/7aZ/OJ3WRU2fHnPobMH5DsDtb+f48w5/M/4lUCLl6tMP1/fI8iFQvYPX/ZD2uIiU74dA+ABvdvPGB856kc2nDc4a01oQhafdDE4TnYI11mv3jBwpbwSaW4jKsnr9utnWMyqtw6HyAdEYlEkmZ4owXN/O2ojOr0ClnyvNql0MNDIlBNtlcKzPEESRNLe5Gdf+9nVNuJ3Ll4N97x84cu82ntL7lw+LLdPrYv3YNqRzNl76pDGJ6umNhD0SkUrpCfNgOTT/+l4Tu17a6qNXm3brKNZws3D1c0zb5D5urhareNRCKRpBZvtCPS4v1GCMX6KoDJqNKsV8ookaYWOQpmszlH4LGjPAAAFsJJREFUgIw5MiRr9owhxsC0/nNBI0EWkmoyF5f7edACu/1EhUcnm016Vz3tP27Bd1tH4+7peCqz0WBkx597GdHiG/qVH8LodpPY/+8RTCaz41alRXmKVyscr+5NLIpewTejD20/bJZs87BF5eblAdvXOkO2dBQsmzdV7JFIJBJHeKMdkTYDm5KzcHaLNxEENH2vHkUqFUx9w5KRZr0b2FyqF4qgVb/GyTrm4fUnbNaPUU0q5/Zd4Palu1bbAGQvmNWh8WIzgWwx/cA39J3cwyl9jPDgcAbV/pLxXaZydNNJrpy4zoE1RxnVZiJftPyWmKgYdHod36wbQdWWFcw+gCBuiyt/qTxM2T021QKbs+TJRP2uNVFsvBddhrVHp9elij0SiUTiCG9cjMjdq/dZM3szF45cxtXNhSY963Lp2FX2rDgUtz3hnc6Ltz5tRefhbV+usclA9gJZ6flVZxaMWmq+UT63QKHoFPKVyk37j5M3Bub+jQdxwmq2CLzxwGbwausPmjDjw3l2x3PzcCUyPMria4oiqNmhSoKquI7wfZ9ZXDxyFXhWYC/236ObTzJ36EIG/PgeXn5efLXycwKu3OPo5lOYjCaKVi5IkUoFUz2w+ZPZfQl9Es6htcfQ6XVxK1KqSaXz0LbJui0lkUgkycEbpSOybu4Wpn4wByHMFU+FMN+X/TL68sVfg9BUDRdXPYUrFsDV/fXaR9/8+04WjV/OnaerEO5ebjTr1YCeYzvj6ZO8KprbFu9mQrdpdtvNPDbJpoMQE22gV/FPbMrp6/QKdd6uTsUmZfmx3xyio2LQ63WoqoZqUqn1VlWG/jbQaWXZ+zce0D3/AJsCdy7uLvx9dy5efi9HOdUasUrB2xbtJuRxKFnyZKbpe/XIUVAWsJNIJKmDFDSzwKld5/is3miL0uCKTsEngzd/XP0pVRUwUxtN07h3LZCYqBiy5M3sVKyEM0SERvJ2tj5ER1iO8RACchTOzq/nptpdMbh/8yHvFhxoNZhWCMH0A99QpFJBwkMi2L5kL7cvBuDl60ntjlUTreC66bcdfPe/n+y2+2b9SCo1KZuoMSQSieR1xZn79xsTI/L39/9Y1Y5QTSrBD0LYvmRvKluVughhrnabp3iuFHNCADx9POg+qqPV1zWgz8RuDm1bZMntz8RNX5ozPZ5rrugUFEXw6S8fxMXxePl60rJvI/p9/y7dR3dMtBMCOJwO7Gg7iUQikVjmjYkRObLxpM2bhlAEhzccp3nvBqlo1evL20NaA/DH138THREdFzPim9GHD2f0onprxyu7lqlbgj+uzGDd3K0c3nAck9FEyRpFadmvMTkLZ08R+4tVK2y3jU6vUNgBPRSJRCKRWOeNcEQ0TbP75KqpWjzlTknSEELQ6fM2tPqgMfv/OULwwxCy5MlE5eblcHF1cbq/DFnT0+3Lt+j25VspYG1C8hTLSdn6JTm965xFMThFp1C3Uw3SZ5bCYBKJRJIU3ghHRAhB4Qr5uXjkCqqVTA6hCIpWKZTKlr3+ePp40OCdWi/bjEQx9PcP+bT2KO5dD4zLAIrdTcpXMhcDp/d6idZJJBLJ68EbEyPS7qPmVp0QBOh0yisvXiZJXvyzZ2Dm0Yn0mdid3MVy4pPBm3yl8zBwem+m7h2Pd7q0lS0jkUgkryJvTNaMpmlM7TebdXO3xqsNotMraKpGrQ5V8cvkS6acGWnYvTb+OTImxxQkSSQ8JIItf+zizN7zCAFl65akXtear3V2k0QikbzqyPRdK2iaxq5lB1g1Yz2Xjl5B76onfWY/bl+8G5eFoaoamqbxzsgO9Bjz9itVafd14+TOs4xqM5GI0EiEEGb9F1XFJ70336wbQdHKcitNIpFI0iLSEXGQecMXsXTSKovaIgAfTOlJ+49bJPu4Evvcux5I7xKDiIk2JFBoVRQFDx935p//MdXk0yUSiUTiOGlGR6R169bkzp0bd3d3smXLRvfu3QkICLB/YgpiNBjZveIg84Yv4q/J/1h1QgAWjV+OIcaxyq7OEhkWyeY/drJ04io2/baD8JCIFBnnVeXfnzdiiDFalIlXVZWI0EjW/bL1JVgmkUgkkuQkRVdEpkyZQrVq1ciWLRt37txh8ODBAOzbt8+h85N7ReTY1tN803UqwQ9CEDqBZrI/9cnbxlCmbokkj/08//y8kblD/yAqPBqdXsFkVHHzcKXn2M50GNRSbgcBPQoN5O6V+zbbFKqQn58PT0wliyQSiUTiKM7cv1M0fXfQoEFx/8+TJw/Dhg2jbdu2GAwGXFyc15JICpeOXWVk8/GYngapOuKEAESGWS6mllg2/LqN6QN/ifs9VqMiOjKG2YN/x8XNhTYDXq3CZCajicMbTnD7YgCePh5Ua1Mpyfoahij7K1HRkTFJGkMikUgkL59U0xF5/PgxixYtonr16ladkOjoaKKjn9UnCQkJSbbxF41fbg5EtVMR9kVyFU0+5U6jwci8EYtttlnw5VKa9W6Aq1vqOmqJ5cimk3z3v594fDcoLhtp2oC5tB7QlL7f9Uh0yfnCFQsQdP+JRTExMGc7FZGqphKJRPLKk+I6IkOHDsXLy4uMGTNy8+ZNVq9ebbXthAkT8PPzi/vJlSvxtUKeJyYqhv2rDztVF0TRKZSpWyJZK5ae3v0fTwKDbbYJexLO8S2nkm3MlOTc/gt80XICQfeeAM/qrpiMKqumreOnj39NdN+t+zex6oTEjtHqgyaJ7l8ikUgkaQOnHZExY8bEpVJa+zly5Ehc+yFDhnD8+HE2bdqETqejR48eVkurDx8+nODg4LifW7duJX5mzxEZFmVdzMwCil7By9eDj2f2SZbxYwl9HOZQu5BHjrV72cz/cimaplm8npoG/87axL3rgYnqu3zD0nQY1BIARXkWM6PozB/ZHqPfpphUwpVIJJJXHqe3ZgYOHEjnzp1ttsmbN2/c//39/fH396dw4cIUK1aMXLlyceDAAapVq5bgPDc3N9zckr8qrHc6Lzx9PYgIibTbVu+io16XmnQf1ZFs+bMkqx1Z82VO1nYvkycPgjmx7YzNNoqisGPpXjoPa+d0/0II+k7uQdHKBVn2wxouHL4MQLGqhXh7cBuqt3G8aJ5EIpFI0i5OOyKxjkViiH1yfj4OJDXQ6XU0792QFT+utbo9IxTBtP3fkLdELtw9k98ZAihUPj95S+bixrnbFmNVhCLImjczJWsWTZHxkxNHVncURRDyKDTRYwghqNupBnU71cBkNBckTGzMiUQikUjSJikWI3Lo0CFmzJjBiRMnuHHjBtu3b6dr164UKFDA4mpIStNleDuy5MmEon9hyk9X/ft8242ilQqmmBMC5hvrxzPfR6fXxdtuALMToiiCT2b3TZH0XU3TuHTsKse2nCLgyr0k95chW3q7ToHJqJIlb/Ks7uj0OumESCQSyWtIijkiHh4erFixggYNGlCkSBHee+89SpYsyc6dO1Nk+8Uevhl9mLZ/PI2610Hv+mwhKEehbAz74yM6Dm6dKnaUrFGUH3Z+TfHqReIdL1qpIN9tHUP5BqWSfcydf+/n3UIf0r/iUIY2Hsu7hT7k0zqjuHLyeqL79PL1pM7b1dC96Ng9h95VR/2uNRM9hkQikUhef95Iiffw4HDuXg3EzdOVnIWzvzQBsbvX7vMoIIgMWdORvUDWFBlj44LtTH7vZ/PKz3NXWtEpuLq7MG3fePKVypOovu9dD2RApWGEPQm3uOU1YNp7tB3YLJGWSyQSieRVRdaakQAQFRHN29l6ExlqWZRN0SmUb1CKCRu+SPQYAVfu8fMn8zm47lico5M1X2Z6ft2ZBu/USnS/EolEInl1STPKqpKXy96Vh6w6IWDW/Tiy+SQPbj8iU86MiRoje4GsjPt3OA/vPCLgyn08fT3IXzoPipLiEjUvjcjwKI5tPkV4cAQ5C2ejWNXCUpZfIpFIEol0RF5j7t94gE6vi8s4sYgGD249TLQjEot/joz450haH2kdTdNYMmElS75dSdRz0v+5i+Xgs3n9KV618Eu0TiKRSF5NXt/HVgnpMvliMtlwQp7il0lueznCgi+XMv+LJfGcEIDbFwIYUn8Ml09cezmGSSQSySuMdEReY2q2r4LeRsqrUASFKuRPVhn715VHd4NYOnGVxddUVcNoMDH/y6Wpa5REIpG8BkhH5DXGN6MPXYa3t/habEhD7wnvpKJFry47lu61WpoAzPE2h9YdI/hh8hVqlEgkkjcB6Yi85nQf3ZF3v+qEq7u5mq94KqSWLrMfY1YMoXzD0i/TvFeGx/eeoNPZ+XPR4MkD6YhIJBKJM8hg1dccIQTdvnyLdh81Y/+/Rwl9HEa2/Fmo1LSsVCp1gozZ02OyU71ZCEH6LH6pZJFEIpG8HkhH5A3By8+Lht1qv2wzXlnqdanJnCF/YFItB/8qOoWqLSvgm8EnlS2TSCSSVxu5NSOROED6zH50H93R4muxKrX/G9clla2SSCSSVx/piEgkDtJ1RHsG/PgePhm84x0vWC4fU3aPJW+JXC/JMolEInl1kRLvEomTGGIMnN71H+EhkeQslDXRtXokEonkdUVKvEskKYiLq4vMNpJIJJJkQm7NSCQSiUQieWlIR0QikUgkEslLQzoiEolEIpFIXhrSEZFIJBKJRPLSkI6IRCKRSCSSl4Z0RCQSiUQikbw0pCMikUgkEonkpSEdEYlEIpFIJC8N6YhIJBKJRCJ5aaRpZdVY9fmQkJCXbIlEIpFIJBJHib1vO1JFJk07IqGhoQDkyiWLiUkkEolE8qoRGhqKn5+fzTZpuuidqqoEBATg4+ODEOJlmxOPkJAQcuXKxa1bt96ognxv6rzhzZ27nPebNW94c+f+ps4bkn/umqYRGhpK9uzZURTbUSBpekVEURRy5sz5ss2wia+v7xv3gYU3d97w5s5dzvvN402d+5s6b0jeudtbCYlFBqtKJBKJRCJ5aUhHRCKRSCQSyUtDOiKJxM3NjdGjR+Pm5vayTUlV3tR5w5s7dznvN2ve8ObO/U2dN7zcuafpYFWJRCKRSCSvN3JFRCKRSCQSyUtDOiISiUQikUheGtIRkUgkEolE8tKQjohEIpFIJJKXhnREJBKJRCKRvDSkI+IA169fp1evXuTLlw8PDw8KFCjA6NGjiYmJsXlez549EULE+6latWoqWZ08JHbumqYxZswYsmfPjoeHB3Xr1uXs2bOpZHXyMH78eKpXr46npyfp0qVz6JzX4ZpD4ub+OlzzoKAgunfvjp+fH35+fnTv3p0nT57YPOdVveY///wz+fLlw93dnQoVKrB7926b7Xfu3EmFChVwd3cnf/78zJo1K5UsTV6cmfeOHTsSXFshBOfPn09Fi5POrl27aNWqFdmzZ0cIwapVq+yek5rXWzoiDnD+/HlUVWX27NmcPXuWKVOmMGvWLEaMGGH33KZNm3L37t24n3Xr1qWCxclHYuc+adIkfvjhB2bMmMHhw4fJmjUrjRo1iitk+CoQExNDx44d+eCDD5w671W/5pC4ub8O17xr166cOHGCDRs2sGHDBk6cOEH37t3tnveqXfM///yTTz75hJEjR3L8+HFq1apFs2bNuHnzpsX2165do3nz5tSqVYvjx48zYsQIPvroI5YvX57KlicNZ+cdy4ULF+Jd30KFCqWSxclDeHg4ZcqUYcaMGQ61T/XrrUkSxaRJk7R8+fLZbPPuu+9qbdq0SR2DUhF7c1dVVcuaNav27bffxh2LiorS/Pz8tFmzZqWGicnK/PnzNT8/P4favm7X3NG5vw7X/Ny5cxqgHThwIO7Y/v37NUA7f/681fNexWteuXJlrV+/fvGOFS1aVBs2bJjF9p9//rlWtGjReMf69u2rVa1aNcVsTAmcnff27ds1QAsKCkoF61IHQFu5cqXNNql9veWKSCIJDg4mQ4YMdtvt2LGDzJkzU7hwYfr06UNgYGAqWJey2Jv7tWvXuHfvHo0bN4475ubmRp06ddi3b19qmPhSeR2vuT1eh2u+f/9+/Pz8qFKlStyxqlWr4ufnZ3cOr9I1j4mJ4ejRo/GuFUDjxo2tznP//v0J2jdp0oQjR45gMBhSzNbkJDHzjqVcuXJky5aNBg0asH379pQ0M02Q2tdbOiKJ4MqVK0yfPp1+/frZbNesWTMWLVrEtm3b+P777zl8+DD169cnOjo6lSxNfhyZ+7179wDIkiVLvONZsmSJe+115XW85o7wOlzze/fukTlz5gTHM2fObHMOr9o1f/jwISaTyalrde/ePYvtjUYjDx8+TDFbk5PEzDtbtmzMmTOH5cuXs2LFCooUKUKDBg3YtWtXapj80kjt6/1GOyJjxoyxGIj0/M+RI0finRMQEEDTpk3p2LEjvXv3ttl/p06daNGiBSVLlqRVq1asX7+eixcvsnbt2pSclkOk9NwBhBDxftc0LcGx1CYx83aG1+2aO8urfs0t2WpvDmn5mtvC2Wtlqb2l42kdZ+ZdpEgR+vTpQ/ny5alWrRo///wzLVq0YPLkyalh6kslNa+3Ptl7fIUYOHAgnTt3ttkmb968cf8PCAigXr16VKtWjTlz5jg9XrZs2ciTJw+XLl1y+tzkJiXnnjVrVsDsVWfLli3ueGBgYAIvO7Vxdt5J5VW+5s7wOlzzU6dOcf/+/QSvPXjwwKk5pKVrbgl/f390Ol2CVQBb1ypr1qwW2+v1ejJmzJhitiYniZm3JapWrcrChQuT27w0RWpf7zfaEfH398ff39+htnfu3KFevXpUqFCB+fPnoyjOLyY9evSIW7duxfuiflmk5Nzz5ctH1qxZ2bx5M+XKlQPM+7M7d+5k4sSJSbY9KTgz7+TgVb3mzvI6XPNq1aoRHBzMoUOHqFy5MgAHDx4kODiY6tWrOzxeWrrmlnB1daVChQps3ryZdu3axR3fvHkzbdq0sXhOtWrV+Pfff+Md27RpExUrVsTFxSVF7U0uEjNvSxw/fjzNXtvkItWvd4qEwL5m3LlzRytYsKBWv3597fbt29rdu3fjfp6nSJEi2ooVKzRN07TQ0FDts88+0/bt26ddu3ZN2759u1atWjUtR44cWkhIyMuYRqJIzNw1TdO+/fZbzc/P7//t2y+v2lAYBvBOtJALFQgSMECChgQ0AUcQfAHSkH4FcHUEiQFFUE0tgj8KDajKI0oqEITiUU2wz8Q2srtdMbgLJ+ueX1LVV5wnb5o8oger1Qqe56HT6SCbzf5T2YMggBACw+EQyWQSQggIIRCG4X0mijsHHs8ORGPnrVYL5XIZruvCdV2USiW02+13M1HY+Xw+h6qqsG0bvu+j1+shkUjgfD4DACzLQrfbvc+fTie8vb2h3+/D933Ytg1VVbFYLGRFeMqjuSeTCdbrNY7HIw6HAyzLgqIoWC6XsiI8JQzD+zesKArG4zGEEAiCAID8fbOI/AHHcaAoyofPzxRFgeM4AIDb7YZms4l0Og1VVZHL5WCaJi6Xi4QEz3smO/DtOudgMEAmk0EsFkO9XofneS8+/eeYpvlh7u12e5+J4s6Bx7MD0dj59XqFYRjQdR26rsMwjN+ubkZl59PpFPl8HpqmoVqtYr/f39+ZpolGo/FufrfboVKpQNM0FAoFzGazF5/473gk92g0QrFYRDweRyqVQq1Ww2azkXDqz/lxDfnXxzRNAPL3/QX4/gcKERER0Yv917dmiIiISC4WESIiIpKGRYSIiIikYREhIiIiaVhEiIiISBoWESIiIpKGRYSIiIikYREhIiIiaVhEiIiISBoWESIiIpKGRYSIiIik+QpHnujr0mKZGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(over_sampled_df['f1'],over_sampled_df['f2'],c=over_sampled_df['t'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e6a2eed",
   "metadata": {},
   "source": [
    "## Q6: What are outliers in a dataset? Why is it essential to handle outliers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "060394ae",
   "metadata": {},
   "source": [
    "Outliers in a dataset are data points that significantly deviate from the rest of the data points. They are observations that lie at an abnormal distance from other data points, often distant from the central tendency (mean or median) of the data. Outliers can be extreme values that are much larger or much smaller than the majority of the data points in a particular feature or attribute.\n",
    "\n",
    "\n",
    "Handling outliers is essential for several reasons:\n",
    "\n",
    "**Data Quality and Accuracy**: Outliers can be the result of data entry errors, measurement errors, or anomalies in the data collection process. Dealing with outliers ensures data quality and accuracy, leading to more reliable analysis and modeling.\n",
    "\n",
    "**Impact on Statistical Analysis**: Outliers can significantly skew statistical analysis and metrics such as mean, standard deviation, and correlation coefficients. Removing or properly handling outliers ensures that these statistics accurately represent the underlying data distribution.\n",
    "\n",
    "**Model Performance**: Outliers can have a disproportionate impact on the model's training process. They can distort the learning algorithm's decision boundaries, leading to poor model performance and predictions. Handling outliers can help build more robust and accurate machine learning models.\n",
    "\n",
    "**Assumptions of Models**: Some machine learning algorithms, such as linear regression, assume that the data follows a normal distribution without significant outliers. Outliers violating these assumptions can lead to biased estimates and incorrect model outputs.\n",
    "\n",
    "**Interpretability and Generalization**: Outliers can cause models to overfit to noise and specific instances in the data, reducing their ability to generalize to new, unseen data. Handling outliers ensures better model interpretability and generalization.\n",
    "\n",
    "\n",
    "Generally the approach used to find outliers is by the boxplot graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4760731",
   "metadata": {},
   "source": [
    "## Q7: You are working on a project that requires analyzing customer data. However, you notice that some of the data is missing. What are some techniques you can use to handle the missing data in your analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "383f3c64",
   "metadata": {},
   "source": [
    "The are several techniques that can be used to handle missing data:\n",
    "\n",
    "#### Deletion:\n",
    "**Listwise Deletion**: Removing entire rows with missing values. It is straightforward but may lead to loss of valuable information if the amount of missing data is significant.\n",
    "<br>**Pairwise Deletion**: Analyzing available data for each pair of variables independently. This method utilizes all available data but may introduce bias in the analysis.\n",
    "\n",
    "#### Imputation:\n",
    "**Mean/Median/Mode Imputation**: Replacing missing values with the mean, median, or mode of the corresponding feature. It is a simple approach but may not be suitable if data follows a skewed distribution.\n",
    "<br>**Regression Imputation**: Using regression models to predict missing values based on other correlated variables.\n",
    "<br>**K-Nearest Neighbors (KNN) Imputation**: Imputing missing values based on the values of the k-nearest neighbors in the feature space.\n",
    "<br>**Multiple Imputation**: Creating multiple imputed datasets to account for uncertainty in the imputation process. This method is particularly useful when the data is missing at random (MAR).\n",
    "\n",
    "#### Forward or Backward Fill:\n",
    "For time series data, missing values can be filled by propagating the last observed value forward or the next observed value backward.\n",
    "\n",
    "#### Interpolation*:\n",
    "Interpolating missing values based on a linear or polynomial relationship between neighboring data points.\n",
    "Using Algorithms Tolerant to Missing Data:\n",
    "\n",
    "Some machine learning algorithms can naturally handle missing data, such as decision trees or random forests.\n",
    "\n",
    "#### Data Augmentation:\n",
    "For certain types of data, data augmentation techniques can be used to generate synthetic data points for missing values.\n",
    "\n",
    "#### Domain Expertise and Business Rules:\n",
    "In some cases, domain knowledge and business rules can guide the handling of missing data. For example, setting missing values to a specific category or default value based on business rules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41874603",
   "metadata": {},
   "source": [
    "## Q8: You are working with a large dataset and find that a small percentage of the data is missing. What are some strategies you can use to determine if the missing data is missing at random or if there is a pattern to the missing data?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1de7188",
   "metadata": {},
   "source": [
    "Different strategies can be employed to investigate the patterns of missing data:\n",
    "\n",
    "**1. Data Visualization**:\n",
    "\n",
    "Plotting the distribution of missing values across different features can reveal patterns. You can use heatmaps or bar plots to visualize the missingness for each feature.\n",
    "Scatter plots or other visualizations can be helpful in identifying potential relationships between missing values and other variables.\n",
    "\n",
    "**2. Descriptive Statistics**:\n",
    "\n",
    "Computing summary statistics like mean, median, or standard deviation for features with and without missing values can provide insights into potential patterns.\n",
    "\n",
    "**3. Correlation Analysis**:\n",
    "\n",
    "Analyzing the correlation between missingness in one variable and other variables in the dataset may reveal potential relationships. Correlation matrices can be used for this purpose.\n",
    "\n",
    "**4. Hypothesis Testing**:\n",
    "\n",
    "Performing statistical tests to assess if the missingness in a particular variable is related to other variables in the dataset. Chi-square tests or t-tests can be used for this purpose.\n",
    "\n",
    "**5. Imputation and Sensitivity Analysis**:\n",
    "\n",
    "Impute the missing data using different methods (e.g., mean imputation, regression imputation, multiple imputation) and assess the impact of different imputation strategies on the results. If the results are robust to different imputation methods, the missing data might be MCAR or MAR.\n",
    "\n",
    "**6. Investigate Data Collection Process**:\n",
    "\n",
    "Understanding how the data was collected and identifying any potential biases or issues in the data collection process can help identify the reasons behind missing data.\n",
    "\n",
    "**7. Machine Learning Models**:\n",
    "\n",
    "Train a machine learning model to predict the presence or absence of missing values in a particular variable based on other variables. The model can give insights into potential relationships and patterns in the missing data.\n",
    "\n",
    "**8. Pattern Analysis**:\n",
    "\n",
    "Investigate if certain subsets of data have higher or lower missingness than others. For example, missingness might be more prevalent in certain geographical regions or for specific customer segments."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e18b52a6",
   "metadata": {},
   "source": [
    "## Q9: Suppose you are working on a medical diagnosis project and find that the majority of patients in the dataset do not have the condition of interest, while a small percentage do. What are some strategies you can use to evaluate the performance of your machine learning model on this imbalanced dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e2fcdc",
   "metadata": {},
   "source": [
    "When dealing with imbalanced datasets in a medical diagnosis project, it is crucial to adopt appropriate evaluation strategies to assess the performance of the machine learning model accurately. \n",
    "\n",
    "The strategies to evaluate the model effectively:\n",
    "\n",
    "**Confusion Matrix**: Use a confusion matrix to analyze the model's predictions. It provides a detailed breakdown of true positives, true negatives, false positives, and false negatives.\n",
    "\n",
    "**Accuracy with Caution**: Be cautious when relying solely on accuracy as an evaluation metric, especially for imbalanced datasets. Accuracy might be misleading, as it can be dominated by the majority class, leading to high accuracy but poor performance on the minority class.\n",
    "\n",
    "**Precision, Recall, and F1-score**: Precision (positive predictive value) and recall (sensitivity) are valuable metrics for imbalanced datasets. Precision measures the proportion of true positives among predicted positives, while recall measures the proportion of true positives among actual positives. The F1-score is the harmonic mean of precision and recall, providing a balanced metric that considers both false positives and false negatives.\n",
    "\n",
    "**Receiver Operating Characteristic (ROC) Curve**: Plot the ROC curve to assess the model's performance across different thresholds. The area under the ROC curve (AUC-ROC) is a valuable metric for imbalanced datasets, providing an aggregate measure of the model's discrimination power.\n",
    "\n",
    "**Precision-Recall (PR) Curve**: Use the PR curve when the positive class is of greater interest. It visualizes the trade-off between precision and recall across different probability thresholds.\n",
    "\n",
    "**Cost-Sensitive Learning**: Incorporate class weights or misclassification costs to give more importance to the minority class during training.\n",
    "\n",
    "**Cross-Validation**: Utilize techniques like stratified k-fold cross-validation to ensure that each fold maintains the original class distribution. This helps in obtaining more robust and unbiased performance estimates.\n",
    "\n",
    "**Resampling Methods**: Evaluate the model's performance using resampling techniques like oversampling, undersampling, or SMOTE to balance the class distribution during cross-validation.\n",
    "\n",
    "**Ensemble Methods**: Consider using ensemble methods like Random Forest or Gradient Boosting, which can handle imbalanced datasets better than single models.\n",
    "\n",
    "**Threshold Adjustment**: In certain cases, adjust the classification threshold based on the desired trade-off between precision and recall to optimize the model's performance for the specific application.\n",
    "\n",
    "**Domain Expertise**: Involve domain experts to interpret the model's performance and validate the clinical significance of the results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ba669a",
   "metadata": {},
   "source": [
    "## Q10: When attempting to estimate customer satisfaction for a project, you discover that the dataset is unbalanced, with the bulk of customers reporting being satisfied. What methods can you employ to balance the dataset and down-sample the majority class?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ebd546",
   "metadata": {},
   "source": [
    "To balance the dataset and down-sample the majority class when attempting to estimate customer satisfaction, you can employ various techniques. \n",
    "\n",
    "\n",
    "Down-sampling involves reducing the number of instances in the majority class to match the number of instances in the minority class some methods to achieve this are :\n",
    "\n",
    "**Random Under-Sampling**: Randomly select a subset of instances from the majority class to match the number of instances in the minority class. This approach is simple but may lead to loss of valuable information.\n",
    "\n",
    "**Cluster-Based Under-Sampling**: Divide the majority class into clusters using clustering algorithms and then select a representative subset of instances from each cluster to maintain diversity.\n",
    "\n",
    "**Tomek Links**: Identify pairs of instances, one from the majority class and one from the minority class, that are the nearest neighbors of each other. Remove the majority class instance of each pair.\n",
    "\n",
    "**Edited Nearest Neighbors**: Remove majority class instances that are misclassified by their k-nearest neighbors from the minority class.\n",
    "\n",
    "**NearMiss**: Select majority class instances that are closest to minority class instances based on distance metrics.\n",
    "\n",
    "**Instance Hardness Threshold**: Calculate instance hardness scores for each data point based on the classifier's confidence. Select instances with lower hardness scores from the majority class.\n",
    "\n",
    "**Ensemble Methods**: Use ensemble techniques like EasyEnsemble or BalanceCascade, which create multiple balanced datasets by combining several random subsets of the majority class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11db81d6",
   "metadata": {},
   "source": [
    "## Q11: You discover that the dataset is unbalanced with a low percentage of occurrences while working on a project that requires you to estimate the occurrence of a rare event. What methods can you employ to balance the dataset and up-sample the minority class?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990d0dbc",
   "metadata": {},
   "source": [
    "When dealing with an imbalanced dataset with a low percentage of occurrences of a rare event, you can employ various techniques to balance the dataset and up-sample the minority class.\n",
    "\n",
    "\n",
    "Up-sampling involves increasing the number of instances in the minority class to match the number of instances in the majority class some methods to achieve upsampling are:\n",
    "\n",
    "**Random Over-Sampling**: Randomly duplicate instances from the minority class to increase its size. This approach is simple but may lead to overfitting and an overestimation of the rare event occurrence.\n",
    "\n",
    "**SMOTE (Synthetic Minority Over-sampling Technique)**: Generate synthetic samples for the minority class by interpolating between existing instances. SMOTE selects a random neighbor for each minority class instance and creates synthetic instances along the line connecting the two instances in the feature space.\n",
    "\n",
    "**ADASYN (Adaptive Synthetic Sampling)**: Similar to SMOTE, ADASYN generates synthetic samples for the minority class, but it assigns different weights to different minority class instances based on their difficulty in learning.\n",
    "\n",
    "**SMOTE-ENN (SMOTE with Edited Nearest Neighbors)**: Combine SMOTE and Edited Nearest Neighbors. SMOTE is used to generate synthetic samples, and Edited Nearest Neighbors is then applied to remove noisy samples from the newly generated minority class instances.\n",
    "\n",
    "**Borderline-SMOTE**: Focus on the borderline instances of the minority class, which are close to the decision boundary, and generate synthetic samples for them.\n",
    "\n",
    "**Cluster-Based Over-Sampling**: Cluster the minority class instances and then generate synthetic samples for each cluster to maintain diversity.\n",
    "\n",
    "**MSMOTE (Modified Synthetic Minority Over-sampling Technique)**: Modify SMOTE to balance the class distribution by considering both the minority and majority class.\n",
    "\n",
    "**ADOMS (Adaptive One-Step SMOTE)**: An improved version of SMOTE that adjusts the synthetic samples' location based on the density of the feature space.\n",
    "\n",
    "While using up-sampling techniques we must be careful, as they may lead to overfitting and noise in the data. The goal is to balance the dataset without introducing bias and to maintain the diversity and representativeness of the minority class.\n",
    "\n",
    "After up-sampling the minority class, you will have a more balanced dataset, which can be used for estimating the occurrence of the rare event more accurately. Always evaluate the model's performance on the up-sampled dataset using appropriate evaluation metrics for imbalanced data, such as precision, recall, F1-score, and area under the ROC curve (AUC-ROC). Additionally, cross-validation should be performed to ensure robustness in the evaluation process."
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