CDC_ML.ipynb

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    "\n",
    "<h1 style=\"text-align: center;\">2020 BRFSS CDC ML Project<h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "# 1. Business Understanding\n",
    "Heart disease is the leading cause of death in the United States. The term \"heart disease\" refers to several types of heart conditions. The most common type of heart disease in the United States is coronary artery disease (CAD), which can lead to a heart attack. Machine learning leads to a better understanding of how we can predict heart disease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# 2. Data Collecting\n",
    "### 2.1 About the dataset\n",
    "\n",
    "The Behavioral Risk Factor Surveillance System (BRFSS) is a collaborative project between all of the states in the United States and participating US territories and the Centers for Disease Control and Prevention (CDC). The BRFSS is a system of ongoing health-related telephone surveys designed to collect data on health-related risk behaviors, chronic health conditions, and the use of preventive services from the non-institutionalized adult population (≥ 18 years) residing in the United States. The BRFSS is administered and supported by CDC's Population Health Surveillance Branch, under the Division of Population Health at CDC's National Center for Chronic Disease Prevention and Health Promotion. \n",
    "\n",
    "Factors assessed by the BRFSS in 2020 included health status and healthy days, exercise, inadequate sleep, chronic health conditions, oral health, tobacco use, cancer screenings, and health-care access (core section). Optional Module topics for 2020 included prediabetes and diabetes, cognitive decline, electronic cigarettes, cancer survivorship (type, treatment, pain management) and sexual orientation/gender identity (SOGI).\n",
    "\n",
    "Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: \"Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.\". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as \"Do you have serious difficulty walking or climbing stairs?\" or \"Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]\". In this dataset, We noticed many different factors (questions) that directly or indirectly influence heart disease, so we decided to select the most relevant variables from it and do some cleaning so that it would be usable for machine learning projects ([1](https://www.kaggle.com/datasets/kamilpytlak/personal-key-indicators-of-heart-disease)).\n",
    "\n",
    "<br>\n",
    "\n",
    "### 2.2 Dataset Description [source](https://www.cdc.gov/brfss/annual_data/2020/pdf/codebook20_llcp-v2-508.pdf)\n",
    "\n",
    "|Category|Label|Question|Value| \n",
    "|-|-|-|-|\n",
    "|<b>HeartDisease</b>|Ever had CHD or MI| <i>-Respondents that have ever reported having coronary <br> -heart disease (CHD) or myocardial infarction (MI)</i>|-Yes<br>-No|\n",
    "|<b>BMI</b>|Computed body mass index|<i>Computed body mass index</i>|Float[1-9999]\n",
    "|<b>Smoking</b>|Smoked at Least 100 Cigarettes|<i>Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]</i>|-Yes<br>-No|\n",
    "|<b>AlcoholDrinking</b>|Heavy Alcohol Consumption Calculated Variable|<i>Heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week)</i>|-Yes<br>-No|\n",
    "|<b>Stroke</b>|Ever Diagnosed with a Stroke|<i>(Ever told) (you had) a stroke.</i>|-Yes<br>-No|\n",
    "|<b>PhysicalHealth</b>|Number of Days Physical Health Not Good|<i>Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good?</i>|Number of days [1-30]|\n",
    "|<b>MentalHealth</b>|Number of Days Mental Health Not Good|<i>Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good?</i>|Number of days [1-30]|\n",
    "|<b>DiffWalking</b>|Difficulty Walking or Climbing Stairs|<i>Do you have serious difficulty walking or climbing stairs?</i>|-Yes<br>-No|\n",
    "|<b>Sex</b>|Are you male or female?|<i>Are you male or female?</i>|-Male<br>-Female|\n",
    "|<b>AgeCategory</b>|Reported age in five-year age categories calculated variable|<i>Fourteen-level age category</i>|-Age [18-79]<br>-Age [80 or older]|\n",
    "|<b>Race</b>|Imputed race/ethnicity value|<i>Imputed race/ethnicity value (This value is the reported race/ethnicity or an imputed race/ethnicity, if the respondent refused to give a race/ethnicity. The value of the imputed race/ethnicity will be the most common race/ethnicity response for that region of the state)</i>|-White<br>-Black<br>-Asian<br>-American Indian/Alaskan Native<br>-Hispanic<br>-Other|\n",
    "|<b>Diabetic</b>|(Ever told) you had diabetes|<i>(Ever told) (you had) diabetes? (If ´Yes´ and respondent is female, ask ´Was this only when you were pregnant?´. If Respondent says pre-diabetes or borderline diabetes, use response code 4.)</i>|-Yes<br>-No<br>-No, borderline diabetes<br>-Yes (during pregnancy)|\n",
    "|<b>PhysicalActivity</b>|Exercise in Past 30 Days|<i>During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?</i>|-Yes<br>-No|\n",
    "|<b>GenHealth</b>|General Health|<i>Would you say that in general your health is:</i>|-Excellent<br>-Very good<br>-Good<br>-Fair<br>-Poor|\n",
    "|<b>SleepTime</b>|How Much Time Do You Sleep|<i>On average, how many hours of sleep do you get in a 24-hour period?</i>|Number of hours [1-24]|\n",
    "|<b>Asthma</b>|Ever Told Had Asthma|<i>(Ever told) (you had) asthma?</i>|-Yes<br>-No|\n",
    "|<b>KidneyDisease</b>|Ever told you have kidney disease?|<i>Not including kidney stones, bladder infection or incontinence, were you ever told you had kidney disease?</i>|-Yes<br>-No|\n",
    "|<b>SkinCancer</b>|(Ever told) you had skin cancer?|<i>(Ever told) (you had) skin cancer?</i>|-Yes<br>-No|"
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Smoking</th>\n",
       "      <th>AlcoholDrinking</th>\n",
       "      <th>Stroke</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>DiffWalking</th>\n",
       "      <th>Sex</th>\n",
       "      <th>AgeCategory</th>\n",
       "      <th>Race</th>\n",
       "      <th>Diabetic</th>\n",
       "      <th>PhysicalActivity</th>\n",
       "      <th>GenHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Asthma</th>\n",
       "      <th>KidneyDisease</th>\n",
       "      <th>SkinCancer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>16.60</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>55-59</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>20.34</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>7</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>26.58</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>65-69</td>\n",
       "      <td>White</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Fair</td>\n",
       "      <td>8</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>24.21</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>75-79</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>23.71</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Female</td>\n",
       "      <td>40-44</td>\n",
       "      <td>White</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319790</th>\n",
       "      <td>Yes</td>\n",
       "      <td>27.41</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Male</td>\n",
       "      <td>60-64</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fair</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319791</th>\n",
       "      <td>No</td>\n",
       "      <td>29.84</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Male</td>\n",
       "      <td>35-39</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Very good</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319792</th>\n",
       "      <td>No</td>\n",
       "      <td>24.24</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>45-49</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>6</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319793</th>\n",
       "      <td>No</td>\n",
       "      <td>32.81</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>25-29</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Good</td>\n",
       "      <td>12</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319794</th>\n",
       "      <td>No</td>\n",
       "      <td>46.56</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>Female</td>\n",
       "      <td>80 or older</td>\n",
       "      <td>Hispanic</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Good</td>\n",
       "      <td>8</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>319795 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",
       "0                No  16.60     Yes              No     No               3   \n",
       "1                No  20.34      No              No    Yes               0   \n",
       "2                No  26.58     Yes              No     No              20   \n",
       "3                No  24.21      No              No     No               0   \n",
       "4                No  23.71      No              No     No              28   \n",
       "...             ...    ...     ...             ...    ...             ...   \n",
       "319790          Yes  27.41     Yes              No     No               7   \n",
       "319791           No  29.84     Yes              No     No               0   \n",
       "319792           No  24.24      No              No     No               0   \n",
       "319793           No  32.81      No              No     No               0   \n",
       "319794           No  46.56      No              No     No               0   \n",
       "\n",
       "        MentalHealth DiffWalking     Sex  AgeCategory      Race Diabetic  \\\n",
       "0                 30          No  Female        55-59     White      Yes   \n",
       "1                  0          No  Female  80 or older     White       No   \n",
       "2                 30          No    Male        65-69     White      Yes   \n",
       "3                  0          No  Female        75-79     White       No   \n",
       "4                  0         Yes  Female        40-44     White       No   \n",
       "...              ...         ...     ...          ...       ...      ...   \n",
       "319790             0         Yes    Male        60-64  Hispanic      Yes   \n",
       "319791             0          No    Male        35-39  Hispanic       No   \n",
       "319792             0          No  Female        45-49  Hispanic       No   \n",
       "319793             0          No  Female        25-29  Hispanic       No   \n",
       "319794             0          No  Female  80 or older  Hispanic       No   \n",
       "\n",
       "       PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",
       "0                   Yes  Very good          5    Yes            No        Yes  \n",
       "1                   Yes  Very good          7     No            No         No  \n",
       "2                   Yes       Fair          8    Yes            No         No  \n",
       "3                    No       Good          6     No            No        Yes  \n",
       "4                   Yes  Very good          8     No            No         No  \n",
       "...                 ...        ...        ...    ...           ...        ...  \n",
       "319790               No       Fair          6    Yes            No         No  \n",
       "319791              Yes  Very good          5    Yes            No         No  \n",
       "319792              Yes       Good          6     No            No         No  \n",
       "319793               No       Good         12     No            No         No  \n",
       "319794              Yes       Good          8     No            No         No  \n",
       "\n",
       "[319795 rows x 18 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import pandas as pd for data manipulation\n",
    "import pandas as pd\n",
    "df = pd.read_csv('https://raw.githubusercontent.com/Machine-Learning-Projects1/2020-BRFSS-Codebook-CDC/main/dataset/heart_2020_cleaned.csv')\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# 3. Data Understanding\n",
    "### 3.1 Listing the unique values of each column\n",
    "Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages, and makes importing and analyzing data much easier.\n",
    "\n",
    "While analyzing the data, many times the user wants to see the unique values in a particular column, which can be done using Pandas unique() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HeartDisease : ['No' 'Yes']\n",
      "BMI : [16.6  20.34 26.58 ... 62.42 51.46 46.56]\n",
      "Smoking : ['Yes' 'No']\n",
      "AlcoholDrinking : ['No' 'Yes']\n",
      "Stroke : ['No' 'Yes']\n",
      "PhysicalHealth : [ 3  0 20 28  6 15  5 30  7  1  2 21  4 10 14 18  8 25 16 29 27 17 24 12\n",
      " 23 26 22 19  9 13 11]\n",
      "MentalHealth : [30  0  2  5 15  8  4  3 10 14 20  1  7 24  9 28 16 12  6 25 17 18 21 29\n",
      " 22 13 23 27 26 11 19]\n",
      "DiffWalking : ['No' 'Yes']\n",
      "Sex : ['Female' 'Male']\n",
      "AgeCategory : ['55-59' '80 or older' '65-69' '75-79' '40-44' '70-74' '60-64' '50-54'\n",
      " '45-49' '18-24' '35-39' '30-34' '25-29']\n",
      "Race : ['White' 'Black' 'Asian' 'American Indian/Alaskan Native' 'Other'\n",
      " 'Hispanic']\n",
      "Diabetic : ['Yes' 'No' 'No, borderline diabetes' 'Yes (during pregnancy)']\n",
      "PhysicalActivity : ['Yes' 'No']\n",
      "GenHealth : ['Very good' 'Fair' 'Good' 'Poor' 'Excellent']\n",
      "SleepTime : [ 5  7  8  6 12  4  9 10 15  3  2  1 16 18 14 20 11 13 17 24 19 21 22 23]\n",
      "Asthma : ['Yes' 'No']\n",
      "KidneyDisease : ['No' 'Yes']\n",
      "SkinCancer : ['Yes' 'No']\n"
     ]
    }
   ],
   "source": [
    "# retrieve all labels and store in a list\n",
    "columns_df = list(df.columns.values)\n",
    "# iterate over the list to print all unique values of each column in the dataframe\n",
    "for column in columns_df:\n",
    "    print(column, ':', str(df[column].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Continuous vs Categorical\n",
    "Every dataset has two type of variables Continuous(Numerical) and Categorical. Regression based algorithms use continuous and categorical features to build the models. You can’t fit categorical variables into a regression equation in their raw form in most of the ML Libraries.\n",
    "\n",
    "### need to move after descriptive statistics\n",
    "#### 3.2.1 Return a subset of the DataFrame’s columns based on the column dtypes\n",
    "`DataFrame.select_dtypes(include=None, exclude=None)`\n",
    "- To select all numeric types, use `np.number` or `number`.\n",
    "- To select strings you must use the `object` dtype, but note that this will return all object dtype columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy for array operations and select all numerical columns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of numerical features\n",
    "numeric_features = df.select_dtypes(include=[np.number])\n",
    "numeric_features.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HeartDisease', 'Smoking', 'AlcoholDrinking', 'Stroke', 'DiffWalking',\n",
       "       'Sex', 'AgeCategory', 'Race', 'Diabetic', 'PhysicalActivity',\n",
       "       'GenHealth', 'Asthma', 'KidneyDisease', 'SkinCancer'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# list of categorical features\n",
    "categorical_features = df.select_dtypes(include=[object])\n",
    "categorical_features.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Generate descriptive statistics\n",
    "Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.\n",
    "- `DataFrame.describe(percentiles=None, include=None, exclude=None, datetime_is_numeric=False)`\n",
    "\n",
    "Before generating statistics on our dataset, if you look at the 'AgeCategory' column, you notice that unique values are in the range of [a-b] where a is minimum, and b is maximum.\n",
    "- AgeCategory : [ '18-24', '25-29', '30-34', '35-39', '40-44', '45-49', '50-54', '55-59', '60-64', '65-69', '70-74', '75-79', '80 or older']\n",
    "\n",
    "We have to encode the column and do some calculations so that it would be usable for generating statistics. \n",
    "- One of the easiest methods to convert [a-b] to an integer is to calculate the mean and replace it with the original value. \n",
    "- For example, if we have [55-59], the mean equals 57. Hence, all values from 55 to 59 will replace with 57."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         57\n",
       "1         80\n",
       "2         67\n",
       "3         77\n",
       "4         42\n",
       "          ..\n",
       "319790    62\n",
       "319791    37\n",
       "319792    47\n",
       "319793    27\n",
       "319794    80\n",
       "Name: AgeCategory, Length: 319795, dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode 'AgeCategory' column\n",
    "encode_AgeCategory = {'55-59':57, '80 or older':80, '65-69':67,\n",
    "                      '75-79':77,'40-44':42,'70-74':72,'60-64':62,\n",
    "                      '50-54':52,'45-49':47,'18-24':21,'35-39':37,\n",
    "                      '30-34':32,'25-29':27}\n",
    "df['AgeCategory'] = df['AgeCategory'].apply(lambda x: encode_AgeCategory[x])\n",
    "df['AgeCategory'] = df['AgeCategory'].astype(int)\n",
    "df['AgeCategory']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_56708_row0_col0, #T_56708_row0_col2, #T_56708_row0_col3, #T_56708_row0_col4, #T_56708_row0_col5, #T_56708_row0_col6, #T_56708_row1_col1, #T_56708_row2_col1 {\n",
       "  background-color: #08306b;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_56708_row0_col1 {\n",
       "  background-color: #206fb4;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_56708_row1_col0, #T_56708_row1_col2, #T_56708_row1_col3, #T_56708_row1_col4, #T_56708_row1_col5, #T_56708_row2_col2, #T_56708_row2_col3, #T_56708_row2_col4, #T_56708_row3_col1, #T_56708_row3_col6 {\n",
       "  background-color: #f7fbff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row1_col6, #T_56708_row2_col6, #T_56708_row3_col2 {\n",
       "  background-color: #e7f0fa;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row2_col0 {\n",
       "  background-color: #f3f8fe;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row2_col5 {\n",
       "  background-color: #f1f7fd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row3_col0 {\n",
       "  background-color: #d9e8f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row3_col3 {\n",
       "  background-color: #c7dbef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row3_col4 {\n",
       "  background-color: #c4daee;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_56708_row3_col5 {\n",
       "  background-color: #cfe1f2;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_56708\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_56708_level0_col0\" class=\"col_heading level0 col0\" >mean</th>\n",
       "      <th id=\"T_56708_level0_col1\" class=\"col_heading level0 col1\" >std</th>\n",
       "      <th id=\"T_56708_level0_col2\" class=\"col_heading level0 col2\" >min</th>\n",
       "      <th id=\"T_56708_level0_col3\" class=\"col_heading level0 col3\" >25%</th>\n",
       "      <th id=\"T_56708_level0_col4\" class=\"col_heading level0 col4\" >50%</th>\n",
       "      <th id=\"T_56708_level0_col5\" class=\"col_heading level0 col5\" >75%</th>\n",
       "      <th id=\"T_56708_level0_col6\" class=\"col_heading level0 col6\" >max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_56708_level0_row0\" class=\"row_heading level0 row0\" >BMI</th>\n",
       "      <td id=\"T_56708_row0_col0\" class=\"data row0 col0\" >28.325399</td>\n",
       "      <td id=\"T_56708_row0_col1\" class=\"data row0 col1\" >6.356100</td>\n",
       "      <td id=\"T_56708_row0_col2\" class=\"data row0 col2\" >12.020000</td>\n",
       "      <td id=\"T_56708_row0_col3\" class=\"data row0 col3\" >24.030000</td>\n",
       "      <td id=\"T_56708_row0_col4\" class=\"data row0 col4\" >27.340000</td>\n",
       "      <td id=\"T_56708_row0_col5\" class=\"data row0 col5\" >31.420000</td>\n",
       "      <td id=\"T_56708_row0_col6\" class=\"data row0 col6\" >94.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56708_level0_row1\" class=\"row_heading level0 row1\" >PhysicalHealth</th>\n",
       "      <td id=\"T_56708_row1_col0\" class=\"data row1 col0\" >3.371710</td>\n",
       "      <td id=\"T_56708_row1_col1\" class=\"data row1 col1\" >7.950850</td>\n",
       "      <td id=\"T_56708_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "      <td id=\"T_56708_row1_col3\" class=\"data row1 col3\" >0.000000</td>\n",
       "      <td id=\"T_56708_row1_col4\" class=\"data row1 col4\" >0.000000</td>\n",
       "      <td id=\"T_56708_row1_col5\" class=\"data row1 col5\" >2.000000</td>\n",
       "      <td id=\"T_56708_row1_col6\" class=\"data row1 col6\" >30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56708_level0_row2\" class=\"row_heading level0 row2\" >MentalHealth</th>\n",
       "      <td id=\"T_56708_row2_col0\" class=\"data row2 col0\" >3.898366</td>\n",
       "      <td id=\"T_56708_row2_col1\" class=\"data row2 col1\" >7.955235</td>\n",
       "      <td id=\"T_56708_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_56708_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
       "      <td id=\"T_56708_row2_col4\" class=\"data row2 col4\" >0.000000</td>\n",
       "      <td id=\"T_56708_row2_col5\" class=\"data row2 col5\" >3.000000</td>\n",
       "      <td id=\"T_56708_row2_col6\" class=\"data row2 col6\" >30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_56708_level0_row3\" class=\"row_heading level0 row3\" >SleepTime</th>\n",
       "      <td id=\"T_56708_row3_col0\" class=\"data row3 col0\" >7.097075</td>\n",
       "      <td id=\"T_56708_row3_col1\" class=\"data row3 col1\" >1.436007</td>\n",
       "      <td id=\"T_56708_row3_col2\" class=\"data row3 col2\" >1.000000</td>\n",
       "      <td id=\"T_56708_row3_col3\" class=\"data row3 col3\" >6.000000</td>\n",
       "      <td id=\"T_56708_row3_col4\" class=\"data row3 col4\" >7.000000</td>\n",
       "      <td id=\"T_56708_row3_col5\" class=\"data row3 col5\" >8.000000</td>\n",
       "      <td id=\"T_56708_row3_col6\" class=\"data row3 col6\" >24.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27bbfb9f070>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate descriptive statistics\n",
    "df.describe()[1:][list(numeric_features)].T.style.background_gradient(cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# 4. Exploratory Data Analysis\n",
    "Exploratory Data Analysis(EDA) is an approach to analyse the data , to summarize its characteristics , often with visual methods.\n",
    "\n",
    "Exploratory Data Analysis is majorly performed using the following methods:\n",
    "- Univariate visualization — provides summary statistics for each field in the raw data set.\n",
    "- Bivariate visualization — is performed to find the relationship between each variable in the dataset and the target variable of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Univariate visualization\n",
    "#### 4.1.1 Univariate visualization of categorical features\n",
    "provides summary statistics for each categorical field in the raw data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sXwGHb0jbETlDSZIkSZIkSVJHdCLp/RXgjPK8AlbQJL0PrarqpqqqLqqqanNgP+BbdeMXwMZVVW1V2t5Y9p8PHLyBbSVJkiRJkiRJXWrjkf6CdV0/CVAS21cBM4FNgS/WdX1HVVV/B5wJLAEeb9n1N8AWQFXXdd0Wm7gBbZ+jqqrpwHSAKVOmDPIMJUlSr4uIM4Gjy+a1mfnhiLgY2B94qsRnZeY1EXEwzd1pE4ArM3NmOcZUYA7N+5ebgJMyc0VETAHmAVsDCUzLzCcjYhJwKbAjsBg4OjMfHf6zlSRJkqTRpSM1rquqehnwbeDLdV1fBlxT1/Ud5eVrgNcBS4HNW3bbnCYRvqqf2Ia0fY66rr9Q1/UedV3vsdVWWw3onCRJkgBKEvtNNO9npgK7R8SRwJ7AAZk5tTyuiYgJwMU0Jdh2BfaMiEPKoeYBp2bmzjR3x51Y4rOB2Zm5C3A7q++g+ySwIDN3pUmWnzfMpypJkiRJo1InFrJ8KfAt4PS6ri8u4W/2LT4JvBG4A7gF+NOqqjaqqmoKsFFd148Bd1ZVdVBpewiwYAPbSpIkDadFwAcz85nMXA7cC0wpjzkR8aOImBURGwF7Afdn5gOZuYIm0X1URLwcmJCZt5Vjzi3x8cABNHfLPRsvzw+lmekNcDlwSGkvSZIkST1lxMubAB8FXgScUVVV38ykvwHOqapqOfAoML2u66VVVS0AvkuTnD+ltP0gMKeqqk1oPkReVdf1yvVtO/ynJ0mSellm3t33PCJeCRxDs/7IQcB7gSeBrwPvKc8Xtey+CJgMbLeW+JbA0pIgb43Tuk8pg7IU2Ap4pLV/pQzKpLZuT0aSJEmSukQnanp/APhAPy/t20/bjwMfb4v9BDhwMG0lSZKGW0S8GrgWOC0zEziy5bULgONoFvhut4qmnMmGxHme11rNoFk/RZIkSZK6UkdqekuSJHWziNgXuB74SGZeEhGviYi3tjSpgOXAw8A2LfFtaWZmry2+GJgYEePa4rTuExEb89yFvvucC+zQ9th/QCcqSZIkSaNQJ8qbSJIkda2IeBnwNeCYzLyhhCvg3Ii4gaakyXTgEuB7zS6xE/AAcCxwcWYujIhlEbFvZt5CMyt8fmYuj4gFNCVTLuuLl69xXdn+VHl9QakpvobMXELb4t4RMURnL2k0iIgzgaPL5rWZ+eGIuJjmAtdTJT6rLKh7MHA2MAG4MjNnlmNMpVkUdwvgJuCkUjppCs36A1sDCUzLzCdL6aRLgR1pLtAdnZmPDv/ZSpIkPZczvSVJkobWacBmwNkR8cOI+CHwR8BZNItv3wP8MDMvz8xlwPHA1SV+H6vXIJkGnBMR9wIvAM4v8ZOB6RFxD00Ca2aJnwHsExF3lzZ9a5xI6iElif0m4HXAVGD3iDgS2BM4IDOnlsc1ETEBuBg4HNgV2DMiDimHmgecmpk701y4O7HEZwOzM3MX4HaasQfgkzQX23alSZafN8ynKkmStFbO9JYkSRpCmbm29UugSRa1t78e2K2f+F3AXv3EF9IsitkefwI4bAO7K6n7LAI+mJnPAJQLZ1PKY06ZqX0NMItmjLk/Mx8obecBR5WLahMy87ZyzLnArIj4InAAcERL/EbgdODQ8hrA5cCFETG+vztOJEmShptJb0mSJEnqEpl5d9/ziHglTbmj/Wgulr2XpsTS14H3lOeLWnZfBEwGtltLfEtgaWauaIvTuk8pg7IU2IrV6w5QSqBMauvyZCRJkoaYSW9JXSsiJgK3Am/JzAetZSlJknpFRLwauBY4LTMTOLLltQto1gD4Sj+7rqIpZ7IhcZ7ntT4zgDPX2XFJkqQhYE1vSV0pIvYGbgZ2bglby1KSJHW9iNgXuB74SGZeEhGviYi3tjSpgOXAw8A2LfFtaWZmry2+GJgYEePa4rTuExEbAxOBx9u6di6wQ9tj/wGfqCRJ0lo401tStzqRZhG3LwNExAsYJbUsvbVXkiQNl4h4GfA14JjMvKGEK+DciLiBpqTJdOAS4HvNLrET8ABwLHBxZi6MiGURsW9m3kIzK3x+Zi6PiAU0JVMu64uXr3Fd2f5UeX1B+3ugzFwCLGnr7xCevSRJUsOkt6SulJknwBofpF4K3ECHa1kWM/DWXkmSNDxOAzYDzm55H/Q54CzgFmA8cHVmXg4QEccDV5d9rgOuKvtMo5kssDlwJ3B+iZ8MXBIRM4FfAG8v8TOAuRFxN01ie9rwnJ4kSdLzM+ktqSdk5s8ZHbUsobm1d25bbDKwoJ+2kiRJ6y0zPwB8YC0vz+6n/fXAbv3E76K5I649vpBmUcz2+BPAYRvYXUmSpGFh0ltST4iI1wA7Z+bVJTToWpaZuZL+a1k+tI5alt7aK0mSJEmSNIxcyFJSr+irZfmiiBhPU8vyGlpqWZZFmY6lqVm5EFhWFoKCllqWNDOyj2mNl+d9tSxhLbUsJUmSJEmSNLxMekvqCZn5I1bXsrwH+GFmXp6Zy4DjaWpZ3gPcx5q1LM+JiHuBF7BmLcvpZbHL/YGZJX4GsE+pZXkyzUKakiRJkiRJGkGWN5HU1TJz+5bns7GWpSRJkiRJUldzprckSZIkSZIkqWuY9JYkSZIkSZIkdQ2T3pIkSZIkSZKkrmHSW5IkSZIkSZLUNUx6S5IkSZIkSZK6hklvSZIkSZIkSVLXMOktSZIkSZIkSeoaG3e6A5IkSZIkSRr7IuJM4OiyeW1mfjgiDgbOBiYAV2bmzNJ2KjAH2AK4CTgpM1dExBRgHrA1kMC0zHwyIiYBlwI7AouBozPz0YjYBLgI2AP4HXBsZt43IicsadRyprckSZIkSZIGpSS33wS8DpgK7B4RbwcuBg4HdgX2jIhDyi7zgFMzc2egAk4s8dnA7MzcBbgdOKPEPwksyMxdaZLl55X4+4GnSnwGcMlwnaOkscOktyRJkiRJkgZrEfDBzHwmM5cD9wI7A/dn5gOZuYIm0X1URLwcmJCZt5V955b4eOAA4KrWeHl+KM1Mb4DLgUNK+2fjmXkTsGWZLS6ph1neRJIkSZIkSYOSmXf3PY+IVwLHAOfTJMP7LAImA9utJb4lsLQkyFvjtO5TyqAsBbZax7F+0dq/Uh5lUlu3JyOpK5n0liRJkiRJ0pCIiFcD1wKnAcuBaGuyiqacSbt1xRngPq1mAGf2E5fUhSxvIkmSJEmSpEGLiH2B64GPZOYlwMPANi1NtgUeWUd8MTAxIsa1xWndJyI2BiYCj6/jWO3OBXZoe+w/gNOUNAaY9JYkSZIkSdKgRMTLgK8Bx2bmFSX8veal2Kkkso8F5mfmQmBZSZIDHFfiy4EFNKVRno2X59eVbcrrC0r7Z+MRsR+wLDPXKG0CkJlLMvPB1gfw0BCdvqRRxvImkiRJkiRJGqzTgM2AsyOerWjyOeB44Ory2nWsXqRyGjAnIjYH7qSp/w1wMnBJRMykqcv99hI/A5gbEXcDS8r+ABcAny/xp4F3DsO5SRpjTHpLkiRJkiRpUDLzA8AH1vLybv20vwvYq5/4QuCgfuJPAIf1E18GvGsDuyupy1neRJLGqJWr+lubRSPF778kSZIkSaPTiM/0rqpqPHAxsD2wKfBJ4B5gLlADPwZOqet6VVVVZwKHAiuAGXVdf7+qqp0G23aETlWShtW4jTbimjt+2ulu9Kwjd9+p012QJEmSJEn96MRM73cAj9d1vT/wZ8C/AGcDM0usAg6vquoPgQOBvYG/AC4s+w+q7QicnyRJkiRJkiSpQzqR9P4KzeID0CSiVwC7AzeW2HzgYGA/4Ft14xfAxlVVbTUEbSVJkiRJkiRJXWrEy5vUdf0kQFVVm9Os2DsT+Oe6ruvS5DfAFsBE4PGWXfvi1SDbPkdVVdOB6QBTpkwZzOlJkiQREWcCR5fNazPzwxFxMM1daBOAKzNzZmk7FZhD8z7lJuCkzFwREVOAecDWQALTMvPJiJgEXArsCCwGjs7MRyNiE+AiYA/gd8CxmXnfiJywJEmSJI0iHVnIsqqqlwHfBr5c1/VlQGud7c2BJcDS8rw9Pti2z1HX9Rfqut6jrus9ttpqqw09HUmjVERMjIgfR8T2ZXt62f7viPjXkiAiIj4WEQsj4oflcUqJT42IH0TETyLiixGxcYlPiYibIuK+iPh/EfHCEp8UEddGxL3l9W06dOqSOqgkt98EvA6YCuweEW+nWdPkcGBXYM+IOKTsMg84NTN3prkL7sQSnw3MzsxdgNtZfafcJ4EFmbkrTbL8vBJ/P/BUic8ALhmuc5QkSZKk0WzEk95VVb0U+BZwel3XF5fwnVVVHVSeHwIsAG4B/rSqqo2qqpoCbFTX9WND0FZSD4iIvYGbgZ3L9s7Ah4A/Al5LM/6dUprvCfxFZk4tj751AYYqESWptywCPpiZz2TmcuBemrHo/sx8IDNX0IwvR0XEy4EJmXlb2XduiY8HDqC5K+7ZeHl+KM1Mb4DLgUNK+2fjmXkTsGWZLb6GcoFu+9YHMHnoTl+SJEmSOmvEy5sAHwVeBJxRVVVfougDwPlVVW1C88HwqrquV1ZVtQD4Lmsmpz4IzBlo2+E/PUmjxIk0Y8GXy/bTwPsycylARPw30JcM2gM4PSJ2pCktcBrwUp6biJoVEV+kSUQd0RK/ETidJuF0QIlfDlwYEeNL0ktSj8jMu/ueR8QrgWOA82mS4X0W0SSat1tLfEtgaUmQt8Zp3aeUQVkKbLWOY/2irYszgDMHdnaSJEmSNPp1oqb3B2iS3O0O7Kftx4GPt8V+Mti2krpfZp4AEBF92wuBhSW2FfBXwPGlNMmdNInuB2mS2GcAX2foElGPtPat1OOd1NZlZ1lKXSYiXg1cSzO+LAeirckqmrtI2q0rzgD3aXUuzVjXajLeESdJkiSpS3RiprckdUxE/D4wH7goM79Twm9uef0zNHV3r+1n94EmotrNwFmWUleLiH2Bq4EZmXlFRBwItNb535bmgtjDa4kvBiZGxLjMXNkSp2Wfh8paA30LevfFf9p2rDVk5hLa1jnpu0AoaexzIV1JkqQOLWQpSZ0QEbvQrAFwSWb+3xKbEhHvbmlW0czIfN5EVFuc1n3aElHtzgV2aHvsP8jTkzRKRMTLgK/RJH2uKOHvNS/FTmX8OBaYX+5CWVaS5ADHlfhympnXx7TGy/Pryjbl9QWl/bPxiNgPWJaZ7aVNJHUxF9KVJElqONNbUk+IiM1pFtH9aGbOa3npd8A/RcS3acqbnAJck5kLI2JZROybmbfQkoiKiL5E1GX0n4j6FGsmotbgLEup650GbAac3fJ/+3PA8TSzvzejGS/61hqZBswp49SdNPW/AU4GLomImTR1ud9e4mcAcyPibpqxZFqJXwB8vsSfBt45DOcmaXR7diFdgIhYYyHdEutbSPcehmj9khL/GDQL6UbElhExpf3CmyXeJEnSSDHpLalXnECzOOVpEXFaif17Zn4sIt4L/AewCXAz8Jny+lAloiT1kMxc2/olALv10/4uYK9+4guBg/qJPwEc1k98GfCuDeyupC7iQrqSJEkNk96Sulpmbl+enlMe/bW5mmb2ZXt8SBJRkiRJI8mFdCVJUq8z6S1JkiRJXcKFdCVJklzIUpIkSZK6ggvpSpIkNZzpLUmSJEndwYV0JUmSMOktSZIkSV3BhXQlSZIaljeRJEmSJEmSJHUNk96SJEmSJEmSpK5h0luSJEmSJEmS1DVMekuSJEmSJEmSuoZJb0mSJEmSJElS1zDpLUmSJEmSJEnqGia9JUmSJEmSJEldw6S3JEmSJEmSJKlrmPSWJEmSJEmSJHUNk96SJEmSJEmSpK5h0lvSqPbLX/7yObGf/vSnHeiJpF7kGCSpkxyDJHWK44+ksc6kt6RRacmSJSxZsoQTTzyRX//6189uP/bYY5x88smd7p6kLlc/8zvHIEkd4/sgSZ3i+COpW2w82ANUVXVCXddfbNl+f13X5w/2uJJ62wc/+EFuueUWAPbee+9n4xtvvDEHH3xwp7olqUcsu+Nr7LPPOYBjkKSR5/sgSZ3i+COpWww46V1V1duBw4A3VFX1xyU8DvgDwKS3pEG56KKLAPjbv/1bzjrrrA73RlKvmfD6t3PHp49zDJLUEb4PktQpjj+SusVgZnp/A1gEvAT4fImtAn422E5JUp+zzjqLhx9+mF//+tfUdf1s/NWvfnUHeyWpVzgGSeokxyBJneL4I2msG3DSu67rXwHfAb5TVdXWwGaDPaYktfvnf/5nvvzlL/OSl7zk2VhVVVx//fUd7JWkXuEYJKmTHIMkdcpgxp+ImAjcCrwlMx+MiIuB/YGnSpNZmXlNRBwMnA1MAK7MzJll/6nAHGAL4CbgpMxcERFTgHnA1kAC0zLzyYiYBFwK7AgsBo7OzEcH+z2QNLYNRU3vC4FDgUeACqiBPxrscSUJ4LrrruNb3/oWL33pSzvdFUk9yDFIUic5BknqlIGOPxGxN03CeueW8J7AAZm5qKXdBOBi4EDgf4BrI+KQzJxPk9g+ITNvi4iLgBOBzwKzgdmZeUVEnAGcAZwOfBJYkJmHRsQ7gfOAYwZ04pK6xlDMyt4b2LGu61VDcCxJWsO222474A96/cwwGNaZBBGxCXARsAfwO+DYzLxvYGcuaTQYzBgkSYPlGCSpUwYx/pwInAJ8GSAiXgBMAeaUz1fXALOAvYD7M/OB0m4ecFRE3ANMyMzbyvHmArMi4ovAAcARLfEbaZLeh5bXAC4HLoyI8Zm5vLVj5XPcpLb+Th7ISUoa/TYagmP8lNWlTSRpSL3+9a/nn/7pn7jjjju4++67n308nzLD4GbKDIOWmQSHA7sCe0bEIaX5PODUzNyZ5o6VE0u8bybBLsDtNDMJYPVMgl1pkuXnlfj7gadKfAZwycDPXNJoMNAxSJKGgmOQpE4Z6PiTmSdk5oKW0EuBG4B3A/vQlDl5D7AdzTpxfRbRJKDXFt8SWJqZK9ritO5TXl8KbNVP92YAD7Q9FvTTTlIXGIqZ3lOAhVVV/bRs13VdW95E0pD46le/CsA3vvGNZ2PrWUtujRkGjMBMghL/GEBm3hQRW0bElMz8xYaet6TRYRBjkCQNmmOQpE4ZqvEnM38OHNm3HREXAMcBX+mn+SqaSUgbEud5Xmt1Ls3nulaTMfEtdaWhSHq/fQiOIUn9uuGGGwa0X2aeABARfaFhm0kQEX0zCdZ2rDWS3t5WJ40dAx2DJGkoOAZJ6pShGn8i4jXAzpl5dQlVwHLgYWCblqbb0qwVt7b4YmBiRIzLzJUtcVr2eSgiNgYmAo+39yUzlwBL2vo3iLOTNJoNRdL7Xf3EPjEEx5Uk/vVf/7Xf+F/+5V9u6KE2dMbAQGYSrO8MgxnAmf3EJY0yQzgGSdIGcwyS1ClD/Dns3Ii4AXgSmE5TBvJ7QETETjRlRo4FLs7MhRGxLCL2zcxbaGaFz8/M5RGxgGaBysv64uVrXFe2P1VeX9Bez1tS7xmKpPcvy78V8IesZ53wqqr2Bv6xruuDqqp6HfB14P7y8mfrur6yqqozacoFrABm1HX9/aqqdqK5HaUGfgycUtf1qg1pO+gzljRifvKTnzz7/JlnnuGOO+5g7733HsihRmImQV/8p23Hancu3lYnjQlDOAZJ0gZzDJLUKUM1/mTmjyLiLOAWYDxwdWZeDhARxwNX06wTdx1wVdltGs3Cl5sDdwLnl/jJwCURMZPmbtq+ygNnAHMj4m6amdzTNrijkrrOoJPedV1/vnW7qqr5a2vb0ubDwDuBp0pod+Dsuq4/09LmD4EDgb2Bl9EMhHsCZwMz67r+TlVVnwMOr6pq4fq2pVkpWNIYcdZZZ62x/cQTT/DhD394IIca9pkEEdEXvzki9gOW9VfP29vqpLFjCMcgSdpgjkGSOmWw409mbt/yfDYwu5821wO79RO/i2ZNpvb4QuCgfuJPAIetd+ck9YRBJ72rqtq5ZXNb4OXrsdvPgP/D6gXmdgeiqqrDaWZ7zwD2A75V13UN/KKqqo2rqtqqtL2x7DcfeBOQG9DWpLc0hr34xS/m4Ycf3uD9MnPZCMwkuAD4fIk/TXNxT1IX2ZAxKCImArcCb8nMByPiYmB/Vl/0n5WZ10TEwTQX6icAV2bmzLL/VGAOsAVwE3BSWUdgCjAP2JrmPdC0zHyyrBdwKbAjzd0qR2fmo0Nw2pJGiYG+D5KkwXL8kTTWDEV5k9aZ3suADz7fDnVdX11V1fYtoe8DX6zr+o6qqv6OptbtEtZceOA3NB/6qpLcbo21L1KwrrbPUVXVdJq6UkyZMuX5ui9pBLXWkqvrmh//+Me85CUvWe/922YYDOtMgsxcRv/rHEgaowY6BkXE3jQJ69bJAXsCB2TmopZ2E4CLae5Y+x/g2og4JDPn0yS2T8jM2yLiIuBE4LM0M6VmZ+YVEXEGzYW404FP0tx5cmhEvBM4j+ZuFElj1GDfB0nSQDn+SBrrhqK8yRuqqnoJ8Arg53VdPzaAw1xT1/WSvuc0syX/H7B5S5vNaRLhq/qJLd2Atv2dwxeALwDssccedX9tJHVGay05gG233dbbeiWNmEGMQScCp1DuaouIFwBTaO4qmULzfmcWzQW3+zPzgdJuHnBURNwDTMjM28rx5gKzIuKLwAHAES3xG2mS3oeW1wAuBy6MiPEu5CSNXb4PktQpjj+SxrqhKG9yFM3MonuBP6iq6uN1Xc/bwMN8s6qqU+u6/j7wRuAOmkUO/qmqqn+mWeBto7quH6uq6s6qqg6q6/o7wCHAt2kWjVvftpLGkL5acg8//DArVqzg5S9fnwpKkjQ0BjoGZeYJsEa9/pcCNwDvBZ6kWcD7PeX5opZdF9G8l9luLfEtgaWZuaItTus+pQzKUmAr2hbULWVQJrV1eTKSRh3fB0nqFMcfSWPdUJQ3+Rtg97qun6yqanOaD3QbmvR+H3BBVVXLgUeB6XVdL62qagHwXWAjmtlS0JRPmVNV1SY0ifar6rpeub5tB3yWkjpi4cKFnHzyyfzv//4vq1at4kUvehGf//znecUrXtHprknqAUM1BmXmz4Ej+7Yj4gKahW+/0k/zVUC1gXGe57VWM2hKyUka5QY6BrmmgKTB8nOYpLFuoyE4xqq6rp8EqOv6NzR1vZ9XXdcP1nW9T3n+X3Vd71vX9UF1Xf9FXddLS/zjdV3vXdf1nnVd31xiP6nr+sC6rl9f1/W767peuaFtJY0dn/jEJzjhhBP4wQ9+wB133MH73vc+Zs2a1eluSeoRQzUGRcRrIuKtLaEKWA48DGzTEt+WZmb22uKLgYkRMa4tTus+EbExz13zpM+5wA5tj/03+KQkDbuBjEFlTYGb6X9NganlcU3LmgKHA7sCe0bEIaX9PODUzNyZZrw6scT71hTYBbidZk0BWL2mwK40yfLzBnnqkjrMz2GSxrqhSHr/vKqqz1RVdXgpL/KzITimJAHw+OOPc+SRz06O5K1vfSu/+tWvOtgjSb1kCMegCjg3Il4UEeNpFtC+BvgeEBGxU0lkHwvMLwvpLouIfcv+x5X4cmABqxeoPA6YX55fV7Ypry/or553Zi7JzAdbH8BDAzkpScNrgGNQ35oCj8Bz1hT4UUTMioiNaFlToJRM6ltT4OU8d02Bo8rYdQCr756dCxxVnh9KM9MbmjUFDint1xARkyJi+9YHlleSRiU/h0ka64Yi6f154AngT4C/BP5lCI4pSQCsXLmSJUuWPLv9xBNPdK4zknrOUI1Bmfkj4CyaNUvuAX6YmZdn5jLgeODqEr+P1QmlacA5EXEv8ALg/BI/GZheFrvcH5hZ4mcA+0TE3aVNX7k3SWPUQMagzDwhMxe0hPrWFHg3sA/NuPEe1r52wKDXFAD61hRoNwN4oO2xoJ92kjrMz2GSxrqhqOl9DvAXdV3/rKqqs2mu+B8wBMeVJN7xjndwzDHHcMghzd228+fP513veleHeyWpVwx2DMrM7Vuez6YpDdDe5npgt37id9HMxGyPLwQO6if+BHDYendO0qg3FO+DRtmaAufSfF5sNRkT39Ko4+cwSWPdUMz0Xl7X9c8A6rr+Of2/uZGkATnwwAMBWL58OT//+c/55S9/yZ/8yZ90uFeSeoVjkKROGooxaDStKWB5JWns8D2QpLFuKGZ6L6yq6lPAd2lmIz08BMeUJAA+8pGPMG3aNI477jiefvppLr/8cj760Y8yZ86cTndNUg9wDJLUSUM0BvWtKXAD8CTNmgKX0LKmAE2ZkWOBizNzYUQsi4h9M/MWWtYUiIi+NQUuo/81BT7FOtYUkDR2+B5I0lg3FDO9/xL4X+DNNFf/3z0Ex5QkAH71q19x3HHNumybbropxx9/PIsXL+5wryT1CscgSZ00FGOQawpIGgjfA0ka6wY907uu62U0tdkkacitXLmSX/7yl7z0pS8F4LHHHqOu6w73SlKvcAyS1EmDGYNcU0DSYPgeSNJYNxTlTSRp2Bx//PEcccQR7L///lRVxa233sqHP/zhTndLUo9wDJLUSY5BkjrF8UfSWGfSW9Ko9ra3vY0/+IM/4LbbbmPcuHG85z3vYeedd+50tyT1CMcgSZ3kGCSpUxx/JI11Jr0ljXq77LILu+yyS6e7IalHOQZJ6iTHIEmd4vgjaSwbioUsJUmSJEmSJEkaFUx6S5IkSZIkSZK6hklvSZIkSZIkSVLXMOktSZIkSZIkSeoaJr0lSZIkSZIkSV3DpLckSZIkSZIkqWuY9JYkSZIkSZIkdQ2T3pIkSZIkSZKkrrFxpzsgSSMpIk4A/qoltAPwZeD3gP2Bp0p8VmZeExEHA2cDE4ArM3NmOc5UYA6wBXATcFJmroiIKcA8YGsggWmZ+eSwn5gkSZIkSZIAZ3pL6jGZ+cXMnJqZU4FpwP8CHwf2BA7oe60kvCcAFwOHA7sCe0bEIeVQ84BTM3NnoAJOLPHZwOzM3AW4HThjhE5NkiRJkiRJONNbUm/7LPBR4HfAFGBOmal9DTAL2Au4PzMfAIiIecBREXEPMCEzbyvHmQvMiogvAgcAR7TEbwROb/2iETEJmNTWl8lDd1qSJEmSJEm9y5neknpSKVsyITO/ArwUuAF4N7APTZmT9wDbAYtadltEk5xeW3xLYGlmrmiLt5sBPND2WDAU5yVJkiRJktTrnOktqVe9l6ZWN5n5c+DIvhci4gLgOOAr/ey3iqacyYbE251LMwu81WRMfEuSJEmSJA2aSW9JPSciNgEOBI4v268Bds7Mq0uTClgOPAxs07LrtsAj64gvBiZGxLjMXNkSX0NmLgGWtPVpkGclSZIkSZIksLyJpN70WuAnmflU2a6AcyPiRRExHphOU9f7e0BExE4RMQ44FpifmQuBZRGxb9n/uBJfTjNb+5jW+MickiRJkiRJksCkt6TetCPwUN9GZv4IOAu4BbgH+GFmXp6Zy2hmg19d4vcBV5XdpgHnRMS9wAuA80v8ZGB6Wexyf2DmsJ+NJEmSJEmSnmV5E0k9JzP/Dfi3tthsYHY/ba8HdusnfhewVz/xhcBBQ9VXSZIkSRpLImIicCvwlsx8MCIOpllPaQJwZWbOLO2mAnOALYCbgJMyc0VETAHmAVsDCUzLzCcjYhJwKc0kpsXA0Zn5aClfeRGwB/A74NjMvG/ETljSqORMb0mSJEmSJA1aROwN3AzsXLYnABcDhwO7AntGxCGl+Tzg1Mzcmabk5IklPhuYnZm7ALcDZ5T4J4EFmbkrTbL8vBJ/P/BUic8ALhm2E5Q0Zpj0liRJkiRJ0lA4ETgFeKRs7wXcn5kPZOYKmkT3URHxcmBCZt5W2s0t8fHAAawuKzkXOKo8P5RmpjfA5cAhpf2z8cy8CdiyzBaX1MMsbyJJkiRJkqRBy8wTACKiL7QdsKilySJg8jriWwJLS4K8Nb7GsUoZlKXAVus41i9a+1bKo0xq6/JkJHUlk96SJEmSJEkaDlU/sVUDiA/kWO1mAGf2E5fUhTpW3qSqqr2rqvpOeb5TVVU3V1W1oKqqz1ZVtVGJn1lV1ferqrq1qqq9hqqtJEmSJEmSht3DwDYt29vSlD5ZW3wxMDEixrXF1zhWRGwMTAQeX8ex2p0L7ND22H9gpyVptOvITO+qqj4MvBN4qoTOBmbWdf2dqqo+BxxeVdVC4EBgb+BlwNXAnoNtC1wzUucpSZJ6V0RMBG4F3pKZD0bEwTTvTSYAV2bmzNJuKs1iTFsANwEnlVt2p9DUvdwaSGBaZj5Zbs29FNiR5oPh0Zn5aERsAlwE7AH8Djg2M+8bsROWJEl6ru8BERE7AQ8AxwIXZ+bCiFgWEftm5i3AccD8zFweEQuAY4DL+uLlWNeV7U+V1xeU9n3xmyNiP2BZZq5R2gQgM5cAS1pjLWVYJHWZTs18/hnwf1q2dwduLM/nAwcD+wHfqhu/ADauqmqrIWgrSZI0rCJib+BmYOeyPQG4mOYC/K7AnhFxSGk+Dzg1M3emuT33xBKfDczOzF2A24EzSvyTNB/ydqVJlp9X4u8HnirxGcAlw3aCkiRJ6yEzlwHH00xOvAe4j9WLVE4DzomIe4EXAOeX+MnA9Ii4h2Ym9swSPwPYJyLuLm1OKfELgE1L/HyaSZaSelxHZnrXdX11VVXbt4Squq7r8vw3NDOd+m5ToS0+2LbPUVXVdGA6wJQpLvArSZIG7USaD2JfLtt7Afdn5gMAETEPOKp8mJuQmbeVdnOBWRHxReAA4IiW+I3A6cCh5TWAy4ELI2J8iX8MIDNviogtI2JKfzOdJEmShlNmbt/y/Hpgt37a3EXzHqk9vhA4qJ/4E8Bh/cSXAe8aVIcldZ3RspBl6wIDm9PcbrK0PG+PD7btc9R1/QXgCwB77LFH3V8bSZKk9ZWZJ8Aat8xuByxqabIImLyO+JbA0sxc0RZf41ilDMpSYKt1HGuNpHcpjzKprcuTkdQ1LK8kSZJ63WhZ2PHOqqoOKs8PARYAtwB/WlXVRlVVTQE2quv6sSFoK0mSNNKqfmKrBhAfyLHazaCpqdn68D2S1CUsryRJkjR6Znp/EJhTVdUmwL3AVXVdr6yqagHwXZrk/ClD0XbEzkiSJGm1h4FtWra3BR5ZR3wxMDEixmXmypZ467EeioiNWV3mrS/+07ZjtTuXplxKq8mY+Ja6xagtr+SdJpIkaaR0LOld1/WDwD7l+U+AA/tp83Hg422xQbeVJEkaYd8DIiJ2oplZfSxwcWYujIhlEbFvZt4CHAfMz8zlEbEAOAa4rC9ejnVd2f5UeX1Bad8Xvzki9gOW9VfPOzOX0FbyraUMi6QxbjSXV6KZBX7mwM5MkiRp/Y2W8iaSJEldqyywdDxwNXAPcB+r70CbBpwTEfcCLwDOL/GTgellNub+wMwSPwPYJyLuLm367nC7ANi0xM8H3jmc5yRpzBhN5ZXOBXZoe+zfTztJkqRBGS3lTSRJkrpOZm7f8vx6YLd+2txFU36gPb4QOKif+BPAYf3ElwHvGlSHJXWjUVNeyTtNJEnSSHGmtyRJkiR1r2fLK0XEOJrySvPLhbVlEbFvafdseSWaGv/HtMbL874yStBSXqk1vq7ySpIkSSPFpLckSZIkdSnLK0mSpF5keRNJkiRJ6jKWV5IkSb3Mmd6SJEmSJEmSpK5h0luSJEmSJEmS1DVMekuSJEmSJEmSuoZJb0mSJEmSJElS13AhS0k9JyJuAF4KLC+h9wKvAGYCmwDnZOaFpe3BwNnABODKzJxZ4lOBOcAWwE3ASZm5IiKmAPOArYEEpmXmkyN0apIkSZIkST3Pmd6SekpEVMAuwG6ZOTUzpwIPAX8P7AfsBkyPiFdFxATgYuBwYFdgz4g4pBxqHnBqZu4MVMCJJT4bmJ2ZuwC3A2eMzJlJkiRJkiQJTHpL6j0B1MD8iLgrIv4KOBi4ITOfyMyngKuAtwF7Afdn5gOZuYIm0X1URLwcmJCZt5Vjzi3x8cABZf9n4yN0XpIkSZIkScLyJpJ6z4uA64H30ZQs+Q5wJbCopc0imoT3dv3EJ68jviWwtCTIW+NriIhJwKS28HPaSZIkSZIkacOZ9JbUUzLzu8B3y+ZTEXERTc3uv29ruoqmbEm7gcTbzQDOXJ/+SpIkSZIkacNY3kRST4mI/SLijS2hCngQ2KYlti3wCPDwBsYXAxMjYlxbvN25wA5tj/0HdEKSJEmSJElagzO9JfWaScAnIuKPgPHAu4B3APMiYivgKeCtwHTgR0BExE7AA8CxwMWZuTAilkXEvpl5C3AcMD8zl0fEAuAY4LK+eHsHMnMJsKQ1FhHDcKqSJEmSJEm9x5neknpKZn4duBa4E7iDJol9C/B3wLeBHwKXZeb3M3MZcDxwNXAPcB+rF6mcBpwTEfcCLwDOL/GTgekRcQ/N7O2ZI3BakiRJkiRJKpzpLannZOYZwBltsctoZme3t70e2K2f+F00i122xxcCBw1VXyVJkiRJkrRhnOktSZIkSZIkSeoaJr0lSZIkSZIkSV3DpLckSZIkSZIkqWuY9JYkSZIkSZIkdQ2T3pIkSZIkSZKkrmHSW5IkSZIkSZLUNUx6S5IkSZIkSZK6hklvSZIkSZIkSVLXMOktSZIkSZIkSeoaJr0lSZIkSZIkSV3DpLckSZIkSZIkqWts3OkOSJIkSZIkqXtFxA3AS4HlJfRe4BXATGAT4JzMvLC0PRg4G5gAXJmZM0t8KjAH2AK4CTgpM1dExBRgHrA1kMC0zHxyhE5N0ijlTG9JkiRJkiQNi4iogF2A3TJzamZOBR4C/h7YD9gNmB4Rr4qICcDFwOHArsCeEXFIOdQ84NTM3BmogBNLfDYwOzN3AW4HzhiZM5M0mo2qmd5VVf0XsLRsPgB8HjgPWAF8q67rWVVVbUQzoO0GPA2cUNf1T6uq2md9247oSUmSJEmSJPWuAGpgfkRsTTNb+zfADZn5BEBEXAW8DbgRuD8zHyjxecBREXEPMCEzbyvHnAvMiogvAgcAR7TEbwROf04nIiYBk9rCk4fiBCWNPqMm6V1V1WZAVdf1QS2xHwJvBX4OXFtV1euAHYDN6rp+fUl0f4bmCuDnNqCtJEnSiPPWXkmS1INeBFwPvI/mfc13gCuBRS1tFgF7Adv1E5+8jviWwNLMXNEW788M4MyBn4aksWTUJL1pZmP/XlVV36Lp18eBTeu6/hlAVVXfBA4GtgW+AVDX9W1VVe1RVdXE9W07sqckSZLUaLm1d0rfB7OI+H3gCmB3mrvSbo2Ib9Pc8XYxcCDwP8C1EXFIZs6nSWyfkJm3RcRFNLf2fpbVt/ZeERFn0Nza+5xZTpJ6lxfeJHVCZn4X+G7ZfKq8fzmbprxJq1U0ZUvaDSTen3NpZoK3mgwsWEt7SWPYaEp6/xb4Z+CLwCuB+cCSltd/A+wITAR+3RJfWWJL16dtVVUb13W9AkmSpJHlrb2SOsYLb5I6JSL2AzbNzOtLqAIeBLZpabYt8Ajw8AbGFwMTI2JcZq5siT9HZi5hzTwTETGQU5I0BoympPdPgJ/WdV0DP6mq6tfAi1te35xmcPq98rzPRjQJ783Xp21/Ce+qqqYD0wGmTJky2POQJEnqj7f2Suqkjl9486Kb1LMmAZ+IiD8CxgPvAt4BzIuIrYCnaMrVTgd+BERE7ERzAe5Y4OLMXBgRyyJi38y8BTgOmJ+ZyyNiAXAMcFlffGRPT9JotFGnO9Di3TQ1t6mqajuahPVTVVW9oqqqCvhTmltObgHeXNrtA/x3XddLgWfWp21/X7iu6y/Udb1HXdd7bLXVVsN5jpIkqUdl5ncz87jMfCozHwMuAj7RT9ORuLV3h7bH/uvuvaQu0Hfh7QjgjcBJwBQ27ALbYC+8zaBJYrU+LCsgdbnM/DpwLXAncAdNEvsW4O+AbwM/BC7LzO9n5jLgeOBq4B7gPuCqcqhpwDkRcS/wAuD8Ej8ZmF4uzO1PU7JJUo8bTTO9LwLmVlV1M80MhHfTfFi7FBgHfKuu6+9VVfUD4E+qqrqV5gPeX5b9T9qAtpIkSSPKW3slddIoqal7LtbTlXpSZvaVPWqNXUYzO7u97fU06761x++iuSOuPb4QOGio+qre9vTylWw6flynu9HThupnMGqS3nVdP0Nz20q7fdraraJJcLfvf9v6tpUkSeqASXhrr6QOGQ0X3rzoJkka7TYdP47dP/SlTnejp93x6eOG5DijqbyJJElS1/LWXkkdNgn4dERsFhGbs/rC2xsjYquI+D2aC2/fAL5HufAWEeNoLrzNL7Mpl0XEvuWYz154o5mtfUxrfKROTJIkqd2omektSSMlIs4Eji6b12bmhyPiYpok0VMlPiszr4mIg2lu/Z0AXJmZM8sxptIsALUFcBNwUmauiIgpwDxgayCBaZn55AidmqRRzlt7JXVKZn49IvamufA2DrgwM2+JiL4Lb5sAX8zM7wNExPE0F942A65jzQtvc0ri/E7WvPB2SUTMBH4BvH1ETkySJKkfJr0l9ZSSxH4T8Dqa9QO+ERFHAnsCB2Tmopa2E4CLgQOB/wGujYhDMnM+TWL7hMy8rdTEPBH4LDAbmJ2ZV0REX3Lr9JE7Q0mSpP554U2SJPUKk96Ses0i4IOZ+QxAKQ8wpTzmlJna1wCzaD7Q3Z+ZD5S284CjSumACZl5WznmXGBWRHwROAA4oiV+I21J74iYRHOLcavJQ3WCkiRJkiRJvcykt6Sekpl39z2PiFfS1J7cj2Zm0nuBJ4GvA+8pzxe17L6IJjm93VriWwJLM3NFW7zdDODMQZ+MJEmSJEmSnsOkt6SeFBGvpllQ7rTMTODIltcuoFmA6Sv97LoKqDYw3u5cmlngrSbTLAAlSZIkSZKkQTDpLannRMS+NAszzSi1t18D7JyZV5cmFbAceBjYpmXXbYFH1hFfDEyMiHGZubIlvobMXAIsaevT4E9MkiRJkiRJbNTpDkjSSIqIlwFfA47NzCtKuALOjYgXRcR4YDpNXe/vNbvEThExDjgWmF8WalpWkufQzAqfn5nLaWZrH9MaH4nzkiRJkiRJUsOZ3pJ6zWnAZsDZLbOrPwecBdwCjAeuzszLASLieJpZ4ZsB1wFXlX2m0Sx8uTlwJ3B+iZ8MXBIRM4FfAG8f5vORJEmSJElSC5PeknpKZn4A+MBaXp7dT/vrgd36id8F7NVPfCHNopiSJEmSJEnqAMubSJIkSZIkSZK6hklvSZIkSZIkSVLXMOktSZIkSZIkSeoaJr2ldXh6+cpOd6Gn+f2XJEmSJEnShnIhS2kdNh0/jt0/9KVOd6Nn3fHp4zrdBUmSJEmSJI0xzvSWJEmSJEmSJHUNk96SJEmSJEmSpK5h0luSJEmSJEmS1DVMekuSJElag4tJd5bff0mSpMFxIUtJkiRJa3Ax785yMW9JkqTBcaa3JEmSJEmSJKlrmPSWJEmSJEmSJHUNk96SJEmSJEmSpK5h0luSJEmSJEmS1DVMekuSJEmSJEmSuoZJb0mSJEmSJElS1zDpLUmSJEmSRo2nl6/sdBd6mt9/Sd1g4053QJIkSZIkqc+m48ex+4e+1Olu9Kw7Pn1cp7sgSYPmTG9JkiRJkiRJUtcw6S1JkiRJkiRJ6homvSVJkiRJkiRJXcOa3pI0xCLiWGAmsAlwTmZe2OEuSeohjkGSOsXxR1InOQZJatXVM72rqtqoqqrPVVX13aqqvlNV1U6d7pOk7hYRvw/8PbAfsBswPSJe1dleSeoVjkGSOsXxR1InOQZJatfVSW/gCGCzuq5fD3wE+ExnuyOpBxwM3JCZT2TmU8BVwNs63CdJvcMxSFKnOP5I6iTHIElr6PbyJvsB3wCo6/q2qqr26HB/JHW/7YBFLduLgL1aG0TEJGBS234vB3j00Uc36Is9sfiXG9o/DZGHHtps2L/Gqt8uGfavobV76KGH1rtty//dccPSmfXnGNQjHIO6m+PP83P86SzHoO7mGPT8HIM6x/Gn+w3VGNTtSe+JwK9btldWVbVxXdcrWhtVVTUdmF42n6yqKkeqg6PAlsBjne6Ehs2Y/vlW//yuDd3l5cPRjw1U9RNb1bY9Azizv52nTZs21P3RMPmnTndAw+6N/9+AykBuC/xsiLuyIRyDeoRjUHdz/NFo5xjU3RyDNJo5/nS/oRqDuj3pvRTYvGV7o/aEN0Bd118AvjBivRpFqqq6va5rZ8B3KX++HfEwsH/L9rbAI21tzgXmtsU2AXYE7gdWDlPfRpPJwAKa79X6X8bVWNJrP+NxNP/ff9DhfjgGrZ9e+/3sNb3283X8GXt67Xe01/Taz9cxaGzptd/PXtOLP9+1jkHdnvS+Bfhz4N+qqtoH+O8O90dS9/v/gI9HxFbAU8BbWX0nCQCZuQRY0s++Pxnuzo0WEdH39KHMfLCDXdEw6dGfcSdnN/VxDFoPPfr72TN69Ofr+DOG9OjvaM/o0Z+vY9AY0aO/nz2jh3++/Y5B3b6Q5TXAsqqqbgXOAf66w/2R1OUy82Hg74BvAz8ELsvM73e0U5J6hmOQpE5x/JHUSY5Bktp19Uzvuq5XASd1uh+jXE+Wdekh/nw7IDMvAy7rdD8k9SbHIEmd4vgjqZMcgyS16vaZ3noepZ65upQ/X0mSJEmSJPUak96SpE5YAsyi/5p66g5L8Ges0WsJ/n52syX489XotgR/R7vZEvz5avRagr+f3WwJ/nyfVdV13ek+SJIkSZIkSZI0JJzpLUmSJEmSJEnqGia9u1hVVf58u1hVVePKv+OrqurqRWklSZIkSZKk9WVStEtVVbVRXderqqraqqqq6VVVHVhV1Y6d7peGRvn5rqyqajLwL8CfVVX1gk73S5IkSZIkSeo0k95dqKqqbUvCezvgP4BXAScAx1dVFZ3tnYZC+fluCcwBvgM8A+xVVdWrO9oxST0hIqrWf6XRxN9PSZ3kGCSpkxyDpNVMeneZqqr+CnhXVVWvAKYBl9V1PQM4C3gZ4GzvMaytZM1+wDbAfcCZwD7A7KqqJnSibxKs+eYqIjbpZF80PCJio8zsWwV7Yon5plqjgr+f3c2/MRrtHIO6m2OQRjvHoO7l+DMwVV3Xz99KY0ZVVX8KvAn4CfBaoK7r+q/Ka/8I/G9d15/pYBc1QC0lazYHngJeChwGbAn8OzAeeD8wva7rZzrXUwki4l3AgcDczLyp0/3R0IuI6cCfAr8Cfgack5nLOtsrqeHvZ3fzb4xGO8eg7uYYpNHOMah7Of5sGGd6d5m6rr8JfAUI4B6grqrq3Kqq3kGTDP9aB7unAagap7WUrLkG+DvgQuBq4FvAXwKfAf7ZhLc6LSLeR1NS6Tpgo4h4cUS8sMPd0iBFxE4tz98B/BXwSeBW4PeBsyPCRXXVEf5+9g7/xmg0cgzqHY5BGo0cg3qD48+GM+ndBaqquraqqvlVVf1jVVWHAY8DtwEvAn4KjANeAby9ruufdbCrGpgtgbdXVbUz8CXgXOB6mlI17wUWA7cAJ9R1/eNOdVICKH9096Ipr/QgzVXo7wOfiIgpHeyaBigiqojYDPj3iDi73Fr3WuAzmXkncCnN2LQdsNM6DiUNOX8/e4t/YzTaOAb1FscgjTaOQb3D8WdgTHqPcVVVjaNZyPBlwP40i1bOA94JnEhTy/te4B/rur6vQ93U4CwDFgI18E3gN8D/Bd4HPAqMq+v6ai9oqBPaa8Rl5pM0F9vuBP4FeAT4G5r689YeG4Mysy63Q/458EaaO00eAvaPiG0y8+nM/D7Nz3diB7uqHuTvZ3fzb4xGO8eg7uYYpNHOMah7Of4MDW9vGOPqul5ZVdX5NEnRNwP/Xtf1P1RVtQXwauB1wLfqurZ+0xhSVVUFvKqu67vruv5NVVULgL2B1wOnAMfTzOD/K5ryJtKIi4jIzCzPTwKmAFsAHwMuAx7NzN9FxP7Ay4HfdayzGrTM/FlEvJWmrNKTNCW03hYRN9DcTbQtzZtsacT5+9l9/BujscQxqPs4BmkscQzqLo4/Q8eFLLtEVVW/BxxNk/j+fF3X13e4SxqEsujovkAFPAb8Hk3dpjnAlTS3sbwReG9d1/d2qp/qXRHxYuAHwAHA22gWVf1rVteZ/0CJf4zmboXjM/NHnemtBiIi3kRz+9y9wE3AU5l5V0S8APhPYFPgZuA1wErgtMy8q1P9VW/x97O7+TdGo51jUHdzDNJo5xjUvRx/hpYzvbtEXde/rarqSmAF8MGqqp6p63pBp/ulAfuHuq5/VVXVHsBLgf2AfYCf1HV9aFVVmwKz67pe3NFeqpctp1k/YAtgK5o/xicBPwI+CnwC+DTN7VePZebjHeqnBu4FNKWyAM4D/iIifgLcBdxN80b7u8D/AerM/HVHeqle5e9nd/NvjEY7x6Du5hik0c4xqHs5/gwhZ3p3mTLj+zBgQV3XD3e6PxqYqqqquuU/Z1VVmwF7AqcDX6nr+pKOdU4qImIOcAfwJppbrh4AjqWpP38dcERm/rZzPdRgRcQbgQuAM4H/AF4JvJWmvNIRNDXkds3MxzrVR/Uufz+7m39jNNo5BnU3xyCNdo5B3cvxZ+g407vL9M34rr2aMaa1//zqul5WVdUdNFf07u9Mr9TrIuI1wCTgB2XBlJ+V7Y8B1wD/mZnLI+J4YEtgAuAf4zEsM6+PiNNp3lBPyMwvAf8NEBGzgE18w6VO8fezu/g3RmONY1B3cQzSWOMY1D0cf4aPM70lSc8rIl4IzKWpNX838Bvgf2gWzjiCZsXw2cD3aGYZvD0z7+5EXzX0IuLPgXOBMzNzXoe7I63B38+xz78xGsscg8Y+xyCNZY5BY5vjz/Ay6S1JWi8RsRFNbbEX0yyesRkwA3hvZv5bRGxNs2jK05n5vx3rqIZFRBwKXApMz8x/63R/pFb+fo59/o3RWOYYNPY5Bmkscwwa2xx/ho9Jb0nSgEXEEcA/AJ/OzIs63B0Ns4j4M+CnmfnTTvdFaufvZ/fxb4zGEseg7uMYpLHEMai7OP4MDZPekqQNFhEVQGbWEXEM8LfAAZm5tLM9kySNdf6NkdRJjkGSOsXxZ2iZ9JYkDVhEVOUP8gsz88lO90eS1D38GyOpkxyDJHWK48/Q2KjTHZAkjV2Z2Xfl9KmOdkSS1HX8GyOpkxyDJHWK48/QcKa3JEmSJEmSJKlrONNbHVFV1UFVVV3RFvuHqqqOH8QxX1NV1QHl+Xeqqvp++feWqqrmVVX1kvLauVVVTRnUCUiSJEmSJEkalUx6q5u8FXhVy/ZxdV0fVNf1vsB84AsAdV3PqOv6F53ooCRJkiRJkqThtXGnOyC1q6rqLGB/YBxwdl3XX6mq6kDgTJoLNS8EjgWeAf4DeBz4NnA88ExVVf/Vfsy6ri+tqurvq6raDPgGcBLwEuAzwHLgt8DbgGXA54BXlq81s67r71RV9TbgFGA8UANHAhVwZWm3GXBSXdc/rKrq1NK/Griiruvzh/Y7JEmSJEmSJGltTHqrk/64qqrvtGzvCJwF7FDX9X4lQX1bVVX/CbwaeEdd149UVfVR4CjgUmAbYPe6rp+pqqoCHq3r+vvN0+f4FTCpZfsI4N+Ac4HDgBcBhwKP1XX9nlIO5abytXcGDq3r+rdVVX0e+FNgCU3C/TiaGeYvqKrqVcAxwH7la/xnVVXfrOs6B/g9kiRJkiRJkrQBTHqrk26o6/ov+jaqqvoHYHNg95Zk+Hhge+Bh4Pyqqp4Efh+4pbz+QF3XzzzfFyoJ8W2A/20Jfwr4O+D6cvzvAa8B9q+qau/SZuOqqrYs+11Svv4uwHdpSqa8Evh/NLPFPwn8AfDyckxoEumvBEx6S2NIROxDcxHuJTR3c/wPcFpm3j0Ex54L/Dgz/7kt/gngp5n5pcF+DUmdExHjgYXAjzLzz0rsIOBfMvMPBnjMGtgqMx/bgH2+U77mVWX/HwMrae5UA/hy+zjUsu9JwKTM/Id1HP8g+jmniNgD+Ehmvm19+ypp9FvXe6OI+BZw7AaOUQcxiHFR0tgUEdsDPwP+u4Q2osmnnJeZX2r9TBQRxwOfAO6luUt/DvAETd5lz8z8YTnmdODzwJ9l5jdL7CjgQ5m51zr6UgNbAW8B3paZb2l7/TDg4Mx8/xCcujrApLdGm2XAt+u6nl5V1UbAGTQD4reAV9R1/Zuqqi5h9Qe2VS37rmLtderfA1xf1/Wqllng7wDm1nV9WlVVfwtMB+4DHqrr+lNVVU2gSYovB2YBfYtf/mf5+gcBi+q6flNVVa+nSaLPAO4GDqnruq6q6q+BHw382yFppEXEpsDXgTdl5n+V2DuA+RGxQ2auHI6vm5kfG47jShpxR9L87d89InbNzHs73aHiDX0JqYjYEvh6RNSZ+Zn2hpn5uYF+kcy8naZknKQu8XzvjYA/6WT/JI05v8vMqX0bEfFy4PqIeKrtM9FxwEczc15EXAzMycxPRsRXafIxPyzt/pym9O1hwDdL7I3AtYPpZGb+O/DvgzmGOsukt0ab3wBPVlW1gKZ29zUl0T0PWFBV1VPAL4Ht+tn3DuDTVVX1fbj8UmkPzUzuU9rafx/4Ymmziibp/Qgwp6qqG4GJwGxgKc3M8u8CK2jKpGxHM/hdUVXV+2j+L32iruu7qqq6Hri5qqpNy9d4eHDfEkkj7PdoSiG9sCV2Kc1Y8MaImEUzVryaZj2AM4H3AwFcnZl/Dc/OOHg/zczKXwJ/lZk/af1CEXE2sBtwOPAvlBngEbEM+AeaD5Hb0cx8ODcixgGfpnlD92uaO1RelZkHDfH3QNLAnQxcAfyU5mL4e1tfjIgXAhcA+9K8r/gazUX2icCFwFSadUHm03zQW1F2nVVmWr4E+HRmXliOdwbw9nKsn9CMNY+uq4OZ+VhE/A1wdRmHzgReD2xLk7D/KbBlZv5VRDwIzKX58DgFuDIzP9x2TvvRjJNvBzahzN4sd7YspbmT7mU0kwv+IjOfjIg3A/9IM0b+EDgY2C8zH1xX3yV1xLreG32xbH+7/L9eQPP+5LXAR2nGpX+hGbtq4DPtd7W1jiGZeWtE/Dkwk2Y8+S3NjPLvDs+pSeq0zFwYER8DPlT+//+Y5g7/vYAdImJbmvK0v4uILWjeIx0KnBsRE0q7N9Cs39aX93kj8PaI2Jnm/dULaT5X/RA4JjOX9deXiHgbzfuTN9O8N3pbZr6l3EH3XZr3b1Noxrp3ZeaqMiP9I8DvgBuAD2Sm+dZRYG2zYqVhVdf1d1pLm5TYR+q6/te6rv+mruv967p+XV3Xnyiv/U1d11Prut63ruv/U9f1iXVdP1jX9T4t+19b1/Wr6rr+dl3XB9V1vVdd128oj3fUdf3r0u6guq7vq+v6e3Vd71PX9Rvruv6Tuq4fqOv66bquj6vr+sDy9efUjaPL8f6orutD67r++7quHy/7HVTX9X51XX+rHP/TpZ971HV9cl3XwzIrVNLwyMxfAR8GvhERP4+ILwN/Cfx/NAvo7gl8MjN3oUlm/y3Nm64/BE6JiO0i4o/LMd6QmbsBlwFfi4i+W02qiLiQpnzTmzPzybZubAo8lpn70syY/IeI2Aw4Adid5pa+1wOvGJZvgqQBiYhXAfvQrBlyCfDOiHhJW7NP0CyAvStNgntf4EDgfJq1Ql4D7EFzQey0lv1+npm708wk/0xEjI+IvwQOobnF97U0HxLnrmd376Ip/bZl2X458IeZ+Y5+2r4wM/cH/gg4tczs7DvnN5Sv+eeZeWs/++4O/Fk53+2Ao8r35MvAO8pMr2/TfLiVNAqt671RZv5lafaGzPyf8vzHmbkrzczLfwcuKGPUIcCnIuL1fcduH0Mi4pU0d9C+OTNfRzMx6asR8YLhP1NJHXQXzXsgAMpEottpSpR8mmYsOSczP0ST9N4/IjaiuWh+S2beA/w2Il4XEVNoSufeAZwIXJKZrwd2Anag+ez2HBFxLPBx4KDM7K9E7StoZpi/Bvhj4MDy3u8facqgvI7mYuC4wXwjNHRMekuS1CYzzwZeSjNTexFwOnAnsAXwQGbeWZr+DPh2Zj5TygYsBV5Mk+C5MjMXl+PNpUnobF/2+xvgJODMzHx6Ld34f+Xf/6JJgr+AZsbBlzJzWWY+Q1O7TtLo8T7g2sx8IjN/ADxA20xvmg9nF2XmyjJ2HJiZ36FJBv1LZtZlXPhcifW5rPz7Q5oxYWJ5/V8zs+/OtvNo7kjZZD36Wpd/f1f+va1lVnm7/weQmQ/TrHPy4hKfTFPy4GuZubZybt/IzKczczlN/c4XAwcA92TmXeW4l9CMn5JGqbW9NyqzLtstKP/uDGyWmV8tx3gEuJrmfRL0P4b8Cc1dJ9dHxA9pZoCvoklWSepeNc2dHc8rMx+iufP2tTSlTb5eXvo68CaahPR1mVnTjFWLI+LDwGdpLsC/8DkHbSY2fQn4XMsFvHb/kZmrMvM3NHfFvRj4U+BbpU/Q3M2nUcKktyRJLSJi34j4UGb+JjO/Xm7jfzXNB67xQHuSenk/h+nv72tV9ge4kabswdyy6F1/fgdQ3qz17b+C1WsaQFMWQNIoUGYhHgfsFxEPlrIg29LcZtv6/3wFqxPORMTLyszn9nFjo7b9lsNzxoT+9tmYNceJtdmT5iJe350m7XectPpdy/O65fgraBJU74qItS0U1d++7WMZrLlOi6RR5HneG/VXz7tvPOnv/VDr2NbfGDIOuD4zp/Y9aO6g+fEQnY6k0WlPVi9uuT7m09wp92ZW1+6+FtiPJundF7uc5o6RhcA5NBOK+nuftIQmYf7xsthmf9bnPY2fz0YRk96SJK1pMTCz1Jfssy3NTOv2MgVr803gmIjYCqCUIHicZkYANLfq/QvNm6uPb0DfrgXeERGbRsTGwPG0JM8kddQ04DFgu8zcPjO3B3akmU20dUu7/48mwbNRWRzuKpoPbd+kKZFUlfh0msWz1+WbwF+23Pb/fuCmddxBAkBEbEdzK+4/b8gJ9uPRUtLkNGBeRPzeeu53C7BzRLy29OetNPWCHc+k0Wld743+mybJ099F/ASeiYj/A8+OPW9l9djW3xhyA/CmiNil7PNmmrUGNhvys5I0KpS622cAz1lcex3mA+8GHsnMX5bYApoykH/E6nHmT4FPZOaVNO8z9qb/8iP3Z+YNNDO1v1RKp6yPbwIHR0RfmbYTNuAcNMxMekuS1KIsNnkETc3Jn0fEPTT1eafTfHhbn2P8J81Mghsi4m7gXcBbMnNVS5ua5o3ayRHxR+vZvbk0i0PdCdxKU2N8vW4DlDTs3gecnZnPzvDJzCU0tbpntLSbRfN/9y6a/8vXlVv/30+THP/v8kjg75/na15Ek0T/fkTcS7O2wLS1tP12RPwwIu6gqYv5pcycvSEnuDalPMl9rOeH1cx8gmbRyy9FxH/RfCBdgeOZNCqt671RqXv7VeDmiPiDtv2Wl/0+EBE/ohmvPpGZ325r9+wYkpl307znuiIi7gL+L3BYSxknSWPfhPKe5IflfcBc4G8z89rn2a/VzTT1uftKm1DKtP0AWJiZfWXTPgpcExG305SOu5F1l0v6e5oLeh9an06U8fGvgW+Wr7Ervp8ZNaq6dkKFJEljQUS8Cdg6M+eV7fOAZZl5emd7JknrLyImAjOBj2fmbyPiD2nuZNmupXyLJEnSqFYW9z4O+L+Zuarc2XJ6Zu7d4a6JpuafJEkaG+4GPhQRH6L5G34XzexSSRozMnNpRDwD/CAiltPUKz/ahLckSRpjHqJZHPO/I2IF8Guau3k1CjjTW5IkSZIkSZLUNazpLUmSJEmSJEnqGia9JUmSJEmSJEldw6S3JEmSJEmSJKlruJBli4jYFNgTWASs7HB3JG2YccC2wA8y8+lOd2ZDOf5IY55jkKROcfyR1EmOQZI6aa1jkEnvNe0JLOh0JyQNyv7AzZ3uxAA4/kjdwTFIUqc4/kjqJMcgSZ30nDHIpPeaFgFceumlbLPNNp3ui6QN8OijjzJt2jQo/4/HIMcfaQxzDJLUKY4/kjrJMUhSJ61rDDLpvaaVANtssw2TJ0/udF8kDczz3pIWEZ8A3gbUwEWZeXZEXExzZfCp0mxWZl4TEQcDZwMTgCszc2Y5xlRgDrAFcBNwUmauiIgpwDxgayCBaZn55Pr22/FHGvPG6m2xjkHS2Of4I6mTHIMkddJzxiCT3pJ6SkQcCPwx8FpgPHBPRFxLc1vbAZm5qKXtBOBi4EDgf4BrI+KQzJxPk9g+ITNvi4iLgBOBzwKzgdmZeUVEnAGcAZw+cmcoaTSIiDOBo8vmtZn54eG+uBYRk4BLgR2BxcDRmfno8J+tJEmSJI0uG3W6A5I0kjLzRuANmbmCJmG0MbAMmALMiYgfRcSsiNgI2Au4PzMfKO3nAUdFxMuBCZl5Wzns3BIfDxwAXNUab+9DREyKiO1bH4DTCqQuUZLYbwJeB0wFdo+II1l9cW1qeVzTcnHtcGBXYM+IOKQcah5wambuDFQ0F9dg9cW1XYDbaS6uAXwSWJCZu9Iky88b5lOVJEmSpFHJmd6Sek5mLo+IWcBpwFdoxsIbgPcCTwJfB95TnrfWhVpEk5zebi3xLYGlJUHeGm83AzhziE5H0uizCPhgZj4DEBH30lxY67u4NgW4BphFy8W10rbv4to9PPfi2qyI+CLNxbUjWuI30txRcmh5DeBy4MKIGJ+Zy1s7V2aET2rrsxfeJEmSJHUNk96SelJmnhkR/wj8B/DGzDyy77WIuAA4jiYh3m4VzYzLDYm3O5cmUdVqMq4aLnWFzLy773lEvBI4BtgPOIjhvbj27D6lDMpSYCvgkbYuzsALb5IkaRhY4k3SaGF5E0k9JSJ2KW+iyMzfAl8FjomIt7Y0q4DlwMNA6xLe29Ikj9YWXwxMjIhxbfE1ZOaSzHyw9QE8NASnJ2kUiYhXA/8JnJaNIzPzf8vYcwHwZjb8Itq6Lq5tyIW3Hdoe+6/jVCRJkp6XJd4kjSbO9JbUa3akKRGwH1DTvMm6ETg3Im6gmXU5HbgE+B4QEbET8ABwLHBxZi6MiGURsW9m3kIzK3x+KZuygGZW52V98RE+P0mjQETsC1wNzCgL274G2Dkzry5NBn1xLTNXsubFtb59HoqIjYGJwOPtfcvMJcCStv4O+FwlSZIKS7xJGjWc6S2pp2TmdcB1wJ3AHcCtmfkJ4CzgFuAe4IeZeXlmLgOOp0lc3QPcx+pFKqcB55Q3ci8Azi/xk4Hp5c3a/sDMkTgvSaNHRLwM+BpwbGZeUcIVzcW1F5VFb6fTfOh79uJauUvkWJqLaAuBZSV5Di0X12hKIR3TGi/PryvblNcXtH/YkyRJGi6ZeXdfsrqlxNs3aNZPejewD81npPew9lJugy7xBvSVeGs3g2YyU+vDEpNSl3Kmt6Sek5ln0lbPNjNn09wu1972emC3fuJ30cxOaI8vpKnbK6l3nQZsBpzdMoP6c6y+uDYeuDozLweIiONpLq5tRpO4br24NiciNqe5UNd6ce2SiJgJ/AJ4e4mfAcyNiLtpZnJPG57TkyRJWrtS4u1aSok3YLjXT3JtJUnPYdJ7kFauWsW4jZww3yl+/yVp5KxYWbPxuP4+UwzPfmNVZn4A+MBaXh62i2uZ+QRw2AZ2d606/Te2019fkjrh6eUr2XT8uOdvOEqN9f5r8HqhxJvvUTrL77/Wl0nvQRq30UZcc8dPO92NnnXk7jt1uguS1DM2Hldx9jWPbvB+f3PkNs/fSKNOp9/j+DdeUi/adPw4dv/QlzrdjQG749PHPX8jda2WEm/HZOYNJdxX4m0410/qK/H2KUagxFun3yP1Ot8jan2Z9JYkSZIkSdJgWeJN0qhh0luSpC72zMpVbDJuYLf/DWZfSZIk9ZZuKfEmqTuY9JYkqYttMm4j3nr19we079Vvfc5nDUmSJEmSRj2nb0mSJEmSJEmSuoZJb0mSJEmSJElS1xj28iYRMRG4FXhLZj4YEa8HzgE2B34EvCszn4mIqcAcYAvgJuCkzFwREVOAecDWQALTMvPJiJgEXArsCCwGjs7MRyNiE+AiYA/gd8CxmXnfcJ+nJEmSJEmSJKnzhnWmd0TsDdwM7Fy2JwJfBaZn5qtLs/eUf+cBp2bmzkAFnFjis4HZmbkLcDvNqrwAnwQWZOauNMny80r8/cBTJT4DuGR4zk6SJEmSJEmSNNoMd3mTE4FTgEfK9p8A383MH5XtU4FrIuLlwITMvK3E5wJHRcR44ADgqtZ4eX4ozUxvgMuBQ0r7Z+OZeROwZZktLkmSJEmSJEnqcsNa3iQzTwCIiL7QTsCTEXEN8ApgAfBB4HXAopZdFwGTgS2BpZm5oi0OsF3fPqUMylJgq9Z42z6/aO1bKY8yqa3Lk5EkSZIkSZIkjVkjvZDlxsCfAn9Dk+h+AfARmnIm7VatI84A92k1A3ig7bFg7V2XJEmSJEmSJI12I530fhS4LTMfyMyVwL8BewEPA9u0tNuWpiTKYmBiRIxri9O6T0RsDEwEHl/HsdqdC+zQ9th/cKcnSZIkSZIkSeqkYS1v0o9vAbMi4mWZ+T/AW4A7MnNhRCyLiH0z8xbgOGB+Zi6PiAXAMcBlffFyrOvK9qfK6wtK+774zRGxH7AsM9cobQKQmUuAJa2xljIskiRJkjQmRcSZwNFl89rM/HBEXEwzyeepEp+VmddExMHA2cAE4MrMnFmOMRWYA2wB3AScVMpKTgHmAVsDCUzLzCdL+chLgR1pJi8dnZmPDv/ZSpIkPdeIzvQuie73Av8REfcBLwbOKi9PA86JiHtpyp6cX+InA9Mj4h6aN2kzS/wMYJ+IuLu0OaXELwA2LfHzgXcO71lJkiRJ0uhQkthvoiknORXYPSKOBPYEDsjMqeVxTURMAC4GDgd2BfaMiEPKoeYBp2bmzjQlJE8s8dnA7MzcBbid5nMZwCdpJiLtSpMsP2+YT1WSJGmtRmSmd2Zu3/L8WuDaftrcRVPqpD2+EDion/gTwGH9xJcB7xpUhyVJkiRpbFoEfDAznwEok4qmlMecMlP7GmAWzeev+zPzgdJ2HnBUmXA0ITNvK8ecS3PH7heBA4AjWuI3AqcDh5bXAC4HLoyI8Zm5fPhOVZIkqX8jXd5EkiRJkjRMMvPuvucR8UqaUpD70Uwkei/wJPB14D3l+aKW3RcBk4Ht1hLfEliamSva4rTuU8qgLAW2omV9pVICZVJblycjSZI0xEx6S5IkSVKXiYhX09xhe1pmJnBky2sX0KyD9JV+dl1FU85kQ+I8z2t9ZgBnrrPjkiRJQ2BEa3pLkiRJkoZXROwLXA98JDMviYjXRMRbW5pUwHLgYWCblvi2NDOz1xZfDEyMiHFtcVr3iYiNgYnA421dOxfYoe2x/4BPVJIkaS2c6S1JkiRJXSIiXgZ8DTgmM28o4Qo4NyJuoClpMh24BPhes0vsBDwAHAtcnJkLI2JZROybmbfQzAqfn5nLI2IBTcmUy/ri5WtcV7Y/VV5f0F7POzOXAEva+juEZy9JktQw6S1JkiRJ3eM0YDPg7JaE8ueAs4BbgPHA1Zl5OUBEHA9cXfa5Driq7DONZuHLzYE7gfNL/GTgkoiYCfwCeHuJnwHMjYi7aRLb04bn9CRJkp6fSW9pHZ5evpJNx497/oYaFn7/pe60cmXNuHH9lX4d3n0lqRdk5geAD6zl5dn9tL8e2K2f+F3AXv3EF9IsitkefwI4bAO7K0mSNCxMekvrsOn4cez+oS91uhs9645PH9fpLkgaBuPGVXxn3uIB7XvQO7Ya4t5IkiRJkrqNC1lKkiRJkiRJkrqGM70l9ZyI+ATwNqAGLsrMsyPiYOBsYAJwZWbOLG2nAnOALYCbgJMyc0VETAHmAVsDCUzLzCcjYhJwKbAjsBg4OjMfHcnzkyRJkiRJ6mXO9JbUUyLiQOCPgdcCewCnRsRuwMXA4cCuwJ4RcUjZZR5wambuDFTAiSU+G5idmbsAt9Ms3gTwSWBBZu5Kkyw/b/jPSpIkSZIkSX1MekvqKZl5I/CGzFxBM0t7Y2AScH9mPlDi84CjIuLlwITMvK3sPrfExwMHAFe1xsvzQ2lmegNcDhxS2kuSJEmSJGkEWN5EUs/JzOURMQs4DfgKsB2wqKXJImDyOuJbAktLgrw1Tus+pQzKUmAr4JG+g5QSKJPaujUZSZIkSZIkDZozvSX1pMw8kyYZ/TLglf00WUVTzmRD4jzPa31mAA+0PRY8b6clSZIkSZL0vEx6S+opEbFLWZySzPwt8FXgDcA2Lc22pZmZ/fBa4ouBiRExri1O6z4RsTEwEXi8rRvnAju0PfYf9MmpqzyzcuWI7idJkiRJUrewvImkXrMjMCsi9gNqmsUrPw98OiJ2opl1fSxwcWYujIhlEbFvZt4CHAfML+VRFgDHAJf1xcvxryvbnyqvL8jM5a0dyMwlwJLWWEQMx7lqDNtk3Dj+/KprNni//3jbkcPQG0mSJEmSxg5nekvqKZl5HU1i+k7gDuDWzLwCOB64GrgHuI/Vi1ROA86JiHuBFwDnl/jJwPSIuIdmlvbMEj8D2Cci7i5tThnuc5IkSZIkSdJqzvSW1HNKPe8z22LXA7v10/YuYK9+4guBg/qJPwEcNlR9lSRJkiRJ0oZxprckSZIkSZIkqWsM60zviJgI3Aq8JTMfbImfAhyVmQeV7SnAPGBrIIFpmflkREwCLqWpwbsYODozH42ITYCLgD2A3wHHZuZ9EVEBnwbeAqwCTix1eCVJkiRJkiRJPWDYZnpHxN7AzcDObfFXAX/b1nw2MDszdwFup6mJC/BJmkXgdgXmAOeV+PuBp0p8BnBJib8V2BV4FXAEcElEWMJFkiRJkiRJknrEcJY3OZFmAbdH+gIRsSnweVYntYmI8cABrF40bi5wVHl+KM1Mb4DLgUNK+2fjmXkTsGWZLX4ocEVmrsrMnwALgT8ajpOTJEmSJEmSJI0+wzYLOjNPAIiI1vBZwMXAAy2xLYGlmbmibC8CJpfn25VtMnNFRCwFtmqNt+2ztvhzlNIpk9rC/baVJEmSJEmSJI0NI7aQZUT8CTAlM/+17aWqn+arnue1DY33ZwZN8r31sWAtbSVJkiRJkiRJY8BI1rt+O/DqiPgh8EJgm4i4EngHMDEixmXmSmBbVpdEeRjYBnio1OaeCDzeEv9pade3T1+ctnh/zqUppdJqMia+JUnSIEXEmcDRZfPazPxwRBwMnA1MAK7MzJml7VSatUu2AG4CTip3uA3JQt8jcsKSJEmSNIqM2EzvzHx3Zu6amVOBE4DbM/OYzFxOk2g+pjQ9Dphfnl9XtimvLyjtn41HxH7Assz8RYlPi4hxEbETzSKaP1hLf5Zk5oOtD+ChoT1rSZLUa0py+03A64CpwO4R8XaaEm+H0yy6vWdEHFJ2mQecmpk709y1dmKJD9VC35IkSZLUU0Zypve6nAxcEhEzgV/QzAqH5sPd3Ii4G1gCTCvxC4DPl/jTwDtL/Cpgb+BHZfs9mfm74e++JEnSsxYBH8zMZwAi4l6aC/H3Z+YDJTYPOCoi7gEmZOZtZd+5wKyI+CLNQt9HtMRvBE6nWbj7gBK/HLiwZaHvj0Gz0HdEbBkRU8rEgGe5rokkSRou3u0mabQY9qR3Zm7fT+w7wEEt2wtbt1viTwCH9RNfBryrn3gNnFYekiRJIy4z7+57HhGvpLlb7Xw2bBHuoVzoe42kN80s8DMHdnaSJEn9a7vbrQa+Ue52+0fgQOB/gGsj4pDMnE+T2D4hM2+LiIto7nb7LKvvdrsiIs6gmRB5Oqvvdjs0It5Jc7fbMbTc7RYRB9Dc7bb3yJ25pNFotMz0liRJ6ioR8WrgWpqL8cuBaGsy0MW5B7ug97m4rokkSRp63u0madQw6S1JkjTEImJf4GpgRpmldCD9L7a9tkW4FzN0C32vITOX0JSNa+3vQE9VkiQJ8G43SaPLiC1kKUmS1Asi4mXA12jqSV5Rwt9rXoqdImIccCwwv5R4W1aS5FAW9B7ihb4lSZJGTLnb7T9p7nb7WT9NOnm32w5tj/37aSepCzjTW5IkaWidBmz2/7N35/FS1uX/x1+HTclEVNwRl9RLM9PK7ZvikluEueRCgiIluGAqpakVZJhLq6ImaihBomhuLQpmognuaS6/XK40FQWxUDwiKMr2++P6DNyMw+Esc+Y+Z+b9fDzmwcxn7rnPdc9w7nPP9fl8rg9wSWYE9dXAIGL09+pEgvrW9NwAYIyZrQk8RYyIgvIt9C0iIiJSEZrtJiJthZLeIiIiImXk7mcAZ6zk6R1LbP8MsGuJ9rIs9C0iIiJSCZnZbv3c/b7UvGy2G/AqMdttrLtPN7MFZraHuz9EZrabmRVmu91I6dluF5GZ7WZmhfYHNdtNRAqU9BYRERERERERkZbSbDcRaTOU9BYRERERERERkRbRbDcRaUu0kKWIiIiIiIiIiIiIVA0lvUVERERERERERESkaijpLSIiIiIiIiIiIiJVQ0lvEREREREREREREakaSnqLiIiIiIiIiIiISNXolHcAIiIiIiIiUh5mdh5wdHp4l7ufbWb7A5cAXYGb3X142nYnYAywFjAVONndF5lZL2ACsD7gwAB3n2dm3YEbgC2B2cDR7v6WmXUBrgN2Bj4E+rv7ixU5YBEREZESNNJbRERERESkCqTk9oHAF4CdgC+Z2THAWOBQYDtgFzPrk14yATjN3bcB6oAhqX00MNrdtwWeAEak9guAae6+HZEsvyy1nw7MT+3DgPGtdYwiIiIijaGkt4iIiIiISHWYBZzp7h+7+0LgBWAb4CV3f9XdFxGJ7qPMbDOgq7s/ml47LrV3BvYCbs22p/t9iZHeABOBPmn7Ze3uPhXokUaLi4iIiORC5U1EpOasZNrvWKA3MD+1j3T3O8o1HbhChyYiIiI1zN2fK9w3s62BfsDlRDK8YBbQE9h4Je09gLkpQZ5tJ/uadN0zF1ivgX29no0vlUfpXhR2T0RERETKTCO9RaSmrGTa7+HALsBe7r5Tut1hZl0p33RgqQEfL1606o1a4bUiIiJZZrY98DfgLOA/JTZZQly/NKWdZr4maxjwatFtWontRERERFpEI71FpNYsm/YLYGYvAL3SbUwaqX0HMBLYlTQdOG1bmA78PJ+cDjzSzK4lpgMflml/ADgnG4BGOVWvLh070fe2Mc167V1HDFn1RiIiIqtgZnsAtwHD3P0mM9sb2DCzyUbAm8DMlbTPBrqZWUd3X5xpJ/OaGWbWCegGvJNpf7loX8VGEddHWT1R4ltERETKrNWT3mbWDXgYONjdXzOzE4mFTpYSoyBPcvePtXK4iFTCSqb97gnsA5wEzAPuBE5I98s1HThrGHBeiw9GREREJMPMNgX+CPRz9/tS82PxlG1FjKzuD4x19+lmtsDM9nD3h4CBwGR3X2hm04hrpBsL7Wlfk9Lji9Lz09L2hfYHzWxPYIG7r1DaBMDd64H6opjLdvwiIiIiBa1a3sTMdgMeJBZPwcy2Ab4PfBn4fPr5p6bNtXK4iFRMdtqvh8Pd/X/u/gFwBfA1mj6Ft7FTe0cBWxTdejf5IERERERWdBawOnCJmT1tZk8Dg9LtNuB54EWWL1I5ALg0zXxbg6j/DTAUODHNbusNDE/tI4Ddzey5tE3hu9wVwGqp/XLguFY6PhEREZFGae2R3kOIC6Hr0+OPgFPcfS6Amf0/oNdKVg5fVamAvuk5iJXDr8ysHP5jiJXDzayHmfUqNdJARGpTiWm/OwDbuPttaZM6YCErn/bbnOnAy2iUk4iIiLQGdz8DOGMlT+9YYvtniHJuxe3TiVlwxe1zgENKtC8Ajm9iuCIiIiKtplWT3u4+GJYnc9LF0/TUth7wHWLUgVYOF5GKWMm03zpglJndR5Q0OZGYJVLO6cAiIiIiIiIiIlIBuSxkaWabEImg69z972b25RKbVWLlcNXUFak92Wm/hbargYuBh4DOwG3uPhHAzAYRo8JXJ+pYZqcDjzGzNYGnWHE68HgzG050th3TyscjIiIiIiIiIiIZFU96m9m2wN3AFe7+69SslcNFpCJWMe13dIntp1CG6cAiIiIiIiIiIlIZrbqQZbE0IvIeYHgm4V1IEi1IdXYhUyqASEL3y7an+4UVwiGzcni2fVUrh7v7a9kbMKN8RysiIiLtxdJFpSaFtf5rRUREREREpPwqPdJ7MLABcJaZnZXa/uzuP6bppQJGAOPSCuH16fUQK4dfk9o/QiuHi4iIyCrUderAfy9/sFmv3eD0PcscjYiIiIiIiLRERZLe7r55untpupXaRiuHi4iIiIiIiIiIiEiLVLS8iYiIiIiIiIiIiIhIa1LSW0RERERERERERESqhpLeIiIiIiIiIiIiIlI1lPQWERERERERERERkaqhpLeIiIiIiIiIiIiIVA0lvUVERERERERERESkaijpLSIiIiIiIiIiIiJVQ0lvEREREREREREREakaSnqLiIiIiIiIiIiISNVQ0ltEREREREREREREqoaS3iIiIiIiIiIiIiJSNZT0FhEREREREREREZGqoaS3iIiIiIiIiIiIiFQNJb1FRKTmfbx4YUVfJyIiIiIiIiKtp1PeAYiIiOStS8fOfO2PP2zy6yYddlErRCMiIiIiIiIiLaGkt4iIiLRbSxctpa5TXcVe1xRm1g14GDjY3V8zs7FAb2B+2mSku99hZvsDlwBdgZvdfXh6/U7AGGAtYCpwsrsvMrNewARgfcCBAe4+z8y6AzcAWwKzgaPd/a1WPUgREREREZE2SElvERERabfqOtUx6xezmvy6jc7eqBWiWc7MdiMS1ttkmncB9nL3WZntugJjgb2BN4C7zKyPu08mEtuD3f1RM7sOGAJcBYwGRrv7TWY2AhgBnANcAExz975mdhxwGdCvVQ9URERERESkDVLSW0SkjVm6aDF1nTpW/LUiUlZDgFOB6wHMbA2gFzAmjdS+AxgJ7Aq85O6vpu0mAEeZ2fNAV3d/NO1vHDDSzK4F9gIOy7Q/QCS9+6bnACYCV5pZZ3dX8XkRERGpGM12E5G2QElvEZE2pq5TR2ZfNaFZr13vlGPLHE3btXDxx3Tu2KXirxVpDHcfDGBmhaYNgPuAk4B5wJ3ACel+dqj6LKAnsPFK2nsAc919UVE72dekL4ZzgfWAN7OxpS+G3YtC7omIiIhIC2m2m4i0Fa2e9C7Rw9eqPXlm1gW4DtgZ+BDo7+4vtvZxiohIZXXu2IUf3fLVZr32wqPuLnM0Ig1z91eAwwuPzewKYCBwS4nNlwClCo431M4qnssaBpzXQLgiIiIizdVmZ7up41+ktnRozZ2nHr4HST18mZ68Q4HtgF3MrE/afAJwmrtvQ3xpG5LaCz152wJPED15sLwnbzsiWX5Zaj8dmJ/ahwHjW+0ARURERBrBzHYwsyMyTXXAQmAmsGGmfSNiZPbK2mcD3cysY1E72deYWSegG/BOiXBGAVsU3Xo389BERERElnH3we4+LdNUmO32bWB34prjBFY+q63Fs92Awmy3YsOAV4tu00psJyJVoLVHeq/Qw0cFevJS+48B3H2qmfUws17u/no2MPXwidQmMzsPODo9vMvdz1YtucpatPhjOjWjtEhzXyfSRtQBo8zsPqKkyYlEx/xjgJnZVsQXr/7AWHefbmYLzGwPd3+IGBU+2d0Xmtk0YsrujYX29DMmpccXpeenlarn7e71QH22LVOGRURERKRs2thst1FEXimrJ0p8i1SlVk16l6hn2Wo9eZm6lSvb1wpJbzS1V6TmpOT2gcAXgKXA3WZ2DPBzVEuuYjp17MK48Qc2+XWDjr+nFaIRqQx3f9bMLgYeAjoDt7n7RAAzGwTcBqxOJK5vTS8bQEwFXhN4Crg8tQ8FxpvZcOL65pjUPgIYZ2bPEUntAa18WCIiIiINMrMdgG3c/bbU1OLZbu6+mNKz3WY0NNtNHf8itaXSC1k2tceuOT156uETkZWZBZzp7h8DmNkLRPmlitaSE5Ha4e6bZ+6PJjrNireZAuxYov0ZYpZccft0YJ8S7XOAQ1oUsIiIiEh5tZnZbiJSWyqd9K5ET16h/eWifa1APXwitcfdnyvcN7OtiQuiy2n9GSgrnINUXklERERERGqBZruJSF4qnfRu9Z48Myu0P2hmewILiut5i0htM7PtgbuAs4ipdcU9Xq1dS24YKq8kIiIiIiJVSrPdRCRvHRqzkZltUqLts039Ye6+ABhE9OQ9D7zIij15l6ZyA2uwYk/eianUQG9geGofAeyeevKGEgtmAlwBrJbaLweOa2qcItI+/Pe//y3VvFVDrzGzPYApwLnuPp4WzEApaif7moZqyRHllbYouvVuKG4Rycfsee9+ou21OZ+YQCYi0ipKXeu8/PLLJbYUESkvnX9EpL1rcKS3ma2T7k4ys31YPoqxM/AnYOvG/JCiHr5W7clLifXjGxOXiLRP9fX1AAwZMoTrr7+epUuXMnfu3MLTY4gk8ieY2abAH4F+7n5faq54LTmVVxJp++YumAfA2XeO4vLDzmYpSwFYtGQxP5h0BROPvTjP8ESkypW61gFYtGgRQ4cO5Z57tLiziLQOnX9EpFqsqrzJROCAdD87WnERcEerRCQisgpnnnkmDz30EAC77bZb8dP/auClZxH14i7JJJmvZvkMFNWSExEARt7zW55443kADr7ujGXtHTt0oPeWX8wrLBGpESu71unUqRP777//Kl9vZt2Ah4GD3f01MxtLzCqbnzYZ6e53mNn+wCVAV+Bmdx+eXr8TMZBgLWAqcHJar6QXMAFYH3BggLvPS+uV3ABsScyKO9rd32rRmyAiuWjp+UdEpK1oMOnt7gcBmNlYd/92ZUISEWnYddddB8APfvADLr44RlvOmDGD/fbbD+C0lb3O3c8AzljJ06olJyLL/PqQ7wHwsyljOXc/XQKJSGWVutZpLDPbjUhYb5Np3gXYy91nZbbrCowF9gbeAO4ysz7uPplIbA9290fN7DpgCHAVUZN3tLvfZGYjiA7/c4ALiBlufc3sOOAyYtabiLQzLTn/iIi0JY1ayNLdv21mmwHrkFmozd3/2VqBiYisysUXX8zMmTN57733eOutZYOJtgdeyy8qEakm5+73bd6a+zZzP5qfCpwEW2+z3GISkdqRvdYplBgA2H777Rt62RBivaPrAcxsDaAXMXutFzFjdyTRsf+Su7+atpsAHJXWUurq7o+m/Y0DRprZtcBewGGZ9geIpHff9BzEbOErzaxzcZm3NCK8e1G8PVfxNohIDpp5/hERaTMalfQ2s58BpwP/g2Xf+ZYS09dERHLxq1/9iuuvv551112XxYsXF5qvIr7YiYi02NWP3Mptz05h7a5rUuj3r6uDm4/7eb6BiUhNyF7rFNTV1TFlypSVvsbdB8MK64VsANwHnATMA+4ETkj3Z2VeOotIQG+8kvYewFx3X1TUTvY1qQzKXGA9li/2XTAMOK/hoxaRtqA55x8RkbakUUlvYmraVu5efNEiIpKbSZMmcc8997DBBhtky5vstarXiYg01n0v/YOJx15EjzXWzjsUEalB2Wud5nL3V4DDC4/N7Api4e1bSmy+hMzM3ka2s4rnskYRI8SzegLTSmwrIjkqx/lHRCRPjU16v6GEt4i0NRtttJEuwipk8aKP6dipS8VfK5K39T+9thLeIpKbclzrmNkOwDbufltqqgMWAjOBDbM/jhiZvbL22UA3M+vo7osz7WReM8PMOgHdgHeKY3H3emKx72x8LTg6EWkt+q4lIu1dY5PeU8zsF8CfgA8LjarpLSJ5+r//+z9+8YtfsN9++/Hee+8VmlXTuxV07NSF+6/t26zX7jv4rjJHI1I5X+q5HVc9fAt7brETXTKdN6rpLSKVkL3WWX311Ze1N7Gmbh0wyszuI0qanAiMBx4DzMy2Al4F+gNj3X26mS0wsz3c/SFiVPhkd19oZtOIWcA3FtrTz5iUHl+Unp9WXM9bRNqXMp1/RERy09ik96D071GZNtX0FpFc3X777QDcfffdquktIq1i8osPA3D/y08sa1NNbxGplOy1TkFTa+q6+7NmdjHwENAZuM3dJwKY2SDgNmB1InF9a3rZAGLhyzWBp4DLU/tQYLyZDQdeB45J7SOAcWb2HDGSe0DTjlRE2ppynH9ERPLUqKS3u2/R2oGIiDTVfffdt+y+anqLSGv4w0Alt0UkP9lrnaZy980z90cDo0tsMwXYsUT7M8CuJdqnA/uUaJ8DHNLsYEWkzWnJ+UdEpC1oVNLbzL5Xqt3dLylvOCIijfe73/1u2f36+vrC3ROI0UYiIi1289N/Ldneb6eDKhyJiNSi7LVO1re+9a0KRyIitUbnHxFp7xpb3mSHzP0uQG/g/vKHIyLSeP/+97+X3c/U9N4ul2BEpCq98s7MZfcXLl7Es7Ne4gubbJtjRCJSS7LXOh9//DFPPvkku+22W44RiUit0PlHRNq7xpY3WaErz8x6ANe3SkQiIo108cUXL7s/Y8aMQn25HrkFJCJV5wf7fXuFx/Ufvs+F916bUzQiUmuy1zoAc+bM4eyzz84pGhGpJTr/iEh716E5L3L3t4HNyxuKiEhZ9Mw7ABGpXt27rsms99/JOwwRqVHrrLMOM2fOXPWGIiJlpvOPiLQ3zanpXQfsDPyvVSISEWmkbJ25d999t3D37VyCEZGqlK3pvXQp+OzXWLvrmjlGJCK1JHuts3TpUv71r3+x7rrr5hiRiNQKnX9EpL1rTk3vpcDrwPfLH46ISONl68x98MEHhbvD8ohFRKpTtqZ3HbD+p9fhlC8flV9AIlJTstc6ABtttJHKC4hIRej8IyLtXZNqepvZZkBnd3+5VaMSEWmEQp25mTNnMmPGDO6++26At3INSkSqSqGm91tz32bRksX07L5BzhGJSC3JXussWrSIzTbbLOeIRKRW6PwjIu1dY8ubbAX8CdgY6GBmbwMHu/sLrRmciEhDpk+fztChQ/nf//7H4sWLC82fAV7LLyoRqSYz6v/LDyf9hrc/qGfp0qWstfqn+Vnf09l8nY3zDk1EakD2WmfJkiWsvfbaXHPNNXzmM5/JOzQRqXI6/4hIe9fYhSx/A/zC3dd297WAC4ArWy8sEZFVO//88xk8eDD/+Mc/+POf/1xo/mmeMbU1SxctrOjrRKrNqGk3cswXv8qkwVcwechvGLjzwVw69Ya8wxKRGpG91nnyySc55ZRTGDlyZN5hiUgN0PlHRNq7xtb03sDdxxceuPvviha3bBIzOxb4QXo42d3PMrOdgDHAWsBU4GR3X2RmvYAJwPqAAwPcfZ6ZdQduALYEZgNHu/tbZtYFuI5YbPNDoL+7v9jcWEWk7XrnnXc4/PDDi5vXySOWtqquU2dm/ObbTX5dz++MbYVoRNqfdz94jz7b7rHs8de225Obn74nx4hEpJYUX+scccQRjBs3Lr+ARKRm6PwjIu1dY0d6dzKzZYkkM+tBLGjZZGb2KeByYG9gR6C3me1PJLZPc/dtiLWihqSXjAZGu/u2wBPAiNR+ATDN3bcjkuWXpfbTgfmpfRiwLFkvItVl8eLF1NfXFzc369wkIlLKoiVLmLtg3rLH9R++T11dXY4RiUgtKb7WmTNnTn7BiEhN0flHRNq7xo70vgJ41MxuTo/7AZc282d2JJLtawDzgc7AQqCruz+athkHjDSza4G9gMMy7Q8A5wB903MAE4Erzaxzav8xgLtPNbMeZtbL3V/PBpFGincviq1nM49JRHJw7LHH0q9fP/r06cP7779faJ6QZ0wiUl2O+Px+nHzrRXxl610AuO+lf3D0TgfkHJWI1IrstQ7A5MmTOf7443OOSkRqgc4/ItLeNXak9yRi9GQXYFtgE+CO5vxAd3+fGK39IjCTWHDuY2BWZrNZRAK6BzDX3RcVtUMsqjkr7XMRMBdYL9te4jVZw4BXi27TmnNMIpKPvffeG4CFCxfy+uvL+rVUd0BEymb3zXagrg4WLl7E6+/O4u3579J7iy/mHZaI1Ijstc4rr7zCf//7Xw44QB1vItL6dP4RkfausSO9xwFXuvvlZrY6cDIwFvhaU3+gmX0e+DawGfAeMSrzwBKbLiHKnJRqp4HnGnpN1ijiuLJ6osS3SLtx7rnnMmDAAAYOHMgrr7zC1KlTAX4B7JtzaCJSJS6eMpbDP/cVjtxxfz5atJA/Pfd3fnbf7/jl14flHZqI1IDstc5HH33ExIkT+eEPf8iYMWPyDk1EqpzOPyLS3jV2pHcPd78cwN0XuPsoYKNm/syDgCnu/j93/4hIPO8DbJjZZiPgTWKBym5m1rGoHWKU+IYAZtYJ6Aa8k20v8Zpl3L3e3V/L3oAZzTwmEcnBu+++y8CBAwHo0qVLoXn93AISkarz3ofzOHLH/QFYrVNnjt7xAOZ8UJ9vUCJSM7LXOqutthqDBg1i9uzZOUclIrVA5x8Rae+aspDlxoUHZrYBpUdUN8YzwP5mtoaZ1QFfJ+p0LzCzPdI2A4HJ7r6QGHndL9ue7k9Kj0nPT0vbL2s3sz2BBcX1vEWkOixevJj//ve/xc1aYU5Eymbx0sW8Pf/dZY/nfPAeS7VcrohUSPG1zttvv81SnYREpAJ0/hGR9q6x5U0uAZ42s7uJ2t77A99vzg9093vM7AvAk8QClo8DPyNqhI8xszWBp4DL00uGAuPNbDjwOnBMah8BjDOz54B6YEBqvwK4JrV/BBzXnDhFpO0bNGgQhx12GL179+aDDz4oNF+dZ0wiUl2O3vFAvn3zSHbr9TmgjidnPM8pXz4q77BEpEZkr3Xq6up4+OGHOfvss/MOS0RqgM4/ItLeNSrp7e5jzewJYD9gEfBLd/9Xc3+ou/8c+HlR8zPAriW2nU6UPylunwMcUqJ9AaAlhUVqwJFHHsnnPvc5Hn30Ud5//33+9re/Afx5Va8zs27Aw8DB7v6amY0FegPz0yYj3f0OM9uf6PTrCtzs7sPT63cCxgBrAVOBk919kZn1ItYpWB9wYIC7zyvjIYtIhfX9bG+2XX9znpzxAh07dOSYLxzEluuWWh9bRKT8stc6HTt25IQTTmCbbbbJOywRqQE6/4hIe9fYkd64+7PAs60Yi4hIk2277bZsu+22zJgxg9/85jer3N7MdiMS1tkrtl2Avdx9Vma7rsSCvXsDbwB3mVkfd59MJLYHu/ujZnYdMAS4ChgNjHb3m8xsBDEj5ZyyHKiI5OYzPTblMz02zTsMEalRhWsdEZFK0/lHRNqzxtb0FhGpFkOAU0kL3JrZGkAvorzSs2Y20sw6EDNPXnL3V919EZHoPsrMNgO6uvujaX/jUntnYC/g1mx7hY5JRERERERERESSRo/0FhGpBu4+GMDMCk0bAPcBJwHzgDuBE9L9WZmXzgJ6AhuvpL0HMDclyLPtn2Bm3YHuRc2qlyAiIiIiIiIiUgZKeotITXP3V4DDC4/N7ApgIHBLic2XAHVNbC9lGHBekwIVEREREREREZFGUXkTEalpZraDmR2RaaoDFgIzgQ0z7RsRJVFW1j4b6GZmHYvaSxkFbFF0692iAxEREREREREREUAjvUVE6oBRZnYfUdLkRGA88BhgZrYV8CrQHxjr7tPNbIGZ7eHuDxGjwie7+0Izmwb0A24stJf6ge5eD9Rn2zLlVkREREREREREpAU00ltEapq7PwtcDDwEPA887e4T3X0BMAi4LbW/yPJFKgcAl5rZC8AawOWpfShwopk9T4zcHl6p4xARERERERERkaCR3iJSk9x988z90cDoEttMAXYs0f4MsGuJ9unAPuWMU0TaLzPrBjwMHOzur5nZ/sAlQFfgZncfnrbbCRgDrAVMBU5290Vm1guYAKwPODDA3eelxXBvALYkSisd7e5vmVkX4DpgZ+BDoL+7v1ixAxYREREREWkjlPQWERERKTMz241IZG+THncFxgJ7A28Ad5lZH3efTCS2B7v7o2Z2HTAEuIrojBvt7jeZ2QhgBHAOcAEwzd37mtlxwGVEaaXTgfnuvp2Z7UWUatqtckctIiIioo5/EWkbVN5EREREpPyGAKeyfEHbXYGX3P1Vd19EfJE7ysw2A7q6+6Npu3GpvTOwF8vLKo0Djkr3+xJf+AAmAn3S9sva3X0q0CN9aVyBmXU3s82zN6BneQ5bREREalnq+H+QT3b8HwpsB+xiZn3S5hOA09x9G2KtpSGpvdDxvy3wBNHxD8s7/rcjkuWXpfZlHf/AMKLjX0RqnJLeIiIiImXm7oPdfVqmaWNgVubxLCLRvLL2HsDclCDPtq+wr/T8XGC9BvZVbBixQG/2Nq3EdiIiIiJNpY5/EWkTlPQWERERaX11JdqWNKO9OfsqNgrYoujWu8R2IiIiIk2ijn8RaStU01tERESk9c0ENsw83ogYAbWy9tlANzPr6O6LM+3Zfc0ws05AN+CdTPvLRftagbvXA/XZNjNr/pGJiIiIrFxb6/gfV9TWEyW+RaqSRnqLiIiItL7HADOzrcysI9AfmOzu04EFZrZH2m5gal9IfAHrl21P9yelx6Tnp6Xtl7Wb2Z7AAnd/vZWPS0RERKQhze74L2pfYV8r6fgv3tcK3L3e3V/L3oAZLTo6EWmzlPQWERERaWXuvgAYBNwGPA+8yPJalQOAS83sBWAN4PLUPhQ40cyeJ8qPDE/tI4Ddzey5tM2pqf0KYLXUfjlwXGsek4iIiEgjqONfRHKh8iYiIiIircTdN8/cnwLsWGKbZ4hFnorbpwP7lGifAxxSon0BcHyLAhaRqmBm3YCHgYPd/TUz2x+4BOgK3Ozuw9N2OwFjgLWAqcDJ7r4oLQA3AVgfcGCAu88zs+7EYnFbEqMxj3b3t8ysC3AdsDPwIdDf3V+s2AGLSJvl7gvMbBDR8b86kaDOdvyPMbM1gadYseN/vJkNB14HjkntI4BxqYO/Pr0eouP/mtT+Eer4FxGU9BYREREREakaZrYbkcjeJj3uCowF9gbeAO4ysz7uPplIbA9290fN7DpgCHAVMBoY7e43mdkIItF0DnABMbKyr5kdB1xGjLY8HZjv7tuZ2V7AeGC3yh21iLQ16vgXkbypvImIiIiIiEj1GEKUPSrUs90VeMndX3X3RUSi+ygz2wzo6u6Ppu3GpfbOwF4sH4k5Djgq3e9LjPQGmAj0Sdsva3f3qUCPNFpcREREJBe5jPQ2s68DPyHqVv7V3c9o7Sl3FTw8ERERERGRXLj7YAAzKzRtDMzKbDIL6NlAew9gbkqQZ9tX2Ff6TjYXWK+Bfa1QUzd9V+teFHJPRERERMqs4iO9zWxL4GrgUGAH4Itm1oeYcncosB2wS2qDSGyf5u7bAHXEyAVYPuVuW+AJYsodLJ9ytx2RLL+s9Y9KRERERESkTaor0bakGe3N2VexYcCrRbdpJbYTERERaZE8ypscTozknpFW2e0HfEDrT7kTERERERGpNTOBDTOPNyJKn6ysfTbQzcw6FrWvsC8z6wR0A95pYF/FRgFbFN16N++wRERERFYuj/ImWwEfm9lfiQujvwDP0fpT7la46NLUOhERERERqQGPAWZmWxEjq/sDY919upktMLM93P0hYCAw2d0Xmtk0YnDSjYX2tK9J6fFF6flpaftC+4NmtiewwN1XKG0C4O71QH22LVOGRURERKRs8kh6dyJGae8DzAP+RIz0LlbuKXfFhgHnNRyqiIiIiIhI++XuC8xsEHAbsDqRuC7MmB0AjDGzNYGngMtT+1BgvJkNJ+pyH5PaRwDjzOw5Ink9ILVfAVyT2j8CjmvNYxIRERFZlTyS3m8B97r7bAAz+yNRmmRxZptGT7lz98WUnnI3o2jKXbFRRFmUrJ6oppyIiIiIiLRz7r555v4UYMcS2zwD7FqifToxSKm4fQ5wSIn2BcDxLQpYREREpIzyqOl9J3CQmXVPdeL6ECMNzMy2Sm39ial104EFZrZHeu2yKXdEcrpftj3dL0ytg8yUu+Ig3L3e3V/L3oAZZT9aEREREREREREREamYiie93f0x4BfAg8DzwHTgKmAQMeXueeBFVpxyd6mZvQCswYpT7k40s+eJxU+Gp/YRwO5pat1Q4NRWPiQRERERERERERERaSPyKG+Cu48FxhY1t+qUOxERERERERERERGpfnmUNxERERERERERERERaRVKeouIiIiIiIiIiIhI1VDSW0RERERERERERESqhpLeIiIiIiIiIiIiIlI1lPQWERERERERERERkaqhpLeIiIiIiIiIiIiIVI1OeQcgIlJpZtYNeBg42N1fM7P9gUuArsDN7j48bbcTMAZYC5gKnOzui8ysFzABWB9wYIC7zzOz7sANwJbAbOBod3+rogcnIiIiIiIiIlLjNNJbRGqKme0GPAhskx53BcYChwLbAbuYWZ+0+QTgNHffBqgDhqT20cBod98WeAIYkdovAKa5+3ZEsvyy1j8iERERERERERHJ0khvEak1Q4BTgevT412Bl9z9VQAzmwAcZWbPA13d/dG03ThgpJldC+wFHJZpfwA4B+ibngOYCFxpZp3dfWE2gDQivHtRXD1bfmgiIiIiIiIiIqKR3iJSU9x9sLtPyzRtDMzKPJ5FJKBX1t4DmOvui4raV9hXen4usF6JMIYBrxbdppXYTkREREREREREmkhJbxGpdXUl2pY0o72hfRUbBWxRdOu9qkBFREREREREpPV8tHBx3iHUvHJ9BipvIiK1biawYebxRsCbDbTPBrqZWUd3X5xpz+5rhpl1AroB7xT/QHevB+qzbWZWhkMRERERERERkeZarXNHvvT93+cdRk178pcDy7IfjfQWkVr3GGBmtpWZdQT6A5PdfTqwwMz2SNsNTO0LiVIk/bLt6f6k9Jj0/LTiet4iIiIiIiIiItK6lPQWkZrm7guAQcBtwPPAi8Ct6ekBwKVm9gKwBnB5ah8KnJgWu+wNDE/tI4Ddzey5tM2plTgGERERERERERFZTuVNRKQmufvmmftTgB1LbPMMsGuJ9unAPiXa5wCHlDNOERERERERERFpGo30FhEREREREREREZGqoaS3iIiIiIiIiIiIiFQNJb1FREREREREREREpGrkWtPbzH4JrOfug8xsJ2AMsBYwFTjZ3ReZWS9gArA+4MAAd59nZt2BG4AtgdnA0e7+lpl1Aa4DdgY+BPq7+4sVPjQRERERERERERERyUFuI73NbD9gUKZpAnCau28D1AFDUvtoYLS7bws8AYxI7RcA09x9OyJZfllqPx2Yn9qHAeNb8TBEREREREREREREpA3JJeltZusAFwIXpcebAV3d/dG0yTjgKDPrDOwF3JptT/f7EiO9ASYCfdL2y9rdfSrQI40WFxEREREREREREZEql1d5k2uAHwGbpscbA7Myz88CegI9gLnuvqiofYXXpDIoc4H1GtjX69kAUnmU7kVx9URERERERERERERE2q2Kj/Q2s8HAG+4+JdNcV2LTJQ20N/c1WcOAV4tu00pHLSIiIiIiIiIiIiLtQR4jvfsBG5nZ08A6wKeBpcCGmW02At4kFqjsZmYd3X1xph1gZnrNDDPrBHQD3sm0v1y0r2KjiHIpWT1R4ltERERERERERESk3ar4SG93P8DdP+fuOwE/Bv7s7t8CFpjZHmmzgcBkd19IJKH7ZdvT/UnpMen5aWn7Ze1mtiewwN1XKG2S4qh399eyN2BGmQ9XRERERERERERERCoor5repQwAxpjZmsBTwOWpfSgw3syGE3W5j0ntI4BxZvYcUJ9eD3AFcE1q/wg4rjLhi4iIiIiIiIiIiEjeck16u/s4UokRd38G2LXENtOBfUq0zwEOKdG+ADi+vJGKiIiItJyZ3QdsACxMTScBnwGGA12AS939yrTt/sAlQFfgZncfntp3AsYAawFTgZPTot69gAnA+oADA9x9XoUOTUREREREpM1oSyO9RURERKqWmdUB2wK93H1RatsEuAn4EjFD7WEzu59YYHsssDfwBnCXmfVx98lEYnuwuz9qZtcBQ4CrgNHAaHe/ycxGELPizqnoQYqIiIiUoI5/Eam0itf0FhEREalRRizePdnMnjGz7wD7A/e5+xx3nw/cChxJzH57yd1fTQnyCcBRZrYZ0NXdH037HJfaOwN7pdcva6/QcYmIiIisVKbjf0d33ymt8TYDuBDYE9gRONHMPmtmXYmO/0OB7YBdzKxP2tUE4DR33waoIzr+YXnH/7bAE0THv4jUOI30FhEREamMtYEpwCnEyKW/AzcDszLbzCIS3huXaO/ZQHsPYG5hBHmm/RPMrDvQvai55LYiIiIiZZDt+F+fGK39PqnjH8DMCh3/D5A6/lN7oeP/eT7Z8T/SzK4lOv4Py7Q/QInZbroGEqktSnqLiIiIVIC7PwI8kh7OT6VJLiFGOWUtIUYvFWtOeynDgPNWFa+IVB+VFxCRnLSJjn90DSRSU1TeRERERKQCzGxPM9sv01QHvAZsmGnbCHgTmNnE9tlANzPrWNReyihgi6Jb7yYfkIi0KyovICJ5cfdH3H2gu89397eB64DzS2za2h3/o9A1kEjN0EhvERERkcroDpxvZl8GOgPHA8cCE8xsPWA+cARwIvAsYGa2FbGoZX9grLtPN7MFZraHuz8EDAQmu/tCM5sG9ANuLLSXCsLd64H6bJuZlflQRaQNahPlBUSk9pjZnsBq7j4lNbVKx7+7L6aBjn9dA4nUFiW9RURERCrA3e80s92Ap4COwJXu/pCZ/Qi4nygtcK27Pw5gZoOA24DVgUksX6RyADDGzNZM+7o8tQ8FxpvZcOB14JiKHJiItBe5lxdQPV2RmtWdNtDxLyK1RUlvERERkQpx9xEUTfl39xuJL2nF204hyg0Utz9DJKWK26cD+5QrVhGpLm1kXYFhqJ6uSM1Rx7+I5EFJbxERERERkSrXRsoLjCJKn2T1BKY1+YBEpF1Rx7+IVJqS3iIiIiIiItWvOzmXF1A9XREREamUDnkHICIiIiIiIq3L3e8E7iJKAjxJJLEfAgrlBZ4GbnT3x919ATCIKC/wPPAiK5YXuNTMXgDWYMXyAiemxS57A8MrcFgiIiIiJWmkt4iIiIiISA1QeQERERGpFUp6i4gkZnYfsAGwMDWdBHyGGKnUBbjU3a9M2+5PLP7UFbjZ3Yen9p2AMcBawFTgZHdfVMHDEBERERERERGpaSpvIiICmFkdsC2wo7vv5O47ATOAC4E9iZFOJ5rZZ82sKzAWOBTYDtjFzPqkXU0ATnP3bYgFooZU9khERERERERERGqbRnqLiAQDlgKTzWx9YrT2+8B97j4HwMxuBY4EHgBecvdXU/sE4KhUw7Kruz+a9jkOGAlcVckDERERERERERGpZUp6i4iEtYEpwClEyZK/AzcDszLbzCJqWG5cor1nA+0rMLPuQPei5k9sJyIiIlLLFi9ZQscO7XtycjUcg4iISHukpLeICODujwCPpIfzzew6omb3hUWbLiHKlhRrqL3YMOC85kUqIiIiUhs6dujAHU++nHcYLXL4l7bKOwQREZGapKS3iAhgZnsCq7n7lNRUB7wGbJjZbCPgTWBmE9uLjSJKn2T1BKY1K3gREREREREREVlGSW8RkdAdON/Mvgx0Bo4HjgUmmNl6wHzgCOBE4FnAzGwr4FWgPzDW3aeb2QIz28PdHwIGApOLf5C71wP12TYza6XDEhERERERERGpLbkkvc3sPODo9PAudz/bzPYnSgl0BW529+Fp252IBeXWAqYCJ7v7IjPrBUwA1gccGODu81Kt3BuALYHZwNHu/lbFDk5E2iV3v9PMdgOeAjoCV7r7Q2b2I+B+oAtwrbs/DmBmg4DbgNWBScCtaVcDgDFmtmba1+UVPRARERERERERkRpX8aR3Sm4fCHwBWArcbWbHAD8H9gbeAO4ysz7uPplIbA9290dTjd0hwFXAaGC0u99kZiOAEcA5wAXANHfva2bHAZcB/Sp7lCLSHrl74VySbbsRuLHEtlOAHUu0P0MsdikiIiIiIiIiIjnIY6T3LOBMd/8YwMxeALYBXnL3V1PbBOAoM3se6Oruj6bXjgNGmtm1wF7AYZn2B4ikd9/0HMBE4Eoz6+zuC7NBpBHh3Yti61mWIxQRERERERERERGRXHSo9A909+cKSWwz25oYhb2ESIYXzCIS0BuvpL0HMNfdFxW1k31Nen4usF6JUIYRtXizNy0iJyIiIiIiIiIiItKOVTzpXWBm2wN/A84C/lNikyVAXRPbWcVzWaOALYpuvRsMWkRERERERERERETatLwWstyDWABuWKrJvTewYWaTjYA3gZkraZ8NdDOzju6+ONNO5jUzzKwT0A14pzgGd68H6oviavGxiYiIiIhIdVvy0Ud0WG21mv35IiIiIm1dHgtZbgr8Eejn7vel5sfiKduKKDPSHxjr7tPNbIGZ7eHuDwEDgcnuvtDMphGlUW4stKd9TUqPL0rPTyuu5y0iIiIiItJcHVZbjX/snN+61bs88XhuP1tERESkPchjpPdZwOrAJZmR1VcDg4jR36sTietb03MDgDFmtibwFHB5ah8KjDez4cDrwDGpfQQwzsyeI0ZyD2jFYxERERERERERERGRNqTiSW93PwM4YyVP71hi+2eATwyjcPfpwD4l2ucAh7QsShERERERERERERFpj3JbyFJEREREpLk+Wri4pn++iIiIiIisXC4LWYqIiIiItMRqnTvype//Pref/+QvB+b2s0VEREREpGEa6S0iIiIiIiIiIiIiVUNJbxERERERERERERGpGkp6i4iIiIiIiIiIiEjVUNJbRERERERERERERKqGkt4iIiIiIiIiIiIiUjWU9BYRERERERERERGRqqGkt4iIiIiIiIiIiIhUDSW9RURERERERERERKRqKOktIiIiIiIiIiIiIlVDSW8REREREWlTli5eXNM/X0RERERaplPeAYiIiIiIiGTVdexI/cQbc/v53Y/pn9vPFhEREZGW00hvEREREREREREREakaSnqLiIiIiJTZ4iWLavrni4iIiIjkSeVNRERERETKrGOHTtz53Ojcfv7B2w/N7WeLiIiIiORNI71FREREREREpOLa+6yU9h6/iEg100hvEREREZEa8/Gij+jSabWa/fki0jbkPSumpTSrRkSk7arKpLeZ9QeGA12AS939ypxDEpEaonOQiORJ5yBpjC6dVmPfK/bI7efff9pDuf1saT06/4hInnQOEpGsqitvYmabABcCewI7Aiea2WfzjUpEaoXOQSKSJ52DRCQvOv+IrNrHiz7KO4QWacvx6xwkIsWqcaT3/sB97j4HwMxuBY4Ezs9uZGbdge5Fr90M4K233mrSD5wz+7/Ni1RabMaM1Vv9Zyz5oL7Vf4aUNmPGjEZvm/m97dgqwTTeKs9BjTn/zHmvvlk//KMS79lbcxc0fUcl9vP2ewubE1LJz7G+vun1D0vtZ/675Ytp4Zz55dnPu++VL6Z355RlPx+/O7tsMb0/p+n7mjHjk5/32/VNP7bY1ye/bP3v/f81eT+LZyz+ZExz325WTAtnzKi6c1BBntc4jfkbn+ff6Mb8jZrzv7kViKS0GWutOr5Fcz/5e1ApjXn/5r5b3/qBrMS8RsQ3e8mSCkRSWvb9q9bzT0F7/67VnO8r7fn7R1Ou3wvyPFe2VGPOtcWOGX9kK0RSGROPv/UTbdV+DlqZ9n5uas+UB6p+5coF1S1durRMIbUNZvYDYA13H54eDwZ2dfcTi7b7CXBe5SMUkVbW290fzOuHN+YcpPOPSFXTOUhE8qLzj4jkSecgEcnTJ85B1TjSu65EW6lhGKOAcUVtXYAtgZeA/Ia+VE5PYBrQG2h6F7W0dbX2+XYENgL+kXMcjTkHjaJ5559yfqbl2pdiUkyKKdTCOagp2vrfIMXXMoqvZcodn84/zdfW/6+Um463uuV1vDoHtS+19ntRa2rx813pOagak94ziQ+3YCPgzeKN3L0eqC/x+n+3SlRtkJkV7s5w99dyDEVaQY1+vv/JOwAacQ5q7vmnnJ9pufalmBSTYlpBVZ+DmqKt/w1SfC2j+FqmleLT+acZ2vr/lXLT8Va3nI9X56B2otZ+L2pNDX++Jc9B1Zj0vhf4iZmtB8wHjgBObPglIiJlo3OQiORJ5yARyYvOPyKSJ52DRGQFHfIOoNzcfSbwI+B+4GngRnd/PNegRKRm6BwkInnSOUhE8qLzj4jkSecgESlWjSO9cfcbgRvzjkNEapPOQSKSJ52DRCQvOv+ISJ50DhKRrKob6S1NUg+MpHRNK2n/6tHnW23qKd9nWq59lWs/5dxXufZTzn2Vaz/l3Fe59lPOfZVrP+XcV7n2U2vqadvvWz2KryXqUXwtUU/bjq+W1FNbn0U9Ot5qVk9tHa80Tz36f1LN6tHnu0zd0qVL845BRERERERERERERKQsNNJbRERERERERERERKqGkt4iIiIiIiIiIiIiUjWU9BYRqUJmVpd3DMUUU/vVFt+nthiTiIiIiIiItA1KeouIVKcu5dhJIbFYpgRjWWIqKFNMq5djX+V8n8ysc0v3kY2lLX12NRCTyCq19f9r+p0QEWnbzEx5HBGRRtDJskrpi4pI+1PGZOd+wG/NrFMZLorXTf92Sftu1rmlXDGZ2SZmtpqZfdrdl7ZwXwcCd5vZp9y9pas6r9nC1xdi2hsYnu5X1WfXhmNaJ/3bKe1b10arUCop2lavO8p1Xi0XM6srnG/MbCsz2zjvmLKy8QFbm9mnzax7njGV0lb/v4nkqRY7rMysY4m2qjx+M+tuZn3N7NNAVR6jiKxaLZ7rW6Ju6dKWfs+XtqboC1VnAHdfmG9UUi5Fn++a7v5+3jFJy5nZjsCBwFh3f6cF+zkI+B0w2913bGFMXwXOBN5It8vcfU5eMaV4zgNeIJKmZ7v7my3Y15XAR+7+2ebGlPbVBzgBeBeYCtzu7vObsZ8DgZuBF9z9yy2MqU19dmWO6avA2DLF1Bf4LvAfYA7wW3d/1cw6uPuSluy7WhX9Dfq0u88r9VxeUjJgfuoUOwGY4+535BlTQfb/lZmdAXwH+H/Ai+7+w1yDK2JmpwNfA14EegDD3P3tfKMKRf8HvwnMdfdJOYe1TCE+M/sysDbQ0d3/nHdctabo/0kXd/8475haU9H5ZS13f68tnJNbk5l1dPfFqbP6EOK64KG842oNKbm1F7AfsB3wMvCAu9+da2DSptTaea8W1eK5vqU0mqnKFJ3ovgtcQYxk3L+tjXaS5sl8vt8Bfm1ml5vZ4Wa2Ws6hScusCVwADDKz9ZuzAzM7GDgfGAA8b2afb24wZvYl4DfAT4E7gY2Az6bnGt2rnJKKLY7JzHYALge+D1xGJJj3b2o8afuDgZHAscA/U9K6Wcxsd+J9Gg38O8XUoxn76Uu81wOBV8ysRwtGQe9MeT67cv5/+jxwVRliOoj4PSlHTNsQfyN/CtwKfATcbmbbuPsSjfguLfM36CTgajO7wMxOzT6XFzPbHrgG2Ck17Zx5ri7vETGZLyl7ANsQSZrLgG3N7OI8Y8tK56Ojifg2ARYCH5vZWrkGlmT+Dx5NdKT9PdeAMlISbmnqnBsH/B+wab5R1abM/5PjiXPVXjmH1Koy55cTgbFmdi1wrpmtnm9krSeT8P4zcDjpuqKgmv6Ou/tSd3/A3X8M/Ii45vxt6twVAWrvvFeLavFc31JV84dAQuZEdwLQFziDmE5+GGkaVN5f+qTlzGwg8YX0h0AfYJd8I5Iy2Bh4FjgIGGhmTUqcmtlGxJf/c939fmAtYPsWxLMFcK+7T3X324FuwAHQ+MSWmW1KJKl/VIaY1gIecfcH3f0ZIkG5S1PiSTH1IM6L57n7I8A8YMvmBGRmXYHdgD+4+33u/ksiufH1Ju7ns8C5wA/c/S/A54CdW5BA3AyY1MLPrmz/n8xsWyL5eGcLY+oL/Az4fpn+j39E/B9/ALgXuBC4A7jBzDbXSO9PKiQQzOzbwHHEe7Y3sF36fciVuz8HLAXONLPtgE8DvTLP55KUz0xD7ZAS85OJ0b8vAI8CvwS2MbPL84ivhI7AbcDxxO/qd4ChxHVlm2BmmwFHAh+4+wepLbfvNWa2pZmtn5JwnwbOIs5Vw939SjPrZmZH5BVfrTKzU4DBwCSgg5mtkz6fqmFmW2XuH0v8vl4APEx0Wl1iZp1yCq8Szgf+4+7HA7PM7IdmNhaWJ4eqhZl91sxOdvd/u/vviFmGl5hZ/7xjk7ajFs57tUjn+uZT0rtKmNmXzeynmaaeRBLlVOBNokd4vJltkfdILGk6M9vLzI7MNG1LjFT9OjEt/5fAcDPrVer10jaZ2efN7EAz+wxRT/gnwAgisfCtxia+zexzwL7A+SkZCDAROMbM1ln5K0vua+s0evY54FMpcQ3wCmnRx7Rdg4sIpmPaATjJ3e9NzTc2NSYz29HMvgG8CqxtZpunp94gkjGF7VbZu52O62vA1zPTQf8CHJ8SKI1mZocStbffBrqZ2Xrpqf8Aq2W2a/DiI42m/j7wbXe/LzWPA75qZp2bksRJ79V+QHdgE1teK7ipn90ORKmdCzL/n5r82aV9HQD8gUgwr92CmL4CXA98twwxfT7FtRFwUPoCuTSVAfs5cDdwokW9cHUSs2xUfDaB0JO4vtiV6Dg6FzjVYoZIHvHVFX5X3P1Y4L0UXw/gjJQA+aOZnWRmFe0kthWnnHZKifkTgQPN7Cvu/hHwD2LE97qZc0nFmdkRZvZLYtr8acAJ7n5QKte0C6nufU6xZevHdyD+BowB3jGzn6YR1nnO0Pg6sIWZdU4lf/5L/H0oxLshMGRV5zopn5Tk2ZWYGfQa0UH3OHB+NVwvp/Pe6sCfzeyS9DvyeeDX7v4UcAPwe2JQxVYN7KpdsU/W8H4OWGRmTwL9iGsLM7MBFQ+ulWTOf18iBscA4O5/A44BBqcOValx1X7eq0W1eq4vJyW9q8d8YICZjUyPuwC/Bb4IHOru7xGLdeliu33aCLgqM0roDeBi4Jvu/lV3f5cY8b12XgFK06Sk6QRi9NxworzJI+7+GHAO8XkOtFWUOkn7uZEY8XaCxXRviCTKB6T/E43p+TWzw4gyD1cQHWWnuPsb6eklQH3a7mjglJUlF9J+bge+R5TgKXxBeRRY0JiY0h/4TsQolpHEiN7+7v5a2qSOOO9hZscBPynxRSi7r87EqIfvAl/NPP0Y8ASw9apiyuzvYKK2+O3pdilRDxqiDEAh0XEYcMTKEqe2vHzIle7+Uuapx9Pxrp6SOCWPq2hfhxAdHT8m/i+dkql33pTP7hDgJmAQ8LlMZ8JjNPKzy+zrQKJDrgcxcvQHRCII4n1qbEwHAb8mPu+NU1sH4n1qzv/xCcAw4v/DScA3LY2SSgnIR4F13X2ROomXfdkeaGa3mtnRZtYTmEuMVj7O3fukJN+hwId5xJc6LZZYlHL7OtGB+BHxe3010ZH0NFEHtdlrJjQ3tnT/dOAPZnYBcBdx3h+TEt+LiJE6J7j77ErFZstH7hfOUdOJ36cPifPJv8zsW+kcuzXwYCViKyXzPg4FfkHM+qgnvuhtCPywkPjOKb7LgNeBp1LHxbPAdWbWNcW0IXEe1HevVlL8tzadl14GniJKfr1JXJdsSBV8H0rnvQVEh8t+xHXbDKC3mW3o7h+5++PEsXZrYFfthi2v4V1nZoemTsx64pr3UmLtgR8Tn3s1rXnUHcDdrwdWN7OzMs89ANxX2Ead9bWl1s57tagWz/Xlpguvdq5wokvT/Q8HvpH+EP6CSHK/QIzWHAhsTox+knYi8/neTCRHf5W+0P+T+EJ/s5ltbmaHA52BWbkFK41mZhsSSbej3f0woif+QHefky7oHwfOJnrp+zWQDMzu5xuAA4Xk3YtEgrEwxXNRI2Pq5+57Awbsl/5/dQE+BbyekoY/AiaXSi5k9vNNd98f2AA42Mw2dfeXU4zjVhVT+gO/CHiGSCIfCRyV2eTTwH/S78N3gfHuvriBfS1M+6oH+prZoPTc28QooZ9YLAyyqvfp88Ao4CtEZ9RviARbP4t6t58G3rSYmXEB8GSpxGnRfjY0s/Fmdr2Z9Xf3h4lajeNT0qzkcWX21SW9N99Kn90f0j43Tcn+TwFvNOKzK+zn2+6+L3BLNNvGKSnvwPj0vq3qfTowHV+ho2Edd38jcyybAK81IqYDiS+yQ4hyARumn7/E3f9N0/6Pr0Ek8493974pho2JpNlZtnx68DpALzP7dK1/eUz/Tzd29+HE6JHriA6LCcBDwCNmtobFbIzVSB0ZlZRJhp5JdI58iygPdCZRtsaAt939POAsd38lh9i+Q/xu3UIsRHYpkfj+EXCbme2dOlkWVCo2oFvmd64wO+UN4lpxe+I68u/EuXcf4NhKvnelWJTu68fyc8LewD3E+/o5YtZMpWNado5w91nA/cCfiDUo7gQeNrNfpceXV/gzrhlmZpnft5PN7CIzu5Lo9NoZ2NfdxxDrgWxGDh10rcXd/wMcQZxj+hElno60KIXxdeJaZUaOIZZF4XooXRM/AOxJrBVyGDFDaypwspndByzxKlk81sy2Bq41s8Jix5cCdYUBEWkmzixiJklHddbXjlo+79WiWjnXt4a6pUt1XmyvikYQrZMSZjsQUxyuIUYg3kQkjHoAQ9O0WmkHbMWVeT/t7vMsFty7kpgavSZRBmMLYiTnme7+bG4BS6OZ2brEojunuPuzKVF8M3AwMC/ze70zsRL99Cbs5w/AIe5en7b5I/CWu5/ciJjuJr48LAReJEb3LiFG0GxKTJd7N/28kueSBvazFJjm7heb2Z+BGe4+tOF3atnI3EFEEmFPYiTde8T//1PT/k9tzLltJfua4+5XmNnfgI/c/eBV7GMzIhF0O5EY+gkxKr87kYDdgUgaLQFObOB9KrWfrxIJ11eJROzVwD/dvcEF7iwWsb2fGBH/CDEC9z3gYyLR34uYEltPw5/dyvazkBhVfRHwR2BmQ59dSp6fC0xx94csRo9fCBxUGH1uZr8hSvK8s7KYGtjPgSm5VNjuTuD1Vf1/MrNPEUnQ64l6xU8Qo3//S3w56EV8HnsQHUn/amh/1S4l835KdOwsIWYlbEp8aeoHfJmYifHZ1HZ66oDPI9YtiM60QzPnvk8RnSTnAYuBk9394wrF8zlgfXe/z6L8zm+JGuhGrMUxg+iM+gGRBH8udQpWIrY64svv5cQXpx2I5OxpxDlpC6LMz7HuPjMlmDqmjsNcpJg7EiVgriXOZ98gao6fSCwk/CXifax457/FQmH/B0whOizPJUZjfYWYXbAB8bfmEVux3I2UQfod+wfxu3QksQDrd4nz/D3EOh5HEjOhFgCD2vP1cuoMHkAMbJoKzHf3Z1LH7t+ITqwHid/txURnXy7n5nJL56PhwGJ3v9DMniauCx4iOr7WAdZz9ysK2+c1+6MlCucJM1ubKGkJ0RH5GHGN2ZMY1f5o2n4t4m/MWE9rHEh1q7XzXi2q5XN9uWmkdztVlPA+A7jTorTJAmKk50nA4WnUXz/gMCW825dMwvt04HYz+zXwJDHi+xpiqv+pRI/f0fpD1q68R3yGH6cL+PWA9YGl6SJ3K4sp0U+sLOHdwH7WAxYBWNS//iGRxGxMTOe4+0ziS/pQd+9DjKJZF3gJ6ErU6G7oXLKy/YwGvmRmmxDJlQsbERPEBf4zHgv2zCam6HUkRvbNp4EkbiP31RPA3Q8AGuwYSNtNJ75YHAzckr5wnE+MOtqASA73IMoUrDSuleznAiLp/GUi0fwb0sjqVcT0EVHi5itEsmqMu3+NGJW7HjCTSLA1+Nk1sJ8xwBeIBT9PZxWfXUosXpwS1R3TMT1Meq+Tx1NMK+0YaGA/m8IKdcBPJ967BqUvgiOJ/393Ade5+yFER+J9RIfIOcA+SngvKxkynHi/LwQuSyPkOwC3ufs0IuG4O1Env2IX2iVG4L9DXPD/X6btdGIhweOBH1Yw4d2ZqH/9rJltQHQULiLep0OIc+HjxO/ZDcBdlUp4Jx09ykQNJr4sLyFGx+9GdJp+lZjVsTksm1WRW8I7KczCmUOUTPo6cLBHKZj+RCmoe3NMeI8mOoUvJM7pvySSDk8B/3X3uzwWTs5tMdUqt5A4B6xF/M07hFgo+VniGuh84K/EjNgDquB6eQ3i+uEiouPqbjN7gCh7+Bxx7dWR6Bg6sr0nQcysr5mdlh4uJc5Pa6ZjnuDutxCDLRa4+03tPeENcZ5IHf33EJ8zxGCS24nSFRsTa/8UFiecT8x4VMK7dtTaea8W1dS5vjUp6d1OZRLe3yaSnj8GjiZWcV1KLGpxtpmN8Jgy+25uwUqTmNnGhYsYMzuVOMldTSSdRgH/DziFSPr1dfd6fb7tS/ry/gfgpXRB3pkYtTLPzI4nRleu1tA+GrGfQUQi8M3GJALSeeK+dP8Bd78p3b+TGAE7Hfiyu7/Qgv2sRox+nJ6S4o2xBOhpZkcRyZjfEcmF/YBdVxXPKvY1FtjczI5JMTZ2StjDxHvr6XWL3f124uJjDrCfR3mZpu5nkbvfQXQy9Ejv35sN7SDjT+l1OwD/S/v7C5Fofh34v0a+V6X2cyfR4dGzsZ9dIUmW3pv3ic6Q7Ij1x4A9VvU+NbSfQhLT3V9p7Pvk7n8lkntPEgkp3P1p4n1a7O5z3P1/jdlXtUqJgmxi7lnid/cci5Xjv522+w+xEGxnd59bwfiynf77paRjR+L3aUdbvpjmdGBx2v6tCsa2kJipsTYxDb0PcCzRcXiAu/+dKE92PzDYV1GWp8zx9SI6sUgxbEX8zr/m7mcRpal2IGaR/bTUPirBzNYtdGyYWT/gdIvFVP8EbEN0xna2WOdkITHgI484P0ssPn2cux9BjJj/KrHQ3KXE3+ct8oitlqS/Dc8QnTifJTqhdwe+SSQDdyZmcrm7V6ymf2tJ1wkHEDPdHib+j32HuP54i1hQux+xcG67LmuZzgOzgF3M7EGiw+s9onPpfnf/Vdr0E2shtNeEN0CauX0BMdDpa6nTrN7dH3b3c4nyTusSSbHC9WM11TCXVai1814tqqVzfWvLbRV2aZk0qnMdotd3KDCPmKa9LvHLMA44kPgyIO2EmX2RmF78fTObQiS6DyF69N8ivsRfTEzt+xrLF4WTdsZXrOv5IfAPi9rspxFT0OpbuJ/vNGU/WWmk4rZEonMD4v/flKYmA4v2sz5RaqCpyae3iYTW+UQZk/ss1ii4txkXcSvb19Sm7MRjwbwbAMxsH6KMyPrp9hd3n9PAy1e1n/WI83iTvry4+xtm9jNi+v/XzQzifd+MGEnaqMXxVrKf1YnRvk2u55sZMXy2md1iZpe5+xnu7i3czyh3H9bUeCC+KJjZM8D+ZjaXKE2zFTFqoqaZ2dqFTlQzO4n43P/l7gPN7BoiwXc+MWV2CHCfxwyBivEVa3ifQHT0TyQWID0QGGlmc4gvfP0qNbK2qGzF6sS57jkigdyF6GSpM7NriY6XY5rQqVUW7v66mb1rZq8Bz7r7IamT/S9mdrzHrIpvE+eyBtcSaC0W9WvPAq43s+2I2Rd3E+/ljsTfyO+mW1dgYI4d/1sQ5amOBp7yKJV1KvH70Qn4ibsvUkmT8ktJwe7AP9J10H/S4x8DdwB/c/eFaQBAD+L/StWMgnX3KWZ2DrHweFd3/z0xKIY087eLt/NRv5kSH/8mZp5sQQycuDZdM21lZuOJ78PvuHuTruXauA5EouufmbZRZvauu//Y3f9qZsOIvyVVUbtcVq3Wz3u1qBbO9ZWgmt7tSKmLZjM7lxgxtztRf7ULMfXpNmC4en3bl1T64QGi5/ZOord2JDGq9UGiHMAviFEPX/MKTdeW1pW+5DtxgXtEE0cvt8Z+VidKPRwHzAXO9WZMmSrjfj4PrOvu96fHnZo7OrLM+1qTKO/wDeL4fpBGDeeyn7SvzYgEzFeIGt4/a+Z7Xpb9pH11SAn+vYlz2vDmjDopsZ8fNbaDocS+Nibe8z2JEa8/bO7xVQsz+xoxhXIIMfL9p0S5mz2Bhzzqp44mFsoZ5g2XXmqN+FYvdPJZ1Of/jrvvn74MnEaU5LmfuA7anFhEtqIxptgKCyz+hZhafDLR+fQIsVDkV4Abm3t+bmZM2dHxfYiySTPc/Yup7Uzicz/J3R+oVFwl4lyDSLZfRCwKvCHwPXd/2cxOIWa7fdHdn0uj1hdUcmZGJgm3PdHZ8gqwP5GA/7O7X5a2OwP4e62fU1pL6qgZR6y/8BzRSfwG8Xt2GDEaeDTx/WhrooOpKjs1LRYvGwWc5+4Tcg6nbCwWZFyc+Z3bhKjZfQzwuLuPNrN9iUEZa7j7b9Pr2mUHU+Y41yRmJXYDbiX+1v4jbTOISHD91sx6EHXMD/ZYaFyqnM57ta1az/WVoqR3O2RmA4g//IW6tKsTX676ECODzwKO9xxqG0rzpSl8XYjFpb5ILFDwHyIB/it338fMDibK2fzAKzRdW1qfxYJrY4Efu/u/895P2lcnoqRBh5Z0npVrP2lfZfsyU659WdSa7kocX7NLPJRrP5n9dQHqWjoKt1z7SftanZiCN6+N7KeO5e95i/ZVDcxsAlEf+Rnib8+v3P1RM9uFGOH9CPAz4NfpuYqNUk6lLW4kRm7/x6LkxeeJEd5HEaOoLyPqn17hOdWuTEmJwcRMm/WJ6ajrER1IWwMT3X1KhWPKJrw3JKbFrkMstrg7sTDsXDP7HtFB+WUimVzRLwgptm8Sn/PaxCj+/Yia+ze4+0dmdjLxpX4vd3+wkvFl4jwUuISYWXUbsbDmDsRn/nd3/0UecdUaixmvaxH/l48kvgsNIzpu/mBm6xPXHh9VsmMkD2bWl1gf4ER3/0Pe8bRUpoO7AzCBqFv8ETHScTciufd4avudpzJo1o5reMOyc8vpxHfBQcS5+AziHPMhcc4+zd3vTdt/SiM8a4vOe7Wt2s71laSkdztQ9IXlm8CZxCIWGwLnEYsk/YAYIbg70N/dn88pXGkiM+vh7m9nHvciRne/R3zx+zdRyuTfxOJYx7j7/8sjVmk9ZtalHCP3y7UfEaktZrYusRDfxkQZiYvd/erUcfUFYnG+v7j7rysc19ru/q6Z/Zaomz+Q+NLXgSgtsaG7/zKNQt+AWNy2Il/2UgfMxylB05VIvE8iZmMdDQwgBiVcS1yf/aGSAxKKrh/PIt67l4hOjOlEB8b2xCCKF4hSNrnMEEwjF88FvkTMWDqHuN5dh6jlfV8a+TkIeKySI+UzMe5KJLwPJQYgXEDMvruBuD47jRgxP709jjZt79IMkJ8Bv3T363IOp6LM7KvAy17ZRXFbTeqUvoMoPzeNGL26IzGTsQ8xg+YNdz8ptyDLKJVyuoooH/Z/xEKE2xOl1w4gZlhNcPd72utodmkdtXzeq0XVdq6vFCW927iiLyxbESOabnb3V8zsp8Tokl8DmxBToabl8UVAmsfMDiAWlbqdmEq+xN3fM7PvEAutbUCMaHiR+Iyv1DQ2EREph1QqZjpR6mKRRR3vpen2A2Cou9+dEt+fB2a7+xsVjO/rROmIRUTNym8Q5Vb6u/trFvWxHyYWbToJGOLu/6lQbPsSixXeCVzn7q+m2VhjiaTtWKIs2S+B8939nyvdWevFWBgxeTyR8D4GuBd4jXg/nycStwcRo+gbswBvuWPcjShH05WYtn0IkYS/nCitNILo5JgETHb3XGqNA1isl7Ev8ZmeRMyI+GGK7Q6i0+Dtle9BWkNKkJLKQ/Qjzl17lWPWlOTDzLYkSpBdTnQa3gX8AzjM3X+YHeXc3pPAZmbEeW6uuw9NbWemtn3c/Wkz61wY0S4COu+JNEWHvAOQhhWN0LkbOJUYBYy7jwD+RXxxme7uv1XCu91ZjRjFdBrwPeAsM/sssUDBSOIi72GiXtdIJbxFRKQczGw9orbzRGCwme0O/AE4ligXcgZwmZkd4u6L3P2fFU54H0B0Bo8gEssXu/vZRKLxGjPbHHidKIExEji9Ugnv5H1ien0vYJqZ/YJYjHZroqb3H4nReRsSpegqxswOMrM+KeG9OlF3ehSwJlH3fC4xKr13ek/3yCnh3Qf4LbAlUChZ051Ich8PbOLuP0ibf5mYyl3J+OrSv59OTa8AtxAzHxa4+++IQQs7AjOV8M6HxyLHS1Py82ZgTyV+2pdU4i1rAbEY8V+A29z9SqIzdnOA9p7wLpxbkpnp9hkzOyDVM/81cDHwTzPrThy7yDI674k0npLe7YCZ7UF8YdmV+HK3vZn9CsDdhxMLH87IL0JpLne/k5gm+2+i/ueTxBflbsRCJoenqUo/cvd384pTRESqzjtEx+rqxEjpXxDTxi8gRtfdT8wk+4nFAoMVY2b7E7Vcj3f3h4A/EwmB8cTo3x2IhMAEopbyHu7+r0rGSCQp5hOztY4iFpS6m6g5fTdRG/3HRK3NinUWJD2Au8zsqx6Lfz5ClMI7nFgcbTDRmX6kmXXLo6Z96mS5nHh/Lgb6EiP57yBqlW4DHGFmg4kFQS9z9/mVjDElFL4O/M7MbgG2IK7XvgB8mMqd7Easo6FBCTnLJD8r+v9EWiYl7RabWQczG5JKGC0gEt5bAHemmv/nAiusZ9ReE97p3NLHzC4Evunu5xDfAb8B7J1m6fwc2NLd672ZC65L9dN5T2TVlPRug7K9vxarVX+LKG2xgbs7sdDQ3mZ2NYC7X5DDFyopE3e/n1h8dH9gHlG/+3NEHbdvp4uj93IMUUREqoSZrW9m66YFv84hRk7PJxKhQ4m/P+8RJUR+C3ylksnGdA30GSK5/aHFAr03A9cQo9L/RyTre6S29939nUrFV5Bqc18PjHb3R4B/EiO6FwHrAnXAce7+TCXiMbO6ws3dbyDqid9lZvu6+2iiTFrfFONXiOTtxTmODDPgdo/FUru4+xJ3nwkMJxYk/Qcx4nsQ8Ky7/7fiAZrtTNTY/Q0xA+9AokxMV2IU6h+AX7v71ErHJivXHhOhtSolfS9KD+8gzk17sHwQ0KXATUQH51vu/r30urpP7KydKCS8ifUBXgd+bGZnu/sPifPzQOJ9gOhMbdfHK5Wh857IyqmmdxtTVMO7i7t/nGp9nUksjDTR3V80s88TF+FHEjU29UG2c2nV7lHAMHf/k5mtBfSo8HRtkaplZp2J+sXPuvtXy7C/LxMjOTcEOqV9n7OqEadmtgtwgruf3NIYRJoi/V25jVgU7F53v9/MjgF6ufvPzWwDombxicSClp8HFlb6GiOV5PgmUTd5E2K20/XpuZ2J37tDib+RFS0dUhRnD2IBqTnECL3T3X1Sem5Dd3+rodeXOZa1ijvIzew4YBxRJ/uvwGRiIcsDgEPyKImXGeV4AbCRu59QVJt0dWIG4yXE/9UuhVIGFY5zK+AnwIfuPiS1HUkk4QcT5em6uPtL7bXEgkjezGx7omTQy8Cr7n5Gav8u0Qn7JWKQXtfU0bhsrYKcQm6xVK7kZuBsokTWxUSJp1uJDoCLgd/nMHtJRKQqKendRpnZ94jpu5sTfwA7ECOA5wI3uftzhaR4flFKuaVFsCYQi4fdmHc8ItXEzI4Gvk18idqrJQkfM9uL+F093N2fTG0DiOn62zaUiEtTd49094Ob+/NFmsvMtiFG/B5L1HV+khhNd767P5jZbpM08jYXZrYaMeLte8DgVOaEtGDT94GD8hjhXczMLiZGzH/NY9HPii84ltYCeZIYzf1XYqT+JOA5YjHsPxAj+BcAOwHP512OIy0EejXx2U4zsw5AB48FVX8F3OPu91Q4pkJCvgPRmXk2cS0+PI3mx8zuBH7u7tMqGZtINUl1qxen+1sR56g57r5/ZtDXWOBGd78387p238GUOp/HEovgHgk8DnyKSHpfRgyeyG3BXhGRaqOkdxthZvsA9R4rNH+LmJb6VWIRwyfcfWga3f1dYiryz4FF7f0Pv3ySmX0VeNndX847FpFqYmZ/J6bJfg7o7O4npfZzgROIhemmAoe5++Zm1oU41+4NdCQW9zvd3eea2f3El7ExRT/jCOAhogTDpcDuxOJxdcTowNfT82sRU/u/lWrGDge6AB8AZ7n7I6msw9VpH/XA8wDuPiiNjvoNUUZhKTHF/vfpb8llRLmKNYiSC7PStNlCYv5Idz+8LG+qtEtpZO3uwBXAVUBvosbzsXkmuoulxPexRFm3HwBrE4tWftvd/1/OsRUSpKsRNcevcffbc4plXeJ9OZRIoLxKfL49iWTSWWnTvbIdG3kys67EQqUbA79197+n9m8Sn/Wh7v5aBePp4LHw5z5Ebfu/EKNPTyVK1vw/oiTM7cARef//E2mvCgnv1Ln0JeAFIuk7hTiP/iZtdz9wqbv/Ob9oWy7zt2IHYr2mV4jyXHVEh9qJ6bvfYcSinX/LL1oRkeqjpHcbYGYHEomNASnRcRaR7P4/YuHK/un5c4CNiOlfFa9tKCLSXqWRkP8kEiyfIabPb0rUZb2MSBC9B1wL7JeS3j8mEtZnpy8sFwHdUyfk+8Bu7v78Sn7e/xEjVPulRMq5xGJ7X8+O9DazrYkkyj7u/k5KZt9L1PQfTkpEAp8GphGJ98GAA99399vNbGMi0XUUMeV+CrH40XQz24kY8dkrjaCcClzo7n8tzzsr7ZmZbU4k+LYgOtUHuvvEXIMqkpLK/YnaykuAr+ZRlqOU1HnQkSghMgP4QV6DEcxsI6IkzIHEAqD/S+eXjYjz3HbAJW3lvQMws57EQqTHEDW85wBfJhZ2q8jUfjNbzd0/SvcPIP4eXAwsdvcbzWxbotOlL9Exer67/629l1gQyUNRwvseotP+fWJW8yxivYb/Ep38a7j7ibkFWwaZhPd+xOzAh4jrue8C3YG/AUcTHdDfcvep1TCaXUSkLemUdwC1LiW8f0ZM73wkTXnahKjt/G9ipMtCM+sGfMrdH80vWhGRdusU4C53nwPMMbNXiXrBGwC3uHs9gJldSXQ2AhxMfCk5IJZWoAsxghsi+bbSxaDT+Xw4cJKZfQbYh/hiV+wAIik1Jf2Mwr63IkpafS8lVuaa2XiixvI2wOqFUaXu/qaZ3UbMDrofeMPdp6fnnk7H2tfM/k0k/StaMkDaLnd/zcyuJa4H1yASj22Ku39kZhOJ34vH3P3FvGMqSImJRWb2UyJJmluiwt1nmdnNxOyP683su6lT7iWgTSZS3H2GmY0gOv6+QHQcXFg4f7W2VFv3DDO7jDg/9yOSUY8Bg83sGeBPRC30OuLvwXspdiW8RZrAzLq6+4fp4Y1EKaY7iQWBRwC/AA4i/g7d7+4/S69rtx1MKeG9MzFj8BvEd/tTiZmAg4Bz07/f9bQgbls7T4s0VhpI8R9iZlRBHdGZvIQylXZM1603ZUsfNeG1PyHWg/lOpu3XwGnEgKEZjdjHYGJNj9FmdjIxIOpnDWw/iZjF+7yZ3UMsFP92U2OX5lPSO0ep1/dmYvTf82a2JTGl8y9EDcbpQPdU53kr0oW2iIg0npmtQdQGXmBmr6XmbsQXj5uIC7KCbB3FjsAZ7j457efTwOrpuUeJ0eErjEZMSfM7iBHXlwG/JpImLxIjtot1BKa4e7/MPjYF3iSm1JeKrVSyvQPQOd2fV/TclUQt838TZQT0hUqyFqUa1KfmHcjKuPsCM/t9W/2/6+5eyZ9XnMAuJIVSB9iVRIfeL83s+4XZKG34vVtIzFR5vJI/N9URPjuVFtiEKDcwhRiNOZNYRPNC4m/HRGLG5XeBw83sOXefX8l4RdozMzue+L2618zWJmbRXUnUtP4TkRC7kCjbtl3h9yud69plwhvi3EwkuNcFxqcZfWOIa7s/ELNIrkqj39tcx6RIM3zo7jsVHqS/r/8CzivXD3D3weXal8Xi2QOJmvrfITqiVmVP0vc/d796VRu7+9cyDw9oRpjSQkp652s14o/eWmbWGbiBqPH6ZzN7DriOWM15Q2KxtDfzC1VEpN0aALwNbOPLF07qTnQs/hMYbma/dPf3iNrehS8dfwW+Y2ZTiAT0GCKhPAS4ALjRzJ7y5QtZDiK+wJ1P6sB096vSBdU5RIKbtK9Cgvo+4Hwz29bdXzSzrxF/C3oS03y/ZWb3Ecn2/sSUXwc+NrNvZMqbHJGOs5RbiRlFOwC7NucNlOrVXr5kt5c4W1s2MWJma7n7e6mEUp27L00jvq8mRu7/1My+6RVeWLOdWEKM5p5NrLWwH/BjYF+izML7RJ3hM4kRXS+kDoX3lPAWabK/AzPN7BCic+kOonzbtsAviQV29wF2dPdboX2P8AYwsx2JY/wqMaDtTGBoOkePIfIwG2Rm5ulvnFQdd59pZi8Rs2U3MrO7gF7Ed6H+xBpEzwE93f29VDbOiZKNnyFKPS4hBv58P5UA+jvwG3e/NQ0OvYAY/DMfONndnzGzHxJ18lcnrofOcvc7SoT4TWJ0+iXAPWZ2vrt/AMsWfr8GWD/FcAHwMXAIMQv4Q2A9otP8z8T6Sjuk13Yn1lfZkihNeSTLB5fcb2bfITrZN0vXcJ8CXgM+5+6FWcVSJiudmi2tz90nESP/JgJvAFe4+y/NrJO7/4eoHziQWFTtuRxDFRFpz04hatkuG8WdyplcDgwjktmPmNkTxAKTH6TNfkpcgDxFJJvriC8tuPs0orb2ZWb2tJk9TySe9/VYc+FqYG8zexZ4hLig2iKN+nkE2NbM7kjn9hOBm9JU+p8Ch6SkysXAAmKa4L1EaZUPUgLrMGJa/rPpufPd/f5SB+/uHxOJ70c0nU6k/SpKeJ9OlDG53sw6ZxMm7v4W8CvgJCW8Pym9X68QpaWGEmvo3EN0TtYBmwNPEgsb/yJ9ga5z95fdfXZOYYu0O2Y2zMz+mxK72xIDC04AbiFGe/cjElXHAdPcfUThte0x4Z0SdphZb+B0omxLb2IG905mdkU6l7wJ/NTdKzrDRaTSLNY42groSiSAz0iJ4alEIvp1oiOsMHBnX+Add3+G6BAb6u47E79L+xTtewMicTzI3T+ftv+ZmW0G7A/sndp/RAxIKuUUYIK7P0F0eB+fee4mogTm9kTJyYtSrH8mFtm9MrPt34BPp3JGEGuV3OXu7xY2cPdvFY7R3R8A3iE6xSCS71OU8G4dGumdM3e/08yWEIunFcqXLLVY6KMwkkRlTUREmsndv7CS9hFm9ifgy+7+WQAz+x6phEmqPbnSkg8ei0GWXBDSo+7wF4uaz0j//ocY/VPY9hbiC2CxQ4Hb3P2ElCy/jeXT6Z4hakQW/9y/A5/LtqXyLnsTyR0RaacyCe9DiFFQZxJrwEwws8Hu/n5mxLcWPC9iZmu6+/uk2Tzu/t/U2XgcUfLqLiJRdT0x8KSru7vKDog0j7uPMrN9zexBd98zzZY4GvgWcDfR2fQP4Cl3Px8+Wb6pPfGo4f0VYrb2CCKJ1o+YNXIg8DBwFTEa9ePcAhVpPV3N7Ol0vxMx03YAsYbS4+7+cnruaaLOPUSpo18Ao4nybFel9puAO9Lo8L+lbbL2AP7l7k8DeKx1dDssK6k0IJUy251YQHYFZvZFYpZJofzIeGJA0dXA2sCORI4Od3+DGHmOLV+DaZn0u38dUaP/CeIcd3bpt2iZK4nZw5PScX9/FdtLMynp3Qa4+yQzOwm43MzWdvcJecckIlIj/g2cY2YnEomQ14mR123Bv4BrzOwiYlrg/aSLr8Yys4OI2URj3f2x8ocoIpWURk0dB0x098fNbA8ieTTGzE5097n5Rtg2pc6/v5vZb939GjPrCPyROK8eTay7sDA9HgwMcfePQGUHRJqqqDTJ/USppYfcfY+UMDqCuK75NTDG3f9Z4nXt1Q7AL919gpmtBewMfI8YxLYnsF2ewYm0shVqehekEpDZmWdLWb5u0b3ApyzWu9uLNNra3X+UEskHkhZ+NbMvZfaxiOUlKQszLXYgcpx/Iurp3wM8wPJEetbQtI8n03mpE7Ax0Ad4MBNnYf9GfE9cmd8BT1kstNk9DURqyA3ARWa2L/BpT4vZSvkp6d1GuPtf0ojvCWb2sbv/Ie+YRESqXUoQHZV3HKW4+7+IUQwt2cdfgXXKE5GIVFqJJNAH6Xagmf3D3f8BHGRmjxKDJ76lJO0nufv81IF4oZktIL5E/9vdR8KyzoTniPV2TiskvEWk6QrrDBCjMx8lZptdaGaPu/uuafbaYOBpd78HqibhDVGu5RQzu9lj4crHiGTfUGLRynvb82h2kXJLo6RHEwN7bvRYvLwT8DLwdXe/2sz+CrzA8jWRAB4DtjOz7VO5yEOJMiZjgSfc/ZLUwT2a5esqActqbh8DHJwtD2lmvwe+mwalPkkk4K81s02Bh4jZtNm1mbLHMTP9vl/DygcpLS681t0/MLMJKd5LGvt+SdOppncb4u53Eb98/8w7FhERERHJT0qMLEn3B5jZD4gvW2cSC/EeVRj15O67Az9WImXl3P02Yvrwr4FN3P1MADPr6u5OjMAcnaYxi0jLrAm8S4x6/qe79wHeSaVO7ibWIrmnsHF7THhnanjvaWanmtk3iO/xk4hFynsQ5ezWAmYCW4Nmj4iUMB7YlEgY4+6LiHWXbjSzfxJlIL+d7ZBOZdwGAONTSZXvEbWxJwI90npLTwLzgHXMbM3MzzseeL7EekgXAPua2eeIhTaPTmXQ/gIMTmumTAZOT9dkxcYAX0jHU8rtwINp/xCjw9cHft/AeyMtVLd0qc65IiIiIiJtkZmdTCy29BfgXKLO5BwigbsacK27P5VfhO2LmfUFLgN+5O43p7YuhRq7GoUp0nIp4fsn4Nep1i5mtgsxOvNMd780tbXr37e0xsJIItl1HDEa9A7gSGA3YoTp0cQCfbsCpwFL2vMxi5SbmX0TOD51jtWE1Gl2DrCZu5+SdzzVTElvEREREZE2KC209BvgMMCIGpU7ALsQ02SPAS539//lFWN7ZGZfB34F/Nzdx+Ydj0g1SomsccAJwONEp90T7l6qvm67Y2ZdiVrBZxOLl/8E+DpRx/teYD3gq0A9cBFwuLu/kEesIm2Vmf2dWOjyCHd/PudwKsbMXgVmA4ekEeTSSpT0FhERERHJWWHEY/ZfYCsiYfR7Yqrtr4ialacC+wGPufuHuQXdjpnZ4cSI792BWRp5KdJ0ZtbR3RcXtXUCFqfz2FHEInTzgHnufkLapl3X8E6lpV4iavGuRSyAN4BY+O53wEHuvjAd/wHAZanusIiIVJBqeouIiIiI5G9TWLao00DgF8BbwM3EiMFPuXs98CLwV+B1Jbybz93vAHZ29zeV8BZpukLC28w6mNkwMzvdzHZM9XgLHXm3EOU9js0kvOvaecL7i8ANQA9iRPfWwG/c/TUi+b16eo50/Kcp4S0ikg8lvUVEREREcmRmawP3mdlZqWk14C13fx94mpgyv5mZHQYMBoa6+yt5xFpNVBZGpPkKCW+idveawBbAvWa2QepI6pg2/cDdF0L7ruFtZnVmtjVwJ3BLOgc/DtwGnGpmE4lZOT9z91npvSG7+J6IiFSWyptkmNlqRI3EWUSdRGlfvkDUVOtOdOjMAi4kpp5J9esIbAT8oz1eXOr8I9Lu6RwkLfVlonTJtcCbwEHAaGAm0Bm4BpgL/BaombqX0ig6/0heDidmqfyWOEfdDfybqGP9n/zCalXfI2p1n0Ic45rECO9NiXN3LZ6f2/U5SESqV6e8A2hjdgGm5R2ElM02wN55ByEV1xt4MO8gmkHnH5HqoHOQtNTFmfv9Szx/aKUCkXZn2fnHzM4HjiRqDF/n7peY2f5EDeKuwM3uPjxtuxMwhqhNPBU42d0XmVkvYAKwPuDAAHefZ2bdifIOWxILcR3t7m+ZWRfgOmIhvw+B/u7+YiPi1vmn/ftu+nevXKOorHvzDqANaq/XQCJSpZT0XtEsgBtuuIENN9ww71hEpAneeustBgwYAOn3uB3S+UekHdM5SETyUnz+MbO9ga8AnydmCTxvZlOAscSAkDeAu8ysj7tPJhLbg939UTO7DhgCXEXMNBjt7jeZ2QhgBHAOcAEwzd37mtlxxIKg/YDTgfnuvp2Z7QWMB3ZrxCHo/CPSjlXBNZCIVCklvVe0GGDDDTekZ8+eecciIs3TXqfF6vwjUh10DhKRvCwGcPcHzGzfNFp7E+I7X3fgJXd/FcDMJgBHmdnzQFd3fzTtYxww0syuJUbtHpZpf4BIevdl+YjeicCVZtY5tf84xTDVzHqYWS93f70xcev8I9LutddrIBGpUkp6i4iIiIiIVBF3X2hmI4GzgFuImsPZUZizgJ4NtPcA5rr7oqJ2sq9JifW5wHoN7GtZ0juVRuleFK4y3SIiIlJ2HfIOQERERERERMrL3c8jktGbAluX2GQJUNfEdpr5moJhwKtFN9XzFhERkbJT0ltERERERKRKmNm2aXFK3P0D4HZgXyBbMHsj4E1g5kraZwPdzKxjUTvZ15hZJ6Ab8E4D+8oaBWxRdOvdrAMVERERaYCS3iIiIiIiItVjS2CMma1mZl2AQ4FrADOzrVIiuz8w2d2nAwvMbI/02oGpfSExArtftj3dn5Qek56flrZf1m5mewILiut5u3u9u7+WvQEzyv0GiIiIiCjpLSIiIiIiUiXcfRKRgH4KeBJ42N1vAgYBtwHPAy8Ct6aXDAAuNbMXgDWAy1P7UODEtNhlb2B4ah8B7G5mz6VtTk3tVwCrpfbLgeNa6xhFREREVkULWYqIiIiIiFSRVM/7vKK2KcCOJbZ9Bti1RPt0YJ8S7XOAQ0q0LwCOb3bQIiIiImWkkd4iIiIiIiIiIiIiUjWU9BYRERERERERERGRqqGkt4iIiIiIiIiIiIhUDSW9V2HxkiV5h7BSbTk2ERFpmY8WLs47hJqnz6Bhug7Jl95/Ealm+hucL73/IlINtJDlKnTs0IE7nnw57zBKOvxLW+UdgoiItJLVOnfkS9//fd5h1LQnfzkw7xDatLZ8jVQLdB0oItVM10H50jWQiFQDJb1FREREyszMzgeOBJYC17n7JWa2P3AJ0BW42d2Hp213AsYAawFTgZPdfZGZ9QImAOsDDgxw93lm1h24AdgSmA0c7e5vmVkX4DpgZ+BDoL+7v1ipYxYREREREWkrVN5EREREpIzMbG/gK8DniQT0aWa2IzAWOBTYDtjFzPqkl0wATnP3bYA6YEhqHw2MdvdtgSeAEan9AmCau29HJMsvS+2nA/NT+zBgfKsdpIiIiIiISBumpLeIiIhIGbn7A8C+7r6IGKXdCegOvOTur6b2CcBRZrYZ0NXdH00vH5faOwN7Abdm29P9vsRIb4CJQJ+0/bJ2d58K9EijxUVERERERGqKypuIiIiIlJm7LzSzkcBZwC3AxsCszCazgJ4NtPcA5qYEebad7GtSGZS5wHoN7Ov1bGypPEr3opB7IiIiIiIiUiU00ltERESkFbj7eUQyelNg6xKbLCHKmTSlnWa+JmsY8GrRbVqJ7URERERERNolJb1FREREysjMtk2LU+LuHwC3A/sCG2Y22wh4E5i5kvbZQDcz61jUTvY1ZtYJ6Aa808C+io0Ctii69W7ygYqIiIiIiLRRSnqLiIiIlNeWwBgzW83MuhCLV14DmJltlRLZ/YHJ7j4dWGBme6TXDkztC4nR1/2y7en+pPSY9Py0tP2ydjPbE1jg7iuUNgFw93p3fy17A2aU8w0QERERERHJk5LeIiIiImXk7pOIBPRTwJPAw+5+EzAIuA14HniR5YtUDgAuNbMXgDWAy1P7UOBEM3ueGIk9PLWPAHY3s+fSNqem9iuA1VL75cBxrXWMIiIiIiIibZkWshQREREps1TP+7yitinAjiW2fQbYtUT7dGCfEu1zgENKtC8Ajm920CIiIiIiIlVCI71FREREREREREREpGoo6S0iIiIiIiIiIiIiVUNJbxERERERERERERGpGkp6i4iIiIiIiIiIiEjVUNJbRERERERERERERKqGkt4iIiIiIiIiIiIiUjWU9BYRERERERERERGRqtEp7wBERFqDmZ0HHJ0e3uXuZ5vZWKA3MD+1j3T3O8xsf+ASoCtws7sPT/vYCRgDrAVMBU5290Vm1guYAKwPODDA3eeZWXfgBmBLYDZwtLu/1fpHKyIiIiIiIiIiBRrpLSJVJyWxDwS+AOwEfMnMDgd2AfZy953S7Q4z6wqMBQ4FtgN2MbM+aVcTgNPcfRugDhiS2kcDo919W+AJYERqvwCY5u7bEcnyy1r5UEVEREREREREpIiS3iJSjWYBZ7r7x+6+EHgB6JVuY8zsWTMbaWYdgF2Bl9z9VXdfRCS6jzKzzYCu7v5o2ue41N4Z2Au4Ndue7vclRnoDTAT6pO1FRERERERERKRCVN5ERKqOuz9XuG9mWwP9gD2BfYCTgHnAncAJ6f6szMtnAT2BjVfS3gOYmxLk2Xayr0llUOYC6wFvZuNLZVC6F4XdExERERERERERaTElvUWkapnZ9sBdwFnu7sDhmeeuAAYCt5R46RKinElT2lnFc1nDgPNWGriIiIiIiIiIiDSbypuISFUysz2AKcC57j7ezHYwsyMym9QBC4GZwIaZ9o2Ikdkra58NdDOzjkXtZF9jZp2AbsA7JcIbBWxRdOvdrAMVEREREREREZEVtPpIbzPrBjwMHOzur6UF5i4BugI3u/vwtN1OxMJvawFTgZNTeYBeRI3d9QEHBrj7vFQe4AZgSyIJdbS7v2VmXYDrgJ2BD4H+7v5iax+niLQdZrYp8Eegn7vfl5rrgFFmdh9R0uREYDzwWLzEtgJeBfoDY919upktMLM93P0hYlT4ZHdfaGbTiJIpNxba08+YlB5flJ6flmqKr8Dd64H6opjLdPQiIiIiIiIiIrWtVUd6m9luwIPANulxV2AscCiwHbCLmfVJm08ATnP3bYjk1JDUPhoY7e7bAk8AI1L7BURCaTsiWX5Zaj8dmJ/ahxFJLRGpLWcBqwOXmNnTZvY08GXgYuAh4HngaXef6O4LgEHAban9RZYvUjkAuNTMXgDWAC5P7UOBE83seWKE9vDUPgLY3cyeS9uc2poHKSIiIiIiIiIin9TaI72HEEmf69PjXYGX3P1VADObAByVEkdd3f3RtN04YKSZXQvsBRyWaX8AOAfom54DmAhcaWadU/uPAdx9qpn1MLNe7v56ax2kiLQt7n4GcMZKnh5dYvspwI4l2p8hzlvF7dOJRTGL2+cAhzQxXBERERERERERKaNWTXq7+2BYYdr+xsCszCazgJ4NtPcA5rr7oqL2FfaVyqDMBdZrYF8rJL1TeZTuRSH3RERERERERERERETarVav6V2krkTbkma0N2dfxYYB55VoFxERERERabfM7Dzg6PTwLnc/28zGEmXZ5qf2ke5+R2uvudT6RysiIiLySa1a07uEmcCGmccbAW820D4b6GZmHYvaV9iXmXUCugHvNLCvYqOALYpuvZt3WCIiIiIiIvlLSewDgS8AOwFfMrPDgV2Avdx9p3S7o0JrLomIiIhUXKVHej8GmJltBbwK9AfGuvt0M1tgZnu4+0PAQGCyuy80s2lAP+DGQnva16T0+KL0/LS0faH9QTPbE1hQqp63u9cD9dm2TBkWERERERGR9mgWcKa7fwyQFuTulW5j0kjtO4CRVGDNJXdfWAhMJSZFRESkUiqa9Hb3BWY2CLgNWJ1IXN+anh5AXIStCTwFXJ7ahwLjzWw4UZf7mNQ+AhhnZs8RyesBqf0K4JrU/hFwXGsek4iIiIiISFvh7s8V7pvZ1sQAoT2JRbhPAuYBdwInpPutveZSdtbtMFRiUkRERCqgIklvd988c38KsGOJbZ4hRhoUt08nLtCK2+cAh5RoXwAc36KARURERERE2jEz2x64CzjL3R04PPPcFcTs2FtKvLTcay5ljSJGh2f1BKaVeK2IiIhIs1W6vImIiIiIiIi0IjPbg5hdO8zdbzKzHYBt3P22tEkdsJAWrLnk7ospvebSjKI1l5ZRiUkRERGplEovZCkiIiIiIiKtxMw2Bf4I9Hf3m1JzHTDKzNY2s87AiURd72VrLplZR2LNpclptu2ClDyHzJpLxKjsftn2dL+wthJk1lxqreMUERERaYhGeouIiIiIiFSPs4j1ky7JjKK+GrgYeAjoDNzm7hMBKrDmkoiIiEjFKektIiIiIiJSJdz9DOCMlTw9usT2rbrmkoiIiEgeVN5ERERERERERERERKqGkt4iIiIiIiIiIiIiUjVU3kRERESkzMzsPODo9PAudz/bzMYCvYH5qX2ku99hZvsDlwBdgZvdfXjax07AGGAtYCpwsrsvMrNewARgfcCBAe4+z8y6AzcAWwKzgaPd/a3WP1oREREREZG2RSO9RURERMooJbEPBL4A7AR8ycwOB3YB9nL3ndLtDjPrCowFDgW2A3Yxsz5pVxOA09x9G6AOGJLaRwOj3X1b4Ali8TiAC4Bp7r4dkSy/rJUPVUREREREpE1S0lvavMVLFuUdQkltNS4REcndLOBMd//Y3RcCLwC90m2MmT1rZiPNrAOxSNxL7v6quy8iEt1HmdlmQFd3fzTtc1xq7wzsBdyabU/3+xIjvQEmAn3S9iIiIiIiIjVF5U2kzevYoRN3PveJheZzd/D2Q/MOQURE2iB3f65w38y2BvoBewL7ACcB84A7gRPS/VmZl88CegIbr6S9BzA3Jciz7WRfk8qgzAXWA97MxpfKoHQvCrsnIiIiIiIiVUJJbxEREZFWYGbbA3cBZ7m7A4dnnrsCGAjcUuKlS4hyJk1pZxXPZQ0Dzltp4CIiIiIiIu2cypuIiIiIlJmZ7QFMAc519/FmtoOZHZHZpA5YCMwENsy0b0SMzF5Z+2ygm5l1LGon+xoz6wR0A94pEd4oYIuiW+9mHaiIiIiIiEgbpJHeIiIiImVkZpsCfwT6uft9qbkOGGVm9xElTU4ExgOPxUtsK+BVoD8w1t2nm9kCM9vD3R8iRoVPdveFZjaNKJlyY6E9/YxJ6fFF6flpqab4Cty9HqgvirlMRy8iIiIiIpI/Jb1FREREyussYHXgkkwy+WrgYuAhoDNwm7tPBDCzQcBt6TWTWL5I5QBi4cs1gaeAy1P7UGC8mQ0HXgeOSe0jgHFm9hyR1B7QOocnIiIiIiLStinpLSIiIlJG7n4GcMZKnv7EyszuPgX4/+3dd5xdZbX4/08KTSREihSRGlggKihdpChFuSjlUiIJTaliIVcQUUEEERT80gSEH0WQriKoNFGwIAhekHJpSxGIlNAZQwuk/f549oSTcZJMJjOzz5zzeb9e88o5z9lnzzozO885s/ba61mrm/F7gfW7GR9PWRSz6/hLwHZzGa4kSZIktRx7ekuSJEmSJEmSWoZJb0mSJEmSJElSyzDpLUmSJEmSJElqGSa9JUmSJEmSJEktw6S3JEmSJEmSJKllmPSWJEmSJEmSJLUMk96SJEmSJEmSpJZh0luSJEmSJEmS1DJMekuSJEmSJEmSWoZJb0mSJEmSJElSyzDpLUmSJEmSJElqGSa9JUmSJEmSJEktY3jdAUhSf4iIo4Bdq7vXZuZhEbElcBKwEHBFZh5Rbbs2cA6wKPAn4MDMnBIRywMXA+8GEhibma9GxEjgEmBl4Hlg18x8JiLmB84D1gXeAMZk5sMD8oIlSZIkSZIEWOktqQVVye2tgQ8BawPrRMRuwPnA9sAawHoRsU31lIuBL2XmasAQYL9q/EzgzMxcHbgTOLIaPxa4JTPXoCTLT63Gvwy8Vo2PAy7sr9coSZIkSZKk7lnpLakVTQAOycy3ACLiIWA14B+Z+Vg1djGwS0Q8CCyUmbdXz70AODoizgU2BXZoGP8j8DVg2+oxgMuAMyJivmr8WwCZ+aeIWCIils/MfzUGV1WKj+wS83Lz/KolSZIkSZJkpbek1pOZD3QmsSNiVWA0MI2SDO80gZJoXnYW40sAEzNzSpdxGp9TPT4RWHI2++pqHPBYl69b5v6VSpIkSZIkqataKr0jYnfg69Xd6zPzUHvqSuprEbEmcC1wKDAZiC6bTKO0M+lqduP08jmNTqFUjjdaDhPfkiRJkiRJ82zAK70j4h3AacBmwFrAJlX/XXvqSuozEbExcBNweGZeCDwFLN2wyTLA07MZfx4YERHDuozT+JyIGA6MAF6czb5mkpkdmfl44xfwZO9frSRJkiRJkjrV0d5kWPV9Fwbmq74m8589dXepeuRuCvy8cby6vS2l0htKT91tGnrqXgKlpy6wRFUtLqlNRMR7gaspV3pcXg3fUR6KUVUiewzlSpPxwKQqSQ6wZzU+mVJ5PbpxvLp9XXWf6vFbqu1njEfER4FJXft5S5IkSZIkqX8NeHuTzHwlIo4EHqa0H/kD8Bbz2FM3IubUU9eF5KT2cSiwIHBSxIyOJmcBewNXVo9dx9sn1MYC50TEIsDdlKtRAA4CLoyIIyhzyG7V+JHABRHxANBRPR/gh8DZ1fibwB798NokSZJmKSKOAnat7l6bmYdVV9aeBCwEXJGZR1Tbro0tJiVJUgsa8KR3RHwQ+BywAvBvyoeprbvZtL976o4DjppDuJIGocw8GDh4Fg+v1c329wLrdzM+Hti8m/GXgO26GZ8E7DWX4UqSJPWJKrm9NfAhYDpwQ0TsBnyf0l7yCeDaiNgmM6+n/C22b2beHhHnUVpM/oi3W0xeXhUsHQl8jbdbTG4bEXtQWkyOpqHFZERsSmkxucHAvXJJkqSZ1dHe5BPATZn5XGa+SWlZsjkD3FOXspDcSl2+NpmnVyZJkiRJ9ZkAHJKZb1Wt1x4CVgP+kZmPVVfQXkxpJbkCtpiUJEktasArvYF7gRMiYmHgdeDTwB+BnSNi48y8lYaeuhHR2VP3UrrvqXscDT11I6Jz/M+z66mbmR2UtgQzNLRBkCRJkqRBJTMf6LwdEatS/k46je7bP86qLaQtJiVJ0qBXR0/vGyPiQ8BdlAUs/wp8D7gKe+pKkiRJ0jyJiDWBaynrnEwGulb3zK4tpC0mJUnSoFdHpTeZ+X1KX7lG9tSVJEmSpHkQERtTFu4eV/Xk3ozu2z/Oqi3kjBaTmTmV7ltMPjmLFpOPdNlXV6dQ2qU0Wg64Za5fqCRJ0mzU0dNbkiRJktTHIuK9wNXAmMy8vBq+ozwUo6q1ksZQWkmOByZVSXJoaDFJSUKPbhyvbne2koSGFpON43NqMZmZjzd+AU/20cuXJEmaoZZKb0mSJElSnzsUWBA4qWG9orOAvSnV3wtSEtSdi1SOxRaTkiSpBZn0liRJkqQWkJkHAwfP4uG1utneFpOSJKklmfSWJEnqYxFxFLBrdffazDwsIrYETgIWAq7IzCOqbdcGzgEWBf4EHJiZUyJieeBi4N1AAmMz89WIGAlcAqxM6b27a2Y+ExHzA+cB6wJvUNobPDwgL1iSJEmSmog9vSVJkvpQldzeGvgQsDawTkTsBpwPbA+sAawXEdtUT7kY+FJmrgYMAfarxs8EzszM1YE7KW0FAI6l9NFdg5IsP7Ua/zLwWjU+Driwv16jJEmSJDUzk96SJEl9awJwSGa+VS3w9hCwGvCPzHwsM6dQEt27RMQKwEKZeXv13Auq8fmATXm77+4FwC7V7W0pld4AlwHbVNvPGM/MPwFLVNXikiRJktRWbG8iSZLUhzLzgc7bEbEqMJqyONyEhs0mAMsBy85ifAlgYpUgbxyn8TlVG5SJwJKz2de/GuOr2qOM7BL2ckiSJElSizDpLUmS1A8iYk3gWuBQYDIQXTaZRmln0tXsxunlcxqNA47qZlySJEmSWoLtTSRJkvpYRGwM3AQcnpkXAk8BSzdssgzw9GzGnwdGRMSwLuM0PicihgMjgBdns6+uTgFW6vK1SS9epiRJkiQ1JZPekiRJfSgi3gtcDYzJzMur4TvKQzGqSmSPAa7PzPHApCpJDrBnNT4ZuIXSGmXGeHX7uuo+1eO3VNvPGI+IjwKTMnOm1iYAmdmRmY83fgFP9tHLlyRJkqTa2d5EkiSpbx0KLAicFDGjo8lZwN7AldVj1/H2IpVjgXMiYhHgbkr/b4CDgAsj4ghKX+7dqvEjgQsi4gGgo3o+wA+Bs6vxN4E9+uG1SZIkSVLTM+ktSZLUhzLzYODgWTy8Vjfb3wus3834eGDzbsZfArbrZnwSsNdchitJkiRJLcf2JpIkSZIkSZKklmHSW5IkSZIkSZLUMkx6S5IkSZIkSZJahklvSZIkSZIkSVLLMOktSZIkSZIkSWoZJr0lSZIkSZIkSS3DpLckSZIkSZIkqWWY9JYkSZIkSZIktQyT3pIkSZIkSZKklmHSW9JsTZ86te4QutWscUmSJEmSJKlew3uyUUS8JzOf6jL2vsx8sH/CktQshgwbRsdll9b2/Z+bOJF3jxgx09ijzz3Phw8+uKaIJLWTZ599lqWWWmqmsUceeYRRo0bVFJGkduIcJEmS1DuzrfSOiMUiYjHguoh4V+f9iFgK+OXAhCipHf379Tf49+tv8D+XXMrEN96Ycf/FV1/lq5dfXnd4klrc9LfeoKOjg/32249///vfdHR00NHRwQsvvMBBBx1Ud3iSWlznnOMcJEmS1DtzqvS+DNiquv1iw/gU4Kp+iUiSgCOvvJK//vOfAGz9/RNmjA8bOpTN11ijrrAktYlJd13NhhueDMAGG2wwY3z48OFsueWWdYUlqU0ccsgh3HrrrYBzkCRJUm/MNumdmZ8AiIjzM/NzAxOSJMFpe+wOwHeu/iVH7rB9zdFIajcLbbQbd524J1//+tc5/vjj6w5HUps577zzAJyDJEmSeqlHPb0z83MRsQKwGDCkYfxv/RWYJAEcucP2TOjoYOIbbzB9+tvjG9YXkqQ2cvzxx/PUU0/x73//m+kNk9Caa65ZY1SS2oVzkCRJUu/0dCHL7wFfBp4DOj9tTQdW7qe4JAmAM377O6644w4WW3hhGFLOuQ0Bfn/IIfUGJqkt/OAHP+Ciiy5i8cUXnzE2ZMgQbrrpphqjktQunIMkSZJ6p0dJb2A0MCozn+7PYCSpq98+8ABXfvnLLDlikbl+bkSMAG4DPpWZj0fE+cAmwGvVJkdn5lURsSVwErAQcEVmHlE9f23gHGBR4E/AgZk5JSKWBy4G3g0kMDYzX42IkcAllBOCzwO7ZuYzvXzpkprAddddx4033shSSy1VdyiS2pBzkCRJUu8M7eF2T5jwllSHpUaM6G3CewPgz8BqDcPrAZtm5trV11URsRBwPrA9sAawXkRsU21/MfClzFyNUmC+XzV+JnBmZq4O3AkcWY0fC9ySmWtQkuWnznXgkprKMsssY7JJUm2cgyRJknqnp5XeN0XECcAvgTc6B+3pLam/rbfySvzwxt+y6erBAsPnmzHeg57e+wFfAC4CiIiFgeWBc6pK7auAo4H1gX9k5mPVdhcDu0TEg8BCmXl7tb8LgKMj4lxgU2CHhvE/Al8Dtq0eA7gMOCMi5svMyY2BVRXhI7vEu9ycX5KkgbbRRhtxwgknsMUWW7DgggvOGLefrqSB4BwkSZLUOz1Neu9d/btLw1ive3pHxKeBbwMLA7/JzINtLyCpO9fccy8ANz344IyxnvT0zsx9ASKic2gp4GbgAOBV4Bpgn+r2hIanTqAkoJedxfgSwMTMnNJlnMbnVPPURGBJoOuVMuOAo2b7AiQ1hV/84hcA3HDDDTPG7KcraaA4B0mSJPVOj5LemblSX33DiFgZOAvYAHgWuLlqJXA2sBnwBHBtRGyTmddTEtv7ZubtEXEepXrzR7zdXuDyiDiS0l7ga7zdXmDbiNiD0l5gdF/FL2lgXT3u4D7ZT2Y+CuzYeT8ifgjsCfysm82nUXLrczPOHB5rdAqlQrzRcsAt3WwrqUY333xz3SFIamPOQZIkSb3To6R3RHylu/HMPKkX33NHSiX3k9W+RwOrMsDtBSQNDpfe9pduxw/abcxc7SciPgCslplXVkNDgMnAU8DSDZsuQ6nMntX488CIiBiWmVMbxml4zpMRMRwYAbzYNZbM7AA6usQ3V69H0sD48Y9/3O34Zz/72QGORFI76u0c5GLekiSp3fW0vckHGm7PT/nA9Ptefs9RwFsR8RtKcujXwAMMcHsBe+pKg8Mjzz034/aUqVO5Z/x41lmpVxefDAFOiYibKS1N9gcuBO4AIiJGAY8BY4DzM3N8REyKiI0z81ZKVfj1mTk5Im6hXEFyaed49T2uq+4fVz1+iyfcpMHt73//+4zbb731FnfddRcbbLBBjRFJaie9mYOqxbzPofvFvCc0bNe5mLdX20qSpJbT0/YmM5USRMQSVIvD9fJ7bgpsTkk8/RJ4vZvt+ru9wDjsqSs1vW/tsP1M9ztee51vX3XVXO8nM++LiOOBW4H5gCsz8zKAiNgbuBJYkJK4/nn1tLGUhS8XAe4GTqvGDwIujIgjgH8Bu1XjRwIXRMQDlErusXMdqKSmcvzxx890/6WXXuKwww6rKRpJ7aaXc1DTLuYtSZI0UHpa6T2TzHwhIlbs5fd8BvhdZj4PEBFXUxbInNqwTb+3F8CeutKgNHLhdzCho6PH22fmig23z6RUKHXd5iZgrW7G76X8Qdh1fDzlxF3X8ZeA7XocnKRBZ7HFFuOpp56qOwxJbaonc1AzL+bt1baSJGmg9Kan9xBgXeC5WWw+J9dQKiRHAq8A21CqKg8fyPYC9tSVBofGnt7Tmc5DT0/gXQsvXGNEktpJYz/d6dOnc//997P44ovXGJGkdtIXc1CTLeY9Dq+2lSRJA6A3Pb2nUy7n/2pvvmFm3hERJwB/prQY+C2lP9zD2F5AUheNPb2HAEsvOoIvbbVVfQFJaiuN/XQBlllmGdubSBowfTEHNdNi3ni1rSRJGiBz1dM7IlYA5svMR+blm2bm+ZRFUxrZXkDSf+js6T2ho4MpU6fx3sUXqzkiSe2ks5/uU089xZQpU1hhhRVqjkhSO+mjOahpFvP2altJkjRQetreZBRlwcllgaER8QLwqcx8qD+Dk6QnXnyJr15+OS+88grTpk9n5Dvewf8bsxsfqjswSW1h/PjxHHTQQTz33HNMmzaNd73rXZx99tmsssoqc3xuRIwAbqN8Zno8Is4HNgFeqzY5OjOviogtgZOAhYArMvOI6vlrA+cAiwJ/Ag6seuUuD1wMvBtIYGxmvlq1jrsEWJlSkblrZj7TRz8KSTWYlzmok4t5S5KkdtTT9ianAydk5oUAEfFZ4Azg4/0VmCQB/OC669hj44+w7dprA/Dru+/mxGuv49IvH1xvYJLawjHHHMO+++7LjjuWdrhXXnklRx99ND/5yU9m+7yI2ICSsF6tYXg9YNPMnNCw3UKUq982A54Aro2IbTLzekpie9/MvD0izgP2o7SEOxM4MzMvj4gjKcmmrwHHUqort42IPYBTKRWXkgap3s5B4GLekiSpvQ3t4XZLdSa8ATLzx5TVuCWpX7302mszEt4An/7Qh+h4/fX6ApLUVl588cUZySaAnXbaiZdffrknT90P+AJVv9uIWBhYnlI5eV9EHB0RQylJpX9k5mOZOYWS6N6laim3UGbeXu3vgmp8PmBT3q7GvADYpbq9LaXSG+AyYJtqe0mD1DzMQZIkSW2tp5XewyNiseoMPhGxBGVBS0nqV1OnTePfr7/Bou9YCICO115nSM0xSWofU6dOpaOjg5EjRwLw0ksv9eh5mbkvzNSrdingZuAASk/da4B9qtsTGp46gbKo27KzGF8CmFglyBvHaXxO1QZlIqVI4emG/VC1QRnZJeTlkNR0ejsHSZIktbueJr1/CNweEVdU90cDJ/dPSJL0tl3WX599zj2XLd+/JgC/u/8Bdttow5qjktQudt99d0aPHs0222wDwPXXX89ee+011/vJzEeBGeWaEfFDyqJvP+tm82nQ7fm92Y0zh8cajQOOmk24kppEX81BkiRJ7aan7U2uo1R2zw+sDrwHuKq/gpKkTh9ZdVWGDBnClKlTGf/Cizz/yitsvvoadYclqU1sttlmAEyePJlHH32UZ599lq222mqu9xMRH4iInRqGhgCTgaeApRvGl6FUZs9q/HlgREQM6zJO43MiYjgwAnixm3BOAVbq8rXJXL8oSf2ur+YgSZKkdtPTSu8LgDMy87SIWBA4kLLo0n/1V2CSBPCdq69m5/XWY/SGG/Dm5Cn84s47+c4vf8kF++9fd2iS2sDhhx/O2LFj2XPPPXnzzTe57LLL+MY3vsE555wzt7saApwSETdTWprsD1wI3AFERIwCHgPGAOdn5viImBQRG2fmrZSq8Oszc3JE3EK56u7SzvHqe1xX3T+uevyWzJzcNZDM7AA6Gsca2rBIaiJ9OAdJkiS1lZ5Wei+RmacBZOakzDyFUlkkSf2q4/XXGb3hBgAsMN9wdttoQ1589dWao5LULl5++WX23HNPABZYYAH23ntvnn/++bneT2beBxwP3Ao8CNyTmZdl5iRgb+DKavxh3l6kcixwckQ8BCwMnFaNHwTsHxEPUiq0j6jGjwQ2jIgHqm2+MNeBSmoqfTUHSZIktZu5Wchy2cx8GiAilqL7vpGS1KemTpvG8xNfYckRiwDw4quvMn266+hKGhhTp07l2WefZamllgLghRdemKs5KDNXbLh9JnBmN9vcBKzVzfi9wPrdjI8HNu9m/CVgux4HJ6npzescJEmS1K56mvQ+CbgnIm6g9PbeEvhqv0UlSZXdNtqIPc46iw1HjWLIkCH876OP8iV7WUoaIHvvvTc77LADm2yyCUOGDOG2227jsMMOqzssSW3COUiSJKl3epT0zszzI+JOYAtgCnBiZt7fr5FJErDdhz/EGssuy52PPcqwoUPZ/SMfYZWl3l13WJLaxM4778z73/9+br/9doYNG8Y+++zDaqutVndYktqEc5AkSVLv9LTSu7MX5X39GIskdWvVpZdi1aWXqjsMSW1q9dVXZ/XVV687DEltyjlIkiRp7vV0IUtJkiRJkiRJkpqeSW9JkiRJkiRJUssw6S1JkiRJkiRJahkmvSVJkiRJkiRJLcOktyRJkiRJkiSpZZj0liRJkiRJkiS1DJPekiRJkiRJkqSWYdJbkiRJkiRJktQyTHpLkiRJkiRJklqGSW9JkiRJkiRJUssw6S1JkiRJkiRJahkmvSVJkiRJkiRJLcOktyRJkiRJkiSpZZj0liRJkiRJkiS1DJPekiRJkiRJkqSWYdJbkiRJkiRJktQyTHpLkiRJkiRJklrG8LoDkKT+EhEjgNuAT2Xm4xGxJXASsBBwRWYeUW23NnAOsCjwJ+DAzJwSEcsDFwPvBhIYm5mvRsRI4BJgZeB5YNfMfCYi5gfOA9YF3gDGZObDA/aCJUmSJEmSZKW3pNYUERsAfwZWq+4vBJwPbA+sAawXEdtUm18MfCkzVwOGAPtV42cCZ2bm6sCdwJHV+LHALZm5BiVZfmo1/mXgtWp8HHBhv71ASZIkSZIkdavWSu+IOBFYMjP3ttJSUh/bD/gCcFF1f33gH5n5GEBEXAzsEhEPAgtl5u3VdhcAR0fEucCmwA4N438EvgZsWz0GcBlwRkTMV41/CyAz/xQRS0TE8pn5r/56kZIkSZIkSZpZbZXeEbEFsHfDkJWWkvpMZu6bmbc0DC0LTGi4PwFYbjbjSwATM3NKl/GZ9lU9PhFYcjb7mklEjIyIFRu/uttOkiRJkiRJc6+WSu+IWAz4LnAcsFZErICVlpL615Buxqb1Yrw3++pqHHBUN+OSJEnzzHVNJElSu6ur0vts4JvAy9V9Ky0l9bengKUb7i8DPD2b8eeBERExrMv4TPuKiOHACODF2eyrq1OAlbp8bdK7lyVJkvQ21zWRJEmqIekdEfsCT2TmTQ3DdVVaPtbl65ZutpPUGu4AIiJGVYnsMcD1mTkemBQRG1fb7VmNT6bMCaMbx6vb11X3qR6/pdp+xnhEfBSY1N1VJpnZkZmPN34BT/bx65UkSe2pc12TzhPvM9Y1qYqFOtc16e5q212qq2c3BX7eOF7d3pZS6Q3latttGq62vQTK1bbAElW1+EwsPJIkSQOljvYmo4FlIuIeYDHgncB05lBpmZlT6b7S8slZVFo+0mVfXZ1C+QDXaDlMfEstKTMnRcTewJXAgpQEdecfc2OBcyJiEeBu4LRq/CDgwog4AvgXsFs1fiRwQUQ8AHRUzwf4IXB2Nf4msEd/viZJkqSuMnNfgIjoHOq3dU0iYk5X23Y9+T8OW7xJkqQBMOBJ78zcqvN2lYDaPDM/GxH3R8TGmXkrDZWWEdFZaXkp3VdaHkdDpWVEdI7/eU6VlpRk1QwNHwwltYjMXLHh9k3AWt1scy+lCqrr+Hhg827GXwK262Z8ErDXPAUsqWXYU1dSk2imdU1OwcIjSZI0AOrq6d2dscDJEfEQsDAzV1ruHxEPUnreHlGNHwlsWFVUHkS5hA9KpeUC1fhpWGkpSZIGmD11JTWRplnXxBZvkiRpoNTR3mSGzLyA6ky/lZaSJKmFdPbUvai6P6OnLkBEdPbUfZD/7Kl7dEScS+mpu0PD+B+Br1F6525ajV8GnNHQU/dbUHrqRsQSEbF8d1e8SWorM9Y1oaxjNAY4PzPHR8SkgbzaVpIkaaDUmvSWJElqRc3cU7dqjzKyS8guJCe1KNc1kSRJ7ciktyRJUv9rpp6643AhOanlua6JJElqZ83U01uSJKlVNU1PXcpCcit1+dqkdy9LkiRJkpqPSW9JkqT+N6OnbpXIHkPpnTsemBQRG1fbzeipC3T21J0xXt3u7J0LDT11G8dn11PXheQkSZIktTqT3pIkSf2suvR/b0pP3QeBh5m5p+7JEfEQsDAz99Tdv1rschPgiGr8SGDDqnfuQZQFM6H01F2gGj8Ne+pKkiRJalP29JYkSeon9tSVJEmSpIFnqCw8JgAAJ8NJREFUpXeLe3Py1LpDmKVmjk2SJEmSJEnS4GSld4tbYL5hrPPVn9QdRrfuOnHPOW8kSZIkSZIkSXPBSm9JkiRJkiRJUssw6S1JkiRJkiRJahkmvSVJkiRJkiRJLcOktyRJkiRJkiSpZZj0liRJkiRJkiS1DJPekiRJkiRJkqSWYdJbkiRJkiRJktQyTHpLkiRJkiRJklqGSW9JkiRJkiRJUssw6S1JkiRJkiRJahkmvSVJkiRJkiRJLcOktyRJkiRJkiSpZZj0liRJkiRJkiS1DJPekiRJkiRJkqSWYdJbkiRJkiRJktQyTHpLkiRJkiRJklqGSW9JkiRJkiRJUssw6S1JkiRJkiRJahkmvSVJkiRJkiRJLcOktyRJkiRJkiSpZZj0liRJkiRJkiS1DJPekiRJkiRJkqSWMbzuACRpoEXEzcBSwORq6ABgFeAIYH7g5Mw8o9p2S+AkYCHgisw8ohpfGzgHWBT4E3BgZk6JiOWBi4F3AwmMzcxXB+ilSZIkSZIktb1akt4RcRSwa3X32sw8rK8SSxExErgEWBl4Htg1M58ZsBcnqalFxBBgdWD5zJxSjb0HuBxYB3gTuC0ifg88BpwPbAY8AVwbEdtk5vWU+WffzLw9Is4D9gN+BJwJnJmZl0fEkcCRwNcG9EVKkiRJkiS1sQFPelfJ7a2BDwHTgRsiYjfg+/RNYulY4JbM3DYi9gBOBUYP7KuU1MSCMvdcHxHvppxUewW4OTNfAoiInwM7A38E/pGZj1XjFwO7RMSDwEKZeXu1zwuAoyPiXGBTYIeG8T9i0luSJDUBr3aTJEntoo6e3hOAQzLzrcycDDwErEaVWKoqLzsTSyvwn4mlXSJiPkpi6eeN49XtbSmV3gCXAdtU20sSwLuAmyiJ6S2AA4HlKXNTpwnAcsCyczm+BDCxs4K8YXwmETEyIlZs/OpuO0mSpL7ScLXbWpm5dmauDTwJfBf4KLAWsH9EvC8iFqJc7bY9sAawXkRsU+3qYuBLmbkaMIRSlARvFyWtDtxJKUqSJEmqxYBXemfmA523I2JVShX2afRdYmnGc6qKg4nAksDTjXFUbVBGdgnPpJPU4jLzL8BfqruvVVeQnET5g6/RNMofcl31ZryrccBRPYlXkiSpj3i1myRJahu1LWQZEWsC1wKHUi6viy6b9DaxZNJJ0ixFxEeBBTLzpmpoCPA4sHTDZstQTpQ9NZfjzwMjImJYZk5tGO/qFMofg42WA26Z6xckaVCxtYCkGnVe7fZ5yrzyB+AK/rPIaH368Wo3LDySJEkDoI72JkTExpQPXIdn5oXMQ2KpyziNz4mI4cAI4MVuwjgFWKnL1ybz+NIkNb+RwIkRsWBELALsBewObBERS0bEO4CdgBuAO4CIiFHVfDMGuD4zxwOTqrkMYM9qfDIlcT26cbxrAJnZkZmPN35RLi+W1MJsLSCpTpn5l8zcMzNfy8wXgPOAY7rZtL+vdnusy5cn/SVJUp+rYyHL9wJXA6Mz8+ZqeEZiifLBZwxwfmaOj4hJEbFxZt5KQ2IpIjoTS5cyc2Lpuur+cdXjt1SJqJlkZgfQ0SW2vnypkppQZl4TERsAdwPDgDMy89aI+Cbwe0ql5bmZ+VeAiNgbuBJYkDK/dK4lMBY4p0qc301p0wRwEHBhRBwB/AvYbUBemKTBoClaC1hpKbUnr3aTJEntpI72JodSkkcnNSSZzwL2pm8SS0cCF0TEA5Sk9th+fC2SBqHMPJIuFZCZeSnlJFrXbW+iVF92Hb+Xcvlv1/HxwOZ9FaukllJ7a4HKOGzxJrWjkcAxEfERYD7evtrt4ohYEniNcrXb/sB99F1R0gwWHkmSpIFSx0KWBwMHz+LheU4sVZVS281blJIkSX2rSRbSBSstpbbk1W6SJKmd1LaQpSRp3kydNo1hQ2tZmkH489fca5LWAlZaSm3Mq90kSVK7MOktSYPUsKFDuequR+oOo23tuM6oukPQ4DOSmlsLSJIkSVI7sERNkiRpAGTmNcC1lHYAd1GS2LcCna0F7gEuzcy/ZuYk3l7v5EHgYWZuLXByRDwELMzMrQX2rxa73AQ4YgBeliRJkiQ1HSu9JUmSBoitBSRJkiSp/1npLUmSJEmSJElqGSa9JUmSJEmSJEktw6S3JEmSJEmSJKllmPSWJEmSJEmSJLUMk96SJEmSJEmSpJZh0luSJEmSJEmS1DJMekuSJEmSJEmSWoZJb0mSJEmSJElSyzDpLfWzt6a8WXcI3WrWuCRJkqTZmTptWt0htD1/B5KkZje87gCkVjf/8AX42A83rjuM//D7L91adwiSJEnSXBs2dChX3fVI3WG0tR3XGVV3CJIkzZaV3pIkSZIkSZKklmHSW5IkSZIkSZLUMkx6S5IkSZIkSZJahklvSZIkSZIkSVLLMOktSZIkSZIkSWoZJr0lSZIkSZIkSS3DpLckSZIkSZIkqWWY9JYkSZIkSZIktQyT3pIkSZIkSZKklmHSW5IkSZIkSZLUMkx6S5IkSZIkSZJahklvSZIkSZIkSVLLMOktSZIkaSZvTp5adwhtzZ+/JEnSvBledwCSJEmSmssC8w1jna/+pO4w2tZdJ+5ZdwiSJEmDmpXekiRJkiRJkqSWYdJbkiRJkiRJktQyTHpLkiRJkiRJklqGSW9JkiRJkiRJUstoyYUsI2IMcAQwP3ByZp5Rc0iS2ohzkKQ6OQdJqovzjyRJahYtV+kdEe8Bvgt8FFgL2D8i3ldvVJLahXOQpDo5B0mqi/OPJElqJq1Y6b0lcHNmvgQQET8HdgaOadwoIkYCI7s8dwWAZ555ZqbBl55/tn8inUdPPrlgj7ab9npH/wbSS08++WSPt33puYn9GEnvPLloz+OfMnFqP0bSO3Pz85/4ckf/BdJLr3aJv+H/7bABD2Zmc5yD5mb+mZNmnZ/aQU/n4HnRrPN3u5ibedI5SAPNOai1Of/MmfNPvZyDWtsgnYMkaSatmPReFpjQcH8CsH43240DjupuB2PHju37qPrBCXUHMI+2+N3gvtrxu/y47hDmyRY/2aLuEObNqafO6pFlgH8OYCRd9WQOGscgn380+OdgzVkv36ecgzQgnINam/OPmp1zUGsbpHOQJM2kFZPeQ7oZm9bN2CnABV3G5gdWBv4B9Edp7nLALcAmQM9PnTYP46+X8c/eMMoHrf/th33PjZ7MQacw8PNPsxnsx7PmrN1+x85Bg0u7HZ/tpt1+v84/g0+7HaPtpt1+v80yB0nSTFox6f0U5c2l0zLA0103yswOoKOb5/+9X6ICIqLz5pOZ+Xh/fZ/+Yvz1Mv4eaYbKgjnOQXXMP81msB/PmrM2/R07Bw0SbXp8to02/f06/wwibXqMto02/f02wxwkSTNpxaT374BvR8SSwGvATsD+9YYkqY04B0mqk3OQpLo4/0iSpKYxtO4A+lpmPgV8E/g9cA9waWb+tdagJLUN5yBJdXIOklQX5x9JktRMWrHSm8y8FLi07jgktSfnIEl1cg6SVBfnH0mS1CxartK7yXUAR9N9H7vBoAPjr1MHxq/W0YHHQ6vrwN+xmlcHHp+trAN/v2puHXiMtrIO/P1KUu2GTJ8+ve4YJEmSJEmSJEnqE1Z6S5IkSZIkSZJahklvSZIkSZIkSVLLMOndhyLiPXXHIEmSJEmSJEntzKR3H4mIocB3I+IvdcfSWxExZHb3m11ELBYR89cdR281/rwH288eICJWjIioOw5JkiRJkiS1N5PefSQzp2Xm3sCLEfGzuuOZWxExNDOnV7d3j4glO+8PBhGxEnA5sEVELFB3PHMrIoY0/Px3BTaoOaS5Uv3MNwd2joi9ImLteiNSXbqcvBm0J6E0e52/58F4gk6tz+Ozdfkeo2bnMdr6fI+RpMHDpHcfaHjj2wx4Blg/Iq6uNai5EBHvyMxp1e3dgAOBYfVGNXcy8zHgBuAAYJOq8n7QaEh4bwt8C7i33ojmTma+CdwD7ACcACxYZzyqT8OxvBdwVkRsWnNI6mONJ0mBEdWYf/ipKXh8tjbfY9TsPEZbm+8xkjS4DJk+fdAU8za1iFgXuBLYC3gL+CKweGZ+otbA5iAi3gd8DDgXWAJ4Avh/mfnViJg/M9+qNcAe6KySjoj/Bo6hfADZF/hjlYwdFCJideBHwHPAXpk5qeaQ5qj64Nd5wmQYcBSwEvAQ8MvMfKDO+FSPiPg8MAY4FXgBuA94KzNfrTUw9amI2B/4BPAy8E/g5MEwb6k9eHy2Lt9j1Ow8Rluf7zGSNDgMqmrYJjcf8KvM/ENm3paZY4D5I+LaugObg4UobUFWAV4BdgLGRcTHM/OtwVAxXSW89wIOo1QaXwHsT6n4btqK9a5VAZn5MHAS5Vj6TEQsVktgc6Eh4b0NsA0laf91YDlgbEQsExEj64tQAy0i3gmsD4wFHgc2A/4KHBMRy9cYmuZRRIxquL075eTuscBtwHuAkyJieE3hqc15fLYH32PU7DxGW5PvMZI0ODV9QrNZddPL63lgTERs2LDZjcAqEfHhgY5vTjqT2Zl5FzAN+BqlrcZ1wO7AjRGxaWZOa+bEd8PPPyiVxY9k5leBW4HTgE9GRFO22mi4/PHzEXFmRPwaeAT4LfBx4L8iYvE6Y5yVLv0K9wHOBkYDN1Fa45xMqbi/ADi9+gNALaibkzevUo7ju4HTgaeBrwBLA/a2HIQiYkg1j/4qIk6qfucfpFwVdDdwCfATYFlg1Gx2JfU5j8/W5nuMmp3HaGvzPUaSBremTWY2s4Z2GlsDF0TEZcCqwEHAryNit4jYDtga2CUz/1ZnvF1V8XdW6C5DqfA+nlL1/W3gasoleX+IiI07t20WXT5cdt5+BFgqIt4LkJknA68BO9PEx3l1adxewI+BB4EfAv8H/ArYDdiyGU86NCTstwTWAjbOzD0oVw1cB0wCjgAuA77j5ZytKSKi4Vg4MCKOi4gzgLOAdYGPZeY5lEs/VwDeqC9a9VZmTq8u2f00sAXwTeBJytU0S2fmm5n5V8of8yNqDFVtyOOzdfkeo2bnMdr6fI+RpMHNnt69VCX7TgK+S6lsPRX4AqUf8xeB6cAlmXl1XTHOSUQcBmwPLE5pTTEcOITSe+5Y4L+AhzLzodqC7KLzhEN1ew/KGfUO4FlK8vgGSj/pRYDPAQdVi1w2pYg4G/hpZt5U3T8Y+BKlcn0McFNmPl1jiDNpOOEzlLJY5VWUXvBfAu7IzKkR8S3g88AmmflIjeGqH1Xtd/4X2JRycmk74H8oaxvcCBxcjX+LchJk78y8r55o1Veqy3uvBF6lnKi7F7iZ0iLrGGDbZpqz1F48PluH7zFqdh6j7cf3GEkafOw71XsfAk7MzCsAIuIhypveBsAuwNAqATgjSVu3iNgc6MjMeyJiX8riG9tSEpd3ABsB3weOBr6amd+pK9ZZaUh470+prD8W2BO4ndIv7z2U38ESwLhmSnjP4lhYjFIJchNAZp4aER8D3pWZFw10jLPTJf7FMvOFiNiR0tpkF+ApYHxmHhMRb1JO/Kh1TQZeBBYFlqT8sXcgZbGmb1A+/J9Iubz3hcx8saY41UvV1UxjKScS/wS8lpn3RsRHKG2YPgy8Dvw3MJXyB71/7GlAeHy2PN9j1Ow8RluY7zGS1BpMevfeSMqZ/YsAMvPuiLgCGFElBqdW402R+KveuM+iVA8DrAF8h3Kp3Z+AO6uvzavt/jnwUc5Z1dpkEUqF+kGZeVtE/AH4MrAA8I1qAc4RmTmxxlBn0qVCfTtKy5XXKL3U/xIRz2TmhRExFliZt9u2NI2G+L8AbB0Rb1CqHA6i/D/4ckSckZmPZub3awxVAyAzX4mIeynz4PuAPwKPAZ+hnPBYF3gzM7O+KDWPFgb2qG6fSllg9++UyqYHKH8M/oXyB9/0zPx3LVGqXXl8tjDfY9TsPEZbnu8xktQCmq5XcDNqWLRyo6pf939TFup7OSJOrh7bgPKhZ2p9kXavSnh/D9g3M2+vLscbAkwEPkmpSPg2pTXLacDdmflsTeH+h8Ye3lVftYmURWGWiIhhmfkCcC7wUUrlNM2U8IaZEsYHUy5zXJOyyOOuwI7AURFxMeWyyN0y8/maQv0PEbFAw+09KDHvT7lUc+PMfIXyofCDwOdcubx1RcQHImKThsVh/0k5AfgtSqXTbzNzMmUx3CUo6wRokMrMq4CtgIeB24CVKO27XgKeofz+RwPD/WNPA83js/X4HqNm5zHaPnyPkaTWYE/vHoqIT1MuU7uJcjZ/aWAfSrX0fMBSwJGZ+evaguxGRGwB/JySnHwwIlYGvg6cR1kw8WeUy/G2oyzQcUyztgSpTiwsDNxP6ZO3MuVn/khVPf0V4NNVErYpRMQHKCcY5qP8vH8PjM7MJyNiBKVa4ARKAnwRgMx8rp5o/1NEBOXnelxmjo+IfSivY11Ka5wdKS1xfkW5/G++zHyirnjVfyLinZTjdGNKhcsrwBOUq0V2oMwfZ1JaJa1KOXnzQB2xqm9V738/BL6VmT9pGB8OzJ+Zr9cWnNqex2dr8D1Gzc5jtD35HiNJg5tJ7x6IiIUpyeFvZead1dhlwLDM3DUi3gtMy8ynmqmHN0BE/BdwKWWhyjsprUx+lpknRcTilA9tv6NUqW/TLB/Ouv4cI+KLlJMM/6CcYDidUnG8IKWn3rLAAZl5bw3hdisitqEsdvogsDrl5MO6wE7VKuBExCeA3TNzj1nuqCYRsRAl7n0pl2keS1m1/DzgD5n5yWq7y4CzM/MPNYWqAVItYLoo5YqKnSn//8ZR/u/9NCLeTWkz9GYznbzRvKv+6DsFOCozL645HGkmHp+twfcYNTuP0fbke4wkDV4mvecgIhbOzNci4q/AIZl5SzW+BqUlyJjMbLqWJo0i4lOUJPGCwFcy89KqLcjUiFiOkoi9t4krvD8AXEhJyj9btdjYmpJQngi8C3gmM5+sLeAuImIrSgX0AZQK6N0p/dKXAJ7OzD2r7b5EOeHwmWY5jqp2MkH5me9BuTRzL2B+SlX6AZRFT/ek/B6+DPx3Zj5aS8CqVUTsQGmfdGJmnldzOOpHEbEtcAmwf2b+tO54pEYen63J9xg1O4/R9uB7jCQNTia9u9GZcK1aO3yO0r97B0rP6O9m5kNVUvMoyoKKLzVTdXd3qorvc4H9MvPaiBgGDK36zjWV6mf7ScpCqzdRKqW/AewHDMnMKRFxBrBgZu5TX6Tda2gps0Fm/r0a2wA4FDgaOJzSIudXwE6UhHdTVNg3ioizKL3HPwu8k5LkXohyHO0IrA9MAw7NzPvrilP16Oy1X82VoyltkzZttn766lsR8Ungkcx8pO5YpK48PluH7zFqdh6j7cf3GEkafEx6z0KVJD4IWAW4GrgLWA74AuUs71hgXGZeW1eMc2swXJpV/dxPobTSeA/wW+AF4DfA5zPz5mq7/YBVMvPwmkKdpeo1XEJpW3JtNXYi5fWMrT4cHwC8CNzXmRhvBtUH+CGZOa26fwrwEWAMpef4GErF91GZ2RERC2Tmm3XFq/o1nCR8Z2a+Wnc8kqTW4XuMmp3HqCRJzcukdzciYk1KFe5/A++l9DVeBPgFsHj19VRm3tZsPbznpLo062Kq3nN1x9OoamPyU0o1+p8bxveltGd5A/guZRHRLSlJ5aasMK5aypxKqe5eg5I43rmzl3cz6tJS5oPAY5n5SkQcQVnodAyl4vsLlLYyXwOmDqbjX/1nsM2FkqTBw/cYNTuPUUmSmo9J725ExMcpva8/Vd3/MHAE8E/g9MwcX2d886pZL82qFn7cNTM/GxFDM3NaRJxKSRr/H6V39FcpidefNlOFdHeqyvpzgFcyc9VqbP7MfKveyGavWjR0R+A+YLXM3DYiTgI2oLT7mR941gV6JEmSJEmS1IyG1h1Ak/o/YP6I2AUgM/8G/J3S6uRTETGsWr17UMrMG5ot4V1ZirIaOlXCexTQAWwDnA1cDvwwM49t9oQ3QGb+mpIkHhYR21VjzZ7w/gTwGUqv+oWBSdXlml8B/pdScf+wCW9JkiRJkiQ1q0GbuO0rnYuQNNwfSmndcAOweUQcHxGbAVsDt1GqXenseaw+dRfw8WoVdIBHgWMycyqwVTU2f9ffWTPLzOsoFeo/iYhd646nq25+lsMoi3COBVYEdgc+HxEHZOY4YI9mXPxUkiRJkiRJ6jS87gDqVi08sgHwmcz8nyqZ/WZEXAysDewG7Fl9zQ9sTqmAdWXuPpaZ/xcRRwFfiYhhmXklQESMAfalLAL5Rq1B9kJmXhMRnwGaqrq+Sw/v5YHplJYm51F61q9bPfYB4C/V056tI1ZJkiRJkiSpp9q6p3fDatsrUxZIHAc8ByUZ3rDddsD6lMX89sjMe2sIty1ExEhgL+A7lMr6F4AA9s7MB2oMrWVFxFeAXYGpwDeBDwEfB64FJlMWrhydmf+oLUhJkiRJkiSph9qy0ruhwnU4Jan3IqWKe9PM/Flny4eG1g93AcsCO5n461+Z2QGcGhG/ofRQfw54MjMn1BpYi4qIrYA9gC2BVzPzzYh4kXLMj6P8/PfyuJckSZIkSdJg0XaV3g3V3ZsCZwHHUCqKlwROAnbPzCfqjFEaKBGxCfC5zPxsRCxQJb2vBH4BXAoMsX+9BBExHzAeuC8zP9mD7W8ExmTmCxHxOLBzZt7Zz2FKqllErAjcn5nvbBgbDZwBnAisl5k7d/O8a4CfZ+YFfRzPtylXbD1VDc1HWTPlkM5FwSPiHmDzqvBAUouLiA2B44HFKWt8PQEcSvl7+PTMfH+X7dcFDu9u7upm35+q9rUopajsfuBQ/76WJNWh7Sq9q4T3J4BPAFcA7we+AvySktB4L/BE1VN6an2RSv0nIvYDXgIeB1aNiDUb2sfcC7xeXQ3RXmfFpFnbkdLzfp2IWCMzH5rD9lvN4XFJbSAiDgCOBLbMzHtqCuOKzPxiQ0x7ADdV7/0TM3PtmuKSNMAiYgHgGmDrzPxbNbY7cD3w2e6eU52070nCewxwBLBdZj5SXTV9OPD7ar55s49ehiRJPdJ2Se+IeB+lonufzLw9IhYELqOckV4bOAH4qAlvtZKIWCQzX2kYWg5YMTOvjIhbgbMj4mfAQsBngMvriFNqYgdR/l88Qmn9c0BEvBP4MbAqMI3SFugAymKwUP7I+6/q9gERcRbwbuCizPxmRGxOqbR6GlgTeB04CvgyZS2DKzPzfyJiKHAysCGwCDAE2Dczb+3XVyxpnkTE4cDelM+Vj1f/50/PzPdHxLLAhZT2eeMpc0Pn8yYB36OcPFsWODUzT6ke24cyHw2ltOf7IvAvSiX3Bg3V278FTu8ursy8qEp8jwHOiojplArP4cBPgCWqTa/NzCNn9X0z8+GIWI1Sxf7OKtZ7KOuATIqIoyknDN+qnrN3Zk6IiDWAUylVpsOA0zLz/Ln+AUvqjXcAIyn/ZztdAkyk/H8EICI+Wo3vRqnY7py7Lqi2/QClWOxh4DOZ+Spljaz9M/MRmFFs9j3KHLdARAwHfgSsBiwGvEK5Ki4j4g/AX4CNgeWBWygtFqdV1ePHUuaf14ADM/PeiPgI8H1gYcrnsG9n5jURsTewTzX+78z8WB/97CRJg8zQugMYSNXZ5g9QFuzbASAzJ1UVrgcCmwGPR8RGtQUp9bGI2JKSRCMiRkfEqsCCVCe9MvNrlEuuF6R8yPzvzj+aJc04Wboh8FNKkmqPiFicksxZpKqSXK/afOXM7KyU+ljD5byTMnNdyqLIh0TEe6vx9YBjM3N14Fng68C2wIeBL1SJsQ0oyaSNMvN9VQyH99sLljTPIuIEykmt0zLz8W42OQO4PTPXpLxHr97w2ALAC5m5MaW68nsRsWBEbEZZ7HuTzPwQpVDjF5n5OmVe2Lf63qtQTpxdM5sQ76V8Jm60H/BoZn4Y2IRyJdiis/q+Dc+5MDM3AkYBKwHbVnPcOEo7l3WBG4ENqqTXzymtEtahfPY+tGq3IKmfZebLwGHADRHxaERcRKnw/h3lBBUR8THgAuDTmXlbN7tZB/gksAbl88ku1eeiFYGZTshn5vTMvDQzJwLbAB2ZuWFmrgb8L+XEXadVgM0pc9PHgc0iYingYspJsw9S/mb5XkS8i1J4sEc1Z20H/Cgilq/2tSalbZMJb0lqYy2f9G5YlHJZShXNr4GjgWUi4uCGTadUHwKgVMFKg17Vyuc0SrUElKqHbwMbAeMi4qSq1ckrwFmZeVBmPlxLsFLz+jyl4vGlzPxf4DFKRfefgTWr6qTDgVM6q5u6cSlAZj5DSW53VnU+lpl3V7f/Cfw+M9/KzBcolVSLZeZfKJcLHxARP6Akwd6JpGa1MCVp81+U5Mza3WyzJSWpRDVv3Nzl8V9W//6NkgRfmHJCbBRwW9WH+wRgsYhYDDgT2LNaf2B/4Nw5XLU4nXJ1SaMbgJ0i4jrKHHd4Zv57Dt/3a8DzEXEYpYJzWcr89BQlsf63at66JzOvplR4rgKcX+3rj5SrzD40m1gl9aHMPAlYinLCbQLl//HdlD7cy1FOmF2dmffNYhc3ZOabmTkZ+D9K1XbnGkCzzC9k5s+BCyLiSxFxKiXB3fh55teZOa26OvWRar8bU9ZJuKfaxy8ycxvK3zLLAFdXc8l1lHntg9W+7qsS7ZKkNtbySe/qsqrtKVUl/x9wNuXN/TpgrepDOpk5NSKWAVagvHlLg1pEbE2pMvtiZv6pOvHzbUpV1ucpi9YsRqkG+w6l0ltSg4hYGNgT+GhEPF4tSrkMZWG4JymJoOOBEcDvImJWPS8nN9yeTmlRAtC1v+XkLveJiG2Ba6u7v6Qswjyk63aSmsYblJ6211Pmh6uqBHGjxnkAYEo3+6BaX4Nq22GU9khrV1eYfBhYF3i5ukLrPmB7YCxw7hxiXI8un3erk3orUT4vrwj8tWofMMvvS2kRuD+lfcHJlCR95yLYm1Hau7wInFwluYZRKj3XbtjfhpSKTUn9LCI2joivZuYrmXlNZh5GqYqeRlnodgqltdJeEbH+LHbzRsPt6ZT/8y8Df6f8f+76PX8aEWtFxOcpLeBepxQDXMbM8+B/7LeKZ3rDvoZExAcpc8lD3cwlv6k2fbUHPw5JUotryaR3RIyKiG9Ut9emnL3elnJp5RqUypPrgJuAtSNiBYDMnAB8ykpXDXYRsQVlodbdM/PmiFgR+C0Qmfl61dLn18D5mXko8InMfLa+iKWmNRZ4AVg2M1fMzBWBlSmVSYdREjU3Vm2CfkNZHBlKG635+iiGrSjVTz+iXAq8Aw19NyU1nWlVBSSU3twPUpI7jZ+7b6Aki6kux+/JJfg3ArtVRRpQWvPd1PD4GZRL/+/IzKdntZOqP/fKlJZNjePfA46sKrIPBh6gVGbP7vt+AjgmM6+gJKY2AIZFxFrA/ZSk1PGUhPhaQAKTqoXzqNqg3E9plyCp/z0PHFH17O60DOVqksWBZ6qWJocCF0fEO+Zi30cDp0bEKICIGBYRR1DWzXqYMl9ckJnnUeaCTzPnzzN3AGtExJrV/e0p7U5up7Rg2rT6XmsD/6BcbSJJEtCiSW9Kr+J9IuIDlKq5q4CdgD0ol4V/mZII/zlwSGaOrxYKo7qMUxrsFqBURyxaXep8KfCjzGyspFqV0pMY/vMSZ0nF54GTGtsEZGYHpW3QDpQ/1h6MiDsp1d6nVpv9AvhzRLyfeXcWpa/lfZRFnv4JrNT5viWpeVWV2ntSii6ObXjoC8D7IuIhSuXjPT3Y128oi7b9tpoPxlDW4eisgryGckLurC5PHR0R90TE3RFxLyXxtHlmTuqy3SmUYpD7gTsprZwum8P3/Qalkv3O6vv+ERiVmfdSkup3Vo99DvifzHyLkrTat9rXjZREuwvzSgOguipkB+C4qqf3g5T/q/tTEtGd211ISVT/v7nY96XAccBlVcuRB4D3AR/PzDeBH1Batd1DOXH2N8oVc7Pb57OUAoQLq+d9hbJw5vOUv+9PrOa1iyj9vcf3NF5JUusbMn369DlvNYhEROfZ4uMpl3neQVncZyiwa2b+KyIOBN6dmcfUFKbU76qVzk+ntC35SvVBtPOxXSkL5o3NzAdrClGSJPWRqhXJOcD7GxLhkiRJUltquSqxzJxaVeTdR1lYbDzl7PVUYOOI+BzwVcolUVLLysxrgIMoPfpe6RyPiD0olyya8JYkqQVExIWUFioHmfCWJEmSWqTSu+pXvBHwQOMq0xFxBnBrZl4aEYdQenwtDVyYmTfWEqw0wCLi05RLlr9EafdzHLCXCW9JkiRJkiS1olZJeq8DHAmsQFnY4leZ+Y+I2A34ZGbu1bDtfA2LC0ltoWp18jPgJWArE96SJEmSJElqVS2R9IaSzKasGH848BylncO3KYv6nJ6Zl1XbDfGyT7WjiNgSeCIzc44bS5IkSZIkSYNUyyS9O0XESGAJ4LvAy8CHgbuAL1a9viVJkiRJkiRJLarlkt6NImItYDPg8cz8Vd3xSJIkSZIkSZL6V0smvbtrYWJbE0mSJEmSJElqfS2Z9JYkSZIkSZIktaehdQcgSZIkSZIkSVJfMektSZIkSZIkSWoZJr0lSZIkSZIkSS3DpLckSZIkSZIkqWWY9JYkSZIkSZIktYzhdQcgAUTEPsD+wAhgfuBR4IjMvKOX+9scOD0z399l/ALg/sz8QS/3+21gicz8YkTsC8yfmWc2jvdmv5IkSZIkSZL6hpXeql1EHAd8Ftg1M9fIzFWA44FrImL5eqObrY8C76g7CEmSJEmSJElvs9JbtYqIpYBxwCqZOaFzPDNvjoivAAtHxHuA04HlgfmAyzPzuIhYEbgJuA7YAFgM+GZmXtHD793tfqvHvgHsACwILAwcmplXNTx3R2A7YKuIeKMaXj0ifg8sAzwLfKbxNUmSJEmSJEnqf1Z6q24bAQ91lxzOzIsy8yHgIuD8zFwHWB/YMiJ2rTZbGfhNZq4PfA04oWEXq0TEPY1flER1p273GxErAFsCm2XmB4FvAsd0ie0q4FfAyZl5RkMsu2bm6sDLwL69/aFIkiRJkiRJ6h0rvVW3IcD0zjsRsQhwS3X3ncCvgc2AxSLiOw3jawN/BSZTKr0B/kap9u70z8xcu/GbVT29iYiFZ7XfzPxpROwFjI2IUcCG1WNz8tvMfL66fS/w7h48R5IkSZIkSVIfMumtut1BaQuyeGa+mJmvUBLanYtGLk9JjH8kM1+vxpcAJgFLAG9l5rRqX9OrbXti2Kz2GxEfBn4JnAzcCPwR+FEP9jm54fbcxCJJkiRJkiSpj9jeRLXKzKeBU4GfNS5aWd3eGHgFuB34SjU+ErgV2H4ev+/E2ex3U+DOzDyJkvDegZIk72oKpRe4JEmSJEmSpCZhpbdql5nfjIixwCUR8U5KInkScAVwBqVNyOkR8X/A/MBlmXlJtZDlvBgzi/0uBewUEQ8Cb1EWy1ysar3S6Prq+fMYhiRJkiRJkqS+MmT69Olz3kqSJEmSJEmSpEHA9iaSJEmSJEmSpJZh0luSJEmSJEmS1DJMekuSJEmSJEmSWoZJb0mSJEmSJElSyzDpLUmSJEmSJElqGSa9JUmSJEmSJEktw6S3JEmSJEmSJKllmPSWJEmSJEmSJLWM/x/WMXI1RvugUAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1080 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib and seaborn for visualization\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Univariate visualization of categorical features\n",
    "def categorical_feature_func():\n",
    "  i = 1\n",
    "  plt.figure(figsize = (25,15))\n",
    "  for feature in categorical_features:\n",
    "      plt.subplot(3,5,i)\n",
    "      sns.set(palette='Paired')\n",
    "      sns.set_style(\"ticks\")\n",
    "      ax = sns.countplot(x = feature, data = df)#, hue = 'Stroke')#, color='#221C35') \n",
    "      ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "      i +=1\n",
    "\n",
    "categorical_feature_func()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.1.2 Univariate visualization of numerical features\n",
    "provides summary statistics for each numerical field in the raw data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2520x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Univariate visualization of numerical features\n",
    "def numeric_features_func():\n",
    "  i=1\n",
    "  plt.figure(figsize = (35,5))\n",
    "  for feature in numeric_features.columns:\n",
    "      plt.subplot(1,5,i)\n",
    "      sns.set(palette='dark')\n",
    "      sns.set_style(\"ticks\")\n",
    "      sns.histplot(df[feature],kde=True)\n",
    "      plt.xlabel(feature)\n",
    "      plt.ylabel(\"Count\")\n",
    "      i+=1\n",
    "\n",
    "numeric_features_func()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Bivariate visualization\n",
    "#### 4.2.1 Bivariate visualization of categorical features\n",
    "relationship between each categorical variable in the dataset and the target (HeartDisease) variable of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1080 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def categorical_feature_func():\n",
    "  i = 1\n",
    "  plt.figure(figsize = (25,15))\n",
    "  for feature in categorical_features:\n",
    "      plt.subplot(3,5,i)\n",
    "      sns.set(palette='Paired')\n",
    "      sns.set_style(\"ticks\")\n",
    "      ax = sns.countplot(x = feature, data = df, hue = 'HeartDisease')\n",
    "      ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n",
    "      i +=1\n",
    "\n",
    "categorical_feature_func()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4.2.2 Bivariate visualization of numerical features\n",
    "relationship between each numerical variable in the dataset and the target (DiffWalking) variable of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2520x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def numeric_features_func(f):\n",
    "  i=1\n",
    "  plt.figure(figsize=(35,5))\n",
    "  sns.set(palette='Paired')\n",
    "  sns.set_style(\"ticks\")\n",
    "  for feature in numeric_features:\n",
    "      plt.subplot(1,5,i)\n",
    "      sns.boxplot(y=df[feature], x = df[f])\n",
    "      i+=1\n",
    "\n",
    "numeric_features_func('DiffWalking')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# 5. Preprocessing\n",
    "\n",
    "### 5.1 Label Encoding\n",
    "When working on some datasets, we found that some of the features are categorical, if we pass that feature directly to our model, our model can't understand those feature variables. We all know that machines can't understand categorical data.\n",
    "<br>\n",
    "Example: \n",
    "\n",
    "- Original: `categorical_features['AlcoholDrinking'] = [Yes, No]`\n",
    "- Encoded: `categorical_features['AlcoholDrinking'] = [1, 0]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>HeartDisease</th>\n",
       "      <th>BMI</th>\n",
       "      <th>PhysicalHealth</th>\n",
       "      <th>MentalHealth</th>\n",
       "      <th>SleepTime</th>\n",
       "      <th>Smoking_No</th>\n",
       "      <th>Smoking_Yes</th>\n",
       "      <th>AlcoholDrinking_No</th>\n",
       "      <th>AlcoholDrinking_Yes</th>\n",
       "      <th>Stroke_No</th>\n",
       "      <th>...</th>\n",
       "      <th>GenHealth_Fair</th>\n",
       "      <th>GenHealth_Good</th>\n",
       "      <th>GenHealth_Poor</th>\n",
       "      <th>GenHealth_Very good</th>\n",
       "      <th>Asthma_No</th>\n",
       "      <th>Asthma_Yes</th>\n",
       "      <th>KidneyDisease_No</th>\n",
       "      <th>KidneyDisease_Yes</th>\n",
       "      <th>SkinCancer_No</th>\n",
       "      <th>SkinCancer_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>16.60</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>20.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>26.58</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>24.21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>23.71</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319790</th>\n",
       "      <td>1</td>\n",
       "      <td>27.41</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319791</th>\n",
       "      <td>0</td>\n",
       "      <td>29.84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319792</th>\n",
       "      <td>0</td>\n",
       "      <td>24.24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319793</th>\n",
       "      <td>0</td>\n",
       "      <td>32.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319794</th>\n",
       "      <td>0</td>\n",
       "      <td>46.56</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>319795 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        HeartDisease    BMI  PhysicalHealth  MentalHealth  SleepTime  \\\n",
       "0                  0  16.60               3            30          5   \n",
       "1                  0  20.34               0             0          7   \n",
       "2                  0  26.58              20            30          8   \n",
       "3                  0  24.21               0             0          6   \n",
       "4                  0  23.71              28             0          8   \n",
       "...              ...    ...             ...           ...        ...   \n",
       "319790             1  27.41               7             0          6   \n",
       "319791             0  29.84               0             0          5   \n",
       "319792             0  24.24               0             0          6   \n",
       "319793             0  32.81               0             0         12   \n",
       "319794             0  46.56               0             0          8   \n",
       "\n",
       "        Smoking_No  Smoking_Yes  AlcoholDrinking_No  AlcoholDrinking_Yes  \\\n",
       "0                0            1                   1                    0   \n",
       "1                1            0                   1                    0   \n",
       "2                0            1                   1                    0   \n",
       "3                1            0                   1                    0   \n",
       "4                1            0                   1                    0   \n",
       "...            ...          ...                 ...                  ...   \n",
       "319790           0            1                   1                    0   \n",
       "319791           0            1                   1                    0   \n",
       "319792           1            0                   1                    0   \n",
       "319793           1            0                   1                    0   \n",
       "319794           1            0                   1                    0   \n",
       "\n",
       "        Stroke_No  ...  GenHealth_Fair  GenHealth_Good  GenHealth_Poor  \\\n",
       "0               1  ...               0               0               0   \n",
       "1               0  ...               0               0               0   \n",
       "2               1  ...               1               0               0   \n",
       "3               1  ...               0               1               0   \n",
       "4               1  ...               0               0               0   \n",
       "...           ...  ...             ...             ...             ...   \n",
       "319790          1  ...               1               0               0   \n",
       "319791          1  ...               0               0               0   \n",
       "319792          1  ...               0               1               0   \n",
       "319793          1  ...               0               1               0   \n",
       "319794          1  ...               0               1               0   \n",
       "\n",
       "        GenHealth_Very good  Asthma_No  Asthma_Yes  KidneyDisease_No  \\\n",
       "0                         1          0           1                 1   \n",
       "1                         1          1           0                 1   \n",
       "2                         0          0           1                 1   \n",
       "3                         0          1           0                 1   \n",
       "4                         1          1           0                 1   \n",
       "...                     ...        ...         ...               ...   \n",
       "319790                    0          0           1                 1   \n",
       "319791                    1          0           1                 1   \n",
       "319792                    0          1           0                 1   \n",
       "319793                    0          1           0                 1   \n",
       "319794                    0          1           0                 1   \n",
       "\n",
       "        KidneyDisease_Yes  SkinCancer_No  SkinCancer_Yes  \n",
       "0                       0              0               1  \n",
       "1                       0              1               0  \n",
       "2                       0              1               0  \n",
       "3                       0              0               1  \n",
       "4                       0              1               0  \n",
       "...                   ...            ...             ...  \n",
       "319790                  0              1               0  \n",
       "319791                  0              1               0  \n",
       "319792                  0              1               0  \n",
       "319793                  0              1               0  \n",
       "319794                  0              1               0  \n",
       "\n",
       "[319795 rows x 51 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode all columns\n",
    "columns_df = list(df.columns.values)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "cat_cols = [\"Smoking\", \"AlcoholDrinking\", \"Stroke\", \"DiffWalking\",\n",
    "                \"Sex\", \"AgeCategory\", \"Race\", \"Diabetic\", \"PhysicalActivity\",\n",
    "                \"GenHealth\", \"Asthma\", \"KidneyDisease\", \"SkinCancer\"]\n",
    "for cat_col in cat_cols:\n",
    "    dummy_col = pd.get_dummies(df[cat_col], prefix=cat_col)\n",
    "    df = pd.concat([df, dummy_col], axis=1)\n",
    "    del df[cat_col]\n",
    "\n",
    "for col in ['HeartDisease']:\n",
    "    if df[col].dtype == 'O':\n",
    "        le = LabelEncoder()\n",
    "        df[col] = le.fit_transform(df[col])\n",
    "\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.1.1 Listing the unique values of each column after encoding\n",
    "Here we have all possible values on each feature, so please take a look at an example.\n",
    "\n",
    "- Original: `Diabetic : ['Yes', 'No', 'No-borderline-diabetes', 'Yes-during pregnancy']`\n",
    "- Encoded: \n",
    "  - `Diabetic_Yes : [0, 1]`\n",
    "  - `Diabetic_No : [0, 1]`\n",
    "  - `Diabetic_No-borderline-diabetes : [0, 1]`\n",
    "  - `Diabetic_Yes-during pregnancy : [0, 1]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HeartDisease : [0 1]\n",
      "BMI : [16.6  20.34 26.58 ... 62.42 51.46 46.56]\n",
      "PhysicalHealth : [ 3  0 20 28  6 15  5 30  7  1  2 21  4 10 14 18  8 25 16 29 27 17 24 12\n",
      " 23 26 22 19  9 13 11]\n",
      "MentalHealth : [30  0  2  5 15  8  4  3 10 14 20  1  7 24  9 28 16 12  6 25 17 18 21 29\n",
      " 22 13 23 27 26 11 19]\n",
      "SleepTime : [ 5  7  8  6 12  4  9 10 15  3  2  1 16 18 14 20 11 13 17 24 19 21 22 23]\n",
      "Smoking_No : [0 1]\n",
      "Smoking_Yes : [1 0]\n",
      "AlcoholDrinking_No : [1 0]\n",
      "AlcoholDrinking_Yes : [0 1]\n",
      "Stroke_No : [1 0]\n",
      "Stroke_Yes : [0 1]\n",
      "DiffWalking_No : [1 0]\n",
      "DiffWalking_Yes : [0 1]\n",
      "Sex_Female : [1 0]\n",
      "Sex_Male : [0 1]\n",
      "AgeCategory_21 : [0 1]\n",
      "AgeCategory_27 : [0 1]\n",
      "AgeCategory_32 : [0 1]\n",
      "AgeCategory_37 : [0 1]\n",
      "AgeCategory_42 : [0 1]\n",
      "AgeCategory_47 : [0 1]\n",
      "AgeCategory_52 : [0 1]\n",
      "AgeCategory_57 : [1 0]\n",
      "AgeCategory_62 : [0 1]\n",
      "AgeCategory_67 : [0 1]\n",
      "AgeCategory_72 : [0 1]\n",
      "AgeCategory_77 : [0 1]\n",
      "AgeCategory_80 : [0 1]\n",
      "Race_American Indian/Alaskan Native : [0 1]\n",
      "Race_Asian : [0 1]\n",
      "Race_Black : [0 1]\n",
      "Race_Hispanic : [0 1]\n",
      "Race_Other : [0 1]\n",
      "Race_White : [1 0]\n",
      "Diabetic_No : [0 1]\n",
      "Diabetic_No, borderline diabetes : [0 1]\n",
      "Diabetic_Yes : [1 0]\n",
      "Diabetic_Yes (during pregnancy) : [0 1]\n",
      "PhysicalActivity_No : [0 1]\n",
      "PhysicalActivity_Yes : [1 0]\n",
      "GenHealth_Excellent : [0 1]\n",
      "GenHealth_Fair : [0 1]\n",
      "GenHealth_Good : [0 1]\n",
      "GenHealth_Poor : [0 1]\n",
      "GenHealth_Very good : [1 0]\n",
      "Asthma_No : [0 1]\n",
      "Asthma_Yes : [1 0]\n",
      "KidneyDisease_No : [1 0]\n",
      "KidneyDisease_Yes : [0 1]\n",
      "SkinCancer_No : [0 1]\n",
      "SkinCancer_Yes : [1 0]\n"
     ]
    }
   ],
   "source": [
    "# iterate over the list to print all unique values of each column in the dataframe\n",
    "for column in list(df.columns.values):\n",
    "    print(column, ':', str(df[column].unique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 Dataset Splitting\n",
    "Here we will show how to split a dataset into Train and Test sets. \n",
    "- The train-test split is used to estimate the performance of machine learning algorithms that are applicable for prediction-based Algorithms/Applications. \n",
    "- By default, the Test set is split into 20% of actual data and the training set is split into 80% of the actual data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((255836, 51), (63959, 51))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import train_test_split from sklearn.model_selection\n",
    "from sklearn.model_selection import train_test_split\n",
    "# split the dataset into train and test set with 80% and 20% respectively\n",
    "train_data, test_data = train_test_split(df, train_size=0.80) \n",
    "train_data.shape, test_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to split a dataset into train and test sets to evaluate how well our machine learning model performs. The train set is used to fit the model, and the statistics of the train set are known. The second set is called the test data set, this set is solely used for predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((255836, 50), (255836,))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the train and test set into features and labels\n",
    "X_train= train_data.drop('HeartDisease', axis=1)\n",
    "y_train= train_data['HeartDisease']\n",
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63959, 50), (63959,))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the train and test set into features and labels\n",
    "X_test= test_data.drop('HeartDisease', axis=1)\n",
    "y_test= test_data['HeartDisease']\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3 Feature Scaling\n",
    "Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "# there is no data-leakage because we are using \n",
    "# information of train in test not test in train\n",
    "X_train=sc.fit_transform(X_train)\n",
    "X_test=sc.transform(X_test)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.7016465 , -0.42317641,  2.02172538, ..., -0.19533667,\n",
       "         0.32134051, -0.32134051],\n",
       "       [-1.08932803,  2.09704412, -0.48990887, ...,  5.1193664 ,\n",
       "         0.32134051, -0.32134051],\n",
       "       [-0.88437496, -0.42317641,  1.39381682, ..., -0.19533667,\n",
       "         0.32134051, -0.32134051],\n",
       "       ...,\n",
       "       [ 0.70322306, -0.42317641, -0.48990887, ..., -0.19533667,\n",
       "         0.32134051, -0.32134051],\n",
       "       [-1.32108266, -0.42317641, -0.48990887, ..., -0.19533667,\n",
       "         0.32134051, -0.32134051],\n",
       "       [-1.14923739, -0.42317641,  0.38916312, ..., -0.19533667,\n",
       "         0.32134051, -0.32134051]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X_train after scaling\n",
    "X_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4 Handling Imbalanced Data\n",
    "Balanced vs Imbalanced Dataset :\n",
    "\n",
    "- Balanced Dataset: In a Balanced dataset, there is approximately equal distribution of classes in the target column.\n",
    "- Imbalanced Dataset: In an Imbalanced dataset, there is a highly unequal distribution of classes in the target column.\n",
    "\n",
    "Let's take a look at our target which is 'HeartDisease':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    292422\n",
       "1     27373\n",
       "Name: HeartDisease, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HeartDisease'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we shown in the code above, there are 292k 'No' and 27k 'Yes'then it represents an Imbalanced dataset as there is highly unequal distribution of the two classes.\n",
    "<br>\n",
    "Problem with Imbalanced dataset:\n",
    "\n",
    "- Algorithms may get biased towards the majority class and thus tend to predict output as the majority class.\n",
    "- Minority class observations look like noise to the model and are ignored by the model.\n",
    "- Imbalanced dataset gives misleading accuracy score.\n",
    "<br>\n",
    "Techniques to deal with Imbalanced dataset :\n",
    "\n",
    "- Under Sampling\n",
    "  - In this technique, we reduce the sample size of Majority class and try to match it with the sample size of Minority Class.\n",
    "    - For example, take an imbalanced training dataset with 1000 records.\n",
    "    - Before Under Sampling :\n",
    "      - Target class 'Yes' = 900\n",
    "      - Target class 'No' = 100\n",
    "    - After Under Sampling :\n",
    "      - Target class 'Yes' = 100\n",
    "      - Target class 'No' = 100\n",
    "    - Now, both classes have the same sample size.\n",
    "\n",
    "- Over Sampling\n",
    "  - In this technique, we increase the sample size of Minority class by replication and try to match it with the sample size of Majority Class.\n",
    "    - For example, Let’s take the same imbalanced training dataset with 1000 records.\n",
    "    - Before Under Sampling :\n",
    "      - Target class 'Yes' = 900\n",
    "      - Target class 'No' = 100\n",
    "    - After Under Sampling :\n",
    "      - Target class 'Yes' = 900\n",
    "      - Target class 'No' = 900\n",
    "    - Now, both classes have the same sample size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use both methods in our project, so we used 'SMOTE' for over_sampling and 'NearMiss' for under_sampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import SMOTE from imblearn.over_sampling\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import NearMiss from imblearn.under_sampling\n",
    "from imblearn.under_sampling import NearMiss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Counter is a collection where elements are stored as \n",
    "# dictionary keys and their counts are stored as dictionary values.\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: Counter({0: 234042, 1: 21794})\n",
      "   SMOTE: Counter({0: 234042, 1: 234042})\n"
     ]
    }
   ],
   "source": [
    "# balance the dataset using SMOTE (Synthetic Minority Oversampling Technique)\n",
    "smote = SMOTE(sampling_strategy='minority')\n",
    "X_train_smote , y_train_smote = smote.fit_resample(X_train,y_train)\n",
    "\n",
    "print('Original: {}'.format(Counter(y_train))) \n",
    "print('   SMOTE: {}'.format(Counter(y_train_smote))) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: Counter({0: 234042, 1: 21794})\n",
      "NearMiss: Counter({0: 21794, 1: 21794})\n"
     ]
    }
   ],
   "source": [
    "# balance the dataset using NearMiss (undersampling)\n",
    "nearmiss = NearMiss(version=3)\n",
    "X_train_nearmiss, y_train_nearmiss = nearmiss.fit_resample(X_train, y_train)\n",
    "\n",
    "print('Original: {}'.format(Counter(y_train))) \n",
    "print('NearMiss: {}'.format(Counter(y_train_nearmiss))) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.5 K-Fold Cross Validation\n",
    "K-Fold CV is where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point. \n",
    "<br>\n",
    "Lets take the scenario of 5-Fold cross validation(K=5). Here, the data set is split into 5 folds. In the first iteration, the first fold is used to test the model and the rest are used to train the model. In the second iteration, 2nd fold is used as the testing set while the rest serve as the training set. This process is repeated until each fold of the 5 folds have been used as the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kfold cross validation\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# make a 10 fold cross validation\n",
    "cv = KFold(n_splits=10, random_state=None,shuffle=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "# 6. Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we compare the classification strength of AdaBoost, Random Forest, Decision Tree, KNN, Naïve Bayes, and Perceptron. After training our models, Naïve Bayes achieved the highest accuracy of 89%, whereas Perceptron reached the lowest accuracy of 69%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 AdaBoost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.1 Build the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "[CV 1/10] END .learning_rate=1, n_estimators=50;, score=0.799 total time= 1.1min\n",
      "[CV 2/10] END .learning_rate=1, n_estimators=50;, score=0.798 total time= 1.2min\n",
      "[CV 3/10] END .learning_rate=1, n_estimators=50;, score=0.794 total time= 1.1min\n",
      "[CV 4/10] END .learning_rate=1, n_estimators=50;, score=0.802 total time= 1.1min\n",
      "[CV 5/10] END .learning_rate=1, n_estimators=50;, score=0.798 total time= 1.2min\n",
      "[CV 6/10] END .learning_rate=1, n_estimators=50;, score=0.841 total time= 1.2min\n",
      "[CV 7/10] END .learning_rate=1, n_estimators=50;, score=0.820 total time= 1.3min\n",
      "[CV 8/10] END .learning_rate=1, n_estimators=50;, score=0.820 total time= 1.1min\n",
      "[CV 9/10] END .learning_rate=1, n_estimators=50;, score=0.830 total time= 1.3min\n",
      "[CV 10/10] END learning_rate=1, n_estimators=50;, score=0.828 total time= 1.2min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'learning_rate': 1, 'n_estimators': 50}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import AdaBoostClassifier from sklearn.ensemble\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "# import GridSearchCV from sklearn.model_selection\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Make a model using AdaBoostClassifier\n",
    "ada_model = AdaBoostClassifier()\n",
    "# Make a dictionary of parameters\n",
    "param_grid = {'n_estimators':[50], 'learning_rate':[1]}\n",
    "# Make a grid search classifier to find the best parameters\n",
    "# cv is the cross validation, and verbose is the level of verbosity\n",
    "ada_cv = GridSearchCV(ada_model, param_grid, cv=cv, verbose=3) \n",
    "#fit the model on train dataset on SMOTE\n",
    "ada_cv = ada_cv.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# print the best parameters\n",
    "ada_cv.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.2 Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.84      0.89     58380\n",
      "           1       0.26      0.60      0.36      5579\n",
      "\n",
      "    accuracy                           0.82     63959\n",
      "   macro avg       0.61      0.72      0.63     63959\n",
      "weighted avg       0.90      0.82      0.85     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# y_pred is the predicted value of y\n",
    "y_pred=ada_cv.predict(X_test)\n",
    "\n",
    "# import classification_report from sklearn.metrics\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "# print the confusion matrix\n",
    "cm = confusion_matrix(y_test,y_pred) # confusion matrix\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.3 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm, annot=True, linewidths=0.8, fmt='d', cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.1.4 Receiver Operating Characteristic (ROC) - Area Under the Curve (AUC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "\n",
    "# import roc_curve from sklearn.metrics\n",
    "from sklearn.metrics import auc, roc_curve\n",
    "\n",
    "y_score_ada=ada_cv.predict_proba(X_test)   #results are probailities for each sample for each class\n",
    "#retrieve the probabilities only for the class1 (yes, positve class)\n",
    "yes_probs=y_score_ada[:,1]\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_ada , tpr_ada, threshold_ada=roc_curve(y_test,yes_probs)  # false positive, true posistive, threshold\n",
    "#AUC\n",
    "auc_ada=auc(fpr_ada , tpr_ada)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_ada, tpr_ada, marker='_', label='AdaBoost (auc=%0.3f)' % auc_ada, color='Orange')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "[CV 1/10] END ................criterion=entropy;, score=0.894 total time= 3.4min\n",
      "[CV 2/10] END ................criterion=entropy;, score=0.891 total time= 2.5min\n",
      "[CV 3/10] END ................criterion=entropy;, score=0.892 total time= 2.5min\n",
      "[CV 4/10] END ................criterion=entropy;, score=0.893 total time= 2.6min\n",
      "[CV 5/10] END ................criterion=entropy;, score=0.893 total time= 3.1min\n",
      "[CV 6/10] END ................criterion=entropy;, score=0.930 total time= 3.4min\n",
      "[CV 7/10] END ................criterion=entropy;, score=0.959 total time= 2.5min\n",
      "[CV 8/10] END ................criterion=entropy;, score=0.956 total time= 3.3min\n",
      "[CV 9/10] END ................criterion=entropy;, score=0.958 total time= 3.4min\n",
      "[CV 10/10] END ...............criterion=entropy;, score=0.957 total time= 3.6min\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy'}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#number of trees in randomm forest\n",
    "#n_estimators=[100,128]\n",
    "# the function to measure the quality of split\n",
    "criterion=['gini','entropy']\n",
    "#number of features to consider at every split\n",
    "#max_features=['sqrt','log2']\n",
    "# maximum numbers of deppth in tree\n",
    "#max_depth=[20,50,100]  # we use this based on the result of Decision Tree algorith hyperparameter tuning\n",
    "# ==>\n",
    "#param_dict={'criterion':criterion}\n",
    "param_dict={'criterion':['entropy']}   #best parameters\n",
    "\n",
    "#make the model\n",
    "rf_model=RandomForestClassifier()\n",
    "rf_cv=GridSearchCV(rf_model,param_dict,cv=cv,verbose=3)\n",
    "\n",
    "rf_cv.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "rf_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.93      0.93     58380\n",
      "           1       0.27      0.26      0.27      5579\n",
      "\n",
      "    accuracy                           0.87     63959\n",
      "   macro avg       0.60      0.60      0.60     63959\n",
      "weighted avg       0.87      0.87      0.87     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=rf_cv.predict(X_test)\n",
    "\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True,linewidths=0.8,fmt='d',cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "y_score_rf=rf_cv.predict_proba(X_test)   #results are probailities for each sample for each class\n",
    "#retrieve the probabilities only for the class1 (yes, positve class)\n",
    "yes_probs=y_score_rf[:,1]\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_rf , tpr_rf, threshold_rf=roc_curve(y_test,yes_probs)  # false positive, true posistive, threshold\n",
    "#AUC\n",
    "auc_rf=auc(fpr_rf , tpr_rf)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_rf, tpr_rf, marker='_', label='Random Forest (auc= %0.3f)' % auc_rf, color='Orange')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.3 Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 100}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "#make the model and parameters\n",
    "tree=DecisionTreeClassifier(random_state=1024)  #model    # for the class weight we use : ,class_weight='balanced'because of imbalnace dat in test dataset\n",
    "#range of hyparameters of model\n",
    "#weights=[{0,1},{1,10.68}]  # two dictionary in a list. Each disctionary is showing a class and it's weigtht\n",
    "param_dict = {\"criterion\":['entropy'], \"max_depth\": [100]} #best values\n",
    "#param_dict = {\"criterion\":['gini','entropy'], \"max_depth\": max_depth}\n",
    "\n",
    "tree_cv=GridSearchCV(tree, param_grid=param_dict, cv=cv,n_jobs=-1,verbose=3 )  # for tuning the model\n",
    "\n",
    "tree_cv.fit(X_train_smote ,y_train_smote)\n",
    "\n",
    "tree_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.90      0.92     58380\n",
      "           1       0.22      0.30      0.25      5579\n",
      "\n",
      "    accuracy                           0.85     63959\n",
      "   macro avg       0.58      0.60      0.58     63959\n",
      "weighted avg       0.87      0.85      0.86     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=tree_cv.predict(X_test)\n",
    "\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YessYlCRJUoFBKWPrTZIkqYQVJUmSVGBBKWNQkiRJBbbeMrbeJEmSSlhRkiRJBRaUMgYlSZJUYOstY1CSJEkF5qSMe5QkSZJKWFGSJEkFTZaUAIOSJEmqwZyUsfUmSZJUwoqSJEkq8K63jEFJkiQVNJmTAFtvkiRJpawoSZKkAltvGYOSJEkqMCdlbL1JkiSVsKIkSZIKKlhSAoOSJEmqwbveMrbeJEmSSlhRkiRJBd71ljEoSZKkAnNSxtabJElSCStKkiSpoMmSEmBQkiRJNZiTMrbeJEmSSlhRkiRJBd71ljEoSZKkAnNSxtabJElSCStKkiSpoFF3vUXEROAQoApcmVK6KCL2BS4CmoEfpZTG5XN3BKYBg4E7gBNTSssjYjgwA9gUSMColNKiiBgCXAu8HVgAfDalNL+z9VhRkiRJBZU6HmsqIj4I7ANsD7wfOCUidgCuAg4CtgN2ioj98ktmAKeklEbkHzU6H58KTE0pbQs8AIzPx88FZqaUtiMLWJesbk0GJUmS1C2klG4H9k4pLSerBvUGhgCPp5Rm5+MzgEMjYmugOaV0b3759Hy8D7AXcGP78fz3B5BVlACuB/bL55ey9SZJkgrqeddb3vIaUuNUS0qppf1ASqk1IiYApwE/AbYA5rWbMg8Y1sn4xsDCPFS1H6f9NXmLbiGwCTC3bO1WlCRJUkFTpX4HMBaYXeMYW+uzU0pnkwWYrYB31piygtpdvc7GWc25mgxKkiRpXZsMvK3GMbn9pIjYNt+gTUppMfAzYG9gs3bTNierAM0pGV8ADIqIXh3GaX9NRPQGBgHPd7ZwW2+SJKmgnq23vL3WsgZT3w5MiIg9yO56Owj4PnBBRGxDVoU6ArgqpfRURCyNiN1TSncBRwO35K27mcBhwHUrx/P3vzl//Y38/MyUUmtnC7KiJEmSCiqV+h1rKqV0M1mYmQU8CNydUroBOBb4KfAo8BivbdQeBVwcEX8F+gPfycdPAk6IiEeBPYFx+fh4YNeIeCSfc/Jq/xyq1eqaf4Pupdo8ckyj1yCtd5bMmsL8hZ3+A0zSOrDZoD5d+mCjo659uG4B4Yejduixz/m29SZJkgr8WW+ZNQ5K+aaoE8k2VQ2h2LarppQ+XL+lSZKkRmkyJwFrV1G6gOw2vj8DT7Ka2+kkSZJ6urUJSkcB56aUzlpXi5EkSd2DrbfM2gSlDYHb1tVCJElS92FMyqzN4wFuAj61rhYiSZLU3XRaUYqIL7V7ORv4Sv7Ap3uAxR2mV1NKF9d5fZIkqQGabL0Bq2+9XVhjbL/86KgKGJQkSXoTMCdlOg1KKSWf3C1JktZbaxyEIuKqiHhbybmIiP+s37IkSVIjVSqVuh092er2KI3ktY3vxwK/i4ihNaYeCHykvkuTJEmN0sPzTd2sbo/SqWQ/pbeaH9NrzFn5R/mD+i1LkiSp8VYXlE4GppGFodvy1492mNMGtACP1HtxkiSpMbzrLbO6zdwvAbcDRMTewIMppUVdsTBJktQ45qTM2jyZe2tg64gonZBSsv0mSZLeNNYmKE0vGa8CrwKLcJ+SJElvCj39brV6WZugVOtutwHAXsA3gCPrsqK1sGTWlK7+SEnAZoP6NHoJktYxH6SYWeOglO9X6ugl4PqI6AdcAry/XgtbEy8ubuvKj5MEDO3Xi6XLG70Kaf3Td21KG6qbev2xPwW8u07vJUmSGszWW2aNg1JEbFRjuAnYAjgTeKJei5IkSY3VZE4C1q6i9BzZxu1aXgUOfePLkSRJ3YFBKbM2Qem4GmNVYCHwu5I9TJIkST3W2gSl9wI/SCk9uK4WI0mSugf3KGXW5u6/0dR+RIAkSXqTaarU7+jJ1iYo3QEcsK4WIkmS1N2sTevtKeCkiDgK+DvwbIfz1ZTSQXVbmSRJahg7b5lOg1JEnAVckVKaC4wA7m53euC6XJgkSWqcJpMSsPqK0tnAb4C5KaW9u2A9kiRJ3cbqgpJxUpKk9ZA/6y2zJnuUyh4yKUmS3qTsvGXWJCidFREL1mBeNaV0/BtdkCRJUnexJkFpBDBsDeZZeZIk6U3CzdyZNQlKR6WU7l/nK5EkSd2GOSnjXi1JkqQSa/PASUmStJ7o6T96pF5WF5SuAdZkI7ckSXoTcY9SptOglFI6rqsWIkmS1N3YepMkSQUWlDIGJUmSVOAepYx3vUmSJJWwoiRJkgoq/rhXwKAkSZJqsPWWsfUmSZJUwoqSJEkqsKKUMShJkqSCis8HAGy9SZIklbKiJEmSCmy9ZQxKkiSpwM5bxtabJElSCStKkiSpoMmSEmBQkiRJNbhHKWPrTZIkqYQVJUmSVGDnLWNQkiRJBU3+UFzA1pskSVIpK0qSJKnA1lvGoCRJkgq86y1j602SJKmEFSVJklTgAyczBiVJklRgTsrYepMkSSphRUmSJBXYessYlCRJUoE5KWPrTZIkqYQVJUmSVGAlJWNQkiRJBRV7b4BBSZIkdSMRcTbw2fzlr1NKX4mIfYGLgGbgRymlcfncHYFpwGDgDuDElNLyiBgOzAA2BRIwKqW0KCKGANcCbwcWAJ9NKc3vbD1W1iRJUkGljseaygPRR4GRwI7A+yLicOAq4CBgO2CniNgvv2QGcEpKaUT+UaPz8anA1JTStsADwPh8/FxgZkppO7KAdcnq1mRFSZIkFdTz8QB5JWdIjVMtKaWWdq/nAaemlJbl1/0VGAE8nlKanY/NAA6NiEeB5pTSvfm104EJEXEFsBdwcLvx24HTgQPycwDXA5dFRJ+UUmvZ2q0oSZKkdW0sMLvGMbb9pJTSIyuDT0S8EzgMWEEWoFaaBwwDtigZ3xhYmFJa3mGc9tfk5xcCm3S2cCtKkiSpoM5buSeTVXY6aqk1OSLeDfwaOA1oBaLDlBXUXmJn46zmXE0GJUmSVFDPm97y9lrLmsyNiN2BnwJjU0o3RMQHgc3aTdkcmAvMKRlfAAyKiF4ppbZ247S75pmI6A0MAp7vbD223iRJUrcQEVsBvwCOSCndkA/fl52KbSKiF3AEcEtK6SlgaR6sAI7Ox1uBmWRtu1Xj+e9vzl+Tn5/Z2f4ksKIkSZJqaNBzlE4D+gIXRazqtn0POJasytSXLOzcmJ8bBUyLiIHALOA7+fhJwDURMQ54Gjg8Hx8PTI+IR8gqXKNWt6BKtVp9Q9+ogaovLm5r9Bqk9c7Qfr1Yunz18yTVV9/e9d421LkfzZpTt4Bw2Mgte+zTK60oSZKkAp/MnXGPkiRJUgkrSpIkqcB6UsagJEmSCmy9ZWy9SZIklbCiJEmSCqykZAxKkiSpwNZbxsAoSZJUwoqSJEkqsJ6UMShJkqQCO28ZW2+SJEklrChJkqSCJptvgEFJkiTVYOstY+tNkiSphBUlSZJUULH1BhiUJElSDbbeMrbeJEmSSlhRkiRJBd71ljEoSZKkAltvGVtvkiRJJawoSZKkAitKGYOSJEkq8PEAGVtvkiRJJawoSZKkgiYLSoBBSZIk1WDrLWPrTZIkqYQVJUmSVOBdbxmDkiRJKrD1lrH1JkmSVMKKkiRJKvCut4xBSZIkFdh6y9h6kyRJKmFFSXXT1tbGeV8/i6eefJJKpcLpZ55Na2sr50+aQJ8N+vDOEdvypa+cQVNTE5defAEPz3qItrY2DvrMoRz86UN5bsECzhn3FVpbWxk0aDDnTDqf/v37N/prST3Cn/70MJdcdCFXTv8hzz//PBPPHsfChQtZ0dbGueedz1bDh/Ot885l1kMPrfp7NfnSqfTq1YtJXz+HOc88Q2trK189Yzz/vv32Df426g686y1jUFLd3HnH7wCYNv1aHnzgfr43ZTILnn2WL33lDLbfcSTfu+wS/uuWm9h007fwzD+f5oofXM+yZcs44pBPsM++H+WH069g/wMPZv9PHMS0703hlz+/kcOPPKbB30rq/q6+cho3/eqXNDc3AzD52xew/4Gf4GMf35/777uX2bP/wVbDh/PoI4/w3cuvYOjQjVZd+93LLmWbbd7JpPPO52/pMVJ6zKAkABtvOVtvqpsP7r0vXx03AYD5c+cycOAgnn12PtvvOBKA7XcYyZ9mPcR7tt+RM885F8j+xdLWtoLevXsz9rSv8vEDPsGKFSt4dv58Bg4c2LDvIvUkW201nIsuuXTV6z/Oeoj/nf+/nHD8sdz861/x/p12ZsWKFTz99FNMPOcsjhn1OX7+sxsBuPuuO+nTpw8njj6ey783ld1237NRX0Pqlrp9UIqIIRHx1o5Ho9el2nr37s3E8V/j2+dP4mP7H8iWW27FQw/8AYA77/g9S5YuYcMNN2TQoMEsb21l4vgzOPjTh9KvX38qlQor2to44pBP8uAD9/O+nXZp8LeReoZ9P/oxevd+rUEwd+4cBg0exOVXTmezzTbn6iunsWTJYg4/4ki+8c0LmHr5Ffz4huv4W3qMlhdfZOHChXxv2pV88EP7cNGF32rgN1F30lSp1O3oybp9UALGArNrHOqmzvr6efzkFzdz3sSz+MoZZ/GDq6cx5gvHMXSjjRgyZCgACxe+xNgxJ/C2t7+DY44/YdW1vfv04Yaf3cRXx53DxPFfa9RXkHq0wYOH8KG99wHgg3vvw6OP/IW+fZsZddTRNDc307//AHbeeVdSeozBQ9rN/dDePPqXvzRy6epGKnU8erIuC0oRMbyzo5NLJwNvq3Gom7nlpl9yzZWXA9C3bzOVpibunPl7Jkw6nynfv5qXWlrYeZcPsHTpUsZ84f9w4EGf5v+c8B+rrj//GxN58A/3AdCvf38qTT0hx0vdz8j3vo+Zd9wOwEMP/IF3bLMNTz35JMcceThtbW20trYya9ZDbPeud79u7oMPZnMlvaZSrVa75IMi4s/AO4G5FANmNaX09rV8y+qLi9vqsjbVx5Ilizn37DN5/rnnWL58OUcf93kqTU1cPvU79O3bzHt32pn/GDOW62dcw5Xfn8o7Y9tV146fMIlly5bxrUkTqFQqNDVVOPX0cbzt7e9o4DdSLUP79WLp8kavQh3NmfMMp5/2JWZc/2Pmzp3DhLPGsWTJEgYMGMA3z/82gwYPZvpVV/Db39xC7z59OPCTB/HZww7npZYWzjl7HM8tWEDv3r0597xvseWWwxr9dVRD395dW5y594mWugWEXd8xpMcWlroyKA0CZgInpZTuqsNbGpSkBjAoSY3R1UHpvideqltA2OUdg3tsUOqy3kZKaSEwGvB+b0mS1CN06XOUUkr3A/d35WdKkqS118NvVqsbHzgpSZIKzEkZbyuSJEkqYUVJkiQVWVICDEqSJKmGikkJsPUmSZJUyoqSJEkq8K63jEFJkiQVmJMytt4kSZJKWFGSJElFlpQAg5IkSarBu94ytt4kSZJKWFGSJEkF3vWWMShJkqQCc1LGoCRJkopMSoB7lCRJkkpZUZIkSQXe9ZYxKEmSpAI3c2dsvUmSJJWwoiRJkgosKGUMSpIkqcikBNh6kyRJKmVFSZIkFXjXW8agJEmSCrzrLWNQkiRJ3UpEDALuBg5MKT0ZEfsCFwHNwI9SSuPyeTsC04DBwB3AiSml5RExHJgBbAokYFRKaVFEDAGuBd4OLAA+m1Ka39la3KMkSZIKKnU81kZE7ALcCYzIXzcDVwEHAdsBO0XEfvn0GcApKaUR+UeNzsenAlNTStsCDwDj8/FzgZkppe3IAtYlq1uPQUmSJBU1KillYedkYG7+emfg8ZTS7JTScrJwdGhEbA00p5TuzedNz8f7AHsBN7Yfz39/AFlFCeB6YL98filbb5IkaZ3KW15DapxqSSm1tB9IKX0+v2bl0BbAvHZT5gHDOhnfGFiYh6r24697r7xFtxDYhNdCWYEVJUmSVFCp4/+AscDsGsfYNVpK0Yp/Ybyz9yplRUmSJBXU+a63yWQtsI5a1uDaOcBm7V5vTlYBKhtfAAyKiF4ppbZ24+3f65mI6A0MAp7v7MMNSpIkaZ3K22st/+Ll9wEREduQVaGOAK5KKT0VEUsjYveU0l3A0cAtKaXWiJgJHAZct3I8f6+b89ffyM/PTCm1dvbhtt4kSVJB4/Zyv15KaSlwLPBT4FHgMV7bqD0KuDgi/gr0B76Tj58EnBARjwJ7AuPy8fHArhHxSD7n5NV9fqVarb7Br9Aw1RcXtzV6DdJ6Z2i/Xixdvvp5kuqrb++ufVT23/53cd0Cwoi39Ouxj6+0oiRJklTCPUqSJKnAn/WWMShJkqQCf9ZbxtabJElSCStKkiSpwIJSxqAkSZKKTEqArTdJkqRSVpQkSVKBd71lDEqSJKnAu94ytt4kSZJKWFGSJEkFFpQyBiVJklRkUgJsvUmSJJWyoiRJkgq86y1jUJIkSQXe9Zax9SZJklTCipIkSSqwoJQxKEmSpAJbbxlbb5IkSSWsKEmSpBosKYFBSZIk1WDrLWPrTZIkqYQVJUmSVGBBKWNQkiRJBbbeMrbeJEmSSlhRkiRJBf6st4xBSZIkFZmTAFtvkiRJpawoSZKkAgtKGYOSJEkq8K63jK03SZKkElaUJElSgXe9ZQxKkiSpyJwE2HqTJEkqZUVJkiQVWFDKGJQkSVKBd71lDEqSJKnAzdwZ9yhJkiSVsKIkSZIKbL1lrChJkiSVMChJkiSVsPUmSZIKbL1lDEqSJKnAu94ytt4kSZJKWFGSJEkFtt4yBiVJklRgTsrYepMkSSphRUmSJBVZUgIMSpIkqQbvesvYepMkSSphRUmSJBV411vGoCRJkgrMSRlbb5IkSSWsKEmSpCJLSoBBSZIk1eBdbxlbb5IkSSWsKEmSpALvestUqtVqo9eg9UhEDAHGApNTSi0NXYy0nvHvn7T2bL2pqw0Bzs5/ldS1huDfP2mtGJQkSZJKGJQkSZJKGJQkSZJKGJQkSZJKGJTU1VqACfmvkrpWC/79k9aKjweQJEkqYUVJkiSphEFJkiSphD/CRF0qIo4AxgEbABenlC5r8JKk9UZEDALuBg5MKT3Z4OVIPYIVJXWZiNgSmATsAewAnBAR72rsqqT1Q0TsAtwJjGj0WqSexKCkrrQvcFtK6YWU0ivAjcAhDV6TtL4YDZwMzG30QqSexNabutIWwLx2r+cBOzdoLdJ6JaX0eYCIaPRSpB7FipK6UqXG2IouX4UkSWvIoKSuNAfYrN3rzbENIEnqxmy9qSv9D3BORGwCvAJ8BjihsUuSJKmcFSV1mZTSHOBM4HfAH4HrUkr3N3RRkiR1wh9hIkmSVMKKkiRJUgmDkiRJUgmDkiRJUgmDkiRJUgmDkqR1LiJqPWxUkro9n6Mk9SAR8Xvggx2GlwCPA9NSSlPW0WcuSikdGBFvBWYDh6aUblyDa4cBVwBHAs+9wXVUgS+nlC58I+8jSWvDipLU89wFfKDd8Ungz8ClETFmHX/2vPwzb1vD+fsCH1t3y5GkdcuKktTztKSU7m0/EBG3Ae8HxgB1ryqtlFJ6Fbh3tRMl6U3CoCS9CaSUVkTEw8AnI+JDZE8/PxGYAPQB3p9Smh0RhwNnACOAZ4DJKaVLV75PRAwALgY+DVSBC9p/Tq3WW/55E4H3Ai3Aj/PP+BxwdX7pgoiYkFI6JyJ6A2cBxwKbAn8BTk8p3druc0YA3wH2AOYDJ7/RPyNJ+lfYepPePN5JFmJWOh0YDYzNQ9IxwHXA7cAngGuAiyPiy+2uuQH4FPBl4HjgcGC3sg+MiJ2B/wZeAg4Dzs6vmwz8Gjg3n/pxsr1KANOAU4FLgIOBx4BbImK3/D0HAb8H3gKMAs7L1ypJXc6KktTzVPKqDEAF2Bz4D2Ak8MV28y5NKf0KICKagG8A16aUVu5j+m2+QXp8REwF3gEcAHwupfSj/Lr7eX346uhr+fmDU0pt+TXNwDHAC8AT+bwHU0rPRcS2ZJWk0SmllcHpNxGxOVmo2ic/vwmwc0rpmfw9XwR+uuZ/RJJUHwYlqefZH2jtMLaErGU2haxdBZDanR8BbAH8ul3IAriFrG22M7Btu7HsDVKaFxH3dLKW3YDrV4ak/Jop+TqIiI7zP5T/enOHddwMnBcRGwC7A39eGZJy/wm0IUldzKAk9Tx38lrlqAosAv6RUmqF14WTZ9td82/5r9flR0ebA0OB1pTSwg7n5gMDS9ayUYfPWZ2V65hTcn7jfB2ve5RASqktIhasxedIUl0YlKSe56WU0gNre03+68nA/TXOzwYOAfpExJCUUku7c/8GLOvkfTdpPxARGwHvI3uMQa35VbJK1PIa558Dnge26/CeFbIAJUldys3c0vrhMbIAMiyl9MDKgywEfR0YTLaBGrI73gCIiKHArp28793AfvkeqJUOA24CelFsl91Jtq9qUId17EtWJVtOdsfeeyLine2u+zCw4Vp8X0mqCytK0nogpbQ8Is4BLspbc7cCbyO7o+xxYHZKqRoRM4DJEdEXeJpss3afTt76G8BM4MaIuBzYCpgETEkpvRwRLfm8T0fEb1NKf4yInwIz8vX8lWzf0jjg/PwxBz8ATgN+FRFnAs3553TclyVJ65wVJWk9kW+yPpHsSd43k23i/glwQEqpmk87nuw2/gnAtcB9wK86ec97gY+S7XH6BVng+Q7w1XzKrcB/AZeShR/Ibvm/miyE/YbsEQRfJXv2EimlpWR3v/0VmE4W5saR3UUnSV2qUq1WVz9LkiRpPWRFSZIkqYRBSZIkqYRBSZIkqYRBSZIkqYRBSZIkqYRBSZIkqYRBSZIkqYRBSZIkqYRBSZIkqcT/B4c8lkjpiXsxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion MAtrix on heatmap\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True,linewidths=0.8,fmt='d',cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "y_score_tree=tree_cv.predict_proba(X_test)   #results are probailities for each sample for each class\n",
    "#retrieve the probabilities only for the class1 (yes, positve class)\n",
    "yes_probs=y_score_tree[:,1]\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_tree , tpr_tree, threshold_tree=roc_curve(y_test,yes_probs)  # false positive, true posistive, threshold\n",
    "#AUC\n",
    "auc_tree=auc(fpr_tree , tpr_tree)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_tree , tpr_tree, marker='_', label='Decision Tree (auc=%0.3f)' % auc_tree, color='Orange')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.4 K-Nearest Neighbours (KNN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "[CV 1/10] END ...................algorithm=auto;, score=0.894 total time=  41.4s\n",
      "[CV 2/10] END ...................algorithm=auto;, score=0.892 total time=  52.2s\n",
      "[CV 3/10] END ...................algorithm=auto;, score=0.891 total time=  43.2s\n",
      "[CV 4/10] END ...................algorithm=auto;, score=0.893 total time=  50.2s\n",
      "[CV 5/10] END ...................algorithm=auto;, score=0.892 total time=  44.2s\n",
      "[CV 6/10] END ...................algorithm=auto;, score=0.894 total time=  48.4s\n",
      "[CV 7/10] END ...................algorithm=auto;, score=0.877 total time=  48.0s\n",
      "[CV 8/10] END ...................algorithm=auto;, score=0.876 total time=  44.7s\n",
      "[CV 9/10] END ...................algorithm=auto;, score=0.874 total time=  42.8s\n",
      "[CV 10/10] END ..................algorithm=auto;, score=0.875 total time=  45.3s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto'}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "param = {'algorithm':['auto']}\n",
    "model_knn = KNeighborsClassifier(n_neighbors=2)\n",
    "model_knn = GridSearchCV(model_knn, param_grid = param, cv=cv, verbose=3)\n",
    "model_knn.fit(X_train_smote, y_train_smote)\n",
    "model_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.91      0.92     58380\n",
      "           1       0.25      0.31      0.27      5579\n",
      "\n",
      "    accuracy                           0.86     63959\n",
      "   macro avg       0.59      0.61      0.60     63959\n",
      "weighted avg       0.87      0.86      0.86     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_knn.predict(X_test)\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion MAtrix on heatmap\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True,linewidths=0.8,fmt='d',cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check\n",
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "y_score_knn=model_knn.predict_proba(X_test)   #results are probailities for each sample for each class\n",
    "#retrieve the probabilities only for the class1 (yes, positve class)\n",
    "yes_probs=y_score_knn[:,1]\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_knn , tpr_knn, threshold_knn=roc_curve(y_test,yes_probs)  # false positive, true posistive, threshold\n",
    "\n",
    "#AUC\n",
    "auc_knn=auc(fpr_knn , tpr_knn)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_knn , tpr_knn, marker='_', label='KNN (AUC=%0.3f)' %auc_knn, color='Orange')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.5 Naïve Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'var_smoothing': 10.0}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb_model=GaussianNB()\n",
    "params_NB = {'var_smoothing': np.logspace(1,10, num=100)}\n",
    "nb_cv=GridSearchCV(nb_model, param_grid=params_NB ,cv=cv, verbose=1) \n",
    "nb_cv.fit(X_train_smote, y_train_smote)\n",
    "nb_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.95      0.94     58380\n",
      "           1       0.37      0.33      0.35      5579\n",
      "\n",
      "    accuracy                           0.89     63959\n",
      "   macro avg       0.65      0.64      0.64     63959\n",
      "weighted avg       0.89      0.89      0.89     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = nb_cv.predict(X_test)\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion MAtrix on heatmap\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True,linewidths=0.8,fmt='d',cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "y_score_nb=nb_cv.predict_proba(X_test)   #results are probailities for each sample for each class\n",
    "#retrieve the probabilities only for the class1 (yes, positve class)\n",
    "yes_probs=y_score_nb[:,1]\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_nb , tpr_nb, threshold_nb=roc_curve(y_test,yes_probs)  # false positive, true posistive, threshold\n",
    "#AUC\n",
    "auc_nb=auc(fpr_nb , tpr_nb)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_nb, tpr_nb, marker='_', label='Naive Bayes (auc=%0.3f)' % auc_nb, color='Orange')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.6 Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 1 candidates, totalling 10 fits\n",
      "[CV 1/10] END .......random_state=2, tol=0.0001;, score=0.729 total time=   2.2s\n",
      "[CV 2/10] END .......random_state=2, tol=0.0001;, score=0.659 total time=   1.3s\n",
      "[CV 3/10] END .......random_state=2, tol=0.0001;, score=0.694 total time=   1.3s\n",
      "[CV 4/10] END .......random_state=2, tol=0.0001;, score=0.637 total time=   2.6s\n",
      "[CV 5/10] END .......random_state=2, tol=0.0001;, score=0.711 total time=   2.0s\n",
      "[CV 6/10] END .......random_state=2, tol=0.0001;, score=0.733 total time=   1.1s\n",
      "[CV 7/10] END .......random_state=2, tol=0.0001;, score=0.697 total time=   1.6s\n",
      "[CV 8/10] END .......random_state=2, tol=0.0001;, score=0.570 total time=   1.7s\n",
      "[CV 9/10] END .......random_state=2, tol=0.0001;, score=0.643 total time=   2.5s\n",
      "[CV 10/10] END ......random_state=2, tol=0.0001;, score=0.555 total time=   2.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'random_state': 2, 'tol': 0.0001}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "param_per = {'tol':[0.0001],\n",
    "             'random_state': [2]}\n",
    " \n",
    "pr_model = GridSearchCV(Perceptron(), param_per, refit = True, cv=cv, verbose = 3)\n",
    " \n",
    "# fitting the model for grid search\n",
    "pr_model.fit(X_train_smote, y_train_smote)\n",
    "pr_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.64      0.77     58380\n",
      "           1       0.16      0.71      0.26      5579\n",
      "\n",
      "    accuracy                           0.65     63959\n",
      "   macro avg       0.56      0.68      0.52     63959\n",
      "weighted avg       0.89      0.65      0.73     63959\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = pr_model.predict(X_test)\n",
    "\n",
    "# print classification report\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(66.5, 0.5, 'Truth')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Confusion MAtrix on heatmap\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(10,7))\n",
    "sn.heatmap(cm,annot=True,linewidths=0.8,fmt='d',cmap='Blues')\n",
    "plt.xlabel('Predicted',fontsize=16)\n",
    "plt.ylabel('Truth',fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC-AUC\n",
    "#predict Probabilities  \n",
    "y_score_pr=pr_model.predict(X_test)   #results are probailities for each sample for each class\n",
    "\n",
    "#Size of the plot\n",
    "plt.figure(figsize=(10,7), dpi=100)\n",
    "#plot \"No-Skill\" on ROC Curve\n",
    "plt.plot([0,1],[0,1],linestyle='--',label='No Skill')\n",
    "# calculate the features of ROC curve\n",
    "fpr_pr , tpr_pr, threshold_pr=roc_curve(y_test,y_score_pr)  # false positive, true posistive, threshold\n",
    "#AUC\n",
    "auc_pr=auc(fpr_pr , tpr_pr)\n",
    "#Plot the ROC Curve\n",
    "plt.plot(fpr_pr, tpr_pr, marker='_', label='Perceptron (auc= %0.2f)' % auc_pr, color='Black')\n",
    "#X-axis label\n",
    "plt.xlabel(\"False Positive Rate\")\n",
    "#Y-axis label\n",
    "plt.ylabel(\"True Positive Rate\")\n",
    "#show the legend\n",
    "plt.legend()\n",
    "#show the plot\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.8 64-bit (microsoft store)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "dbf26edbf9203c5a14d87dcceb59242e73145f762ab2b7c2898a643dffae66ca"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}