Mission740Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e35c934",
   "metadata": {},
   "source": [
    "# Predicting Heart Disease\n",
    "\n",
    "The World Health Organization (WHO) estimates that 17.9 million people die every year because of cardiovascular diseases (CVDs).\n",
    "\n",
    "There are multiple risk factors that could contribute to CVD in an individual such as unhealthy diet, lack of physical activity or mental illnesses. Being able to identify these risk factors in individuals early on could help prevent a lot of premature deaths.\n",
    "\n",
    "In this project, we will use the [Kaggle dataset](https://www.kaggle.com/datasets/fedesoriano/heart-failure-prediction) and build a K-Nearest Neighbors classifier to accurately predict the likelihood of a patient having a heart disease in the future. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7a6956c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6089c814",
   "metadata": {},
   "source": [
    "## EDA: Descriptive Statistics\n",
    "\n",
    "We will start with exploring our dataset. As per the source, each patient has the following information collected about them:\n",
    "\n",
    "\n",
    "1. `Age`: age of the patient [years]\n",
    "2. `Sex`: sex of the patient [M: Male, F: Female]\n",
    "3. `ChestPainType`: chest pain type [TA: Typical Angina, ATA: Atypical Angina, NAP: Non-Anginal Pain, ASY: Asymptomatic]\n",
    "4. `RestingBP`: resting blood pressure [mm Hg]\n",
    "5. `Cholesterol`: serum cholesterol [mm/dl]\n",
    "6. `FastingBS`: fasting blood sugar [1: if FastingBS > 120 mg/dl, 0: otherwise]\n",
    "7. `RestingECG`: resting electrocardiogram results [Normal: Normal, ST: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV), LVH: showing probable or definite left ventricular hypertrophy by Estes' criteria]\n",
    "8. `MaxHR`: maximum heart rate achieved [Numeric value between 60 and 202]\n",
    "9. `ExerciseAngina`: exercise-induced angina [Y: Yes, N: No]\n",
    "10. `Oldpeak`: oldpeak = ST [Numeric value measured in depression]\n",
    "11. `ST_Slope`: the slope of the peak exercise ST segment [Up: upsloping, Flat: flat, Down: downsloping]\n",
    "12. `HeartDisease`: output class [1: heart disease, 0: Normal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d12aa5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load dataset\n",
    "df = pd.read_csv(\"heart_disease_prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc74cce6",
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9a59e51",
   "metadata": {},
   "source": [
    "The dataset seems to contain both numerical and categorical features. Let's look at the datatype for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "344a302b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age                 int64\n",
      "Sex                object\n",
      "ChestPainType      object\n",
      "RestingBP           int64\n",
      "Cholesterol         int64\n",
      "FastingBS           int64\n",
      "RestingECG         object\n",
      "MaxHR               int64\n",
      "ExerciseAngina     object\n",
      "Oldpeak           float64\n",
      "ST_Slope           object\n",
      "HeartDisease        int64\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int64      6\n",
       "object     5\n",
       "float64    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.dtypes)\n",
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aab3ff7b",
   "metadata": {},
   "source": [
    "`7` features in total are numerical while `5` are categorical. However, two of the numerical features, `FastingBS` and `HeartDisease` are categorical as well. \n",
    "\n",
    "We will focus on the numerical variables first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bd0d2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>918.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>53.510893</td>\n",
       "      <td>132.396514</td>\n",
       "      <td>198.799564</td>\n",
       "      <td>0.233115</td>\n",
       "      <td>136.809368</td>\n",
       "      <td>0.887364</td>\n",
       "      <td>0.553377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.432617</td>\n",
       "      <td>18.514154</td>\n",
       "      <td>109.384145</td>\n",
       "      <td>0.423046</td>\n",
       "      <td>25.460334</td>\n",
       "      <td>1.066570</td>\n",
       "      <td>0.497414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>-2.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>173.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>267.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age   RestingBP  Cholesterol   FastingBS       MaxHR  \\\n",
       "count  918.000000  918.000000   918.000000  918.000000  918.000000   \n",
       "mean    53.510893  132.396514   198.799564    0.233115  136.809368   \n",
       "std      9.432617   18.514154   109.384145    0.423046   25.460334   \n",
       "min     28.000000    0.000000     0.000000    0.000000   60.000000   \n",
       "25%     47.000000  120.000000   173.250000    0.000000  120.000000   \n",
       "50%     54.000000  130.000000   223.000000    0.000000  138.000000   \n",
       "75%     60.000000  140.000000   267.000000    0.000000  156.000000   \n",
       "max     77.000000  200.000000   603.000000    1.000000  202.000000   \n",
       "\n",
       "          Oldpeak  HeartDisease  \n",
       "count  918.000000    918.000000  \n",
       "mean     0.887364      0.553377  \n",
       "std      1.066570      0.497414  \n",
       "min     -2.600000      0.000000  \n",
       "25%      0.000000      0.000000  \n",
       "50%      0.600000      1.000000  \n",
       "75%      1.500000      1.000000  \n",
       "max      6.200000      1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc1f5d4",
   "metadata": {},
   "source": [
    "From the table above, we can observe that:\n",
    "\n",
    "- The average age of patients is ~`53` years.\n",
    "- The median for `Cholesterol` is higher than its mean by roughly `25` mm/dl, indicating that it could be a left-skewed distribution with a possibility of outliers skewing the distribution.\n",
    "- `RestingBP` and `Cholesterol` have a minimum value of zero.\n",
    "- There don't seem to be any missing values in these columns. But we will have to confirm it across the entire dataset as well.\n",
    "\n",
    "`RestingBP` can't be `0`. And, as per the [American Heart Association](https://www.heart.org/en/health-topics/cholesterol/about-cholesterol/what-your-cholesterol-levels-mean), serum cholesterol is a composite of different measurements. So, it is unlikely that `Cholesterol` would be `0` as well. We will have to clean both of these up later.\n",
    "\n",
    "Next, we will look at the categorical variables. It would also be beneficial to look at how the target feature, `HeartDisease`, is related to those categories. Before that, let's quickly check if there are any missing values in the dataset or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c436572",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "847a31a8",
   "metadata": {},
   "source": [
    "There are no missing values in this dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75988364",
   "metadata": {},
   "source": [
    "## EDA: Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47977c45",
   "metadata": {},
   "source": [
    "We identified that most of the categorical columns are all of dtype **object**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "304a72e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>ST_Slope</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "      <td>918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>Normal</td>\n",
       "      <td>N</td>\n",
       "      <td>Flat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>725</td>\n",
       "      <td>496</td>\n",
       "      <td>552</td>\n",
       "      <td>547</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sex ChestPainType RestingECG ExerciseAngina ST_Slope\n",
       "count   918           918        918            918      918\n",
       "unique    2             4          3              2        3\n",
       "top       M           ASY     Normal              N     Flat\n",
       "freq    725           496        552            547      460"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=['object'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe32c99",
   "metadata": {},
   "source": [
    "We can confirm that those columns are indeed categorical given the number of unique values in each of them. But, we can't gather much else. Also, `FastingBS` and `HeartDisease` are categorical as well since they only contain binary values. We can confirm that quickly as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99fc3fcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1], dtype=int64), array([0, 1], dtype=int64))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"FastingBS\"].unique(), df[\"HeartDisease\"].unique() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79253815",
   "metadata": {},
   "source": [
    "Let's start looking at the categories in more detail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5af0a32f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorical_cols = [\"Sex\", \"ChestPainType\", \"FastingBS\", \"RestingECG\", \"ExerciseAngina\", \"ST_Slope\", \"HeartDisease\"]\n",
    "\n",
    "fig = plt.figure(figsize=(16,15))\n",
    "\n",
    "for idx, col in enumerate(categorical_cols):\n",
    "    ax = plt.subplot(4, 2, idx+1)\n",
    "    sns.countplot(x=df[col], ax=ax)\n",
    "    # add data labels to each bar\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, label_type=\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d97efb3",
   "metadata": {},
   "source": [
    "- The dataset is highly skewed towards male patients. There are `725` male patients and `193` female patients. This could potentially induce a bias in our model.\n",
    "- `496` patients had `ASY` (asymptotic) chest pain type.\n",
    "- `552` patients had a normal restin ECG.\n",
    "- `704` patients had blood sugar lower than `120` mg/dl\n",
    "\n",
    "Grouping these by `HeartDisease` will give us a better idea about the data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a5f28e00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sCzAE6ApcDjxhZskxzCciIiIiIiIJKmbFrEfsDhZTgy8HrgEmBe2TgEHB82uAKe5e6O6rgJVAn1jlExERERERkcQV02tmzSzZzBYAm4FZ7v4u0MLdNwIEj82DzVsD66J2zw/aDn/NkWaWZ2Z5BQUFsYwvIiIiIiIicSqmxay7l7h7DpAN9DGzbkfZ3Mp7iXJec4K757p7brNmzaooqYiIiIiIiCSSapnN2N23A3OIXAu7ycxaAgSPm4PN8oE2UbtlAxuqI5+IiIiIiIgklljOZtzMzBoFzzOAS4BlwAxgWLDZMGB68HwGMMTM0sysPdARmB+rfCIiIiIiIpK4UmL42i2BScGMxEnAVHefaWZvA1PNbASwFhgM4O6LzWwqsAQoBka5e0kM84mIiIiIiEiCilkx6+4LgZ7ltG8FBlSwz3hgfKwyiYiIiIiISM1QLdfMioiIiIiIiFQlFbMiIiIiIiKScFTMioiIiIiISMJRMSsiIiIiIiIJR8WsiIiIiIiIJJxKFbNm9lpl2kRERKR6qG8WEZHa7qi35jGzdKAu0NTMGgMWrGoAtIpxtlCcfcfTYUdIeM9nhp1ARKTmqo19s4iISHmOdZ/Z7wI/JNI5vscXHeZO4PHYxRIREZEKqG8WERHhGMWsu/8O+J2Zfd/dH62mTCIiIlIB9c0iIiIRxzozC4C7P2pm5wHtovdxd43JFRERCYH6ZhERqe0qVcya2TPA6cACoCRodkAdpoiISAjUN4uISG1XqWIWyAW6uLvHMoyIiIhUmvpmERGp1Sp7n9lFwCmxDCIiIiLHRX2ziIjUapU9M9sUWGJm84HCg43ufnVMUomIiMixqG8WEZFarbLF7LhYhhAREZHjNi7sACIiImGq7GzGr8c6iIiIiFTeifTNZtaGyARRpwClwAR3/52ZZQHPEZkZeTXwDXf/PNhnDDCCyCRTP3D3f1XJNyAiInKSKjub8S4iMyQC1AFSgT3u3iBWwURORkmpc+UfPuGUBqn85bpTefg/m5j83uc0qRf5J3/HgBZcfEYmRSXOT6evZ9HGfRSXwrVnNWJUv2YhpxcRObYT7JuLgR+7+/tmlgm8Z2azgJuA19z9fjMbDYwGfmpmXYAhQFegFTDbzM5w95IKXl9E5Kj2F5Xyjb+s4kCxU1zqXNGlAT+6uAVLPtvHXS9sYO+BUrIb1eF312aTmZ7MvE92c/+szygqcVKTjbsuO4W+p9UP+9uQOFHZM7OZ0ctmNgjoE4tAIlXhqXe20qFZGrsLS8vaRpzblO/2bXrIdi8u3sGBEufVUR3Zd6CUSx5fwdXdG9KmcZ3qjiwiclxOpG92943AxuD5LjNbCrQGrgH6B5tNAuYAPw3ap7h7IbDKzFYG7/F2VX0fIlK7pKUYk4e1o15aMkUlztf//Cn9O2Zyz0sb+dnAUzinXT2ee/9z/vDmFn4yoAWN6ybz1LdOpUWDVJZv2s8Nz6xm/k86h/1tSJyo7GzGh3D3fwIXV20UkaqxcUcR//54F0N6NT7mtmaw90ApxSXO/uJSUpONzLQT+rMQEQnV8fbNZtYO6Am8C7QICt2DBW/zYLPWwLqo3fKDtsNfa6SZ5ZlZXkFBwQnlF5Hawcyol5YMQHGJU1TqmMGnWwv50ql1Abjg9Hq8vHQnAN1aZtCiQSoAZzRPo7DYKSwuLf/Fpdap7DDjr0UtJhG5t53uaydx6d5XNnLXZaewu/DQUXBPz9/KPz78nO6tMrh7YEsaZiRzRZeGzFq2i94PLmNfUSljL29Jo7qVnRdNRCQ8J9M3m1l9YBrwQ3ffaWYVblpO2xHv4e4TgAkAubm5+nwgIkd18HKw1dsOcGPvLHpm1+WM5mnMWr6Lyzo34MXFO9m4o+iI/V5aspOuLdNJS9GJB4mo7Kf2q6KeFxOZHOKaKk8jcpJeW76TJvVS6N4qg7dX7S5rv753E35wYXMMePDfm7nvXxt5cFA2C9bvJclg/k86s2NfCYOf+pTzT6tP2ywNMxaRuHdCfbOZpRIpZP/m7v8ImjeZWUt332hmLYHNQXs+0CZq92xgw8kGF5HaLTnJePl/OrBjXwkjp6xl+ab9PHBNNuNe3sDv5mzm0k4NSE0+9Fjax5v3c/+sz/jrje3CCS1xqbLXzH471kFEqkLe2r3MXr6TOSt2UVjs7Cos4bZp6/jdtV98Fht6dmOGP7sGgOkLd9C/Y31Sk42m9VM4u21dFm7Yp2JWROLeifTNFjkF+2dgqbs/FLVqBjAMuD94nB7V/qyZPURkAqiOwPyTyS0iclDDjGTObVePOSt3892+Tfnrje0B+HRLIf9esatsu407ihg5ZS0PfS2bU7PSwoorcahS5+jNLNvMnjezzWa2ycymmVl2rMOJHK+fXnoK7/64M2/e3olHv57Nee3r87tr27Bp1xdDVf61dCedmqcD0LphKm99ugd3Z++BUj7I38fpTfWfpIjEvxPsm/sCNwAXm9mC4OsKIkXspWa2Arg0WMbdFwNTgSXAK8AozWQsIidj655iduyL/Deyv6iUNz7dTYemddiyuxiA0lLn0bkFXJebBcCOfSV8+29ruPOSFvRuWy+03BKfKjvM+C/As8DgYPn6oO3SWIQSqWq/evUzlny2HzPIblSHX17VCoAb+2Txk3+u59LHV+LA4JxGnHlKerhhRUQq57j7Znd/g/KvgwUYUME+44HxJx5TROQLm3cV86Pn8yl1p9Thyq4NGdCpAU+9vYWn/7sNgMvPbMA3ejYCYNL8razeVsijrxfw6OuRCeaeuaEdTetrjhMBcz/2PA1mtsDdc47VVt1yc3M9Ly+vSl/z7DuertLXq42ez3wg7AgJr+3Yj8KOIFIrmdl77p4bdo7KqE19s4jEt7U/7x52hGPSZ6vEdbS+ubJTgW0xs+vNLDn4uh7YWnURRURE5DipbxYRkVqtssXscOAbwGdEbrb+dUCTQomIiIRHfbOIiNRqlR1sfh8wzN0/BzCzLOBBIh2piIiIVD/1zSIiUqtV9sxsj4OdJYC7bwN6Hm0HM2tjZv8xs6VmttjMbgvas8xslpmtCB4bR+0zxsxWmtlyMxt4It+QiIhILXHcfbOIiEhNUtliNumwojOLY5/VLQZ+7O5nAucAo8ysCzAaeM3dOwKvBcsE64YAXYHLgSfMLPl4vhkREZFa5ET6ZhERkRqjsp3eb4G3zOzvgBO5Rueo0/S7+0Yi1/Dg7rvMbCnQGrgG6B9sNgmYA/w0aJ/i7oXAKjNbCfQB3j6O70dERKS2OO6+WUREpCapVDHr7k+bWR5wMZH7033N3ZdU9k3MrB2RoU/vAi2CQhd332hmzYPNWgPvRO2WH7Qd/lojgZEAbdu2rWwEERGRGuVk+2YREZFEV+nhSEEHedydpJnVB6YBP3T3nWYV3au93Ju4H3ETXHefAEyAyL3sjjePiIhITXGifbOIiEhNUNlrZk+ImaUSKWT/5u7/CJo3mVnLYH1LYHPQng+0ido9G9gQy3wiIiIiIiKSmGJWzFrkFOyfgaXu/lDUqhnAsOD5MGB6VPsQM0szs/ZAR2B+rPKJiIiIiIhI4orlrId9gRuAj8xsQdB2F3A/MNXMRgBrgcEA7r7YzKYSGS5VDIxy95IY5hMREREREZEEFbNi1t3foPzrYAEGVLDPeDQTo4iIiIiIiBxDTK+ZFREREREREYkFFbMiIiIiIiKScFTMioiIiIiISMKJ5QRQIiIiIuU6+46nw45wTO89cGPYEURE5Ch0ZlZEREREREQSjopZERERERERSTgqZkVERERERCThqJgVERERERGRhKNiVkRERERERBKOilkRERERERFJOCpmRUREREREJOHoPrMiIiIiAsT//X91718RiaYzsyIiIiIiIpJwVMyKiIiIiIhIwtEw4zhyYOdWVr88gaI9OzAzmva4iOZnXwbA5vdnUfDBbCwpiQan5ZB94TfZtuQtNv335bL99xWso/ON91K3+alhfQsiIiIiIiLVQsVsHLGkZLL7D6Vui3aUHNjHsmfuIfPUrhTv3cmOle9z5rBfkJSSStGenQBkdTmPrC7nAZFC9pN//k6FrFSbeL+uKhHo2i8RERGRE6diNo6k1m9Eav1GACTXySA9qxVFuz9ny8I5tPjSlSSlpEa2q9fgiH23LXuHxp3Pqc64IiIiIiIiodE1s3GqcEcBezevoV7L0yn8fBO785ez7K/38vGUX7Jn46dHbP/5snfJUjErIiIiIiK1hIrZOFRyYD+fzniU7IuuIzktAy8toWT/XjpdN5bWF36TVS88jruXbb9n4yckpaaR0Sw7xNQiIiIiIiLVR8VsnPGSYj6d8ShZZ55H4zNyAaiTmUWjjmdjZtRreTqYUbxvV9k+ny97R2dlRURERESkVlExG0fcnTX/+jPpWa1okXt5WXvDDr3YtXYpAPu3fYaXlpCSkRnsU8rny/9L485fCiWziIiIiIhIGDQBVBzZs34F25a8RXrTbJZOuhuAVhd8nSbd+7HmlT+x5C93YckptPvyzZgZALvXLSc1M4u0Rs3DjC4iIgnAzJ4CrgQ2u3u3oC0LeA5oB6wGvuHunwfrxgAjgBLgB+7+rxBii4iIlEvFbBypn30GvX4yqdx17b9yS7ntmW3PpPN1Y2MZS0REao6JwGNA9L21RgOvufv9ZjY6WP6pmXUBhgBdgVbAbDM7w91LqjmziIhIuVTMioiI1BLuPtfM2h3WfA3QP3g+CZgD/DRon+LuhcAqM1sJ9AHerpawIgIkxn3dn88MO4HUVrpmVkREpHZr4e4bAYLHg9ettAbWRW2XH7QdwcxGmlmemeUVFBTENKyIiMhBKmZFRESkPFZOm5fThrtPcPdcd89t1qxZjGOJiIhEqJgVERGp3TaZWUuA4HFz0J4PtInaLhvYUM3ZREREKqRiVkREpHabAQwLng8Dpke1DzGzNDNrD3QE5oeQT0REpFwxK2bN7Ckz22xmi6LassxslpmtCB4bR60bY2YrzWy5mQ2MVS4REZHayswmE5nAqZOZ5ZvZCOB+4FIzWwFcGizj7ouBqcAS4BVglGYyFhGReBLL2Ywnoun/RURE4oa7D61g1YAKth8PjI9dIhERkRMXszOz7j4X2HZY8zVEpv0neBwU1T7F3QvdfRVwcPp/ERERERERkSNU931mD5n+38yip/9/J2q7o07/D4wEaNu2bQyjioiISLxY88qf2PHJAlLqNqDLt395yLpN/32J9a8/R4/vPUZK3UxKS4pZ++pf2LtpNWZG9kXXkdn2zJCSi4hIrMTLBFCa/l9EREQqlNX1fDp8/SdHtB/YuZWdaxZTJ7NJWdvWhXMA6HLTeDp8/U7yX5+Me2l1RRURkWpS3cWspv8XERGR45bZpjPJ6fWOaM//z7O07vdNsC+Oi+/buoHMU7sAkFqvASlp9dj72apqyyoiItWjuotZTf8vIiIiVWL7yvdJzWxM3eaHXnZUt1kbdqz8AC8toXB7AXs3rebArsOn8RARkUQXs2tmg+n/+wNNzSwfuIfIdP9Tg1sBrAUGQ2T6fzM7OP1/MZr+X0RERI6itKiQz955gY6D7zhiXZPu/di/bQPLnhlHnQZNqNeqA5aUHELKmq+8a5k3vDGN7SvfxyyJlLqZnPrlm6lTvzHF+3bz6YxH2fvZKpp0PZ82l9wYcnoRSXQxK2Y1/b+IiIjESuH2zRzYUcDSSXcDcGDXNpY+M5bO199Dar1GZF90Xdm2y5+9j7RGLcKKWqNldT2fZj0vYfVLE8raWvS+glbnXwvA5vdf5bO3p9P20puw5FRa9b2WfVvy2b8l/4Teb+3Pu1dJ7lhqO/ajsCOI1BrVPZuxiEiVOJ6ZTfds/IS1r04M1jotzxtEo4651Z5ZRKpORrM29Bj1WNnyogk/pvP14yKzGRcV4g7JddLYuXoRlpRERtNyb5IgJymzTWcKdxQc0pacllH2vLSo8Iv2OmnUzz6Dwu2bqi2fiNRsKmZFJCGVdzYAyp/ZNKNpNp1vGIclJVO0eztLJ/0vDU/vqWGHIglk1cwn2LVuGcX7dvPRkz+kZd+v0rT7heVuW7R3Jyv//iCYUad+Y0798nerOa2sn/d3ti15k+Q6GXT85uiw44hIDaViVkQSUnlnA+CLmU0//efvytqSUtPKnpcWFx0y66mIJIb2V37vqOu7jfxt2fO0hs3oOuLXsY4kR9H6gq/T+oKv89m7L1DwwWxa9f1a2JFEpAaKl/vMioictIpmNgXYs/ETlvxlDEsn/Yy2lw7TWVkRkWrQuPO5bP84L+wYIlJD6cysiNQIR5vZFKBey9Pp8u1fsW/rBta8PIEG7XuQlFKnmlOKiNR8+z//jPTGpwCw45MPSM9qGXKi2qW8OSWK9+1m1cwnOLBjC3UaNqX9VaNIibpv84GdW1nylzG0PG8QLXpfEVZ0keOmYlZEaoRjzWx6UEaTViSlprFvy3rqndI+pLQiIjVDedcy7/x0Ifu3bYxcs9ygKW0vHVa2/aIJP6bkwD68pJjtK9+nw9fv0ORcVay8OSU+m/8imW27cMqXruSzd2ey6d2ZtL7wm2Xr8//zLA3a9wgjrshJUTErIjXC0WY2LdxeQJ0GWVhSMoU7tlC47TPSGjQNMa2ISM1Q3rXMFU3MBYde2yyxUd6cEjtWvs8Z3xwDQJOu5/Pxc78qK2a3r3iPOg2bHTK/hEiiUDErIgnpeGY23b3+YzY9PxNLSgEz2lxyIyl1M6s5sYiISDiK9+4ktX4jAFLrN6J4704ASg4Usmn+i3QYfCeb/vtyiAlFToyKWRFJSMczs2mTrn1p0rVvrCOJiIgklI1v/YPmZw8kuU562FFEToiKWRERERGRGiylbgOKdm8ntX4jinZvJ6VuAwD2bPyU7R/nsX7uVEoK94IZlpxK816XhpxYpHJUzIqIiIiI1GANT+/J1sVvcMqXrmTr4jdo2KEXAJ2G/qxsmw1vPk9ynTQVspJQVMyKiIiISI33yZZCbv2/dWXLaz8/wI8uas5nO4t57eOdpCYbpzauwwODsmmYkbj3Ii9vTolTvnQlq154nK0fzaVOgya0v2pU2DFFqoSKWRGRkKz9efewIyS8tmM/CjuCiCSI05um8fL/dACgpNT50m+XM/DMBny6pZCfXtKClGTjV69+xhPzChhz2Skhpz1xFc0p0fEbPz3qfq36fjUWcURiKinsACIiIiIi1enNT3fTtnEdshvVoV+HTFKSDYCebeqycWdRyOlEpLJUzIqIiIhIrTJj0Q6u7t7wiPap739O/466dZtIolAxKyIiIiK1xoHiUmYv38VXuh5azD76+mZSkuCrPY4sckUkPumaWRERERGpNeas3E23luk0q//Fx+C/L/ic1z7exeRh7TGzENOJyPFQMSsiIgD0fXg59eokkZxkJCfBzO92YPveYkb93zrytxeR3SiVJ77RNqFn+RQRmfHRDq7u3qhsec6KXfz+jS1M/XZ7Mupo0KJIIlExKyIiZabc1J6sel90DU+8sYW+p9Xnexc044l5BQk/y6eI1G77DpQy75Pd/PKqVmVtY1/ayIHiUq5/ejUAPbMz+OVVrUNKKCLHQ8WsiIhUaNaynUz5dnsArs1pxJC/rFIxKyIJK6NOEh+OPvOQtrm3nRFSGklkZ9/xdNgRjuq9B24MO0K1UDErIiJlrn9mNQZcl5vFt3Kz2LKnmBaZqQC0yExly57icAOKVKNEuBe07rUsIrWZilkREQHgHyNOo0WDVLbsLub6p1dzetO0sCOJJKwNOw5w+z/WU7C7mCSDb53dmOHnNi1b/4c3t/DLVz/jgzs7HzK0X0RqtgM7t7L65QkU7dmBmdG0x0U0P/syADa/P4uCD2ZjSUk0OC2H7Au/GXLa+Kf/PUVEBIAWDSJnYJvWT2HgmZksWL+PpvVS2LSriBaZqWzaVURTfegWqZTkJON/B55C91YZ7C4s4co/fML5p9fnjObpbNhxgDc+2U3rhqlhxxSRamZJyWT3H0rdFu0oObCPZc/cQ+apXSneu5MdK9/nzGG/ICkllaI9O0/qfU5mZElFB+OqelLIqhhZoinbRESEvQdK2V1YUvZ87ie76dQ8jUs6NWDagu0ATFuwnUs7NwgxpUjiaJGZSvdWGQDUT0umQ9M0Nu2KDNP/+SufMeayFugOMCK1T2r9RtRt0Q6A5DoZpGe1omj35xQseI0WX7qSpJTIQa7UeuH1twcPxv37+x35582n8fR/t/Hx5v1lk0K+ftsZ9D2tPk/MKwgt40E6xC4iImzZXczIKWsBKC51runekP4dMzmrdQbfm7qO597/nFYNU/n9N9qEnFQk8az7/ACLP9tPTusMZi3bySmZqXQ5JSPsWCISssIdBezdvIZ6LU9n/evPsTt/ORvm/Z2klFRaXziEei1PCyVXi8zUsvkyog/GxeOkkCpmRUSEtll1eOV7HY5ob1w3hck3tQ8hkUjNsKewhFueW8vYy08hJcl4bG4Bz9zYLuxYIhKykgP7+XTGo2RfdB3JaRl4aQkl+/fS6bqx7P3sU1a98Dhdb34QC3kIR/TBuHicFFLDjEVERERioKjEueW5dQzq0Ygvd2nIms8PsG77Ab78+5X0fXg5G3cW8ZU/fMLmXUVhRxWRauQlxXw641GyzjyPxmfkAlAnM4tGHc/GzKjX8nQwo3jfrlBzRh+My0w/8WtjYynuilkzu9zMlpvZSjMbHXYeERGR2k598/Fzd+6cvp4OzdK4+bzILMadW6Tz/p1n8ubtnXjz9k60bJDKi989neaZmghKpLZwd9b868+kZ7WiRe7lZe0NO/Ri19qlAOzf9hleWkJKRmZYMY84GAeUTQoJxM2kkOEniGJmycDjwKVAPvBfM5vh7kvCTSYiIlI7qW8+MXlr9/KPD7fTuUUaX/79SgDuGNCCi88I78OpiIRvz/oVbFvyFulNs1k66W4AWl3wdZp078eaV/7Ekr/chSWn0O7LN4c2xLi8g3FA2aSQ37ugWdxMChlXxSzQB1jp7p8CmNkU4BpAHaaIiEg41DefgN6n1mPNvd2Ous2bt3eqpjQiEi/qZ59Br59MKndd+6/cUs1pylfRwbjvXdA07iaFNHcPO0MZM/s6cLm7fydYvgH4krvfGrXNSGBksNgJWF7tQeVYmgJbwg4hkgD0txJ/TnX3ZmGHiCe1vG/W32j80e8kPun3En9q0u+kwr453s7Mlncu/ZBq290nABOqJ46cCDPLc/fcsHOIxDv9rUiCqLV9s/5G449+J/FJv5f4U1t+J/E2AVQ+EH2+OhvYEFIWERERUd8sIiJxKt6K2f8CHc2svZnVAYYAM0LOJCIiUpupbxYRkbgUV8OM3b3YzG4F/gUkA0+5++KQY8nxq3FDzURiRH8rEvdqed+sv9H4o99JfNLvJf7Uit9JXE0AJSIiIiIiIlIZ8TbMWEREREREROSYVMyKiIiIiIhIwlExKyfNzNzMnolaTjGzAjObGWYukXhlZiVmtiDqq13YmURqKzP7atCPdTazd4O/ybVBP1b2Nxr0bVvM7FdhZ66pgt/Db6OWf2Jm4w7b5kMzm3xY20QzWxX8rt43s3OrKXKtEf13EiwnmdkjZrbIzD4ys/8Gk8Q9a2b/E7Xfl8xsoZnF1Tw9NYmZNYn6v+ozM1sftdzCzIrM7Lth54wVFbNSFfYA3cwsI1i+FFgfYh6ReLfP3XOivlaHHUikFhsKvAEMcfcvuXsOMBZ47rC/0cuA5cA3zKy8e+/KySsEvmZmTctbaWZnEvns2s/M6h22+o7gdzca+ENMU9ZOZX8nwfI3gVZAD3fvDnwV2A7cDtxhZs3MLAl4DPieuxdXf+Tawd23Hvy/CngSeDhq+VrgHSK/vxpJxaxUlZeBrwTPhwKTj7KtiIhI6MysPtAXGMEXH9IrMhT4HbAWOCfG0WqrYiIzsN5ewfpvAc8ArwJXV7DNXKBD1UervSr4O2kJbHT3UgB3z3f3z919E/Ag8BvgFmChu78RQmyJGAr8GMg2s9Zhh4kFFbNSVaYAQ8wsHegBvBtyHpF4lhE1BOj5sMOI1GKDgFfc/WNgm5n1Km+jYOTRAGAmkYO1NfYsRxx4HLjOzBqWs+6bwHMc/XdwFfBRjLLVVoM48u9kKnBV0I/91sx6Rm3/JNAFuAO4s9rTCgBm1gY4xd3nE/l9fTPkSDGhYlaqhLsvBNoR6VxeCjeNSNyLHmb81bDDiNRiQ4kcjCV4rKhAuhL4j7vvBaYBXzWz5GrIV+u4+07gaeAH0e1m1hsocPc1wGtALzNrHLXJA2a2ABhJ5AyiVJ0j/k7cPR/oBIwBSoHXzGwAQHC29g/Ay+6+NYS8EjGESBELR///LaHpYmypSjOIDC3pDzQJN4qIiEjFzKwJcDGROR8cSAbczMo7kzQU6Gtmq4PlJsBFwOzqyFoL/T/gfeAvUW1Dgc5Rv4MGRK4H/FOwfIe7/726AtYWR/s7cfdCIpeZvWxmm4icwX0t2LU0+JLwDAVamNl1wXIrM+vo7ivCDFXVdGZWqtJTwM/dXcN7REQk3n0deNrdT3X3du7eBlgFnB+9kZk1CNraBtu1A0ZRQ89yxAN330bkjNIIiMycCwwmMtnQwd/BNeh3UB0q+jvpZ2atoOz30wNYE2JOiWJmnYB67t466m/mVxx7boCEo2JWqkxw8f/vws4hIiJSCUOBw69Zn0ZkkqFoXwP+HZyFOmg6cLWZpcUwX233W+DgrMb9gPXuHn2nhLlAFzNrWe3JapeK/k4mAi+Y2SJgIZHJux6r3mhyFBX93mrcASBz97AziIiIiIiIiBwXnZkVERERERGRhKNiVkRERERERBKOilkRERERERFJOCpmRUREREREJOGomBUREREREZGEo2JWpIYws5+Z2WIzW2hmC8zsS2FnEhERiTdmdoqZTTGzT8xsiZm9ZGYjzWxmFbx2fzM7L2p5nJmtD/rlRWZ29TH2f8nMGh1l/ePBay0xs33B8wVm9vWTzS6SiFLCDiAiJ8/MzgWuBHq5e6GZNQXqhBxLREQkrpiZEbn/5iR3HxK05QBXVdFb9Ad2A29FtT3s7g+a2ZnAPDNr7u6l5e3s7lcc7cXdfRSAmbUDZrp7TlWEFklUOjMrUjO0BLa4eyGAu29x9w1mdraZvW5m75nZv8yspZk1NLPlZtYJwMwmm9nNoaYXERGpHhcBRe7+5MEGd18AzAPqm9nfzWyZmf0tKHwpry8N2n8QnCFdGJzpbQfcAtwenC29IPqN3X0pUAw0NbN/Bq+32MxGHtzGzFabWVMza2dmS83sj8E2r5pZRnnfkJk9Y2bXRC3/zcyuNrObzGy6mb0S9Pv3RG1zvZnND3L+wcyST/onKxICFbMiNcOrQBsz+9jMnjCzC80sFXgU+Lq7nw08BYx39x3ArcBEMxsCNHb3P4YXXUREpNp0A96rYF1P4IdAF+A0oG9FfWmw/Wigp7v3AG5x99XAk0TOxOa4+7zoFw8u/ykFCoDhwevlAj8wsybl5OkIPO7uXYHtwLUV5P4T8O3gPRoC5wEvBev6ANcBOcBgM8sNzhB/E+gbnNktCbYRSTgaZixSA7j7bjM7G7iAyFHn54BfEOm0ZwUHl5OBjcH2s8xsMPA4cFYooUVEROLLfHfPBzCzBUA7IkVkuX0psBD4m5n9E/jnUV73djO7HtgFfNPdPTir+9VgfRsihevWw/ZbFZw1hkgB3q68F3f314NraZsDXwOmuXtxkHeWu28Nvqd/AOcTOTt8NvDfYJsMYPNR8ovELRWzIjWEu5cAc4A5ZvYRMApY7O7nHr6tmSUBZwL7gCwgvxqjioiIhGUxUNFkSYVRz0uIfE42KuhLga8A/YCrgbvNrGsFr/uwuz94cMHM+gOXAOe6+14zmwOkVyJPucOMA88QObs6BBge1e6HbedEvqdJ7j7mKK8nkhA0zFikBjCzTmbWMaopB1gKNAsmh8LMUqM62tuD9UOBp4JhVCIiIjXdv4G06LkizKw3cGEF2y+nnL40OCjcxt3/A9wJNALqEzn7mnmMDA2Bz4NCtjNwzsl8Q4GJRIZI4+6Lo9ovNbOs4HrbQcCbwGvA14MzuQTrT62CDCLVTmdmRWqG+sCjwXT+xcBKYCQwAXgkuIYmBfh/ZlYEfAfo4+67zGwu8L/APeW+soiISA0RDPH9KpH+cDSwH1hNBcOE3f1AcNubQ/pS4GPgr0GbETn7ut3MXgD+HkzI9P0KYrwC3GJmC4kUy+9Uwfe1ycyWlvN9vEHkrG0H4Fl3zwMws/8FXg2K8iIio7nWnGwOkepm7oePPhARERERkURhZnWBj4jcom9H0HYTkOvut4aZTSSWNMxYRERERCRBmdklwDLg0YOFrEhtoTOzIiIiIiIiknB0ZlZEREREREQSjopZERERERERSTgqZkVERERERCThJPSteZo2bert2rULO4aIiNQQ77333hZ3bxZ2jkSmvllERKrS0frmhC5m27VrR15eXtgxRESkhjAz3WfxJKlvFhGRqnS0vlnDjEVERERERCThqJgVERERERGRhKNiVkRERERERBJOQl8zKyIihyoqKiI/P5/9+/eHHSWupaenk52dTWpqathRRESkhlPfXDkn0jermBURqUHy8/PJzMykXbt2mFnYceKSu7N161by8/Np37592HFERKSGU998bCfaN2uYsYhIDbJ//36aNGmizvIozIwmTZroCLmIiFQL9c3HdqJ9s87MHubsO54OO0LCe++BG8OOIFKrqbM8Nv2MpLqs/Xn3sCPERNuxH4UdQSShqN85thP5GenMrIiIiIiIiCQcFbMiIjVc/fr1D1meOHEit956a5W89urVq3n22WfLlufMmUPDhg3p2bMnnTp1ol+/fsycObNs/ZNPPsnTT2sEjIiI1G7qm6uGhhmLiMgJKS4uLuswv/Wtb5W1X3DBBWWd5IIFCxg0aBAZGRkMGDCAW265Jay4IiIiNV5t65t1ZlZEpBYrKCjg2muvpXfv3vTu3Zs333wTgPnz53PeeefRs2dPzjvvPJYvXw5EjhwPHjyYq666issuu4zRo0czb948cnJyePjhh494/ZycHMaOHctjjz0GwLhx43jwwQcBeOSRR+jSpQs9evRgyJAhAOzZs4fhw4fTu3dvevbsyfTp04HIUeYLLriAXr160atXL9566y0ANm7cSL9+/cjJyaFbt27MmzcPgFdffZVzzz2XXr16MXjwYHbv3h3Dn6KIiEjVUd9ceTE7M2tm6cBcIC14n7+7+z1mNg64GSgINr3L3V8K9hkDjABKgB+4+79ilU9EpLbYt28fOTk5Zcvbtm3j6quvBuC2227j9ttv5/zzz2ft2rUMHDiQpUuX0rlzZ+bOnUtKSgqzZ8/mrrvuYtq0aQC8/fbbLFy4kKysLObMmcODDz5YdrR3zpw5R7x/r169eOCBB45ov//++1m1ahVpaWls374dgPHjx3PxxRfz1FNPsX37dvr06cMll1xC8+bNmTVrFunp6axYsYKhQ4eSl5fHs88+y8CBA/nZz35GSUkJe/fuZcuWLfziF79g9uzZ1KtXj1//+tc89NBDjB07tmp/sCIiIidIfXPV9M2xHGZcCFzs7rvNLBV4w8xeDtY97O4PRm9sZl2AIUBXoBUw28zOcPeSGGYUEanxMjIyWLBgQdnyxIkTycvLA2D27NksWbKkbN3OnTvZtWsXO3bsYNiwYaxYsQIzo6ioqGybSy+9lKysrEq/v7uX296jRw+uu+46Bg0axKBBg4DIUdsZM2aUHSHev38/a9eupVWrVtx6660sWLCA5ORkPv74YwB69+7N8OHDKSoqYtCgQeTk5PD666+zZMkS+vbtC8CBAwc499xzK51XREQk1tQ3V03fHLNi1iM/oYPnjlODr/J/ahHXAFPcvRBYZWYrgT7A27HKKCJS25WWlvL222+TkZFxSPv3v/99LrroIp5//nlWr15N//79y9bVq1fvuN7jgw8+4Mwzzzyi/cUXX2Tu3LnMmDGD++67j8WLF+PuTJs2jU6dOh2y7bhx42jRogUffvghpaWlpKenA9CvXz/mzp3Liy++yA033MAdd9xB48aNufTSS5k8efJx5RQREYkH6psrL6bXzJpZspktADYDs9z93WDVrWa20MyeMrPGQVtrYF3U7vlB2+GvOdLM8swsr6Cg4PDVIiJyHC677LKya2aAsqPEO3bsoHXryH/BEydOrHD/zMxMdu3aVeH6hQsXct999zFq1KhD2ktLS1m3bh0XXXQRv/nNb9i+fTu7d+9m4MCBPProo2VHjD/44IOyPC1btiQpKYlnnnmGkpLIoJ01a9bQvHlzbr75ZkaMGMH777/POeecw5tvvsnKlSsB2Lt3b9nRYhERkXinvrnyYlrMunuJu+cA2UAfM+sG/B44HcgBNgK/DTYv7y65R5zJdfcJ7p7r7rnNmjWLSW4RkdrikUceIS8vjx49etClSxeefPJJAO68807GjBlD3759yzqn8vTo0YOUlBTOOuusskkm5s2bVzb9/6hRo3jkkUcYMGDAIfuVlJRw/fXX0717d3r27Mntt99Oo0aNuPvuuykqKqJHjx5069aNu+++G4Dvfe97TJo0iXPOOYePP/647Aj0nDlzyMnJoWfPnkybNo3bbruNZs2aMXHiRIYOHUqPHj0455xzWLZsWSx+fCIiIlVOfXPlWUXjpauamd0D7Im+VtbM2gEz3b1bMPkT7v6rYN2/gHHuXuEw49zcXD84tryqnH1HYt5jKZ6898CNYUcQqbWWLl1a7rAhOVJ5Pysze8/dc0OKVCPEom9OZGt/3j3sCDHRduxHYUcQSRjqmyvvePvmWM5m3AwocvftZpYBXAL82sxauvvGYLOvAouC5zOAZ83sISITQHUE5scqn4iIiMSPmnow+fnMsBOIiNRcsZzNuCUwycySiQxnnuruM83sGTPLITKEeDXwXQB3X2xmU4ElQDEwSjMZi4iIiIiISHliOZvxQqBnOe03HGWf8cD4WGWS6lFTh1RVJw3fEhERERE5uphOACUiIiIiIiISCypmRUREREREJOGomBUREREREZGEE8sJoEREJMFV9Qyzlb111yuvvMJtt91GSUkJ3/nOdxg9enSV5hAREUlU6pu/oDOzIiISV0pKShg1ahQvv/wyS5YsYfLkySxZsiTsWDWCmbUxs/+Y2VIzW2xmtwXt48xsvZktCL6uiNpnjJmtNLPlZjYwvPQiIhKWeO2bVcyKiEhcmT9/Ph06dOC0006jTp06DBkyhOnTp4cdq6YoBn7s7mcC5wCjzKxLsO5hd88Jvl4CCNYNAboClwNPBLfcExGRWiRe+2YVsyIiElfWr19PmzZtypazs7NZv359iIlqDnff6O7vB893AUuB1kfZ5RpgirsXuvsqYCXQJ/ZJRUQknsRr36xiVkRE4oq7H9FmZiEkqdnMrB2R+8G/GzTdamYLzewpM2sctLUG1kXtlk85xa+ZjTSzPDPLKygoiGVsEREJQbz2zSpmRUQkrmRnZ7Nu3Rf1U35+Pq1atQoxUc1jZvWBacAP3X0n8HvgdCAH2Aj89uCm5ex+xCcad5/g7rnuntusWbPYhBYRkdDEa9+sYlZEROJK7969WbFiBatWreLAgQNMmTKFq6++OuxYNYaZpRIpZP/m7v8AcPdN7l7i7qXAH/liKHE+0CZq92xgQ3XmFRGR8MVr36xb84iISIUqO11/VUpJSeGxxx5j4MCBlJSUMHz4cLp27VrtOWoii4wJ+zOw1N0fimpv6e4bg8WvAouC5zOAZ83sIaAV0BGYX42RRUTkMOqbo3KFHUBERORwV1xxBVdcccWxN5Tj1Re4AfjIzBYEbXcBQ80sh8gQ4tXAdwHcfbGZTQWWEJkJeZS7l1RzZhERiQPx2DermBUREakl3P0Nyr8O9qWj7DMeGB+zUCIiIidI18yKiIiIiIhIwolZMWtm6WY238w+NLPFZnZv0J5lZrPMbEXw2DhqnzFmttLMlpvZwFhlExERERERkcQWyzOzhcDF7n4Wkan+Lzezc4DRwGvu3hF4LVjGzLoAQ4CuwOXAE2aWHMN8IiIiIiIikqBiVsx6xO5gMTX4cuAaYFLQPgkYFDy/Bpji7oXuvgpYyRe3BhAREREREREpE9NrZs0sOZgtcTMwy93fBVocnP4/eGwebN4aWBe1e37QdvhrjjSzPDPLKygoiGV8ERERERERiVMxnc04mL4/x8waAc+bWbejbF7e7IpezmtOACYA5ObmHrFeRESqztqfd6/S12s79qNjbjN8+HBmzpxJ8+bNWbRo0TG3FxERqU3UN3+hWmYzdvftwBwi18JuMrOWELlJO5GzthA5E9smardsYEN15BMRkfhx00038corr4QdQ0RERALx2jfHcjbjZsEZWcwsA7gEWAbMAIYFmw0DpgfPZwBDzCzNzNoDHYH5sconIiLxqV+/fmRlZYUdQ0RERALx2jfHcphxS2BSMCNxEjDV3Wea2dvAVDMbAawFBgO4+2IzmwosAYqBUcEwZREREREREZFDxKyYdfeFQM9y2rcCAyrYZzwwPlaZREREREREpGaolmtmRURERERERKqSilkRERERERFJODG9NY+IiCS2ykzXX9WGDh3KnDlz2LJlC9nZ2dx7772MGDGi2nOIiIjEI/XNX1AxKyIicWXy5MlhRxAREZEo8do3a5ixiIiIiIiIJBwVsyIiIiIiIpJwVMyKiNQw7h52hLinn5GIiFQn9TvHdiI/IxWzIiI1SHp6Olu3blWneRTuztatW0lPTw87ioiI1ALqm4/tRPtmTQAlIlKDZGdnk5+fT0FBQdhR4lp6ejrZ2dlhxxARkVpAfXPlnEjfrGJWRKQGSU1NpX379mHHEBERkYD65tjRMGMRERERERFJOCpmRUREagkza2Nm/zGzpWa22MxuC9qzzGyWma0IHhtH7TPGzFaa2XIzGxheehERkUOpmBUREak9ioEfu/uZwDnAKDPrAowGXnP3jsBrwTLBuiFAV+By4AkzSw4luYiIyGFUzIqIiNQS7r7R3d8Pnu8ClgKtgWuAScFmk4BBwfNrgCnuXujuq4CVQJ9qDS0iIlKBmBWzRxnKNM7M1pvZguDriqh9NJRJRESkGphZO6An8C7Qwt03QqTgBZoHm7UG1kXtlh+0Hf5aI80sz8zyNFuniIhUl1jOZnxwKNP7ZpYJvGdms4J1D7v7g9EbHzaUqRUw28zOcPeSGGYUERGpdcysPjAN+KG77zSzCjctp+2IGyW6+wRgAkBubq5upCgiItUiZmdmjzKUqSIayiQiIhJjZpZKpJD9m7v/I2jeZGYtg/Utgc1Bez7QJmr3bGBDdWUVERE5mmq5ZvawoUwAt5rZQjN7KmrGRA1lEhERiSGLnIL9M7DU3R+KWjUDGBY8HwZMj2ofYmZpZtYe6AjMr668IiIiRxPzYvbwoUzA74HTgRxgI/Dbg5uWs3u5Q5ncPdfdc5s1axab0CIiIjVTX+AG4OLD5q64H7jUzFYAlwbLuPtiYCqwBHgFGKXLf0REJF7E8prZcocyufumqPV/BGYGixrKJCIiEkPu/gblHzwGGFDBPuOB8TELJSIicoJiOZtxuUOZDl6TE/gqsCh4rqFMIiIiIiIiUimxPDN7cCjTR2a2IGi7CxhqZjlEhhCvBr4LkaFMZnZwKFMxGsokIiIiIiIiFYhZMXuUoUwvHWUfDWUSERERERGRY6qW2YxFREREREREqpKKWREREREREUk4KmZFREREREQk4aiYFRERERERkYSjYlZEREREREQSjopZERERERERSTgqZkVERERERCThqJgVERERERGRhKNiVkRERERERBKOilkRERERERFJOJUqZs3stcq0iYiISPVQ3ywiIrVdytFWmlk6UBdoamaNAQtWNQBaxTibiIiIHEZ9s4iISMRRi1ngu8APiXSO7/FFh7kTeDx2sURERKQC6ptFREQ4RjHr7r8Dfmdm33f3R6spk4iIiFRAfbOIiEjEsc7MAuDuj5rZeUC76H3c/emK9jGzNsDTwClAKTDB3X9nZlnAc8FrrQa+4e6fB/uMAUYAJcAP3P1fx/8tiYiI1Hwn0jeLiIjUJJUqZs3sGeB0YAGRQhPAiRSrFSkGfuzu75tZJvCemc0CbgJec/f7zWw0MBr4qZl1AYYAXYkMnZptZme4e0kFry8iIlJrnWDfLCIiUmNUqpgFcoEu7u6VfWF33whsDJ7vMrOlQGvgGqB/sNkkYA7w06B9irsXAqvMbCXQB3i7su8pIiJSixx33ywiIlKTVPY+s4uIDBc+IWbWDugJvAu0CArdgwVv82Cz1sC6qN3yg7bDX2ukmeWZWV5BQcGJRhIREUl0x903m9lTZrbZzBZFtY0zs/VmtiD4uiJq3RgzW2lmy81sYBVmFxEROWmVPTPbFFhiZvOBwoON7n71sXY0s/rANOCH7r7TzCrctJy2I442u/sEYAJAbm6ujkaLiEhtdSJ980TgMY4civywuz8Y3aDLf0REJN5VtpgddyIvbmapRArZv7n7P4LmTWbW0t03mllLYHPQng+0ido9G9hwIu8rIiJSC4w73h3cfW4wWqoydPmPiIjEtcrOZvz68b6wRU7B/hlY6u4PRa2aAQwD7g8ep0e1P2tmDxE5AtwRmH+87ysiIlIbnEjffBS3mtmNQB6RyRs/J3KpzztR25R7+Q9ELgECRgK0bdu2CmOJiIhUrLKzGe/iiyG/dYBUYI+7NzjKbn2BG4CPzGxB0HYXkSJ2qpmNANYCgwHcfbGZTQWWEJkJeVRtG8pUWnyAj6f8Ei8pxktLaHRGb1r1/RoAm9+fRcEHs7GkJBqclkP2hd8EYG/BWta9OpGSA/vAkuh8/T0kpdQJ89sQEZFqcIJ9c3l+D9wXvNZ9wG+B4VTy8h/QJUDVYcOOA9z+j/UU7C4myeBbZzdm+LlNWbxxHz+buYHCYic5CX7xlVbkZNdl3ecHGPDYCk5vmgZAz+wMfnlVucciREQSVmXPzGZGL5vZICJDjY62zxuU3xECDKhgn/HA+MpkqoksOZWO3xhNcp10vKSY5ZPH07B9D0qLi9ix8n3OHPYLklJSKdqzEwAvLWH1i3+g3RXfpW7zthTv240lVXbkuIiIJLIT6ZsreJ1NUa/xR2BmsKjLf+JIcpLxvwNPoXurDHYXlnDlHz7h/NPr86tZn3Fb/+Zc1DGTf3+8i1/N+oznvn0aAKdm1eHl/+kQcnIRkdip7GzGh3D3fwIXV20UMTOS66QDkULVS0vAjIIFr9HiS1eSlJIKQGq9yEH3nasXkdGsDXWbR4Z0pWTUx5JO6FcqIiIJ7kT75mD+ioO+SmSWZIhc/jPEzNLMrD26/CdULTJT6d4qA4D6acl0aJrGpl3FGMbuwlIAdhWW0DwzNcyYIiLVqrLDjL8WtZhE5N52GkYUA15ayrJn7qFw+yaa5QygXsvTKfx8E7vzl7Nh3t9JSkml9YVDqNfyNAo//wwzY8XfH6B47y4ad/4Sp/T5StjfgoiIVIMT6ZvNbDKRe703NbN84B6gv5nlBPuuBr4Luvwnnq37/ACLP9tPTusMxn75FG58Zg3j/7WRUod/fOe0Q7b78u9XkpmWxE8GtKDPqfVCTC0iUvUqOyb1qqjnxUQ6u2uqPI1gSUmcOew+ivfv4dPpj7CvIB8vLaFk/146XTeWvZ99yqoXHqfrzQ/ipSXszv+YztePIym1Dium/pq6LdrR4NSuYX8bIiISe8fdN7v70HKa/3yU7Wv15T/xaE9hCbc8t5axl59CZnoyD/57E3dffgpXdGnIzEU7uHP6ep4d1p7mmSm8/aNONK6bwkcb9nHz5DXMGtWRzPTksL8FEZEqU9lrZr8d6yByqJT0emS26czO1Qupk5lFo45nY2bUa3k6mFG8bxepmVnUb9OZlLqRy6YanHYW+zatUTErIlILqG8u34GdW1n98gSK9uzAzGja4yKan30ZUP5kituWvMWm/75ctv++gnV0vvFe6jY/NaxvoUJFJc4tz61jUI9GfLlLQwCmLdjOuC9HRop/pWsDfjpjPQBpKUmkpUQuPereKoNTs+qwausBerTOCCe8iEgMVHaYcTbwKJEZih14A7jN3fNjmK3WKdq7E0tKJiW9HqVFB9i5Zgmn9PkKSanp7Fq7lMy2Z7J/22d4aQkpGZk0aNedTfNforSoEEtOYfe6ZTQ/e2DY30bo9heV8o2/rOJAsVNc6lzRpQE/urgFD762iVnLd5JkRpN6yfx2UDYtGqRSVOL8dPp6Fm3cR3EpXHtWI0b1axb2tyEiclTqm8tnSclk9x9K3RbtKDmwj2XP3EPmqV0p3ruz3MkUs7qcR1aX84BIIfvJP38Xl4Wsu3Pn9PV0aJbGzec1LWtvnpnKO6v3cG77+ry5ag/tsiJ3NNi6p5hGGckkJxlrtx1g1dYDtG2s62lFpGap7DDjvwDPEtxGB7g+aLs0FqFqq6I921nz8h/x0lJwp3GnPjQ8PYfSkmLWvPInlvzlLiw5hXZfvhkzIyW9Hs1zB7Lsr+MAo8FpZ9Hw9JyQv4vwpaUYk4e1o15aMkUlztf//Cn9O2by3b5N+cmAFgD85Z2t/O71zfzyqta8uHgHB0qcV0d1ZN+BUi55fAVXd29Im8a6xZGIxDX1zeVIrd+I1PqNAEiuk0F6ViuKdn/OloVzyp1MMdq2Ze/QuPM51Rm30vLW7uUfH26nc4s0vvz7lQDcMaAFv766FeNe3khJaaT/u//qyO133l2zh4f+vZmUJCMpCX55VSsa1dUdD0SkZqns/2rN3P0vUcsTzeyHMchTq9Vt1pYzb7zviPak5BTaf+WWcvdp0qUvTbr0jXW0hGJm1EuLXBNUXOIUlTpmHHKd0N4DpVhw5yizyHJxibO/uJTUZCMzTbNCi0jcU998DIU7Cti7eQ31Wp7O+tefK3cyxWifL3uX0wf9MJywx9D71HqsubdbuetevOXI2+9c0aUhVwRDkUVEaqrKFrNbzOx6YHKwPBTYGptIIievpNS58g+fsHrbAW7snUXP7LoA/Gb2Jv7x4edkpicz5ab2QKTDn7VsF70fXMa+olLGXt5SR69FJBGobz6KkgP7+XTGo2RfdB3JaRkVTqZoFjmwuWfjJySlppHRLDvk5CIiUlmV/cQ+HHgMeJjIdTlvAZp4QuJWcpLx8v90YMe+EkZOWcvyTfvp1CKdOy9pwZ2XtODxuQVMencrP7q4BQvW7yXJYP5POrNjXwmDn/qU80+rT9ssDTOW2Fr78+5hR0h4bcd+FHaEMKlvroCXFPPpjEfJOvM8Gp+RC1DxZIp1I8ONP1/2DllxOsRYRETKV9mxlPcBw9y9mbs3J9KBjotZKpEq0jAjmXPb1WPOyt2HtF/ToyEvL41M/jF94Q76d6xParLRtH4KZ7ety8IN+8KIKyJyPNQ3l8PdWfOvP5Oe1YoWuZeXtTfs0Itda5cCHDKZYmSfUj5f/l8ad/5SKJlFROTEVLaY7eHunx9ccPdtQM/YRBI5OVv3FLNjXwkQmdn4jU9306FpHVZtLSzbZtayXZzeNA2A1g1TeevTPbg7ew+U8kH+vrJ1IiJxTH1zOfasX8G2JW+xa+0Slk66m6WT7mbHpx/SpHs/CndsZslf7mLVzCfKJlME2L1uOamZWaQ1ah5yehEROR6VHWacZGaND3aaZpZ1HPuKVKvNu4r50fP5lLpT6nBl14YM6NSA705Zy6dbC0kyaN2wDr+8qhUAN/bJ4if/XM+lj6/EgcE5jTjzlPRwvwkRkWNT31yO+tln0Osnk8pdV9Fkipltz6TzdWNjGUtERGKgsp3eb4G3zOzvRK7L+QYwPmapRE7Cmaek8/L/HDmz4x+GtC13+3ppyfz+m+WvExGJY+qbRUSkVqtUMevuT5tZHnAxYMDX3H1JTJOJiIhIhdQ3i4hIbVfp4UhBB1npTtLMngKuBDa7e7egbRxwM1AQbHaXu78UrBsDjABKgB+4+78q+14iIiK10fH2zSIiIjVJZSeAOhETgcvLaX/Y3XOCr4OFbBdgCNA12OcJM0uOYTYRERERERFJYDErZt19LrCtkptfA0xx90J3XwWsBPrEKpuIiIiIiIgktjBmPbzVzG4E8oAfB7MwtgbeidomP2g7gpmNBEYCtG2rSXtEREREJPGs/Xn3sCPERNuxH4UdQWqRWA4zLs/vgdOBHGAjkZkYITJxxeG8vBdw9wnunuvuuc2aNYtJSBEREREREYlv1VrMuvsmdy9x91Lgj3wxlDgfaBO1aTawoTqziYiIiIiISOKo1mLWzFpGLX4VWBQ8nwEMMbM0M2sPdATmV2c2ERERERERSRwxu2bWzCYD/YGmZpYP3AP0N7McIkOIVwPfBXD3xWY2lcjtBYqBUe5eEqtsIiIiIiIikthiVsy6+9Bymv98lO3HA+NjlUdERERERERqjjBmMxYREREREZFy1NSZrqHqZ7uu7tmMRUREJCRm9pSZbTazRVFtWWY2y8xWBI+No9aNMbOVZrbczAaGk1pERKR8KmZFRERqj4nA5Ye1jQZec/eOwGvBMmbWBRgCdA32ecLMkqsvqoiIyNGpmBUREakl3H0usO2w5muAScHzScCgqPYp7l7o7quAlXxxSz0REZHQqZgVERGp3Vq4+0aA4LF50N4aWBe1XX7QdgQzG2lmeWaWV1BQENOwIiIiB6mYFRERkfJYOW1e3obuPsHdc909t1mzZjGOJSIiEqFiVkREpHbbZGYtAYLHzUF7PtAmartsYEM1ZxMREamQilkREZHabQYwLHg+DJge1T7EzNLMrD3QEZgfQj4REZFy6T6zIiIitYSZTQb6A03NLB+4B7gfmGpmI4C1wGAAd19sZlOBJUAxMMrdS0IJLiIiUg4VsyIiIrWEuw+tYNWACrYfD4yPXSIREZETp2HGIiIiIiIiknBUzIqIiIiIiEjCUTErIiIiIiIiCUfFrIiIiIiIiCScmBWzZvaUmW02s0VRbVlmNsvMVgSPjaPWjTGzlWa23MwGxiqXiIiIiIiIJL5YzmY8EXgMeDqqbTTwmrvfb2ajg+WfmlkXYAjQFWgFzDazM3QLABERERERKc/Zdzx97I0S0POZYSdIHDE7M+vuc4FthzVfA0wKnk8CBkW1T3H3QndfBawE+sQqm4iIiIiIiCS26r5mtoW7bwQIHpsH7a2BdVHb5QdtRzCzkWaWZ2Z5BQUFMQ0rIiIiIiIi8SleJoCyctq8vA3dfYK757p7brNmzWIcS0REREREROJRdRezm8ysJUDwuDlozwfaRG2XDWyo5mwiIiIiIiKSIGI5AVR5ZgDDgPuDx+lR7c+a2UNEJoDqCMyv5mwiIrXKT/6Zz78/3kWTeinMGtURgFFT1/Lp1gMA7NxfQoP0ZF7+nw4APD63gOc++Jxkg3FXtOTCDpqhQkRERMITs2LWzCYD/YGmZpYP3EOkiJ1qZiOAtcBgAHdfbGZTgSVAMTBKMxmLiMTW4JzGDOvThB89n1/W9vg32pY9v++VjTRITwbg4837eWHRDmaN6sCmXcVcN2kVc35wBslJ5V0lIiIiIhJ7MStm3X1oBasGVLD9eGB8rPKIiMihvtSuHus+P1DuOnfnxcU7mHxTewBmLdvFVd0akpaSRNvGdWiXlcaC9fs4u03d6owsIiIiUiZeJoASEZE4Mn/NXprWT6F9kzQAPttVRMuGqWXrT2mQwmc7i8KKJyIiIqJiVkREjjTjox1c3a1R2bKXM7+8aYSxiIiIhKi6J4ASEZE4V1zivLJ0BzO/26GsrWWDVDbu+OJM7Gc7i2mRmVre7iIiUs3Km9DvxcU7ePg/m1m5pZAZN59Oj9YZADy/cDsT3txStu/STft58bun07VlRijZRU6GzsyKiMgh3vh0N6c3TTtkWPGlnTN5YdEOCotLWfv5AVZtKySntT74iIjEg8E5jZl0fbtD2s5onsYfhrTlS6ceOrfBV3s04uX/6cDL/9OBh7+WTXajVBWykrB0ZlZEpJb6/v+t4+3Ve/h8bzFf+u0ybu/fnCFnZ/HCoh1c3b3RIdue0Tydr3RtwCWPrSAlybjvK600k7GIVIuz73g67Agx8XwV3t2svAn9OjZLP+Z+Mz7afsglJSKJRsWsiEgt9ejgNuW2//ar2eW2f//C5nz/wuaxjCQiItXohUU7+NPQU8OOITFU3hD07XuLGfV/68jfXkR2o1Se+EZbGmZEbsW39LP9jHlhPbsLS0kymDHydNJT43cwb/wmExERERGRmPggfy8ZqUl0anHsM7iSuMobgv7EG1voe1p9Xr/tDPqeVp8n5hUAkTkzfviPdfzyqlbMvrUjz327PanJ8T0KS8WsiIiIiEgt88JHO7i6e8OwY0iMfaldPRoFZ10PmrVsJ9fmNALg2pxGvLpsJwBzP9lN5xbpdDklcg1147opcX9JkYpZERERwcxWm9lHZrbAzPKCtiwzm2VmK4LHxmHnFJGTV1rqvLhkh66XraW27PnijgQtMlPZsqcYgFVbCzHghqdXc8WTK3nyjYIQU1aOrpkVERGRgy5y9y1Ry6OB19z9fjMbHSz/NJxoIlKR8ib0a1Q3hXte2sC2PSV8+2+r6XJKBs/c2A6Ad9fsoWWDVNpm1Qk3uMSV4lL479q9vDDydDJSkxg6aRXdWmVw/mn1w45WIRWzIiIiUpFrgP7B80nAHFTMisSdiib0u/zMBuW2n9u+Pv+8OX4LFImtpvVS2LSriBaZqWzaVUTTepGSsGWDFM5pV4+sYPmijpks2rAvrotZDTMWERERAAdeNbP3zGxk0NbC3TcCBI/lTmdtZiPNLM/M8goK4n9YmohIbXZJpwZMW7AdgGkLtnNp58hBjws7ZLJ00372HSiluMR5d80eOjaP7wnCdGZWREREAPq6+wYzaw7MMrNlld3R3ScAEwByc3M9VgFFRKrLprxX2PrR64CR0SybUy//Dhve+Ac7Pl2AJSWT1qg5p17+HVLS64Ud9ajKG4L+vQua8r2p63ju/c9p1TCV338jcma/YUYy3zm3KVdN+ASzyJnZAWdU4Q2RY0DFrIiIiODuG4LHzWb2PNAH2GRmLd19o5m1BDaHGlJEpBoc2LWNgvdn0eXbvyIptQ6fzniMz5e9S4N2XWndbzCWlMz6159j07szaX3hN8OOe1QVDUGffFP7ctu/dlYjvnZWoxgmqlqhDDPWjIkiIiLxw8zqmVnmwefAZcAiYAYwLNhsGDA9nIQiItXLvZTS4gN4aQmlxQdIrd+IBu26Y0mR29zUa3U6B3Z/HnJKCfPMrGZMFBERiQ8tgOfNDCKfDZ5191fM7L/AVDMbAawFBoeYUUSkWtTJzKJF7pdZNOFHJKXUIbNdNxq0637INls+mkfjzn1CSigHxdMwY82YKCIiEgJ3/xQ4q5z2rcCA6k8kIhKe4v172L7yfbre/CApaXX59IXH2brkTZp06QvAxndmYElJZJ15XshJJazZjDVjooiIiIiIxJ1daxaT1rAZqXUbYMkpNOp4NnvWrwRg66I32PnJAtp/5RaC0SwSorDOzGrGRBEREREJVfH+Paz911Ps27oegFMv/w5Fu7ax8a3n2b91I52uv4d6p5Q/UY7UXHUaNGHPxpWUFhViKXXYtWYJdU9pz45VC9k0/0U6DhlDUmpa2DGFkIpZzZgoIiIiImHL//ffaNC+O6dd831KS4opLSokOa0up13zA9a+OjHseBKSei1Pp9EZvVn6zD2YJVG3xak07dGfpRPvorSkmJX/90Bku1an0/bSm8INW8tVezEbzJKY5O67omZM/DlfzJh4P5oxUSTunX3H02FHSHjPx/et20REarSSwn3szl/OqV++GYCk5BSSklPi/r6hUj1a9f0arfp+7ZC2rt95IKQ0UpEwzsxqxkQRERERCVXhjs2k1M1kzSt/Yl/BWuq2aEf2RdeTXEfDR0USRbUXs5oxUURERETC5qWl7N20hjYDbqBey9NZ9++/smn+TFqdf23Y0USkksKazVhEREREJDR1MhtTJzOLei1PB6DxGb3Zu2lNyKlE5HiomBURERGRWie1XiNSM7PYv20jADvXLCG9SauQU4nI8Qjr1jwiIiIiIqFqM+B6Vr/4JKUlxaQ1as6pl3+H7SvyWPfaXynet4tP/vEQGc3b0vHrd4QdVUTKoWJWRGoELy1l2V/vIbV+Yzp87Udl7Zv++xLrX3+OHt97jJS6mj5YRES+ULf5qXS+4d5D2hp1zKVRx9yQEonI8dAwYxGpETa//yrpWYcODzuwcys71yymTmaTkFKJiIiISKyomBWRhHdg1zZ2fvohTXtceEh7/n+epXW/b0LkVmAiIiIiUoOomBWRhJf/77/Rut83gC+K1u0r3yc1szF1m7cNL5iIiIiIxIyKWRFJaDs+WUBK3QbUPaV9WVtpUSGfvfMCrfp+LcRkIiIiIhJLmgBKRBLa7vUfs+OTD1i0aiGlxUWUHNjH6pcmcGBHAUsn3Q1EhiEvfWYsna+/h9R6jcINLCIiIiJVQsWsiCS01v2+EQwxhl1rl7Ip72VOu+b7h2yzaMKP6Xz9OM1mLCIiIlKDaJixiIiIiIiIJBydmRWRGiOz7Zlktj3ziPZuI38bQhoRERERiSWdmRUREREREZGEo2JWREREREREEk7cFbNmdrmZLTezlWY2Ouw8IiIitZ36ZhERiUdxVcyaWTLwOPBloAsw1My6hJtKRESk9lLfLCIi8SquilmgD7DS3T919wPAFOCakDOJiIjUZuqbRUQkLsXbbMatgXVRy/nAl6I3MLORwMhgcbeZLa+mbFJJp0JTYEvYORLaPRZ2AqkG+lupAlX/t3JqVb9gDaC++STU2L9z9VMnTf82pCI19t8GnOi/jwr75ngrZsv77vyQBfcJwITqiSMnwszy3D037Bwi8U5/K5Ig1DefBP2dS0X0b0Mqon8blRdvw4zzgTZRy9nAhpCyiIiIiPpmERGJU/FWzP4X6Ghm7c2sDjAEmBFyJhERkdpMfbOIiMSluBpm7O7FZnYr8C8gGXjK3ReHHEuOn4aaiVSO/lYk7qlvPmn6O5eK6N+GVET/NirJ3P3YW4mIiIiIiIjEkXgbZiwiIiIiIiJyTCpmRUREREREJOGomJUqY2aXm9lyM1tpZqPDziMSr8zsKTPbbGaLws4iIhUzMzez30Yt/8TMxlVzhjlmplt01ABm9jMzW2xmC81sgZn9J3hcaWY7gucLzOy8sLNKbJnZ7qjnN5nZ5MPWNzWzAjNLO/z/ADNrp88PX1AxK1XCzJKBx4EvA12AoWbWJdxUInFrInB52CFE5JgKga+ZWdMT2dnM4mqiTQmPmZ0LXAn0cvcewCXAde6eA3wHmOfuOcHXWyFGler3D+BSM6sb1fZ1YIa7F4aUKWGomJWq0gdY6e6fuvsBYApwTciZROKSu88FtoWdQ0SOqZjIrKK3H77CzE41s9eCs2yvmVnboH2imT1kZv8Bfh0s/z44C/epmV0YjM5YamYTo17v92aWF5y5u7e6vkGpNi2BLQeLE3ff4u66X7Pg7juBucBVUc1DgMnl7yHRVMxKVWkNrItazg/aREREEtnjwHVm1vCw9seAp4OzbH8DHoladwZwibv/OFhuDFxMpCh+AXgY6Ap0N7OcYJufuXsu0AO40Mx6xOKbkdC8CrQxs4/N7AkzuzDsQBJXJhMpYDGzVkT+D/lP1Pq/HRyGDrxU/fHil4pZqSpWTpvu+yQiIgktOGvyNPCDw1adCzwbPH8GOD9q3f+5e0nU8gseuRfiR8Amd//I3UuBxUC7YJtvmNn7wAdECl1dqlODuPtu4GxgJFAAPGdmN4UaSuLJTOB8M2sAfAP4+2H/h1x3cBg6cEUYAeOVilmpKvlAm6jlbEDDZ0REpCb4f8AIoN5Rtok+gLvnsHUHr3srjXp+cDnFzNoDPwEGBGd6XwTSTyawxB93L3H3Oe5+D3ArcG3YmSQ+uPs+4BXgq2iI8XFRMStV5b9ARzNrb2Z1iPwhzgg5k4iIyElz923AVCIF7UFvEQwLBK4D3jiJt2hApADeYWYtiEymKDWImXUys45RTTnAmpDiSHyaDPwIaAG8E3KWhKFiVqqEuxcTOcr4L2ApMNXdF4ebSiQ+BVPwvw10MrN8MxtxrH1EJHS/BaJnNf4B8G0zWwjcANx2oi/s7h8SGV68GHgKePMkckp8qg9MMrMlwb+ZLsC4cCNJiOoG/f/Brx8Rua66FfBccFmCVILpZyUiIiIiIiKJRmdmRUREREREJOGomBUREREREZGEo2JWREREREREEo6KWREREREREUk4KmZFREREREQk4aiYFYkTZlZiZguivtod5/4/NLO6UcsvmVmjE8zS38x2BDkWmtlsM2serOtkZnOCdUvNbMKJvIeIiEg8ieqHF5nZCyfSh5pZjpldEbV8tZmNPolMc8xsedRng79HrbsxyLo4uOXPT6LW/cjMlpnZR2b2oZk9ZGapJ5pDJF6pmBWJH/vcPSfqa/Vx7v9DoKyYdfcr3H37SeSZF+ToAfwXGBW0PwI8HKw7E3j0JN5DREQkXhzsh7sB2/ii3zseOUBZMevuM9z9/pPMdV3UZ4OvA5jZl4n0+5e5e1egF7AjWHcLcBlwjrt3B3oDm4GMk8whEndSwg4gIuUzs/rAdKAxkAr8r7tPN7N6wFQgG0gG7gNaELnR9n/MbIu7X2Rmq4FcIjdqfxl4AzgPWA9c4+77zKw38GdgT7D+y0EnHp3DgExgZdDUEsg/uN7dP4rBty8iIhKmt4EeAGZ2OvA40AzYC9zs7svMbDBwD1BCpJC8BPg5kGFm5wO/IlJA5rr7rWY2EdhJpG8+BbjT3f9uZknAY8CFwCoiJ5uecveys7DlGAP8xN03ALj7fuCPwbqfAf0OHtB29wPAyRbUInFJZ2ZF4kdG1DCi54H9wFfdvRdwEfDboLC8HNjg7mcFhecr7v4IsAG4yN0vKue1OwKPB0dvtwPXBu1/AW5x93OJdMbRLjCzBcBaIh30U0H7w8C/zexlM7v9RIcyi4iIxCMzSwYGADOCpgnA9939bOAnwBNB+1hgoLufBVwdFI1jgeeCs6jPlfPyLYHzgSv5osD8GtAO6A58Bzj3sH3+FvX54IGgrRvwXjnZM4H67r7qOL9tkYSkYlYkfkQPM/4qYMAvzWwhMBtoTeQM7EfAJWb2azO7wN13VOK1V7n7guD5e0C7oAjNdPe3gvZnD9vn4DDjNkSK3t8AuPtfgDOB/wP6A++YWdqJfcsiIiJxIyM4iLsVyAJmBaOkzgP+L1j3ByIFKcCbwEQzu5nISKnK+Ke7l7r7EiJ9OkSK2/8L2j8D/nPYPtHDjO84xusb4GULZgODIni1mZ1XyYwiCUPFrEj8uo7IkKaz3T0H2ASku/vHwNlEitpfmdnYSrxWYdTzEiKXGNhxZJkB9Du44O4b3P0pd78GKCZyhFhERCSR7Qv621OBOkSumU0Cth82p8WZAO5+C/C/QBtggZk1qcR7RPfHdtjj8VhM5LPAIdx9J7DHzNoHy/8KvqdFwfckUqOomBWJXw2Bze5eZGYXEelcMbNWwF53/yvwIJFJHwB2Ebm2tVLc/XNgl5mdEzQNOcrm5wOfBO9/+cEZEc3sFKAJketwRUREEl4w4ukHRIYU7wNWBdfHYhFnBc9Pd/d33X0ssIVIUXtcfXHgDeBaM0sysxZERj0dy6+A3wT9MGaWZmY/iFr3+4OXAQWXKKUfZyaRhKAJoETi19+AF8wsD1gALAvauwMPmFkpUAT8T9A+AXjZzDZWcN1seUYAfzSzPcAcgpkQAwevmbWg/TtB+2XA78xsf7B8RzAsSkREpEZw9w/M7EMiB3qvI1Ic/i+RCRmnAB8S6Ys7EuknXwva1gKjg/7zV5V8u2lErtFdBHwMvMuh/fHfzGxf8HyLu1/i7i8Fhe/soFh1vpjb4vdE7m7wrpkVAruJDIn+4Dh/DCJxz9z92FuJSI1kZvXdfXfwfDTQ0t1vCzmWiIhIrXKwPw6GKs8H+upAscix6cysSO32FTMbQ+T/gjXATeHGERERqZVmBsOC6wD3qZAVqRydmRUREREREZGEowmgREREahkzSzazD8xsZrCcZWazzGxF8Ng4atsxZrbSzJab2cDwUouIiBxKxayIiEjtcxuwNGp5NPCau3ckMpHNaAAz60JkApyuwOXAE2ZW2ftpioiIxJSKWRERkVrEzLKBrwB/imq+BpgUPJ8EDIpqn+Luhe6+ClgJ9KmmqCIiIkeV0BNANW3a1Nu1axd2DBERqSHee++9Le7eLOwcMfb/gDs59F6YLdx9I4C7bzSz5kF7a+CdqO3yg7ZDmNlIYCRAvXr1zu7cuXMMYouISG10tL45oYvZdu3akZeXF3YMERGpIcxsTdgZYsnMrgQ2u/t7Zta/MruU03bEzJHuPoHIva7Jzc119c0iIlJVjtY3J3QxKyIiIselL3C1mV0BpAMNzOyvwCYzaxmclW0JbA62zwfaRO2fDWyo1sQiIiIV0DWzIiIitYS7j3H3bHdvR2Rip3+7+/XADGBYsNkwYHrwfAYwxMzSzKw90BGYX82xRUREyqUzsyIiInI/MNXMRgBrgcEA7r7YzKYCS4BiYJS7l4QXU0RE5AsqZkWqWFFREfn5+ezfvz/sKHEtPT2d7OxsUlNTw44iUiu5+xxgTvB8KzCggu3GA+OrLZiISA2jz4aVcyKfDVXMilSx/Px8MjMzadeuHWblzZ0i7s7WrVvJz8+nffv2YccRERERiRl9Njy2E/1sGLNrZs0s3czmm9mHZrbYzO4N2seZ2XozWxB8XRG1zxgzW2lmy81sYKyyicTS/v37adKkif6zOgozo0mTJjpCKSIiIjWePhse24l+NozlmdlC4GJ3321mqcAbZvZysO5hd38wemMz60JkMoquQCtgtpmdUd3X5px9x9PV+XY10nsP3Bh2hNDpP6tj089IRERqs7U/7x52hJhoO/ajsCPEJX3uObYT+RnF7MysR+wOFlODryPuTRflGmCKuxe6+ypgJdAnVvlEREREREQkccX01jxmlmxmC4jcr26Wu78brLrVzBaa2VNm1jhoaw2si9o9P2g7/DVHmlmemeUVFBTEMr5Ilalfv/4hyxMnTuTWW2+tktdevXo1zz77bNnynDlzaNiwIT179qRTp07069ePmTNnlq1/8sknefppjUAQERERCYs+G1aNmE4AFQwRzjGzRsDzZtYN+D1wH5GztPcBvwWGA+WdVz7iTK67TwAmAOTm5h7tTK9IjVdcXFz2H9a3vvWtsvYLLrig7D+pBQsWMGjQIDIyMhgwYAC33HJLWHFFREREJIZq22fDmJ6ZPcjdtxOZ/v9yd9/k7iXuXgr8kS+GEucDbaJ2ywY2VEc+kTAVFBRw7bXX0rt3b3r37s2bb74JwPz58znvvPPo2bMn5513HsuXLwciR+4GDx7MVVddxWWXXcbo0aOZN28eOTk5PPzww0e8fk5ODmPHjuWxxx4DYNy4cTz4YOSS9UceeYQuXbrQo0cPhgwZAsCePXsYPnw4vXv3pmfPnkyfPh2IHOW74IIL6NWrF7169eKtt94CYOPGjfTr14+cnBy6devGvHnzAHj11Vc599xz6dWrF4MHD2b37t2HRxMRERGRw+izYeXF7MysmTUDitx9u5llAJcAvzazlu6+Mdjsq8Ci4PkM4Fkze4jIBFAdgfmxyidSnfbt20dOTk7Z8rZt27j66qsBuO2227j99ts5//zzWbt2LQMHDmTp0qV07tyZuXPnkpKSwuzZs7nrrruYNm0aAG+//TYLFy4kKyuLOXPm8OCDD5YdbZszZ84R79+rVy8eeOCBI9rvv/9+Vq1aRVpaGtu3bwdg/PjxXHzxxTz11FNs376dPn36cMkll9C8eXNmzZpFeno6K1asYOjQoeTl5fHss88ycOBAfvazn1FSUsLevXvZsmULv/jFL5g9ezb16tXj17/+NQ899BBjx46t2h+siIiISALSZ8Oq+WwYy2HGLYFJZpZM5AzwVHefaWbPmFkOkSHEq4HvArj7YjObCiwBioFR1T2TsUisZGRksGDBgrLliRMnkpeXB8Ds2bNZsmRJ2bqdO3eya9cuduzYwbBhw1ixYgVmRlFRUdk2l156KVlZWZV+f/fyR+T36NGD6667jkGDBjFo0CAgctRsxowZZUfo9u/fz9q1a2nVqhW33norCxYsIDk5mY8//hiA3r17M3z4cIqKihg0aBA5OTm8/vrrLFmyhL59+wJw4MABzj333ErnFREREanJ9Nmwaj4bxqyYdfeFQM9y2m84yj7jgfGxyiQSj0pLS3n77bfJyMg4pP373/8+F110Ec8//zyrV6+mf//+Zevq1at3XO/xwQcfcOaZZx7R/uKLLzJ37lxmzJjBfffdx+LFi3F3pk2bRqdOnQ7Zdty4cbRo0YIPP/yQ0tJS0tPTAejXrx9z587lxRdf5IYbbuCOO+6gcePGXHrppUyePPm4coqIiIjUdvpsWHnVcs2siFTssssuK7tmASg7Srdjxw5at45M6D1x4sQK98/MzGTXrl0Vrl+4cCH33Xcfo0aNOqS9tLSUdevWcdFFF/Gb3/yG7du3s3v3bgYOHMijjz5adsTugw8+KMvTsmVLkpKSeOaZZygpiQycWLNmDc2bN+fmm29mxIgRvP/++5xzzjm8+eabrFy5EoC9e/eWHa0TERERkYrps2HlqZgVCdkjjzxCXl4ePXr0oEuXLjz55JMA3HnnnYwZM4a+ffuW/edQnh49epCSksJZZ51VdpH/vHnzyqZfHzVqFI888ggDBgw4ZL+SkhKuv/56unfvTs+ePbn99ttp1KgRd999N0VFRfTo0YNu3bpx9913A/C9732PSZMmcc455/Dxxx+XHQGcM2cOOTk59OzZk2nTpnHbbbfRrFkzJk6cyNChQ+nRowfnnHMOy5Yti8WPT0RERKRG0WfDyrOKxksngtzcXD84tryqnH1HYt5jKZ6898CNYUcI1dKlS8sdtiFH0s9K4o2ZvefuuWHnSGSx6JtFaqK1P+8edoSYaDv2o7AjxB193qm88n5WR+ubdWZWREREREREEo6KWREREREREUk4KmZFREREREQk4aiYFRERERERkYSjYlZEREREREQSjopZERERERERSTgpYQcQkaOr6ttFVfbWSa+88gq33XYbJSUlfOc732H06NFVmkNEREREjp8+G35BZ2ZF5AglJSWMGjWKl19+mSVLljB58mSWLFkSdiwRERERCUG8fjbUmVkROcL8+fPp0KEDp512GgBDhgxh+vTpdOnSJeRkNcvan3cPO0LCazv2o7AjiIiI1Hjx+tlQZ2ZF5Ajr16+nTZs2ZcvZ2dmsX78+xEQiIiIiEpZ4/WyoYlZEjuDuR7SZWQhJRERERCRs8frZUMWsiBwhOzubdevWlS3n5+fTqlWrEBOJiIiISFji9bNhzIpZM0s3s/lm9qGZLTaze4P2LDObZWYrgsfGUfuMMbOVZrbczAbGKpuIHF3v3r1ZsWIFq1at4sCBA0yZMoWrr7467FgiIiIiEoJ4/WwYywmgCoGL3X23maUCb5jZy8DXgNfc/X4zGw2MBn5qZl2AIUBXoBUw28zOcPeSGGYUiXuVnS69KqWkpPDYY48xcOBASkpKGD58OF27dq32HCIiIiJyKH02jMoVqxf2yMDq3cFiavDlwDVA/6B9EjAH+GnQPsXdC4FVZrYS6AO8HauMIlKxK664giuuuCLsGCIiIiISB+Lxs2FMr5k1s2QzWwBsBma5+7tAC3ffCBA8Ng82bw2si9o9P2g7/DVHmlmemeUVFBTEMr6IiIiIiIjEqZgWs+5e4u45QDbQx8y6HWXz8qbDOmLaLHef4O657p7brFmzKkoqIiIiIiIiiSSW18yWcfftZjYHuBzYZGYt3X2jmbUkctYWImdi20Ttlg1sqI58IiIiEq6z73g67AgxEca1bSIitUUsZzNuZmaNgucZwCXAMmAGMCzYbBgwPXg+AxhiZmlm1h7oCMyPVT4RERERERFJXLE8M9sSmGRmyUSK5qnuPtPM3gammtkIYC0wGMDdF5vZVGAJUAyM0kzGIiIiIiIiUp5Yzma8EOhZTvtWYEAF+4wHxscqk4iIiIiIiNQM1XLNrIicuLU/716lr9d27EfH3Gb48OHMnDmT5s2bs2jRoip9fxERERE5cfps+IWYzmYsIonppptu4pVXXgk7hohUMTNLN7P5ZvahmS02s3uD9iwzm2VmK4LHxlH7jDGzlWa23MwGhpdeRETCEq+fDVXMisgR+vXrR1ZWVtgxRKTqFQIXu/tZQA5wuZmdA4wGXnP3jsBrwTJm1gUYAnQlckeCJ4K5MEREpBaJ18+GKmZFRERqCY/YHSymBl8OXANMCtonAYOC59cAU9y90N1XASuBPtWXWEREpGIqZkVERGoRM0s2swVE7vM+y93fBVq4+0aA4LF5sHlrYF3U7vlB2+GvOdLM8swsr6CgIKb5RUREDlIxKyIiUou4e4m75wDZQB8z63aUza28lyjnNSe4e6675zZr1qyKkoqIiBydilkREZFayN23A3OIXAu7ycxaAgSPm4PN8oE2UbtlAxuqL6WIiEjFdGsekThXmenSq9rQoUOZM2cOW7ZsITs7m3vvvZcRI0ZUew6R/9/encfJVZUJH/896SyddHaTJglNSIAEEhbDSwARUBRRRBQFRRgVFDHqC+PKCOqMoAzKOCjj6PgKjqwqyyhIVFAWjcgmizAQlmhIAoQEspA9oZPuft4/6gY6K52kq6ur+/f9fOpTt07dc+u5oZpTzz3nnqP2FRHDgXWZuTQi+gJvA/4NmAqcClxYPN9UVJkK/DwivguMAsYB93d44JKkV/jb8FUms5I2cc0111Q6BEnlMRK4spiRuAdwfWb+JiLuBa6PiI8DzwIfAMjMxyPieuAJoAk4IzObKxS7JKlCOutvQ5NZSZK6icx8FNh/M+WLgSO3UOcC4IIyhyZJ0jbznllJkiRJUtUxmZXKIHOTyT61Ef+NJElSd+Hvnte2Pf9GJrNSO6utrWXx4sX+T2srMpPFixdTW1tb6VAkSZLKyt+Gr217fxt6z6zUzhoaGpg7dy4LFy6sdCidWm1tLQ0NDZUOQ5Ikqaz8bdg22/Pb0GRWame9evVi7NixlQ5DkiRJnYC/DcunbMOMI2KXiPhjRDwZEY9HxGeL8vMi4vmIeKR4HNOqzpcjYmZEzIiId5QrNkmSJElSdStnz2wT8MXM/GtEDAAeiojbivcuzsyLWu8cEROBk4C9KS3MfntEjHc9O0mSJEnSxsrWM5uZ8zPzr8X2CuBJYOetVDkOuDYzGzNzNjATOKhc8UmSJEmSqleHzGYcEWMoLdL+l6LozIh4NCIui4ghRdnOwHOtqs1lM8lvREyJiAcj4kFvopYkSZKk7qnsyWxE9Ad+CXwuM5cD/w/YHZgEzAe+s37XzVTfZP7qzLw0Mydn5uThw4eXJ2hJkiRJUqdW1mQ2InpRSmR/lpk3AGTmi5nZnJktwI95dSjxXGCXVtUbgHnljE+SJEmSVJ3KOZtxAD8BnszM77YqH9lqt/cB04vtqcBJEdEnIsYC44D7yxWfJEmSJKl6lXM240OBjwCPRcQjRdlXgJMjYhKlIcRzgE8CZObjEXE98ASlmZDPcCZjSZIkSdLmlC2Zzcy72Px9sDdvpc4FwAXlikmSJEmS1DV0yGzGkiRJkiS1J5NZSZIkSVLVMZmVJEmSJFUdk1lJkiRJUtUxmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVHZNZSZIkSVLVMZmVJEmSJFUdk1lJkiRJUtUxmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVnTYlsxFxR1vKJElSx7BtliR1dz239mZE1AL9gGERMQSI4q2BwKgyxyZJkjZi2yxJUslr9cx+EngI2Kt4Xv+4CfivrVWMiF0i4o8R8WREPB4Rny3Kh0bEbRHx9+J5SKs6X46ImRExIyLesSMnJklSF7XdbbMkSV3JVntmM/N7wPci4h8z8/vbeOwm4IuZ+deIGAA8FBG3AR8F7sjMCyPiHOAc4OyImAicBOxN6cry7RExPjObt/FzJUnqsnawbZYkqcvYajK7XmZ+PyLeCIxpXSczr9pKnfnA/GJ7RUQ8CewMHAccUex2JTANOLsovzYzG4HZETETOAi4d5vOSJKkbmB72mZJkrqSNiWzEXE1sDvwCLC+pzSBNjWYETEG2B/4C7BTkeiSmfMjor7YbWfgvlbV5hZlGx9rCjAFYPTo0W35eEmSupwdbZslSap2bUpmgcnAxMzMbf2AiOgP/BL4XGYuj4gt7rqZsk0+LzMvBS4FmDx58jbHI0lSF7HdbbMkSV1BW9eZnQ6M2NaDR0QvSonszzLzhqL4xYgYWbw/ElhQlM8FdmlVvQGYt62fKUlSN7FdbbMkSV1FW5PZYcATEfH7iJi6/rG1ClHqgv0J8GRmfrfVW1OBU4vtUynNvri+/KSI6BMRY4FxwP1tPRFJkrqZ7WmbXWlAktRltHWY8XnbcexDgY8Aj0XEI0XZV4ALgesj4uPAs8AHADLz8Yi4HniC0kzIZziTcXV69hv7VjqEqjf6a49VOgRJnd9521HHlQYkSV1GW2cz/tO2Hjgz72Lz98ECHLmFOhcAF2zrZ0mS1N1sZ9vsSgOSpC6jrbMZr+DVyZh6A72AVZk5sFyBdUctTWv527XfJJubyJZmBo8/kFGHHs+8u29k8WPT6Nm39M896vD3M2i319O4bCFPXP5laoeMBKBu1O6MPuqjlTsBSVKH2dG22ZUGpK7h5XUtnHj5bNY2JU0tyTETB/KFt+7E4/PX8NXfzKOxKanpAf/6rlFMaujHktVNfOq653h03hreP2kw579rVKVPQdpube2ZHdD6dUS8l9KVWbWjqOnFuBPPoaZ3LdncxIxrLmDQ2P0AqD/gHex04DGb1OkzqJ4Jp57f0aFKkipsR9pmVxqQuo4+PYNrTh1DXZ8a1jUn7//JLI4YN4Dv/vFFPntEPW8ZN4A//G0F37rtBa772G706dmDs95az4wFjcxY8HKlw5d2SFsngNpAZv4KeGv7hqKIoKZ3LQDZ0ky2NMOWf2BIkvSKtrbNrjQgdS0RQV2fGgCampN1LUkEBMHKxhYAVjQ2Uz+gFwD9evfgwF3r6NPT35iqfm0dZnx8q5c9KK1t55XXMsiWFp66+lwal77I8ElHUjdyd5bNepSFD9/B4sfvpm7EWHY+4mR61tYBsHbZQp686l+o6d2XUYedQP+GPSt8BpKkjrA9bXMbVhq4kE1XGvh5RHyX0gRQrjQgdULNLcmxlzzNnJfWcsqBQ9m/oR9fe+cITrn6GS74/XxaEm44fbdKhym1u7bOZvzuVttNwBxKk0KonUWPHkw49XyaXl7FrJv+kzUL5zJ80lsZechxEDDvrht4fto17Hr06fSqG8w+n7yYnn37s/qF2Tx9038y8aPfpKZP30qfhiSp/LanbXalAakLqukR3PLpPVi2ppkp1z7LjBdf5ucPvcS/HD2CYyYO4jfTl/Glm57n56eOrXSoUrtq6z2zHyt3INpQz9o6BuyyF8vnPLrBvbLD9nszT99wMQA9evaiR89iyMiIsfQZVM/LS16gboT/o5Kkrm572mZXGpC6tkF9azhkTB3TZq7kl48s5bx3liYJfdfeAzl76vMVjk5qf226ZzYiGiLixohYEBEvRsQvI6Kh3MF1N+tWL6fp5VUAtKxby/JnnqB26CjWrVz6yj5L//4QfYc1vLJ/tpTuhWhcuoDGpS/QZ9DwDo9bktTxbJslASxe1cSyNaUBEy+va+GuWSvZY1hv6gf04r45pd+Vd89exZihvSsZplQWbR1mfDnwc4phR8CHi7KjyhFUd7Vu1VKeueXHpQQ1kyF7HsSg3Scx5+ZLWL3gWQD6DBrG6KNKF+NXzp3B/LtvIHrUQPRgl6M+Ss++/St5CpKkjmPbLIkFK5r4wo1zacmkJeHYvQdx5J4DGVhbw3m3zKe5pTTj8YXveXVVrUMvnsGKxhbWNSe3PrWcqz8yhvH1tRU8C2n7tDWZHZ6Zl7d6fUVEfK4M8XRr/YaPZsIpmy6zM+aYT252/yHjD2TI+APLHZYkqXOybZbEhBG13PLpPTYpP3DXOn77qU3LAe7+vBOGqmto69I8iyLiwxFRUzw+DCwuZ2CSJGmrbJslSd1aW3tmTwN+AFxMadr/ewAnhZKkKvXyuhZOvHw2a5uSppbkmIkD+cJbd+K3jy/j4j8uYOaiRqZ+Ynf22/nV2dGffOFlvvzr51nZ2EKPgKlTdqe213YtV672YdssSerW2prMng+cmplLACJiKHARpYZUklRl+vQMrjl1DHV9aljXnLz/J7M4YtwAxtf34ZKTRvOVX28462VTc/K5G57j4uMbmDiiL0tWN9GrZkuT4qqD2DZLkrq1tiaz+61vLAEy86WI2L9MMUmSyiwiqOtTA5QS1XUtSQSMG775CUDufHole+1Uy8QRpZ7aIf3a2nyojGybJUndWlt/jfSIiCEbXf31l4wkVbHmluTYS55mzktrOeXAoezf0G+L+85e3EgAH7lqDotXN/GefQbxqcNcCqzCbJslSd1aWxu97wD3RMQvKN2XcyIuoC5JVa2mR3DLp/dg2Zpmplz7LDNefJk9d9p8z2xTCzzw7Gp+PWV3+vbqwclXzmafUX05bDeXA6sg22ZJUrfWpmQ2M6+KiAeBtwIBHJ+ZT2ytTkRcBhwLLMjMfYqy84BPAAuL3b6SmTcX730Z+DjQDHwmM3+/7acjSdpWg/rWcMiYOqbNXLnFZHbkwJ68YUwdQ+tKzcZbxg1g+rw1JrMVtD1tc3fQ0rSWv137TbK5iWxpZvD4Axl16PEsmXE/8++5kZcXz2fPD59L3YixALz0xD28+MAtr9Rfs/A59jrl6/Sr37VSpyBJaqM2D0cqGshtaSSvoDTL4lUblV+cmRe1LoiIicBJwN7AKOD2iBifmc3b8HmSpDZavKqJnj2CQX1reHldC3fNWsmnDxu2xf3fvMcAfnT3ItasbaFXTfCXZ1bx8UO2vL86xna0zV1e1PRi3InnUNO7lmxuYsY1FzBo7H7UDmtgt+M+w7O3XrHB/kMnvpGhE98IlBLZp3/1PRNZSaoSZbu3JjPvjIgxbdz9OODazGwEZkfETOAg4N5yxSdJ3dmCFU184ca5tGTSknDs3oM4cs+B/O7J5Zx78zxeWtXMx342h4kj+nL1KWMY1LeG0w8ZxrsvfZqIUs/skeMHVPo0pE1EBDW9SyMMsqWZbGmGCPq+btRr1n3pqfsYstcbyh2iJKmdVGKiiDMj4hTgQeCLxcQVOwP3tdpnblG2iYiYAkwBGD16dJlDlaSuacKIWm759B6blB89YSBHTxi42TrHv34wx79+cJkjk3ZctrTw1NXn0rj0RYZPOpK6kbu3qd6Sp/7C7u/9XHmDkyS1m45e7f7/AbsDk4D5lCavgNK9PhvLzR0gMy/NzMmZOXn4cGfSlCRJG4oePZhw6vns88mLWfXCLNYsnPuadVbNf5oevfrQd3hDB0QoSWoPHZrMZuaLmdmcmS3AjykNJYZST+wurXZtAOZ1ZGySJKlr6Vlbx4Bd9mL5nEdfc98lT93HUIcYS1JV6dBkNiJGtnr5PmB6sT0VOCki+kTEWGAccH9HxiZJkqrfutXLaXp5FQAt69ay/JknqB269ftlM1tYMuMBhux1cEeEKElqJ2W7ZzYirgGOAIZFxFzgXOCIiJhEaQjxHOCTAJn5eERcT2lGxibgDGcyliRJ22rdqqU8c8uPyZYWyGTIngcxaPdJLP37gzx3x09pWrOCp2/4Ln3rRzPu/f8EwMrnZtBrwFD6DK6vcPSSpG1RztmMT95M8U+2sv8FuNi7JEnaAf2Gj2bCKedvUj543GQGj5u82ToDRk9grw99rdyhSZLaWUdPACVJkiRJ0g4zmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVHZNZSZIkSVLVMZmVJEmSJFUdk1lJkiRJUtUxmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVHZNZSZIkSVLVMZmVJEmSJFUdk1lJkiRJUtUxmZUkSZIkVZ2yJbMRcVlELIiI6a3KhkbEbRHx9+J5SKv3vhwRMyNiRkS8o1xxSZIkSZKqX88yHvsK4AfAVa3KzgHuyMwLI+Kc4vXZETEROAnYGxgF3B4R4zOzuYzxqYs661dz+cPfVvC6up7cdsY4AJ54YQ1f+fU8Vq9toWFwb753QgMDamv489MrufC2F1jXnPSqCb7y9hEculv/Cp+BJEmSpNdStp7ZzLwTeGmj4uOAK4vtK4H3tiq/NjMbM3M2MBM4qFyxqWv7wKQhXPnhMRuUnX3TPM45agS3njGOd0wYyCV3LwJgSL8aLvuHXbn1jHF8930NfP6GuRWIWJIkSdK26uh7ZnfKzPkAxXN9Ub4z8Fyr/eYWZZuIiCkR8WBEPLhw4cKyBqvqdPCYOgb3rdmgbNbiRg7etR8Ah+9exy1PLgdgn5F92WlgLwDG1/ehsSlpbGrp2IAlSZIkbbPOMgFUbKYsN7djZl6amZMzc/Lw4cPLHJa6ivH1fbhtxgoAfvv4cuYvW7fJPjc/sZy9R9bSp2dn+bOQpPblfBaSpK6ko3+1vxgRIwGK5wVF+Vxgl1b7NQDzOjg2dWH/flwDV92/mHf9aCarGlvoVbPh9ZO/LXiZC297gW+9e1SFIpSkDnEFcPRGZevnsxgH3FG8ZqP5LI4GfhgRNUiS1El0dDI7FTi12D4VuKlV+UkR0ScixgLjgPs7ODZ1YXsM78NPTxnLbz+1B+/ZdxC7Du39ynvzl61jyrXP8t3jG9h1aJ8KRilJ5eV8FpKkrqScS/NcA9wL7BkRcyPi48CFwFER8XfgqOI1mfk4cD3wBPA74AxnMlZ7WrSyCYCWluT7dy7kQ5OHArBsTTMf+9kzfOltO3Hg6LpKhihJleJ8FpKkqlS2pXky8+QtvHXkFva/ALigXPGo+/jH/3mOe+esYsnqJg7+zlN8/oh6Vq9t4aoHSp0RR08YyIn7DwbgyvsXM+elRr7/p4V8/0+lH2BXf2QMw/qXc9UqSaoK2zSfBXApwOTJkze7jyRJ7c1f7Opyvv+BXTZbftohwzYp+8yb6/nMm+s3s7ckdRsvRsTIzJzvfBaSpGritK2SJHVvzmchSapK9sxKktRNFPNZHAEMi4i5wLmU5q+4vpjb4lngA1CazyIi1s9n0YTzWUiSOhmTWUmSugnns5AkdSUOM5YkSZIkVR2TWUmSJElS1TGZlSRJkiRVHe+ZlSRJKpNnv7FvpUMoi9Ffe6zDPuuAf7qqwz6rI904oNIRSNXPnllJkiRJUtUxmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVHZNZSZIkSVLVMZmVJEmSJFUdk1lJkiRJUtWpyDqzETEHWAE0A02ZOTkihgLXAWOAOcCJmbmkEvFJkiRJkjq3iiSzhbdk5qJWr88B7sjMCyPinOL12ZUJTdJr6aqL2HekGwdUOgJJkqTq1ZmGGR8HXFlsXwm8t3KhSJIkSZI6s0olswncGhEPRcSUomynzJwPUDzXb65iREyJiAcj4sGFCxd2ULiSJEmSpM6kUsOMD83MeRFRD9wWEU+1tWJmXgpcCjB58uQsV4CSJEmSpM6rIslsZs4rnhdExI3AQcCLETEyM+dHxEhgQSVikyRJUtf38kvzmf3rH77yunHZAkYdejz9d5nAc7ddQfO6RnoPHMbYd32Kmj59Kxipuptnv7FvpUMom9Ffe6xdj9fhyWxE1AE9MnNFsf124BvAVOBU4MLi+aaOjk2SJKmze3pRI2f+z3OvvH52yVq+8JZ63jCmjq/+Zh6NTUlND/jXd41iUkO/CkbaudUOHcmEU88HIFtaeOxHn2PQHgcwe+oP2PmIkxiwy14seuxOXnzgZkYddkKFo5W0OZXomd0JuDEi1n/+zzPzdxHxAHB9RHwceBb4QAVikyRJ6tR2H9aHWz69BwDNLcnB35nBOyYM5Jypz/PZI+p5y7gB/OFvK/jWbS9w3cd2q3C01WHFs4/TZ/Bw+gwaxstL5tO/YU8ABu66NzN/cZHJrNRJdXgym5mzgNdvpnwxcGRHxyNJklSt7p61ktFDetMwuDdBsLKxBYAVjc3UD+hV4eiqx5Kn/sKQvd4AQN9hDSx7+mEG7/F/WPK3B1i74qUKRydpSyq5zqwkSZJ2wNTpy3jPvoMA+No7R3DK1c9wwe/n05Jww+n2yrZFS3MTS59+mFGHlwYF7vqOj/PcH37K/Ht/xeDd9ydqaiocoaQtMZmV1CVMv/SL9OhdS0QPokcP9vrI15l3940sfmwaPfsOBGDU4e9n0G6bDAyRpKq0tqmF22es4Oy3jQDgpw+8xL8cPYJjJg7iN9OX8aWbnufnp46tcJSd3/LZj9Kvfld61ZUuCtS+bhTjPvAlAF5+6QWWzfrfSoYnaStMZiV1GeNPPIee/QZsUFZ/wDvY6cBjKhSRJJXPtJkr2WdkLcP7l37O/fKRpZz3zpEAvGvvgZw99flKhlc1ljx5H0OLIcYA61Ytp1fdQDJbeOG+mxj2+rdWMDpJW2MyK0mSVIWmPraM9+w7+JXX9QN6cd+cVRwytj93z17FmKG9KxdclWhZ18jyZ6Yz+u0ffaVsyVP3sfCR2wEYPG4yr9vn8ApFJ5VHc0ty7CVPM2JgLy7/0K488cIavvLreaxe20LD4N5874QGBtRWx/B6k1lJXcbff/HvEDB8v7cw7PVvAWDhw3ew+PG7qRsxlp2POJmetXUVjlKSdtyatS38+emVfPPdo14p+7f3jOK8W+bT3AJ9egYXvmfnCkZYHXr06sPrz/zhBmX1B7yd+gPeXqGIpPK77L7F7DG8zysTxp190zy++o4RvGFMHdf9dQmX3L2Is47cqcJRto3JrKQuYfw//DO9+w9h3arlzPzFt+kzdCTDJ72VkYccBwHz7rqB56ddw65Hn17pUCVph/Xt3YP/PWfCBmUH7lrHbz+1R4UiklQN5i9bxx/+toIz3zSc/753MQCzFjdy8K6lNakP372Oj1xdPclsj0oHIEntoXf/IQD0qhvIoD0OYPULs+hVN4jo0YOIHgzb782smj+rwlFKkiRVztd/N5+vvH0EPeLVsvH1fbhtxgoAfvv4cuYvW1eh6Ladyaykqte8tpHmtWte2V7xzHRqhzWwbuXSV/ZZ+veH6DusoUIRSpIkVdYdM5bzurqe7Duq7wbl/35cA1fdv5h3/Wgmqxpb6FUTWzhC5+MwY0lVr2n1Mmbd9J8AZEszQyYcwqCx+zHn5ktYveBZAPoMGsbooz5WyTAlSZIq5sFnV3P7jOVM+/sKGpuSFY3NfPaXz/G9E3bhp6eUlvGataiRP/x9RYUjbTuTWUlVr8/geiac+q+blI855pMViEaSJKnzOfuoEZx9VGld6ntnr+TSexbzvRN2YdHKJob170lLS/L9OxfyoclDKxxp25nMSpIkSVI3NfWxpVz1wEsAHD1hICfuP7iyAW0D75mVJEmSpMJpp51GfX09++yzzybvXXTRRUQEixYtqkBk7eeQsf25/EO7AnDaIcOY9pnxTPvMeM45agQR3jMrSZIkSWVzwD9dVZbjrlg9gtcdfSZP33zpBp+xdvlinrn1SnoPeB1HnnsdPfsNKMvn31iew3ZJ9sxKkiRJUmHALntRU1u3SfncP/6cnd/0QaiinsuuzmRWkiRJkrZi6cy/0mvAEPrVj650KGql0yWzEXF0RMyIiJkRcU6l45EkqbuzbZbUnbWsa+SF+37NqEOPr3Qo2kinSmYjogb4L+CdwETg5IiYWNmoJEnqvmybJXV3jUsXsHbZQp688l+YfukXWbviJZ68+musW7W00qF1e51tAqiDgJmZOQsgIq4FjgOeqGhUkiR1X7bNkrq1vsN3Yb8zfvDK6+mXfpG9Pnxe2SaAUttFZlY6hldExPuBozPz9OL1R4CDM/PMVvtMAaYUL/cEZnR4oHotw4Dqnq9c6hj+rXQ+u2bm8EoH0ZnYNu8w/861JX43Oq+xwABKHX9NwDw2/G+1L/Bk8V45+N3Y0Bbb5s7WM7u5qcE2yLYz81Lg0o4JR9sjIh7MzMmVjkPq7PxbUZWwbd4B/p1rS/xuaEv8brRdp7pnFpgL7NLqdQOlKyGSJKkybJslSZ1SZ0tmHwDGRcTYiOgNnARMrXBMkiR1Z7bNkqROqVMNM87Mpog4E/g9UANclpmPVzgsbTuHmklt49+KOj3b5h3m37m2xO+GtsTvRht1qgmgJEmSJElqi842zFiSJEmSpNdkMitJkiRJqjoms2oXEZER8Z1Wr8+KiPMqGJLUKUXJXRHxzlZlJ0bE7yoZl6QdExFjImL6RmXnRcRZlYpJlRURzRHxSKvHmIg4IiJ+8xr1JkXEMR0Vp8qr1ffg8Yj434j4QkSYg7UT/yHVXhqB4yNiWKUDkTqzLE1U8CnguxFRGxF1wAXAGZWNTJLUztZk5qRWjzltrDcJMJntOtZ/D/YGjqL03/bcCsfUZZjMqr00UZp57fOVDkTq7DJzOvBr4GxKDdpVmfl0ZaOSVC4RMS0i/iMi7omI6RFxUKVjUuVFxEHFd+Lh4nnPYvmrbwAfLHrzPljpONV+MnMBMAU4sxipVRsRl0fEY8X34C0AEXFzROxXbD8cEV8rts+PiNOLHv5pEfGLiHgqIn4WEVG5M6ucTrU0j6refwGPRsS3Kx2IVAW+DvwVWAtMrnAsksqvLjPfGBFvAi4D9ql0QCq7vhHxSLE9OzPft9H7TwFvKpa/ehvwzcw8oUhcJmfmmR0ZrDpGZs4qhhnXAx8uyvaNiL2AWyNiPHAncHhEzKHUYXRoUf0w4KfASGB/YG9gHnB3sc9dHXgqnYLJrNpNZi6PiKuAzwBrKh2P1Jll5qqIuA5YmZmNlY5H0g7b0lqH68uvAcjMOyNiYEQMzsylHRKZKmVNZk7ayvuDgCsjYhyl70mvDolKncH6XtTDgO8DZOZTEfEMMB74M6Xf07OB3wJHRUQ/YExmzoiIkcD9mTkXoLhoMoZumMw6zFjt7T+AjwN1FY5DqgYtxUNS9VsMDNmobCiwqNjeONndUvKr7uN84I+ZuQ/wbqC2wvGoA0TEbkAzsIBXk9qNPUBp1NbhlHppHwY+ATzUap/WF8Kb6aadlCazaleZ+RJwPaWEVpKkbiEzVwLzI+JIgIgYChzNqz0lHyzKDwOWZeayigSqzmQQ8Hyx/dFW5SuAAR0ejcouIoYDPwJ+UEwIeSfwoeK98cBoYEZmrgWeA04E7qPUU3tW8axWTGZVDt8BnNVYktTdnAL8czHk7w/A11tN7rYkIu6h9EPWC74C+DbwrYi4G6hpVf5HYKITQHUZfdcvzQPcDtxKad4MgB8CNRHxGHAd8NFWtx79GXgxM1cX2w2YzG4iShcFJEmSVA4RMQ04KzMfrHQsktSV2DMrSZIkSao69sxKkiRJkqqOPbOSJEmSpKpjMitJkiRJqjoms5IkSZKkqmMyK7WziGgupmBf/zinjJ/1jYh42w7Uvyki7m2HOP47Iibu6HEkSZKktnICKKmdRcTKzOzfjscLSn+rLe11zOK4g4HHgJXAMZk5uz2PL0lSVxERXwX+AWgGWoAlwBCgPzAcWN+G/t/MvGcz9Y8FzqfUkdQL+F5mXhIR5wErM/Oisp+E1AXZMyt1gIgYFBEzImLP4vU1EfGJYvufIuKBiHg0Ir5elI2JiCcj4ofAX4FdIuJLEfFYRPxvRFxY7HdFRLy/2L4wIp4ojnNRUTY8In5ZHP+BiDi0VVgnAL8GrgVOahXrFRHxnxFxT0TManX8HhHxw4h4PCJ+ExE3t3pvWkRMLrZXRsQFRZz3RcRORfm7I+IvEfFwRNy+vlySpM4sIg4BjgX+T2buB7wN+FBmTgJOB/6cmZOKx+YS2V7ApcC7M/P1wP7AtI6KX+rKTGal9td3o2HGH8zMZcCZwBURcRIwJDN/HBFvB8YBBwGTgAMi4k3FcfYErsrM/YGJwHuBg4uG8NutPzAihgLvA/YuGtp/Ld76HnBxZh5IKXn971bVTgauKR4nb3QOI4HDKDXeFxZlxwNjgH0pNd6HbOH864D7ijjvBD5RlN8FvKE4n2uBL22hviRJnclIYFFmNgJk5qLMnLcN9QcAPYHFRf3GzJyx8U4RMam4CPxoRNwYEUOK8mkR8R/FRebpEXFQUV4XEZcVF6sfjojjdvREpWrTs9IBSF3QmuJq7QYy87aI+ADwX8Dri+K3F4+Hi9f9KSW3zwLPZOZ9RfnbgMszc3VxrJc2Ovxy4GXgvyPit8BvWtWbWBqpDMDAiBgA9AP2AO7KzIyIpojYJzOnF/v9qhjW/ESrHtTDgP8pyl+IiD9u4fzXtvr8h4Cjiu0G4LqIGAn05tUhWZIkdWa3Al+LiL8BtwPXZeaf2lo5M1+KiKnAMxFxB6U28prN3D50FfCPmfmniPgGcC7wueK9usx8Y3HB+zJgH+CrwB8y87Ti1qH7I+L2zFy1/acqVRd7ZqUOEhE9gAnAGmDo+mLgW62GJ+2RmT8p3mvdGAWwxRvcM7OJUu/uLyn14P6ueKsHcEir4++cmSuAD1K612d2RMyh1ON6UqtDNm702a2fX8u6fPVm/GZevWj2feAHmbkv8Emgto3HkySpYjJzJXAAMAVYSOnC7Ee38RinA0cC9wNnUUpIXxERg4DBrZLkK4E3tdrlmuI4d1K6MD2Y0sXwcyLiEUrDlmuB0dsSl1TtTGaljvN54ElKQ3ovK+6h+T1wWkT0B4iInSOifjN1by3261fsN7T1m0X9QZl5M6WruJNa1Tuz1X7ry08Gjs7MMZk5hlIj3TqZ3Zy7gBOKe2d3Ao547VPewCDg+WL71G2sK0lSxWRmc2ZOy8xzKbWrJ2zHMR7LzIspjVja1vobX9BOSheZT2h1wXp0Zj65rXFJ1cxkVmp/G98ze2FEjKd0n+kXM/PPlO4l/efMvBX4OXBvRDwG/ILSvTUbyMzfAVOBB4srsGdttMsA4DcR8SjwJ0qJM8BngMnF/TdPAJ+KiDGUrtyuH8JMMZPx8og4eCvn9UtgLjAduAT4C7Cszf8qcB7wPxHxZ2DRNtSTJKliImLPiBjXqmgS8Mw21O8fEUdsrX4xt8aSiDi8KPoIpfZ8vQ8WxzoMWFbs/3vgH6O4lygi9m9rTFJX4dI8ktosIvpn5sqIeB2loVKHZuYLlY5LkqRyiYgDKN0qMxhoAmYCUzJzUZGknpWZx26l/gDgOmB3SrcarQI+m5kPRquleYrRUz+iNK/FLOBjmbkkIqYB9wJvBgYCp2Xm/RHRF/gP4I2UemnnbC0OqSsymZXUZkWDOpjSBE7fzswrKhmPJEldXdH2npWZD1Y6FqmzcTZjSW2WmUdUOgZJkiQJ7JmVJEmS2kVE3AiM3aj47Mz8fSXikbo6k1lJkiRJUtVxNmNJkiRJUtUxmZUkSZIkVR2TWUmSJElS1TGZlSRJkiRVnf8Pu+V74k3kJ7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16,15))\n",
    "\n",
    "for idx, col in enumerate(categorical_cols[:-1]):\n",
    "    ax = plt.subplot(4, 2, idx+1)\n",
    "    # group by HeartDisease\n",
    "    sns.countplot(x=df[col], hue=df[\"HeartDisease\"], ax=ax)\n",
    "    # add data labels to each bar\n",
    "    for container in ax.containers:\n",
    "        ax.bar_label(container, label_type=\"center\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b44da4c",
   "metadata": {},
   "source": [
    "- We can further notice how skewed the dataset is towards male patients. Only `50` female patients in the dataset have been diagnosed with heart disease.\n",
    "- A significant number of patients, `392`, diagnosed with heart disease have asymptomatic (ASY) chest pain. While chest pain could be a relevant feature for our model, asymptomatic implies that those patients who had a heart disease did not have chest pain as a symptom. \n",
    "- A high number (`170`) of patients with blood sugar greater than 120 mg/dl were diagnosed with heart disease in relation to those who were not diagnosed as such.\n",
    "- Out of all patients who had an exercise-induced angina, `316` were diagnosed with a heart disease.\n",
    "- Out of all patients with a flat ST slope, `381` were diagnosed with a heart disease.\n",
    "\n",
    "Looking at the data distribution from the above plots, we can start to identify some features that could be relevant to us. We will clean up the dataset a bit first before narrowing down on our features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bb5c3c5",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "\n",
    "We identified that there are no missing values. However, as we noticed earlier, a couple of columns have 0 values which don't make sense.\n",
    "\n",
    "We will look at how many zero values `RestingBP` and `Cholesterol` contain and decide how to handle those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3ad28327",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>55</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>155</td>\n",
       "      <td>N</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "449   55   M           NAP          0            0          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "449    155              N      1.5     Flat             1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"RestingBP\"] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c4efa8b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>65</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>93</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>95</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>127</td>\n",
       "      <td>N</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>61</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>105</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>110</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>50</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>139</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.7</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>110</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>131</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>43</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>122</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>120</td>\n",
       "      <td>N</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Up</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>63</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>160</td>\n",
       "      <td>N</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>48</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>110</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Down</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>56</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>122</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>62</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>ST</td>\n",
       "      <td>119</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>172 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "293   65   M           ASY        115            0          0     Normal   \n",
       "294   32   M            TA         95            0          1     Normal   \n",
       "295   61   M           ASY        105            0          1     Normal   \n",
       "296   50   M           ASY        145            0          1     Normal   \n",
       "297   57   M           ASY        110            0          1         ST   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "514   43   M           ASY        122            0          0     Normal   \n",
       "515   63   M           NAP        130            0          1         ST   \n",
       "518   48   M           NAP        102            0          1         ST   \n",
       "535   56   M           ASY        130            0          0        LVH   \n",
       "536   62   M           NAP        133            0          1         ST   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "293     93              Y      0.0     Flat             1  \n",
       "294    127              N      0.7       Up             1  \n",
       "295    110              Y      1.5       Up             1  \n",
       "296    139              Y      0.7     Flat             1  \n",
       "297    131              Y      1.4       Up             1  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "514    120              N      0.5       Up             1  \n",
       "515    160              N      3.0     Flat             0  \n",
       "518    110              Y      1.0     Down             1  \n",
       "535    122              Y      1.0     Flat             1  \n",
       "536    119              Y      1.2     Flat             1  \n",
       "\n",
       "[172 rows x 12 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Cholesterol\"] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1dbe3eca",
   "metadata": {},
   "source": [
    "`RestingBP` has only one zero value. We can remove that row from consideration. There are `172` zero values for `Cholesterol`. That's a relatively high number. We can't remove them all and replacing those values with the median might not be an ideal approach, but that's what we will go for now.\n",
    "\n",
    "To be more accurate, we will replace the zero values in `Cholesterol` in relation to `HeartDisease`. So, the 0 values in `Cholesterol` for patients who were diagnosed with a heart disease will be replaced by the median of the non-zero values for patients who were diagnosed with a heart disase. And we'll do the same for those who were not diagnosed with a heart disease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "61c9e24a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df.copy()\n",
    "\n",
    "# only keep non-zero values for RestingBP\n",
    "df_clean = df_clean[df_clean[\"RestingBP\"] != 0]\n",
    "\n",
    "heartdisease_mask = df_clean[\"HeartDisease\"]==0\n",
    "\n",
    "cholesterol_without_heartdisease = df_clean.loc[heartdisease_mask, \"Cholesterol\"]\n",
    "cholesterol_with_heartdisease = df_clean.loc[~heartdisease_mask, \"Cholesterol\"]\n",
    "\n",
    "df_clean.loc[heartdisease_mask, \"Cholesterol\"] = cholesterol_without_heartdisease.replace(to_replace = 0, value = cholesterol_without_heartdisease.median())\n",
    "df_clean.loc[~heartdisease_mask, \"Cholesterol\"] = cholesterol_with_heartdisease.replace(to_replace = 0, value = cholesterol_with_heartdisease.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6408d9a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>RestingBP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>917.000000</td>\n",
       "      <td>917.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>239.700109</td>\n",
       "      <td>132.540894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>54.352727</td>\n",
       "      <td>17.999749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>85.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>214.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>225.000000</td>\n",
       "      <td>130.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>267.000000</td>\n",
       "      <td>140.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>603.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cholesterol   RestingBP\n",
       "count   917.000000  917.000000\n",
       "mean    239.700109  132.540894\n",
       "std      54.352727   17.999749\n",
       "min      85.000000   80.000000\n",
       "25%     214.000000  120.000000\n",
       "50%     225.000000  130.000000\n",
       "75%     267.000000  140.000000\n",
       "max     603.000000  200.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean[[\"Cholesterol\", \"RestingBP\"]].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "512c073a",
   "metadata": {},
   "source": [
    "The minimum values for both have changed! There are no more zero values in either of those."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c71332d",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Thanks to our EDA and a general understanding of the features, we can identify some of the features that we could start with:\n",
    "\n",
    "- `Age`\n",
    "- `Sex`\n",
    "- `ChestPainType`\n",
    "- `Cholesterol`\n",
    "- `FastingBS`\n",
    "\n",
    "\n",
    "We will also identify how stronly the feature columns are correlated to the target colummn. That should help us narrow down on the features.\n",
    "\n",
    "In order to do that, we will first convert our categorical columns into dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22829bb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <th>RestingECG_Normal</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "      <th>ExerciseAngina_Y</th>\n",
       "      <th>ST_Slope_Flat</th>\n",
       "      <th>ST_Slope_Up</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>1.0</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1.5</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0   40        140          289          0    172      0.0             0   \n",
       "1   49        160          180          0    156      1.0             1   \n",
       "2   37        130          283          0     98      0.0             0   \n",
       "3   48        138          214          0    108      1.5             1   \n",
       "4   54        150          195          0    122      0.0             0   \n",
       "\n",
       "   Sex_M  ChestPainType_ATA  ChestPainType_NAP  ChestPainType_TA  \\\n",
       "0      1                  1                  0                 0   \n",
       "1      0                  0                  1                 0   \n",
       "2      1                  1                  0                 0   \n",
       "3      0                  0                  0                 0   \n",
       "4      1                  0                  1                 0   \n",
       "\n",
       "   RestingECG_Normal  RestingECG_ST  ExerciseAngina_Y  ST_Slope_Flat  \\\n",
       "0                  1              0                 0              0   \n",
       "1                  1              0                 0              1   \n",
       "2                  0              1                 0              0   \n",
       "3                  1              0                 1              1   \n",
       "4                  1              0                 0              0   \n",
       "\n",
       "   ST_Slope_Up  \n",
       "0            1  \n",
       "1            0  \n",
       "2            1  \n",
       "3            0  \n",
       "4            1  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = pd.get_dummies(df_clean, drop_first=True)\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97ccf85c",
   "metadata": {},
   "source": [
    "Now, we can find how they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e48e3b51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlations = abs(df_clean.corr())\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(correlations, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fbd10db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B7sA7QR/2LOAEQsn1mGDeV8CdwUPeBe4NDtisC3QDrjOzHwhV5LNe9WiPfxA6KHM+oST+yTw/AXJIRvTpzpQRd3Nc7USWj+vNPy5tHumQREREpAixUK4qhzn9IwZKNuoZ6RAASJk7INIhiIiIHKoINmXsVeHvb+V7nrPl/7pFZN+KVMVbRERERCRaHUlnNRERERGRw0wke7LzmxJvEREREYlaRSnxVquJiIiIiEghUMVbRERERKJX0Sl4q+ItIiIiIlIYVPEWERERkailHm8REREREckTVbxFREREJGoVpYq3Em8pUqLlipHRcAXNaHkuREREDkVRSrzVaiIiIiIiUghU8RYRERGR6FV0Ct6qeIuIiIiIFAZVvEVEREQkahWlHm8l3iIiIiIStYpS4q1WExERERGRQqCKt4iIiIhELVW8RUREREQkT46oxNvM0sxsXtitTh4ff4eZlQqbHmtm5Q8ylrZmti2IY76ZTTCzysGy481sSrBsiZkNOZhtyOFp0GPd+HViH+aM7BXpUERERCLOzPL9FilHVOINpLh7w7Dbyjw+/g4gI/F29wvcfeshxDMtiONU4FvglmD+y8CLwbITgVcOYRtymHlz9Cw63TIw0mGIiIhEByuAW4QcaYl3JmZW2swmmtn3ZrbAzDoF8482s8/M7AczW2hmXc3sNqAaMNnMJgfjVppZvJnVCSrTQ81skZl9aWYlgzGnBxXtmWb2vJktzCYOA8oAW4JZVYGkPcvdfUHBPhMSTWZ8v4LN2/6MdBgiIiKSz460xLtkWJvJx8BOoLO7NwbaAS8ESfD5wFp3P83dGwDj3P1lYC3Qzt3bZbPu+sBAdz8Z2ApcHsx/A7jR3ZsDaVke08rM5gGrgA7AsGD+i8AkM/vczO482HYWERERkcOdWk0OX+GtJp0J/bHhGTObD0wAqgOJwAKgg5k9a2at3H1bLtb9i7vPC+5/B9QJEuYy7v51MP/tLI/Z02pSk1CC/hyAu78BnAiMBNoCs8ys+MHtsoiIiIhEgyMt8c6qG5AANHH3hsB6oIS7LwOaEErA+5jZo7lY166w+2mETtWYl59UnwKt90y4+1p3H+bunYDdQIPwwWbWw8zmmNmcIUN07KWIiIgUTUWp4n2kn8e7HLDB3VPNrB1QG8DMqgGb3f3/zOx3oHswfgehXuyNuVm5u28xsx1mdqa7zwKu3M/wlsCKYPvnAxODuKoAlYA1WdY9BNiTcXtu4hERERE53EQyUc5vR3ri/RYw2szmAPOAH4P5pwDPm1k6kArcFMwfAnxuZr/l0OedneuAoWb2BzAFCG9b2dPjbcH864P55wL9zWxnMH2vu6/L477JYWpEn+60alKf+PKlWT6uN70HjWXEqJmRDktEREQOkbmrWFqQzKy0u/8e3H8AqOrut+fzZvSPGGVKNuoZ6RBImTsg0iGIiMjhLSpKzTV7fpLvec7qAZ0ism9HesW7MFxoZg8Seq5/ZW/bioiIiIgcQZR4FzB3fw94L9JxiIiIiByOilKP95F+VhMRERERkUKhireIiIiIRK2iVPFW4i0iIiIiUasoJd5qNRERERERKQSqeIuIiIhI1FLFW0RERERE8kQVbxERERGJXkWn4K3Euyj4ddOuSIcAQO1KxSMdQtSIhqtGRsPVMyE6novlG1IiHQIA9SqXjHQIIlEtactfkQ6BEnHR0Qzw+67dkQ6BOpVKRDoEQK0mIiIiIiKSR6p4i4iIiEjUUsVbRERERETyRBVvEREREYlaRajgrcRbRERERKKXWk1ERERERCRPVPEWERERkahVhAreqniLiIiIiBQGVbxFREREJGoVpR5vJd4HYGYO/J+7XxNMxwK/AbPd/aKDWF93oKm79wybNwW4x93nmNlKYAfgwBbgWnf/9VD24dtZ03ntpWdJT0vn/Isv48prr8u0/OupkxkxdABWrBgxMTHcdPt9NDitMQAfvvsm40Z/BMAxdetzz0O9Oaq4rlBZ1A16rBsdWzcgefMOml7xTKTDKVRzZs9gSP/nSE9P59yLOvO3v/8r23HLlizk7huv5f7Hn6Vlu3MKOUqRI883M6cz8MVnSU9P44JLLuOqa6/PtHzG1Em8MXgAxYLvspvvuJ9TGjZm9a+/0PvhezPG/bYmie49buHyK6/Jcwyzvp7GS/36kp6WxsWXXs41/7wh0/JpUyYx9LVXsGJGTEwst999P6c1apKxPC0tjeuu+RsJCYk83//VPG9/j29nzWDQS8+SlpZOx4s70zWb7/X/DR2Y8b1+4+33Znyvf/Tum3w++iMM45i69bn7oSf1vV6IlHgf2B9AAzMr6e4pwDnAmgLeZjt332hmTwAPAzcc6AE5SUtLY0C/Z+jbfwjxlRO59bqraN6qLbWPqZsxplHTZjRv1RYz4+fly3jq4XsY9u6nbExez6iRb/H626MoXrwETz18D1MmjOPcCzvlwy5KNHtz9CwGvfcVr/e+NtKhFKq0tDRe+08fnnpxEPEJidx5QzfObNGGWmHvlz3j3hjUn8ZnNI9QpCJHlrS0NF7u9zTPvTyEhMpVuPmfV9K8VTvqhL03Gzc9k7NatcPMWPHTUno/fA/D3xtNzdrHMOTNDzLW0/Xi9rRs0/6gYnih79O89OpQKicmcv01XWnZph3HHFsvY0yTM5rRsk0ohuU/LeWR++/mnY/GZCwf+c6b1KlzLH/88cchPRcD+z1Dn/6Dg+/1qznzAN/rTz98L/9995Pge/1thr79cfC9fu9h8b1ehAre6vHOpc+BC4P7VwHv7FlgZmeY2ddmNjf4//HB/LvMbFhw/xQzW2hmpfK43ZlA9UMJfOnihVSrUYuq1WsQFxdHmw7n8/W0yZnGlCxVKuPPODtTUjL9SSctLY1du3aRtns3u3bupGJ8wqGEI4eJGd+vYPO2PyMdRqFbtmQh1arXpGq10PuldfvzmDV9yj7jRn/4Di3atKdc+YqFH6TIEejHxQuoXqMW1arXJC4ujnbndOTrqfv5LtuZgrFvtjZ3zmyqVa9JYtVqeY5hyaIF1KhZk+o1ahIXdxTtz72AaVMyx1Cq1NE5fp9uWL+Or6dP5eJLL8/ztsOFvtdrZnyvt+1wPjOnTck0Jvff6ylUOgy+14sVs3y/RYoq3rnzLvComY0BTgWGAa2CZT8Crd19t5l1AJ4BLgdeAqaYWWfgIeDf7v5n8OLvamYtw9Zfj+ydD4w6lMA3Jq8nITExYzohIZEfFy/YZ9z0ryYy7LX+bNuymd79BgIQn5DIFVf9g793PpfixUvQ+IzmNG121qGEIxLVNiVvIL5ylYzp+IREli7J/H7ZmLyemVMn80z/ISxbsqiwQxQ5Im1M3kBC2HszoXIiSxbN32fc9CkTef21l9i6ZTNPvzBwn+WTx3/O2ed2PKgYkjesp3Ji1YzpyomJLFq4bwxfTZrAoAEvsWXLJvr1fy1jfv8X+nLz7Xfz5yFUuyH0OZWQGP45VTnb7/UZX01k2Gsvs3XLZnr3GxCMTaTLVf/gms7nZXyvN9H3eo7M7HygPxADvO7ufbMsLwf8H1CLUE7dz93f2N86VfHOBXefD9QhVO0em2VxOWCkmS0EXgRODh6TDnQH3gS+cvcZYY95z90b7rkBc7Ksc7KZbQA6AG9nF5OZ9TCzOWY25+0Rr+dpf7I7SKFlm/YMe/dTHuv7EiOGht6gO7Zv5+tpk/nfB5/zzqcT2JmSwoRxY/Z5rEhR4Xg2czO/X4a8/Dz/vOl2YmJiCicoEQHf972ZXUW7Zdv2DH9vNE8+25/hgwdkWpaamsrX06bQ+uxz8yuEbL9P25zdgXc+GkPfF15h6GuvADBj6hQqVKjICSeefFDbzhRHNp9T2cXRok17/vvuJzze9yVGDA39CNmxfTszp01mxAdjefvT8exMSWHiYfC9bpb/twNv02KAgUBH4CTgKjM7KcuwW4DF7n4a0BZ4wcyO2t96lXjn3qdAP8LaTAK9gcnu3gC4GCgRtqw+8DuQ179ptQNqA4uAJ7Mb4O5D3L2puze9+h/XZzcECP26TV6/PmM6OXn9fttFTm3UlLVrVrNt6xbmzplFlWo1KF+hIrGxcbRs257FC+blcVdEDh/xCYls3LAuY3pj8vp9/gy7fOlinn38fv55RUdmfDWBV//zDDOnTirsUEWOKPGVE0kOe28mb1hPpYTKOY4PfZclsW3rlox538ycRv3jT6RipfiDiqFyYiIb1v+WMb1h/Xri43OOoWHjpqxJWs3WLVuY/8Ncpk+dwuUXncNjve7hu29n88TD9x9UHKHv9fDPqQ1U2k8cpzRqwm+ZvterZ3yvt2jbnsULfjioOI4AZwDL3f1nd/+LUPdD1mZ4B8pY6JdPaWAzsHt/K1XinXvDgCfdPevfc8qx92DL7ntmBn9+6A+0BiqZWZe8bCw4kPMO4FozO+hG0uNPPJk1Sb/y29okUlNT+WrCOJq3bJtpzJqkVXjwU/6npYvZnbqbsuXKk5BYhR8XzWfnzhTcnblzZlOrzrEHG4pI1DvuhJNZk7SKdWvXkJqaytSJX9CsZZtMY4a9P5Y3Rn7OGyM/p0WbDtx8Vy+atz47QhGLHBlOOLEBa1bv/S6bPP5zzmrVNtOYNav3fpct+3ExqbtTKVuufMbySV8efJsJwAknNSBp9SrWrkkiNfUvJn45lpZt2mUak7T614wYli5ZTGpqKuXKl+emW+9k1OeT+HDMeJ54ph9NTm/GY089e1BxhL7XV7EueC6mTBjHmVk+pzJ/ry9hd2rouaicWIUlYd/r8+bMpladYw4qjsJkZgVxy+gcCG49smy2OrA6bDqJfY+7GwCcCKwFFgC3Bx0POVKPdy65exKhRDqr54ARZnYXEF72ehF41d2Xmdl1hNpHpuZxm7+Z2TuE/pTR+2DijomNpeddveh1502kp6Vx3kWXUufYeoz5+H0ALur8N6ZPnsCEcaOJiY2l+FHFeaj3c5gZJ558Kq3adeDm7l2JiYmh3nEnckGnPP1+kMPUiD7dadWkPvHlS7N8XG96DxrLiFEzIx1WgYuJjeWmOx/gkbtvIj09nXMu7ETtY+oxdtRIAC649IoIRyhyZIqJjeXWe3px/+03kp6eRseLOlPn2HqM/ij0XXbxZX9j6uTxjP98NLGxsRxVvDiP9H4+08GW330zkzsfePSgY4iNjeXO+x7irp49SEtL56JOnTm2bj0+/uA9ADp36cqUieP5/LNPiY2NpXjxEjzZp1++n4M6JjaWW+56MPheT+fc/Xyvx8bGUfyo4vQKvtdPOPlUWrU7h1u6Xxl8r59Ax8Pge70gzmri7kOAIfvbbHYPyzJ9HjAPOBuoC4w3s2nuvj3HlXp2TUtyWPl1066o+EesXUnnAY0mJRv1PPCgQpAyd8CBBxWw5RtSIh0CAPUql4x0CCJRLWnLX5EOgRJx0dEM8Puu/XYsFIo6lUpExYn8TnlkfL7nOQt6n7PffTOz5sDj7n5eMP0ggLv3CRvzGdDX3acF05OAB9z9m5zWGx2vLhERERGRbBREq0kufAvUN7NjggMmryR0vF+4VUD7IMZE4Hjg5/2tVK0mIiIiIiJhgtNE9wS+IHQ6wWHuvsjMbgyWDyLUBjzczBYQak2539037m+9SrxFREREJGrld598brn7WLKcRjpIuPfcXwvk6fyUSrxFREREJGrpkvEiIiIiIpInqniLiIiISNSKVKtJQVDFW0RERESkEKjiLSIiIiJRqwgVvFXxFhEREREpDKp4FwG6YqRkJxquGAnRcQXN+eOej3QIIpILf0bB1RprVCgV6RAAWLXpz0iHQJ1KJSIdAlC0eryVeIuIiIhI1CpCebdaTURERERECoMq3iIiIiIStYpSq4kq3iIiIiIihUAVbxERERGJWkWo4K3EW0RERESil1pNREREREQkT1TxFhEREZGoVYQK3qp4hzOzGmb2iZn9ZGYrzKy/mR1lZm3NbEwOj1lpZvH5HMdwM+uSn+sUORwNeqwbv07sw5yRvQptm9/NnsG/u3XihqsuZuT/Dctx3LIlC7mkbWOmTxlfaLGJHMm+mz2DG/9+KT2uvoSRb+3vvbmITu2aMCPsvdm/7+P8vdPZ3NK9aHy1zvv2a+761+Xc0b0zn7w7PMdxK5Yu4urzmzF76sSMebdecwn39biSB268ml63XFsI0Uo4Jd4BCzUQfQSMcvf6wHFAaeDpiAYmcgR7c/QsOt0ysNC2l5aWxmsv9uGJ5wfy6v8+4quJ41i1ckW244YP6k+j05sXWmwiR7K0tDQGvdSXx58bwMARHzJ1P+/NEYP3fW+273gxjz9feJ8lBSk9LY03BjzH/U/3p9/Q9/l6ypck/fpztuPefn0ApzU5c59lDz8/iL6D3uaZgf8rjJAPmZnl+y1SlHjvdTaw093fAHD3NOBO4F9AxvVjzaySmX1pZnPNbDBgwfw6ZvajmY0ws/lm9oGZlQqWNTGzr8zsOzP7wsyqBvNvMLNvzewHM/twz/hwZtY7qIDr30qOODO+X8HmbYV32eRlSxZStXpNqlSrQVxcHK3bn8es6VP2GTfmw3c4q017yleoWGixiRzJfsr63jz7PGZn99786F3OatOeclnemw1Oa0KZMuUKKdqCtXzpIqpUq0li1RrExsXRvM05zPn6q33GjfvkPZq1akfZ8hUiEGX+Msv/W6QomdvrZOC78Bnuvh1YBdQLm/0YMN3dGwGfArXClh0PDHH3U4HtwM1mFge8AnRx9ybAMPZW0T9y99Pd/TRgCXBd+PbN7DmgMvBPd0/Pn90UkZxs2riBhMpVMqbjExLZlLwh05iNyeuZOW0yHTtdUdjhiRyxNm3cQHzlxIzpSgmJbNqYnHlM8gZmTpvE+ZcUjXaSnGzZmEylhMzPxZZNmZ+LzRs38O2MKXS48PJ9Hm8YfR7sSa+br2HiZx8VeLySmQ6u3MsAz8X81sBlAO7+mZltCVu22t1nBPf/D7gNGAc0AMYHf9qIAX4LxjQws6eA8oTaWr4IW9cjwGx375FtsGY9gB4AgwcPpkePbIeJSF74vh8BWf8kOfSV5+l+4+3ExMQUVlQiR7xs3ppkLVoOfeV5uv+76L83PbtUJcvn1P9e+w9XX38rxbJ5Lh5/6XUqVkpg25bNPPNgT6rVrMOJpzYuqHDzRVE6naAS770WAZl+GppZWaAmkLWRLLsEPbv5TuizYZG7Z9cMOhy41N1/MLPuQNuwZd8CTcysortv3mdD7kOAIQeIR0TyoFJCIskb1mVMb0xeT8X4hExjlv+4mOeeuB+A7du2MmfWdGJiYmje6uxCjVXkSBKfUJmNG9ZnTG/K5r3509LFPP/kA0DovfndrOkUi4mleat2hRprQasYX5lNyZmfiwoVM5/j4edlS3j5mYcA2LFtK/O++ZpiMTGc3qItFSuFnrdyFSpy+lltWbF0UdQn3kWJEu+9JgJ9zexad/+fmcUALxBKjsObTKcC3YCnzKwjEN48VcvMmrv7TOAqYDqwFEjYMz9oPTnO3RcBZYDfgnndgDVh6xpHqAL+mZmd6+47CmKnRWSv4044mbVJq1i3dg2VEiozdeIX3PvoM5nG/Pf9sRn3X3zmEU4/q7WSbpECVn/Pe/O3NVSKr8zUSV9wzyN9Mo3573ufZdx/sc+jnNG8VZFLugHqHn8S69asYsNva6gYX5mZX42n5wO9M415+c1PMu6/9vzjNG7WitNbtGVnSgru6ZQsdTQ7U1KY//0sLut2fWHvQp4VoYK3Eu893N3NrDPwqpk9Qqj/fSzQCwivVj8BvGNm3wNfEeoB32MJ8I/goMufgNfc/a/g1IAvm1k5Qs/5S4Qq7I8As4FfgQWEEvHwmEaaWRngUzO7wN1T8nu/RaLZiD7dadWkPvHlS7N8XG96DxrLiFEzC2x7MbGx3HjHAzx6z02kp6dzzgWdqH1MPcZ+MhKAC9TXLRIRoffm/Tx2z82kp6fT4YJO1D6mLp8H780DHXPx/BMPsGDed2zftpXuXc7j6n/eyLkXdi6M0PNdTEws3XveR59et5Genkbb8y6hZp26jB/zIQDnXLRvX/ce27Zu4j9P3AdAWtpuWrQ7n4ann1UocUuIeXaNU5JnZlYHGOPuDSKwef0jStQq2ahnpENg/rjnIx0CAPUTS0Y6BJGotmxd4Z3FKCfHVdnnBGMR8f2v2yMdAo1rl42KWnOrF6bne54z7e6WEdk3VbxFREREJGrp4ErZh7uvJHT2EhERERGRfSjxFhEREZGoVYQK3rqAjoiIiIhIYVDFW0RERESilnq8RUREREQKQRHKu9VqIiIiIiJSGFTxFhEREZGoVZRaTVTxFhEREREpBKp4ixRRyzekRDoEIDquGnnq+fdGOgQANn8zINIhsGZLdLwu6lXWVTxlX0lbI//6/GbN5kiHAMDS5Mg/F41rl410CEDR6vFW4i0iIiIiUatYEcq81WoiIiIiIlIIVPEWERERkahVhAreqniLiIiIiBQGVbxFREREJGrpdIIiIiIiIpInqniLiIiISNQqVnQK3kq8RURERCR6FaVWk6hPvM3sd3cvHTbdHWjq7j3zYd11gLPc/e1gui3wCfAzUApYDzzn7mOC5TcCf7r7/w512yLRZM7sGQzp/xzp6emce1Fn/vb3f2U7btmShdx947Xc//iztGx3Tr7G8N3sGQx5OYjhws5csZ8Y7rnpWu57/Flats3fGLIz6LFudGzdgOTNO2h6xTMFtp0Z06fyXN+nSU9Lp/PlV/Cv63tkWj550gRefaU/VqwYsTEx3PtALxo1bgrAYw8/yNSpU6hYsRIfjhqTbzFFw+tCJDuLvp/F+0NfwtPTaHHOxZzX5dpMy5ct+J7Xnrmf+MRqADQ8sw0XXpn96zcvlv/wDV/8byCenk6jdhfQ4pKrMi1fuXge77/wKOUrVwHghNNb0vqyUGwv33Y1R5UsRbFixShWLIbrn37toOP4bcl3zPtoCJ6ezjFnnsuJ51yxz5gNP81n3sdDSU9Lo/jRZWl3W1/+3JLM7P/7Dzt3bMGsGMc2P4/j2nY66Dgk76I+8S4oZhYL1AGuBt4OWzTN3S8KxjQERplZirtPdPdBhR6oSAFLS0vjtf/04akXBxGfkMidN3TjzBZtqHVM3X3GvTGoP43PaF4wMbzYh6f+M4hKCYnc2aMbzVq2oVadfWMYPqg/jU7P/xhy8uboWQx67yte733tgQcfpLS0NPo89SSDhr5BYpVEunXtQpt2Z1O3br2MMc3ObE7bdu0xM5Yt/ZH77rmDUaPHAXDJpZdx5dV/5+Fe9+drTJF+XYhkJz0tjXcH9+O2J/pToVJl+t5zHaee0YqqtY7JNK7eSadxyyP98m+76WmMe+Nluj34HGUrJfD6wzdzXOPmJNSok2lcrRMacOW92f9Iv/ahFyhVttwhx/H9yNdoc/NTlCxfiQkv3Em1U5pRrkqtjDF//fk73498jVY3PsHRFSuzc8dWAKxYDA0vvY4KNeuRuvNPxve7g8QTGmV6bDQqQgXvw/vgSjNLMLMPzezb4NYimH+GmX1tZnOD/x8fzO9uZiPNbDTwJdAXaGVm88zszqzrd/d5wJNAz+Dxj5vZPcH928xssZnNN7N3g3lHm9mwIJa5ZtYpmF/HzKaZ2ffB7axgflUzmxpsf6GZtQrmn2tmM4OxI82sdNbYRPLLsiULqVa9JlWr1SAuLo7W7c9j1vQp+4wb/eE7tGjTnnLlKxZIDFWr16TKAWIY8+E7nNWmPeUr5H8MOZnx/Qo2b/uzQLexcMF8ataqTY2aNYmLO4rzOl7IlEkTM40pVerojD+3pqSkYOz9JmrS9HTKlju0L/OsouF1IZKdlT8tJqFKDRKqVCc2Lo6mrTrwwzfTCny7a5f/SIXE6lRIrEZMbBwnN2/H0u++LvDtZrX512WUTqhK6fgqxMTGUatxa9YumJVpzKrvvqL6aWdxdMXKAJQoUx6AkuUqUqFm6Ad9XIlSlE2sScrWTYUa/5HucEi8SwaJ6Twzm0coEd6jP/Ciu58OXA68Hsz/EWjt7o2AR4Hwn57NgX+4+9nAA4Qq3A3d/cUctv89cEI28x8AGrn7qcCNwbyHgElBPO2A583saGADcI67Nwa6Ai8H468GvnD3hsBpwDwziwceBjoE4+cAd+3/KRI5eJuSNxAf/FkUID4hkU0bN2QaszF5PTOnTqZjp33/nJkvMWzcQELWGJKziWFawcUQSRs2rKdKlb37n5iYyIYN6/cZN2nCeC69+HxuvfnfPN674NpeIDpeFyLZ2bopmQrxiRnTFSolsHVT8j7jflm6kKduv5ZXnriLtat+PuTtbt+ykbKVEjKmy1ZMYMfmjfuMS/ppMYMfuIG3n32ADUkrM+abGW/1vY+hvW7k+4kH3xKWsm0TpcrvjaNk+XhStmVOnnckr+GvP39n8isPMP7521n5zcSsq+GPTevZmvQzleocf9CxFBYrgP8i5XBoNUkJElNgb493MNkBOCms6b6smZUBygEjzKw+4EBc2PrGu/vmPGw/p3+d+cBbZjYKGBXMOxe4ZE9VHCgB1ALWAgOC1pU04Lhg+bfAMDOLA0a5+zwzawOcBMwI9usoYOY+QZn1AHoADB48mB49emQdIpIrjmczN/PLfsjLz/PPm24nJiamgILYN4asB9MMfeV5ut9YgDFEkOdi/wHO7nAOZ3c4h+/mfMurA/oz+PXhBRdTNLwuRLKR7Sszy/ulZt3jeWroR5QoWYqFc75m0DMP8OSg9/N9w1m3W7VOfW57+R2OKlGSn+bOZuQLj3LLi6HDwro/3p8yFeL5Y9sW/q/PfVSqVovaJ56aL3FkfW96ehpbVi+n7S1Pk5a6i4kv3kOlOidQpnJ1AFJ3pfD1sGdoeNkNxJUolfcYCpnOahI9igHN3T0lfKaZvQJMdvfOwQGUU8IW/5HHbTQClmQz/0KgNXAJ8IiZnUzolX+5uy/NEs/jhA7UPC2IeSeAu081s9bBut40s+eBLYR+HGQ+YiMLdx8CDNkzmcd9EskQn5DIxg3rMqY3Jq+nUnxCpjHLly7m2cdD/cPbt21lzqzpxMTE0Lz12fkSQ6WERJKzxFAxaww/Lua5J7KJoVX+xBBJiYlVWLdu7/6vX7+ehITKOY5v0vR0Vq9exZYtm6lQQG030fC6EMlOhUoJbNm49y9CWzYlU65ifKYxJUsdnXG/QdOzeGdwP37fvpXSZcsf9HbLVoxne1hlffvmZEpXqJRpTPGw7dZv1IzP3+jPn9u3UapsOcpUCMV4dLkKnNC0JWtX/HhQiXfJ8pX4c+veOFK2bqRkucyfAyXLxVPlxLLEFi9BbPESJNRtwNY1v1CmcnXS03bz9bBnqNW0LTVOOyvP25dDczi0muzPlwT915BxMCSEKt5rgvvd9/P4HUCZnBaa2anAI8DALPOLATXdfTJwH1AeKA18AdxqwU9gM2sUFs9v7p4OXAPEBMtrAxvcfSjwX6AxMAtoYWb1gjGlzGxPhVwk3x13wsmsSVrFurVrSE1NZerEL2jWsk2mMcPeH8sbIz/njZGf06JNB26+q1e+JlfHnXAya7PG0CJzDP99fyzD3v+cYe+HYrjprl5FIukGOLnBKaxatZI1SatJTf2LLz7/jDbtMu/bqlW/ZlTGlyxeRGpqKuXLVyiwmKLhdSGSndr1T2TDb0lsXL+W3ampzJk2gVPPaJlpzLYtmzLeLyuXLcbTnaPLHNpxENXqnsDmdWvYsuE30nansmjmZI5rkjlx/X3r5oztrln+I+5OyTJl+WtnCrtSQseK/LUzhZ8XzCGhZp2DiqNireP4PXktv29aR9ruVFZ9P5VqDZplGlP9lDPZuGIR6Wlp7P5rJ5t+XUrZxBq4O9++05+yiTU5vl3ng9p+JJhZvt8i5XCveN8GDDSz+YT2ZSqhfuvnCLWa3AVM2s/j5wO7zewHYDgwl9DBlnMJnU5wA3Cbu2dtjooB/s/MyhGqcr/o7lvNrDfwEjA/SL5XAhcBrwIfmtkVwGT2Vt3bAveaWSrwO3CtuycH7TTvmFnxYNzDwLI8PjciuRITG8tNdz7AI3ffRHp6Oudc2Inax9Rj7KiRAFxwacH378bExnLjHQ/w6D1BDBcEMXwSxBDBHuIRfbrTqkl94suXZvm43vQeNJYRo/bp/joksbGxPNDrUW769/Wkp6XRqfPl1KtXn5HvvQPAFV2vYuL4Lxj96SfExsZSokQJnuv3YsaXxwP33sWcb79h69YtnNu+NTfdfCudLz+05ywaXhci2YmJieXKHnfxyuN3kp6exlntL6JarWOZ+vnHALTu2Jm5X09m6ucfUywmhrijinPdPU8ecrJVLCaG87vfytt978fT0zmtbUcq16jDdxNGA9Ckw8UsmT2VORM+zdjuZbc+jJnxx7YtvP/iY0DorCwNWrSn3mlnHHQcjS+/kamvPRqcTvAcylWtzfLpYwGo1/ICylapSZUTm/Dlsz3BjGObn0e5anVIXrGIX7+dTLmqdfjyuVsBOOXCa6l68umH9NxI7ll2vYVy2NE/ouxj+YaUAw8qBNHwEXPq+fdGOgQANn8zINIhsGZLdLwu6lUuGekQJApN+jHyZ9hY+0d0vEeWJkc+jt7n14+K7upLX5+T798ko65vGpF9O9wr3iIiIiJShBUrQifyPtx7vEVEREREDgtKvEVEREQkapnl/y1327XzzWypmS03sweyWX5v2LVmFppZmpnt91RTSrxFRERERMKYWQyhs9p1JHR9lavM7KTwMe7+fHARxobAg8BXB7pWjHq8RURERCRqRej0f2cAy9395yCGd4FOwOIcxl8FvHOglariLSIiIiJHFDPrYWZzwm5ZLwFeHVgdNp0UzMtuXaWA84EPD7RdVbxFREREJGoVRME7yxXAs91sdg/LYezFwIwDtZmAEm8RERERiWIROp1gElAzbLoGsDaHsVeSizYTUKuJiIiIiEhW3wL1zewYMzuKUHL9adZBwVXM2wCf5GalqniLFFG6MuBe0XDFSICKZ/SMdAikzI2O50IkO2efUCnSIUgUikS92913m1lP4AsgBhjm7ovM7MZg+aBgaGfgS3f/IzfrVeItIiIiIpKFu48FxmaZNyjL9HBgeG7XqcRbRERERKJWhE4nWCCUeIuIiIhI1CpWdPJuHVwpIiIiIlIYVPEWERERkahVlFpNVPEWERERESkEqniLiIiISNQqQgVvJd4iIiIiEr3UaiIiUoTMmD6VThedx8Udz2HY60P2WT550gSu6Hwxf7u8E1f/7TLmfj8nY9ljDz9Iu9bNufzSiwosvkGPdePXiX2YM7JXgW1DREQKnhLvPDKzh8xskZnNN7N5ZtYsn9a70symZZk3z8wW5sf6RSR7aWlp9HnqSQa+9jofffoZ48aOYcWK5ZnGNDuzOe9/9Cnvf/gJj/d+hiceezhj2SWXXsarg14v0BjfHD2LTrcMLNBtiIhEq2KW/7eI7UvkNn34MbPmwEVAY3c/FegArM7HTZQxs5rBtk7Mx/WKSA4WLphPzVq1qVGzJnFxR3FexwuZMmlipjGlSh2d8afOlJQULOwCxk2ank7ZcuUKNMYZ369g87Y/C3QbIiJS8JR4501VYKO77wJw943uvtbMmpjZV2b2nZl9YWZVzaycmS01s+MBzOwdM7vhAOt/H+ga3L8KeKfA9kREANiwYT1VqlTJmE5MTGTDhvX7jJs0YTyXXnw+t978bx7v/UxhhigickQzs3y/RYoS77z5EqhpZsvM7FUza2NmccArQBd3bwIMA552921AT2C4mV0JVHD3oQdY/wfAZcH9i4HRBbMbIrKHu+8zL7sP5bM7nMOo0eN48eWBvDqgf2GEJiIiRYwS7zxw99+BJkAPIBl4D/g30AAYb2bzgIeBGsH48cACYCBwfS42sRnYEiTqS4Ac/7ZsZj3MbI6ZzRkyZN+DwUQkdxITq7Bu3bqM6fXr15OQUDnH8U2ans7q1avYsmVzYYQnInLEswK4RYpOJ5hH7p4GTAGmmNkC4BZgkbs3zzrWzIoBJwIpQEUgKRebeI9Qot79AHEMAfZk3PuW7EQkV05ucAqrVq1kTdJqKicm8sXnn/HMcy9kGrNq1a/UrFkLM2PJ4kWkpqZSvnyFCEUsInJkKVaETieoxDsPgn7tdHf/KZjVkFBl+lwza+7uM4PWk+PcfRFwZ7C8FzAsGJN6gM18TKiX/AugWkHsh4jsFRsbywO9HuWmf19PeloanTpfTr169Rn5XugQiyu6XsXE8V8w+tNPiI2NpUSJEjzX78WMdpQH7r2LOd9+w9atWzi3fWtuuvlWOl9+Rb7GOKJPd1o1qU98+dIsH9eb3oPGMmLUzHzdhoiIFDzLrr9RsmdmTQj1c5cHdgPLCbWd1ABeBsoR+jHzEvAV8AlwhrvvMLP/ADvc/bEc1r0SaOruG8Pm1QHGuHuDA4Smf0SR/Ug50M/dQlLxjJ6RDoGUuQMiHYKIHD6iotR8w/sL8z3PGfq3BhHZN1W888DdvwPOymbRRqB1NvMzTgno7ncdYN11spm3klD/uIiIiIgc5pR4i4iIiEjUKkqXjFfiXcjMbDZQPMvsa9x9QSTiEREREYlmRSjvVuJd2Nw9Xy4xLyIiIiKHFyXeIiIiIhK1itLpBHUBHRERERGRQqCKt4iIiIhErSJU8FbiLSIiIiLRqyid1UStJiIiIiIihUAVbxEp8tZsSYl0CEB0XDWyZKPIXz0TouO5kOizc3ekI4BouaD3tii45G6VsnGRDgEoWlXiorQvIiIiIiJRSxVvEREREYla6vEWEREREZE8UcVbRERERKJWsaJT8FbiLSIiIiLRqygl3mo1EREREREpBKp4i4iIiEjU0sGVIiIiIiKSJ0q8RUTCzJk9gx5Xd+L6Ky/m/f8bluO4ZUsWcnGbxkyfPL4Qo4usQY9149eJfZgzslekQ5Ej3IxpU7nkwvO46Pxz+O/QIfssnzxpAl06X8zfLuvEVX+7jO+/m1MwcUyfSqeLzuPijucw7PXs47ii88X87fJOXP23y5j7ff7EMfvr6fz98ou4unNH3hr++j7Lp381iX9e1Znrrr6cHtf+jfnzvs9YtmPHdh69/06u6XIx11xxMQvnz8uXmApSMcv/W6QcVKuJmVUBXgJOB3YBK4FRwCXuftGhBGRmbYG/3P3rYPpx4AYgOYi3l7t/up/HjwWudvetOSwfCLQAjgKOAZYGi55y9w8OJfb9MbNGwPfA+e7+hZl9HGy/NJAA/BIMvdndvzazH4DF7n5VQcUkIpmlpaXx2n/68NSLg4hPSOTOG7pxZos21Dqm7j7j3hjUn8ZnNI9QpJHx5uhZDHrvK17vfW2kQ5EjWFpaGs88/SSDh75BYmIiV3ftQtt2Z1O3Xr2MMc2aNadtu/aYGcuW/si9d9/BJ2PG5XscfZ56kkFD3yCxSiLdunahTbuzqVs3LI4zM8dx3z13MGr0ocWRlpbGS889xQsDhpKQWIV//6MrLVq3o86xez+nGp9+Ji1at8PMWPHTUh5/8B7e/GA0AK+80JczmrfgyWdfJDU1lZ07o+PKvvtThDpN8l7xtlCjzcfAFHev6+4nAb2AxHyKqS1wVpZ5L7p7Q+AKYJiZ5Ri3u1+QU9IdLL8lWNcFwAp3bxjcCizpDlwFTA/+j7t3DuK4HpgWFsfXZnYioX+b1mZ2dAHHJSKBZUsWUq16TapWq0FcXByt25/HrOlT9hk3+sN3aNGmPeXKVyz8ICNoxvcr2Lztz0iHIUe4hQvmU7NmbWrUrEncUUdx/gUXMmXyxExjSh19dEZfcEpKSoH0CC9cMJ+atYI44o7ivI4XMmVSljhKZYmDQ49jyaIFVK9Zi2o1ahIXF8fZ53Rk+leTsmy3VKbt7tnsH7//zg9zv+PCTpcDEBcXR5kyZQ85Jsm9g2k1aQekuvugPTPcfR4wDShtZh+Y2Y9m9laQpGNmTczsKzP7zsy+MLOqwfzbzGyxmc03s3fNrA5wI3Cnmc0zs1bhG3b3JcBuIN7MRgXrW2RmPfaMMbOVZhZvZnXMbImZDQ3GfGlmJbPbITN708w6hU2/ZWaXmFl3M/vEzMaZ2VIzeyxszN/N7JsgzsFmFpPTExY8D12A7sC5ZlbiAM/x1cCbwJfAJQcYKyL5ZFPyBuIrV8mYjk9IZNPGDZnGbExez8ypk+nY6YrCDk9EgA3r11Ol6t73aeXERNavX7/PuIkTxtPpovPpedO/eaL3M/kfx4b1VKmyN47ExEQ2bNg3jkkTxnPpxedz683/5vF8iGNj8gYqJ+7dbkJiIhuTN+wzburkCVzT5WIeuPNm7n+kNwBr1yRRvnwF+j7xMNd168JzTz1KSkr0/5guZpbvt4jty0E8pgHwXQ7LGgF3ACcBxwItzCwOeAXo4u5NgGHA08H4B4BG7n4qcKO7rwQGEVS43X1a+MrNrBmQTqjt5F/B+poCt5lZpWziqQ8MdPeTga3A5TnE/Trwz2Ab5QhV3McGy84AugENgSvMrGlQke4KtAiq1mnBmJy0AH5x9xXAFELV9v3pCrwHvENQIReRgud4NnMzf0APefl5/nnT7cTE5PhbW0QKUHbv0+wq2u07nMMnY8bx0isDGfhK//yPw3MXx9kdzmHU6HG8+PJAXh1w6HFkt93sejFat+vAmx+M5unnX2bYoAEApKXt5qelS+jUpSv/fesDSpQoydvD/3vIMUnu5ffpBL9x9yQAM5sH1CGU8DYAxgcvyBjgt2D8fOAtMxtFqEc8J3ea2d+BHUBXd/egWt45WF6TUJK9Kcvjfgmq8RD6sVAnu5W7+1dmNtDMKgOXAR+6++4g3vHuvinYp4+AloSq7k2Ab4MxJYF9f27udRXwbnD/XeAa4KPsBprZ6UCyu/9qZkmEWmsquPuWLON6AD0ABg8eTI8ePbJZm4jkRXxCIhs3rMuY3pi8nkrxCZnGLF+6mGcfvx+A7du2MmfWdGJiYmje+uxCjVXkSJWYWIV1v+19n25Yv57KlSvnOL5J09NZvXoVW7ZspkKF/GsPS0yswrp1e+NYv349CQkFH0dC5UQ2rN+73eT164nP8jkV7rTGTVmzZjVbt24hoXIVEionclKDUwFo0/5c3h6x78GZ0aYonQnkYPZlEaGkMzu7wu6nEUrsDVgU1sN8irufG4y5EBgYrO87M8vph8CeCngrd58WHIDZAWju7qcBc4Hs2jeyiycnbxKqWv8TeCNsftaflh7s04iwfTre3R/PbqVBC8rlwKNmtpJQ9b+jmZXJIY6rgBOCsSuAsmRTqXf3Ie7e1N2bKukWyR/HnXAya5JWsW7tGlJTU5k68QuatWyTacyw98fyxsjPeWPk57Ro04Gb7+qlpFukEJ3c4BRWrVpJUtJqUv/6i3FjP6NNu8zvwVW//ppRGV6yeBGpqamUL1+hQOJYk7Sa1NS/+OLzbOJYlf9xnHBSA5JWreK3NUmkpqYyafzntGjdLtOYpNWrMra77MfF7E5NpVy58lSKjychsQqrVobO5/D9t7Ook+Xg8Whklv+3SDmYivck4Bkzu8Hdh0JGlbZNDuOXAglm1tzdZwatJ8cBS4Ca7j7ZzKYT6msuTaiqfaBO/3LAFnf/08xOAM48iP3IajjwDbDO3ReFzT/HzCoCKcClwL+AP4FPzOxFd98QLC/j7r9ms94OwA/uft6eGWY2IljXm+EDg4NGrwBOdfc1wbx2wMOE2mFEpADFxMZy050P8MjdN5Gens45F3ai9jH1GDtqJAAXXHpk93WP6NOdVk3qE1++NMvH9ab3oLGMGDUz0mHJESY2NpYHH3qUm3pcT3p6Gpd2vpx69erz/nvvAPC3rlcxYfwXjP70E+JiYyleogTP9Xsx3w+wjI2N5YFej3LTv68nPS2NTkEcI4M4ruh6FRODOGJjYymRT3HExsZyx329uOe2f5OelsYFl3TmmLr1+OTD9wDodHlXpk4azxeffUpsbCxHlSjBY8/0y9ju7ff04qlH7yc1NZVq1WvywKO9D+2JkDyxbHuFDvQgs2qETifYBNjJ3tMJdtpzOkEzGwDMcffhZtYQeJlQwhwbPHY4MDmYZ8D/uXtfMzsO+IBQL/etQHvgd3fvF7b94sH2qhMk9sDj7j4lqBQ3JZTEj3H3BsFj7gFK76lMBwdyZiwP5o0DRu05cNTMuhPqxz4aqAe87e5PBMu6Ag8S+qtBKnCLu8/K5rkaDswKPxjVzC4BbnL3jkH1/h53vyi439fdzwwbGwMkAY3d/Teyl/d/RJEjyPIN0XG6rHqVsz2+u1CVbNQz0iEAkDJ3QKRDkCi0c3ekI4CDSIsKxLaU1EiHQJWycVFxIr9Hxv2U7/8qvc+vH5F9O6jEuygys1LAAkIJ7rZgXnegqbtHxzdVzvSPKLIfSrz3UuIt0UyJ915KvPcqSol3UepXP2hm1gH4EXhlT9ItIiIiIpF3pPd4FznuPgGolc384YRaYnLFzGYDxbPMvsbdFxxKfCIiIiJHqkhe4j2/KfHOR+7eLNIxiIiIiEh0UuItIiIiIlErkleazG/q8RYRERERKQSqeIuIiIhI1CpCBW9VvEVERERECoMq3iIiIiIStXRWExGRw0g0XLgmWkTLhWui4UI+0fJcyF4llJVkKBkXF+kQooZRdDJvtZqIiIiIiGRhZueb2VIzW25mD+Qwpq2ZzTOzRWb21YHWqd+WIiIiIhK1ItFqYmYxwEDgHCAJ+NbMPnX3xWFjygOvAue7+yozq3yg9ariLSIiIiKS2RnAcnf/2d3/At4FOmUZczXwkbuvAnD3DQdaqRJvEREREYlaxSz/b7lQHVgdNp0UzAt3HFDBzKaY2Xdmdu2BVqpWExERERGJWlYAJ/I2sx5Aj7BZQ9x9SPiQbB7mWaZjgSZAe6AkMNPMZrn7spy2q8RbRERERI4oQZI9ZD9DkoCaYdM1gLXZjNno7n8Af5jZVOA0IMfEW60mIiIiIhK1ItRq8i1Q38yOMbOjgCuBT7OM+QRoZWaxZlYKaAYs2d9KVfEWEREREQnj7rvNrCfwBRADDHP3RWZ2Y7B8kLsvMbNxwHwgHXjd3Rfub71KvEVE5LAx6LFudGzdgOTNO2h6xTORDkdECkEBtHjniruPBcZmmTcoy/TzwPO5XWeuW03MrIqZvWtmK8xssZmNNbMeZjYmt+vYz7rbmtlZYdOPm9ma4ITkC83skgM8fmxwLsWclg8M1rXYzFKC+/PMrMuhxr6fba40sw/DpruY2fAsYz4xs5lZ5uVp30VEjiRvjp5Fp1sGRjoMESlExczy/RYpuap4W+hw0o+BEe5+ZTCvIXBxPsXRFvgd+Dps3ovu3s/MTgSmmVlld0/P7sHufsH+Vu7utwCYWR1gjLs3zI+gc6GpmZ3s7ouyLgh+KDQGfjezY9z9l7DFud53EZEjyYzvV1CrasVIhyEiclByW/FuB6SGl9fdfR4wDShtZh+Y2Y9m9laQpGNmTczsq+C8hl+YWdVg/m1B5Xl+UEGvA9wI3BlUeVuFb9jdlwC7gXgzGxWsb1FwGhiCda40s3gzq2NmS8xsaDDmSzMrmd0OmdmbZtYpbPotM7vEzLoHlehxwWVCHwsb83cz+yaIc3BwVaP96Qf0ymHZ5cBoQidkvzK7AeH7foDtiIiIiBRJETq4smD2JZfjGgDf5bCsEXAHcBJwLNDCzOKAV4Au7t4EGAY8HYx/AGjk7qcCN7r7SmAQoSpvQ3efFr5yM2tGqGE9GfhXsL6mwG1mVimbeOoDA939ZGAroQQ3O68D/wy2UQ44i719PGcA3YCGwBVm1jSoPncFWgQV87RgzP68DzQ2s3rZLLsKeCe4XZXdg7Psu4iIiIgcxvLj4Mpv3D0JwMzmAXUIJbwNgPFBATwG+C0YPx94y8xGAaP2s947zezvwA6gq7t7UC3vHCyvSSjJ3pTlcb8E1XgI/Viok93K3f2roPe7MnAZ8GFwBCvAeHffFOzTR0BLQpXnJsC3wZiSwIEuDZpGqOH+QeDzPTPNLBGoB0wP9mu3mTUIOxJ2n33PumILO/H74MGD6dGjR9YhIiIiIoe9CLZk57vcJt6LgJwORNwVdj8tWKcBi9y9eTbjLwRaA5cAj5jZyTms90V377dnwszaAh2A5u7+p5lNAUrkIp5sW00CbxKqWl8J/CtsftZE1wnt0wh3f3A/68tpGw8Seg736ApUAH4JkviyQQwPB8sz7Xt2spz4fZ/EXERERESiS25bTSYBxc3shj0zzOx0oE0O45cCCWbWPBgbZ2Ynm1kxoKa7TwbuA8oDpQlVdsscIIZywJYg6T4BODOXse/PcEJtMmQ5APIcM6sY9IdfCswAJgJdggo5wfLaB9qAu6cCL+7ZTuAq4Hx3r+PudQhV0rPt8xYRkb1G9OnOlBF3c1ztRJaP680/Ls2uviMiRUkxLN9vkZKrinfQDtEZeMnMHgB2AivJoVXE3f8KTtX3ctA/HQu8ROgSmv8XzDNCld2tZjYa+CA42PHWHMIYB9xoZvMJJfazcreL+92v9Wa2JJv9mE6oUl0PeNvd5wCY2cPAl8EPiFTgFuDXXGzqvwTV7OBg0lrh8bv7L2a2PejpFhGRHPzjweGRDkFECllRajWxbNqHjxgWurznAqCxu28L5nUHmrp7z0jGlkdH7j+iiByWSjaK/EdsytwBkQ5BJNpFRcr76tcr8z3PufmsOhHZt1xfQKeoMbMOwI/AK3uSbhERERGJLkXpdIJH7CXj3X0CoZaPrPOHE+r9zhUzmw0UzzL7GndfcCjxiYiIiEjRcsQm3vnF3dWXLSIiIlJAInmJ9/ymxFtEREREolYRyruP3B5vEREREZHCpIq3iIiIiEStotRqooq3iIiIiEghUMVbRERERKJWESp4K/EWEZHCFw0Xr4mGi/hAdDwXItGsKLVnFKV9ERERERGJWqp4i4iIiEjUsiLUa6KKt4iIiIhIIVDFW0RERESiVtGpd6viLSIiIiJSKFTxFhEREZGoVZQuoKPEW0RERESiVtFJu9VqIiIiIiJSKFTxFhEREZGoVYQ6TfK34m1mVczsXTNbYWaLzWysmfUwszH5sO62ZnZW2PTjZrbGzOaZ2UIzu+QAjx9rZuX3s3xgsK7FZpYS3J9nZl0ONfYctjc7WP8qM0sO214dM4s1s41m1qcgti0iIodm0GPd+HViH+aM7BXpUETkMJJvibeFzm7+MTDF3eu6+0lALyAxnzbRFjgry7wX3b0hcAUwzMxy3B93v8Ddt+5n+S3Bui4AVrh7w+D2waEGnsP2mgXbexR4L2x7K4FzgaXA36wonTVeRKSIeHP0LDrdMjDSYYgcEcws32+Rkp8V73ZAqrsP2jPD3ecB04DSZvaBmf1oZm/tSSbNrImZfWVm35nZF2ZWNZh/W1B5nh9U0OsANwJ3BlXhVuEbdvclwG4g3sxGBetbZGY99owxs5VmFh9UlJeY2dBgzJdmVjK7HTKzN82sU9j0W2Z2iZl1N7NPzGycmS01s8fCxvzdzL4J4hxsZjEH8VxeBfQHVgFnHsTjRUSkAM34fgWbt/0Z6TBEjgjFCuAWKfm57QbAdzksawTcAZwEHAu0MLM44BWgi7s3AYYBTwfjHwAaufupwI1BFXgQQYXb3aeFr9zMmgHpQDLwr2B9TYHbzKxSNvHUBwa6+8nAVuDyHOJ+HfhnsI1yhCruY4NlZwDdgIbAFWbW1MxOBLoCLYJqdlowJteCHwHtgTHAO4SScBERERE5zBVW0v+Nuye5ezowD6gDHE8oWR9vZvOAh4Eawfj5wFtm9ndCleyc3Bk8th/Q1d2dULL9AzALqEkoyc7ql6AaD6EfC3WyW7m7fwXUM7PKhBLgD919Tzzj3X2Tu6cAHwEtCSXMTYBvg7jaE/qhkRcXAZPd/U/gQ6BzdlXzoHd+jpnNGTJkSB43ISIiInJ4KEqtJvl5VpNFQE4HIu4Ku58WbNeARe7ePJvxFwKtgUuAR8zs5BzW+6K799szYWZtgQ5Ac3f/08ymACVyEU+2rSaBNwlVra8E/hU237OMc0L7NMLdH9zP+g7kKkJ/EVgZTFci1MYzIdPG3IcAezLurLGIiIiISJTJz4r3JKC4md2wZ4aZnQ60yWH8UiDBzJoHY+PM7OTgAMma7j4ZuA8oD5QGdgBlDhBDOWBLkHSfQP70Rw8n1CaDuy8Km3+OmVUMWkMuBWYAE4EuQYWcYHnt3G7IzMoSqpzXcvc67l4HuAW1m4iIiMgRygrgFin5lngHbR6dCSWkK8xsEfA4sDaH8X8RqpA/G7SGzCPUQx0D/J+ZLQDmEqpqbwVGE2q72OfgyjDjgFgzmw/0JtRucqj7tR5YAryRZdF0QtXweYRaUOa4+2JCLTNfBjGMB6rmYXOXAZPcPbwi/wlwiZkVP8hdEBGRfDaiT3emjLib42onsnxcb/5xaXZ/vBWR/FCUWk0slC9LTsysFLAAaOzu24J53YGm7t4zkrGF0T+iiEgelWwUHR/hKXMHRDoEkZxExSmNP/jht3zPc7qcVjUi+6ZLxu+HmXUAfgRe2ZN0i4iIiEjhKUqnE9Ql4/fD3ScAtbKZP5xQ73eumNlsIGuryDXuvuBQ4hMRERGRw4cS70Lg7s0iHYOIiIjI4SiSPdn5Ta0mIiIiIiKFQBVvEREREYlaRafercRbRERERKJYEeo0UauJiIiIiEhhUMVbRERERKJWsSLUbKKKt4iIiIhIIVDFW0REjkjRcsXIaLiC5mMv3BnpEAB44Oy6kQ5BolBR6vFW4i0iIiIiUcvUaiIiIiIiInmhireIiIiIRK2i1GqiireIiIiISCFQxVtEREREolZROp2gEm8RERERiVpqNRERERERkTxR4i0iIiIiUcss/2+5266db2ZLzWy5mT2QzfK2ZrbNzOYFt0cPtM4DtpqYWRqwIBj7C3CNu2/NXcgZ62gIVHP3scH0JcBJ7t43L+sJW98UoCqQEsxa7u5dgmXXAvcBFtyGuXu/YNldQA8gFUgHJgL3u3tqDttZCXzn7pcH012Ai9y9+8HEfTDMbDgwxt0/KKxtiohI9Bv0WDc6tm5A8uYdNL3imULbbtKiOcx+fzDu6RzX4jxOPe9v+4z5bdl8vhk5hPS03RQvXZYL7nqu0OITyQ9mFgMMBM4BkoBvzexTd1+cZeg0d78ot+vNTY93irs3DIIYAdwCPJ3bDQQaAk2BsQDu/inwaR7XkVU3d58TPsPMOgJ3AOe6+1ozKwFcEyy7ETgXONPdt5rZUcBdQElCiXhOmprZye6+KK8Bmlmsu+/O6+NEREQO5M3Rsxj03le83vvaQttmenoas959lfNue5pSFeIZ3fcOap16JuWr1soYs+vP35n5zkDOvbU3pStWJmX71kKLT4qmCF1A5wxChd2fAczsXaATkDXxzpO8Hlw5Ezg1CKAuoV8CCcCfwA3u/qOZXQE8BqQB24AOwJNASTNrCfQhlOw2dfeeQUV3O6HEvApwn7t/YGbFgAFAG0KV9mKEqtf7q/w+CNzj7msB3H0nMDRY9hDQek+13t3/AnJTce8H9AK6hc80s4rAMODYYP97uPt8M3scqAbUATaa2TLgGEIV+uMIJftnAh2BNcDF7p4a/Hni4uC5+Rr4t7t7LuITEZEj0IzvV1CrasVC3ebGlcsok1CNMglVATi2aWtW/TAzU+L987dTqN3wLEpXrAxAybLlCzVGkXxSHVgdNp0ENMtmXHMz+wFYSygH3W+hNtc93kHJvT17K9VDgFvdvQlwD/BqMP9R4Dx3Pw24JEhwHwXec/eG7v5eNquvCrQELmJvMnwZoeT1FOB6oHmWx7wV1lPzfDCvAfBdNrGXAUq7+y+53d8w7wONzaxelvlPAHPd/VRCifn/wpY1ATq5+9XBdF3gQkK/lP4PmOzupxBqlbkwGDPA3U939waEku9c/9lCRESkMPy5dRNHV4jPmC5VIZ4/tm7KNGb7+jX89efvfP6f+/n0mdtYPmtiYYcpRUwxy/+bmfUwszlhtx5ZNptdmT1rQfR7oHaQ874CjDrgvuRif0ua2TxgE1ARGG9mpYGzgJHBssGEkmeAGcBwM7sBiMnF+gFGuXt60DeTGMxrCYwM5q8DJmd5TLcgkW/o7vceYP1G2JNlZucFCftKMzvrAI9NA54nVE0P1xJ4E8DdJwGVzKxcsOxTd08JG/t50Ee+gNBzMi6Yv4DQjwuAdmY228wWAGcDJ+93h8JeMEOGDDnALoiIiBy67P4Qa1mOVEtPT2PTquV0uOUJzr2tN/PGvsO29UmFFaIUQVYA/7n7EHdvGnbLmkwlATXDpmsQqmpncPft7v57cH8sEGdm8exHrnu8g6RyDKEe7+HA1j2931mCuNHMmhGq5M4LDqw8kF1h9y3L//NiEaFq86QsMW03sz/M7Bh3/8XdvwC+MLMxwFG5WO+bhBLv8D8f7O+X0B9Z5u8K4kg3s9SwFpJ0IDboRX+VUPvN6qBdpcT+AgpeIHteJGpJERGRAnd0hXj+2LIxY/rPLRspVa7iPmNKlC5LXPESxBUvQZX6Ddic9AvlEmsUdrgih+JboL6ZHUOoNfhK4OrwAWZWBVjv7m5mZxAqaG/aZ01hct1q4u7bgNsItZWkAL8E/dxYyGnB/bruPtvdHwU2Evq1sAMok9ttBaYDl5tZMTNLBNrm4jF9gOeCJwIzK25mt4Ute83Myu+JmQMkt3sE1eoXCR24ucdUgr5vM2sLbHT37blZXzb2xLEx+GtCl4Ncj4iISIGJr30c2zesZcfGdaTtTuXnOVOpeeqZmcbUOvVM1i9fRHpaGrv/2knyL0spX6VmDmsUObBInE4wODlGT+ALYAnwvrsvMrMbgxN2QChfWxj0eL8MXHmg4/PydHClu88NVn4loaTzNTN7GIgD3gV+AJ43s/qEKsITg3mrgAeCtpQ+udzch4R6yhcCy4DZhA7W3OMtM9vTzrHR3Tu4+9ggSZ8QJNZO6ABIgNeAUsBsM9sF/E6oLWZuLuP5L/Bw2PTjwBtmNp/QwZX/yOV69hGcZWUoodaTlYR+ZYmIiORoRJ/utGpSn/jypVk+rje9B41lxKiZBbrNYjExnHnlTXz5ysN4ejr1zzqXCtVq8+PUzwA4ofWFlK9ai+onNWHUUzdjVozjWpxHhep1CjQukYIQtI+MzTJvUNj9AYROBJJrFs0nzjCz0u7+u5lVAr4BWgT93pJZ9P4jiojIfpVs1DPSIfDYC3dGOgQAHji7bqRDkMyi4mLtU5Zuzvc8p+3xFSOyb3k9nWBhGxO0hhwF9FbSLSIiInJkKRYV6X/+iOrE293bFsZ2zGw2UDzL7GvcfUFhbF9EREREir6oTrwLi7tnd0J0EREREYmwCF25skDk+qwmIiIiIiJy8FTxFhEREZGolZvT/x0ulHiLiIiISNQqQnm3Wk1ERERERAqDKt4iIiIiErWKFaFeE1W8RUREREQKQVRfuVJyTf+IIiKHqb6TVkQ6BJ64+8VIhwBAytw8XX1bCl5UlJpnLd+a73nOmfXKR2TfVPEWERERESkE6vEWERERkegVFXX3/KHEW0RERESilq5cKSIiIiIieaKKt4iIiIhErSJ0NkFVvEVERERECoMq3iIiIiIStYpQwVuJt4iIiIhEsSKUeUc88TazNGBBEMsvwDXuvjWP62gIVHP3scH0JcBJ7t73IGOaAlQFUoJZy929S7DsWuA+Qi8DA4a5e79g2V1ADyAVSAcmAve7e2oO2/kXcCehC+AUAx4CzgVaAEcBxwBLg+FPufsHB7M/IiJy+EtaNIfZ7w/GPZ3jWpzHqef9bZ8xvy2bzzcjh5CetpvipctywV3PFXhcgx7rRsfWDUjevIOmVzxT4NsTOZxFPPEGUty9IYCZjQBuAZ7O4zoaAk2BsQDu/inw6SHG1c3d54TPMLOOwB3Aue6+1sxKANcEy24klDSf6e5bzewo4C6gJKFEnCzrqkEo0W7s7tvMrDSQ4O6fBMvrAGP2PDciInLkSk9PY9a7r3LebU9TqkI8o/veQa1Tz6R81VoZY3b9+Tsz3xnIubf2pnTFyqRs31oosb05ehaD3vuK13tfWyjbkyOPTidYcGYC1QHMrK6ZjTOz78xsmpmdEMy/wswWmtkPZjY1SHCfBLqa2Twz62pm3c1sQDB+uJm9bGZfm9nPZrancl3MzF41s0VmNsbMxu5Zth8PAve4+1oAd9/p7kODZQ8BN+2p1rv7X+7e192357CuysAO4Pdg/O/u/stBPWsiIlKkbVy5jDIJ1SiTUJWY2DiObdqaVT/MzDTm52+nULvhWZSuWBmAkmXLF0psM75fweZtfxbKtkQOd9FQ8QbAzGKA9sB/g1lDgBvd/Sczawa8CpwNPAqc5+5rzKy8u/9lZo8CTd29Z7Cu7llWXxVoCZxAqBL+AXAZUAc4hVASvAQYFvaYt8xsT6vJeHe/F2gAfJdN7GWA0nlMnH8A1gO/mNlE4CN3H52Hx4uIyBHiz62bOLpCfMZ0qQrxJP+yNNOY7evXkJ62m8//cz+pO1M46exO1DuzfWGHKpLvdDrB/FXSzOYBm4CKwPig7eIsYGSwbDCh5BlgBjDczG4AYnK5jVHunu7ui4HEYF5LYGQwfx0wOctjurl7w+B27wHWb4T6tEMTZucF1feVZnZWdg9w9zTgfKALsAx40cwez+X+YGY9zGyOmc0ZMmRIbh8mIiKHIXffZ55lyUbS09PYtGo5HW55gnNv6828se+wbX1SYYUoUmCsAG6REg0V7xR3b2hm5YAxhHq8hwNbs+tvdvcbgwr4hcC84MDKA9kVdt+y/D8vFgFNgElZYtpuZn+Y2THu/ou7fwF8YWZjCB0kmS0PfZJ+A3xjZuOBN4DHcxOIuw8h9FcBCEv6RUSk6Dm6Qjx/bNmYMf3nlo2UKldxnzElSpclrngJ4oqXoEr9BmxO+oVyiTUKO1wRyUE0VLwBcPdtwG3APYTOJvKLmV0BYCGnBffruvtsd38U2AjUJNQrXSaPm5wOXB70eicCbXPxmD7Ac2ZWJYiluJndFrbsNTMrvydmoEROKzKzambWOGxWQ+DXPO6DiIgcAeJrH8f2DWvZsXEdabtT+XnOVGqeemamMbVOPZP1yxeRnpbG7r92kvzLUspXqRmhiEXyUREqeUdDxTuDu881sx+AK4FuhBLZh4E44F1CfdHPm1l9Qk/bxGDeKuCBoC2lTy439yGhnvKFhFo9ZgPbwpaH93hvdPcO7j42SNInBIm1s7cv/DWgFDDbzHYROmhyBjA3h+3HAf3MrBqwE0gGbsxl7CIicgQpFhPDmVfexJevPIynp1P/rHOpUK02P079DIATWl9I+aq1qH5SE0Y9dTNmxTiuxXlUqF6nwGMb0ac7rZrUJ758aZaP603vQWMZMWrmgR8ocgSy7PrGjhRmVtrdfzezSoRaPloE/d6HmyP3H1FE5DDXd9KKSIfAE3e/GOkQAEiZOyDSIUhmUXFY49xfd+R7ntOodpmI7FtUVbwjYEzQGnIU0PswTbpFRERE5DBwRCfe7t62MLZjZrOB4llmX+PuCwpj+yIiIiKHq6J0OsEjOvEuLO7eLNIxiIiIiByOilDeHT1nNRERERERKcpU8RYRERGR6FWESt6qeIuIiIiIFAJVvEVEREQkalkRKnkr8RYRERGRqFWUzmqiVhMRERERkUJwRF+5sgjRP6LsI2nLX5EOAYA/d+2OdAgkbU2JdAgAnH1CpUiHwM7I/3MAUEJ/b5VslGzUM9IhsGRCv0iHAMDG7ZH/DG96TNmoqDUvTPo93/OcBjVKR2TfVPEWERERESkEqjmIiIiISPSKirp7/lDiLSIiIiJRqyid1UStJiIiIiIihUAVbxERERGJWjqdoIiIiIiI5Ikq3iIiIiIStYpQwVsVbxERERGRwqCKt8gR6puZ0xn44rOkp6dxwSWXcdW112daPmPqJN4YPIBixYoRExPDzXfczykNG7P611/o/fC9GeN+W5NE9x63cPmV1+Q5hu9mz2DoK8+Tnp7OORdeyhXd/pXtuGVLFnHvzddy32N9adH2HAD6932cb2dOpVyFigwc/kGet52TRd/P4v2hL+HpabQ452LO63Jt5lgWfM9rz9xPfGI1ABqe2YYLr8w+7sPdjGlTebbv06SnpdP58iu47oYemZZPnjSBga/0p5gVIyY2hnvv70XjJk0jFK0cyQY91o2OrRuQvHkHTa94psC28+2sGQx66VnS0tLpeHFnul57XablX0+dzP+GDsSCz80bb7+XBqc1BuCjd9/k89EfYRjH1K3P3Q89yVHFix9UHD/M+Zo3X3uB9PR02p7fiUu6ds923Iqli3jszn9x64PP0KxVewD++H0HQ196iqSVKzAzetz5CPVPOvWg4ig0Rajkne+Jt5mlAQvCZr3r7n3zezvBtp4Eprr7hIN8/CdAZXdvfohxvA78x90XH8p6gnWdDHwMnObuKcG8z4A33f3dQ12/CEBaWhov93ua514eQkLlKtz8zytp3qoddY6pmzGmcdMzOatVO8yMFT8tpffD9zD8vdHUrH0MQ978IGM9XS9uT8s27Q8qhkEv9aX3C69RKSGRu/7djWYt2lCrTt19xo0Y3J9Gp2d+m7bveDEXXtaVF5955CCegeylp6Xx7uB+3PZEfypUqkzfe67j1DNaUbXWMZnG1TvpNG55JDqubldQ0tLSeObpJxk89A0SExO5umsX2rY7m7r16mWMadasOW3btcfMWLb0R+69+w4+GTMuglHLkerN0bMY9N5XvN772gMPPkhpaWkM7PcMffoPJr5yIrdedzVntmpL7bDPzUZNm9G8VVvMjJ+XL+Pph+/lv+9+wsbk9Ywa+TZD3/6Y4sVL8NTD9zJlwjjOvbBTnuNIT0tj+MDnePCZAVSMT+SR2/5B4zNbU6P2sfuMe3fYAE5tcmam+W8OeoHTmjTnjoefZXdqKrt27Ty4J6QQ6XSC+5fi7g3DboeUdFtItnG6+6OHkHSXBxoD5c3smAMM3y93vz4/ku5gXYuAj4CHAMzsUiBOSbfkpx8XL6B6jVpUq16TuLg42p3Tka+nTs40pmSpUlhwKPnOnSnZfvDNnTObatVrkli1Wp5j+GnJQqpWr0mVajWIi4uj9dnnMXv6lH3GjfnoXc5q055yFSpmmt/gtCaUKVMuz9vdn5U/LSahSg0SqlQnNi6Opq068MM30/J1G4eLhQvmU7NmbWrUrEncUUdx/gUXMmXyxExjSh19dMZrJCUlJeO+SGGb8f0KNm/7s0C3sXTxQqrVqEnV6qHPrLYdzmfmtCmZxmT63MzynkhLS2PXrl2k7d7Nrp0pVIpPOKg4VixdRGLVmlSuWoPYuDjObHMO3838ap9xX3z6Hqe3aEfZchUy5v35x+/8uGAubc8PJfyxcXEcXbrMQcUhB6dQerzNrJyZLTWz44Ppd8zshuD+vWb2rZnNN7Mngnl1zGyJmb0KfA/UNLP7zGyBmf1gZn2DccPNrEtwv6+ZLQ7W0y+Yl2BmHwbr/9bMWoSFdTkwGngXuDIs1uFm9rKZfW1mP4etv5iZvWpmi8xsjJmNDVs2xcyaBvd/N7OngzhnmVliMP9iM5ttZnPNbMKe+Tl4ErjCzBoCfYFbDukfQCSLjckbSKhcJWM6oXIiG5PX7zNu+pSJdO96MQ/dfQv3PPzkPssnj/+cs8/teFAxbNq4gfjKe98GlRIS2bQxOfOY5A3MnDaJ8y/pclDbyKutm5KpEL83pgqVEti6KXmfcb8sXchTt1/LK0/cxdpVPxdKbIVtw/r1VKm69zVSOTGR9ev3fY1MnDCeThedT8+b/s0TvQvuT/wikbYpeQMJiXvfE/EJlbP93Jzx1USuu7ITj9zTk7t6PRGMTaTLVf/gms7ncdUlHTi6dBmaNDvroOLYvCmZSgl7P6cqxieyJcvn1OaNG5jz9RQ6XHh5pvkb1q2hTLnyDH7hCXrd0o2hLz7Fzp0pBxVHYTLL/1ukFETiXdLM5oXdurr7NqAnMNzMrgQquPtQMzsXqA+cATQEmphZ62A9xwP/c/dGwEnApUAzdz8NeC58g2ZWEegMnOzupwJPBYv6Ay+6++mEEu3Xwx52FfBOcLsqyz5UBVoCFxFKfAEuA+oApwDXAzm1pxwNzArinArcEMyfDpwZ7M+7wH05PB53/xO4J3j8u+7+U05jRQ6K+z6zsqtot2zbnuHvjebJZ/szfPCATMtSU1P5etoUWp99bn6FsE8EQ195nu7/vp2YmJiD2kaeY8pmXtYqbs26x/PU0I94uP//aHdhFwY980ChxFbYPJtnI7uKdvsO5/DJmHG89MpABr7SvzBCE4mI3L4nWrRpz3/f/YTH+77EiKEDAdixfTszp01mxAdjefvT8exMSWHiuDEHGciB43hz0H+48l+3UizLZ2d6Whorly+lw0VdeGbgWxQvUYLR7w0/uDjkoBTEwZUp7t4w60x3H29mVwADgdOC2ecGt7nBdGlCifgq4Fd3nxXM7wC8ESSkuPvmLKvfDuwEXg/6oceEPe6ksBdkWTMrA5QC6gHT3d3NbLeZNXD3hcG4Ue6eDiwOq0y3BEYG89eZWea/y+/1V9j2vwPOCe7XAN4zs6rAUcAvOTyeYB9Hm9lW4NXslptZD6AHwODBg+nRo0d2w0SyFV85keQN6zKmkzesp1JC5RzHn9qoKWvXJLFt6xbKlQ/92fKbmdOof/yJVKwUf3AxJFRm44a91aJNyeupmOVPrz8tXczzT4YS2+3btvLdrOkUi4mleat2B7XNA6lQKYEtG/fGtGVTMuUqZt6/kqWOzrjfoOlZvDO4H79v30rpsuULJKZISUyswrrf9r5GNqxfT+XKOb9GmjQ9ndWrV7Fly2YqZGkLEikK4hMSSV6/9z2xMXkDleJzfk+c0qgJvz21mm1bt/DD999SpVp1ygfvjRZt27N4wQ+0P/+iPMdRMb4ym8Iq7Zs3rqd8ls+pX35awoA+DwGwY/tWfvj2a2JiYqh3QgMqxlem3gkNADijVXtGvzcizzEUtqLUxFZopxMM+rRPBFKAPZ/KBvQJ6wev5+7/DZb9Ef5wsi9GAeDuuwlVzT8kVBnfc3RPMaB52Pqru/sOoCtQAfjFzFYSqmRfGbbKXVm2Hf7/A0l1z/g5msbeHzevAAPc/RTg30CJXKwrPbjtw92HuHtTd2+qpFvy6oQTG7Bm9a/8tjaJ1NRUJo//nLNatc00Zs3qVex5KS/7cTGpu1MpW658xvJJXx58mwlA/RNOZm3SKtb9tobU1FSmTvqCM1pkjuG/733Gf98by3/fG8tZbTpw050PFljSDVC7/ols+C2JjevXsjs1lTnTJnDqGS0zjdm2ZVPG87Jy2WI83Tk6n3vNo8HJDU5h1aqVJCWtJvWvvxg39jPatDs705hVv/6a8VwsWbyI1NRUypevkN3qRA57x594MmuSVrEu+NycMmEcZ7Zsk2nMmqS9n5s/LV3C7tTQ52blxCosWTSfnTtTcHfmzZlNrToHd3jZscefxLq1q9iwbg27U1OZ9dV4mpzZOtOYl0Z8Qv//fUr//33KGS3PpnvP+2l6VlvKV4ynUkIia1evBGDR3G+pXuvg4ihUVgC3CCnM0wneCSwBegHDzKw58AXQ28zecvffzaw6kJrNY78EHjWzt939TzOrGF71NrPSQCl3H2tms4DlYY/rCTwfjGvo7vMItZac7+4zg/nHAOOBh/cT/3TgH2Y2AkgA2gJv52H/ywFrgvv/yMPjRPJdTGwst97Ti/tvv5H09DQ6XtSZOsfWY/RH7wNw8WV/Y+rk8Yz/fDSxsbEcVbw4j/R+PtPBlt99M5M7H3j0kGK48Y77eeyem0lPT6fDBZ2ofUxdPv9kJAAdO12x38c//8QDLJj3Hdu3baV7l/O4+p83cu6FnQ86HoCYmFiu7HEXrzx+J+npaZzV/iKq1TqWqZ9/DEDrjp2Z+/Vkpn7+McViYog7qjjX3fNkkTyoMDY2lgcfepSbelxPenoal3a+nHr16vP+e+8A8LeuVzFh/BeM/vQT4mJjKV6iBM/1e7FIPhcS/Ub06U6rJvWJL1+a5eN603vQWEaMmpmv24iJjeWWux6k1503kZ6WzrkXXUqdY+sx5uPQ5+ZFnf/G9MkTmDBuNLGxcRQ/qji9ej+HmXHCyafSqt053NL9ylDl+bgT6Njp4I5diYmJpfvN9/HsQ7eRnp5Gm3MvoUadukz47EOAffq6s7r25nt49blH2Z2aSuWq1fn3XQf/OV7Umdn5hNqWY4DXczphiJmdDswCurr7fs9va55do+WhBZn1dILjgGHAJ8AZ7r7DzP4D7HD3x8zsdkI90wC/A38nVCke4+4Nwtb7AHAtoVaOse7ey8yGE2rrmBGsvwSh3zH93H2EmcUTam05kdCPjKmEerZnADXCKtOY2ffATcFtzJ4nzsx+d/fSQcX+VaA1sAwoTugUguPNbApwj7vP2TM+eGwX4CJ3725mnYAXCSXfs4DT3b3tAZ7LlUBTd9+4/2c9578GyJEractfkQ4BgD937Y50CCRtjY6Dh84+oVKkQ2Bn5P85ACihq0hINko26hnpEFgyITpOFbpxe+Q/w5seUzYqfkn/tD4l3/Oc+okl97tvZhZDKN87B0gCvgWuynoWu2DceEItz8MKPfEuysysdFCZrwR8A7Rw93UHelwh0D+i7EOJ915KvPdS4i3RTIn3Xkq894pQ4t0ceNzdzwumHwRw9z5Zxt1BqFvjdMIKtznRR1/ejAnO/30U0DtKkm4RERGRIitCHWzVgdVh00lAs/ABQYt0Z+BsQon3ASnxzoMDtYbklZn9E7g9y+wZ7q7zdouIiIhQMMdChp8dLjDE3YccYLNZK+8vAfe7e1puj29R4h1B7v4G8Eak4xARERE5kgRJ9pD9DEkCaoZN1wDWZhnTFHg3SLrjgQvMbLe7j8pppUq8RURERCR6RabV5FugfnDmuzWETjt9dfgAd884F+OeE37sL+kGJd4iIiIiIpm4+24z60no1NcxhM5YssjMbgyWDzqY9SrxFhEREZGoZREqebv7WGBslnnZJtzu3j0361TiLSIiIiJRqyhdl6vQLhkvIiIiInIkU8VbpIgqERcdv6trVCgV6RD4Zs3mSIcQNXTNNIlm0XDxmhM73BPpEAAo3bBVpEMg+Y2ukQ4BiNSxlQUjOr6ZRURERESKOFW8RURERCR6FaGStyreIiIiIiKFQBVvEREREYlakTqdYEFQ4i0iIiIiUUunExQRERERkTxRxVtEREREolYRKnir4i0iIiIiUhhU8RY5Qs36ehov9etLeloaF196Odf884ZMy6dNmcTQ117BihkxMbHcfvf9nNaoScbytLQ0rrvmbyQkJPJ8/1cLO/x8tfyHb/jifwPx9HQatbuAFpdclWn5ysXzeP+FRylfuQoAJ5zektaXXQvAy7ddzVElS1GsWDGKFYvh+qdfK/T4C8qM6VN5ru/TpKel0/nyK/jX9T0yLZ88aQKvvtIfK1aM2JgY7n2gF40aN41QtFLUfTtrBoNeepa0tHQ6XtyZrtdel2n511Mn87+hA7FixYiJieHG2++lwWmNAfjo3Tf5fPRHGMYxdetz90NPclTx4vke46DHutGxdQOSN++g6RXP5Pv69zi7QRWevroRMcWM/5v6My+P/THT8jIl43itRzOqVzya2Bjj1XFLeWf6L9StUobXb2qeMa52Qmme/Xghg8cvK7BY80NR6vGO+sTbzB4CrgbSgHRgC1ABKA0kAL8EQ29296+zefxFQG9C1f04oL+7Dzazx4Hf3b1AL5NlZm2BT8Li3OjuHXKzfTO7FFjm7osLMkY58qSlpfFC36d56dWhVE5M5PprutKyTTuOObZexpgmZzSjZZt2mBnLf1rKI/ffzTsfjclYPvKdN6lT51j++OOPSOxCvklPT2PcGy/T7cHnKFspgdcfvpnjGjcnoUadTONqndCAK+/N/ov02odeoFTZcoUQbeFJS0ujz1NPMmjoGyRWSaRb1y60aXc2devufY00O7M5bdu1x8xYtvRH7rvnDkaNHhfBqKWoSktLY2C/Z+jTfzDxlRO59bqrObNVW2ofUzdjTKOmzWjeqi1mxs/Ll/H0w/fy33c/YWPyekaNfJuhb39M8eIleOrhe5kyYRznXtgp3+N8c/QsBr33Fa/3vjbf171HMTP6XtOEK/pNYe3mFL589BzGzVvLsrXbM8Zcd3Y9lq7dzt/7T6dSmeLMfKYjH8z8lRXrdtDusS8z1rPgxYv57PukAos1/xSdzDuqW03MrDlwEdDY3U8FOgDd3L0hcD0wzd0bBrfsku44YAhwsbufBjQCphRW/GHC4+yQh8ddCpxUQDHJEWzJogXUqFmT6jVqEhd3FO3PvYBpUyZnGlOq1NFYUGbYmZKScR9gw/p1fD19Khdfenmhxl0Q1i7/kQqJ1amQWI2Y2DhObt6Opd/t83FyxFm4YD41a9WmRs3Qa+S8jhcyZdLETGPCXyMpKSlF6pRfEl2WLl5ItRo1qVq9BnFxcbTtcD4zp03JNKZkqVI5fmalpaWxa9cu0nbvZtfOFCrFJxRInDO+X8HmbX8WyLr3aHxsRVZu2MGvyX+QmpbOqG9W0bFR9UxjHChdIg6Ao4vHsvWPv9idnp5pTOuTKrNywx8kbSrYeCWzaK94VyVUId4F4O4b8/j4MoT2cVPw+F3A0qyDzKwhMAgoBawA/uXuW8xsCjAPOAMoG8z/xsyOBl4BTgnW/7i7f5LXnQvb/g1AD+AoYDlwDdAQuARoY2YPA5e7+4qD3YZIuOQN66mcWDVjunJiIosWzt9n3FeTJjBowEts2bKJfv33tlD0f6EvN99+N38e5tVugO1bNlK20t4v4bIVE1izfMk+45J+WszgB26gTIVKdOh2I5WDiriZ8Vbf+wCjSfuLaNz+okKKvGBt2LCeKlWqZEwnJiayYMG+r5FJE8bzcv8X2LxpM6+8OrgwQ5QjyKbkDSQk7n09xidU5sfFC/YZN+OriQx77WW2btlM734DgrGJdLnqH1zT+TyKFy9B4zOa06TZWYUWe36rWqEkazanZEyv3fwnTepWyjTm9Yk/8X+3tWThi5dQukQsN7w2E/fM6+ncrBYfzf61MEI+ZEWp1SSqK97Al0BNM1tmZq+aWZu8PNjdNwOfAr+a2Ttm1s3Mstvn/wH3B1X1BcBjYcuOdvezgJuBYcG8h4BJ7n460A54PkjGc9LKzOYFt4eyWf6Ru58eVOWXANcFFfxPgXuDSrmSbsk3WT+AgUzVoT3anN2Bdz4aQ98XXmHoa68AMGPqFCpUqMgJJ55c0GEWjlw8F1Xr1Oe2l9/h332Hcvq5nRn5wqMZy7o/3p8bnhnM1ff34dvxn/Drkn2T08ORZ/Miye41cnaHcxg1ehwvvjyQVwf0L4zQ5Ajk2bxRs3s9tmjTnv+++wmP932JEUMHArBj+3ZmTpvMiA/G8van49mZksLEcWP2eezhIrscNOvb9ewGVVi4aisN7vyUdo99SZ+/N6Z0ib211riYYpzXsDqffru6YIOVfUR14u3uvwNNCFWDk4H3zKx7HtdxPdAe+Aa4h73JMwBmVg4o7+5fBbNGAK3DhrwTrGcqUNbMygPnAg+Y2TxCrSslgFr7CSO81eTpbJY3MLNpZrYA6AYcMKMxsx5mNsfM5gwZMuRAw0UyqZyYyIb1v2VMb1i/nvj4yjmOb9i4KWuSVrN1yxbm/zCX6VOncPlF5/BYr3v47tvZPPHw/YURdoEoWzGe7ZuSM6a3b06mdIXM1aPipY7mqBIlAajfqBlpabv5c/s2AMpUiAfg6HIVOKFpS9auyHyQ0+EqMbEK69aty5hev349CQk5v0aaND2d1atXsWXL5sIIT44w8QmJJK/f+3rcmLyBSvv5zDqlURN+W7OabVu3MHfOLKpUq075ChWJjY2jRdv2LF7wQ2GEXSDWbkmhesWSGdPVKpZi3daUTGOuankMn30X6t3+ZcPvrNr4B/Wrls1Y3v7UKsz/dQvJ23cVTtCHyArgFilRnXgDuHuau09x98eAnkCem0rdfYG7vwiccxCPz/oz2wn9m10elkzXcvd9/zade8OBnu5+CvAEoUR+/0G5D3H3pu7etEePHgcaLpLJCSc1IGn1KtauSSI19S8mfjmWlm3aZRqTtPrXjKrn0iWLSU1NpVz58tx0652M+nwSH44ZzxPP9KPJ6c147KlnI7Eb+aJa3RPYvG4NWzb8RtruVBbNnMxxTTL/Gfr3rZsznos1y3/E3SlZpix/7UxhV0qoP/KvnSn8vGAOCTXrFPYuFIiTG5zCqlUrWZO0mtTUv/ji889o0+7sTGNWrdr7GlmyeBGpqamUL18hEuFKEXf8iSezJmkV69YmkZqaypQJ4zizZeY/gq9JWpXxevxp6RJ2p6ZStlx5KidWYcmi+ezcmYK7M2/ObGrVOSYSu5Ev5v6ymWMql6FW/NHExRTj0jNqMW7umkxjkjb9SauTEgFIKFucelXK8Gvy7xnLL2tWm49nryrUuA+FWf7fIiWqe7zN7Hgg3d1/CmY1BHLdkGRmpYGm7j4lp8e7+zYz22Jmrdx9GqH+6q/ChnQFJptZS2BbMP4L4FYzu9Xd3cwaufvcg9jFPcoAvwUHg3YD9ryDdgTLRPJVbGwsd973EHf17EFaWjoXderMsXXr8fEH7wHQuUtXpkwcz+ef/X975x0nVXn94eewixRpIk1RRLGLomBBUQG7sVckxhI1pthii7FEDVaMXWON3VijBtDEDtgVUCyI2MCCUbpUFXa/vz/eO+zsMFvg57z3wp7Hz36YW2bP19mZO+ee95ShlJeX06RJUwZddmXRpd3lnUZlZexx9Ek8cPlZqLKSHv32pMMaXRnz/DAAeu2yD+PffInRzw+lUVkZjVdqwoEnnYeZMe/7mTxyTchMq6yooHufnVm3x9Zp/u/8bJSXl/Pnc87n9789jsqKCvY74CDWXXc9Hn34QQAOGTCQF557hmFDh1BeXk7Tpk254sprVsj3iJM+ZeXlnHDa2Zxz6u+prKhkt733p+s66/LkE48AsPcBh/LK8Od5/ulhlJc3pslKTTjnoiswMzbcZDN26L8rJxx9GGVlZay7/obsud/BJdF5z2VHs0Ov9WjXpgWfPn0RF93yH+759+s/q42KSnH2P9/mkdP70qiR8eDLnzPhm9kc1S90eLlnxGdcNWwcNxy7DSMv2h3DGPToe8yY+xMAzVYqo+8mHTn9ntE/qy6nflixPL6sYGa9CEWMbYBFhMLD4yVNS9r0nSGpxkomM2sJPAx0AxYA84BTJI3Ob+dXUFz5OfDrvOLK14G+VC+ubAZcC2xHiH5PqklHTToL7P8e+BPhpuB9oKWko82sD3A78CNwcC153tn9IzqpMW3uorQlANCuRfr39/ePyUa7rF/1WiNtCSxYmLaCQLPGaStwssik6T+kLYGNdjkjbQkAtNh8h7QlMPWuAZm4k/72+4U/u5/TqXXjVP7fMu14p03ieJ8hKeu3hf5HdJbAHe8q3PGuwh1vJ8u4412FO95VrEiOd/rfiI7jOI7jOI5TE5lw/38eVhjH28yeAAqrJc6S9Myy/k5J/ZbC/u5AYYXZREkHLKt9x3Ecx3EcZ8VhhXG803ZwEwd/mZ18x3Ecx3EcZ0lWoID3iuN4O47jOI7jOCseK1KzpMz38XYcx3Ecx3GcFQGPeDuO4ziO4ziZxVagZBOPeDuO4ziO4zhOBDzi7TiO4ziO42SXFSfg7Y6346yozP0xGwN0vpw+P20JTJi6IG0JmeH7jEzQadbYJ+g4SzJt9k9pS8jE4BqAuWNfTlsCMCBtAcAK5Xd7qonjOI7jOI7jxMAj3o7jOI7jOE5m8XaCjuM4juM4juMsFR7xdhzHcRzHcTLLitRO0B1vx3Ecx3EcJ7N4qonjOI7jOI7jOEuFO96O4ziO4ziOEwF3vB3HcRzHcRwnAp7j7TgNlFFvvMot1w6moqKSPfc5gAFHHlvt+GsvDefe2/+ONWpEWVkZvzvlTLr36AnA4w/dx3+HPY5hrN1tPU4/dxArNWmy1BrGjnqNe2++isrKSvrvsR/7HXZ00fM+mzCOv5xyDKeccynb7LgzACcdsS/NmjWnUaNGNCor59K/37vU9nP8b/wYxj5+G6qsZO3eu7HRrocscc6UT95j7BO3U1lRQZOVW9H/5MuZP3Mqb95/NT/MmYlZI9bZdnfW77ffMutImzdfe4UbrrqcysoK9trvIA4/+rhqx18Z+SJ33HIDjawRZeVlnHjan9ls8/CemDNnNn+7+AImfvYpGJz1l4vovtnmKfxfOCsq745+jfuS60W/PfZj3wFHFz3vswnjuODUYzjp7EvZZodwvZg3dw63X3sxX0/6DDPj+FP/wnobb7bUGnbq3olLfrkFZY2M+1/6nOv/81G14y2bNebm47ehc9uVKS8zbnp6Ag++MpFunVryj99vu/i8tdq3YPATH3Drcx8vtYa6uOWCw9lzx+5MnTGHLQ+59Gf//WmwIuV4L5eOt5mdC/wSqAAqgZnAKkALoD0wMTn1D5JeK/L8vYGLCBH/xsB1km41swuBuZKuLLH+o4EtJZ2Yt28EcIak0aW07TgAFRUV/P3KS7nsultp16EjJx37S3rv0I+11u62+JwtttyGbXfoh5nx+acfc8l5Z3LHQ0OYNvU7/v3oA9z+wBM0adKUi887kxHPP81uey2dw1lZUcFdN17BOZffyKrtOnLuSUfRa9sdWWOtdZY474F/3EiPXr2X+B3n/e0WWrVus0yvweLfX1nB24/eTN8/XEyzNqvy/FWnsvqm29C6U5fF5/w0fy5vP3ozO/zur6zctgM/zJkFgDUqY/P9j2WVNddl4Q/zee7KP9Jxwy2qPXd5oaKigmuvuJirbryd9h078dujBtBnx/50XafqPdFzq9702bE/ZsZnn0zgwrPP4L5/DQPghqsuZ+tt+zBo8DUsXLiQH37waaHOz0dlRQV3//0Kzr70Rtq268hfTj6Knr2LXy8euvNGNiu4Xtx3y1X06LUtfzxvMIsWLuTHH39Yag2NzLj8iF4ccuUIvpmxgGfP35Wnx37Dx9/MXnzOsTuty4RvZvOr615h1ZZNeP3SPfnX61/w2bdz6H/Bs4t/z/vX7MNTb3+9DK9E3dw37A1ueXgk/7joyJL8fuf/x3KXamJm2wJ7Az0lbQbsAhwuaXPgOOBlSZsnP8Wc7sbAbcA+knoAWwAjYul3nCww4cMPWH2NNVmt8xo0btyYfrvswesvj6h2TrPmzbEkzPDDggWLH0Nw0n788UcqFi3ixx8WsGq79kut4dMJ4+i0+pp0XG0Nyhs3Ztu+uzL6tZFLnPf0kIfZZof+tGqzylLbqA8zvviYFu1Xo0W7TpSVN6ZLzx355v03qp3z5ZiRdO6xHSu37QBA05ZtAGjWui2rrLkuAI2bNqdVxzVZMGt6SXSWmvHj3qfzml1YfY01ady4MTvtuievjHyx2jnN894TCxYsWDzHed7cubz7zhj22u8gABo3bkzLlq2i6ndWbD6bMI6Oq61Jh+R60bvvrox5fcnrxTNDH2arPv1p1brqejF/3lw+ev8d+u0RggPljRuzcouWS62h5zptmTRlDl9MncfCikr+/daX7LlF52rnCGjRtDEAKzcpZ9a8n1hUWVntnB037sCkKfP4evr8pdZQH159+zNmfF+a350WVoL/0mJ5jHivBkyT9COApGlL+fyWhP/v6cnzfwQmFJ5kZpsDtwDNgc+AYyTNTCLTY4GtgVbJ/rfMbGXgBmDT5PdfKGnI0v7PJbbnArcC/QnR/MMkTV2W3+U4xZg+dQrtO3ZavN2ufQc++vD9Jc57deQL3Hnz9cyaOYOLrrwxObcjBw88iiMO2J0mTZrSc+tt6bXNdkutYea0qazavuPi7VXbd+TTjz6ods6MaVMY9eoI/nLFzXw24cNqxwzjsrNPxDB23usAdt7rwKXWALDg++k0b1N149CsTTtmfFH9kjBn6mQqKyoYfsOfWfTDAtbruy9dt9652jnzpn/HrK8/Z9WuGyyTjrSZNnUKHfLeE+07dmT8B0u+J14a/jy3//06Zs6czuXX3ATAN5O/pk2bVbj8r+fx6ScT2GCjjTnp9D/TrFnzaPqdFZsZ06tfL9q268hnE5a8Xox+bQTnXn4zt31cdb2Y8u1kWrZuw61X/ZUvJ37C2utuxBG/P52mTZstlYbVVmnG5BlVKznfzJhPr26rVjvnHy98wv0nb88H1+xLi6bl/Obm15Gq/54DtunC429+sVS2GzorUqrJchfxBp4F1jSzj83sJjPruzRPljQDGAp8YWYPmtnhZlbsdbgXOCuJqr8PXJB3bGVJ2wF/AO5M9p0LvChpK4LD/LfEGV8WVgbeltQTGFlg23H+3wgtsc+KXNn69N2ZOx4awoWXX8s9t/8dgDmzZ/P6y8O551//4YGhz/HDggW88PSTP4uGwqvrvTdfzS+PO4lGZWVLnHrhtf/gspvu56xLruPZYf9i/HtvL7WGREgRqutQZQUzv/qUHY6/kB1/P4gPn3mIOVMmLz6+8McFvHbnpWx+4G9o3HT5dDZV6B1A0W+7Hfvvwn3/GsYlf7ueO28JN2MVFYv4ZMJ49jt4AHf88180bdqMB+6+o9SSnYZEkfdn4TXrvluu5rBjlrxeVFZUMOnTCeyy98Fc+vd/0qRpU4Y9fPdSSyjm+xXK2ql7Jz74chbdTx1K/wue5bJf9aRF06oYZ+OyRuy+eWeGjvpqqe07KwbLneMtaS7QCzgemAo8nORML83vOA7YGXgLOIMq5xkAM2sNtJGUW8e6B9gx75QHk9/zEtDKzNoAuwF/NrOxhNSVpkBNiZ5Fv+rz9lcCDyeP7we2LzzRzI43s9FmNvq2226r4dc5TnHate/I1O++Xbw9beoUVm3XocbzN92iF/+b/BXfz5rJO6PfoNPqnWmzSlvKyxvTp9/OfPj+u0utoW27Dkyf+t3i7elTv2OVtu2qnfP5x+O5/tJzOemIfXnz5Re584bBjHp1RHj+qiFK3XqVtmy1XT8+mzBuqTUANGuzKvNnVS0oLZg1jWat21Y/p3U7Om3Uk/ImTWnSojXtu3Vn1uRQSlJZsYjX7ryULlv2Y40eSx/5zwrtO3RkSt57Yup339GulhSiHj23ZPLkr5g1aybtO3SifYeObNw9FKv13Xk3Pi5YoXCc/w+F14sZ076jTcH1YuIn47nxsnM55ch9eeuVF7n7xsGMfm0Ebdt1oG27Dqy7YXcAtt5hZyZ9usRCd518M3MBndtWRclXb9ucb2dVr2UYuP3aPDUm5G5PnDKXL6fNY73VqtKudt6sE+99MZOps39cavsNGSvBT1osd443gKQKSSMkXQCcCBy0DL/jfUnXALsuw/MLHWcR/o4H5eWXd5E0vobnTycUg+bTFqgpbWYJR13SbZK2lLTl8ccfvzTaHYcNNtqEyV9/ybfffM3ChQsZ8fzT9N6++uLR5K+/XBwF/WTCeBYtXEir1m3o0LET48e9xw8/LEASY0e/SZeuay+1hm4bbMy3k79kyv8ms2jhQl4f+Ry9tt2x2jnX3zeEG+4byg33DWWbHXbimJPOYqs+/fhhwQIWzJ8HhPzz995+gzW6ditmpk7adlmfuVO/Ye70b6lYtJAv336J1btvU+2czpv2Ztpn46isqGDRTz8w/YsJtOq4BpIY9eB1tOq4Jhv0P2CZ7GeFDTfuztdffsn/Jof3xIvP/Zc+O/avds7XX1W9Jz7+6EMWLVxI69ZtWLVdO9p37MSXk8LNyNuj3qDr2sv293CcYqyzwcZ8+82XTPk2XC/eGPkcvXpXv15ce88Qrrt3KNfdO5Stt9+Jo088iy2360ebtu1YtX1HvvlqEgDj3hlF5y5Lf816Z+IM1u7Qki7tVqZxWSP237oLT78zudo5X0+fzw4bh5SY9q2asG6nlnwxde7i4wdusxZPvPnlUtt2VhyWuxxvM9sAqJT0SbJrc6DeyVJm1oLQUWRETc+X9L2ZzTSzHSS9DBxBSPnIMQAYbmbbA98n5z8DnGRmJ0mSmW0h6Z0aZIwCbjSzTpK+NbMtgSZAbu2pEXAw8BChe8sr9f3/c5z6UFZezgmnnc05p/6eyopKdtt7f7qusy5PPvEIAHsfcCivDH+e558eRnl5Y5qs1IRzLroCM2PDTTZjh/67csLRh1FWVsa662/InvsdvPQayso5+sQ/cdk5J1NZWUG/3fdlza7deO7JxwDYde+a74e/nzWdq//6JyCkOfTpvwebb7Vs0eZGZWX0POh3vHTz+Uk7wV1pvdpafPrKfwBYd/tf0KrTmnTaqBfPDj4RzFhn291pvXpXpn42ji9GDaf1al159oqTANh0ryNZbZOtlklLmpSXl/PHP53DGSf/lsqKCn6x7wGs3W1dhjwWFt/2O2gAL734HM88NZTy8nJWatqUCy69cvFy/ylnnMPF55/FwoULWb3zmvz5/IvS/N9xVjDKyso5+g9/YvC54XrRd7d9WaNrN55/Klwvdtmr9vjZkX84g5uuOJ9FCxfSYbXO/Pa085daQ0WlOPufb/PI6X1p1Mh48OXPmfDNbI7qF24y7xnxGVcNG8cNx27DyIt2xzAGPfoeM+b+BECzlcrou0lHTr+ntM3L7rnsaHbotR7t2rTg06cv4qJb/sM9/369pDZLzgqU421F8/oyjJn1IhQxtgEWAZ8Cx0uaZmb9CC359q7l+S0JaRzdgAXAPOAUSaPz2wkWFFd+Dvw6r7jydaAv1YsrmwHXAtsR3iKT6tCxHyF3uxEwFzhZ0tvJsbnANcAvgO+BAXUUVy5ff0QnCpOmL327rFKQ+9JJkyfGf1f3SRG4aI/10pbAt7MXpi0BgE6tGqctwckgoyfOrvukErPnoP+mLQGAuWNfTlsCC965MRMu79wff35ntUWTdEo2l7uIt6QxBOe22LER1NEaUNIcgkNb7NiFeY/HAks2Dg48JunsgucuAH5bm+2C84cANXY9kfQX4C/1/X2O4ziO4zhOtlnuHG/HcRzHcRyn4bAitRNcoR1vM3sCKKygOEvSM8v6OyX1Wwr7uwODC3ZPlFRrFZakFssgzXEcx3Ecx8kwK7TjXZeDG8H+M8AyO/mO4ziO4zgNnRUo4L1iO96O4ziO4zjOcs4K5Hkvl328HcdxHMdxHGd5wyPejuM4juM4TmaxFSjk7RFvx3Ecx3Ecx4mAR7wdx3Ecx3GczLIitRP0iLfjOI7jOI7jRGC5GxnvlAYzO17Sba4jGxqyoiMLGrKiIwsasqIjCxqyosM1ZEtHFjRkRUcWNDhL4hFvJ8fxaQtIyIKOLGiAbOjIggbIho4saIBs6MiCBsiGDtdQRRZ0ZEEDZENHFjQ4Bbjj7TiO4ziO4zgRcMfbcRzHcRzHcSLgjreTIyt5YFnQkQUNkA0dWdAA2dCRBQ2QDR1Z0ADZ0OEaqsiCjixogGzoyIIGpwAvrnQcx3Ecx3GcCHjE23Ecx3Ecx3Ei4I634ziO4ziO40TAHW/HcZYLzKxJ2hqc6phZx7Q1ZAUza5y2hpiY2YFpa8gaZrZ2ffY5DRvP8W6gJF+YlwKrS9rTzDYGtpV0R0QNHYBzgHWB94HLJM2OZT/RUOuXh6THY2nJJ/kS7w5MljQlDQ15WtoAJ0i6JKLNOyUdk7fdAhgiaedYGhK7zYHTgS6SfmNm6wEbSHoypo4sYWatgYOAXwIbSeoc0XY7SdNi2asLMzOgP+G12EdSlBuRLFy3zOxtST1LbaceOg6V9EjaOqD4a2JmYyT1iqhhVeBCoA8g4BVgkKTpsTQ4tVOetgAnNe4G7gLOTbY/Bh4GojnewL3AGOAGYG/geuDoiPYB9qnlmIAojreZ3QLcIGlc4ti8DlQAbc3sDEkPRtCwJvAXYHXg38ADwEXAEUDJ7Rcw2cxulvR7M1sFeAq4PbIGCJ+RMcC2yfbXwKNAFMfbzN6r6RAgSZtF0tEM2JfgYPYEWgL7Ay9Fsr8PcCewyMwqgEMlvRbDdg16tiG8FgcAbYETgDMjSsjEdSsjHGlmxwB/kPR5GgLMbENgE6B1wU1RK6BpZDkPET6XByXbhxO+23eJrMOpAY94N1DMbJSkrczsHUlbJPvGSto8ooZq9rISQUkDMxsnaZPk8R+BfpL2N7NOwH9zf6MSaxgOjCQ4/XsAOwPjgFMlfVtq+0X0DAZaA72AyyU9loKG0ZK2LPicvCupRyT7YwmO1APAMGBB/nFJX0TQ8E9gR+BZwpf6i8CnkqItoSc3IIdK+ihxeq+Q1DeW/TwdlwCHAl8SbkafAEbHfC2ygpnNBz4tdoiIN4WJlv2Bywifk5uBytwxSTMi2N+PcCO6LzA079Ac4KGYN4nFIuy561gsDU7teMS74TIvWZISgJn1Br6PrMGSaKYl22X52zEumHlCWgMXEBwMCA7oIEmxXpOf8h7vSoiqIunbsJodhbaSLkweP2Nm3wFbSfoxloCCaNFbhAj8W4DM7MAUUn9+SqK9uc9JNyDa6yFp8ySaNpDgVHyY/PuspEWRZHQHZgLjgY8kVZhZ7IjNIkkfAUh608xaRraf43hgAsG5e1LSDym8FtUws70I0dbFkVVJgyKYnkjtkfdoSPq3mU0kRHqPJfm8Jv+uE8H+EGCImW0r6fVS26uD4WZ2GJBLvzmYsGLoZASPeDdQzKwnIcWjO/AB0B44WFJNS9ul0DCJEJko5llKUskvmHlaHiO8Dvcku44AekiKUkCURJuvAiYDw4ENE6e7HPhA0oYRNLwL9KPq7zE8fztS5OiuWg4rP+87Bma2K3AesDEh4tsHOFrSiJg68vQMAP4ODJb0t4h2NySkVgwApgAbApvGWgkxs6+Bq/N2nZa/LenqJZ5UGh1lwG6EG6GdCJ+RXYA1I94I5eu5BWhOyDP/B8HJekvSsRFsL14FSpOk6Po8wv/7mWnWX5hZU4LjX3gjFO26ZWZzgJUJqYpGaKIxr0qKWsXS4hTHHe8GTOLUbUD4cE6QtDBlSalRLM0mZuqNma1PyHHvBFwr6e5k/+7AbpJOj6BhEhm5EcoSycpQb8Lr8kbs4j4z6wwcRsgnnkmIZD0haW4k+70lvZG3vSXB8TwE+FrSdhE0XFDLYUWK8GJmXSR9mTxuSqhNGQhsD7wg6ZcxdOTpeU/SZnn/tgAel7RbBNs3Sjqx1HbqoWMC8BhwkaQFdZ1fYi2PAh8RblIHEfKrx0s6JU1dTrZwx7uBUkNV/PfA+zG7aCTOf4UkJcV92xDyR8fG0pDoeJ0QLXkl2e4DXClp29qf6ZSCLESOEh19gLGS5pnZrwiFhdfFyK1O7I8kFDI+AvwLqLbqEGkVomjtRdLRY0dJI0utoTbMbCtJoyLZqum1aAUcIOmeIk8rpZ43JW1jZm8ABwLTCStk60WwvQ/wXu6zYGbnEwr6vgBOkTSx1BoSuxtL+rAe590g6aQSa3lH0hZ5N0KNgWck7VRKu4ntwvelgGmSviq1bWfp8BzvhsuxhE4Nw5PtfsAbwPpmNkjSfaUWYGa/AQYDc83sIkJXgLeBLSy0kxtcag15/A64N8n1hhBZPCqWcTNrR+iMMIPQSeNvwA7AZ8DpkooVMf3cGmotbJX0dqk15HEfIXK0O3mRo4j2c9wM9DCzHoT3552EbjyxCvvWInyB/paQX5zDiJS/WhMKUZtUnG4L7U8PI0SbvwdiFY4VLbhQaIMa1elOeNJCu8+/Ea6dIqScxOASwkoQZrY38CvC32ML4BbCZ7fk1MfpTuhTUiGB3KrxLDPrDnwLdI1gF0KqYiFtzWwlYGDsYJZTMx7xbqCY2TDgOEnfJdsdCU7GccBLkrpH0DCOsETbkuBUrSVpmoXeyaNyXT4i6CgjdM04M4lc5b5Io2FmzwKjCa/FzgTnexjB+T5cUr8IGobnbfYitNHLoRhRmzwtqUWOCnS8LalnEs2bLOmOmqKeaWJmm0gaV6LfPYta2gZK2rcUdovoWIvg2A0EFhFuSraUNCmG/UTDFEJnl6JIOjmWlkKSXOemsQrC87v7mNmdhHTFwcl2Fj8jJddkZscR0l42I1zDWwDnS7qllHbr0LQlcLWkHes82YmCR7wbLl1zTnfCFGB9STPMLFau90+SZgIzzezTXO6spPlm9lMdz/3ZSLo09EoeR3W48+go6Zxk+f6LvMK5j8zshBgCJPXPPU4c3/61nV9i0owc5TPHzM4mRPN2TG7Ssjih8D5CGkwpmErxaFo0zOw1QmvJhwhF4J+Y2cSYTnfCAqrfkKZK8n7ci/DZKE/2xSo2tSSnfD4hWHBT3rHYvaszgaTcasNIUlyNykfS6OTv5GQEd7wbLi+b2ZMkbesIuXkvmdnKwKxIGpqZ2RaEquuVkseW/MS+cL9jZkMJr0euAjzm5MqKxJ7MrLB4r7LI+aUm7aWw2yy0lvwLoS9uC+D8FHQMIBRKHZt0melCWNbPGqXsOTkn7TxugvO/BtCR0IHpE9J5j06PncddB8OAHwiTf2NfJ64FxgKzCQWEowGS6/j/ImupDyX7jJjZabUdj9V1pxjJanba13MnD081aaAkkdUDCakeEIpyVpMUJbqaaBhe2/GYEdca2tgpVjFf3nK+EdJLckv7BmwvaZUYOvL0ZG6p2KmZUv69zOxxFbTVTG7QDyDkju5VCrtFdORG1Q8E1gXaALtLeiuG/UTDG5J61+O8kqX+FNh5TxEH1RSx3xnoALwrqTLZtxrQOK/7S5TXoi7M7Ohct6gS/O7auu4g6a+lsFug4QaWdLDbAtsRil2HlVqDUz/c8W7AmNnmhGjeoYRhCI9JujFVUQ0UM6u1WC9GxLHgwn0YBbmsMfNXkyjNpcDqkvZMium2lXRHLA2Jjt6EfvcbASsBZcBcSa1rfWJkIuWvrgT8gnDN2IOQy/p4Gl/oyftjAOF9uqakNWNrqI1YN64Wpru+IOnZUttaViK+Fu2Bswg99/M7IUWtC0kLMytsBiBCQG1UzE5lTt14qkkDw0K/6Fw3gOnAw4QbsNTyeS0DrQ2T1+VmQq51dzPbDNhX0sUx7GdgKR9CcWeOtPNY7yYUJ52bbH9MeK9GdbyBGwmfl0cJnTOOBEreqm0ZKFlNhIUhQgMJXSqGE/LJt5b061LZrIukPuV64Pqk6DJrxBo3+wbwhJk1ItRF5Ma1Z2lISqzX4p+Ea8RehC5VRxFSlEqOmT2rpHe6mZ0t6bIYdvOpbwqUmT0m6aBS63FqxiPeDQwzqwReJuSsfprs+1wpDkcxs6eoobUhYWx7jNaGIwnt4m5VMo3NzD6I0d2lQMf7LLlc+D3BKb5Y0vQIGppK+qFgXztFHBxjZqMkbWV50/Es4kCjPB2jJW2Zv6RvZq8pwtCYAh3H5kf7k6K68yItYeeuGUcr6c0c+5qRpILV9GUlRZjUuDREjPJ+DuxPCFJk8ss84msxRlKvgs/qSEklb/1ZcJ3KdJqeZWTiaEPGI94Nj4MIEbzhZvY0IZ0gVkSiJiqBjbRka8NtCLnOJXe8geaS3gqp74uJPgIa+C+h0PKBZPswwt/ne0IUeJ8IGt4ys+OVTCs0s4OAywg3QrGYZ2FipBINvQmvQWzmJykWY83sCkLR2Mop6Ng5+TscC6xKWA2ItUrSi/A+fD5x9B4ipNzEpNgY8C7AH4mvJUt8QhiYk0mnOzK5Tkj/M7O9gG8IBbkxWJ5e/+VJ6wqJO94NDElPEJYmVyZESk4FOprZzYQx1GnkCmahteE0M+tGlaN3MOlU5veRlD/o4X0ze1VSHwuTE2NwOHCnmY0AVic4erHzJE8jdDPpZmavEjpZHBxZA8ARhK47JxI+K2sSbl6jIumXZjaA0L1iPqGo8dVItt8B3gHOsjDJcyChC9F/CdeM2yJoeCz32MzWAc4BdgQuJ376UX2I1Q71f8CI5G/xY25nml00ihDrtbg4KcA9nVCX0YrwmY3BOklXLMt7vBhF6nXvLB94qomDmbUFDgEGpFGIYmY3EaJX+a0NvyakfjwZI/88+TK/jVABPpNQbHq4Io0Gz9PxLnC8pDeT7a2B2yX1iLlEaGb7E1Ya5hDGgpd8cmYRDeXABoQvswmSYt2EFepoBnSRNCEN+4mG9QiTEd8nFHp+CJwmaX5KehoBuwKH5XK9S929wsw2IuT8b0Fo6Xi/pDRWpXJdoQ4H1pE0KGkz2Slmh5VER9FuGpFSkMqAZpLmJtu9CQXIAO9ImlNqDVkhC8Xx9cVTTdLHHW8ndZIvsYMII30NeIXQYSXam9PM1pY0MVkJaCRpTm5fLA2Jjq0IY8lbEF6L2YRpouOAvSQ9EkHDHUA34NeE9JJrgRsl/b3UtvM0NAX+QGh3KUKO8S2FuecRdOwDXAmsJGntpBPQoNgRLDP7CDhR0vPJ5+U04BhFmu5aH0qZ22pmueLWK4FHSPre55A0oxR2a9FzMyFFbidJG1noOf+spK0iaigD7pEUayWs0P6VwBRJVyTbE4EPCB1F3pZ0VmQ97YHfkDdMCECRWsLWh1iFjbUFC8xstyx3wWkIuOPtOBR3GnLFOinpaU34fM5KwfapwLW5G59Ey9UxC9jM7BFCtP3+ZNdAYBVJh8TSkOgYQ0izGZFXPBW9d7KZtVLBVFUzW0/SJzF11EYpI2lmNomq3NTcv7mCDMUuDs9dLwqK6haPUI+o4xlgH0nRJv3m2X4H2Cq36pB7LZIbw5clbV/7b/jZ9bxGuEEfQ96NWX6aUtrEiDZnJVjg1IzneDupY6Gd4GDCIIbc5EopQkssM9sQ2ARobdXbGrYihbHHZtaEEP3vCpTnij0lDYqlQdI1BdvfE4r6YrJBgRMzPEnDic0iSd8XFN2mQTMzuwboLGkPS/qaE4rrskLJojiSutbnvFKnu+SxMIk4525O25POhNlJwKtJTnH+xN0YOd6NClJ9zkpsy9IZUd48dpR9GYgR6bwQ2BoYASBprJl1jWDXqSeN0hbgOMAVhJ7ZrSW1ktQyhtOdsAGwN2EK3j55Pz0Jy5axGQLsR+ioMi/vJxpmtp6Z/cvMPjSzz3M/MTUA7yQ5ozlN2wBRigkL+MDMfgmUJa/LDcBrKei4G3gGWC3Z/pjQ0cOpTowOSBB6iD9BKEy/hJAed2kk2/l8Q+j40ghomfcTg5XMbLGtXPpCskIWPWgBPGlmv0jBbtZYlARLnIziEW8nC3wnaXwahiUNAYaY2baSXk9DQwFrSNojZQ13ARcA1wD9CbnesUO+2wBHmtmXyXYXYLwlfc4jpnqcRCjo+xF4kOD8XhTJdj7tJD1iZmcDSFpkZhV1PSky0dMdihDlfSrpn0ka0s7Jrv3TuIbliigTB1i5QsdI3A48bGa/U9V4+LUIrWBvj6gjxynAOWb2Iw17mFC1YAFwMukEC5wacMfbyQKjzexh4N9Ub4n1eEQNB5jZOGAB8DTQA/ijpPtrf9rPzmtmtqmk9yPbzaeZpBfMzJKuLhea2csEZzwWad98AJB0DTkXODdJLVg5doFnQup9zevq5CGpd62/IA4xi5aaE3qIC2gW0e5izKw7IcrfNtmeBhwZI91G0tVmNh94JSlKF2F17nJJN5fafhE9sSL9tVJHF6QYqTBZCRY4NeDFlU7qWJhKV4hiVqNbMhXRzA6gqr/58BSKpT4E1iW0M/yRqqhNtGI+C32zdwD+BbwITCZ8mW4QwXbb2o6n0L3iAcL46QpC0Vau0PRvkXX0JPQm7k7oHNEeOFjSexE1pN7Joy5K2VmlwM75hBasjxE+o/sDj0q6uNS2C3S8BpwraXiy3Q+4VPEnq7Yg+BPRWwia2YaSPko+I0sg6e2IWjJT2GhmrQjfHQ2mrePygjvejgOY2ThJm5jZ7YRWhk+n1KVgrWL7FbGfuIWWhuMJee8XEZzNK5RMsiyx7YmEyJkR0ktmJo/bAF9KWrvUGgr05G7IDidMcDwLGBPrRij5W3wl6VsLfc1/Syi+/RA4P+aNSFY6edSGmb0RI/JuZuOBLXKrH0mU821JG5XadoGOJV7/WH8TMzsN+F7SHQX7TwLKJF1bag2JvdskHW9mw4scliLOpshCFySrakmbWwH4ntB6dEwsDU7teKqJkxpm9idJVyQFa0vcAUo6OaKcYRZ6JS8A/pB0KYiWUmBV7eJSj05IGpU8nEvI745pe20AM7sFGCrpP8n2nsAuMbUkNDazxoSI5o2SFppZzGjFrVT9f29HWEI+CdicMPAp5jTP1Dt5ZCjdZRKhgDB3jWgCfBbJdj6fm9lfqCoq/RVhtSwGxxCK0Au5DRhF6P9fciQdn/xb8kFr9SALXZDuAP4g6WUAM9ueULcTtQWqUzPueDtpkitGGp2qCkDSn81sMDBbUkWSu7hfRAkPELqrjKEq4rtYHlDyPsVWMOa4kMjLpVtJ+l2e7f+aWRp5ircSnKx3gZeSFYnZtT7j56UsL6o9ALhNoS/xY2Y2NqIOWLKTx8HAeZE13ESS7gIMItyoPgbETnf5ERhnZs8RPp+7EnKdr4eoQYNjgL8CjxOuGS8R72ZZKtI/XNKPloLnadXbweb4Hnhf0pRIMrJQ2Dgn53QDSHrFzFIP6DhVeKqJkzpmdoikR+vaV2INzQnTALsky5brEXpJPxlLQ9qY2VTgK0JBzpsUVOAr4thjC4NBXiYM0BEhkrejpN1jaagJMytXpFHlZvYBsHnSxeQj4HhJL+WOSeoeQ0eeng2p6uTxYuxOHllJdzGzo2o7LumeWFrSIukwtIuk7wr2dwSel7RpZD1PEXrb51JO+gFvEKbvDpJU8laTyffIucBuhOvnM8BFMQuyLfT7b064jotwwz6TcIMaNefdKY5HvJ0scDZQ6GQX21dK7iJEm3NFSV8n9qM63mb2gqSd69pXIjoRIncDgV8CTwEPxuiQUISBhC4qTyTbLyX7omBmv5J0f5LHWowYA0ogfHmOTLpVLCDcjGBm6xK5q0lC2p08Uk93SZgO/EdSGrZzBek1Rc2kOFNm/wY8ZWanAzlnrhdhLsOVEewXUglslLsRSG4Abia0Jn2JCD3ec12QktXTtAobN0/+LexCtR3hPRMt590pjjveTmokebu/ADrnlmgTWhEGyMSkm6QBZjYQQNKCmMulZtaU4NS0S7pF5Gy3AlaPoUFSBaGV4tMWJmgOBEaY2SBJN8TQkKdlBqEvb1qsnPybaosySZeY2QuEwTnPqmqJshEh1zsaRTp53GVmsTt5ZCHdBeAw4Dozewy4K3bkn+IBgS6EoUplMQRIujdZJRtE6LYjYBxwgaT/xtBQQNeC6PsUYH1JM8xsYQwBhYWNZha9sDEjue5OLXiqiZMaZtaDcHc+CDg/79AcQiu/mRG1vEZYQn81WcruRoj2bh3J/imEL83VCe37co73bOB2STdG0tEE2IvgdHcFhgJ3Spocyf4waunFnEZbLieQoU4eqaa75OloRfic/Jrwnr2LcM2IGuU0s3WAc4AdCUOv7iiWe72iY2Y3EW4+ciulBxFWLs8EnozhkJrZe8AJBYWNN0XuatKaEO3eMdk1kpBq49MsM4I73k7qmFljSQuTx6sAaypif+LE7q6EyNnGwLNAH+BoSSMi6zgpdnQ5z/Y9hMjVf4GHJH2Qgoa+ycPmhH7mlYRuEQsgep55f+BEYMNk13hCZ5MRsTRkCTP7LzBQ0qxkuw1wv6S9I+voCWxPcHZfTTNn1czaEeoP/kh4f6wLXB/jM2xmGxHyibcgpH3cH6v2ILH/iKRDk8eDJZ2Vd+xZSbvF0pLYNIKz3YcQuHiF0Bo2mpNjZq9K6lPXvhJreIzQ6z9XZ3AE0ENSseJTJwXc8XZSx8xGAPsSUp/GAlOBkZJqyq8tlY5Vgd6Ei/YbkqbFtJ9oOAR4WtIcMzuP0K7r4hjOhZlVEibPQfWoc7TRy0nrvksI3Rq+TGyvAdwNnJO7QYugYy/gRsJqzNuJjp6Em7MTc20OGxJm9m9C95BqnTwIS/pROnkUSXfZn4iDa8zsQEmPWxiUcgzQjZA7fI+kKUlx3XhJRfvx/4w6HgW2JORSP0IY8LQYRejvXlDgWm1wUf6xhkQWChstmT1Q1z4nPdzxdlInd5E2s+MI0e4LLNLQAath2lmO2NG03P93skR5GeGL9RxJ28TUkRbJF1cL4LTckn2ypH8lMF/SHyPpGAGcIundgv2bATdI6lv0iSswWejkkXa6S15XlXuBf+Q6zBScs7OkF0qsYxJVN8e5f3PpaZIUo/3oYme7iOMdZYJogZ4DgcFAB8JrES1gkKeh2BCfHFKEYT5m9jpwpqRXku0+wJWSti21bad+eHGlkwXKzWw14FDC0mlMrqrlWBoV4LnI1V7AzZKGmNmFkTWkyd6EgqjFEQFJs83s98BHhCX9GHQqdLoTLe8l3RIaIql28kiYRAYG10g6spZjJXW6Extd63OemW2i0nUlam5mWxAKfZslj3MObxodb64A9kkr5x8yU9j4e+CeJNfbgBnA0akqcqrhjreTBQYR+p2+KmlUUiz0SQzDGblQ5jPZzHLTCgcnxY6NUtYUExXLyVQYahRzeW7eMh5bkUm7kwekP7hmw6SArpBcdDVr0wHvo/h0yZ+D/1HVVvNbqrfY/LZENmvjuzSdbshGYaOksUCPZKUQhYnITobwVBPHYXFu8e+pumCOAG6NlVOcp6M5sAdh2tonyUrAppKejakjLZI84scl3Vuw/1fAobG6mpjZLELv3yUOAdtLWiWGjqyRdiePtNNdzGwcoQVqTfa/KKX9paUh5Vqb2XWEWQT/JtygASDp8YgaUitstJpnDgAgKdbsAacO3PF2UsfM1icMOugoqXuSR7tvrIKpRMM/gMZUv2BWSDouloYCPR0IS+oASPoyDR2xMbPOhPHXCwgDjUQo6GsGHKB4bQ1rzeGO2V0la6TcyWNv0h1cs1w5sjFyrS0bo9pzQ4UKkaRjImpIrbDRzAoH5lRD0l9LrcGpH+54O6ljZiMJvVZvzauSjzoO24qMnS62L4KOfQl556sTukV0AT6StElMHWljZjsBmxAizONi5M3WoOMUSdfVtW9FJiudPBIt9xPGgqeS7mJmN0o6MabN/w+RHO/UR7XXhJltJWlURHte2OjUied4O1mguaS3rPqgyNiTKyvMrJukz2DxUIqKOp5TCi4itDR8Pun00p+Io9KzgqQXgRfT1gEcBRQ62UcX2bcicx5hFeIQ4JrCTh6S5ptZlKiipF/lpbvcleT9R0t3yTndSe3FQYQhU+V5xweVWsNSEmOQTuqj2vMxs40J9QgDCZH3LSOaT62w0cx+A4xIUhQNuIPwHv0COErSOzF0OHXjjreTBaZZmBQpADM7mFC4E5MzgeFm9jnhgrkWIY81NgslTTezRmbWSNJwMxucgo4GjZkNBH4JrGNmQ/MOtSR092hwpN3JI8/W7CSXthkh3eUA4Ewzi5LukjCE4NSNIS+fOBZmVgY0kzQ32e4NrJQcfid3EyKpdwQ5WRjVvhbB0R5ICNqsBWwpaVIM+zlSLmw8hTDvAMLr0ANYhzBg6Xpgh4hanFpwx9vJAicAtxE6BkwGJgKHxxQg6QUzWw/YgOB4fyQp+hcqMMvMWhAiRf80synEj/478Brh5q8d1VtOzgGiTlXNAKl38qgl3WXr/HQXIJbjvYakPSLZKsZggoN7RbL9IKGorylh4NNZNTyvFLxsZk9SfVT7S2a2MjCr1MbN7DWgNfAQcHAS8Z0Y0+muqbAxt4obqbBxUV4zgL2BeyVNB543sytqeZ4TGXe8ndSR9DmwS3KhbkQorBtAWCKLSS+qlo57mBmF3TVKhZl1SQoo9yP8/59KuPloTWi36ERE0hdm9jUwryEXUiZMBPZJWUNm0l0SXjOzTSW9H9FmPjsTio5zzJK0T5Ji8HJkLSdQfVT7vVSNao/RrnUqYbptR6A9oRVt7OK1lpHtFaMy6YI1k/D+uCTvWBp91Z0a8OJKJzWS5bgTgM6Epdvnk+0zgHcl7RdRy32EKNpYqnK7FaEvcM5+/hS4xyQdFMOuUztJmskRMfvwZo0sdPKIUSS4NJjZh4RuLhMJqSZR+3gXFn6b2W65lqOxumhkiSSn+iBCisW6QBtgd0lvpakrJknHn1uBMmCYpN8k+/sCf5K0V5r6nCrc8XZSw8yGEO7OXyfcoa9CyFM8JcmVi6llPLCxUvpA5Ds3WXB0nICZPUIodn2OvME5sW7IskAWOnmY2Xzg02KHSGFwTZJTvASK1Mc7uV5tXVhQmjigb0raMIaOxGbqo9oL9HQgrJgOBNaUtGYEm5kobDSzcqClpJl5+1Ym+Hq5eoBdJT0XQ49THE81cdJkHUmbwuI+2tOALjG6ExThA8LwhdhFnTlUw2MnXZ5KfhosGenkkYV0F8ysVVIwl8Y1Kp/bgYfN7HdJilruZuDm5FhMUh/VXsC8pMj2hppukEpAJgobJS0iBLPy9xVO2h1MCCQ4KeGOt5MmiyveFUaCT4ztdJvZMIKj2xL40MzeovrUsyiTEgk55bMJ0aJmyWNIOXrU0FGJJyEuZ6TZyeOnWNHkOniAULiWG+6U3wNVBGer5Ei6OlkFeCWJaIqwInO5pJtjaMgj9VHtAGa2HfAPoAXQxcx6AL8F/hDB/PJU2Gh1n+KUEne8nTTpUeBgNstzPmM5m0MJRTmFBUl9gShTEgEklcWy5dSfpNPNZcDGVJ8kGsXByhhpdvJ4NSW71ZC0d/Lv2hnQcgtwS9IFyVJaKQQYbWYPk+Ko9oRrgN0J13QkvWtmO0ayvTwVNvqKasq44+2kRkaczf2AcyRVa5dmZvOACwi5ek7D5S7C++AaQoeGX9NwI0apdfLISLpLNcxsFWA9qt+QvVTzM35W26cB30u6I5e7m+w/CSiTdG0MHQmtgPnAbnn7ROhCExVJXxUMYos1BO18YDShsHGopHGwuLDx80ganOUEL650GjRWy2h6M3s/l4PuNEzMbIykXvnvBTN7WVKDG0aRdiePRMPTVKW7LHaqJF1V45NKo+M4Ql7vGoROSL2B1yXtFMn+B0BPST8V7G8CjIpdbJoFzOxfwNXAjYS/x8mEITqHRbKfamGjmfWW9EY9zntc0oGl0ODUD494Ow2dprUcy9oSoROfH8ysEfCJmZ1ISD/qkLKmtNgzbQGkP7gmxymEPtpvSOpvZhsCf41oX4VOd7LzRysI+ZYKM/uTpCvM7AaKpC+k0Pnnd8B1hPa0XwPPEtrTRiEDhY03AXW23HSnO33c8XYaOqPM7DeSqnUCMLNjCVE1p2HzR6A5IXp2ESHd5Kg0BcUmQ508IP3BNTl+kPSDmWFmTSR9ZGYbxBRgZh1VfVQ7ZtYxooRcQeXoiDZrRNI0Ik88XgYaapqak4c73k5D54/AE2Z2OFWO9paEfuIHpCXKyQaSRgGYmST9Om09KZGJTh4J2wNHm1lq6S4JX5tZG0JB4XNmNhP4JqL9vwFPmdnphBHxECbvXgFcGUOApGHJw/mSHs0/ZmaHxNBQYPMK4GLC5N+nCS39/ijp/thaaqGUub3rJAO/ihuO16HLqQPP8XYcwMz6A7lc73GSXkxTj5MNzGxbQoFtC0mLW5RJitGizCkg7cE1eTraJRHWXAFda+DpYukfJdSwJ/BnwnVLwDhCO8H/xtKQ6Fhiqmgak0ZzEzvN7ABgf+BUYLjyJnymTSlfFzP7BDiupuOSRpbCrrP0eMTbcQBJw4HhaetwMse1pNeiLHOk1ckjK+kuZrYPcCew0MwqgUPTcmgSBzuqk51P4vj/AuhsZtfnHWoFLEpBUuPk318AD0qaESndvd6FjcCkEsqY68718kGjtAU4juNkGUlfFeyK1aIsUySdPF4CniEUEj4DXBjJ/APJv2MIOcVj8n5i5hhfAuwgaXVCW8PLItpejJk9kvd4cMGxZyPJ+Ibw2v9A9b/HUMLNamyGmdlHhFTBF8ysfaItBjfV56QSFzbONLNOuQ0zO9LMhpjZ9WbWtoR2naXEI96O4zg181UyEU9mthKhyDL1KX0pkVonjwwNrlkk6aNEy5tm1jIlHevlPd4VOCtvu30MAZLeBd41swdyUxuTFZE181vqxULSn5ObkNnJJOR5hDkNDYU2wE8Ayarc5cBJwObAbcDBaQlzquOOt+M4Ts2k2qIsY6TeyQPSHVwDdEiG1xTdlnR1JB21FWfFLtx6zsz2JfgTY4GpZjZS0mm1P+3nwcx2kvSimR2Yty//lBiDfLJQ2NhI0ozk8QDgNkmPAY+Z2dgI9p164o634zhODSwnLcpikXYnjxoH1wBRBtcAtwMta9iO6fA2N7MtCOmizZLHlvzEnj/QWtLs5G9zl6QLzOy9Op/189EXeBHYp8ixWBM0pwJRhzgVodzMypN+4jsDx+cfS0mTUwTvauI4jlNATUNBcqQwHCR1MtLJ432q0l02z6W7SBoQS0Oio4+kV+vaV0L7tRaCS+ofQ0ei5X3CuPh7gHMljTKz9xrS9Ewze0fSFilrOJdQWDoN6EKYbCozWxe4R1KfNPU5VfhdkOM4zpLkF+z9FbggLSFpk6VOHmQk3QW4gSWnBBbbVxJiOtb1YBCh0PbVxOleB/gktggzuxS4QtKsZHsV4HRJ50UwP9PMOkn6NrF9JKH49gvgwrwUkJIh6RIzewFYDXhWVVHVRoRcbycjeMTbcRynFrIQzUqTJG3g0MTJ3Ybg3PRNScsTwK8Jg692IozobizpF5Hsbwtsl9i/Ju9QK+CA2D2j8/Oa8/geeF/SlJha0qbY5zRWP3EzexvYJWlhuCPwEFWFjRtJ8sJGZzEe8XYcx6mdhh6dyEonD4DfJBHNC5N0i9aEKYWxWAloQfjuzH8dZpNO14hjgW2pmkHQD3gDWN/MBkm6r9QCzGx94Gago6TuZrYZsK+ki0ttu4CyZBXkx0RXM6BJJNte2OjUG3e8HcdxnNpIvZNHVtJdEpsjzezu3LRMM2tEmGw6O7YeoJIQUf0u0dKR4ARvQ+i5XnLHm1BgeiZwK4Ck98zsAcL49pjcT+jffRfhZvkYQt55DLyw0ak3/oZwHMcpwMzmUBXpbm5mOafKAElqlY6yVMhCJ4/c4JrF6S6EbhZpcZmZ/Y4wTGkM0NrMrpb0t8g6uuac7oQpwPpJysPCSBqaS3qroIVf1MmVFow/CLwH7EL4nF4k6ZlIEh4k3JBNAxYALye61iWk/jjOYtzxdhzHKUBSmukUmULSX6HmTh6RZGQp3QVg46SF3uHAfwgDbMYAsR3vl83sSeDRZPsg4CUzWxmYFUnDNDPrRnITZmYHA/+LZBsId8Jm9m9JvYibepSz74WNTr3x4krHcRynTooVqkUsXvsayE9pOS1/O+LgmpyecYTCuQeAGyWNNLN3UyiuNIKz3YcQ5X0FeEwRv9iTLia3EYpOZwITgcNzqTgRdfwduFvSqJh2HWdp8Yi34ziOUyN5nTzaF+R6twLKIsnIQrpLPrcCk4B3CRHmtQgFllFJHOx/JT+pIOlzYJckyt6IkGoxgNBKLyb9gd+Z2SRgHlVpYQ2mn7izfOARb8dxHKdGkmE5/YDfAbfkHZoDDJMUrWdz2oNraiOvuC6mzQOBwUAHqiZXRqlBMLNWwAlAZ2AI8HyyfQbwrqT9Sq2hQM9axfbHjrw7Tl244+04juPUiZmtlXYnjzTTXQpsdgQuBVaXtKeZbQxsK+mOyDo+BfaRND6m3cT2EEJqyeuETh6rENotniJpbGw9iabtgfUk3WVm7Qnv0YlpaHGcmvBUE8dxHKc+pNbJIyPpLvncDdwFnJtsfww8DER1vIHv0nC6E9aRtCmAmf2DZFS5pDlpiDGzC4AtgQ0If5vGhBaDPirdyRSN0hbgOI7jLBdsnES49yd08ugCHBHJduHgmtxPWoNr2kl6hNBHmyTFpCIFHaPN7GEzG2hmB+Z+Itle3K5QUgUwMS2nO+EAYF9CfjeSvqF6XYDjZAKPeDuO4zj1obGZNSY43jdKWmhmUXIVMzi4Zp6ZrUpVC73epNOvuRUwH9gtb5+AxyPY7lHQ375Zsp1Wr/ufkraCub/JypHtO069cMfbcRzHqQ9Z6OSRlcE1pwFDgW5m9irQnhQi75J+Hdtmnu00Unxq4xEzuxVoY2a/IUyu/EfKmhxnCby40nEcx1kmYnfyMLOxkjZPBtf0Ihlck0bLODMrJ+QTGzBBUqxJkZjZnyRdYWY3UKSdoqSTY2nJEma2KyH6b8Azkp5LWZLjLIFHvB3HcZw6qamTB3ELClNLdynC1kBXwvdoTzND0r2RbOcKKkdHspd5zOzYpKvMc8l2mZldkJu86jhZwR1vx3Ecpz7cTfqdPLKQ7oKZ3Qd0A8ZSVVQpIIrjLWlY8nC+pEfzj5nZITE0ZJCdzewg4FhgVcJ7dWS6khxnSTzVxHEcx6kTMxslaSsze0fSFsm+sZI2T1lXGoNrxhO6vKT6BZqVvuZZwcwGAH8nFJwOzMJgJccpxCPejuM4Tn1IvZNHRtJdAD4AOgH/i2wXADPbE/gF0NnMrs871AqIehOSFcxsPeAU4DFgI+CI5CZxfrrKHKc67ng7juM49SELnTzuJsV0FzMbRrjxaAl8aGZvAT/mjkvaN4YO4BtCfve+hO4uOeYAp0bSkDWGASdIesHMjPB+HQVskq4sx6mOp5o4juM49SLNTh6J/VTTXZI2dR2BlwsO9QUmpzAyvnHub2BmqwBrSnovpoasYGatCnu6m9l6kj5JS5PjFMMnVzqO4zj1ZWugB9ATGGhmR0a2n3a6y37AUEkj838Ikzz3j6gjx3Nm1srM2hIKTu8ys6tT0JEaZvYnAEmzixSWptbn3HFqwiPejuM4Tp3U1MkjZs9oM+sJ3AB0J+RZtwcOjhXlNbMPJHWv4dj7kjaNoSPP5juStjCz4wjR7gvM7L00+pqnRX4xaWFhaUMuNHWyi+d4O47jOPVhS1Lu5CHpbTPrS3rpLk1rOdYsmooqys1sNeBQqvLeGxpWw+Ni246TOp5q4jiO49SHXCePtEkz3WVUkuddDTM7lupFjrEYBDwDfCZplJmtAzS0nGbV8LjYtuOkjqeaOI7jODVS0MljcyCtTh6pp7sk7QyfAH6iytHeElgJOEDStzF0OFWYWQUwjxDdbkbo4U2y3VRS47S0OU4xPNXEcRzHqY2h1NLJI7KWVNNdJH0HbGdm/Ql55gBPSXoxDT1mtj5wM9BRUncz2wzYV9LFaehJA0llaWtwnKXBI96O4zhOjZjZk8A5hQWMZrYlcIGkfSJqeRQ4WVIqg2uyhpmNBM4Ebs1rr1hjAajjOOnjEW/HcRynNroW6xoiabSZdY0hIEODa7JGc0lvhXkxi2mQkysdZ3nBHW/HcRynNrLQySNL6S5ZYpqZdaOqr/nBpDTG3nGc+uGOt+M4jlMbo8zsN5Juz98ZuZPHfhRPd5kHXECkkfEZ5ATgNmBDM5sMTAQOT1eS4zi14TnejuM4To1koZNH1gbXZA0zW5nQHngBMEDSP1OW5DhODXjE23Ecx6mRjHTyyEK6S2Yws1aEaHdnYAjwfLJ9BmF0vDvejpNRPOLtOI7jZBozexB4sYZ0l90kDUhHWTqY2RBgJvA6sDOwCmEF4hRJY1OU5jhOHbjj7TiO42SaLKS7ZIn89BozKwOmAV0kzUlXmeM4deGpJo7jOE6myUi6S5ZYmHsgqcLMJrrT7TjLBx7xdhzHcZzliLwx6VB9VLoBktQqLW2O49SOO96O4ziO4ziOE4FGaQtwHMdxHMdxnIaAO96O4ziO4ziOEwF3vB3HcRzHcRwnAu54O47jOI7jOE4E3PF2HMdxHMdxnAj8H3e5Izuj5/yTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(correlations[correlations > 0.3], annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b81078d",
   "metadata": {},
   "source": [
    "From our correlation heatmap, we can identify the following features to be positively correlated (correlation coefficient greater than 0.3) to `HeartDisease`:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `MaxHR`\n",
    "- `ChestPainType_ATA` \n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "The correlation coefficient threshold was chosen arbitrarily. Surprisingly, `Cholesterol` is not strongly correlated to `HeartDisease`. We can consider ignoring the feature for now.\n",
    "\n",
    "Given everything we have attempted so far, we can narrow down our features to the following:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `Sex_M`\n",
    "    - It has a relatively low value for the coefficient, but given what we observed in our EDA, let's also take it into account.\n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "Time to create our model using these features!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143bf6f0",
   "metadata": {},
   "source": [
    "## Building a Classifier with One Feature\n",
    "\n",
    "We will split our dataset into a training and a test set first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6b14956b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
    "y = df_clean[\"HeartDisease\"]\n",
    "\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.15, random_state = 417)\n",
    "\n",
    "features = [\n",
    "    \"Oldpeak\",\n",
    "    \"Sex_M\",\n",
    "    \"ExerciseAngina_Y\",\n",
    "    \"ST_Slope_Flat\",\n",
    "    \"ST_Slope_Up\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2131229",
   "metadata": {},
   "source": [
    "We will start with creating a model for each of the features above and evaluate their performance using accuracy as a metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ddcfc26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The k-NN classifier trained on Oldpeak and with k = 3 has an accuracy of 58.70%\n",
      "The k-NN classifier trained on Sex_M and with k = 3 has an accuracy of 61.59%\n",
      "The k-NN classifier trained on ExerciseAngina_Y and with k = 3 has an accuracy of 73.19%\n",
      "The k-NN classifier trained on ST_Slope_Flat and with k = 3 has an accuracy of 81.88%\n",
      "The k-NN classifier trained on ST_Slope_Up and with k = 3 has an accuracy of 55.07%\n"
     ]
    }
   ],
   "source": [
    "for feature in features:\n",
    "    knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "    knn.fit(X_train[[feature]], y_train)\n",
    "    accuracy = knn.score(X_val[[feature]], y_val)\n",
    "    print(f\"The k-NN classifier trained on {feature} and with k = 3 has an accuracy of {accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76171c65",
   "metadata": {},
   "source": [
    "Our best forming model, with an accuracy of ~`82%`, was trained on the `ST_Slope_Flat` feature with `ExerciseAngina_Y` being a close second. These make sense given the data distributions we saw previously.\n",
    "\n",
    "We will train a model using all of these features next."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cacc6c3",
   "metadata": {},
   "source": [
    "## Building a Classifier with Multiple Features\n",
    "\n",
    "Before training on all of the above features, we need to normalize the data first. We will use scikit-learn's MinMaxScaler to scale the values between 0 and 1, and then train the model again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cbbf5de2",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train[features])\n",
    "X_val_scaled = scaler.transform(X_val[features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "96a81789",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 83.33\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "accuracy = knn.score(X_val_scaled, y_val)\n",
    "print(f\"Accuracy: {accuracy*100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d51e1f",
   "metadata": {},
   "source": [
    "The model's accuracy jumped to ~`83%`! That's not a significant improvement, but still a good start. Using all these features results in an improved model, but, let's see what parameters/hyperparameters might be optimal."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f4b42d",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization\n",
    "\n",
    "Let's prepare our data first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "531aaa84",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_clean.drop([\"HeartDisease\"], axis=1)\n",
    "y = df_clean[\"HeartDisease\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state = 417)\n",
    "\n",
    "features = [\n",
    "    \"Oldpeak\",\n",
    "    \"Sex_M\",\n",
    "    \"ExerciseAngina_Y\",\n",
    "    \"ST_Slope_Flat\",\n",
    "    \"ST_Slope_Up\"\n",
    "]\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train[features])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "364195b9",
   "metadata": {},
   "source": [
    "We will use grid search to explore the following range of values for a couple of hyperparameters:\n",
    "\n",
    "- For `k` (`n_neighbors` in scikit-learn) we will use values in the range of 1 to 20.\n",
    "- We will use two distance metrics - `minkwoski` and `manhattan`.\n",
    "\n",
    "`minkwoski` is the default metric for KNeighborsClassifier in sklearn, so we don't expect `manhattan` to do better. But, let's see what happens!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8e1a3cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=KNeighborsClassifier(),\n",
       "             param_grid={'metric': ['minkowski', 'manhattan'],\n",
       "                         'n_neighbors': range(1, 20)},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_params = {\"n_neighbors\": range(1, 20),\n",
    "                \"metric\": [\"minkowski\", \"manhattan\"]\n",
    "              }\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(knn, grid_params, scoring='accuracy')\n",
    "knn_grid.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2a22a9e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83.43507030603806, {'metric': 'minkowski', 'n_neighbors': 19})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_score_*100, knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4334d3",
   "metadata": {},
   "source": [
    "Our best model had an accuracy of ~`83%` with `19` `n_neighbors` and `minkowski` as the distance metric. \n",
    "\n",
    "The accuracy of this model is only slightly better. Since `GridSearchCV` employs a cross-validation approach, it is reasonable to assume that this is a better estimate of how the model performs compared to our prior attempt.\n",
    "\n",
    "We will evaluate our model on our test set now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52c42a3c",
   "metadata": {},
   "source": [
    "## Model Evaluation on Test Set\n",
    "\n",
    "We need to first normalize our test set similar to how we scaled our training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2086c66d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Model Accuracy on test set: 86.96\n"
     ]
    }
   ],
   "source": [
    "X_test_scaled = scaler.transform(X_test[features])\n",
    "predictions = knn_grid.best_estimator_.predict(X_test_scaled)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f\" Model Accuracy on test set: {accuracy*100:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf618cba",
   "metadata": {},
   "source": [
    "Our model got an accuracy of ~`87`%. That's really good! This means that our model is likely to correctly predict whether a patient is at risk for a heart disease ~`87`% of the time.\n",
    "\n",
    "However, the accuracy being higher than the one before raises some flags.\n",
    "\n",
    "One explanation that could explain is to look at how the data is distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d05278ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of patients by their sex in the entire dataset\n",
      "1    724\n",
      "0    193\n",
      "Name: Sex_M, dtype: int64\n",
      "\n",
      "Distribution of patients by their sex in the training dataset\n",
      "1    615\n",
      "0    164\n",
      "Name: Sex_M, dtype: int64\n",
      "\n",
      "Distribution of patients by their sex in the test dataset\n",
      "1    109\n",
      "0     29\n",
      "Name: Sex_M, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Distribution of patients by their sex in the entire dataset\")\n",
    "print(X.Sex_M.value_counts())\n",
    "\n",
    "print(\"\\nDistribution of patients by their sex in the training dataset\")\n",
    "print(X_train.Sex_M.value_counts())\n",
    "\n",
    "print(\"\\nDistribution of patients by their sex in the test dataset\")\n",
    "print(X_test.Sex_M.value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4818a3b",
   "metadata": {},
   "source": [
    "We used `Sex` as one of our features for training the model.\n",
    "\n",
    "- `X` had `724` males and `193` females.\n",
    "- `X_train` had `615` males and `164` females.\n",
    "- `X_test` had `109` males and `29` females.\n",
    "\n",
    "\n",
    "We can see that the above datasets have a significantly higher number of male patients than female ones. We briefly mentioned previously that this could present a bias because of this imbalance in our dataset and we can see it potentially impacts our model. If the test dataset doesn't have that many female patients and the model was trained on a dataset with more male patients, then it is understandable it has better accuracy on the test set. Of course, there could be other factors contributing to this discrepancy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08f92bf4",
   "metadata": {},
   "source": [
    "## Summary and Next Steps\n",
    "\n",
    "Our final model was trained using the following features:\n",
    "\n",
    "- `Oldpeak`\n",
    "- `Sex_M`\n",
    "- `ExerciseAngina_Y`\n",
    "- `ST_Slope_Flat`\n",
    "- `ST_Slope_Up`\n",
    "\n",
    "and had a test set accuracy of `86.96`%. However, given the limitations of our data this accuracy might not be indicative of a well performing model.\n",
    "\n",
    "There are quite a few things we could try next to get better results:\n",
    "\n",
    "- Try out different features.\n",
    "- Expand the grid search parameters to identify more optimal hyperparameters.\n",
    "- Explore other algorithms that might perform better than k-NN.\n",
    "- Try and collect more data."
   ]
  }
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