end-to-end-heart-disease classification .ipynb

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    "# Predicting heart disease using machine learning\n",
    "\n",
    "This notebook looks into using various python based machine leaning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes. ta\n",
    "\n",
    "We're going to take the following approach:\n",
    "1. Problem defination\n",
    "2. Data\n",
    "3. Evaluation\n",
    "4. Features\n",
    "5. Modelling\n",
    "6. Experimentation\n",
    "\n",
    "## 1. Problem Defination\n",
    "\n",
    "In a statement,\n",
    "> Give clinical parameters about a patient, can we predict whether or not they have heart disease?\n",
    "\n",
    "## 2. Data\n",
    "\n",
    "The original data came from the Cleavland data from the UCI Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/heart+disease\n",
    "\n",
    "There is also a version available on Kaggle. https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data?select=heart_disease_uci.csv\n",
    "\n",
    "## 3. Evaluation\n",
    "\n",
    "> If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we will oursue the project.\n",
    "\n",
    "## 4. Features\n",
    "\n",
    "**Create a data dictionary**\n",
    "\n",
    "1. age - age in years \n",
    "2. sex - (1 = male; 0 = female) \n",
    "3. cp - chest pain type \n",
    "    * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "    * 1: Atypical angina: chest pain not related to heart\n",
    "    * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "    * 3: Asymptomatic: chest pain not showing signs of disease\n",
    "4. trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "    * anything above 130-140 is typically cause for concern\n",
    "5. chol - serum cholestoral in mg/dl \n",
    "    * serum = LDL + HDL + .2 * triglycerides\n",
    "    * above 200 is cause for concern\n",
    "6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) \n",
    "    * '>126' mg/dL signals diabetes\n",
    "7. restecg - resting electrocardiographic results\n",
    "    * 0: Nothing to note\n",
    "    * 1: ST-T Wave abnormality\n",
    "        - can range from mild symptoms to severe problems\n",
    "        - signals non-normal heart beat\n",
    "    * 2: Possible or definite left ventricular hypertrophy\n",
    "        - Enlarged heart's main pumping chamber\n",
    "8. thalach - maximum heart rate achieved \n",
    "9. exang - exercise induced angina (1 = yes; 0 = no) \n",
    "10. oldpeak - ST depression induced by exercise relative to rest \n",
    "    * looks at stress of heart during excercise\n",
    "    * unhealthy heart will stress more\n",
    "11. slope - the slope of the peak exercise ST segment\n",
    "    * 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "    * 1: Flatsloping: minimal change (typical healthy heart)\n",
    "    * 2: Downslopins: signs of unhealthy heart\n",
    "12. ca - number of major vessels (0-3) colored by flourosopy \n",
    "    * colored vessel means the doctor can see the blood passing through\n",
    "    * the more blood movement the better (no clots)\n",
    "13. thal - thalium stress result\n",
    "    * 1,3: normal\n",
    "    * 6: fixed defect: used to be defect but ok now\n",
    "    * 7: reversable defect: no proper blood movement when excercising \n",
    "14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2890599f",
   "metadata": {},
   "source": [
    "## Preparing the tools\n",
    "\n",
    "We're going to use pandas, Matplotlib and NumPy for the data analysis and manipulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd4b4570",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all the tools we need\n",
    "\n",
    "# Regular EDA (exploratory data analysis) and plotting libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "# we want our plots to appear inside the notebook\n",
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Import models from Scikit-Learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model Evaluation\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import plot_roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ddf8cb6",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8930d4e5",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>age</th>\n",
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       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
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       "      <th>exang</th>\n",
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       "      <th>slope</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "df = pd.read_csv(\"heart-disease.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ced581e5",
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     "data": {
      "text/plain": [
       "(303, 14)"
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     "execution_count": 3,
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   "source": [
    "df.shape #(rows, columns)"
   ]
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  {
   "cell_type": "markdown",
   "id": "6e7ef97f",
   "metadata": {},
   "source": [
    "## Data Exploration (Exploratory Data Analysis or EDA)\n",
    "\n",
    "The goal here is toh finf more about the dataset and become a subject matter expert on the dataset we are working with.\n",
    "\n",
    "1. What question(s) are we trying to solve (or prove wrong)?\n",
    "2. What kind of data do we have and how do we treat different types?\n",
    "3. What's missing from the data and how do we deal with it?\n",
    "4. Where are the outliers and why should we care about them?\n",
    "5. How can we add, change or remove features to get more out of our data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5eae37d6",
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       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "df.head()"
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  {
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       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  target  \n",
       "298      1   0     3       0  \n",
       "299      1   0     3       0  \n",
       "300      1   2     3       0  \n",
       "301      1   1     3       0  \n",
       "302      1   1     2       0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a31cd9df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    165\n",
       "0    138\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find out how many of each class there are\n",
    "df[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "feb6d67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"target\"].value_counts().plot(kind = \"bar\", color = [\"salmon\", \"lightblue\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6213f3b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3034667b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Are there any missing values?\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "651c6f30",
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04924404",
   "metadata": {},
   "source": [
    "### Heart Disease Frequency acoording to Sex "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5f1fa4ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    207\n",
       "0     96\n",
       "Name: sex, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9fd334b",
   "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>sex</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex      0    1\n",
       "target         \n",
       "0       24  114\n",
       "1       72   93"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare target column with sex column\n",
    "pd.crosstab(df.target, df.sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0324713b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.sex).plot(kind = \"bar\",\n",
    "                                    figsize = (10, 6),\n",
    "                                    color = [\"salmon\", \"lightblue\"]);\n",
    "plt.title(\"Heart Disease Frequency for Sex\")\n",
    "plt.xlabel(\"0 = No Disease,  1 = Disease\")\n",
    "plt.ylabel(\"Amount\")\n",
    "plt.legend([\"Female\", \"Male\"])\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "328041ef",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>False</th>\n",
       "      <td>29</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>109</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "age            \n",
       "False    29  66\n",
       "True    109  99"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare age and target\n",
    "pd.crosstab(df.age > 50, df.target).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1d18aaf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Af/mXfwFq324cNWoUW2+9NZtvvjmzZs1q8vqxhoYGhgwZslDbkUceyfDhw/njH//IYYcdRp8+fdh8883p168f6667LgDXXXcdn/vc57jwwgv54IMPGDp06IcC2Y033sjw4cPp2LEjXbp0YcSIEUQEHTt25PLLL+eggw5i3rx5nHTSSey4444tcj5jccN6q4J+/fpl/YV60pI4QtY0R8ikVc+kSZPYYYcdSpexUpszZw5du3blb3/7GwMHDuTKK69kt912a7PjN/UZRcSEzOzX1PqOkEmSpHbn1FNP5emnn+b9999n2LBhbRrGloeBTJIktTstdW1XW/F3yCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJC2X3/3ud0TEQjfovu+++/j0pz/dpnXMmDGDTp06ccUVV7TYPl9++WW6dOlC37596du3L6effnpj34QJE9h5553Zeuut+eIXv7jYOwMsC79lKUnSKq6lf2exub9P2NDQwN57782IESO44IILWrSGZXHDDTew55570tDQsMRbNi2rrbbaqsnbLH3uc5/jyiuvZM899+RTn/oUd9xxB4cccsgKHcsRMkmStMzmzJnDgw8+yFVXXcWIESMW6ps9ezZDhgyhd+/enH766cyfPx+oBbidd96ZnXbaiXPPPReA4cOH87Wvfa1x26uvvpovfOELAPzmN7+hf//+9O3bl9NOO4158+Y1WUtDQwOXXHIJU6dOZdq0aY3tV111Fdtuuy377rsvn/3sZ/n85z8P1EbUjjzySHbffXd23313HnzwwWa/7+nTpzN79mwGDBhARHDiiSdyyy23NHv7xTGQSZKkZXbLLbdw8MEHs+2227LBBhvw6KOPNvaNGzeOSy65hCeeeIIXXniBm2++mddee41zzz2Xe++9l4kTJ/LII49wyy23cNRRR3HzzTc3bjty5EiOPfZYJk2axMiRI3nwwQeZOHEiHTp04LrrrvtQHVOmTOH111+nf//+HHPMMYwcORKA1157je9+97s8/PDD3HXXXQtNq37pS1/i7LPP5pFHHuGmm27ilFNOafI9vvTSS+y6664MGjSIBx54AIBp06bRs2fPxnV69uy5UAhcXk5ZSpKkZdbQ0MBZZ50FwNChQ2loaGj8Nfz+/fuz5ZZbAnDccccxZswYOnXqxL777ku3bt0AOP744xk9ejSDBw9myy235OGHH2abbbbh2WefZa+99uKnP/0pEyZMYPfddwdq986svx/mAiNGjOCYY45prOPkk0/my1/+MuPGjWPQoEFssMEGABx99NE899xzQO3G5E8//XTjPmbPns27777L2muv3djWvXt3Xn31VTbccEMmTJjA4MGDeeqpp5q8XiwiVuhcgoFMkiQto5kzZ3Lvvffy5JNPEhHMmzev8Sbh8OGAEhFLvPD92GOP5frrr2f77bdnyJAhjesPGzaM73//+0uspaGhgTfeeKNx9Oy1115j8uTJSzze/PnzGTt2LF26dFnsOmuttRZrrbUWAB//+MfZaquteO655+jZsydTp05tXG/q1KlsuummS6yxOZyylCRJy+TGG2/kxBNP5JVXXuHll19mypQp9OrVizFjxgC1KcuXXnqJ+fPnM3LkSPbee2/22GMP7r//ft566y3mzZtHQ0MDgwYNAuCII47glltuoaGhgWOPPRaA/fbbjxtvvJE333wTgFmzZvHKK68sVMezzz7Le++9x7Rp03j55Zd5+eWXOf/88xkxYgT9+/fn/vvv5+2332bu3LncdNNNjdsdeOCBXH755Y3LTV24P2PGjMZr1l588UUmT57MlltuSffu3Vl77bV5+OGHyUyuvfZaDj/88BU+pwYySZK0TBoaGhgyZMhCbUceeWTj/SMHDBjAeeedx0477USvXr0YMmQI3bt35/vf/z6f+MQn6NOnD7vttltjkFl//fXp3bs3r7zyCv379wegd+/eXHjhhRx44IHssssuHHDAAUyfPr1ZdTQ0NNCjRw++/vWvs8cee7D//vvTu3dv1l13XQB+/OMfM378eHbZZRd69+7Nz372sw+9x9GjR7PLLrvQp08fjjrqKH72s581Tn8OHz6cU045ha233pqtttpqhb9hCRAt8dsZpfTr1y/Hjx9fugytIlr6a+HtRXO/3i5p5TFp0iR22GGH0mWs9ObMmUPXrl2ZO3cuQ4YM4aSTTvpQgGstTX1GETEhM/s1tb4jZJIkqV264IIL6Nu3b+NI3eDBg0uXtFhe1C9Jktqliy++uHQJzeYImSRJUmEGMkmSVkGr8jXg7d3yfDatFsgi4pcR8WZEPFnXtkFE3BURk6vn9ev6zo+I5yPi2Yg4qLXqkiRpVde5c2dmzpxpKFsJZSYzZ86kc+fOy7Rda15DdjVwOXBtXdt5wD2ZeVFEnFctnxsRvYGhwI7ApsDdEbFtZjZ90ypJklZjC36cdMaMGaVLURM6d+680O2VmqPVAllmjo6ILRZpPhzYt3p9DXAfcG7VPiIz/wG8FBHPA/2Bsa1VnyRJq6pOnTrRq1ev0mWoBbX1NWSbZOZ0gOp5wU2pegBT6tabWrVJkiS1eyvLRf1N3ZWzyYnxiDg1IsZHxHiHaiVJUnvQ1oHsjYjoDlA9v1m1TwU2q1uvJ/BaUzvIzCszs19m9ltwx3hJkqRVWVsHstuAYdXrYcCtde1DI2KtiOgFbAOMa+PaJEmSimi1i/ojooHaBfwbRcRU4FvARcD1EXEy8CpwNEBmPhUR1wNPA3OBM/2GpSRpVeB9cpvmfXKXTWt+y/K4xXTtt5j1vwd8r7XqkSRJWlmtLBf1S5IkrbYMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmFFAllEnB0RT0XEkxHREBGdI2KDiLgrIiZXz+uXqE2SJKmttXkgi4gewBeBfpm5E9ABGAqcB9yTmdsA91TLkiRJ7V6pKcuOQJeI6Ah8BHgNOBy4puq/BhhcpjRJkqS21eaBLDOnARcDrwLTgb9m5ihgk8ycXq0zHdi4rWuTJEkqocSU5frURsN6AZsCH42IE5Zh+1MjYnxEjJ8xY0ZrlSlJktRmSkxZ7g+8lJkzMvMD4GbgX4E3IqI7QPX8ZlMbZ+aVmdkvM/t169atzYqWJElqLSUC2avAnhHxkYgIYD9gEnAbMKxaZxhwa4HaJEmS2lzHtj5gZv45Im4EHgXmAo8BVwJdgesj4mRqoe3otq5NkiSphDYPZACZ+S3gW4s0/4PaaJkkSdJqxV/qlyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKmwjqULkCStGi57+7LSJayUzihdgNoFR8gkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVFiRQBYR60XEjRHxTERMiogBEbFBRNwVEZOr5/VL1CZJktTWSo2QXQbckZnbA32AScB5wD2ZuQ1wT7UsSZLU7rV5IIuIdYCBwFUAmfm/mfkOcDhwTbXaNcDgtq5NkiSphBIjZFsCM4BfRcRjEfGLiPgosElmTgeonjcuUJskSVKbKxHIOgK7AcMzc1fgPZZhejIiTo2I8RExfsaMGa1VoyRJUptZaiCLiE0i4qqI+GO13DsiTl6BY04Fpmbmn6vlG6kFtDciont1jO7Am01tnJlXZma/zOzXrVu3FShDkiRp5dCcEbKrgTuBTavl54CzlveAmfk6MCUitqua9gOeBm4DhlVtw4Bbl/cYkiRJq5KOzVhno8y8PiLOB8jMuRExbwWP+wXguohYE3gR+E9q4fD6avTtVeDoFTyGJEnSKqE5gey9iNgQSICI2BP464ocNDMnAv2a6NpvRfYrSZK0KmpOIPsytenErSLiQaAbcFSrViVJkrQaWWogy8xHI2IQsB0QwLOZ+UGrVyZJkrSaWGogi4gjFmnaNiL+CjyRmU1+E1KSJEnN15wpy5OBAcCfquV9gYepBbPvZOavW6k2SZKk1UJzAtl8YIfMfANqv0sGDAf2AEYDBjJJkqQV0JzfIdtiQRirvAlsm5mzAK8lkyRJWkHNGSF7ICJ+D9xQLR8JjK7uP/lOaxUmSZK0umhOIDsTOALYu1oeB3TPzPeAT7RWYZIkSauLpU5ZZmYCL1CbnhxC7cdbJ7VyXZIkSauNxY6QRcS2wFDgOGAmMBKIzHRUTJIkqQUtacryGeAB4NDMfB4gIs5uk6okSZJWI0uasjwSeB34U0T8PCL2o/ZL/ZIkSWpBiw1kmfm7zDwW2B64Dzgb2CQihkfEgW1UnyRJUrvXnIv638vM6zLz00BPYCJwXmsXJkmStLpozg/DNsrMWZl5RWZ+srUKkiRJWt0sUyCTJElSyzOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgorFsgiokNEPBYRv6+WN4iIuyJicvW8fqnaJEmS2lLJEbIvAZPqls8D7snMbYB7qmVJkqR2r0ggi4iewL8Bv6hrPhy4pnp9DTC4jcuSJEkqotQI2aXA14D5dW2bZOZ0gOp54wJ1SZIktbk2D2QR8WngzcycsJzbnxoR4yNi/IwZM1q4OkmSpLZXYoRsL+CwiHgZGAF8MiJ+A7wREd0Bquc3m9o4M6/MzH6Z2a9bt25tVbMkSVKrafNAlpnnZ2bPzNwCGArcm5knALcBw6rVhgG3tnVtkiRJJaxMv0N2EXBAREwGDqiWJUmS2r2OJQ+emfcB91WvZwL7laxHkiSphJVphEySJGm1ZCCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqrGPpAtTyLnv7stIlrJTOKF2AJEmL4QiZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQV1uaBLCI2i4g/RcSkiHgqIr5UtW8QEXdFxOTqef22rk2SJKmEEiNkc4GvZOYOwJ7AmRHRGzgPuCcztwHuqZYlSZLavTYPZJk5PTMfrV6/C0wCegCHA9dUq10DDG7r2iRJkkooeg1ZRGwB7Ar8GdgkM6dDLbQBGxcsTZIkqc0UC2QR0RW4CTgrM2cvw3anRsT4iBg/Y8aM1itQkiSpjRQJZBHRiVoYuy4zb66a34iI7lV/d+DNprbNzCszs19m9uvWrVvbFCxJktSKSnzLMoCrgEmZ+aO6rtuAYdXrYcCtbV2bJElSCR0LHHMv4D+AJyJiYtX2deAi4PqIOBl4FTi6QG2SJEltrs0DWWaOAWIx3fu1ZS2SJEkrA3+pX5IkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVJiBTJIkqTADmSRJUmEGMkmSpMIMZJIkSYUZyCRJkgozkEmSJBVmIJMkSSrMQCZJklSYgUySJKkwA5kkSVJhBjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpMAOZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgkyRJKsxAJkmSVNhKF8gi4uCIeDYino+I80rXI0mS1NpWqkAWER2AnwKHAL2B4yKid9mqJEmSWtdKFciA/sDzmfliZv4vMAI4vHBNkiRJrWplC2Q9gCl1y1OrNkmSpHYrMrN0DY0i4mjgoMw8pVr+D6B/Zn6hbp1TgVOrxe2AZ9u8UK2qNgLeKl2EpHbHf1vUXJtnZremOjq2dSVLMRXYrG65J/Ba/QqZeSVwZVsWpfYhIsZnZr/SdUhqX/y3RS1hZZuyfATYJiJ6RcSawFDgtsI1SZIktaqVaoQsM+dGxOeBO4EOwC8z86nCZUmSJLWqlSqQAWTm7cDtpetQu+RUt6TW4L8tWmEr1UX9kiRJq6OV7RoySZKk1Y6BTO2et+OS1Boi4pcR8WZEPFm6Fq36DGRq17wdl6RWdDVwcOki1D4YyNTeeTsuSa0iM0cDs0rXofbBQKb2zttxSZJWegYytXfRRJtfLZYkrVQMZGrvlno7LkmSSjOQqb3zdlySpJWegUztWmbOBRbcjmsScL2345LUEiKiARgLbBcRUyPi5NI1adXlL/VLkiQV5giZJElSYQYySZKkwgxkkiRJhRnIJEmSCjOQSZIkFWYgk1ZDEXFwRDwbEc9HxHkttM+MiEvqlr8aERcsw/afiYgZEfFYREyOiDsj4l/r+r8TEfu3RK2tJSIGRsSjETE3Io5qoX1eEBHTImJidV5ujojedf2/qF+WtGoykEmrmYjoAPwUOAToDRzXQv+h/wM4IiI2WoF9jMzMXTNzG+Ai4OaI2AEgM/8rM+9ugTpb06vAZ4DftvB+/29m9q3Oy0jg3ojoBpCZp2Tm0y18PEltzEAmrX76A89n5ouZ+b/ACODwFtjvXOBK4OxFOyJi84i4JyIer54/trSdZeafqv2dWu3j6gWjThFxUUQ8Xe3v4qqtW0TcFBGPVI+9qvb+EfFQNfL2UERsV7XvGBHjqpGnxyNim6r9hLr2K6oA2yyZ+XJmPg7Mb+42yyozRwKjgH8HiIj7IqJfRHSoztGTEfFERJxd9W8VEXdExISIeCAitq/aD42IP1fn5e6I2KRqH1S994lV39pV+znVeX08Ir7dWu9PWl11LF2ApDbXA5hStzwV2GPRlSLieOCcJrZ/PjMXNx33U+DxiPjBIu2XA9dm5jURcRLwY2BwM2p9FDhtkbo2AIYA22dmRsR6Vddl1EaSxlSB705gB+AZYGBmzq2mPP8PcCRwOnBZZl5X3VarQzUadyywV2Z+EBH/AxwPXNuMWpstIh4A1m6i66vNHAV8FNh+kba+QI/M3Kk6xnpV+5XA6Zk5OSL2AP4H+CQwBtizOoenAF8DvgJ8FTgzMx+MiK7A+xFxILANtTAfwG0RMTAzRzf7TUtaIgOZtPqJJto+dMuOzLwOuG5ZdpyZsyPiWuCLwN/rugYAR1Svfw0sGtgWp6laZwPvA7+IiD8Av6/a9wd6RzRusk41urMucE01ApZAp6p/LPD/RURP4OYqsOwHfBx4pNpPF+DNZtbabJm5zwruoqnz8iKwZUT8BPgDMKoKVP8K3FB3XtaqnnsCIyOiO7Am8FLV/iDwo4i4jtp5mVoFsgOBx6p1ulILaAYyqYUYyKTVz1Rgs7rlnsBri660nCNkAJdSG8H51RLWae4923aldg/Sf25YG+nqD+xH7Wbxn6c24rMGMCAz64MgVUD5U2YOiYgtgPuq/fw2Iv4M/BtwZzVKFMA1mXl+M+tbLi0wQrYrML6+ITPfjog+wEHAmcAxwFnAO5nZt4l9/AT4UWbeFhH7AhdU+7moCrqfAh6uRhUD+H5mXtGM2iQtB68hk1Y/jwDbRESvaqpuKHDboitl5nXVheSLPpb47cHMnAVcD9TfaPmh6jhQmwIcs7QiI2IQtevHfr5Ie1dg3cy8nVrg6Ft1jaIWzhast6B9XWBa9fozdf1bAi9m5o+pvf9dgHuAoyJi42qdDSJi8+r1tVUQXGGZuc9izu1Sw1hEHElttKphkfaNgDUy8ybgm8BumTkbeCkijq7WiSq0wcLnZVjdfrbKzCcy87+phb7tqU3/nlSdeyKix4JzJKllOEImrWaqEabPU/tPtgPwy8x8qoUPcwl14YjaFOYvI+IcYAbwn4vZ7tiI2Bv4CLUptCMzc9Ii66wN3BoRnamN3Cz4EsEXgZ9GxOPU/m0bTe06sR9Qm7L8MnBv/bGAEyLiA+B14DuZOSsivkFtum8N4ANqo02vUAts05f0piNid+B3wPrAoRHx7czccUnbNNPZEXEC8FHgSeCTmTljkXV6AL+q6gZYMMp3PDC8el+dqH2J4y/URsRuiIhpwMNAr2r9syLiE8A84Gngj5n5j+r6urHV1Occ4ARaYTpXWl1FZnNnDiRp9RQR6wBXZebRpWuR1D4ZyCRJkgrzGjJJkqTCDGSSJEmFGcgkSZIKM5BJkiQVZiCTJEkqzEAmSZJUmIFMkiSpsP8Ha3SyJY/RXT8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot of crosstab\n",
    "pd.crosstab(df.target, df.age > 50).plot(kind = \"bar\",\n",
    "                                    figsize = (10, 6),\n",
    "                                     color = [\"lightgreen\", \"salmon\"]);\n",
    "                                         \n",
    "plt.title(\"Heart Disease Frequency for Age\")\n",
    "plt.xlabel(\"0 = No Disease,  1 = Disease\")\n",
    "plt.ylabel(\"Age\")\n",
    "plt.legend([\"Below Age 50\", \"Above Age 50\"])\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b528c2f5",
   "metadata": {},
   "source": [
    "### Age vs. Max Heart Rate for Heart Disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6437ce18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create another figure\n",
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "# Scatter with positive examples\n",
    "plt.scatter(df.age[df.target == 1],\n",
    "            df.thalach[df.target == 1],\n",
    "            c = \"salmon\")\n",
    "\n",
    "# Scatter with negative examples\n",
    "plt.scatter(df.age[df.target == 0],\n",
    "            df.thalach[df.target == 0],\n",
    "            c = \"darkblue\")\n",
    "\n",
    "# Add some helpful info\n",
    "plt.title(\"Heart DIsease in function of Age and Max Heart Rate\")\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Max Heart Rate\")\n",
    "plt.legend([\"Disease\", \"No Disease\"]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6b50d33a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check the distribution of the age clumn with a histogram\n",
    "df.age.plot.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "909abf8c",
   "metadata": {},
   "source": [
    "It's a normal distribution. Normal Distribution is a graph showing a Bell Curve."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af588de1",
   "metadata": {},
   "source": [
    "### Heart Disease frequency per Chest Pain type\n",
    "\n",
    "cp - chest pain type\n",
    "\n",
    "  * 0: Typical angina: chest pain related decrease blood supply to the heart\n",
    "  * 1: Atypical angina: chest pain not related to heart\n",
    "  * 2: Non-anginal pain: typically esophageal spasms (non heart related)\n",
    "  * 3: Asymptomatic: chest pain not showing signs of disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0f88dbcd",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df.cp, df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a5e04f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make the cross tab more visual\n",
    "pd.crosstab(df.cp, df.target).plot(kind = \"bar\",\n",
    "                                   figsize = (10, 6),\n",
    "                                   color = [\"salmon\", \"darkblue\"])\n",
    "# Add some communication\n",
    "plt.title(\"Heart Disease with Chest Pain Type\")\n",
    "plt.xlabel(\"Chest Pain\")\n",
    "plt.ylabel(\"Types\")\n",
    "plt.legend([\"No Disease\", \"Disease\"])\n",
    "plt.xticks(rotation = 0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8157dedd",
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.098447</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.279351</td>\n",
       "      <td>0.213678</td>\n",
       "      <td>0.121308</td>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.398522</td>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.210013</td>\n",
       "      <td>-0.168814</td>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.068001</td>\n",
       "      <td>-0.225439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>-0.098447</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.280937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.049353</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.433798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>0.279351</td>\n",
       "      <td>-0.056769</td>\n",
       "      <td>0.047608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>-0.144931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>0.213678</td>\n",
       "      <td>-0.197912</td>\n",
       "      <td>-0.076904</td>\n",
       "      <td>0.123174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.085239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>0.121308</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>0.094444</td>\n",
       "      <td>0.177531</td>\n",
       "      <td>0.013294</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.028046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>-0.116211</td>\n",
       "      <td>-0.058196</td>\n",
       "      <td>0.044421</td>\n",
       "      <td>-0.114103</td>\n",
       "      <td>-0.151040</td>\n",
       "      <td>-0.084189</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>0.137230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>-0.398522</td>\n",
       "      <td>-0.044020</td>\n",
       "      <td>0.295762</td>\n",
       "      <td>-0.046698</td>\n",
       "      <td>-0.009940</td>\n",
       "      <td>-0.008567</td>\n",
       "      <td>0.044123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.421741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>0.096801</td>\n",
       "      <td>0.141664</td>\n",
       "      <td>-0.394280</td>\n",
       "      <td>0.067616</td>\n",
       "      <td>0.067023</td>\n",
       "      <td>0.025665</td>\n",
       "      <td>-0.070733</td>\n",
       "      <td>-0.378812</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>-0.436757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>0.210013</td>\n",
       "      <td>0.096093</td>\n",
       "      <td>-0.149230</td>\n",
       "      <td>0.193216</td>\n",
       "      <td>0.053952</td>\n",
       "      <td>0.005747</td>\n",
       "      <td>-0.058770</td>\n",
       "      <td>-0.344187</td>\n",
       "      <td>0.288223</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.430696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>-0.168814</td>\n",
       "      <td>-0.030711</td>\n",
       "      <td>0.119717</td>\n",
       "      <td>-0.121475</td>\n",
       "      <td>-0.004038</td>\n",
       "      <td>-0.059894</td>\n",
       "      <td>0.093045</td>\n",
       "      <td>0.386784</td>\n",
       "      <td>-0.257748</td>\n",
       "      <td>-0.577537</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.345877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>0.276326</td>\n",
       "      <td>0.118261</td>\n",
       "      <td>-0.181053</td>\n",
       "      <td>0.101389</td>\n",
       "      <td>0.070511</td>\n",
       "      <td>0.137979</td>\n",
       "      <td>-0.072042</td>\n",
       "      <td>-0.213177</td>\n",
       "      <td>0.115739</td>\n",
       "      <td>0.222682</td>\n",
       "      <td>-0.080155</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>-0.391724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>0.068001</td>\n",
       "      <td>0.210041</td>\n",
       "      <td>-0.161736</td>\n",
       "      <td>0.062210</td>\n",
       "      <td>0.098803</td>\n",
       "      <td>-0.032019</td>\n",
       "      <td>-0.011981</td>\n",
       "      <td>-0.096439</td>\n",
       "      <td>0.206754</td>\n",
       "      <td>0.210244</td>\n",
       "      <td>-0.104764</td>\n",
       "      <td>0.151832</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>-0.225439</td>\n",
       "      <td>-0.280937</td>\n",
       "      <td>0.433798</td>\n",
       "      <td>-0.144931</td>\n",
       "      <td>-0.085239</td>\n",
       "      <td>-0.028046</td>\n",
       "      <td>0.137230</td>\n",
       "      <td>0.421741</td>\n",
       "      <td>-0.436757</td>\n",
       "      <td>-0.430696</td>\n",
       "      <td>0.345877</td>\n",
       "      <td>-0.391724</td>\n",
       "      <td>-0.344029</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age       sex        cp  trestbps      chol       fbs  \\\n",
       "age       1.000000 -0.098447 -0.068653  0.279351  0.213678  0.121308   \n",
       "sex      -0.098447  1.000000 -0.049353 -0.056769 -0.197912  0.045032   \n",
       "cp       -0.068653 -0.049353  1.000000  0.047608 -0.076904  0.094444   \n",
       "trestbps  0.279351 -0.056769  0.047608  1.000000  0.123174  0.177531   \n",
       "chol      0.213678 -0.197912 -0.076904  0.123174  1.000000  0.013294   \n",
       "fbs       0.121308  0.045032  0.094444  0.177531  0.013294  1.000000   \n",
       "restecg  -0.116211 -0.058196  0.044421 -0.114103 -0.151040 -0.084189   \n",
       "thalach  -0.398522 -0.044020  0.295762 -0.046698 -0.009940 -0.008567   \n",
       "exang     0.096801  0.141664 -0.394280  0.067616  0.067023  0.025665   \n",
       "oldpeak   0.210013  0.096093 -0.149230  0.193216  0.053952  0.005747   \n",
       "slope    -0.168814 -0.030711  0.119717 -0.121475 -0.004038 -0.059894   \n",
       "ca        0.276326  0.118261 -0.181053  0.101389  0.070511  0.137979   \n",
       "thal      0.068001  0.210041 -0.161736  0.062210  0.098803 -0.032019   \n",
       "target   -0.225439 -0.280937  0.433798 -0.144931 -0.085239 -0.028046   \n",
       "\n",
       "           restecg   thalach     exang   oldpeak     slope        ca  \\\n",
       "age      -0.116211 -0.398522  0.096801  0.210013 -0.168814  0.276326   \n",
       "sex      -0.058196 -0.044020  0.141664  0.096093 -0.030711  0.118261   \n",
       "cp        0.044421  0.295762 -0.394280 -0.149230  0.119717 -0.181053   \n",
       "trestbps -0.114103 -0.046698  0.067616  0.193216 -0.121475  0.101389   \n",
       "chol     -0.151040 -0.009940  0.067023  0.053952 -0.004038  0.070511   \n",
       "fbs      -0.084189 -0.008567  0.025665  0.005747 -0.059894  0.137979   \n",
       "restecg   1.000000  0.044123 -0.070733 -0.058770  0.093045 -0.072042   \n",
       "thalach   0.044123  1.000000 -0.378812 -0.344187  0.386784 -0.213177   \n",
       "exang    -0.070733 -0.378812  1.000000  0.288223 -0.257748  0.115739   \n",
       "oldpeak  -0.058770 -0.344187  0.288223  1.000000 -0.577537  0.222682   \n",
       "slope     0.093045  0.386784 -0.257748 -0.577537  1.000000 -0.080155   \n",
       "ca       -0.072042 -0.213177  0.115739  0.222682 -0.080155  1.000000   \n",
       "thal     -0.011981 -0.096439  0.206754  0.210244 -0.104764  0.151832   \n",
       "target    0.137230  0.421741 -0.436757 -0.430696  0.345877 -0.391724   \n",
       "\n",
       "              thal    target  \n",
       "age       0.068001 -0.225439  \n",
       "sex       0.210041 -0.280937  \n",
       "cp       -0.161736  0.433798  \n",
       "trestbps  0.062210 -0.144931  \n",
       "chol      0.098803 -0.085239  \n",
       "fbs      -0.032019 -0.028046  \n",
       "restecg  -0.011981  0.137230  \n",
       "thalach  -0.096439  0.421741  \n",
       "exang     0.206754 -0.436757  \n",
       "oldpeak   0.210244 -0.430696  \n",
       "slope    -0.104764  0.345877  \n",
       "ca        0.151832 -0.391724  \n",
       "thal      1.000000 -0.344029  \n",
       "target   -0.344029  1.000000  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a Correlaion Matrix\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "02df736b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m7Wnbd4eFB/HZrgRjA51BjXnr8OffuiqeqLaRhp2KBafoZk7RLir6/tgEwNb39wvz5do/1jP8t3W4OZq43Mi+v4btfrZfZmctLSV18zaCu3U6X6nOytl2/Z+/OY9W0U1oFX3iuo9u/drx7pyJjH/tdr777G87pBQXktpemO4KtASOnxh9AxAP3KmUGqC1fqRyYa31dOD46EM//NLi8xC1qiPJGYSHnJhuDAv2JSklC2cnR8IrnXoTFuLH0dSz/xS2Ltb+tpwNf9uuiQhrHkF2uqViWXZ6Nl5+XrX+XV7+5nOqXxcb/1jG1vm2/CHNIsittP7cDAuevlU/MXXz8qQ4rxBrWRkOJlONZbYvM/ZULIDUwhKC3E98+hjk5kJ6DadURXm782zXKB5eFk92ie0UiG5BZo7mF2Eptj1enJhBe38v/jqUZkx4bJ/IBVY6RSPAzZmM4ur5mzRwZ3y7pkzcsL3eT+GozNnHh5LMzIrHJRYLTmZzretbS0vZM20a/t2749vJuDfTVb8uZ+2ftvbfsEUElrQT/YYl3XLW+1/S/qP88O4c7nz5bjzsfCD2/ewV/DLPlr112whSki0Vy1JTLAQEVs2+a2ciiYfS+c8VLwNQVHSMG4a/zLw/nyEwyHzG+vbi7GOmOPPEdi/OysKllm3nXOqeL+lFJVVOrwpwPfW++3i7pjy1/sLYdxf9tILlv68BoHGLhhWnxwBkpVkw+3ufouaprf93M90NPBULILWgat8f6O5SZWbquChvd57pEsUjy2vo+8sfLz6SQXs/L/42qO939fWhqFL7LcrMwsVcdbu7+pgpyjipjI+54nHatni8GkXg4m3M/gowf94K/i2/pqNpy4ZkVJo9ykjLxucUbef7z+eTY8nj8Sdvq3F5645N+filOeRY8vAqv+nBpeZCvlbDKLXdAlHAQK31B1rrD4DLgFbAdcCQ09a0kz8WbOKmG2yf4nXrGEVObgHJqRY2bN1HVGQwjRoG4ORkYsRVPfljwUZDMnW/qg/3fzSB+z+aQKue7djy73q01hzekYCrhysNfGvfkbfs0fac6tdF5yv6VlxM3qxHe+IWrUNrzZGdB3Bxd602wFBKEdG+GTtXbgEg9t91NOvermJ5UX4hh+P20qxHO4y0PTOXCE83Qj1ccHRQDI4IYNnRzCplgtxdeKNXK55fu5tDeUUVzycXFNPOrwEuJtuu0TXImwM5BYbm35mdS5iHG8FuLjgqxcCQAFalVM0f6OrMlE4teXXrHhLzi07xm+qHZ+PGFKWmUpSejrW0lMz16/HpULvT8bTWHJg1C7eQkGoXrdtbr6v78Oi0CTw6bQJterVj0wLb/ndwRwJuHm54+dV+/8tKzWLWlC8YNeFmAsID7ZjaZsToGL7+4Qm+/uEJ+g5sy1+/2rLHbk3A09Ot2ilXMX3b8NeSKfw8fxI/z5+Eq6sT8/58BoA+A9qcsb69eEU2pjAllcI0W9tJXbcBv+jatZ1zqXu+nLzvDggJYFVq9X33hY7l+27BhbHvDrwupuJi8o592rF6/ga01uyLT8DNwxXzWQ7AC/IK2bV1H9Exbe2UuGbbs3Jp6OlGqLtt+w9pGMDyk/t+Nxde79WK59dV7/vb+lbq+wO9Scg1ru/3jmxEfkoqBeXtN2ntBgI7Vj2VLbBje46sXIPWmqy9+3F0c8O10kAlac16QnsYew3s0BtieGOm7WLyrn3bsuzvjWit2R13EHcPV3z8q7edf39dw7a1u3h4yi04OJw4DE1OTK+Yidu/K5HSY6U08DZuJk0Yr7YzIWGAB3D85D4PIFRrXaaUKj51tbqb+cGD9OnZCn+fBuxd+yEvvvMDTk62uP/7eiF/L9rM0AHRxC+fSkFhMXeP/xSwXWz56HMz+O2rpzCZHJj53RJ27E483arsonnX1uxev51373gRJ1dnrn/0popls56bxrWPjMbLz5vVvyxlxff/kpeVy0f3vU7zrq259pHRp61vhKZdWrN/QzyfjpuCk4szwx8eU7Fs7gvTGPbgaBr4eTPgtqv55Y0ZLPv6D4KahNN+SI+KcrtXbyOyY0ucT3NOtD2UaXhj0z7e79sWk4JfD6SwP6eA65sGA/DjvmTuat0QbxcnnuzUFIBSrbl14VbiM/P4NzGDrwdHU6Y1u7Ly+Wl/sqH5rRrej9/PG93a4AD8lZhKQl4hV0XY8v92KJmxzSLwcnbikbZNKl7zPSttFzE+G92caF9vvJ0dmTugCzP2HOLPxFTD8iuTicajR7Nr6lS01UpA7964h4aSsnQpAEH9+lGSnU3cyy9TVlSEUoqkhQtpP3kyhYmJpK9Zg1tYGLFTpgDQ8LrrMLczdiDbsltrdq7bweu3vYSzizMjxo+uWPb5M5/yn8dG4e3nzYqflrL0+0XkZubyzt1v0LJba0Y8NoqFX8+nICefnz6wTR47mEw8/NHjhmTv3ac1q5bt4IbhL+Pq6sxzL42qWPbIvdN5ZvJIAgJPPag4XX17UyYTUWNGEfvue2irleCY3niEhXJ0ia3thPa3tZ2NL75CWWERKEXiwn/p+uILOLq51VjXSFYNH2zfz+td2+CgbPvuwbxCrmxo23d/P5zMLVG2fffhNif23ftW2fbdZzo0p0P5vjtnQBdm7jnEXwbuuwDterQids0Onr7pFZxdnLh94om2P3XCdG6bMBKzvzcLf1jG/DmLyc7M5YU73qJdj1bcNsF2Dv/m5bG06doCFzfj+/43N9v6fgcFvx3v+5uU9/37y/t+5xN9f5lVc+u/J/r+ry4r7/stxvb9DiYTrW8Zxfo3P0BbrYT37UWD8FAOLVoGQMTAvgR0aEvatjiWPjEJk4sz7e8ae+K1F5eQHreTNreNOdUq7K5jr1ZsXr2Dh0e8irOrE/c+c6LvePXxz7h74o34BnjzvzfnERDkw7Pj3gdsp2D9544hrF28jWV/b8DkaMLZ2YlHXrzlrE9JExcXdarzVasUUupO4FlgCbazQ/sCrwCzgRe01k+cprp2ixh9msUXtsJDs5m7/+I9L/HGJpfz5e759R2jzm5vPpSuc1fUd4w6W39jDAP+XFnfMepk8fDejC0fOFyMZvXrxy8H/6rvGHV2TaNhWEr+rO8YdWZ2Hs64FUvqO0adTY/pz6C/Ls59999hvVme/Ed9x6izPsFX0O37i7ffXzcihkfWLKrvGHU2tcdAtmT8Xt8x6iza78qLYuTi1/whw65ky9j9/gW5TWo1E6K1/lwp9RdwC7AT+AdI1FrnA6cbgAghhBBCCCFEFbUahCil7gIeBsKBLUAPYDUw0G7JhBBCCCGEuATJhem1vzD9YaArcFBrPQDoCBh3uyAhhBBCCCHEJaO2F6YXaa2Lyr/h0kVrvVMp1cKuyYQQQgghhLgE1eW7py41tR2EJCqlzMDPwAKlVBZw1F6hhBBCCCGEEJeu2l6Yfl35f19QSi0GvIGL95ZRQgghhBBC1BO5JqT2MyEVtNYX7z07hRBCCCGEEPXurAchQgghhBBCiLqTmZDa3x1LCCGEEEIIIc4LmQkRQgghhBDCQDITIjMhQgghhBBCCIPJTIgQQgghhBCGknkA2QJCCCGEEEIIQ8lMiBBCCCGEEAaSa0JAaa3tvQ67r0AIIYQQQghA1XeA2ghp84xhx8dJ8S9fkNvEkJmQufsv3i9Xv7HJ5bhFjK7vGHVWeGg203b8U98x6uyeVkOYvnN+fceos3Eth7I+7Y/6jlEnXQOuYMK6RfUdo87e6Dbwom87H2+/ePfd+1oPYdTiZfUdo87mDOjLuBVL6jtGnUyP6c978Rdv23m4zRBuW3bxfi/yjL79GPTXyvqOUWf/DuvNnuzf6ztGnTXzvrK+I4haktOxhBBCCCGEMJCcjiUXpgshhBBCCCEMJjMhQgghhBBCGEjJPIBsASGEEEIIIYSxZCZECCGEEEIIA8k1ITITIoQQQgghhDCYzIQIIYQQQghhIKUuyK/uMJTMhAghhBBCCCEMJTMhQgghhBBCGEiuCZGZECGEEEIIIYTBZCZECCGEEEIIA8n3hMhMiBBCCCGEEMJgtRqEKKXuPOmxSSn1vH0iCSGEEEIIcelSysGwnwtVbU/HGqSUugG4E/ADvgSW2i1VOa01f077kd3rt+Pk4sT1j48hNKphtXJrfl3G6p+XkpmUzsQ5L+Ph7XlW9e1h2pt3M2xQR9IycugyeEKNZd6efCtDB0RTUFjCuMc/YUtcAgCD+3XgrRfGYjI5MGPOYt76+FdDMlemtWbJ/+ZxYGM8Ti7ODHnoZoKaVt922Snp/PnWDIryCghsEs7lj4zF5OTI4dg9/PrqdLwD/QCI6tmBHiOHGZp/8WfzOLBxO44uzlz+8JhT5M/g9zdP5B/+6C2YnGy7xeHYPSz+/EespWW4eXkw8pWHDc3/1Xs/sWX1DlxcnRn39GgiW4RXK/fx5K/Zv/Mwjo4mmrSK4I4JI3B0NNW6vr2kbIsn9qu5YNVE9O9N86uGVlmutSb2q7mkbo3H5OJMx3FjMTeOAOCfR5/B0dUV5eCAMjnQf8pThuWunO9ibT9aa5Z+Po+EjfE4ujgz5MGbCTzFvvvX2yeyD314bEV2gOQ9B5k78W2GPX47zXp1NCQ7QE58HIlz56CtVvx69yH48qr9RlFyEgdnzqDw8CFCrr6WoCEntS2rlV2vvoST2UzT+x8yLPdxmbFx7J09F62thPSJIWL45VWWFyQls/OLGeQdOkzkddfQ8PIhta5rBK01Kz6fx8FNtvYz6IGbCaih/eSkpPPPOzMozivAPzKcy8rbz4F121g7+w+UUjiYHIi54wZCWjU1LH9WXBwJc75DW60E9YkhbFjV9lOYlMTeGTPJP3SIiGuvJXSobfsXZ2ay94svOJadA0oR1LcvIZcNMiw3QFd/M/e3aoKDgj8TU5iz/0iV5YNCAxgVGWZ7HWVlTI3fx/7cAgDGt4uiR4APlpJj3LVii6G5j9NaM/3tn9mwyva+88ikUUS1rP6+8+ZzX7N3RyImRxPN2zTkgads71v5eYW8Nelb0pKzsJZZue7m/gy+qls9vBJhlFoNj7TWNwEzgVjgT+ARrfV4ewYD2LN+OxlH03jk82e55qFR/Pbh9zWWi2jdhNtevQ9zoG+d6tvDV98v5Zqxr51y+dAB0TRtHEzbvo/ywMTPeP9l22STg4Ni6ku3c82tr9Nx0HhGXN2Lls3CjIpdIWHjdixJqdz+ySQuu28Ui6Z9V2O55TN/pdPVA7j9k0m4eLoTt3B1xbKw1k25eepEbp460dABCMCBjdvJSkrjjmnPMfj+kSz8ZG6N5ZbN/IXOV/fnzmnP4erpTmx5/qK8AhZOm8u1z/yX2z58mqsm3GFkfLau2UHy4XTenvM0dz4xghlv/VBjuV5DOvHmtxN5ddYTlBQfY8lva86qvj1oq5VtM+fQ84kHGPj6JI6sXk/OkaQqZVK3xpOfksqgtybT4Y6b2Prl7CrLez/9KANefqZeBiBwcbefhE3bsRxN5daPJzHo3lEs+rTmfXflrF/peNUAbvt4Ei4e7sT/e2LftZZZWTnrFyKiWxkVG7C1ncOzv6XpAw/T6vkpZK1fR+HRo1XKmNw9CB85isDLhtT4O9IWLcQ1OMSIuNVoq5U938ym3aMP0vXFF0hdu578k/I7ergTddMoGg4dfNZ1jXBo03ayk1IZ89Ek+t8ziqXTa24/q7/6lQ5XDWDMR7a+f0d5+wlv14KR70xk5DsTGXD/GBZ//K1h2bXVyoFvv6XVww8RPWUy6evWU1Bt+3sQOWoUoUOqbn/l4ECjESOIfnEK7Z5+iuTFi6vVtScH4KE2TXhqQzx3LN/MwJAAGnm6VSmTVFDEo2tj+e/KLXy99zCPtY2qWDY/MZWnNmw3LG9NNqzaydHD6Uyf9xQPPDWCj1+fV2O5/pd3Ztr3T/LR7PGUFB/jn5/XAvDH9yuJiAziw2/H8+q0+/j8vV85dqzUyJdgKJkJqf3pWM2Ah4F5QAJwi1LK3Y65ANixJo7oQV1RStGwVWMK8wrJzcyuVi40KhyfIL8617eHlet2kmnJO+XyK4d05tt5ywFYt3kv3l7uBAea6Rodxb6EZBIOpXLsWBnf/7aaK4d0MSRzZfvWxdKqfzeUUoS0iKQ4v5C8k7ad1prDsbtp1isagNYDurNv7TbDs9Zk37pYWg+w5Q89Tf5D2/bQvHc0AG0GdmPvmlgAdi7bSLOeHfAKsA1s3c0NDM2/cXkcMZd3QSlFVNvG5OcVkpWeU61cdM/WKKVQStG0dQSZqdlnVd8esvYl4BEUgEdgAA6OjoT16ELyxq1VyiRt2krDmB4opfCNasKxggKKLMbsm7VxMbef/etiaTWg6r6bf5b77tY/lxLVMxr38llloxQkHMAlMACXAFvb8enalextW6qUcfLywqNxJMpkqla/JCuT7NhY/HrHGJS4qpz9B3ALDMStPH9gty5kbK7a9p29vPCKbFwtf23qGuHAulhalPf9wS0iKTlF+zkSu5umPaMBaDmgOwfW2dqPk5tLxZewlRaXAMZ9IVvegQO4BgTiWr4N/bt2JWtL1W3o5OWFZw3b39lsxrNRIwBMrq64hYRQYrEYFZ2W5gYcyS8iqbCYUq1ZnJRGr5M+WN1uySWvtKzi/wGuzhXLYrNyyKnnA/a1y+IYOLwzSilatmtEfm4hmTW873Tt3arifat56wjSUy22BUpRWFCM1prCgmIaeLljMl24B9Di3NX2r/sbMElrfTfQD9gDrLdbqnI5GRa8/c0Vj739vclJr/2ByrnWt6fQYF8SkzIqHh9JziQ02JfQYB8Sj1Z6PimDsCAfw/PlZVpo4H9ivZ5+5moHYUW5+bh4uOFQ3pk3OKlM0q4DfPXIq/w05WPSD1X9JNze8jKyaVDpb9/A30xeRtX8hbn5uFbKX/k1Zh1NpSivgO+eeZ+vHnuD+EXrDMsOkJWeg1+gueKxb6CZrNO03dLSMlbM30D7Hi3rVP98Ksqy4OZ7ou24+fpQlGU5Y5nCTFsZhWL16++z5LlXSFi03IjI1VzM7Scvw4Kn39ntu57+ZvLLX19ehoV9a7bRbqjxB/IlWRacfU4ceDmbfTh2Uts5nSNzvyPs+v9APX3yV2Kx4FKpXbv4+FBcywPZc6l7PuVnWvCs1Pd7+JmrDUKKcvNxrtR+PPxOtB+A/Wu28u2DL/LHy9MY+MAYY4JzfBtWaj8+ZootWWf9e4rS08k/fAjPyMjzGe+0/F2dSSsqqXicVlSCv6vLKcsPaxjEujSLAclqLyM1G/8gc8Vjv0BvMlJP/761+K+NdOppe9+6ckRvDiekMHb4ZB646S3GPXYtDg4yCLmU1faakG5a6xwArbUG3lZKnfJCBaXUOGAcwKeffor5soi6pdM1/nLj6tuRquHTIa11xSdIVZ83ItGZV3pyMn2aYIFNw7lz+hSc3Vw4sCGe3179jNs/mXSeQ55aTdmqbdrTlLGWWUndd5gRLz7AsZJjzJ7wLiEtGuMbFmiHtNXVmP805We8/QMtOzShZYcmdap/PtXYLk7e+KcpEjNpPG4+Zoqzc1j1+vt4hgbj37LZ+Q96Ghd3+6lp455Uosa/ke2fpZ/Po/fYq3Gol08gz5z9VLK3bcWxgRfujRqRu2vX+Y1VW+fSV9dHP1+D07WNSoVOW6ZJjw406dGBo/F7WTf7d65+4cHzmvGUauz3zq7nKysqYvcn02g8ciSObm5nrmBHp3qPjfb1Zlh4EI+Uz7xeKM72kOvj1+fRpmMT2na0vW9tWrOLJs3CeOXje0lKzOC5Bz6lTXQT3D1d7RO4nsktems/CHFTSr0LhGmtL1dKtQZ6YpsRqUZrPR2Yfvzh3P1/1zrQ2t+Ws+Fv27mlYc0jyE63VCzLTs/Gy8+r1r/Ly998TvXt6UhyBuEhJ04hCwv2JSklC2cnR8JDKz0f4sfR1LP/JKcutvy5jLh/VgEQ1CyC3PQT683LsODh612lvJuXJ8X5hVjLynAwmcjNsOBZXsbF/UTnHdmlDYs+nUthTh5uXvY7vWPzH8uIXWBrO8FREeRW+tvnptecv6hS/rwMCx4+tjKefmbcvDxwcnXBydWF8DZNSUs4YteDyAXzVrC4/JqOJq0aknF8ihrITLVg9veusd6PX8wn15LPHS+PqHjON8C71vXPN9usxom2U5iZhau56rpdfc3Vy/iYbfXL/3Xx9iKkSzSWfQmGDEIu5vaz9c9lxC0o33ejIsjLqLrvevqcft/Nq/T6Uvcd4q+3ZwBQlJtHwsbtOJgcaNq9g12yV+bs40NJVmbF4xJLFk5mc63q5u/bR/a2LeTExWItPUZZYREJX/yPxnfcZae01Tn7mCmu1K6Ls7JwqWX+c6l7rmL/Wsb28vYTGBVBXqW+P79Suz7O1cuTkkrtJ7+G9weA0DZRZH+Qbve+/zhnHx+KMyu1nywLzmexDa2lpez6ZBr+3bvj16mTHRKeWnpRSZXTqwJcnckoLqlWrkkDdx5v15Sn1m+v99OvAH7/fgXzy6/paNa6IekploplGanZ+AbU/L7z7WfzycnK44Gnbqt4buHv6/nP2IG202Ab+hMU6svhg6m0aFPHD7LFBa+2w7AZwHzg+NV+u4FH7JCH7lf14f6PJnD/RxNo1bMdW/5dbzt/eUcCrh6uNKihozuVlj3anlN9e/pjwSZuuqEPAN06RpGTW0ByqoUNW/cRFRlMo4YBODmZGHFVT/5YsNGQTNHD+1ZcSN60e3t2LFmH1pqkXQdw9nCtGGAcp5SiYbtm7Fm1BYDti9fStFs7APKzcio+xUnenYDWGtcGHnbN3/GKvoyd+iRjpz5JVI/2bF9sy3901wFcTpE/ol0zdq+05Y9ftI6o7rb8Ud3bcWT7fqxlZRwrLiFp90H8woPsmn/wDTG8MmM8r8wYT+c+7Vjx9wa01uyNS8Dd0xUf/+oD6MW/rSF23S7uf+HmKtPWnWLa1qq+PZibNCI/OZX81HSspaUcWbOB4E7tq5QJ7tSewyvWoLUmc+9+nNzdcDV7U1pUzLHCIgBKi4pJjd1Bg4ahhuS+mNtPh+F9GfPuRMa8W77vLj6x77q4u1Y7QFRKEd626r7bpHzfvf3Tydwx3fYT1TOaAXffaMgABMC9UWOKU1MpTk/DWlpK1vr1eLev3bpDr7uetq+9SZtXXqPxneNo0LKFoQMQAK/IxhSmpFKYZmv7qes24Bddu/znUvdctRvWt+Ji8shu7dlV3vcn7zqA8ynaT1jbZuxbvQWAnYvXEtnV1n6yk9Iq+v60fYexlpbZve8/zrNxY4pSUykq34bp69fj06F221Brzb6Zs3ALCal20boRdmbnEubhRrCbC45KMSAkgFWpmVXKBLo680LHlry6dQ+JBUWGZ6zJlSNi+OCbx/ngm8fp2a8ti/7ciNaanbEHcfd0xbeG9535P69h05pdPPHSLVXetwKCzGxdb/tsOysjl8RDqQSH+Varf8lQDsb9XKBqOxPir7Weq5R6CkBrXaqUKrNjLgCad23N7vXbefeOF3Fydeb6R2+qWDbruWlc+8hovPy8Wf3LUlZ8/y95Wbl8dN/rNO/ammsfGX3a+vY284MH6dOzFf4+Ddi79kNefOcHnMpvf/m/rxfy96LNDB0QTfzyqRQUFnP3+E8BKCuz8uhzM/jtq6cwmRyY+d0SduxONCz3cZGd25CwcTtf3jMFRxcnhjx0c8Wyn6Z8wuAHbsLT15uYsdfw59tfsvKb3wlsEk6bwT0B2LNqM1v/XoGDyQFHZ2eGj7+txlPN7Je/Nfs3xPP5PVNwcnFm6IMnzkv+cco0htw/Gk8/b/rcejV/vDWDld/8QWCTcNoO7gGAX8NgGndsxcyHXkM5ONBucA/8GxlzMAwQ3bMVW1fv4PGRr+Ds6sS4p0dXLHtz/HTumjgSH39vvnzrB/yDfHjh7vcB6NqvHdfdPvS09e3NwWSi/dhRrH7zA7TVSkTfXniFh3Lg32UARA7qS1CHtqRsiWPh+EmYnJ3p+N+xABTn5LBuqm1f0FYrYT27EtS+jWHZj7uY20/j8n135r22fXfwgyf23Z9f/ITL7j+x7/719pes/vZ3AiLDaXNZT0PynY4ymQgfeRP73p+Ktmr8evXGLTSM9GVLAPDv259j2dnsevUlyoqKUEqRtmghrZ6fgqmeT50BW/6oMaOIffc9tNVKcExvPMJCObrEdkf70P79KMnOZuOLr1BWWARKkbjwX7q++AKObm411jVao85tOLRpO9/cZ2s/Ax840X5+f+kTBtx3Ex6+3vS45RoWvPMla8vbT6vy9rNv9RZ2LV2Hg8mEo7MTQx6/3bC+X5lMRN40mh1Tp6K1lcDevXEPCyW5fPsHl2//2JdepqzItv2TFi6kw5TJFCQmkr5mDe5hYWydPAWAiOuvw6ddO0OyWzV8sH0/r3dtg4OCvxJTOZhXyJUNgwH4/XAyt0RF4OXsxMNtbKcvlWm4b5XtwvtnOjSng6833s6OzBnQhZl7DvFXYqoh2Y/r0rsVG1bt4L/Xv4qLqxOPPDeqYtnzj3zGQ8/ciF+ANx+9Po/AYB/G32l73+o1oB2j7xrCqDsHM3XKHO4f/SZaw+0PXIm32dibYwhjqdOd119RSKklwA3AAq11J6VUD+B1rXW/WqzjrE7HutDc2ORy3CKMO4A73woPzWbajn/qO0ad3dNqCNN3zq/vGHU2ruVQ1qf9Ud8x6qRrwBVMWLeovmPU2RvdBl70befj7Rfvvntf6yGMWrysvmPU2ZwBfRm3Ykl9x6iT6TH9eS/+4m07D7cZwm3L7P5VZHYzo28/Bv21sr5j1Nm/w3qzJ/v3+o5RZ828r7wwLv49g6adpxp2Jdi+jY9ckNuktjMhjwG/Ak2VUiuBAOA/dkslhBBCCCGEuGTVdhDSFBgGNMQ2I9L9LOoKIYQQQgghyhl5ivqFqrZXqzxXfoteH+AybHe++sRuqYQQQgghhBCXrNoOQo5fhH4FME1r/QvgfJryQgghhBBCiBooHAz7OWMWpS5XSu1SSu1VSk2sYbm3Uuo3pdRWpVS8Uur287ENajsIOaKU+hS4EfhTKeVyFnWFEEIIIYQQFxillAn4CNtlF62B0eXfB1jZ/cB2rXUHoD+2Ly0/58mI2l7XcSNwOfCW1tqilAoBnjjXlQshhBBCCPH/jbpwvr+jG7BXa70fQCk1B7gG2F6pjAYaKNuFLJ5AJnDO35ZZq0GI1roA+LHS4yQg6VxXLoQQQgghhLAfpdQ4YFylp6ZrraeX/z8MOFxpWSK2G1BV9iG2u+QeBRoAI7XW1nPNJXe4EkIIIYQQwkgG3h2rfMAx/RSLawpy8neYDAW2AAOx3TF3gVJqeflNq+rsgpkLEkIIIYQQQhgqEdtXcBwXjm3Go7LbgR+1zV7gANDyXFcsgxAhhBBCCCGM5GDgz+mtB5oppSLLLzYfhe3Uq8oOAYMAlFJBQAtgf11edmVyOpYQQgghhBD/D2mtS5VSDwDzARPwhdY6Xil1T/nyacCLwAylVCy207ee1Fqnn+u6ZRAihBBCCCHE/1Na6z+BP096blql/x8Fhpzv9cogRAghhBBCCCMZeGH6hUquCRFCCCGEEEIYSmZChBBCCCGEMJLMhKC0PvlWwOed3VcghBBCCCEENX/vxQWnea9phh0f7151zwW5TQyZCfly93wjVmMXtzcfyrQd/9R3jDq7p9UQ3CJG13eMOis8NJsJ6xbVd4w6e6PbQGbuuTjb/63NhjJuxZL6jlFn02P68+T6f+s7Rp293nUQnWcvr+8YdbZxdB8sJX+eueAFyuw8nIVHLs78l4UNZ+zSpfUdo85m9evH6tQ/6jtGnfUMvIKlSRdn2wHoFzKcRm9evO+7B58YWN8RakcuiJBNIIQQQgghhDCWXBMihBBCCCGEgbRcEyIzIUIIIYQQQghjyUyIEEIIIYQQRpKJEJkJEUIIIYQQQhhLZkKEEEIIIYQwkoNMhchMiBBCCCGEEMJQMhMihBBCCCGEkeTuWDITIoQQQgghhDCWzIQIIYQQQghhJJkIkZkQIYQQQgghhLFkECKEEEIIIYQwlJyOJYQQQgghhJHkFr0X9iBEa83C6fPYt3E7Ti7OXPHwGIKjGlYrZ0nO4Jc3Z1CUW0BQ03CueuwWTE6OrP3xX+KXbADAWmYlIzGZh75+BbcGHoZkX/K/eRzYGI+TizNDHrqZoKbVs2enpPPnWzMoyisgsEk4lz8yFpOTI4dj9/Drq9PxDvQDIKpnB3qMHGb33MdNe/Nuhg3qSFpGDl0GT6ixzNuTb2XogGgKCksY9/gnbIlLAGBwvw689cJYTCYHZsxZzFsf/2pY7uNStsUT+9VcsGoi+vem+VVDqyzXWhP71VxSt8ZjcnGm47ixmBtHAPDPo8/g6OqKcnBAmRzoP+Upw/NrrVkwfR77NmzH0cWZqx45ddv/+Y0ZFOYWEBwVztXlbb8ov5Bf35pFTloWVquV7tcNpMPgHoblz4yNY+/suWhtJaRPDBHDL6+yXGvNvtnfkREbh8nZmRZ33EaDRrbtn7jgX5KWrQA0IX1jCB98mWG5j0vZGk/sV9+jrZpG/XvR/Oqa2s/3pGyJx+TiRKdxYzFHRpxYbrWy5LnXcPUx03P8fYZm7xniw/hOTTApxc/7kpmxI7HK8n5hvtzbvjFWrSmzat7etJ8t6Tm1qmsErTXvvPYTq5bvwNXViedeGk3L1tXb/kuT5rAj/jBoTcPGAUx66Sbc3V3IyS7gpUlzOHI4HWcXJ56dMoqmzUIMzf/9hz8Rv3YHzq5O3DJhNBHNq+df8tNyFs9bRvrRdF7/6UU8vT0B2L1lL58+9zl+wb4ARPdpz/CxQ6vVtxdLXBwHv/sObbUSGBND6LCq7zuFSUnsnzmT/EOHaHjttYQMGQJAcWYm+774gmM5OSilCOzbl+BBgwzLfZzWmm/e+4lta3bg7OLMXU+PpnGL8GrlFs5bzj/fLyP1SAYf/DaFBmbb9i/IK+TTF78hMyWLsjIrw0YNoM8V3QzN/90HPxG7xtZ+bps4mkY1tJ9FPy7n3x+WkXY0nbd/frEiP8CuzXv57sOfKCsrw9Pbkyfee8CQ7P0a+/L8oGaYlGLOtiQ+WXewxnLtgxvw85guPPBbHH/uTsPF5MDc0Z1wNikcHRR/7k7j3ZUHDMks6letByFKKWegJaCBXVrrErulKrd/43ayjqZx96fPcXRXAvM/mcutbz9erdySGb/Q9Zr+tO7bmb8/+o6tC1bTaXgful8/iO7X2zrBPetiWf/LEkMGIAAJG7djSUrl9k8mkbw7gUXTvmP0m+OrlVs+81c6XT2AFn06s/CTOcQtXE2HYX0ACGvdlGufvceQvCf76vulTJs5n/+9W/MB1NAB0TRtHEzbvo/SrWMU7798J32veQ4HB8XUl27nijGvcCQpgxW/vczvCzayc88Rw7Jrq5VtM+fQ68mHcPP1Yemk1wju1B6vsBMHIqlb48lPSWXQW5PJ2neArV/Opt/kJyuW9376UVwaeNb06w2xb8N2Mo+mcc90W9v/++O53PZO9ba/qLztt+nXmb8+/I4tC1bTeXgfNv6xHP+IYG58/m7ys3P59O6Xadu/CyYn+3/uoK1W9nwzm/aPP4KLjw+bXnwVv+j2eISGVpTJjI2jICWVbq+8SO7+A+z56hs6PfsU+YlHSFq2gk7PPoWDo4lt776Pb/t2uAcF2T135fxbZ35H74kP4eZrZsmk1wnuXLX9pGyNJy85lcvefoGsfQlsnTGHfpNPDNb3/b2YBqHBHCssMiw32D5Ym9i5KfctjiOlsJivhkSz9EgmB3IKKsqsS7Gw9K9NAESZ3Xm9dytu+GNjreoaYdXyHRw+mMYPfzxN3LaDvPHSD3zx7aPVyj0y4Vo8PV0BmPrGz3z/7XJuvesyZvxvIc1bhvLGe3eQsD+FN1+Zx0f/M24gGL92B2lH0njhq6dJ2HGQOVN/YMLH1fM3bRtJu55tmProh9WWRbVrwr2v/NeIuFVoq5WEb7+l5aOP4uzjQ/wrr2Du0AH3Svuuo4cHjUaNImvz5ip1lYMDjUaMwKNRI8qKioh76SW8WrWqUtcI29bsICUxnddnP82+7QeZ9fYPTJr+SLVyzdpF0qFXG1576KMqz//740rCGgfx6Ot3kZOVx1NjXqXnkE44GtB3AsSt3UFKYhovffM0B7Yf5Jt3f+DpT6q3n6h2kbTv2Ya3H6nafgpyC/l26g889Mbd+AX5kJOVa0huBwUvDm7BmLmbSc4t5tdburBwXxp7MgqqlXuqbxTLEjIqnisuszL6u80UHCvD0UHxw+hOLNmfweakHEOy1xu5RW/trglRSl0B7APeBz4E9iql7P6x/J41sbQd2A2lFGEtIynOLyQvM7tKGa01B7ftoWXvaADaDerGnjWx1X7XjqWbaN23s70jV9i3LpZW/W3ZQ1qcOvvh2N006xUNQOsB3dm3dpthGU9n5bqdZFryTrn8yiGd+XbecgDWbd6Lt5c7wYFmukZHsS8hmYRDqRw7Vsb3v63myiFdjIoNQNa+BDyCAvAIDMDB0ZGwHl1I3ri1SpmkTVtpGNMDpRS+UU04VlBAkSX7FL/ReLvXxtKuUtsvOk3bbxUTDdja/u7VtravgJLCYrTWHCsswa2BOw4mYy4By9l/ALfAQNwCbNs/sFsXMjZX3f4ZW7YS3Mu2/b2aNqG0oJBiSzYFScl4NY3E5OKMMpkwt2hO+qYthuQ+LmtfAp5BAXgE+uPg6Eh4j87V2k/yxm1ExHQvbz+RHMsvoCjL9vcpzMgieUscjfr3NjQ3QBvfBhzOK+JIfhGlVs0/h9LoH+5bpUxhqbXi/24mE1rXvq4Rli2OY9jVXVFK0a5DY3JzC0lPq75vHh+AaK0pLj6GKn9DP7AvmS7dmwPQuEkQSUcyyUg35kAMYNuqOLoPtuWPbN2YwrxCsjOq52/YLLxituNCkXfgAK6BgbiW77u+XbuStbVq23fy8sKzcWOUyVTleWezGY9GjQAwubriGhLCMYvFqOgVNq+Io/flXVBKEdWmMQV5hVjSqx/MNmoeTkBI9e2vFBQV2PrO4sJiPLyM6zsBtqyMo+dQW/tp0sbWfiw1tJ+IZuH415B/3b8b6dinPX5BPgB4+TSwe2aA6BAvErIKOJxdxDGr5redqQyOCqhW7rZO4fy1J5X0gmNVni84VgaAo4PCyeSANiS1qG+13bPeBgZorftrrfsBA4B37RfLJjcjmwb+5orHDfzM5J60Mxbm5OPi6YZDeYdYU5ljRSXs37SDFr062DtyhbxMCw38fSoee/qZqx1EFuXm4+JRNXvlMkm7DvDVI6/y05SPST+UZEzwWgoN9iUx6cQnGUeSMwkN9iU02IfEo5WeT8ogLMinpl9hN0VZFtx8T6zTzdeHoizLGcsUZtrKKBSrX3+fJc+9QsKi5UZEriYvIxuvWrR910rtx8v/RJnOV/Yl/XAy7499js8eeJXB425AORjzRlpiseBSadu6+PhQfNLBSHGWBRdf30plzJRYsnAPCyV79x6O5eVRVlxCxrZYijMzDcl9XOFJbcPV14fCrOzqZfxOLmMBIPbrH2g7+rp6+ZQr0N2FlILiiscpBSUEuLlUKzcg3I95V3TmvX5tmLx291nVtbe01GyCgs0VjwODzKSl1vwBwZRnZzOs/yQSDqRy4022GeRmLcJYstD2YU587EGSk7JITbHYO3aF7PRszIHmisfmADOW9LP7gOPA9gReuetNPpr4KUcPGNf3l1gsOFfaL53NZo5lZZ317ylOT6fg0CE8IiPPZ7xayUrLwbfS9vcJMJN1Ftt/0A0xHD2YwiPXvsCzt73JTQ9dh4NBfSeAJS0bnwBzxWOfADOWGgbhp5JyOI2CvALeevhDXhr3Nqvnr7dDyuqCPV1Iyj3RfyTlFhPsWbX/CPJ0ZmizAL7eUv3MCAcFf97alU33x7A8IZMtl/osCNg+LTTq5wJV2/nFVK313kqP9wOppyqslBoHjAP49NNPcerfqI7xqo+Fq7+vn7nM3vVxhLWKNOxULICKjxcrOTm6rqHMcYFNw7lz+hSc3Vw4sCGe3179jNs/mXSeQ9adqqFVa60rPo2s+rwRiarmqObkXKcpEjNpPG4+Zoqzc1j1+vt4hgbj37LZ+Q96GjW/hpPKnKbt79+0g6Am4Yx55UGyktKZ/dxHNGzTBBd3NzukPUlt/t41NgqFR2gIDYcNZdvbUzG5uODZsGG1T1ztro75lVIkb47FxcsTc2QEadt3n/9sZ1DTe01NL2dxYgaLEzPoGODFve0bcd/iuFrXtbea+8Wa30UnvTSasjIrb7/6Iwv+3sxV13Vn7J2DeOe1n7j5P2/StFkIzVuGYXI07iCypvw19Yun0rBZOFNmT8LVzYW4NduZPukLXvjqmfMZ8dRq03eeQVlREbunTaPRyJE4uhnQ35yk5u1f+/pxa3cRERXGk+/dR+qRdN587FNadGiCm4freUx5ajX367V/AWVlVg7uSuSxd+6lpPgYr9//Hk1aNyKoYeD5jFkrJ7+W5wc257Wl+7DW0MysGobPXI+XiyPTr21Hc38PdqfnG5RU1JfaDkLilVJ/AnOxvS+NANYrpa4H0Fr/WLmw1no6MP34wy93z691oI1/LGPr/NUAhDSLIDfdUrEsN8OCp693lfJuXp4U5xViLSvDwWSqscz2ZcacirXlz2XE/bMKgKBmEeSmn/gEKS/DgkdN2fNrzl75YDGySxsWfTqXwpw83Lzq7zqFyo4kZxAe4lfxOCzYl6SULJydHAkPrfR8iB9HU8/+k7RzYZvVOLHOwswsXM1Vt72rr7l6GR+zrX75vy7eXoR0icayL8GQQciG35expbzthzaLIOektt/gpPbj7uVJUaX2k5N+ov1sW7iWnv8ZbDtdKDQAc5AfGYdTCW1R1w8Eas/Zx0xxpW1bnJWFi9lcpYyLr0+VGY7iLAvO5WVC+sQQ0icGgP3zfsLFx9iZNLeT2kZRZhZuPiftu74+FGZULeNq9ubIuk0kbYoleWs81mOllBYWsuHjL+ly3+2GZE8pKCbI/cSnj0HuzqQXFp+y/Oa0HMI93TA7O5513fPp+9kr+GWere23bhtBSrKlYllqioWAQK9T1jWZHLhsaDRfz1jMVdd1x9PTlUkvjQZsB6TXXf4ioWF+p6x/Piz9eQUr/7Dlb9QiAkvqifyWNAvefqfOf7LKB7tte7Tmu/d+IC87r+LCdXty9vGhpNJ+WWKx4HTSvns61tJS9kybhn/37vh26mSHhDVb+OMKlv62BoDIlg3JrLT9s9IsmP28T1GzuuV/ruOKmwehlCIoPICAEF+SDqbQpLX9+s7FP61g+e+29tO4ZQRZaZaKZVlpFrz9a99+fAK88fT2wMXNBRc3F5p1aMrhfUftPghJzismpMGJ/iOkgQspeVUvHW4f1IAPrmoDgK+bEwMi/Wynfu5NryiTU1zK6sNZ9I/0vfQHIXJ3rFqfjuUKpAD9gP5AGuADXAVceT4Ddb6iL3e8/yR3vP8kzXq0J27ROrTWHNl5ABd312oDDKUUEe2bsXPlFgBi/11Hs+7tKpYX5RdyOG4vzXq0w96ih/fl5qkTuXnqRJp2b8+OJbbsSbsO4OxRc/aG7ZqxZ5Ut+/bFa2nazZYzPyun4hOd5N0JaK1xNXIm5wz+WLCJm26wnf7QrWMUObkFJKda2LB1H1GRwTRqGICTk4kRV/XkjwUbDc1mbtKI/ORU8lPTsZaWcmTNBoI7ta9SJrhTew6vWIPWmsy9+3Fyd8PV7E1pUXHFxcSlRcWkxu6gQUNjLqzscmVf7vrgSe764Ema92xPbC3afqN2zdixYgtga/vNy9u5V4APCVt3AZCXlUNGYirmYPseiB3nFdmYwpRUCtNs2z913Qb8oqueCunXoQPJq2zbP2fffhzd3XApHyiW5Nim4YsyMknftJnA7l0NyX2cuUkj8iq1n8Q1G2toP+04tGJtefs5gKO7G64+3rQZeS2Xf/AKQ6e+RJf778C/dQvDBiAA2zNzadjAlVAPFxwdFEMiAliaWPV0tnDPEwe5LX08cHJQWEpKa1XXXkaMjuHrH57g6x+eoO/Atvz163rbHci2JuDp6YZ/QNW2r7Xm8KG0iv8vXxpPo0jbQVZuTiHHjpUC8Mu8NUR3blpx/Yi99Ls2hqc/e4KnP3uCDjFtWbvAlv/A9gTcPNzwPouD4OzME31/wo6DaK3x8DKm7/ds3Jii1FSK0m1tP3P9enw61O40Zq01B2bNwi0khJDBg+2ctKrLro/hxS/H8+KX4+nUpx0r/96A1pq98Qm4ebpiPouDeL8gH7ZvtM1iZmfmknQolYBQ+/adA66LYdLnTzDp8yeIjmnL6vm29rM/3tZ+zmYQFR3Tjr2x+ykrLaO4qIQD2w8SEmH/G3tsTcol0sedht6uODkormoZyIJKgwuAmM9WEzPd9vPn7jSeW7iLf/am4+vmhJeL7TNxF0cHYhr5sjfD2BtiiPpR25kQB+BhrbUFQCnlA7yttbbru2vTLq3ZvyGeT8dNwcnFmeEPj6lYNveFaQx7cDQN/LwZcNvV/PLGDJZ9/QdBTcJpP+TErUh3r95GZMeWOLsae25zZOc2JGzczpf3TMHRxYkhD91cseynKZ8w+IGb8PT1JmbsNfz59pes/OZ3ApuE02ZwTwD2rNrM1r9X4GBywNHZmeHjbzurKdlzNfODB+nTsxX+Pg3Yu/ZDXnznB5zK7w7yv68X8veizQwdEE388qkUFBZz9/hPAdtU8KPPzeC3r57CZHJg5ndL2LHb2Nt8OphMtB87itVvfoC2Wono2wuv8FAO/LsMgMhBfQnq0JaULXEsHD8Jk7MzHf87FoDinBzWTbW9Fm21EtazK0Ht2xiaH2xtf++GeD75r63tX/nIibb/3fPTGP5Qedu//Wp+fv1E2+9Q3vZjRl3O71O/5rP7X0VrGHj71bgb8EkqgDKZiBozith330NbrQTH9MYjLJSjS5YCENq/H77t25IZG8u6p54tv0XvrRX14z/+lNK8fJTJRLMxo3HyMHbw7WAy0f7Wkax640O01Uqjfj2rt5/otqRsjWfB48/j6OxMx3G3GJrxVMo0vLFhHx/2b4tJKX7Zn8L+nAJuiAoGYN7eZAY19OeKyEBKrZriMitPrdx52rpG692nNauW7eCG4S/j6urMcy+Nqlj2yL3TeWbySPz8GzDlmW/JzytGo2nWPJQJz40AIGF/Ci888w0mBwcimwbxzORRp1qVXbTp3pr4tTt44eaXcXZ15uYJJ9b/0cTpjBk/ErO/N4t/XMbCOYvIyczllbvepE33VowZP4rNS7ey/NeVmEwmnFycuOPZsYb1/cpkovHo0eyaOhVttRLQuzfuoaGkLLXtu0H9+lGSnU3cyy9TVlSEUoqkhQtpP3kyhYmJpK9Zg1tYGLFTpgDQ8LrrMLez/weAlXXo2Ypta3YwYdQruLg6cedToyuWvfPEdG5/ciQ+/t4s+GEZf367mOzMXJ677S3a92jFHRNHcvVtg/nfK7N59tY30BpuvOfKKre/tbd2PVoTt3YHz4x5GWcXZ2578kT7ef/J6Yx9wtZ+/p23jPmzbe1nyp1v0q57K8ZOGEVIoyDadGvJlDvfRClFzBU9CGti/1tUl2nNpIW7mfWfaEwOirmxR9mTkc+YDrYP8b7ZevSUdQM9nXlnWGscHBQOwO+7Ulm0P+OU5S8ZMhGCOt11CRWFlNqste54pudO4axOx7rQ3N58KNN2/FPfMersnlZDcIsYfeaCF6jCQ7OZsG5Rfceosze6DWTmnouz/d/abCjjViyp7xh1Nj2mP0+u/7e+Y9TZ610H0Xl2/dwY4XzYOLoPlpI/6ztGnZmdh7PwyMWZ/7Kw4YwtHzhcjGb168fq1D/qO0ad9Qy8gqVJF2fbAegXMpxGb16877sHnxh4URzeNxv2hWGX3e35644LcpvUeiZEKeWjtc4CUEr5nkVdIYQQQgghRDkt3xNS64HE28AqpdQP2C5MvxF42W6phBBCCCGEEJesWg1CtNazlFIbgIHYzmK7Xmu93a7JhBBCCCGEuBTJ3bFqf0pV+aBDBh5CCCGEEEKIcyLXdQghhBBCCGEkmQip9feECCGEEEIIIcR5ITMhQgghhBBCGEnujiUzIUIIIYQQQghjySBECCGEEEIIYSg5HUsIIYQQQggjyS16ZSZECCGEEEIIYSyZCRFCCCGEEMJIMhEiMyFCCCGEEEIIY8lMiBBCCCGEEEaSW/TKTIgQQgghhBDCWEprbe912H0FQgghhBBCcJFcbRF1w9eGHR/vnXfzBblNzup0LKWUD9BQa73tbOp1nbvirEJdSNbfGMP0nfPrO0adjWs5lAnrFtV3jDp7o9tA3CJG13eMOis8NJuxS5fWd4w6mdWvH1ctWF7fMerst8F9+Cnhr/qOUWfXNR7Gx9v/qe8YdXZf6yE8subi7Xum9hjI0PkX53vX/KEx/HLw4m371zQaxl0rltR3jDr7X0x/Hlt78bb9d7oP5J8jf9Z3jDobEja8viOIWjrjIEQptQS4urzsFiBNKbVUa/2YfaMJIYQQQghxCZILImq1Cby11jnA9cCXWuvOwGX2jSWEEEIIIYS4VNXmdCxHpVQIcCPwjJ3zCCGEEEIIcWmTu2PVaiZkCjAf2Ke1Xq+UagLssW8sIYQQQgghxKXqjDMhWuvvge8rPd4P3GDPUEIIIYQQQlyyZCLkzDMhSqkmSqnflFJpSqlUpdQvSqlII8IJIYQQQgghLj21OR3rW2AuEAKEYpsVmWPPUEIIIYQQQlyqtIMy7OdCVZtBiNJaf6W1Li3/+Rr5AkIhhBBCCCFEHdXm7liLlVITsc1+aGAk8IdSyhdAa51px3xCCCGEEEKIS0xtBiEjy/+9+6Tn78A2KGlyXhMJIYQQQghxKZNb9Nbq7lhyEboQQgghhBDivDnjIEQp5QrcB8Rgm/lYDkzTWhfZOZsQQgghhBCXHpkIqdXpWLOAXOCD8sejga+AEfYKJYQQQgghhLh01WYQ0kJr3aHS48VKqa32ClRZz2Azj0c3wUEpfjmQwsydiVWWXx4RwNiW4QAUlpbx2sZ97MnOB2B081CujQxCA3uzC5iybjclVuNu6qW1ZvFn8ziwcTuOLs5c/vAYgpo2rFYuOyWD39+cQVFeAYFNwhn+6C2YnGx/lsOxe1j8+Y9YS8tw8/Jg5CsPG5Y/ZVs8sV/NBasmon9vml81tMpyrTWxX80ldWs8JhdnOo4bi7lxBAD/PPoMjq6uKAcHlMmB/lOeMiz3cdPevJthgzqSlpFDl8ETaizz9uRbGTogmoLCEsY9/glb4hIAGNyvA2+9MBaTyYEZcxbz1se/GpjcxhIXx8HvvkNbrQTGxBA6bFiV5YVJSeyfOZP8Q4doeO21hAwZAkBxZib7vviCYzk5KKUI7NuX4EGDDM/fyc+H/7aw7bsLjiTzQ0LVfTfc3Y2H2zSnqZcnX+1N4KeDRyqWXRMRypCwYDSQkJfPe/G7OWbgvgu29v3bJz+ya90OnFydGPH4TYQ1q77/rvplOSt/WkpGUjrPzX0JD29PAFIPpfDDO99yZG8iQ2+9gr4jBhqafenn80jYGI+jizNDHryZwBr7nnT+evtE3zP04bGYnBxJjNvDb69OxyvQD4CoHh3oPnJYtfr2krYtnh3fzEVbNeH9etP0yup9z45v5pK2NR6TszPt/jsW78YR5CUls+XjzyvKFaSm0+z6K4kcamz77+Jv5p6WTTApxV+JKcw9ULXtN/Rw47G2zYjy8mTmnoP8kHCi7V8bEcqw8CCUgr8SU/jp4FFDs4Nt+/768Y/sXL8DJxcnbhx/E+E1tP2VvyxnxU9LyTiazvPfn2j7m/7dwJK5/wLg4ubCdQ+OILRpmGH5M2Pj2D97LlpbCe4TQ8Phl1dZXpCUzO4vZpB36DCNr7uG8MuH1LquvaVui2f717a237Bfb6JqeN/d/vWJ990O5W0f4Fh+Adu++JrcxKOAosNdt+DTzNhLdrXWzPvwJ+LX7sDZ1YmbJ4ymYfPqbWfpT8tZMm8Z6UfTefWnF/EsbzvHHdx5iLcfmMrtz42lY79og9LXgwv41rlGqc0gZLNSqofWeg2AUqo7sNK+sWx/mwmdmvLA0jhSCkuYeVk0y45mcCCnsKLM0fwi7l68jdxjZfQK9uHpLlHc/u9WAtycGRkVysj5mygus/JKzxYMiQjg94RUe8eucGDjdrKS0rhj2nMk7U5g4SdzGfPW49XKLZv5C52v7k/Lvp1Z8PF3xC5cTfSwPhTlFbBw2lxueOFevAJ8KbDkGpZdW61smzmHXk8+hJuvD0snvUZwp/Z4hYVUlEndGk9+SiqD3ppM1r4DbP1yNv0mP1mxvPfTj+LSwLOmX2+Ir75fyrSZ8/nfu/fVuHzogGiaNg6mbd9H6dYxivdfvpO+1zyHg4Ni6ku3c8WYVziSlMGK317m9wUb2bnnSI2/xx601UrCt9/S8tFHcfbxIf6VVzB36IB7aGhFGUcPDxqNGkXW5s1V6ioHBxqNGIFHo0aUFRUR99JLeLVqVaWuvTkA97RsynOb4sgoKuad7tGsTcvkcH5BRZncY6VM37WPHgF+Ver6ujhzVUQY963aSInVypPtWtI3KIB/k4zbdwF2rd9B+pE0xn/5DId3HuTnD77n/vcfq1auUZtIWnZvzfQJH1Z53t3LnavuvYHtq2KNilwhYdN2LEdTufXjSSTvTmDRp98x6o3x1cqtnPUrHa8aQIs+nfn3kznE/7ua9pf3ASC0VVOuefYeo6OjrVbiZ82h24SHcPX1YdULrxHYsT0NKvU9adviyU9Ope8bk7HsO0D8zNn0ev5JPEOCiXnxmYrfs+iRpwjuHG1ofgfg/lZNeWpDHOlFJXzQM5o1qRkcyj/xvpVzrJRPduynV2DVtt/I051h4UE8tGYrx7SVVzq3ZW1aJkcLjD3zeWd525/w5TMc2nmQn97/ngc/qN72G7eJpFX31nz6RNW27xvsxz1vPYh7A3d2rtvOvKnf1VjfHrTVyr5vZtP28Udw8fFhy4uv4hvdHo8qfac7TW8aRcbmLWdd197Z42fNoXt521/x/GsEdaqh7aek0v9NW9uPmzGb3i/Y3nfjv55LQLvWdH5wHNbSUsqKSwzJXdn2tTtIPZLGpK+eJmHHQb6b+gPjP360WrkmbSNp27MN7z/6YbVl1jIrv0z/jVZdWhoRWdSzU35PiFIqVim1DegOrFJKJSilDgCrgb72DtbGtwGH84o4kl9MqVWz4FAa/UKrdtrbMnLJPVYGQGxGDoFuzhXLHB0ULiYHTApcTSbSCo3dIfeti6X1gG4opQhtEUlxfiF5mdlVymitObRtD817RwPQZmA39q6xHbTsXLaRZj074BXgC4C7uYFh2bP2JeARFIBHYAAOjo6E9ehC8saqk19Jm7bSMKYHSil8o5pwrKCAIkv2KX6j8Vau20mmJe+Uy68c0plv5y0HYN3mvXh7uRMcaKZrdBT7EpJJOJTKsWNlfP/baq4c0sWo2ADkHTiAa2AgrgG27e/btStZW6tufycvLzwbN0aZTFWedzab8WjUCACTqyuuISEcs1iMig5AM+8GJBUUkVJYRKnWLEtOo3t5Oz4u+9gx9uTkUaqrz3A4KIWzgwMOClxMDmTWx5vp6lg6XdYVpRQRrRpTmF9ITkb19h0WFY5vsF+15z3NDWjYIgIHx9p8FdP5tX9dLK3K+56Q8r4nv4a+53Dsbpr1igag9YDu7Fu7zfCsJ7Pst/U97uV9T0j3LqRuqtr2UzdtJay3re/xiWpCaQ19T3r8TtwD/HHzr/63sacW3g04WlBEcmExpVqzJCmNnicNNrJLjrG7hrYf4eHGjuxciq1WrBq2ZWbTO9DY/ADbV8XSabCt7TeqQ9tv3CYS9wbuAES0akx2unHvC7n7bX2nW3nfGdCtC5mbq7YfZy8vGkRW7ztrU9eeLPsScA880fZDe3Qh5aS2n3JS2z/+vnussJDMXXtp2K83AA6Ojjh5uBuW/bjYVXF0K287ka0bU5hXSHYNbadhs3D8gn1r+A22WZLovh3w9Km/DzENo5RxPxeo071DXglcBVwORAL9gP7l/7/C3sEC3JxJKSiueJxSWExApUHGya5pEsyq5CwA0gpL+HrXEX67oit/XdWd/GOlrE2x2DtyFXkZ2TTwN1c8buBvJu+knbEwNx9XDzccyjtDTz9zxUAl62gqRXkFfPfM+3z12BvEL1pnWPaiLAtuvj4Vj918fSjKspyxTGGmrYxCsfr191ny3CskLFpuROSzFhrsS2JSRsXjI8mZhAb7EhrsQ+LRSs8nZRAW5FPTr7CbEosFZ98THbSz2cyxrKyz/j3F6ekUHDqER6SxN7jzc3EhvfjEvptRXIKfi0ut6mYWl/BTQiJf9OnGrL49yC8tY3N5uzJSTno25oATf3dvf3ONB2IXorwMC55+J7JX7leOK8rNx6Vy3+NvJr/S60vedYBvHn2Vn6d8TMahJGOCY+tXXCv1K66n6Htc/aqWKT6pTNLaDYT26GrPqDXyc3UmrehE208vKsbf9dTvW5Ul5BXQzsebBk6OuDg40DXAhwDX2u0351N2RtW2b/Y313ggWRvr/15Di66tzle0Myq2WHCp1H6cfXworuWHMOdS93woyrLg5neGtp9Z9X3X1deHokwLBanpOHt5su2zWSx/9mW2ff4VpZX6YKNY0rPxCTRXPDYHmM9qEGpJs7BtRSwxV/WyQzpxOkqpy5VSu5RSe8u/G7CmMv2VUluUUvFKqaXnY72nPB1La32wfKVfaa1vOSnIV8AtNVa0LR8HjAP49NNPwdz6rIPVNG471VnhnQO8uToyiP8usn2S18DJRN9QX675cz25JWW81qslwyIC+OtQ2lnnqCtdwye81QajpyljLbOSuu8wI158gGMlx5g94V1CWjTGNyzQDmlPjlXDlj45/GmKxEwaj5uPmeLsHFa9/j6eocH4t2x2/oOeA1VDC9Nao2r4xKCmzWFXtdn+Z1BWVMTuadNoNHIkjm5u5ylY7ZzNvnsyD0dHugf6cdeK9eSXljKxfUv6BwewJNm4fRdOkfcC/jSpqpraz0klamxjtn8CmoRz+/QpOLu5cGBjPL+99hm3fTzp/MesSY194tltd2tpKambt9FixLXnKVTt1dj2a9n4D+cXMvdAIq92aUtRaRkHcvMpM7zz4RTN5+zb/t4te1j/9xrufde4axlr3dGc77rnRU0B1JlLKNBlVnISDtPmlpH4NI0k/uu57PttPi3+c7Vdkp5KrY4dTmPeRz9z9bgrcTAZP4NcLy6QtxSllAn4CBgMJALrlVK/aq23VypjBj4GLtdaH1JKnZeD0dpcE9LmpLCOQOfTVdBaTwemH3/42dwVZx0stbCEIPcTnwIFubmQXsMpVVHe7jzbNYqHl8WTXVIKQLcgM0fzi7AU2x4vTsygvb+X3Qchm/9YRuyC1QAER0WQm26pWJabbsHD17tKeTcvT4ryC7GWleFgMpGXYcHDx1bG08+Mm5cHTq4uOLm6EN6mKWkJRwwZhNhmNU588l6YmYWruWp2V19z9TI+Zlv98n9dvL0I6RKNZV/CBTcIOZKcQXjIiVMJwoJ9SUrJwtnJkfBKp/2FhfhxNPXsZyHOhbOPDyWZmRWPSywWnMzmWte3lpayZ9o0/Lt3x7dTJzskPL304mL8K818+Lk4k1nLT+Wifc2kFBaRc+wYAKtSM2hl9jJkELL61+Ws+8u2/4Y3j8CSduLvnp1uwcvXy+4Z6mrrn8uIW7AKgKCoCPIyTmTPy7Dg6VO97ymu3PdU6p9c3E8MWiM7t2Hxp3MpzMnDzcv+p0fYPtk9kb0oMwuXk/seHzNFGSeVKe9zwHbevFejCFy8jf97pReVVJm98Hd1IeMsTiecfySF+UdSALi9WaMqsyr2tOrX5az909b2G7ao2vYt6Ra8/M5uWybtP8oP787hzpfvxsPL47xmPR0XHzPFldpPSVYWLrXsO8+l7vng6uND4Unt2vXk/fak993Kbd/V14xPU9usd0jXjuz9/R/7hwaW/byCVX/Y2k5EiwiyUi0VyyxpFrzPou0c2n2YGS/OAiAvO5/ta3fgYDLRIabdec0squkG7NVa7wdQSs0BrgG2VypzE/Cj1voQgNb6vFyoebprQp5SSuUC7ZVSOeU/uUAK8Mv5WPnpbM/MJcLTjVAPFxwdFIMjAlh2NLNKmSB3F97o1Yrn1+7mUN6Ji/eSC4pp59cAl/LRdNcgbw7kFGBvHa/oy9ipTzJ26pNE9WjP9sXr0FpzdNcBXDxc8TxpEKKUIqJdM3av3AJA/KJ1RHW37WxR3dtxZPt+rGVlHCsuIWn3QfzCg+z+GgDMTRqRn5xKfmo61tJSjqzZQHCn9lXKBHdqz+EVa9Bak7l3P07ubriavSktKuZYoe1vUVpUTGrsDho0NO6i6Nr6Y8EmbrrBdhFut45R5OQWkJxqYcPWfURFBtOoYQBOTiZGXNWTPxZsNDSbZ+PGFKWmUpRu2/6Z69fj06HDmSti+yTqwKxZuIWEEDJ4sJ2T1mxPTi6h7q4EubrgqBR9gwNYl5Z55opAWlExLb0b4OJg23c7+Jo5XOmiXnvqeXUfHv5kAg9/MoE2vdqxaeF623VbOxJwdXfDy8/7zL+knnQY3pcx705kzLsTadq9PTvK+56kXQdwcXet9gGIUorwts3Ys2oLANsXr6VJN1vfk5+VU/GJZvLuBLTWuDYw5kDSO7IR+SmpFKTZ2n7S2g0Edqza9wR2bM+Rlba+J2vvfhzd3Kp8SJK0Zj2hPYy9juu4XTm5hLm7EeRma/v9QwJYk1q7tg/g7ewEQICrC70D/ViSZMwMYK+r+/DotAk8Oq287S+wtf2DOxJw8zi7tp+VmsWsKV8wasLNBITb/0OzyhpENqYoJZWi8vaTtm4DvtG16zvPpe754N2kats/umYDQWdq++Xvu65mb1x9fchLSgYgPX4XDUKDDcnd99oYJn72BBM/e4L2MW1ZV952DmxPwNXDDe+zaDuTv32OybMnMXn2JKL7deDGh2+4tAcgDsqwH6XUOKXUhko/4yolCQMOV3qcWP5cZc0BH6XUEqXURqXU2POxCU53OtarwKtKqVe11obfY7VMwxub9vF+37aYFPx6IIX9OQVc39S2Y/24L5m7WjfE28WJJzs1BaBUa25duJX4zDz+Tczg68HRlGnNrqx8ftqfbGj+yM6t2b8hns/vmYKTizNDHxxTsezHKdMYcv9oPP286XPr1fzx1gxWfvMHgU3CaTu4BwB+DYNp3LEVMx96DeXgQLvBPfBvZMzBvIPJRPuxo1j95gdoq5WIvr3wCg/lwL/LbK9tUF+COrQlZUscC8dPwuTsTMf/2tpjcU4O66Z+Ctju9hHWsytB7duccl32MvODB+nTsxX+Pg3Yu/ZDXnznB5zKb338v68X8veizQwdEE388qkUFBZz93hb5rIyK48+N4PfvnoKk8mBmd8tYcfuxNOt6rxTJhONR49m19SpaKuVgN69cQ8NJWWp7RTMoH79KMnOJu7llykrKkIpRdLChbSfPJnCxETS16zBLSyM2ClTAGh43XWY2xnXkVs1TNu1j8md2uKgFAuPpnAov4DLw2377t+JyZidnXi3e0fcHU1YNVxdfkes3Tm5rExJZ2qPjpRpzf6cPP5ONO6ahONadGvNzvU7ePP2l3BycWbE46Mrln357Kfc8OgovPy8WfnzUpZ+v4i8zFym3vMGLbq15j+PjiI3M4cPHnyb4gLb32fFz0t5bPpTuHq42j17485tSNi4nZn3TsHRxYnBD95cseznFz/hsvtvwtPXm5ix1/DX21+y+tvfCYgMp81lPQHYu3oz2/5egYPJAUdnZ4Y9fttZnxJVVw4mE61vGcX68r4nvG8vGoSHcmiRre+JGNiXgA5tSdsWx9InJmFycab9XSfeC8uKS0iP20mb28acahV2ZdXw0Y59vNK5LQ4K/jmSwsH8Aq4ob/t/JCbj4+zEBz2jcXc0oTVc2yiUcSs2UVBWxqToljRwcqJMaz7csY+80jLDX0PLbq3ZuW4Hr9/2Es4uzowYf6Ltf/7Mp/znsVF4+3mz4idb28/NzOWdu9+gZbfWjHhsFAu/nk9BTj4/ffA9YPubPvxR9TtD2oMymWg6ZhRx776HtloJiumNR1goSUtsfWdIf1vfufnFVygrLAKlOLLwXzq/+AKObm411jWKg8lE27GjWPfGB2h9ou0fLG/7jQb2JbBDW9K2xrHkCdv7buW23+aWkWz55EusZWW4B/jT4b+nPGPebtp0b832tTuYcvPLOLk6c/OEURXLPpk4nZvGj8Tb35slPy7j3zmLyMnM5dW73qRN91bcNH7UaX6zOFcnnaF0stqcRX38LKhBgBuwWim1Rmu9+1xyqRrP4atcQKnewBatdb5S6magE/De8WtGakF3rcPpWBeK9TfGMH3n/PqOUWfjWg5lwrpF9R2jzt7oNhC3iNFnLniBKjw0m7FLz8v1W4ab1a8fVy24MG8sUBu/De7DTwl/1XeMOruu8TA+3m7MKRX2cF/rITyy5uLte6b2GMjQ+Rfne9f8oTH8cvDibfvXNBrGXSuW1HeMOvtfTH8eW3vxtv13ug/knyN/1neMOhsSNvwCudri9Jre+b1hVyLt+3zEKbeJUqon8ILWemj546egYjLieJmJgKvW+oXyx58Df2utvz+XXLW5+ucToEAp1QGYABzE9i3qQgghhBBCiIvXeqCZUipSKeUMjAJO/pbmX4A+SilHpZQ7tq/v2HGuK67NhemlWmutlLoG2wzI50qpW891xUIIIYQQQvx/pC+Q+RqtdalS6gFgPmACvtBaxyul7ilfPk1rvUMp9TewDbAC/9Nax53rumszCMktn5q5BdsoyAQ4neuKhRBCCCGEEPVLa/0n8OdJz0076fGbwJvnc721OR1rJFAM3KG1TsZ2xfx5DSGEEEIIIYT4/+OMMyFa62Sl1Dzg+Bc9pAM/2TWVEEIIIYQQlyqHC+R8rHp0xpkQpdR/gR+AT8ufCgN+tmMmIYQQQgghxCWsNteE3I/t2xTXAmit95yvr2sXQgghhBDi/x2Dvn/pQlaba0KKtdYlxx8opRyp/iUmQgghhBBCCFErtZkJWaqUehpwU0oNBu4DfrNvLCGEEEIIIS5Rck1IrWZCngTSgFjgbmy38HrWnqGEEEIIIYQQl67TzoQopRyAbVrrtsBnxkQSQgghhBDiElabaYBL3Gk3gdbaCmxVSkUYlEcIIYQQQghxiavNNSEhQLxSah2Qf/xJrfXVdkslhBBCCCHEpUrujlWrQYgncGWlxwp43T5xhBBCCCGEEJe62gxCHLXWSys/oZRys1MeIYQQQgghLm1ydyyU1jV/5YdS6l5st+NtAuyrtKgBsFJrfXMt1yHfKSKEEEIIIYxwURzdN3noZ8OOj/e/f+0FuU1ONxPyLfAX8CowsdLzuVrrzLNZyYA/V9Yh2oVh8fDerE/7o75j1FnXgCuYuWd+fceos1ubDWXs0qVnLniBmtWvH24Ro+s7Rp0UHprN5M0L6ztGnT3f8TIWHvmzvmPU2WVhw3lt64L6jlFnEzsM5pE1i+o7Rp1N7TGQ8WsvzvxvdR/IK1su3rbzdPRgHlq9uL5j1Nn7PQcwbsWS+o5RZ9Nj+vPtvr/rO0ad3dT08vqOUCtargk59SBEa50NZAMX5xGUEEIIIYQQ4oJUm2tChBBCCCGEEOeLfE+IbAIhhBBCCCGEsWQQIoQQQgghhDCUnI4lhBBCCCGEkeQWvTITIoQQQgghhDCWzIQIIYQQQghhJLlFr8yECCGEEEIIIYwlMyFCCCGEEEIYSa4JkZkQIYQQQgghhLFOOxOilIoFdE2LAK21bm+XVEIIIYQQQlyqZCLkjKdjXWlICiGEEEIIIcT/G6cdhGitDx7/v1IqCOha/nCd1jrVnsGEEEIIIYS4FGm5JqR2F6YrpW4E3gSWYJtA+kAp9YTW+gc7ZqOrv5kHWjfBpOCPwynM3n+kyvLLQgMY1SQMgMKyMqbG7WNfbgEAE9pF0SPQB0vJMe5YvsWeMWuktear935iy+oduLg6M+7p0US2CK9W7uPJX7N/52EcHU00aRXBHRNG4OhoqnV9e+ZfMH0e+zZsx9HFmaseGUNwVMNq5SzJGfz8xgwKcwsIjgrn6sduweTkSFF+Ib++NYuctCysVivdrxtIh8E9DMtviYvj4Hffoa1WAmNiCB02rMrywqQk9s+cSf6hQzS89lpChgwBoDgzk31ffMGxnByUUgT27UvwoEGG5QaY9ubdDBvUkbSMHLoMnlBjmbcn38rQAdEUFJYw7vFP2BKXAMDgfh1464WxmEwOzJizmLc+/tXA5Ccc3RLPxpk/oK1Wmg7sTZtrhlRZrrVm48zvObo5HkcXZ3rcewu+kREArJn2FUc2xeHq1YAr3nq2PuKjteb7D38ifu0OnF2duGXCaCKaV2//S35azuJ5y0g/ms7rP72Ip7cnALu37OXT5z7HL9gXgOg+7Rk+dqhh2dd++QOJ5ds25r5b8G9SPXtuajpLpn5JcV4BfpEN6fvgWEyOjliOJLPi46/JOJBIp1FX0u7qywzJfVzatnh2fDMXbdWE9+tN0yurbjetNTu+mUva1nhMzs60++9YvBtHkJeUzJaPP68oV5CaTrPrryRyqLH7b+q2eOK+tuWP6NebZldVzx//9VxStsZjcnEm+r9jMTe2tf2Fjz2Do6srysEB5eBA3ylPGZr9eL51M37gSHn76X3vLfidov0se+9E+4l5wNZ+alvfXtK2xbPz27loq5Xwvr1pcuXlVZZrrdn5zVzStsXZ2s9dt+LVOIL8pGS2fvy/inIFaelEXXcVjQ1sP5mxceydPRetrYT0iSFieNXsBUnJ7PxiBnmHDhN53TU0vHxIresaQWvN35/+yJ7123FyceLax8YQUsNxQ1ZyBvNem0lhXj4hTRty3fibMTk5UphbwK9TvyUzKR1HZyeueWQ0gY1DDX8dwji1vTD9GaCr1vpWrfVYoBvwnP1i2YI93KYJE9fHc9uyzQwKDaCRp1uVMkkFRTyyJpa7Vmzhq72HebxdVMWyvxNTeXL9dntGPK2ta3aQfDidt+c8zZ1PjGDGWzWP13oN6cSb307k1VlPUFJ8jCW/rTmr+vayb8N2Mo+mcc/05xj+wEj+/nhujeUWzfiFrtf0597PnsPVw50tC1YDsPGP5fhHBHPXhxMZ8+qD/Pv5z5QdKzUku7ZaSfj2W1o89BDtJ08mY/16Co4erVLG0cODRqNGETJ4cJXnlYMDjUaMoMOUKbR56ilSFi+uVtfevvp+KdeMfe2Uy4cOiKZp42Da9n2UByZ+xvsv3wmAg4Ni6ku3c82tr9Nx0HhGXN2Lls3CjIpdwWq1suGLuQyYeD9XvP0cB1duIDsxqUqZo1viyU1K46qpL9Dtvzex/n9zKpY16deDAU/db3TsKuLX7iDtSBovfPU0Nz12I3Om1rz/NW0byUNv3YtvkE+1ZVHtmvD0Z0/w9GdPGDYAAUjcvJ2c5DRueP95eo0bzepK27ayDV//QpsrBvCf95/HxcONPYts+66Lpwfdbx9B26sGGpb5OG21Ej9rDl0ef4A+r04iac16co9UbTtp2+LJT06l7xuTaXP7TcTPnA2AZ0gwMS8+Q8yLz9B78lOYXJwJ7hxteP7YWXPoPv4BBrw2iaM15E/dFk9eSioD35xMh9tvInbG7CrLez71KP1eeqZeBiAAR7ZsJzc5jevee56e/x3Nms9rbj8bv/mF1sMHcP17z+Ncqf3Utr49aKuVHV/NpvNjDxDzyvMkrV1P3pGq/Xf6tjgKUlLp8/oU2tw2hu2zvgXAIySYXi8+S68Xn6Xn5KcxOTsTZGD70VYre76ZTbtHH6Triy+QunY9+dXet9yJumkUDYcOPuu6Rti7YTuZR9J48H/PctVDo/jjw+9rLLfwi1/pcV1/Hvzfc7h6urHpH9txz/K5CwhqEsa9H0/kusdv5u9PfzQwfT1wUMb9XKBqOwhxOOn0q4yzqFsnLc0NOFpQRFJhMaVasygpjd5BvlXKxFtyySstA2B7Vi7+rs4Vy7Zl5ZBj0EFvTTYujyPm8i4opYhq25j8vEKy0nOqlYvu2RqlFEopmraOIDM1+6zq28vutbG0G9gNpRRhLSMpyi8kLzO7ShmtNQe37aFVTDQA7QZ1Y/fqWMA2XVZSWIzWmmOFJbg1cMfBZMzN2PIOHMA1MBDXgAAcHB3x7dqVrK1bq5Rx8vLCs3FjlMlU5XlnsxmPRo0AMLm64hoSwjGLxZDcx61ct5NMS94pl185pDPfzlsOwLrNe/H2cic40EzX6Cj2JSSTcCiVY8fK+P631Vw5pItRsStk7E3AMzgAzyB/TI6ONOrVmcQN26qUObJhG5F9u6OUwr9ZJCUFhRRm2dpXYKtmOHt4GJ67sm2r4ug+uCtKKSJbN6Ywr5DsjOxq5Ro2C6+Y7bhQHNqwjai+tn03sHkkJfmFFGRV33eT4nfTuEdHAKL6d+fgets+4ubdgICoRjictG8YwbI/AY+gANwDbftuSPcupG6quu+mbtpKWO8eKKXwiWpCaUEBRZaqry89fifuAf64+fsZGZ+sfQl4BAbgUZ4/tEcXkk/Kn7xpKw0r5T9WQ/76dHj9NpqUt5+A07Sf5PjdNCpvP037dedwefupTX17yd6fgHtQYKX205XUzVX7ntTN2wgt3/7mqCYcKyik+KTtn7F9J+6BxrafnP0HcAsMxK38fSuwWxcyNldtO85eXnhFVn/fqk1dI+xcE0f7QbZ+M7xlY4ryC8mt4bjhwLY9tI7pAECHy7qxq/y4If1QMk2imwPg3zAIS0omeVnGHfcI49X2qPBvpdR8pdRtSqnbgD+AP+0XC/xdnUktKql4nFZYgr+LyynLD28YxLo0iz0jnZWs9Bz8As0Vj30DzWSln7ojLi0tY8X8DbTv0bJO9c+3vIxsvPxPrL+Bn5nckw7CCnPycfVwqzhY8fI/UabzlX1JP5zM+2Of47MHXmXwuBtQDsYMQkosFpx9TxwYOpvNHMvKOuvfU5yeTsGhQ3hERp7PeOcsNNiXxKSMisdHkjMJDfYlNNiHxKOVnk/KIKyGT+jtrTDTgoffifW6+5opyLRUKVOQmY27n/m0ZepTdno25kr7nznAjOUs978D2xN45a43+Wjipxw9kHTmCudJQaYFD/8T29/Dr/q2Lc7Nx9n9xL7r7utDQWb9HwgXZVlw9T2R3dXXh6IsS/UyflXLFJ9UJmntBkJ7dMVoRVkW3PzOkD+z6mt08/WhqOLvo1jzxvssm/QKBxcvt3/gGhRknbT/1qL9eFRqP7Wpby9FWVlV24+PmaKT+v7ik9uYj7na3yh57QaCDW4/JRYLLpVyufj4UFzLD8DOpe75lJtuwTvAXPHYy9+b3PQzHzfkZFgACIoMZcdK26DxyK6DWFKzyDHwuMdwShn3c4Gq1TUhWusnlFI3AL2xfcg9XWv906nKK6XGAeMAPv30Uwhvc9bBatpkusa7BUO0rzfDGwbxUPlo+kKgdfWsp2sGM97+gZYdmtCyQ5M61T/falr/yQFq+nscb+v7N+0gqEk4Y155kKykdGY/9xEN2zTBxd2tWp3zrsbsZ7f1yoqK2D1tGo1GjsTRzYDMZ0HV0BK01qgaXmNNm6I+VM9WU9u5cDrKGve/s8jXsFk4U2ZPwtXNhbg125k+6Qte+OqZ8xnx1Gq8qfqZt/8F4Ry3O4C1tJTUzdtoMeLa8xTqbJzqjvZnUF4k5rnxuPqYKc7JYc3r7+MZEoxfy2bnNeGZ1Nx91qL9qLOpbyc1xqq67jO9t9naz1aa/efa85vtTM5ll7xAd2egWt9T8x5iKxNz42D+njaPaQ+8QVCjEEKahhl2BoWoH7X+xnSt9TxgXi3LTgemH384+8+VZx0sraiEwEqnVwW4OZNRXFKtXJMG7oxv15SJG7bX6+lXAAvmrWBx+TUdTVo1JCPVUrEsM9WC2d+7xno/fjGfXEs+d7w8ouI53wDvWtc/Xzb8vowt823n9YY2iyAn/cT6czMsNPCtun53L0+K8guxlpXhYDKRk27Bs7zMtoVr6fmfwSil8A0NwBzkR8bhVEJbNLLrawBw9vGhJDOz4nGJxYKT2Vzr+tbSUvZMm4Z/9+74dupkh4Tn5khyBuEhJ04TCAv2JSklC2cnR8JDKz0f4sfR1LOfATpXbr5m8jNOrLcg04Kbz0ltx9dMQfmnX6cqY7SlP69g5R+29t+oRQSWSvufJc2Ct59XrX+Xm4drxf/b9mjNd+/9QF52XsWF6+fbjr+XsvvfVQD4N21EfvqJ7Z+fYcH9pG3r0sCTkoIT+25BZhbuvvW7/aF85iDzRPaizCxczFVzufqYKco4qYyPueJx2rZ4vBpF4OJd+7/X+eLq40PhSdlcT9r2rr7mKq+xMDML1/L8x/918fIiuHM0lv0JhgxCds4/qf1U3n8zqu+bJ7ef/MysijbmcfL+X0N9e6nWfrIsVdrGqcq4Vnp/SN8WVy/tx9nHTHGlXMVZWbjU8n3rXOqeq3W/LWdTpeOG7EpnpOSkZ9PgpH7T3cuj2nFDAz9b+3Bxd+Wax8YAtsHie7dPwSfY2FMqDSXjq9ptAqXU9UqpPUqpbKVUjlIqVyll1xP1dmbnEubhRrCbC45KMTAkgFUpmVXKBLo6M6VTS17duofE/CJ7xqmVwTfE8MqM8bwyYzyd+7Rjxd8b0FqzNy4Bd09XfPyrd2qLf1tD7Lpd3P/CzThUOl2pU0zbWtU/n7pc2Ze7PniSuz54kuY92xO7aB1aa47sPICLu2vFAOM4pRSN2jVjx4otAMT+u47mPdoB4BXgQ8LWXQDkZeWQkZiK2aDOxLNxY4pSUylKT8daWkrm+vX4dOhQq7paaw7MmoVbSEi1i9YvFH8s2MRNN/QBoFvHKHJyC0hOtbBh6z6iIoNp1DAAJycTI67qyR8LNhqez69pI3KTU8lLTaestJSDqzYS1rldlTJhndtzYNlatNak7zmAk7tbvQ9C+l0bU3EheYeYtqxdsN7WHrYn4Obhhrdf7fNlZ+ZUfOKasOMgWms8vOx3nUury/txzZtPcc2bTxHRrT17l9n23dTdB3B2d6s2CFFKEdKmOQlrNgOwd8laIrrU/3fPekc2Ij8llYI0276btHYDgR2r5grs2J4jK9egtSZr734c3dxwrTRQSVqzntAexl8LBWBuUjX/0TUbCD4pf3DH9hyulN/J3Za/tLiY0kLb+1hpcTFpcTtoEG7MnYFaDu3H1W88xdVvPEVE1/bsL28/abtt+2ZN7Se4dXMOlreffUvX0rC8/TTs0u6M9e3FK7IRBVXaz/rq7Se6PUfLt79l734c3VyrDHST1mwgpB5O5fOKbExhSiqF5dlT123AL7p271vnUvdcdbuqD/d8OIF7PpxAy57t2Pavrd9M3JmAi4drtQ8vlVJEtm/G9hW2a1a2LlxHix5tASjKK6i4gc2m+atp1LYpLu6uiEtXbWdC3gCu0lrvsGeYyqwa3o/fzxvd2uAA/JWYSkJeIVdFBAPw26FkxjaLwMvZiUfa2k5hKtNwz0pbw342ujnRvt54Ozsyd0AXZuw5xJ+Jxn21SXTPVmxdvYPHR76Cs6sT454eXbHszfHTuWviSHz8vfnyrR/wD/LhhbvfB6Brv3Zcd/vQ09Y3QtMurdm7IZ5P/jsFJxdnrnxkTMWy756fxvCHRtPAz5sBt1/Nz6/PYNnXfxDUJJwOQ2y34Y0ZdTm/T/2az+5/Fa1h4O1X426nT4FPpkwmGo8eza6pU9FWKwG9e+MeGkrK0qUABPXrR0l2NnEvv0xZURFKKZIWLqT95MkUJiaSvmYNbmFhxE6ZAkDD667D3K7d6VZ5Xs384EH69GyFv08D9q79kBff+QEnJ9uu+r+vF/L3os0MHRBN/PKpFBQWc/f4TwEoK7Py6HMz+O2rpzCZHJj53RJ27E40LPdxDiYTXW6/kcWvfIS2WmkyoCfmhqHsWWA7x73Z4D6EdmzD0S3x/PbwC5hcnOlxz80V9Ve+/wUp2/dQnJvHT/c9Q/v/XEHTgb0MfQ1turcmfu0OXrj5ZZxdnbl5wqiKZR9NnM6Y8SMx+3uz+MdlLJyziJzMXF65603adG/FmPGj2Lx0K8t/XYnJZMLJxYk7nh1r2Ckp4R3bkLgpnnkPTcbk7ESf+05s239e/ZiYu2/C3ddMlzHXsGTql2ya8zt+kQ1pPrAnAAWWHH6b+AbHCm37xvY/l3DdO8/gbMCplA4mE61vGcX6Nz8ov8VqLxqEh3Jo0TIAIgb2JaBDW9K2xbH0iUmYXJxpf9fYivplxSWkx+2kzW1jTrUKu+dvO3YUa974AK2tNCzPn1Cev/HAvgR2aEvq1jgWPTEJk7Mz0eX5i7Nz2PCebV+2Wq2E9exKYPuzP5X5XIV1bEPi5nh+fHgyjs5O9L73RPtZ+OrH9CpvP53HXMPS975k83e/49u4Ic3K28/p6tubg8lEq5tHsvGt99FWK2F9euEZFsrh8u3fcGBf/Mvbz/IJz2FycabtnbdW1C8rLiEjfget66H9KJOJqDGjiH33PbTVSnBMbzzCQjm6xPa+Fdrf9r618cVXKCssAqVIXPgvXV98AUc3txrrGq1Z19bsWb+dD+58EScXZ6559KaKZd9MmsbVD9uOGy67/Sp+eH0mi2b9QUjTcDoOtbWdtMMp/Pz21ygHBwIigrn6YWOPe4TxVI3nR55cSKmVWuvedVyHHlCH07EuFIuH92Z92h/1HaPOugZcwcw98+s7Rp3d2mwoY8sHDxejWf364RZxcXakhYdmM3nzwvqOUWfPd7yMhUfsev8Mu7osbDivbV1Q3zHqbGKHwTyyZlF9x6izqT0GMn7txZn/re4DeWXLxdt2no4ezEOrF9d3jDp7v+cAxq1YUt8x6mx6TH++3fd3fceos5uaXn7hXGB4Go2f/9uwq3kSJl+Y2+S0MyFKqevL/7tBKfUd8DNQfHy51voSv4mzEEIIIYQQ4nw70+lYV5X/q4ECoPLXHmtABiFCCCGEEEKcjQv4SwSNctpBiNb6dgCl1EzgYa21pfyxD/C23dMJIYQQQgghLjm1vTC9/fEBCIDWOksp1dE+kYQQQgghhLiEyUxIre9S7FA++wGAUsqXs/iOESGEEEIIIYQ4rrYDibeBVUqpH7BdC3Ij8LLdUgkhhBBCCHGJ0gbdtv1CVqtBiNZ6llJqAzAQUMD1Wuvtdk0mhBBCCCGEuCTV+pSq8kGHDDyEEEIIIYQ4F7W9IOISJptACCGEEEIIYSi5uFwIIYQQQggjyTUhMhMihBBCCCGEMJbMhAghhBBCCGEk+Z4QmQkRQgghhBBCGEtmQoQQQgghhDCSzISgtNb2XofdVyCEEEIIIQS277O74DV6c5Fhx8cHnxh4QW4TQ2ZCxi5dasRq7GJWv35MWLeovmPU2RvdBjJuxZL6jlFn02P6c9WC5fUdo85+G9yHyZsX1neMOnm+42W4RYyu7xh1VnhoNl/tnV/fMerslqihvLZ1QX3HqLOJHQbz3MaLs+0DvNj5Ml66SPfdZztexvSdF2/bH9dyKKMWL6vvGHU2Z0Bf3ov/p75j1NnDbYbwbtzF2/c82nZwfUcQtSSnYwkhhBBCCGGkC3JuwlhyYboQQgghhBDCUDITIoQQQgghhIG0XJguMyFCCCGEEEIIY8lMiBBCCCGEEEZSMhMiMyFCCCGEEEIIQ8lMiBBCCCGEEEaSa0JkJkQIIYQQQghhLBmECCGEEEIIYSRl4M+Zoih1uVJql1Jqr1Jq4mnKdVVKlSml/nP2L7g6GYQIIYQQQgjx/5BSygR8BAwDWgOjlVKtT1HudWD++Vq3XBMihBBCCCGEgRwunGmAbsBerfV+AKXUHOAaYPtJ5R4E5gFdz9eKL5xNIIQQQgghhDivlFLjlFIbKv2Mq7Q4DDhc6XFi+XOV64cB1wHTzmcumQkRQgghhBDCQEZ+TYjWejow/VRRaqpy0uOpwJNa6zJ1HoOfcRCilOoNbNFa5yulbgY6Ae9prQ+etxRCCCGEEEIIoyUCDSs9DgeOnlSmCzCnfADiDwxXSpVqrX8+lxXXZibkE6CDUqoDMAH4HJgF9DuXFdeGJS6Og999h7ZaCYyJIXTYsCrLC5OS2D9zJvmHDtHw2msJGTIEgOLMTPZ98QXHcnJQShHYty/BgwbZO24VKdviif1qLlg1Ef170/yqoVWWa62J/WouqVvjMbk403HcWMyNIwD459FncHR1RTk4oEwO9J/ylKHZATJj49g7ey5aWwnpE0PE8Mur5d83+zsyYuMwOTvT4o7baNDIlj9xwb8kLVsBaEL6xhA++DLD83fy8+G/LZrgoBQLjiTzQ0JileXh7m483KY5Tb08+WpvAj8dPFKx7JqIUIaEBaOBhLx83ovfzTHryR8K2NfRLfFsnPkD2mql6cDetLlmSJXlWms2zvyeo5vjcXRxpse9t+Abadv+a6Z9xZFNcbh6NeCKt541NDfAtDfvZtigjqRl5NBl8IQay7w9+VaGDoimoLCEcY9/wpa4BAAG9+vAWy+MxWRyYMacxbz18a8GJj9Ba80/n85j74btOLk4c9WjYwiJalitXFZyBj+9PoPCvAJCmoZzzeO3YHJypCi/kF/emkV2WhbWMis9rh9I9OAehmVf++UPJJa3jZj7bsG/SfXsuanpLJn6JcV5BfhFNqTvg2MxOTpiOZLMio+/JuNAIp1GXUm7q43df5O2xrNllq3tRw7oTaurq7f9zbO+J3lLPCZnZ7rdcws+kRGUlRxj8ZR3KSstRZeVEd69I23/c6Wh2QGObIlnQ/m+GzWwN21r2HfXl++7Jhdnet17C37l++6qaV+RWL7vXl0P++7xfIs/m8eBjdtxdHHm8ofHENS0evvJTsng9zdnUJRXQGCTcIY/amv7AIdj97D48x+xlpbh5uXByFceNix/TnwciXPnoK1W/Hr3IfjyqscNRclJHJw5g8LDhwi5+lqChpz03my1suvVl3Aym2l6/0OG5a5Yv9as+HweBzfZ9t9BD9xMQA3bPyclnX/emUFxXgH+keFc9vBYTE6OHFi3jbWz/0AphYPJgZg7biCkVVPDsq/84gcObYrH0dmZAQ/eQkANfU9OSjoL3/2SotwCApo0ZOBDJ7Kvn/07ysGWvdft/zEse324gL4wfT3QTCkVCRwBRgE3VS6gtY48/n+l1Azg93MdgEDtrgkp1VprbBepvKe1fg9ocK4rPhNttZLw7be0eOj/2Lvv8Ciqvo3j35NNr5seQkgIhN577wKigpUHEMWOXR/LoyiiNBs2xAbYK0VQQbHRe68h9BIgkJ5set/z/rEhJCTgEtkN5P19rouLzM6Z7L2TM7NzZs6ceYLWkyaRunUruWcqNswcPTyIGDmSOgMHVnhdOTgQMXw4bSZPpsULL5C4cmWlZW2dfc/Xc+n2v8fo/+bLnN64lczT8RXKJO2OIScxiQFvT6LNvbez+8s5Feb3ePEp+r06vkYaINps5vD3c2j11ON0mjKRpM1byTlv/aVF7yU3MYnOr02h8Zg7OPzt9wDkxJ0mfs062r/0Ah0nTiB1dzS5iYl2ze8APNS0IRN3xvDohu30Dgmknod7hTJZRcXMPniUn89rnPi5ODM0vC5Pbd7FYxt3YEDROzjQjunBbDaz7Yv59Bv3KNe/M4ET67eREVex/pzZFUNWfDJDp0+k8wO3s/WzuWXzGvTpSr8XHrVr5vK+/XE1N45544LzB/drS8P6IbTs/RSPjfuUGa/eB4CDg2L61Hu48a43aTfgWYYP607TRnUv+Hts6ei2faSdSeaRTydw3eMj+OOj+VWWW/HlIrrc1JdHP52Aq6c7u/7eCMC239YSUC+EsR+O4843HmfZZ79QUlRsl+xxO/eRmZDMrTNeofvYUWwsVzfK2/bdIlpc34/bZryCi4cbh1dYsrt4etDlnuG0HNrfLnnLM5vN7PhyPr2ee5TBb03g5IbKdT9hVwzZCckMeXciHe+/ne1fWD6fg5MjfV56gsFvvMig118kYfc+Ug8ft3v+LV/Mp/+4Rxn6zgRi12/DdIFt98bpE+n6wO1sLvf3adinKwNqcNsFOL59H+nxydw7cwIDHx3Bsk+qrvtrvl5Eh2F9uW+mpe5HL7PUn/zsXJbNnM9N4x/g7g9fZOhz99otuzabOTXnBxo+9iTNXplM+tYt5J333WVw9yBsxEiCrhlU5e9IXrEM15A69ohbpZM79pERn8Toj16m70MjWT17XpXlNn67mDZD+zH6o5dx8XRn/3LL+g9r1YQR745jxLvj6PfoaFZ+/IOdsycz6sNX6PPwKNbOrnrfs+nbRbS+oR+3f/QKLp5uHCiXffi7LzD8nRfo+8gdrLZj9v/PtNbFwGNYRr3aD8zXWscopR5SSj1ky/e2phGSpZR6AbgTWFI6RJeTLUMBZB8/jmtQEK6BgTg4OuLXqRPpu3dXKOPk7Y1n/foog6HC685GIx4REQAYXF1xrVOHIpPJ1pHLpB+NxSM4EI8gS/a6XTuSsL1i9vgdu6nXsytKKfyiGlCUm0u+KcNuGS8m89hx3IKCcCtd90GdO5K6s2L+1F27Celuye/dsAHFuXkUmDLIjU/Au2EkBhdnlMGAsUljUnbssmv+Rj5exOfmk5iXT7HWrElIpkugX4UyGUVFHM7MplhXvsLhoBTODg44KHAxOJBWUGiv6ACkHonFMyQQz+AADI6ORHTvQNy2PRXKnN62h8jeXVBKEdAoksLcPPLSLfUnqFkjnD087Jq5vPVbDpBmyr7g/BsGdeCHhWsB2LLzCD7e7oQEGenUNoqjsQnEnkyiqKiEH3/dyA2DOtordgUHN0XTqn9nlFKENY0kPyePrLSK26fWmtg9h2nWsy0ArQd05uCmaMByhqswrwCtNYV5hbh5ueNgsM84ICe37SGqtyV7UONICnPyyE2vnD0+5hD1u7YDIKpvF05stWzjbj5eBEZF4HDeftUe0o7E4hl8ru6Hd+vAme3n1f3te6jfy1L3/RtFUlRa95VSOLm6AmAuKcFcYrZqfPzLKfVILF4hgXiV23ZPnbftntq2hwal225gaf6zf5/gZo1wqcFtF+Dolmia97PUn9AmkRTk5JFdRd0/uecwjXu0BaBF/84cKa37B9Zsp1G3NniX7nPdjTY/Z1kmN/Y4LkGBuJR+d/l26kTGnl0Vyjh5e+NRP7LScQNAYXoaGdHR+PfoaafElR3fEk2Tvpb1H9LEsv3mVLH+T0cfomG3tgA07deF41ss9czJzYWzffaLCwqx50YQu3UPjftYsgc3ttSdnCr2PWf2HqJBN8u+p3HfLhzfsrtS9qKCArtvv/+faa1/11o31lo31Fq/WvraTK11pRvRtdZ3a60XXI73taY71ggsl2Xu1VonKKXCgbcux5tfTKHJhLPfuQNHZ6ORnOOXflarICWF3JMn8YiM/OfCl0l+ugk3P9+yaTc/X9KPHv/HMnlpJlyNPigUG9+cAQrq9+tF/f697JYdLOvepVw2F19fMs9b9wXpJlzK/X1cfI0UmtJxrxvK8Z9/oSg7GwcnZ1L3RONVP8Ju2QH8XVxIKSgom04tKKSxt3VfhGkFhfwcG8cXvTpTaDazMzWdnWkmGyWtWl6aCQ//c+vf3c9IypHYCmVy0zJw9zdWKJObZsLN18dOKasvNMSPuPjUsunTCWmEhvgRGuJL3Jlyr8en0rltVE1EJCs1A+9AY9m0d4CRrNQMvPzOrd+8zBxcPdzKDta9SssAdLyhN/Mnz+b9OydQkJfPLc/fg7LTeIy5aSY8As7VHw9/S91wL1c3CrJycHY/l93dz5fctJo/CZKXbsLdv/x+0UjaeXU/Lz0DNz9jhTJ56Za6bzabWTb+DbITkmk4qA/+Ufbb70Ppui+X3+MC267Hedtu3nl/n5qUnZqBV4CxbNorwEh2agae5et+VsW67+lvLGuopJ9JoqS4hHnjZ1CYl0/7G/rSon9nu2QvTDfh7Fv+uMH3ko4bTs+fR91bbqMkP98W8aySk2bC87ztNyctA49y6z8/Kwfncuvfw99ITuq57ffYpt1s+n4xeRnZXD/epieyL5rd099ITqoJD98LZ/f0963QyDq+eTebv1tMXmYWQ160X/aacDlv8L5a/WMjpLTh8QPQWSk1FNiqtf7mYsuUDv01FmDWrFnQpMmlJ6viDPWldqAryc/n0MyZRIwYgaOb26VnqCZtTfaLFOn58rO4+RopyMhkw5sz8AwNIaBpo8sf9EKsuf2hqs+IwiO0DvWGDGbPO9MxuLjgWa9elWecbMmaYR4uxMPRkS5B/ty/bis5xcWMa92UviGBrEpIvpwRL1nlnVXlT3S17NBUFX8hrXWV+ausZvZQxRtX/gtcONyxHfsJbhDGHa8/Tnp8Ct+/9BHhLRvg4m6H/VCVm+Y/158rgjXZL7DvAXBwcGDQ6y9SmJPL+vdmk3HqDD71Qi97zEtizbq/grbdqr6/Kn+EC5cxl5hJOnqK4VMeo6iwiDnPvUedJvXxqxtkg7Tnq2rdWrdkxp7dOHp54x4RQdbBg5c31iWo+vihUqGLlmnQtQ0NurbhTMwRtsz5jWETH7+sGS+oyqr9z9tv+SKRXdoQ2cWSfeucJQy1V3ZRI6wZHet+4GVgBZZq/oFSarLW+osLLXPeUGB63erVlxzM2deXwrS0sulCkwkno9Hq5c3FxRyeOZOALl3wa9/+kt//37Bc1Ugvm85LS8fVWPEsl6ufsXIZX6Nl+dL/XXy8qdOxLaajsXZthDj7Gikol60gPR2X89a9i58vBeX+PgXpJpxLy9Tp1ZM6vSyXs48t/BkXX1/sKaWggAAXl7Jpfxdn0spdGbmYtn5GEvPyySwqAmBDUirNjN52bYS4+RnJST23/qu6wuHuZyQ31XTRMleq0wmphNXxL5uuG+JHfGI6zk6OhIWWe72OP2eS0qv6FTax7bc17PzT0je5TuNwMpNNZfMyU0x4+p/3N/D2JD8nD3NJCQ4GA1kpJrxKy+xeupnuwwdauluGBmIM9iflVBJ1m9jmquD+P1dzaPkGAAIaRpCTcm695aRWPsvu4uVJYe657Llp6bj71Xz9cfMzkptafr9YuV67lV45uFgZZw93gpo1In73Prs2QtzP23ZzLrDt5lxh2+7OJWuIXmqp+yFR4WSlmMrmZaWYKpyFB3A7r+5nlzvb7elvxM3bAydXF5xcXQhr0ZDk2NN2aYQ4+/pSmF7+uCHd6uOGnKNHydizi8y90ZiLiyjJyyf2i8+of+/9Nkp7TvQfa9i31LL9BkWFk33e9utxXv1w9faksNz6z0mt/DcCCG0RRcYHKeRlZuPm7WmT7Hv/WM3+ZZbsgVERFbJnp5oq7VfOz56dml7lVcDQFlGs/NC22WvaFXTuocZY0z/gf0C70j5gdwEdgOdtGws869cnPymJ/JQUzMXFpG3dim+bNlYtq7Xm+Dff4FanTqWb1u3B2CCCnIQkcpIs2U9v2kZI+9YVyoS0b82pdZvQWpN25BhO7m64Gn0ozi+gKM9yKbg4v4Ck6P142flMnndkffISk8hLtuRP2rIN/7YV171/mzYkbLDkzzx6DEd3N1xKG1qFmZkA5KemkbJjJ0FdLtvDNa1yODOLUHdXgl1dcFSK3iGBbElO++cFgeT8Apr6eOFS2nWmjZ+RUzl5toxbiX/DCLISkshOSqGkuJgTG7ZTt0OrCmXqdmjN8TWb0VqTcvg4Tu5uNX4gY60lS3dw+62WLoad20WRmZVLQpKJbbuPEhUZQkS9QJycDAwf2o0lS7fbLVfHG3rzwIfP88CHz9Oka2uiV2xBa03cgeO4erhW6IoFljN89Vs1Yv+6XQDsWb6Fxl0sfyfvIF+O77acTc1OzyTtdBK+If7YSrNr+3DjWy9w41svEN65NUfWWLInHTqOs7tbpS95pRR1WjQmdtNOAI6s2kx4x9ZV/Wq78msYQXa5un9y43ZCz6v7oR1aE7vWUvdTDx/Hyc1S9/MzsyjMyQWguLCQxL0H8Q4Ntmv+s9tuVrltt955+cM6tOZY6babXLrt1nRXrHbX92bM9OcZM/15orq2Zt9KS/05c/A4Lh6uFbpigaX+hLdqxKH1uwCIWbGFqNK6H9WlFaf3HcNcUkJRQSHxh07gH2afv4N7RH0KkpIoSEnGXFxM+tat+LS27rgh9OZbaPnGW7R47Q3q3zcWr6ZN7NIAAWg1pHfZzeSRnVtzcJVl/SccPI6zu2ulBoZSirotG3F04y4ADqzcTGQny/rPiE8uu5qSfPQU5uISXL1sd59RyyF9GP6O5WbyyM6tObTakj2xdN9zfgNKKUVoy8Yc22jZ9xxatZn6nVtXzn7sFCXFxTbNLmqeNfeExAFZ5aazqPhkRZtQBgP1R43i4PTpaLOZwB49cA8NJbH0qkpwnz4UZmSw99VXKcnPRylF/LJltJ40iby4OFI2bcKtbl2iJ08GoN7NN2Ns1epib3nZOBgMtB4zko1vfYA2mwnv3R3vsFCOL18DQOSA3gS3aUnirr0se/ZlDM7OtHtgDAAFmZlsmT4LsIz0UbdbJ4Jbt7BL7rOUwUDU6JFEv/c+2mwmpGcPPOqGcmaVZd2H9u2DX+uWpEVHs+WFl0qH6L2rbPmYj2dRnJ2DMhhoNHoUTna+0dKsYebBo0xq3xIHpVh2JpGTOblcGxYCwJ9xCRidnXivSzvcHQ2YNQwLr8sjG7ZzKDOL9YkpTO/ajhKtOZaZzZ/njW5jaw4GAx3v+Q8rX/sIbTbToF83jPVCObzUcjN3o4G9CG3XgjO7Yvj1yYkYXJzp+tAdZcuvn/EFifsOU5CVzc+PjKf1bdfTsH93u+X/+oPH6dWtGQG+XhzZ/CFT3l2AU+nQnZ99t4w/V+xkcL+2xKydTm5eAQ8+a6nvJSVmnprwFb9++wIGgwNfz1vF/kNxF3srm4nq1Jwj22L46P7JZUP0njXnlZnc8MQovPx96H/PMH6e9hWrvl1CSIMw2g62DMPba+S1LH7vO2Y98joA/e8ehruPfc7mhbVrQdyOGBY+MQmDsxO9HjlXN/5+/WN6Png77n5GOo6+kVXTv2TH3N/wj6xH4/7dAMg1ZfLruGkU5Vn2q/t+X8XN747H2Q5dyRwMBtrf/R/WvGGp+5F9u+ETFsqRZZa6H3VNL+q0bUH8rhh+f2oiji7OdHrQ8vnyTZls+eQbtNmM1pp6XdsT2t4++/zy+Tvf8x+Wl267UaXb7qHSbbfxwF7UbdeC07ti+OVJS/7u5bbdtaXbbn5WNgtLt91Gdtx2ASI7NOfYthg+f8hS9wc/fq7u/zR5JoMeHYWnvw+97hrGkre/Yv33SwhqEEbL0iGo/euFUL9dM75+4g2UgwOtBnYlIMI+J9KUwUDYiNs5OmM62qzx794Dt9C6pKxZBUBA774UZWRw8PWpZccNySuW0eyVyRjs2GX7YiI6tODkjn18/8hkHF2c6P/Yufrx29RP6PfI7Xj4+dD1zhtZ+u6XbP7hNwIjw2h2jWX7PbpxFwdXb8HBYMDR2YlBz9xjt6664e1bcHJHDHMenYSjixN9Hz2XfcnUj+n7yO14+BnpeseNLH3vS7bM+Y2AyHo0G2DJfmzTLg6t2oyDoyX7wKfvvWq6GVdHLf5oVlNV9j8ElFJPl/7YFmgFLMLS4+9GYIvW2to7hvSYanTHulJ806cPz21ZUdMxqm1a5/6MXbeqpmNU2+yefRla+gV+Nfp1YC8m7VxW0zGq5ZV21+AWPqqmY1Rb3sk5fHvkr5qOUW13Rg3mjd1LazpGtY1rM5AJ26/Oug8wpcM1TL1Kt92X2l3D7ANXb90f23QwI1euqekY1Ta3X2/ej/m7pmNU25MtBvHe3qt33/NUy4FXxeF9o1lr7HZz3uEHe1+R6+RiV0LaAUeAoVge137WIlsGEkIIIYQQojZT9hkw8Yp2sUZIB+Al4FbgA/vEEUIIIYQQQtR2F2uEzAT+BCKBbeVeV1i6ZTWwYS4hhBBCCCFqJbkn5CKjY2mtZ2itmwFfaq0blPsXqbWWBogQQgghhBCiWqx5WOHD9ggihBBCCCHE/wcOciXEqueECCGEEEIIIcRlY81zQoQQQgghhBCXidwTIldChBBCCCGEEHYmV0KEEEIIIYSwI7kSIldChBBCCCGEEHYmjRAhhBBCCCGEXUl3LCGEEEIIIexISX8suRIihBBCCCGEsC+5EiKEEEIIIYQdKbkMgNJa2/o9bP4GQgghhBBCAFdFP6dW36y12/Fx9JheV+Q6scuVkEUn/rDH29jEjRFDmH3gr5qOUW1jmw7m+a3LazpGtb3ZaQA/x1699efm+kNYdvr3mo5RLdfUvY5vj1y9df/OqMG4hY+q6RjVlndyzlW/7X556OqtP/c0HnzV7vvHNh3MQ+tX1nSMapvZo99VX3em7Vla0zGq7bnWA7l91eqajlFtP/TtU9MRrCK3hMg9IUIIIYQQQgg7k3tChBBCCCGEsCO5EmJlI0Qp1b6KlzOAE1rr4ssbSQghhBBCCFGbWXsl5GOgPbAHyw0/LUt/9ldKPaS1/ttG+YQQQgghhKhV5EqI9feExALttNYdtdYdgHbAXuAaYJqNsgkhhBBCCCFqIWuvhDTVWsecndBa71NKtdNaH5MnPgohhBBCCGE9Bzl8troRclAp9Qkwt3R6BHBIKeUCFNkkmRBCCCGEEKJWsrYRcjfwCPBfLPeErAOexdIA6WeLYEIIIYQQQtRG0pHI+kaIAzBda/0OgFLKALhorc1Atq3CCSGEEEIIIWofa29MXw64lZt2A5Zd/jhCCCGEEELUbkrZ79+VytpGiKvWuuyKR+nP7raJJIQQQgghhKjNrG2E5JR/YKFSqgOQZ5tIQgghhBBCiNrM2ntC/gv8qJQ6UzpdB8sIWUIIIYQQQohLoGSMXusaIVrrrUqppkATLKNjHdBay9C8QgghhBBCiEtmVSNEKeUOPA1EaK0fUEo1Uko10Vr/ZstwWmsWf/wTB7bux8nFif88ezthjepVKrd+0VrW/bya1DMpvPLjVDx8PAHYsXwbq+YvB8DFzYWbHx9OaMO6toxcIfvKTxdyfPs+HF2cufbJ0QQ3rJw9IzGV3976ivzsXIIahHHdU3dicLL8WU5FH2bl5z9hLi7BzduDEa89aZfsAIm7Y4j+9ke0WRPRtzuNhw2uMF9rTfS3P5K4KwaDixPtx47BGBl+br7ZzKoJb+Dqa6Tbs4/YLXf5fL9+8hMHt+zHydWJ4c/cTt0q6s6GRWtZ//NqUuNTmDD/XN1JOpnIgnd/4PSROAbfdT29h/e3e/4fP/yZmM37cXZ14s7nRhHeuHL+VT+vZeXCNaScSeHNn6fgWZr/0K4jzJrwOf4hfgC07dWa68YMrrS8LfP/PWshR7btw8nFmaFPjaZOVOX86Qmp/PzmV+Rl51KnYRg3PmOp//k5eSx6+xsyktMxl5jpekt/2g7sapfsM996kCED2pGcmknHgc9VWeadSXcxuF9bcvMKGfvMJ+zaGwvAwD5teHviGAwGB76au5K3P15sl8zl1YZtd9nshRzdbqk71z85mpAq6o4pIZVFb31FflYuwQ3DGPq0pe5s/mk5Mau2AWAuMZMal8AT372Gm5eH3fJfzfv+1OgYDv8wH7SZOr16EHH9tRXma605/MN80qL34uDsTLP77sIrwlJ/Ti1dTvya9WitCe3dk3qDBtgtd/l8V3v92fTlAk7tiMHRxZnej95JQIPK+bMSU1g5/UsKsnPxj6xHn8fHYHByxHQ6gTUffUfq8Tg6jrqBVsOusUtugIy9ezk1fx6YzQT07EnItUMqzM9PiCf2q6/JPXWS0BtvImTQoArztdnM/tdexdloJOqxx+2Wu6ZcyTeM24u194R8CRQC3Uqn44CpNklUzoGt+0k5ncxzX47n1v+O4OcZP1ZZrn6LSB5442F8g30rvO4X4s9Dbz/O07OeZ8Dtg1g4fZ6tI5c5vn0f6fHJ3DtzAgMfHcGyT+ZXWW7N14voMKwv982cgKunO9HLNgKQn53LspnzuWn8A9z94YsMfe5eu2XXZjO7v55Ht+ceY8C0CcRt2kbm6fgKZRJ3x5CdkMQ170yk7X2j2f3V3Arzj/65Eq/QELtlPt/B0rrz7JfjueXJEfzyQdV1J6JFJPe98TDG8+qOu7c7Qx++ld632rfxcVbM5v0kn05m4rcvcvvT/2Hu9AVVlmvYMpIn3n4Yv/PyA0S1asCLn/6PFz/9n10bIABHt+0j7Uwyj3w6geseH8EfH1Vd/1d8uYguN/Xl0U8t9X/X35b6v+23tQTUC2Hsh+O4843HWfbZL5QUFdsl+7c/rubGMW9ccP7gfm1pWD+Elr2f4rFxnzLj1fsAcHBQTJ96Dzfe9SbtBjzL8GHdadrIPic9zqoN2+6x7ftIP5PMg7MmcO2jI/jrAvvOVV8totONfXlwtqXu7F5qqTtdbhnAvTOe594Zz9Pnrhuo1zLKbgeQcPXv+w99N4c2Tz1G56mvkLh5Kzmnz1Qokxa9l7zEJLq8Ppkmd43m4Dc/AJAdd5r4Nevp8NI4Ok16idTd0eQmJtot+1lXe/2J27mPzPhkhn/wCj0fHMWGT+dWWW7r94tocUM/hn/wCi6ebhxaYcnv4ulBt3uH02qonU+cmc2cnPMDjR5/guYTJ5G2dSt5ZyrWHYO7B/VGjiR44MAqf0fS8uW4htSxR1xxhbC2EdJQaz2N0qeja63zsHTLsql9G6JpP7ATSikimtUnLyePzNSMSuXqRoXhF+Jf6fX6LSJx97IM4hXerD4ZKZWXtZWjW6Jp3q8zSilCm0RSkJNHdlrF99dac3LPYRr3aAtAi/6dObIpGoADa7bTqFsbvAMtZ7LdjV52y55+NBbP4EA8ggJwcHQkrGsHErbvrlAmYfsewnt2QSmFX1QkRTm55KdbPl9eajoJu/YS0beH3TKfb9/GaNpfY6k74dWoO55GL+o1CcfB0dpN5PLas2EvXUrrfmTz+uRl55FRRf56jcLKrnZcSQ5uiqZVf0v9D2saSX5OHllV1P/YPYdp1rMtAK0HdOZgaf1XCgrzCtBaU5hXiJuXOw4G+/wt1m85QJrpwo8/umFQB35YuBaALTuP4OPtTkiQkU5tozgam0DsySSKikr48deN3DCoo10yn1Ubtt3Dm6JpWVp36ja98L7zxJ7DNC3dd7Ya0JnDpXWnvP2rd9C8dwd7xC5zNe/7M4/F4hYUhFtQIA6OjgR36UTKrj0VyqTs3ENI964opfBp2IDi3DwKTBnkxifg3SASg4szDgYDxiaNSN6xy27Zz7ra68+JrXuI6mPJH9Q4ksKcPHLTK+c/s/cQkV3bARDVpwsntlq2czcfLwKjInBwNNg1d87x47gGBeESaKk7vh07Ydpdcd/j5O2NR/36KEPlbIXp6WRERxPQs6e9Itc4GaLX+kZIoVLKDdAASqmGQIHNUpXKSM3AGHjuDK8xwFjlgZg1tv65iSadml2uaP8oOzUDrwBj2bRXgJHs87LnZeXg6uGGQ+kG6elvLNtZpp9JIj87l3njZ/Dt09OIWbHFbtnz0k24+Z1b765+vuSdtxPMSzfh5n9+GRMA0d8toOWom2u05memVKw7PgHGKhshV6qMlAyMQcayaWOgEdMlNqKP74vltfvf4qNxszhzPP6fF7iMslIz8A40lk17BxjJOr/+Z1as/17lynS8oTcppxJ4/84JzH70dQaNvRXlUDMNwvOFhvgRF59aNn06IY3QED9CQ3yJO1Pu9fhU6lZxhcqWasO2m3X+vtO/6rrj4lmu7lRRpii/kGM79tOkexubZy7vat73F5jScS1Xf1x8jRSkp1csk27CpXwZPyMF6SY86oZiOnSYouxsSgoKSY3eS0FaxWXt4WqvP7lpJjzKbZ/u/kZy0kwVyhRk5eDsfi6/h78vOWk1+/1WZDLh5HvuhJizr5Eik/V//1Pz51H31ltRV/IRs7jsrB0dayLwJ1BPKfU90AO450KFlVJjgbEAs2bNInhw5f6MVtFV/O5qXIA5suswW//cxMPv2a9frdaVw1fati5SxlxiJunoKYZPeYyiwiLmPPcedZrUx69ukA3Snp/LmjJVZVck7IzGxdsTY2Q4yfsOXf5sVqryI1xFO7eq64/1+es1CmPynJdxdXNh76Z9zH75CyZ+O/5yRry4qvKfX+QiFe3Yjv0ENwjjjtcfJz0+he9f+ojwlg1wcXe74DL2UtU+SGtd5d+nitVgW7Vg263qQ1Retf9c5sjWvdRtFmnXrjRQC/f954WvartVCjxC6xA+ZDC73n4fg6sLnvXCaujEwdVefyq/dv4+p8o6ZqtAVquy8li1pGnPHpy8vPCIiCDr4MHLG+sKdhUdktiMtaNj/a2U2g50xVKrntRap1yk/Gxg9tnJRSf+sDrQhsVr2fy7pW9jvSbhmJLPtaRNKSa8/b2t/l0A8cfOsOC9udz36oN4eNt2Z7JzyRqiS/uVhkSFk5ViKpuXlWLCw8+nQnk3b0/yc/Iwl5TgYDCQnWrCw9dSxtPfiJu3B06uLji5uhDWoiHJsaft8kXk5mckr9wZrPy0dNx8z8vu50teasUyrkYfTm/ZQfyOaBJ2x2AuKqY4L49tH39Jx0cu2Ga9bDYuXsuWPyzrP6xxxbqTkWLC2+/S6o69rf5lHeuXWPJHNAnHlGQqm2dKNuFzCXXfzcO17OeWXZsz7/0FZGdkl924bgvbflvDzj8t+es0Dicz2VQ2LzPFhKd/xTrkfl79z0ox4VVaZvfSzXQfPtDSZSg0EGOwPymnkqjbJMJm+a11OiGVsDrnuvDVDfEjPjEdZydHwkLLvV7HnzNJ9j0TfLVuu9uXrGH3X6V1p9F5+85UE55V7DsLssvVnSrK7Ftjv640tWXf7+LrS365+lOQbsLFaKxQxtXXt8IVjoI0E86lZUJ79yC0t6Ur39GFv+DqW3FZW7na68++P1dzcNkGAAKiIsgpt33mpppwPy+bq7cnhbnn8uekplcqY29ORl+K0tPKpgvTTTidV3cuJOfoEUy7d5Oxdy/moiJK8vI4/vnnRN53n43SiiuFtaNjLddaDwCWVPHaZdV9WC+6D+sFwP7NMWxYtJa2fdtz8sAJ3Dzc8Pa3fkNLT0rnm8lfMPK5OwgMs/0OvN31vWl3fW8Ajm2LYeeSNTTt1Z74Q7G4eLhW2skppQhv1YhD63fRtHcHYlZsIapLKwCiurRixewFmEtKKCkuIf7QCToM62vzzwBgbBBBdkISOUkpuPkZidu0vdKBSEj7Vhxbupq63TqSfjQWR3c3XH19aDHiJlqMuAmA5H2HOPL7MrscxAB0G9aLbqV158DmGDYsXkubvu05deAEru6XVndqQp+betLnJkt/2L2bYlj9yzo69G9H7H5L3fe5hPwZaZl4+3qhlCJ2/wm01jZvhHe8oTcdb7DU/8NbYtj22xpa9GnP6YOxuHq44lVF/a/fqhH71+2iRZ8O7Fm+hcal9d87yJfjuw8S3rIh2emZpJ1OwreKe3dqwpKlO3jorkHMX7yBzu2iyMzKJSHJRHJqJlGRIUTUC+RMQhrDh3bj7ic+tGu2q3Xb7XB9bzqU7juPbI1hx29raNa7PWcOxuLifoF9Z+tGHFi/i+a9OxC9fAuNSusOQH5OHqf2HmHoM3faJX9t2fd7RUaQl5hEXnIKLr5GEjdvpcWDFQ8E/du25vTyVQR16UjmseM4urviYrR8vsLMTJy9vclPTSNl+07aj696dLnL7WqvP82v7UPza/sAcHL7Xvb/uYYGPTqQfDgWJ3c33H0r56/TojHHN+2kYY+OHFm9mfBOre2S9UI86tcnPymJgpQUnIxG0rdtJfK++61atu7Nt1D35lsAyDp4kMSlf/+/aIDIY0L+oRGilHIF3IEApZQv566teQOhNs5G087NObBlP2/ePRVnF2eGPzuqbN7n42dx29Mj8fH3Yd3Pq1n94wqy0rJ498FpNO3cnOFPj2TZd3+Rm5nDz6UjIzkYDDz50TO2jg1AZIfmHNsWw+cPTcbJxZnBj48um/fT5JkMenQUnv4+9LprGEve/or13y8hqEEYLUuHIfWvF0L9ds34+ok3UA4OtBrYlYAIm69ywLKeWt81gg3TPkSbzUT06YZ3WCjHl6+xfLYBvQlu25LE3TEsfeYVHJ2daTfWPjtrazXp3JwDW/fz1j1TcXJxZvgz5+rOly/N4tanRuLt78P6Xyx1Jzsti+kPTaNJ5+bc9tRIstIy+eDxdyjIzUcpxbpfVvP07BdwLXeFwZZadGlOzOb9TLzjVZxdnbnjuZFl8z4aN5vRz47AGODDyp/WsGzuCjLTsnjt/rdo0aUZo58dyc7Vu1m7eD0GgwEnFyfufWmMXfvaRnVqzpFtMXx0/+SyIXrPmvPKTG54YhRe/j70v2cYP0/7ilXfLiGkQRhtB1vqf6+R17L4ve+Y9cjrAPS/exjuNryKU97XHzxOr27NCPD14sjmD5ny7gKcSodO/ey7Zfy5YieD+7UlZu10cvMKePDZWQCUlJh5asJX/PrtCxgMDnw9bxX7D8XZJfNZtWHbbdjRsu+cNdZSd6578lzdmT9xJkMet9SdfncPY9G0r1jz3RKCG4TRetC5IZwPbdxDZLumOLu62D3/1b7vb3zHCHa/OwNtNlOnZ3c86oZyeqWl/tTt1xv/1i1J27OXTeMmYHB2pum9d5Utv/ej2RRlZ6MMBhrdMQonD/t2ZYKrv/7Ua9+CuJ0x/Pj4JBydnej16B1l8/567WN6PnQ7Hn5GOt1xIyvf+5Ltc37DP7IeTfpbBi/NTc9k0bhpFOVZvrv2LlnFre+Nx9nGXVmVwUD4yFEcfn862mwmoEcP3EJDSV69GoDAPn0oyshg/2uvUpJvyZa0fBktJk7C4Fbz3WxFzVBV9S0sm6nUk1ielh4KnOZcIyQT+FRrbc1pvkvqjnWluTFiCLMP/FXTMaptbNPBPL91eU3HqLY3Ow3g59irt/7cXH8Iy07/XtMxquWautfx7ZGrt+7fGTUYt/BR/1zwCpV3cs5Vv+1+eejqrT/3NB581e77xzYdzEPrV9Z0jGqb2aPfVV93pu1ZWtMxqu251gO5fdXqmo5RbT/07XNVXGPo8fM6u901uP7mnlfkOrnolRCt9fvA+0qpx7XWH9gpkxBCCCGEEKIWs3boigSllBeAUuolpdRPSqn2NswlhBBCCCFEraQc7PfvSmVttAla6yylVE9gMPA18IntYgkhhBBCCCFqK2sbISWl/18PfKK1XgQ42yaSEEIIIYQQtZc8Md36RshppdQs4D/A70opl0tYVgghhBBCCCHKWNuQ+A/wF3Ct1toE+AH/s1UoIYQQQgghRO1l7RPTc5VSSUBP4DBQXPq/EEIIIYQQ4hLY89ldVyqrroQopV4BngdeKH3JCfjOVqGEEEIIIYQQtZdVV0KAm4F2wA4ArfWZs0P2CiGEEEIIIawnF0KsvyekUFsera4BlFIetoskhBBCCCGEqM3+8UqIsnRa+610dCyjUuoB4F7gU1uHE0IIIYQQoraRKyFWNEK01lopdROWe0IygSbAy1rrpTbOJoQQQgghhKiFrL0nZCNg0lrLsLxCCCGEEEL8C3IlxPpGSD/gQaXUCSDn7Ita69Y2SSWEEEIIIYSotZTlfvN/KKRURFWva61PWPEe//wGQgghhBBC/HtXxTWGAX+st9vx8fIhPa7IdWLtwwqtaWxckKnw93+zeI0yOl/Hx/v+rukY1fZI80F0mLO2pmNU2/ZRva769f/G7qvz9qlxbQZetdnBkv/5rctrOka1vdlpAG7ho2o6RrXlnZxDWsHimo5RbX4uw0jIuzrzh7gNo8HHq2s6RrUde6QPO1KW1HSMamsfcD1zj/5Z0zGqbWTDawl/d2VNx6i2k0/3q+kIVx2l1LXA+4AB+Exr/cZ580djuTccIBt4WGu9+9++r7XdsYQQQgghhBCXgcMVcm1CKWUAPgIGAnHAVqXUYq31vnLFjgN9tNbpSqkhwGygy799b2ufEyKEEEIIIYSoXToDR7TWx7TWhcBc4MbyBbTWG7TW6aWTm4Cwy/HGVl0JUUp1B+qXL6+1/uZyBBBCCCGEEOL/Ewdlv1umlVJjgbHlXpqttZ5d+nNd4FS5eXFc/CrHfcAflyOXNQ8r/BZoCOwCSkpf1oA0QoQQQgghhLiClTY4Zl9gdlUdw6psISml+mFphPS8HLmsuRLSEWiurRlGSwghhBBCCHFRV8o9IViufNQrNx0GnDm/kFKqNfAZMERrnXo53tiae0L2AiGX482EEEIIIYQQV4ytQCOlVKRSyhkYCVQYGlApFQ78BNyptT50ud74gldClFK/Yrkc4wXsU0ptAQrOztdaD7tcIYQQQgghhPj/4koZGUprXayUegz4C8sQvV9orWOUUg+Vzp8JvAz4Ax8ry6Pei7XWHf/te1+sO9bb//aXCyGEEEIIIa5cWuvfgd/Pe21muZ/vB+6/3O97wUaI1no1gFIqEojXWueXTrsBwZc7iBBCCCGEEOL/B2uuBv0ImMtNl5S+JoQQQgghhLhEDkrb7d+VyppGiGPpw0sAKP3Z2XaRhBBCCCGEELWZNY2QZKVU2U3oSqkbgRTbRRJCCCGEEKL2clD2+3elsuY5IQ8B3yulPsTyQJNTwBibphJCCCGEEELUWv/YCNFaHwW6KqU8AaW1zrJ9rLL35t03fmbD2v24ujoxYeoomjavd8Hyb7+2kN9+2cKqLW9Wa/nLnX315wuJ3R6Do4szgx6/g6CGld87IzGFP975ivzsXIIahDH4yTEYnM79WRIOn2D+uHcY8sw9NOrezi7ZAbrV8eXZ9g0wKMUvRxP4an9chfl96vrxcOv6mLWmxKx5Z8cxdqVkWrWsPfzb9R+39zC/vj4b7yB/AKK6tqHLiCF2zb/5ywXE7bTk7/nInQQ0qJw/KymFVdO/pCA7F//IevR+fAwGR0dMpxNY9/F3pB6Po/3IG2g17Bq7Za8N+RN3xxD97Y9osyaib3caDxtcYb7WmuhvfyRxVwwGFyfajx2DMTL83HyzmVUT3sDV10i3Zx+xa/aZbz3IkAHtSE7NpOPA56os886kuxjcry25eYWMfeYTdu2NBWBgnza8PXEMBoMDX81dydsfL65yeVvSWvPem4vYsPaAZb89ZQRNmodVKvfqK/M5EBOH1prwiEBemjoCd3cXsrPymPjCHBITTJSUmLn9rj7ccFMnu+afMW0Rm9cdwMXViRcmj6Bxs8r535w4n4P7LPnrRQQybrIl/1n7957ikTEf8Mqbd9B3YGu75e9dz5eXe0bh4KCYvy+emTtPVZh/TX1/nu5SH7OGErNmyrojbEuw7Pvvbl2XEc3qoBTM2xfPl3tO2y33WVprvp7+M7s27sfZ1ZmHx48isknl9f/hxO84duAUBkcDDZuHc/9zw3F0NHD6RCKzXp3L8UNxjBh7HTfc3s/u+f+Y9ROHt+7DycWJm54eTWhU5X3n5l/XsOmX1aTFp/DcnFfx8PG8pOVtoU99Pyb2bYTBAeZGx/Px1pNVlmsd7MWiUR14dEkMvx9Opo6nC+8NaUaguzNaww/RZ/hip/2PG+ztShmityZZtQ6UUtcDjwBPKaVeVkq9bNtYFhvW7ufUiWQWLHmRca/8h2lTF1yw7P6Yk2Rl5VV7+cstdsc+TGeSuOvjlxnw8EhWzJpXZbn13yym3dB+3P3xy7h4uBOzfGPZPHOJmfXfLCK8bTN7xQYsl+7GdWjIE6tiuO337QyOCCTS271CmS2JJkb+sYPb/9zJpC2HmNClkdXL2sPlWP+hzRoy+r1xjH5vnF0bIABxO/eRmZDMrTNeofvYUWz8bG6V5bZ9t4gW1/fjthmv4OLhxuEVlvwunh50uWc4LYf2t2fsMldzfm02s/vreXR77jEGTJtA3KZtZJ6Or1AmcXcM2QlJXPPORNreN5rdX1X8fEf/XIlXaM084/XbH1dz45g3Ljh/cL+2NKwfQsveT/HYuE+Z8ep9ADg4KKZPvYcb73qTdgOeZfiw7jRtVNdesctsXHeAUydS+PG35xn38m1Mm/pTleX++79hfLvgab5b+AzBdYwsmLMegAVzNxDZMJhvFzzNR58/xIy3f6WoqNhu+TevO0DcyRS+X/w8z064jXdfrTr/Y88O44v5T/Plj88QFGLk57nry+aVlJiZ9f4SOnVrYq/YgGX/Pal3I+5ZEs3gOVsZ2iiIKN+K++8NcelcN287N8zfzvMrD/J6P0vGxn7ujGhWh5sX7uD6edvoH+FPfR83u+YH2LVxPwlxKbw370UeeG44n79d9fd+j0HteWfOOKZ9+z8KC4pY+esmADy93bnrqZu5YZR9Gx9nHd62j9TTyTzx2UsMfWIkv31Y9ThA4c0bMOa1RzAG+VVr+cvNQcHU/o256+fdDPhqC8OaBtPIr/J3v4OCF3o1ZPWJtLLXSrRm6uojDPh6CzfO2c6YtnWrXFbUPv/YCFFKzQRGAI9j6Y41HIiwcS4A1qzcy5BhnVBK0apNfbKy8khJzqhUrqTEzIx3fuXxp4dWa3lbOLYlmmb9OqOUok6TSApy8shJq/jeWmtORR+iUfe2ADTv14Wjm/eUzd/9+2qiurXFvfQMh7208PPiVHY+p3PyKTZr/j6ZTN+wiju6vOJzA6a5GQxobf2y9nA51n9NOrltD1G9LfmDGkdSmJNHbnrl/PExh6jf1XKFLKpvF05s3Q2Am48XgVEROBgMds8OV3f+9KOxeAYH4hEUgIOjI2FdO5CwfXeFMgnb9xDeswtKKfyiIinKySW/9PPlpaaTsGsvEX172D07wPotB0gzZV9w/g2DOvDDwrUAbNl5BB9vd0KCjHRqG8XR2ARiTyZRVFTCj79u5IZB//pZVJdszcoYhgztgFKKlm0iyM7KJyU5s1I5D09XwFKPCvKLKH2AFkopcnMK0FqTl1uIt487BoP9zjmuWxXD4Bss+Vu0tuRP/af8BefyA/w0Zz19BrTC18/DbrkB2gR5cyIjj1OZ+RSZNb8dSWJgpH+FMrnl9/2OBjSWnX9DX3d2JWaSX2ymRMPmMyYGRQbYNT/A9nV76XVtR5RSNGpZn9ysPNJTKq//dt2bo5RCKUVUs3DSkizbr4+vFw2bhWNwrJnz1Ac27aXtAMtxS72m9cnPySMrrfJxS52GYfgG+1d7+cutbYg3saY8TmZY6s6vBxIZ1LDy3/+etmH8cTiZ1Nyy8Y5Iyilkb5Jln5VTVMKR1BxCPF0qLVvbyD0h1l0J6a61HgOka60nAd0Au1zbS07KIDjEWDYdFGwkOanyxvTjnLX07tuCgECfai1vC9mpJjz9fcumPf2NZJ+3I8jPysHFw63sQMszwEhOakbZ8kc37aHV4J52yVtekLsLibkFZdOJuYUEulXeIfQL82fh9R14v08LJm0+dEnL2tq/Xf8ACQeP8/1Tr/PL5I9JPVnxTLit5aaZ8Ag4l9/D30humqlCmYKsHJzdz+V39/Ml1w5fNta4mvPnpZtw8zuX3dXPl7zzGlB56Sbc/M8vYwIg+rsFtBx1M6grc88fGuJHXHxq2fTphDRCQ/wIDfEl7ky51+NTqRvsW9WvsKnkpMwK++3AYJ8L7renTpjH9f0mcyI2meGjLI2+20Z1J/Z4EkMHTOGOW9/hqedvxMHBfgeUKUmZBFmZ//WX53HzgMmcPJ7MLSMt+ZMTM1i7ci/DhnezR9wKQjycic8+t/+Ozy4g2KPy/ntQpD9LR3Xi8+tb8vwKy77/UFounUN9MLo44uroQN8If+rUwIFkWnIm/kHGsmm/ICNpFzn5WFxcwtq/ttGmS1M7pPtnWSkmvAONZdPeAT5kpli/X/y3y1dXiKcLZ7Lyy6bjswsI9qr49w/2dGZwo0C+u0g3vTBvV1oEebEzoXLDUdQ+1uyZz/ZxylVKhQJFQOTFFlBKjVVKbVNKbZs9e3a1w2ld1djGFb/Yk5MyWP73bobf3qtay9tOFe993ltXma+0zOrPF9JjzDAc7HgG77wIFVS1JlfGpXLrku08s3YfD7eOuKRlbe/frf/ABmHcM3syo997gTbX9+HXNz69/BEvpsqqe/7avXLH/r6q81sTq4q6o5QiYWc0Lt6eFe4PudKoKrZSrXWFM/HnXrdHon9+06qyAbw0ZQS/Lp9A/cgglv1luVq1ef0hGjUJ5dflE/j6x6d457WfycnOr3J5W6hqv3Kh/C9MHsHCpROIiAxiRWn+D95azINPXmfXqzdlqohZVR34+3gqA+ds5cE/Yni6c30AjqbnMmvnKb4Z1pqvbmjFgdRsSmqgAlW9/i9c/ou3F9C0TQOatm1gw1TWq3rXaf1xy79dvrqq/O4/L8zEvo14fe1RzBeoFu5OBmYNbcmkVYfJLiy57BmvNEppu/27UlkzOtZvSikj8BawA0sd/+xiC2itZwNnWx/aVPj7xYpX8OOcdSxaaOkX3rxlOIkJprJ5SYkmAoO8K5Q/eCCOuJMp3Hb9qwDk5xdx63WvsvD38QQFG/9x+ctp9+9r2Lt0AwDBUeFkp6aXzctONeHpW/FKjZu3JwU5eZhLSnAwGMhOMeHhZymTdPQkf7zzleUzZWUTu30fDgYHGnZpY7P8ZyXmFhBc7gbJYHdnUvIKLlh+Z3ImYZ5uGJ0dL3nZy+lyrn8X93N9mSM7tGDlrPnkZWbj5m27rnH7/1zNoeWW/AENI8hJOZc/J9WE+3n5Xbw8Kcw9lz83LR13v4pl7Olqz3+Wm5+RvLRz2fPT0nE7v+74+ZKXWrGMq9GH01t2EL8jmoTdMZiLiinOy2Pbx1/S8ZF77Jb/n5xOSCWszrluHHVD/IhPTMfZyZGw0HKv1/HnTFJ6Vb/islswdz2LF24GoFmLehX228mJGQQEXni/bTA4MODaNnz/1WpuuKkTSxZt5c57+1m6o4QHEFrXj9jjSbRoZbuG4c9z1/PbT5b8TVrUI+kS8/cf3Ia5X6/mups6cXDfKSY//z0AGaYcNq07gMHgQK/+LW2W/6yE7MIKVy/qeLqQlHvh/ffW+AzCfVzxdXUkPb+Y+fsTmL8/AYBnu0SSkG2fff/fC9exYrHlno4GzeqRmmQqm5eWZMI3oOr9yoIv/iLLlMP9rw23R8wL2vzrWnb8ZTnuCW0UTmayqWxeZkoGXv7WH7d4Bxj/1fLVFZ9dQKiXa9l0HU8Xks77+7cK9uLD65oD4OfmRL9If0u37aMpODooZg1tyc/7E/nziDwF4v8La0bHmlL640Kl1G+Aq9baZtf2ho/qyfBRli5I69bEsOCHdQwa0o69e07g6elWqctVz94t+GPV5LLpvp2fZ+Hv4wHo1a/FPy5/ObW5rjdtrusNwPFte9n9+xoa9+xAwqFYXNxdyw5wz1JKEdayEYc37KJJrw7sW7mZBp1bAXDPrEll5f6e8S2RHVvapQECsC8ti3peroR6uJCUV8ig8EDGbzhYoUyYpytxpWcXm/p64OSgMBUWk2XFsrZyOdd/Tnom7kYvy9ntQ7ForXH1sm3/7GbX9qHZtX0AOLVjL/v/XENkjw4kH47F2d2t0kG8Uoo6LRoTu2knDXp05MiqzYR3tN8oOue72vOfZWwQQXZCEjlJKbj5GYnbtL1SIyKkfSuOLV1N3W4dST8ai6O7G66+PrQYcRMtRtwEQPK+Qxz5fdkV1QABWLJ0Bw/dNYj5izfQuV0UmVm5JCSZSE7NJCoyhIh6gZxJSGP40G7c/cSHdsl028ge3FbaHWn9mv0smLOegUPaErPnJB5erpUO4rXWxJ1KpV54AFpr1q3aR0T9QACCQ4xs23yEth0akJaaxYkTydQNq9x3/nK6eWQPbi7Nv3HNfn6at54B17ZlX/RJPDxd8a8i/+lTqYSV5t+wZh/hkZb8835/sazc6xPm0q13c7s0QAD2JGVS38eNMC9XEnMKuCEqiP8u3V+hTIS3KycyLfv+FgGeODk4kJ5vufHf382J1LwiQj1dGNwggFt/2mmX3INu7cmgWy3HDTs27OPvhevofk07jsScwN3TFd+AygfhKxZvYs/mg7w04yG7dterSpehvegy1NKb49CWGDb/upaWfdoTd/AErh6ueF3CyZmmXVr+q+Wra3dCFpFGN+p5u5KQXcDQpsE88XtMhTI9P99U9vM7g5uy/Fgqfx+1NDjeGtSUI2k5fLaj4mhstdmVfK+GvVywEaKUuuUi89BaVz3kx2XUo1dzNqzZz63XvYqrqzMTpo4sm/ffh2czftIIAoMuvHFdbHlbq9+hBbHb9/H1w5NxdHFi4ON3lM37ZconXPPo7Xj6+dBzzI388c6XbPzhNwIjw2hxjf37AZ+vRMO0bUf5sG9LDEqx6FgixzJzuTXKMtrPwiMJDKgXwPWRQRSbNQUlZl5Yf+Ciy9rbv13/RzbuZM+f63AwOODo7MyQZ+62yyXts8LatSBuRwwLn5iEwdmJXo+cy//36x/T88Hbcfcz0nH0jaya/iU75v6Gf2Q9Gve35M81ZfLruGkU5eWjlGLf76u4+d3xOLvbZ7Saqzm/g8FA67tGsGHah2izmYg+3fAOC+X48jUARA7oTXDbliTujmHpM6/g6OxMu7F32jyXtb7+4HF6dWtGgK8XRzZ/yJR3F+BUOuz3Z98t488VOxncry0xa6eTm1fAg8/OAiwDfDw14St+/fYFDAYHvp63iv2H7D9MZvdeTdmwdj/Dr38DF1dnXpryn7J5Tz/yOS9MvA3/AC+mvDSXnOwC0JqoJqE895LlK+ueB69h6oR5jL7lHdCaR/97HUZf+93g3bVXUzat28/tQy35x006l/+5Rz/nuVduwy/Ai9cnzCUnx5K/YeNQnh5/wa9cuynRMHHtEb4e2goHpfjxQAKH03O5vUUdAH6IiefahoHc3CSYYrMmv9jME3/vK1v+48EtMLo6UmzWvLLmMJkF9huV7Kx23Zqxa+N+/vuf13BxdeLBF0eVzXvzmdk8MG4EfoE+fP72AgKCfXl57AwAOvVpxa33DsaUmsn4+94jLycf5aD4Y/4a3vr+edw9XC/0lpdVo07NObR1H+/fNwUnF2dueur2snnfvTyTYU+Owtvfh02LVrN+wXKy07P45NE3adSxOTf+d9RFl7elEq2ZsPIQ397aBoNSzNsbz6HUXO5oHWrJvufMBZftFOrDrc1D2J+czR93WAbDmLb+GCuPp11wGVE7qKrvmwCl1JcXWU5rre+18j0uqTvWlcbofB0f7/u7pmNU2yPNB9FhztqajlFt20f1uurX/xu7l9Z0jGoZ12bgVZsdLPmf37q8pmNU25udBuAWPuqfC16h8k7OIa3A/s8ZuVz8XIaRkHd15g9xG0aDj1fXdIxqO/ZIH3akLKnpGNXWPuB65h79s6ZjVNvIhtcS/u7Kmo5RbSef7ndVXGMYuXKN3W7WmNuv9xW5Ti54JURrfWX1IRBCCCGEEELUCtbcmH72YYUtgLLrkVrryRdeQgghhBBCCFEVhyt41Cp7uaIfViiEEEIIIYSofa7ohxUKIYQQQgghah9rumOd/7DCVP7hYYVCCCGEEEKIqskQvTZ6WKEQQgghhBBCXMgV97BCIYQQQggharOafUTmlcHa0bG6A/XPli99WOE3NswlhBBCCCGEqKX+sRGilPoWaAjsAkpKX9aANEKEEEIIIYS4RHJPiHVXQjoCzfWFHq0uhBBCCCGEEJfAmkbIXiAEiLdxFiGEEEIIIWo9eVjhRRohSqlfsXS78gL2KaW2AAVn52uth9k+nhBCCCGEEKK2udiVkLexPCH9TeCmcq+ffU0IIYQQQghxieSekIs0QrTWqwGUUk5nfz5LKeVm62BCCCGEEEKI2uli3bEeBh4BGiil9pSb5QWst3UwIYQQQgghaiN5TgioCw16pZTyAXyB14Fx5WZlaa3TLuE95M4bIYQQQghhD1dFR6ex61bZ7fh4ds++V+Q6uVh3rAwgAxj1b99k7LpV//ZX1JjZPfsycuWamo5RbXP79cZU+HtNx6g2o/N1/HfTipqOUW3Tu/a/avNP79qfCduX1XSMapvS4Rq+PPRXTceotnsaDyatYHFNx6g2P5dhuIX/66+PGpN3cg4d566t6RjVsm1kL/abfqvpGNXWzHgDd69Z/c8Fr1Bf9e7DgD+u3g4jy4f0ILvo6vzeAvB06l/TEawio2PJ1SAhhBBCCCGEnVnznBAhhBBCCCHEZSKjY8mVECGEEEIIIYSdSSNECCGEEEIIYVfSHUsIIYQQQgg7ku5YciVECCGEEEIIYWdWXQlRSkVT+XkfGcA2YKrWOvVyBxNCCCGEEKI2kqsA1nfH+gMoAX4onR5Z+n8m8BUw9PLGEkIIIYQQQtRW1jZCemite5SbjlZKrdda91BK3WGLYEIIIYQQQtRG8rBC668GeSqlupydUEp1BjxLJ4sveyohhBBCCCFErWXtlZD7gS+UUp6AwtIN636llAfwuq3CCSGEEEIIUdvI6FhWNkK01luBVkopH0BprU3lZs+3RTAhhBBCCCFE7WTt6FguwK1AfcBRKUvzTWs92WbJhBBCCCGEqIVkdCzru2MtwjIk73agwHZxKkqL3suROfPR2kydXj0Jv+7aCvNz4xM48MVXZJ88ReTNN1Lv2kFWL2trmTF7iZs/F20249+jFyHXDqkwPz8hnhNff0XeqZPUGXYTwYMGV5ivzWYOvj4VJ6ORho8+Yc/olvfXmnff+JkNa/fj6urEhKmjaNq8XqVyU1+ey/6YU6A19eoH8vLU23F3dyEzI5epL8/l9KkUnF2ceGnySBo2qmO3/Ml7Ytj//Xy0WRPWpwcNbzhv/WrN/u/nk7w7BoOzM60eGINP/XCy4xPY9fHnZeVyk1JodMsNRA4eYLfstSF//O4Ydn2zAG02E9mvB82GDaowX2vNzm9+JGGXJX/nh+7ENzKcksIiVk5+j5LiYnRJCWFd2tHythvsmv1svmWzF3J0+z6cXJy5/snRhERVrv+mhFQWvfUV+Vm5BDcMY+jTd2JwcmTzT8uJWbUNAHOJmdS4BJ747jXcvDzskv29NxexYe0By7Y7ZQRNmodVKvfqK/M5EBOH1prwiEBemjoCd3cXsrPymPjCHBITTJSUmLn9rj7ccFMnm+c+a+ZbDzJkQDuSUzPpOPC5Ksu8M+kuBvdrS25eIWOf+YRde2MBGNinDW9PHIPB4MBXc1fy9seL7Zb7rG4hvjzbvgEOSvHLsQS+3h9XYf61EYHc1cxSl3KLS3hj2xEOm3IAGNk4lJsbhICCX44mMOfQGbvn11rz2bu/sH3DflxcnXliwkgaNq1cf959+TuO7I/D0dFAo+b1ePiF4Tg6GgCI3n6Ez99bRElxCd5GD16d+ajd8qfv3Uvs3Hlos5ngXj2pO6Tid29efDxHvvqanJMnCb/pJkIHW/ZNBWlpHPniC4oyMkEpgnv3ps419t1vdgow8mizBjgo+D0ukbnHTleYPyA0kJGRdS2fo6SE6TFHOZaVC8CzraLoGuiLqbCI+9ftsmvus7TWvPX6fNavjcHV1ZmJr46hWfPwC5af9to8Fv+8kXVbp1d4PSY6lrtHT+P1t+/nmkHtbZxa1CRrGyFhWmu7HsVrs5nD38+h9TP/xcXXlx1TXse/bWs8QkPLyjh6uBN1+0hSd+665GVtnf3UnB+IevIpnHx9Ofj6q/i0boNbufc3uHsQNmIkGbt2Vfk7klcswzWkDiX5eXbJfL4Na/dz6kQyC5a8yN49J5g2dQFf/PBUpXL/fe4mPD1dAZg+7Rd+/GEtd91/DV99tozGTUOZ9v69xB5L5K3XFvLRZ4/YJbs2m4n5Zi6dn3sCVz9fNkx8g6B2rfGqe64RlLwnhpyEJHpPm4Tp6HFivp5D91eex7NOCD2njC/7PSv++wIhHdraJXdtyW82m9nx5Xz6vPA4bv5Glr00jdD2rfAJO5c/YVcM2QnJDHl3ImlHYtn+xVyumfIcDk6O9HnpCZxcXTEXl7Bi0jvUadMC/0aRdv0Mx7bvI/1MMg/OmsCZg7H89cl87nrnmUrlVn21iE439qV57w78+dE8di/dSPvretHllgF0ucVyAHN4SzRbF62ySwMEYOO6A5w6kcKPvz1PzJ6TTJv6E5//UPlExn//NwyP0m33/bcWs2DOesbc158FczcQ2TCYtz+8l/S0bEYMm8bg69vh5GTt18W/8+2Pq5n59V989l7V+4vB/drSsH4ILXs/Red2Ucx49T563zgBBwfF9Kn3cP3o1zgdn8q6X1/lt6XbOXD4dJW/xxYcFDzfsSGPrtxLYl4B3wxsy5rTaRzPzC0rcyY7n7HL95BVVEz3Or6M7xTF3Ut309DHnZsbhDBm6S6KzWZm9GnJujNpnMrOt1t+gO0bDhB/KoVPFrzAob0nmTltIW998WSlcr0Hd+CpSaMBeHfCdyxdtJkht3YnOyuPWdN+4pX3HyAwxBdTWpbdsmuzmeM//EDzp57C2deX6Fdfw7dNG9wrHDd4EDlyJGm7dlZYVjk4EDF8OJ4REZTk57NnylR8mjersKwtOQBPtGjAc1tiSM4v5OPubdiYlMaJ7HPHAPG5+Ty1OZrs4hI6Bxh5umUUj23cA8BfcUksOhHP860b2SVvVdavjeHUySR++X0Se/cc5/Upc/hmzvNVlt239wRZ5baLs0pKzMx472e69Whu67g1Tu4Jsf5q0AalVCubJjlP5rHjuAUF4RYYiIOjI0GdO5K6c3eFMs7e3nhH1kcZDJe8rC3lxh7HJSgQl9L39+3UiYw9uyqUcfL2xqN+ZKXsAIXpaWRER+Pfo6edEle2ZuVehgzrhFKKVm3qk5WVR0pyRqVyZxsgWmsKCoo421Xv+NEEOnZpDED9BsHEn04jNcU+X0amY7F4BAfiHmRZ/3W6dCRpR8W/f9KO3dTt0RWlFL5RDSjOzSXfVPHzpcQcwD0wALcAf7vkPutqz592JBbP4EA8gwMwODoS3q0DZ7bvqVDm9PY91O/VBaUU/o0iKcrNIy89A6UUTq6WOmUuKcFcYrYMhWFnhzdF07J/Z5RS1G0aSUFOHtlpFdev1poTew7TtEdbAFoN6MzhTdGVftf+1Tto3ruDPWIDsGZlDEOGdkApRcs2EWRn5ZOSnFmpnEf5bTf/3LarlCI3pwCtNXm5hXj7uGMw2K/jwPotB0gzZV9w/g2DOvDDwrUAbNl5BB9vd0KCjHRqG8XR2ARiTyZRVFTCj79u5IZBHe0VG4AWfl6cysrndE4+xWbN3yeT6VPXr0KZPalZZBVZBpWMTskiyM0FgPre7kSnZlFQYqZEw47kDPqFBdg1P8CWNXvpO8RSf5q0iiAnK4+0lMr1p2OPZiilUErRqEU4qUkmANb8tYNu/VoRGOILgNHPy27Zs48fxzUwCNfS796ATp1I31Vx3+nk7Y1nFccNzkYjnhERABhcXXGrU4dCk8le0Wlq9OJ0Tj7xeQUUa83K+GS6B1WsO/tMWWQXl5T9HOjqXDYvOj2TzKKaHax09crdXD+sa+lxQwOys3JJruK4oaTEzPR3fuKJZ26pNG/eDysZMLAdvnasN6LmWPvN0hPYrpQ6qJTao5SKVkrt+cel/oVCkwkXP9+yaRdfXwqs3CH8m2Uvh8J0E86+53YezkZfitKtf//T8+dR95bbQNVcj8HkpAyCQ4xl00HBRpKTKu9MACa/NIchfV8m9ngS/7m9FwCNmtRl1TJLFYmJPkFCfDpJiSZbxwYgP92Ea7m/v6ufL/nnrf/8dBOu/hXLFJxXJn7zNkK72q8byllXe/68dBPu5bK5+RnJSzOdVyYDNz9jxTKl+c1mM3+/8BqLH3qe4FZN8Y+y71UQgKzUDLwCzuXz8jeSlVqx/udl5uDi6YZD6cFMVWWK8gs5tmM/Tbq3sXnms5KTMitsu4HBPhfcdqdOmMf1/SZzIjaZ4aMsj4K6bVR3Yo8nMXTAFO649R2eev5GHByunN7LoSF+xMWnlk2fTkgjNMSP0BBf4s6Uez0+lbrBvlX9CpsJcnMhMfdcj+WkvMKyRkZVbmwQzIb4dACOZuTQLtAbH2dHXAwO9KjjR7D7hZe1lbTkDAKCjWXT/kE+pFVxIHlWcXEJq/7YTruuTQE4czKZ7Mxcxj/8MU+PeY+Vv2+zdeQylu/+ct+9vkYKTOmX/HvyU1LIOXUSz0j77XsCXJ1Jzi8sm07OLyTA9cJ//yH1gtmSbLJDMuslJZoIDjm3zQUF+5Jcxff+vB9W0adfawIDfSotv3L5bm79T29bR70iKKXt9u9KZe03yxCgETAIy9PRb+AiT0lXSo1VSm1TSm2bPXt29ZL9m3VW4+u7igBWns3N2LMbRy9v3EvPyNQUrataiVV/iJenjmLJiklENghm6Z+WS9xj7htAZmYed9z2FvN/WEvjpnUxONrpQKaK7GfP8lrLXFxM0s49hHSugf6oV33+Kl47P/9F6peDgwODXn+RGz58lbSjsWScsn+/+Ko+ROU/wT+XObJ1L3WbRdqtKxZwSfXnpSkj+HX5BOpHBrHsL8sZ483rD9GoSSi/Lp/A1z8+xTuv/UyOnbsEXYyqYj+kta7yM1ZZzWypitV8oQgdgny4sUEIH+w+DkBsZh7fHIjjo76t+KBPSw6bciix+we49HU2a9pCmrdtQIt2DQDLPVBHD8Qx4d37mDjjAeZ/vpTTJ5NtkLQKVdX9S7yUWpKfz6FPZlJ/xAgc3dwuV7Jqqfp7GNr6+TAkLJhPD8baN9A/qCru+ZtlcpKJZX/vYMTtfSuVffvNH3niqZvseuVV1Cxrh+g9AaCUCgJcrSg/Gzjb+tDb1q265GDOvkYK0s6dwShIT8fFaLT5speDs68vhelpZdOFpnScrHz/nKNHydizi8y90ZiLiyjJyyf2i8+of+/9Nkp7zo9z1rFo4UYAmrcMJzHBVDYvKdFEYJD3BZc1GBy4ZnBbvvtqJUNv7oKnpysvTx0FWHakN187hdC69ukW5OrnS365v39+WjouxopnXFx9jeSnnlfG11g2nbwnBu+IcFx8LvyZbeVqz+/mZyS3XLa8NBNuvj6VypS/OlJVGWcPd4KaNSJ+9z586tm+X/b2JWvY/Zel/tdpFE5Wyrl8WakmPP3O+wzenhRk52EuKcHBYKiyzL419umKtWDuehYv3AxAsxb1Kmy7yYkZBARefNsdcG0bvv9qNTfc1Ikli7Zy5739UEpRLzyA0Lp+xB5PokWrC99gak+nE1IJq3NuX1I3xI/4xHScnRwJCy33eh1/ziRd+lnwfyMpt6DC1YsgN2eS8yqP5RLl486Ezo14YnUMGYXnutAsOpbIomOJADzSOoKk3MJKy9rC7z+u4+9FlvrTqHk9UsqdvU5NysDvvDPWZ8397C8y0rMZ9+bdZa/5B/ngZfTA1c0FVzcXmrdrQOzhM9QND7TlRwAs370FaeW+e9NNOF/Cd7+5uJiDn8wkoEsX/Nvb9wROSn5hhe5Vga7OpBZU/vs38HLnmVYNeWHrvhrvfgUwf84qfl6wHoDmLSNITDi3zSUlphMQZKxQ/sD+U8SdTOam614GID+/kBuHvMyiPyazP+YEL/zPMrCKKT2H9Wv3YjA40G9AW7t8FnuTe0KsvBKilBqmlDoMHAdWA7HAHzbMhXdkffISk8hLTrGc1d2yDf+21nVp+DfLXg7uEfUpSEqiICUZc3Ex6Vu34tPauvcPvfkWWr7xFi1ee4P6943Fq2kTuzRAAIaP6sl3C/7Hdwv+R+/+Lflj8Va01kTvjsXT042A876ItNacKj3DpbVm7eoYIiKDAMjKzKOodAe5aOEm2nZoWHb/iK35REaQk5hEbunfP37zNoLata5QJqhda06v34TWmvQjx3B0c8O13IF+/KathHa1b3/ys672/H4NI8hOSCI7KYWS4mJObtxOaIeKt5SFdmhN7NrNaK1JPXwcJzc33Hx9yM/MojDHcrNicWEhiXsP4h0abJfcHa7vzb0znufeGc/TqGtr9q7Ygtaa0weO4+LuWqmBoZQivHUjDqzfBUD08i006nLuc+bn5HFq7xEadbX97XS3jezBNz8+zTc/Pm3Zdn/djtaavbtP4OHlWqkRYtl2U8p+XrdqHxH1LQeJwSFGtm0+AkBaahYnTiRTN8y+9xVdzJKlO7j9Vku3z87tosjMyiUhycS23UeJigwhol4gTk4Ghg/txpKl2+2abV9aFvW8XAn1cMHRQTEoPJA1p9MqlAl2d+Gtns15eeNBTmZVHHjE18WprEz/sAD+OmGfKwjXDe/J9O+eYfp3z9Cld0tW/WGpPwejT+Dh6YpfQOVG7NJFm9i56SDPTLmzQne9zr1bsm/XMUqKSyjIL+RwzEnC6gfZ5XN41q9PflIS+aX7zpStW/FtY913r9aao19/g1udOoQOGmjjpJUdyMiirocbIW4uOCpFvzqBbEiqWHeCXJ2Z2K4pr+8+TFzulXF18j+j+jJn4XjmLBxP3/5tWLJ4U+lxwzE8Pd0qdbnq1acVf69+k9/+fpXf/n4VV1dnFv1hedrDr39NLXt9wKB2jHtpVK1tgAgLa4c7mQJ0BZZprdsppfoBo2wXC5TBQNTokUS/9z7abCakZw886oZyZtVqAEL79qEwI4PtU16jJC8flCJu2XI6TZmIo5tblcvaizIYCBtxO0dnTEebNf7de+AWWpeUNasACOjdl6KMDA6+PpWS/HyUUiSvWEazVyZjqOHLv2f16NWcDWv2c+t1lp3EhKkjy+b99+HZjJ80Av8ALyaP/4Gc7AI0mkaNQ3luwnAAYo8lMnH89xgcHIhsGMz4SSMv9FaXnYPBQPM7R7L1rQ/QZjNhvbvjFRbKyRVrAAjv35vANi1J3rOX1f97GYOLM63vH1O2fElBISl7D9Di7tF2y1zb8re/+z+seeMjyxC9fbvhExbKkWWWm4mjrulFnbYtiN8Vw+9PTcTRxZlOD94BQL4pky2ffIM2m9FaU69re0Lb23VMDAAadmzOsW0xzBo7GScXZ6578ty6nD9xJkMeH4WXvw/97h7Gomlfsea7JQQ3CKP1oK5l5Q5t3ENku6Y4X6Rfty1079WUDWv3M/z6N3BxdealKf8pm/f0I5/zwsTb8A/wYspLc8nJLgCtiWoSynMvWW4SvefBa5g6YR6jb3kHtObR/16H0dd+3cm+/uBxenVrRoCvF0c2f8iUdxeUjcz12XfL+HPFTgb3a0vM2unk5hXw4LOzAMvNrk9N+Ipfv30Bg8GBr+etYv+huIu91WVXouGt7Uf5oE9LDA6KxccSOZaZy60NQwBYeDSBB1qE4+PiyPMdo0qX0Yz5excA03o2w8fZiWKzmTe3Hy27gd2eOvRoxvYN+3no1tdxcXXiiQnn9t2T//spj43/D36BPnzy5kICQ3x5/v4ZAHTr24oR9w+iXmQw7bs25cnR7+DgoLhmWBciGtpneHZlMBB5+yj2T5+O1maCevTAvW4oCaXHDSGlxw3RU1+lJN9y3BC/bBltJk8iNy6OlE2bcK9bl92TLAfF4bfcjG8r++x/zBo+2HeMNzu1wEHBH3FJnMjO44Z6lrrz26kE7owKx9vZiSdbWLq+lWh4ZIOlG+X4No1p4+eDj7Mjc/t15OvDJ/kjLsku2c/q2bsl69fu5cYhL+Pq5szEKee+l554+EMmTLqDwPOujIj/39SF+hxWKKTUNq11R6XUbqCd1tqslNqite5sxXvosdXojnWlmN2zLyNXrqnpGNU2t19vTIW/13SMajM6X8d/N62o6RjVNr1r/6s2//Su/ZmwfVlNx6i2KR2u4ctDf9V0jGq7p/Fg0grs/5yLy8XPZRhu4TY9V2VTeSfn0HHu2pqOUS3bRvZiv+m3mo5Rbc2MN3D3mtU1HaPavurdhwF/rK/pGNW2fEgPsouuzu8tAE+n/ldFR6fx25bb7aavVzsOuCLXibVXQkxKKU9gDfC9UioJqPnOiEIIIYQQQoirjrWNkBuBfOApYDTgA0y2VSghhBBCCCFqK4creOhce7F2HLQIrXWJ1rpYa/211noGYP+O2kIIIYQQQoirnrWNkPlKqeeVhZtS6gPgdVsGE0IIIYQQojZyUPb7d6WythHSBagHbAC2AmeAHrYKJYQQQgghhKi9rL0npAjIA9ywPKzwuNbabLNUQgghhBBC1FJX8hUKe7H2SshWLI2QjkBPYJRSaoHNUgkhhBBCCCFqLWsbIQ8Ah4EXtdYJwOPALluFEkIIIYQQorYy2PHfP1FKXauUOqiUOqKUGlfFfKWUmlE6f49Sqn21P3g51jZC7sHyxPSzT57KwjJsrxBCCCGEEOIqpJQyAB8BQ4DmWHo7NT+v2BCgUem/scAnl+O9rb0npIvWur1SaieA1jpdKeV0OQIIIYQQQgjx/8kV9JyQzsARrfUxAKXUXCwXGvaVK3Mj8I3WWgOblFJGpVQdrXX8v3lja6+EFJW2lHRpwMCzPwshhBBCCCGuTEqpsUqpbeX+jS03uy5wqtx0XOlrXGKZS2btlZAZwM9AkFLqVeA24KV/++ZCCCGEEEL8f2PP0bG01rOB2ReYXVWS8y80WFPmklnVCNFaf6+U2g4MKA1yk9Z6/799cyGEEEIIIUSNicPyLMCzwrA8D/BSy1wya6+EoLU+ABz4t28ohBBCCCHE/2dX0HNCtgKNlFKRwGlgJHD7eWUWA4+V3i/SBcj4t/eDwCU0QoQQQgghhBC1h9a6WCn1GPAXlhF9v9BaxyilHiqdPxP4HbgOOALkYhk1919TlhvdbUpuYBdCCCGEEPZw5VxjuIhpe5ba7fj4udYDr8h1YpcrIQP+WG+Pt7GJ5UN6MHbdqpqOUW2ze/Zl2enfazpGtV1T9zoG/7WupmNU21+De/Ls5hU1HaNa3u7Sn6k7l9V0jGp7qd01zD7wV03HqLaxTQeTkLe4pmNUW4jbMDrOXVvTMapt28heuIWP+ueCV6C8k3OAQzUd419oTN8lV+9xw6rre1z1xz1ZRctrOka1eTkNqOkIVjFckc0C+7JqiF6llF8Vr0Ve/jhCCCGEEEKI2s7a54T8qpTyPjtR+iTFX20TSQghhBBCiNrLQdnv35XK2kbIa1gaIp5KqQ7Aj8AdtoslhBBCCCGEqK2sfU7IEqWUE/A34IXlOSGHbZpMCCGEEEKIWshBybhNF22EKKU+oOLoVt7AMeBxpRRa6ydsGU4IIYQQQghR+/zTlZBt501vt1UQIYQQQggh/j+4ku/VsJeLNkK01l/bK4gQQgghhBDi/wer7glRSjUCXgeaA65nX9daN7BRLiGEEEIIIWolQ00HuAJYOzrWl8AnQDHQD/gG+NZWoYQQQgghhBC1l7WNEDet9XJAaa1PaK0nAv1tF0sIIYQQQojaSZ4TYmV3LCBfKeUAHFZKPQacBoJsF0sIIYQQQghRW1nbCPkv4A48AUzB0iXrLhtlEkIIIYQQotaS54RY/7DCrQBKKa21vse2kYQQQgghhBC1mbWjY3UDPgc8gXClVBvgQa31I7YM1ynAyKPNGuCg4Pe4ROYeO11h/oDQQEZG1gUgr6SE6TFHOZaVC8CzraLoGuiLqbCI+9ftsmXMKqVF7+XInPlobaZOr56EX3dthfm58Qkc+OIrsk+eIvLmG6l37SCrl7UHrTU/fvgzMZv34+zqxJ3PjSK8cb1K5Vb9vJaVC9eQciaFN3+egqePJwCHdh1h1oTP8Q/xA6Btr9ZcN2aw3fJ3DDDyUNMGGJTij7hE5h+PqzC/nocbT7dsRJS3J18fPsGC2HN166bwUIaEBaMU/BGXyM8nztgt91lJe2LY+918tFkT3qcHjYZWXHdaa2K+m0/i7hgMLs60fWAMxvrhACx7ejyOrq4oBweUgwO9J79g9/ynd8Ww7esFaLOZqP49aHnjoArztdZs/fpHzuy05O/+8J34R1ryb5j5LXE79uLq7cWwt1+ye/az+VZ+upDj2/fh6OLMtU+OJrhh5fqfkZjKb299RX52LkENwrjuqTsxOFl2q6eiD7Py858wF5fg5u3BiNeetFv2GdMWsXndAVxcnXhh8ggaNwurVO7NifM5uC8OrTX1IgIZN3kE7u4uZfP37z3FI2M+4JU376DvwNZ2yQ7QLcSXZ9s3wEEpfjmWwNf7K26710YEclczy98it7iEN7Yd4bApB4CRjUO5uUEIKPjlaAJzDtl/25351oMMGdCO5NRMOg58rsoy70y6i8H92pKbV8jYZz5h195YAAb2acPbE8dgMDjw1dyVvP3xYjsmt9Ba8+qrs1m9ejuuri688caTtGgRVancuHHvsWXLXry8PAB4443/0qxZA7Kycvjf/97hzJlkSkpKuPfeW7j11mvslr9zoJHHmjfAoGDJqUR+OFrxuOGa0EBGNTx33PBe9FGOZuUS6OrMi20b4+fihFnDbycTWBgbb7fccHUf84Cl7rz9+o+sXxuDq6sTE18dQ9Pm4RcsP+21efz68ybWbn0PgFUrdjPzg19xcHDAYHDgmXG30bZ95bpXWxiu4Hs17MXa7ljTgcHAYgCt9W6lVG9bhQLLHfNPtGjAc1tiSM4v5OPubdiYlMaJ7LyyMvG5+Ty1OZrs4hI6Bxh5umUUj23cA8BfcUksOhHP860b2TJmlbTZzOHv59D6mf/i4uvLjimv49+2NR6hoWVlHD3cibp9JKk7d13ysvYQs3k/yaeTmfjti8TuP8Hc6Qt47uOnKpVr2DKSVt1aMP2pDyvNi2rVgIdfe8AecStwAB5t1pAXtu0lJb+QD7q1ZVNSKidzztWdzKJiPtl/jO5B/hWWjfB0Z0hYME9s2k2RNvNah5ZsTk7jTG6+3fJrs5nob+bS9bkncPPzZe0rbxDSvjVedeuUlUnaE0N2YhL935qE6ehxor+aQ6+Jz5fN7/bCU7h4edotc3lms5ktX8znmvGP4+5v5I8XpxHWoRXGsHP5z+yKISs+mRunTyTlSCybP5vLda9aDtga9ulKk8F9WP/RNzWSH+D49n2kxydz78wJxB+KZdkn8xn99jOVyq35ehEdhvWlae8OLP14HtHLNtJ2SC/ys3NZNnM+t058GO9AP3JNWXbLvnndAeJOpvD94ufZF32Sd1/9iZnfPVGp3GPPDsPD0zLi+odvL+bnuesZfa9lvJGSEjOz3l9Cp25N7JYbLDdQPt+xIY+u3EtiXgHfDGzLmtNpHM/MLStzJjufscv3kFVUTPc6vozvFMXdS3fT0MedmxuEMGbpLorNZmb0acm6M2mcyrbftgvw7Y+rmfn1X3z2XtXn6Ab3a0vD+iG07P0UndtFMePV++h94wQcHBTTp97D9aNf43R8Kut+fZXflm7nwOHTVf4eW1mzZjuxsWf4++9Z7N59kIkTP+HHH9+psuxzz93Ltdf2qPDa998voWHDcGbOfJm0tAyuvfYhhg7tg7Ozk82zOwBPtmjAs5stxw0ze7ZhfeJ5xw15+Ty5sfS4IdDIM62ieGTDHkq05uN9xzmcmYObwcDsnm3YlmKqsKyts1+txzxnrV8bw6mTSfz8+0T27onl9Slz+XpO1Q3xfXtPkJVZcd127tqEPv1ao5Ti8ME4xj37OQt/fcUe0UUNsXZ0LLTWp857qeQyZ6mgqdGL0zn5xOcVUKw1K+OT6R7kV6HMPlMW2cUlZT8HujqXzYtOzySzqNiWES8o89hx3IKCcAsMxMHRkaDOHUndubtCGWdvb7wj66MMhkte1h72bNhLl4GdUEoR2bw+edl5ZKRmVCpXr1FY2dWOK0UTHy/O5OaTUFp3VsUn0+28xkZGYRGHMrMp1hX7ZIZ7uLE/I4sCsxmzhj1pGfQ4b1lbSz8ai0dQIB5BljoQ2rUjCTsq1oGEHbup16MrSil8oxpQlJtLvqny36cmpB6JxSskEK/gAAyOjkR078CpbXsqlDm1bQ8NendBKUVgo0iKcvPITbfkD27WCBcPj5qIXubolmia9+uMUorQJpEU5OSRnVZx/WqtObnnMI17tAWgRf/OHNkUDcCBNdtp1K0N3oGWbcPd6GW37OtWxTD4hg4opWjROoLsrHxSkzMrlTvbANFaU1BQhFLnTsv9NGc9fQa0wtfPvn+HFn5enMrK53ROPsVmzd8nk+lTt+L+ZU9qFlml+/bolCyC3CxXb+p7uxOdmkVBiZkSDTuSM+gXFmDX/ADrtxwgzZR9wfk3DOrADwvXArBl5xF8vN0JCTLSqW0UR2MTiD2ZRFFRCT/+upEbBnW0V+wyy5dv4qab+qOUom3bpmRm5pCUlGb18kopcnJy0VqTk5OHj48Xjo72eSJCU6MXp3PPHTesOJNMj+CK9ScmvdxxQ3oWgW6W44a0giIOZ1quqOWVlHAiO5eAcscUdsl+lR7znLV65R6uG2bZr7dqE0lWVi4pyZW/l0pKzLz/zk88+czNFV53d3ct2w/l5RUiFwpqP2sbIaeUUt0BrZRyVko9C+y3YS4CXJ1Jzi8sm07OLyTA1eWC5YfUC2ZLssmWkaxWaDLh4udbNu3i60uByWTzZS+njJQMjEHGsmljoBFTyqUd5B7fF8tr97/FR+Nmcea4/S5r+7s6k5xfUDadkl9g9ZdJbHYurXx98HJyxMXBgU6BvgRepN7ZQn66CTf/c3XA1c+X/HRTxTJpJlzL1RM3P1/y086WUWyaNoM1L7/GiZVrbR/4PLlpJjzK5ffwM5JXlu1smQw8/I1l0+5VlKlJ2akZeAUYy6a9Aoxkn9cIz8vKwdXDDYfSEwme/sayhkr6mSTys3OZN34G3z49jZgVW+yWPSUpk6CQc9kDg31ITqp623395XncPGAyJ48nc8tIyxnt5MQM1q7cy7Dh3ewRt4IgNxcSc89tu0l5hWWNjKrc2CCYDfHpABzNyKFdoDc+zo64GBzoUcePYHf7brvWCA3xIy4+tWz6dEIaoSF+hIb4Enem3OvxqdQN9q3qV9hUYmIqISHnGm8hIf4kJqZWWfa9975l6NDHee21TyksLAJg9OjrOXo0jl697mLYsMcZP/4BHBysPt/5rwS6OpOcV/G44WL77+vDg9mSZKr0eoibC418PNl/kcbk5XY1H/OclZxoIiTkXJ0NDvYlKdFUqdz8H1bRu19rAgJ9Ks1buWwXtw6dxH8f+ZiXp9xpy7g1Tobotb471kPA+0BdLMPz/gU8eqHCSqmxwFiAWbNmQb0W/zKmhdZVjyTQ1s+HIWHB/Lf0LGSN+zcDHlwhgyVUta7Lnyn9J/UahTF5zsu4urmwd9M+Zr/8BRO/HX85I15QVSkvUHUqOZWTx/zjcbzesSX5xSUcz8qhxNqFL5uq3s+KdV9apOeEZ3H1NVKQmcmmN2fgWScE/6Y1d4neku38/FV8xkuoX7ZWdf2vVOiCZcwlZpKOnmL4lMcoKixiznPvUadJffzq2n5k80vZdl+YPMJyVvKNX1jx126uu6kTH7y1mAefvA6DwT4HjhVUEfNCW1+HIB9ubBDC/cstVwljM/P45kAcH/VtRW5xCYdNNbHt/jNVxYfUWlf5N6qJ+FW9Z1XZnn76LgIDfSkqKmbChA+ZPXsBjz02inXrdtKsWSTffPMqJ0/Gc889E+jYsQWenu52SF+ZvkANauvvw3X1gnl8Q8XjBjeDA5M6NOXDfcfILbZph49/dNUc85SyZt+TnGRi2d87mfXlf6v8Hf2uaUu/a9qyY9thZn74Kx9/Zp976UTNsHZ0rBRgtLW/VGs9G5h9dnLeH+svOVhKfmGFS42Brs6kFhRWKtfAy51nWjXkha37avxS5FnOvkYK0tLLpgvS03ExGm2+7L+1+pd1rF+yEYCIJuGYyp0hMiWb8PH3tvp3uXm4lv3csmtz5r2/gOyM7LIb120p5byzXwGuLlXWnQv563Qif51OBOCeRhEVrqrYg6uvL3mp5+pAflo6rr4Vzxi5+hnJL1dP8tLScfU1li5v+d/F25uQDm0xHYu1ayPE3c9ITrn8OWkm3M7LbyljKpvOraKMve1csobopZb6HxIVTlaKqWxeVooJD7+K+dy8PcnPycNcUoKDwUB2qgmP0s/g6W/EzdsDJ1cXnFxdCGvRkOTY0zZrhPw8dz2//bQZgCYt6pGUcC57cmIGAYEX3nYNBgf6D27D3K9Xc91NnTi47xSTn/8egAxTDpvWHcBgcKBX/5Y2yV5eUm5BhasXQW7OJOdV3v6ifNyZ0LkRT6yOIaPw3H5/0bFEFh2zbLuPtI4gKdf67d5eTiekElbnXBfPuiF+xCem4+zkSFhoudfr+HMmKb2qX3HZff/9EubP/wuAVq0akZCQUjYvISGVoKDKXW7Pvubs7MQtt1zDF1/8BMBPPy1j7NjbUEoRERFKWFgIx47F0bp1Y5t/juT8wrLuVWA5bkjJr/q44X+tGvL8eccNBqWY1KEpy04nszbB+i5ol8PVeswzf85qfllgOcZr3jKChIRzdTYxMZ3AoIr7zYP7TxF3Mpmbr7Pc65GfX8hNQ17hlz8mVSjXvmMj4k6lYErPxuhbM/c32tqVfIXCXqw61aWUaqCU+lUplayUSlJKLVJKNbBlsAMZWdT1cCPEzQVHpehXJ5AN5/VLDXJ1ZmK7pry++zBxdrxx+J94R9YnLzGJvOQUzMXFJG3Zhn/bNjZf9t/qc1NPXvz0f7z46f9o07Mlm5duRWvN8X2xuHm44eNv/UFiRlpm2VmR2P0n0Frj4W2f/uUHM7Oo6+5GcGnd6VsnkE2X0KfZp/QGykBXF3oE+bMqPtlWUatkbBBBTmISuaV14MymbYS0qzg6UUi71pxavwmtNelHjuHk7oar0YfiggKK8yzbQnFBAcl79+MVZt9BDfwbRpCVkERWUgolxcWc2LCdeh1aVSgT1qE1x9ZsRmtN8uHjOLm74V7DjZB21/dmzPTnGTP9eaK6tmbfyi1orTlz8DguHq54ntcIUUoR3qoRh9bvAiBmxRaiulg+Z1SXVpzedwxzSQlFBYXEHzqBf1iwzbLfPLIHn89/ms/nP02vfi3567ftlhHU9pzAw9MV//MaIVpr4k6mlP28Yc0+wiMDAZj3+4vM+8Pyr881rXjqxVvs0gAB2JeWRT0vV0I9XHB0UAwKD2TN6YrbbrC7C2/1bM7LGw9yMqvija2+Lk5lZfqHBfDXCftuu9ZYsnQHt9/aC4DO7aLIzMolIcnEtt1HiYoMIaJeIE5OBoYP7caSpdvtkmn06OtZtGgGixbN4JpruvLLLyvQWrNr1wG8vNyrbIScvU9Ea82yZZto1CgCgDp1Atm40XJ1KiUlnePH4wizYd0v72BGFmHljhv6hwayIbHyccOUDk15bfdh4nIqHjc81zqKk9l5/Hjc/qOqXa3HPP8Z1YcfFr7IDwtfpG//1vy+2LJfj959HE9Pt0pdrnr2acVfq9/g17+n8uvfU3F1dS5rgJw6mVR23HBg30mKiorxMdbs/YHCtqztjvUD8BFw9i6ikcAcoIstQgGYNXyw7xhvdmqBg4I/4pI4kZ3HDfVCAPjtVAJ3RoXj7ezEky0s7aESDY9ssOz8xrdpTBs/H3ycHZnbryNfHz7JH3FJtopbgTIYiBo9kuj33kebzYT07IFH3VDOrFoNQGjfPhRmZLB9ymuU5OWDUsQtW06nKRNxdHOrcll7a9GlOTGb9zPxjldxdnXmjudGls37aNxsRj87AmOADyt/WsOyuSvITMvitfvfokWXZox+diQ7V+9m7eL1GAwGnFycuPelMZfUnevfMGv4aP9RXuvQEgcFf59O5EROLteHWerOkrgEfJ2d+KBbW9wdDWgNN0WEMnbdDnJLSni5bVO8nJwo0ZoP9x8tuxHQXhwMBlqOGcmmaR+gtZl6vbvjFRZK7Io1ANTv35ugNi1J2r2XFf97GYOzM23vHwNAQUYm296fZVkPZjN1u3UiqPXl6Q55Kfk73/Mflr/2kWWI3n7dMNYL5dBSy/0pjQf2om67FpzeFcMvT07E0cWZ7g/dUbb82hlfkLjvMPlZ2Sx8ZDytb7ueRv272/UzRHZozrFtMXz+0GScXJwZ/Pi5C8E/TZ7JoEdH4envQ6+7hrHk7a9Y//0SghqE0XJgVwD864VQv10zvn7iDZSDA60GdiUgwj7bcddeTdm0bj+3D30DF1dnxk36T9m85x79nOdeuQ2/AC9enzCXnJwC0JqGjUN5evwtdsl3MSUa3tp+lA/6tMTgoFh8LJFjmbnc2tCy7S48msADLcLxcXHk+Y5Rpctoxvy9C4BpPZvh4+xEsdnMm9uPlt3Abk9ff/A4vbo1I8DXiyObP2TKuwtwKh22+bPvlvHnip0M7teWmLXTyc0r4MFnLdtrSYmZpyZ8xa/fvoDB4MDX81ax/1Dcxd7KJvr06cjq1dsYOHAsbm4uvFZuaOkHHpjI1KmPExzsz7PPvkN6egZaa5o2bcCkSZbRwB55ZAQvvDCdoUMfQ2vNs8/ejZ+ffU4wlGh4f+8x3up87rghNjuPYeGW+rP4ZAJ3NbIcNzxV7rjhwfW7aeXrxeCwII5m5vBZT8uJv08PnmRzsn2uRl3Nxzxn9ejdkvVrY7hpyCu4ujnzSrl7Op54+CMmTBpNYLl7Tc+3fOkufl+8GUdHAy6uTrz+9n12O26oCXIlBNSF+hxWKKTUZq11l/Ne26S17mrFe+gB1eiOdaVYPqQHY9etqukY1Ta7Z1+Wnf69pmNU2zV1r2PwX+tqOka1/TW4J89uXlHTMarl7S79mbpzWU3HqLaX2l3D7AN/1XSMahvbdDAJefZ/TsTlEuI2jI5z7T8wwuWybWQv3MJH1XSMask7OQc4VNMx/oXG9F1y9R43rLq+B1f7cU9W0fKajlFtXk4DrorD+2+P/GW3u77ujBp8Ra4Ta6+ErFRKjQPmYrlPcASwRCnlB6C1tm/nSSGEEEIIIa5SBnXlDZxhb9Y2QkaU/v/gea/fi6VRYtP7Q4QQQgghhBC1h7WjY0XaOogQQgghhBD/H9TAIOhXnIs2QpRSF71TUWv90+WNI4QQQgghhKjt/ulKyNDS/4OA7sDZO2z7AasAaYQIIYQQQghxCWR0rH9ohGit7wFQSv0GNNdax5dO18EyZK8QQgghhBBCXBJrb0yvf7YBUioRsP3jT4UQQgghhKhl5EqI9Y2QVUqpv7A8oFBjeVjhSpulEkIIIYQQQtRa1o6O9VjpTeq9Sl+arbX+2XaxhBBCCCGEqJ3kOSHWXwk5OxKW3IguhBBCCCGE+Ff+aYjeLCzdr1Tp/2WzAK219rZhNiGEEEIIIUQt9E+jY3md/Vkp1ZZz3bHWaK132zCXEEIIIYQQtZLcmG7lAxuVUk8A3wIBQCDwrVLqcVsGE0IIIYQQQtRO1t4Tcj/QVWudA6CUehPYCHxgq2BCCCGEEELURnIlBJTW/3x3vlIqGuiktc4vnXYFtmqtW1nxHnL7vxBCCCGEsIer4vD+15N/2O34eGj4kCtynVh7JeRLYLNS6uywvDcBn1v7JmsTllxirCtHr5DreT/m75qOUW1PthjEmNWrazpGtX3Tpw+LTvxR0zGq7caIIby2a2lNx6iWF9sOZPaBv2o6RrWNbTqYh9ZfvY8zmtmjHw0+vnq33WOP9GG/6beajlFtzYw3AIdqOkY1NcYtfFRNh6i2vJNz+PbI1bvvuTNqMAuO/1nTMarttshraf7FmpqOUW377u1d0xGsIldCrH9OyLtKqVVATywtzHu01jttGUwIIYQQQghRO13Kc0J2ADtsmEUIIYQQQohazyBXQqwbHUsIIYQQQgghLherr4QIIYQQQggh/j0HJeM2yZUQIYQQQgghhF3JlRAhhBBCCCHsSK4CyDoQQgghhBBC2JlcCRFCCCGEEMKO5DkhVl4JUUo1VkotV0rtLZ1urZR6ybbRhBBCCCGEELWRtd2xPgVeAIoAtNZ7gJG2CiWEEEIIIURtZVD2+3elsrYR4q613nLea8WXO4wQQgghhBCi9rO2EZKilGoIaACl1G1AvM1SCSGEEEIIIWota29MfxSYDTRVSp0GjgOjbZZKCCGEEEKIWkoeVmhlI0RrfQy4RinlAThorbNsG0sIIYQQQghRW1nVCFFK+QOvAD0BrZRaB0zWWqfaMpzWmjkzfiZ6836cXZy594VRRDQOq1RuxU9rWbpgDcmnU3lv0WS8jJ4A/DlnBZuX7QCgpMRM/IlE3ls0GU9vD1vGLsu+7vOFnNgRg6OLMwMeu4PAhvUqlctMTOHvd7+iIDuXgMgwrnlyDAYnR45v2cPmOUtQSuFgcKDnvbdSp1lDm+c+y7R3LyfmzUObzQT17EnokCEV5ufFx3Ps66/JOXmSejfdRJ1BgwAoSEvj6BdfUJSZiVKKoN69CRkwwG65z9Jas/jjnziwdT9OLk7859nbCWtUef2vX7SWdT+vJvVMCq/8OBUPH0vd2bF8G6vmLwfAxc2Fmx8fTmjDunbNv+WrBZzeaak/PR6+E/8GlfNnJaWw5v0vKcjOxT+yHj0fG4PB0dHq5W2Zf+WnCzm+fR+OLs5c++Rogquo/xmJqfz21lfkZ+cS1CCM6566E4OTZbd0KvowKz//CXNxCW7eHox47Um75U+NjuHwD/NBm6nTqwcR119bYb7WmsM/zCctei8Ozs40u+8uvCLCLbmXLid+zXq01oT27km9Qfat/73r+fJyzygcHBTz98Uzc+epCvOvqe/P013qY9ZQYtZMWXeEbQmZANzdui4jmtVBKZi3L54v95y2a3awrNvP3v2F7Rv24+LqzBMTRtKwaeX9/rsvf8eR/XE4Ohpo1LweD78wHEdHAwDR24/w+XuLKCkuwdvowaszH7Vr/ldfnc3q1dtxdXXhjTeepEWLqErlxo17jy1b9uLlZfk+euON/9KsWQOysnL43//e4cyZZEpKSrj33lu49dZr7JJ95lsPMmRAO5JTM+k48Lkqy7wz6S4G92tLbl4hY5/5hF17YwEY2KcNb08cg8HgwFdzV/L2x4vtkvl8Wmv+nrWQI9v24eTizNCnRlMnqvK+Jz0hlZ/f/Iq87FzqNAzjxmcs+578nDwWvf0NGcnpmEvMdL2lP20HdrVr/iWf/MTBrftwcnHi1mdGU7eK766Ni9ew4efVpMWn8OK8V8u+u6xd3hZ61vXlha4NMSjFgkMJfLan4r6nf7g/j7ePQGso1po3Nh9lR6Jl37N0eGdyikowa02x1vxn8U67ZK5JMkSv9feEzAWSgVuB20p/nmerUGdFb95PUlwKr33/ImOeHc537y6oslxUy0ieeedh/EN8K7x+7aj+vPL5s7zy+bPc8sD1NGnT0C4NEICTO/aREZ/E6I9epu9DI1k9u+rVtfHbxbQZ2o/RH72Mi6c7+5dvBCCsVRNGvDuOEe+Oo9+jo1n58Q92yQ2gzWZif/iBJk88QetJk0jdupXcM2cqlHH08CBi5EjqDBxY4XXl4EDE8OG0mTyZFi+8QOLKlZWWtYcDW/eTcjqZ574cz63/HcHPM36sslz9FpE88MbD+AZXrDt+If489PbjPD3reQbcPoiF021e3Ss4vWsfWQnJ3Pz+K3R7YBSbPp9bZbnt3y+i+XX9uOX9V3D2cOPwio2XtLytHN++j/T4ZO6dOYGBj45g2Sfzqyy35utFdBjWl/tmTsDV053oZZb8+dm5LJs5n5vGP8DdH77I0OfutVt2bTZz6Ls5tHnqMTpPfYXEzVvJOV2xDqdF7yUvMYkur0+myV2jOfiNZfvMjjtN/Jr1dHhpHJ0mvUTq7mhyExPtlt1BwaTejbhnSTSD52xlaKMgonzdK5TZEJfOdfO2c8P87Ty/8iCv92sCQGM/d0Y0q8PNC3dw/bxt9I/wp76Pm92yn7V9wwHiT6XwyYIXeGTccGZOW1hlud6DO/DR/Od5/4dnKSwoYumizQBkZ+Uxa9pPjH/7Xj6Y+xz/e22MPeOzZs12YmPP8Pffs5gy5VEmTvzkgmWfe+5eFi2awaJFM2jWrAEA33+/hIYNw1m8+AO+/fZ13nzzcwoLi+yS/dsfV3PjmDcuOH9wv7Y0rB9Cy95P8di4T5nx6n0AODgopk+9hxvvepN2A55l+LDuNG1kv5M25R3dto+0M8k88ukErnt8BH98VPW+Z8WXi+hyU18e/dSy79n1t2Xfs+23tQTUC2Hsh+O4843HWfbZL5QU2W8cnkNb95FyJpmnv3iJm54cyeIPq/7uimjegHtffwRjkF+1lr/cHBS81C2KB//ey9CftnFdg0AaGivuezadSefmX3Zwy6IdvLT2EJN7NK4w/+4/dnPLoh3/LxogwsLaRoif1nqK1vp46b+pgNGGuQDYtW4v3QZ3RClFwxb1yc3Ow5SaWalceOMwAur4VfEbztmyfAedB7SzVdRKjm+JpknfziilCGkSSWFOHjlpGRXKaK05HX2Iht3aAtC0XxeOb9kDgJObC0pZmsnFBYWA/ZrM2ceP4xoUhGtgIA6Ojvh16kT67t0Vyjh5e+NZvz7KYKjwurPRiEdEBAAGV1dc69ShyGSyV/Qy+zZE035gJ5RSRDSrJufVCAAAaTxJREFUT15OHpmpGZXK1Y0Kwy/Ev9Lr9VtE4u5l2YGGN6tPRkrlZW3p1NY9NOhtqT+BjS31Jze9cv1JiDlERFdLvW7Ypwuntu62enlbOrolmub9LO8f2iSSgpw8squo/yf3HKZxj7YAtOjfmSObogE4sGY7jbq1wTvQsl27G73slj3zWCxuQUG4BVnqf3CXTqTs2lOhTMrOPYR074pSCp+GDSjOzaPAlEFufALeDSIxuDjjYDBgbNKI5B277Ja9TZA3JzLyOJWZT5FZ89uRJAZGVqzfucXmsp/dHA1oy3gjNPR1Z1diJvnFZko0bD5jYlBkgN2yn7VlzV76DumAUoomrSLIycojLaXyfr9jj2YopVBK0ahFOKlJJgDW/LWDbv1aEVh6UsroZ7+6A7B8+SZuuqk/Sinatm1KZmYOSUlpVi+vlCInJxetNTk5efj4eJVd4bG19VsOkGbKvuD8GwZ14IeFawHYsvMIPt7uhAQZ6dQ2iqOxCcSeTKKoqIQff93IDYM62iXz+Q5uiqZVf8u+J6xpJPk5eWRVse+J3XOYZj3bAtB6QGcOlu57lILCvAK01hTmFeLm5Y6DwdpDpX9v/8a9tBtg+e4Kb1af/Oyqv7tCo8LwreK7y9rlL7dWAV6czMwjLsuy7/njWDL9wy+273Hg//sdEQ7Kfv+uVNZuWSuVUiOVUg6l//4DLLFlMABTSiZ+Qcayad9AI6bkS9+YCvIL2bvlAO37tL6M6S4uJ82EZ8C5s+se/sZKjZD8rBycPdxwKD2Q9/A3klNuZ3Fs025+eHwKS16dSf/H7DcOQKHJhLPfuUads9FIUXr6Jf+egpQUck+exCMy8nLGs0pGagbGwHPr3xhgJKOaO+Ktf26iSadmlyuaVXLTTXj4n8vv7m8kN81UoUxBVg7O7uXqj58vuaV1zJrlbSk7NQOvAGPZtFeAkezz1n9eVg6u5eq/p7+xrKGSfiaJ/Oxc5o2fwbdPTyNmxfkjhNtOgSkdV79z687F10jBefW/IN2ES/kyfkYK0k141A3FdOgwRdnZlPxfe/cdHkXVt3H8e7LpvTdq6L0oVToIiAJWpNj7Yy/wqKgoRcQudsXyiIWOBUFFepMqNaH3lpDeCyE57x+z6RsIITshvL8PVy52Z87s3jt75uzOnDOzOWdJ2BVJTuLFbzuVFerhTHR6TuH96PQcQjxcypQbEBHAkpEd+eaGVrywfD8A+xMz6RTug6+LI66ODvSuF0CYZ9ll7S0xLoXAEN/C+wHBPiSep90/dy6PlX/+S/suzQA4fTyO9NRMXn70M567+wNW/LHF3pFLOHMmgdDQop230NAAzpyxPXL5gw9+YMiQJ3njja8KezvuuOMGDh06SY8e9zB06JO8/PJDODiY9yX4fMJD/TkZXfRaTsUkEh7qT3ioHydPF5senUCtUr3LZklLSME7yLfwvnegL2ml257Ukm2PV7EyHQb3JP5EDB/eNY5pj09hwMO3okxc/6kJyfgUzx/kc1E7EZe6fGWFeLgQk1HU9sRk5BDs7lymXL96ASy8pQNfDGjFK2v2FU7XwNcDWzN3aHuGNQ21e15xeajo1bEeAZ4DfrTedwAylFLPAVpr7V28sFLqYeBhgC+//JLmQyvXLau1jf3kSuzR7fgnikatIkwbigUVzH6BMg26tKVBl7acjjrIppkLGTr+ySrNWC6buS5uxedlZ7P/iy+oN3w4jm7mD+mwdYhFVaLyHNx+gM1/beDRD8w7HwHKewvKVCAbhS5mefuxVf/Lxi+/TH5ePrGHTjBs0hPkns1l5vMfENa0Pv61gu2QtnQuG9NKhdc2CikFHuFh1B00kO3vfojF1QXPOrVN/QJjq4rbqgt/H0ng7yMJdAzz4blO9bnr950cSsrky20n+H5oGzJz89ibkE6erYXt7GKf8su359OiXQNatjeGM+Xn5XNo70kmfvofzubk8sIDH9OkVT1q1Q2yQ9qyKrrtPffcPQQF+ZGbe45x4z5h2rR5PPHESNau3Ubz5hF8//1kjh+P5r77xtGhQ0s8Pd3LPrDJbLWhWmubr68aqk65T1y25Sw/3OGtewhpUJs7pzxJUnQ8P73yKXVbNcDF3ZzPsUttu6ur7a/oMyw7lsCyYwlcHeLDU1fX54G/jB6oOxZuJy7rLP6uTnx9XWsOJ2fx7xlzRyCY7fI4tFC9Knp1rIvqz9ZaT8O4pC+AXhNT8U6T5b+sZc3CDQDUb1qHRGsXO0BSXDK+gT4XEwWAzcu20dmEoVi7/lzN7iX/ABDcqC7p8UVHQDMSkvHwK5nd1duTsxlZ5Ofl4WCxGGX8y76+8JaNSPk4nqzUdNy8Pe37IgBnPz/OJhYNHzibnIyTr2+Fl88/d44DX3xBYOfO+F91lR0S2vbPgjVs/MMY11unaV2S44rWf3J8Mt4B3uUtalP04dPM+2AWD0x+BA8TdmD3Ll7F/mVG/QlsWI+MhKL8mQnJuJWqPy5enpzNLFZ/EpNwt5bx8Pe94PJVbdui1exaYqz/0EZ1SYtPLpyXFl+2brt5e5JdrP6nF9tGPAN8cfP2wMnVBSdXF2q3bEjc0VOm7IS4+PmRXaz3IicpGZdS9d/Vz69ED0dOYjLO1jLhPbsR3rMbAIfm/4qrX8ll7Skm/WyJ3oswTxdiM3PKLb85OoW6Pq74uTqSlH2OOXtimLMnBoAxnSOISS9/2ar0x9y1/G09p6NxizrEn0kunJcQm4J/kO26O+vrxaQkpfPiW/cWTgsI9sHL1wNXNxdc3Vxo0b4BRw+ctutOyE8/LWLOnMUAtG7dmJiY+MJ5MTEJBAeXHS5cMM3Z2YlbbrmWb7/9GYCff17Kww/fZgwlrRdO7dqhHD58kjZtmpR5DLOdikmgdljREJtaof5En0nC2cmR2uHFpocFcDrWvB7ALQtXs+0vo+0Ja1KX1Ljkwnmp8cl4BpSsP+6l2p60+GS8rGV2LNnINcP6o5TCPzwI35AA4k/EUqtpPbvl37BgDZut+Ws3qUtK8fxxKXj5V/yzyyfQ95KWr6yYjBxCi/W6hnq4EJt5ttzy/55JoY6XG74ujiTnnCMuyyibmJ3LsmMJtAnyuuJ3QsRF7IgppYYqpd61/g22V6C+N3cvPJm8fY/WrF+8Ba01h6KO4ubhiu9FfpHMTM9i345DtOveyk6Ji7Qe1LPwZPKITm3Yt3KTMW5/3xGc3V3LfAlTSlGrVWMOrd8OwN4VG4no2BqAlOi4wqPJcYdOkH8uD1cvc3pyPOvXJzs2luz4ePLPnSNx82b82rat0LJaa458/z1uYWFlTlq3t2uG9uDZL57n2S+ep+U1rdm6ZDNaa47tOYqbhxveARX/Ep4Um8T3E79lxPN3ElTbhKPvQLOBvRj69liGvj2Wuh3bcHi1UX/i9h/Byd2tcAejgFKK0BZNOLbBOInv0KqN1OlgDDms06H1BZevau1v6MndU1/g7qkv0KhLG3avMJ7/9L4juHi44mmj/tdt3Zj967YDELV8E406G/W/UefWnNp9mPy8PHJzzhK9/xgBtUPsmr+AV0Q9ss7EkhVn1P8zGzcT2K7kUM6Adm2I+WcDWmtSDh3G0d0VF1/j9Z1NNc5fyE5IJP7fbQR37mhKboCdsanU93GjtpcrTg6KwY2CWXqk5FCget6uhbdbBnri5OBAUrZx4m2AmxMA4Z4uDGwQyIKDsabkvn5Yd6b+OJqpP46mc89WrPzzX7TW7Nt1DA9PV/wDy7b7S37bwLYN+xg96a4Sw5U69WzF7u2HyTuXR072WQ5EHad2fftuw3fccUPhCebXXtuFX39djtaa7dv34uXlbnMnpOA8Ea01S5duoHFj40tuWFgQ69cb53bFxydx5MhJaptU9y9k0ZKtjLq1BwCd2jciNS2TmNhktuw4RKOIUOrVCcLJycKwIV1ZtORf03J1GNyThz55gYc+eYGmXdqwa7nR9pzcewRXD1e8bLQ99Vs3Zs/a7QDsXLaJJta2xzvYjyM7jGFC6UmpJJ6KtXnuRVXqMrQHT372PE9+9jzNu7Zm2zLjs+v4nqO4eLhe1GdXsy6tLmn5yoqMT6Oejxu1PI22Z1CDIFYcL9n21PUqanuaB3ji5KBIzjmHm6MD7tbzntwcHbgm3JcDSRl2z1zdlDLv73JV0Uv0vgl0BH6yTnpaKdVda/2i3ZIBrbs0Z9eGPbw06g2cXZy478WRhfOmPj+Ne58fjm+gD0vnrWbxrBWkJKYx/v53ad2lOfc+PxyAbWt20bJjU1zczB3bXO/qlhzfupufHpuIo4sTfZ+4s3Dewtc/p89jo/Dw96HLXTey5P3/sXHGQoIiatP82q4AHFq/nX2rNuFgseDo7MSA0feZNpxGWSzUHzmSfVOnovPzCerWDffwcM6sWgVASK9enE1JIXLyZPKys1FKEb10KW0mTCDr5EniN2zArVYtdk2cCECdm2/Gt3VrU7IXaNapBXs37eGte1/H2cWZYWOK6s43L3/Jbc+NwCfAh7W/rGLV3OWkJabx/iNv06xTC4Y9N4KlPy4mMzWDXz42riziYLHw9KejTctfq31LTm6L4uenJ+Do7ES3R4vqz9Ipn3HNI6Nw9/fl6jtuZNWH/2Pb7IX4169D475dL7i8GSKubsHhLVF885+JOLk4M/DJonOafp74BQMeH4lngA897hnKone/Y91PiwhuUJtW1kthBtQJpX775kx/6k2UgwOt+3chsF64KdkdLBaa3DmcHe9/hM7PJ6z7NXjUCufUitUA1OrTk4A2rUjcGcmGF8dhcXam2f33FC4f+ek0ctPTURYLje8ciZOHecNA8zSMX3OQ6UNa46AUc/fGcCApk1EtwwCYERXNdQ2DuLlpCOfyNdnn8nnq792Fy382sCW+ro6cy9e8tvoAqTnmXRWowNXdmvPvP3v4z61TcHF14qlxIwrnTXzmK554+Xb8g3z4/K35BIX68cKDHwHQtXdrhj84gDoRIVzVpRlP3/EeDg6Ka4d2pl7DMNPy9+rVgVWrttC//8O4ubnwRrFLSz/00Hhef/1JQkICGDPmPZKSUtBa06xZAyZMeAyAxx4bztixUxky5Am01owZcy/+NnrI7WH6x0/So2tzAv28OLjxEya9Pw8n6yWzv/5xKX8t38bAPu2IWjOVzKwcHhnzJWBcAv/Zcd/x+w9jsVgcmD57JXv2nzQlc2mNOrbg4JYoPn1wYuElegvMfO0LBj81Eq8AH/reN5Rf3v6OlT8sIrRBbdoNNNqeHiOuY8EHP/LlY1MA6HvvUNx97D8CoUDTTi3Yv3k3798/CScXZ255blThvOnjvuDmZ0biHeDDP7+uYs28ZaQnpvHxo2/RpGMLbnl25HmXt6c8DZPXH+Srga1wUIpfDsRwMDmT4U2NbW/2vmj61w/kxkbWticvn9Er9wAQ4ObMR/1aAOCoFIsOx7L2lHk9aeL8lFL+GFfErQ8cBW7XWieVKlMH+B4IBfKBaVrrDy/42DbPXSgbYCfQTmudb71vAbZprStypvdFDce63PQIvYEPo/6u7hiV9nTLAdxt3Xmoib7v1Yvfjv1Z3TEq7cZ6g3hj+5LqjlEpL7Xrz7S9i6s7RqU93Gwg/1m3orpjVNoX3frQ4LOau+0efqwXe5IXVneMSmvuOxjYX90xKqkJbnVHXrjYZSrr+Ex+OFhz2567Gg1k3pG/qjtGpd0WcR0tvl1d3TEqbff9PS/jY/9FNsctMu3MqY5BN1R6nSil3gYStdZvKqVeBPy01i+UKhMGhGmttyqlvIB/gZu01rttPGShizkvxrfYbXMOywghhBBCCCGqy43AdOvt6cBNpQtoraO11lutt9OAPcAFr0pV0atjTQG2KaVWYFwEoScwtoLLCiGEEEIIIazMPFej+FVrraZZLyJVESFa62gwdjaUUuc9yU4pVR9oD2y80ANX9OpYM5VSKzHOC1HAC1rrmIosK4QQQgghhKgepa5aW4ZSainG+RylvXwxz6OU8gTmA89orcv+ymwp590JUUqVvr5qwZlm4Uqp8IKuFyGEEEIIIUTFXE6/E6K1vra8eUqpM0qpMGsvSBhg87KJSiknjB2Qn7TWP1fkeS/UE/KerazFbvetyJMIIYQQQgghapwFwD3Am9b/fytdQBmXb/0G2KO1fr+iD3zeHTGtdR+tdR/gc+BG6+0VQAowpsLxhRBCCCGEEDXNm0B/pdQBoL/1PkqpcKXUH9Yy3YC7gL5Kqe3Wv+sv9MAVPTH9Fa31HKVUd2uA9zB2TDpf5AsRQgghhBDi/zWlTLtC7yXRWicA/WxMPw1cb729FuOc8YtS0SFpedb/bwC+0Fr/Bjhf7JMJIYQQQgghREV3Qk4ppb4Ebgf+UEq5XMSyQgghhBBCCCtl4t/lqqI7ErcDi4HrtNbJgD/wX3uFEkIIIYQQQly5Kvo7IZnAz8XuRwPR9golhBBCCCHElcrMHyu8XMmQKiGEEEIIIYSpKnp1LCGEEEIIIUQVkI4Q6QkRQgghhBBCmExpbffrFNeMCyELIYQQQoiarkZ0MkQmLTTt+3Erv8GX5ToxZThWp7lrzXgau9g0rDv3rl5V3TEq7buevVgfu6i6Y1Ra1+AbeHDtyuqOUWlfd+/NU+tXVHeMSvmoax9GrFhd3TEqbVafnvxv/+LqjlFp9zUZyNb4mrvtXhV4Q41vO3svWlfdMSpl5Q3d+OFgza37dzUaiFvdkdUdo9Kyjs/ktuU1t+2c17cnSTkLqztGpfm5DK7uCKKC5JwQIYQQQgghTHRZdk2YTM4JEUIIIYQQQphKekKEEEIIIYQwkfxOiPSECCGEEEIIIUwmPSFCCCGEEEKYSDpCpCdECCGEEEIIYTLZCRFCCCGEEEKYSoZjCSGEEEIIYSIZjiU9IUIIIYQQQgiTSU+IEEIIIYQQJnKQrhDpCRFCCCGEEEKYS3pChBBCCCGEMJF0hEhPiBBCCCGEEMJk0hMihBBCCCGEiZTS1R2h2l3WOyFdQnwZ3b4BDkrx2+EzfL/vZIn5A+sGcXfT2gBkncvjra2HOJCSAcDIxuHcGBGCBg6mZDJp837O5pv3hidFRnJ01mx0fj4hPbpTa9CgEvOzoqM5+N10Mo4fp+5NNxE+cAAAOYmJHPz2W3JTUkEpQnr2JOzafqblLqC15qcPf2Hnhj04uzjz4EsjqW9d18Utnb+Gv+euJvZUAh//PhEvX08AMtOz+HLSTySeSSIvL59BI/rQ44ZOpuVP3BXJ4Zlz0Dqf0B7dqXP9dSXmZ0bHsP/b70g/foL6N99I7esGVHhZM8TtjGLvjDno/Hxq9+xGg8ElM2it2fvTHOJ2RmJxdqb1g/fgXb8uGdEx7Pjs68JymXHxNLp5CPUHmluHUqMiOTlnFjo/n4BuPQi9rmT9z46J5tj078g6cZywoTcRMmBgifk6P599U17HydeXho8/ZWZ04/m1Zum0+Rz6dzdOLs7c8PQdhDaqU6ZcckwCv73zHdlpmYQ0rM2Q5+7C4uTIxp+XEbVyCwD5efkknIzhqR/fwM3Lw5Ts06f+wvb1e3B2debRl0cSYWPb/WT8jxzeewKLo4WGLery4PPDcHS0cOrYGb6cPIsj+08y/OHrGTyqj90zF1fT285OQb480aIBFgWLTpxhxqFTJeZfGx7EyIa1jNeSl8cHuw5xKC2TIFdnXmrXBH8XJ/I1LDwew/yj0abn11rz95fzObjFqPtDnr2DMBt1PykmgV/e+o6s9EzCGtbmxtFG3c/OyOK3d78nJS6J/Lx8utzSl3b9u5iS/Yt3HmFQv/bEJaTSof/zNsu8N+EeBvZpR2bWWR4e/TnbI48C0L9XW94dfzcWiwPfzVrBu58tMCVzcWlRkZyeMwt0Pn7dehA8sGy7efL778g+cZyQoTcR1L+o3dz78os4uLqiHBTKwUKjsa+YHR+tNe+/9Svr1+zBxdWZcZNG0KxF2bZn8muz2RN1Aq2hbr0gxr0+And3F1JTM5n86mxOnkjAxcWRlycMp2HjMNNfhzDPZbsT4gA8f1VDnlgdSWzmWaZf2441pxM4kpZVWOZ0Rjb/WbmTtNw8uob6MfbqRty/fAdBrs4MbxzO8L+2kpOfzxtdmtK/ThCLjsWakl3n53NkxgxaPPsszn5+7Jr8Bn5t2+IeHl5YxtHDg4gRI0jcvq3EssrBgXrDhuFZrx552dnsnPQ6Pi2al1jWDDs37OHMyXjemvkSh3Yf4/v35vHqtGfKlGvcOoK217Tkzac+LTF92c/rqFU/hGffepDUpHTG3jGFrgOuwtHJ/lVO5+dz6KeZtBr9DC5+fmyfNAX/dm3wKLH+3Wk4agQJ27Zf9LJm5N/zw0w6/PdpXP39WD9hCsHt2+BZqyhD/M5IMs/E0uOtiaQcOsLu72fQ5dUX8QgL5ZpJrxQ+zspnXiTk6namZS943hMzZ9Do6Wdx8vNj35TJ+LRpi1uxdWhx96D28BGkbN9u8zHili/FNTSMvOwsm/Pt7fC/u0k6HccjX47j9L6jLP58Dve8N7pMuZXf/UbHG3vToufV/PXpbHYsWc9V1/eg8y396HyL8QX4wKZdbP5tpSk7IADb1+8h5mQ8H8x+iYNRx/jm3Xm8/tUzZcp1G3AVj792BwAfj/+RFb9voP/N3fD0dueeZ29my+pIU/IWV9PbTgfg6ZYNGLMxirjss3zRvS3rziRyLL2oHkdnZfP0+l2kn8ujU5Avo1s34rF/dpKnNZ/tPsKB1AzcLBamdW/LlvjkEsua4dCW3SSejuOxr8Zxat9R/vx0Dvd/ULbuL//fb3S+qTcte13NH5/MZvvf67n6hh5sWbiGwDqhDH/tETJS0vj84cm07t0Biwlt/w9zV/HF9MV8/cFjNucP7NOOhvVDadXzWTq1b8RHkx+g543jcHBQTH39Pm644w1ORSew9vfJLFzyL3sPnLL5OPag8/M5PWsGEU89i6OfH4fenIx3m7a4hhWr++4ehN8+gtQd220+RoNnR+Po6WVS4rLWr93LiWPxzF04lqidx3n79fl8O+PpMuWe+e+NeHi6AjD1nd+YN3Mtdz/Qj+lfLaNx03DemnofR4+c4d3JP/PJ14+a/TJMI+eEVPCcEKVUkFLqXaXUH0qp5QV/9gzW0t+Lk+nZnM7I4ZzW/H0ijp61AkqU2ZWQRlpuHgCRCakEuzsXzrMohYvFAYsCV4uF+Oyz9oxbQvqRI7gGBeMaFISDoyOBHTuStH1HiTJO3t54RtRHWSwlpjv7+uJZr57xGlxdcQsL42xyslnRC21bG0m36zqglKJRy/pkpmeRHJ9aply9JrUJCvMvM10pyM7MQWtNTlYOHt7uOFjMOQUp7fARXIODcbOu/6BOHUjcVnL9O3t742Vj/VdkWXtLOXwU95Bg3IONDGGdOxK7bWeJMrHbdhLerQtKKXwbNSA3M4uc5JQSZRJ278U9OBC3wJLbjb1lHj2CS3AQLtZ16NexIyk7t5co4+TtjUf9iDLrH+BsUiIpu3YR0K27SYnLOrBhF636dkIpRa1mEeRkZJGeWHL9aq05tvMAzbq1A6B1v04c2LCrzGPtWbWVFj2vNiM2AP+ujaSHddtt3Ko+mWlZJNnYdttf0wKllLGNN69LYqzx+nz8vGjYvC4WR/NPGazpbWczXy9OZWYTnWV8bi0/HUe3kJLtY1RSGunnjM+t3UlpBLkZn1uJObkcSDV68rPy8jiWnkmgqzNm27dhF62tdb92swiyM7JIs1H3j+48QPPu7QBo068T+6x1Xyk4m2W0/WezzuLmZV7bv27TXhKT08udP3jA1cyYvwaATdsO4uPtTmiwLx3bNeLQ0RiOHo8lNzePub+vZ/CADqZkLpB59AjOQUE4W+u+T4eOZXY2HL29cS+n3bwcrF4RyfVDrkYpRau29UhPyyI+rmzbU7ADorUmJzvXqDTAkcNn6NC5MQD1I0KIPp1EQkKaeS9AmK6iLcNPwB4gApgAHAU22ykTAEFuzpzJzCm8H5uZU9hY2zI0IpT10UkAxGWf5cd9p1gwuCN/DOlMeu45Np5JtmfcEs4mJ+PiX/TB4+znS05y0kU/TnZ8PBknjuMZEVGV8SokKS4V/2Dfwvt+Qb4kxaeUv0Ap/W7tzuljZ3jmpvG8cu87jHrqZhwczPkgyklOxsXfr/C+s58fORX8MnIpy1aV7KQkXItlcPXzJTupZP3JSUq2USa5RJmYjVsI7dLRrlltOZuUjLNfsfrv60duqWznc2rObGrdchuo6rtuRlpCCl6BvoX3vQJ8SUsoWf+zUjNw8XTDwfqFwFaZ3OyzHN66h6bXtLV75gKJcakEFNt2/YN9SYwrf9s9dy6PNYu30LZzMxPSnV9NbzuDXJ2Jyyo64BWXfZYgV5dyy99QN4RNscllpoe6udDYx5M95/lCbS9pCSl4B/kW3vcOtF33XT2K1f1iZToM7kn8iRg+vGsc0x6fwoCHb0WZ1PZfSHioPyejEwrvn4pJJDzUn/BQP06eLjY9OoFaIX62HsJuziUn41Ss3XTy8yP3Yj57FBz5aCoH3phE4prVVR+wAuJiUwgO9S28HxziQ1ys7bZn0rhZXN9nPMeOxnL7SOOAU+Mm4axcZuzMRu06Tkx0EnEmfnczm1Lm/V2uKtoyBGitvwFytdartNb3A+UO8lRKPayU2qKU2jJt2rRKBbO50so5pePqIB+GRoTwya6jAHg5WehVy5+bFm3m+t834eZo4bq6QZXKUSm6bFB1kR1vednZ7P/8C+oPH46jm1tVJaswbes1XMRLiNy4j7qNajH11/FM/HY0P079mayM7CpMeB6XcurP5XCemI0MpeuPrfeneJH8c+eI3baD0I7mHYEvcv5s55OycweOXt64W49oV5+K1P8Llzm4OZJazSNMG4oFF7/tfvvuPJq1bUCzdg3smKqCroC2szRdTqPSLsCH6+uE8OXeoyWmu1kcmHB1Mz7ZfZhMa4+JqWy+B6WKnKehPLx1DyENavP0D5N46OMX+OuLueRkVs+wytJs1SWtNcrGBmKribWrS/zMbTjmRRq/NI6IJ54mYdUKMg7sr8JwFWPzY6mc1zBu0ggWLnuN+hEhLF28HYC7H+hLWmoWdw17j7kz19KkWS0sl2mvj6gaFR2kmWv9P1opdQNwGih7tpGV1noaULD3ob+eu/aig8VmniXEvegIUrC7C3E2hlQ18nHn5Q6NeGZNFClnzwHQKcSX0xnZJFvvrziVQJsAb/46HnfROSrD2c+PnMTEwvtnk5Jx9vWt8PL5586x7/MvCOzcmYCrrrJDQtuW/ryWVb9vACCiWR0Six2hS4pLxjfAp8KPteaPTdxwZz+UUoTUDiIozJ/oY2do0ML+Xy5d/HzJSSw6eno2KQmXCq7/S1m2qrj6+5FdLEN2UjIufr4XLONaLGf8zki869XFxcfb3nHLcPbz42xSsfqfnIRTBddhxqFDpOzcTmrkLvLP5ZKXlc3Rb7+m/v0P2iltkX8XrWbH4vUAhDWuS1p8cuG8tIRkPP1L1n83b09y0rPIz8vDwWKxWWb3anOGYv09fy3LFxjbboPmdUgotu0mxibjF2h725337WLSkjN48I1hds9YETW17SwQl322RI99kKuzzaHADbzc+W/rhryweTepuecKp1uUYsLVzVh6Ko41MYlllrOXLQtXs+0va91vUpfUuOTCeanxyXiWavvdvT3JzihW9+OT8bKW2bFkI9cM649SCv/wIHxDAog/EUutptV9YAFOxSRQO6xoeGqtUH+izyTh7ORI7fBi08MCOB178T1wl8LRz4/cYu1mblISjj6+FV6+oI119PbGu117Mo8ewaNxkypOWda8WWv5bf5GAJq3rENsTHLhvNgzKQQGlf+9wWJx4Nrr2vHTdysYfFMnPDxdGTdpBGDsHN48aDLhtcoO975SXB79g9WrouvgdaWUDzAaGAN8DTxjr1BgjJWt4+lGuLsLjkoxoE4Qa06XbJRD3Fx465rmvLZpP8fTi46yx2Tm0MrfCxfrONSOwT4cTcu0Z9wSPOvXJzs2luy4ePLPnSN+82b82lZsOIbWmkPTv8ctLIzwAf3tnLSka2/pzqT/jWHS/8ZwVY/WrPtrC1prDkYdxc3TFd/Ain+hDQjxY/e/xpGYlMQ0oo/HEhRuzrkJXhH1yT5TtP7jNm3Bv13F1v+lLFtVvCPqkXkmlkxrhuiNmwlu36ZEmeB2bTi9bgNaa5IPHsbRzRUX36LGPnrDFsKqYSgWgHu9+uTExpITH0f+uXMkbd6MT5uKrcPwm2+h1Zvv0PKNN6n/wMN4NWtqyg4IwNU39OT+j17g/o9eoHGXNkQu34TWmlN7j+Di7lpmB0MpRd02jdm7bjsAu5ZtonHn1oXzszOyOBF5kMZdWmNvA27tzpvTx/Dm9DF06NmaNdZt90DkUdw9XfGzse0uX7CBnRv38eSEO00bKnkhNbXtLLAvJY3aHm6EuhmfW33Dg/jnTMnPrWBXZyZd3Yw3dhzgZKne4efbNOJ4ehZzj5w2MzYdBvfkoU9e4KFPXqBplzbsstb9k3uP4OrhipeNul+/dWP2rN0OwM5lm2hirfvewX4c2bEPgPSkVBJPxeIXau55aeVZtGQro27tAUCn9o1ITcskJjaZLTsO0SgilHp1gnBysjBsSFcWLfnX1GwF7eZZa7uZsmUz3hVsN/NzcsjLzi68nb5nN67htewZt9BtI7rzw9zR/DB3NL36tuKP3/9Fa03kjmN4erkSGFSy7dFac+J4fOHttSujqFc/GIC01CxyrTvlv83fSPurGhSePyKuTBXtCRkGrNVaRwJ9lFL+wLvA7/YKlqfhnW2H+KhnKxwU/H7kDIdTM7mlQSgAPx+O4cEWdfBxduKFqxoay+Rr7lm2g6jEdJadTOCHa9uRpzX7kjP45XCMvaKWoSwWIkaNZM/UqWidT3C3brjXCidm5SoAQnv34mxKCrten2w0HEoRvXQpbSdOIPPkSeI3bMC9Vi12TJgIQN1bbsavtf2/yBTXtmtzdm7Yw/Mj3sDF1YkHxo4snPf+f6dx3wvD8Qv0Ycm81fwxYwUpiWmMu/dd2nRpzv0vDmfovf35+o2ZvHLP22gNt/9ncOHle+1NWSw0vGMEkR98aFzms3s3PGqFE21d/2HW9b9t0hvkZRnr/9TSZVw9aTyObm42lzWTg8VC8zuH8++7H6Hz86nV4xo8a4VzYrkxzrdO354Etm1F3M5I1jw/DouLM60euKdw+bycsyRE7aHFvXeYmruAslioPXwUhz6ais7XBFzTDbfwWsSvXglAYM/e5KaksG/K6+RlZ6OUIm75Upq/NhHLZTB8BqBhhxYc3hLFlw9PxMnFmeufLlqXc8Z/waAnR+IV4EOfe4fy29vfsfrHRYQ0qE2bAUWjVPev30lE+2Y4n+ecAHto37U529fv4ZnbjW33kZeKtt23Rk/joReH4x/kwzfvziMwxI9XH/4IgI69WnPr/QNJTkjl5Qc+ICsjG+Wg+HPOat756QXcPez/ZaCmt515Gj6MPMw7nVrioODPk7EcTc9iaF3jc2vB8RjuaVwXb2cnnm3ZoHCZR9btoLWfFwNrB3MoNYOvuxtfPr/ad5yNceYekW/UsQUHt0Tx6YMTCy/RW2Dma18w+Cmj7ve9byi/vP0dK39YRGiD2rQbaNT9HiOuY8EHP/LlY1MA6HvvUNx9zGn7p3/8JD26NifQz4uDGz9h0vvzcLJelevrH5fy1/JtDOzTjqg1U8nMyuGRMV8CkJeXz7PjvuP3H8ZisTgwffZK9uw/eb6nqnLKYiF8xCiOfDwV8jV+13TDNbwWCdZ2M8Dabh5883XyrXU/fvlSmrw6kbz0dI59+RkAOj8P346d8WrZytT8ANf0aM4/a/Zw2w1TcHV14hVrrwbAs499xUvjbycg0IuJr8wkMz0braFR0zBeeOU2AI4eOcOEl2dicVDUbxjKyxNuN/01mOlyPlfDLMrm2PLShZTaprVuf6Fp5dCdKjEc63KxaVh37l29qrpjVNp3PXuxPnZRdceotK7BN/Dg2pXVHaPSvu7em6fWr6juGJXyUdc+jFhRPSc4VoVZfXryv/2LqztGpd3XZCBb42vutntV4A01vu3svWhddceolJU3dOOHgzW37t/VaCBudUdeuOBlKuv4TG5bXnPbznl9e5KUs7C6Y1San8vgGvH1/lj676adeVTPc8hluU4q2hPioJTy01onAVh7Qi7b3xgRQgghhBDicnVZ7hWYrKI7Eu8B/yil5mFcEuZ2YLLdUgkhhBBCCCGuWBXaCdFaf6+U2gL0xdh5u0VrvduuyYQQQgghhBBXpAoPqbLudMiOhxBCCCGEEJdATkyXyxQLIYQQQgghTCYnlwshhBBCCGEi6QiRnhAhhBBCCCGEyaQnRAghhBBCCBM5SFeI9IQIIYQQQgghzCU9IUIIIYQQQphIOkKkJ0QIIYQQQghhMukJEUIIIYQQwkRK6eqOUO2kJ0QIIYQQQghhKukJEUIIIYQQwkRyTggore3eHST9TUIIIYQQwgw14vv9mawFpn0/DnEbelmuE1N6Qp7ZsNyMp7GLqV360u/PddUdo9KWDerGqug/qjtGpfUKu57nNtbc+vN+5748vHZldceolGnde/Nh1N/VHaPSnm45gLd3LqnuGJX2fJv+zDr0V3XHqLQRDa+r8W1nTc2/bFA35h2puXXntojruG356uqOUWnz+vbEre7I6o5RaVnHZxKTtaC6Y1RaqNvQ6o5QIeqy3C0wl5wTIoQQQgghhDCVnBMihBBCCCGEiaQjRHpChBBCCCGEECaTnRAhhBBCCCGEqWQ4lhBCCCGEECaSXgBZB0IIIYQQQgiTSU+IEEIIIYQQJpJL9EpPiBBCCCGEEMJk0hMihBBCCCGEqaQrRHpChBBCCCGEEKaSnhAhhBBCCCFMpKQnpPydEKXULedbUGv9c9XHEUIIIYQQQlzpztcTMuQ88zQgOyFCCCGEEEJcJKVqxhkRSil/YDZQHzgK3K61TiqnrAXYApzSWg++0GOXuxOitb6vMmGFEEIIIYQQV4QXgWVa6zeVUi9a779QTtmngT2Ad0UeuELnhCilbgBaAq4F07TWEyuy7KWI2xnFnp/moPM1tXt1o+HggSXma63Z89Mc4nZEYXF2pvVDd+NTvy7p0TFs/+ybwnKZsfE0vmUwEQP72TtyoY6BvjzevAEOCv44eYZZh0+VmN8vPIgREbUAyMrLY2rUIQ6nZQIwpnUjugT5kXw2lwfXbjctc3Faa2Z//Au7NuzB2dWJe18cSb0mdcqUW/7zGpbNW03c6Xje+3USXr6ehfP2bTvI7E9+IS8vD08fT/774ROm5Y/dGcXuH426U6dXNxoNKVt3dv84h9gdUVhcnGlrrTsAuRmZ7Pz2R9JOngYUbR+8C7/GDUzLDpC4K5KDM+egdT5hPbpT9/rrSszPjI5h77ffkX78BBE330id6wZUeFkzaK1Z+818jm2NwtHFmX5P3ElQw7L1J/VMPH+//x056ZkERtTm2qfvxuLkyJFNO9k4cxFKKRwsDnS//1bCmjc0Nf+G/83jhDV/z8fvIrBB2fxpZ+JZMfV/5KRnEhBRh15PGvmTT8Ww+tMfSThykg4jB9N66LWmZv/zy585sHk3Ti5O3PTcHYQ3Kpt94++r2fDrKhKj43l+5mQ8fDwvanl7qeltZ03Pr7Vm0ec/s8/6/t86+g5qNS77/q9fsJp/fjHqz0uzS9afiixvL2lRkZyeMwt0Pn7dehA8cFCJ+dkx0Zz8/juyTxwnZOhNBPUv+mzY+/KLOLi6ohwUysFCo7GvmJYb4It3HmFQv/bEJaTSof/zNsu8N+EeBvZpR2bWWR4e/TnbI48C0L9XW94dfzcWiwPfzVrBu58tMDG5QWvNR2//xsa1e3FxdWLsxOE0aV67TLm3xs9h3+6TaK2pUy+IFycOx93dpXD+nsgTPHb3x7z21p307t/GzJdgshpzTsiNQG/r7enASmzshCilagM3AJOB5yrywBfsC1JKfQEMB57EWGPDgHoVefBLofPzifp+Fh1GP0GPKa8SvWEzaaeiS5SJ2xlFRkwsPd+eQMv7RhE1fSYAnmGhdJ/0Mt0nvUy3CWOxuDgTenU7e0cu5AA81bIBY7dEcf+abfQNC6Kep1uJMtGZ2Ty7cRcPrdvOjwdP8FyrRoXzFp+MZeyW3abltSVy4x7OnIzj9Z9e4q7Rt/PTB/NslmvUOoJn33uUgBC/EtMz07KYMXUej7/xIBO+e5FHxt9jRmygqO50GvMEvd58ldPl1Z0zsfR+ZwKt7xtF5HczC+dF/TiHoNYt6P3WeHpOfhnP8FDTshfkP/DTTFo/+yQdJ40nduNmMk6fLlHG0cOdRqNGUGdg/4te1gzHt+4mJTqWOz59ld7/GcGqabNtllv/wwLaDunDHZ++iounO3uWrQegduumDH//RYa//yJ9Hr+DFZ/NMDM+J7ftJjU6jmEfv0b3R0byz1ezbJbb/NNvtBzch2Efv4aLpxv7lxv5XTw96Hr/MFoP6WtmbAAObNlNwqk4nvr6FYY8NYKFn8y1Wa5uiwbc/cZj+Ab7V2p5e6jpbWdNzw+wf/Nu4k/H8dy3r3DT0yNYUM77X69FA+6fUrb+VHR5e9D5+ZyeNYOIJ56m8asTSdm8iezoUm2nuwfht48g8NoBNh+jwbOjafzya6bvgAD8MHcVN979ZrnzB/ZpR8P6obTq+SxPvPgVH01+AAAHB8XU1+/jxnveon2/MQwbeg3NGtcyK3ahjWv3cvJ4PD8teIEx427j/cm2R+0/MWYo3855jv/NHU1wqC+/zFpXOC8vL58vP1xEx65NzYr9/4JS6mGl1JZifw9fxOIhWutoAOv/weWUmwo8D+RX9IErMiDtGq313UCS1noC0BWw+2GN5MNH8QgJwj04CAdHR8I6dyB2644SZWK37qBWty4opfBr1IBzmZlkJ6eUKBMftRf3oEDcAgPsHblQM18vTmVkE52VwzmtWREdxzWlGurdyWmkn8srvB3k6lw4b1dSKqm550zLa8v2dZF0HdgRpRQNWtYnKz2L5ISUMuXqNq5NYJh/memblv1L+x5tCndOvP287J65QPKho7gHF9Wd8C4dOFOq7pwpVXdyrXUnNyuLxH0HqdOrGwAOjo44ebiblh0g9fAR3IKDcQsy8gd36kDCtpL5nb298Y6oj7JYLnpZMxzZtIumvTuhlCK0aQRnM7LISCxZf7TWnNq1n4Zd2wHQrE9njmzaCYCTmwvK+nOy53LOYvYRo2Obd9Kol5E/uImRPzOpbP7TkfuJ6NIegEa9OnNss7Gu3Xy8CGpUDwdHS5nHtre9GyJp18/Ydus0q092RhZpiWW33bCGtfELKdsuVnR5e6jpbWdNzw+wZ30k7a3vf93m9clOzyLVRtsf3qg2fqFl609Fl7eHzKNHcA4Kwtna/vl06Ejqju0lyjh6e+NeP6JM23k5WLdpL4nJ6eXOHzzgambMXwPApm0H8fF2JzTYl47tGnHoaAxHj8eSm5vH3N/XM3hAB7NiF1q7MoqBg69GKUXLNvVIT8smIS61TDkPT2NQjdaanJzcwrYe4OeZ6+jVrzV+/h6m5a4uysR/WutpWusOxf6mlcii1FKlVKSNvxsr9FqUGgzEaq3/vZh1UJHhWFnW/zOVUuFAAhBxMU9SGdlJybj6Fx1dd/X3I/nQkbJlAkqWyUlKxtXXp3Ba9MYthHfpaO+4JQS6OhOXfbbwflz2WZr7lv8lfFCdEDbFJZuQrOKS41LwC/ItvO8X5EtyXAq+AT7lL1TMmRNx5OXl8e7Tn5CdlUO/W3vSdaA570N2UjJuAReoO4nJuJWqX9mJySiLA87enuz86ntSj5/EJ6IuLe68HUcXF8xyNjkZl2LZXPz8SD1y5DxLVM2yVSkjMRnPwKIcHgG+ZCSm4OFfVH+y0zJw9nDDwfplwCPAl4xiX1YOb9jBhp8WkJWSzg0v/8e88EBmYjIexeqQe4AvGYnJuPsV5c9Jy8DZvXh+vzI7WtUhLT4Z72LbrnegD6nxKXj5V2zbvdTlL0VNbztren6A1IRkfIq//0E+pCak4F3Btv9Sl78U55KTcfIr2ulz8vMj82LaPwVHPpoKQECPXvj36FnFCS9NeKg/J6MTCu+fikkkPNSf8FA/Tp4uNj06gU7tGtl6CLuKj00lONS38H5QiA9xsSkEBJU9PWDKq7PZuHYv9RqE8PhzxnWQ4s6ksGZFJB9Me4S9USfMii0ArXW5Y4aVUmeUUmFa62ilVBgQa6NYN2CoUup6jFM3vJVSP2qt7zzf81akJ2ShUsoXeAfYinFmvO2xCUWBC7t9pk2bdr6i5dPa1uNe1EPknztH7LadhHa6qnIZqpC28XoA2vn7MKh2CF/tO2puoAvQXNr6z8vL59i+kzz55kM8/fYjLPr+b86csFVv7cHWulYXLqFA5+WTevQEdfv1pMfrL2NxceHQ74vtkrJctquK/ZetQjbre+nqc4EyDbq0ZdTH4xj0wkNsmrmwSvNdiO1opeqQrTbKXoEugu26XfFkl7p8VatpbWdpNS2/zbp/MfXnEpe/JDa/N1R88YZjXqTxS+OIeOJpElatIOPA/ioMd+ls/a6E1trm+i2n2tmVzTaxnDdg7MThzF8yjnoRwSxfbPQgf/zOAh55+noslppx1ahLp0z8uyQLgIIx9fcAv5UuoLUeq7WurbWuD4wAll9oBwQq0BOitZ5kvTlfKbUQcNVan/dwn7Wbp2DvQz+zYfmFnqYM48h00RXAshOTcPEteSTF1c+X7IRSZfx8C+/H7YzCu15dXHwqdJJ+lYnPPluiiz3I1ZmEnLNlyjXwcmd064aM3by72rvgAVb8spY1C40x7fWb1SWp2BG6pLhkfAIrvh79gnzw9PHAxc0FFzcXGrdtyIlDpwmpU95Qwqrj6udHVql64epXsu64+fuSVbp+WeuOq78vfg2Nzr6wju05uPBvu2cuztnPl5xi2XKSknDx9bX7spdq15+r2b3kHwCCG9UlPb4oR0ZCMh6l3gNXb0/OZmSRn5eHg8VilLFxtD28ZSNSPo4nKzUdN2/PMvOryu6/VrFvqZE/sFE9MorVocyEZNz9beTPLJ4/qUwZs2z8fQ1bFxvbbnjjuqQW23ZT41PwCqj4tusd6HtJy1+Kmtp2Fqip+TcsWMPmv6znYzWpS0rx9z8uBS//ir//PoG+l7T8pXD08yM3KbHwfm5SEo4+vhVe3snaVjp6e+Pdrj2ZR4/g0bhJFaesvFMxCdQOKxoCVyvUn+gzSTg7OVI7vNj0sABOx9q8gmqV+2XWOhb+vBGApi3rEBuTXDgv7kwKgTZ6QQpYLA70HdiWWdNXcf1NHdm3+wQTX/gJgJTkDDas3YvF4kCPvq3s+hrEBb0JzFFKPQAcxzg3HOvoqK+11tdX9oErtLuplLpGKTUK4wT1G5VSd1f2CSvKJ6IeGWdiyYyLJ//cOaI3biG4fcmrJAS3b8OpdRvQWpN08DCObm4lh2Jt2Ex4F/PHRe5NSaOWhxuhbi44KkWfsCD+iU0sUSbY1Znx7ZsxZccBTmZmm57Rlj43d+fVb/7Lq9/8l3bdW7F+8Wa01hyOOoqbh1uFh2IBtOvemoO7DpN3Lo+c7LMc2X2MsLohdkxfxKdBybpzesMWQi5Ud9yNuuPq64Orvx/p0TEAxEftw8vkE9O9I+qTdSaWLGv+2E1bCGjX1u7LXqrWg3oWnkwe0akN+1ZuQmtNzL4jOLu7ltnBUEpRq1VjDq3fDsDeFRuJ6NgagJTouMKjanGHTpB/Lg9XL/uOEW5xXS9ufncsN787lnod23BwlZE/dv8RnNzdSgzFKsgf1rIJRzZsA+Dgqo3U7Vg9V3LpPKQHj37yPI9+8jzNu7Zm+zJj2z2x9yiuHq4XNZSqWedWl7T8paipbWeBmpq/y9AePPnZ8zz5mVF/tlnf/+N7juLi4XpRQ6madWl1SctfCvd69cmJjeVsfBz5586RsmUz3m0q1v7l5+SQl51deDt9z25cw80/uft8Fi3ZyqhbewDQqX0jUtMyiYlNZsuOQzSKCKVenSCcnCwMG9KVRUsuamh+pd08ohvfzHmOb+Y8R48+rVi88F+01kTtPIaHp2uZoVhaa04ejy+8/c/q3dSNCAJg9h8vMftP46/Xta159qVbZAfkMqC1TtBa99NaN7b+n2idftrWDojWemVFfiMEKtATopT6AWgIbAfyCp4D+L6iL6AyHCwWWtw1gs3vfIzOz6d2z2vwqh3O8eWrAajbtydBbVsRtzOSVf99FYuLM20eLNo3yss5S3zkXlree4c9Y9qUr+Hj3Yd5q2NLHBT8eTKWY+lZDK5jfJldeCKGuxrVxdvZiadbGpd+zdPw2D9Gl+TLbZvQ1t8HH2dHZvXpwPQDx/nzpFlDmQytu7QgcuMeXr5jMs4uztz7wojCeR+9MI27/zsc30Afls1fzeKZy0lNTGPiA+/QunNz7n5+BGH1QmjZqRkTH3gHpRTdb+hCrQZhpmR3sFhodfcINr39MVoX1Z1j1rpTr29Pgtu2Im5HJCv/+yoW55J1p+Vdw9n++f/Iz8vDPSiQtg/dZUruAspiodEdI9j1wYfo/HxCu3fDo1Y4p1euAiC8dy/OpqTw76Q3yMvKBqU4uXQZHSeNx9HNzeayZqt3dUuOb93NT49NxNHFib5PFPXKLnz9c/o8NgoPfx+63HUjS97/HxtnLCQoojbNr+0KwKH129m3ahMOFguOzk4MGH2fqUOC6lzVkpPbopj75AQcnZ3o8XhR/sVvfEb3/4zCw9+XjnfeyIoP/se/MxcSEFGHpn2N/JlJqfz24tvkZmWjlCJy0Upu/eBlnN3dynvKKtO4Ywv2b97Nhw9MwsnFmZueHVU478dXv2Do0yPxDvBhw2+rWDdvGelJaXz++Fs07tCCG58Zed7l7a2mt501PT9A007G+//+/cb7f8tzRe//9HFfcPMzRv3559dVrJm3jPTEND5+9C2adGzBLc+OPO/y9qYsFsJHjOLIx1MhX+N3TTdcw2uRsHolAAE9e5ObksLBN18nP9toO+OXL6XJqxPJS0/n2JefAaDz8/Dt2BmvluZ+AZ7+8ZP06NqcQD8vDm78hEnvz8PJyfia9vWPS/lr+TYG9mlH1JqpZGbl8MiYLwFj+POz477j9x/GYrE4MH32SvbsP2lqdoAuPZqxYe0eRg15ExdXZ16ccHvhvOcf/4bnX7sN/0AvpoybRUZGDmhNwybhPPfyLaZnvRzUlB8rtCdV3njVwgJK7QFa6AsVLF+lhmNdLqZ26Uu/P9dduOBlatmgbqyK/qO6Y1Rar7DreW5jza0/73fuy8NrV1Z3jEqZ1r03H0aZOxStKj3dcgBv71xS3TEq7fk2/Zl16K/qjlFpIxpeV+Pbzpqaf9mgbsw7UnPrzm0R13Gb9aBRTTSvb0/c6o6s7hiVlnV8JjFZ5v/OSFUJdRt6OZyed0GpuUtMO3PH26n/ZblOKnJ1rEggFIi+UEEhhBBCCCHEhVyW+wWmKncnRCn1O8awKy9gt1JqE5BTMF9rPdT+8YQQQgghhBBXmvP1hLyLsZv2FnBTsekF04QQQgghhBAXydYll/+/KXcnRGu9CkAp5VRwu4BSyv5nVwohhBBCCCGuSOcbjvUo8BjQQCm1s9gsL6Bmnq0nhBBCCCFENZOekPMPx5oB/AlMAV4sNj2t4BrBQgghhBBCCHGxzjccKwVIAWrudeaEEEIIIYS47MjvhMgaEEIIIYQQQpiqIr8TIoQQQgghhKgiSsk5IdITIoQQQgghhDCV9IQIIYQQQghhKukJkZ4QIYQQQgghhKmkJ0QIIYQQQggTye+ESE+IEEIIIYQQwmRKa23v57D7EwghhBBCCEENOdki89wa074fuzv2uCzXiSnDsbYnLDTjaeyiXcBgDqTU3PyNfQZT753l1R2j0o79ty9/n/qjumNU2oBa1zPj0F/VHaNSRjW8jg8il1R3jEp7tlV/Rq1cVd0xKm1G717UfX9FdceotOPP9SE9t+a2PZ5OfUnLXVbdMSrFy6kfLb5dXd0xKm33/T1Jyqm5n7t+LoOJyVpQ3TEqLdRtKG51a+7vVGcdn1ndESpIBiPJGhBCCCGEEEKYSk5MF0IIIYQQwkRyYrr0hAghhBBCCCFMJj0hQgghhBBCmEgp6QmRnhAhhBBCCCGEqS64E6KUiqjINCGEEEIIIURFKBP/Lk8V6QmZb2PavKoOIoQQQgghhPj/odxzQpRSzYCWgI9S6pZis7wBV3sHE0IIIYQQ4kqk5IyI856Y3hQYDPgCQ4pNTwMesmMmIYQQQgghxBWs3J0QrfVvwG9Kqa5a6/UmZhJCCCGEEOIKdvmeq2GWivQFJSillimlIgGUUm2UUq/YOZcQQgghhBDiClWRnZCvgLFALoDWeicwwp6hhBBCCCGEuFIppUz7u1xVZCfEXWu9qdS0c/YII4QQQgghhLjyVeQX0+OVUg0BDaCUug2ItmsqK601333wK9vW78HF1ZlHXxlBg6a1y5T7aPyPHN57EovFQqMWdXjohWE4OlrYvDqSOV/9hXJQWCwO3PP0jTRr28CM6Gitmfber2z5x8j+zKsjaNSsbPZ3xv3IwT0nsThaaNKyDk+MNbJnpGfx7qsziItJIj8vn5vv7E3/IZ1MyQ7Qq74/r/VrjEUpZu2M5vNNx2yWaxPqxa93dOCJ3yP5Y38cLhYH5oy8CmeLwtFB8cf+OD5Yd8S03AW01sz/5BeiNu7B2dWJO58fSZ0mdcqUW/XLGlbOX0386Xim/DIJTx/PEvOP7T3Oe09M5b5xd9O+VzuT0hv5//ryZw5s3o2TixM3PXcHYY3K5k+KSWD+m9PJSs8grGEdbh5zJxYnR7LSMlkwdQaJ0fE4Ojtx4zMjCa4fbmr+dd/O4/jWKBydnenz5F0ENSibP/VMPEs/+B/ZaZkENahD36fuxuLkyJFNO9k8cyHKQeFgceCa+24jrHlD0/KnREZyYs5syM8nsHt3Qq8bVGJ+dkw0R7+bTuaJ44TfeBOhAwaUmK/z89nzxmScfX1p9MSTpuUGY9sd37sxFgeYtSuazzYft1muTYgXv428mscXRfHHgTjCPF34YFBzgtyd0Rpm7DrNt9tOmpodjLrzzpQ5rFsThaurM+Mn303zFnXLLf/2G7NZ8Mt61m6eWmJ61K6j3HvH20x590GuHXCVnVMX0Vrz7pS51vxOjJ98N80ukP/3XzawZvMHAKxcvoMvPv4dBwcHLBYHRr94G+2uamRWfLrX8mNsl4ZYlGLe/hi+3nmixPy+dQN48qp6aA3ntObNjYfYeiYVgCXDOpGRm0e+1pzTmtsXbDMtdwGtNe+/9Svr1xifveMmjaBZi7KfvZNfm82eqBNoDXXrBTHu9RG4u7uQmprJ5Fdnc/JEAi4ujrw8YTgNG4eZmv+jt39j49q9uLg6MXbicJo0L5v/rfFz2Lf7JFpr6tQL4sWJw3F3dymcvyfyBI/d/TGvvXUnvfu3MSX7F+88wqB+7YlLSKVD/+dtlnlvwj0M7NOOzKyzPDz6c7ZHHgWgf6+2vDv+biwWB76btYJ3P1tgSubqdfn2UJilIj0hjwNfAs2UUqeAZ4BH7RmqwPb1e4k5Gc+Hc8by0AvD+OYdWz9ZAj0GXM0HM1/g3R/HcDYnl+ULNgLQukNj3v5+NG9PH81/XhrOl1PmmBEbgC3/7OX0iXimzR/LE2OH8dlbtrP3vu5qvpj7Ap/ONLL//auRfdHcddSNCOGTGWOY8sVjfPPhAnJzzemAclAwqX9T7pm3g2u/3cjQ5sE0DnC3WW5sz0asPppQOC0nL5+Rs7cxaPpmBk3fTK/6/rQP8zYld3G7N+4h9lQcr/7wEiOeu53ZU23/tE2DVhE88e6j+If4lZmXn5fPb9N+p3mHZvaOW8bBLbtJPBXHk1+/wpCnRrDok7k2yy39dgFdbu7Nk1+Pw9XTja1/bwBgzZwlhDSoxaOfvcjNo+/kry9/NjE9HN+6m5ToOEZ+8hq9Hh3JmmmzbJbb8MNvtBnch1GfvoaLpxt7lxnXwKjduinD3h/LsPfG0vuxO1n12QzTsuv8fI7PnEHjJ5+ixfgJJG7eTNbp0yXKWNw9qDNiBCH9+9t8jNhly3ANNe+LSwEHBa/3bcI9v+yg33ebGNoshMb+5Wy7PRqy6lhi4bQ8rXl91UH6Td/EjTP/5e52tWwua2/r1kRx4ngsv/4xgVfGj2LKpJnllt0deYy01Mwy0/Py8vnog1/o2q2FPaPaVJD/lz/G8/L4O5gyyXbdh4L8WSWmderSlJk/v8yM+S/x6qQ7mfTaT/aOXMhBwStdG/HI35EM+XkL1zcIoqFvyTqw4XQSN/+6lVt+28ora/YzsVuTEvPv/XMHt/y2tVp2QADWr93LiWPxzF04lrGvDuPt121/9j7z3xv5cd4Yfpo/hpAwX+bNXAvA9K+W0bhpOD/NH8Ork0fywVu/mpgeNq7dy8nj8fy04AXGjLuN9yfbbrufGDOUb+c8x//mjiY41JdfZq0rnJeXl8+XHy6iY9emZsUG4Ie5q7jx7jfLnT+wTzsa1g+lVc9neeLFr/ho8gMAODgopr5+Hzfe8xbt+41h2NBraNa4llmxRTW64E6I1vqw1vpaIAhoprXurrU+avdkwOY1kfS87mqUUjRpVY+M9CyS4lPLlGt/TfPCcW+NWtQlMTYZAFd3l8KxcDlZZ8HEcXEbV0fS93oje7PW9chIyyLRRvaO3YqyN2lRl3hrdpQiKzMHrTVZmTl4ebtjsZhzTel2Yd4cTcrkREo2ufma3/fG0r9RUJly915Vmz8PxBKfmVtiemZuHgCODgoni4PRhWayXf9E0ql/R5RSRLSoT1Z6FikJKWXK1Wlcm4BQf5uPseqXNbTr2RZPP0+b8+1p74ZI2vQz8tduVp/sjCzSEkvm11pzZOcBWnRvC0Dbazuxb/0uAOKPx9CgnfHlILBOCMlnEklPKlv/7OXo5p006dUJpRQhTSLIycgiI6ls/tOR+2nQtT0ATXp35simHQA4uRVtu7k5OaYeMMo4cgTX4GBcgoJwcHTEr0NHknfsKFHGydsbj/r1URZLmeXPJiWRsmsXgd27mxW5ULtQb44mZ3G8cNs9w4CGgWXK3deuNn8eiCMh82zhtNiMs0TGpgOQkZvHwYQMQj1dyixrb6tW7OCGoV1QStG6bQPS0zKJiyu77ebl5TP1vZ95avQtZebNnrGCfv3b4+fvZUbkElat2Mn1Qztb80eQlpZJfDn5P3zvZ54efXOJ6e7uroV1PyvrrKnHSlsHenE8NYuTaUb9+fNwHH3rBpQok3kuv/C2m2P1tO/ns3pFJNcPMT57W7WtR3paFvFxZds+D0/j58601uRk5xZ+Pzhy+AwdOjcGoH5ECNGnk0hISDMt/9qVUQwcbORv2aYe6WnZJFwof05uiXH/P89cR69+rfHz9zAtN8C6TXtJTE4vd/7gAVczY/4aADZtO4iPtzuhwb50bNeIQ0djOHo8ltzcPOb+vp7BAzqYFVtUowt+q1VKPaeUeg54BHjIev8BpVQ7e4dLikshIMS38H5AkA+JNhrzAufO5bH6r39p26XoyPWmVbt4dsSbvDnmax59abg945aQEJtCYPHswT4kxJ4/+4o//+Wqrkb2wcO6ceLoGe6+fgJPjHqXh5+7CQcHc3ZCQj1diE7LKbwfnZZT5stIiKczAxsH8eP2U2WWd1Dwxz0d2fp4d9YcTWR7tHlffgskx6fgF+xbeN83yJeU+PLXf5nl45LZuXYX3YdcY4d0F5YWn4xPkG/hfe9AH9JK5c9KzcDVww0H6xdh70BfUhOSAQiJCGfPup0AnNp3jOTYJFIv4vVfqozEZDwDi3qXPAN8ybBmK5CdloFzsfyeAX5kFNvROrJxB7OenMSfb3xB78fvMCU3QG5yMk5+RTumzn6+5CYnVXj5E3NmU+vWW6vlZMBQTxdOp2UX3o9OzyHEq5xtd2fZbbdAbW9XWgZ7sS3G/G039kwyIaFFdSc4xI+4M8llys2esZJefdoQFORTZvkVy3Zw6+097R3VprgzyYQWyx8S4kesjfxzZqykZ582BJbKD7Bi6XZuHTKBZx77jFcn3WXPuCWEeLgQk1HU9sdk5BDs7lymXL96ASy8pQNfDGjFK2v2FU7XwNcDWzN3aHuGNQ01I3IZcbEpBIf6Ft4PDvEhrpzP3knjZnF9n/EcOxrL7SONgwaNm4SzcplxMCdq13FiopNs1j97iY9NLZE/6Dz5p7w6m5v7TeT4kThuGdENgLgzKaxZEcnQYV3NiHtRwkP9ORldNHLiVEwi4aH+hIf6cfJ0senRCdSyMTrhSqNwMO3vclWRZB2A/wC1rH8PA72Br5RSNgf9KaUeVkptUUptmTZtWqXDaRuHWM73uf7NO/Np3q4BzdsVnffRqVdrPpj1ImPevI/ZX/1V6SwXy9bRofNl/+yt+bRs34BW7Y3sWzfso0HjWnz/x2t89ONovnjnFzLTs8t/ADvTpV7Ra32b8OaqQ+TbeKH5Gq6fvpkuX/xDuzBvmgSaezQGjKNDZVzEl8L5n/7K0IcH42BS71OFlMpvs45Zj5t2v70/2emZfPHE22xasJqwhrXMfS02V3+p9W/jPSpeJKJzW0Z8PI6Bzz/M5pmLqjjg+dhesxWRvHMnTl5eeNSrV7WRKshWytKreXzvxkxZY3vbBXB3svDlkFZMWHmA9LN5VZ7xQiqy6cbFJrP0760MH9W7TNl335rLU8/eZFrPcWm22p7Sdd/Iv81mfoA+17Zj/u+v8e5Hj/DFJ7/bI6ZNFW0hlx1LYPDPW3hi6W6eurp+4fQ7Fm7ntgXbeOTvSEY2D+fqkLI7WPZ2MU3/uEkjWLjsNepHhLB08XYA7n6gL2mpWdw17D3mzlxLk2a1sNjo8bSXitSfAmMnDmf+knHUiwhm+WKjt/bjdxbwyNPXV1v9Px9lo4ZprW2+Plvvo7jyVOTE9ADgKq11OoBS6jVgHtAT+Bd4u/QCWutpQMHeh96esLDCgRbPX8sy6zkdDZvVIaHYEYiEuBT8Am03anO/WUxqcjqjX7jX5vwW7Rvy2euzSE1Ox9vXPsNrFs5dy2LrOR2NW9Qhvnj22BT8bRzxApjx1WJSk9J5Yuy9hdOWLtzMbXf3RSlFeJ1AQsL9OXEslqYtyz/BsarEpOcQVuzoaZiXC2fSz5Yo0ybEi4+HtATA382JPhEBnMvX/H0wvrBMas451p9IoneEP/vjM+yee/Wva/lnkXFOQd2mdUkqGNqG0bPhE1Dxc1OO7z/Bd5O+ByA9JYPdG/fgYLHQtnvrKs1c3Kbf17B1sZE/vHFdUuKSC+elxqfgVSq/u7cH2RlZ5Ofl4WCxkBqfjFeAUcdc3F258Tmj90BrzYf3TcQvtOSwiqoW+ecq9iz9B4CgRvVIjy/qPUhPSMbdv2T9d/X25Gyx/OkJSbj7ld1Gwls2YsUn8WSlpuPmbf+hcU6+fuQmFZ0rcTYpGSdf3wotm3HoIMk7dpASGUl+bi55WVkc+eYbIh54wE5pS4pOzyHcy7XwfpinC7HpOSXKtA7x4pPrjXMlSmy7h+JxdFB8OaQVv+w5w1/FtmV7mzNzJb/MM8a0t2hVjzMxRXUn9kwSgcV6NQH27jnByeNx3HT9qwBkZ5/lxkGv8tufE9kTdYyx//0GgOSkDNaticRicaBPv3Z2zL+KX4vljymW/8yZJIKCS9brfdb8N1//WmH+mwa9xq9/TihR7qoOjTl5Ip7kpHR8TRgWGpORQ6hHUdsf6uFCbObZcsv/eyaFOl5u+Lo4kpxzjrgso2xidi7LjiXQJsiLf8/Yvwd23qy1/Dbf+Oxt3rIOsTHJhfNiz6TY7G0qYLE4cO117fjpuxUMvqkTHp6ujJtk/AqB1pqbB00mvJbtIbtV5ZdZ61j4s5G/aan8cWdSCAwq/7PLYnGg78C2zJq+iutv6si+3SeY+IJxHlFKcgYb1u7FYnGgR99Wdn0NFXEqJoHaYUWfQ7VC/Yk+k4SzkyO1w4tNDwvgdGzFe59rLjkxvSI7IXWB4q1QLlBPa52llMopZ5lKG3hrdwbeanSLbl23m8Xz13FN//YciDqOu4crfoFlN8ZlCzawc+M+xn38aIkhSzEn4wmpFYBSisP7TnIu9xxePvY7Kj94WHcGDzOyb167m4Vz19FzQHv2RR7H3dMVfxvZF/+6ga0b9jH505LZg0J82bH5AK3aNyApIY2Tx2MJtXNDWGBHdBoRfu7U8XElJi2HIc2CeWrh7hJlun+1vvD2u4Oas/xQPH8fjMffzYlz+ZrUnHO4ODrQvZ4/n2+0fWWtqtbzpu70vMlY/5Ebolj961qu7tueo3uO4erhhk9AxY/KTZgxrvD2D2/NoFWXFnbdAQHoNKQHnYb0AGD/pig2/76GVr2u4tS+Y7h4uOJV6ku8UoqINo3ZvXYHrXpdxY6lm2jaxfigyU7PxMnFGYuTI1sXr6deq4a4uLuWec6q1GpQL1oN6gXAsX8jifxzNY26X03sgaM4u7vh4Vc2f3irJhxev41G3Tuwf+VG6ncyruKSEh2Hd2ggSiniDp8g79w5XL3M6VHzqF+f7NhYcuLjcfL1JWnLZiIeeLBCy9a6+RZq3Wyco5C2bx9nlvxt2g4IwI6YNCJ83ajj7UpMeg5DmoXw1B9RJcp0/2ZD4e33BjZj2eEE/j5k7HC8M6AZBxMz+HprySsi2dvtI3tz+8jeAKxZtYs5M1cycFAHIncewdPTrcyQqx69WvP3qrcK73fv+Ay//TkRgN8Xv144/bWXp9OjV2u77oAY+Xtx+0ij7q9dtYs5M1dZ8x/F09OtzJfg7r1as3hV0Qm8PTo+W7gDcuJ4LLXrBKGUYu/u4+TmnsPH15y6HxmfRj0fN2p5uhKbmcOgBkE8v3JviTJ1vVw5bh3y1zzAEycHRXLOOdwcHVAoMs/l4ebowDXhvny+3faV2arabSO6c9sIo+1ft3o3c2euo/+g9kTtPI6nl2uZL/Faa06eSKBO3UC01qxdGUW9+sEApKVm4ermhJOTI7/N30j7qxoUnn9hLzeP6MbN1uFU61fv4efZ6+h3XTt27zqOh6crATbynzqRQG1r/n9W76ZuhHHe5uw/XiosN2XcLLr2bHFZ7IAALFqylf/cM4A5C/6hU/tGpKZlEhObTFxCKo0iQqlXJ4jTMYkMG9KVe5/6pLrjChNUZCdkBrBBKfWb9f4QYKZSygPYXf5il679Nc3Ztn4PTw+bgrOrE4++XPQbiVNGf8UjL96Of5APX78zn6AQP155+CPAGIJ12/0D2LhiJ6v/2oLF0YKzsxPPTLrLtHHaHbo1Z8s/e3jolim4uDrxzLii7K898xVPvXw7AUE+fPrWfIJD/RjzgJH9mj6tGfngAEY80J+pE2fx+Mh30Brue2IwPnbqwSktT2teXbqf729rh8VBMWfXaQ4kZHBHW+MSrz/tOF3ussGezrw/qAUODsYoxIX7Yll+OKHc8vbSsnMLdm/cw8Q7J+Pk6sydzxet/89fnMaoMcPxCfRh5c+rWTZrOamJaUx58B1adm7OqDHV/1ucjTu24MDm3Xz8wCScXJy58dlRhfN+evULhj49Eq8AH669bwjz3prO8u8XEdawNu0HGuOA406c4df3fkQ5OBBUN5ShT480NX/dq1pyfGsUMx+fgKOLE70fv7Nw3qLXP6P3Y6Pw8Pely503suSD/7Fp5kICI+rQvJ+R//CG7exfuREHRwuOzk70f+5+07ZdZbFQd8RIDnw4FZ2fT2C3briFhxO3ahUAQb16kZuSwp43JpOXnY1SithlS2k5fgIWNzdTMpYnT2vGrdjPD7e2xaIUsyOj2Z+QyZ1tjG33x53lb7sdw324tUUoe+LS+fNO46TQt9cdZsWRxHKXsYfuPVuxbk0kNw56FVc3Z8ZPurtw3lOPfsK4CXcSVKpn5HLSrWcr1q2J4qZBr+Hq5sxrxc7peOrRTxk34Y7z5l+2ZDt/LNiIo6MFF1cnprz7gGl1P0/D5PUH+WpgKxyU4pcDMRxMzmR4U+NKb7P3RdO/fiA3NgrhXL4mOy+f0Sv3ABDg5sxH/YweNkelWHQ4lrWnzD+afU2P5vyzZg+33TAFV1cnXplU1J4/+9hXvDT+dgICvZj4ykwy07PRGho1DeOFV24D4OiRM0x4eSYWB0X9hqG8POF2U/N36dGMDWv3MGrIm7i4OvNised//vFveP612/AP9GLKuFlkZOSA1jRsEs5zL5e9QIPZpn/8JD26NifQz4uDGz9h0vvzcHIyvmZ+/eNS/lq+jYF92hG1ZiqZWTk8MuZLwLhIw7PjvuP3H8ZisTgwffZK9uw3//LgZrM1PO3/G2Vz7HzBTKPlqw0EA90x+o7Waq23XMRzXNRwrMtNu4DBHEipufkb+wym3jvLqztGpR37b1/+PvVHdceotAG1rmfGIfPORapKoxpexweRS6o7RqU926o/o1auqu4YlTajdy/qvr+iumNU2vHn+pCeW3PbHk+nvqTlLqvuGJXi5dSPFt+uru4Ylbb7/p4k5dTcz10/l8HEZNXc37kIdRuKW11zD1xVpazjM2vEt/tz+TtMO/PF0aHtZblOztsTorXWSqlftdZXY5z/IYQQQgghhLgE1XEFxctNRS6fsEEp1dHuSYQQQgghhBD/L1TknJA+wCNKqWNABsaQLK21bmPXZEIIIYQQQlyRLr/LKJutIjshg+yeQgghhBBCCPH/xgV3QrTWxwCUUsGAfa9TJ4QQQgghxBVOro5Vgb4gpdRQpdQB4AiwCjgK/GnnXEIIIYQQQogrVEUGpE0CugD7tdYRQD9gnV1TCSGEEEIIccVSJv5dniqyE5KrtU4AHJRSDlrrFUA7+8YSQgghhBBCXKkqcmJ6slLKE1gN/KSUigVy7RtLCCGEEEKIK5P8TkjFdkJ2AJnAs8AdgA/gac9QQgghhBBCiCtXhX4nRGudD+QD0wGUUjvtmkoIIYQQQghxxSp3J0Qp9SjwGNCw1E6HF3JiuhBCCCGEEJUkP1Z4vp6QGRiX4p0CvFhseprWOtGuqYQQQgghhBBXrHJ3QrTWKUAKMNK8OEIIIYQQQlzZ5McKpS9ICCGEEEIIYTKlta7uDJdEKfWw1npadeeoLMlfvWpy/pqcHSR/dZP81asm56/J2UHyV7eanl9UnSuhJ+Th6g5wiSR/9arJ+WtydpD81U3yV6+anL8mZwfJX91qen5RRa6EnRAhhBBCCCFEDSI7IUIIIYQQQghTXQk7ITV9XKHkr141OX9Nzg6Sv7pJ/upVk/PX5Owg+atbTc8vqkiNPzFdCCGEEEIIUbNcCT0hQgghhBBCiBpEdkKEEEIIIYQQppKdEFFjKKV8lVKPVdFjvVTsdn2lVGRVPK49KaW+U0rddhHlL4vXpZR6Sim1Ryn1k1JqTHXnuRRKqWeUUu7VnKFwO1BK9VZKLbzI5S+qHhVb7qKf6/8DpVR6OdMrtZ4v8Fz3KqU+qcrHPM9zrVRKdTDjuUSR6tq+L1ZVfh5f4HluUkq1sPfziOohOyGiJvEFyjR6SilLJR7rpQsXEVXkMeB64EB1BylNGS6mHXwGqNadEMrZDoQQVwRfasb27ctF5KxEW1vgJkB2Qq5Ql/1OiFLqV6XUv0qpKKXUw9ZpDyil9luP1HxVcGRIKRWklJqvlNps/etWvelBKeWhlFqklNqhlIpUSg1XSl2tlFplfV2LlVJhSikfpdQ+pVRT63IzlVIPVXf+4pRSdyuldlpfyw/WIy5fKKXWWN+PwXaO8CbQUCm13fr+rlBKzQB2KaUsSql3rNN3KqUesWYOU0qtti4TqZTqoZR6E3CzTvvJ+tiOSqnp1mXnFRztVkodVUq9pZTaZP1rZJ0+zPp4O5RSq+3xYkuvb+vknkqpf5RShwuOdlkb93eseXYppYbbI09lKKW+ABoAC4BngbZKqeVKqQMF9dvWe2TnTPWV0TPzGbAVGFes3kywlrG13T4FhAMrlFIrrOUGKKXWK6W2KqXmKqU8rdM7Wt+nHdZ646WUcldKzbE+z2yl1EZVuSPNhdsB8A7gaa2ze5XR26SsGV61vq5IpdS0guml1oXNMkqpRkqppdb8W5VSDa2L2HyuS6GUutO6jrYrpb5USnW2riNX6/sQpZRqpZTyVEots+bZpZS60bp8wfv5lbXs30opN+u8jtbHWl+wjVxi1ues6ypSKfVMqXlKKfWJUmq3UmoREFxsXnntiM3PLKVUJ2v92Wb9v6mNLDdYX1fgpbwm62OVqe+l5o+0rvNIpdRbxaanK6Xes74ny5RSQdbpDZVSfynjM26NUqrZpWa8FKrsZ9cQ6/a3zVrPQ6ozXylVtn2blVMp9cEFts2CtraOUmqc9bUsUcb3nDHWsmXqjFLqGmAo8I71eRqWm0bUTFrry/oP8Lf+7wZEArWAo4A/4ASsAT6xlpkBdLfergvsuQzy3wp8Vey+D/APEGS9Pxz41nq7P7AeGAH8Vd3ZS72OlsA+ILDgfQG+A/7C2JltDJwEXO2YoT4Qab3dG8gAIqz3HwZesd52AbYAEcBo4GXrdAvgZb2dXupxNdDNev9bYIz19tFiy98NLLTe3gXUst72NXF9z7Wu7xbAwWJ1bIn19YUAx4Gw4uurmuvOUSAQGA/ssG7LgcAJjC/1Nt8jO9ejfKALMADjcpHKul4XAj1tbbfFX4v1diCwGvCw3n8BeBVwBg4DHa3TvQFHYAzwpXVaK+Ac0KEKtoMUoLY1/3qK2kD/Ysv8AAyx3v4OuO0CZTYCN1tvu2L0/pT7XJfwXjQHfgecrPc/s25nrwPvAp8CY63zHAHvYuv+oPV9q29dl+2s8+YAd1pvRwLXWG+/eSnbA3A1xnbvAXgCUUB7rG0JcAtF22E4kFxsPR/Fdjti8zOroM5Yb18LzLfevhf4BLgZ47PPr4q2CVufUyuBDtbXchwIsr4Hy4GbrOU0cIf19qsUfRYvAxpbb3cGlttzm77Aa7PVlvpRdHXQB4H3qiufjbz1qaLt28Sc59s284Eu1nkdgO0YnwFeGL3jBZ+1NuuMWa9H/qrnz5HL31NKqZutt+sAdwGrtNaJAEqpuUAT6/xrgRbFDgh4K6W8tNZpZgYuZRfwrvXo0UIgCeMLyBJrTgsQDaC1XqKUGobxwdu2euKWqy8wT2sdD6C1TrTmn6O1zgcOKKUOA80wGhkzbNJaH7HeHgC0UUVjYX0wdow2A98qpZyAX7XW5WU7obVeZ739I/AUxpcggJnF/v/Aensd8J1Sag7wc1W8mFLKW9+/Wtf37mJH77oDM7XWecAZpdQqoCOw0w65LtVvWussIEsZvQmdqPh7VJWOaa03KKXexag726zTPTHqzRqKbbda6zU2HqMLxs7gOut744zxJaEpEK213gygtU4FUEp1Bz60TotUSlXV+7NJa33S+hzbMT741wJ9lFLPY+xA+GN8af691LJlyiilVmLsYP9izZptfezzPVdl9cP4cr/Z+vhuQCwwEaNeZGNsi2B8qXlDKdUT44tNLYydboAjxerNv0B9pZQvxg7tP9bpM4BL6a3tDvyitc4AUEr9DBTvtetJ0XZ4Wim1vNTyttoRm59ZGO3XdKVUY4wv+k7FHqcPxpe5AQV1qwqU+JzSWq8plqkjsFJrHQegjN7jnsCvGO/DbGu5H4GfldEbeA0wt9hjuFRRzsqw1Za2BmYrpcIwttsj53uAanYp27dZzrdtHtNab7De7k7RZwBKqd+t/19udUaY5LLeCVFK9cZopLtqrTOtH477MI6e2eJgLZtlSsAK0FrvV0pdjTEmfgrGkbIorXXX0mWVMV6yOZCF0aicNDPrBSiMD8PSSk8z84dnMordVsCTWuvFpQtZG8YbgB+UUu9orb+38Vjnex1lbmut/6OU6mx93O1KqXZa64TKvIhylLe+c0qVKf5/TVBmPWutV1fwPapKBXVHAVO01l+WLlB8u1VK/a21nli6CLBEaz2y1HJtsP3e2et9Kl4n8jCGFrpi9Cp00FqfUEqNx+jRKJ6zvDLny1nmuS4xuwKma63HlsoWirFD6GTNlAHcgXE0/mqtda5S6ihFr6l0Ljeqfn1X5PHO1/7ZalNsfmYppT4GVmitb1ZK1cfolShwGGOIYxOMHt9LVvpzSin1d/E4F/NQGK8pWWvdriqyVQFbbenHwPta6wXW7xnjTc50MSq1fZvsfNtm6c9pWy63OiNMcrmfE+IDJFl3QJphHHl0B3oppfyUUo4Y3cgF/gaeKLijlGpnZlhblFLhQKbW+keMI+udgSClVFfrfCelVEtr8WeBPcBIio4MXy6WAbcrpQIAlFL+1unDlFIO1rGaDTB2Eu0lDaML15bFwKMF60wp1UQZ45zrAbFa66+Ab4CrrOVzS63fugXvCcb6L350d3ix/9dbH7+h1nqj1vpVIB6jl64qlbe+bVkNDFfGeTFBGEcpN1VxnqpyozLG+gdgDDXYfJ73yAyLgftV0bkctZRSwTa224JMxevgBqCbKhrf766UagLsBcKVUh2t072sbdVa4HbrtBZA60pmPt92UKDgC0C89bXZulqOzTLWo+snlVI3WbO6KPtdEWwZcJtSKtj6XP7W+jANGAf8BBScg+CDUU9ylVJ9gHrne2CtdRKQppTqYp004hKzrgZusr7PHhQNiSo+f4R1OwzD6LEorkw7QvmfWT7AKevte0s9zjGMoV/fF/vsuCTnqe9gDM3rpZQKVMZFQEYCq6zzHCiqW6OAtdb6c0QZvfoF58pUZ8++rba0+Pq9p7qClaOqtm97K56zotvmWmCI9TPAE+PAExeoMxVZH6KGuqx7QjDON/iPMoYt7MP40D8FvIHRMJ4GdmOMmQSj2/5Ta3lHjA+F/5gdupTWGCdV5QO5wKMY45c/Ukr5YOScqpTKxRib2klrnaaMk51fAV6rptwlaK2jlFKTgVVKqTyKhq/sw/hACgH+UzB0w04ZEpRS65RxcmkWcKbY7K8xuqm3KqM/Nw7jqhq9gf9a1286xnhsML7k7FRKbQVextj5u0cp9SXGONXPiz22i1JqI8YHbsFR73eUMVRCYXzI7aji11re+rblF6CrNYMGntdax1iPoF5uNgGLMMa/T9Jan1ZK3YPt98jutNZ/K6WaA+uNakM6cCfQiLLbLRj15k+lVLTWuo9S6l5gplKqYOjAK9ajysOBj5VxgnQWRo/uZxhDbHZivJ87KWq7Libz+baDgjLJSqmvMIbZHMUY2nQxZe4CvlRKTbS+/mEXm7MitNa7lVKvAH8royc4F/gNOKe1nmH90vuPUqovxg7J70qpLRhDPvdW4CkeAL5SSmVg9CZc9PoulnWrUuo7inbwv9Zab1NFw0d+wRj6swvYT9EX9QK22pHyPrPexqgrz2Gcg1E6yz6l1B0Yw1eGaK0PVfZ1Wdn6nHrX+lzRSqmxwAqM9u4PrfVv1uUygJZKqX8x1m3BjtYdwOfW99YJmEUVt5EVVU5bOh5j3Z3C+F4RUR3ZbKmq7dveSuXcDDS70Laptd6slFqAUReOYfTkFWyT5dWZWRjb8FMY54Zcal0Xl5GCE7NqFKWUp9Y63Xp08ReME7t/qe5c/99YP5AXaq3nVXcWe1FGt3KHgvHEQlSW9Qu1k9Y6Wxk9h8uAJlrrs9Uc7YpV8Flhvf0iEKa1froachzlCmxHlFLpWmvP6s4hao5i39/cMXa6H9Zab63uXKJ6XO49IeUZr5S6FqNL8m+ME+SEEOJy5o5xeV8njCPKj8oOiN3dYD2K74hx5PXe6o0jxP9706zDUV0xzgeTHZD/x2pkT4gQQgghhBCi5rrcT0wXQgghhBBCXGFkJ0QIIYQQQghhKtkJEUIIIYQQQphKdkKEEEIIIYQQppKdECGEEEIIIYSp/g+HMRNadYTongAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's make our correlation matrix more visual\n",
    "corr_matrix = df.corr()\n",
    "fig, ax = plt.subplots(figsize = (15, 10))\n",
    "ax = sns.heatmap(corr_matrix, \n",
    "                 annot = True,\n",
    "                 linewidths = 0.5,\n",
    "                 fmt = \".2f\",\n",
    "                 cmap = \"YlGnBu\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "36ac9650",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into x and y\n",
    "x = df.drop(\"target\", axis = 1)\n",
    "y = df[\"target\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "d4461790",
   "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>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "0     63    1   3       145   233    1        0      150      0      2.3   \n",
       "1     37    1   2       130   250    0        1      187      0      3.5   \n",
       "2     41    0   1       130   204    0        0      172      0      1.4   \n",
       "3     56    1   1       120   236    0        1      178      0      0.8   \n",
       "4     57    0   0       120   354    0        1      163      1      0.6   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "298   57    0   0       140   241    0        1      123      1      0.2   \n",
       "299   45    1   3       110   264    0        1      132      0      1.2   \n",
       "300   68    1   0       144   193    1        1      141      0      3.4   \n",
       "301   57    1   0       130   131    0        1      115      1      1.2   \n",
       "302   57    0   1       130   236    0        0      174      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "0        0   0     1  \n",
       "1        0   0     2  \n",
       "2        2   0     2  \n",
       "3        2   0     2  \n",
       "4        2   0     2  \n",
       "..     ...  ..   ...  \n",
       "298      1   0     3  \n",
       "299      1   0     3  \n",
       "300      1   2     3  \n",
       "301      1   1     3  \n",
       "302      1   1     2  \n",
       "\n",
       "[303 rows x 13 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2b1e4764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "298    0\n",
       "299    0\n",
       "300    0\n",
       "301    0\n",
       "302    0\n",
       "Name: target, Length: 303, dtype: int64"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "04b10614",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into train and test sets\n",
    "np.random.seed(42)\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c8c40150",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>sex</th>\n",
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       "      <th>thal</th>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>46</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>75</th>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>135</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>117</td>\n",
       "      <td>230</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>94</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>69</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>160</td>\n",
       "      <td>234</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>249</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>140</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>242 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
       "132   42    1   1       120   295    0        1      162      0      0.0   \n",
       "202   58    1   0       150   270    0        0      111      1      0.8   \n",
       "196   46    1   2       150   231    0        1      147      0      3.6   \n",
       "75    55    0   1       135   250    0        0      161      0      1.4   \n",
       "176   60    1   0       117   230    1        1      160      1      1.4   \n",
       "..   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
       "188   50    1   2       140   233    0        1      163      0      0.6   \n",
       "71    51    1   2        94   227    0        1      154      1      0.0   \n",
       "106   69    1   3       160   234    1        0      131      0      0.1   \n",
       "270   46    1   0       120   249    0        0      144      0      0.8   \n",
       "102   63    0   1       140   195    0        1      179      0      0.0   \n",
       "\n",
       "     slope  ca  thal  \n",
       "132      2   0     2  \n",
       "202      2   0     3  \n",
       "196      1   0     2  \n",
       "75       1   0     2  \n",
       "176      2   2     3  \n",
       "..     ...  ..   ...  \n",
       "188      1   1     3  \n",
       "71       2   1     3  \n",
       "106      1   1     2  \n",
       "270      2   0     3  \n",
       "102      2   2     2  \n",
       "\n",
       "[242 rows x 13 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e19531b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132    1\n",
       " 202    0\n",
       " 196    0\n",
       " 75     1\n",
       " 176    0\n",
       "       ..\n",
       " 188    0\n",
       " 71     1\n",
       " 106    1\n",
       " 270    0\n",
       " 102    1\n",
       " Name: target, Length: 242, dtype: int64,\n",
       " 242)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train, len(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "774da178",
   "metadata": {},
   "source": [
    "Now we have got our data into training and test sets, it's time to build a machine leaning model.\n",
    "\n",
    "We will train it (find the patterns) on the training set.\n",
    "\n",
    "And we will test it (use the patterns) on the test set.\n",
    "\n",
    "We are going to try 3 different machine learning models:\n",
    "1. Logistic Regression\n",
    "2. K-Nearest Neighbours Classifier\n",
    "3. Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "98087f35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put models ina dictionary\n",
    "models = {\"Logistic Regression\" : LogisticRegression(),\n",
    "          \"KNN\" : KNeighborsClassifier(),\n",
    "          \"Random Forest\" : RandomForestClassifier()}\n",
    "\n",
    "# Create a function to fit and score models \n",
    "def fit_and_score(models, x_train, x_test, y_train, y_test):\n",
    "\n",
    "    \"\"\"\n",
    "    Fits and Evaluate given machine learning models.\n",
    "    models: a dict of different Scikit-Learn machine learning models\n",
    "    x_train: training data (no labels)\n",
    "    x_test: testing data (no labels)\n",
    "    y_train: training labels\n",
    "    y_test: test labels\n",
    "    \"\"\"\n",
    "    # Set random seed\n",
    "    np.random.seed(42)\n",
    "\n",
    "    # Make a dictionary\n",
    "    model_score = {}\n",
    "    \n",
    "    # Loop through models\n",
    "    for name, model in models.items():\n",
    "        # Fit the model to the data\n",
    "        model.fit(x_train, y_train)\n",
    "        # Evaluate the model and append its score to the model_score\n",
    "        model_score[name] = model.score(x_test, y_test) \n",
    "    return model_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "34294b70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Win10\\Desktop\\data-science-projects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Logistic Regression': 0.8852459016393442,\n",
       " 'KNN': 0.6885245901639344,\n",
       " 'Random Forest': 0.8360655737704918}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_score = fit_and_score(models = models,\n",
    "                            x_train = x_train,\n",
    "                            x_test = x_test,\n",
    "                            y_train = y_train,\n",
    "                            y_test = y_test)\n",
    "model_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05752538",
   "metadata": {},
   "source": [
    "### Model Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ba64f706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:>,)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_compare = pd.DataFrame(model_score, index = [\"accuracy\"])\n",
    "model_compare.T.plot.bar(),"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "578364dc",
   "metadata": {},
   "source": [
    "Now we have a baseline model and we know a model's first prediction aren't always what we should base our next stepps off.\n",
    "What should we do?\n",
    "\n",
    "Let's look at the following:\n",
    "* Hyperparameter Tuning\n",
    "* Feature Importance\n",
    "* Confusion Matrix\n",
    "* Cross-validation\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Score\n",
    "* Classification Report\n",
    "* ROC Curve\n",
    "* Area Under the Curve (AUC)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2efd291",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning (by hand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "84a5513f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's tune KNN\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "\n",
    "# Create a list of different values for n_neighbors\n",
    "neighbors = range(1, 21)\n",
    "\n",
    "# Setup KNN instance\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Loop through different n-neighbors \n",
    "for i in neighbors:\n",
    "    knn.set_params(n_neighbors = i)\n",
    "    \n",
    "    # Fit the algorithm\n",
    "    knn.fit(x_train, y_train)\n",
    "    \n",
    "    # Update the training score list\n",
    "    train_scores.append(knn.score(x_train, y_train))\n",
    "    \n",
    "    # Update the test score list\n",
    "    test_scores.append(knn.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bc74c7db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.8099173553719008,\n",
       " 0.7727272727272727,\n",
       " 0.743801652892562,\n",
       " 0.7603305785123967,\n",
       " 0.7520661157024794,\n",
       " 0.743801652892562,\n",
       " 0.7231404958677686,\n",
       " 0.71900826446281,\n",
       " 0.6942148760330579,\n",
       " 0.7272727272727273,\n",
       " 0.6983471074380165,\n",
       " 0.6900826446280992,\n",
       " 0.6942148760330579,\n",
       " 0.6859504132231405,\n",
       " 0.6735537190082644,\n",
       " 0.6859504132231405,\n",
       " 0.6652892561983471,\n",
       " 0.6818181818181818,\n",
       " 0.6694214876033058]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "46eeebb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.6229508196721312,\n",
       " 0.639344262295082,\n",
       " 0.6557377049180327,\n",
       " 0.6721311475409836,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.7049180327868853,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.7540983606557377,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.7377049180327869,\n",
       " 0.6885245901639344,\n",
       " 0.7213114754098361,\n",
       " 0.6885245901639344,\n",
       " 0.6885245901639344,\n",
       " 0.7049180327868853,\n",
       " 0.6557377049180327]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3c07ce8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximun KNN Score on the test data: 75.41%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's visualize it\n",
    "plt.plot(neighbors, train_scores, label = \"Train score\")\n",
    "plt.plot(neighbors, test_scores, label = \"Test score\")\n",
    "plt.xticks(np.arange(1, 21, 1))\n",
    "plt.xlabel(\"Number of neighbors\")\n",
    "plt.ylabel(\"Model Scores\")\n",
    "plt.legend()\n",
    "\n",
    "print(f\"Maximun KNN Score on the test data: {max(test_scores)*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f5b898",
   "metadata": {},
   "source": [
    "## Hyperparamter Tuning with RandomizedSearchCV\n",
    "\n",
    "We are going to tune:\n",
    "* LogisticRegression()\n",
    "* RandomForestClassifier()\n",
    "\n",
    "...using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "73c2a509",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a hyperparameter grid for LogisticRegression()\n",
    "log_reg_grid = {\"C\" : np.logspace(-4, 4, 20),\n",
    "                \"solver\" : [\"liblinear\"]}\n",
    "\n",
    "# Create hyperparameter grid for RandomForestClassifier()\n",
    "rf_grid = {\"n_estimators\" : np.arange(10, 1000, 50),\n",
    "           \"max_depth\" : [None, 3, 5, 10],\n",
    "           \"min_samples_leaf\" : np.arange(1, 20, 2),\n",
    "           \"min_samples_split\" : np.arange(2, 20, 2)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b190750d",
   "metadata": {},
   "source": [
    "Now we have got hyperparameter grid setup for each of our models, let's tune them using RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7878fafa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={&#x27;C&#x27;: array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=LogisticRegression(), n_iter=20,\n",
       "                   param_distributions={'C': array([1.00000000e-04, 2.63665090e-04, 6.95192796e-04, 1.83298071e-03,\n",
       "       4.83293024e-03, 1.27427499e-02, 3.35981829e-02, 8.85866790e-02,\n",
       "       2.33572147e-01, 6.15848211e-01, 1.62377674e+00, 4.28133240e+00,\n",
       "       1.12883789e+01, 2.97635144e+01, 7.84759970e+01, 2.06913808e+02,\n",
       "       5.45559478e+02, 1.43844989e+03, 3.79269019e+03, 1.00000000e+04]),\n",
       "                                        'solver': ['liblinear']},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tune LogisticRegression\n",
    "\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparameter search for RandomizedSearchCV\n",
    "rs_log_reg = RandomizedSearchCV(LogisticRegression(),\n",
    "                                param_distributions = log_reg_grid,\n",
    "                                cv = 5,\n",
    "                                n_iter = 20,\n",
    "                                verbose = True)\n",
    "\n",
    "# Fit random hyperparameter search model to LogisticRegression\n",
    "rs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6e79a6f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'solver': 'liblinear', 'C': 0.23357214690901212}"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "380393e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30e0c38d",
   "metadata": {},
   "source": [
    "Now we have tuned LogisticRegression(), there is no change in the score. Let's do the same for RandomForestClassifier()..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "140ae349",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 3, 5, 10],\n",
       "                                        &#x27;min_samples_leaf&#x27;: array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        &#x27;min_samples_split&#x27;: array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        &#x27;n_estimators&#x27;: array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_iter=20,\n",
       "                   param_distributions={'max_depth': [None, 3, 5, 10],\n",
       "                                        'min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'n_estimators': array([ 10,  60, 110, 160, 210, 260, 310, 360, 410, 460, 510, 560, 610,\n",
       "       660, 710, 760, 810, 860, 910, 960])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setup a random seed\n",
    "np.random.seed(42)\n",
    "\n",
    "# Setup random hyperparamter search for RandomForestClassifier()\n",
    "rs_rf = RandomizedSearchCV(RandomForestClassifier(),\n",
    "                           param_distributions = rf_grid,\n",
    "                           cv = 5,\n",
    "                           n_iter = 20,\n",
    "                           verbose = True)\n",
    "\n",
    "# Fit random hyperparamter search model for RandomForestClassifier()\n",
    "rs_rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "122a3a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 210,\n",
       " 'min_samples_split': 4,\n",
       " 'min_samples_leaf': 19,\n",
       " 'max_depth': 3}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "4d66caeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs_rf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bcab60e",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning with GridSearchCV()\n",
    "\n",
    "Since our LogisticRegression() model provides the best score so far, we will try and improve them again using GridSearchCV()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "80855aee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 30 candidates, totalling 150 fits\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         &#x27;solver&#x27;: [&#x27;liblinear&#x27;]},\n",
       "             verbose=True)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
       "             param_grid={'C': array([1.00000000e-04, 1.88739182e-04, 3.56224789e-04, 6.72335754e-04,\n",
       "       1.26896100e-03, 2.39502662e-03, 4.52035366e-03, 8.53167852e-03,\n",
       "       1.61026203e-02, 3.03919538e-02, 5.73615251e-02, 1.08263673e-01,\n",
       "       2.04335972e-01, 3.85662042e-01, 7.27895384e-01, 1.37382380e+00,\n",
       "       2.59294380e+00, 4.89390092e+00, 9.23670857e+00, 1.74332882e+01,\n",
       "       3.29034456e+01, 6.21016942e+01, 1.17210230e+02, 2.21221629e+02,\n",
       "       4.17531894e+02, 7.88046282e+02, 1.48735211e+03, 2.80721620e+03,\n",
       "       5.29831691e+03, 1.00000000e+04]),\n",
       "                         'solver': ['liblinear']},\n",
       "             verbose=True)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Different hyperparameters for our LogisticRegression()\n",
    "log_reg_grid = {\"C\" : np.logspace(-4, 4, 30),\n",
    "                \"solver\" : [\"liblinear\"]}\n",
    "\n",
    "# Setup rid hyperparameter search for LogisticRegression\n",
    "gs_log_reg = GridSearchCV(LogisticRegression(),\n",
    "                          param_grid = log_reg_grid,\n",
    "                          cv = 5,\n",
    "                          verbose = True)\n",
    "\n",
    "# Fit grid hyperparameters search model\n",
    "gs_log_reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "6ebcbdc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check best_params\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "4ac5c912",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_log_reg.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5acde51e",
   "metadata": {},
   "source": [
    "## Evaluating our tuned machine learning classifier, beyond accuracy\n",
    "\n",
    "* ROC curve and AUC score\n",
    "* Confusion matrix\n",
    "* Classification report\n",
    "* Precision\n",
    "* Recall\n",
    "* F1 Score\n",
    "\n",
    "...and it would be great if cross-validation was used where possible.\n",
    "\n",
    "To make comparisions and evaluate our trained model, first we need to make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8870af2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make predictions with tuned models\n",
    "y_preds = gs_log_reg.predict(x_test)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "876c092e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "179    0\n",
       "228    0\n",
       "111    1\n",
       "246    0\n",
       "60     1\n",
       "      ..\n",
       "249    0\n",
       "104    1\n",
       "300    0\n",
       "193    0\n",
       "184    0\n",
       "Name: target, Length: 61, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "01e1e52b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Win10\\Desktop\\data-science-projects\\heart-disease-project\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function plot_roc_curve is deprecated; Function :func:`plot_roc_curve` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: :meth:`sklearn.metric.RocCurveDisplay.from_predictions` or :meth:`sklearn.metric.RocCurveDisplay.from_estimator`.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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QmZkVV9ZEsD4iPihoJGZmVi+y9hqaL+kUoJmk7sAFwIzChWVmZsWStUZwPsl8xf8C7iIZjvoHBYrJzMyKKGuNYO+IuBS4tJDBmJlZ8WWtEfxK0t8k/a+kngWNyMzMiipTIoiIocAQYCUwXtKrkn5ayMDMzKw4Mj9QFhH/jIgbgbNJnikYXaigzMyseLI+ULavpDGS5pNMUTmDZH4BMzNr4LI2Fv8/4G7gKxFReXIZMzNrwDIlgoj4cqEDMTOz+lFjIpD0x4j4pqRX2Xi+4SwzlJmZWQOQr0bw3+nvYwodiJmZ1Y8aG4sjYkX68tyIeCv3Bzi38OGZmVmhZe0+engV646qy0DMzKx+5GsjOIfkm/+ekublbNoJeL6QgZmZWXHkayO4C/gL8Avg4pz1ayPi/YJFZWZmRZMvEURELJE0svIGSbs4GZiZNXxZagTHAHNIuo8qZ1sAexYoLjMzK5IaE0FEHJP+7lqccMzMrNiyjjX0n5J2TF+fKulXkjoXNjQzMyuGrN1HbwU+ltQH+DHwFvD7gkVlZmZFU5vJ6wM4FrghIm4g6UJqZmYNXNbRR9dKugQ4DRgkqRmwXeHCMjOzYslaIziZZOL670TEP4EOwHUFi8rMzIom61SV/wQmATtLOgZYFxF3FjQyMzMriqy9hr4JvAScBHwTeFHSiRn2O1LSa5IWS7q4iu3fkjQv/ZmRNkabmVkRZW0juBQYEBHvAkjaFfgrcG91O6TtCDeTDFhXBsySNC0iFuYU+wdwSESslnQUMB44oPaXYWZmmytrG8E25UkgtSrDvgOBxRHxZkR8Ckwm6XVUISJmRMTqdHEmngfZzKzostYIpkt6hGTeYkgajx/Os08HYGnOchk1f9v/LskAd5uQNAIYAdC5s59jMzOrS1nnLL5Q0jeAg0nGGxofEffn2U1VrIsq1iFpKEkiOLia848nuW1E//79qzyGmZltnnzzEXQHxgLdgFeBURGxLOOxy4BOOcsdgeVVnKM3cDtwVESsynhsMzOrI/nu808AHgJOIBmB9KZaHHsW0F1SV0nNgWHAtNwC6XhF9wGnRcTrtTi2mZnVkXy3hnaKiNvS169JejnrgSNivaTzgEeAZsCEiFgg6ex0+zhgNNAGuEUSJENZ9K/tRZiZ2ebLlwhaSPoS/77f3zJ3OSJqTAwR8TCVGpXTBFD++nvA92obtJmZ1Z18iWAF8Kuc5X/mLAdwaCGCMjOz4sk3Mc3QYgViZmb1I+sDZWZm1kg5EZiZNXFOBGZmTVzW0UeVzlU8Ol3uLGlgYUMzM7NiyFojuAU4EBieLq8lGVnUzMwauKyDzh0QEftLmguQDhvdvIBxmZlZkWStEXyWzi8QUDEfwecFi8rMzIomayK4Ebgf2E3SVcBzwM8LFpWZmRVN1mGoJ0maAxxGMrzEcRGxqKCRmZlZUWRKBOkooR8DD+aui4i3CxWYmZkVR9bG4j+TtA8IaAF0BV4DehYoLjMzK5Kst4b2y12WtD9wVkEiMjOzotqsJ4vT4acH1HEsZmZWD7K2EfwoZ3EbYH9gZUEiMjOzosraRrBTzuv1JG0GU+o+HDMzK7a8iSB9kKxVRFxYhHjMzKzIamwjkLRtRGwguRVkZmaNUL4awUskSaBU0jTgT8BH5Rsj4r4CxmZmZkWQtY1gF2AVyRzF5c8TBOBEYGbWwOVLBLulPYbm8+8EUC4KFpXZVuKzzz6jrKyMdevW1XcoZpm0aNGCjh07st1222XeJ18iaAa0YuMEUM6JwBq9srIydtppJ7p06YJU1X8Ds61HRLBq1SrKysro2rVr5v3yJYIVEXHFloVm1nCtW7fOScAaDEm0adOGlStr95hXvieL/a/fmjwnAWtINuffa75EcNjmhWJmZg1FjYkgIt4vViBmVrV33nmHU045hT333JN+/fpx4IEHcv/991dZdvny5Zx44olVbhsyZAizZ88GYMKECey333707t2bXr16MXXq1ILFv2TJEnr16lXt9rFjx7LPPvvQq1cv+vTpw5133smYMWO45JJLNipXWlrKvvvuW+UxTjzxRN58882K5blz5yKJRx55pMY4xowZw9ixY2uMZUtNnDiR7t270717dyZOnFhlmbfeeovDDjuM3r17M2TIEMrKyirW9+vXj759+9KzZ0/GjRtXsc+wYcN44403tjg+2MxB58ysOCKC4447jsGDB/Pmm28yZ84cJk+eXPFBkWv9+vW0b9+ee++9t8ZjlpWVcdVVV/Hcc88xb948Zs6cSe/evbc41vXr19d6n3HjxvHYY4/x0ksvMX/+fJ555hkiguHDh3PPPfdsVHby5MmccsopmxxjwYIFbNiwgT333LNi3d13383BBx/M3XffvcWxbIn333+fyy+/nBdffJGXXnqJyy+/nNWrV29SbtSoUZx++unMmzeP0aNHVyTBdu3aMWPGDEpLS3nxxRe5+uqrWb58OQDnnHMO11577RbFVy7rcwRmTd7lDy5g4fI1dXrMHu1LuOxr1U/r8cQTT9C8eXPOPvvsinV77LEH559/PgB33HEHf/7zn1m3bh0fffQREyZM4JhjjmH+/Pl88sknnHnmmSxcuJB9992XTz75BIB3332XnXbaiVatWgHQqlWritd///vfGTlyJCtXrmSHHXbgtttuY5999uHBBx/kyiuv5NNPP6VNmzZMmjSJ3XffnTFjxrB8+XKWLFlC27Ztuf766zn77LMrvp3feuuttG/fng0bNvD973+fGTNm0KFDB6ZOnUrLli35+c9/zpNPPklJSQkAO++8M9/+9rcBaN26NS+++CIHHHAAAH/84x83+oZfbtKkSRx77LEVyxHBvffey2OPPcagQYNYt24dLVq0yPu3qCmWzfXII49w+OGHs8suuwBw+OGHM336dIYPH75RuYULF3L99dcDMHToUI477jgAmjdvXlHmX//6F59//u+p4gcNGsQZZ5zB+vXr2XbbLfsod43AbCu2YMEC9t+/5hFeXnjhBSZOnMgTTzyx0fpbb72VHXbYgXnz5nHppZcyZ84cAPr06cPuu+9O165dOfPMM3nwwYqJBxkxYgQ33XQTc+bMYezYsZx77rkAHHzwwcycOZO5c+cybNiwjb6Jzpkzh6lTp3LXXXdxwQUXcMghh/DKK6/w8ssv07NnkuTeeOMNRo4cyYIFC2jdujVTpkxh7dq1rF27lm7dulV5XcOHD2fy5MkAzJw5kzZt2tC9e/dNyj3//PP069dvo+WuXbvSrVs3hgwZwsMPP1zj+wfkjSXXddddR9++fTf5ueCCCzYpu2zZMjp16lSx3LFjR5YtW7ZJuT59+jBlSjKO5/3338/atWtZtWoVAEuXLqV379506tSJiy66iPbt2wOwzTbb8MUvfpFXXnklb8z5uEZgllFN39yLZeTIkTz33HM0b96cWbNmAWz0jTPXM888U/Hh1Lt374rbP82aNWP69OnMmjWLxx9/nB/+8IfMmTOHUaNGMWPGDE466aSKY/zrX/8CkttJJ598MitWrODTTz/dqI/617/+dVq2bAkkNZjy++rNmjVj5513ZvXq1XTt2pW+ffsC0K9fP5YsWUJE1NjDZdiwYRx00EH88pe/ZPLkyZt8iy63YsUKdt1114rlu+++m2HDhlUc4/e//z3f+MY3qj2XpLyx5Lrwwgu58MJsY3BWdWupqvOMHTuW8847jzvuuIPBgwfToUOHim/5nTp1Yt68eSxfvpzjjjuOE088kd133x2A3XbbjeXLl2+UCDdHQWsEko6U9JqkxZIurmK7JN2Ybp+XznxmZqmePXvy8ssvVyzffPPNPP744xv1E99xxx2r3b+mD7+BAwdyySWXMHnyZKZMmcLnn39O69atKS0trfhZtGgRAOeffz7nnXcer776Kr/97W83etK6pvOX23777SteN2vWjPXr11NSUsKOO+64USNvrk6dOtGlSxeefvpppkyZwje/+c0qy7Vs2bIing0bNjBlyhSuuOIKunTpwvnnn89f/vIX1q5dS5s2bTa5P//+++/Ttm3bvLHkqk2NoGPHjixdurRiuaysrOIbfa727dtz3333MXfuXK666ioguTVVuUzPnj159tlnK9atW7euIglviYIlgnT46puBo4AewHBJPSoVOwronv6MAG4tVDxmDdGhhx7KunXruPXWf//X+PjjjzPtO3jwYCZNmgTA/PnzmTdvHpD0LMpNLqWlpeyxxx6UlJTQtWtX/vSnPwHJt9ny2w4ffPABHTp0AKi25wvAYYcdVhHrhg0bWLOm5jaVSy65hJEjR1aUW7NmDePHj6/YPnz4cH74wx/SrVs3OnbsWOUx9t13XxYvXgzAX//6V/r06cPSpUtZsmQJb731FieccAIPPPAArVq1ol27djz++ONAkgSmT5/OwQcfnCmWchdeeOFGybL858Ybb9yk7BFHHMGjjz7K6tWrWb16NY8++ihHHHHEJuXee++9ivv/v/jFL/jOd74DJImjvG1n9erVPP/88+y9994V+73++usVt9+2RCFrBAOBxRHxZkR8CkwGjq1U5ljgzkjMBFpLalfAmMwaFEk88MADPP3003Tt2pWBAwfy7W9/m2uuuSbvvueccw4ffvghvXv35tprr2XgwIFAMn7SqFGj2Geffejbty/33HMPN9xwA5A0vP7ud7+jT58+9OzZs6Jb6ZgxYzjppJMYNGgQbdu2rfacN9xwA08++ST77bcf/fr1Y8GCBXljHDp0KAMGDKBXr14ccsgh7LDDDhXbTzrpJBYsWFBxq6cqX/3qV3nqqaeA5LbQ8ccfv9H2E044gbvuuguAO++8kyuvvJK+ffty6KGHctlll1W0C+SLZXPssssu/OxnP2PAgAEMGDCA0aNHV9zGGz16NNOmTQPgqaeeYu+992avvfbinXfe4dJLLwVg0aJFHHDAAfTp04dDDjmEUaNGsd9+yRTy77zzDi1btqRduy3/yNSWdo+q9sDSicCREfG9dPk04ICIOC+nzEPA1RHxXLr8OHBRRMyudKwRJDUGOnfu3O+tt96qdTyXP5j8g9wa7vNaw7Fo0aJq+67b1uGTTz5h6NChPP/88zRr1qy+wyma66+/npKSEr773e9usq2qf7eS5kRE/6qOVcjG4iwD1WUazC4ixgPjAfr3779ZmcsJwKxxatmyJZdffjnLli2jc+fO9R1O0bRu3ZrTTjutTo5VyERQBnTKWe4ILN+MMmZmNarqvntjd+aZZ9bZsQrZRjAL6C6pq6TmwDBgWqUy04DT095DXwY+iIgVBYzJrNYKdfvUrBA2599rwWoEEbFe0nnAIyTzGkyIiAWSzk63jwMeBo4GFgMfA3WX4szqQIsWLVi1ahVt2rTxKKS21SufjyDLk9S5CtZYXCj9+/eP8oGzzArNM5RZQ1PdDGX11Vhs1uBtt912tZrpyawh8lhDZmZNnBOBmVkT50RgZtbENbjGYkkrgdo/WpxoC7xXh+E0BL7mpsHX3DRsyTXvERG7VrWhwSWCLSFpdnWt5o2Vr7lp8DU3DYW6Zt8aMjNr4pwIzMyauKaWCDYdXLzx8zU3Db7mpqEg19yk2gjMzGxTTa1GYGZmlTgRmJk1cY0yEUg6UtJrkhZLuriK7ZJ0Y7p9nqT96yPOupThmr+VXus8STMk9amPOOtSvmvOKTdA0oZ01rwGLcs1SxoiqVTSAklPFzvGupbh3/bOkh6U9Ep6zQ16FGNJEyS9K2l+Ndvr/vMrIhrVD8mQ138H9gSaA68APSqVORr4C8kMaV8GXqzvuItwzQcBX0hfH9UUrjmn3BMkQ56fWN9xF+Hv3BpYCHROl3er77iLcM0/Aa5JX+8KvA80r+/Yt+CaBwP7A/Or2V7nn1+NsUYwEFgcEW9GxKfAZODYSmWOBe6MxEygtaQtnwG6/uS95oiYERGr08WZJLPBNWRZ/s4A5wNTgHeLGVyBZLnmU4D7IuJtgIho6Ned5ZoD2EnJhBGtSBLB+uKGWXci4hmSa6hOnX9+NcZE0AFYmrNclq6rbZmGpLbX812SbxQNWd5rltQBOB4YV8S4CinL33kv4AuSnpI0R9LpRYuuMLJc82+AfUmmuX0V+O+I+Lw44dWLOv/8aozzEVQ1jVTlPrJZyjQkma9H0lCSRHBwQSMqvCzX/GvgoojY0EhmF8tyzdsC/YDDgJbAC5JmRsTrhQ6uQLJc8xFAKXAo0A14TNKzEbGmwLHVlzr//GqMiaAM6JSz3JHkm0JtyzQkma5HUm/gduCoiFhVpNgKJcs19wcmp0mgLXC0pPUR8UBRIqx7Wf9tvxcRHwEfSXoG6AM01ESQ5ZrPBK6O5Ab6Ykn/APYBXipOiEVX559fjfHW0Cygu6SukpoDw4BplcpMA05PW9+/DHwQESuKHWgdynvNkjoD9wGnNeBvh7nyXnNEdI2ILhHRBbgXOLcBJwHI9m97KjBI0raSdgAOABYVOc66lOWa3yapASFpd2Bv4M2iRllcdf751ehqBBGxXtJ5wCMkPQ4mRMQCSWen28eR9CA5GlgMfEzyjaLBynjNo4E2wC3pN+T10YBHbsx4zY1KlmuOiEWSpgPzgM+B2yOiym6IDUHGv/P/AndIepXktslFEdFgh6eWdDcwBGgrqQy4DNgOCvf55SEmzMyauMZ4a8jMzGrBicDMrIlzIjAza+KcCMzMmjgnAjOzJs6JoAlIR94szfnpUkPZD+vgfHdI+kd6rpclHbgZx7hdUo/09U8qbZuxpTGmxyl/X+ano1e2zlO+r6SjN+M87SQ9lL4eIukDSXMlLZJ02WYc7+vlo3BKOq78fUqXr5D0X7U9ZhXnuEN5RmtNh7HI3AU5vfaHMpSrcvRNSWMlHZr1fJadE0HT8ElE9M35WVKEc14YEX2Bi4Hf1nbniPheRCxMF39SadtBWx4e8O/3pRfJIF8j85TvS9J/u7Z+BNyWs/xsRHyJ5MnnUyX1q83BImJaRFydLh4H9MjZNjoi/roZMW5N7gCOrGL9TST/nqyOORE0QZJaSXo8/bb+qqRNRu1Mv8U+k/ONeVC6/iuSXkj3/ZOkVnlO9wzwxXTfH6XHmi/pB+m6HSX9WclY8vMlnZyuf0pSf0lXAy3TOCal2z5Mf9+T+w09/RZ7gqRmkq6TNEvJeO1nZXhbXiAduEvSQCVzNsxNf++dPtV6BXByGsvJaewT0vPMrep9TJ0ATK+8Mh0GYg7QLa1tzEzjvV/SF9JYLpC0MF0/OV13hqTfSDoI+DpwXRpTt/Jv8pKOkvTHnPdmiKQH09e1+htKGp1e43xJ46WNBm46NX2P5ksamJbP+r5UqbrRNyPiLaCNpP+ozfEsg2KNse2f+vsBNpAMylUK3E/yRHlJuq0tyROK5Q8Xfpj+/h/g0vR1M2CntOwzwI7p+ouA0VWc7w7Ssf+Bk4AXSQZCexXYkWSo4AXAl0g+JG/L2Xfn9PdTQP/cmHLKlMd4PDAxfd2cZETGlsAI4Kfp+u2B2UDXKuL8MOf6/gQcmS6XANumr/8LmJK+PgP4Tc7+PwdOTV+3JhnPZ8dK5+gKzMlZHgI8lL5uAywBepI8CXxIuv4K4Nfp6+XA9uXnqBxH7nudu5z+jd/O+VvdCpy6mX/DXXLW/x74Ws7f6Lb09WDS8fOre18qXXt/kqeeq/s324UqxuMnqVmdUN//pxrbT6MbYsKq9Ekkt2kAkLQd8HNJg0mGIegA7A78M2efWcCEtOwDEVEq6RCS2xDPp18Km5N8k67KdZJ+CqwkGe30MOD+SL4FI+k+YBDJN+Wxkq4h+ZB4thbX9RfgRknbk9xKeCYiPpH0FaB3zj3unYHuwD8q7d9SUinJh84c4LGc8hMldScZ1XG7as7/FeDrkkalyy2Azmw8tk+79D3INUjSXJL3/mqSQcRaR0T5bGITSRITJAlikqQHgAeqiWMTkQzNMB34mqR7ga8CPwZq8zcsN1TSj4EdgF1IkviD6ba70/M9I6lESTtLde9Lbnyzge9lvZ4c7wLtN2M/q4ETQdP0LZKZnPpFxGeSlpD8Z62Q/sceTPIB8ntJ1wGrgcciYniGc1wYEfeWL6iaBsyIeD29R3408AtJj0bEFVkuIiLWSXqKZBjik0k/lEjGmzk/Ih7Jc4hPIqKvpJ2Bh0jaCG4kGbvmyYg4XknD+lPV7C+Sb6ev1XQOKr23JG0Ex1QcJDl/db5K8m3768DPJPWsoWxl95Bc0/vArIhYm97Wyfo3RFIL4BaS2tlSSWPY+Hoqj1ETVPO+KBkQbku1IHlPrQ65jaBp2hl4N00CQ4E9KheQtEda5jbgdyRT580E/lNS+T3/HSTtlfGczwDHpfvsSHJb51lJ7YGPI+IPwNj0PJV9ltZMqjKZZNCtQSQDk5H+Pqd8H0l7peesUkR8AFwAjEr32RlYlm4+I6foWpJbZOUeAc4vv2cu6UtVHP51khpHtdLzr1baDgOcBjwtaRugU0Q8SfJtvjXJbbVclWPK9RTJ+/l9kqQAtf8bln/ov5e2JVTuSVTepnMwySiYH5DtfdlcewENdhC9rZUTQdM0CegvaTZJ7eBvVZQZApSmtzBOAG6IiJUkH4x3S5pH8qGyT5YTRsTLJPedXyJpM7g9IuYC+wEvpbdoLgWurGL38cA8pY3FlTxK8o35r5FMZQjJnAsLgZeVdEH8LXlqv2ksr5AMc3wtSe3keZL2g3JPAj3KG4tJag7bpbHNT5crH/cj4O/lH7w1+DbJ7bR5JL2TrkjP/Qclo2rOBa6PiP+rtN9k4MK0UbZbpXNvIKnpHJX+prZ/w/R8t5G07zxAcssw12ol3XnHkdwChAzvi5KOALdXdU4lo2++AOwtqUzSd9P125F0PJhdXby2eTz6qFmBSTqe5DbcT+s7loYsfR/3j4if1XcsjY3bCMwKLCLul9SmvuNoBLYFflnfQTRGrhGYmTVxbiMwM2vinAjMzJo4JwIzsybOicDMrIlzIjAza+L+P0WfPhzFNnstAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve and calculate AUC metric\n",
    "plot_roc_curve(gs_log_reg, x_test, y_test);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "04942bea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[25  4]\n",
      " [ 3 29]]\n"
     ]
    }
   ],
   "source": [
    "# Confusion matrix\n",
    "print(confusion_matrix(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d29659ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Improve visualization\n",
    "sns.set(font_scale = 1.5)\n",
    "def plot_conf_mat(y_test, y_preds):\n",
    "    \"\"\"\n",
    "    Plots a nice looking confusion matrix using seaborn's heatmap\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots(figsize = (3, 3))\n",
    "    ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n",
    "                     annot = True,\n",
    "                     cbar = False)\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.ylabel(\"True Label\")\n",
    "    \n",
    "plot_conf_mat(y_test, y_preds);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85d37ea8",
   "metadata": {},
   "source": [
    "Now we have got ROC curve, ab AUC metric and a confusion matrix, let's get a classification report as well as cross-validated precision, recall and f1 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "bffa4c88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.86      0.88        29\n",
      "           1       0.88      0.91      0.89        32\n",
      "\n",
      "    accuracy                           0.89        61\n",
      "   macro avg       0.89      0.88      0.88        61\n",
      "weighted avg       0.89      0.89      0.89        61\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4c0fa9c",
   "metadata": {},
   "source": [
    "### Calculate evaluationbmetrics using cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ed01974",
   "metadata": {},
   "source": [
    "We are going to calculate accuracy precision, recall and f1 score of our model using cross-validation and to do so we'll be using `cross_val_score()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "cbc3eba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.20433597178569418, 'solver': 'liblinear'}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the best hyperparameters\n",
    "gs_log_reg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "11688e12",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new classifier with best params\n",
    "clf = LogisticRegression(C = 0.20433597178569418,\n",
    "                         solver = \"liblinear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "825e01e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446994535519124"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated accuracy\n",
    "\n",
    "cv_accuracy = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv = 5,\n",
    "                         scoring = \"accuracy\")\n",
    "\n",
    "cv_accuracy = np.mean(cv_accuracy)\n",
    "cv_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "35141c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8207936507936507"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated precision\n",
    "\n",
    "cv_precision = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv = 5,\n",
    "                         scoring = \"precision\")\n",
    "\n",
    "cv_precision = np.mean(cv_precision)\n",
    "cv_precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "9f936d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9212121212121213"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated recall\n",
    "\n",
    "cv_recall = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv = 5,\n",
    "                         scoring = \"recall\")\n",
    "\n",
    "cv_recall = np.mean(cv_recall)\n",
    "cv_recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "2e3c3481",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8673007976269721"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cross-validated f1 score\n",
    "\n",
    "cv_f1_score = cross_val_score(clf,\n",
    "                         x,\n",
    "                         y,\n",
    "                         cv = 5,\n",
    "                         scoring = \"f1\")\n",
    "\n",
    "cv_f1 = np.mean(cv_f1_score)\n",
    "cv_f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "8d2a13ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SEhIgl8uRkpICAGjcuHGJtnn37l3Y29vna69bty5q1qyp3Nd53p7qqaWlBaB4B2OfffaZ8v957+23fzcrVaoE4PXr8fTpU2RkZODo0aM4evRogY95//79Irf79jbelvdefPMAEHi9D+rWrQsAGDRoEE6ePIktW7Zgy5YtMDAwgLOzM/r37/9RZ0NJKvQ1NDRgaWmJK1eu4NWrV8o3zdsWL16MpKQkBAYGKl+wvDdSHlHI1xDkLcs7b9C7d2/Y29vj6NGjOH78OE6fPo2TJ09i69atiIyMhJaWFjp16oSffvpJ+e/UqVPKTwNRUVF4/vy58hxBHhsbG+W4ZZ8+ffDDDz8gOTkZf/zxB54/f45evXop+8bHx+Obb75BvXr10LFjR+XJ1ZMnT2LNmjUl2odvnoB+1/64cuUKBg8eDG1tbXTu3Bmenp5o3bo15HI5goODC92GQqGAjo5OgePqAJR/KDU0NAo8gVycP2Dv82kvOTkZXl5eeP78Oezs7ODu7o7PP/8cQgj4+/sr+1WpUgVLly7F9evXceTIEZw4cQJRUVHYuXMnJk+ejJEjRwIAZsyYAV9fXxw6dAgnTpzAoUOHsH//fnh5eRW5j4p6XnmfqN5l4cKFWLt2LVq3bg0LCwt88cUXsLS0xOzZs4sVgm8q7HdBoVDkO39Wmutf3v49LOrx8vZHjx494O3tXWCf4oylF7U/c3Nzi3xeurq62LJlCy5fvoyjR4/ixIkT2Lx5M8LDwzFv3rwCz+N8CJIKfeD1hSVnz57FwYMH0adPn3zLX758iZ07dyI3N1dlKORteUdDCQkJ+Zbdvn0bAJQf565du4aWLVuif//+6N+/P7KzszF//nxs2rQJJ0+ehJ2dHa5du4YGDRqgZ8+e6NmzJxQKBTZu3Ih58+bhwIEDGDhwYL4ZOm8ekbq7uyMkJATHjh3DhQsXYGhoCAsLC+Xy2bNnw8jICLt27UL16tWV7fv27SvWfstjYGCA8+fP5/uj+fYVtfPmzYOWlhYOHDigcmS3evXqIrfRuHFjnDx5EqampvmOug8dOqR8XQwNDREfH4/U1FSVbRTn6t5GjRop+745BPXw4UP88MMP+T6mA8Dy5cuRkpKCmJgYlU8Ib+/De/fu4d69e7C2toaJiQnGjh2LBw8eYMiQIdiwYQNGjhyJJ0+e4MaNG+jUqRNGjBiBESNG4OnTp/D398eOHTvw73//GzVq1CjyebytTp060NbWxp07d/It27BhA548eYJBgwZh7dq1+OKLLzBv3jyVPk+ePCnxNhs3blzg78Hjx4+RmZmJhg0blvgxy4qenh6qVauGV69eKYc689y7dw9//vmnysVv76tRo0Y4ffo0nj17pvLJ8+rVqwgLC8OYMWNQqVIlZGRkwMLCAhYWFpgyZQpu3rwJHx8fbNy48aOFvqTG9AHAy8sLjRs3RkhICP7++2+VZbm5uZg5cyaePHmCESNGFDrDp02bNqhbty4iIiJUphFmZmZi69atqFu3LkxNTXHjxg34+PioTMXT0tJC69atAbw+cklLS4OXl5fKEbempqZytoSmpiaqVq2Kzp07q/wzNTVV9s87gs87snz7DZSWloZGjRqpBP79+/dx+PBh5XMvDhcXF2RkZCAyMlLZlpOTgx07duTbnp6enkoYZ2RkYPfu3fm2p6mpqXJ0njc1cNWqVSqPGRcXh3HjxilDtnv37gCgMrtJCIGtW7cW+TwcHR0BABERESrtUVFRiImJyfcxPe85VatWTfkHA3g9pp93TUfec1q9ejWGDh2qcjl/gwYNUL9+feURY1RUFIYOHYo//vhD2adOnTowMjKChoZGkUeW71K5cmXY2tri+PHjKkfs6enp2LBhA+RyuXIacIsWLVTWPX78OBITE/Hq1StlW14dhX16cnZ2RkJCQr7hk7Vr1wJAqYarSqty5cpwcHDA8ePH8ddff6ksmzt3Lvz9/ZXTRd8cFiopR0dHKBQKld8L4PX7KyYmBp999hnmzJmDb775Bs+ePVMub9asGWrWrPner/f7kNyRftWqVbF8+XIMHz4c/fv3R+/evWFmZoa0tDTExsbi2rVrcHV1VZn+WJAqVarg22+/xYQJE+Dp6Yn+/fsDAHbu3IlHjx5h6dKl0NTURNu2bWFtbY3Fixfj/v37MDExwf3797FlyxY0a9YMnTp1gpaWFnr37o2tW7fixYsXsLS0RFpaGrZs2YLPPvss34mvd+ndu7dyXvWbQzsA4ODggIMHD+K7776DmZkZ7t69ix07dijnpL/5RizMF198gR07dmD27Nm4desWmjZtir179+Lx48f5trdu3TqMHz8ednZ2ePz4MXbu3Kk8knxze3p6ejh79ix27NgBOzs7ODo6omvXrggLC8Pdu3fRuXNnJCcnIzw8HI0aNYKfnx8AoEOHDnBzc8O6devw+PFjmJubIy4uDlevXi3yeXz++ecYMGAANm/ejEePHqFTp064efMmtm3bhr59+6JVq1b57qvi4OCAuLg4jBo1Cq6ursjIyEB0dDTkcrnKc/Lx8cGePXvg4+MDLy8v1KpVC2fOnMGvv/6KcePGAQD69u2LjRs3YvTo0fjyyy9Rv359XLlyRTlNtTQXX02ePBkDBgzAgAEDlBMNduzYgefPn2PChAkwMjJCo0aNsHr1amRlZaFBgwb4/fffsXv3blStWjXfawO8Dq8nT54UeDQ6atQoHD58GBMmTMCXX36Jpk2b4syZMzh8+DBcXFyUf2DVZcqUKfj111/h4+MDHx8fNGrUCPHx8fjpp5/g5eWFli1bAnj9Rxd4fZJbCFGiq8S7dOkCW1tbzJ07Fzdu3ICZmRkuXbqE6Oho+Pv7o3bt2hg2bBhGjBgBHx8f5fm8o0ePQi6XIyQk5IM89wJ9tHlCn5gHDx6IuXPnCnd3d2FhYSHatm0rBg4cKHbu3KkypUqI/NPm3nT69GkxaNAg0bZtW2FlZSWGDx8uzp07p9Ln6dOnYvbs2aJLly7C1NRU2NraiunTp4tHjx4p+7x48UKEhoYKFxcXYWZmJmxsbMT48eNVpt0VJSMjQ5iZmYl+/frlW5aWliaCgoKEra2tMDc3Fy4uLmLu3LniwoULQiaTiQ0bNgghip6ymbed4OBg0blzZ2FhYSHGjRunnIKY1+/ly5di7ty5wsHBQZiZmYmuXbuK6dOni1u3bolWrVqJ4OBg5eNFRUUJW1tbYWpqKnbv3i2EECI7O1usXLlSuLi4iDZt2gh7e3sxdepUlWmpef2WLFkiHB0dhZmZmRg2bJg4depUkVM2hRAiNzdXrFu3TrmN7t27ixUrViin+r09hU+hUIjVq1eLrl27ClNTU+Hk5CQmTJggbt++LTp06CBGjRqlfOwLFy6I4cOHi44dOwpTU1PRq1cvsXnzZpX31t9//y3Gjh0rbG1tRZs2bYSLi4tYvny5cvtCvN+UzbzHHj16tGjXrp2wtrYWw4cPF1evXlVZPnz4cGFtbS2srKxEv379RHh4uPjf//4nZDKZ+OOPP5T7N28KaPv27cXLly8LrOHx48di+vTponPnzsLU1FS4ubmJ9evXi1evXhVae2Htxenz9nTfd/VNTEwUkyZNEh06dBBmZmbC3d1dbNy4UaU+IYSYPXu2sLS0FBYWFuLOnTvv3G5BvxcvXrwQCxcuFE5OTsLU1FS4u7uLLVu2iNzcXGWfn376SXh7e4v27dsLc3Nz4enpKfbv3//O5/0haAjBL0YnIpIKyY3pExFJGUOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgk5JO/OOvp02dQKD7tWaX6+rpIScksuiMVC/dn2eL+LDvlYV9qamqgTp13X9z3yYe+QiE++dAHUC5qLE+4P8sW92fZKe/7ksM7REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUnIJz9Pn0hKatSsBu2qZf9rWbduyb9vtzAvs14h458XZfqY9HEw9Ik+IdpVK6P35D3qLqNI+xZ+gQx1F0HvhcM7REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUkIQ5+ISEIY+kREEsLQJyKSEN6GgYgqJN7HqGAMfSKqkHgfo4JxeIeISEIY+kREEsLQJyKSEMmN6fPkDhFJmeRCnyd3iEjKOLxDRCQhDH0iIglh6BMRSUixQ3///v3o2bMnzM3N4ebmhujo6EL7p6amIjAwEHZ2drCxscGoUaOQmJhYynKJiKg0inUiNyYmBlOmTIGvry/s7e1x9OhRBAQEQFtbG66urvn6CyHg7+8PuVyOf//736hduzaWLl0KX19f7Nu3D7Vq1SrzJ0LqwdlQROVLsX5bFy1aBDc3NwQFBQEA7O3tkZ6ejtDQ0AJDPzExERcvXkRISAj69u0LAGjevDm6deuGuLg49OvXr+yeAakVZ0MRlS9FDu8kJSVBLpfDxcVFpb1Hjx5ISEhAUlJSvnWysrIAADo6Osq2vKP7tLS00tRLRESlUGToJyQkAACMjY1V2o2MjAAAt2/fzrdOq1at0KFDB6xYsQK3bt1Camoq5syZg+rVq6Nbt25lUTcREb2HIod3MjJefyjW1dVVac87is/MzCxwvZkzZ+Lrr7+Gu7s7AEBLSwsrVqyAoaFhiQrU19ctulMFVdbj2lLH/Vm2uD/Lzsfcl0WGvhACAKChoVFgu6Zm/g8Lt27dgre3N5o0aYKgoCBoa2tjx44dGDduHNavXw9ra+tiF5iSkgmFQhS7f1HK0xv18eNPfxSa+7NscX+WHanuS01NjUIPlosM/Ro1Xu+4t4/onz17prL8TT/++CMAICwsTDmWb2tri6+++gr//e9/ERUVVbzqiYioTBU5pp83li+Xy1Xa79y5o7L8Tffu3UPz5s1VpmZqaGjAysoKN2/eLFXBRET0/ooMfSMjIxgYGCA2Nlal/fDhw2jatCkaNWqUbx1jY2PcuHED6enpKu2//fYbGjduXMqSiYjofRVrnr6/vz8CAwNRq1YtODk5IS4uDjExMVi8eDGA11ffyuVytGjRArq6uhg6dCj27t0LPz8/jBw5Etra2tizZw/Onj2rXIeIiD6+YoW+h4cHsrOzERYWhsjISBgaGiIkJEQ5Myc+Ph6BgYHYtGkTOnToAAMDA0RERGD+/PmYNm0aNDU1IZPJsHHjRnTu3PmDPiEiInq3Yl8/7+3tDW9v7wKXeXh4wMPDQ6WtefPmWL16demqIyKiMsW7bBIRSQhDn4hIQhj6REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUkIQ5+ISEIY+kREEsLQJyKSEIY+EZGEMPSJiCSEoU9EJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkhKFPRCQhDH0iIglh6BMRSQhDn4hIQhj6REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJKXbo79+/Hz179oS5uTnc3NwQHR1daH+FQoFVq1aha9euMDc3R+/evXHgwIHS1ktERKVQuTidYmJiMGXKFPj6+sLe3h5Hjx5FQEAAtLW14erqWuA6//3vf7F9+3ZMmjQJrVq1woEDBzB58mTo6urC0dGxTJ8EEREVT7FCf9GiRXBzc0NQUBAAwN7eHunp6QgNDS0w9OVyOcLDwxEcHIwBAwYAADp16oTExET8/PPPDH0iIjUpMvSTkpIgl8sxadIklfYePXogJiYGSUlJMDQ0VFl29OhRaGtro2/fvirtW7ZsKX3FRET03ooc009ISAAAGBsbq7QbGRkBAG7fvp1vnevXr8PY2BinT59Gnz590Lp1a7i4uODgwYNlUTMREb2nIo/0MzIyAAC6uroq7To6OgCAzMzMfOukpqbi/v37CAoKwvjx42FgYIDIyEhMnDgRenp66NixY7EL1NfXLbpTBVW3bg11l1ChcH+WLe7PsvMx92WRoS+EAABoaGgU2K6pmf/DQk5ODlJTU7F69Wo4OzsDeD2mn5CQgOXLl5co9FNSMqFQiGL3L0p5eqM+fpyh7hKKxP1Ztrg/y45U96WmpkahB8tFDu/UqPF6x719RP/s2TOV5W/S0dFBpUqVYGtrq2zT0NBA586dcf369eJVTkREZa7I0M8by5fL5Srtd+7cUVn+JiMjIygUCrx69UqlPScnJ98nBiIi+niKDH0jIyMYGBggNjZWpf3w4cNo2rQpGjVqlG8de3t7CCEQExOjbHv16hV+/vlnWFlZlUHZRET0Poo1T9/f3x+BgYGoVasWnJycEBcXh5iYGCxevBjA6xO3crkcLVq0gK6uLjp16gRHR0fMmTMHz58/R9OmTbF161YkJydj4cKFH/QJERHRuxUr9D08PJCdnY2wsDBERkbC0NAQISEhcHd3BwDEx8cjMDAQmzZtQocOHQAAS5cuRWhoKNauXYv09HS0bt0aYWFhMDU1/XDPhoiIClWs0AcAb29veHt7F7jMw8MDHh4eKm3a2toICAhAQEBA6SokIqIyw7tsEhFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkhKFPRCQhDH0iIglh6BMRSQhDn4hIQhj6REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUkIQ5+ISEIY+kREEsLQJyKSEIY+EZGEMPSJiCSEoU9EJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkhKFPRCQhDH0iIgkpdujv378fPXv2hLm5Odzc3BAdHV3sjdy/fx9WVlZYuXLl+9RIRERlpFihHxMTgylTpsDW1hYrVqyAjY0NAgICEBsbW+S6QggEBQUhMzOz1MUSEVHpVC5Op0WLFsHNzQ1BQUEAAHt7e6SnpyM0NBSurq6Frrt161YkJCSUvlIiIiq1Io/0k5KSIJfL4eLiotLeo0cPJCQkICkpqdB1FyxYgNmzZ5e+UiIiKrUiQz/vKN3Y2Fil3cjICABw+/btAtdTKBSYNm0a3Nzc4ODgUNo6iYioDBQ5vJORkQEA0NXVVWnX0dEBgHeO1f/vf/9DUlISVq9eXaoC9fV1i+5UQdWtW0PdJVQo3J9li/uz7HzMfVlk6AshAAAaGhoFtmtq5v+wkJCQgCVLlmDp0qWoUaN0TyYlJRMKhSjVY7ypPL1RHz/OUHcJReL+LFvcn2VHqvtSU1Oj0IPlIod38kL77SP6Z8+eqSzPk5ubi2nTpsHV1RW2trZ49eoVXr16BeD1kE/e/4mI6OMrMvTzxvLlcrlK+507d1SW57l//z5+++03REdHo02bNsp/ALBs2TLl/4mI6OMrcnjHyMgIBgYGiI2NRffu3ZXthw8fRtOmTdGoUSOV/vXq1cPOnTvzPU7//v3x5ZdfwtPTswzKJiKi91Gsefr+/v4IDAxErVq14OTkhLi4OMTExGDx4sUAgNTUVMjlcrRo0QK6urowMzMr8HHq1av3zmVERPThFeuKXA8PD8yaNQsnT56Ev78/zp49i5CQELi7uwMA4uPj4eXlhatXr37QYomIqHSKdaQPAN7e3vD29i5wmYeHBzw8PApd//r16yWrjIiIyhzvsklEJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkhKFPRCQhDH0iIglh6BMRSQhDn4hIQhj6REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUkIQ5+ISEIY+kREEsLQJyKSEIY+EZGEMPSJiCSEoU9EJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkpNihv3//fvTs2RPm5uZwc3NDdHR0of0fP36MGTNmwNnZGZaWlvDw8EBMTExp6yUiolKoXJxOMTExmDJlCnx9fWFvb4+jR48iICAA2tracHV1zdc/OzsbX3/9NTIyMjBu3DjUq1cPhw4dwoQJE5Cbm4tevXqV+RMhIqKiFSv0Fy1aBDc3NwQFBQEA7O3tkZ6ejtDQ0AJD/8SJE/jrr78QGRkJc3NzAICtrS3u3buHdevWMfSJiNSkyOGdpKQkyOVyuLi4qLT36NEDCQkJSEpKyreOjo4OvLy8YGZmptLerFkzyOXyUpZMRETvq8gj/YSEBACAsbGxSruRkREA4Pbt2zA0NFRZ1qlTJ3Tq1EmlLScnB8ePH0fLli1LVTAREb2/IkM/IyMDAKCrq6vSrqOjAwDIzMws1oYWLFiAxMRErFixokQF6uvrFt2pgqpbt4a6S6hQuD/LFvdn2fmY+7LI0BdCAAA0NDQKbNfULHyESAiB+fPn48cff4Sfnx+6detWogJTUjKhUIgSrVOY8vRGffw4Q90lFIn7s2xxf5Ydqe5LTU2NQg+Wiwz9GjVe77i3j+ifPXumsrwg2dnZmDZtGg4cOAA/Pz9MnTq1WEUTEdGHUWTo543ly+VymJiYKNvv3LmjsvxtmZmZGDVqFC5evIigoCAMGTKkLOolIqJSKHL2jpGREQwMDBAbG6vSfvjwYTRt2hSNGjXKt05ubi7GjBmD3377DYsWLWLgExF9Ioo1T9/f3x+BgYGoVasWnJycEBcXh5iYGCxevBgAkJqaCrlcjhYtWkBXVxfbtm3D2bNn4eXlhYYNG+Ly5cvKx9LQ0EDbtm0/yJMhIqLCFSv0PTw8kJ2djbCwMERGRsLQ0BAhISFwd3cHAMTHxyMwMBCbNm1Chw4dcOjQIQDA9u3bsX37dpXHqlSpEv78888yfhpERFQcxQp9APD29oa3t3eByzw8PODh4aH8edOmTaWvjIiIyhzvsklEJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkhKFPRCQhDH0iIglh6BMRSQhDn4hIQhj6REQSwtAnIpIQhj4RkYQw9ImIJIShT0QkIQx9IiIJYegTEUkIQ5+ISEIY+kREEsLQJyKSEIY+EZGEMPSJiCSEoU9EJCEMfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikhCGPhGRhDD0iYgkpNihv3//fvTs2RPm5uZwc3NDdHR0of2fPXuGWbNmwdbWFpaWlhgxYgQSExNLWS4REZVGsUI/JiYGU6ZMga2tLVasWAEbGxsEBAQgNjb2netMnDgRsbGxmDJlCkJCQvDw4UP4+voiIyOjzIonIqKSqVycTosWLYKbmxuCgoIAAPb29khPT0doaChcXV3z9T9//jyOHz+OdevWwcHBAQBgbW2Nrl27IiIiAiNHjizDp0BERMVV5JF+UlIS5HI5XFxcVNp79OiBhIQEJCUl5Vvn1KlT0NHRga2trbJNT08P7du3x4kTJ8qgbCIieh9FHuknJCQAAIyNjVXajYyMAAC3b9+GoaFhvnWMjIxQqVIllfYmTZogJiamRAVqamqUqH9x1KtTrcwf80P4EM/9Q+D+LFvcn2VHivuyqMcqMvTzxuB1dXVV2nV0dAAAmZmZ+dbJzMzM1z9vnYL6F6ZOHZ0S9S+ODTNciu70CdDXz78PP0Xcn2WL+7PscF/mV+TwjhACAKChoVFgu6Zm/ofIW1bgBgvoT0REH0eRCVyjRg0A+Y/onz17prL8Tbq6usrlb69T0CcAIiL6OIoM/byxfLlcrtJ+584dleVvr5OUlJTviP/OnTsF9icioo+jyNA3MjKCgYFBvjn5hw8fRtOmTdGoUaN869jZ2eGff/7B6dOnlW2pqak4f/48OnfuXAZlExHR+yjWPH1/f38EBgaiVq1acHJyQlxcHGJiYrB48WIArwNdLpejRYsW0NXVRfv27WFjY4NJkyZhypQpqF27NpYtW4YaNWrgyy+//KBPiIiI3k1DFHbW9Q3btm1DWFgY7t+/D0NDQ4wcORJ9+/YFAERFRSEwMBCbNm1Chw4dAADp6emYO3cujh49CoVCASsrK0ybNg3NmjX7YE+GiIgKV+zQJyKi8o/zJ4mIJIShT0QkIQx9IiIJYeiX0IQJE3DixIlCrzomIvpUMfRL6N69exg5ciQcHBywYMEC3Lp1S90lEREVG2fvvIfbt28jOjoa+/btw/3792FmZoZ+/fqhV69eBd6WgoonMzMTL168gEKhyLesfv36aqiIqOJh6JfSmTNnEBsbi/j4eDx9+hTdunVD//790alTJ3WXVm7I5XIEBQXhwoUL7+xz7dq1j1hR+TJ8+PBi99XQ0MCGDRs+YDX0qSvWFbn0bjVq1ICOjg60tLSQnZ2Nv//+G35+fmjVqhXmz5+P5s2bq7vET15wcDBu3ryJsWPHokGDBrwTawnl5OSou4QK5eHDhyXqX94+hfJI/z3cu3cP+/btw969e5GQkICGDRuiX79+6NevHwwMDHD37l2MHj0ampqa2Lt3r7rL/eRZWFhgzpw56NWrl7pLIYKpqSlyc3OL3b+8fQrlkX4JDR48GBcuXECVKlXQrVs3TJ8+HZ06dVL5vgEDAwO4urpi48aNaqy0/NDR0UGtWrXUXUa5VdGPTD+2yMhIjBo1CtnZ2Zg8eTIqV65YMckj/RLy9PSEp6cnevfuXehJ22vXriErKwsWFhYfr7hyKiQkBLdv38aqVavyfVkPFa1Vq1Yl2m/l7chUHRISEjBgwAD4+fnhm2++UXc5ZYqh/x5evnyJq1evwsrKCgDw4MEDnD17Fi4uLtDW1lZzdeXPihUr8OOPP6JOnTowNzdHtWqq32uqoaGB4OBgNVX36YuKiipR6Pfr1+8DVlNxhIeHY+HChTh69Cj09PTUXU6ZYeiXUFJSEoYNGwaFQoG4uDgAwKlTp/D111+jRYsWWL9+PT8+l1CXLl0KXa6hoYFjx459pGqIXsvNzcWFCxfQokULhr6U+fv74+7duwgNDUXTpk2V7cnJyRg7diyaN2+OBQsWqK9Akrw//vgD586dQ05OjvLKcYVCgRcvXuD8+fOIiIhQc4Wftl9++QXm5ubQ0dFRdykfRMU6Q/ERnD9/HiEhISqBDwCNGzeGv78/vvvuO/UUVgGkp6fj8uXLyMzMhJ6eHszMzPidyiUUERGB4ODgAm8ToqmpCTs7OzVUVb4MHz4c27dvh7m5ubJt+/btcHFxQZ06ddRYWdlg6JeQEALZ2dnvXP7y5cuPWE3FsWrVKqxevRpZWVnKNi0tLYwcORJjx45VY2Xly+bNm+Hg4IB58+ZhzZo1yMzMRFBQEI4fP45p06ahT58+6i7xk/f2H8zc3FzMnDkTpqamFSL0eRVMCbVv3x4rV65EWlqaSvs///yDNWvWwMbGRj2FlWM7duzA0qVL4eHhga1bt+Lw4cPYsmULPDw8sHLlSkRGRqq7xHIjKSkJX331FWrVqgVTU1NcuHAB2tra6NGjB0aOHIlNmzapu8RyqSKNgvNIv4SmTJmCgQMHokuXLmjXrh309fWRmpqKixcvonLlypg7d666Syx3/ve//2Hw4MEICgpStjVp0gTW1tbQ0tLC5s2bMWDAADVWWH5UqVJFOYPMyMgId+7cQU5ODqpUqQIrKyteO0I80i8pY2Nj7N+/H15eXsjIyMDly5eRmpoKT09PREdH87YL7yEpKQlOTk4FLnNycsKdO3c+bkHlWKtWrRAfHw/g9XtVoVDgt99+A1Dyi7ioYuKR/nuoX78+AgIC1F1GhdGwYUPcunULnTt3zrfsxo0bvFq3BIYMGYLx48cjIyMDc+bMQdeuXTF16lS4ublhz549ymtLqOQqyoWDDP338OjRI1y6dOmdU+I4ZbNk3N3dERoaigYNGqB79+7K9sOHD2P58uXw8PBQY3XlS48ePbBixQokJCQAeH0zu8mTJyM8PBxmZmacXVZM48ePh5aWlkqbv79/vjYAOHTo0Mcqq0xwnn4JHT58GJMnT0ZOTo7yL78QQvn/Zs2a4cCBA+ossdzJysrC119/jXPnzkFLSwv6+vpISUlBTk4OrK2tsXbt2nxX6VLx5c02KyiwKL/AwMAS9f/hhx8+UCUfBkO/hDw8PFClShV8//33CA8PR25uLkaMGIHjx49j8eLFWL16NWxtbdVdZrkUHx+Pc+fO4Z9//kHNmjVhY2MDBweHCvOx+mNQKBRYtGgRLl26hPDwcACvLzby9/fH8OHDOf2VOLxTUrdu3cLChQvRunVrdOjQAWFhYWjevDmaN2+OJ0+eMPRLwcnJ6Z0ndKl4VqxYgU2bNmHUqFHKNplMBj8/P6xbtw66uroYOnSo+goktWPol5CmpqbyxKKRkRESEhKgUCigqakJe3t77N69W80Vlg/Dhw/HjBkz0KxZsyK/+Ynf9lR8u3fvxtSpUzFo0CBlm76+Pvz9/aGjo4OIiAiGvsRxymYJGRsb49KlSwBej99nZ2fjr7/+AgA8e/as0Kt16f+8eRI8Jyen0H/cp8WXmpqKZs2aFbjMxMQE9+/f/8gV0aeGR/ol5OXlheDgYDx//hwTJkxAx44dMX36dAwYMACbN29GmzZt1F1iubB58+YC/0+lY2xsjCNHjhQ4/TUuLg5NmjRRQ1X0KWHol5CXlxeysrKQnJwMAJg9ezZGjBiB4OBgNG7cGNOnT1dzheXX8+fPUb16dQDAkSNH8ODBAzg5OcHQ0FDNlZUfQ4YMwbRp05Ceno5u3bpBT08Pqamp+Omnn3DgwAHMmTNH3SWSmnH2TgldvHgRZmZmqFKlirJNCIGnT59WqHtuf0wJCQkYPXo03N3dMWHCBCxZsgSrV68GAGhrayMsLAzt2rVTc5XlR3h4OFauXImUlBRlW+3atTF27FiVsX6SJoZ+Cdnb22Py5Mno27evukupMPz9/ZGQkIB58+bBxMQEdnZ2sLOzQ3BwsPKolUNAJSOEwO3bt5GWloYaNWqgWbNmqFSpkrrLok8AT+SWUOXKlXmP9zJ27tw5TJo0CWZmZjh79iwyMjLg5eUFXV1deHt748qVK+ousdzJzs5GSkoK7t+/j3r16uHx48fqLok+ERzTL6ExY8bgu+++w/Xr1yGTyaCvr5+vD4ciSiYnJ0c5DfbEiROoVq2a8h4xubm5qFyZb9OSCA8PR2hoKP755x9oaGhg586dCA0NRXZ2NlauXKk8b0LSxN+mEsq7d8myZcsAqN6EKe92DNeuXVNLbeWVTCbD4cOHYWxsjNjYWNjZ2aFy5crIyclBeHg4ZDKZukssN3bu3Ik5c+bA19cXzs7Oyjn5/fv3R2BgIJYtW8abBUocQ7+E+CUUZW/cuHHw9/dHeHg4tLS0MGLECACvbx6WkpKiPKlLRduwYQOGDRuGqVOnIjc3V9nu4uKCR48eISwsjKEvcQz9EuI3Y5U9W1tb7Nu3D3/88Qfatm2Lxo0bA3h91W7Hjh3RokULNVdYfty9e/ed34PbsmVLju0TQ7+kinPUOXr06I9QScViaGiYbz4+pxeWXIMGDfD7778XeHHWtWvX0KBBAzVURZ8Shn4JLVmy5J3LdHV1Ua9ePYZ+MfDeOx+Gp6cnVq5cCW1tbTg7OwMAXr58iWPHjmHVqlUYPHiwmiskdWPol1DefXbe9Pz5c5w/fx4zZ87Et99+q4aqyp+3771DZWPUqFG4d+8eQkJCEBISAuD1JyYhBHr27IkxY8aouUJSN16cVYZ27dqFLVu28E6bpHaJiYk4c+aM8uIsa2trtGzZEhEREfDx8VF3eaRGPNIvQ40aNcKtW7fUXUa5dPz4cZw5c0Y5s+T333/H4sWLMWrUKHTs2FHN1X36Tpw4gd27d0NDQwNffPEFHB0d0bRpU+Xy8+fPw8PDA9evX2foSxyvyC0jDx8+xPr165UzT6j4Dh48iNGjR6v8waxWrRoUCgX8/Pxw4sQJNVb36du7dy9GjhyJY8eO4fjx4xg9ejSOHDkCAEhLS8OUKVMwePBg3Lx5E8OGDVNztaRuHN4poTZt2uT7+j6FQgEhBIQQmDdvHvr06aOm6sqnPn36wMbGBjNmzMi3bPbs2fjtt9+wc+dONVRWPnh6eqJKlSpYv349tLS0EBgYiISEBCxevBjDhw/HvXv3YG9vj6CgIBgbG6u7XFIzDu+U0OjRowv8zlZdXV04OTmpfKSm4pHL5QgKCipwWbdu3RAVFfWRKypfEhMTMWfOHOU9ofz9/dGzZ0/4+/sjKysLoaGh6NGjh5qrpE8FQ7+E/vWvfwFQvfd7ZmYmMjMzOQf6Penr6+Pq1asFjt1fv35deV8eKtjz58/RsGFD5c8GBgYQQqBSpUrYu3dvgfeHIunimH4JvXjxAhMnTsTAgQOVbZcvX4aTkxMCAwP51X7voXfv3li+fDm2bduGJ0+eQAiBlJQUREZGYtmyZejdu7e6S/ykCSGgqfl/v8p5t1CeMGECA5/yYeiX0MKFC3H69GmVE2KWlpaYO3cu4uPjsXLlSjVWVz75+/vD3t4eM2fOhL29PVq3bg07Ozt8++23sLW1xbhx49RdYrlUv359dZdAnyAO75TQ4cOHMW3aNPTr10/ZpqOjg759+yInJwerVq3ChAkT1FdgOVSlShUsXboUf//9Ny5cuID09HTUqFEDVlZWaNWqlbrLK7cKOvdExNAvoYyMjHd+ZG7YsKHKV9RRychkMjRr1gxPnz5FnTp1eB/9EnjzRG7ehLxZs2ZBR0dHpR9vaUEc3ikhExOTd15xu2fPHrRs2fIjV1QxXLlyBX5+frC0tISjoyOuX7+OadOmYcWKFeou7ZPXvn17VK1aFTk5OcjJycGrV6/Qvn17aGlpKdvy/vGcE/FQqoTGjBmD0aNH4969e+jatSv09fWRmpqKn376CZcvX+aY/nu4ePEihg4dipYtW2LkyJHKfdigQQMsX74cderUwVdffaXmKj9d/P5gKglenPUefvrpJyxbtgzXrl1TfpRu1aoVxo0bhy5duqi5uvLnq6++Qs2aNbF69Wq8evUKpqam2LVrF9q0aYO5c+fi1KlT2Ldvn7rLJKoQeKT/HpydneHs7IysrCykpaVBR0eHX5ZeClevXsXSpUsB5D/56OzsjG3btqmjLKIKiWP67yEiIgKTJk1C1apVUb9+fVy7dg0uLi68u+Z70tHReecJ8IcPH+Y7GUlE74+hX0JbtmxBcHCwypF9gwYNYG1tjRkzZmDPnj1qrK586tKlC5YsWYI///xT2aahoYHHjx9jzZo1cHR0VGN1RBULx/RLqEePHujTpw/8/f3zLVu+fDkOHTrE8ecSSktLw5AhQ3Djxg3Ur18f9+/fR4sWLZCcnIx69eohIiICenp66i6TqELgmH4JPXjwAO3atStwmZWVFdatW/eRKyr/ateujcjISERHR+PMmTMwNjaGrq4uvL294eHhobzHERGVHkO/hBo1aoRff/0VnTp1yrfswoULvPT9PYSEhMDd3R0DBw5UuacREZU9hn4JeXl5YcGCBcjNzUW3bt2gp6eHp0+fIi4uDhs2bMD48ePVXWK5s2PHDtjZ2am7DCJJYOiX0NChQ/Hw4UNs3LgR69evB/D6svfKlStj8ODB+Prrr9VcYfnTpk0bnD59Gra2tuouhajC44nc95SRkYFLly4pbw5mbm6OmJgY7NixgzN4SigkJASbN29Gw4YN0aJFC3z22WcqyzU0NBAcHKym6ogqFoZ+Kf3+++/Yvn07Dh48iBcvXkBPTw+nT59Wd1nlSlFXMWtoaODYsWMfqRqiio2h/x6ePXuGffv2Yfv27fjrr79QpUoVODs7o2/fvnBwcFB+iQUV7cmTJ7h37x6aNGmC2rVrq7scogqPY/olcPXqVWzfvh379+/Hixcv0Lp1awDAmjVrCpzNQ++WlZWFoKAgxMTEKO9f5Obmhu+//55fj0j0ATH0iyEyMhLbtm3D1atXUa9ePfj4+MDDwwP6+vqwsbHhfd/fw5IlSxATEwNPT0+0bt0at2/fxvbt26FQKLBkyRJ1l0dUYTGtiuHbb7+FiYkJ1q1bBzs7O+VNwTIyMtRcWfl15MgR+Pv7q1zZbGJigu+//x5ZWVmoWrWqGqsjqrh4751i6N69OxISEjBp0iRMmjQJ8fHxUCgU6i6rXHv48CFsbGxU2hwdHfHq1SvcvXtXTVURVXw80i+GZcuW4enTp9i7dy92796N0aNH47PPPkP37t2hoaHB7yJ9Dzk5OfmO5uvUqQPg9Xg/EX0YPNIvpjp16mDIkCGIjo7G7t274erqqjwJOWPGDCxfvhy3b99Wd5kVAieUEX04DP338Pnnn2PGjBn4+eefsWTJEhgZGWHVqlVwd3eHh4eHussr9/jJiejD4Tz9MvL48WPs3r0b0dHROHjwoLrL+eS1atUK5ubmKt9LIITAL7/8grZt26p8cYqGhgY2bNigjjKJKhyGPqnF4MGDS9SfX/5NVDYY+kREEsIxfSIiCWHoExFJCEOfiEhCGPpERBLC0CcikpD/B1xYakMYv0+zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize our cross_validated metrics\n",
    "cv_metrics = pd.DataFrame({\"Accuracy\" : cv_accuracy,\n",
    "                           \"Precision\" : cv_precision,\n",
    "                           \"Recall\" : cv_recall,\n",
    "                           \"F1\" : cv_f1},\n",
    "                            index = [0])\n",
    "cv_metrics.T.plot.bar(title = \"Cross-validated classification metrics\", legend = False);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed7517ac",
   "metadata": {},
   "source": [
    "### Feature Importance\n",
    "\n",
    "Feature Importance is another way of asking, \"which features contributed most to the outcomes of the model and how did they contributed?\"\n",
    "\n",
    "Finding feature importance is different for each machine learning model. One way to find feature importance is to search for \"(MODEL NAME) feature importance\".\n",
    "\n",
    "Let's find the feature importance for our LogisticRegression model..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "9eeb47bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.20433597178569418, solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(C=0.20433597178569418, solver='liblinear')"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit an instance of Logistic Regression\n",
    "clf = LogisticRegression(C =  0.20433597178569418,\n",
    "                                solver = \"liblinear\")\n",
    "clf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "548873b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.00316728, -0.86044655,  0.66067042, -0.01156993, -0.00166374,\n",
       "         0.04386109,  0.31275847,  0.02459361, -0.60413083, -0.56862804,\n",
       "         0.4505163 , -0.63609898, -0.67663378]])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check coef_\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "71c51afb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': 0.0031672809701328098,\n",
       " 'sex': -0.8604465542018854,\n",
       " 'cp': 0.6606704161071124,\n",
       " 'trestbps': -0.011569931839584581,\n",
       " 'chol': -0.0016637442846940298,\n",
       " 'fbs': 0.043861090099753856,\n",
       " 'restecg': 0.3127584688139112,\n",
       " 'thalach': 0.024593614036076978,\n",
       " 'exang': -0.6041308274033194,\n",
       " 'oldpeak': -0.5686280446250761,\n",
       " 'slope': 0.45051629703183155,\n",
       " 'ca': -0.6360989766185763,\n",
       " 'thal': -0.6766337834775279}"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Match coef's of the features to columns\n",
    "feature_dict = dict(zip(df.columns, list(clf.coef_[0])))\n",
    "feature_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "57f0290b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize feature importance\n",
    "feature_df = pd.DataFrame(feature_dict, index = [0])\n",
    "feature_df.T.plot.bar(title = \"Feature Importance\", legend = False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "59430147",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "sex            \n",
       "0        24  72\n",
       "1       114  93"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"sex\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "2d0754aa",
   "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>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0    1\n",
       "slope          \n",
       "0       12    9\n",
       "1       91   49\n",
       "2       35  107"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df[\"slope\"], df[\"target\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ad322e",
   "metadata": {},
   "source": [
    "slope - the slope of the peak exercise ST segment\n",
    "* 0: Upsloping: better heart rate with excercise (uncommon)\n",
    "* 1: Flatsloping: minimal change (typical healthy heart)\n",
    "* 2: Downslopins: signs of unhealthy heart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33261b26",
   "metadata": {},
   "source": [
    "# 6. Experimentation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cb95ff8",
   "metadata": {},
   "source": [
    "If we haven't hit our evaluation metric yet... we can ask questions to ourselves\n",
    "* 1. Could we collect more data? (Could try)\n",
    "* 2. Could we try a better model? If we are working with structured data, we might want to look into CatBoost or XGBoost.\n",
    "* 3. Could we improve the current models (beyond what we've done so far)?\n",
    "* 4. If our model is good enough, how would we export it and share it with others?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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