patient-treatment-classification-main file.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# Pogaku Ravi Narasimha     ||        12102758       ||                 RK21B63\n",
    "\n",
    "\n",
    "\n",
    "# VED PRAKASH CHOUBEY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"font-size:30px\">Patient Treatment Classification</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Context\n",
    "In hospitals, medical treatments and surgeries can be categorized into inpatient and outpatient procedures.<br> For patients, it is important to understand the difference between these two types of care, because they impact the length of a patient’s stay in a medical facility and the cost of a procedure.\n",
    "\n",
    "<b>Inpatient Care (Incare Patient) and Outpatient Care (Outcare Patient)</b>\n",
    "\n",
    "The difference between an inpatient and outpatient care is how long a patient must remain in the facility where they have the procedure done.\n",
    "\n",
    "Inpatient care requires overnight hospitalization. Patients must stay at the medical facility where their procedure was done (which is usually a hospital) for at least one night. During this time, they remain under the supervision of a nurse or doctor.\n",
    "\n",
    "Patients receiving outpatient care do not need to spend a night in a hospital. They are free to leave the hospital once the procedure is over. In some exceptional cases, they need to wait while anesthesia wears off or to make sure there are not any complications. As long as there are not any serious complications, patients do not have to spend the night being supervised.<br>\n",
    "<i>[source of information: pbmhealth]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement\n",
    "In today’s world of automation, the skills and knowledge of a person could be utilized at the best places possible by automating tasks wherever possible. As a part of the hospital automation system, one can build a system that would predict and estimate whether the patient should be categorized as an incare patient or an outcare patient with the help of several data points about the patients, their conditions and lab tests."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. About Data\n",
    "The dataset contains Electronic Health Record predictions collected from a private Hospital in Indonesia. It contains the patient's laboratory test results used to determine the next patient's treatment whether <b>in care</b> or <b>out care.</b>\n",
    "\n",
    "### Attribute information\n",
    "- HAEMATOCRIT : Patient laboratory test result of haematocrit\n",
    "\n",
    "- HAEMOGLOBINS : Patient laboratory test result of haemoglobins\n",
    "\n",
    "- ERYTHROCYTE : Patient laboratory test result of erythrocyte\n",
    "\n",
    "- LEUCOCYTE : Patient laboratory test result of leucocyte\n",
    "\n",
    "- THROMBOCYTE : Patient laboratory test result of thrombocyte\n",
    "\n",
    "- MCH : Patient laboratory test result of MCH\n",
    "\n",
    "- MCHC : Patient laboratory test result of MCHC\n",
    "\n",
    "- MCV : Patient laboratory test result of MCV\n",
    "\n",
    "- AGE : Patient age\n",
    "\n",
    "- SEX : Patient gender\n",
    "\n",
    "- SOURCE : Target ( Binary :in/out )\n",
    "\n",
    "#### Acknowledgements\n",
    "This dataset was originally downloaded from Mendeley Data.\n",
    "Sadikin, Mujiono (2020), “EHR Dataset for Patient Treatment Classification”, Mendeley Data, V1, doi: 10.17632/7kv3rctx7m.1<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Objective:\n",
    "- Build a machine learning model to predict if the patient should be classified as <b>in care</b> or <b>out care</b> based on the patient's laboratory test result.\n",
    "\n",
    "\n",
    "## Machine Learning Solution\n",
    "- It is a Supervised Machine Learning Problem\n",
    "- Binary Classification task (0 or 1)\n",
    "- Performance metrics\n",
    "    - Precision\n",
    "    - Recall\n",
    "    - Accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "import pickle\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "plt.style.use('seaborn-whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Load and split data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    {
     "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>HAEMATOCRIT</th>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SOURCE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.1</td>\n",
       "      <td>11.8</td>\n",
       "      <td>4.65</td>\n",
       "      <td>6.3</td>\n",
       "      <td>310</td>\n",
       "      <td>25.4</td>\n",
       "      <td>33.6</td>\n",
       "      <td>75.5</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>43.5</td>\n",
       "      <td>14.8</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.7</td>\n",
       "      <td>334</td>\n",
       "      <td>27.5</td>\n",
       "      <td>34.0</td>\n",
       "      <td>80.7</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>out</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33.5</td>\n",
       "      <td>11.3</td>\n",
       "      <td>4.74</td>\n",
       "      <td>13.2</td>\n",
       "      <td>305</td>\n",
       "      <td>23.8</td>\n",
       "      <td>33.7</td>\n",
       "      <td>70.7</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>out</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE   MCH  MCHC  \\\n",
       "0         35.1          11.8         4.65        6.3          310  25.4  33.6   \n",
       "1         43.5          14.8         5.39       12.7          334  27.5  34.0   \n",
       "2         33.5          11.3         4.74       13.2          305  23.8  33.7   \n",
       "\n",
       "    MCV  AGE SEX SOURCE  \n",
       "0  75.5    1   F    out  \n",
       "1  80.7    1   F    out  \n",
       "2  70.7    1   F    out  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load dataset into the memory\n",
    "\n",
    "data = pd.read_csv('patientdata.csv')\n",
    "data.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's divide the data into train and test sets.<br>\n",
    "Train : 80% of data<br>\n",
    "Test : 20% of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-05-21T04:41:35.962005Z"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3529, 10), (883, 10))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Label encoding\n",
    "# (1=in care patient), (0=out care patient)\n",
    "data['SOURCE'] = data.SOURCE.replace({\"in\":1, 'out':0})\n",
    "\n",
    "\n",
    "# get all the features\n",
    "features = [feat for feat in data.columns if feat !='SOURCE']\n",
    "\n",
    "X = data[features] # feature set\n",
    "y = data['SOURCE'] # target\n",
    "\n",
    "# Splitting data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)\n",
    "\n",
    "# train and test datasets dimensions\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The dataset contains 4412 rows and 11 columns\n",
      "Total number of features : 10\n",
      "Number of numerical features : 9\n",
      "Number of categorical features : 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"The dataset contains {data.shape[0]} rows and {data.shape[1]} columns\")\n",
    "\n",
    "num_features = [feat for feat in features if data[feat].dtype != object]\n",
    "cat_features = [feat for feat in features if data[feat].dtype == object]\n",
    "\n",
    "print(f\"Total number of features : {len(features)}\")\n",
    "print(f\"Number of numerical features : {len(num_features)}\")\n",
    "print(f\"Number of categorical features : {len(cat_features)}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
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   "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>Missing %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HAEMATOCRIT</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCH</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCHC</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCV</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOURCE</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Missing %\n",
       "HAEMATOCRIT         0.0\n",
       "HAEMOGLOBINS        0.0\n",
       "ERYTHROCYTE         0.0\n",
       "LEUCOCYTE           0.0\n",
       "THROMBOCYTE         0.0\n",
       "MCH                 0.0\n",
       "MCHC                0.0\n",
       "MCV                 0.0\n",
       "AGE                 0.0\n",
       "SEX                 0.0\n",
       "SOURCE              0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the percentage of missing values in each feature\n",
    "\n",
    "data.isna().mean().to_frame(name='Missing %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation : Luckily, we didn't find any missing values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># of unique values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HAEMATOCRIT</th>\n",
       "      <td>326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCH</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCHC</th>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCV</th>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AGE</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SEX</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SOURCE</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              # of unique values\n",
       "HAEMATOCRIT                  326\n",
       "HAEMOGLOBINS                 128\n",
       "ERYTHROCYTE                  433\n",
       "LEUCOCYTE                    276\n",
       "THROMBOCYTE                  554\n",
       "MCH                          189\n",
       "MCHC                         105\n",
       "MCV                          406\n",
       "AGE                           95\n",
       "SEX                            2\n",
       "SOURCE                         2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To count number unique values in each feature\n",
    "\n",
    "data.nunique().to_frame(name='# of unique values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.1 Univariate analysis \n",
    "Exploring each feature individually\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.1 Target \n",
    "name : SOURCE<br>\n",
    "There are two classes in the target\n",
    "- 0 : Outcare patient\n",
    "- 1 : Incare patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    {
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\n",
      "text/plain": [
       "<Figure size 700x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Target distribution\n",
    "\n",
    "plt.figure(figsize=(7,4))\n",
    "fig = sns.histplot(x='SOURCE', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:36.344785Z",
     "iopub.status.busy": "2021-05-21T04:41:36.344464Z",
     "iopub.status.idle": "2021-05-21T04:41:36.355764Z",
     "shell.execute_reply": "2021-05-21T04:41:36.354246Z",
     "shell.execute_reply.started": "2021-05-21T04:41:36.344756Z"
    }
   },
   "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>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count\n",
       "0   2628\n",
       "1   1784"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check no. of data points for each class\n",
    "\n",
    "data.SOURCE.value_counts().to_frame(name='Count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation : \n",
    "- data points belongs to class 0 : 1992\n",
    "- data points belongs to class 1 : 1317\n",
    "- Not an imbalanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.2 Numerical features\n",
    "names :\n",
    "- HAEMATOCRIT\n",
    "- HAEMOGLOBINS\n",
    "- ERYTHROCYTE\n",
    "- LEUCOCYTE\n",
    "- THROMBOCYTE\n",
    "- MCH\n",
    "- MCHC\n",
    "- MCV\n",
    "- AGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:36.357677Z",
     "iopub.status.busy": "2021-05-21T04:41:36.357183Z",
     "iopub.status.idle": "2021-05-21T04:41:36.409377Z",
     "shell.execute_reply": "2021-05-21T04:41:36.408043Z",
     "shell.execute_reply.started": "2021-05-21T04:41:36.357634Z"
    }
   },
   "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>HAEMATOCRIT</th>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>AGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "      <td>4412.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>38.197688</td>\n",
       "      <td>12.741727</td>\n",
       "      <td>4.541260</td>\n",
       "      <td>8.718608</td>\n",
       "      <td>257.524479</td>\n",
       "      <td>28.234701</td>\n",
       "      <td>33.343042</td>\n",
       "      <td>84.612942</td>\n",
       "      <td>46.626473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.974784</td>\n",
       "      <td>2.079903</td>\n",
       "      <td>0.784091</td>\n",
       "      <td>5.049041</td>\n",
       "      <td>113.972365</td>\n",
       "      <td>2.672639</td>\n",
       "      <td>1.228664</td>\n",
       "      <td>6.859101</td>\n",
       "      <td>21.731218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.700000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.480000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.900000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.375000</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>4.040000</td>\n",
       "      <td>5.675000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>27.200000</td>\n",
       "      <td>32.700000</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.600000</td>\n",
       "      <td>12.900000</td>\n",
       "      <td>4.570000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>85.400000</td>\n",
       "      <td>47.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>42.500000</td>\n",
       "      <td>14.200000</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>10.300000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>29.800000</td>\n",
       "      <td>34.100000</td>\n",
       "      <td>88.700000</td>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>69.000000</td>\n",
       "      <td>18.900000</td>\n",
       "      <td>7.860000</td>\n",
       "      <td>76.600000</td>\n",
       "      <td>1183.000000</td>\n",
       "      <td>40.800000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>115.600000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE    LEUCOCYTE  THROMBOCYTE  \\\n",
       "count  4412.000000   4412.000000  4412.000000  4412.000000  4412.000000   \n",
       "mean     38.197688     12.741727     4.541260     8.718608   257.524479   \n",
       "std       5.974784      2.079903     0.784091     5.049041   113.972365   \n",
       "min      13.700000      3.800000     1.480000     1.100000     8.000000   \n",
       "25%      34.375000     11.400000     4.040000     5.675000   188.000000   \n",
       "50%      38.600000     12.900000     4.570000     7.600000   256.000000   \n",
       "75%      42.500000     14.200000     5.050000    10.300000   321.000000   \n",
       "max      69.000000     18.900000     7.860000    76.600000  1183.000000   \n",
       "\n",
       "               MCH         MCHC          MCV          AGE  \n",
       "count  4412.000000  4412.000000  4412.000000  4412.000000  \n",
       "mean     28.234701    33.343042    84.612942    46.626473  \n",
       "std       2.672639     1.228664     6.859101    21.731218  \n",
       "min      14.900000    26.000000    54.000000     1.000000  \n",
       "25%      27.200000    32.700000    81.500000    29.000000  \n",
       "50%      28.700000    33.400000    85.400000    47.000000  \n",
       "75%      29.800000    34.100000    88.700000    64.000000  \n",
       "max      40.800000    39.000000   115.600000    99.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[num_features].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:36.410908Z",
     "iopub.status.busy": "2021-05-21T04:41:36.410624Z",
     "iopub.status.idle": "2021-05-21T04:41:37.435311Z",
     "shell.execute_reply": "2021-05-21T04:41:37.434023Z",
     "shell.execute_reply.started": "2021-05-21T04:41:36.410881Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KzMzUyJEjtXDhQkkq8wL21rEDdf3NF5VZD/48Ho927NihLl26NJh3Jn/IOq57lmxVn57d1LNDS6fLqVREdoT0g9Q1tqu6t+1e7fXcbre2bt2quLi4eh25InRPrrRyjZ66Ok6do5rXWzu2aIjPF0n6bmtTXfs3qW/PboqPjy9zjSg5tGa7du18//eep0v+pdV7LfDOk6p3jarudam8bVXldD1fGqtTPXZ1dVxOnDhx6m/I1upGoWqo6p78goICM2DAALN06VKzb98+388XX3xhunbtanbs2GGMqdk9+cYYs2LFCjN06FCzdu1aM27cOGOM/33s1W3XmIrvc3/99ddNv379zIEDB8rU8umnn5rY2Fizbds2Y4wxDz30kElOTi5zb5bH4zFXXnmlmT17dpl98PrPf/5j4uLizDPPPOObxj353JPPPfm11xDvZW1I95FzT75dGuLzxRjuyUftNOR78h0P+UuWLDEJCQllniTGGJOcnGz+93//1xhT85BfXFxsBg0aZJKSksyiRYuMMf7BuLrtGlNxcC4uLjY33nijSUpKMmvXrjWFhYXm6NGjZunSpWbAgAHm6aef9i2bnZ1tLr74YnPnnXeazMxMY4wx+/fvN/fee6+56KKLfMNoVvTB28WLF5tu3bqZNWvWlNmXkgIl5Bvj/2n01atXm6NHj5rVq1eXO7pOYmKi+eSTT8wnn3xS7ug65a1f3dF1Klu3OsuVN7rOggULqjW6zp/+9Kdqja4zZ86cMkH+//2//1fp6DreH0bXOXUN8eLYkIIpId8uDfH5YkzVo+usXr263NF1Bg4c6De6Trdu3UxQUJCZM2dOmdF1qnONqu51qaYa6nFpSE7l2BHyhw83V155pXn44YfLXXfRokUmLi7OHDp0qNJx8m+99VZjTNmAPHfuXBMXF2dyck6ewEsG4+q2a0zlwbm4uNj85S9/MZdffrnp06eP6d+/v/nNb35jPvnkkzLLHjx40MyePdsMGzbMxMfHm8GDB5vp06f7Qn95+1DSrbfeai6++GKTk5PTIEK+MeWPK9upU6cqx8mPjo6ucAzikuvXpu2aLFfZePal96eicfLbtGnDOPkBpiFeHBtSMCXk26UhPl+MqZ9x8r3n/Zpeo2pzTatIQz0uDU1Nj10ghPwgY8oZugPWO3HihL777jt17969Xu7JL4lvvP3vN95eeOGFFW63Lr7x9qKLLtKGDRvUt2/favVPY+d2u7V582bFx8c3mHtZv9mTq6Rnv1Tq1MEBf0/+f7L/o4mpE/Vm0pvq0bZHtdc7XcelIfVlIGiIzxdJ2rhxoy688MJyz402fONtQz0uDVFNjl1dHZfa5LWA+OAt7BYSElLpkGAhISG65JJLdMkll5zS+rVpuybL1WRbpffH+2Sv7nZHjx5d7nLTpk2rsN2NGzdWWRsA4L/KO/9Wdj2q7jbqcnkEjoZ27BrOx+MBVKpVq1YKDg4+5eHXAMBGnBvRWPFOPmCJ8847TwcOHPAbAg4AGjvOjWiseCcfsAgXMQAoi3MjGiNCPgAAAGAZQj4AAABgGUI+AAAAYBk+eAsADdA3e3KdLqFKPx49Lkn64eBxeQqqX6/b7dbOI8UK3ZNbr+N+7zh4vN62DQBOI+QDQAPi8pz8/sJ7397qcCVVCwrLVrPOQbrz79tkirNqvoEVa+q+qHI0i+BSCMA+nNkAoAGJP7eV3pmSqNDgIKdLqZajRUPVYlSrGq3jdruVkZGh2NjYev8Gz2YRoerUrlm9tgEATiDkA0ADE39uK6dLqIGWNV7D7XbLlRWmnh1a1nvIBwBb8cFbAAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKhThcAZ3g8HklSfn6+w5U0Dm63W5J04sQJhYSEOFwNvDgugYnjEpg4LoGJ4xKY6uq4eHOaN7fVRJAxxpxyy2iwsrOzlZmZ6XQZAAAAqEJMTIzatm1bo3UI+Y2Uy+VSbm6uIiIiFBzMXVsAAACBxuPxqLCwUC1btlRoaM1uwCHkAwAAAJbhLVwAAADAMoR8AAAAwDKEfKCObdu2TTfddJMGDBigxMREzZgxQ4cPH5YkbdmyRVdffbX69OmjESNGaMmSJQ5X23isWbNGV199tfr27avExEQ99NBDKigokMRxCQRut1s33HCD7r33Xt80jotzPvjgA/Xo0UN9+vTx/aSkpEjiuDgpJydHM2bMUEJCgvr376/Jkyfr4MGDkjguTnnvvff8nid9+vRRz5491bNnT0kOHxcDoM7k5+ebxMRE86c//ckUFhaaw4cPm9/+9rfm1ltvNTk5OWbAgAFm0aJFpri42Kxevdr06dPHbNmyxemyrZednW3i4uLM0qVLjdvtNgcOHDBJSUnmT3/6E8clQDzzzDOmW7duZubMmcYYw3Fx2Ny5c829995bZjrHxVnXX3+9mTJlisnNzTXHjh0zd9xxh7nllls4LgFk//79JjEx0bzzzjuOHxfeyQfq0N69e9WtWzdNmTJF4eHhat26tSZOnKivvvpKH3/8sVq1aqVJkyYpNDRUF110kZKTk7V48WKny7ZemzZttHr1al111VUKCgpSTk6OCgsL1aZNG45LAFizZo0+/vhjjR492jeN4+KsrVu3+t6JLInj4pxvvvlGW7Zs0dy5c9WiRQs1b95cDz30kKZPn85xCRDGGKWkpGjYsGG64oorHD8uhHygDp133nl6+eWX/b744qOPPtIFF1yg7du3KzY21m/5Ll26aNu2bae7zEapefPmkqShQ4cqOTlZUVFRuuqqqzguDsvOztasWbP05JNPKjIy0jed4+Icj8ejb7/9VmlpaRo+fLguvvhizZ49W7m5uRwXB3399dfq0qWL/vnPf2rUqFEaPHiwHnvsMUVFRXFcAsS7776rHTt2+G47dPq4EPKBemKM0dNPP63PP/9cs2bNUl5enl+IkaQmTZroxIkTDlXYOH388cdauXKlgoODdeedd3JcHOTxeJSSkqKbbrpJ3bp185vHcXHO4cOH1aNHD40ZM0YffPCB3njjDWVmZiolJYXj4qDc3Fx9//33yszM1LJly/TOO+/owIEDmjlzJsclAHg8Hr344ou67bbbfG8qOX1cCPlAPTh+/LjuvPNOLV++XIsWLVLXrl0VGRnp+6CnV0FBgZo1a+ZQlY1TkyZN1L59e6WkpGjVqlUcFwctWLBA4eHhuuGGG8rM47g4p127dlq8eLEmTJigyMhInX322UpJSdHKlStljOG4OCQ8PFySNGvWLDVv3lzt2rXTtGnT9MUXX3BcAsC6det08OBBTZgwwTfN6fMYIR+oYz///LPGjx+v48eP66233lLXrl0lSbGxsdq+fbvfsjt27ND555/vRJmNysaNG/XLX/5SRUVFvmlFRUUKCwtTly5dOC4Oeffdd7V+/Xr169dP/fr1U2pqqlJTU9WvXz+eLw7atm2b5s2bJ1PiuzKLiooUHBysXr16cVwc0qVLF3k8HhUXF/umeTweSVL37t05Lg776KOPNGrUKDVt2tQ3zenzGCEfqEO5ubn6zW9+o759++ovf/mL2rRp45s3atQoHTp0SK+++qqKi4u1du1aLV++XOPHj3ew4saha9euKigo0JNPPqmioiLt2bNHjz32mCZMmKAxY8ZwXBzyr3/9Sxs3blR6errS09OVlJSkpKQkpaen83xxUKtWrbR48WK9/PLLcrlc2rt3r5544gldeeWVPF8cNGjQIJ177rm6//77lZeXp8OHD+vpp5/WyJEjlZSUxHFx2IYNG9S/f3+/aU6fx4JMyZfqAGrllVde0dy5cxUZGamgoCC/eZs2bdLWrVv1yCOPKCMjQ23atNHkyZN11VVXOVRt47Jjxw7NmTNHW7du1RlnnKHk5GTfKEgcl8Dg/bDa3LlzJYnj4qD169frqaeeUkZGhiIiIjR27FilpKQoIiKC4+KgAwcOaO7cufrqq69UWFioESNGaNasWWrRogXHxWF9+vTRM888o6FDh/pNd/K4EPIBAAAAy3C7DgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgmVCnC4AzXC6XcnNzFRERoeBgXusBAAAEGo/Ho8LCQrVs2VKhoTWL7YT8Rio3N1eZmZlOlwEAAIAqxMTEqG3btjVah5DfSEVEREg6+aCJjIx0uBrJ7XYrIyNDsbGxCgkJcbqcgEU/VR99VX30VfXQT9VHX1UffVU9jbWf8vPzlZmZ6cttNUHIb6S8t+hERkaqadOmDldz8skrSU2bNm1UT96aop+qj76qPvqqeuin6qOvqo++qp7G3k+ncms1N2MDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAlgl1ugAAAOraj4fylFfoqtNtut1u7TxSrNA9uQoJCanTbdfU0aIctQhv5WgNlamqr5pFhKpTu2YOVAY0HoR8AIBVfjyUp+Hz0uqvgRVr6m/b1RAUlq1mnecp74fpMsVtHa2lSpX01efThxH0gXpEyAcAWMX7Dv4zE+PVJbp5nW3X7XYrIyNDsbGxjr6T/+PR73X/eqP513VTpxZdHaujMpX11Y6DxzXtzc11/pcWAP4I+QAAK3WJbq6eHVrW2fbcbrdcWWHq2aGloyE/uMnJFy6do5urR9u627+6FCh9BTRmfPAWAAAAsAwhHwAAALAMIR8AAACwDCEfwGl36NAhp0sAAFiM6wwhH8BptnPnTrVv3147d+50uhQAgIW4zpxEyAdwWuXk5Mjj8SgnJ8fpUgAAFuI6cxJDaKLeud1urVq1Svv27dNZZ52lIUOG1NmQapVtuzbtll530KBBWr16db3sQ032Jy0tTWvXrlVOTo6GDBniV1NlNRYVFemFF17QDz/8oM6dO+vWW2/VunXrqrXsTTfdpFmzZmn79u06//zzNXfuXKWnp1erz8urCQCAhsCpLFBnTA0MHz7cLF26tMz0pUuXmuHDh/tNW7RokYmNjTWvvPJKmeVnzpxpevToYeLj48v8vPjii8YYY+bPn29iY2PN3XffXWb9wsJCk5CQYGJjY/2mf/PNN2bq1KkmISHBxMfHm5EjR5q5c+eaI0eOlNlGenq6ueOOO0xiYqLp1auXGTx4sElJSTE//vijb5n58+eb66+/vtI+yczMNDNmzDCJiYmmd+/eZujQoWb27Nlmz549vmV27dplYmNjTe/evU18fLzp1auXSUhIML/73e9Mbm6uX7/MnDnTb53f//73ZdosXdfevXvN9OnTzUUXXWR69+5thg0bZv74xz/6bbu0vLw8k56ebvLy8irdv9paunSpiYmJMZJ8PzExMWUeRy6Xy6SnpxuXy1Un265uu9Xdbmho6CltqyZquj+la6qoxpSUlDLzSv/UZNmK1q1uvz3xxBNGktmwYUOd9l9pp/KYaqxs66utu3NMx5mpZuvunDrdbqD007eHvjU9X+1pvj30raN1VKayvqqv49NQBcrjKtDVpJ82bNhQ6+uMU1mgtNrktXq7XWfx4sW69tpr9dprr8nlKvutdsnJydq0aVOZn9tuu823TOvWrbVixQodO3bMb93PPvtMxcXFftM+//xzXXfdderUqZPeffddbdy4US+99JJ27dqlcePG6cCBA75lly1bpv/5n/9Rr1699NZbb2nz5s1asmSJoqOjNXHiRL9lK7N161ZdeeWVioiI0D/+8Q9t2rRJixcvliRdccUV+v777/2WT01N1aZNm7Rlyxalpqbqp59+0iOPPFJpG2+88YY++OCDCud7PB79v//3/9SyZUv961//0ubNm/Xaa68pIyNDd955Z7X2o768/fbbmjBhguLi4rRmzRodO3ZMa9asUVxcnCZMmKC333673rY9fvz4U2q39HYXLVqkoKAgtW178qvjFy1aVGf7cCr78+WXX+qhhx7yq+muu+6SJLVt21ZBQUF+NY4fP15PPPGE2rZtq4ULF+rll19WUFCQwsPDfetWtOyYMWP8arzkkkskSUFBQZKksWPH+q1but8qqmnGjBl10mcAANQHp7JAnavJK4LqvpO/evVqM2DAAJOfn2+GDh1qUlNT/ZYv+Y51RbzvVl9++eXmjTfe8Jt38803m9mzZ/veyS8sLDSDBg0yzz//fJntuN1uM3HiRHPXXXcZY4zJzc01vXv3NosXL66w3e+++86vhopcccUV5v777y933t13320mTJhgjPnvu/K7du3yW+a1114zl112me/38t7Jf+ihh0zfvn1NZmZmmb4xxphDhw6Z2NhYs3HjRr9tb9u2zdx3330VvuKt73fyXS6XiYmJMcnJycbtdvvNc7vdJjk52XTq1MlXX01eoVe27aKiIhMZGWmaNm1qioqKqmy3su2W/L24uNhv3aq2VRM12Z/CwkJz9tlnm6SkJFNcXGySkpJMaGio7/eSNeXn55ugoCATEhJiCgoK/NopLCw07du3N6GhoaawsLDMsidOnDCSTHh4uMnLyzPt27c3kszYsWNNfn6+CQ8PN5LMiRMnytRYVb9dfPHFRpJZv359rfqtOv3Ku2PVY1tf8U6+83gnv/oC5XEV6E7XO/lOZYGK1Cav1fie/D/84Q+aM2eO37Ti4mLfqxtJev3113XNNdeoSZMmuu666/TXv/5VY8eOPaUXIVdeeaWWLVumiRMnSpIOHDigrVu36oYbbtCbb74pSdq0aZMOHTqkcePGlVk/ODhYEyZM0IMPPiiXy6XPP/9cLpdLV111VbntTZ06tVp17d69W999951mzZpV7vyrr75aN954o/bu3Vvu/KysLH3yyScaPXp0pe3ceOON2r17t6ZNm6Y333zT9w6sV9u2bTVw4EDdcccdSk5OVr9+/dS7d2917dq1zHEqj9vtltvtrnK5mkpLS1NmZqYWLVokY0yZNmbMmKEhQ4YoLS1Nw4YN882vTi2VbXvlypXKz8/3/X/YsGGVtlvZdkv+HhQUVGbdyrZVEzXZH4/Ho7179+qNN95QUFCQRo8erdTUVI0ePbpMjZs3b/Zt78svv5QkXzshISF68MEHdfvtt+vZZ5+VJL9l33rrLUnStGnTFBERoeuuu05PP/20zjvvPIWFhemuu+7SE088oXvuuUcTJkzwq7FkO+X1269//WutXLlSy1d8qfD2nU+536ri8Xi080ixgncdUXAwYwxUxra++iHruKS6P7/V5DxVnzxuj+9fp2upSGV95Z2WsT83YOs/nWx7/tWXmvRTxr5cSad2DnAqC1SkNs+RGof8Bx54oExAfvvtt/Xcc89Jkvbs2aNVq1bp97//vSTpmmuu0fPPP6/169drwIABvnVSU1O1YsWKMtt/7733dPbZZ/t+v/zyyzVv3jz9+OOP6tSpk95++21ddtllioiI8C1z8OBBSVK7du3KrTk6OlrFxcU6cuSIDhw4oFatWqlJkya++c8995xeffVVSZLL5VJSUpIefvjhSvuhOm16l/Muc/nllys4OFhut1snTpzQWWedpQceeKDSdiRp7ty5uvLKK/Xoo4+Wu/zChQu1ZMkSffLJJ3rjjTeUn5+vbt26afr06VV+0DEjI6PK9k/F2rVrJZ18Um7evLnMfI/H41uuVatWvulbt26t1ba988rbdmXtlrfd0r+XXreybdVETfanpM2bNysrK0vSyReNpWvcsmVLuet624mJiZEkrVu3rkybmzZtkiRddNFF2rx5s8LCwiSdfHG7efNmDRw4UNLJF9jnnHNOpe2U7qfjhSdPWPM/3KTXcmOr7J9aW5Fd/23YwrK+2vTNNrmyIqpesIaqc56qT5n5mZKk7zO+V2FkoaO1VKW8vtp64GTN9yxxth8DjmXPv3pTjX4q3L9DkrTxm201fuHkVBaoD3U+us7f//53uVwuXXHFFb5pLpdLf/3rX/1CflJSkubOnVvl9tq0aaNhw4bpnXfe0d13361ly5bpmWee8btPPyoqSpK0d+9eX3Apaffu3QoLC1Pr1q0VFRWlnJwcFRUV+d4Vv+OOO3THHXdIku69995yP0NQWsk2O3XqVG6b3uWMMZJOvoDxBqLjx49r/vz5uuaaa/TBBx+offv2FbbVqlUrPfXUU7r++uuVkJBQZn54eLgmTZqkSZMmye12a9u2bfr73/+u2267TcuXL9d5551X4bZjY2PVtGnTKve3przDVgUHBys+Pr7M/DVr1kiSBg4cqPj4eLndbm3dulVxcXFVflK9sm2XHC7Lu+3K2q1su6V/L71uZduqiZrsj/dkIknx8fFavXq1pJOPs9I1NWnSREuWLPH97uVtZ+HChZLke0yVXHb37t1at26d1qxZo+TkZN9nTc455xzFx8f7/orWp08fv22X107pfvK+kLnz0j4aP+miGvZW9Xk8Hu3YsUNdunTh3bEq2NZXP2Qd1z1LtqpPz27q2aFlnW23Juep+hSRHSH9IHWN7arubbs7VkdlKuur0D250so1eurqOHWOau5QhYHDtudffalJP323tamu/ZvUt2e3Gl+fncoCFTlx4sSpvyFbk3t7qronv6CgwAwYMMAsXbrU7Nu3z/fzxRdfmK5du5odO3YYY2p2T74xxqxYscIMHTrUrF271owbN84YY8zatWt99+QXFRWZxMRE88QTT5TZjsvlMuPHjzdTp041xhhz+PBhExcXZ958881y2y1ZW1X35F955ZVm2rRp5c674447zJVXXmmMqfie/GPHjpnY2Fjz/vvvl2m7vHX+/Oc/m759+5rp06f76nrzzTfNkCFDyr0XbMCAAebDDz8stz7uyeeefO7Jh5dtfcU9+c7jnvzqC5THVaDjnnyHR9dZvny5goKClJycrDPPPNP3c/HFFys2NtZ3S0xNDR06VMXFxXr44Yc1YcKEMvPDwsL06KOPatGiRXr66ad14MAB3yu+O+64Q/v379d9990n6eSIPX/84x/16KOP6i9/+Yuys0/+2efAgQN68cUX9eGHH/putZFOjhm+f/9+vx/vq7o5c+b4bk3avXu3PB6Pdu3apf/93//Vv//970pHziksLNTf/vY3NWnSRHFxcdXqh5tvvlkXXnih3nvvPd+0YcOGqbCwUA888IAyMzPldruVk5OjV155RZL8/npyOoWEhOjJJ59Uamqqxo0b5zdizLhx45Samqp58+ad0rthlW17/PjxKigo0IkTJzR+/PgatVt6u+vXr9fDDz+s1NRUnXPOOVq+fLkeeughrV+/vtb7cKr7s379et1+++16//33dc455yg1NVVTpkzx1Ziamuqr8ZprrvHdZ9+xY0f95S9/0axZs5SamqozzjhDBw4c0JQpU7Rhw4Yyy77++usaM2aMioqK1KxZMx04cEAjRozQ+++/r6ZNm6qoqEhjx47V5s2bNX78eOXn5ys/P99X48MPP6zly5eXqWncuHFatWqVb78BAAgkTmWBelGTVwRVvZN/5ZVXmocffrjcdRctWmTi4uLMoUOHKh0n/9ZbbzXGlH0Xfe7cuSYuLs7k5Jx85V/ynXyv7777zkybNs03Zv0ll1xi5syZY7Kzs8vU8/XXX5t77rnHDB482PTu3dsMHDjQ3Hbbbeazzz7zLeMdq7/0T8l373/++Wdz3333mYsvvtj06tXLXHzxxWbWrFl+78CXHic/Pj7e9O3b11x33XVm9erVvuWqeiffGGOys7PNxRdf7Nc3O3fu9O1LXFycGTBggJk6darvLyflcXKc/E6dOtXbOPnebVe33eput/TYuNXdVk3UdH+qGiffu251xr6vybIVrVvdfmOc/MBjW1/xTr7zeCe/+gLlcRXoAnGc/PrIAqXVJq8FGfN/N4yjUTlx4oS+++47de/evV7uyS+pOt8863a7tXnzZsXHx9fo1XBj/cbbgQMHNthvvN2yZYsuvPBCbdiwQX379q3zPizZl6fymGqMbOurb/bkKunZL5U6dXCd35MfCP30n+z/aGLqRL2Z9KZ6tO3hWB2Vqayv6uv4NFSB8rgKdDXpp40bN9bJdSYQvvG2Nnmtzj94C5QWEhJSL8NKVbXt2rRb3rr1tQ9VtVt6XqtWrXwnuerWGB4ermnTpp3yst7Rs6pa16l+AwCgrjX0axof4wZwWrVq1UrBwcEBN9QYAMAOXGdO4p18AKfVeeedpwMHDlT4HRMAANQG15mTeCcfwGnX2E+8AID6xXWGkA8AAABYh5APAAAAWIZ78gEAVvpmT26dbs/tdmvnkWKF7sl1dKjDH48elyT9cPC4PAV1u491pbK+2nHwuENVAY0LIR8AYBWX5+TXv9z79tb6aWDFmvrZbjUFhWWrWecg3fn3bTLFWY7WUqVK+qpZBBEEqE88wwAAVok/t5XemZKo0OCgOt2u2+1WRkaGYmNjHf/SoqNFQ9ViVCtHa6hMVX3VLCJUndo1c6AyoPEg5AMArBN/bqs636bb7ZYrK0w9O7R0PORLgf1NsYHVV0DjxAdvAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLhDpdAJzh8XgkSfn5+Q5XcpLb7ZYknThxQiEhIQ5XE7jop+qjr6qPvqoe+qn66Kvqo6+qp7H2kzeneXNbTQQZY0xdF4TAl52drczMTKfLAAAAQBViYmLUtm3bGq1DyG+kXC6XcnNzFRERoeBg7toCAAAINB6PR4WFhWrZsqVCQ2t2Aw4hHwAAALAMb+ECAAAAliHkAwAAAJYh5CMguN1u3XDDDbr33nudLiVg5eTkaMaMGUpISFD//v01efJkHTx40OmyAtK3336rSZMmqV+/fho8eLAefvhhFRUVOV1WwDh8+LBGjRqldevW+aZt2bJFV199tfr06aMRI0ZoyZIlDlYYOMrrq48++khXXHGF+vbtqxEjRui55547pZEvbFNeX3kdPHhQgwYN0ttvv+1AZYGlvH7atm2bfvOb36hPnz4aNGiQHn30UblcLgerDAzl9dX777+vSy+9VH379tWYMWP0j3/8w8EKAxshHwHhueeeU3p6utNlBLSpU6fqxIkT+uSTT/T5558rJCREs2fPdrqsgOPxeHTrrbdqzJgxWr9+vd566y19+eWXWrhwodOlBYQNGzZo4sSJ+vnnn33TcnNzdcstt2jcuHH66quv9Mgjj+jRRx/V119/7WClziuvr7755hvNmDFD06ZNU3p6uhYuXKi3335br776qnOFBoDy+srL4/Fo+vTpOnLkiAOVBZby+unw4cO68cYbNWjQIK1fv17//Oc/lZaWpr/97W8OVuq88voqIyNDs2bN0qOPPqqNGzfq0Ucf1SOPPEJ+qAAhH45bs2aNPv74Y40ePdrpUgLWN998oy1btmju3Llq0aKFmjdvroceekjTp093urSAk5ubq6ysLHk8HnnHFQgODlZkZKTDlTlv2bJlmj59uu6++26/6R9//LFatWqlSZMmKTQ0VBdddJGSk5O1ePFihyp1XkV9tWfPHv3qV7/S8OHDFRwcrM6dO2vUqFH66quvHKrUeRX1ldfzzz+vM888U2edddZpriywVNRP77zzjmJiYnTrrbcqLCxM55xzjv7617/q0ksvdahS51XUV5mZmXK5XL7ze1BQkEJCQhQeHu5QpYGNkA9HZWdna9asWXryyScJYZX4+uuv1aVLF/3zn//UqFGjNHjwYD322GOKiopyurSA07p1a91444167LHHFBcXp6FDhyomJkY33nij06U5bvDgwfrkk0902WWX+U3fvn27YmNj/aZ16dJF27ZtO53lBZSK+mrMmDG67777fL8XFBQoLS1NF1xwwekuMWBU1FeStHbtWr3//vt64IEHHKgssFTUT19//bViY2P1+9//XomJiRo5cqTee+89nXnmmQ5V6ryK+mrw4MGKj4/XtddeqwsuuEC/+tWvdNddd6lXr14OVRrYCPlwjMfjUUpKim666SZ169bN6XICWm5urr7//ntlZmZq2bJleuedd3TgwAHNnDnT6dICjsfjUZMmTTR79mxt3rxZqamp+uGHHzR//nynS3NcVFRUueMs5+XllXmR3aRJE504ceJ0lRZwKuqrko4fP64pU6aoSZMmjfpFZEV9lZ2drfvvv1/z5s1Ts2bNHKgssFTUT7m5uXr77bfVq1cvpaWl6bnnntObb76pV155xYEqA0NFfVVUVKRzzjlHr7zyirZs2aIFCxbo2Wef1ZdffulAlYGPkA/HLFiwQOHh4brhhhucLiXgef8UOWvWLDVv3lzt2rXTtGnT9MUXXygvL8/h6gLLJ598oo8++kjXXXedwsPDdf7552vKlCl8OKsSkZGRKigo8JtWUFBAMKvEzp079atf/Uoul0uvvfaamjdv7nRJAcUYoxkzZuiGG25Qz549nS4noIWHhysuLk4TJkxQWFiYunXrpuuvv14ffvih06UFnGeffVbh4eEaNGiQwsLCNGzYMI0dO1Zvvvmm06UFpJp9dRZQh959910dPHhQ/fr1kyRfyFixYgUfoimlS5cu8ng8Ki4uVkREhCT5RvPg++z87du3r8xIOqGhoQoLC3OoosAXGxurf//7337TduzYofPPP9+higLbF198oXvuuUfXXHONfve739X4Wygbg3379mn9+vXasmWLnn/+eUkn//Lxhz/8QR999JEWLFjgcIWBo3PnzmVGJCr5mSL81969e9WqVSu/aZzfK8Y7+XDMv/71L23cuFHp6elKT09XUlKSkpKSCPjlGDRokM4991zdf//9ysvL0+HDh/X0009r5MiRvINYyuDBg5WVlaWXXnpJbrdbu3bt0osvvqjk5GSnSwtYo0aN0qFDh/Tqq6+quLhYa9eu1fLlyzV+/HinSws4mzdv1pQpU3Tfffdp5syZBPwKnH322dq6davv/J6enq6zzz5bDzzwAAG/lPHjxysjI0MLFy6U2+3W999/r0WLFumKK65wurSAM2LECH3wwQdatWqVjDFav3693nvvPc7vFeDsBDQAYWFhev311zV37lyNGTNGhYWFGjFihGbNmuV0aQGnS5cuWrBggZ555hm9/PLLOuOMM3T55ZdrypQpTpcWsFq3bq2//vWveuSRRzR//ny1adNG//u//6uBAwc6XVrAeemll+RyufTII4/okUce8U2/8MIL9fLLLztYGRqqzp07a9GiRXr88cf15z//WU2aNNG1117LrazluPrqq1VQUKCHH35YWVlZOvvss/Xggw9q+PDhTpcWkIIMfw8CAAAArMLtOgAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZUKdLgDOcLlcys3NVUREhIKDea0HAAAQaDwejwoLC9WyZUuFhtYsthPyG6nc3FxlZmY6XQYAAACqEBMTo7Zt29ZoHUJ+IxURESHp5IMmMjLS4WoCl9vtVkZGhmJjYxUSEuJ0Odahf+sffVy/6N/6Rf/WP/q4ftW2f/Pz85WZmenLbTVByG+kvLfoREZGqmnTpg5XE7jcbrckqWnTppz86gH9W//o4/pF/9Yv+rf+0cf1q67691RureZmbAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyoU4XAAA4fX48lKe8QpfTZZySo0U5ahHeyuky/Ljdbu08UqzQPbkKCQnxTW8WEapO7Zo5WBmAxo6QDwCNxI+H8jR8XprTZZySoLBsNes8T3k/TJcpbut0OWWtWFNm0ufThxH0ATiGkA8AjYT3HfxnJsarS3Rzh6upmR+Pfq/71xvNv66bOrXo6nQ5Pm63WxkZGYqNjfW9k7/j4HFNe3Nzg/2LCQA7EPIBoJHpEt1cPTu0dLqMGglucvJFSefo5urRNnBqd7vdcmWFqWeHln636wCA0/jgLQAAAGAZQj4AAABgGUI+AAAAYBlCPmCxQ4cOOV0CAECcj3H6EfIBS+3cuVPt27fXzp07nS4FABo1zsdwAiEfsFROTo48Ho9ycnKcLgUAGjXOx3ACQ2ii3rndbq1atUr79u3TWWedpSFDhtR6qLnKtlnT9kovP2jQIK1evVr79u1TdHS0mjZtqrS0NB08eNBve0VFRXrhhRf0ww8/qHPnzpo8ebJCQkKqbLsm67ndbqWlpemzzz7Tzz//rF/84hcaMWKEhg0b5qvh2Wef1apVq5SXl6eoqCj94he/0NGjR5WVleVrDwDgvPXr1+vNN9/UihUrdOzYMTVr1kytW7dW8+Ynh4g966yzFBYWpoSEBJ177rlKSEjQggULtH37dgUFBSkhIUFnnnmmtmzZon//+98644wzNGnSJIWGhmr//v3KyspSVFSUOnTooEGDBmnVqlVKS0uTJA0ZMkSStGrVKknSsGHDNGzYMEknr4OfffaZVqxYoRUrVigoKEj9+/fXU089pcjISL/rVkxMjOLi4pSdne27XklSWlqa0tLS5PF41LZtW7Vv314dOnTwa7fkNa68aRVdqyu6rnun79mzp9J99+5rfWaPgGTqyPDhw03Pnj1NfHx8mZ+vvvrKzJw50/To0cNveq9evczYsWPN8uXLjTHG/PnPfza9e/c2O3bsKLP9qVOnmnHjxpl169b5bSM2NtbExcX5fp89e7YxxpjY2Fizdu3aMtuZP3++uf76632/l16/d+/epl+/fubmm282P//8s9+633zzjZk6dapJSEgw8fHxZuTIkWbu3LnmyJEjZdpZtWqVufnmm01CQoLp27evufzyy80//vEPY4wxHo/H/Pa3vzWXXXaZyc/P91vv2LFjZsSIEebBBx80L774oq+uuLg4Exsb67fv7777rlm7dm2Z6d6fyy67rMLjlZeXZ9LT001eXl6Fy9SFpUuXmpiYGCPJ9xMTE2OWLl1aL9usaXvlLR8aGur3e0hISJntXXHFFWWWCw4ONi1atKi07ZSUlGqvl5KSYqKjo/2me3+ioqLMFVdcYYKDg8udX7r+lJSUU+7v+uRyuUx6erpxuVxOl2Ktkn28dXeO6Tgz1WzdneN0WTX27aFvTc9Xe5pvD33rdCl+ynsMN+R+DjS2nCOeeOKJKs/Vdf1TnetDdHS0mT59umndunWFy3Tu3LnMdav0Nkpfw0pfr0pfy6Kjo01UVFS1rtUVXddTUlLKTK9s36Ojo+ste1Smto/h2uS1Og35le3ozJkzzcyZM/2m5efnm/nz55tu3bqZnTt3Go/HY/7nf/7HXHHFFaawsNC33Jtvvmn69etXJnQbU3GYr0nIL73crl27zNVXX22uvfZa37TPPvvM9OrVyzz11FNm//79xuPxmB07dpgpU6aYoUOHmv379/uWfeWVV0y/fv3Me++9Z/Lz843b7Tbr1q0zgwcPNo899pgxxpjs7GwzZMgQ88ADD/i1fc8995irrrrKb/+NMb4wX1pF06tyOkL+0qVLTVBQkElOTjZr1qwxx44dM2vWrDHJyckmKCjolJ5slW1TUo3aK72tRYsWmaCgINO+fXsjydx1111GkmnTpo0JCgoyixYtMmvWrDGdO3c2kkzLli3NwoULzb59+8ztt9/ue9JPmjSp3LZTUlKMJNO+ffsq1+vXr5/fieSiiy4yzz77rLnooosqPJG2bNmy3OnNmzc3kgIy6NtyAQ9khPz6RcivXzacI7zXmpoEdG+o9obV3r17m4SEBN/88PBwvzdyvP+//fbbTZ8+ffy2dc0115TZ/oMPPmgGDx5cZnpYWJj51a9+ZS688EK/6eHh4b7rVa9evUybNm2MJDNkyBDfMh06dDCSzMCBA03v3r3LbPvRRx81x44dM48++miZaRVdqyu65nuvkZ07dzZBQUHm0ksvNQsXLiyz7w8++KD59NNPfftaH9mjqm022pBvjDHHjx83sbGx5sMPPzTGnAy/iYmJ5tFHHzXGGPPDDz+Y+Ph488knn5S73foI+cYY8/rrr5v4+HhjjDGFhYVm0KBB5vnnny+znNvtNhMnTjR33XWXMcaY/fv3mx49epj333+/zLKrVq0yM2bMMEVFRcYYY9atW2e6d+9uVqxYYYwxZtmyZaZ///5m165dZdZtaCHf5XKZmJgYk5ycbNxut988t9ttkpOTTadOnWr0oK9sm0VFRSYyMtI0bdrU17+VtVd6WyV/Ly4uNklJSSY0NNSMHTvWrF271iQlJZlOnTqZEydOmNDQUBMeHm46duxoXC6Xb92xY8ea9u3bm9DQUN+LNG/bHTt2NKGhoaZ9+/amuLjYr4bS63mne0/uY8eO9e2v2+02l112md9JrEmTJuayyy4zISEhvndMgoKCfBeBs846y0RHR/vVFShsuIAHOkJ+/SLk16+Gfo7wns9LhvKwsDATFBRU7l9qw8LCyrwLHR0dbX7xi1+YkJAQ37k9JCTEd837xS9+YcLCwowk07FjR9OxY0cTHBxsmjRpYsaOHetbduzYsSYpKck0bdrUxMTEmPz8fNOkSRO/tkteu0pfa37xi1/4rpnFxcW+N8TCw8N915ikpCTjdruN2+02Y8eONUFBQb62O3XqZAoLC01MTIxJSkryXVe9x7b0tbqia77L5TIdO3b0XTe9bbpcLvOLX/zCBAcH+7XpcrmM2+327XtdZo/q5BknQ76j9+QfP35cL730ks444wz17dtXktSmTRs98cQTuvnmm3XJJZfo8ccf17XXXquRI0fWePu33XZbmXulCgsLFR8fX+l6O3fu1NKlS3XxxRdLkjZt2qRDhw5p3LhxZZYNDg7WhAkT9OCDD8rlcmnlypUKCQnRqFGjyiw7ePBgDR482Pf7gAEDNHnyZD3wwAM655xzNGfOHD3++OM655xzaryvp8rtdsvtdtf5dtPS0pSZmalFixbJGFOmjRkzZmjIkCFKS0vz3RNYm22uXLlS+fn5vv+X3mbp9kpvq+TvQUFBGj16tFJTUzV69GiFhoYqJSVFQ4cO1T333COXy6UZM2bo8ccf993v511369atuv322/Xss89q2rRpfm1L0h/+8AcFBQX57rUvb734+HhlZmb6ah8zZozf/nbu3Nlv3woKCtS5c2e53W499NBDMsbo9ttv992Lv2/fPt3w28l6feELmvXwE7r+5tur1d+ng8fj0c4jxQredUTBwYwDUB9K9vGP2Sck1d/zvj553B7fv4FUu7eWkjV5/5+xPzegam2IGvo54qs1X/qdzyWpuLhYkjR06FAtWbKk3HmSdP7552v79u1llps2bZoef/xx3zXv559/VkpKip544gn99NNPvuUKCgrUqVMnud1u5efn67777pMxRqmpqcrMzNTzzz+vgoIC3/JXXXWV7z53qey15ueff9bf//53GWMUFBSk6667Tk8//bSKioo0adIkPf300xo9erSMMZJOXrvef/995efn+/7/7LPP+l17S+eAktdqSeVe89PS0vTTTz/p7rvv9mszLS1NP//8syT5tend/syZM5Wamqoff/yxzrJH6ZrL22Z554iaqM05pE5D/h/+8AfNmTPHb9pZZ52l5cuXS5JSU1O1YsUKud1uFRcXq2nTprr44ov1j3/8Q9HR0b51LrroIt1yyy26+eab1bNnT91zzz2nVM9LL72khIQEv2nPPvus1q9f7zfN+2KguLhYxcXFOu+88zRmzBjdfPPNkqSDBw9Kktq1a1duO9HR0SouLtaRI0d05MgRtWzZUmFhYdWqcfLkyVq3bp2uueYa/eY3v6n2g660fv36lZl2yy236JZbbql0vYyMjFNqrypr166VdPIEvXnz5jLzPR6Pb7lWrVrVepveeRVts3R7pbdV+nfvh1azs7P9trNp0yZJ0sCBA8u06/F4FBMTI0lat26dr0Zv25IUExPjm16yzZLrlTzpSlJWVpbf/u7evVuleafFxMT4TrAlpWaerGHB+2u1pCi+zHzHrciuehnUTok+3vTNNrmyIhwspuYy8zMlSd9nfK/CyEJniynH1q1b//v/Ayfru2fJ1ooWR0010HNE3n++rHBe6XN9aREREeUuVzrXSCdzU3lKXi88Ho/f9WHdunV+y3bo0KHKa03J62/JnOP9/6FDh3zzvdfRkv/3tlmylpLX7JLX6vLaLDkvPDzcr82S65Rs07v9ktfiusoepWuubJslzxGnS52G/AceeEBXXXVVhfOTkpI0d+5cSdIXX3yhlJQUxcbG6vzzzy+z7B133KEXXnhBd9xxh0JD6/cPDt4XA3l5efrTn/6kDz74QKNHj1ZkZKQkKSoqSpK0d+9eXxgraffu3QoLC1Pr1q0VFRWlnJwcFRUV+R6AXt7hs9q0aeObFhwcrMmTJ+vGG2/UXXfddcr7kJ6efkrrxcbGqmnTpqfcbkW8w4QFBweX+5eTNWvWSDoZlqv6y0p1tllyWLLytlm6vdLbKv376tWrJUlt27b1206fPn20bt0635PeG/a963rfsUlISPDV4G1bOvmuhPevUiXbLG89r6ioKL9p5f2lxzstMzOz3JCfFBOs1yXdOnagrr+5/IuBEzwej3bs2KEuXbo0yHfpGoKSffxj9gnds2Sr+vTspp4dWjpdWo1EZEdIP0hdY7uqe9vuTpfj43a7tXXrVsXFxfn+chy6J1dauUZPXR2nzlHNHa6wYWvo54iv1rj12+VPlDuvSZMmla5bWFhY7nKlw7nkf50pqeT1Ijg42O/6kJCQ4PcXgj179lR5rSl5/V28eLFvuvcvEO3atfPN915Hpf/mKG+bJWspec0uea0ur03pv9dO71+rvW2WHp7U26Z3+yX7qK6yR+may5tf3jmiJk6cOHHqb8ie0g1C5TiVe/LT0tJMt27dzJIlS8pdp6L75auzTG3uyb/77rtNYmKi78O0RUVFJjEx0TzxxBNltudyucz48ePN1KlTjTHGZGVlmQsuuMB88MEHZZZdsWKF6d69u9m7d6/f9OrcV889+dyT790e9+SjJrgnv35xT379aujnCO7J5578Rv3B28cff9z06tWr3GEznQr5x48fNyNHjjS//vWvjcfjMcYYs3LlStO7d2/f6Dput9ts377d3HbbbSYxMdEvuC9YsMAMGDDALF++3BQUFJiioiLz+eefm4EDB5q5c+eWqcnGkG+M/6fRV69ebY4ePWpWr15dZ6PrlN6mSoyuU532Sm+rOqPrrF692m90nQULFpg9e/aY2267zXcivO6668ptu+ToOlWtV3p0nYSEBPOnP/3JDBw4sMLRGM444wxG10EZhPz6RcivXzacI+pqdJ3+/ftXObrOrbfeWmaEmauvvrrM9h944AGTmJhY7ouMa665xvTt27fM6Dre61WvXr18Q25WNLpOr169ymx7zpw55ujRo2bOnDllplV0ra7oml/e6DoLFiwos++///3vzSeffOLb1/rIHo1mdJ2Kxsn/85//XGHILyoqMldeeaUZN25cmXcZnQr5xhizefNm06NHD7Nw4ULftO+++85MmzbNJCYmmt69e5tLLrnEzJkzx2RnZ5dZf/ny5eZXv/qVGTBggOnTp48ZN26ceeONN8rdh7oI+eX1e3x8vN/QniU5OU5+p06d6nysWu82a9reqYyT36lTp2qPk1+67eqOk9+pU6dKx8mPjo5mnHxUCyG/fhHy65ct54jGPE5+dHR0tcbJr+haXdF1PRDGya9OnnEy5AcZU84NvLDeiRMn9N1336l79+71ck9+STZ84+2JEyca5DfevvXWW1qzZo3f/Y2BxO12a/PmzYqPjw/sbw1swEr28Xf7jyvp2S+VOnVwg7sn/z/Z/9HE1Il6M+lN9Wjbw+lyfMp7DH+zJ7fB9nOgseUcsXHjRl144YV68cUX9eOPPwbcN95u2LBBubm5fONtFU4lz9T2MVybvEbIb6ROZ8hvyBryBcZ7UdmwYYNviNpA05D7t6Eg5NcvQn79suUcEcjnY1v6OFA5GfIb3kfVAVRLq1atFBwcXO1hwgAA9YPzMZzg6JdhAag/5513ng4cOFDh9zsAAE4PzsdwAu/kAxbjggIAgYHzMU43Qj4AAABgGUI+AAAAYBnuyQeARuabPblOl1BjPx49Lkn64eBxeQoCp363262dR4oVuifXN3LGjoPHHa4KAAj5ANBouDwnR0y+9+2tDldSc0Fh2WrWOUh3/n2bTHGW0+WUtWJNmUnNIrjEAnAOZyAAaCTiz22ld6YkKjQ4yOlSTsnRoqFqMaqV02X4cbvdysjIUGxsrN8Y2M0iQtWpXTMHKwPQ2BHyAaARiT+3ldMl1ELgfbGU2+2WKytMPTu05IuEAAQUPngLAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFgm1OkC4AyPxyNJys/Pd7iSwOZ2uyVJJ06cUEhIiMPV2If+rX/0cf2if+sX/Vv/6OP6Vdv+9eY0b26riSBjjKnxWmjwsrOzlZmZ6XQZAAAAqEJMTIzatm1bo3UI+Y2Uy+VSbm6uIiIiFBzMXVsAAACBxuPxqLCwUC1btlRoaM1uwCHkAwAAAJbhLVwAAADAMoR8AAAAwDKEfKAC27Zt00033aQBAwYoMTFRM2bM0OHDh50uyxpr1qzR1Vdfrb59+yoxMVEPPfSQCgoKnC7LOm63WzfccIPuvfdep0uxzgcffKAePXqoT58+vp+UlBSny7JGTk6OZsyYoYSEBPXv31+TJ0/WwYMHnS7LGu+9957fY7dPnz7q2bOnevbs6XRp1vj22281adIk9evXT4MHD9bDDz+soqKi09Y+IR8oR0FBgW6++Wb16dNHX375pVJTU5WTk6P777/f6dKscPjwYd1666269tprlZ6ermXLlmn9+vX685//7HRp1nnuueeUnp7udBlW2rp1q6644gpt2rTJ9/PEE084XZY1pk6dqhMnTuiTTz7R559/rpCQEM2ePdvpsqxx+eWX+z12//Wvf6lVq1Z65JFHnC7NCh6PR7feeqvGjBmj9evX66233tKXX36phQsXnrYaGCcfKMfevXvVrVs3TZkyRSEhIQoPD9fEiRM1Y8YMp0uzQps2bbR69Wo1b95cxhjl5OSosLBQbdq0cbo0q6xZs0Yff/yxRo8e7XQpVtq6dasuvfRSp8uw0jfffKMtW7b4zhOS9NBDDykrK8vhyuxkjFFKSoqGDRumK664wulyrJCbm6usrCx5PB55x7gJDg5WZGTkaauBd/KBcpx33nl6+eWX/b644qOPPtIFF1zgYFV28V64hw4dquTkZEVFRemqq65yuCp7ZGdna9asWXryySdP60WlsfB4PPr222+Vlpam4cOH6+KLL9bs2bOVm5vrdGlW+Prrr9WlSxf985//1KhRozR48GA99thjioqKcro0K7377rvasWMHt/XVodatW+vGG2/UY489pri4OA0dOlQxMTG68cYbT1sNhHygCsYYPf300/r88881a9Ysp8uxzscff6yVK1cqODhYd955p9PlWMHj8SglJUU33XSTunXr5nQ5Vjp8+LB69OihMWPG6IMPPtAbb7yhzMxM7smvI7m5ufr++++VmZmpZcuW6Z133tGBAwc0c+ZMp0uzjsfj0YsvvqjbbrvN9+YLas/j8ahJkyaaPXu2Nm/erNTUVP3www+aP3/+aauBkA9U4vjx47rzzju1fPlyLVq0SF27dnW6JOs0adJE7du3V0pKilatWsU7oXVgwYIFCg8P1w033OB0KdZq166dFi9erAkTJigyMlJnn322UlJStHLlSh0/ftzp8hq88PBwSdKsWbPUvHlztWvXTtOmTdMXX3yhvLw8h6uzy7p163Tw4EFNmDDB6VKs8sknn+ijjz7Sddddp/DwcJ1//vmaMmWK/vGPf5y2Ggj5QAV+/vlnjR8/XsePH9dbb71FwK9DGzdu1C9/+Uu/UQaKiooUFhbGrSV14N1339X69evVr18/9evXT6mpqUpNTVW/fv2cLs0a27Zt07x581Ty+ySLiooUHBzsC6g4dV26dJHH41FxcbFvmsfjkSTxHZ5166OPPtKoUaPUtGlTp0uxyr59+8qMpBMaGqqwsLDTVgMhHyhHbm6ufvOb36hv3776y1/+wgdC61jXrl1VUFCgJ598UkVFRdqzZ48ee+wxTZgwgYBUB/71r39p48aNSk9PV3p6upKSkpSUlMQoO3WoVatWWrx4sV5++WW5XC7t3btXTzzxhK688koew3Vg0KBBOvfcc3X//fcrLy9Phw8f1tNPP62RI0dyS0kd27Bhg/r37+90GdYZPHiwsrKy9NJLL8ntdmvXrl168cUXlZycfNpqIOQD5Xj77be1d+9effjhh7rwwgv9xhFG7TVr1kwvv/yytm/frsTERN1www0aNGgQQ5SiwTjzzDO1YMECffrppxowYIDGjx+vuLg4/f73v3e6NCuEhYXp9ddfV0hIiMaMGaMxY8bozDPP1Jw5c5wuzTq7d+9WdHS002VYp0uXLlqwYIE+++wzJSQk6Ne//rVGjBihu++++7TVEGT4uxcAAABgFd7JBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsE+p0AXCGy+VSbm6uIiIiFBzMaz0AAIBA4/F4VFhYqJYtWyo0tGaxnZDfSOXm5iozM9PpMgAAAFCFmJgYtW3btkbrEPIbqYiICEknHzSRkZG13p7b7VZGRoZiY2MVEhJS6+3ZjL6qHvqp+uir6qOvqod+qj76qvroq+op2U9FRUXKzMz05baaIOQ3Ut5bdCIjI9W0adNab8/tdkuSmjZtyhO3CvRV9dBP1UdfVR99VT30U/XRV9VHX1VPef10KrdWczM2AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYJlQpwsATrcfD+Upr9B1SuseLcpRi/BWtWrf7XZr55Fihe7JVYumEerUrlmttgcAAFAaIR+Nyo+H8jR8XtoprRsUlq1mnecp74fpMsVta1/MijWSpM+nDyPoAwCAOkXIR6PifQf/mYnx6hLdvEbr/nj0e92/3mj+dd3UqUXXU67B7XYrIyNDIa076J4lX5/yXxUAAAAqQshHo9Qlurl6dmhZo3WCm5x8UdA5url6tK3ZuiW53W65ssIUGsW79wAAoH7wwVsAAADAMoR8AAAAwDKEfAAAAMAyhHycNocOHXK6BNQjji8AAIGDkI/TYufOnWrfvr127tzpdCmoBxxfAAACCyEfp0VOTo48Ho9ycnKcLgX1gOMLAEBgYQhNALWWlZUlSbrwwgtrtZ3Q0FBFREQoLy/PN61Zs2YaOXKkvv/+e/30008qLCxUcHCwXK6T3y8QHByssLAwud1uhYeHq3379goNDVWTJk104sQJuVwuGWN8y/Tv319t2578MjOXy6X09HTt379fzZo107nnnqsWLVqoffv22r59u7755hsZY9SyZUu1bdtWR44c8W2zU6dOioqKUlBQkFq1aqVJkyYpNDRU+/fv1/79+3X48GFJ0hlnnKFPP/1UR44c0dlnn63x48frnHPOkdvt1ueff6709HQ1a9ZMQ4YM0eTJk7V69WqlpaXJ4/Gobdu2at++vTp06KAhQ4bI7XbrhRdeUEZGhvbs2ePbfs+ePZWRkaEzzzxTLVu21NatW3Xs2DEZY9ShQwd16dJFcXFxys7OVnR0tCTp4MGDvv/v379fWVlZioqK8rUlSatWrdK+ffvUrl07bd26VT/++KM6deqkuLg4HTp0SGeddZaGDBmikJAQSSeHh/3000/12muv6aefflLHjh3161//WpdccolvGUkqKirSCy+8oB9++KHS7dUVt9vt25fo6GidccYZ5c7fs2dPmX6o61rqW8l9ra/+bCh1OdkXgXocUDsN7riaABUbG2vWrl1b7ry1a9ea2NhYEx8fX+bnsssuq3Ib8+fPN9dff73ftC1btpipU6eaiy66yPTp08f88pe/NC+99JIpLi72W+6bb74xU6dONQkJCSY+Pt6MHDnSzJ071xw5cqRMO6tWrTI333yzSUhIMH379jWXX365+cc//mGMMcbj8Zjf/va35rLLLjP5+fl+6x07dsyMGDHCPPjgg+bFF1/07VtcXFyZ/X733Xer3R8l5eXlmfT0dJOXl1fu/JpyuVwmPT3duFyucudv2LDBSDIbNmyok/ZO1dbdOabjzFSzdXdOjdf99tC3puerPc23h76tVQ3evtr8U/Yp1xJIWrZsaSTxU88/LVq0MMHBwaelraioKBMdHV2tZWNiYszSpUvN0qVLTYsWLSqsfenSpcYYY1JSUkxoaGiV26srS5cuNTExMX5tnH322WbJkiUVzq+vWupbeftyqvtQ1TndqboCsc3SfeXE/jYUdfm4Ot2cekzVJq816JB/qtsoHfI//PBD07t3b/Paa6+ZY8eOGY/HY7799lszduxYc+edd/qW++yzz0yvXr3MU089Zfbv3288Ho/ZsWOHmTJlihk6dKjZv3+/b9lXXnnF9OvXz7z33nsmPz/fuN1us27dOjN48GDz2GOPGWOMyc7ONkOGDDEPPPCAX3333HOPueqqq0xhYWG19ru6/VESIZ+QXxcI+P/9CQoK8gvJ5S3TvHnzMtOSk5NNly5dykwfOHCg6d27d4XttWnTpso6SrYXFhZmJJkOHTqY7t27+/7vnX/77bebSy+91G9bQ4YMMZJM69atTceOHY0k07FjR9O6dWsjyUyaNMkkJyf7tSnJXHDBBWbevHnmggsu8Jt+xRVXGEmmffv25vbbbzdBQUGmd+/e5W6vLi6cS5cuNUFBQSY5OdmsWbPGHDt2zHz55ZdmyJAhJigoyKSkpJigoCDTr18/ExQUZC699FKzcOFCXz94pzeEcFbevq5Zs+aU+7Ouwlhd1xWIbZbsKyf2tyFpqCHfyccUIb+G2ygZ8gsKCsyAAQPMn//85zLLfffdd2bq1KnmyJEjprCw0AwaNMg8//zzZZZzu91m4sSJ5q677jLGGLN//37To0cP8/7775dZdtWqVWbGjBmmqKjIGGPMunXrTPfu3c2KFSuMMcYsW7bM9O/f3+zatava+03Irz5Cft05ePCg48E6UH9CQkJ8gTs4ONhER0ebyMjIMmH4l7/8pYmJiTEdO3b0e5c+KCjI9+bAZZddZiT5zb/sssvML37xCxMREWHCw8PLtB8WFmYiIyNNaGioadeunW/d0NBQExoaas4991wTHR1tQkNDzdixY01SUpLp1KmTyc/PN5GRkSYyMtIXdKOjo01BQYHp2LGjad++venUqZMpKCgw7du3N6Ghoeb48eOmSZMmvhrHjh1r3G63MebkuXHs2LEmODjYt+/t27c3BQUFJiYmxiQnJxu3222Ki4t928vPzzfJycmmU6dOtQoCLpfLr42S09evX2/Gjh3r2//Sy7ndbpOcnGxiYmJ8fRPIoaSifTXmv/tS032oizBWH3UFapvp6emmsLDwtLfd0DTEkO/kY6q2Ib/R35O/ceNG5eTkKCkpqcy8bt26af78+ZKkdevW6dChQxo3blyZ5YKDgzVhwgQ9+OCDcrlcWrlypUJCQjRq1Kgyyw4ePFiDBw/2/T5gwABNnjxZDzzwgM455xzNmTNHjz/+uM4555y628lKuN1uud3uOtlOyX8rmp+xL1chPx+udXun6oes4756arrfHrfH929t+sy7rsdzcnsZ+3Pr5Bicbpcl1u7++0DSt29fbdy4sc625z2el1xyiVasWKFJkybp6aefLrNcZGSkMjMzy0w3xuj555/XtGnT1LlzZ0n/fbxIUufOnfXBBx9IkiZOnKg333zTb/3x48frjTfekCTdcMMNvraHDBmizz//XLt27dLdd9+tp59+WmPGjFGfPn00ZMgQPf/888rPz5ckRURESJImTZqkL7/8Uj/99JNeeukl3Xbbbfryyy/14IMP6vbbb9f06dNVUFDgq/G+++6TMcbXB/fee6/ef/99X23XXXedvvzyS2VmZmrRokUyxigoKMi3veeff14zZszQkCFDlJaWpmHDhtWs8/9PWlqaXxslz1HBwcEaNWqU3n//fZ133nl6//33yyznreGee+5RampqrWqpbxXtq9ep9GdV53Sn6grENr1tfPHFF6e97YamLh5Xp5uTj6naZrQGHfL79etXZtott9yiW265pdrb8H44rl27dpUud/DgwUqXi46OVnFxsY4cOaIjR46oZcuWCgsLq1YNkydP1rp163TNNdfoN7/5zSk/SE6lPzIyMk6prYps3bq13Okbv9kmSbp7ydeKWHWiTts8FZu+2SZXVkSN1snMz5QkfZ/xvQojC2tdw5b/nOz7e5aU32eBbu+BLKdLqDPeD+LWta5du2rFihUKDw8vd/6uXbsqXHfdunXavHmzdu/eXWZeyWlnnnlmmflnnXWW7/8l227SpEmZ6VlZWb4XEOvWrSvTRnh4uNauXStJiomJkSStXbvW9yHrTZs2+bXt8Xi0efNmv99LioiI8G2v5LLeba9bt079+/f3tdOqVasy+1cd5bVRUnZ2tiT5PsBcUd3eD5XXppb6VtW+evflVPahonO603UFUpte69evd6zthqY2j6vTzcnHVG37qUGH/PT09Ernh4eHl/sKyOVy+S5wUVFRkk6eyM8+++wyy3pHWvAut3fvXt/FqKTdu3crLCxMrVu3VlRUlHJyclRUVFTm4u4dZrBNmza+acHBwZo8ebJuvPFG3XXXXZXvdCWq6o/yxMbGqmnTpqfcppfb7dbWrVsVFxdX7ifNvU+Cp6/upe5xvWvd3qn6Ieu47lmyVX16dlPPDi1rtG5EdoT0g9Q1tqu6t+1+yjV4+6p3j1hp5To9dXWcOkc1P+XtOeWyxVHau7vikNqQeANfXfv+++8lnRxNpjznnntuhc/bhIQExcfHl/tXvZLT9u/fX2b+vn37fP8v2bb3HfeS06OiohQcHOxrc8mSJb420tPTVVRUpIEDB0qS768OAwcO1Pbt2yVJffr08XtxEBwcrPj4eN/va9as8autsLBQI0eOLLPswoULfTV46xk4cKDftmrCO5xr6Xq8zz/vC7sOHTpUWrf33F+bWupbRfvq5d2XmuxDVed0p+oKxDa9fTVgwIDT3nZDUxePq9PNycdUXFycCgsLT/0N2Tq7gaiO1cU9+aNGjTL//Oc/y0yfNm2amT17tjHGmMLCQjNgwACzcOHCMst99913JjY21mzcuNEUFRWZxMRE88QTT5RZzuVymfHjx5upU6caY4zJysoyF1xwgfnggw/KLLtixQrTvXt3s3fv3hrvE/fk1x735Ncd7snnnnzuyQ8M3JPvfJvck1893JNf/Tat/+DtBx98YPbt2+f3U1xcXO1Q++yzz5pLLrnEbNmyxRhjTH5+vnnvvfdMr169/MLme++9Z3r37m0WLVpkjh8/blwul/nqq6/M6NGjfcHdGGNWrlxpevfu7Rtdx+12m+3bt5vbbrvNJCYm+gX3BQsWmAEDBpjly5ebgoICU1RUZD7//HMzcOBAM3fu3DK1EvJPD0J+3WJ0nf/+VGd0nWbNmpWZlpSUZDp37lxm+sCBA02vXr0qbK+i0XVK/lQ0uk63bt18//fOv+2222o8us51111X4eg6jz32WKWj69x6660mKCjI9OrVq9zt1fXoOqtXrzZHjx41q1atqnR0nQULFjT40XW8+7p69eqAGl2nLuoKxDYrGl3ndO1vQ9IQQ74xzj6mrA355f3s2LGj0nHh4+PjfUNZulwu3wm7T58+pm/fvubaa681X3zxRZn2Vq1aZW666Sbf+Pfek713FByv7777zkybNs0kJiaa3r17m0suucTMmTPHZGdnl9nm8uXLza9+9SszYMAA06dPHzNu3DjzxhtvlLu/dRHyq+qPkgj5hPy6QtA/PT+nc5z86Ojoao+T36lTpzodJ9+7vbpS3tjWHTp0qNY4+XVdS30rb19OdR/qe5z8+u7b09lmdcbJb2iPpfrSUEO+Mc49pmqT14KMMUZodE6cOKHvvvtO3bt3r7N78jdv3qz4+Phy77PbuHGjLrzwQm3YsEF9+/atdXun6ps9uUp69kulTh1c43vy/5P9H01Mnag3k95Uj7Y9TrkGb1+FRnXSFS+sOaVaAs1HH32kX/7yl7XeDt94yzfenq5vvL3wwgv96uYbb8tup7JzulN1BWKb5fVVg/tm1NOkrh9Xp5sTj6nCwsJTzmsN+oO3AAKD98OJdfkirqFfDEq69957y50+evToMtMuueQSXXLJJeUuHxISomnTppWZXl99VXKkr/KGBC6vvtGjR5e7XyWFh4eXux/1JSQkxLcv3r6qaH5DF6j74kRdTvZFoB4H1E5DO67BTheAxqFVq1YKDg5u9MOG2YrjCwBAYOGdfJwW5513ng4cOFDl9xGgYeL4AgAQWHgnH6cNAdBuHF8AAAIHIR8AAACwDCEfAAAAsAz35KNR+mZPbo3X+fHocUnSDwePy1NQ8/W93G63dh4pVojyql4YAADgFBDy0ai4PCe/FuLet7fWeN2gsGw16xykO/++TaY4qw6qyZYkNYvgaQgAAOoW6QKNSvy5rfTOlESFBged0vpHi4aqxahWtarB7XYrIyNDsbGxatE0Qp3aNavV9gAAAEoj5KPRiT+3VS3Wrv0307rdbrmywtSzQ8sG/yVPAAAgMPHBWwAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMqFOFwBneDweSVJ+fn6dbM/tdkuSTpw4oZCQkDrZpq3oq+qhn6qPvqo++qp66Kfqo6+qj76qnpL9VFRUJOm/ua0mgowxpk4rQ4OQnZ2tzMxMp8sAAABAFWJiYtS2bdsarUPIb6RcLpdyc3MVERGh4GDu2gIAAAg0Ho9HhYWFatmypUJDa3YDDiEfAAAAsAxv4QIAAACWIeQDAAAAliHko9ays7M1efJk9evXTwkJCXrkkUfkcrmcLitgHD58WKNGjdK6det807Zs2aKrr75affr00YgRI7RkyRIHK3Tetm3bdNNNN2nAgAFKTEzUjBkzdPjwYUn0VWlr1qzR1Vdfrb59+yoxMVEPPfSQCgoKJNFX5XG73brhhht07733+qbRT/4++OAD9ejRQ3369PH9pKSkSKKvSsvJydGMGTOUkJCg/v37a/LkyTp48KAk+srrvffe83ss9enTRz179lTPnj0l0U+lffvtt5o0aZL69eunwYMH6+GHH/aNqFPrvjJALV1//fXmd7/7nTlx4oT5+eefzdixY83ChQudLisgpKenm5EjR5rY2Fizdu1aY4wxOTk5ZsCAAWbRokWmuLjYrF692vTp08ds2bLF4WqdkZ+fbxITE82f/vQnU1hYaA4fPmx++9vfmltvvZW+KiU7O9vExcWZpUuXGrfbbQ4cOGCSkpLMn/70J/qqAs8884zp1q2bmTlzpjGG51955s6da+69994y0+mrsq6//nozZcoUk5uba44dO2buuOMOc8stt9BXldi/f79JTEw077zzDv1UitvtNomJieZvf/ubcbvdZt++fWbMmDHmueeeq5O+4p181MpPP/2k9evXKyUlRZGRkTr33HM1efJkLV682OnSHLds2TJNnz5dd999t9/0jz/+WK1atdKkSZMUGhqqiy66SMnJyY22z/bu3atu3bppypQpCg8PV+vWrTVx4kR99dVX9FUpbdq00erVq3XVVVcpKChIOTk5KiwsVJs2beircqxZs0Yff/yxRo8e7ZtGP5W1detW37usJdFX/r755htt2bJFc+fOVYsWLdS8eXM99NBDmj59On1VAWOMUlJSNGzYMF1xxRX0Uym5ubnKysqSx+OR+b9xcIKDgxUZGVknfUXIR61s375drVq1Uvv27X3TOnfurL179+ro0aMOVua8wYMH65NPPtFll13mN3379u2KjY31m9alSxdt27btdJYXMM477zy9/PLLfl+M8tFHH+mCCy6gr8rRvHlzSdLQoUOVnJysqKgoXXXVVfRVKdnZ2Zo1a5aefPJJRUZG+qbTT/48Ho++/fZbpaWlafjw4br44os1e/Zs5ebm0lelfP311+rSpYv++c9/atSoURo8eLAee+wxRUVF0VcVePfdd7Vjxw7f7XL0k7/WrVvrxhtv1GOPPaa4uDgNHTpUMTExuvHGG+ukrwj5qJW8vDy/C6gk3+8nTpxwoqSAERUVVe6YtuX1WZMmTRp9f0kn3/V5+umn9fnnn2vWrFn0VSU+/vhjrVy5UsHBwbrzzjvpqxI8Ho9SUlJ00003qVu3bn7z6Cd/hw8fVo8ePTRmzBh98MEHeuONN5SZmamUlBT6qpTc3Fx9//33yszM1LJly/TOO+/owIEDmjlzJn1VDo/HoxdffFG33Xab780J+smfx+NRkyZNNHv2bG3evFmpqan64YcfNH/+/DrpK0I+aqVp06bKz8/3m+b9vVmzZk6UFPAiIyN9H5T0KigoaPT9dfz4cd15551avny5Fi1apK5du9JXlWjSpInat2+vlJQUrVq1ir4qYcGCBQoPD9cNN9xQZh795K9du3ZavHixJkyYoMjISJ199tlKSUnRypUrZYyhr0oIDw+XJM2aNUvNmzdXu3btNG3aNH3xxRf0VTnWrVungwcPasKECb5pPP/8ffLJJ/roo4903XXXKTw8XOeff76mTJmif/zjH3XSV4R81Mr555+vnJwcHTp0yDfthx9+0JlnnqkzzjjDwcoCV2xsrLZv3+43bceOHTr//PMdqsh5P//8s8aPH6/jx4/rrbfeUteuXSXRV6Vt3LhRv/zlL30jL0hSUVGRwsLC1KVLF/rq/7z77rtav369+vXrp379+ik1NVWpqanq168fj6lStm3bpnnz5vnuB5ZOPqaCg4PVq1cv+qqELl26yOPxqLi42DfN4/FIkrp3705flfLRRx9p1KhRatq0qW8azz9/+/bt8zufS1JoaKjCwsLqpK8I+aiVmJgYXXjhhZozZ46OHz+uXbt26YUXXvB75Q5/o0aN0qFDh/Tqq6+quLhYa9eu1fLlyzV+/HinS3NEbm6ufvOb36hv3776y1/+ojZt2vjm0Vf+unbtqoKCAj355JMqKirSnj179Nhjj2nChAkaM2YMffV//vWvf2njxo1KT09Xenq6kpKSlJSUpPT0dB5TpbRq1UqLFy/Wyy+/LJfLpb179+qJJ57QlVdeyWOqlEGDBuncc8/V/fffr7y8PB0+fFhPP/20Ro4cqaSkJPqqlA0bNqh///5+03j++Rs8eLCysrL00ksvye12a9euXXrxxReVnJxcJ30VZEq+fAdOwaFDh/THP/5R69atU3BwsMaNG6fp06f7fZCysevatatee+01JSQkSDo5msUjjzyijIwMtWnTRpMnT9ZVV13lcJXOeOWVVzR37lxFRkYqKCjIb96mTZvoq1J27NihOXPmaOvWrTrjjDOUnJzsG5mIviqf90N/c+fOlcTzr7T169frqaeeUkZGhiIiIjR27FilpKQoIiKCvirlwIEDmjt3rr766isVFhZqxIgRmjVrllq0aEFfldKnTx8988wzGjp0qN90+snf6tWr9cwzz2jnzp0644wzdPnll9fZOZ2QDwAAAFiG23UAAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMuEOl0AnOFyuZSbm6uIiAgFB/NaDwAAINB4PB4VFhaqZcuWCg2tWWwn5DdSubm5yszMdLoMAAAAVCEmJkZt27at0TqE/EYqIiJC0skHTWRkZJ1v3+12KyMjQ7GxsQoJCanz7aNy9L+z6H9n0f/Oov+dRf87q677Pz8/X5mZmb7cVhOE/EbKe4tOZGSkmjZtWufbd7vdkqSmTZtyknEA/e8s+t9Z9L+z6H9n0f/Oqq/+P5Vbq7kZGwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALBMqNMFAKi9Hw/lKa/Q5fvd7XZr55Fihe7JVUhISL23f7QoRy3CW9V7O+VpFhGqTu2aOdI2AACBipAPNHA/HsrT8Hlp5c9csabe2w8Ky1azzvOU98N0meK29d5eeT6fPoygDwBACYR8oIHzvoP/zMR4dYluLunkO/kZGRmKjY2t93fyfzz6ve5fbzT/um7q1KJrvbZV2o6DxzXtzc1+f8UAAACEfMAaXaKbq2eHlpJOhnxXVph6dmhZ7yE/uMnJFxado5urR9uW9doWAACoHj54CwAAAFiGkA8AAABYhpAPAAAAWIaQj9Pm0KFDTpcAoA7xnAaAwEXIx2mxc+dOtW/fXjt37nS6FAB1gOc0AAQ2Qj5Oi5ycHHk8HuXk5DhdCoA6wHMaAAIbQ2gCAGrM7XZLkl544QW1bdtW//nPf7Rt2zYdO3ZMbdq00ZlnniljjA4ePChJatKkic4++2ydc845GjRokM4991wNGTLEN8RrUVGRnnzySb300ks6ePCgmjVrpv79+6uwsFBHjx7VWWedpR49eig8PFzDhg3TkCFDtHr1au3bt09nnXWW37ZK1piWlqa0tDRJ0sUXXyxjjL744gtlZmYqKChIHTt21LBhwxQcHKyDBw8qOjpakrR//35lZWUpKipKHTp0qHD7q1at0p49e5SVlaW2bdsqOztbbdu21cGDB3Xw4EFt2rRJTZs2VWJionr37q3s7Oxy6y1d67BhwzRo0CAtWLBA27dvV1BQkBISEnz9JqlM2wcPHtShQ4e0a9cuSVLHjh01YsQIDRs27LR883XJ/jh69Kiys7PL3V/vciWPXen9qazfa1pPZY+R6ixb3e3UpL362IfGxKl+aXDHw5yi2bNnm/j4eBMfH2969uxpunbt6vs9Pj7ePPfcc2b48OHlrjt8+HCzdOlSY4wxS5cuLbNur169zLBhw8y8efOMx+PxrefxeMwbb7xhrrrqKhMfH2/69etnJk6caJYtW+a3/ZkzZ5rY2Fgzb968Mm0fOnTIXPD/2bvz+Kiqw///78megICsCgIJxICsSYgEIQiiiEBSlUVcoNqftS5Uq1YQ9Wutn7a4tbV1aesHqxbRqoBojVZELUJLQEGCUdkxInsIEgLZZ87vDz5zO5ONJGQyycnr+XjMg8yde88599y597zncufOwIFO27777juTkJBghg4d6tSdmppqfv7zn5uCggK/ZXNzc83cuXPNqFGjzNChQ82YMWPMgw8+aPbu3Vulnk2bNpnbb7/dXHDBBSYpKclcdtll5i9/+YspLy83xhjz0EMPmZEjR5rDhw/7LVdeXm6mT59ufvKTn5i3337b6ZOhQ4f6tTMxMdH8+c9/rtL+yo+anDhxwqxfv96cOHGixnlOR0VFhVm/fr2pqKgwxhizYcMGI8ls2LAhIPW1Zjl7jpre92aanD1HnWmV+z+Qvjr8lRn00iDz1eGvAl5XZdWte3PQlP0fDEuXLjVnnnmmkXRaj9jYWLN06VIzZ86cei8bEhJSbVnGnOz/xx9/3HTp0uW021hd+d4+iI2NbZTyli5dWq+2dunSxXTt2rVe8/u2PVDvidr6w7u+1c3XtWvXGte/cr+fTntqKqu2eetaTn3qC8Q6+GoNx5/G7uvGrLex+/908lqDQ76vpUuXVgn01U3zqhzyK89XUVFhPvroIzNw4EDzxhtvONPvvvtuc9FFF5kVK1aYEydOmLKyMvPhhx+aCy+80Nx3333OfPfee69JTU01o0ePNm6326/s559/3iQnJ1cJ+d99950zT15enpk2bZqZO3euM+2LL74wSUlJ5sEHHzS7d+82Ho/H7Nmzxzz44IMmJSXFbNmyxZn3n//8pxk6dKhZuHChKSwsNB6Px3z11Vdm8uTJ5o477jDGGFNSUmJ+8IMfmJtuusmvfb///e/NRRddZL7//nu/6dW1s7bpp0LItwchn5DflJYuXdpowblv374NDvXex6JFi0xWVpbJyMgwLpfLLF261CxevNh5PS0tzfzyl7+sslyvXr1Mz549q0zv0aOH8/ett95qJk6caCSZlJQUp/ylS5cal8tlUlJSjCSTlJRkXC6XadeunV9ZoaGh1bb5uuuuc9rr+wEnLS3NfPTRR+aqq67ym3/IkCFm3rx5VYK9y+UySUlJ1dZR3boFKgR5+yMjI8M88sgjRpIZOnSoGTVqlHG5XOaRRx4xGRkZTpszMjJMVlaWKSwsdOb3vjZx4kSzYMGCavu9Ie3x1lP5PVKXeatrb3Xl1Ke+QKxDZbYffxq7rxu7XkL+KUK+15VXXmkefvhhY4wxK1asMAMHDjS7d++uMt/27dvNeeedZ1auXGmMORny7777bjNixAizevVqv3knTZpkHnzwwVpDvjHGLFy40EyaNMl5fvnll5v777+/2nbeddddZtq0acaYk+F9+PDh5n//93+rzLd582Zz++23OwF+586dJjEx0SxcuNAYY8y6devMkCFDzKZNm6osS8hHTQj5hPymUlFRYXr37m2io6PNyJEj6xzEvWHJ5XL5zRsWFuY3T3h4uOnVq5fzt+/8ksxll13m/D1x4kQTExNjYmNjTUVFhXG73SYjI8PExcWZXr16mcjISDN58mRTVlZmYmNjTVRUlImKijIhISEmJCTE9O7d28TGxppu3bqZyMhIvzZNnjzZpKenm7i4OFNWVmYyMjJMbGysM613794mPT3d+df7Ws+ePZ02R0REmF69epno6GgTExNjJk2aZKKjo01oaKgJCwszxcXFJj093YSFhZmoqCiTnp5u3G63KS0tNWFhYaZr164mMjLShIaGmri4OFNRUWFKSkqcPo6OjjbR0dGmV69eJioqykRHR5uQkBATFRVlJk+ebOLi4szkyZOd+Xz7qrHfE7GxsSYjI8Pp6/T0dPPpp586fRcXF2eKi4uddpSVlfkt622n72ve7enb73Vpu297Kp/k832PVFRU1DpvWVlZlTZVV05paWmd62tInzakTJuPP43d14GotzmF/IBek79v3z6lpKRUmX78+PFalysrK9NHH32krVu36mc/+5kk6cMPP1RycrJ69uxZZf74+HglJSXp/fff15gxYyRJ4eHhSk9P17Jly5SWliZJys7OljFGiYmJ+ve//11j/Xl5eVqxYoUuvfRSSdKePXu0efNmPfDAA9XOP336dN1www3at2+fvv32Wx09elTp6elV5uvfv7+eeuop53mfPn30i1/8Qg8//LBSUlI0b948zZ07V0OGDKm1fxqT2+12rq1t7HKr+3fb/gKF7j7S6PW1ZjvzTu5Pvtuy8r+B5HF7nH+boj5fzvvqQEGT110bj8ejXd+XK+S77xUSYs/9DT7L+re+/fZbSVK5qXodqsfjqXFZY0yVeSvPP3XqVL322mtV/vYqLCx0/o6KilJRUZFyc3O1cuVKjR07VnPnznWu75ake++9V6tWrVJubm6V9njX4y9/+YtuueUWvzZNmDBBSUlJGj16tFatWuWUe/fddyszM1OS9POf/1yZmZnOv76vSdKUKVP82n/ZZZfpvffec54/++yzuvTSS5WZmamKigrNmzdPxhg9/fTTqqio0HXXXacnn3xSkvTNN9841+p7+6y4uFiStHv3br/1Kikp0YQJE/Tuu+/qrrvu0rvvvuu85ttXjWXlypXKzc3VokWLnL5euHChQkJCZIxx+u7ZZ5912rxq1SqNHTvWWfbuu+922ul9TVKVfq9L233bY4ypclzwluntz5rmXbVqVZX2VlfO008/Xef66trv9VmH6spsyuN/Uzrdfmmqehu7/0+nnICG/O7du+vjjz+uMn3cuHF+z70fBowxKisrkzFGI0eO1NNPP+2E9kOHDqlLly411tW1a1fnC15eU6dO1YwZM1RYWKgzzjhDS5Ys0dSpU6td/gc/+IFCQkLkdrtVVFSks88+Ww899JBTtyR17ty5xrq98x05cqTWeSu78sorlZWVpauvvlqXXHKJrrvuujotV1P7faWnp+uXv/xlrctt27atQfXVVU5OjiTp8y+3SJLuWvyFIlcXBbTO1mrjl1tUkRfpN83b/4GUW5wrSdq6batKo0sDXp+vnIMn67t7ceDXs0E+zA92CxrVia//e3Ik+5sDjV5+9+7dq/3ba9++fc7f3i+XStLatWvVoUOHaj9krF27ttY6e/fuXWXa4cOHnbLWrl3rfHDIy8tz5vH+7f2tAN/XJOnss8/2e1759XXr1ql///7Oc4/Ho+zsbK1bt06SFBERUa/1qK6uynV6y+nQoUOdyzoVb7s8Hk+VNubk5Dj96F0v3zZ45/f9vQXf9nmX9a5HXdru257s7Owqr/tuV99plef1fb26eiuvV13qq2u/12cdaiuzKY7/Tamx+qWp6m0O/d8s7q7j+2Fg586duvPOOxUREeF3RqZLly7OmZfq7NmzR/Hx8X7T+vfvrz59+uif//ynMjIytGLFCr333nv65JNPqiz/j3/8Q+ecc46kk//T8NRTT+mqq67Se++953y42Ldvn+Li4qqt29vGsrIySScPStUNUt67Bvj66U9/qrffftv5X4uG8G1/fSQkJCgmJqbB9dbE7XYrJydHgwcPVmhoqLMTPDl9iM4bPLTR62vNduYd192Lc5Q0qL8G9WgvqWr/B1JkfqS0U+qX0E/ndTovoHVVFra3QFqVpd9PH6y+Xdo2ad218Xg82rFjh+Lj4y07k+/WTe88IUlKjDtLnx3Y3qjl+4Z437+9unfvrm+++UaS1LNnT61fv16SNGLECCUmJiorK6vKMiNGjKi1zurGlc6dOzvbbcSIEc7fvsdu79/eEzqVj+v79+/3e1759dTUVL8gHxISosTERKWmpmrx4sXOWFLX9aiurupOjHn7qrF4b6EaEhJSpY2DBw/Wp59+KknOevm2wbus70kx3/Z5t6d3PerSdt/2VDevt0zftlY3r++tYaur11uOd73qUl9d+70+61Dd6015/G9Kp9svTVVvY/d/UVFRw0/INsb1Qo19Tf7+/fvNiBEjzJw5c5xpH3/8sRk4cKDfF1y9vvrqKzNgwADz8ccfG2NOXpN/7733GmOM+dvf/mauvvpqs2zZMjN79uwqddZ0TXthYaFJSEgw7777rjHm5PcD7rzzzmrX56c//am58sorjTHGlJaWmuHDh5sFCxZUmW/z5s0mISHBfP75537T63JdPdfkoyZck881+U2Fa/K5Jr+69wTX5HNNflPgmnyLvni7cuVKk5CQYN58801n2rx588xFF11kPvzwQ3PixAlz4sQJs2LFCjNmzBi/DwS+If/IkSNm0KBBZvLkyeZf//pXlTqrC8klJSXmmWeeMUOGDHG+6Lt582YzbNgw8+CDD5rvvvvOuN1us3v3bvPAAw+YpKQk8/XXXzvL/+Mf/zBDhw41ixYtMsePHzcVFRXms88+M5deeqm5/fbbq6wrIR+ng5BPyG9KzfHuOmvWrKn17joPPfRQtXefOeecc6pM9727zi233BKQu+tce+21Nd5dZ8WKFWb69Ol+8w8ZMsTMnTu3RdxdZ/78+Ubyv7vO/Pnzq9ytZs2aNebYsWPO/N7XJk6caJ577rlGu7uOt57K75G6zFtde6srpz71BWIdKrP9+NPYfd3Y9RLy63h3nf/5n/8xSUlJfnfUWbx4sbnqqqvMsGHDzLBhw8yMGTPMkiVL/O6n7xvyjTHm9ttvN2lpaU6HVxfyfe8zn5ycbK699lqzZs0av/bs3r3b3HfffebCCy80Q4YMMRdeeKF54IEHqg3Yq1evNj/60Y9MamqqSUxMdA5clc8I+LbhdEJ+TffJr/y/Bl6EfHsQ8gn5Ta2x7pMfFxfXaPfJ95ZlTGDuk+9bvrcPTuc++b7l1fc++V27dq3XffK7du0a9Pvke9e3vvfJr9zvp9Oemsqqbd66llOf+gKxDr5aw/Gnsfu6MettTiHfZUylWx6gVSgqKtLmzZt13nnnBeya/OzsbCUmJio0NFSff/65hg0bpg0bNig5ObnR62vNvtxboPSn/63M29P8rsn37f9A+jr/a83InKHX01/XgE4DAlpXZdWte3PQlP0fLJ999pmGDx+uG2+8sdn94q23/wcPHqzVq1fzi7dB+sXbvn378ou3QfjF29Zw/GnOv3jb2P1/OnmtWXzxFvbr0KGDQkJCGvUb7wCCp1OnTgoJCdH999+vPn36nHZ5ERERuu+++3TffffVeZlT3SYvNDRUF198sS6++GK/6d7bI5+u0NDQRrtVX01tvfPOO2tcpjFvE9gYvP1xqpBTU7819vrUZ/vUNm9dy2nM90Mgy7RBsPqlpW0PQj6aRJ8+fXTw4ME631oUQPPGPg0AzZs993ZDs0cYAOzCPg0AzRchHwAAALAMIR8AAACwDCEfAAAAsAxfvAUs8eXeAudvt9utXd+XK2xvQcBvK/bNseOSpJ2HjstTUnCKuRvXjkPHm7Q+AABaCkI+0MJVeE7+1MW8N3OqvvhhVsDrd4Xnq01fl+54dYtMeV7A66tOm0gOZQAA+GJkBFq4xJ4d9NbsUQoLcTnT3G63tm3bpoSEhCb5gZBjZWPUbnyHgNdTnTaRYYrr3CYodQMA0FwR8gELJPbs4Pfc7XarIi9cg3q0b6JfPGw+vzYLAAD44i0AAABgHUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgmbBgNwDB4fF4JEnFxcUBKd/tdkuSioqKFBoaGpA6UDP6P7jo/+Ci/4OL/g8u+j+4Grv/vTnNm9vqw2WMMafdArQ4+fn5ys3NDXYzAAAAcAqxsbHq1KlTvZYh5LdSFRUVKigoUGRkpEJCuGoLAACgufF4PCotLVX79u0VFla/C3AI+QAAAIBlOIULAAAAWIaQDwAAAFiGkA8AAABYhpCPRpefn6/bbrtNKSkpSk1N1W9+8xtVVFQEu1nW2LJli370ox9p+PDhGjVqlObOnasjR45IkjZt2qTp06crKSlJ48aN0+LFi/2WXbZsmcaPH6/ExERNmTJFGzduDMYqWMHtdmvWrFmaN2+eM43+D7yjR49q7ty5Sk1N1fnnn6/bbrtNhw4dkkT/N4WvvvpK1113nVJSUpSWlqZf//rXKisrk0T/B9KRI0c0fvx4rVu3zpl2Ov3tdrv12GOPaeTIkUpKStKtt97q7Eeoqrr+X758uS6//HIlJydr3LhxeuaZZ/xuc9ks+t8AjWzmzJnm5z//uSkqKjK7d+82kydPNgsWLAh2s6xQXFxsRo0aZf74xz+a0tJSc+TIEXPTTTeZm2++2Rw9etQMHz7cLFq0yJSXl5s1a9aYpKQks2nTJmOMMWvXrjVJSUlm/fr1pqyszLz44osmNTXVFBUVBXmtWqY//OEPpn///ubee+81xhj6v4nMnDnTzJ492xQUFJjCwkLz05/+1PzkJz+h/5uA2+02o0aNMn/729+M2+02+/fvNxMmTDDPPPMM/R9A69evN5dccolJSEgwa9euNcac/vHm6aefNhkZGWbfvn2msLDQ3Hnnneamm24K2jo2Z9X1f05OjhkyZIj5+OOPjdvtNjt27DAXXXSR+etf/2qMaT79z5l8NKpvv/1Wn376qebMmaPo6Gj17NlTt912m1555ZVgN80K+/btU//+/TV79mxFRETozDPP1IwZM/TZZ5/pgw8+UIcOHXTdddcpLCxMF1xwgTIyMpy+X7x4sSZPnqxhw4YpPDxcN9xwg84880y99957QV6rlicrK0sffPCBLr30Umca/R94X375pTZt2qRHH31U7dq1U9u2bfWrX/1K99xzD/3fBAoKCpSXlyePxyPzfzfmCwkJUXR0NP0fIMuWLdM999yju+66y2/66fb34sWLddNNN+nss89W27Zt9cADD2jVqlX67rvvmnwdm7Oa+n/v3r26+uqrddFFFykkJER9+/bV+PHj9dlnn0lqPv1PyEej2r59uzp06KBu3bo50/r27at9+/bp2LFjQWyZHfr06aPnn3/e71f0li9froEDB2r79u1KSEjwmz8+Pl5btmyRJO3YsaPW11E3+fn5euCBB/S73/1O0dHRznT6P/C++OILxcfH64033tD48eOVlpamxx57TF26dKH/m8CZZ56pG264QY899pgGDx6sMWPGKDY2VjfccAP9HyBpaWlasWKFJk2a5Df9dPq7sLBQBw4c8Hu9c+fOat++vbZu3RqgNWmZaur/CRMm6L777nOel5SUaOXKlRo4cKCk5tP/hHw0qhMnTvgFH0nO86KiomA0yVrGGD355JP617/+pQceeKDavo+KinL6/VSv49Q8Ho/mzJmjH/3oR+rfv7/fa/R/4BUUFGjr1q3Kzc3VsmXL9NZbb+ngwYO699576f8m4PF4FBUVpQcffFDZ2dnKzMzUzp079dRTT9H/AdKlS5dqfwDpdPr7xIkTkqSYmJgqr3tfw0k19b+v48ePa/bs2YqKitINN9wgqfn0PyEfjSomJkbFxcV+07zP27RpE4wmWen48eO644479M4772jRokXq16+foqOjVVJS4jdfSUmJ0++neh2n9txzzykiIkKzZs2q8hr9H3gRERGSpAceeEBt27ZV586ddeedd+qTTz6RMYb+D7AVK1Zo+fLluvbaaxUREaFzzz1Xs2fP1t///nfe/03sdPrbGz4rj9Vsj/rbtWuXrr76alVUVGjhwoVq27atpObT/4R8NKpzzz1XR48e1eHDh51pO3fu1FlnnaUzzjgjiC2zx+7duzV16lQdP35cS5YsUb9+/SRJCQkJ2r59u9+8O3bs0Lnnnivp5Lap7XWc2ttvv61PP/1UKSkpSklJUWZmpjIzM5WSkkL/N4H4+Hh5PB6Vl5c707x3szjvvPPo/wDbv3+/cycdr7CwMIWHh/P+b2Kn09/t27dXt27dtGPHDue1vLw8HT16tMolJqjZJ598ounTp2v06NH661//qvbt2zuvNZv+b/Sv8qLVu+aaa8xdd91lCgsLnbvrPPXUU8FulhWOHj1qxo4da+bNm2fcbrffa0eOHDEpKSnmxRdfNGVlZSYrK8skJSWZrKwsY4xx7r6QlZXlfNv//PPPN99//30Q1sQO9957r3N3Hfo/8MrKysz48ePN7bffbo4fP27y8/PND3/4QzN79mz6vwls377dDBo0yPz5z382FRUVZvfu3SY9Pd08+uij9H8T8L27y+n295NPPmnS09PN7t27nbu7zJw5M1ir1iL49v/GjRvNwIEDzeLFi6udt7n0PyEfjS4vL8/cfvvtZvjw4WbEiBHm0UcfNRUVFcFulhVeeOEFk5CQYIYOHWoSExP9HsYY88UXX5gZM2aYpKQkc/HFF5ulS5f6Lf/WW2+ZCRMmmMTERDNt2jSTnZ0djNWwhm/IN4b+bwoHDhwwd955pxk1apRJSUkxc+fONQUFBcYY+r8p/Oc//zHTp083w4YNM2PHjjW///3vTWlpqTGG/g8035BpzOn1d1lZmXniiSfM6NGjTXJysrn11lvN4cOHm2xdWiLf/r/55ptNv379qozDN954ozN/c+h/lzH/dx8sAAAAAFbgmnwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDKEfAAAAMAyhHwAAADAMoR8AAAAwDJhwW4AgqOiokIFBQWKjIxUSAif9QAAAJobj8ej0tJStW/fXmFh9YvthPxWqqCgQLm5ucFuBgAAAE4hNjZWnTp1qtcyhPxWKjIyUtLJN010dHST1et2u7Vt2zYlJCQoNDS0yepFw7HNWh62WcvDNmt52GYtS0vdXsXFxcrNzXVyW30Q8lsp7yU60dHRiomJabJ63W63JCkmJqZF7WStGdus5WGbtTxss5aHbdaytPTt1ZBLq7kYGwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALBMWLAbAABAZd8cPqETpRVBq/9Y2VG1i+gQsPLbRIYprnObgJUPAIR8AECz8s3hE7rotyuDVr8rPF9t+v5WJ3beI1PeKWD1/OuesQR9AAFDyAcANCveM/h/mJGo+K5tm7z+b45t1f2fGj11bX/FtevX6OXvOHRcd76eHdT/qQBgP0I+AKBZiu/aVoN6tG/yekOiTn6w6Nu1rQZ0avr6AaAx8MVbAAAAwDKEfAAAAMAyhHwAAADAMoR8ADhNhw8fDnYTgFaP/RDwR8gHgNOwa9cudevWTbt27Qp2U4BWi/0QqIqQDwCn4ejRo/J4PDp69GiwmwK0WuyHQFXcQhMB53a7tXr1au3fv19du3bVGWecUa9lzj77bI0ePVqhoaF+r+3du1d5eXnq0qWLevTooZEjR2rNmjXVLlO53MrLjh49Wm63W3/605+0fft2uVwunX/++fr+++/VqVMn5efnO//6LlO5TTXV7ft6hw4d9I9//EM7d+5UmzZtdMUVV6h3795KTU3Vc889p23btmn//v0666yzFB8fr8GDBysvL095eXk688wz9dlnn8ntduvAgQPq3LmzNm/erPDwcOXm5iomJkZ9+vTRWWedpW+++UYHDx5Ux44dtXv3bkVHR6u4uFi9evXS7t27FRMTozPPPFM33nijOnXqpPvvv1/79++XMUYDBw7Unj17FBERod27d8sYI5fLpd69eys2NlabN29Wfn6+XC6XBgwYoK+//loVFRUqLy9XdHS0Tpw4IUkKCwvTiBEjFB8fr82bN2vPnj0KCwvTxRdfrKioKA0cOFCPP/64jh49qoqKCvXs2VP9+vXTyy+/rOjoaK1evVo7d+7UH/7wBx0+fFiHDh2Sx+Op8n6JiorSD37wAx0+fFi7du1SeHi4vvvuO7lcLhlj1LNnT0VGRmrEiBGKjIxUamqq2rZtq7vvvlsHDx5UcXGxJMnlcunMM89UQUGBQkJC5PF41LFjR7Vp00bjx4/XWWedpWeeeUbff/+9U3fl9xiA4Hn00UdVUFCgPn36OMeEdevWye12KzQ0VMOGDdOGDRvk8XgUEhKi1NRUde/eXZJ04MCBaseU6sYL32P/ypUrdejQIb9jf21jjaQaX6vv8aS6scdbfm1jYUOcapxrDVpcHxg0qYSEBJOQkGB27txZ5bUXXnjBJCQkmKeeesqZ5na7zSuvvGKmTp1qhg0bZoYPH25++MMfmjVr1jjzrF271iQkJFRb38yZM/3K8zpx4oRZv369OXHiRCOsVc2WLl1qYmNjjSTn0b17d7N48eJ6LRMbG2uWLl1a7WveR1hYWLXL1Fau99GuXTsTEhJS7Ws1PWprk2/dtdXLo+ZHRERE0NtQ30dzUVFRYdavX28qKiqC3ZQGydlz1PS+N9Pk7DkalPq/OvyVGfTSIPPV4a8CUn5169fSt1mwjRkzptH358pjivfhPb4vXrzYdO/evcprc+bMqfGY36VLF9O1a9day62r6saWrl27mi5dupxWuXWtqzHKbUqnu48Fqw9OJ69xuU4QnHnmmVq2bFmV6W+++abatv3vrzsaY3T77bfr73//u+bNm6e1a9dq9erVSk9P1y233KKPPvqoKZtdb2+++aamTZumwYMHKysrS4WFhfr3v/+tvn376qqrrtKbb75Zp2WysrI0ePBgTZs2TVOnTlXnzp0lSRMnTtSCBQuUlJQkSWrT5uTPwy9atMhvmTfffNMpt/KyEydOlCQdO3bMOUM8dOhQSf89QxsRESFJateunSQpKSlJLpdLnTt31tSpU2tt79y5c53XvWdYfD/1jxkzpspZgDPOOENjxoyptk9DQlrPLltWVhbsJtSby+UKdhOAVueKK67QJ598Ikm66KKLJEkdOnTwmycqKsrveXR0dLVl3Xrrrc6Y4j32JyUl+Y0XnTt31rRp0zR9+nT17dtX//73v51jf+fOnfXEE084x3XfscblcikvL0+HDh2q9jVvudWNjZVVN1Y+8sgjOnTokPLy8vTII49UGY/qUm5d62qMcluSFtsHjf+ZA7VJSEgwv/71r83o0aON2+12pm/atMlceumlfmfe33vvPTNo0CCzd+/eKuUsWLDAPPfcc8aY5nkmv6KiwsTGxpqMjAy/9ayoqDCffvqpSU9PN3FxcX6fqGtaxhhjysrKTHR0tImJiTG9evVy5vEu061bNxMbG+tXrtvtNhkZGSYuLs707t3bpKenm969e/uVX1xcbFwulwkJCfErPz093fTq1cs5m9y1a1cTFxdn0tPTnXpiY2OdZcrKyvza63a7TXp6ugkLCzPp6enm+PHjztlpb/neMlwul9+Zo9jYWNO7d28zadKkKv+7EBISUu0Z7vr+L0Rze4SGhprJkyebnj17Br0tdXl06NDBhIWFmf3791d5bcuWLQHZp+qjpZ8V5kw+6qqoqMhIMuHh4UY6+T/FkydPNmFhYX5ntENDQ01ERIRzDPUe86OiokxISIhxuVxm0qRJJi4uzvTq1ct07drVhIWFOWOL75jie+zPyspytpnveBQaGmrS09OdscY7hkVHR5vIyEi/ccN3rKpubKysurHSO807tviW4Vt+fd9ftY3Lp1NuMDR0Hwt2H5xOXuOa/CAYO3asMjMztWbNGqWlpUmSlixZomnTpmnVqlXOfB9//LGSk5Od6wV9/fjHP64yLSUlpcq0oqIiDR8+vMa2uN1uud3uhqxGrVauXKnc3FwtWrRIxhinDrfbrZCQEM2ZM0djxozRypUrNXbs2FqXkaRVq1Y5103v3r1br776qowxzjJ/+ctfdMstt+juu+9WZmamU+7cuXOdM+g///nPlZmZqVdeecUp/9lnn5UxRsYYv/LvueceZWZmasaMGXr99dd14YUXasmSJbrrrruUmZnp1OPbPu96eF166aXKzMzUpZdeqnvuuUeSNGXKFL322mtO+71tS0hI0LZt2zRo0CBlZ2c77X3vvff8yvR4PBowYICys7PVuXNnHT58WP3799eWLVtq3R7t27dXQUGB2rRpoxMnTuicc87Rnj17al3Gez17eHi4ysvLa523soiIiFOeifdetxoTE6OioiL16dNH7777bp3K97atNt26ddPBgweddY+KilJJSUmd16E2HTt21NGjR/XKK6+oY8eOOnLkiPPawIEDtWHXoUapp6E8Ho92fV+ukO++b5H/+7Mz77ikwB2fTsXj9jj/BqJ+b5nbDhQ4f7f0bRYs8//fHEnSxCum6R+L/659+/Zp6tSpqqio0MyZM/Xkk09KOtnn06dP12uvveYch73HfK++ffs6x9y77rpLTz75pK699lo9+eST1Y4pkrRp0yYlJydL+u8Y5l320ksvdcYa3zHMy3fc8JbrHWN8x8bKqhsrK08bPXq0Xxne8msrt651+WpoucHgm0PqI9h9cDrHIEJ+EISFhSkjI0PLli1TWlqaSkpKtHz5cmVmZvqF/CNHjjiXl9TF+vXrq0ybNWtWrcts27at7g2vh7Vr10o6OXB5Q2tN83n/W7W2ZbyveXnn8U7v3bu3JCkvL8+vXN8vaXpf8y1/3bp11bbLe7/ls846S5JUWlrqV4b33+rWo3J9hw8f1saNGyXJ+cDm8Xj8QmqPHj20bds2v/bWdM9n73LeHf+CCy44ZciPjo5WQUGBzjjjDJ04ccLvsrCatG/fXkePHlVYWFi9Q/5ZZ52l3bt3n7L8I0eOKDw8XJJO+aHD1/Dhw2vcdl7x8fE6ePCgs+7t2rU7Zcivy4cTSSooKJB08v0zKf0KLVr4gvOa2+3W5X/KqsNaNIEP84PdgtOy8cstqsiLbPJ6c4tzJUlbt21VaXRpo5efc/BkmXcvzqn6YgvfZk3t4OqTx9b/VPRxpu3du1fSfy+38Tr77LMl/fe4XpnvMci7rPff6sYU6eRxOicnx5nHd5m8vDxnrKk8hvmWKckpt/IYVp3qxkrfad4xorryayu3rnX5ami5weTdXnXVkvuAkB8kU6ZM0YwZM3T8+HF9+OGHSk5OVpcuXfzm6dq1q3Owquz48eMKDQ2t8brCukpISFBMTMxplVEd723MQkJClJiY6Ex3u91+O9iIESOc12taxvc1L+883unffvutJDl96C03K+u/Ycv7mm/5qampWrx4cZX2ez9cHThwQJIUGRnpV0blbeW7Hl5r1qxxykpKStK6deu0b98+pw2+Id+7nX3P4NX0Ac973bf3mk/fdayJ9wxSYWGhpJPvn1PxBtmKiopTzluZt9/qUr73A8Q555xT5/I//fTTU86zY8cOSf9d92PHjp1ymbp+D6B9+/bKz89Xamqq5s+f7/daaGio3r7tgjqVEygej0c7duxQfHx8izwrvDPvuO5enKOkQf01qEf7Jq8/Mj9S2in1S+in8zqd1+jlh+0tkFZl6ffTB6tvl5MfuFv6NguW+fuS9EbuRo0K26V//N+0Hj16SKq6P+/fv19Szccn32OQd1nvv9WNKdLJ4/TgwYMVGhrqjEfeZbp06VJlfPPlO254y608hlWnurHSd5p3bKmu/NrKrWtdvhpabjB484d3e9VVsPugqKio4SdkT/daIdRPQkKCWbt2rTHGmCuuuMK88cYbZtasWebDDz80xvhfQ798+XIzaNAgs3///irlzJ8/30yYMMF4PB6uyeeafK7J55p8Py39+m6uyUddcU0+1+TXVWu8Jp+Q38R8Q/7ChQvNpEmTTFpamikvLzfG+Idyj8djbrzxRpORkWE2bNhg3G63KSwsNC+++KIZOHCgWbFihTGmeX7x1piTt5tyuVwmIyPDrFmzxhw7dsysXr3ajB492rhcrmpvO1XdMmvWrDEZGRlOGE5JSTGSzMSJE81zzz1nkpKSjCTTvn17I8ksWrTIbxnvbS5dLleVZSdOnFglpA0dOtQZFLzBXDp5m01JJikpya+s2to7Z84c5/XRo0f7lSvJjB492u+5JHPGGWc489oW5lvDozlo6YGRkI/6uPzyy539b+zYsUY6+UHcd7+Mioryex4dHV3t/nvzzTc7Y0pMTIyRTh7zfceLlJQUZzwaPXq0Wb16tXPs944Lffv2NZL/WON7Qqe617zl1uWWjNWNlfPnz3fKnj9/fpXxqKG3ejzVuNxSbqN5OvtYMPuAkN+C+Ib8I0eOmIEDB5rHH3/ceb1yKC8tLTXPPvusmTx5sklOTjbDhw83N9xwQ4u+T36PHj3qfZ/8uLi4et8n37tMbeV6Hw25T35tbfKtm/vkN+zBffIbrqUHRkI+6qsp75PvPb5Xd5/8uLi4Wu+T37Vr1xrvk195zDqVut4nv77l1rWuxii3KQXiPvlN0Qenk9dcxpziFhWwUlFRkTZv3qzzzjsvINfk+6ruF2+HDRtW6zVx/OItv3jbkn7x1u12a8OGDc5dNoLN7XYrOztbiYmJzfvXGGvw5d4CpT/9b2XenhaUa/K/zv9aMzJn6PX01zWg04BGL7+69Wvp2yzYPv/8cw0bNkzTp09vsl+89e73hYWF/OJtC9AY+1gw+uB08hohv5VqypDvi4Gs5WGb1W7Xrl0699xztX37dvXp0+fUCzSBlr7NCPktb5sFWzD2Q7ZZy9JSt9fp5DXurgMAp6FPnz46ePBgvW53C6BxsR8CVXGfLgA4TQQLIPjYDwF/hHwAAADAMoR8AAAAwDKEfAAAAMAyfPEWANAsfbm3ICj1fnPsuCRp56Hj8pQ0fht2HDre6GUCQGWEfABAs1LhOXln53lv5gSlfld4vtr0demOV7fIlOcFrJ42kQzBAAKHIwwAoFlJ7NlBb80epbAQV9DacKxsjNqN7xCw8ttEhimuc5uAlQ8AhHwAQLOT2LNDkFvQ9D/CBQCNiS/eAgAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYJC3YDEBwej0eSVFxc3KT1ut1uSVJRUZFCQ0ObtG40DNus5WGbtTxss5aHbdaytNTt5c1p3txWHy5jjGnsBqH5y8/PV25ubrCbAQAAgFOIjY1Vp06d6rUMIb+VqqioUEFBgSIjIxUSwlVbAAAAzY3H41Fpaanat2+vsLD6XYBDyAcAAAAswylcAAAAwDKEfAAAAMAyhHwAAADAMoR8BNyRI0c0fvx4rVu3zpn20EMPadCgQUpKSnIer7/+ehBbCUnasmWLfvSjH2n48OEaNWqU5s6dqyNHjkiSNm3apOnTpyspKUnjxo3T4sWLg9xaSLVvM/az5ikrK0vTp09XcnKyRo0apV/96lcqKSmRxH7WHNW2vdjHmje3261Zs2Zp3rx5zrRWtY8ZIIDWr19vLrnkEpOQkGDWrl3rTL/yyivNm2++GcSWobLi4mIzatQo88c//tGUlpaaI0eOmJtuusncfPPN5ujRo2b48OFm0aJFpry83KxZs8YkJSWZTZs2BbvZrVpt28wY9rPmKD8/3wwePNgsXbrUuN1uc/DgQZOenm7++Mc/sp81Q7VtL2PYx5q7P/zhD6Z///7m3nvvNcaYVrePcSYfAbNs2TLdc889uuuuu/yml5WVadu2bRo0aFCQWobq7Nu3T/3799fs2bMVERGhM888UzNmzNBnn32mDz74QB06dNB1112nsLAwXXDBBcrIyNArr7wS7Ga3arVtM/az5qljx45as2aNpkyZIpfLpaNHj6q0tFQdO3ZkP2uGatte7GPNW1ZWlj744ANdeumlzrTWto8R8hEwaWlpWrFihSZNmuQ3fcuWLaqoqNBTTz2lkSNHasKECfrf//3fBv2aGxpPnz599Pzzz/v9EuDy5cs1cOBAbd++XQkJCX7zx8fHa8uWLU3dTPiobZuxnzVfbdu2lSSNGTNGGRkZ6tKli6ZMmcJ+1kzVtL3Yx5qv/Px8PfDAA/rd736n6OhoZ3pr28cI+QiYLl26VPvDDYWFhRo+fLhmzZqlTz75RE888YRefvllvfDCC0FoJapjjNGTTz6pf/3rX3rggQd04sQJvwOlJEVFRamoqChILURllbcZ+1nz98EHH2jVqlUKCQnRHXfcwX7WzFXeXuxjzZPH49GcOXP0ox/9SP379/d7rbXtY4R8NLlRo0Zp4cKFGj58uMLDwzVkyBBdf/31eu+994LdNEg6fvy47rjjDr3zzjtatGiR+vXrp+joaOeLZl4lJSVq06ZNkFoJX9VtM/az5i8qKkrdunXTnDlztHr1avazZq7y9ho0aBD7WDP03HPPKSIiQrNmzaryWmvbxwj5aHIffvihXnvtNb9pZWVlioqKClKL4LV7925NnTpVx48f15IlS9SvXz9JUkJCgrZv3+43744dO3TuuecGo5nwUdM2Yz9rnj7//HNddtllKisrc6aVlZUpPDxc8fHx7GfNTG3b6z//+Q/7WDP09ttv69NPP1VKSopSUlKUmZmpzMxMpaSktLqxjJCPJmeM0SOPPKKsrCwZY7Rx40YtXLhQM2bMCHbTWrWCggJdf/31Sk5O1l//+ld17NjReW38+PE6fPiwXnrpJZWXl2vt2rV65513NHXq1CC2GLVtM/az5qlfv34qKSnR7373O5WVlWnv3r167LHHNG3aNE2YMIH9rJmpbXuFh4ezjzVD77//vj7//HOtX79e69evV3p6utLT07V+/fpWN5a5jDEm2I2A/fr166eFCxcqNTVVkvTaa6/pxRdf1MGDB9W5c2f96Ec/0nXXXRfkVrZuL774oh599FFFR0fL5XL5vbZx40bl5OToN7/5jbZt26aOHTvqtttu05QpU4LUWkin3mbsZ83Tjh07NH/+fOXk5OiMM85QRkaGc4ck9rPmp7btxT7W/Hnvkf/oo49KUqvaxwj5AAAAgGW4XAcAAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsAwhHwAAALAMIR8AAACwDCEfAAAAsExYsBuA4KioqFBBQYEiIyMVEsJnPQAAgObG4/GotLRU7du3V1hY/WI7Ib+VKigoUG5ubrCbAQAAgFOIjY1Vp06d6rUMIb+VioyMlHTyTRMdHR3w+txut7Zt26aEhASFhoYGvL7WhL4NHPo2MOjXwKFvA4e+DQz6tXbFxcXKzc11clt9EPJbKe8lOtHR0YqJiQl4fW63W5IUExPDTtzI6NvAoW8Dg34NHPo2cOjbwKBf66Yhl1ZzMTYAAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgGUI+AAAAYBlCPgAAAGAZQj4AAABgmbBgNwAAgKb0zeETOlFa0eT1His7qnYRHQJah9vt1q7vyxW2t0ChoaGSpDaRYYrr3Cag9QJofgj5AIBW45vDJ3TRb1c2eb2u8Hy16ftbndh5j0x5p8BX+GGW39N/3TOWoA+0MoR8AECr4T2D/4cZiYrv2rbJ6v3m2Fbd/6nRU9f2V1y7fgGrx+12a9u2bUpISFBoaKh2HDquO1/PDsr/XAAILkI+AKDVie/aVoN6tG+y+kKiTn6g6Nu1rQZ0Cly9brdbFXnhGtSjvXO5DoDWiS/eAgAAAJYh5AMAAACWIeQDAAAAliHkA0AzdPjw4WA3AUA9sd+iOSHkA0Azs2vXLnXr1k27du0KdlMA1BH7LZob7q6DgHO73Vq5cqXWrl2ro0ePauzYsZKk1atXa//+/Tr77LM1evToU94Jwu1213uZxmr/6dbbkDLKysr0pz/9STt37lTfvn112223KSIiwm+egoICTZw40ZknMzNTX3zxRb3qKS4u1pw5c7R9+3ade+65euKJJxQREeHX3pEjR2rNmjW1llt5HSsvk5KSonnz5vnVI6lK3dHR0bX2m9vt9uuXm2++WevWrXPmGThwoMaNG6d9+/ape/fu+uSTTxQREaFZs2Y5y/zxj3/U5MmTnXneffdd/exnP3Nef/nllyVJ1157rb7++msNGDBA//u//6tbb73Vmed///d/9fDDD/u1fffu3Ro4cKDcbrdCQ0P1yiuv6Oqrr3bW55///KduuOEGHT16VB06dFB2draOHDmiwYMHy+PxKCQkRDk5OSopKZHH49HRo0dP+d4C0DwcPXrUb789cuSIxowZ4xxn3n//fT3yyCPOMeN//ud/9Itf/KLWY+J9992nyy67zCnj448/1ldffeV3TCwrK/NbpnK51dXjdrv9jomVj2ePPvqo1q9ff1rH/NGjR0s6/bE+NTVVzz33XI3H/IbW0xDByiENZtAqnThxwqxfv96cOHEioPUsXbrUxMbGGknOo0uXLqZr165+02JjY83SpUvrVc6plglU++tbb0PKmDNnjgkLC/NbJiwszMyZM8eZp2/fvn6vV/c4VT2XX355tctFR0dXqbu2cqtbx8rL1PVx+eWX11hmu3btTEhISIPKbYmPDRs21Pl91pJUVFSY9evXm4qKiiavO2fPUdP73kyTs+dok9b71eGvzKCXBpmvDn8V0Hoq922w1tdGp3rfbtiwwdlvu3Xr1iTHiJiYmCappyHH/LqO9b79Wl25p3p07drVdOnS5ZT1nK5g5ZDTyWuE/EaQkJBgEhISzM6dO6u89sILL5iEhATz1FNPOdPcbrd55ZVXzNSpU82wYcPM8OHDzQ9/+EOzZs0aZ561a9eahISEauubOXOmX3nGGJOZmWlmzpxphg8fblJSUsxVV11l/vnPf9bY5qYI+UuXLjUul8tkZGSYf//732bVqlXmN7/5jbNzPPLII6awsNBkZWWZjIwM43K5qt1ZfMvJysqq0zKN3f6G1tuQMubMmWMkmW7dupkFCxaY/fv3mwULFjiDxpw5c/wC/oQJE8xtt93md+CJi4s7ZT3egB8REWHmzZtnduzYYa688kqnjNGjR5tFixYZl8vl1L1o0aIq5VZex0WLFjntd7lcJjk52UgyLpfLSDLPPPOMiY+Pd+qJj483O3bsMPPmzTMRERFGkjn//POr9Nt1113nLHPrrbea559/3rhcLmeZyh9MHnroIb8BMCoqynTq1MlvnsjISL/nnTp18punU6dOpk+fPn7ztGnTxu/5kCFD/J5717O+jwcffNCEh4f7TSPkNz5CPhqqriHf9xgyYsQIM3LkSL/9uvJ+fvHFF1c5Jnbu3NlvnoSEhCoBevTo0c7zK6+80lx88cVVjmeV6znrrLOc5+ecc44ZM2aM3zy9evXyO45Nnjy53sf8RYsWmUceeaTOY723XxcvXuxX7s9+9jNnjHK5XObWW291nksyP/vZz+pVz+kIVg4xhpAfdAkJCSY1NdX89re/rfJaenq6SU5OdkK5x+Mxt912m0lPTzefffaZKS8vN6WlpeaNN94wQ4YMMR9++KExpn4h/1e/+pW58MILzcqVK01paakpLy83K1asMMnJyWbRokXVlhHokF9RUWFiY2NNRkaGcbvdpqKiwqxbt87Exsaa9PR0k56ebuLi4pyDpdvtNhkZGX7TqivHV03LBKL9Dam3IWWUlpaasLAw061bN1NeXu63THl5uenWrZsJDQ11DmonTpwwpaWlpnv37iY9Pd0UFhY6rx09erTGeoqKipyDZWlpqV97J02a5BxEe/XqZTIyMkx5eblfOb7l9u7d2287e9e5vLzcTJo0yamnuLjYZGRkOINIWFiYU09RUZGz/t5pkyZNcvrNt18mT55sYmNjnXpKS0tNx44dnfUuKCgwGRkZtZ4NysvL8wvlvs+rC+sul8t8//33zvOIiAhTUFBwykD/n//8x+/5xo0ba5x3//79xhhjvvvuO2faK6+8Ut+3botAyA8cQn7g1DXkex+FhYV+x9r8/Hy/Y0phYaFzvJNkQkND/Z5LMvn5+c6HgkmTJpmCgoIqHxi8x0RvPYWFhc4y3ueVy/V9Xvl4NnHiRFNcXOx3fPYe832PvdUd833nqetY75sPvOX6HvNLS0tNenq6CQsLM+np6aa0tNR069bNhIWFmV69etUrUzR0uwcjh3idTl7jmvxGkpGRobffflt33XWXQkJOfp/5iy++UFlZmQYMGODM9/7772vVqlVavny5unfv7kyfPn26CgoKtHPnTl188cV1rveLL77Qyy+/rDfeeENDhw51pl9yySV68MEH9fXXX9e6vNvtltvtrnN9dbVy5Url5uZq0aJFMsbI7XZr48aNftNGjx6tlStXOtfoz507t8q06srxVd0ygWp/fettSBlPP/20Kioq9PDDD8vlcvkt43K59Mtf/lK33nqrJGnChAmKjIzUxx9/rH379um1115TdHS0xo8frxUrVmjixIlavXp1tfXcfffdkqQ777xToaGhzvcmvO0dOHCgc435q6++KpfLVaUc73NJeuWVV2SM8SvD5XIpMjJSkjRlyhSFh4f7LXP33XfLGKMnnnhCd999t5555hmFhobqyiuv1Ouvv67IyEin33z7ZeDAgU4ZixYtUmhoqFNPr1691KZNG796Zs6cqa1bt+qzzz6TJKWmpurMM89Ujx49tHfvXnXv3l1nnnmmzj//fGeemTNnyuPx6NVXX5V08tr8M844Q+eee662b9/u1HPNNdc480RGRuqOO+5wrqsNCQlRamqq3zYfPHiwQkJC5PF4JEnh4eHq3r27vv32WyUmJmrv3r06++yznW0yc+ZMDUy7rNr3V0vm8Xi06/tyhXz3vXO8bCo7845LCtyxryYet8f5N5D1esuu/O+2AwVNur42OtX7dtv+Aufv1NRURUdH66c//amkk8fa9u3bq0uXLsrLy1PHjh0VHR2tn/3sZ84x4+c//7lzTPSW0b59e02ZMsU5JrZp08bvWHXXXXcpIyPDOd7deeedio6O1sCBA5Wdna2BAwdWqWfmzJk6++yzned33nmn2rRpo3Hjxumjjz5SYWGhwsPDnWW8x2ff46p3XKt8zK9unlON9dXlA99jfmhoqC699FJlZmbq0ksvVWhoqDMWeseoumaKhghWDvE6nf2WkN9Ixo4dq8zMTK1Zs0ZpaWmSpCVLlmjatGlatWqVM9/HH3+s5ORkv4Dv9eMf/7jKtJSUlCrTioqKNHz4cKe8nj17+gV8ryuuuEJXXHFFre3etm1bra831Nq1ayWdPChmZ2dL+u+txTwej4wxznwdOnRwpleeVl05vqpbJlDtr2+9DSlj3bp1kqTY2Nhql4mNjXX+njFjhrKzs/Xpp58607Kzs3XVVVdpxYoV2rlzp7Kzs6utZ+PGjZKkCy64wKnHt70jRozwa2d15Xif+85TeZ2/++47SVL37t39yvDW7X2+ceNGpx1nnXWWJGnPnj3ONN9+qa7e48dPBrfevXtXqWfSpElKTU11BsXrr7/er29dLpeys7P1wx/+0Jln0qRJfiF/8uTJys7Odj5MlJaWKjs7W5MmTXLmueGGG/z67aqrrqqyDbOzs3XllVdq6dKlkqRZs2YpLi5ODz74oI4cOeLMP+7iCVrxwXsyxujyP2XJWh/mB63qjV9uUUVeZJPVl1ucK0naum2rSqNLA15fTk7OyX8Pnqzr7sU5Aa+z1ajhfVt6YIfzt/c4U/lY26FDB+Xl5TnHEN9jhu8x0beMs88+W5L03XffVTlWVV7GW493mvcY6VvPpEmTnGOZ7zIJCQn66KOPtG/fPr9lvMfnuhzzK89T17HeNx9kZ2dXGQvz8vKc+bKzs/3GwvrU0xDByiGNgZDfSMLCwpSRkaFly5YpLS1NJSUlWr58uTIzM/1C/pEjR9S5c+c6l7t+/foq02bNmtXg8ipLSEhQTExMg5eviffuAiEhIUpMTJTb7XbWJSQkxNkhR4wYocTERElSVlZWlWmVy6msumUC0f6G1NuQMlJTU7V48WLl5ubqkksuqbLMggULnL9ff/11/fCHP9SRI0ecaYmJibrvvvskSX379lViYmK19SQlJWndunXKyspSRkZGlfZ6D2q+7a9cjve57zyV17lnz55av3699u3bV2WZrKws532QlJTktM17dumcc86ptl8GDhxYpd62bduqsLDQOSPuW897772nrVu3Os//9re/6Sc/+Ynz3BijxMRE539IvMv4DlbvvvuurrrqKpWWngxMkZGRSkxM1O9//3tnnpdeeknt2rVznr/xxhtatGiRfCUmJmrZsmXO85dfftn5wN+xY0dnfT/+aLmkkx9A3r7tAtnG4/Fox44dio+PD8qZ/LsX5yhpUH8N6tG+yeqNzI+Udkr9EvrpvE7nBawet9utnJwcDR48WKGhoQrbWyCtytLvpw9W3y5tA1Zva3Cq9+3mnBhd87eTf3uPM5WPtd5jpPcY8vrrrzvL+x4TfcvwHhN79uxZ5Vjlewz3fe5tn/cY6VvPe++953xw8F3Ge9Kve/fufst4j891OeZXnqcuY33lfJCYmFhlLFyzZo0kqXPnzkpMTPQbC+uTKRoiWDnEq6ioqOEnZE/jMiH8n4SEBLN27VqzefNmM2TIEFNYWGiWLVtmbrnlFmOM/zX08+bNM7Nmzaq2HO/1e8bU/Zr8p59+2owbN67a+UpKSsyxY8eqfY1r8uvX/obUyzX5XJPPNfnV45r8wOGa/MDhmnyuyW9p1+QT8huBN+QbY8wVV1xh3njjDTNr1iznS7S+oXz58uVm0KBBzoDua/78+WbChAnG4/HUOeTn5OSYhIQEs2nTpirz/e1vfzMpKSlOgPLV1HfXWb16tfnkk0/Mr3/9a+fAMn/+fHPs2DGzZs2aOt9dZ82aNXVaprHb39B6G1KG7911nnvuObN3717z3HPP1Xh3nfHjxzt3HfA+4uLiTlmP79115s6da7Zu3WquuOIKp4ya7q5TudzK63iqu+s8/fTTVe4ksXXrVjN37txq767j7bdrr73WWeaWW24xCxYsqPXuOv/v//2/KnfX8f0wUHmgk07ejcJ3no4dO5q4uDi/eSrfXWfw4ME1fjioz2Pu3LncXacJEPLRUA25u87w4cNNampqlWDu+7wud9c599xz/Z5XvrvOFVdcUe+76/To0aPK3XV69uzpdxybPHlyvY/5ixYtMvPnz6/zWF/d3XXWrFlTp7vr1Kee0xGsHGIMIT/ofEP+woULzaRJk0xaWppzJtY3lHs8HnPjjTeajIwMs2HDBuN2u01hYaF58cUXzcCBA82KFSuMMfW7u86DDz5oxo4daz755BNTXl5uSkpKzFtvvWWGDh1qFi5cWG0ZwbxPfteuXavcOzcuLq7e98k/1TKBan99621IGY11n/xT1dPQ++RXLpf75AfmQchvfIR8NFRrvk9+Q475dR3rA3Gf/EDkg2DlEO6u04ykp6frscce0/XXX6+wsKrd63K59Kc//UnPP/+8fvGLX2j//v0KCwvTgAEDtGDBAl1wQf2vv3344Yf16quv6g9/+IPz7fz4+Hg99thjmjBhQmOsVoNNmTJFl19+ufOLtyNGjGjQL956y2nqX5prjHobUsbjjz+uX//617X+4u2OHTtO+xdv33rrrUb5xdvq1vF0f/G2pn5rLb94O2zYsDq/xwA0LwcOHLD6F2/rcsxvyC/RVlduc/nF22DlkNPhMsbnWx5oNYqKirR582add955AfnibWVut1vZ2dlKTExs1jtES0TfBk6w+vbzzz/XsGHDtGHDBiUnJzdZvU0lmO/ZL/cWKP3pfyvz9rQm/eLt1/lfa0bmDL2e/roGdBpw6gUaqHLfBmt9bXSq963t+22gMIbV7nTyWtPe1gAAcEodOnRQSEhIs7sdG4Casd+iueFyHQBoZvr06aODBw+e1u1xATQt9ls0N5zJB4BmiKAAtDzst2hOCPkAAACAZQj5AAAAgGUI+QAAAIBl+OItAKDV+XJvQZPW982x45KknYeOy1MSuLrdbrd2fV+usL0FCg0N1Y5DxwNWF4DmjZAPAGg1Kjwnfxpm3ps5TVqvKzxfbfq6dMerW2TK8wJf4YdZfk/bRDLcA60Nez0AoNVI7NlBb80epbAQV5PXfaxsjNqN7xDQOtxut7Zt26aEhATnh4XaRIYprnObgNYLoPkh5AMAWpXEnh2CVHPgf3HW7XarIi9cg3q059dDgVaOL94CAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAlgkLdgMQHB6PR5JUXFzcJPW53W5JUlFRkUJDQ5ukztaCvg0c+jYw6NfAoW8Dh74NDPq1dt6c5s1t9eEyxpjGbhCav/z8fOXm5ga7GQAAADiF2NhYderUqV7LEPJbqYqKChUUFCgyMlIhIVy1BQAA0Nx4PB6Vlpaqffv2Cgur3wU4hHwAAADAMpzCBQAAACxDyAcAAAAsQ8gHAAAALEPIR6PbsmWLfvSjH2n48OEaNWqU5s6dqyNHjjivXX/99UpKStLIkSP1yCOPqKKiIsgtbjlq69t3331XEydOVHJysiZMmKC///3vQW5ty5GVlaXp06crOTlZo0aN0q9+9SuVlJRIkjZt2qTp06crKSlJ48aN0+LFi4Pc2paltr5dvny5Lr/8ciUnJ2vcuHF65plnGnSbuNaqtr71OnTokEaOHKk333wzSK1smWrrW8axhqutXxnDAsAAjai4uNiMGjXK/PGPfzSlpaXmyJEj5qabbjI333yzyc/PN6mpqeYvf/mLKSsrM99995259NJLzfPPPx/sZrcItfXt1q1bzdChQ83GjRuNMcZs2LDBDBw40Hz22WfBbXQLkJ+fbwYPHmyWLl1q3G63OXjwoElPTzd//OMfzdGjR83w4cPNokWLTHl5uVmzZo1JSkoymzZtCnazW4Ta+jYnJ8cMGTLEfPzxx8btdpsdO3aYiy66yPz1r38NdrNbhNr61svtdptZs2aZ/v37m6VLlwaxtS1LbX3LONZwtfUrY1hg8GNYaFT79u1T//79NXv2bIWGhioiIkIzZszQ3Llz9dZbbyk2NlY333yzJOmcc87RCy+8IJfLFeRWtwy19W1ubq4qKirk8XhkjJHL5XLmQe06duyoNWvWqG3btjLG6OjRoyotLVXHjh31wQcfqEOHDrruuuskSRdccIEyMjL0yiuvaMiQIUFuefNXW9/u3btXV199tS666CJJUt++fTV+/Hh99tln+v/+v/8vyC1v/mrrW69nn31WZ511ls4+++wgtrTlqa1vGccarrZ+ZQwLDEI+GlWfPn30/PPP+01bvny5Bg4cqC+++EIJCQn6xS9+oY8++kjR0dGaOnWqc7BE7Wrr27S0NCUmJuqaa65RaGio3G637r33XoJoHbVt21aSNGbMGB08eFApKSmaMmWK/vCHPyghIcFv3vj4eC1ZsiQYzWyRaurbmJgYTZgwwZmvpKREK1euVEZGRrCa2uLU1LeStHbtWr377rtaunQpfdoANfXt/fffzzh2Gmp7zzKGNT6uyUfAGGP05JNP6l//+pceeOABFRQU6M0339SQIUO0cuVKPfPMM3r99df14osvBrupLU7lvi0rK9M555yjF198UZs2bdJzzz2np59+Wv/+97+D3dQW5YMPPtCqVasUEhKiO+64QydOnFB0dLTfPFFRUSoqKgpSC1uuyn3r6/jx45o9e7aioqJ0ww03BKeBLVjlvs3Pz9f999+v3/72t2rTpk2wm9eiVe5bxrHGUblfGcMCg5CPgDh+/LjuuOMOvfPOO1q0aJH69euniIgIDR48WNOmTVN4eLj69++vmTNn6p///Gewm9uiVNe3Tz/9tCIiIjRy5EiFh4dr7Nixmjx5sl5//fVgN7dFiYqKUrdu3TRnzhytXr1a0dHRVb7IWFJSQnBqgMp9W1BQIEnatWuXrr76alVUVGjhwoXOmT7UXeW+nTt3rmbNmqVBgwYFu2ktXuW+DQ8PZxxrBJX7lTEsMAj5aHS7d+/W1KlTdfz4cS1ZskT9+vWTdPKa27KyMr95vdffoW5q6tt9+/apvLzcb96wsDCFh4cHo5ktyueff67LLrvM771ZVlam8PBwxcfHa/v27X7z79ixQ+eee25TN7NFqq1vo6Oj9cknn2j69OkaPXq0/vrXv6p9+/ZBbG3LUlPfStJ//vMfPfvss0pJSVFKSor27dunhx9+mEtK6qi2921cXBzjWAPV1q/ffvstY1gAEPLRqAoKCnT99dcrOTlZf/3rX/2+BDZ16lRt27ZNCxYskNvt1tatW7Vo0SJdfvnlQWxxy1Fb344bN07vvfeeVq9eLWOMPv30U/3jH//gWtw66Nevn0pKSvS73/1OZWVl2rt3rx577DFNmzZNEyZM0OHDh/XSSy+pvLxca9eu1TvvvKOpU6cGu9ktQm19+/XXX2v27Nm67777dO+99yosjK+I1UdNfXvNNddoy5YtWr9+vfPo3r27HnroIT333HPBbnaLUNv79qqrrmIca6BTHWsZwxqfy/DxE43oxRdf1KOPPqro6OgqdxvYuHGjNm3apMcff1zbtm1TVFSUrrnmGt16663cmaAOTtW3L7/8shYtWqS8vDx1795dP/nJT/SDH/wgSK1tWXbs2KH58+crJydHZ5xxhjIyMjR79mxFREQoJydHv/nNb7Rt2zZ17NhRt912m/NFMZxaTX17xx13aOXKlVW+8zBs2LAqXzBH9Wp73/oaN26cfvrTn/K+rYfa+pZxrOFq61fGsMZHyAcAAAAsw+U6AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlwoLdAARHRUWFCgoKFBkZqZAQPusBAAA0Nx6PR6WlpWrfvr3CwuoX2wn5rVRBQYFyc3OD3QwAAACcQmxsrDp16lSvZQj5rVRkZKSkk2+a6OjoILemZXO73dq2bZsSEhIUGhoa7OZYj/5uOvR106K/mw593bTo74YrLi5Wbm6uk9vqg5DfSnkv0YmOjlZMTEyQW9Oyud1uSVJMTAwHryZAfzcd+rpp0d9Nh75uWvT36WvIpdVcjA0AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYhpAPAAAAWIaQDwAAAFiGkA8AAABYJizYDQAA2Oebwyd0orQi2M2QJB0rO6p2ER2CUnebyDDFdW4TlLoBtG6EfABAo/rm8Ald9NuVwW6GJMkVnq82fX+rEzvvkSnvFJQ2/OuesQR9AE2OkA8AaFTeM/h/mJGo+K5tg9qWb45t1f2fGj11bX/FtevXpHXvOHRcd76e3Wz+RwNA60LIBwAERHzXthrUo31Q2xASdfJDRt+ubTWgU3DbAgBNiS/eAgAAAJYh5AMAAACWIeQDAAAAliHkA7DG4cOHg90EAM0Qxwa0RoR8AFbYtWuXunXrpl27dgW7KQCaEY4NaK24uw4Czu12a+XKlVq5cqUkaezYsRo7dqxCQ0PrXEZZWZn+9Kc/aefOnerbt69uu+02hYaGavXq1dq/f7+6du0qSTp06JDz94EDB5SXl6cuXbqoR48eGj16dL3q9G37xx9/rJ07d2r79u2SpOjoaPXv31+HDh2SMUbl5eUaM2aMjh8/rpCQEF144YUyxmj16tV+6yxJH330kV566SXl5OSoXbt2GjRokEaOHKkDBw7o/fffV05Ojlwul3r06KEOHTro4MGD+v7779WmTRsVFhbq+PHj8ng8iouL08iRI1VcXKzi4mKNGjVKCQkJeuaZZ3To0CEdOHBAoaGhat++vQYOHKj9+/frnHPO0RlnnKGcnBx98803Ki4ulsvl0plnnqnU1FQNHTpUy5cvV3Z2tjwejyIiItSjRw9VVFQoMjJSeXl5ioiIUElJicrLyxUREaH27durrKxMx48fV+/evdW5c2cdOXJEBw4ckDFGbdq00aBBgxQTE6OPP/5Yhw4dksvlUmxsrHr16qU9e/aorKxMYWFhKi4u1oEDB+RyuRQdHa2YmBgdO3ZMYWFhioyMVHFxsY4ePVrttnK5XDLGaM+ePerTp0+9tjMAex06dEgej0dXXnmljh07pq5duyokJERut1tdu3ZV27ZtVVpaqgsuuEAlJSV68cUXVVJSopSUFC1atEgbN27U/v37dfbZZ9c4jviOcx6PR506dVKXLl2Un5+vTp066dChQzp8+LC+++47SVLv3r01evRoffXVV9q1a5dcLpdSU1PVs2dPjRw5Up988olefvllHTt2TN27d9cFF1ygnj17OvW73W5n/PO2S1KVafUd81C76vq9WfexQat04sQJs379enPixImA1rN06VLTpUsXI8nv0bVrV7N06dI6lTFnzhwTFhbmt3xISIhp165dlXJre8TGxta5ztra3tBH+/btTXR0dKOVx6Pmx/nnn9/Qtyx8VFRUmPXr15uKiop6LZez56jpfW+mydlzNEAtq7uvDn9lBr00yHx1+Ksmr7u+/dDQ/kbN5syZY1wuV6MdW6obR5YuXWq6du3aaHXU1t7Y2FgzZ84cExsb6ze9S5cuVdpQ3zEvkGx4by9durRKvzdFH59OXuNynSbWr18/9evXr9r/NnzxxRfVr18/Pf300840j8ejV199VdOmTVNKSopSU1N1/fXXKysrS5K0f/9+DRgwQB999FG19V199dX6zW9+E5iVOYU333xTU6dOVV5entLS0vTRRx/po48+Ulpamg4dOqRp06bpzTffrLWMuXPn6oknnlCnTp20YMEC7d+/X7feeqs8Ho+OHTum0aNHy+VyKS0tTf3795ck9ejRw1n+1ltv1cSJE+VyudS5c+c61Vm57V4ul6vWZWp6/Ze//KX69++vgoICFRcXS5KioqIkSZGRkadsC+rvs88+0/Dhw4PdDABB5B0/jDGSpJCQukeefv38fzgtOTlZWVlZGjx4sN848uabb2ratGk6dOiQMwYlJCTUWnbbtlV/IO7KK69Uu3btJMlpryQNHz5c8fHxzvOSkhI98cQT6ty5s7KyslRYWKhHHnlEeXl5OnTokB555BEVFhZW21Y0nHc7Dx482On3FtHHjf6RA7VKSEgwqamp5re//W2V19LT001ycrJ56qmnjDHGeDwec9ttt5n09HTz2WefmfLyclNaWmreeOMNM2TIEPPhhx8aY4y5/fbbzc0331ylvM2bN5v+/fubb7/9tsprgT6TX1FRYXr37m2io6NNenq6cbvdzmtut9ukp6ebmJgYExcXV+Mn+9LSUhMWFma6detmysvLnXJjY2PN5MmTnbMWkydPNmVlZSY2NtZ069bNhIaGmsmTJ5v09HQTFxdnysrKTEZGhomLi3Om1XY2wdv2qKgoExUVZSSZiIgIExISUuOZ/dDQUONyuZwzMFFRUWbSpEkmJibGxMbGml69ejnzhoSEmN69e5v09HTTo0ePoJ7xDg8PD/pZ90A+CgsLG/eN3cpwJv/0cCY/eEpLS01oaGiVY0JERIQ5++yzazxmXHLJJSYkJMRIMhMnTvQ75hcWFhq32+2MJ6WlpSY2NtZER0ebyZMnm9jYWJOenu6MUd7xw3vcj46ONpMmTTKSnP+dDgkJMVFRUSY2Ntb07NnTmT8qKsrExMSYsrIyZ8z0rk/Xrl2dccw7Jqanp1cZ33zbGuz3VEt+b3v7OCMjwy/LGNM0fXw6eY1r8oMgIyNDb7/9tu666y7nzMIXX3yhsrIyDRgwwJnv/fff16pVq7R8+XJ1797dmT59+nQVFBRo586duvjiizVz5kzdcMMNOnjwoLp16+bM9+qrr2rMmDHq1atXjW1xu91yu92Nvo4rV67Ut99+K0maN2+ejDF+9dx7773KzMzUN998o5UrVzrXq/t6+umnVVFRoYcfflgul8u55jE3N1eLFi3S0qVL9eSTT6pPnz5atWqVcnNzddddd+nJJ5/UhAkTlJSUpNGjR2vVqlWaO3euRo8erbvuukuZmZk11lm57V5paWn6+OOPNWbMGC1ZsqTKMvHx8dq6davzvKSkRJdddpnee+895ebm+s3r8Xj07bff6uc//7kyMzPr2KPV69y582ndNaK8vPy06j8dXbp08fufkkDImHKV/vD8ooDWYTOPx6Nd35cr5Lvv63UWdGfecUmBO77Uh8ftcf5t6rZ469t2oKBOdTe0v1HVouf/XG2fT5kyRa+99lqNy51xxhnyeE6+Z+Lj4/WDH/xAt956qyTp2muv1bJly5zx5Omnn3aO7xMmTNC7776ru+++W5mZmbr77rv17rvvOuV6PB4VFxcrIiJCkjR69Gj961//ksfjUUlJSZVxoqSkRJK0atUqjR071hkzJenCCy/UkiVLnO+5ecdEY4xGjx7tN75521rbmNcUvNsi2MeDhvDNHZWzjBT4Pj6dPiPkB8HYsWOVmZmpNWvWKC0tTZK0ZMkSTZs2TatWrXLm+/jjj5WcnOwX8L1+/OMfO397/ztvyZIlmj17tiTp+PHjeuedd/Tss8/W2pZt27Y1xipVsXbtWudvj8ej7Oxsv9e9B1HvvB06dKhSxrp16yRJsbGxzvLecj0ej8LDwyVJe/bscaZ7D6B5eXlOHWvXrnW+lOQNlTXVWbntXt7La0pLS6tdprrLbk4VYBsj4HrXtyUKCwv84ec/G7/W5X/KCng91vswv0GLbfxyiyrygntJWm5xriRp67atKo2ufv8NlJyDJ+u7e3FO/RZsYH/jv46sqHocl6Szzz671uW8X4yVTo4tkyZNcp5//fXXzk0JpP+OUdJ/b9Hp/bem4/uePXsk/XdMORXvWOU7Zno/AFQeZ83/XebjO775joM1jXlNKSennvtCM+CbOypnGe9073zNoY99EfKDICwsTBkZGVq2bJnS0tJUUlKi5cuXKzMz0y/kHzlyRJ07d65TmbNmzdKf//xn3XrrrQoJCdHbb7+tHj16aOTIkbUul5CQoJiYmNNan+r43gElJCREiYmJfq97v1MgSSNGjKjyuiSlpqZq8eLFys3N1SWXXOJXbkhIiHMW+pxzztGIESMknbwLj3TyLLH3TNiIESOcv7t06VJrnZXb7uU9qNZ0DX114d9bV01O9XpdeNe3JaqoqAh4HaOSBugPt10Q8Hps5fF4tGPHDsXHx9f7TP7di3OUNKi/BvVoH8AWnlpkfqS0U+qX0E/ndTqvSesO21sgrcrS76cPVt8uVa/Drqyh/Y2qFkVk67efV/2f0v3799e6XM+ePbV+/XpJJ8cW3zPsAwYMUGJiojN+eccoSc5Y7f23puP7Oeeco/Xr1ztjyql4xyrfMdP7AcE77kknx0RvyPcd37zL1TbmNQW3262cnBwNHjy4ed+Nphq+uaO6Pgx0HxcVFTX8hGzjXjmEU0lISDBr1641mzdvNkOGDDGFhYVm2bJl5pZbbjHGGDNz5kznmvx58+aZWbNmVVtOYWGhKSoqcp4XFxeb4cOHm1WrVhljTl7f/9prr9XYDq7J55p8cU0+ToFr8k8P1+QHD9fkc01+Y2nJ1+QT8puYN+QbY8wVV1xh3njjDTNr1iznS7S+IX/58uVm0KBBZv/+/VXKmT9/vpkwYYLxeDzOtMcee8zcfvvtZv369eb888/3+xBQWVPcQnPp0qXOASstLc2sWLHCrFixwowaNcpIJ28TdqpbT82ZM8dIMt26dTPPPfec2bt3r7nlllucckePHm1cLpdJS0sz/fv3N5L8gvMtt9xiJk6caFwul0lJSalTnZXb7n2c6jZsNb3+0EMPOW3zPiIjI/3+5dH4D26jefoI+aeHkB9c3vHDN2jX9fhx7rnn+j1PTk42a9asMRkZGX7jyNKlS51jv/c4n5CQUGvZbdu2rTLt8ssvr/a20Oeff77p27ev8/yss84ykkxKSopZs2aNOXbsmJk/f77z+vz5882xY8eqbWswtfT3tnc7Z2RkOP3eVH1MyG9BfEP+woULzaRJk0xaWppzpto35Hs8HnPjjTeajIwMs2HDBuN2u01hYaF58cUXzcCBA82KFSv8yt6zZ48ZOnSo+dnPfmYef/zxWtvRGu+THxcXx33yW8mDgN84CPmnh5AffI19n/zqxpGmvE9+XFxctffJ79q1a5U21HfMCyQb3tvV3Se/KfqYu+u0UOnp6Xrsscd0/fXXV/slRJfLpT/96U96/vnn9Ytf/EL79+9XWFiYBgwYoAULFuiCC/yvNe7Ro4dGjRqlDz74QB9++GFTrUatpkyZossvv/y0fvH28ccf169//esm/8Vb37bzi7ct5xdvP/nkE1144YV13s4A7PX4449rypQpuuCCCzRkyJCA/OJt5XGuKX7x9pFHHuEXb5uYdzu3pD52GePzqwtoNYqKirR582add955AfnibWvidruVnZ2txMTEZr2z26Km/v788881bNgwbdiwQcnJyUFsoT0a+t7+cm+B0p/+tzJvTwv6F2+/zv9aMzJn6PX01zWg04BTL9CI6tsPHEsCo7pjA33dtOjvhjudvMbX9wFYoUOHDgoJCWl2tzADEFwcG9BacbkOACv06dNHBw8erPNtZwG0Dhwb0FpxJh+ANRjEAVSHYwNaI0I+AAAAYBlCPgAAAGAZQj4AAABgGb54CwAIiC/3FgS7Cfrm2HFJ0s5Dx+Upadr27Dh0vEnrAwBfhHwAQKOq8Jz8+ZV5b+YEuSWSKzxfbfq6dMerW2TK84LShjaRDLUAmh5HHgBAo0rs2UFvzR6lsBBXsJsiSTpWNkbtxncISt1tIsMU17lNUOoG0LoR8gEAjS6xZ4dgN8FHcH91FwCCgS/eAgAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYh5AMAAACWIeQDAAAAliHkAwAAAJYJC3YDEBwej0eSVFxcHOSWtHxut1uSVFRUpNDQ0CC3xn70d9Ohr5sW/d106OumRX83nDeneXNbfbiMMaaxG4TmLz8/X7m5ucFuBgAAAE4hNjZWnTp1qtcyhPxWqqKiQgUFBYqMjFRICFdtAQAANDcej0elpaVq3769wsLqdwEOIR8AAACwDKdwAQAAAMsQ8gEAAADLEPIBAAAAyxDygXo4evSo5s6dq9TUVJ1//vm67bbbdOjQIUnSpk2bNH36dCUlJWncuHFavHhxkFvbcv3jH/9QUlKS32PQoEEaNGiQJPo6EL766itdd911SklJUVpamn7961+rrKxMEv3d2Hbu3Kkbb7xRKSkpGjt2rP785z87t8ejrxvPkSNHNH78eK1bt86Zdqr+XbZsmcaPH6/ExERNmTJFGzdubOpmt1jV9bckbdy4UYMHD64yP33dBAyAOps5c6aZPXu2KSgoMIWFheanP/2p+clPfmKOHj1qhg8fbhYtWmTKy8vNmjVrTFJSktm0aVOwm2yFAwcOmFGjRpm33nqLvg4At9ttRo0aZf72t78Zt9tt9u/fbyZMmGCeeeYZ+ruRHT9+3IwdO9Y88MAD5sSJE2bPnj0mPT3dPP300/R1I1q/fr255JJLTEJCglm7dq0xxpyyf9euXWuSkpLM+vXrTVlZmXnxxRdNamqqKSoqCuaqtAjV9bfH4zGLFy82iYmJJiEhwW9++rppcCYfqKMvv/xSmzZt0qOPPqp27dqpbdu2+tWvfqV77rlHH3zwgTp06KDrrrtOYWFhuuCCC5SRkaFXXnkl2M1u8YwxmjNnjsaOHavLL7+cvg6AgoIC5eXlyePxyPzfDddCQkIUHR1NfzeyDRs2KD8/X7/4xS8UExOjHj166NZbb9Xf//53LV++nL5uBMuWLdM999yju+66y2/6qd7Lixcv1uTJkzVs2DCFh4frhhtu0Jlnnqn33nsvGKvRYtTU3/fff78WL16sO+64o8oy9HXTIOQDdfTFF18oPj5eb7zxhsaPH6+0tDQ99thj6tKli7Zv366EhAS/+ePj47Vly5YgtdYeb7/9tnbs2KF58+ZJEn0dAGeeeaZuuOEGPfbYYxo8eLDGjBmj2NhY3XDDDfR3I/N4PAoPD1d4eLgzzeVy6fDhw9q4cSN93QjS0tK0YsUKTZo0yW/6qd7LO3bsoP8boKb+/tnPfqbXX39dAwYMqLIMfd00CPlAHRUUFGjr1q3Kzc3VsmXL9NZbb+ngwYO69957deLECUVHR/vNHxUVpaKioiC11g4ej0d//vOfdcstt6ht27aSRF8HgMfjUVRUlB588EFlZ2crMzNTO3fu1FNPPUV/N7Lk5GRFRUXpd7/7nYqLi7V371799a9/dV6nr09fly5dqv3RoFO9l3mvN0xN/X3WWWfVuAx93TQI+UAdRURESJIeeOABtW3bVp07d9add96pTz75RMYYlZSU+M1fUlKiNm3aBKOp1li3bp0OHTqkadOmOdOio6Pp60a2YsUKLV++XNdee60iIiJ07rnnavbs2fr73/9Ofzeydu3aacGCBdq0aZPGjh2rO++8U1dccYUkKTQ0lL4OoFO9l3mvNx36umkQ8oE6io+Pl8fjUXl5uTPNe0eM8847T9u3b/ebf8eOHTr33HObtI22Wb58ucaPH6+YmBhnWkJCAn3dyPbv3+/cSccrLCxM4eHh9HcjKysrU0VFhRYuXKh169Zp8eLFCgkJUXx8vIYMGUJfB9Cp3svnnnsu/d9E6OumQcgH6mjkyJHq2bOn7r//fp04cUJHjhzRk08+qUsuuUTp6ek6fPiwXnrpJZWXl2vt2rV65513NHXq1GA3u0XbsGGDzj//fL9p48ePp68bWVpamvLy8vSXv/xFbrdb3333nf785z8rIyOD/g6AG2+8UUuWLJExRl9++aX+8pe/6Prrr6evA+xU/Ttt2jS98847Wrt2rcrLy/XSSy8pPz9f48ePD3LL7UNfN5Hg3twHaFkOHDhg7rzzTjNq1CiTkpJi5s6dawoKCowxxnzxxRdmxowZJikpyVx88cVm6dKlQW5ty5eYmGhWrlxZZTp93fj+85//mOnTp5thw4aZsWPHmt///vemtLTUGEN/N7ZPP/3UXHnllSYxMdFcfPHFZuHChc5r9HXj8r2lozGn7t+33nrLTJgwwSQmJppp06aZ7Ozspm5yi1a5v405ebvMyrfQNIa+bgouY/7vfmkAAAAArMDlOgAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZQj5AAAAgGUI+QAAAIBlCPkAAACAZcKC3QAER0VFhQoKChQZGamQED7rAQAANDcej0elpaVq3769wsLqF9sJ+a1UQUGBcnNzg90MAAAAnEJsbKw6depUr2UI+a1UZGSkpJNvmujo6EYr1+12a9u2bUpISFBoaGijlYumx7a0B9vSLmxPe7At7RGobVlcXKzc3Fwnt9UHIb+V8l6iEx0drZiYmEYr1+12S5JiYmI4YLVwbEt7sC3twva0B9vSHoHelg25tJqLsQEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLEPIBAAAAyxDyAQAAAMsQ8gEAAADLhAW7AQDQVL45fEInSiuC3Ywm53a7tev7coXtLVBoaGiwm1OjY2VH1S6iQ7Cb0ewFenu2iQxTXOc2jV4ugKZFyAfQKnxz+IQu+u3KYDcjuD7MCnYLauQKz1ebvr/ViZ33yJR3CnZzWoYAbs9/3TOWoA+0cIR8AK2C9wz+H2YkKr5r2yC3pmm53W5t27ZNCQkJzfZM/jfHtur+T42eura/4tr1C3ZzmrVAbs8dh47rztezW+X/eAG2IeQDaFXiu7bVoB7tg92MJuV2u1WRF65BPdo325AfEnXyg1ffrm01oFPr2j711RK2J4Dg44u3AAAAgGUI+QAAAIBlCPkAAACAZQj5aDKHDx8OdhMAAABOS0vJM4R8NIldu3apW7du2rVrV7CbAgAA0CAtKc8Q8tEkjh49Ko/Ho6NHjwa7KQAAAA3SkvIMIR8AAACwDPfJbwZeeeUV/c///I/uu+8+3XDDDX6vFRcX64UXXtD777+vvXv3yhijvn37asqUKbrmmmvkcrkkSePGjVNeXp7Cwqpu0gULFiglJaUpVgUAAADNACG/GXjllVd0zTXXaOHChZo5c6YT1IuKinT11VcrJiZGv/zlLzVw4EAZY5STk6MHHnhA+/bt0z333OOU8/DDD2vKlCnBWg0AAAA0E4T8IMvKylJ+fr7mzZunlStXavny5Zo8ebIk6bnnntOJEyf02muvKSYmxllm+PDheuyxx/T555+fdv1ut1tut/u0y/Etz/ffytO37S9Q6O4jjVYfAsfj8WjX9+UK+e57hYS0/Cv7duYdl9T47/mWoKb9sjnxuD3Ov825nc1BILenc6w+UMB2aAK2HWdbg237CyRVHUsCtV+eTnmE/CB7+eWXddVVVykqKkrXXnutXnjhBSfkv/fee/rBD37gF/C9kpOTlZycfNr1b9u27bTLqE5OTo7f88+/3CJJumvxF4pcXRSQOhEgH+YHuwWNauOXW1SRFxnsZgRF5f2yOcktzpUkbd22VaXRpcFtTAsRiO2Zc/Bk39+9uPm+V6xk2XHWZqUHdkg6mWuq+2DWnI6zhPwg2rt3r1avXq1f/OIXkqSrrrpKzz77rD799FMNHz5cBw4c0FlnneXMX1ZWppEjR0qSjDEqKyvT+++/rx49ekg6ebnO/Pnz/eo4++yz9c4779TYhoSEhGo/RDSU2+1WTk6OBg8erNDQUGe6x3PyLN2T04fovMFDG60+BI7H49GOHTsUHx9vxRmmnXnHdffiHCUN6q9BPdoHuzlNqqb9sjmJzI+Udkr9EvrpvE7nBbs5zVogt2fY3gJpVZZ+P32w+nZp26hloyrbjrOtweacGF3zNyl5UH8lJiY60wO1XxYVFTX4hCwhP4heffVVVVRU6PLLL3emVVRU6IUXXtDw4cPVpUsXHTx40HktIiJC69evlyTt2bNHF198sYwxzusPPfRQva/JDw0NDcigX7lc798JZ7fX0F4dG70+ND632y1PfriG9Dyz2QbD+vCuQ6De8y1Bc173kNAQ59/m2sbmJhDb0zlWn9W+1X0YDgbbjrOtgfvwyf2ipv2vsffL0ymLkB8kpaWlWrJkiX7zm984Z+elk5fP/OQnP9HOnTs1YcIEZWZm6qabblJ0dHQQWwsAAICWhP8bCpJ33nlHLpdLGRkZOuuss5zHhRdeqISEBL300kv66U9/qjZt2ujGG2/U559/LrfbrYqKCmVlZWnOnDk644wzCP8AAACogpAfJK+++qoyMjIUHh5e5bUZM2bo7bffVklJiV5//XWNGzdOv/71rzVixAidf/75mj9/voYPH673339fnTp1cpZ76KGHlJSUVOWxYMGCplw1AAAABBmX6wTJm2++WeNr1113na677jrn+Y9//GP9+Mc/rrW8jz/+uNHaBgAAgJaNM/loEh06dFBISIg6dOgQ7KYAAAA0SEvKM5zJR5Po06ePDh48qM6dOwe7KQAAAA3SkvIMZ/LRZFrCDgEAAFCblpJnCPkAAACAZQj5AAAAgGUI+QAAAIBl+OItgFbly70FwW5Ck3O73dr1fbnC9hY06s+tN6Zvjh2XJO08dFyekta3jeojkNtzx6HjjVoegOAh5ANoFSo8RpI0782cILckiD7MCnYLauQKz1ebvi7d8eoWmfK8YDenZQjg9mwTSTwAWjr2YgCtQmLPDnpr9iiFhbiC3ZQm53a7tW3bNiUkJDTbM/mSdKxsjNqN7xDsZjR7gd6ebSLDFNe5TaOXC6BpEfIBtBqJPTsEuwlB4Xa7VZEXrkE92jfrkC+1D3YDWoSWsz0BBBNfvAUAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsQ8gHAAAALEPIBwAAACxDyAcAAAAsExbsBiA4PB6PJKm4uLhRy3W73ZKkoqIihYaGNmrZaFpsS3uwLe3C9rQH29IegdqW3pzmzW314TLGmEZrCVqM/Px85ebmBrsZAAAAOIXY2Fh16tSpXssQ8lupiooKFRQUKDIyUiEhXLUFAADQ3Hg8HpWWlqp9+/YKC6vfBTiEfADA/9/e/YU0ucdhAH8SHa5UVJK6EaQ2NSLKXI7aIBBXkBqhRpBK86KCGVE0NVrdVMa8CC0iiwotjCADE0ekXkQ3NnVk9gcsNUpBWc6xlc4/a/udm9NgnHNY5xxxvuP53L3f9734wsM7Hsbed0REFGH4FS4RERERUYRhySciIiIiijAs+UREREREEYYln5bM9PQ0DAYDVCoV1Go1amtr8fPnz3CvRb9haGgIFRUVyMnJgUajQXV1NZxOJwBgcHAQBw8eRFZWFnJzc9Ha2hrmbel3+Hw+lJeX4+zZs4EZs5QWl8uF6upqqNVq7NixAwaDAd++fQPALKXmw4cPKC0thUqlglarxeXLl7G4uAiAWUqF0+mETqdDb29vYBYqu7a2klbsHgAABYtJREFUNuh0Omzbtg1FRUUYGBhY3qUF0RIpKysTZ86cER6PR4yNjYn8/Hxx586dcK9FIczNzQmNRiOuXbsmFhYWhNPpFEePHhXHjx8XLpdL5OTkiJaWFuH1ekVPT4/IysoSg4OD4V6bQmhoaBCZmZmipqZGCCGYpQSVlZWJyspK4Xa7xY8fP8SJEyfEsWPHmKXE+Hw+odFoxP3794XP5xOTk5Ni79694saNG8xSImw2m8jLyxPp6enCarUKIUJ/plqtVpGVlSVsNptYXFwUTU1NQq1WC4/Hs2x785t8WhJfv35FX18fqqqqIJfLkZqaCoPBgIcPH4Z7NQphYmICmZmZqKyshEwmQ1JSEg4dOoT+/n50dXUhMTERpaWliI6Oxs6dO1FYWMhcV7hXr16hq6sLe/bsCcyYpbS8f/8eg4ODMJvNSEhIQFxcHC5dugSj0cgsJcbtdmNqagp+vx/izxcaRkVFQS6XM0sJaGtrg9FoxOnTp4PmobJrbW1Ffn4+srOzERMTA71ej6SkJDx79mzZdmfJpyUxPDyMxMRErFu3LjDbuHEjJiYm8P379zBuRqFs2LABd+/eDfqHvs7OTmzevBnDw8NIT08Pul6hUGBoaGi516TfND09DZPJhKtXr0IulwfmzFJa3r59C4VCgcePH0On00Gr1aKurg4pKSnMUmKSkpKg1+tRV1eHLVu2YPfu3UhLS4Ner2eWEqDVatHd3Y19+/YFzUNlNzIyEvZsWfJpSczOzgYVCgCBY4/HE46V6D8QQqC+vh4vXryAyWT621xjY2OZ6Qrl9/tRVVWFiooKZGZmBp1jltLidrvx8eNHfPnyBW1tbXj69CnsdjtqamqYpcT4/X7ExsbiwoULePPmDSwWC0ZHR3H9+nVmKQEpKSl/+ydUobJbCdmy5NOSWL16Nebm5oJmv47XrFkTjpXoX5qZmcHJkyfR0dGBlpYWZGRkQC6XY35+Pui6+fl5ZrpC3b59GzKZDOXl5X85xyylRSaTAQBMJhPi4uKwdu1anDp1Ci9fvoQQgllKSHd3Nzo7O3H48GHIZDIolUpUVlbi0aNHvC8lLFR2KyFblnxaEkqlEi6XCw6HIzAbHR3F+vXrER8fH8bN6HeMjY2huLgYMzMzePLkCTIyMgAA6enpGB4eDrp2ZGQESqUyHGtSCO3t7ejr64NKpYJKpYLFYoHFYoFKpWKWEqNQKOD3++H1egMzv98PANi0aROzlJDJycnAm3R+iY6ORkxMDO9LCQuVnVKpDHu2LPm0JNLS0pCdnY0rV65gZmYG4+PjuHnzJkpKSsK9GoXgdrtx5MgRbN++Hffu3UNycnLgnE6ng8PhQHNzM7xeL6xWKzo6OlBcXBzGjemfPH/+HK9fv4bNZoPNZkNBQQEKCgpgs9mYpcTs2rULqampOHfuHGZnZ+F0OlFfX4+8vDwUFBQwSwnRarWYmprCrVu34PP5MD4+jsbGRhQWFvK+lLBQ2ZWUlKCjowNWqxVerxfNzc2Ynp6GTqdbth1XiV+PehP9Tw6HAxcvXkRvby+ioqJw4MABGI3GoAc6aeVpamqC2WyGXC7HqlWrgs4NDAzg3bt3qK2txadPn5CcnAyDwYCioqIwbUv/xq935JvNZgBglhJjt9thNpvR39+PhYUF5ObmwmQyISEhgVlKTE9PDxoaGvD582fEx8dj//79gTeaMUvpyMjIwIMHD6BWqwGE/kxtb29HY2Mj7HY7FAoFzp8/j61bty7bviz5REREREQRhj/XISIiIiKKMCz5REREREQRhiWfiIiIiCjCsOQTEREREUUYlnwiIiIiogjDkk9EREREFGFY8omIiIiIIgxLPhERERFRhGHJJyIiIiKKMCz5REREREQRhiWfiIiIiCjC/AEIPdWnhaVDWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x2500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot for each numerical feature\n",
    "\n",
    "fig, axes = plt.subplots(9, 1, figsize=(8, 25))\n",
    "for i, c in enumerate(num_features):\n",
    "    f = data[[c]].boxplot(ax=axes[i], vert=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Density plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:37.438602Z",
     "iopub.status.busy": "2021-05-21T04:41:37.438277Z",
     "iopub.status.idle": "2021-05-21T04:41:39.741107Z",
     "shell.execute_reply": "2021-05-21T04:41:39.739734Z",
     "shell.execute_reply.started": "2021-05-21T04:41:37.438573Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x2500 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Kernel Density Estimation plot for each numerical feature\n",
    "\n",
    "fig, axes = plt.subplots(9, 1, figsize=(8, 25))\n",
    "for i, c in enumerate(num_features):\n",
    "    f = data[[c]].plot(kind='kde',ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "- Few outliers are present in some numerical features\n",
    "- Most of the features follows the Gaussian distribution (approx.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1.3 Categorical feature(s)\n",
    "name : SEX<br>\n",
    "labels : F, M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:39.74547Z",
     "iopub.status.busy": "2021-05-21T04:41:39.745125Z",
     "iopub.status.idle": "2021-05-21T04:41:39.953222Z",
     "shell.execute_reply": "2021-05-21T04:41:39.952011Z",
     "shell.execute_reply.started": "2021-05-21T04:41:39.74544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar and count plot for Categorical feature\n",
    "fig, axes = plt.subplots(1, 2, figsize=(20, 6))\n",
    "df = data['SEX'].value_counts()\n",
    "barplot = df.plot(kind='pie', ax=axes[0], title='SEX', autopct=\"%.2f\", fontsize=14, ylabel='')\n",
    "countplot = sns.countplot(x='SEX', data=data, ax=axes[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.2 Bivariate analysis\n",
    "\n",
    "### 6.2.1 Numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### KDE plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:39.954889Z",
     "iopub.status.busy": "2021-05-21T04:41:39.954601Z",
     "iopub.status.idle": "2021-05-21T04:41:41.87552Z",
     "shell.execute_reply": "2021-05-21T04:41:41.874626Z",
     "shell.execute_reply.started": "2021-05-21T04:41:39.954862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x2200 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDEplot for each numerical feature w.r.t target\n",
    "\n",
    "fig, axes = plt.subplots(5,2, figsize=(14,22))\n",
    "axes = [ax for axes_row in axes for ax in axes_row]\n",
    "for i,c in enumerate(data[num_features]):\n",
    "    plot = sns.kdeplot(data=data, x=c, hue='SOURCE', multiple='fill', ax=axes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:41.877378Z",
     "iopub.status.busy": "2021-05-21T04:41:41.876961Z",
     "iopub.status.idle": "2021-05-21T04:41:43.159542Z",
     "shell.execute_reply": "2021-05-21T04:41:43.158322Z",
     "shell.execute_reply.started": "2021-05-21T04:41:41.877346Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x2400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relationship between target and mean of each numerical features\n",
    "\n",
    "fig, axes = plt.subplots(5,2, figsize=(14,24))\n",
    "axes = [ax for axes_row in axes for ax in axes_row]\n",
    "for i,c in enumerate(data[num_features]):\n",
    "    df = data.groupby(\"SOURCE\")[c].mean()\n",
    "    plot = df.plot(kind='bar', title=c, ax=axes[i], ylabel=f'Mean {c}', color=('orange','skyblue'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "- On average, The quantity of HAEMOTOCRIT, HAEMOGLOBINS, ERYTHROCYTE, and THROMBOCYTE features are lesser in In care patients.\n",
    "- MCH, MCHV, MCV are less informative to differentiate classes\n",
    "- The mean age of Incare patients is greater than the Outcare patients."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation - matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:43.16168Z",
     "iopub.status.busy": "2021-05-21T04:41:43.161236Z",
     "iopub.status.idle": "2021-05-21T04:41:44.071759Z",
     "shell.execute_reply": "2021-05-21T04:41:44.070846Z",
     "shell.execute_reply.started": "2021-05-21T04:41:43.161636Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matt = data[num_features].corr(method='pearson')\n",
    "plt.figure(figsize=(8,8))\n",
    "corr = sns.heatmap(corr_matt, annot=True, cmap='Greens', cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.073532Z",
     "iopub.status.busy": "2021-05-21T04:41:44.073243Z",
     "iopub.status.idle": "2021-05-21T04:41:44.226903Z",
     "shell.execute_reply": "2021-05-21T04:41:44.225722Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.073505Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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j/X/SNWvW4OTkVBVLERXs1vVbbFkSw6/fnsPA0ACn7h3wHuWBoaHhA885lXyauNXxhHw6Re/Y9hU7uXO7kNfff60ypy2qkIT4YyAqKopBgwaxceNGXn/9dW0QFxQU8Nprr1GjRg1mzpxJmzZt0Gg0fPfdd0ydOpXLly8zYcIEbT+hoaH07du3upYhKsH62Z9iZVOL2VtncPPaLdZMX8cXMcl0G9hVr7astIxDMcnEr9uDlU0tnWO3b9xm+0dxnDhwko4vyxP6v4lsp1Szo0ePkpubS3BwMGq1mr1792qPRUREcPv2bdatW0eHDh0wMzPD3Nycjh07smDBAqysrKpv4qLS5WT+zq/fnsPLrzcmZibYNKhDz9d7kLzzSLn1H01azS+nfqXHIHed9qI7RcwZvgBzSzPadn62KqYuqpBciVezTZs2MWDAAMzMzBg8eDDr1q2jd+/eACQkJNCnTx9q1Kihd1779u1p3759VU9XVKErF69So2YNat1zVW33VD2uZ+dRkH+HGpbmOvVDJw/C2taKY4nf6LQbmRgx+ZOJPFG7Jp8u2FIlcxdVR67Eq1FmZiYpKSkMGTIEgAEDBvDrr79y/PhxAK5evYqdnZ22vri4GCcnJ5ycnOjQoQPPPvssmZmZ2uOhoaHa43/cPD09q3ZRosIUFRRham6i02ZsZnz32J0ivXprW6ty+zE0NOSJ2jUrfH7i8SBX4tUoOjqa0tJSvLy8tG2lpaWsW7eOjh07YmtrS1ZWlvaYiYkJJ06cACAjI4Nu3bqh0Wi0x0NCQmRP/F/ExNyE4sJinbaSwhIAzMzlq/XEXRLi1aSoqIiYmBjmzp3Liy++qG3/+eef8fPz49y5c/Ts2ZP4+HjefvttzM3NH9Kb+Deq/7Qdt28WcPPaLe2V9NX0LKxsa2FuKT8P4i7ZTqkmu3fvRqVS4enpiZ2dnfbWpUsXWrZsSWRkJAEBAVhYWDBixAhOnjxJWVkZpaWlHD16lIkTJ1KzZk0J93+xuo1safpME2I/jqOwoJDcK7ns/XQ/nV7pWN1TE48RCfFqEh0djaenJ8bGxnrHBg4cSFxcHIWFhXz22We4u7szZ84cOnXqxPPPP09YWBgdO3YkMTGROnXqaM8LCQnB0dFR77ZmzZqqXJqoQG+FvIG6TE3okDA+CFhOq+cd6PV6DwAm9J7CN0knq3mGorqpNPduqor/lL0Zu6t7CkKIB+jZ6NE+lCBX4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWDy9Wz/YTZmNtU9BSHEPyRX4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4hXA3t4ee3t7zp8/r3ds/fr12NvbEx4erm1Tq9VER0fj6+uLk5MTzs7ODBs2jKNHj2prjh07hr29fbnjDR06VKc/gM8//5yhQ4fi7OzM888/z8CBA0lMTKygFYqqcOP6TT4IXsbInu/g92oAGz+Moqy0rNzatK++5f2h03izmx8TBk/m5JFT5dZt+DCKVXPW6LRlZWazYPwS3u71LqN6B/Lx7NXcvnW7opcjqoiEeAWxtrZmx44deu2xsbFYWlpq72s0GgIDA9m8eTPBwcF8/fXXpKSk4OHhwahRozhw4MBfHnvOnDksXLiQkSNHkpKSwtGjR3n77beZOnUqUVFR/2hdouqET/8YM3MzPor7kNlrZvD9iR9J+GyvXt2V367y4dQV+L7tw9q9K/Ed4cPy6R9zLee6tubWjXw+Co1g77b9euevmLmKRk0asDJ+GYs3z+P3q7lErdhSqWsTlUdCvIJ4enoSFxeHWq3Wtp0+fZri4mJat26tbUtMTCQ5OZmIiAicnJwwMjLCxMSE/v37ExgYyLlz5/7SuKdPn2bTpk0sX74cV1dXTExMMDIyonv37kyfPp309PQKW6OoPFczsvgx7ScGjx6AqZkp9RrWxWd4H/Zv139ST9lzBIe2LXm+SwcMjQzp1K0jrRztORj3BQCFBYWMHxRMDcsadHRz0jv/8sXLaDQaNGoNGg0YqFSYmJpU9hJFJZEvhaggbm5uxMfH89VXX+Hi4gJATEwMvr6+JCcna+sOHjxI+/btadCggV4fI0eO1GtzctL/JSwoKKBjx47a/ho3bkzbtm316ry9vfH29v67SxJVKONCJpZPWGBta61ta9ikAb9n5XL71m0salro1DZu1kjn/IZPNyD910sAGJsYs+jTudSqXUtvKwWg7whvtqzcRuK2/ajL1DRv04xBowdU0spEZZMr8QpiZGSEp6endkulsLCQvXv36oXotWvXsLF59G/UOXHihN6tQ4cOf7s/8XgqLCjE1NxUp83U9O79wjtFOu13CgoxNdOtNTE10dYZGhlSq3atB45loFLhM7wPa/euZNn2xQB8sjDyny5BVBMJ8QrUt29fkpKSyM/PJzExkfbt22Nra6tTU7duXXJycso9Pz8/nzt37vylMR/WX1FREbdu3fpL/YnqYWpmSlFhsU5bUdHdUDavYabTbmZmSnGRbm1xUbFeXXnO/3SRrWti8RrqgZm5KbZ2NgwJeI0j+76m4PZf+9kTjwcJ8Qrk4OBA06ZN2bNnD7Gxsfj6+urVdO3albS0NK5evap3LDw8HB8fHzQazSOP6ebmRkZGBqdPn9Y79tlnn+Hu7v6XnxhE1WvUtCH5N/K5ce2Gti3zwmVq161NDcsaerUZFzJ12jIvXqZRk4Z/Ok5uVi7qMrXOezeGRoagAkNDiQMlkn+1Cta3b18iIyO5cOECrq6uesd79OiBs7Mzfn5+nDx5ErVaTX5+PpGRkURFRTFhwgRUKtUjj/fMM88wcOBA3nvvPZKTkyktLaWoqIi4uDiWLFnCmDFjMDc3r8glikpQv7Ed9s+1ZOOyaO7cvkP25Rx2RO7CzaOzXq1Lrxf58eRPfH3gOGWlZXx94Dg/nvyJzr1e+tNx7J9rgamZCZuWb6a4qJgb12/y2aptPO/aQW+LRiiDvLFZwTw8PFiwYAHDhg3DyEj/4VWpVHz88cesXbuWGTNmcOXKFYyMjGjdujVr1qzhhRde+MtjhoaGEh0dzYcffsj48ePRaDQ0b96cBQsW0LNnz4pYlqgCQXPfZf2STbzXfyIGKhWdX3mJvsO9AHizuz8jJg7DpeeLNHyqAePmj2HLx1tZPW8dNnZ1CJobQP0n7f50jCesnyB46UQ2r9zKu15jMTE1pr2Lo7yxqWAqzV957S7+VVJ/P/rnRUKIatHB5tEu6GQ7RQghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFEz+nvh/mKmhfAmAEEonV+JCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJCCKFgEuJVzN7eHnt7e86fP693bP369djb2xMeHq5tU6vVREdH4+vri5OTE87OzgwbNoyjR49qa44dO4a9vX254w0dOlSnP/H4S/0qjbFDJjHYbThjBo7nxJcnH1hbVqZmQ3gUb73izxD3N5k/cTHXf7+uV3fj+k3e9Q3i+9Qfddr3xOzjXd+xDOk6nHd9x5KwbW+Fr0dULgnxamBtbc2OHTv02mNjY7G0tNTe12g0BAYGsnnzZoKDg/n6669JSUnBw8ODUaNGceDAgaqctqgCly9dYfHkpbzm159NSZ8w8G1fPpi6jNzsa+XWb18fy7fHTrMwMow1uz/CxNSEj8NW69T89O1Zprw9g6sZWTrt36SksmX1VsbNCSTqUCRjZwWwaUUU36X+UGnrExVPQrwaeHp6EhcXh1qt1radPn2a4uJiWrdurW1LTEwkOTmZiIgInJycMDIywsTEhP79+xMYGMi5c+eqY/qiEn2RkEyrtg44uz6PoZEhL3V/gTaOrdgfV/4TdtKuQ/gM7YNNvTrUsKjBW2PfIO3ot1zNvBvYhz4/zNKQFQz2H6h37vOdO7BqRzjNHJpSVlrGzbxbgAoLyxqVuURRweRLIaqBm5sb8fHxfPXVV7i4uAAQExODr68vycnJ2rqDBw/Svn17GjRooNfHyJEj9dqcnJz02goKCujYsWMFzl5Upt8uZPBks8Y6bY2aNOTiL5f0am/nF5CbfU2n3qqOFRY1LUj/9RJ2DevRrlNbuvR0wdDIkCXTl+v1YW5hTmb6ZYIGT0RdpsZz0Ks0tW9S8QsTlUZCvBoYGRnh6enJjh07cHFxobCwkL179xIfH68T4teuXcPGxuaR+z1x4oRe29ChQytkzqJq3LldiKm5mU6bqZkphQWFerWFBXfuHjc3va/eRFtvXcfqT8es17Aumw9v4OIv6SyY9AG1rGvh80afv7kCUdUkxKtJ3759GThwIPn5+SQlJdG+fXtsbW11aurWrUtmZma55+fn52NoaIi5uXlVTFdUku2RO4ndsFN7v0Wb5hQXFunUFBUWYW5hxv1Mze6Gd3Fh8X31xZhbPPrPhZHR3Rho3qoZvQf0InnvEQlxBZE98Wri4OBA06ZN2bNnD7Gxsfj6+urVdO3albS0NK5evap3LDw8HB8fHzQaTVVMV1SSfsO9iToUqb21aNOC385n6NRkXMjkyaaN9c61fMKS2ra1deqv5+aRfzO/3Pr77d6cwAdTl+m0lZSUYvmE5QPOEI8jCfFq1LdvXyIjI7lw4QKurq56x3v06IGzszN+fn6cPHkStVpNfn4+kZGRREVFMWHCBFQqVTXMXFQW11dc+CHtR44kHaWstIwjSUf5Ie1HXF/pXG69u4crMZE7yLqczZ3bd1i/dCNtHFth16jen47V2tGB48knOJJ0FLVazU/fnuXzz/bQs2/3il6WqESynVKNPDw8WLBgAcOGDdO+pL2XSqXi448/Zu3atcyYMYMrV65gZGRE69atWbNmDS+88EI1zFpUpkZPN2TSgvF8+tFmPg5bja2dDRPmjaXBk/UBSE78kogFa4k6FAlA/xF9KS0tY5p/KIUFd2jToQ3j5773SGM1c2jKhLAgNkdsZWXYGmzr2/DW2GG81F1+rpREpZHX4/9Z319/8H8iEUJUr2es2z9SnWynCCGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgkmICyGEgsmXQvyHqZBvBRJC6eRKXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCXAghFExCvIrZ29tjb2/P+fPn9Y6tX78ee3t7wsPDtW1qtZro6Gh8fX1xcnLC2dmZYcOGcfToUQCuXLlC69atOXDgQLnjvfbaa8ydO7dyFiMqRepXaQQNmcggt2EEDhzHiS9TH1hbVqZmQ/invPmKP4PdhzNv4iKu/X5de/zCL+nMDJzLGy+P4K1X/VkW+hE3825qj++J2cu7vkEM7jqMd32DSNiWWKlrExVPQrwaWFtbs2PHDr322NhYLC0ttfc1Gg2BgYFs3ryZ4OBgvv76a1JSUvDw8GDUqFEcOHCA+vXr0717d7Zt26bX308//cS3337L0KFDK3U9ouJcvnSFRZOXMMhvAJ8mreO1t/uzeOoycrOvlVsfsz6WU8dOsyhyLmt3f4yJqQkfh60GoKiwmDlj5+PwXEs++TyCZZsXc+tGPitmrwLgm5RUNq/eyrg5Y4g+tIGgWYFsXBHFd6k/VNl6xT8nIV4NPD09iYuLQ61Wa9tOnz5NcXExrVu31rYlJiaSnJxMREQETk5OGBkZYWJiQv/+/QkMDOTcuXMAvP766yQnJ5OVlaUzTnR0NK6urjz55JNVszDxj32RkEyrtg44uz6PoZEhL3V/gTaOrdgfV/4rraRdB/EZ2gebejbUsKjBiLHDSDt6iquZWfye9TtPN3+S/m/1w9jYiJq1avKydzd+PHUGgOc7dyBixwqaOTSlrLSMW3k3ARUWljWqcMXin5IQrwZubm6UlJTw1VdfadtiYmLw9fXVqTt48CDt27enQYMGen2MHDkSPz8/ADp27Ejz5s2JiYnRHs/Pz2f37t288cYblbQKURkuXcjgyWa6T7qNmjTi4i/perW38wvIzb7GU/fUW9WxwqKmBem/XqLhUw2Y/uFkDA3/92t+9NAxmjo01d43tzAnM/0yA12HMmfcAnr27UFT+yaVsDJRWSTEq4GRkRGenp7aLZXCwkL27t2Lt7e3Tt21a9ewsbF5pD6HDh3K9u3btVf3cXFxNGzYkBdffLFC5y4qV+HtO5iZm+q0mZqZUFhQqFd7p+DO3eOPUK/RaIhe9RknvjzJiLHDdI7Va1iXLYc3snD9XI4kfUXsxriKWIqoIhLi1aRv374kJSWRn59PYmIi7du3x9bWVqembt265OTklHt+fn4+d+7c0d739PTk9u3bHDlyBIAtW7bIXrgCxETuYHDXYdqbBg1FhUU6NUWFxZhZmOuda2Zmqj1+f735PfUFtwtYNHkphxNTmLMyhKea617pGxkZYWRkRPNWzeg94BVS9h2pqOWJKiAhXk0cHBxo2rQpe/bsITY2Vm8rBaBr166kpaVx9epVvWPh4eH4+Pig0WgAMDMzo1+/fmzbto3U1FSysrLo06dPpa9D/DO+w32IPrRBe2vZpgW/nc/Qqcm4kMGTTRvpnWv5hCW1bWvz2/nftG3Xc/PIv5mvrb+acZVJb06l4PYdFkWG6QT47s2fs3jqhzp9lpSUUPMJS4RySIhXo759+xIZGcmFCxdwdXXVO96jRw+cnZ3x8/Pj5MmTqNVq8vPziYyMJCoqigkTJqBS/e/LjocMGUJycjKbNm2if//+mJvrX72Jx5vrK535Ie1HjiQdpay0jCNJR/kh7UfcXulcbr27hysxkTvIupzNndt3WLd0A20cW2HXyI78m/nMCJiD/bMtmbFsMk9YPaFzbmvHVhxPPsGRpKOo1WrOfHuWzz9LpGffHlWxVFFBVJo/LuVElbC3t2fjxo04Oztz/fp1OnfuzLBhw5g4cSJwd2+7Y8eOBAYGAlBcXMzatWtJSEjgypUrGBkZ0bp1a/z8/HjhhRf0+n/33Xc5dOgQSUlJ5b4heq8frqdV/ALFP5b29bds+iiaq5lZ2NrZ8EbAEDq86AjA4cQviViwhuhDGwAoLS1lc8RWDid+SWHBHZ7p0IZRwW9jVbsWu6I/J3L5JkzNTLnnuR5Ae/43KalER3xG9uUcbOvb4DPUC9deLlW6XlG+NtaOj1QnIf4fJiEuxOPrUUNctlOEEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELBJMSFEELB5E/R/ofll9yo7ikIIR7A0rjWI9XJlbgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYhLgQQiiYUUV15O7uTk5ODkZG+l2uWbOGmJgYdu/ejYmJibZdrVbTuHFjRo0ahYeHB2vWrOGjjz5i+/btNGvWTKePMWPG8NtvvzF58mT8/f217QUFBZiammJoaAiAp6cns2bNwt7eno0bN+Ls7KzTT3h4OMePH2fTpk0A2Nvb65yv0WgwNjamXbt2zJgxg8aNG2vP/eGHH4iIiOD48eMUFRVhY2ND9+7d8ff3x8rKSmecL7/8kg0bNvDdd99RUlJCo0aNGDRoEK+99hoajQZ/f38yMzPZvn07ZmZm2vPy8/Px8vKiS5cu1KtXj4iICADKysooKiqiRo0a2trQ0FDq1avHG2+8odP+hwYNGvD555+X868llOTL5CMsX7qCzIxM7OzseG98IF3cOpdbm5eXx9JFyzj65VGKS0pwaGXP2IlB2Du0BOD4sW9Y8eHHXDx/ETMzU7r37MaYcYE6P4NCWSrsT9G6u7sTEBBA3759yz0eHBwMwPz587VthYWFrFmzho8//piEhASefvpp3n77bX7//Xe2bt2qDfytW7eyaNEiYmNjdUIVeGBY/5UQv78uIyODcePGYWRkRHR0NACHDh0iKCiI4cOHM3jwYOrWrcv58+dZunQp33//PZ999hn16tUDIDIyko8++ogZM2bQo0cPTExMOHHiBOPHj8fT05NJkyZx7do1vL29cXd3Z+bMmdqxx48fz8WLF9m8ebPOE96xY8d44403OHv2rM56HtT+KORP0T7+LqVf4rW+Q5i7cDadXV04mHSImdNmsfPz7dStV1evftyYCZSVlhEaFoKFhQVrI9axY9sO4vbsoLCwkN49+hA8/X08+rxKbu413vULxL1bV0YF+FXD6sTDKOJP0ZqZmfHWW2+hVqs5e/YsKpWKhQsX8vvvv7NkyRIAzp8/z7x585g3b55egFeWRo0a0adPH86cOQNAcXEx06ZNw9/fn7Fjx1KvXj1UKhXNmjVj+fLl2NnZMW/ePACysrJYtGgRoaGheHp6YmZmhoGBAR07dmTevHnk5uZSUlJC7dq1Wbx4MVu3buXAgQMA7Ny5k5SUFJYtW6YT4OK/Kz7uc9q1b0fXbm4YGRnxcq8edHBqT+y2nXq1Go0GFSreCRyFlZUVxsbGDB0+hNzca6SnX8K6tjX7kxPp4+2BSqXiRt4NiouKsa5tVeXrEhWnwrZT/o78/HxWrVpFzZo1ad++PQC1a9dm0aJFjBw5km7durFw4UIGDRpE9+7d/3L/o0aN0m6T/KGoqIh27do99Lzz58+zfft2unTpAkBaWhq///473t7eerUGBgb4+voyc+ZMSktLSU5OxtDQkB49eujVuri44OLior3fsWNHRo8eTUhICI0aNSIsLIyFCxfSqFGjv7xW8e907tfzNG+pu7XYpFkTfv75Z71alUrFB8sX6bQd2HcQc3Nznnr6KQAsLCwAeLW7J9lZ2Th2aEcfb89Kmr2oChUa4qGhoYSFhem01a9fn927dwMQHx9PUlISZWVllJSUUKNGDbp06cLmzZupW/d/Lw1feOEF/Pz8GDlyJM888wzjxo37W/NZtWrVA7dT7vVH2JeUlFBSUkLTpk3p2bMnI0eOBCA7OxsAGxubcsepW7cuJSUlXL9+nevXr1OrVi2MjY0faY6jR4/m2LFjDBgwgGHDhuHm5vYXV3mXk5OTXpufnx9+fvIyWckKCgowNzfXaTMzM+NOwZ0/PffwoWQWzlvM5GnvY26uu+e94/MYbt28xdT3ZzBpXDDhq5ZV6LxF1anQEA8JCXngnjiAh4eHdk/88OHDTJw4kZYtW9KiRQu92oCAAD7++GMCAgLKfbO0Iv0R9rdv32bZsmUkJCTw8ssva395bG1tAbh8+TJPP/203vkZGRkYGxtjbW2Nra0teXl5FBcX622JqNVq8vLyqF27trbNwMCA0aNHM3z4cN57772/vYYTJ0787XPF42Pd6vWsWxOpvf/Mc20ovFOoU1NYWFjuG9l/0Gg0fLJ6HZFrNxIyazovv6L/qtDMzAwzMzPGjAtg2KA3uXnjJk/UeqLC1iGqTrXtibu6urJo0SKWLl1KTEyM3vE/tkEMDKpuihYWFkyZMoWOHTsyYsQIsrKyAOjQoQO2trblzrOsrIzY2Fjc3d0xMjKic+fOaDQa7T73vQ4dOoSLiwtXrlzRaf9jjfdv/Yj/nrf83uTLbw5rb88+9wznz53Xqblw7gLNWjQr9/w7dwoZFziBXTt2s3bjap0A/zbtNH09+1NSUqJtKykuxtjYGPMa5uV1JxSgWt/YdHV15a233mL27NmcO3euOqeiY/bs2ZibmzNp0iTtRw7nzZvHp59+ytKlS8nKykKtVvPrr78SEBDA1atXmTx5MnB3y2XMmDHMnDmT+Ph4ioqKKCkp4YsvvmDatGkMGzaM+vXrV/MKhVK86vkqqd+cZF/ifkpLS9mXuJ/Ub07S2/OVcuunTJxK1tUsNn22AYdW9jrHWtg3p/BOIeFLV1BSUsKVy1dYung5Xn37PPL2n3j8VPh2yuzZs/XaR48e/cBzgoKCOHr0KBMmTOCzzz57LD6VYWFhweLFixk8eDCffPIJI0eOpHPnzmzZsoWIiAj69etHfn4+NjY2dOvWjblz5+pskfj5+dGgQQOioqKYPXs2JSUlPPXUUwQFBTFw4MBKmbOjo2O57YmJidqPPgrladL0aRYvX0j4khXMnjGX+g3sWLh0vvaNyoT4RMJC5/HlN4c58+NPJH+RgomJCb2799HpJ3zVhzh2cGRFxDIWz19KD9deWFpa8qpHL0aOGlEdSxMVpMI+Jy6URz4nLsTjSxGfExdCCPHPSIgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCSYgLIYSCVeu33YvqpVLJc7gQSie/xUIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWB/+68Yzpgxg927dwNQWlpKSUkJ5ubm2uMjR45k+/btHDx4UO9cd3d3AgIC6Nu3L7GxsUyZMkXnXLVaTe3atfHw8GDcuHGoVCoANBoNW7duZevWrZw/fx4jIyOaNWvGa6+9hre3t/b84OBgduzYgZ+fH+PHj9cZOzc3F1dXV+rWrcvBgwfJyMigW7dumJubo1KpUKvVmJub4+LiwowZM3jiiSe056anp/Pxxx9z5MgR8vPzsbKyokuXLowaNYoGDRrojHP69GnWrl3LiRMnKCwspF69enh7ezNixAiMjIyYOXMm+/fvZ9euXdSpU0d7XmlpKYMHD8ba2prevXsTEhKiXfudO3e08wTw9/fHw8NDZ/73S0tLe/g/pHjsfZn8JcuXhJORkYldfTuCxr9HF7fO5dbm5eWxdOGHfPXlUYpLinFo5cC4iUHYt7LXqSsrK2PUiHdo0KABoWEzq2AVorL87SvxWbNmkZaWRlpaGqGhoTRo0EB7Py0tjfr16z9yX/efe/LkSaZPn8769euJiYnR1k2YMIGIiAjeeecdjhw5wldffcXbb7/N0qVLmTJlik6f1tbWxMXFoVarddp37tyJqamp3hzi4+NJS0vj22+/JT4+nvT0dObOnas9/t133+Hj44OpqSmbN28mLS2NqKgoALy8vDh79qy2NjExkTfeeIPnn3+effv2kZqaygcffMDu3bu1TyqTJ0/GxsaGyZMn68wjPDyc33//nQULFtCnTx/tYxIfH68zz7S0NEaNGqU3//tvQtkupV9iYtD7vBP4Dslff8God/0JHh9MdlZ2ufWzps/mel4e23Z9RtLhfbRzbEuA/xjuFNzRqVv98RrSUk9VwQpEZXsst1MMDQ1xd3enZcuWnDlzBoCkpCT27t3Lhg0b6N69OzVq1MDY2Jhu3brxySefsHPnTg4fPqzt46WXXqKkpISvvvpKp+/Y2Fh69+790PFtbGzo06cP33//vbZt+vTpvPLKK8yaNYvGjRujUqlo2LAhs2bNonPnzkybNg2AoqIiQkJCePfddxk6dCiWlpaoVCpat27N4sWL0Wg05OXlYWpqytKlS/nmm2/YtGkTAMePHycyMpIPP/wQKyuringohcLt3hmPY4d2dO3mhpGRES/36kF7pw5s37ZDr1aj0aBSqRgdOAorKyuMTYwZ+ubr5Obmkp6erq07/vU3HNh/kG493KtyKaKSVGqIX758GScnJ73b5cuXH3pecXExe/bs4ezZs7i6ugJ3Q7x9+/Y0btxYr7558+Y4OjqSmJiobTM2NsbDw4MdO/73w37q1Ck0Gg3t2rV76Pg5OTns37+fl19+GYCMjAzOnDmjs2Vzr/79+3P69GkuX77MyZMnycvLw8PDQ6/OwcGB5cuXawO6adOmzJgxgw8++IAzZ84QHBzMpEmTeO655x46P/Hfcf7ceZq3aK7T1rRZE345+7NerUql4oPli3Fo5aBtS9p3AHNzc556+mkAruVeY9aM2YQtnIOZmVmlzl1UjUr9Zp8GDRo8cE/8Xn+EvUajobi4GI1Gw4svvkh4eLg2xLOzs7G1tX3gWHXr1iU7W/clZr9+/Rg4cCC3bt2iZs2axMTE0K9fv3LP79OnDwYGBpSVlVFQUED9+vW1+9F/9GtjY/PAsf+ou3bt2kNr7+fj48PRo0d57bXX6N69O0OGDHmk8x40/3t5eHgwc+bMv9WfeDzcvl2g834RgJmZGQX3bY+U5/DBwywMW8SU6cGYm5uhVquZFjyd14cNpqVDy8qasqhij8XXs90b9ufOnSMoKAgTExM6d/7fmze2trY6Lwnvl5GRQfPmulcsDg4ONG3alD179uDp6cn+/ftJSEjQ2Xb5w65du2jUqBEA+fn5LF++nAEDBpCQkKB98rh8+TJNmjQpd+w/5lhcXAzcvZq//83OP9rvfzIKCAggLi6O995774Hr+zP3zl8o1yer17Fu9Xrt/Weee4bCwkKdmsLCQiwsajywD41GwycRn7B+7QZCZs+g5yt3X1GuW7MeExMTXhvyWuVMXlSLx25PvFmzZqxZs4bU1FSmTp2qbe/Vqxfff/+9zhuIf/jxxx/58ccftdsf9/Lx8WHHjh3s3buX559/XueTIA9iaWnJmDFjyM/PJzU1lcaNG9OmTRudN1nvFRMTQ5s2bWjYsCHt2rXDysqKhIQEvbqffvoJFxcXvTcc/7iCvv9KWvz3jPB7iyMnUrS3Z9s+w7lfz+vUnD93gWbNm5V7/p07hYwNGEfcjt18smmNNsABEnYnkPrNSbp0cqNLJzf2fJ7Ins8T6dLJrTKXJCrZY5kadnZ2zJ8/n7i4OO2edteuXfH09OSdd97hwIEDFBQUUFBQQFJSEqNHj6Z379507dpVry9PT0++//571q5di6+v7yONX1RUxIYNGzAzM+PZZ58FICwsjJSUFGbMmEFGRgZqtZrffvuNadOmceTIEe0nWUxMTJg2bRorVqwgKiqK27dvU1ZWxokTJ3jvvffo2bMnjo6OFfRIiX+73p69Sf0mlX2J+yktLWVf4n5Sv0mld59Xy62fPGEyWVez+PSzjTp74wCx8dtJOX6Y5K+/IPnrL3ildy9e6d2L5K+/qIKViMryWGynlMfV1ZXXX3+d2bNn4+TkROPGjZk3bx4xMTGsXr2a999/H7j7pmZgYCB9+/Yttx9ra2u6du1KWlqazvbM/Tw8PLSfszYwMMDBwYFVq1Zp30h1cHBgx44drFy5kiFDhpCXl4eVlRWdO3fW28rw9PTE2tqadevWER4eTlFREfXr16dfv368+eabFfUQPXD+91q3bp08aShYk6ZP88HyxSxfEs6s6bOp38CORR8u4KmnnwIgIX4Pc2eGceRECmd+/InkL1IwMTHh1e66b6yHRyynfQf5Ofg3Umk0Gk11T0JUj9ult6p7CkKIB7AwqvlIdY/ldooQQohHIyEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAKJiEuhBAK9tj+PXFR+VTo//1xIYSyyJW4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIo2F8KcXd3d2JjY/XaY2NjcXd312mLiorC3t6eyMhIvfrg4GDatGmDo6Oj3m3VqlUAhIeHY29vz7hx4/TOLy4uplOnTtjb2+u0//DDD4wZM4ZOnTrh6OhIjx49WLBgAXl5eXp9pKamEhgYiIuLC23btqVz585MmjSJixcvamvCw8MZOnToQx+T9PR03n//fVxcXGjXrh1ubm7MmDGDy5cva2syMjKwt7enXbt2ODo60rZtWzp16sSECRO4efOmzuMSHBysc05ISIjemPfP68qVK0ycOJEXX3yRdu3a0bVrV2bPnq3Tt1CulOQv6e89gBecXqKvZz+Sv0h+YG1eXh4zpoTQvcvLdHnBDf+3RnH2zFnt8Rt5N5g2eQZuL7rT5QU3xgaOIycnpyqWISpJpV2JR0VFMWjQIDZu3EhpaanecU9PT9LS0vRuo0aN0tZYW1uTlJTErVu3dM49ePAgJSUlOm2HDh1i8ODBNGnShLi4OE6ePMmqVav47bff8Pb2JisrS1u7Y8cORowYwXPPPUdMTAynTp1i27Zt1K1bl4EDB+rUPsx3332Hj48PpqambN68mbS0NKKiogDw8vLi7NmzOvXx8fGkpaXx7bffEh8fT3p6OnPnzn3oGFu2bCEhIeGBx9VqNW+99Ra1atUiMTGRU6dOsXHjRn7++WfGjBnzSOsQj6/09EtMDJrE6MB3SPn6MKPe9ef98cFkZ2WXWx86fRZ5eXnE7NrKgcP7aevYlnf9A7lTcAeACUETuVNQwO7EOPYkfY6BgSGzQ+ZU5ZJEBauUED969Ci5ubkEBwejVqvZu3fv3+qnRYsWNGnSRC/Etm/fTu/evbX3i4uLmTZtGv7+/owdO5Z69eqhUqlo1qwZy5cvx87Ojnnz5gFw8+ZNQkNDmTRpEm+//TZ2dnaoVCrs7OyYMGECr7/+OtevX3+k+U2fPp1XXnmFWbNm0bhxY1QqFQ0bNmTWrFl07tyZadOmPfBcGxsb+vTpw/fff//QMYYOHcr06dNJT08v9/j169c5f/48vXv35oknngCgcePGTJs2jQYNGlBWVvZIaxGPp/id8Th2aEfXbl0xMjLi5V4v096pA9u36b8i1mg0qFQqRge+g5WVFcYmxrzx5lByc3NJT0/nxx/O8N3p7wmdO5OaT9TEwsKCGaHTeG+cPNkr2V8O8dDQUJycnHRuoaGhOjWbNm1iwIABmJmZMXjwYNatW/e3J+jj48OOHTu097Oysvjuu+/o3r27ti0tLY3ff/8db29vvfMNDAzw9fUlKSmJ0tJSDh06RGlpKX379i13vMDAQBwcHP50XhkZGZw5c6bcMQH69+/P6dOndbZV7pWTk8P+/ft5+eWXHzrO8OHDef755wkKCqK4uFjveJ06dejUqRMBAQHMnz+fpKQkcnJysLe3JywsDENDwz9di3h8nTt3juYtmuu0NW3WhJ/P/qxXq1KpWLL8Axxa/e/nN2nfAczNzXnq6af4/rvvadqsCbExO+jTy4seri+zZNFSbGxsKn0dovL85RAPCQnhxIkTOrd7920zMzNJSUlhyJAhAAwYMIBff/2V48eP6/QTHx+v92Tg5OSkF3p/XK1euHABuLv//uqrr2Jqaqqtyc6++9LyQT+MdevWpaSkhOvXr5OVlYWVlRVmZmba4ytWrNCO365du4deQf+VMe+t+2MtTk5OODo64uLiwqVLl/Dw8PjTsebPn09eXp721cT91qxZw+jRo/npp5+YMGECLi4ueHl5kZKS8qd9i8fb7dsFmJub67SZmZlR8P/bIw/zxcHDLAhbyOTpwZibm3Pzxk1++fkXLqX/xubt0WzZvpns7GymT5lRWdMXVaDCt1Oio6MpLS3Fy8sLZ2dnevbsSWlpqd7VuIeHh96TwYkTJ2jQoIFOXe3atXFzc2Pnzp3A3f1sX19fnRpbW1uAB171ZmRkYGxsjLW1Nba2tuTl5elc1QYEBGjH79WrV7l7+Pd7lDHvrQPYtWsXJ06cIC0tjdTUVF5++WUGDBjwp3vwVlZWLFmyhK1bt5KYmKh33MTEhCFDhhAZGUlqaiqxsbE888wzjBo1ivPnz//pWsTj45PV63jRyUV7Q6OhsLBQp6awsBALixoP7EOj0bBm1VqmTJrKzNkheHrdvVAwMTEGYGLweCwsLKhjU4eAMe/yZfIRCm4XVN6iRKWq0BAvKioiJiaGuXPnEhcXp72tXLmSL774gnPnzv2tfn18fIiLi+PYsWNYWFjQunVrneMdOnTA1taWmJgYvXPLysq0n54xMjLCzc0NAwMD7ZPC39W4cWPatGlT7pgAMTExtGnThoYNG5Z73NLSkjFjxpCfn09qauqfjufo6EhQUBBTp07l0qVL2vatW7fSpUsX7d63oaEhbdq0Ye7cuVhaWvLzz/ovu8Xja4TfW3x14kvt7dm2z3LuV90n4vPnLtCsebNyz79z5w5BAeOI27GLdZvW0vOV/23XNWnWFLVao/OhgDK1GgANmkpYjagKFRriu3fvRqVS4enpiZ2dnfbWpUsXWrZsWe7HDR+Fq6srJSUlzJkzR+8qHMDY2Jh58+bx6aefsnTpUrKyslCr1fz6668EBARw9epVJk+eDNz9xMusWbOYN28en3zyCbm5ucDdvfaVK1eyZ88e7VYI3H3T9OrVqzq3Pz6yGBYWRkpKCjNmzCAjIwO1Ws1vv/3GtGnTOHLkyEM/eVJUVMSGDRswMzPj2WeffaTHYeTIkXTo0IFdu3Zp29zc3CgqKiIkJISLFy9SVlZGXl4e69evB6Bjx46P1Ld4PPX2fJXUb1LZl7iP0tJS9iXuI/WbVDz69C63PnjCFLKuZhH12SadvXGATi8407BRQ2ZOm0XB7QKuXbvOR8s+oms3NywsLKpiOaISVOi33UdHR+Pp6YmxsbHesYEDB7JgwQKCgoKAu4Ff3qdWnJ2dtZ8V107SyIg+ffoQFRX1wD3kzp07s2XLFiIiIujXrx/5+fnY2NjQrVs35s6dS+3atbW13t7eNGvWjMjISCIjI7l16xbm5ua0a9eODz/8kK5du2prT506haurq85Yr776KkuXLsXBwYEdO3awcuVKhgwZQl5eHlZWVnTu3Jldu3bRqFEjnfM8PDxQqe5+w7yBgQEODg6sWrWKxo0bP+RR/R+VSsX8+fPx8fHRttWtW5ctW7awYsUKhg4dyo0bNzA3N8fZ2Zno6GiddQvladK0CUuWL2bZknBCp8+mfoP6LPpwIU89/RQACfEJzJkZxlcnvuTMj2dI/iIZExMTXumuG/IrIsJp38GRtRvW8MHCJXi96k1RcTGubl2YNHlidSxNVBCVRqOR11H/UQWl+dU9BSHEA9QwsnykOvlv90IIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAS4kIIoWAV+qUQQlkMVPIcLoTSyW+xEEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIomIS4EEIoWKWHuLu7O7GxsXrtsbGxuLu767RFRUVhb29PZGSkXn1wcDBt2rTB0dFR77Zq1SoAwsPDsbe3Z9y4cXrnFxcX06lTJ+zt7fWOlTfurl27tP23a9cOe3t72rVrpzdmcXExq1evxtPTk3bt2uHs7Mybb77JoUOH9Ma5desWH3zwAT179sTR0REXFxcmTJjApUuXtDXh4eG0atVKZ31t27alR48eOvM7duyYdi2POlehTCmHU+jn1R/nDi/g7dGXw18kP7A2Ly+P6VNm4N65Oy6duvD2m/78dOYsAJ/vTqBThxd1bh2eex6nth2raimiEjxWf4o2KiqKQYMGsXHjRl5//XWMjHSn5+npyfz58x/ah7W1NUlJSdy6dYuaNWtq2w8ePEhJSckjj9unTx/69OkDQEZGBt26dSM+Pp5GjRppzysuLmbEiBHcuHGDKVOm0KFDB9RqNXv27OH9999n2LBhvPvuuwBcu3aNgQMH0qpVK1atWsXTTz9NXl4eK1asYODAgcTExNCwYUMAnJyc2LRpk3ackpISdu7cybRp07C3t+eFF17Qmf+jzFUoU/rFdMYHTWT+onl0cevMgf0HmTTufXbtiaNevbp69TOnhVJaVkbs7hgsaliwOmINo/3eJX7vbnp7vkpvz1e1tVlZ2QwZMISg8UFVuCJR0R6b7ZSjR4+Sm5tLcHAwarWavXv3/q1+WrRoQZMmTUhISNBp3759O717967QcTdv3swvv/zCp59+ygsvvICJiQlmZmb4+PjwwQcfEB4ezi+//ALcvcI2MzNj6dKlNGnSBJVKhbW1NdOnT8fNzY2zZ88+cBxjY2P69++PlZUVP/744yPPTyjf7rh4HDs44t69K0ZGRvR85WU6OLVn+9bterUajQZUKt4NHI2VlRXGJsYMe/MNcnNzSb+Yrlc7NXganV0749FH//dCKEeVhHhoaChOTk46t9DQUJ2aTZs2MWDAAMzMzBg8eDDr1q372+P5+PiwY8cO7f2srCy+++47unfvrlf7T8ZNSkqia9euPPHEE3rHOnfujJ2dnfZJ4eDBg/Tq1QtDQ0O92nnz5ultLd2rsLCQTZs2cfv2bV566aVHnp9Qvl9/PUeLFs112po2a8rPZ3/Wq1WpVHwYvoRWrR20bfv3JmFubs7TTZ7SqY3f/Tnnfj3HhPfHV87ERZWpkhAPCQnhxIkTOreQkBDt8czMTFJSUhgyZAgAAwYM4Ndff+X48eM6/cTHx+s9GTg5OXH58mWduj59+vD9999z4cIF4O7++6uvvoqpqalO3aOO+yDZ2dnY2to+8HjdunXJzs4G7m6nPKz2XqmpqTg5OdG+fXueffZZOnXqxPHjx9mwYQMODg5/3oH41yi4fRvzGuY6bWbmZhQUFPzpuV8c/IL5YQuYOmMy5ub/60OtVrN65RpG+o3AwsKiwucsqtZjsSceHR1NaWkpXl5e2rbS0lLWrVtHx47/e9PFw8PjT/fEAWrXro2bmxs7d+5k7Nix7Nixgw8//JBbt279rXEfxNbWlszMzAcez8jIoEuXLtraPwL9fteuXaNWrVraq/QOHTpo98RPnTrFe++9h62tLR06dPjTOQllWxvxCWtXf6K9/+xzz1J4p1CnpvBOITUeEr4ajYY1EWtZt2Y9oXNm0uuVnjrHvzn2Db/n/I5PP5+KnbyoFtUe4kVFRcTExDB37lxefPFFbfvPP/+Mn58f586do1mzZn+5Xx8fH2bPns2LL76IhYUFrVu35tixYxU6bq9evVi2bBnZ2dnUrav7JtPBgwfJzc2lR48ewN1P6ezbt4933nlHZ0tFo9EwcuRInnnmGWbNmqU3Rrt27Vi1ahUDBw6kVq1avPfee3/5sRDKMdJ/BCP9R2jvh3+4gjM/ntGpOX/uPK2faV3u+Xfu3OH9CcH8+ss51m9ap7O18oek/Qdw796VGvdd4QtlqvY3Nnfv3o1KpcLT0xM7OzvtrUuXLrRs2bLcjxs+CldXV0pKSpgzZw6+vr6VMu5rr73GM888w4gRIzh27BjFxcXcunWL2NhYJk+ezDvvvKP9GODo0aO5ceMG48aNIz397ptMWVlZTJkyhatXrzJy5MgHjtOqVSuCg4NZtWoVX3/99d96PIQyefTpzYlvUtm7Zx+lpaXs3bOPE9+k4tHHo9z69ycEc/VKFpu3RpUb4ABpJ0/Rwal9ZU5bVKFqD/Ho6Gg8PT0xNjbWOzZw4EDi4uLIzc0F7gZveZ8THzVqlN65f3xMMD09HQ8P/R/4vzLugxgZGbFmzRp8fHwICwujU6dOdOvWjV27djF37lyCgoK0tbVr1yYmJoZatWoxfPhwHB0d8fX1pbS0lM2bN/Pkk08+dKzBgwfj6urK+++/z40bNx5aK/49mjRtwtLwJXyy+hM6d3IlYuVqPvhwEU8/ffeNyj8++w1w5sczHD6UzIXzF+jZ7RWdz4OfPHFS22fGbxl6rxyFcqk0Go2muichqkdh2Z+/OSaEqB5mhjUeqa7ar8SFEEL8fRLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYBLiQgihYPKnaIUQQsHkSlwIIRRMQlwIIRRMQlwIIRRMQlwIIRRMQlwIIRTMqLonIMS/1Y0bN1i6dCmHDh3ixo0bWFpa8tJLLzF27Fjs7OwAyMnJYeXKlXzxxRfk5uZSs2ZNnJ2d8ff3p2XLltq+3N3dCQgIoG/fvjpjxMbGsmLFCg4ePKity8nJwcjo7q+2RqPBwMCAVq1aMXXqVFq3bq09Nzs7m5UrV3L48GGuX79OrVq1cHV1JTAwEBsbGwCCg4PZvXs3JiYmeuvz9/dn1KhRFfugib9MrsSFqCRjx47l+vXrxMTEcOrUKXbu3ElxcTFvvvkmpaWlZGRk4OPjQ05ODhEREaSlpREXF0fjxo0ZMGAAR44c+VvjhoaGkpaWRlpaGqdOnWLfvn3UrFmTgIAA1Go1AOnp6Xh6eqJWq4mKiiItLY0tW7aQl5fHoEGDyM/P1/bn6emp7e/emwT440FCXIhKkpqaSo8ePbC1tQXAxsaGKVOm0LZtW27evMm8efNo0aIFy5cvp0WLFhgYGFCnTh2CgoIYOnQowcHBlJaW/uN52NjYMHDgQDIzM8nLywNg7ty5PPfcc4SGhlK/fn0A7OzsWLhwIa1ateKXX375x+OKqiEhLkQl6d27NyEhIcycOZOEhAQyMzOxtbVl/vz5WFpacvjwYby8vFCpVHrnDhgwgOzsbNLS0v7xPK5cucKnn37Ks88+S+3atSkuLiYlJYXevXvr1ZqamrJ8+XIcHR3/8biiasieuBCVZM6cOTg7O5OQkMCMGTO4desWTz75JIGBgTg7O1NSUqLde75f3bp1gbv71n9VaGgoYWFhlJaWUlJSgp2dHT169MDf3x+4u1evVqu1rxD+THx8PElJSXrtu3btokGDBn95fqJiSYgLUUkMDAzw8vLCy8sLjUbDuXPniIuLY9KkSaxZswYjIyMuX75c7rkZGRkA2qA1MTGhrKxMr66srEzvTceQkBD69u1LcXExGzduZNWqVbi6umJtbQ2AlZUVxsbG5OTklDt2bm4utWvX1r5C8PDwYP78+X/vQRCVTrZThKgEKSkpODo6avegVSoVzZs3Z/z48bRu3Zqff/6Zbt26ERsbq32z8V7btm3D1tZWu61Rv359MjMz9erS09Np2LBhuXMwMTFh5MiRDBo0iNGjR/PTTz8BYGxsjIuLCwkJCXrnFBcX4+XlxerVq//u0kUVkxAXohI8//zz1KlTh8mTJ3P27FlKSkrIz89n165dXLx4ETc3N6ZOncrly5cZM2YM586dQ61Wk5WVxZIlS4iOjiYsLAxjY2MAvLy82Lx5M0eOHEGtVlNcXExycjLbtm3T+9jh/YKCgrC3t2fcuHEUFhYCMGnSJFJTU5k9ezZZWVkAXLx4kYCAACwsLBg4cGDlPkCiwshfMRSikmRnZ7NixQq+/PJLcnNzMTY2pl27dgQGBtK2bVsArl27xsqVKzl48CC5ublYWlrSsWNH/Pz8cHBw0Olv27ZtREdHc+nSJdRqNU2aNGHYsGF4eXlpax70efLffvsNb29v+vTpQ0hICAAXLlzgo48+4tixY+Tn52NlZYWrqysBAQGP9DlxZ2dnVq1aVaGPmfjrJMSFEELBZDtFCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEUTEJcCCEU7P8A8DtokF0RLQkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_matt = data.corr(method='pearson')[['SOURCE']].sort_values(by='SOURCE',ascending=False)\n",
    "plt.figure(figsize=(3,5))\n",
    "corr = sns.heatmap(corr_matt, annot=True, cmap='Greens', cbar=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "- LEUCOCYTE and AGE are postively correlated with target\n",
    "- THROMBOCYTE, ERYTHROCYTE, HAEMOGLOBINS and HAEMATOCRIT are negatively correlated with target\n",
    "- No notable correlation found between MCHC, MCH, MCV, and target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2.2 Categorical feature(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.228818Z",
     "iopub.status.busy": "2021-05-21T04:41:44.22852Z",
     "iopub.status.idle": "2021-05-21T04:41:44.36223Z",
     "shell.execute_reply": "2021-05-21T04:41:44.361234Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.22879Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mean of target==1 w.r.t SEX\n",
    "\n",
    "df = data.groupby('SEX')['SOURCE'].mean().to_frame().reset_index()\n",
    "plot = df.plot(kind='bar', x='SEX', y='SOURCE', color=('orange', 'skyblue'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observation:\n",
    "- Male Incare patients are more in number than Female Incare patients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.363935Z",
     "iopub.status.busy": "2021-05-21T04:41:44.36361Z",
     "iopub.status.idle": "2021-05-21T04:41:44.373703Z",
     "shell.execute_reply": "2021-05-21T04:41:44.372789Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.3639Z"
    }
   },
   "outputs": [],
   "source": [
    "X_train.SEX.replace({'F':0, 'M':1}, inplace=True)\n",
    "X_test.SEX.replace({'F':0, 'M':1}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Scaling\n",
    "\n",
    "Transform all the numerical features into a range [0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.375393Z",
     "iopub.status.busy": "2021-05-21T04:41:44.375037Z",
     "iopub.status.idle": "2021-05-21T04:41:44.409085Z",
     "shell.execute_reply": "2021-05-21T04:41:44.408238Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.375362Z"
    }
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "X_train[num_features] = scaler.fit_transform(X_train[num_features]) #fit and transform the train set\n",
    "X_test[num_features] = scaler.transform(X_test[num_features]) #transform the test test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.410738Z",
     "iopub.status.busy": "2021-05-21T04:41:44.41043Z",
     "iopub.status.idle": "2021-05-21T04:41:44.429709Z",
     "shell.execute_reply": "2021-05-21T04:41:44.428713Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.410708Z"
    }
   },
   "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>HAEMATOCRIT</th>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <th>MCH</th>\n",
       "      <th>MCHC</th>\n",
       "      <th>MCV</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.675497</td>\n",
       "      <td>0.504702</td>\n",
       "      <td>0.052980</td>\n",
       "      <td>0.139574</td>\n",
       "      <td>0.575290</td>\n",
       "      <td>0.684615</td>\n",
       "      <td>0.508117</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>0.373874</td>\n",
       "      <td>0.403974</td>\n",
       "      <td>0.330721</td>\n",
       "      <td>0.213245</td>\n",
       "      <td>0.233191</td>\n",
       "      <td>0.490347</td>\n",
       "      <td>0.515385</td>\n",
       "      <td>0.493506</td>\n",
       "      <td>0.316327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>0.295045</td>\n",
       "      <td>0.337748</td>\n",
       "      <td>0.329154</td>\n",
       "      <td>0.075497</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.386100</td>\n",
       "      <td>0.553846</td>\n",
       "      <td>0.339286</td>\n",
       "      <td>0.540816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
       "2902     0.594595      0.675497     0.504702   0.052980     0.139574   \n",
       "1282     0.373874      0.403974     0.330721   0.213245     0.233191   \n",
       "2571     0.295045      0.337748     0.329154   0.075497     0.001702   \n",
       "\n",
       "           MCH      MCHC       MCV       AGE  SEX  \n",
       "2902  0.575290  0.684615  0.508117  0.591837    1  \n",
       "1282  0.490347  0.515385  0.493506  0.316327    0  \n",
       "2571  0.386100  0.553846  0.339286  0.540816    0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Feature selection\n",
    "In exploratory data analysis, we found out that the features MCH, MCHC and MCV are not very useful in predicting the target. Let's remove those features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.431752Z",
     "iopub.status.busy": "2021-05-21T04:41:44.431326Z",
     "iopub.status.idle": "2021-05-21T04:41:44.449885Z",
     "shell.execute_reply": "2021-05-21T04:41:44.44896Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.431711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HAEMATOCRIT</th>\n",
       "      <th>HAEMOGLOBINS</th>\n",
       "      <th>ERYTHROCYTE</th>\n",
       "      <th>LEUCOCYTE</th>\n",
       "      <th>THROMBOCYTE</th>\n",
       "      <th>AGE</th>\n",
       "      <th>SEX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>0.594595</td>\n",
       "      <td>0.675497</td>\n",
       "      <td>0.504702</td>\n",
       "      <td>0.052980</td>\n",
       "      <td>0.139574</td>\n",
       "      <td>0.591837</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>0.373874</td>\n",
       "      <td>0.403974</td>\n",
       "      <td>0.330721</td>\n",
       "      <td>0.213245</td>\n",
       "      <td>0.233191</td>\n",
       "      <td>0.316327</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2571</th>\n",
       "      <td>0.295045</td>\n",
       "      <td>0.337748</td>\n",
       "      <td>0.329154</td>\n",
       "      <td>0.075497</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.540816</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      HAEMATOCRIT  HAEMOGLOBINS  ERYTHROCYTE  LEUCOCYTE  THROMBOCYTE  \\\n",
       "2902     0.594595      0.675497     0.504702   0.052980     0.139574   \n",
       "1282     0.373874      0.403974     0.330721   0.213245     0.233191   \n",
       "2571     0.295045      0.337748     0.329154   0.075497     0.001702   \n",
       "\n",
       "           AGE  SEX  \n",
       "2902  0.591837    1  \n",
       "1282  0.316327    0  \n",
       "2571  0.540816    0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.drop(['MCH', 'MCHC','MCV'], axis=1, inplace=True)\n",
    "X_test.drop(['MCH', 'MCHC','MCV'], axis=1, inplace=True)\n",
    "\n",
    "# final train set\n",
    "X_train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.451784Z",
     "iopub.status.busy": "2021-05-21T04:41:44.451461Z",
     "iopub.status.idle": "2021-05-21T04:41:44.498389Z",
     "shell.execute_reply": "2021-05-21T04:41:44.497328Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.451744Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  1.0\n",
      "Test accuracy :  0.6783691959229898\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=1)\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, tree.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, tree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning of Decision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:44.500341Z",
     "iopub.status.busy": "2021-05-21T04:41:44.499909Z",
     "iopub.status.idle": "2021-05-21T04:41:45.490431Z",
     "shell.execute_reply": "2021-05-21T04:41:45.489082Z",
     "shell.execute_reply.started": "2021-05-21T04:41:44.500298Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'min_samples_split': 2,\n",
       " 'max_leaf_nodes': 8,\n",
       " 'max_features': 0.6,\n",
       " 'max_depth': 4,\n",
       " 'criterion': 'entropy',\n",
       " 'class_weight': {0: 1, 1: 2}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distribution = {'max_depth': [4, 6, 8, 10, 12, 14, 16],\n",
    "                'criterion': ['gini', 'entropy'],\n",
    "                'min_samples_split': [2, 10, 20, 30, 40],\n",
    "                'max_features': [0.2, 0.4, 0.6, 0.8, 1],\n",
    "                'max_leaf_nodes': [8, 16, 32, 64, 128,256],\n",
    "                'class_weight': [{0: 1, 1: 2}, {0: 1, 1: 3}, {0: 1, 1: 4}, {0: 1, 1: 5}]\n",
    "               }\n",
    "search = RandomizedSearchCV(DecisionTreeClassifier(random_state=1),\n",
    "                         distribution,\n",
    "                         scoring='accuracy',\n",
    "                         cv=3,\n",
    "                         verbose=1,\n",
    "                         random_state=1,\n",
    "                         n_iter=30)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:45.492597Z",
     "iopub.status.busy": "2021-05-21T04:41:45.492166Z",
     "iopub.status.idle": "2021-05-21T04:41:45.515761Z",
     "shell.execute_reply": "2021-05-21T04:41:45.514519Z",
     "shell.execute_reply.started": "2021-05-21T04:41:45.492553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Best train accuracy :  0.7169169736469255\n",
      " Best test accuracy :  0.6783691959229898\n"
     ]
    }
   ],
   "source": [
    "best_tree = search.best_estimator_\n",
    "best_tree.fit(X_train, y_train)\n",
    "print(\" Best train accuracy : \", accuracy_score(y_train, best_tree.predict(X_train)))\n",
    "print(\" Best test accuracy : \", accuracy_score(y_test, best_tree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:45.517837Z",
     "iopub.status.busy": "2021-05-21T04:41:45.517411Z",
     "iopub.status.idle": "2021-05-21T04:41:45.532715Z",
     "shell.execute_reply": "2021-05-21T04:41:45.531913Z",
     "shell.execute_reply.started": "2021-05-21T04:41:45.517793Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.69      0.72       526\n",
      "           1       0.59      0.66      0.63       357\n",
      "\n",
      "    accuracy                           0.68       883\n",
      "   macro avg       0.67      0.68      0.67       883\n",
      "weighted avg       0.69      0.68      0.68       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, best_tree.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:45.53461Z",
     "iopub.status.busy": "2021-05-21T04:41:45.534048Z",
     "iopub.status.idle": "2021-05-21T04:41:45.5951Z",
     "shell.execute_reply": "2021-05-21T04:41:45.593602Z",
     "shell.execute_reply.started": "2021-05-21T04:41:45.534576Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  0.7163502408614338\n",
      "Test accuracy :  0.7089467723669309\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression()\n",
    "\n",
    "logreg.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, logreg.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning for Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:45.597612Z",
     "iopub.status.busy": "2021-05-21T04:41:45.597127Z",
     "iopub.status.idle": "2021-05-21T04:41:48.056359Z",
     "shell.execute_reply": "2021-05-21T04:41:48.055221Z",
     "shell.execute_reply.started": "2021-05-21T04:41:45.597565Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'penalty': 'none',\n",
       " 'max_iter': 300,\n",
       " 'fit_intercept': True,\n",
       " 'class_weight': {0: 1, 1: 1},\n",
       " 'C': 0.01}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameters\n",
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
    "              'penalty':['l1', 'l2', 'elasticnet', 'none'],\n",
    "              'fit_intercept':[True, False],\n",
    "              'max_iter':[100, 200, 300],\n",
    "              'class_weight': [{0: 1, 1: 1}, {0: 1, 1: 2}, {0: 1, 1: 4}, {0: 1, 1: 5}]\n",
    "             }\n",
    "\n",
    "# Random search for best hyperparameters\n",
    "search = RandomizedSearchCV(LogisticRegression(random_state=1),\n",
    "                         param_grid,\n",
    "                         scoring='accuracy',\n",
    "                         cv=3,\n",
    "                         verbose=1,\n",
    "                         random_state=1,\n",
    "                         n_iter=30)\n",
    "\n",
    "search.fit(X_train, y_train)\n",
    "\n",
    "# Best parameters for Logistic regression classifier\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:48.058824Z",
     "iopub.status.busy": "2021-05-21T04:41:48.058037Z",
     "iopub.status.idle": "2021-05-21T04:41:48.135738Z",
     "shell.execute_reply": "2021-05-21T04:41:48.134572Z",
     "shell.execute_reply.started": "2021-05-21T04:41:48.058772Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train accuracy :  0.7285349957495041\n",
      "Best test accuracy :  0.7032842582106456\n"
     ]
    }
   ],
   "source": [
    "# Retrain with best model\n",
    "\n",
    "best_logreg = search.best_estimator_\n",
    "\n",
    "best_logreg.fit(X_train, y_train)\n",
    "print(\"Best train accuracy : \", accuracy_score(y_train, best_logreg.predict(X_train)))\n",
    "print(\"Best test accuracy : \", accuracy_score(y_test, best_logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:48.138375Z",
     "iopub.status.busy": "2021-05-21T04:41:48.137574Z",
     "iopub.status.idle": "2021-05-21T04:41:48.160867Z",
     "shell.execute_reply": "2021-05-21T04:41:48.159706Z",
     "shell.execute_reply.started": "2021-05-21T04:41:48.138321Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.85      0.77       526\n",
      "           1       0.69      0.49      0.57       357\n",
      "\n",
      "    accuracy                           0.70       883\n",
      "   macro avg       0.70      0.67      0.67       883\n",
      "weighted avg       0.70      0.70      0.69       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, best_logreg.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:48.163667Z",
     "iopub.status.busy": "2021-05-21T04:41:48.162896Z",
     "iopub.status.idle": "2021-05-21T04:41:49.364515Z",
     "shell.execute_reply": "2021-05-21T04:41:49.363505Z",
     "shell.execute_reply.started": "2021-05-21T04:41:48.163616Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  0.7557381694531029\n",
      "Test accuracy :  0.7180067950169875\n"
     ]
    }
   ],
   "source": [
    "svc = SVC(random_state=1)\n",
    "\n",
    "svc.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, svc.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning - Support vector classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:41:49.366529Z",
     "iopub.status.busy": "2021-05-21T04:41:49.366083Z",
     "iopub.status.idle": "2021-05-21T04:42:14.417065Z",
     "shell.execute_reply": "2021-05-21T04:42:14.4159Z",
     "shell.execute_reply.started": "2021-05-21T04:41:49.366485Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 30 candidates, totalling 90 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'kernel': 'rbf', 'gamma': 1, 'degree': 4, 'C': 100}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
    "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
    "              'kernel': ['linear', 'rbf', 'poly'],\n",
    "              'degree':[0, 1, 2, 3, 4, 5, 6]\n",
    "             } \n",
    "search = RandomizedSearchCV(SVC(random_state=1),\n",
    "                         param_grid,\n",
    "                         scoring='accuracy',\n",
    "                         cv=3,\n",
    "                         verbose=1,\n",
    "                         random_state=1,\n",
    "                         n_iter=30)\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:42:14.418686Z",
     "iopub.status.busy": "2021-05-21T04:42:14.418366Z",
     "iopub.status.idle": "2021-05-21T04:42:15.700765Z",
     "shell.execute_reply": "2021-05-21T04:42:15.699441Z",
     "shell.execute_reply.started": "2021-05-21T04:42:14.418654Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train accuracy :  0.768206290733919\n",
      "Best test accuracy :  0.7338618346545867\n"
     ]
    }
   ],
   "source": [
    "# Retrain with best model\n",
    "\n",
    "best_svc = search.best_estimator_\n",
    "\n",
    "best_svc.fit(X_train, y_train)\n",
    "print(\"Best train accuracy : \", accuracy_score(y_train, best_svc.predict(X_train)))\n",
    "print(\"Best test accuracy : \", accuracy_score(y_test, best_svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:42:15.702859Z",
     "iopub.status.busy": "2021-05-21T04:42:15.702443Z",
     "iopub.status.idle": "2021-05-21T04:42:15.829654Z",
     "shell.execute_reply": "2021-05-21T04:42:15.828597Z",
     "shell.execute_reply.started": "2021-05-21T04:42:15.702823Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.85      0.79       526\n",
      "           1       0.72      0.56      0.63       357\n",
      "\n",
      "    accuracy                           0.73       883\n",
      "   macro avg       0.73      0.71      0.71       883\n",
      "weighted avg       0.73      0.73      0.73       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, best_svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random forest classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:42:15.831762Z",
     "iopub.status.busy": "2021-05-21T04:42:15.831303Z",
     "iopub.status.idle": "2021-05-21T04:42:16.554645Z",
     "shell.execute_reply": "2021-05-21T04:42:16.553726Z",
     "shell.execute_reply.started": "2021-05-21T04:42:15.831716Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  1.0\n",
      "Test accuracy :  0.7383918459796149\n"
     ]
    }
   ],
   "source": [
    "forest = RandomForestClassifier(random_state=1)\n",
    "forest.fit(X_train, y_train)\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, forest.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, forest.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperparameter tuning - Random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:44:27.641669Z",
     "iopub.status.busy": "2021-05-21T04:44:27.641314Z",
     "iopub.status.idle": "2021-05-21T04:44:52.565565Z",
     "shell.execute_reply": "2021-05-21T04:44:52.564563Z",
     "shell.execute_reply.started": "2021-05-21T04:44:27.641641Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 20 candidates, totalling 60 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 10,\n",
       " 'bootstrap': False}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_grid = {'bootstrap': [True, False],\n",
    "             'max_depth': [2, 5, 10, 20, None],\n",
    "             'max_features': ['auto', 'sqrt'],\n",
    "             'min_samples_leaf': [1, 2, 4],\n",
    "             'min_samples_split': [2, 5, 10],\n",
    "             'n_estimators': [50, 100, 150, 200]}\n",
    "search = RandomizedSearchCV(RandomForestClassifier(random_state=1),\n",
    "                         params_grid,\n",
    "                         scoring='accuracy',\n",
    "                         cv=3,\n",
    "                         verbose=1,\n",
    "                         random_state=1,\n",
    "                         n_iter=20)\n",
    "\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:44:58.223463Z",
     "iopub.status.busy": "2021-05-21T04:44:58.223123Z",
     "iopub.status.idle": "2021-05-21T04:44:58.893465Z",
     "shell.execute_reply": "2021-05-21T04:44:58.892391Z",
     "shell.execute_reply.started": "2021-05-21T04:44:58.223435Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best train accuracy :  0.8994049305752337\n",
      "Best test accuracy :  0.7372593431483578\n"
     ]
    }
   ],
   "source": [
    "best_forest = search.best_estimator_\n",
    "best_forest.fit(X_train, y_train)\n",
    "print(\"Best train accuracy : \", accuracy_score(y_train, best_forest.predict(X_train)))\n",
    "print(\"Best test accuracy : \", accuracy_score(y_test, best_forest.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:45:03.056252Z",
     "iopub.status.busy": "2021-05-21T04:45:03.05585Z",
     "iopub.status.idle": "2021-05-21T04:45:03.097257Z",
     "shell.execute_reply": "2021-05-21T04:45:03.09588Z",
     "shell.execute_reply.started": "2021-05-21T04:45:03.056217Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.85      0.79       526\n",
      "           1       0.72      0.57      0.64       357\n",
      "\n",
      "    accuracy                           0.74       883\n",
      "   macro avg       0.73      0.71      0.72       883\n",
      "weighted avg       0.74      0.74      0.73       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, best_forest.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stacking classifier\n",
    "Combine all the best models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:45:09.019065Z",
     "iopub.status.busy": "2021-05-21T04:45:09.018711Z",
     "iopub.status.idle": "2021-05-21T04:45:17.114416Z",
     "shell.execute_reply": "2021-05-21T04:45:17.113112Z",
     "shell.execute_reply.started": "2021-05-21T04:45:09.019036Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  0.8506659110229526\n",
      "Test accuracy :  0.7349943374858438\n"
     ]
    }
   ],
   "source": [
    "stack = StackingClassifier(estimators=[('best tree classifier', best_tree),\n",
    "                                       ('best logreg', best_logreg),\n",
    "                                       ('best svc', best_svc),\n",
    "                                       ('best forest classifier', best_forest)],\n",
    "                           \n",
    "                           final_estimator=LogisticRegression(),\n",
    "                           passthrough=True)\n",
    "\n",
    "stack.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, stack.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, stack.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:45:20.76847Z",
     "iopub.status.busy": "2021-05-21T04:45:20.768065Z",
     "iopub.status.idle": "2021-05-21T04:45:20.928944Z",
     "shell.execute_reply": "2021-05-21T04:45:20.927746Z",
     "shell.execute_reply.started": "2021-05-21T04:45:20.768435Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.83      0.79       526\n",
      "           1       0.71      0.59      0.64       357\n",
      "\n",
      "    accuracy                           0.73       883\n",
      "   macro avg       0.73      0.71      0.72       883\n",
      "weighted avg       0.73      0.73      0.73       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, stack.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the final pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-05-21T04:46:46.264354Z",
     "iopub.status.busy": "2021-05-21T04:46:46.263689Z",
     "iopub.status.idle": "2021-05-21T04:46:53.44836Z",
     "shell.execute_reply": "2021-05-21T04:46:53.447163Z",
     "shell.execute_reply.started": "2021-05-21T04:46:46.264313Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final model trained Successfully!\n",
      "Pickled and Saved Successfully!\n"
     ]
    }
   ],
   "source": [
    "# final features\n",
    "features = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'AGE', 'SEX']\n",
    "num_features = ['HAEMATOCRIT', 'HAEMOGLOBINS', 'ERYTHROCYTE', 'LEUCOCYTE','THROMBOCYTE', 'AGE']\n",
    "\n",
    "X = data[features] # feature set\n",
    "y = data['SOURCE'] # target\n",
    "\n",
    "# Splitting data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1, stratify=y)\n",
    "\n",
    "# Label encoding\n",
    "X_train.SEX.replace({'M':1, 'F':0}, inplace=True)\n",
    "X_test.SEX.replace({'M':1, 'F':0}, inplace=True)\n",
    "\n",
    "# Feature scaling\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "X_train[num_features] = scaler.fit_transform(X_train[num_features])\n",
    "X_test[num_features] = scaler.transform(X_test[num_features])\n",
    "\n",
    "# Train the final model again!\n",
    "model = StackingClassifier(estimators=[('best tree classifier', best_tree),\n",
    "                                       ('best logreg', best_logreg),\n",
    "                                       ('best svc', best_svc),\n",
    "                                       ('best forest classifier', best_forest)],\n",
    "                           \n",
    "                           final_estimator=LogisticRegression(),\n",
    "                           passthrough=True)\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "print(\"Final model trained Successfully!\")\n",
    "\n",
    "# Pickle scaler object\n",
    "with open(\"scaler.pkl\", 'wb') as file:\n",
    "    pickle.dump(scaler, file)\n",
    "    \n",
    "# Pickle model object\n",
    "with open(\"model.pkl\", 'wb') as file:\n",
    "    pickle.dump(model, file)\n",
    "\n",
    "print(\"Pickled and Saved Successfully!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### --------------------------- THE END ----------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gaussian Navie Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train accuracy :  0.6911306319070558\n",
      "Test accuracy :  0.6908267270668177\n",
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
      " Best train accuracy :  0.6911306319070558\n",
      " Best test accuracy :  0.6908267270668177\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.83      0.76       526\n",
      "           1       0.66      0.49      0.56       357\n",
      "\n",
      "    accuracy                           0.69       883\n",
      "   macro avg       0.68      0.66      0.66       883\n",
      "weighted avg       0.69      0.69      0.68       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(X_train, y_train)\n",
    "\n",
    "print(\"Train accuracy : \", accuracy_score(y_train, nb.predict(X_train)))\n",
    "print(\"Test accuracy : \", accuracy_score(y_test, nb.predict(X_test)))\n",
    "\n",
    "distribution = {'var_smoothing': [1e-1, 1e-3, 1e-5, 1e-7, 1e-9]}\n",
    "\n",
    "search = RandomizedSearchCV(GaussianNB(),\n",
    "                         distribution,\n",
    "                         scoring='accuracy',\n",
    "                         cv=3,\n",
    "                         verbose=1,\n",
    "                         random_state=1,\n",
    "                         n_iter=5)\n",
    "\n",
    "search.fit(X_train, y_train)\n",
    "search.best_params_\n",
    "\n",
    "best_nb = search.best_estimator_\n",
    "best_nb.fit(X_train, y_train)\n",
    "print(\" Best train accuracy : \", accuracy_score(y_train, best_nb.predict(X_train)))\n",
    "print(\" Best test accuracy : \", accuracy_score(y_test, best_nb.predict(X_test)))\n",
    "print(classification_report(y_test, best_nb.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DOnDggO1sWGG4++671atXL0VFRSklJUXh4eH64YcfFBUVpfDwcLVo0cJ2LLca34iICC1fvlxPPPGEhg4dqho1auirr77S4sWL9dhjj8nd3T3L/ps2bar/+7//02uvvaaWLVvq6tWrioqKUu3atdWgQYNCOUYgN4QY/OWtX79e9erVy/E/3cDAQNWtW1dr167Vzp075eXlpSVLlmjNmjWqVq2aBg4cqKeeekqSdO+99+q9997Tv/71L40ePVre3t5q1aqVnnvuOZUqVUqSNHv2bE2dOlVjxoxR5cqVNWDAAP38889Znt3yZ0OGDNGlS5e0bNkyzZ07VzVq1FCPHj3k4uKihQsX6sqVKypXrpzeeOMNRUVF6b333tOlS5dUp04dvfnmm3rwwQftttemTRt99dVX6tmzp8Nj5unpaTvO2bNnKyEhQXXr1tWUKVPsLm/lVVhYmBYvXqyZM2dq2LBhuvPOOzV16lQNHTrU4W3drEGDBlqxYoVmzZql559/XoZhyM/PT3Pnzs1yo+ufNW/eXEuWLFFUVJRGjBghd3d3+fv765133lFQUJBDdQwdOlTz5s3T4MGDtXXrVt1xxx0FOCp7U6ZM0d/+9jetXbtWS5YsUdWqVdW/f38988wztjNAeRlfLy8vrVixQv/617/0xhtv6Nq1a7b7WwYOHJjtvh9++GGlp6dr1apVWrlypUqXLq377rtPY8eOzTb0AIXNxcjP3WcATG/w4MGyWCxZnusCAGbBmRjgL2bu3Lk6efKkdu3apeXLlzu7HADIN0IM8Bezfft2nTp1SmPHjlXjxo2dXQ4A5BuXkwAAgCnxFmsAAGBKhBgAAGBKhBgAAGBKt9WNvRkZGbpy5Yo8PDyK7AmZAACgcGVmZio1NVXlypXL9SNB/uy2CjFXrlxRXFycs8sAAAD5ULt2bYc+RPe2CjE3Plm2du3ado9xt1qtio2NlZ+fX7af/4FbYwwLhvErOMawYBi/gmMMCya38UtOTlZcXFyWT4i/ldsqxNy4hOTp6SkvLy9bu9VqlXT9sdpMvPxhDAuG8Ss4xrBgGL+CYwwLJi/j5+itINw4AgAATIkQAwAATIkQAwAATIkQAwAATIkQAwAATIkQAwAATMmpIebixYvq0KGDoqOjb9k3NjZWjRo1ylNfAABw+3NaiDl06JD69u2r06dP37JvcnKynn32WaWkpBRDZQAAwAycEmLWr1+v5557TqNHj85T/1deeUXt27cv4qoAAICZOOWJvc2bN1e3bt3k5uZ2yyCzYcMGnTp1SlOmTNG8efPytH2r1Wp7MuCN5Zv/huMYw4Jh/AqOMSyY4hw/35c+LvJ93OzElAeLZT/MwYLJbfzyO6ZOCTFVqlTJU78TJ05o1qxZev/99x16xHNsbGy27TExMXneBrLHGBYM41dwjGHB3I7jd+TIkWLd3+04hsWpMMevxH52UmpqqkaPHq0XX3xRd9xxh0Pr+vn5ZfnspJiYGAUEBPB5F/nEGBYM41dwjGHBFOv4fVi8Z2KCgoLy3HfixInavHmzpOtjkp6ertKlS9teX7hwocLCwrJdN6cxfOqppxQaGqohQ4bk7wD+InKbg0lJSTmegMhNiQ0xMTExiouL00svvaSXXnrJ1j506FD16NFDEydOzHFdi8WS7TdpTu3IO8awYBi/gmMMC+Z2HD9Hjmfy5MmaPHmyJGndunWKiorS9u3bHd7fzftcsmSJQ+v/1WU3B/M7J0vsc2LCwsL0zTff6ODBg7Y/krRgwYJcAwwAAPn1yy+/qH79+po+fboaN26sV155RWlpaXrttdfUtWtXDRw4UM2bN9fkyZNlGIYkqX///pozZ44kafz48ZowYYKGDh2q4OBgtWvXTsuWLctxf19//bX+8Y9/qHnz5goICFBERITd5bE9e/aoT58+Cg4OVtu2bbV8+XLba5s3b1bXrl0VHBysTp06aevWrZKkOXPmqH///nb7adu2rdatW2erd/z48WrTpo1at26thIQEbd++XQ8//LDuu+8+NWrUSI899pji4uJy3VdqaqoaN25sO7MlSWlpaQoPD9fevXvz9w/goBIXYoKDg7Vp0yZnlwEA+AtLTEzUnj17NHr0aL377rvavXu33nnnHb399tuKiorSqlWrtG/fvmzXXbdunfr3768DBw5o8ODBmj59us6dO5elX0pKip5++ml17NhRu3btUnR0tO666y69/vrrkqSTJ09q6NChevjhh3XgwAHNnj1bM2fO1O7duxUdHa0XX3xRY8eO1aFDh/TCCy/o+eef1/Hjx/N0fF999ZVWrVqlTZs2KSEhQSNHjtRTTz2lvXv3aufOnTIMQ3PnzpWkHPd15swZdenSRRs3brRtd8eOHfL29lbTpk0dHfJ8cfrlpJ9++slu+fDhw3nuW6wmlnPevksAi6RQSdp8i463s4lXnLx/5iBz0CxzcGWRlpFFfr83fvaULpexXz/BIqmKel6cr1JTZ6uUpIfSXNTL30WV5teXa4qrTlxxk7dreZ1b0FPaliLFVZSStksXXpaOlFN4JVfd/2ln6VOpd6b0f9bqOv2Kv6pVTbfbvXumtLq5RX+LjVTqhEj9muim8se8FPNbKWliOW2J8ZZ/WQ/1+XaQ9O0gNZS0soWbqm7ppjeO+OiBGi5qtaO7tENqKWllGzdVeztM+slb+qOU/XFdriJtiJS+eUKKq6iWPhmqNt9PklTaKm15wKK7dvVSwucu+j3Bogq/l9G50y7SxEXasK/s//bV6opatmyplStXqlq1aurdu7f69u2r8+fPq0qVKlq/fr0iIiLk4uKSv38TBzk9xAAAUNJU9cy0fZ2c4aJJB8vqwB+lVN3LqnsrZMgwpEwj+x/UVW5a1/2/1zuy62txlaLPldLgnd5KynDR3eUy5OYiGbre948Ui+7wsn/rcYMKGddfS7bo3gr2oSiwUka+js/dVfrPqdJaddxLLi6SX7kMJaS7yO2/tWe7r8BASVJAQIB8fX21ZcsWdevWTV9++aVefvnlPNdRUIQYAAD+5ObI8fL+cipXKlNf9vpDHhYp05Aar61a4H0cjXfX5ENltarDBTWseD2AvP2Dl05eu/6juYaXVV9c9rBbZ+3Pnqrkkaka3ladTbK/GfbtH7wUVDldri6G0v+XUZRpSJdT7UPUzUsfnS6t5bFeer/DRf3N53pomnzQR7FX3K/Xkd2+3n5bQUFBCgkJUe/evbVlyxa5u7srLCxMd955Z77HxFEl7p4YAABKkoR0F3lYDLm6XP/69cM+Skh3tQsK+XEt3UWuLlJpy/UbhI/Eu2tZrLfS/rvdLnel6PtL7tpwsrSsmdK3F900/Wsfubka6lUnWZ/+Ulpf/lZKmYa0+7dSmvNtGfmUMuRbNkM/XXbTsStuysiU/v3D9TM9udbher0Ow5B2nS2lDXGetuOz21dmpnbv3q05c+bIx8dHktS9e3f9+OOP+vDDDxUREVGwQXEQZ2IAAIUqrvSjzi6hUL0celUT9pdTk7VV5e1uqPUdqWpRI1Wxlwv2I/T+6ml69O4k9fuskjIN6c4yVvX3S9S/jvooPtlVd/lYtajVJf3raBlNPlRWlTwyNT7kmprXSJMkvdb0sl477KNfEy2q6W3VzGZXVK9chur4ZOircyl6fHsFZRpSzzopCq2SnmMdveok69D5UuqytbIsLlLdshkaUD9JK2K9lGaVQquk/29fYWGqWbOmZs6cqXr16kmSKlasqFatWmnfvn164IEHCjQmjnIxbrxH7DaQlJSkH374Qffcc0+Wh90dOXJEQUFB+X8+wl/8pkqoQDdVMgdRKJiDcLYc5uC0adOUkpKiV155JcdVc5uDOf38vhXOxAAAgHz57bffFBcXpw0bNmjp0qXFvn9CDAAAyJcPPvhAS5cu1ZNPPql77rmn2PdPiAEAAPkycuRIjRw50mn7591JAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAAAUgrhr+XyIIPKNt1gDAP4yJhwoq81xpSVJGZkuSs+UPN3+9+D6xa0uKaxqzo/oz8n3F9300KeV9G3fc4VWK26NEAMA+MuY1PiqJjW+Kkla97Onor4to+3dzxd4u9fSXZWemfOHLKJoEGIAAPiv09csmvq1jw7Hl5KXm6HutZP1TMMElbJc/wTrf+4vq69+95Cbq6EG5TP0YshVlbJIg7+oIEkK/rCq3m5zScGV7c/mnEty1bTDPvrmgrsupFhUubRVT/snqo9vsiTpTIJFUw756MD5UirlaqhjrVTbtr+76Kbph8vq24tu8nY39Pe6yRoRkKD9f5TSP7ZX1E+P/G7bz/h91z/fanrTK5oTU0aH4911Jc1VZxIsmtvikip6ZOr1Iz766bK7Lqa66k5vq8YGXVObmqmSct7Xkzsr6A5vqybfdExDhgzRvffey8PuAABwtqQMFz2+o6LqlcvQrp5/aGX7C/rqXCnNiSkjSXr7R28lpLvqix5/aEf386rimakZR31Uq4xVi1tdkiQd/vsfWQKMJL28v5zcXaUtneP1dZ9zeswvSZMPlVViuosyMqVBOyqoimemdvU4r/90vqAj8e6aE1NGl1NdNHBHRYVXTVV0xB9a2e6i1p301OoTnnk6pr3nSum5Rte0o/t5BVdO1/AvK8ivXIY+7XZeB3ufU/MaqZp4sKwk5bqv3r7J+vh0aaWlXf8E7fj4eO3Zs0cRERGFMfT5xpkYAAAk7TzrobRMaUyjBLm4SDW8MzUyIEEjviyvZ4MSVNpi6MfLbtoQ56n7q6dpavgVuebxCtKrTa7I292Qu6t0NskibzdDKVYXXUlz0S+Jbvo10aIXQ67K003ydjcU1eKyMg1px68e8rAYeqZholxcpLt8rHqnzUV5uRk6de3WP8JreVt1X/U02/LCVpdUzdMqw5B+TbSobKlMnUu6fj4jt31V9MjUKy5ltX37dj344IPavHmzgoODVatWrXyNdWEhxAAAIOnXBIsupriq8dqqtjZDUnqmiy6kuGrwPYkq5WpozQlPTTpYVrXKWPVso2t6oFbqLbd9JsGi14/4KO6am2r7ZOhvPlZJUqbhovPJFlXwyJTnTT+R7yxz/fWPz5RWDS+rXG4KS3XLXn/t1LVbH1NVz0y75R8vuSlyV3mdT7HIt2yGKnpkytD1jZ9PseS4L0nq+rdkbdy4UQ8++KDWr1+vgQMH3rqAIkaIAQBAUnUvq+4qY9XHXeNtbQnp1wNMRY9M/XTZTW1rpurxBkm6luailce8NHpPee2L+CPX7aZnSkN2VdCYwAQ9Wi9JLi7StxfdtCnO07bfS6muSs6QLcgc/MNd3150V3Uvq35LssgwZAsXn/3ioYR0F93pfT1gpFmlUv99d/elVBdV8Pjfu61uDiTnklw1ck95RbW4rLb/vQdm2xkPffJLaVsdOe2rZ50U9a6brIe279bhw4f1yy+/qGPHjvka58LEPTEAAEhqUzNViRku+vcPXkqzSlfTXDRuXzmN3lNeLi7Shyc89fy+crqQ4qoy7obKlMqUl5uhUhZDHpbrweFaWtbrS+mZLkrJcFFpN0MuLtLZRFe9ccTnv69JgZXSVdsnQ68dLqvkDCk+2VXTDpfVxVRXtb4jVRmZ0oLvvZVm/d+Nx6lWF93lY5Wbi6Etp66HkK9+L6V95zxyPL7EDBdZDRd5/rfW41csmvvt9ft90qzKdV+SdG/FDN19992aNGmSOnfuLE/PvN2XU5QIMQAASCrjbmhpm4uKPuehlhurqv3mKnKVNL/l9Zt2xzRK0N/KWNVla2WFrKmqdT97aV7LS/KwSH7lMxRaJU0tNlTRF2dL2W3Xy83Q1PArmvutt4I/rKp/bK+o+6unqXJpq2KvuMndVVrQ6rLOJbuq9caq6vFxJTWukqYRAQkqW8rQkjaXtPf3Umq+oar6b6+oh+9OVt+7k1XVM1Mvhl7VvO/KKOTDqloe66WIukk5Hl/dslY9H3RVY/eWU+iaqhr5ZXn1rpssd1dDsVfcct3XDREREfr+++/Vu3fvIvk3cBSXkwAAf0kRdZMVUTfZrs23nFWLW1/Ktr+3u6HX7ruS7WteboZWtr+Y47561ElRjzopdm1P3Zto+7pWGavmt7yc7br3VMjQsnbZ19SvXrL61UvO9rXhAQlZ2gbdk6RB99gHnQH1/7ec274kqWbNmqpbt66Cg4Nz7FOcCDEAACBXl1Jd9HuSRfPnz9cjjzzi7HJsuJwEAABy9e1Fdz38aSVVqVJFDz/8sLPLseFMDAAAyFWLGmk6+tA5aeJ8Z5dihzMxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlAgxAADAlJwaYi5evKgOHTooOjo6xz7vv/++OnbsqODgYHXs2FErVqwoxgoBAEBJ5easHR86dEjjx4/X6dOnc+zz2WefaebMmVq8eLEaNWqkI0eO6KmnnlLlypXVsWPHYqwWAACUNE45E7N+/Xo999xzGj16dK79zp07p8GDBysoKEguLi4KDg5WeHi4Dhw4UEyVAgCAksopZ2KaN2+ubt26yc3NLdcg069fP7vlCxcu6MCBA3rhhRdy3b7VapXVarVbvvnv/LDke03cLgoyf5iDKAzMQThbUc3B/G7XKSGmSpUqDq9z/vx5DRkyRA0bNlTXrl1z7RsbG5tte0xMjMP7vSE032vidnHkyJECb4M5iIJgDsLZnD0H/8xp98Q44siRIxo5cqTCwsI0bdo0ubnlXrafn5+8vLxsy1arVTExMQoICJDFks/fJTbnbzXcPoKCgvK9LnMQhYE5CGcrqjmYlJSU4wmI3JT4ELNmzRq9+uqrGjFihAYOHJindSwWS7bfpDm1A3lRGHOHOYiCYA7C2YpqDuZ3uyU6xGzbtk0TJ07U/Pnz1aJFC2eXAwAASpAS97C74OBgbdq0SZIUFRUlq9WqESNGKDg42PZnwoQJTq4SAAA4m9PPxPz00092y4cPH7Z9vXkzF2ABAED2StyZGAAAgLwgxAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFMixAAAAFNyaoi5ePGiOnTooOjo6Bz7fPHFF+rWrZuCgoLUqVMn7dixoxgrBAAAJZXTQsyhQ4fUt29fnT59Osc+cXFxGj58uEaOHKmDBw9q+PDhGjVqlM6dO1eMlQIAgJLIKSFm/fr1eu655zR69Ohb9gsLC1P79u3l5uamzp07q3Hjxlq9enUxVQoAAEoqN2fstHnz5urWrZvc3NxyDTLHjx+Xn5+fXdvdd9+tH3/8MdftW61WWa1Wu+Wb/84PS77XxO2iIPOHOYjCwByEsxXVHMzvdp0SYqpUqZKnfomJifL09LRrK126tJKSknJdLzY2Ntv2mJiYvBWYjdB8r4nbxZEjRwq8DeYgCoI5CGdz9hz8M6eEmLzy9PRUSkqKXVtKSoq8vb1zXc/Pz09eXl62ZavVqpiYGAUEBMhiyefvEpvztxpuH0FBQflelzmIwsAchLMV1RxMSkrK8QREbkp0iPHz89N3331n13b8+HE1bNgw1/UsFku236Q5tQN5URhzhzmIgmAOwtmKag7md7sl+jkx3bt31/79+7V161ZlZGRo69at2r9/v3r06OHs0gAAgJOVuBATHBysTZs2SZJ8fX01d+5cLVy4UI0bN9a8efM0Z84c1alTx8lVAgAAZ3P65aSffvrJbvnw4cN2yy1atFCLFi2KsyQAAGACJe5MDAAAQF4QYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCkRYgAAgCk5FGLGjx+vAwcOFFUtAAAAeeZQiPHy8tLw4cPVoUMHzZs3T7///ntR1QUAAJArh0LMhAkTtHv3bo0dO1YxMTF64IEHNGjQIG3dulVpaWlFVSMAAEAWDt8T4+7urgceeEDz58/XsmXLdOnSJY0ZM0YtWrTQa6+9pmvXrhVFnQAAAHYcDjHnz5/XO++8o549e6p///664447NG/ePL377rs6efKknn766Vtu48KFC4qMjFRYWJjCw8M1ZcoUZWRkZNv33XffVdu2bRUSEqJu3bpp27ZtjpYMAABuQ26OdB40aJD27dununXrKiIiQj169FDFihVtr48ZM0Z9+/a95XZGjRqlatWqaffu3YqPj9fTTz+tpUuX6sknn7Tr98UXX2jhwoVavny56tatq23btmnUqFH69NNPdeeddzpSOgAAuM04FGLuvPNOvf/++woMDMz29Zo1a2rNmjW5buPUqVPav3+/du3aJU9PT9WqVUuRkZF64403soSYn3/+WYZh2P5YLBa5u7vLzc2hsgEAwG3IoTTw0ksvae7cuapQoYJq1aqld999VxcvXtTIkSPl6uoqb29v+fr65rqNY8eOqXz58qpWrZqtzdfXV2fPntXVq1dVtmxZW3uXLl20bt06de7cWRaLRS4uLnrjjTdUvXr1XPdhtVpltVrtlm/+Oz8s+V4Tt4uCzB/mIAoDcxDOVlRzML/bdSjETJ8+XUeOHLFdMvL399f06dOVnp6u559/Pk/bSExMlKenp13bjeWkpCS7EJOenq4GDRpoypQpatCggTZv3qyXXnpJvr6+ql+/fo77iI2NzbY9JiYmTzVmJzTfa+J2ceTIkQJvgzmIgmAOwtmcPQf/zKEQs23bNm3evNl2H0xYWJgWLFignj175jnEeHl5KTk52a7txrK3t7dd++TJkxUSEmK7fNW7d2/95z//0fr16zV+/Pgc9+Hn5ycvLy/bstVqVUxMjAICAmSx5PN3ic35Ww23j6CgoHyvyxxEYWAOwtmKag4mJSXleAIiNw6FmNTUVLtwIEllypTJ8Z1F2alXr54uX76s+Ph4Va5cWZJ04sQJVa9eXT4+PnZ9z549q4YNG9oX7OYmd3f3XPdhsViy/SbNqR3Ii8KYO8xBFARzEM5WVHMwv9t16C3WYWFhmjZtmu3BdqmpqXr99dcVEhKS523Url1boaGhmjp1qhISEnTmzBnNmzdPffr0ydK3bdu2Wr58ub777jtlZmbq448/VnR0tDp37uxI2QAA4Dbk8I29Tz75pEJCQlShQgVdunRJderU0YIFCxza6ezZszVp0iS1a9dOrq6u6tmzpyIjIyVJwcHBeuWVV9S9e3cNGzZMFotFw4cP15UrV/S3v/1Nc+fO1T333OPQ/gAAwO3HoRBTq1Ytbd26VYcOHVJ8fLyqV6+uwMBAh9/yXLlyZc2ePTvb1w4fPvy/4tzcNHz4cA0fPtyh7QMAgNufww9cSUtL01133WV72Nyvv/6q2NhYdejQodCLAwAAyIlDIWbt2rWaPHmyUlNT7dorVapEiAEAAMXKoRCzYMECjRo1St7e3jpw4IAGDBigN954Q/fff39R1QcAAJAth96ddP78eQ0YMED33XefTp8+LX9/f02dOlUffvhhUdUHAACQLYdCTKVKlZSenq4aNWro5MmTkqQ77rhDFy5cKJLiAAAAcuJQiAkMDNSECROUkpKi2rVr6/3339f69etVvnz5IioPAAAgew7dE/PCCy/o5ZdfVmJiosaOHauhQ4cqJSVF06ZNK6r6AAAAsuVQiDlw4IDmzJkjDw8PVa1aVfv27VN6enqWD3QEAAAoag5dTnrllVfk6vq/Vdzc3AgwAADAKRwKMQEBAdq6dWtR1QIAAJBnDl1Ounz5ssaNG6d//vOfqly5slxcXGyvff7554VeHAAAQE4cCjGPPfZYUdUBAADgEIdCTK9evYqqDgAAAIc4FGL69+9vdwnpZsuWLSuUggAAAPLCoRATHh5ut3zp0iV9/PHH6tu3b6EWBQAAcCsOhZhhw4ZlaYuIiNDrr79eaAUBAADkhUNvsc6Ov7+/vv3228KoBQAAIM8cOhNz9uxZu+X09HRt2bJFNWrUKNSiAAAAbsWhENO2bVu7G3sNw1C5cuX06quvFnphAAAAuXEoxPz5gXYWi0WVKlWSu7t7oRYFAABwKw7dE1O1alV98MEHyszMVM2aNbVt2zbNnTtXmZmZRVUfAABAthwKMVOnTtWuXbtksVgkXb+p98svv9SMGTOKpDgAAICcOHQ56ZNPPtHmzZtVsWJFSVJYWJgWLFignj176vnnny+SAkuK2ikrnV0CnCzOyftnDiLOyftnDiLO2QX8iUNnYlJTU+Xl5WXXVqZMGWVkZBRqUQAAALfiUIgJCwvTtGnTlJaWJul6qHn99dcVEhJSJMUBAADkxKHLSS+99JIGDRqkkJAQVahQQZcuXVKdOnW0YMGCoqoPAAAgWw6FmFq1aumjjz7S119/rfPnz6t69eoKDAyUm5tDmwEAACgwhy4nXb16Vc8//7wqVqyozp07a/fu3XrhhReUmJhYVPUBAABky6EQM3HiRF25ckXly5eXJHXt2lXXrl3T1KlTi6I2AACAHDl0Heirr77S559/Lm9vb0mSr6+vZsyYoQ4dOhRJcQAAADlx6ExMZmamrFarXZthGLaH3wEAABQXh0JMy5YtNW7cOJ0+fVrp6ek6ffq0XnjhBd1///1FVR8AAEC2HAoxL774ohISEvTAAw8oMDBQHTt2VHJyssaNG1dU9QEAAGTLoXtiKlasqPfee09nz57V+fPnZbVatWHDBrVt21ZHjhwpohIBAACyytcDXs6ePaslS5boiy++UL169TR27NjCrgsAACBXeQ4xmZmZ+vjjj/XOO+/o2LFjysjI0MKFC9WiRYuirA8AACBbebon5t1331WHDh30xhtvqEOHDtq5c6fKlCkjPz+/oq4PAAAgW3k6EzNt2jQ9+uijGj9+vEqVKlXUNQEAANxSns7E/POf/1R0dLRatWqlWbNm6dy5c3JxcSnq2gAAAHKUpxDTr18/bdmyRTNnztTx48fVoUMHXb16VXv37s3y8DsAAIDi4NBzYu677z7NnTtXH330kR5//HFNnz5dLVq00PTp04uqPgAAgGw5FGJuqFmzpsaOHatdu3ZpzJgx2r9/v0PrX7hwQZGRkQoLC1N4eLimTJmijIyMbPvu379ff//73xUcHKxWrVpp4cKF+SkZAADcZvIVYm4oVaqU+vTpo3Xr1jm03qhRo+Tl5aXdu3drzZo12rt3r5YuXZql34kTJ/TUU0/p0Ucf1ddff62FCxfq7bff1scff1yQsgEAwG2gQCEmP06dOqX9+/dr7Nix8vT0VK1atRQZGakVK1Zk6bty5Uq1a9dOvXr1kouLixo0aKBVq1YpNDS0uMsGAAAlTL6e2FsQx44dU/ny5VWtWjVbm6+vr86ePaurV6+qbNmytvZvvvlGzZo105gxY7Rnzx5VrFhRjz/+uPr27ZvrPqxWq90Nxze+5iZkFERB5g9zEIWBOQhnK6o5mN/tFnuISUxMlKenp13bjeWkpCS7EHPlyhUtW7ZMs2bN0uuvv67Dhw9ryJAhKleunB588MEc9xEbG5tte0xMTCEcAf6qCuPzwZiDKAjmIJytpM3BYg8xXl5eSk5Otmu7sezt7W3XXqpUKbVr106tW7eWJDVu3Fg9evTQRx99lGuI8fPzk5eXl23ZarUqJiZGAQEBslgs+Sv8Q+7D+asLCgrK97rMQRQG5iCcrajmYFJSUo4nIHJT7CGmXr16unz5suLj41W5cmVJ12/grV69unx8fOz6+vr6Ki0tza7NarXKMIxc92GxWLL9Js2pHciLwpg7zEEUBHMQzlZUczC/2y32G3tr166t0NBQTZ06VQkJCTpz5ozmzZunPn36ZOn78MMP6/PPP9fGjRtlGIYOHDigzZs3q0ePHsVdNgAAKGGKPcRI0uzZs5WRkaF27drpoYceUosWLRQZGSlJCg4O1qZNmyRdf7jevHnztGzZMoWGhuqFF17QuHHj1K5dO2eUDQAASpBiv5wkSZUrV9bs2bOzfe3w4cN2y61atVKrVq2KoywAAGAiTjkTAwAAUFCEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEqEGAAAYEpOCTEXLlxQZGSkwsLCFB4erilTpigjIyPXdWJjY9WoUSNFR0cXU5UAAKAkc0qIGTVqlLy8vLR7926tWbNGe/fu1dKlS3Psn5ycrGeffVYpKSnFVyQAACjRij3EnDp1Svv379fYsWPl6empWrVqKTIyUitWrMhxnVdeeUXt27cvxioBAEBJ51bcOzx27JjKly+vatWq2dp8fX119uxZXb16VWXLlrXrv2HDBp06dUpTpkzRvHnz8rQPq9Uqq9Vqt3zz30B+FGT+MAdRGJiDcLaimoP53W6xh5jExER5enratd1YTkpKsgsxJ06c0KxZs/T+++/LYrHkeR+xsbHZtsfExOSjYuC6I0eOFHgbzEEUBHMQzlbS5mCxhxgvLy8lJyfbtd1Y9vb2trWlpqZq9OjRevHFF3XHHXc4tA8/Pz95eXnZlq1Wq2JiYhQQEOBQGLLz4cf5Ww+3jaCgoHyvyxxEYWAOwtmKag4mJSXleAIiN8UeYurVq6fLly8rPj5elStXlnT9jEv16tXl4+Nj6xcTE6O4uDi99NJLeumll2ztQ4cOVY8ePTRx4sQc92GxWLL9Js2pHciLwpg7zEEUBHMQzlZUczC/2y32EFO7dm2FhoZq6tSpmjRpki5duqR58+apT58+dv3CwsL0zTff2LXVr19fCxYsUHh4eHGWDAAASiCnvMV69uzZysjIULt27fTQQw+pRYsWioyMlCQFBwdr06ZNzigLAACYSLGfiZGkypUra/bs2dm+dvjw4RzX++mnn4qqJAAAYDJ87AAAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlQgwAADAlp4SYCxcuKDIyUmFhYQoPD9eUKVOUkZGRbd/3339fHTt2VHBwsDp27KgVK1YUc7UAAKAkckqIGTVqlLy8vLR7926tWbNGe/fu1dKlS7P0++yzzzRz5ky99tpr+vrrrzV9+nS9+eab2rZtW/EXDQAASpRiDzGnTp3S/v37NXbsWHl6eqpWrVqKjIzM9gzLuXPnNHjwYAUFBcnFxUXBwcEKDw/XgQMHirtsAABQwrgV9w6PHTum8uXLq1q1arY2X19fnT17VlevXlXZsmVt7f369bNb98KFCzpw4IBeeOGFXPdhtVpltVrtlm/+G8iPgswf5iAKA3MQzlZUczC/2y32EJOYmChPT0+7thvLSUlJdiHmZufPn9eQIUPUsGFDde3aNdd9xMbGZtseExOTj4qB644cOVLgbTAHURDMQThbSZuDxR5ivLy8lJycbNd2Y9nb2zvbdY4cOaKRI0cqLCxM06ZNk5tb7mX7+fnJy8vLtmy1WhUTE6OAgABZLJb8Ff7hx/lbD7eNoKCgfK/LHERhYA7C2YpqDiYlJeV4AiI3xR5i6tWrp8uXLys+Pl6VK1eWJJ04cULVq1eXj49Plv5r1qzRq6++qhEjRmjgwIF52ofFYsn2mzSndiAvCmPuMAdREMxBOFtRzcH8brfYb+ytXbu2QkNDNXXqVCUkJOjMmTOaN2+e+vTpk6Xvtm3bNHHiRM2ZMyfPAQYAAPw1OOUt1rNnz1ZGRobatWunhx56SC1atFBkZKQkKTg4WJs2bZIkRUVFyWq1asSIEQoODrb9mTBhgjPKBgAAJUixX06SpMqVK2v27NnZvnb48GHb15s3by6ukgAAgMnwsQMAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUCDEAAMCUnBJiLly4oMjISIWFhSk8PFxTpkxRRkZGtn2/+OILdevWTUFBQerUqZN27NhRzNUCAICSyCkhZtSoUfLy8tLu3bu1Zs0a7d27V0uXLs3SLy4uTsOHD9fIkSN18OBBDR8+XKNGjdK5c+eKv2gAAFCiFHuIOXXqlPbv36+xY8fK09NTtWrVUmRkpFasWJGl7/r16xUWFqb27dvLzc1NnTt3VuPGjbV69eriLhsAAJQwbsW9w2PHjql8+fKqVq2arc3X11dnz57V1atXVbZsWVv78ePH5efnZ7f+3XffrR9//DHbbWdmZkqSEhMTZbVas7QnJCTI1TV/ua1O+WIfKpQw165dy/e6zEEUBuYgnK2o5mBKSopdn7wq9hmZmJgoT09Pu7Yby0lJSXYhJru+pUuXVlJSUrbbTk1NlSSdPn0629ePHz+e77pndKic73Vxe4iNjS3wNpiDKAjmIJytqOdgamqqypQpk+dtFXuI8fLyUnJysl3bjWVvb2+7dk9PT1s6uyElJSVLvxvKlSun2rVry8PDI9+/aQAAgOKVmZmp1NRUlStXzqH1ij3E1KtXT5cvX1Z8fLwqV76e6k+cOKHq1avLx8fHrq+fn5++++47u7bjx4+rYcOG2W7bzc1NlSpVKprCAQBAkXHkDMwNxX66onbt2goNDdXUqVOVkJCgM2fOaN68eerTp0+Wvt27d9f+/fu1detWZWRkaOvWrdq/f7969OhR3GUDAIASxsUwDKO4dxofH69JkyYpOjparq6u6tmzp5577jlZLBYFBwfrlVdeUffu3SVJu3fv1owZM3T69GnVrFlTY8eOVatWrYq7ZAAAUNIYt6HExERj/PjxRpMmTYyQkBBj7NixRkJCQo79J0yYYPj7+xtBQUG2P6tWrSrGikuG+Ph44+mnnzZCQ0ONJk2aGK+++qqRnp6ebd+dO3caXbt2NRo1amQ8+OCDxvbt24u52pLHkfEbNGiQ0bBhQ7s598UXXxRzxSXXhQsXjPbt2xv79u3LsQ9zMGd5GT/mYPZ++OEH4/HHHzcaN25sNGvWzBg7dqxx4cKFbPsyB7NyZPwKYw7eliFm/PjxxoABA4xLly4Z8fHxxmOPPWZMnDgxx/69evUy1q1bV4wVlkyPPfaY8eyzzxpJSUnG6dOnjS5duhiLFy/O0u/kyZNGQECA8emnnxrp6enGli1bjMDAQOP33393QtUlR17HzzAMIzw83IiOji7mCs3h4MGDRvv27Q0/P78cfwgzB3OWl/EzDOZgdpKTk43777/feOutt4zU1FTj4sWLxuDBg40hQ4Zk6csczMqR8TOMwpmDt12ISUpKMvz9/Y1Dhw7Z2o4cOWIEBgYaSUlJWfqnpqYa/v7+RmxsbHGWWeLExcUZfn5+dt+AW7ZsMVq3bp2l78yZM40nnnjCrm3QoEHGW2+9VeR1llSOjN/p06eNBg0aGNeuXSvOEk1h3bp1RuvWrY0tW7bk+kOYOZi9vI4fczB7J06cMAYNGmRkZGTY2j777DMjJCQkS1/mYFaOjF9hzUFTvg85JSVFp06dyvFPenq63UPyfH19lZKSori4uCzb+vHHH5WRkaHZs2erWbNm6tixoxYtWuTwA3fM7lYPIbyZow8h/CtwZPxiYmLk7e2t0aNHq2nTpuratavWrFlT3CWXSM2bN9enn36qzp0759qPOZi9vI4fczB7devW1b///W9ZLBZb27Zt2+Tv75+lL3MwK0fGr7DmoCkfv3j06FH94x//yPa1kSNHSrr+PJobbjwwLzExMUv/a9euqUmTJurfv79mzpypH374Qc8884xcXV315JNPFkH1JVNRPoTwr8CR8UtLS1NQUJBGjx6tevXqKTo6WsOHD5e3t7c6depUrHWXNFWqVMlTP+Zg9vI6fszBWzMMQ2+++aZ27Nih5cuXZ3mdOZi7W41fYc1BU4aY8PBw/fTTT9m+9v333+utt95ScnKy7aF4Nx6ml9170O+//37df//9tuXAwEANGDBAW7du/UuFmKJ8COFfgSPj17NnT/Xs2dO23Lx5c/Xs2VMfffQRP0DyiDlYMMzB3CUkJOiFF17Qd999p+XLl6t+/fpZ+jAHc5aX8SusOWjKy0m5qVOnjtzd3e0ea3zixAm5u7urdu3aWfp/9tlnWrVqlV1bWlqaSpcuXdSllig3P4TwhtweQnjs2DG7tuPHj6tevXrFUmtJ5Mj4rVmzRh999JFdW1pamjw8PIql1tsBc7BgmIM5O336tHr37q2EhAStWbMm2x/AEnMwJ3kdv8Kag7ddiPH09FSnTp00Y8YMXbx4URcvXtSMGTPUtWvXbIOJYRiaNm2a9u7dK8MwdPjwYS1btkx9+/Z1QvXOw0MIC8aR8UtISNDkyZP1/fffKzMzUzt37tR//vOfv9ycKwjmYMEwB7N35coVDRgwQCEhIVqyZIkqVqyYY1/mYFaOjF+hzcEC3RZcQl27ds14+eWXjWbNmhmNGzc2xo8fbyQmJtpe79y5szF//nzb8vvvv2888MADRqNGjYx27doZy5cvd0bZTnf+/Hlj+PDhRpMmTYymTZsa06dPt91lHhQUZGzcuNHWd9euXUb37t2NoKAgo0uXLsbOnTudVXaJkdfxy8zMNObOnWu0adPGCAwMNLp06WJ89NFHziy9RPrzu2uYg47JbfyYg9l7++23DT8/P6NRo0Z2zy4JCgoyDIM5eCuOjF9hzUGnPLEXAACgoG67y0kAAOCvgRADAABMiRADAABMiRADAABMiRADAABMiRADAABMiRADAABMiRADoMjVr19f9evX188//5zltXfeeUf169fXnDlz8rXt6OjoHB9t/mfr1q1T27Zt87UfACUPIQZAsahQoYLWr1+fpX3dunXZfjgrANwKIQZAsejWrZs2btyozMxMW9s333yjtLQ03Xvvvba2zMxMLVq0SO3bt1doaKj69Omj3bt3217/448/NHToUIWEhKhdu3bas2eP3X5Onz6toUOHKjw8XG3atNGsWbOUlpZW9AcIoNgRYgAUi9atWys9PV1fffWVrW3NmjVZPiRz7ty5WrFihd566y1FR0dr4MCBioyM1DfffCNJGj16tNzc3LRr1y4tX75cu3btsq2blJSkxx9/XPXq1dOuXbu0cuVKffXVV/m+VAWgZCPEACgWbm5u6tatm+2SUkpKirZt26aePXva9Vu7dq2eeuop+fv7y83NTZ07d1bbtm21Zs0a/frrrzp48KCee+45lSlTRjVq1NCwYcNs6+7cuVNpaWkaM2aMPDw8VKNGDY0cOVIrVqwozkMFUEzcnF0AgL+OiIgI9e3bVwkJCfrss88UEhKiKlWq2PWJj49XrVq17NruvPNO/fjjjzp37pwk6Y477rC9dtddd9m+/vXXX3Xx4kU1btzY1mYYhtLT03XhwoWiOCQATkSIAVBsGjRooLp16+qjjz7S5s2bNWDAgCx9atasqTNnzti1nTlzRlWrVlX16tVty76+vpKk33//3davevXquuuuu/Txxx/b2hISEnThwgVVrFixKA4JgBNxOQlAsYqIiNDSpUt18uRJtWrVKsvrf//737Vo0SJ99913slqt+uijj7R9+3b16tVLd9xxh5o3b65p06bpypUrOn/+vKKiomzrtmnTRomJifr3v/+ttLQ0Xb16VePGjdPo0aPl4uJSnIcJoBgQYgAUq65du+rUqVPq3r273Nyyngx+4okn1K9fP40ePVphYWFauHChZs6cqSZNmkiS/vWvf8nHx0dt2rRR79691axZM9u6ZcqU0dKlSxUdHa2WLVuqffv2cnV11fz584vt+AAUHxfDMAxnFwEAAOAozsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABTIsQAAABT+n9/U5S4zxPz4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Get the accuracy scores for the different models\n",
    "train_accuracy = [accuracy_score(y_train, nb.predict(X_train)),\n",
    "                  accuracy_score(y_train, search.predict(X_train)),\n",
    "                  accuracy_score(y_train, best_nb.predict(X_train))]\n",
    "test_accuracy = [accuracy_score(y_test, nb.predict(X_test)),\n",
    "                accuracy_score(y_test, search.predict(X_test)),\n",
    "                accuracy_score(y_test, best_nb.predict(X_test))]\n",
    "\n",
    "# Create the bar plot\n",
    "plt.bar(range(len(train_accuracy)), train_accuracy, label='Train accuracy')\n",
    "plt.bar(range(len(train_accuracy)), test_accuracy, bottom=train_accuracy, label='Test accuracy')\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.title('Accuracy for different models')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN CLASSIFIER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
      "Best hyperparameters: {'weights': 'uniform', 'p': 1, 'n_neighbors': 11}\n",
      " Best train accuracy :  0.7769906489090393\n",
      " Best test accuracy :  0.7180067950169875\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.74      0.82      0.78       526\n",
      "           1       0.68      0.57      0.62       357\n",
      "\n",
      "    accuracy                           0.72       883\n",
      "   macro avg       0.71      0.69      0.70       883\n",
      "weighted avg       0.71      0.72      0.71       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Initialize KNN classifier\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# Define the hyperparameter distribution\n",
    "param_dist = {'n_neighbors': [3, 5, 7, 9, 11],\n",
    "              'weights': ['uniform', 'distance'],\n",
    "              'p': [1, 2]}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "search = RandomizedSearchCV(knn, param_dist, cv=3, scoring='accuracy', verbose=1, n_iter=5, random_state=1)\n",
    "\n",
    "# Fit the model to the training data\n",
    "search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best hyperparameters:\", search.best_params_)\n",
    "\n",
    "# Fit the best model to the training data\n",
    "best_knn = search.best_estimator_\n",
    "\n",
    "# Print the accuracy of the best model\n",
    "print(\" Best train accuracy : \", accuracy_score(y_train, best_knn.predict(X_train)))\n",
    "print(\" Best test accuracy : \", accuracy_score(y_test, best_knn.predict(X_test)))\n",
    "print(classification_report(y_test, best_knn.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boost classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
      "Best hyperparameters: {'n_estimators': 100, 'min_samples_split': 10, 'min_samples_leaf': 2, 'max_depth': 7, 'learning_rate': 0.01}\n",
      " Best train accuracy :  0.8387645225276282\n",
      " Best test accuracy :  0.7338618346545867\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.73      0.87      0.80       526\n",
      "           1       0.74      0.53      0.62       357\n",
      "\n",
      "    accuracy                           0.73       883\n",
      "   macro avg       0.74      0.70      0.71       883\n",
      "weighted avg       0.73      0.73      0.72       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Initialize Gradient Boosting Classifier\n",
    "gbc = GradientBoostingClassifier()\n",
    "\n",
    "# Define hyperparameter distribution\n",
    "param_dist = {'learning_rate': [0.01, 0.1, 0.5, 1],\n",
    "              'n_estimators': [50, 100, 200, 500],\n",
    "              'max_depth': [1, 3, 5, 7],\n",
    "              'min_samples_split': [2, 5, 10],\n",
    "              'min_samples_leaf': [1, 2, 4]}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "search = RandomizedSearchCV(gbc, param_dist, cv=3, scoring='accuracy', verbose=1, random_state=1, n_iter=5)\n",
    "\n",
    "# Fit the model to the training data\n",
    "search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best hyperparameters:\", search.best_params_)\n",
    "\n",
    "# Fit the best model to the training data\n",
    "best_gbc = search.best_estimator_\n",
    "\n",
    "# Print the accuracy of the best model\n",
    "print(\" Best train accuracy : \", accuracy_score(y_train, best_gbc.predict(X_train)))\n",
    "print(\" Best test accuracy : \", accuracy_score(y_test, best_gbc.predict(X_test)))\n",
    "print(classification_report(y_test, best_gbc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MLP classifer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
      "Best hyperparameters: {'solver': 'adam', 'learning_rate': 'constant', 'hidden_layer_sizes': (50,), 'alpha': 0.001, 'activation': 'relu'}\n",
      " Best train accuracy :  0.752337772740153\n",
      " Best test accuracy :  0.7089467723669309\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.83      0.77       526\n",
      "           1       0.68      0.53      0.59       357\n",
      "\n",
      "    accuracy                           0.71       883\n",
      "   macro avg       0.70      0.68      0.68       883\n",
      "weighted avg       0.71      0.71      0.70       883\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "# Initialize MLP Classifier\n",
    "mlp = MLPClassifier()\n",
    "\n",
    "# Define hyperparameter distribution\n",
    "param_dist = {'hidden_layer_sizes': [(50,), (100,), (150,)],\n",
    "              'activation': ['tanh', 'relu'],\n",
    "              'solver': ['lbfgs', 'sgd', 'adam'],\n",
    "              'alpha': [0.0001, 0.001, 0.01],\n",
    "              'learning_rate': ['constant', 'invscaling', 'adaptive']}\n",
    "\n",
    "# Initialize RandomizedSearchCV\n",
    "search = RandomizedSearchCV(mlp, param_dist, cv=3, scoring='accuracy', verbose=1, random_state=1, n_iter=5)\n",
    "\n",
    "# Fit the model to the training data\n",
    "search.fit(X_train, y_train)\n",
    "\n",
    "# Print the best hyperparameters\n",
    "print(\"Best hyperparameters:\", search.best_params_)\n",
    "\n",
    "# Fit the best model to the training data\n",
    "best_mlp = search.best_estimator_\n",
    "\n",
    "# Print the accuracy of the best model\n",
    "print(\" Best train accuracy : \", accuracy_score(y_train, best_mlp.predict(X_train)))\n",
    "print(\" Best test accuracy : \", accuracy_score(y_test, best_mlp.predict(X_test)))\n",
    "print(classification_report(y_test, best_mlp.predict(X_test)))"
   ]
  }
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