05_Classification.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "fiscal-perspective",
   "metadata": {},
   "source": [
    "# Flight Delay Prediction\n",
    "## Classification - Is the flight delayed or not?\n",
    "\n",
    "Target: **ArrDel15**\n",
    "\n",
    "- <strong>Environment setup:</strong> import libraries and load the dataset\n",
    "- <strong>Data Analysis:</strong> understand the meaning and the predictive power of the variables \n",
    "- <strong>Feature Selection:</strong> evaluate and keep only the most relevant variables\n",
    "- <strong>Preprocessing:</strong> encode categorical variables and partition the dataset for training and testing\n",
    "- <strong>Model design:</strong> train and test various models\n",
    "- <strong>Performance evaluation:</strong> evaluate the models based on their metrics\n",
    "- <strong>Explainability:</strong> understand how the model produces results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "loose-player",
   "metadata": {},
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "streaming-destruction",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pre-requisites\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Store the classifier models to save time\n",
    "import joblib\n",
    "\n",
    "# Preprocessing\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Classifiers from scikit-learn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# Performance metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "from scipy.stats import pointbiserialr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "wrapped-finland",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nive/anaconda3/lib/python3.8/site-packages/numpy/lib/arraysetops.py:583: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1851436 entries, 0 to 1851435\n",
      "Data columns (total 33 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   Year             int64  \n",
      " 1   Quarter          int64  \n",
      " 2   Month            int64  \n",
      " 3   DayofMonth       int64  \n",
      " 4   FlightDate       object \n",
      " 5   OriginAirportID  int64  \n",
      " 6   Origin           object \n",
      " 7   DestAirportID    int64  \n",
      " 8   Dest             object \n",
      " 9   CRSDepTime       int64  \n",
      " 10  DepTime          float64\n",
      " 11  DepDelayMinutes  float64\n",
      " 12  DepDel15         float64\n",
      " 13  CRSArrTime       int64  \n",
      " 14  ArrTime          float64\n",
      " 15  ArrDelayMinutes  float64\n",
      " 16  ArrDel15         float64\n",
      " 17  Time_new         int64  \n",
      " 18  windspeedKmph    int64  \n",
      " 19  winddirDegree    int64  \n",
      " 20  weatherCode      int64  \n",
      " 21  precipMM         float64\n",
      " 22  visibility       int64  \n",
      " 23  pressure         int64  \n",
      " 24  cloudcover       int64  \n",
      " 25  DewPointF        int64  \n",
      " 26  WindGustKmph     int64  \n",
      " 27  tempF            int64  \n",
      " 28  WindChillF       int64  \n",
      " 29  humidity         int64  \n",
      " 30  date             object \n",
      " 31  time             int64  \n",
      " 32  airport          object \n",
      "dtypes: float64(7), int64(21), object(5)\n",
      "memory usage: 480.3+ MB\n",
      "None\n",
      "\n",
      "Shape: (1851436, 33)\n"
     ]
    }
   ],
   "source": [
    "# Loading the combined flight and weather data\n",
    "filepath = \"Data/flight_and_weather.csv\"\n",
    "df = pd.read_csv(filepath, index_col=0)\n",
    "\n",
    "# Understanding the column data\n",
    "print(df.info())\n",
    "print(f\"\\nShape: {df.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "sophisticated-transsexual",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style>\n",
       "table {float:left}\n",
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "%%html\n",
    "<style>\n",
    "table {float:left}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pediatric-stretch",
   "metadata": {},
   "source": [
    "### Eliminating Redundancy\n",
    "|Column|Reason for elimination|\n",
    "|:-|:-|\n",
    "|FlightDate| The columns Year, Month and DayofMonth give this information in separate columns|\n",
    "|OriginAirportID| It gives the same information as Origin|\n",
    "|DestAirportID| It gives the same information as Dest|\n",
    "|**CRSArrTime, ArrTime and ArrDelayMinutes**| They **leak information about target ArrDel15**|\n",
    "|Time_new|It is a duplicate of time|\n",
    "|date |It is a duplicate of FlightDate|\n",
    "|airport|It is a duplicate of Origin|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "executed-violation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Shape: (1851436, 24)\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1851436 entries, 0 to 1851435\n",
      "Data columns (total 24 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   Year             int64  \n",
      " 1   Quarter          int64  \n",
      " 2   Month            int64  \n",
      " 3   DayofMonth       int64  \n",
      " 4   Origin           object \n",
      " 5   Dest             object \n",
      " 6   CRSDepTime       int64  \n",
      " 7   DepTime          float64\n",
      " 8   DepDelayMinutes  float64\n",
      " 9   DepDel15         float64\n",
      " 10  ArrDel15         float64\n",
      " 11  windspeedKmph    int64  \n",
      " 12  winddirDegree    int64  \n",
      " 13  weatherCode      int64  \n",
      " 14  precipMM         float64\n",
      " 15  visibility       int64  \n",
      " 16  pressure         int64  \n",
      " 17  cloudcover       int64  \n",
      " 18  DewPointF        int64  \n",
      " 19  WindGustKmph     int64  \n",
      " 20  tempF            int64  \n",
      " 21  WindChillF       int64  \n",
      " 22  humidity         int64  \n",
      " 23  time             int64  \n",
      "dtypes: float64(5), int64(17), object(2)\n",
      "memory usage: 353.1+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Dropping columns with redundant or duplicate data\n",
    "df.drop(columns=[\"FlightDate\",\n",
    "                 \"OriginAirportID\",\n",
    "                 \"DestAirportID\",\n",
    "                 \"CRSArrTime\",\n",
    "                 \"ArrTime\",\n",
    "                 \"ArrDelayMinutes\",\n",
    "                 \"Time_new\",\n",
    "                 \"date\",\n",
    "                 \"airport\"],\n",
    "        inplace=True)\n",
    "print(f\"\\nShape: {df.shape}\", end=\"\\n\\n\")\n",
    "print(df.info())\n",
    "# df.to_csv(\"./Data/flight_and_weather_without_redundant_info.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "french-segment",
   "metadata": {},
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "athletic-pottery",
   "metadata": {},
   "source": [
    "Plotting the percentage of observations that are **Delayed (1.0)** and **Not Delayed (0.0).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "regulated-dynamics",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0    1463378\n",
      "1.0     388058\n",
      "Name: ArrDel15, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Plotting the percentage of observations that fall under each class\n",
    "ax = df[\"ArrDel15\"].value_counts().sort_values().plot(kind=\"barh\", color=[\"r\", \"g\"])\n",
    "totals= []\n",
    "for i in ax.patches:\n",
    "    totals.append(i.get_width())\n",
    "total = sum(totals)\n",
    "for i in ax.patches:\n",
    "     ax.text(i.get_width()+.3, i.get_y()+.20, \n",
    "     str(round((i.get_width()/total)*100, 2))+'%', \n",
    "     fontsize=10, color='black')\n",
    "plt.title(\"ArrDel15\", fontsize=20)\n",
    "plt.xlabel(\"Count\", fontsize=14)\n",
    "plt.ylabel(\"Class\", fontsize=14)\n",
    "plt.show()\n",
    "print(df[\"ArrDel15\"].value_counts())\n",
    "fig = ax.get_figure()\n",
    "fig.savefig(\"Img/05_01_ArrDel15_Class_Imbalance.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interesting-beverage",
   "metadata": {},
   "source": [
    "### Point Biserial Correlation for Quantitative, Continuous Features with ArrDel15\n",
    "A **correlation coefficient** measures the extent to which two variables tend to change together. The coefficient describes both the strength and the direction of the relationship.<br>\n",
    "A **point-biserial correlation** is used to measure the strength and direction of the association that exists between **one continuous variable and one dichotomous variable.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "durable-portfolio",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>CorrCoeff</th>\n",
       "      <th>pValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DepDelayMinutes</td>\n",
       "      <td>0.546150</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>precipMM</td>\n",
       "      <td>0.088363</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>windspeedKmph</td>\n",
       "      <td>0.085062</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WindGustKmph</td>\n",
       "      <td>0.080279</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DewPointF</td>\n",
       "      <td>0.038158</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>cloudcover</td>\n",
       "      <td>0.037821</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tempF</td>\n",
       "      <td>0.035412</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WindChillF</td>\n",
       "      <td>0.032019</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>humidity</td>\n",
       "      <td>-0.000729</td>\n",
       "      <td>3.213589e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>visibility</td>\n",
       "      <td>-0.021870</td>\n",
       "      <td>1.255175e-194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>pressure</td>\n",
       "      <td>-0.073902</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Feature  CorrCoeff         pValue\n",
       "0   DepDelayMinutes   0.546150   0.000000e+00\n",
       "1          precipMM   0.088363   0.000000e+00\n",
       "2     windspeedKmph   0.085062   0.000000e+00\n",
       "3      WindGustKmph   0.080279   0.000000e+00\n",
       "4         DewPointF   0.038158   0.000000e+00\n",
       "5        cloudcover   0.037821   0.000000e+00\n",
       "6             tempF   0.035412   0.000000e+00\n",
       "7        WindChillF   0.032019   0.000000e+00\n",
       "8          humidity  -0.000729   3.213589e-01\n",
       "9        visibility  -0.021870  1.255175e-194\n",
       "10         pressure  -0.073902   0.000000e+00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# encode categorical data into numeric values\n",
    "labelEncoder = LabelEncoder()\n",
    "df[\"Origin\"] = labelEncoder.fit_transform(df[\"Origin\"])\n",
    "df[\"Dest\"] = labelEncoder.fit_transform(df[\"Dest\"])\n",
    "\n",
    "# get continuous and dichotomous data\n",
    "categorical = [\"Year\", \"Quarter\", \"Month\", \"DayofMonth\", \"Origin\", \"Dest\", \"CRSDepTime\", \"DepTime\", \"DepDel15\", \"winddirDegree\", \"weatherCode\", \"precipMM\", \"time\", \"ArrDel15\"]\n",
    "numeric = [\"DepDelayMinutes\", \"windspeedKmph\", \"precipMM\", \"visibility\", \"pressure\", \"cloudcover\", \"DewPointF\", \"WindGustKmph\", \"tempF\", \"WindChillF\", \"humidity\"]\n",
    "target = df[\"ArrDel15\"]\n",
    "\n",
    "# pbc of first question\n",
    "pbc = list()\n",
    "for col in numeric:\n",
    "    ans = pointbiserialr(df[col], target)\n",
    "    pbc.append([col, ans[0], ans[1]])\n",
    "    \n",
    "pbc_corr = pd.DataFrame(pbc, columns=[\"Feature\", \"CorrCoeff\", \"pValue\"]).sort_values(by=\"CorrCoeff\", ascending=False).reset_index(drop=True)\n",
    "pbc_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "functioning-blair",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RNM0a7sTPm4Kg/ofdkpHEui2baZBUlzqC33JzaZBUl0N2b1E4S9+5HY0SZIJelQR7SSnA48AjZmaSpgGXSno3DLx/AFaFyQ+TtDdBN/7pxJiEJ6kJ8Bjwupn9HJY3UtKLZrZBUktgazhXoMAuQFZ4vB7AXhH7XgNuJbhz4KwYp3A/MMvM1mzHP4wVwGXhEEFLgiGCAlsl1TOzrcA7wCRJD5pZVjgXoSmwEdhiZq9I+gZ4Nt4KOBevfDP+vXAeI7sfRx2JGcu/4rt1v3DCvsH4+5tff0GX1m3ou98B5OUbW/JyuSe8GGjWcCf+2a0XAEl1xKwVy/noh1UlHcq56lVDW+rxqsxgv1PYHV1w693zwAPhvqcIWuUfK4ie2cDAcN884C6gIzCbIBAXeC9MXyfcPhLAzKaHY97zwmC8ATiboBehwIvAfyUtBBYBnxfsCFvv7wG/hMMHRYQvItjelxHMJbiFbwmQQTDMUGA0sFjSx2Y2SNJwYHp4YbCVoCX/G/BMxETFYi1/5yrDwh++Z+Eb3xfZ9ubXXxQuT/nqM6Z89VmxfD9uXM8Vb71e2dVzrmIkSLBX8Dz+HUPYjT/UzPptI2lFH7cOQRA+zcy+qspjV4YTxz294/xRnXOuAr1x5oUV2u9e5/T+cX9f5k+YXOP6/hPjkqYUkg4keM3gO7Uh0DvnnHPRdqjH5ZrZTGBmFR9zGcGsd+ecc4kmQbrxd6hg75xzzlWpSnif/Y7Ig71zzrnEVUOfiBcvD/bOOecSl3fjO+ecc7WcB3tXUz3W9+TqroJzztUM3o3vnHPO1XI+Qc8555yr5bwb3znnnKvlvBvfOeecq93kLXvnnHOudqvjwd4555yr3Sor2Es6HngYSAKeMrO7ovZfCwwKV+sC7YAUM/tJ0gpgPZAH5JpZ5/LWx4O9c865hFUZwV5SEvAo0Bv4HlggaXL4LhYAzOxe4N4w/UnA38zsp4hiephZTkXVyYO9c67Qgnnz+Pf9D5Kfn8/xA/pzxnnnFtlvZjx2/wMsSJ9Hg4YNGHrTjex3wAEAvDJ2HG9NmgwSe+/blqE3Dqd+gwZ88+WXPHzX3WzZvIWkpCSuuO5aDmjfvjpOz7liKqllfxjwtZktB5A0HhgALCsh/ZnAuMqoSIHEGKyoIpI6Sxq1jTRtJJmkkRHbkiVtlfRIuD4iTLNvRJq/hdvK3Z3jXCx5eXk8cs993P7wgzw5YRwzp03nf8u/LZJmQfo8Vq1cyTOvvMzV11/PqLvvASAnK4vXJ7zEI2Oe4cnxY8nPy2fmjBkAPPmvRzj7/y7i8Ref57yLh/DUvx6p8nNzriR16tSJ+1MGLYGVEevfh9uKkdQIOB54JWKzAdMlfSRpyHaeWhEe7EsRdsWUmZktNLMry5B0OdAvYv00YGlUmiXAGRHrp1LyVaFz5fbF0mW0aNWKPVq2pF69ehzTpzfps2cXSZM+eza9+/ZFEu06dmDj+g2syQl6GvPy8ti8eTN5ubls3rSJ3ZJTABDi140bAdi4YQPNw+3O7QjqSHF/JA2RtDDiEx2QYz2px0qowknA3Kgu/C5mdghwAvBXSUeX+zzLW0BNFbawP5c0RtJiSRMlNZK0QtJNkuYAp0nqI2mepI8lvSypSZj/T5LSJX0qab6kppK6S5oS7h8h6XlJ70r6StLgiMP/BnwW0Uo/HXgpqoqvE3T7IGkfYC2QXXm/EZfocrKzSUlLLVxPSU1lTXbRf3JrsoqmSU5NZU1WNsmpqZx29iDO7j+QM/r2o1GTxnQ+4nAALv371Tw56hHO6tef0aP+xYV/vbRqTsi5SmJmo82sc8RndFSS74HWEeutgNUlFHcGUV34ZrY6/JkFvEYwLFAuCRvsQ/sDo83sIGAdcFm4fZOZdQXeBoYDx4ZXWQuBv0uqD0wArjKzg4FjCQJ4tIOAE4EjgZsktYjYNx44Q1IrghmX0f8Q1gErJXUgGM+ZUO6zda40VrzhEd08sViNE4n169aRPms2z73+KuOmTmHTb5t4+803AfjvK69yyd+uYuyUyVxy9VU8cNvtlVB557ZPJXXjLwD2k7R3GC/OACZHJ5K0C3AMMCliW2NJTQuWgT5ARrnPs7wF1HArzWxuuPwC0DVcLgisRwAHAnMlLQLOA/YiuEj4wcwWAJjZOjPLjVH+JDP7LZxR+R5Fr87eIpipWVogH0/wj2QgwdVdiSK7lcY++2xpSZ2LKTk1lezMrML17KwsdktJKTVNTlYWzVOS+WT+AnZv0YJdmzWjbt26dO3RnWWLlwAw442pdO3RA4Cjj+3FF8t8NMrtOCoj2Ifx4HJgGvAZ8JKZLZV0iaRLIpKeDEw3s40R29KAOZI+BeYDb5jZW+U9z0SfjR/dTClYL/jFC5hhZmdGJpJ0UIy88ZSPmW2R9BFwDdCeYNwm2n8Jbs1YaGbrpJJf2BB2I40G+N/an8tSN+eK2P/AdqxauZIfVq0mOTWFWdNnMGzkrUXSHNmtG5NefpnufXrzecZSGjdpQvPkZFJ2T+PzjAw2bdpEgwYN+GTBQv7QLpil3zwlmcUff8zBhx7KogULadG6dazDO1ctKus+ezObCkyN2vZ41PqzwLNR25YDB1d0fRI92O8p6Ugzm0fQwp4D/DFi/wfAo5L2NbOvw1mTrYDPgRaS/mRmC8Iul1jd+AMk3Qk0BroDw4D6EfvvB2aZ2ZpYgdzMfpN0HfBluc/UuW1IqluXy68dyg1XXkV+fj7HndSPNm33YcorrwLQ78+ncFiXo5ifns75p5xKg4YNGXrjcADadehAt149ueyc80hKSmLf/f9A35MHAvC3G67nsQceJD83j3oN6nP19ddX1yk6V0yiPEFPFmOcLhFIakNw1TUbOAr4CjiHYMZ754KHGUjqCdwNNAizDjezyZL+BPwL2Ikg0B8LdAaGmlk/SSOAFkBbYE/gHjN7MjzuFDPrEFWf88PjXh7m3WBm90WlmRmWv7C0c/OWvXOuttprl2YV+k7aPW68Nu7vyx9G3lvj3oub6MG+WNCtwPJHECNgVwUP9s652qqig33Lm6+L+/ty1S1317hgn+jd+M455xJYUoJ04ydssDezFUCltOrD8kdUVtnOOecqRqKM2SdssHfOOec82DvnnHO1nAd755xzrpbzYO+cc87Vch7snXPOuVquTilPJq1NPNjXQolxneqcc+XnLXvnnHOulvNg75xzztVyiRLsE+MsnXPOuQTmLXvnnHMJK1Fa9h7snXPOJSwP9s4551wt58HeOeecq+X8rXfOuYQzf948Hrv/QfLz8zlhQH/OPO/cIvvNjEfvf4D56fNo0LAB/7jpRvY74AAAJo4dx5uTJiOJvfdty7U3Dqd+gwaMGf0kUydNZtdddwXgwssu5fAuR1X1qTkXU6IE+x3qLCVNlbRrHOnbSMqoxCqVduwNseogabCkjyU1q4BjrJCUXN5ynCuLvLw8/nXPfdzx8IP8Z8I43ps2nf8t/7ZImvnp81i1ciVjXnmZv11/PQ/ffQ8AOVlZvD7hJR4b8wxPjR9LXl4+782YUZjvz2eewRMvPs8TLz7vgd7tUJLq1In7UxPtUC17M+tb3XUoD0nnAFcAPc3s5+quj3Px+GLpMlq0akWLli0B6N6nN3Nnz2avffYuTJM+eza9+/ZFEgd27MCG9RtYk5MDBBcLmzdvpm7dumzetInmySnVch7OxaOmBu94VelZSvqHpCvD5QclvRsu95L0QkFLNmwtfybpSUlLJU2XtFOY9lBJn0qaB/w1ouz2kuZLWiRpsaT9wnI+lzQm3DZRUqOIcmZJ+kjSNEl7hNvbSnor3P6+pAPC7XtLmidpgaSRMc7tL8AwoI+Z5YTbZobnOTs8nz9JelXSV5JuC9OUWMfQFWFPwZKCujhXGXKys0lNSy1cT0lNZU12dtE0WdmkRKXJycomOTWV084exFn9B/KXvv1o3KQxnY84vDDdpJdfZvBZg7h35G2sX7eu8k/GuTJKSqoT96cmqupazwa6hcudgSaS6gFdgfej0u4HPGpm7YFfgD+H258BrjSzI6PSXwI8bGadwrK/D7fvD4w2s4OAdcBl4TH/BZxqZocCTwO3h+lHA1eE24cCj4XbHwb+bWZ/An6MOvZewCMEgT563xYzOxp4HJhEcIHSAThfUvOS6hiRP8fMDgH+HdbHuUphZttOQ/E0kli/bh3ps2bzwuuvMmHqFDb9tom333wTgP5/PoXnXn2FJ154nubNm/P4w6MqvO7Oba9E6cav6lp/BBwqqSmwGZhHEJi7UTzYf2tmiyLytZG0C7Crmc0Ktz8fkX4ecIOk64C9zOy3cPtKM5sbLr9AcGGxP0HAnSFpETAcaCWpCXAU8HK4/QlgjzBvF2BcjOMCZAPfAX+Jcc6Tw59LgKVm9oOZbQaWA61LqWOBVyN/BzHKB0DSEEkLJS188dlnS0rmXIlSUlPJyswqXM/OyqJ5SkqxNNnF0iTz8fwF7N6iBbs2a0bdunXp2qM7SxcvAaBZ8+YkJSVRp04d+g4cwBdLl1XJ+ThXFh7sK4GZbQVWABcA6QQBvgfQFvgsKvnmiOU8gvkFghhNi6DssUB/4DdgmqSeBbuik4blLDWzTuGno5n1Ifh9/BKxvZOZtYvKG8uvwAnAJZIGlXAe+VHnlM/vcyZi1TE6f8HvICYzG21mnc2s86Dzzy8pmXMl2v/AdqxauZIfVq1m69atzJw+g6O6dSuS5shu3ZgxdSpmxrIlGTRu0oTmycmk7p7GZxkZbNq0CTPjkwUL2bNNG4DCMX2AOTNn0abtPlV5Ws6VKlGCfXVM0JtN0B19IUFr9wHgIzMzbeO9wmb2i6S1krqa2RygMLBK2gdYbmajwuWDCFrPe0o60szmAWcCc4AvgJSC7WG3/h/MbKmkbyWdZmYvK6jQQWb2KTAXOIOg5R0d0DGzbEnHAzMl5ZjZtDh+J7Hq6FyVSqpblyuuHcqwK68iPz+f40/qR5u2+/DfV4LOpZP+fAqHdzmK+enpnHvKqTRo2JBrbxwOQLsOHTi6V08uPec8kpKS2Hf/P3DiyQMBePJfj/D1l18hwe577MHV1w+rrlN0rpjKeqhOGA8eBpKAp8zsrqj93QmGdgtueXnVzG4tS97tUR3B/n3gn8A8M9soaRPFu/BLcwHwtKRfgciAejpwtqStBGPqtwI7E/QYnCfpCeArgnH3LZJOBUaFQwN1gYeApQSB/N+ShgP1gPHAp8BVwFhJVwGvxKqYmX0rqT8wVdIpcZxTsTrGkde5CnN4l6OK3Rp30p9//6csiSv/cW3MvOcNGcx5QwYX2z7slhEVWkfnKlJltNQlJQGPAr0J5o8tkDTZzKLHsN43s37bmTe+OpVlUk5NJakNMMXMOlR3XUpSGXVcufbn2vtHdc4ltNa7NCu9CzhOf3ltbNzfly+dfFapdZB0JDDCzI4L168HMLM7I9J0B4bGCPbbzLs9aubgg3POOVcBKmnMviWwMmL9+3BbtCPDW8nflNQ+zrxx2aEeqlPRzGwFwaz7HVZNqKNzztVWSUnxdxRIGgIMidg02sxGRyaJkS26B+FjgjvHNkjqC7xOcMt5WfLGrVYHe+ecc66ihYF9dClJvuf3W6sBWgGro8pYF7E8VdJjCh6Pvs2828ODvXPOuYRVSbfSLQD2k7Q3sIrgTq6zIhNI2h3IDO9EO4xgWH0NwUPkSs27PTzYO+ecS1iVEezNLFfS5QR3jCUBT4e3dl8S7n8cOBW4VFIuwfNhzrBgxnzMvOWtU62ejZ+ofDa+c662qujZ+IOnvxb39+WTfU6u0DpUBW/ZO+ecS1g19Yl48fJg75xzLmF5sHfOOedqOQ/2zjnnXC3nwd4555yr5TzYO+ecc7VcUpIHe+ecc65W85a9c845V8t5sHfOOedqOQ/2zrmENn/ePB67/0Hy8/M5YUB/zjzv3CL7zYxH73+A+enzaNCwAf+46Ub2O+AAACaOHcebkyYjib33bcu1Nw6nfoMG1XEazpWqToIE+x3iLCU9KOnqiPVpkp6KWL9f0k2ShsVZ7rOSTg2X60q6Q9JXkhaFn39uZ327SzoqYn2EpKHhckNJMyTdvD1lxzjOlPKW41y88vLy+Nc993HHww/ynwnjeG/adP63/Nsiaeanz2PVypWMeeVl/nb99Tx89z0A5GRl8fqEl3hszDM8NX4seXn5vDdjRnWchnPblCTF/amJdohgD6QDRwFIqgMkA+0j9h8FTDOzu8pxjNuAFkBHM+sEdAPqbWdZ3cM6FSGpPvAK8JGZ3bKdZTtX7b5YuowWrVrRomVL6tWrR/c+vZk7e3aRNOmzZ9O7b18kcWDHDmxYv4E1OTlAcLGwefNm8nJz2bxpE82TU6rjNJzbpiTViftTE+0otZ7L78GzPZABrJfUTFIDoB1wsKRHoLDFPkpSuqTlEa13SXpE0jJJbwCp4fZGwGDgCjPbBGBm681sRLi/jaSMgspIGiqpYN+VYXmLJY2X1Aa4BPhb2DvQLcxWFxgPfGVmwyLK/VzSU5IyJL0o6VhJc8MehsPCdCMkPS/p3XD74IjfTRNJE8NyXpRq6GWlq1FysrNJTUstXE9JTWVNdnbRNFnZpESlycnKJjk1ldPOHsRZ/Qfyl779aNykMZ2POLzK6u5cPLxlX4XMbDXBa/32JAj684APgSOBzsBiYEtUtj2ArkA/oKDFfzKwP9CRILgXXEDsC3xnZuu3o3rDgD+a2UHAJWa2AngceNDMOpnZ+2G6fwC5ZnZ1VP59gYeBg4ADCN5L3BUYCtwQke4g4MTwnG+S1CLc/kfgauBAYB+gS6xKShoiaaGkhS8+++x2nKZzvyvL2zCN4mkksX7dOtJnzeaF119lwtQpbPptE2+/+WZlVNO5cvNgX/UKWvcFwX5exHp6jPSvm1m+mS0D0sJtRwPjzCwvvIB4N9aBJF0QtspXSmq9jXotBl6UdDaQW0q6OcCRkv4Qtf1bM1tiZvnAUuCd8J3FS4A2EekmmdlvZpYDvAccFm6fb2bfh/kXReUpZGajzayzmXUedP752zgl50qXkppKVmZW4Xp2VhbNU1KKpckuliaZj+cvYPcWLdi1WTPq1q1L1x7dWbp4SVVV3TkXw44U7AvG7TsSdON/QNDKPYrgQiDa5ojlyEutWE2Sr4E9JTUFMLNnwnH7tUASQRCP/F00jFg+EXgUOBT4SFJJdzDMJmiBvxnRKo+uZ37Eej5F74aIrnfBemT+PPwOClcF9j+wHatWruSHVavZunUrM6fP4Khu3YqkObJbN2ZMnYqZsWxJBo2bNKF5cjKpu6fxWUYGmzZtwsz4ZMFC9mzTpnpOxLltqKM6cX9qoh2p1nMJuuR/ClvmPwG7EgT8eWUsYzZwhqQkSXsAPQDM7FfgP8AjkhoCSEoC6of5MoFUSc3DOQL9wjR1gNZm9h5BN/2uQBNgPdA0+uBm9gpwL/CWpF3jOnsYEM7kb04wAXBBnPmdqzBJdetyxbVDGXblVVz4lzM45thetGm7D/995VX++8qrABze5Sj2aNmSc085lQfuuJMr/3EtAO06dODoXj259JzzGHzmIMzyOfHkgdV4Ns6VLFG68XekVuISgln4Y6O2NTGznDLOS3sN6Bnm+xKYFbHvn8BIIEPSeuA3YAyw2sy2SrqVYJ7At8DnYZ4k4AVJuxD0HjxoZr9I+i8wUdIA4IrICpjZ45J2ByYDQ8p89jAfeAPYExhpZqtjDAk4V2UO73IUh3cpetPJSX8+pXBZUmGAj3bekMGcN2RwzH3O7UgS5aE6KstEHFe5wpn/G8zsvooob+Xan/2P6pyrlVrv0qxCm9ZPfP5x3N+XFx9wSI1r3u9ILXvnnHOuStWpod3y8fJgvwMouN/fOedc1aqpD8mJlwd755xzCaumTriLlwd755xzCctb9s4551wt5y1755xzrpbzCXquxmqc5H9W55wri8q6z17S8QTvRUkCnop+a6ukQcB14eoG4FIz+zTct4Lg4W15BO9c6Vze+nhUcM45l7Aqoxs/fELro0Bv4HtggaTJ4btcCnwLHGNmP0s6ARgNRL4eskf4rpQK4cHeOedcwqqkCXqHAV+b2XIASeOBAUBhsDezyBe8fQC0qoyKFEiMaYjOOedcDJX0bPyWwMqI9e/DbSW5CIh8D7QB0yV9JCmex66XyFv2zjnnEtb2vMUuDMCRQXi0mY2OTBIjW8zH8krqQRDsu0Zs7hK+HyUVmCHpczObHXdFI3iwd845l7C2Z8w+DOyjS0nyPdA6Yr0VsDo6kaSDgKeAE8xsTUT5q8OfWZJeIxgWKFew925855xzrmItAPaTtLek+sAZBG9CLSRpT+BV4Bwz+zJie2NJTQuWgT5ARnkr5C1755xzCasyZuObWa6ky4FpBLfePW1mSyVdEu5/HLgJaA48Fr7CveAWuzTgtXBbXWCsmb1V3jr5K25roZ82rPc/qtsu89LTeei++8jLy6f/wIGce8H5RfabGQ/eex/pc+fSsGFDbhwxgv3bHcDmzZu5dPBgtm7ZSl5eHj169WLwJRcD8OUXX3DPHXeyZcsWkpKSGDrsOtp36FANZ+dqg92aNK3Q6Dzzx+/i/r7svvueNe5JPAnTjS8pT9IiSUslfSrp71L577mIKDdD0suSGpWStr+kYdsor42ksyLWu0taGx5jkaS3y1tn52LJy8vj/rvu5oFRoxg38WVmTJvGt8uXF0kzb+5cVq5cycuvv8aw4f/knjvvBKB+/fo88vjjPD9+HM+NHcsH6elkLFkCwKMPj+KiIYN5btxYBl9yMY+OGlXl5+ZcSepIcX9qooQJ9sBvZtbJzNoTPOigL3BzBZbbAdgCXFJSQjObHP0UpRjaAGdFbXs/PEYnMzu2fNV1LrZlS5fSqnVrWrZqRb169Ti2Tx9mz5xVJM3sWbM44cS+SKJDx45s2LCenOwcJNGoUXCdm5ubS25uLgonJEti48aNAGzYsIHk5JSqPTHnSpGkOnF/aqKaWetyMrMsgtsmLlcgSdK9khZIWizpYgBJj0nqHy6/JunpcPkiSbfFKPp9YF9Ju0l6PSzrg3DGJZLOl/RIuPyspFGS0iUtl3RqWMZdQLewFf+3yv1NOPe77KwsUtPSCtdT01LJzs6KSpNNWtruhespqWmFafLy8jj3zLPo27s3hx1xOO07Bl31Vw+9hkceepgBfU/kXw89zKVXXF4FZ+Nc2VTSffY7nIQM9gDhk43qAKkE9ziuNbM/AX8CBkvam+BWh25hlpbAgeFyV4LAXkhSXeAEYAlwC/CJmR0E3AA8V0I19gjL6kcQ5AGG8XtL/sFwW0HwXyTpn+U4bedKFGv6jqK/2GIkKkiTlJTEc+PGMunNqSzLWMo3X38NwKsvT+Sqa/7OpKlvcNXf/84dt46s8Lo7t73qoLg/NVHCBvtQwV+tD3CupEXAhwQzJPcjCOjdJB1I8JjDTEl7AEcCBY863CnMtxD4DvgPQQB/HsDM3gWaS9olxvFfN7P88HnJaTH2F4jsxr895olIQyQtlLRwzNPPlPH0nftdaloqWZmZhetZmVnFutxT0lLJzPyxcD07K7NYmqZNm3JI50P5IH0eAFOnTKF7z54A9Op9LMuWLq2sU3AubnUU/6cmSthgL2kfgjcKZREE/SsiAureZjbdzFYBzYDjCVr57wN/ATaY2fqwqN8i8l1hZlso+9OTNkdWqTznY2ajzayzmXU+78ILylOUS1DtDjyQlStXsnrVKrZu3crb06fT7Ziji6TpdvQxvPnGVMyMjCVLaNykCckpyfz888+sXx/8L7Fp0yYWfDifvdq0ASA5JYVPPvoIgIULFtC6dWuc21FoO/6riRLyPntJKcDjwCNmZpKmAZdKetfMtkr6A7DKzDYC84CrgZ4ELf6J4ac0s4FBwEhJ3YEcM1tXrEs0tvVA0/jPyrnyqVu3Ltf841quvvwK8vPy6DegP/u0bcurE4N/7qeceipHde1C+ty5nDZgIA0aNmT4iGCO65qcHG69+Wby8/Ixy6fnsb3penQwAnb98OE8eN995OXlUb9+fYYN95Eot+OoqbPr45Uw99lLyiMYT68H5BJ0sz9gZvnhLXi3AScRtLCzgYFmtlbSRcBIM2shqR7wC8ETj14Ny91gZk2ijrUb8AywN/ArMMTMFks6H+hsZpdLehaYYmYTI8sJj/EWkAw8C3wCDDWzfmU9V7/P3jlXW1X0ffafrsmM+/vy4OZpNe4KIWGCfSLxYO+cq60qOtgv+Skr7u/Ljrul1rhgn5Dd+M455xwkzsQ1D/bOOecSVk2dcBcvD/bOOecSVqJM0CtzD4aknSTtX5mVcc4556pSne341ERlqrekk4BFBLPEkdRJ0uRSMznnnHM7OH8RTlEjgMMIbjvDzBYRvLDFOeecczu4so7Z54b3nFdqZZxzzrmqVFOfdR+vsgb7jPAd60mS9gOu5PdnwzvnnHM1UqK0YcvajX8F0J7gWe5jgbUEj5B1zjnnaqxEeevdNlv2kpKAyWZ2LOAPtXbOOVdr1NTZ9fHaZrA3szxJv0raxczWVkWlnHPOuapQU2fXx6usY/abgCWSZgAbCzaa2ZWVUivnnHOuCtTUbvl4lTXYvxF+nHPOuVojQRr2ZQv2ZjamsivinHPOVTVv2UeQ9C1Q7DWAZrZPhdfIOeecqyI+Zl9U54jlhsBpwG4VXx3nXHWal57OQ/fdR15ePv0HDuTcC84vst/MePDe+0ifO5eGDRty44gR7N/uADZv3sylgwezdctW8vLy6NGrF4MvuRiAL7/4gnvuuJMtW7aQlJTE0GHX0b5Dh2o4O+eKS5TZ+GU6TzNbE/FZZWYPAT0rt2qVQ9IISUMrqKyZkjpvO6VzO768vDzuv+tuHhg1inETX2bGtGl8u3x5kTTz5s5l5cqVvPz6awwb/k/uufNOAOrXr88jjz/O8+PH8dzYsXyQnk7GkiUAPPrwKC4aMpjnxo1l8CUX8+ioUVV+bs6VpLLus5d0vKQvJH0taViM/ZI0Kty/WNIhZc27PcrajX9IxGodgpZ+04qogIuPpLpmllvd9XC1z7KlS2nVujUtW7UC4Ng+fZg9cxZ77/P7aN3sWbM44cS+SKJDx45s2LCenOwcklOSadSoEQC5ubnk5uYWvidcEhs3BjfxbNiwgeTklCo+M+dKVhm9+OHzaR4FegPfAwskTTazZRHJTgD2Cz+HA/8GDi9j3riVtRv//ojlXOBb4C/lOXBVkXQuMJRgzsFi4JuIfZ2Ax4FG4fYLzexnSTOBoWa2UFIysNDM2kjaCXgGOBD4DNgpoqzjgTuAJCDHzHpJ2g14GtgH+BUYAmQAy4FOZvZLmPdroAuQH9Znz7DYq81srqQRQAuClw/lAGdV3G/IuUB2VhapaWmF66lpqSzNyIhKk01a2u6F6ympaWRnZ5GckkxeXh4XnH0O369cyZ//chrtOwZd9VcPvYar/3o5/3roYfLz8xn9zNNVc0LOlUElTdA7DPjazJYDSBoPDAAiA/YA4DkzM+ADSbtK2oPge35beeNW1uGKi8ysR/jpbWZDgC3lOXBVkNSe4Kl/Pc3sYOCqqCTPAdeZ2UHAEuDmbRR5KfBrmP524NDwOCnAk8Cfw+OcFqa/BfgkTH8DwR82H5gEnBzmPRxYYWaZwMPAg2b2J+DPwFMRxz4UGGBmHuhdpbBiU3CDVvm2EhWkSUpK4rlxY5n05lSWZSzlm6+/BuDVlydy1TV/Z9LUN7jq73/njltHVnjdndtelfSK25bAyoj178NtZUlTlrxxK2uwn1jGbTuansBEM8sBMLOfCnZI2gXY1cxmhZvGAEdvo7yjgRfCshYT9BQAHAHMNrNvo47TFXg+3PYu0Dw87gTg9DDNGeE6wLHAI5IWAZOBnSUVDJdMNrPfSqqYpCGSFkpaOObpZ7ZxGs4Vl5qWSlZmZuF6VmZWsS73lLRUMjN/LFzPzsoslqZp06Yc0vlQPkifB8DUKVPo3jOY4tOr97EsW7q0sk7BubgJi/8T8X0bfoYUK7a46CvlktKUJW/cSu3Gl3QAwQtwdpF0SsSunQlm5e/oxPb9knL5/UIo+jxjlVfScUr6o80D9g17BAYCt4X76gBHRgf1sOW0kVKY2WhgNMBPG9aX+x+GSzztDjyQlStXsnrVKlJSU3l7+nRuuf22Imm6HX0ME196id7HHcfSjAwaN2lCckoyP//8M3Xr1qVp06Zs2rSJBR/O5+zzzgMgOSWFTz76iEM6d2bhggW0bt26Ok7PudgsP/4sEd+3JfgeiPyH3gpYXcY09cuQN27bGrPfH+gH7AqcFLF9PTC4vAevAu8Ar0l60MzWhGPoAJjZWkk/S+pmZu8D5wAFrfwVBN3m84FTI8qbDQwC3pPUATgo3D4PeFTS3mb2raTdwtZ9QfqRkroTjOWvA5D0GvAA8JmZrQnLmQ5cDtwbpulkZosq7tfhXMnq1q3LNf+4lqsvv4L8vDz6DejPPm3b8urEoBPvlFNP5aiuXUifO5fTBgykQcOGDB8RjHytycnh1ptvJj8vH7N8eh7bm65HdwPg+uHDefC++8jLy6N+/foMG+7v03K13gJgP0l7A6sIenCjh2AnA5eHY/KHA2vN7AdJ2WXIGzdZrIG66ETSkWY2r7wHqw6SzgOuBfKATwgC+QYzuy9qgt5y4IJwgt4BwEvABuBd4OwYE/QWAfsCV4YT+U4gmKBXB8gys97hxcUzwN6EE/TC7n/CW/YWAOcXPKEwnAz4KNCO4EJstpldEk7Q22Bm95XlnL1l75yrrXZr0rRCZ9T9unFt3N+XjRrvss06SOoLPEQwaftpM7td0iUAZva4gi7bR4DjCeLDBWa2sKS88daxWH3KGOwbAhcRdOkXdmub2YXlrYCreB7snXO1VYUH+w0/xx/smzSrcY/dK+sEveeB3YHjCLq6WxF05TvnnHM1WP52fGqesgb7fc3sRmBj2OV8ItCx8qrlnHPOVQHLj/9TA5X1oTpbw5+/hBPTfiS48d8555yrwWpm8I5XWYP9aEnNgBsJZhA2AW6qtFo555xzVaGGttTjVaYJeq5m8Ql6zrnaqsIn6K1bFf8EvZ1b1s4JepLSJP1H0pvh+oGSLqrcqjnnnHOVLEHG7Ms6Qe9ZYBrBy1gAvgSuroT6OOecc1XIZ+NHSjazlwjPMnzFal6l1co555yrCgnSsi/rBL2NkpoTPv9d0hHA2kqrlXPOOVclambwjldZg/3fCWbht5U0F0ih6DPjnXPOuRpHCTJJfVtvvdvTzL4zs48lHUPwYhwBX5jZ1tLyOuecczs+b9kDvA4cEi5PMLM/V251nHPOuSpUQ8fg47WtYB95L+E+lVkR55xzrup5sIdwQl6MZeecc67m85Y9AAdLWkfQwt8pXCZcNzPbuVJr55xzzrlyKzXYm1lSVVXEOVf95qWn89B995GXl0//gQM594Lzi+w3Mx689z7S586lYcOG3DhiBPu3O4DNmzdz6eDBbN2ylby8PHr06sXgSy4G4KknnmDSa6/TrFkzAC7562Uc1bVrVZ+acyXwlr0rhaRdgbPM7LFKPEYb4DPgi4jNh5nZlso6pktceXl53H/X3Tz82KOkpqVx4Tnn0u2Yo9l7n9+n68ybO5eVK1fy8uuvsTQjg3vuvJP/PDeG+vXr88jjj9OoUSNyt+Zy8UUXcWSXo+jQMXgT9hlnncWgc8+prlNzrmQJ0o1f1ifoueJ2BS6rguN8Y2adIj4e6F2lWLZ0Ka1at6Zlq1bUq1ePY/v0YfbMWUXSzJ41ixNO7IskOnTsyIYN68nJzkESjRo1AiA3N5fc3FxEjXtXiEtI/rhcV7q7CB4ytEjSvZKulbRA0mJJt0DQMpf0uaSnJGVIelHSsZLmSvpK0mFhuhGSnpf0brh9cLWemUtI2VlZpKalFa6npqWSnZ0VlSabtLTdC9dTUtMK0+Tl5XHumWfRt3dvDjvicNp37FCYbuJLL3H26Wdw2y23sG7dOpzbYSTI43I92G+/YYStbmAGsB9wGNAJOFTS0WG6fYGHgYOAA4CzgK7AUOCGiPIOAk4EjgRuklTw0qGCC4pFkh6t1DNyCS3Wg8QkbTNRQZqkpCSeGzeWSW9OZVnGUr75+msATjn1VCZOep3nxo0lOTmZUQ8+WOF1d277ecvelV2f8PMJ8DFBUN8v3PetmS0xs3xgKfCOmRmwBGgTUcYkM/vNzHKA9wguHKBoN/5fS6qApCGSFkpaOObpZyr05FxiSE1LJSszs3A9KzOL5OSUImlS0lLJzPyxcD07K7NYmqZNm3JI50P5IH0eALs1b05SUhJ16tRhwMkn89nSpZV4Fs7FyVv2Lg4C7owIyvua2X/CfZsj0uVHrOdTdIJkdJMprucamNloM+tsZp3Pu/CCeLI6B0C7Aw9k5cqVrF61iq1bt/L29Ol0O+boImm6HX0Mb74xFTMjY8kSGjdpQnJKMj///DPr168HYNOmTSz4cD57tWkDQE52TmH+me+9xz5t21bZOTm3bYnRsvfZ+NtvPdA0XJ4GjJT0opltkNQSiPfdAQMk3Qk0BroTDBPUr6jKOrctdevW5Zp/XMvVl19Bfl4e/Qb0Z5+2bXl14kQg6I4/qmsX0ufO5bQBA2nQsCHDR9wMwJqcHG69+Wby8/Ixy6fnsb3penQ3AB4d9TBffvElktijxR5cd8M/q+0cnSumhrbU4yVLkDf+VAZJYwnG2t8Evgf+L9y1ATgbyAOmmFmHMP2z4frE8La6KWbWQdIIoAXQFtgTuMfMnoxME0+9ftqw3v+ozrlaabcmTSv0No9ff5wV9/dlo92PqXG3mniw3wGEwX6Dmd1XEeV5sHfO1VYVHew3rn4v7u/Lxi161Lhg72P2zjnnEpZZXtyf8pC0m6QZ4W3WMyQ1i5GmtaT3JH0maamkqyL2jZC0KuIurb5lOa6P2e8AzGxEddfBOecSkeWXL3hvh2EEd2XdJWlYuH5dVJpc4Boz+1hSU+AjSTPMbFm4/8F4e4K9Ze+ccy5hVXXLHhgAjAmXxwADi9fJfjCzj8Pl9QSPTW9ZnoN6sHfOOZe48vPj/5RPmpn9AEFQB1JLSxxO1P4j8GHE5svDp7U+HWsYIBYP9s455xLW9rTsIx9iFn6GRJYp6e3wEenRnwHx1E1SE+AV4GozK3jO9L8J7tzqBPwA3F+WsnzM3jnnXMLanjF7MxsNjC5l/7El7ZOUKWkPM/tB0h5AVgnp6hEE+hfN7NWIsjMj0jwJTClLnb1l75xzLmFVw5j9ZOC8cPk8YFJ0AgUvnPgP8JmZPRC1b4+I1ZOBjLIc1IO9c845V3XuAnpL+groHa4jqYWkqWGaLsA5QM8Yt9jdI2mJpMVAD+BvZTmod+M755xLWFb+CXfxHc9sDdArxvbVQN9weQ7BO1di5T9ne47rwd4551zCqoBu+RrBg71zzrmEVQ0P1akWHuydc84lLG/ZO+ecc7VcVY/ZVxcP9s455xKWt+ydc8652s7H7J1ziWZeejoP3XcfeXn59B84kHMvOL/IfjPjwXvvI33uXBo2bMiNI0awf7sD2Lx5M5cOHszWLVvJy8ujR69eDL7kYgD+9dDDzJk9m3r16tGyVSuGj7iZpk2bVsPZOVdcorTsE+KhOpIelHR1xPo0SU9FrN8v6abwdYPxlPuspFPD5XqS7grfUZwhab6kE8J9G0rIf4mkc2OUNVNS53B5RfgAhYIHKxwV5+k7VyZ5eXncf9fdPDBqFOMmvsyMadP4dvnyImnmzZ3LypUrefn11xg2/J/cc+edANSvX59HHn+c58eP47mxY/kgPZ2MJUsAOOzww3nxpQm8MGE8e+61J88980yVn5tzJbH8/Lg/NVFCBHsgHTgKQFIdIBloH7H/KGCamd1VjmOMBPYAOphZB+AkoNTmi5k9bmbPlaHsHmbWKfykl6OOzpVo2dKltGrdmpatWlGvXj2O7dOH2TNnFUkze9YsTjixL5Lo0LEjGzasJyc7B0k0atQIgNzcXHJzc1H4TJDDjzyCunWDTsT2HTqSlRnzUeDOVYtqeFxutUiUYD+XMNgTBPkMYL2kZpIaAO2AgyU9AoWt7FGS0iUtj2hxS9IjkpZJeoPw1YSSGgGDgSvMbDMELysws5cKKiDpdkmfSvpAUlq4bYSkoVXyG3BuG7KzskhNSytcT01LJTs7KypNNmlpuxeup6SmFabJy8vj3DPPom/v3hx2xOG079ih2DGmTJ7MkV28c8rtOCw/L+5PTZQQwT58DGGupD0Jgv48gncDHwl0BhYDW6Ky7QF0BfoRPruY4KUD+wMdCYJ7wbfWvsB3Ea8gjNYY+MDMDgZmh3nj8V7Yhf9hSQkiX7k45mnvJnXxMyu+LXgfR+mJCtIkJSXx3LixTHpzKssylvLN118XSffsf/5DUlISx51wQoXV2bnySpSWfSJN0Cto3R8FPAC0DJfXEnTzR3vdzPKBZQUtceBoYJwFf+3Vkt4t47G38PtrCD8iePlBPHqYWU5pCSJfufjThvUxvradK11qWipZmYVvzyQrM4vk5JQiaVLSUsnM/LFwPTsrs1iapk2bckjnQ/kgfR5t990XgDf+O4W578/hX//+d/ELCOeqUU0dg49XQrTsQwXj9h0JuvE/IGjZH0VwIRBtc8Ry5LdTrED6NbCnpJLG6LeaFTaJ8kisiyxXQ7Q78EBWrlzJ6lWr2Lp1K29Pn063Y44ukqbb0cfw5htTMTMyliyhcZMmJKck8/PPP7N+/XoANm3axIIP57NXmzZAMMP/hTFjuOfBB2i4U8OqPi3nSuUt+9pnLnANsDxsmf8kaVeCMfzBBN312zIbuFjScwTj9T2AsWb2q6T/AKMkXWxmW8J3Dvcysxcq42Scq2h169blmn9cy9WXX0F+Xh79BvRnn7ZteXXiRABOOfVUjurahfS5czltwEAaNGzI8BE3A7AmJ4dbb76Z/Lx8zPLpeWxvuh7dDYD7776HrVu3ctVlfwWgfccOXHfDDdVzks5Fqalj8PFKpGC/hGAW/tiobU3MLKeMXYuvAT3DfF8CkVOVhwO3EXT7bwI2AjdVQL2dqzJHde3KUV27Ftl2yqmnFi5L4tph1xXLt+9++/Hc2LHFtgNMnPR6hdbRuYpUU1vq8ZLFmpXjajQfs3fO1Va7NWlaoZM+Vsy5Oe7vyzZdb6lxE08SqWXvnHPOFeHd+M4551wtZ3ke7J1zzrlazVv2zjnnXC3nLXvnnHOulsv3lr1zzjlXu3nL3tVYDesk0oMRnXNu+3mwd84552o5y8+t7ipUCQ/2zjnnEla+t+ydc8652q2qb72TtBswAWgDrAD+YmY/x0i3AlhP8PK0XDPrHE/+aD6465xzLmFZXl7cn3IaBrxjZvsB74TrJelhZp0KAv125C/kwd4551zCsvy8uD/lNAAYEy6PAQZWRX7vxnfOOZewqmHMPs3MfgAwsx8kpZaQzoDpkgx4wsxGx5m/CA/2zjnnEtb2tNQlDQGGRGwaHRGMkfQ2sHuMrP+M4zBdzGx1GMxnSPrczGbHXdmQB3vnEpCZcc899zJ37hwaNmzILbfcQrt27YqlW7VqFcOGXc/atWtp1+4AbrvtNurVq1dq/rlz53LvvfeRn5/HwIEnc+GFFwBw3XXXsWLF/wBYv349TZs2ZcKE8WRkZDBy5G2F9brkkovp2bNnFf0mXKLbnjH4MLCPLmX/sSXtk5QpaY+wVb4HkFVCGavDn1mSXgMOA2YDZcofzcfsQ5LaSMqohHJvlVTsDy+pu6Qp4XJ/ScPC5YGSDqzoejgXac6cuXz33XdMmjSJ4cOHc8cdd8ZM9/DDoxg0aBCTJ0+iadOdee2110vNn5eXx1133c0jj/yLV155hbfeeotvvlkOwN13382ECeOZMGE8vXr1Kgzobdu25cUXX2DChPE8+ugj3Hbb7eTmJsa9z676WV5u3J9ymgycFy6fB0yKTiCpsaSmBctAHyCjrPlj8WBfyczsJjN7extpJpvZXeHqQMCDvatUs2bNpF+/fkjioIMOYv369WRnZxdJY2YsWLCAY4/tBcBJJ/Vj5sz3Ss2fkZFB69ataNWqFfXq1eO4445j5syZxcqdMWMGxx9/PAA77bQTdesGnYxbtmxBUiWfvXPV6i6gt6SvgN7hOpJaSJoapkkD5kj6FJgPvGFmb5WWf1u8G7+oJElPAkcBqwhmPb4JDDWzhZKSgYVm1kbS+QSBOQnoANwP1AfOATYDfc3sJ0nPAlPMbKKk44GHgBzg44KDhmV1BsYC/YFjJA0H/gy8bGaHhOn2A8ab2aGV+UtwtV9WVha7755WuJ6WlkpWVjYpKSmF23755ReaNm1SGIjT0tLIysouNX9WVjZpabsX2Z6RUbTD7OOPP2a33XZjr732LNy2ZMkSRoy4hR9++IHbbhtZeEznKltVvwjHzNYAvWJsXw30DZeXAwfHk39bvGVf1H7Ao2bWHviFINiWpgNwFsFYyu3Ar2b2R2AecG5kQkkNgSeBk4BuxJi8YWbpBF0014b3Vn4DrJXUKUxyAfDs9pyYc5HMim+LblDHTqNt5I+xg6IFv/XWtMJWfYGOHTvyyisTeeGF53n66WfYvHlzyZV3rgJVw3321cKDfVHfmtmicPkjgicUleY9M1tvZtnAWuC/4fYlMfIeEJb/lZkZ8EIZ6/QUcIGkJOB0gtZ/MZKGSFooaeHTTz9dxqJdIpkwYQKnn34Gp59+BikpKfz4Y2bhvszMrCKteoBmzXZl/foNhePnmZmZpKQkA0GLPVb+1NRUMjN/LLHc3Nxc3n33XY47rk/MOu6zzz7stNNOfP31N+U/YefKoBrus68WHuyLimxO5BEMc+Ty+++pYSnp8yPW84k9RBKr2bMtrwAnAP2Aj8IunOIFm402s85m1vnCCy/cjsO42u70008vnCDXo0d3pkyZgpmxePFimjRpUizYS6Jz5868/fY7APz3v1Po3r07AMccc0zM/O3bt+e771ayatUqtm7dyrRp0+je/ZjCMj/88EPatGlDWtrvQwCrVq0qvKBYvXo1K1asoEWLPSr1d+FcgURp2fvA2LatAA4lmCRxajnK+RzYW1LbsHv+zBLSrQeaFqyY2SZJ04B/AxeV4/jOFeratStz5syhf/8BNGzYkBEjRhTuu/zyK7jppptITU3hqquuZNiw63nssUfZf/8DGDhwYKn569aty3XXXcdll/2V/Px8BgzoT9u2bQvLnjZterEu/E8++YRnnnmWunXrUqdOHW644XqaNWtW2b8C54CqH7OvLrJYg28JSFIbgol0HcL1oUATYDzwErABeBc4O2KCXmczuzxMvyJcz4ncV8oEvTlABzPrF5W+C8HY/mbgVDP7RtIRBC38Pc1sm/8yf/11o/9RnXO1UqNGjSv0do2593WJ+/uyy9C5Ne6WEQ/2NUB44bGLmd1YlvQe7J1ztVVFB/s59xwe9/dl1398WOOCvXfj7+DCJye1BfyRYs45V8Fq6hh8vDzY7+DM7OTqroNzztVWiTJm78HeOedcwvKWvXPOOVfL5ecnxhQnD/bOOecSVn5+fnVXoUp4sHfOOZewvGXvnHPO1XIe7F3NZf4ucOecK4t8825855xzrlZLlJa9vwjHOeecq+W8Ze+ccy5h+Wx855xzrpZLlG58D/bOOecSlgd755xzrpbzbnznnHOulvOWvXMu4ZgZ99x7P3PnpNOwYUNuueUm2rU7oFi6VatWMez64axdu452B+zPbbfdQr169Zg69S2effY5AHZqtBM33HAd+//hD/z4YyY33jSCNTlrUB3x51NO5qyzzqjq03OumEQJ9n7rXRlIaiFp4jbSpIc/u0uaUkKaqZJ2DZc3RJctqZOkvhVaeefiMGduOt99t5JJk15h+PDruePOu2Ome3jUIwwadCaTJ71C052b8trrkwBo0bIFTz31OC+9NJbBgy/ittvuBCApKYm//+0qXn31JZ4b8zQTXnqZb5Yvr7Lzcq4k+ZYf96cm8mBfBma22sxO3Uaao8pQTl8z+6WUsjsBHuxdtZk1czb9+vVFEgcd1JH169eTnZ1TJI2ZsWDBQo7t1ROAk/qdyMz3ZgHQ6eCD2HnnnQE4qGMHMjOzAEhJSS7sIWjcuDF777032VnZVXVazpUoP9/i/tREHuyjSLpb0mUR6yMkXSMpI1xvL2m+pEWSFkvaL9y+IaKYnSW9JmmZpMcl1QnTrJCUHHW8NpIyJNUHbgVOD8s+XdJXklLCdHUkfR2d37mKlJWVxe5paYXraampZGVnFUnzyy9radqkKXXrBqOAaWlpZGUXD9yvvz6ZLl2OLLZ99erVfPHFF3To0L6Ca+9c/PLz8+P+1EQe7IsbD5wesf4XYEHE+iXAw2bWCegMfB+jjMOAa4COQFvglG0d1My2ADcBE8ysk5lNAF4ABoVJjgU+NbOckspwrrxitVmEotIUTyUVTbNgwUJef30yV115eZHtv/76K0OHDmPoNX+nSZMm5a6vc+VV1S17SbtJmhE25mZIahYjzf5ho6/gs07S1eG+EZJWRewrU2+wB/soZvYJkBqOpR8M/Ax8F5FkHnCDpOuAvczstxjFzDez5WaWB4wDum5ndZ4Gzg2XLwSeKSmhpCGSFkpa+PTTz27n4VwimjDhZU4/YxCnnzGIlJRkfszMLNyXmZVFSkpKkfTNdt2V9RvWk5sbvHApMzOTlOTfO5y+/PIrbh15Ow8+eC+77rpr4fatW3MZOvQ6Tuh7HL169ajck3KujKqhG38Y8I6Z7Qe8E64XYWZfhI2+TsChwK/AaxFJHizYb2ZTy3JQD/axTQROJWjhj4/cYWZjgf7Ab8A0ST1j5I/+17Bd/zrMbCWQGR7jcODNUtKONrPOZtb5wgvP357DuQR1+umnMWH8i0wY/yI9uh/DlClTMTMWL15CkyZNSEkpOnIkic6dD+Xtd94F4L9T3qB792MA+OGHHxk69DpGjryFvfbaqzCPmXHLrSPZe++9OefsQTi3o6iGbvwBwJhweQwwcBvpewHfmNn/ynNQD/axjQfOIAj4RWbhS9oHWG5mo4DJwEEx8h8mae9wrP50YE4Zj7seaBq17SmC7vyXwp4C5ypN165daNWyJf0HnMLI2+7g+uv/Ubjv8iuuLhybv+rKK3jhhbH0738Ka9euZeDA/gCMfvIpflm7ljvvvJvTzxjEWYOCjqlFiz7ljTfeZMGChYW9CO/PmVv1J+hclGpo2aeZ2Q8A4c/UbaQ/g6CHONLl4Zyxp2MNA8Qis5o5s7CySVoC5JhZD0ltgClm1kHS9cDZwFbgR+AsM/tJ0gYzayKpO8HYezbBmP1s4DIzy5e0AuhsZjkR6SPL3g2YBtQD7jSzCZLqAWuAw8zs87LU/deNa/2P6pyrlRo13kXbTlV2j5/dPO7vy0tf/OliYEjEptFmNrpgRdLbwO4xsv4TGGNmu0ak/dnMYgbscOL2aqC9mWWG29KAHIIe45HAHmZ24bbq7MF+ByepM8H4TLey5vFg75yrrSo62D92VrO4vy8vG/vzdtdB0hdAdzP7QdIewEwz27+EtAOAv5pZnxL2tyFsLG7ruN6NvwOTNAx4Bbi+uuvinHO1Ub5Z3J9ymgycFy6fB0wqJe2ZRHXhhxcIBU4GMspyUA/2OzAzu8vM9jKzso75O+ec27HdBfSW9BXQO1wveJpq4cx6SY3C/a9G5b9H0hJJi4EewN/KclB/Nr5zzrmEVdUPyTGzNQQz7KO3rybiCapm9ivQPEa6c7bnuB7snXPOJaya+vjbeHmwd845l7A82DvnnHO1nAd755xzrpZLkFjvwb5Wyl1X3TVwzrlKskuFlubB3jnnnKvl8hIk2nuwd845l7ASJNZ7sHfOOZe4PNg755xztZwHe+ecc66Wq+IH6FUbD/bOOecSVl6CvPnVg71zzrmE5d34zrmEY2bcc/+jzE2fT8OGDbjlpn/Q7oD9iqVbteoHhg2/nbXr1tNu/3257ZZh1KtXjzHPT2DqW+8CkJeXx7crvuPdaRPZZZedC7cNOu8yUlOSGfXg7VV6bs7Fkijd+P6KW+dcoTnp8/lu5SomvTKG4df/jTvufjhmuocfeZJBZ/6Zya+MoWnTprw26U0AzjvndCa8+AQTXnyCK/56EYf+8aDCQA8wdvxr7N1mzyo5F+fKIt/i/9REHuzjJCmpNh7LOYBZs9Pp17c3kjio44GsX7+B7Jw1RdKYGQsWLuLYnkcDcNKJfZg5a26xst6a9i7HH9ejcD0zM5s5cz/k5AF9i6V1rrp4sE9AktpI+lzSGEmLJU2U1EjSCkk3SZoDnCapj6R5kj6W9LKkJmH+uyQtC/PeF247TVKGpE8lzQ63nS/pkYjjTpHUPVzeIOlWSR8CR0o6W9J8SYskPeEXAK4yZWXlsHtaSuF6WmoKWVk5RdL8snYdTZs2oW7d4J9iWloyWdlFLwh+27SJ9A8W0qtHt8Jt9z74GFddMZg6dVSJZ+BcfPLM4v7URB7si9sfGG1mBwHrgMvC7ZvMrCvwNjAcONbMDgEWAn+XtBtwMtA+zHtbmO8m4DgzOxjoX4bjNwYyzOxwYA1wOtDFzDoBecCgWJkkDZG0UNLCp599Me6Tdg7AKP5FJhUNzhbjyy4qCbPfn0eng9oXduHPfv8Ddmu2Kwe2+0PFVdY5V2Y+Qa+4lWZW0Cf5AnBluDwh/HkEcCAwN/wSrA/MI7gw2AQ8JekNYEqYfi7wrKSXgFfLcPw84JVwuRdwKLAgPNZOQFasTGY2GhgN8OvalTXz0tNViwkvT+LV16cC0P7AP/BjZnbhvsysbFJSmhdJ32zXXVi/fgO5uXnUrZtEZmYOKclF00ybPpPj+/zehb9ocQaz3p/HnPT5bNm8hY0bf+WfN93J7bdeX4ln5ty2JcoEPQ/2xUUHyoL1jeFPATPM7MzojJIOIwjQZwCXAz3N7BJJhwMnAoskdQJyKdqr0jBieZOZ5UUca4yZ+TeiqzSnnzaA008bAMD7cz5g/MuTOL5PD5ZkfEaTJo2LBXJJdD60E2+/O5vj+/Tgv29Mp/sxRxXuX79hAx99spjbbx1WuO3Kv/4fV/71/wBY+NEinnvhZQ/0bodQU8fg4+Xd+MXtKenIcPlMYE7U/g+ALpL2BQjH9P8QjtvvYmZTgauBTuH+tmb2oZndBOQArYEVQCdJdSS1Bg4roS7vAKdKSg3L2k3SXhV0ns4V07XL4bRquQf9TzmXkXc8wPX/uLJw3+VX30BWdjB+f9UV/8cLYyfS/5RzWbt2HQP7n1CY7r2Zczni8EPZaaedqrz+zsUrUSboKdb4W6KS1AaYCswGjgK+As4BlgGdzSwnTNcTuBtoEGYdDiwAJhG00gXcZ2ZjJL0K7Bdue4fgQgCCIYJOQAaQBowws5mSNphZk4g6nQ5cT3BhthX4q5l9UNp5eDe+c662arRL6wqd4Xlxl4Zxf18+MXdTjZtl6sE+Qhjsp5hZh+quS3l4sHfO1VYVHez/76gGcX9fPpW+ucYFex+zd845l7Bqard8vDzYRzCzFUCNbtU755wrO5+N75xzztVyidKy99n4zjnnElZVz8YPn6q6VFK+pM6lpDte0heSvpY0LGL7bpJmSPoq/NmsLMf1YO+ccy5hVcPjcjOAUwju+oopfCz6o8AJBA9xO1PSgeHuYcA7ZrYfwR1ew2KXUpQHe+eccwkrPz/+T3mY2Wdm9sU2kh0GfG1my81sCzAeGBDuGwCMCZfHAAPLclwfs3fOOZewdtAx+5bAyoj174HDw+U0M/sBwMx+KHjo2rZ4sK+FKvo+VOdKI2lI+G4G52qclz/ZEvf3paQhwJCITaMj/x+Q9Dawe4ys/zSzSWU5RIxt5bos8WDvnCuvIYQvYXIuEUS+eKyE/ceW8xDfEzxavUArYHW4nClpj7BVvwclvBwtmo/ZO+ecczuWBcB+kvaWVJ/g5WqTw32TgfPC5fMIHtO+TR7snXPOuSoi6WRJ3wNHAm9ImhZubyFpKoCZ5RK8OXUa8BnwkpktDYu4C+gt6Sugd7i+7eP6s/Gdc+XhY/bO7fg82DvnnHO1nHfjO+ecc7WcB3vnEoSk3SWNl/SNpGWSpkr6w45QnqQrJX0m6UVJDSS9LWmRpNO3t37Oud/5rXfOJQBJAl4DxpjZGeG2TkAa8OU28iaZWV7kOpC/veWV4DLgBDP7VtIRQD0z67Qd5TjnYvCWvXOJoQew1cweL9hgZouAOZLulZQhaUlBS1pSd0nvSRoLLIleL6k8M3tfgWJlhuVeK2mBpMWSbgm3PQ7sA0yWdB3wAtApbNm3rfTfjHMJwFv2ziWGDsBHMbafAnQCDgaSgQWSCl7QcRjQIWxtd49av7KE8korsyOwX1iOCIL70WZ2iaTjgR5mliPpQ2ComfUr5zk750Ie7J1LbF2BcWE3faakWcCfgHXAfDP7NiJt9Hq8ZR4N9AE+CdM1IQj+Jb79yzlXMTzYO5cYlgKnxthe2nPBN5ayXlJ5pZUp4E4ze6KUYzrnKoGP2TuXGN4FGkgaXLBB0p+An4HTJSVJSiFofc/f3vIkHUPQUo9V5jTgQklNwvQty/rGLudc+XjL3rkEYGYm6WTgIUnDgE3ACuBqgu70TwneqvUPM/tR0gHlKG82waNAi5QJ/CipHTAvuDmADcDZlPFFHs657edP0HPOOedqOe/Gd84552o5D/bOOedcLefB3jnnnKvlPNg755xztZwHe+ecc66W82DvnHPO1XIe7J1zzrlazoO9c845V8v9Pzbl8/CoINSFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "pbc_corr = pbc_corr.set_index(\"Feature\")\n",
    "heatmap = sns.heatmap(pbc_corr[[\"CorrCoeff\"]].sort_values(by=\"CorrCoeff\", ascending=False), vmin=-1, vmax=1, annot=True, cmap=\"BrBG\")\n",
    "heatmap.set_title(\"PBC with ArrDel15\", fontdict={\"fontsize\":18}, pad=16);\n",
    "plt.savefig(\"Img/05_02_PBC_ArrDel15.png\")\n",
    "del pbc_corr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "requested-script",
   "metadata": {},
   "source": [
    "### Spearman Correlation for Monotonic Relationships with Categorical Features\n",
    "The **Spearman correlation** evaluates the monotonic relationship between **two continuous or ordinal variables**. In a monotonic relationship, the variables tend to change together, but not necessarily at a constant rate. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "alive-grain",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nE/2ITwfOLiHtEEq4fYGomXVsCp9HfaJryBOIrlutCe/v60TNthlx6Y8muld6RZgmEXer1/rKUdL7Evde3EJ0TesPooA4neh2sQ7reR+T/lxD+iOAz8J+rLDMJX2uRCfcV4bvVGHnrdHATil8L4p9fkSPLxwZyryCqJPPF0TXeTdO8nNsTPS/ZERPTIpdNzcsX+e2lESfBVHnof+G70FhC0LX9XynSzzeuM9mPlEHpYxS0rUk6sT2Vnn+/+Le68LpT6LfnU+IKgGdSinH1kT3BM8O2y0iOnm/IfY9jv884/4HrITp0Zh0mxE9ROTr8NmvAX4O34kdkv1fri2TwpvmKpEkA0aaWb+qLotzzrmq5dd4nXPOuTTywOucc86lkQde55xzLo38Gq9zzjmXRl7jdc4559LIA69zzjmXRh54nXPOuTTywOucc86lkQde55xzLo088DrnnHNp5IHXOeecSyMPvM455zZYkh6WlCtpRgnrJeluSd9LmiZp15h1h0n6JqyrsOFWPfA655zbkD0KHFbK+sOJhjVsRzR84f0AkjKIRqE7HOgAnCSpQ0UUyAOvc865DZaZjSca+rIkvYjGUDcz+whoLGlzYA/gezObZWZ/Eo2r3KsiyuSB1znnXG3WEpgTMz83LCtpebnVrYhMXMm03y7+MGzn3AbJPvhcFZlfmX4vJ0w9h6iJuNAIMxuRym4TLLNSlpebB17nnHM1VgiyqQTaeHOBLWPmWwHzgY1KWF5u3tTsnHOueqhTJ/Wp/F4BTg29m/cClprZL8BkoJ2kNpI2Ak4MacvNa7zOOeeqB1V8XVDS00BXoLmkucA1QCaAmT0AvAEcAXwPrAROD+vWShoAjAEygIfNbGZFlMkDr3POueqhToVeMgbAzE5az3oDzi9h3RtEgblCeVOzcxuA/w66hgWvvMv0kc9XdVGcK7uqaWpOu2pbaklHSzJJO6SwTb6kqZJmSvpC0qVS6W0XkloXPtFEUjNJ70taLml4XLqx4QkmU8OUU7Yjc67iPfq/VzlsYMKTdudqDtVJfaqBqnNT80nABKIL2kNiV0jKMLP8BPOrzGznsCwHeArYlKhNPxl/AP8HdAxTvL5mNiW1w3Cu8n3wxWdsvdnmVV0M58qnhtZgU1Utj1JSFrAvcCZR4EVS11AbfQqYHj8fn4eZ5RLd2zUg9FbLkHSLpMnheZznJNhmhZlNIArAzjnn0qmWNDVX1xpvb+BNM/tW0m8xD63eA+hoZj9K6ho7nygTM5sVmppziB71tdTMdpe0MTBR0lukdkP0I5LygVHAsHBR3jnnXEVQxXeuqo6q6+nCSUTPxST8LeyV9klckI2fT6TwkzyE6F6tqcDHQDOih2Inq6+Z7QTsF6ZTStyh1F/SFElT+HVhCrtwzrlazGu8VUNSM+BAoKMkI7p/yoi6dK+ISx4/H5/XNkA+kEsUgC8wszFxaVonUy4zmxf+LgvN23sAj5WQtuhJKv7ISOecS1IN7SyVqup4lMcSjRSxtZm1NrMtgR+BLqlkIikbeAAYHpqExwDnScoM67eT1CDJvOpKah5eZwI9gIRjOzpXFZ665gY+fGAk22+1NXNGvckZR/au6iI5lzqv8VaZk4Ab45aNAs4DfljPtpuEpuRMYC3wOHB7WPcQ0Br4TJKAPKJrycVImg00AjaS1JuoifonYEwIuhnAO8CDKR2Vc5Xo5GsHV3URnCu/GhpIUyXvH1S5vKnZObehqujRier0OSzl38uCF9+scT2yqmON1znnXG1US2q8Hnidc85VDx54nXPOuTTyXs3OOeecq2he461km3U9uKqL4JxzNYM3NTvnnHNpVAnj8VZHHnidc85VD7XkGq8HXuecc9WDNzU755xzaeSB1zlXXXTbdnv+fWRPMlSHpz79hOEfvF9s/Xn7HkCfv0WjZ9atU4d22Tl0vHEIS1at4vbex9F9+w4sXLGcbsNvq4riO5ccb2pOjzC+7XT+er7ySOBOMysoY36zgWVhNgN4ERhqZqvXs91yM8sKr98E9gImmFmPmDSPAgcAS8OifmY2tSzldC5ZdSSuP+poTnh0BL/8vpT/nXshb309k2/zcovS3D9xHPdPHAdA9+3b03+f/VmyahUAz30+hUc+nsTdx5xYJeV3Lmm1pHNVdTi9WGVmO5vZjkB34AjgmnLm2S2MnbsHsA1hiL4U3ELJ4+1eHsq7swddlw67tNqK2YsW8vPi31iTn8/L06dyaPsdS0zfu9MujJ72edH8Rz/9yOJVK9NRVOfKp5aMTlStSm1muUB/YIAiGZJukTRZ0jRJ5wBI6ippvKSXJH0p6QFp3TYKM1sOnAv0ltQ0bHt5TH7XllCOd/mr1uxcldqsUSPmLV1SNP/L0qVs1nDThGk3ycyk27bb8/qX09NUOucqkOqkPtVA1a7UZjaLqFw5wJnAUjPbHdgdOFtSm5B0D+AyYCegLdCnhPx+JxrPt52kQ4B2Ydudgd0k7Z9iEa8LQfsOSRunuK1zKRPrNr8ZiQdx6b59Byb/PLuomdm5mkR16qQ81UTVtdSFvzSHAKeGMXY/BpoRBU6AT8xslpnlA08DXZLM7xDgc+AzYIeY/JIxOGyzO9AUuDLhzqT+kqZImrLysy9SyN65df3y+1Jabtq4aH7zTTdlwbLfE6btvdPOjJ7+ecJ1zlV3derUSXmqiapdqSVtA+QDuUQB84KYa6ptzOytkDT+lD9hFUBSQ6A18G3I74aY/LY1s/8mWzYz+8Uiq4FHiGrOidKNMLPOZta5/q5/SzZ75xKaOm8ObZo1Z8vGTcjMyKDXTjsz5usv10nXcON67NV6G978amYVlNK58quswCvpMEnfSPpe0qAE6y+XNDVMMyTlx1yenC1pelg3pUKOsyIyqSiSsoEHgOFmZsAY4DxJmWH9dpIahOR7SGoTru2eAExIkF8WcB8w2swWh/zOCMuR1FJSTgrl2zz8FdAbmFG2I3UuefkFBVz12miePu1sxl94Oa/O+IJvcxdw6u57ceruexWlO7xDR8b98C2r1qwptv19x53Ma/0H0LZ5Np8O/Ccn7bp7ug/BuaRURuCVlAHcCxwOdABOktQhNo2Z3VJYISNq2RxnZr/FJOkW1neuiOOs8tuJgE1CU3Lh7USPA7eHdQ8R1VY/C8EujyjgAXwI3Eh0jXc88FJMnu+H9HXC8qEAZvaWpPbAh9FqlgN/J6pdF5H0AVGTcpakucCZZjYGeDKcHAiYStRxy7lK9953X/PeXV8XW/bY5I+KzT/3+RSe+3zdE/J/PP9UpZbNuYpSSU3HewDfh/5DSHoG6AWs22wUOYno8mWlqfLAa2YZpawrAK4KU5EQNFea2QkJtmm9nv3dBdyVYHlWzOv9Stj2wNLyds45V3aVFHhbAnNi5ucCeyZKKKk+cBgwIGaxAW9JMuA/Zpbq7anrqPLA65xzzkH0sJhUSepPdBtqoRFxwTFRpolvC4CjgIlxzcz7mtn8cFnybUlfm9n4lAsao0YGXjMbC4yt4mI455yrYiHIllYLnQtsGTPfCphfQtoTiWtmNrP54W+upJeImq7LFXirVecq55xztVcl9WqeTPQchzaSNiIKrq/EJ5K0KdEjgV+OWdYg3BlD6Nh7CBXQqbZG1nidc85teCrjGq+ZrZU0gOiulgzgYTObKencsP6BkPRo4C0zWxGzeQvgpdCvqC7wlJm9Wd4yKbprx1WWzxct8DfYObdB2qVZiwod1SB78CUp/17m3XBHjRtZwWu8zjnnqoWa+iSqVHngdc45Vy144HXOOefSyAOvc845l0YZHnidc8659PEar3Ou2pj60ceMvPNuCvILOPCoI+l16t+LrZ83+yceuO5Gfvz2W0445yyOOvmkonUrli3jPzfczNxZP4Lg3KsGsd1OHdN9CM6tlwfeKiQpH5jOXwMnjATuDM9uTjWvj4GNicbP3QSYF1b9A7jUzI6tkEI7V0kK8vN5+NY7+Oddt9MsJ5urzuzPbvt1oVWb1kVpsho1ot8lFzJ5/DqDdDHyzrvZea89ufT6oaxds4bVf/yRxtI7lzwPvFVrVRieifB8zKeATYFrUs3IzPYM+fQDOptZ7MOvJ5W7pM5Vsu+//IrNWrWkRcstANjn4IOY8sGEYoF306ZN2LRpEz6b9GGxbVeuWMFXU7/gvKujcUbqZmZSNzMzbWV3LhW1JfBW+6M0s1yiB2APUCRD0i2SJkuaJukcAEldJY2X9JKkLyU9EMbqTUhSa0kzwut+kkZLelXSj5IGSLpU0ueSPooZELmtpDclfSrpA0k7pOM9cLXbb3kLadbir2Gjm2Zn81teXlLb5s6bT6PGjbn/uhsYdNqZ/OeGm/hj1arKKqpz5VJJj4ysdmpEqcM4inWAHOBMYKmZ7Q7sDpwtqU1IugdwGdEYvW2BPinspiNwcsjjOqJhB3chGvf31JBmBHCBme0GDATuK89xOZecdR/moyRHccnPz+fHb7+j+9G9uXHkf9m4Xj1efvzJii6gcxWijpTyVBPViMAbFL7DhwCnSpoKfAw0A9qFdZ+Y2SwzyycaYaJLCvm/b2bLzCwPWAq8GpZPB1pLygL2AZ4P+/4PsHnCgkr9JU2RNGXUyMdTKIJz62qanc2iBblF87/l5dGkefOktm2Wk03T7Gza7dgBgD27dWX2N99WRjGdK7faUuOtrtd4i5G0DZAP5BIF4AvMbExcmq6sWzVI5bmfq2NeF8TMFxC9T3WAJYXXnksTO0yVP6vZlVfb9jvw69y55M6fT9PsbCa98y4XDPlXUts2btaMZi1ymP/Tz2yx9VbMmPIpLWOuDTtXndTUQJqqah94JWUDDwDDzcwkjQHOk/Sema2RtB1/9VTeIzQ7/wScQOljNKbEzH4P13+PM7PnFbX1dTKzLypqH84lklG3LqdfejHXXzKQgvwCuvU4gi23acPbL0Wjl3U/uhdLFi3iqjP6s2rFClSnDv979gVufeox6jdowOmXXMTwa6MezTlbbMG5/xxcxUfkXGIeeKvWJqE5t/B2oseB28O6h4DWwGch+OUBvcO6D4Ebia7xjgdequBy9QXul3R1KNszgAdeV+l22Wdvdtln72LLuh/dq+h142bNuO/lUQm3bb1dO65/+MFKLZ9zLnkbzLCAoal5oJn1qOKiFONNzc65DVVFDwu46/BbU/69/GzAwBrXw6q61nidc87VMt7UXMOY2VhgbBUXwznnXBl54HXOOefSyEcncs4559LIA6+rEJvUyajqIjjnXI3ggdc555xLIw+8zjnnXBplZHjgdc4559LGa7zOOedcGnngdc5VG599+BEP3nEnBQUFdO95FMeeekqx9XNn/8Tdw67jh2++5e/n9ufovicD8Ofq1Vx13vms+XMN+flr2efAbpx89llVcQjOrVdtCbwVdpSSNpP0jKQfwkD0b0jaTtIqSVPDssckZYb09SU9KWm6pBmSJoSh95CUH7aZKemLMCh9mcoq6eOQ18+S8sLrqZL2kfRCRR2/c5UlPz+f/9x6G9fccRvDn36SD956h59//LFYmqxGjTj70kvoffJJxZZnbrQRQ4ffzV1PjOTOx0fy2Ycf882MGeksvnNJq6xhASUdJukbSd9LGpRgfVdJS2Piw7+S3bYsKqTGGwYreAkYaWYnhmU7Ay2AH8xsZ0kZwNvA8cCTwEXAAjPbKaTfHlgTslxVOPyepBzgKWBT4JpUy2Zme4Z8+gGdzWxAzOpJqebnXLp99+VXbNaqFZu1bAnAft0P4pPxH7BVmzZFaRo3bULjpk2YMrH4V1oSm9SvD0D+2rXkr13LX0NbO1e9VEaNN8See4HuwFxgsqRXzOzLuKQfxD/rP4VtU1JRR9kNWGNmDxQuMLOpwJyY+XzgE6BlWLQ5fw3nh5l9Y2axY+IWLs8F+gMDFMmQdIukyZKmSToHis5Yxkt6KdSuHyitliyptaQZ4XU/SaMlvRqG/hsQatmfS/pIUtOQrq2kNyV9KukDSTuU+R1zLkmL8vJonpNTNN8sJ4dFeXlJb5+fn8/Fp5zGqYf3YOc9dmf7jjtWRjGdK7eMOnVSnpKwB/C9mc0ysz+JRpXrtZ5tKmLbElVU4O0IfFpaAkn1gD2BN8Oih4ErJX0oaZikdiVta2azQllzgDOBpWa2O7A7cHYYgxeiN+kyomEB2wJ9UjyGk0Me1wErzWwXoqEGTw1pRgAXmNluwEDgvhTyd65sEowgphRqrRkZGdz5+Ej++8pLfPvll/z0w6yKLJ1zFaaSAm9LYiqBRDXXlgnS7R0ubf5PUuHZabLbpiQdV7LbhrF1FwE/m9k0KKoRbwPcAjQlqsK3LyWfwl+aQ4BTQ54fA82AwqD9STgzyQeeBrqkUM73zWyZmeUBS4FXw/LpQOtw/Xkf4Pmw7/8Q1drXLajUX9IUSVOee/SxFIrg3Lqa5eSwMDe3aH5Rbi5Ns5unnE9Ww4bstOuufPbRRxVZPOcqTEaGUp5if2/D1D8u20RnqfFns58BW5vZ34B7gNEpbJuyiurVPBM4toR1hdd4NwfGSuppZq8AmNly4EXgRUkFwBHAV/EZSNoGyAdyid6IC8xsTFyarqz7hqTyBsU2cxfEzBcQvU91gCWF155LY2YjiGrHfL14oY/H68qlXfsd+GXOXBbMn0/T7Gw+ePtdLvt3ct0dli5eTEbdumQ1bMjqP1bzxeTJ9Dnl75VcYufSJ/b3tgRzgS1j5lsB8+Py+D3m9RuS7pPUPJlty6KiAu97wPWSzjazBwEk7Q7UL0xgZr+EHmGDgVck7Qt8aWaLJW0EdCDBsH6SsoEHgOFmZpLGAOdJes/M1kjajr+uFe8Rmp1/Ak6g9A8jJWb2e7j+e5yZPR86lHUysy8qah/OJZJRty79B17CkIsupaAgn4N69GCrbbbhfy++BMDhfY5m8aJFXNbvTFauWEGdOnV49ZnnGP7MkyxeuIg7hw6jIL8AswL2PehAdu+ybxUfkXOJVdLtRJOBdiE2zANOJLqsWETSZkSdfU3SHkQVrUXAkvVtWxYVEnhDYY8G7gzB9Q9gNnBxXNLRwBBJ+wFtgPtDAKsDvA6MCuk2Cc25mcBa4HHg9rDuIaA18FnYNg/oHdZ9CNxIdI13PFFP64rUN5T56lC2ZwAPvK7Sdd5nHzrvs0+xZYf3ObrodZNmzXj41dHrbNe63bbc+dijlVw65ypGZQReM1sraQAwBsgAHjazmZLODesfIGqxPU/SWmAVcKKZGZBw2/KWSZag40ZNFJqaB8Z3B69q3tTsnNtQ7dCkeYXem3b2Wy+l/Hv54CFH17j74/zJVc4556qF2vLkqg0m8JrZWBJcI3bOOVczeOB1zjnn0sgDr3POOZdGHnidc865NPLA65xzzqVRRoYHXueccy5tvMbrnHPOpZEHXueccy6NPPA655xzaVTHA69zrrr47MOPePCOOykoKKB7z6M49tRTiq2fO/sn7h52HT988y1/P7c/R/eNnuP+5+rVXHXe+az5cw35+WvZ58BunHz2WVVxCM6tV4Zq3NMfy6TWBF5JjYGTzew+SVsAd5tZSUMZOldt5Ofn859bb+Pau++kWU4OA08/iz3268JWbdoUpclq1IizL72Ej8aNL7Zt5kYbMXT43WxSvz5r165lUP/z2G3vvdi+Y8d0H4Zz65Wh2lHjrR1HGWkM/APAzOZ70HU1xXdffsVmrVqxWcuWZGZmsl/3g/hk/AfF0jRu2oR2HdpTt27xc2lJbFI/Gp0zf+1a8teuJfHY3s5VvQwp5akmqjU1XqLhAtuG4Qa/A9qbWUdJ/YiGFcwAOgK3ARsBpwCrgSPM7DdJbYF7gWxgJXC2mX2d7oNwtc+ivDya5+QUzTfLyeHbmcmPTJafn89l/c7gl7nzOOKYPmzfccfKKKZz5VZTA2mqalONdxDwg5ntDFwet64j0eDGewDXASvNbBei8X1PDWlGABeY2W7AQOC+knYkqb+kKZKmPPfoYxV7FK72STB0p1KotWZkZHDn4yP57ysv8e2XX/LTD7MqsnTOuRTVphpvad43s2XAMklLgVfD8ulAJ0lZwD7A8/rrjGzjkjIzsxFEgdrH43Xl1iwnh4W5uUXzi3JzaZrdPOV8sho2ZKddd+Wzjz5i67bbVGQRnasQdfwab62yOuZ1Qcx8AdHJSR1giZntHDO1T3chXe3Urv0O/DJnLgvmz2fNmjV88Pa77LFfl6S2Xbp4McuXLQNg9R+r+WLyZFptvXVlFte5MvNrvBueZUDDsmxoZr9L+lHScWb2vKJqbycz+6Jii+jcujLq1qX/wEsYctGlFBTkc1CPHmy1zTb878WXADi8z9EsXrSIy/qdycoVK6hTpw6vPvMcw595ksULF3Hn0GEU5BdgVsC+Bx3I7l32reIjci6x2vIADVmC60cbKklPAZ2AryjeuaqzmQ0IaWaH+YWx6yS1Ae4HNgcygWfM7N/r26c3NTvnNlQ7NGleoVXO/3z9Wcq/l+fssGuNq/bWqsBbFTzwOuc2VBUdeB/85vOUfy/P3n6XGhd4a1NTs3POuWqstjxAwwOvc865aqGmdpZKlQde55xz1YLXeJ1zzrk08hqvqxAb1ZIzOOecK686tSTwelRwzjlXLWTUqZPylAxJh0n6RtL3kgYlWN9X0rQwTZL0t5h1syVNlzRV0pSKOE6v8TrnnKsWKqOpWVIG0QA33YG5wGRJr5jZlzHJfgQOMLPFkg4neuTvnjHru5nZwooqkwde55xz1UIlda7aA/jezGYBSHoG6AUUBV4zmxST/iOgVWUUpJA3NTvnnKsWKulZzS2BOTHzc8OykpwJ/C9m3oC3JH0qqX/KB5WAB17naoApH37IWcedwBnHHMtzI9cdanLO7NlccubZHNVlf1544sli624fOowTDzuCc0/qm67iOlcmdVQn5Sl2GNYwxQfHRNE54ROyJHUjCrxXxize18x2BQ4Hzpe0f7mPs7wZVFeSOku6ez1pWksySUNjljWXtEbS8DA/JKTZNibNJWFZ58o7Auci+fn53HvLbQy983b+88zTjH3rbX6a9WOxNA0bNeLcyy7hmL4nr7N99x5HMuzOO9JVXOfKrCw1XjMbYWadY6YRcdnOBbaMmW8FzI/ft6ROwENALzNbVLjczOaHv7nAS0RN1+VSYwJvuECeNDObYmYXJpF0FtAjZv44YGZcmunAiTHzxxJzfcC5yvTtl1+yRatWbN6yJZmZmRzQ/WA+Gj++WJrGTZuyfYcO1K27breNnXbZhYaNGqWruM5VN5OBdpLaSNqI6Lf8ldgEkrYCXgROMbNvY5Y3kNSw8DVwCDCjvAWqFoE31Dy/ljQydOd+QVL90I37X5ImAMdJOkTSh5I+k/R8GKAeSbuHLuBfSPpEUkNJXSW9FtYPkfS4pPckfSfp7JjdrwK+iqm9ngA8F1fE0UQX45G0DbAUyKu8d8S5vyzMzSO7RU7RfPOcHBbl+dfPbXgq4xqvma0FBgBjiEame87MZko6V9K5Idm/gGbAfXG3DbUAJkj6AvgEeN3M3izvcVanXs3bA2ea2URJDwP/CMv/MLMukpoTnZEcbGYrJF0JXCrpRuBZ4AQzmyypEVEwjdcJ2AtoAHwu6fWYdc8AJ0r6FcgnaobYImb978AcSR2JAvCzwOkVdNzOrUeCy1G15EEDrnaprPF4zewN4I24ZQ/EvD4LOCvBdrOAv8UvL69qUeMN5pjZxPD6CaBLeP1s+LsX0AGYKGkqcBqwNVHA/sXMJkM0aH04w4n3spmtCvdivU/xdvo3ie7xOilmf/GeIWqi6E3Uzl+i2Iv9Tz86srSkzq1X85wc8hbkFs0vzM2lWfPmVVgi5ypHHSnlqSaqTjXe+NP6wvkV4a+At83spNhE4YJ4MmM4lpQ/ZvanpE+By4AdgaMSbP8qcAswxcx+VykfeLi4PwJg1pLffDxeVy7btW/P/Dlz+HX+fJplZzPu7Xe4cui1VV0s5yqcD5KQfltJ2tvMPiSqeU4AdolZ/xFwr6Rtzex7SfWJeqd9DWwhaffQ1NyQxE3NvSTdQNTU3BUYBGwUs/42YJyZLUoUVM1sVWje/nadlc5Vooy6dTlv4GVcfeHF5BcUcMhRPdh6m214/cUXATiyTx9+W7SIC087nZUrVlCnTh1GP/Ms/3nmaRpkNeDGq//FtM8+4/clS/h7j56c0v8sDu3Zs4qPyrl1+SAJ6fcVcJqk/wDfAfcDFxSuNLM8Sf2ApyVtHBZfbWbfSjoBuEfSJkRB9+AE+X8CvA5sBQw1s/mSWsfkP5N1ezMXY2bPlPXgnCuPPfbdhz323afYsiP79Cl63bRZM5547ZX4zQAYNOzflVo25ypKnYS33G54ZFb1LaEhAL5mZh0rKf8hwHIzu7Uy8i+NNzU75zZU2zRuWqGRcsrCX1L+vezcfPMaF62rU43XOedcLaZaUuOtFoHXzGYDlVLbDfkPqay8nXPOVYya2ks5VdUi8DrnnHO1o0+zB17nnHPVhNd4XYXYpE5Kj5h2zrlay2u8zjnnXBp55yrnnHMujbyp2TnnnEsjb2p2zjnn0shrvM65KvXxpA8Zfttt5BcUcGSvXvTtd1qx9WbGPbfdxkcTJ1GvXj0GXfMvttthBwCWLVvGLcOu48cffkASV/7f1ezYqRP/vf8BJo4fjySaNG3KoGv+RfPs7Ko4POdqrWpVs5fUT9IWMfOzwzi85clzO0lvSPpe0leSnpPUIoXtx0rqXJ4yOJeq/Px87rr5Zm666y5GPvcs7701htmzZhVL8/GkScz9eQ5PvjiKy64azB033lS0bvhtt7HH3nvx+AvP89+nnmSrNm0AOPGUv/Pw00/x36eeZO8uXRj50ENpPS7nSlMHpTzVRNUq8AL9KD4AfZlJqiupHtHACPeb2bZm1p5o8AU/xXfV2tczZ9Jyy1Zs0aolmZmZHNj9ECaOG18szcRx4zn0yCOQxI477cTyZctYtHAhK5Yv54vPP+fIXr0AyMzMpGHDhgA0yMoq2v6PVasobXhL59JNSn2qicrV1CzpCuAPM7tb0h3A38zsQEkHAacDjwHXAhsDPwCnm9lySf8iGvN2E2AScA5wDNAZeFLSKmDvsJsLJB0FZALHmdnXkhoA9wA7hWMYYmYvh9GLjgTqEQ3/9wTwoZm9WlhmM3s/lL0eURDuDKwFLjWz98MIR48AHYhGTNok5ngPSXQ85XkPnUskLy+P7BZ/Ncxkt8jhyxkz49LkFk+Tk0Nebi4ZGXVp3LgJN177b3747ju2a78DF1x2GZtsEn2VH7rvPsa8/gYNsrK484H703NAziWhptZgU1XeGu94YL/wujOQJSkT6AJMB64GDjazXYEpwKUh7XAz2z2MRrQJ0MPMXghp+prZzmZWOKbuwrD9/cDAsOyfwHtmtjvQDbglBGOIAvZpZnYg0fOfPy2h7OcDmNlOROP/jgzB+DxgpZl1Aq4DdgMITd4lHY9zFSvBqGHrnN0nGMdFEvn5a/n2m2/odewxPPTkE2xSbxOeenRkUZqz/vEPnn/9NbofdhgvPfd8BRfcubKrU4apJipvuT8FdguDz68GPiQKwPsRjYvbAZgoaSpwGrB12K6bpI8lTQcOBHYsZR8vxuyrdXh9CDAo5DuWqIa7VVj3tpn9lkTZuwCPA5jZ18BPwHbA/kQ1ZcxsGjAtpN+rlOMpRlJ/SVMkTXnikUeTKIpzxWXn5JC3YEHRfN6CXJo3zy49TW4uzbOzyc7JITsnhw4do3FHDjjoQL775pt19nHQYYcy7r33KukInEtdHSnlqSYqV1Ozma2RNJuoWXkSUZDqBrQFfiQKgifFbhNqlfcBnc1sThgrt14pu1kd/ubHlFfAMWZW7NdE0p7AiphFM4EDSsi3tE8s0ZiQIsHxJNzYbAQwAuCX35f6eLwuZdt36MDcn+fwy7x5NM/J4b233+LqoUOLpdln//146bnnOfCQQ/hyxgwaZGXRrHnUFzGnRQ4/z/6JrVpvzaeTJ7N16Fw19+efabVVdI46afx4tmrdOq3H5VxpaktTc0XcTjSeqAn4DKLm5duJaqcfAfdK2tbMvpdUH2gF5IbtFkrKAo4FXgjLlgENk9jnGKJrvxeYmUnaxcw+T5DuKWCwpCPN7HUASYcB80K5+wLvSdqOqMb8Tczy9yV1BDqFvBIej5l9m9S75FwK6taty0VXXM7lF15IQX4Bh/c8ijZt2/LyqFEA9DrmGPbad18+njiJvkf3YeN69bjyX/9XtP2FAy9n2L/+j7Vr1rJ5yy0Y9K9/ATBi+L38/NNP1KlThxabbcalgwdVyfE5l0gNrcCmTJbgWlJKGUQdqd4EGpvZCknfAg+Y2e2SDgRuIuqMBHC1mb0iaRhwIjAbmAP8ZGZDJB0DXE/UTL03Ueemzma2MNzSc6uZdQ0doO4E9iGqic42sx6hc1VnMxsQU74dQtq2wBqiWvlFwFLgAaJruCV1rpoKbAtcaGZTSjqe0t4fr/E65zZUmzfatEJDZe6y31P+vcxp2KjGhetyB15XOg+8zrkNVUUH3oXLl6X8e9k8q2GNC7z+5CrnnHPVQk3tpZyq2nKczjnnqrnKenKVpMMkfROeYLhOxwZF7g7rp0naNdlty3aczjnnXDVQGU+ukpQB3AscTtR35yRJHeKSHQ60C1N/oudGJLttyjzwOuecqxYqqca7B/C9mc0ysz+BZ4BecWl6AY9Z5COgsaTNk9w2ZX6Nt5Jl1Jb+8c45V06V9ECMlkR3zxSaC+yZRJqWSW6bMq/xOuecqxaEpT7FPCkwTP3XyXZd8b2nS0qTzLYp8xqvc8656sEKUt8k5kmBJZgLbBkz3wqYn2SajZLYNmVe43XOObchmwy0k9RG0kZED2+Kf/DRK8CpoXfzXsBSM/slyW1T5jVe55xz1UTqNd71MbO1kgYQPWo4A3jYzGZKOjesfwB4AzgC+B5YSTT+QInblrdM/uSqSlaWR6A551xNUNGPa1y5fHHKv5f1s5rUuB6sXuN1rpr6eNIk7rr1NgoKCujRuxd/79ev2Hoz465bb+OjiRPZuF49rhpyDdvvsAMAxx3Vk/r161Mnow4ZGXV56PHHAHj/nXd4eMQIfvpxNiNGPsoOHcp9S6JzFajia7zVUa0KvJLyiUZQyiQaGGEkcKdZ6lf0JV1lZtdXcBGdAyA/P5/bb7qZO+4dTnaLFpx96mnsu//+tNlmm6I0H02cxNw5P/P0Sy/y5YwZ3HbDjYwY+WjR+rv+8wCNGzculm+btm257uabueX6G9J0JM6loAydq2qi2ta5apWZ7WxmOwLdidr0ryljXldVXLGcK+6rmTNpueWWbNGqFZmZmRx0SHcmjBtXLM2EceM47IgjkcSOO+3E8mXLWLhwYan5tm7TxsfgddVYQRmmmqe2Bd4iZpZL9GiwAaEnW4akWyRNDs/qPAdA0uaSxkuaKmmGpP0k3QhsEpY9WaUH4jZIebl55LRoUTSfndOChbl5xdPk5ZGzWUyaFjkszI2Gu5bEpecP4My/n8IrL76YnkI7V15WkPpUA9WqpuZ4ZjZLUh0gh+gxYEvNbHdJGwMTJb0F9AHGmNl14bmd9c3sA0kDzGznqiu927Al6GMS91SfRB0jFdLc99+HaJ6dzeLffuOS8wewVevW7Lzrruukd656qZmBNFW1tsYbo/DX7BCi+7imAh8DzYgemD0ZOF3SEGAnM1u23gxjnqTy2COPVE6p3QYtOyeH3AULiubzchfQPLt5sTQ5OTnk/hqTZkEuzbKzAWge/jZp2pT9u3blq5nlvgPCucpXS2q8tTrwStoGyAdyiQLwBeEa8M5m1sbM3jKz8cD+wDzgcUmnri9fMxthZp3NrPOpp59eqcfgNkw7dOjA3Dk/M3/ePNasWcO7b71Nl/33L5Zm3wP25803XsfMmDl9OllZWTRv3pxVq1axcsUKAFatWsXkjz9im7Ztq+IwnEtR7bjGW2ubmiVlAw8Aw83MJI0BzpP0npmtkbQdUbBtDswzswclNQB2BR4D1kjKNLM1VXYQboNVt25dLrn8Ci674EIK8vM5smdP2rRty+gXRgHQ+9hj2Hvffflo4kRO7H009erVY/A1/wJg8aJFXHX5FQDk56+l+6GHsec++wAw/v33ufOWW1myeDFXXHwJ2263HbcPv6dqDtK5eDW0BpuqWvUAjQS3Ez0O3G5mBeFa7zDgKKLabx7QO0yXA2uA5cCpZvajpJuAnsBnZta3pH36AzSccxuqCn+AxpIfUn+ARuO2Ne4BGrUq8FYFD7zOuQ1VRQfeVYu/T/n3cpMm29a4wFtrm5qdc85VN7WjqdkDr3POueqhllzj9cDrnHOumvDA65xzzqWP13hdRdi4Tq2+Vdo551wcD7zOOeeqCa/xOuecc+njTc3OOedcOnngdc4559KnltR4veePc9XUhxMncezRfejTsxcjE4xyZWbcevPN9OnZi5OPP4Gvv/oKgAW//sp5/ftzfJ9jOOHY43jmqaeKtnnn7bc54djj2HO3znz55ZdpOxbnklM7BknY4AKvpFaSXpb0naQfJN0laaME6baQ9EIS+b0hqXGlFNa5EuTn53PzTTdy1z138+yoFxjz5hhmzZpVLM2kiROZ8/McRr08msFXX81NN9wAQEZGBhddcgnPvTiKh0c+yvPPPV+0bdu223Lzrbewi4/N66ojHxaw5lE0CviLwGgzawdsB2QB18Wlq2tm883s2PXlaWZHmNmSyiivcyWZOWMmrVptSctWrcjMzOSQQw9h/NixxdKMHzuOI3ociSR26rQTy5YtZ2FeHs2zs9mhfXsAGjRoQJs2bcjLzQWgzTZt2Lp16zQfjXPJ8hpvTXQg8IeZPQJgZvnAJcAZkv4h6XlJrwJvSWotaQaApPqSnpM0TdKzkj6W1Dmsmy2peUj/laQHJc2U9JakTarqQN2GLS8vlxabtSiaz8lpQV5uXrE0ubm5tGgRmyaH3LziaebPn88333zNjh07Vm6BnasIXuOtkXYEPo1dYGa/Az8TdSTbGzjNzA6M2+4fwGIz6wQMBXYrIf92wL1mtiOwBDim4oru3F8Sjhqm+EFY1k0j/kqzcuVKBg28nEsvG0hWVlYFl9C5imeWn/JUE21ogVck+jX6a/nbZvZbgvVdgGcAzGwGMK2E/H80s6nh9adA64SFkPpLmiJpyqMPP5x86Z0LcnJasODXBUXzubkLyM5uvm6aBbFpcovSrF2zhisHXs6hRxxOt4PizzOdq56soCDlqSba0ALvTKBz7AJJjYAtgXxgRQnbJTue4+qY1/mUcDuWmY0ws85m1rnfGWckmbVzf+mwYwfmzJnDvHnzWLNmDW+NeYv9DjigWJr9DtifN157HTNj+rTpZGVl0Tw7GzNj6L+H0qZNG/r+/e9VdATOpS7dNV5JTSW9HTrjvi2pSYI0W0p6P1xqnCnpoph1QyTNkzQ1TEcks98NLfC+C9SXdCqApAzgNuBRYGUp200Ajg/bdAB2qtxiOle6unXrcvmVV3Dh+QM4/phjOLh7d9q2bcuoF15g1AtRZ/x9u3ShZcuW9OnVi+uHDeWKwYMA+GLqVP73+utMmTyZvieeRN8TT2LihAkAvP/ee/Q47HCmT5vGpRdexAX/OL/KjtG5eFaQn/JUToOAd0Nn3HfDfLy1wGVm1h7YCzg/xIlCd5jZzmF6I5mdKuG1pBpM0pbAfcAORCcWbwADgZOAzmY2IKRrDbxmZh0lNQBGEvWC/hzoCJxoZt9Jmk1Ui84qTB+2HwhkmdmQ0sqzdMXyDesNds65YNMGWcm2Fibl95/fSPn3stFWR5S5DJK+Abqa2S+SNgfGmtn269nmZWC4mb0taQiw3MxuTWm/G1rgLYtQM840sz8ktSU689nOzP4sb94eeJ1zG6oKD7yzX0s98LbuUZ7Au8TMGsfMLzazdZqbY9a3BsYDHc3s9xB4+wG/A1OIasaL17dff2RkpD7wvqRMouu951VE0HXOOZe8slyzldQf6B+zaISZjYhZ/w6wWYJN/5nifrKAUcDF4W4ZgPuJ7oSx8Pc2YL0dezzwAma2jLhOWc4559KrLNdsQ5AdUcr6g0taJ2mBpM1jmppzS0iXSRR0nzSzF2PyXhCT5kHgtWTKvKF1rnLOOVdDVcF9vK8Ap4XXpwEvxycIT0T8L/CVmd0et27zmNmjgRnJ7NQDr3POudrqRqC7pO+A7mG+8Fn+hT2U9wVOAQ5McNvQzZKmS5oGdCN6UuJ6eVOzc865aiHdD8Qws0XAQQmWzweOCK8nUMKzHszslLLs1wOvc865aqGmPgIyVR54nXPOVQsV8ECMGsEDr3POuWrBa7zOOedcGtXUQQ9S5YHXOedctVBbarx+O5Fz1dSHEydx7NF96NOzFyMfeWSd9WbGrTffTJ+evTj5+BP4+quvitYNHXIthx50MCced3yxbb795hvOOPU0+p54Eqf2/TszZyR126Fz6VGQn/pUA5U78Ep6Q1LjFNK3lpTwv13SWEmd15evpNnh3qnpkr6UNEzSxmUpv3PVUX5+PjffdCN33XM3z456gTFvjmHWrFnF0kyaOJE5P89h1MujGXz11dx0ww1F64486ijuGn7POvnec9ddnHVOf5585mnOOe9c7rnr7ko/FueSVQUP0KgS5Q68ZnaEmS2pgLKsN19FCsvczcx2AvYAtqGUR4alQpI3v7sqN3PGTFq12pKWrVqRmZnJIYcewvixY4ulGT92HEf0OBJJ7NRpJ5YtW87CvDwAdt1tVxptummCnMWK5dGw1MuXL6d5dvNKPhLnkpfMwPfxU0203iAj6QrgDzO7W9IdwN/M7EBJBwGnA134a9i8/xGNbbsPMA/oZWarJO0GPEw0Ju6EmLw3AR4BOgBfAZvErJsdl+/7wN5A79jymdlySecCcyQ1NbPfJF1ONL7uxsBLZnZNyPP/gL7AHGAh8KmZ3SppLDCJ6Aklr4T528O+FwL9wrM82wL3AtnhWM42s6/X9x46l6q8vFxabNaiaD4np8U6zcK5ubm0aBGbJofcvDyaZ2eXmO+lAwdy4YDzuevOO7GCAh5K0ITtXFWpqTXYVCVT4x0P7BdedwaywgOjuwAfxKVtB9xrZjsCS4BjwvJHgAvNbO+49OcBK82sE3AdsFsJZdgeeMzMdjGzn+JXhpEifgTaSToklGMPYGdgN0n7hybsY4BdgD6sOyhCYzM7ALgbuAc41swKTxiuC2lGABeE5QOJxv1dh6T+kqZImvLoww+XcEjOlSzhcJ2Kf3jOummU+AE7RUa98DyXXHYZr/3vDS6+7FKG/fvf5SilcxUrmYHv46eaKJlm1U+JgldDYDXwGVHQ2g+4EBgck/ZHM5sas11rSZsSBbVxYfnjwOHh9f5EgQ4zmxaed5nIT2b20XrKWfiLc0iYPg/zWUSBuCHwspmtApD0atz2z4a/2wMdgbejZ2OTAfwShoTaB3hef/0AJryuHDtaho/H68oiJ6cFC34tGviE3NwFZMc1C+fktGDBgtg0ueukiff6a69x2eWXA3Bw9+5cP3RYBZbaufLxGm9gZmuA2UTNypOIarndgLZEzcOxVse8zicK7CLRqXnMLpIo54rSVoaTgtbAt2F/N5jZzmHa1sz+SwnP2kywDwEzY7bfycwOIXqvlsQs39nM2idRdudS1mHHDsyZM4d58+axZs0a3hrzFvsdcECxNPsdsD9vvPY6Zsb0adPJysoqtZkZILt5Np99+ikAkz+ZzJZbbllpx+Bcqvwab3HjiZpWzwCmE13//NTMTOs0fxVnZkskLZXUJTxsum9cvn2JBqHvCHRK9QBCTfQ+YLSZLZY0Bhgq6clw/bclsIbo2vJ/JN1AdNxHAg8myPIbIFvS3mb2YWhW387MZkr6UdJxZvZ8GCqqk5l9kWqZnVufunXrcvmVV3Dh+QMoKMjnqJ69aNu2LaNeeAGAY449ln27dGHShIn06dWLevXq8X9DhhRtf/Xgq/j00yksWbKEHocdztnnnkOv3r256v+u5vZbbmVtfj4bb7wRg6++uoqO0Ll11ZYab7KB9wPgn8CHZrZC0h+se323NKcDD0taCYyJWX4/8EhoYp4KfJJCnu+H4FcHeAkYCmBmb0lqD3wYTgqWA383s8mSXgG+AH4CpgBL4zM1sz8lHQvcHZrJ6wJ3AjOJThLul3Q1kAk8E/JzrsLt26UL+3bpUmzZMcceW/RaElcMHpRw22E3XJ9w+c677MJjTz1ZcYV0rgLV1Gu2qVLCThwbKElZoRZcn6i23d/MPqvMffo1XufchmrTBlnru4SXktkTrkn597J1l2srtAzpUNvuWR0hqQNQDxhZ2UHXOeeci1erAq+ZnVzVZXDOOZdYbWlqrlWB1znnXPVl+R54nXPOubTxGq+rEHXWe/uwc8458Bqvc845l1YFXuN1zjnn0sdrvM4551waeeB1zjnn0sgK1lZ1EdIimWEBnXNVbNLEifQ5+mh69+zJownG0DUzbrn5Znr37MmJxx/P11/9NX7JtUOG0P2ggzj+uOPSWWTnUlaQn5/yVBPVusCryARJh8csO17Sm1VZLudKkp+fz0033cTd99zD86NGMebNN5k1a1axNBMnTmTOzz/z0ssv88+rr+aGG24oWnfUUUdxz/Dh6S62cylL93i8kppKelvSd+FvkxLSzZY0XdJUSVNS3T5erQu8Fj2c+lzgdkn1JDUgGuj+/LLkJymjIsvnXLyZM2awZatWtGrViszMTA459FDGjR1bLM24sWM5okcPJLFTp04sW7aMhXl5AOy622402nTTKii5c6mx/PyUp3IaBLxrZu2Ad8N8SbqF4WA7l3H7IrUu8AKY2QzgVeBK4BrgCeCfkiZL+lxSLwBJrSV9IOmzMO0TlneV9L6kp4iGSXSu0uTm5dFis82K5nNycsjNzS2WJi83l81atCiab5GTQ24IvM7VFOmu8QK9gJHh9Uigdzq2r82dq64FPgP+BF4D3jOzMyQ1Bj6R9A6QC3Q3sz8ktQOeBgrPdvYAOprZj+kvuqtVEowgFj8OdqIhXfzRLa6mqYJrti3M7BcAM/tFUk4J6Qx4S5IB/zGzESluX0ytDbxhXOFnicbrPR44StLAsLoesBUwHxguaWcgH9guJotPSgq6kvoD/QHuuvtuTj/jjMo5CFcr5OTksODXX4vmc3Nzyc7OXifNrwsWFM0vSJDGuequLDXY2N/bYERMYCRUojZbZ8NojPlk7Wtm80NgfVvS12Y2PuXCBrU28AYFYRJwjJl9E7tS0hBgAfA3omb5P2JWrygp0/ChjwBYtmKFj8fryqXDjjsyZ84c5s2bR05ODm+NGcOw64sPdH/AAQfw3LPPcuihhzJj+nSysrJo7oHX1TBluWYb+3tbwvqDS1onaYGkzUNtdXOiVs5EecwPf3MlvUTU4jkeSGr7eLXyGm8CY4ALFNrvJO0Slm8K/GJmBcApgHekcmlXt25dLr/ySi44/3yOPeYYDu7enbZt2/LCCy/wwgsvALBvly60bNmS3r16MWzYMAYNHly0/VWDB3N6v3789NNPHHHYYYwePbqKjsS50ln+2pSncnoFOC28Pg14OT6BpAaSGha+Bg4BZiS7fSKyBNePaotQo10O3AvcCexDVPudbWY9wnXdUcBK4H3gAjPLktQVGGhmPda3D6/xOuc2VA0bNKjQrgSfPnhUyr+Xu539apnLIKkZ8BzRpcWfgePM7DdJWwAPmdkRkrYBXgqb1AWeMrPrStt+vfutzYE3HTzwOuc2VBUdeCf/54iUfy93P+eNGtePsLZf43XOOVdN+LOanXPOuTSqgPtyawQPvM4556oFr/E655xzaVTgNV5XETJq3GV/55yrGl7jdc4559KotozH64HXOedcteA1Xueccy6N/Bqvc845l0Ze43XOpZ2ZcfPNtzBx4gTq1avHtddeS/v27ddJN2/ePAYNGszSpUtp334Hhg0bRmZmZonbz549myuvHFRs+/POO5e+ffsC8PTTz/Dss8+SkZHBfvt14eKLL07XITtXpKCgdjzor1IGSZCUL2mqpJmSvpB0qaQK35ekpyVNk3SJpEclrSx8mHVYf5ckk9S8jPn3C8/sLJyfXda8nEvGhAkT+fnnn3n55Ze5+uqruf76GxKmu+uuu+nbty+vvPIyDRs24qWXRpe6fevWrXn22Wd49tlneOqpJ6lXrx7dunUDYPLkyYwdO5bnnnuWUaNe4NRTT03LsToXr6CgIOWpJqqs0YlWmdnOZrYj0B04ArimIncgaTNgHzPrZGZ3hMXfA73C+jpAN2BeOXbTD9hifYmcqyjjxo2lR48eSKJTp04sW7aMvLy8YmnMjMmTJ3PwwQcBcNRRPRg79v2kt//kk09o1aoVW2wRfbWff/4FTj/9dDbaaCMAmjZtWtmH6VxCBQWW8lQTVfqwgGaWSzRI8QBFWkv6QNJnYdoHQNLjknoVbifpSUk9JdWT9Iik6ZI+l9QtJHkLyAk16/3CsqeBE8LrrsBEYG1MnpdKmhGmi8Oy1pK+kvRgqKG/JWkTSccCnYEnwz42CdlcEMo9XdIOlfGeudorNzeXzTZrUTTfokUOubnFA+eSJUto2DCLunXrhjQtitIks/2YMWM47LBDi+Z/+uknPv/8M0455VTOPPMsZs6cWeHH5VwyPPBWIDObFfaVQzRQcHcz25UoSN4dkj0EnA4gaVOiIfreAM4PeewEnASMlFQP6An8EGrWH4Q8vgOyJTUJaZ8pLIOk3UL+ewJ7AWfHjLvbDrg31NCXAMeY2QvAFKBv2MeqkHZhKPv9wMAKeoucAyDRYGFSMmmU1PZr1qxh3LjxdO/evWhZfn4+v/++jMceG8kll1zMFVdciY9a5qpCgRWkPNVEaQm8QeG/fybwoKTpwPNABwAzGwdsKymHKGiOMrO1QBfg8ZDma+AnYLtS9vMicCJRgP0gZnkX4CUzW2Fmy0O6wpryj2Y2Nbz+FGi9nvxLTSepv6QpkqY8/PDDpWTlHDz77LOccMKJnHDCiWRnZ/PrrwuK1i1YkEt2dnax9E2aNGbZsuWsXbs2pFlAdnbU9aBFi5xSt58wYSI77LADzZo1K1rWokUOBx10IJLo2LEjderUYfHiJZVxqM6Vymu8FSgMJJxPVNu9BFgA/I2oKXejmKSPA32JaqaPFG6e4u6eAYYCb5sVOx0qLZ/VMa/zKb239+r1pTOzEWbW2cw6n3HGGUkU2dVmJ5xwQlHHp27duvLaa69hZkybNo2srKx1Aq8kOnfuzDvvvAvAq6++RteuXQE44IADSt3+zTffLNbMDNC1azc++WQyEDU7r1mzhiZNGlfW4TpX61V64JWUDTwADLeo/WpT4JcQFE8BMmKSPwpcDGBmhReaxhMFYyRtB2wFfFPS/szsZ+CfwH1xq8YDvSXVl9QAOJriNeJElgEN15PGuQrTpUsXWrVqSc+evRg6dBiDBw8uWjdgwAVF12svuuhCnnjiCXr27MnSpUvp3bv3erdftWoVH3/8MQceeGCxffbu3Yt58+Zy7LHHMWjQYP7972uLmq6dS6fa0qtZlXEtR1I+MJ2oWXktUU32djMrkNQOGAWsBN4HLjCzrJht3wRGm9kDYb4eUeDeLeR1qZm9L6k18JqZdQzpHg3zL8SVZTbQ2cwWSroUKKyCPmRmdybIZyCQZWZDJB0DXA+sAvYGvorJqzNwq5l1Le29WLlyRc1sC3HOufWoX79BhZ6hjb6sbcq/l71v+6HGnSVWSuAtK0n1iQL2rma2tKrLUxE88DrnNlQVHXhfvGSblH8v+9wxq8YF3mrz5CpJBwMPE9WMN4ig65xzLnk1tek4VdUm8JrZO0TXb51zztVCNbWXcqqqTeB1zjlXu3ngdc4559Kopj4QI1UeeCubrV1/Guecc17jdc4559KptnSuSucjI51zzrkSpfuRkZKaSnpb0nfhb5MEabYPA+UUTr/HDLIzRNK8mHVHJLNfD7zOOeeqhSp4VvMg4F0zawe8G+aLMbNvwkA5OxM9yGkl8FJMkjsK15vZG8ns1AOvc865aqEKHhnZCxgZXo8Eeq8n/UFEo+L9VJ6deuB1zjlXLVRBjbeFmf0CEP7mrCf9iUTjvscaIGmapIcTNVUn4p2rnKumzIybb7mNiRMmUa9ePa699l+0b7/DOunmzZvHoMFXs3Tp77TfYXuGDbuWzMxM3njjTR599DEANqm/CVdddSXbb7cdq1ev5syzzuHPP/8kPz+fgw86iPPO65/uw3NuHWUJpJL6A7Ff4BFmNiJm/TvAZgk2/WeK+9mIaBz4wTGL7ycaDc/C39v4azyAEtW4wCupFXAv0Ti+GcAbwGVmtrrUDZPLux/wlpnNL29ezpXXhImT+PnnObz88iimT5/B9TfcxOOPPbJOurvuHk7fvidx2KGHMOy6G3hp9Mscf9yxbNFyCx566AEaNWrEhImTGDbsBh5/7BE22mgjRvznPurXr8+aNWs548yz2XffvenUaacqOErn/lKWpuMQZEeUsv7gktZJWiBpczP7RdLmREPXluRw4DMzKxrwOva1pAeB15Ipc41qalY0VtmLRKMXtQPaAZsAN1dA3hlAP2CLFLercScvrmYYN3Y8PXocgSQ6ddqJZcuWkZe3sFgaM2Py5CkcfFA01N9RPY5k7PvjANj5b51o1KgRAJ126siCBdFviiTq168PwNq1a1m7dq0PA+iqhQKzlKdyegU4Lbw+DXi5lLQnEdfMHIJ1oaOBGcnstEYFXuBA4A8zewTAzPKBS4BTJQ2QNLwwoaTXJHUNr++XNEXSTEnXxqSZLelfkiYQvamdgSdDt/BNJO0maZykTyWNKXyTJY2VdL2kccBFaTp2V8vk5uayWYsWRfMtcnLIzSt+Qr5kyVIaZjWkbt3o/K9Fixbk5uWtk9fo0a+w7757F83n5+dzwol9OejgQ9lrzz3YaaeOlXQUzlVrNwLdJX0HdA/zSNpCUlEP5TByXneiil+smyVNlzQN6EYUj9arptXWdgQ+jV1gZr+HMXdLO5Z/mtlvoVb7rqROZjYtrPvDzLoASDoLGGhmUyRlAvcAvcwsT9IJwHX81X7f2MwOqLhDc664ROfyQnFp1k0VX3udPHkKo0e/wsMP/9Ual5GRwbPPPMmyZcu49LIr+P77H9h227YVUm7nyirdD9Aws0VEPZXjl88HjoiZXwk0S5DulLLst6bVeEVJv0elO17SZ8DnRMG7Q8y6Z0vYZnugI/C2pKnA1UCrJLZDUv9Qw57y8MOPrqdozv3l2Wef54QT+3LCiX3Jzm7OrwuKLiGxIDeX7OzsYumbNG7MsuXLWLs2ejTpggULyG7evGj9t99+x7+HXscdd9xC48aN19lfw4YN6bzbrkya9GHlHJBzKaiCXs1VoqbVeGcCx8QukNQIaAEsAraLWVUvrG8DDAR2N7PFkh4tXBesKGFfAmaa2d4lrC9pu2IX+1euWFozvxmuSpxwwnGccMJxAHzwwQSeefZ5Djv0EKZPn0FWVhbZ2c2LpZdE58678c6773HYoYfw6muv07Vr1BDzyy+/MnDglQwdei1bb7110Ta/LV5MZt26NGzYkD/++IOPP/6Efv1OTd9BOleCmhpIU1XTarzvAvUlnQpFHaJuA4YDPwI7S6ojaUtgj7BNI6IguVRSC6KeaSVZBjQMr78BsiXtHfaVKWnHij4g50rSpcu+tGrZkp69+jB02PUMHnxF0boBF1xcdC33ogsv4IknnqJnzz4sXbqU3r17AjDiwYdYsnQpN9xwEyec2JeT+0bBdWHeQs7ufx7HH38yfz+lH3vutSf7779f+g/QuTi1pcYrK3+vsLQKQfVeoD2QDTxrZueEHs9PADsT9SxrAQwxs7GhlrsnMAtYDbxiZo+Ga8OdzWxhyPsY4HpgFbA3UXPz3cCmRK0Dd5rZg5LGEq4Fr6+8XuN1zm2o6jfYtEK7w9/cu0HKv5dXjF5R47rk17jAG0vSPkTdu/uY2afrS18VPPA65zZUFR14b+yVeuAd9HLNC7w17RpvMWY2Cdh6vQmdc85Ve/k1tOk4VTU68DrnnNtw1JK464HXOedc9eCB1znnnEsjD7yuYlh6n8TinHM1VZofXFVlPPA655yrFvJr8F02qfDA65xzrlrwpmbnnHMujbyp2TnnnEsjr/E656qUmXHzLbczceKH1Ku3MdcO+T/at99hnXTz5s1n0OCrWfr777TfYXuGDR1CZmYmb7zxJo+OfByATerX56rBV7D9du2KtsvPz6fvKaeTk53N3Xfdlrbjcq4ktSXw1rRBEkokySQ9HjNfV1KepNfKmF9jSf+Ime9a1rycK4sJEz/k5zlzeHn081x99WCuv+HmhOnuuvte+vY9iVdGv0DDRo14afQrAGzRcgseevB+nnv2Sc4+63SGDbuh2HZPPf0sbVq3ruzDcC5p+WYpTzXRBhN4iUYg6ihpkzDfHZhXjvwaA/9YXyLnKsu4cePpceQRSKLTTh1Ztnw5eXkLi6UxMyZPnsLBB3UD4KgeRzB27HgAdv5bJxo1agRAp506siA3r2i7BQtymTBhEkeHkYycc+mzIQVegP8BR4bXJxENoACApKaSRkuaJukjSZ3C8iGSHpY0VtIsSReGTW4E2kqaKumWsCxL0guSvpb0ZBgRyblKkZubx2YtcormW+TkFA0FWGjJkqU0bNiQunXrlpgGYPToV9l3n72K5m+57Q4uumgAder4V9hVHwUFqU810YYWeJ8BTpRUD+gEfByz7lrgczPrBFwFPBazbgfgUKIxfK+RlAkMAn4ws53N7PKQbhfgYqADsA2wbyUei6vlEo0cFn+uZyRIQ/E0kyd/yuiXX+GiCwcAMH78BJo2aUKHBNeLnatKBZb6VBNtUJ2rzGyapNZEtd034lZ3AY4J6d6T1EzSpmHd62a2GlgtKZdoLN9EPjGzuQCSpgKtgQnxiST1B/oD3HPX7ZxxRr9yHJWrTZ597gVefOllAHbs0J5fF+QWrVuQm0t28+bF0jdp3Jhly5axdu1a6tatG6XJ/ivNt999x7+HXs/we+6gcePo6z71i2mMG/8BEyZO4s8//2TF8hX88+pruG7YtWk4QudKVlMDaao2qMAbvALcCnQFmsUsT9SmVvgxr45Zlk/J70tS6cxsBDACYOXyxbXkq+QqwgnHH8sJxx8LwAcfTOSZ557nsEO7M33GTLKysooFVYhqwJ0778Y7777PYYd259XX3qDrAfsB8MsvvzJw4GCGDr2GrbfeqmibCy/4BxdeEHVfmDLlUx57/CkPuq5aqKlNx6na0JqaAR4G/m1m0+OWjwf6QtRDGVhoZr+Xks8yoGFlFNC5ZHTpsg+tWrakZ69jGTr0BgYPurxo3YALLym6lnvRhefzxJNP07PXsSxdspTeocPUiAf/y5KlS7nhxls44aRTOPnv/ariMJxLWm3p1axE15FqIknLzSwrbllXYKCZ9ZDUFHgEaAOsBPqHpukhwHIzuzVsMwPoYWazJT1FdK34f8DrhXmFdMOBKWb2aGnl8hqvc25DVT+rSYX2zjtj741T/r18+MPVNa6H4AYTeKsrD7zOuQ1VRQfefnumHngf/bjmBd4N8Rqvc865Gqi2dK7aEK/xOuecq4HSfTuRpOMkzZRUIKlzKekOk/SNpO8lDYpZ3lTS25K+C3+bJLNfD7zOOeeqhSroXDUD6EPU+TYhSRnAvcDhRM9wOElSh7B6EPCumbUD3g3z6+WB1znnXLWQ7idXmdlXZvbNepLtAXxvZrPM7E+iBzX1Cut6ASPD65FA72T269d4nXPOVQvV9BpvS2BOzPxcYM/wuoWZ/QJgZr9IyonfOBEPvJWsonv9OVcaSf3DA1ycq3Ge//zPlH8vY58UGIyI/R+Q9A6wWYJN/2lmLyeziwTLynWK4IHXuQ1Lf8JT05yrDWKfFFjC+oPLuYu5wJYx862A+eH1Akmbh9ru5kDuOlsn4Nd4nXPOuZJNBtpJaiNpI+BEokcTE/6eFl6fBiRTg/bA65xzrnaSdLSkucDewOuSxoTlW0h6A8DM1gIDgDHAV8BzZjYzZHEj0F3Sd0RjwN+Y1H79yVXObTj8Gq9z1Z8HXueccy6NvKnZOeecSyMPvM5VoXCNySTtkMI2+ZKmhkfdfSHpUkml/i9Lah1G3kJSM0nvS1oeRtmKTTc2PBpvapiSui/ROZc8v53Iuap1EjCBqKfkkNgVkjLMLD/B/Coz2zksywGeAjYFrklyn38A/wd0DFO8vmY2JbXDcM4ly2u8zlURSVnAvsCZRIEXSV1DbfQpYHr8fHweZpZLdO/uAEUyJN0iabKkaZLOSbDNCjObQBSAnXNp5jVe56pOb+BNM/tW0m+Sdg3L9wA6mtmPkrrGzifKxMxmhabmHKJnxy41s90lbQxMlPQWqT1p5xFJ+cAoYJh5D0znKpTXeJ2rOicRPXCd8Pek8PqTuCAbP59I4WPtDgFOlTQV+BhoBrRLoUx9zWwnYL8wnZLCts65JHiN17kqIKkZcCDQUZIBGUS10jeAFXHJ4+fj89oGyCd6XJ2AC8xsTFya1smUy8zmhb/LQvP2HsBjyWzrnEuO13idqxrHAo+Z2dZm1trMtgR+BLqkkomkbOABYHhoEh4DnCcpM6zfTlKDJPOqK6l5eJ0J9CAar9Q5V4G8xutc1TiJdR8vNwo4D/hhPdtuEpqSM4G1wOPA7WHdQ0Br4DNJAvJIMEaopNlAI2AjSb2Jmqh/AsaEoJsBvAM8mNJROefWy59c5ZxzzqWRNzU755xzaeSB1znnnEsjD7zOOedcGnngdc4559LIA69zzjmXRh54nXPOuTTywOucc86lkQde55xzLo3+H9xIbIZrUkfbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7, 5))\n",
    "heatmap = sns.heatmap(df[categorical].corr(method=\"spearman\")[[\"ArrDel15\"]].sort_values(by=\"ArrDel15\", ascending=False), vmin=-1, vmax=1, annot=True, cmap=\"BrBG\")\n",
    "heatmap.set_title(\"Spearman Correlations with ArrDel15\", fontdict={\"fontsize\":18}, pad=16)\n",
    "plt.savefig(\"Img/05_03_Spearman_ArrDel15.png\")\n",
    "del heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-flooring",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "A random forest classifier has been modeled to rank the importance of the features in the entire dataset to visualize which features provide relevant information.<br>\n",
    "**Note: Uncomment the below code if you want to re-train the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "charitable-scottish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0048397 , 0.00523481, 0.01395445, 0.02608681, 0.01526924,\n",
       "       0.03741584, 0.05008399, 0.06126179, 0.30946817, 0.19999289,\n",
       "       0.02242114, 0.03434113, 0.01204071, 0.0100903 , 0.006044  ,\n",
       "       0.02369297, 0.02119237, 0.02618649, 0.0244507 , 0.02539917,\n",
       "       0.02574743, 0.02882069, 0.01596521])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.loc[:, df.columns != \"ArrDel15\"]\n",
    "y = np.array(df.loc[:, df.columns == \"ArrDel15\"][\"ArrDel15\"])\n",
    "\n",
    "# model = RandomForestClassifier(n_estimators=100,\n",
    "#                       criterion=\"entropy\", random_state=42, n_jobs=-1)\n",
    "\n",
    "# model.fit(X, y)\n",
    "# joblib.dump(model, \"./Data/feature_importance_model.joblib\")\n",
    "model = joblib.load(\"./Data/feature_importance_model.joblib\")\n",
    "importances = model.feature_importances_\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "direct-rebel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = pd.DataFrame({\n",
    "    \"Feature\": list(X.columns),\n",
    "    \"Importance\": model.feature_importances_\n",
    "})\n",
    "importances = importances.sort_values(by=\"Importance\", ascending=False)\n",
    "importances = importances.set_index(\"Feature\")\n",
    "importances\n",
    "plt.figure(figsize=(8, 6), dpi=80)\n",
    "plt.barh(importances.index, importances.Importance)\n",
    "plt.title(\"Feature Importance Ranking obtained from Random Forest Classifier\", fontsize=12)\n",
    "plt.xlabel(\"Importances\")\n",
    "plt.ylabel(\"Features\")\n",
    "plt.savefig(\"Img/05_04_Feature_Importances_RFC.png\")\n",
    "del importances\n",
    "del model\n",
    "del X\n",
    "del y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "operating-klein",
   "metadata": {},
   "source": [
    "## Preprocessing for Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "sixth-disability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Year', 'Quarter', 'Month', 'DayofMonth', 'Origin', 'Dest',\n",
      "       'CRSDepTime', 'DepTime', 'DepDelayMinutes', 'DepDel15', 'windspeedKmph',\n",
      "       'winddirDegree', 'weatherCode', 'precipMM', 'visibility', 'pressure',\n",
      "       'cloudcover', 'DewPointF', 'WindGustKmph', 'tempF', 'WindChillF',\n",
      "       'humidity', 'time'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>Origin</th>\n",
       "      <th>Dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851431</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851432</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851433</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851434</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1851435</th>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1851436 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Origin  Dest\n",
       "0             6     8\n",
       "1             6     1\n",
       "2             6    13\n",
       "3             6     9\n",
       "4             6    14\n",
       "...         ...   ...\n",
       "1851431       5     8\n",
       "1851432       5     3\n",
       "1851433       5     2\n",
       "1851434      11     2\n",
       "1851435      11    13\n",
       "\n",
       "[1851436 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode the names of the 15 airports which are strings into a number range: 0 - 14\n",
    "labelEncoder = LabelEncoder()\n",
    "df[\"Origin\"] = labelEncoder.fit_transform(df[\"Origin\"])\n",
    "df[\"Dest\"] = labelEncoder.fit_transform(df[\"Dest\"])\n",
    "features = df.loc[:, df.columns != \"ArrDel15\"]\n",
    "labels = np.asarray(df[\"ArrDel15\"])\n",
    "print(features.columns)\n",
    "df[[\"Origin\", \"Dest\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "neutral-massachusetts",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Dataset shape: (1851436, 24)\n",
      "features_train shape: (1481148, 23) | features_test shape: (370288, 23)\n",
      "labels_train shape: (1481148,) | labels_test shape: (370288,)\n"
     ]
    }
   ],
   "source": [
    "# Number of samples/observations/rows is greater than 100,000\n",
    "print(f\"\\nDataset shape: {df.shape}\")\n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.20, random_state=42)\n",
    "print(f\"features_train shape: {features_train.shape} | features_test shape: {features_test.shape}\")\n",
    "print(f\"labels_train shape: {labels_train.shape} | labels_test shape: {labels_test.shape}\")\n",
    "# print(f\"{features_train.shape[1]} Features: {features_train.columns.to_list()}\")\n",
    "# df.to_csv(\"Data/flight_and_weather_encoded.csv\")\n",
    "del df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "genetic-humanitarian",
   "metadata": {},
   "source": [
    "## Modeling Different Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "primary-shower",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "afraid-scene",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    292735\n",
      "         1.0       0.89      0.68      0.77     77553\n",
      "\n",
      "    accuracy                           0.92    370288\n",
      "   macro avg       0.91      0.83      0.86    370288\n",
      "weighted avg       0.92      0.92      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f6284a68c70>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = LogisticRegression(n_jobs=-1)\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./Classifiers/LogisticRegression.joblib\")\n",
    "model = joblib.load(\"./Classifiers/LogisticRegression.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Logistic Regression\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/05_05_LR_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "engaged-color",
   "metadata": {},
   "source": [
    "### Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "israeli-backup",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.91      0.92    292735\n",
      "         1.0       0.68      0.71      0.69     77553\n",
      "\n",
      "    accuracy                           0.87    370288\n",
      "   macro avg       0.80      0.81      0.80    370288\n",
      "weighted avg       0.87      0.87      0.87    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f6284a6f070>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = DecisionTreeClassifier()\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./Classifiers/DecisionTreeClassifier.joblib\")\n",
    "model = joblib.load(\"./Classifiers/DecisionTreeClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Decision Tree Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/05_06_DTC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "representative-chance",
   "metadata": {},
   "source": [
    "### XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "perfect-regression",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.92      0.98      0.95    292735\n",
      "         1.0       0.90      0.68      0.78     77553\n",
      "\n",
      "    accuracy                           0.92    370288\n",
      "   macro avg       0.91      0.83      0.86    370288\n",
      "weighted avg       0.92      0.92      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f628488f670>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = GradientBoostingClassifier()\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./Classifiers/GradientBoostingClassifier.joblib\")\n",
    "model = joblib.load(\"./Classifiers/GradientBoostingClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Gradient Boosting Classifier (XGBoost)\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/05_07_GBC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "higher-afghanistan",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "vietnamese-vulnerability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.97      0.95    292735\n",
      "         1.0       0.88      0.70      0.78     77553\n",
      "\n",
      "    accuracy                           0.92    370288\n",
      "   macro avg       0.90      0.84      0.87    370288\n",
      "weighted avg       0.92      0.92      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f6284965c70>\n"
     ]
    },
    {
     "data": {
      "image/png": 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KHXbIAmZ/VMn74zvWmX//o+ZzySkDAZj2QTv6bb+C3v0+Ztb0Sj596ELatmtdf+yy0f/W+R+xnO3zrYHJRT9PSWnrmwdJZ0gaK2nsrDlVNTdvEpYtacNlpw3krEunsnnnav5xew/O/OlU/vjyeM68ZBrXfGcAAN16reQPL43nhtHvcOYlU/nFOduwZNGar6FqFfz8nG04+tTZ9Nkm6xsddvACbh8znhufeJvdP7uIX347K6uqCt4Y04nTfzKN6x9+h+mT2jH6rm6Nf/ItTNvKaoYdspCn/96F9h2rOeH8mYy6aqs6839i9yWsWNaGD9/Ogu7iBW25/gdb88MbP+Tq+yYwY3I7qlY1Vu03DYWb/0tZWppyBtXaPq2af65LyUNE3BQRe0XEXj27b3p//VathMtOG8jnvzRv9WXe6L90W72+/5HzeefVzQBo1z7Yolv2h2HQrsvoOzC7VCz41ff6s/W2K/jS6WtaqVt0q6Jd++xjOezrc3j3taysHn1WssPOy+izzcdUtIVPH7qACa/X3Zqy0uz9+UVMeL0j82dX0mebFWw14GNGPv42t48ZT88+K/nto+/Qteea7pwDjl5z6V8wZnQXLjhiEP/vqEFMfq89Uye2p7VprZf/5QyqU4D+RT/3A6ZtQJ5NWgRcc+EA+g9awZfPXBMIu/deyWvPdwLg1Wc70XfbbLR//pwKqlJje/qH7Zg6sR1bDchapLdduRVLFlVw1qVrD0jNmbGml+aFx7owYNByAHYcspRFCyqYP6di9XEG7LiiPCfaihxwzPzVl/4fvNWRr+76KU7eZzAn7zOYWdMrOXf4jsyblfV3S8Fnj1jAU/dvuVYZXbpnQbdTl1Uc+c3ZPPKn7o16Dk2tMPrfGluq5exTfQkYJGlbYCowAvhajTwPAOel/tZ9gAXNrT913Iub88Q93dj2k8s4+wvZyPEpP5jGt6+azMifbE1VlWjXvppvX5X1crz+QidGXbUVFW2hok1w/i+msEXXKmZNq+TPv96K/jss59xDsnIKt07df0tPnn9sCyraQuctV3HhtZMAqKiA0388lYu+sgMRWcv3sK/PaZoPooVo37GaPT67iF9/v19J+XcZtoTZ0yv5aNLaLdGzL5vGdoOzAcw/Xtt7rauR1qK1jv4r6hrOzKNw6XDgV2S3VN0aEZdLOgsgIm5Mt1T9BjiU7JaqUyJibH1l7rVbh3jx0f71ZbFNzPC+Q5q6CraeHo97Xo6IvTZ0/6479YrP33pcSXn/ut/IjTrWpqas96lGxEPAQzXSbixaD+DcctbBzJpGS7y0L4WfqDKz3PmJKjOznDmompnlpDVPUt06h+fMrOzyuk9VUn9J/5T0pqRxki5I6d0kjZb0bvq3a9E+P5A0QdLbkoYXpe8p6fW07bo0WI6k9pLuSuljJA0s2ufkdIx3JZ3cUH0dVM0sdxGwqrpNSUsJVgEXRsQngWHAuWkekYuAJyJiEPBE+pm0bQTwKbI7i25Ic5FANr/IGayZc+TQlH4qMC8idgCuBa5MZXUDLia75XMocHFx8K6Ng6qZlUVeN/9HxPTC7HURsQh4k+xx9qOB21O224Fj0vrRwJ0RsSIiJgITgKGS+gBbRMTz6c6jUTX2KZR1D3BQasUOB0ZHxNyImAeMZk0grpX7VM0sd+vZp9pDUvH96TdFxE21ZUyX5bsDY4DehYeFImK6pF4p29bAC0W7FeYUWZnWa6YX9pmcylolaQHQnRLnJynmoGpmZRGlB9XZpdz8L6kTcC/w7YhYmLpDa81aW3XqSd/QfWrly38zK4s8J1SRVEkWUP8YEX9NyTPSJT3p38JrGOqaU2RKWq+ZvtY+ktoCXYC59ZRVJwdVM8tdRH59qqlv8xbgzYi4pmjTA0BhNP5k4P6i9BFpRH9bsgGpF1NXwSJJw1KZJ9XYp1DWccCTqd/1UeAQSV3TANUhKa1Ovvw3szIQVfm9ono/4ETgdUmvprQfAr8A7pZ0KjAJOB4gIsZJuptsQvxVwLkRUZiI+WzgNqAj8HBaIAvad0iaQNZCHZHKmivpMrIJogAujYi59VXWQdXMymI9+lQbKCeepfa+TYCD6tjncuDyWtLHAjvXkr6cFJRr2XYrcGup9XVQNbPc+dl/M7M8BXW+JLGlc1A1s7Joia9KKYWDqpnlLvIdqGpWHFTNrCx8+W9mlqO8Rv+bGwdVM8tdhIOqmVmufEuVmVmO3KdqZpaTQFR79N/MLD+ttKHqoGpmZeCBKjOznLXSpqqDqpmVhVuqNUi6nnr+1kTE+WWpkZk1ewFUVzuo1jS2nm1mZnULwC3VtUXE7cU/S9o8IpaUv0pm1hK01vtUG7yRTNK+ksaTvWsbSbtJuqHsNTOz5i1KXFqYUu7O/RUwHJgDEBH/AfYvY53MrNkTEaUtLU1Jo/8RMbnGO7ar6sprZga0yFZoKUoJqpMlfRoISe2A80ldAWZmtQqIVjr6X8rl/1nAucDWwFRgSPrZzKweKnFpWRpsqUbEbODrjVAXM2tJWunlfymj/9tJ+rukWZJmSrpf0naNUTkza8Y8+l+nPwF3A32AvsBfgD+Xs1Jm1swVbv4vZWlhSgmqiog7ImJVWv5Ai/z7YmZ5yl6p0vDS0tT37H+3tPpPSRcBd5IF068CDzZC3cysOWulo//1DVS9TBZEC5/MmUXbArisXJUys+ZPLbAVWor6nv3ftjErYmYtSAsdhCpFSU9USdoZGAx0KKRFxKhyVcrMmruWOQhVilJuqboYuD4tBwL/AxxV5nqZWXOX0y1Vkm5Nt3O+UZR2iaSpkl5Ny+FF234gaYKktyUNL0rfU9Lradt1Ss/eS2ov6a6UPkbSwKJ9Tpb0blpOLuW0Sxn9Pw44CPgoIk4BdgPal1K4mbVi1SUuDbsNOLSW9GsjYkhaHgKQNBgYAXwq7XODpIqUfyRwBjAoLYUyTwXmRcQOwLXAlamsbsDFwD7AUOBiSV0bqmwpQXVZRFQDqyRtAcwEfPO/mdUtx/tUI+JpYG6JRz4auDMiVkTERGACMFRSH2CLiHg+IgIYBRxTtE9h/uh7gINSK3Y4MDoi5kbEPGA0tQf3tZQSVMdK2hL4PdkdAa8AL5Z4gmbWSilKW4AeksYWLWeUeIjzJL2WugcKLcitgclFeaaktK3Tes30tfaJiFXAAqB7PWXVq5Rn/89JqzdKeoQs2r/W0H5m1sqVPvo/OyL2Ws/SR5Ld1lm4vfNq4FvUPkNL1JPOBu5Tp/pu/t+jvm0R8UpDhZuZlUNEzCisS/o98I/04xSgf1HWfsC0lN6vlvTifaZIagt0IetumAIcUGOfpxqqW30t1avr2RbA5xsqvBzeHd+Zw3c9qCkObRso9u3XcCbbtDx3z0YXUc6b/yX1iYjp6cdjgcKdAQ8Af5J0DdlcJYOAFyOiStIiScOAMcBJZHc0FfY5GXiebGD+yYgISY8CVxR1LRwC/KChutV38/+B63OSZmarBbk9pirpz2Qtxh6SppCNyB8gaUg60gekJz4jYpyku4HxwCrg3IgovKnkbLI7CToCD6cF4BbgDkkTyFqoI1JZcyVdBryU8l0aEQ0OmJV087+Z2XrLqaUaESfUknxLPfkvBy6vJX0ssHMt6cuB4+so61bg1pIri4OqmZWJn/03M8tTKw2qpTymKknfkPST9PMASUPLXzUza9Y883+dbgD2BQr9GouA35atRmbW7JV6439L7CIo5fJ/n4jYQ9K/ASJiXnpVtZlZ3TxJdZ1WpgkJAkBST0qdBsHMWq2W2AotRSmX/9cB9wG9JF0OPAtcUdZamVnz10r7VEt59v+Pkl4mm/5PwDER8WbZa2ZmzVcL7S8tRYNBVdIAYCnw9+K0iJhUzoqZWTPnoFqnB1kzY0sHYFvgbbJJYM3MaqVWOvJSyuX/LsU/p9mrzqwju5lZq7beT1RFxCuS9i5HZcysBfHlf+0kfafoxzbAHsCsstXIzJo/D1TVq3PR+iqyPtZ7y1MdM2sxHFTXlW767xQR32uk+phZS+GgujZJbSNiVX2vVTEzq43w6H9tXiTrP31V0gPAX4AlhY0R8dcy183Mmiv3qdarGzCH7J1UhftVA3BQNbO6Oaiuo1ca+X+DdV/X2ko/LjMrWSuNEvUF1QqgExv47msza918+b+u6RFxaaPVxMxaFgfVdbTOGWbNbOOFR/9rc1Cj1cLMWh63VNcWEXMbsyJm1rK4T9XMLE8OqmZmOWmhr0ophYOqmeVO+PLfzCxXDqpmZnlyUDUzy1ErDaptmroCZtYCpVmqSlkaIulWSTMlvVGU1k3SaEnvpn+7Fm37gaQJkt6WNLwofU9Jr6dt10lSSm8v6a6UPkbSwKJ9Tk7HeFfSyaWcuoOqmZVHlLg07Dbg0BppFwFPRMQg4In0M5IGAyPI3vZ8KHBDmmwfYCRwBjAoLYUyTwXmRcQOwLXAlamsbsDFwD7AUODi4uBdFwdVMysLVZe2NCQingZqPox0NHB7Wr8dOKYo/c6IWBERE4EJwFBJfYAtIuL5iAhgVI19CmXdAxyUWrHDgdERMTci5gGjWTe4r8N9qmZWFusx+t9D0tiin2+KiJsa2Kd3REwHiIjpknql9K2BF4ryTUlpK9N6zfTCPpNTWaskLQC6F6fXsk+dHFTNLH/rd/P/7IjYK6cj1zVVaX1TmG7IPnXy5b+ZlUd+faq1mZEu6Un/zkzpU4D+Rfn6AdNSer9a0tfaR1JboAtZd0NdZdXLQdXMcld4oiqP0f86PAAURuNPBu4vSh+RRvS3JRuQejF1FSySNCz1l55UY59CWccBT6Z+10eBQyR1TQNUh6S0evny38zKQtX53Kgq6c/AAWR9r1PIRuR/Adwt6VRgEnA8QESMk3Q3MB5YBZwbEVWpqLPJ7iToCDycFoBbgDskTSBroY5IZc2VdBnwUsp3aSmz9zmomln+cpxQJSJOqGNTrXM+R8TlwOW1pI8Fdq4lfTkpKNey7Vbg1pIri4OqmZWJn/03M8uTg6qZWX7cUjUzy5ODqplZTvw2VTOz/HjmfzOzvEXrjKoOqmZWFm6p2kbr0Xs5F14+nq49PiaqxSP39uX+P/bnxHPfZ9iBs6iuFgvmVnLNjwczd1Z7AL5y6gcccux0qqvFjb8YxCvPdQegbdtqzv7hO+y61zyqQ4y6fjv+9Xgvem61nO/8bDydOq+iTUXwv7/anrHP9mjK0272Ro28l2XLKqmuFlVVbTjvv7+4ettxR43jjJNf5rhvfoWFizqsTu/ZYzE3/+oB7rh7N+554FMAHPCZiZzwpdcJYM7czbjy159h4aIOHHzgBE4/8WXmzN0MgPsf3olHnhjUqOfY6Pw21fxJuhU4ApgZEes8xZCev/01cDiwFPhmRLxSrvo0hqoqcfPVg3jvzc503GwV1935Eq883417bhvAHb/dDoCjvjaZr505kd/8bCf6b7eE/Q+dyVnH7kP3Xiu44qZ/c/qR+1JdLb56xgcsmFvJ6UftixR07rISgBFnfMAzj/Xiobv70X+7JVz62/9wymEOqhvrexcfslbQBOjZfQl77DaNGbM2Xyf/WaeM5aV/r5kFrk2bas751kucdsFRLFzUgdNOfJmjD3uLO+4eAsD/PTeQ3968T1nPYVPTWgeqyjmhym3UP6HrYayZgfsMslm5m7V5s9vz3pudAVi2tC2TJm5Oj14rWLZkzd+uDh2riDSj2L4HzuLpR3qxamUbZkztyLRJm7HjzgsBOOSY6dx1y0AAIsTC+e3SOmy2efYo8+adVjFnVrvGOr1W56xTXuLmUXuu0zX46aGT+GhGJz6c3GV1WvZijqBDh1VAsFnHlcyZt1ljVneTk9ck1c1N2VqqEfF08bteanE0MCrNBvOCpC0l9SlMPNvc9eq7jO13WsRbr28BwEn/9R4HHfkRSxa35aJTdwege68VvPXaml/M2TPa0733CjbvnLVKTzr3fXbZex7TJ3dk5BWfYP7cdvxx5LZc/rtXOeprU2jfsYofnT6k0c+txQnx8588DgEPjt6Rh0bvyLC9JjN77ma8/2G3tbJ2aL+SrxzzBhddejDHHzVudXpVVRuuv2kYv7vm7yxf0ZZp0zvzm5uHrt7+mWGT2GXwDKZO24Ib/3dvZs1Zt/XbogStdqCqKaf+K3lWbUlnSBoraezH1csbpXIbo0PHVfzomje46X8GrW6ljrp+e04+ZD+eerA3R56QTUCuOqbAragIem61gvGvduH8rw7lrf904bQL3wXggMNmMPr+Ppx08H5cfM5ufPeK8ai1jgjk5Ns/OpRzv3cEP/rZQRx56NvsMngGX/vy69x+55B18p741f/w138MZvnyyrXSKyqqOWL425zz3SM44bTjeP/Drow4NntP3Qsv9eOks77EWd85ilde68P3/utfjXFaTa7MU/9tspoyqJY8q3ZE3BQRe0XEXu3adKgtyyajom01P7rmDZ56sDfPPdFrne1PPdSb/b4wC8hapj23WvNHokfvFcyZ2Z6F8ytZvqwNzz3RE4BnHuvF9p9cDMAhx07nmUezct96rQuV7avZouvKcp9WizY3XabPX9iR58b0Z9fBM9iq92JuvPrvjBp5Lz27L+WGq/5B1y2XsdOg2Zx24suMGnkvxx7xJiO+9DpHHfYW2w/MZoSbPqMzIJ5+biCDd8q+50WLO7ByVfbuuYcfH8Sg7eY0yXk2uvJOUr3JasrR/w2aVXvTFnz7p28xeeJm3HfHgNWpfQcsZdqk7Bd3nwNmM2Vitv7CUz34/i/G89dRA+jeawV9t1nKO29sAYgxT/Vg173n8Z8XuzFkn3lMej/bZ9ZH7Rmyzzwef6AP/bddQrt21SyYW7lOTaw0HdqvRIJlyyvp0H4le+w2nT/+ZVe+8q2vrM4zauS9nPf9L7JwUQcu/PGaYYITv/Iqy5ZX8sDDO9Gt61IG9F9Aly2Ws2BhB/bYdRqTpmRdO922XMrc+dn3t+9eU5g0tQstnW/+bxoPAOdJupPsFbALmnt/6uDdF3DQkR8x8Z3Nuf7uFwG4/brtGP6l6Ww9cClRDTOnd+A3l+0EwKT3OvHMY7343d9eoKqqDSOv+ATV1VkD/n9/tT3fvWI8Z3z/XRbMa8e1P/4kAL//5SAuuPgtjjlxMhFwzY8/Se2NfivFllsu5+LvPwVkl/D/fGZbxr7a4Lvd1jF33mb84e5dufqyR1lVJWbO6sRV138agGO++BbD9p5MVVUbFi1uxy9/s1+ep7BpishtkurmRlGmzuTi2bqBGWSzdVcCRMSN6Zaq35DdIbAUOCVNIluvLpU9Y9+uXy5Lna08Vg3q13Am26Q88dyPX96Yl/F13rJf7L7/BSXlfebv39+oY21qyjn6X9ds3YXtAZxbruObWdPy5b+ZWV4CaKWX/w6qZlYerTOmOqiaWXn48t/MLEetdfTfQdXM8tdCb+wvhYOqmeUuu/m/dUZVB1UzK48WOANVKRxUzaws3FI1M8uL+1TNzPLUep/9d1A1s/Lw5b+ZWU6iZb4qpRRNOUm1mbVkEaUtJZD0gaTXJb0qaWxK6yZptKR3079di/L/QNIESW9LGl6UvmcqZ4Kk69JseUhqL+mulD6mgVdB1ctB1czKI/+Z/w+MiCFF0wReBDwREYOAJ9LPSBoMjAA+RTa16A2SKtI+I8leNFp46Whh1vFTgXkRsQNwLXDlep7tag6qZlYWqq4uadkIRwO3p/XbgWOK0u+MiBURMRGYAAyV1AfYIiKeT1OPjqqxT6Gse4CDCq3Y9eWgamb5C7Kb/0tZSi/xMUkvSzojpfUuvC0k/Vt4KVxdLxXdOq3XTF9rn4hYBSwAupdcuyIeqDKz3IlYn5v/exT6SZObIuKmGnn2i4hpknoBoyW9Ve/h1xX1pNe3z3pzUDWz8ig9qM5u6HUqETEt/TtT0n3AUGCGpD4RMT1d2s9M2et6qeiUtF4zvXifKZLaAl2AuaWeQDFf/ptZeeQ0+i9pc0mdC+vAIcAbZC8PPTllOxm4P60/AIxII/rbkg1IvZi6CBZJGpb6S0+qsU+hrOOAJ2MDX+DnlqqZ5a/Qp5qP3sB9adyoLfCniHhE0kvA3ZJOBSYBxwNExDhJdwPjgVXAuRFRlco6G7gN6Ag8nBaAW4A7JE0ga6GO2NDKOqiaWVls5Mj+ahHxPrBbLelzgIPq2Ody4PJa0scCO9eSvpwUlDeWg6qZlUHpN/a3NA6qZpa/wEHVzCxXrfTZfwdVMysLT1JtZpYnB1Uzs5xEQFXrvP53UDWz8nBL1cwsRw6qZmY5CcDvqDIzy0tAuE/VzCwfgQeqzMxy5T5VM7McOaiameXFE6qYmeUngJym/mtuHFTNrDzcUjUzy4sfUzUzy09A+D5VM7Mc+YkqM7McuU/VzCwnER79NzPLlVuqZmZ5CaKqqqkr0SQcVM0sf576z8wsZ76lyswsHwGEW6pmZjkJT1JtZpar1jpQpWhmtz1ImgV82NT1KJMewOymroSVrCV/X9tERM8N3VnSI2SfTylmR8ShG3qsTU2zC6otmaSxEbFXU9fDSuPvy2rTpqkrYGbWkjiompnlyEF103JTU1fA1ou/L1uH+1TNzHLklqqZWY4cVM3McuSg2sgkHSrpbUkTJF1Uy3ZJui5tf03SHk1RT8tIulXSTElv1LHd35etxUG1EUmqAH4LHAYMBk6QNLhGtsOAQWk5AxjZqJW0mm4D6rsx3d+XrcVBtXENBSZExPsR8TFwJ3B0jTxHA6Mi8wKwpaQ+jV1Ry0TE08DcerL4+7K1OKg2rq2ByUU/T0lp65vHNh3+vmwtDqqNS7Wk1bynrZQ8tunw92VrcVBtXFOA/kU/9wOmbUAe23T4+7K1OKg2rpeAQZK2ldQOGAE8UCPPA8BJaVR5GLAgIqY3dkWtZP6+bC2eT7URRcQqSecBjwIVwK0RMU7SWWn7jcBDwOHABGApcEpT1ddA0p+BA4AekqYAFwOV4O/LaufHVM3McuTLfzOzHDmompnlyEHVzCxHDqpmZjlyUDUzy5GDagskqUrSq5LekPQXSZttRFm3STourd9cywQwxXkPkPTpDTjGB5LWefNmXek18ixez2NdIum761tHs1I5qLZMyyJiSETsDHwMnFW8Mc2Wtd4i4rSIGF9PlgOA9Q6qZi2Jg2rL9wywQ2pF/lPSn4DXJVVIukrSS2ke0DNh9fygv5E0XtKDQK9CQZKekrRXWj9U0iuS/iPpCUkDyYL3/0ut5M9K6inp3nSMlyTtl/btLukxSf+W9Dtqf35+LZL+JullSeMknVFj29WpLk9I6pnStpf0SNrnGUk75fJpmjXAT1S1YJLaks33+UhKGgrsHBETU2BaEBF7S2oP/EvSY8DuwCeAXYDewHjg1hrl9gR+D+yfyuoWEXMl3Qgsjohfpnx/Aq6NiGclDSB7kuyTZE8lPRsRl0r6Itk8pA35VjpGR+AlSfdGxBxgc+CViLhQ0k9S2eeRvZTvrIh4V9I+wA3A5zfgYzRbLw6qLVNHSa+m9WeAW8guy1+MiIkp/RBg10J/KdCFbKLl/YE/R0QVME3Sk7WUPwx4ulBWRNQ13+gXgMHS6oboFpI6p2N8Ke37oKR5JZzT+ZKOTev9U13nANXAXSn9D8BfJXVK5/uXomO3L+EYZhvNQbVlWhYRQ4oTUnBZUpwE/FdEPFoj3+E0PHWdSsgDWffSvhGxrJa6lPx8tKQDyAL0vhGxVNJTQIc6skc67vyan4FZY3Cfauv1KHC2pEoASTtK2hx4GhiR+lz7AAfWsu/zwOckbZv27ZbSFwGdi/I9RnYpTso3JK0+DXw9pR0GdG2grl2AeSmg7kTWUi5oAxRa218j61ZYCEyUdHw6hiTt1sAxzHLhoNp63UzWX/qKspfa/Y7syuU+4F3gdbL3Lf1fzR0jYhZZP+hfJf2HNZfffweOLQxUAecDe6WBsPGsuQvhp8D+kl4h64aY1EBdHwHaSnoNuAx4oWjbEuBTkl4m6zO9NKV/HTg11W8c6762xqwsPEuVmVmO3FI1M8uRg6qZWY4cVM3McuSgamaWIwdVM7McOaiameXIQdXMLEf/H/dg134MmFugAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = RandomForestClassifier(n_jobs=-1)\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./Classifiers/RandomForestClassifier.joblib\")\n",
    "model = joblib.load(\"./Classifiers/RandomForestClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Random Forest Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/05_08_RFC_Confusion_Matrix\")\n",
    "del model\n",
    "del model_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "valued-adelaide",
   "metadata": {},
   "source": [
    "### Extra Trees Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "certified-render",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.93      0.96      0.94    292735\n",
      "         1.0       0.82      0.73      0.77     77553\n",
      "\n",
      "    accuracy                           0.91    370288\n",
      "   macro avg       0.87      0.85      0.86    370288\n",
      "weighted avg       0.91      0.91      0.91    370288\n",
      "\n",
      "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay object at 0x7f6284a518e0>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# model = ExtraTreesClassifier(n_jobs=-1)\n",
    "# model.fit(features_train, labels_train)\n",
    "# joblib.dump(model, \"./Classifiers/ExtraTreesClassifier.joblib\")\n",
    "model = joblib.load(\"./Classifiers/ExtraTreesClassifier.joblib\")\n",
    "model_pred = model.predict(features_test)\n",
    "# print(confusion_matrix(labels_test, model_pred))\n",
    "print(classification_report(labels_test, model_pred))\n",
    "conf_mat_plot = plot_confusion_matrix(model, features_test, labels_test)\n",
    "plt.title(\"Extra Trees Classifier\")\n",
    "print(conf_mat_plot)\n",
    "plt.savefig(\"Img/05_09_ETC_Confusion_Matrix\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quiet-phrase",
   "metadata": {},
   "source": [
    "## Performance Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e68b09b4",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <thead>\n",
    "        <tr>\n",
    "            <th rowspan=\"3\">Classification Model</th>\n",
    "            <th colspan=\"8\">Performance Metric</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th colspan=\"2\">Precision</th>\n",
    "            <th colspan=\"2\">Recall</th>\n",
    "            <th colspan=\"2\">F1 Score</th>\n",
    "            <th rowspan=\"2\" colspan=\"2\">Accuracy</th>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "            <th>0.0</th>\n",
    "            <th>1.0</th>\n",
    "        </tr>\n",
    "    </thead>\n",
    "    <tbody>\n",
    "        <tr>\n",
    "            <th>Logistic Regression</th>\n",
    "            <td>0.92</td>\n",
    "            <td>0.89</td>\n",
    "            <td>0.98</td>\n",
    "            <td>0.68</td>\n",
    "            <td>0.95</td>\n",
    "            <td>0.77</td>\n",
    "            <td>0.92</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Decision Tree Classifier</th>\n",
    "            <td>0.92</td>\n",
    "            <td>0.68</td>\n",
    "            <td>0.91</td>\n",
    "            <td>0.71</td>\n",
    "            <td>0.92</td>\n",
    "            <td>0.69</td>\n",
    "            <td>0.87</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Extra Trees Classifier</th>\n",
    "            <td>0.93</td>\n",
    "            <td>0.82</td>\n",
    "            <td>0.96</td>\n",
    "            <td>0.73</td>\n",
    "            <td>0.94</td>\n",
    "            <td>0.77</td>\n",
    "            <td>0.91</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Gradient Boosting Classifier</th>\n",
    "            <td>0.92</td>\n",
    "            <td>0.90</td>\n",
    "            <td>0.88</td>\n",
    "            <td>0.68</td>\n",
    "            <td>0.95</td>\n",
    "            <td>0.78</td>\n",
    "            <td>0.92</td>\n",
    "        </tr>\n",
    "        <tr>\n",
    "            <th>Random Forest Classifier</th>\n",
    "            <td>0.93</td>\n",
    "            <td>0.86</td>\n",
    "            <td>0.97</td>\n",
    "            <td>0.70</td>\n",
    "            <td>0.95</td>\n",
    "            <td>0.78</td>\n",
    "            <td>0.92</td>\n",
    "        </tr>\n",
    "    </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "tired-jenny",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup\n",
    "perf_df = pd.DataFrame(columns=[\"classifiers\", \"fpr\", \"tpr\", \"auc\"])\n",
    "\n",
    "# Handling Extra Trees Classifier separately due to memory constraints\n",
    "fpr, tpr, _ = roc_curve(labels_test,  model_pred)\n",
    "auc = roc_auc_score(labels_test, model_pred)\n",
    "# Record the results\n",
    "perf_df = perf_df.append({\"classifiers\": \"ExtraTreesClassifier\",\n",
    "                                        \"fpr\": fpr, \n",
    "                                        \"tpr\": tpr, \n",
    "                                        \"auc\": auc}, ignore_index=True)\n",
    "del model\n",
    "del model_pred\n",
    "\n",
    "classifiers = [\"LogisticRegression\", \"DecisionTreeClassifier\", \"GradientBoostingClassifier\", \"RandomForestClassifier\"]\n",
    "for clf in classifiers:\n",
    "    # Load the trained classifiers\n",
    "    model = joblib.load(f\"./Classifiers/{clf}.joblib\")\n",
    "    model_pred = model.predict(features_test)\n",
    "    fpr, tpr, _ = roc_curve(labels_test,  model_pred)\n",
    "    auc = roc_auc_score(labels_test, model_pred)\n",
    "    # Record the results\n",
    "    perf_df = perf_df.append({\"classifiers\": clf,\n",
    "                                        \"fpr\": fpr, \n",
    "                                        \"tpr\": tpr, \n",
    "                                        \"auc\": auc}, ignore_index=True)\n",
    "    del model\n",
    "    del model_pred\n",
    "\n",
    "# Set name of the classifiers as index labels\n",
    "perf_df.set_index(\"classifiers\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "czech-debate",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6), dpi=80)\n",
    "for clf_name in perf_df.index:\n",
    "    plt.plot(perf_df.loc[clf_name][\"fpr\"], \n",
    "             perf_df.loc[clf_name][\"tpr\"], \n",
    "             label=\"{}, AUC={:.3f}\".format(clf_name, perf_df.loc[clf_name][\"auc\"]))\n",
    "    \n",
    "plt.plot([0,1], [0,1], color='orange', linestyle='--')\n",
    "\n",
    "plt.xticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.xlabel(\"Flase Positive Rate\", fontsize=15)\n",
    "\n",
    "plt.yticks(np.arange(0.0, 1.1, step=0.1))\n",
    "plt.ylabel(\"True Positive Rate\", fontsize=15)\n",
    "\n",
    "plt.title(\"ROC AUC Analysis\", fontweight=\"bold\", fontsize=15)\n",
    "plt.legend(prop={\"size\":13}, loc=\"lower right\")\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"Img/05_10_Classifiers_ROC_AUC.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-interface",
   "metadata": {},
   "source": [
    "## Explanation\n",
    "The <strong>Receiver Operator Characteristic (ROC) curve</strong> is an evaluation metric for binary classification problems. It is a probability curve that plots the TPR against FPR at various threshold values and essentially separates the ‘signal’ from the ‘noise’. The <strong>Area Under the Curve (AUC)</strong> is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve.\n",
    "<br><br>\n",
    "**The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.**\n",
    "\n",
    "|AUC Value|Meaning|\n",
    "|:--|:--|\n",
    "|AUC = 1| The classifier is able to **perfectly distinguish** between all the Positive and the Negative class points correctly.|\n",
    "|AUC = 0| The classifier would be predicting **all Negatives as Positives, and all Positives as Negatives**|\n",
    "|0.5 < AUC < 1| There is a high chance that the classifier will be able to distinguish the classes i.e. more numbers of True positives and True negatives than False negatives and False positives|\n",
    "|AUC=0.5| The classifier is not able to distinguish between Positive and Negative class points i.e. either the classifier is predicting random class or constant class for all the data points|"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
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}