dataset_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third Assignment -Big Data Mining\n",
    "\n",
    "##### Theodora Koutsothanasi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the problem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions we need to answer:\n",
    "\n",
    "- Provide an overview of the dataset size, features, and distribution of feature values.\n",
    "- Describe the average delays per airport/airline.\n",
    "- Identify and report the most prominent rules of association between delays and point of origin AND/OR point of arrival.\n",
    "- Try to predict the delay given all other features and report the appropriate performance on cross-validation.\n",
    "- Identify patterns/rules regarding delays and try to explain when delays should be expected, based on these patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import important libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>b'UA'</td>\n",
       "      <td>b'899.0'</td>\n",
       "      <td>b'ORD'</td>\n",
       "      <td>b'IAD'</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>b'DL'</td>\n",
       "      <td>b'357.0'</td>\n",
       "      <td>b'IAD'</td>\n",
       "      <td>b'ATL'</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>b'UA'</td>\n",
       "      <td>b'341.0'</td>\n",
       "      <td>b'IAD'</td>\n",
       "      <td>b'DEN'</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>b'CO'</td>\n",
       "      <td>b'581.0'</td>\n",
       "      <td>b'IAD'</td>\n",
       "      <td>b'DEN'</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>b'UA'</td>\n",
       "      <td>b'902.0'</td>\n",
       "      <td>b'CLE'</td>\n",
       "      <td>b'IAD'</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DayofWeek  CRSDepTime UniqueCarrier FlightNum  Origin    Dest  ArrDelay\n",
       "0        5.0       600.0         b'UA'  b'899.0'  b'ORD'  b'IAD'      -3.0\n",
       "1        5.0       615.0         b'DL'  b'357.0'  b'IAD'  b'ATL'       5.0\n",
       "2        5.0       615.0         b'UA'  b'341.0'  b'IAD'  b'DEN'       8.0\n",
       "3        5.0       625.0         b'CO'  b'581.0'  b'IAD'  b'DEN'      44.0\n",
       "4        5.0       630.0         b'UA'  b'902.0'  b'CLE'  b'IAD'     -10.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Load the dataset\n",
    "from scipy.io import arff\n",
    "import pandas as pd\n",
    "\n",
    "data = arff.loadarff(r'D:\\Desktop\\MscDataScience\\Big Data Mining\\Dataset3 - Final.arff')\n",
    "df = pd.DataFrame(data[0])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove b in from of unique carrier,FlightNum and Dest names\n",
    "\n",
    "df['UniqueCarrier'] = df['UniqueCarrier'].str.decode('utf-8')\n",
    "df['FlightNum'] = df['FlightNum'].str.decode('utf-8')\n",
    "df['Dest'] = df['Dest'].str.decode('utf-8')\n",
    "df['Origin'] = df['Origin'].str.decode('utf-8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>899.0</td>\n",
       "      <td>ORD</td>\n",
       "      <td>IAD</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>357.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>ATL</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>341.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>581.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>902.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>IAD</td>\n",
       "      <td>-10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>EA</td>\n",
       "      <td>931.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>IAD</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>635.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>563.0</td>\n",
       "      <td>EWR</td>\n",
       "      <td>IAD</td>\n",
       "      <td>-12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>636.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>951.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>RIC</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>DTW</td>\n",
       "      <td>IAD</td>\n",
       "      <td>-7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.0</td>\n",
       "      <td>645.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>BOS</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DayofWeek  CRSDepTime UniqueCarrier FlightNum Origin Dest  ArrDelay\n",
       "0        5.0       600.0            UA     899.0    ORD  IAD      -3.0\n",
       "1        5.0       615.0            DL     357.0    IAD  ATL       5.0\n",
       "2        5.0       615.0            UA     341.0    IAD  DEN       8.0\n",
       "3        5.0       625.0            CO     581.0    IAD  DEN      44.0\n",
       "4        5.0       630.0            UA     902.0    CLE  IAD     -10.0\n",
       "5        5.0       635.0            EA     931.0    CLE  IAD     -13.0\n",
       "6        5.0       635.0            UA     563.0    EWR  IAD     -12.0\n",
       "7        5.0       636.0            UA     951.0    IAD  RIC       1.0\n",
       "8        5.0       645.0            UA    1095.0    DTW  IAD      -7.0\n",
       "9        5.0       645.0            UA    1021.0    BOS  IAD       0.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100161 entries, 0 to 100160\n",
      "Data columns (total 7 columns):\n",
      "DayofWeek        100161 non-null float64\n",
      "CRSDepTime       100161 non-null float64\n",
      "UniqueCarrier    100161 non-null object\n",
      "FlightNum        100161 non-null object\n",
      "Origin           100161 non-null object\n",
      "Dest             100161 non-null object\n",
      "ArrDelay         100161 non-null float64\n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exploratory Analysis\n",
    "##### Overview of the dataset size, features, and distribution of feature values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100161.000000</td>\n",
       "      <td>100161.000000</td>\n",
       "      <td>100161.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.952626</td>\n",
       "      <td>1357.918262</td>\n",
       "      <td>4.638462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.985074</td>\n",
       "      <td>460.022924</td>\n",
       "      <td>23.397863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>-72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>925.000000</td>\n",
       "      <td>-7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1335.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>1735.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>667.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           DayofWeek     CRSDepTime       ArrDelay\n",
       "count  100161.000000  100161.000000  100161.000000\n",
       "mean        3.952626    1357.918262       4.638462\n",
       "std         1.985074     460.022924      23.397863\n",
       "min         1.000000       5.000000     -72.000000\n",
       "25%         2.000000     925.000000      -7.000000\n",
       "50%         4.000000    1335.000000       0.000000\n",
       "75%         6.000000    1735.000000       9.000000\n",
       "max         7.000000    2359.000000     667.000000"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      dtype=object)"
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot the histograms\n",
    "df.hist(figsize=(8,8),ylabelsize=8,xlabelsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Univariate Density Plots\n",
    "from matplotlib import pyplot\n",
    "df.plot(kind='density', layout=(1,3),figsize=(15,4),subplots=True, sharex=False)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box Plots\n",
    "df.plot(kind='box', layout=(1,3),figsize=(15,4),subplots=True, sharex=False)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of delays above 250\n",
    "arr=df['ArrDelay']>250\n",
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47453"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of delays below 0\n",
    "arr2=df['ArrDelay']<0\n",
    "arr2.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UA          63706\n",
      "CO           9219\n",
      "AA           8620\n",
      "NW           5521\n",
      "DL           4793\n",
      "US           3513\n",
      "TW           3056\n",
      "EA           1420\n",
      "'PA (1)'      313\n",
      "Name: UniqueCarrier, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Number of flights per Carrier\n",
    "carrier_count = df['UniqueCarrier'].value_counts()\n",
    "print(carrier_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Graphical representation of carriers scheduled flights in numbers and %\n",
    "import seaborn as sns\n",
    "f,ax=plt.subplots(1,2,figsize=(15,5))\n",
    "carrier_count.plot.pie(autopct='%1.0f%%',ax=ax[1],shadow=False)                                                  \n",
    "ax[1].set_title('Flight % of Carrier')\n",
    "ax[1].set_ylabel('')\n",
    "sns.countplot('UniqueCarrier',order = carrier_count.index, data=df,ax=ax[0])\n",
    "ax[0].set_title('Frequency Distribution of Carriers')\n",
    "ax[0].set_ylabel('Number of Flights')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "UA carrier possess the 64% of flights of this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0    14780\n",
      "3.0    14686\n",
      "4.0    14576\n",
      "2.0    14554\n",
      "1.0    14509\n",
      "7.0    13684\n",
      "6.0    13372\n",
      "Name: DayofWeek, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Number of flights per weekday\n",
    "No_flights_weekday=df['DayofWeek'].value_counts()\n",
    "\n",
    "print(No_flights_weekday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IAD    50229\n",
      "DEN     2911\n",
      "ATL     2820\n",
      "ORD     2753\n",
      "LAX     2591\n",
      "LGA     2364\n",
      "BOS     2210\n",
      "DTW     2176\n",
      "DFW     2145\n",
      "EWR     1943\n",
      "BDL     1795\n",
      "PIT     1701\n",
      "MCO     1681\n",
      "CLE     1458\n",
      "SFO     1397\n",
      "TPA     1184\n",
      "ROC     1177\n",
      "IAH     1124\n",
      "MSY      912\n",
      "MEM      903\n",
      "STL      902\n",
      "FLL      859\n",
      "BUF      835\n",
      "PHL      783\n",
      "JAX      740\n",
      "MIA      735\n",
      "RDU      722\n",
      "PBI      688\n",
      "SLC      686\n",
      "MSP      644\n",
      "CHS      534\n",
      "ORF      531\n",
      "MHT      447\n",
      "CAE      446\n",
      "IND      439\n",
      "SYR      364\n",
      "PHF      355\n",
      "GSO      329\n",
      "BTV      326\n",
      "SDF      324\n",
      "RSW      322\n",
      "PWM      303\n",
      "SEA      299\n",
      "PVD      296\n",
      "BNA      294\n",
      "SJU      277\n",
      "HPN      228\n",
      "HOU      201\n",
      "SAV      201\n",
      "ISP      146\n",
      "JFK      142\n",
      "PHX       91\n",
      "SRQ       85\n",
      "MDW       62\n",
      "BWI       43\n",
      "RIC        4\n",
      "DCA        3\n",
      "MDT        1\n",
      "Name: Origin, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "no_origins=df['Origin'].value_counts()\n",
    "print(no_origins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO0mSJEmSJEmSJGkEmLiTJEmSJEmSJEmSRoCJO2lE3XDDDWy++ebsueeebL755txwww2T/ZYkSZIkSZIkSdIALTXsgBHxXOBiYCbw0VLKBcN+D9Kou+GGGzjzzDOZP38+f/rTn5g5cyZHH300AAcffPAkvztJkiRJkiRJkjQIQ51xFxEzgQ8AzwOeDhwcEU8f5nuQlgTnn38+8+fPZ968eSy11FLMmzeP+fPnc/7550/2W5MkSZIkSZIkSQMy7KUytwceLKX8pJTyB+BGYN8hvwdp5D3wwAPsuuuuC2zbddddeeCBBybpHUmSJEmSJEmSpEEbduJuHeBnrcf/UrdJatl000257777Fth23333semmm07SO5IkSZIkSZIkSYMWpZThBYs4ENi7lHJMfXw4sH0p5YTWc44FjgVYY401tr3xxhsXK9bDDz/M7Nmz+3/TxjXuJMS8++67mT9/PqeeeiobbLAB//RP/8SFF17I0UcfzZ577jmU9+B3a1zjLlkxjTv14s6bN2+hf7/nnnsG/h6m+mc8neNOp3017tSNadypHXc67et0izud9tW4Uzemcad23Om0r8adujGNO/px582b961Synbj/rGUMrR/wE7A51uPTwdOn+j52267bVlc99xzz2L/334Yd+rGHXbM66+/vmy22WZlxowZZbPNNivXX3/9UOP73RrXuEtWTONO7bjTaV+NO3VjGndqx51O+2rcqRvTuFM3pnGndtzptK/GnboxjTu1406nfTXuYwfcXybIjS212GnExfNNYKOI2AD4V+Ag4JAhvwdpiXDwwQdz8MEHc++99zJ37tzJfjuSJEmSJEmSJGnAhpq4K6X8MSKOBz4PzAQuL6X8/TDfgyRJkiRJkiRJkjSKhj3jjlLK54DPDTuuJEmSJEmSJEmSNMpmTPYbkCRJkiRJkiRJkmTiTpIk7XA4kwAAIABJREFUSZIkSZIkSRoJJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBJu4kSZIkSZIkSZKkEWDiTpIkSZIkSZIkSRoBUUqZ7PcwoYj4d+ChxfzvTwL+o8O3Y1zjTqd9nW5xp9O+GnfqxjTu1I47nfbVuFM3pnGndtzptK/GnboxjTt1Yxp3asedTvtq3Kkb07hTO+502lfjPnbrl1JWG+8PI52460dE3F9K2c64xl2SYxp36sY07tSOO5321bhTN6Zxp3bc6bSvxp26MY07teNOp32dbnGn074ad+rGNO7Ujjud9tW4UzemcZfsuC6VKUmSJEmSJEmSJI0AE3eSJEmSJEmSJEnSCJjKibsPG9e4UyCmcaduTONO7bjTaV+NO3VjGndqx51O+2rcqRvTuFM77nTa1+kWdzrtq3GnbkzjTu2402lfjTt1Yxp3CY47Ze9xJ0mSJEmSJEmSJC1JpvKMO0mSJEmSJEmSJGmJYeJOkjTpIiIm+z1IWriIsN44xUTEzMl+D5IkSVqy2Z6XpO7ZAaMlWkSsFBEnRsSqQ467bkS8LCJWGHLcJ0bErCHHXDcinhcRywwz7mSIiHkRceQkxh96mRxpt2HHrbFPjYhLAcqQ122OiJWHGa8n9rRp1Ax7XyNinWHGqzGj/XOqiohdgOdGxNKTFH9o5eNk7GNEbBYRLx1yzHnAm+rvk3L8RsQTJvGYmtRzdhLKx/0mqYzcLCKuGnbcyRAR20xCzGnTno+IGRFx0GS/D3UvIlaeDm3NyRIRZ0bE1pP8HiarnrF1RCw7SbGHss8RselkXQtqufwsmJT2/CERsc+QYi0fEZdHxHbDiNeKu05EbDLMmKNiiOfPZJ07W9d+1qEOopzs9o8evyld0Z/EysFy9edkdMIvHxFL1d+HVgBExPoR8fGI2GxYMautgEOB/zesgDXJcQcwq5TyyBDjzgL+Crh0iDF3Be4CVi+l/GGIcZ9cGxjrDjHm4cB7gf8aVsxW7CdGxHKllD8PMWZTPr4UeEVE7DWs2C1fADaahArwwcAHhxhvZkQcHRE7RMTKpZQyFStM9bw9JyKOiogDYLgNuIjYD7gmItYYYswZwFcj4hX1e53Ks5eeAxwAPHNYASNio4hYIyJWGlb5GBEfBa6KiPMj4slDiNeUBdsBW9dtA60/tmLuD/wWht/ZUt/H6sCFwFpDjPmUJnk1CR1MMyPilIg4riZ4hjLorMa9CTgQ+OUwYta4zXH8CLBKRDxniLFfVxPTw4o3MyI+CZw4xI6mDQGGWXescdeLiLdGxIER8ZRhxgaeT16LJrXjaRixa51q/jCuQ62YfxURLxpWvFbc3YHrgc0nIfbQj6OImB0RF9b6zcD7iyLiCrLu9sNBx5og/kwYu+YOs48sIk4C7gdeOcSYERFviYhlh1HPiIgLgEuAFw47QRkR6wNfBY4d5jW3xl4V2AZ4Sn086HN5NvCn+nMox3Hti/sStX0wLFEHUUxCQmmziNglIp4Ow6mnR8TmwBnD7ruPiFcC1wDbAusPMzYw5fqhJhI5QHTYx/FqXb/mlEzcRcTxEbFf7UQb9gm4FfC2iFillPLnIVdMtgC+BrwzImaUUv5Utw+6A2g34NPAt4H/GGSsXqWULwH/CpwzjHgR8TLgQ8DppZTL67ZhXbR3BT4JlIg4cwgxjwXeDZxYSrmqbltxCHF3B24jk7HNMTzQi0tEnAecCLy4lHJL5EzO9QYZsxV7HeBu4KaIeHrUmWBDuMA0x+3XgO8De0fEwDvkI2dwblgf/hj4G2D7GO6MizuA3ev5PFC1w+VO4GXkMfb+Wj4PpCIaEUcOM3HVirs7cC+wHLAR8PaIeOcwyoyWvwO+Abx1WNfe2mF6PvCuiNi2lPKnQZ67EXFa/VznR8RLh1zHeSt5zh4YQxj5WTsiPgJcDdwXOcv9SQOMt0ZE3AH8GrgOWAY4b9BlU6ssWBbYrG4bdEf80+rPWcCvYNI6wX8P7AisPehAtSNtA+BB4Nqogwvafx9w/DnAV4CNyU6ms8j6+kAHvNW6zP3ACqWUQ0spfxzid91c6/8d+A7wjPo9DPqz3gW4CHh1DGFgUP2Mvw78vpRyxJA6mo4Bvh45WGbl1vZBt/nmkm2RFYCjyM7aDRf6n7r1S2CbiHjCkD7nZmDqtRHxqYg4qKnDDfI4rnWqm4DvllL+eVBxxvF7sh9j42EFjIijyfbmB0opfzvEuOvC5AxaIevJu5VSfjnI632t13wV2AQ4rJTyyCR0YO4OvKfWW0+NiKfWPrJhvY9vkO2DrWu5OXD1mNoduGzQsSLiOjLhfSDwlVLK/w46Ziv2FmSb+ppSymFkn8Iw4q4aEbNKKf9VYx4WEU8cdLlcSvl3crDbyfXxQOvqEfFa4F3Aa0opNwwyVk/cA4APRsSarf7kYQxWeTVwM3AScEm95g5jpbH1gTnAsUOIBTz63b4aeEEp5a2llJ8MMfaZwEcj4sMR8eIhxdxg2NeeGvcosl/5wxFxYN026DbI9sAvap/Nzl297pRL3EWO9Pg52XG3yZArBgArkZ09r46IqPGH1UD+BbA0OSL+oxFxaAx4Fk9ErAKcB7ytlHJ+KeWXdfsgOy57O+7eD6wYEasM4bPeDvhRKeWzkbOk7iM/70E7GDiylPIdMnG4XS2IBiIi1gZeA3y1lPL5WkH6CjDQUZi1Y+u9wBmllItKKf8GC4zQ6/z7jYgtgecBF5VSHowcdXM7mSgdhl8D/wtsARwEfCginjTIilJEvBw4OyLWLqX8jOykfgTYLyKe2nW8VtwgG+f3Rc5wDOA+YJ/6+8A6nSJHiL8sItYppfwaOIXc34HN6oyIjYC/Bq4qpewFvB34H+AZA4o3m6x0Hll/H4qIWInshD61lHJ6KeUMYC6wA9mxN8jv9YnN76WUh4ArgT8DbxtEvFbcZ0fErHqNvY28Dt4WOWjnT4PY34i4kvxcP0eWG/uT+zsQEbFCRLyjXg8opfwR+DfyOz04BjjTIiKuAZ4K7Evu5wfJmRaHRcTyAwp7JrBhKeXU+p3eQtbnBtZojIgTasNpC+DvgX+q25euPwdR/h8KnFPrUv9HHSAzqHgTvIdNImK9UsrvyYTHH+v2gdUdS/onYD7wXbKdcGbUJZYG2XkbOQL+bOBjpZRXllJeD5xBft9nxYCWe48c9PVp8thavXV9H8aAs+OAH0bEC8jr+2eBI4F1hzDr/DtkveZn5PVwo0EFikxk3QI8DPxz810O6prX8gDwO7JefkZEnAFjHYldf76RNga+CFxaSjmZPKa3Aga6OkZE7BMRT631mh+SnfD/M8iYNe7uZCfi3wAXAN8CXlB/H1iZERE7AZ8B3ldKubhu23mA9aiVIhM8y5dS5pMz3+YPox5ZO7XeSCbtbqvtzSNqvXKQcXcFzqzf8dBEHYVfSvk74FcRse0AY21LDgx9kLzm7RG5esGfFv4/O30PLydXCvoaee6uBdw1yLpyjfvoClRkOflLcgDL0yNi30HErHHbA73mAZtGDg4eVLynApRS9iml/IZsGwzTFsDVpZQP1vfxv4O+9kUOTr0J+EhEzCylfJK8Jny0vodOy+WI2Lanr+BNwAoxnNmFhwMPl1LurP2cn6xl16DtC7yC7E8+OjLZPujBKoeQs2J3L6UcWEp5NjkY+O1DuBbdRfb9bR01uTME6wGvK6U8FGOzG4dRN78eeBZ5Dn2HHKhzXNPWH1DMF5ODcIe6Ml9EvJnss3gz2d/4pojYeAiDdR4BvgfMBK6MHPDVdz15yiTuaoXzTmCfWoBfQxY2s2vFYJnWcwfREbI8PDoD7ONk1v5VdVuJAY7Ybu3P78nK9vFkw2pr4PORGe5BLd/5R+BHwLci16Y/PiLeA3whInaNjteqj7xHxmci4uQYWzrk5+TyD+sMapZlRLyidqy9EXgkcimt24HbSymf6DpeK24z6+skcoTpUaWUr5GdtS+OwS079O/AG8iOntPIY/oLpZTrBhSvOTZnAv9YE6Mr1oLu9Ii4OOpyE12fvzUZeg056+vc+vt7e0c2DaATZNka/xFyJsvNwI3k7LePRSbenzSgi8uryIrnOyPiYmAd8nh+BDhoUJ2IdV8uJ2cdrEY25u4A/kCOjh/k6LWjyfLxvMgE8d1kA+cZMJDvdyuyU/a35IAKSinfI0eprxqthFNH8ZYqpTxMVlCeA7wkhrfMxfJkJ9rtNd5yNel+GtkpvXnX32vtRFwP+LtYcKbog+QsrTkR8ZouY7Zi70wu8/plMtG+Tu1c+yC5nAldDtqJTBDeX1/3uaWUe0spp5Dn8JwBdgqsDDyBTDAQEccDxwCvBdYAnhcdz+6MvE/GhcAGwCtKKb8tpTxSSrmUnNG5NdlR0GXMreqvpwD/FRFvqo8LuZThQM6fen7eT547pwBXAC+rx/PK0G0HRERsXOtN95P1tteRDcdHB8gMaQbL84F3AndHJnf2BHaq72GgK0XUc/I3ZNJjD3KliAtr3XXQM5XXJTv+ASil/AD4BLlSxZ5dBqrl4ypkx8uZdST8TcDVkTOVBjozuPoHMim7CXkteAi4lkycDqJzbZMYm734e/K8Whn4AfCqGMB9bSNncL6HrC8fRbarX1+vgZ2vuhILDlr4LlmfeiuZ5NkuIm6q16dBJJWWKqX8kBz9v3+NcT95/K7ecaxH1eN0P3JwzK3AEcDewPaDilnjNsmzC0spF5ZSvk8OCPoosHHkLLGBhQc+BsyMiNUi4gvAswZRP44cGf5Vsky+KSKilPI2skPvmq7j9cTejOxwvwmYHbkKxueBNUspvx1g3E3J8+c/gT0jB2w2fxvYPYtr5+WHIuKyyM7hWdS+uAG0QbYlO73PLqUcQc462x/YIcYSWgMVEWeRAxZ3LqXcWEp5Z034fxG4BwbT5qv7/i2yzKCU8g/kMb4b2cZ+fj3uu467PXmtmVXj/olcbeWgyARmZ3Wb1utsCmzWPO4t9wdxHPeYQ84efVRr8MigBqD9jmy/7wacFJl8fwdZf+90gHnkffvOIGfrNEn2P5Dl41r1OYOsS80FnhIRHyb74v62lHLfAOM13kZOjPgi2U64trZNZsHAjqtVgctKKb+MsUTdoeS1vtNlbiNi6Yi4oTlGS85S/SI5SGevGM6Ajk3ICSGUejui1rnzhEEEjBxotmIpZe9Syh2llA+QeYPdyMHWnX63EbFWRNxMJrG+StaP12/9fUbP87uMvRV53LyhlHJnKeUKsg65dOs5gzp3HyD7/34OvJz8nt8eEXv386JTInFXL5RfBr5VR7FCdgJ/m1rpbE6IiNiDrKR11aG2VkTcCFwREddFxPE1efd54Jm1YkYp5f8ibz75hi7ituIfCexVYzT3W3tNKeVashK+G1n4XhQ5hbyTClKMjRj7b7JT61yyI2gX4L/JhvIbgU6WtYpMzH6uxrgAWBO4PSJ2KaU8QHbIv6m+p647iJcnlzs4mLyonEA2Ur9fSjm/Pqezc6l2uCwdEbcAl0XE7qWU/wNOB54T2Vl9O/Ap4LXtBkefcVdpfq/x/pos4E4C7iulvKX13DUiorN70kTOHjyQTHTsHLkW/zfJET9PB7akJgW66Ixovq8YS569j+x82Be4uJRyU+u523QVt/Wa7yMvXs358Qg5Cv0fyPv97EjOvrs7MgHeVUX/dbUysDvZqJlBfq7vJY/vp5IV0Zd0eTGLiDNqWQX5vS5NXsC/QnaCfBDYMgawVGdrP95JfrZ/JGetLkcux7ovPDrAoqvrwno13lZkh9MZEbFH5MyDfcilFT8bEWc1x1cfsTaAnA0VOfLwh2QZeQQ5unbZVmf44dHhEoeRHfCQS+2tSe18LqX8v8hE4tfJJbU6rwDXHMPPyGVoLo3a6V7L/6ZDc5sYzL1hvk02Ev+W7Iw/PSI+C1wF/CkiLmveY0fxZpP79JtmQ+1ce5Cc6bhVDGDmaCnl58DFwDIR8ffAc4EDSinXkwmH7YF9oqPlUGu9YiZZJj0IzI3WqPta6V6RWufpKOZZZP1tq1pPPBQ4IiLeT5aLHyml/GdX8dpKKX8opXy9lHJi7Vh7LfldnwJ8OvJeQy/pIlZtLHwB2K6WEV8k62fPBT4ZuRTcdbVD8cRBlMWNUsrnSikvIpeI+Q+yE+TcWh7Oq8/pqq66euQ95TaPnE1SgA8DryfL/1+Q9bkXk/XkN3YRtxV//8jVP/6XTFw1ZfFS8GjyLuj+vk4rkcmrY0spd9Rtl5BJ26Zd1Plsh4jYIvJ+QpCJ9p+QybMvkmXGl4F/bV07uoq7KZkovCXyXqcAbyGv878lk/Cv7TjmK8jk7wtrp8BD5Kjt2U2sjgdwrEKOjD6svvZ/k8s7PbOU8lXy/H4+2S74VHR4n6OIuJpsg0Bec/4jIi6PiJPJZV+/2VWsnrjXAieVUo4ppRxKlsmPkN/phjGgQWZNeDJ59qQYuz/Xn8l9vYOx5Ya7C1jrKyUHaH6CnLHzXeDuUsoFA4h3JNlf8haybfs7sjMRsoycUdsrXceN2j9yHvBkskzekKw3X11KeUfXMXvivoVs61xGDqZ7UasO1ZSJyzb/p6PYTyPb1CeSSYftyc/6wtqO7+T2DJGDn2aRM9y2KTmYvak//SM5Q3cYywdvS5bJv6N2lEYdTFhKeQXwyxjAspWRt5/4X3JA4XG1/XkgOSv6HvIa+FNy9YauV5h5OnktfzR5VK8LryK/5y26qNvUduOn68Mvk8nvLVp/bwZtPocB3D84cjBB07b+HvBwtGZE1T6sZcjPf5mu2kIR8bzIWxL8D9nn933yevsM4FRyRue6EbFMv3WbGEuEfpksG+8CLo+IU8ky6x5ylY5HB5x1odZZXxU5y+1Fte64J9lH9sNSylvb76/DuGtGxLtibFnKP5B148+XUs4lb5PwCeCCiNixw+/0STGWJNuI7P+ilPJw7Uf4L/Ja9NJo9VP2GXOZ2s85i/xeqTF/Tfbff4/sb+18sFeN33x3dwOzmv6L5pyK7F89qKtrT49Vqfe5jrEBBneSg45PqY+7+m63J5eo/HIp5R9LKafVP51Yv/cZrUTl07qKHRHLRsRt5Iob88nl3Jtj5/nA8RFxdtRJTV2o5+2ZEbFnRDytlglvAfYopTRLNT+fvF3OWbGYCbwlPnEXOYLlcrIS/73IG903Szo1o8Qvqj9fT47q+s+ODowdGLu32zvJg/M1EfHWWlH6Bpm1f1pkwvA2Olzaox5w25H3atoWoI6SezhyxPoXyVGgZ5MN50M6iLl2RNzFWKLylFLKMeTMmVcDry6lnF1KOY4sEPu+70GMJWb/vuQawJ8tpbyBnHL76oj4GDlD7N+a778LTYHZqiCsTs7aeYS8WG8QES+J7BjvLFlYO6T/j+z0WJ+s7O1Advr8AtiidjDeSh5750WfMxsjO0fvj4ibo973q5TyO/KYvYAcXbpefe6+ZNKwq6TsMmSDZWeyEbMNedF8DzlK4vBSylzgCdHB0my1gXQLPLqkQ/PZvYdMJK3VxIlcf/rmqMmRDmKvERFfJsveO6nLg5VS7gaWqxeavwHOLaW8kFxa6mn9Hl8xNiN5s1LK72r5eBC55M/vyc6BW8mRcweQU8o7WVoqctTy/WTyquk4u4hMXl1BJoifT3aC7BUdjgStZf6JEbFpHdjwb+Q14DryXP4RueTfS6DTRMsjZOfOrrVj9o1keXU+2bB7LpkQ36H+Wyy1onNFbwWglHIXObPhJOrNjiNH6/0V8F+LG68Vd82I+CJwaESsXo/Pa8j7JDadWU394o/Ue2h1pZa7+0fe3+Bt5Ll0fYx1hv+BbMCtSS6b2UXMNSM7Q68hk70fICu/d5CJ5zvIGUy/JiuJnXWylVyC+p1kBf/tdVtzrP47ea52Mrs9It4QOcioScb9mJzB+B/kDPNfRSaI7yKvyxuQDbx+465DXmsOKaV8kfxOD6CODm91Evw1Hc3uiIj3ko3EfUop3waoydBXkCPTLyulfDKyI6yrzrs9IuJjEbFhc7zGWGfWXeRxdD1ZZqxALuXV172GIpNRbyOTrk0n3pdqnA8zNvDpevJ73oCsU3cmcuWHp9bfmw6RL5ZSPkbOxrqHbFCeHBFfipxl32+9ZkcyCbwmWRf9U437I3LVgsvJEcwHkR0jV5IJ407UuumryNlBkIn3Zom9P7audV8nk4hdxb2GrJP/iFwStRnc9wcy6fLHyJn2g5jtsGqNeSbZWXsqWaf7B3L09inkNXCvDs+pDUoO4HsT2dm+H9nRcyDZGfF1si67bq0TdOVH5ACZnVsdAF8mj+WnxViCrYv25uq1U+nHwG4xtkTXW+rjW8k22G5k3WYmWZ/rN+5qte74O3LpxKXqcXQqmbB7Yyll51LKT6PbwV5N3N+QK1A0ZeSnSy7leAp5zu4RHd//JhZMnn2c7Ih/VfP3Wpf8DTlopZOOn1q/uBO4IbLzdAPyOLqN7Ev4Zn1eZ7N1ImIFsu7yzVoOr0C2ww6JiPNKKf+PHIm/dUSc3VHcpl1dyE73r9UYD5MD6m4Hfhdj9/ruqoxYWNwbyGvESyJixTqg4SDy/pHL9Xv+RjqQrP9vXXLA2VmllFPJ/d2YTAzcErnCzOH9xAPWKLkE9U5k/0B74Ny7yGvNC6OjQb+96v4eChxd6xtXkyslzS6l/KF1Xf8ZORCry9hzyWtcs6T7T8kE3hPJFW2OI7/zj9f/8ryO4q4AUEq5kjxfd44cqEndfi95/Xt9R8f0g+SAuotKzkwt5CzC5p6NTZ38MHLGViei1QdItj3PJOvq25AD3prB0P9H3rf4heSMwC5ib0gO2rs4IraubevryO/6E2RfxivJVUGe1E/dJnLg2qUR8TzIQYyllHeT9fJlGVuh6Fe97e9+RPb1fZFMRO4IfKDW339GtkkOjYjmVhtdThjYnpxd/qtadlByWfmHgFMi+9qfSQ60+BE52K/vPsAa9x4yEQo5+HWliOhdgeJ3wE9qHajfmJuSS6yuWXIA4czI21AAUEr5F7LP6plk2dGJiNgqxia9NMfm35ADOPaK1i1ygJeQZVNnicpWufMfZL1pnVLK71v9JpcCv61t077LqIg4mFxJbFny2t44kezvOwYevUXOG4CrImLNfuPCo7MnHwGWKaWcVd/D2yP7rf6RHAy1E5k4vaiDtub25C1MNiH7D06K7NN4CPjfiHgrOcFpX8ZuDbR49yEtpSyx/8iG+B315xZkR+XhwEqt5zyZbLj9lOzwmdNR7G3JDsEX9Gx/KlnAvJg8UF5HHiC/BOZ1FPuJwMqt/Xsv2WDdpG47G/gXco3gLj/vp9d9OZHsONuRbIy/Z5znPoecjbBpnzFfTlay/4YcCb96z9/nkMnJh+r38cKO9nUb4JKebc8kO6dfWR/vT47M2AOIDo+rrevvy5H3mTuPHCl9ODl75YM9x/cGHcRdqe7LTWTH0m1kYm4mWbE+n+wIOpmcKTW3g5gzWr+vWF/7/cBTxnnui8gOkSf1GfNQshJ5J/Du1val6s+nkDPAjicrEHcA63Xx3dbXfw9wTs+2mfXnLuTo9Jd1Fa++7nbkheqVrW1b1p/7kB20zePVyArjSzqIu0b9nN9XH+9Vj+Or6/l1OHnfRup3civZmO1qv99BdszOr8fus+v5cmc93jcnG3aPkNeIfo+tJ5Ad33Pq4z3JBPRa9fFxwHd6/08f8XauMV9JDiDZtH0s19/PJhs0f13P6ZkdfK5zyIbp8T3btyJnNH6IvE8YZIPxB8AuHX2na5MDGq4lZxfc3JxPZKP8o2RFDfIadTf1uthn3GeSgyROIpPdvyJnET69HtPHA0uRDak9yFH6R/UZ8w3kspAr1sdLkxX8m4DjWs9bpm5brYP9XL/u2xfqfm1Xt88gkw+fAfbv4rucIP7xZEfic+rjU+rjXVrPuaYpN/qM9aR63qxZH69DJqya6+9BZB2m7+tr6zNcirrcNlk+faTnOcuRibRdW9uW7jPuHPK6eld9vAbZadU0Sg+qn/GLxnvPHex3kHX0n5PXoV0YKxNn1L+/DPho6//MBTbqM+6OZAfD/s37qD9n159Hk0nvR99Lh8fxcmTd4dL6eGbrb/eRdYt16uMNybLlsA7irkl2KH2ArFM9n0yK3kEuVdY8b12yTv2mDvd5ZeCJrX36NNlJ/ByysXpM/duuZEf5N/o9tuvrfYJcJQEy8fAecgT1U8k67f/UzyDIJNsr+4y3fM/j3epnuTtjdbknkHXNq8Y7rxYj5hnkdXTLes6eStZpNqt/fx95rVu+31jjxD6LvPdYe1tzTXoamQA5qD7upB20qLitx0eT1/29u4hdz587yTbOu8jrwVLAS8ly+cDWc/cil9DsYl+3I5dVOqx+pjcBr61/eyLZwfUROmzTM1bubU125B3D2CCkrciBT2+uz9mVvH1AF3G3qj+bc2VjsuP2PLKOszd5vTqCVl12gHHPrXGb9sm+5MzSHwDP6zNmtH7flOwPuoRWm5K89jXf9R5kgnix6ljkoInPkuXfReSA2H3J5UDXbj1vDXKQypuBVbr6jHvey/PJMn7Z+vhS4FM9z7mQ7tu7m5PX2Ln18bFk0nt2/aw/Duxd/9ZF22CtWgbdSCaRXl23v75+B+3r7iHAGX3GW4d63azf79fJ+8auRfYPnUeWV2uS1+Cb6ahuw/h9gA/U82Xz+l7Oru/nAPK+ukd3EHfN+tluQl5bX08OYHkO2YZ/Xeu5byVX/eorLlkvfqSWA2fSUx8lO+c/TvY7ntvR53sA2d+5X2vbLvU7PKE+fh3Zn71iFzFbcf+eVr8POUuoOb6/WT+HzVvH11V4AAAgAElEQVR/77tfrLW/7birktf+i6ntz7r9JLJdvQJ9XOvr99pMunk32ce5LLna1xk9z/0MtY7Vwb4+mezDPIPaBmn97aVkv9gN5HXvsnouddL3WM/PG8hrT9PueBdZb1y+9bzlyIT85sCsPmOeQE6E2JPs272m/Zq1LLmr7vvl5Gy0OR3s6xNav99G3m4Dsn3yceCWnufvATy1z5gHkYNhnlcf70D2Bc6tj68kk3RzWv9nucWO18VBMRn/6oe9Qs+2Q+sHtA+1slC370x29PXdadl6zRXJzq23tLY1HYYHkpWnZRi7t8OTO4q7Uz1A7iQvmiuTF9CP1BNlZbKS/ZPW/+lkv8lC9k0921YjR4I0lZVnkB2O36bP5A7Z+L+dbIC3E7N/0dldC4d3NIVSB/u6NXmxPLVn+4vICkTTuXduLQz7bmCQF4/ryUTDq+q2E2rMp9XC4ZNkRaGvzodWzMOB3ervh5EF7SyyUL+0+b7Jjp6PkRfvLi7YmwO39my7ob7+OdRKEtlRcSq5rOPufca8hmyo7Vjjfxl4ff1bMNbA2Kme29d1eO4E2Tj8fLMfTXnRes7G9Xhfuz7u4phamqzk/qC17QpyxuRy9fHJZOJ7sZNI48RtkoXH9mxfk1xC6l/r+XpOVzFbMdYgKwQfqI/XJgcA/Ji8Ge9JtDrMyYr+aR3EnUfOiL2SscTD28hZSs1zLiMblYv93ZIjsL5ITeCT5f/p9fxcpW5rvtuZ5LXhbR1+vpsA17Ueb012tjyDLIfPJDt95pONvD07ijuLvK6+vrVtdzJZdxaZBLiF7GB7J3kN6it5VmPMps7SbG07gVw+BHKQziX1GGsa1ItdKav/f33GT6DNrJ/1ra1y5LPA/I4+4yArt/PJTo6f1P2bU/9+WH1PO3V4PB1Dq2yv5+cHyUEsM5tygkw0fYwsuxa7I4K8MXZTxn6ULB+PJztevk5eay6un8V76rYuEt7r1p+7kI3EHck6zeXkdbg5dt4OvLX9nfQRc39yNuRTySTCJ8hO6b9qPWdVsrPwY8DGXX2v47yXD9fy4NJ6fM1t/W3lehyv3mG8I+npUCHrUNeTbYIgy+J9B7Cv6wI3tx4vzdjgoOXqOXYbWV49SDedWyuTA1A+2bN9LcauD09sbX8m3Q10u6Z+rg+Rg7y2IhMOJ5HXqmvJdtCM+vy+OgVa+3U7NWnXczx/ocaeUb/zZ9e/rdBnzFVqmXBEa9srydlXd5D1yiZBvDZ5nei7rCTLv1+QnU1zyGv+W8iOp5XIa89dHR/DTQfPSfXfbHJG37vr93wO2Rm1G5mUf9aA415U457dLqfI+utfDPZbjLgLS549ibxOfZhMwOxCtlX6HuBWX//UGvsJ9fHu5DW+qcetRV6nriNX4Ogn1njJnReQ7cpTWs/bjKzH9TWgrfV6M+p3+QfGOqFnk+3su8hrbtMeO5TsyNxjEuJ+o363czqIPV6y8O567jbJ74OBL3UQa2tyoNGR5ACvcxgbNHkB8EDP87eko8HkrdeMnscfp9ZlyD6zTwEX1cfHk/WBvo7n+lqrkGXtqvXxCfXYnVUfX1SP+ajHQ1/XgVbcHchBkaeRg1EPIQezn1P/fi7ZOf5isu35t+SqB4t73t5GDly8hLFO4e3r57gbWe94TY3zMeBdHX+/4/UBrk6W//uSZckxjK1qM7eDmNuTdfK3k4NWmjpEM8jv3eTAmZfU7cvRx/WA7NOcQfY/Xkz2p15J1lvP6nnuWuT1Yt0O9nNdsg5xSWtbs68vJZNnT66PPw7c2NF32sRtyopZZL30/Pr4CWTb6JD6ePn6s6+BMhPEvZ287mxO1lm/S5ZjV9djoK9+dLLu9h2yDrV2LRPOrn/biBzgcHR9Dx8my6tl+olZX/u6erzuQfbdH9H7umRd6iiyzLiA7voeLyPLjRfUY/i2uv2ZdR8/QQ5QWqN+Hj8m2/2b9xHzQ/W1nlIfP40cjPOenuftTbaFb+lif8m6+I1ke3YT8np+UOvvG5PX/ENpTfDqM+YZZJn8beD61vZPUNuW9Xi7juz/7H+AWRdvfNj/yJkLf64Hwkt7/vb6eqDu1sUHNE7snRib2bAu2fA/pT5uGmx7kw3ovjrvJog/i6zQ/w95Ifsymag8geyAfnnroDm249jvZMHOy6aj6VnkhXM2eZF9G/0XsDv2FhxkpehKWonZrgq3VoydGZstsivZUH55z3MuojVyrJMTMRuCzf19XkSOYj2NvIDdw9gFey/y4vLWDmJeQ15E9iJHca1KdvIHeRF5iByFczvZsbhxR/vanvX2lrrt5hp7p3r8vI6s6D+7fgeLfTyRI2avAq7q2b4dWdi+uLVtY7LQ37HL46r1+hf3npf1816Z7Oi6mtbMg45iPpGsDNxIXsAv7T1vyIvKTR3FGy9Z+C4W7Ox6OTnC5s/A8zvc1x3IWRbfp6f8reXG35GdJJ8BdugoZnNuLk0mRW8lK/PvIhtz72XBEWWfYDFH+JCN879jwRlXM8iOpYuAD7W2v4RsQK29OLHGiX18/XcUWeE7tjluyE6nz5FL20IOtNiMbjviVyOvq6u3ts0ky+nra7zVyOUlTiTv+9NvzD3IARV7kw3Eo+r2I2g1isnK/hXUDuIO4j6WBNpXyM6Jt/cZazPqrLP6eCfGZmftSFZ0v0xe259MdqLu1dF+7l/LgN/XMuPguv0CchWBjcjr0uVkg+KdHcR8FvCL+vvzyGvDD8lyfwfyuvBx4Bn1Oc/oIOZ+dT9Xqo/fz9hIwPOBfyavcxvU83ixOncmiP0Bsj6xfi072oMWmg7FOV2cLxPEbzofXkx2xD+FHI3/C7LeuiXZ2fJjOuokrvGOac7Rej5tQ17vryTL5G3IOvPhHcZsEsJrkh0tGy7kuWvU462LUf9r1J8nUDubev6+JTkwqu8ER8/rjjfD73RygFIzuG0eOUL8z3Q0o56sL91La/QsOXvk2Pr7xuRSR4e1j8E+Y65efx5fz98dyTbRHWTb55x6bD259X8Wf1TtgrM0n1m/1zPI6+2KdR/fS85KXp5si3aVPDuDvKZvRrZNbiUHYn2ZnDF8JHm9f019/n7NMTikuK/u8jiusR9L8uxkslP8H7r4rMlr7brktfVV1PY1WVZ+h2wX7UPWa+aQnfKL3eHEwpM7bwW+23ruC+r+951kbz6/+nMbsg17JFlOnFu3zyPbJofXxy+km/bm4437XPosL1h0svDdwJl1++rAZzrYzx2AP7ceP5vsPG36Sj5BaxBh1//I9sCraNWXarlwIWOD2psVsB4gy82uBrSfTs6OupKxGfWXUAdek22zq4CPd7i/E626tRG56ta+ZLl8GJkAuIXFTJT2nLfbkoMX3tz6bl9MJjm2qI9XZDCzr9/B+H2Ac8k+wGYlsKDPgceM9Yt8mgkGSNRj/g3kDLsH6H/mzNVkOdgkgO8g76/alAt/JuvrXc8SvY5ss84j+8WOojX5pD7n88B57c9nAHFPJ9sK5/U87zCyDdrJ4O4J4t4HXNDzvOeQfY+n0GdfLzm49+M92zYkB5Y1/ebPItvxN7DgilyL9VnX8/CLZDu+qVfMJeuue5GTe5q8wXp0u7rXDLK99YNW7CeS+ZHlW4+vJyeE3MnYyhVr9bG/95ADYmf1vJcta6w39PyfLTra38vIa9yLycESt5MrCfTO8t6bLCu36iDm5eQy483jH5KDcl5ODvhtEpfr1eO7k37lTg6QYf+rJ/t9ZKfLg2RjtT0d/c31S9y2w5ir1AP7FrIR0ZzozRJaB7SeuxOZcV62w/jthOF69aA8jZzl8EGyQ75Zt/2ldLR8Y4379Na+XsnYsnpRC56Navy+RzHVz/kusmP9S/R0qpAJncvIztoul2Xp/X6b0SX7kY2Xea3n7kBevLv4fNtxv8LYkjMbkEmzY8hC/yOMjdjess+YEyWyliOTOx8kK4lvrNvPopXc6jN276y3+8h7brVHeu5DFrwndPj9Xs5YA669lOC+ZCG+ej2+/52eJQD7jHs4mah8cX38yvr5btzzvC3IBs5aHR1Xh5OVrf3q49XIykjvUo3LMzYqtMvzqZ0svJWsDPUmC3cjR0B1NUt2d7KScmCNdx61I5ixytG8GvPPZGKg31FjW9ZyqrkerFfP5bn13L2PbKSf3tH32ts4vwQ4sf6+GVlheh2ZSPoJHS1rWF/3c2TFdluyYXFtPZ+fTSZXXkReBzq77tXYe5INuBlkQqUZWNEkBGaTFcIDO477tHocNY3/Q8mO0mvJStkWrefOqmVJP7OjHk8CbT2y4fOaDvbzWcAvW4+fQl5jV2Js1OOZ5DXhLGqjts+Yj+4r2bj4p3pMfaaeLxeTyYDTyaVR5tBRUrTGfC0Ldvov3fP32+l+NPoHgPvr7y+vn+Xudd9fVj/zD9HdDNX2Ml13MZbcvp+xGcF/0bnSRTlVX2dGz+OdyDrqDLLe8zuyPvltMuHz3C5jksn19mznJ7TKjCvJ68+cDr/fJumwM9lR+ClanfuMdV4eTl2qq8O4zTKKK5AJiMtoLVNP1vke6Djuwmb4nUGO+G+SXduTjeq+O7xYMEn5rvra7yQ7SNZpPW8eHc0Em+Az/iF19HJz7JEdfx+mdmD2EW8r8tq6c33ctIuOqu+lWU1gpxrzjR0fy+eQt3f4NJmcnUXWaWYy1jF+FK3OrSU1Lo8vebZu/ay7WHXkJvJ6fjNZBp5I1ue+TnaCP6ce15fVz6Tv2dA8tuTOp8k69F/TTZm8sBl+7dUTViTrWB+jz2WS+4jb9+yv+pqPJVn4fnK5vZXoWTK7j7ivYazOOI/WCi5128/oaFm/nrjL1n18C3l7gBeQ15w55HVny9Zzdyb7H7qYYdF0SM9kbFbO2WS/zXH192algzXJDuon001b7An1O13YqltNmbUc/Q3i6D1v5wFX9jznZLI/tLPBT/V112nt16L6AGd3GbvGuIKaHCUHzbyXrFsd0npOc5/zxRrUwIIJllmM9bVtTbZ9DyHL6AvIPpz7yfti97tvjyWx0wyuez89SeKO4+5az58Le547h+xP7mvZ4McTt4uyYZzY72ZsufYd6zn6yvq53kqrfcmCSafFfi/kag+Xt/e//nwjea3dpj7emxy42Pey6vX1tqz7u0Pdt2ZG+RpkPfZE/rJvvT3gbHETlb37O5scVN7s9+5kfefIDr/XdrJwpZ7tLyb7fntXzusrgUb2b36WnCRwLmMDqtcm62rtFQ+bNueu/cRcIH5XLzTsf7XgPJCsSJ9cT/wj6t9WIDvBOxldythol2Z04aFko7HpDH4J2bm1dj1hHqCD+1XU115YwvB7jK1DvEnd599QO3I7ivuZGvdgsuHyfnJk/Pqt5z6tPq/fe0Qt9HNuPe/N1KW0BvT9HsaCS0k1HfC7kw2d73Tx/U4Q9wTGRjFtTo5wupGc/dbl8mQLJLJahcsu5GzOV7We28V9SCZKFm5LjqidWx8359QxdDTroB63P2BsBHjv8XR23ed/o4PKSX3NVcglUZrkxpcZu2/gNWRFc/f63KeSy7OcOYC4XyIrSkvX4+laxmYIzyE7CPru/K+vN1Gy8NvtY6n38+/wmN6GsREuzb3sXstfzrxbhez86Wt5C8ZGjL2I7CA8irwGvJSxmVkn1e/2z3S3Xvlx9Tu+pn6fzeipGWSF9HvkPU67WIJm5njnbf1bU/FuGnUbkR0Fa/YbtxXjaWQl//T6+BMsuJRI08A6j45H4ddj9blkQ/Gg+vkeR3ZoHdD7HjqI93gTaJ3dm4ScRXdn6/HVwMNkp9dqddsOdJC0a+3rr1qPPwBcXX/fi+z8/kk9b07u8nttxZzPgjNUZ5CdMdfW46zv61593XYC7W6yI2JZcpDIr1nw/n3rj/f/Oor9w3qezK3H0ECWxWTBpOxSPX87n+wA+Qk10U4uv9TXrLeemEu3tn+NVkOybtuRvC52OqueBZczXJ3sFLiXnBnVnj31GVozpjuOuwF53X0z2XBv6ngrk4mCrmY4PNYZfhu2tnWxPGaTQNuKsQTaP9JKoLWeG2QHchczwXo/45XJpOHbaQ1YJOt3fd83sB6jf6YOIiQHaG5PJna2rWXIafW5L+jiXGb8GX5n1uNm1Z7nNnXWvld1may49fUeb/Ksi9mxS9Vz44r6eBmyg+nbZIfe7fQsbU4daNHRPi8qufMjcgb4hLOFH0esRc3w6x1I+CRaAw6WwLiPN1l4E61rfkff7/x6LH2FscR/U09egW4HlDfLN36BvObsTM6UbGYVbk4mwz9Ch/eQrbFfRPaPNLf62JO8HhxDtvs+S3botgddd3H7id57zE206ta9dDRbtb5m+7x9QT1H55Mzzpap5crrWMzZMgv5bu8ly+RdyA7rDzBxH2BX7YP2Z3wqmVT/eT1f3lLP5+toDXKjv0GT4yUcnkj2KVxLJrz3b/29k7bXOHHbiZ2PM5bY2ZlcLaOTVTEWEvdk8trUxJ1L9nceNKy4dNTuGif2y8jBxzeR7YIbyH71j5C3D7iZsXsGN+VlvzOvn0PWL5pl0z9A1lcvIcvoC8m+wIfoLim7f33tk+vjzck+wNPJvMnHyLL6p/UYO64+L9o/O9rf95GDCa5l7P5vh9TPuq/BbQs5pmaT7a+mX2x3csDGkR3FW5asM72RbAucT7ZL1q9/35EclNpMeOp0IHspZclL3DGWYDiAsRlBN5OV7K+SnZk79XPwjROzd7TL5+uJcBtj9555HdnR9V26Wy5rUQnDA2u8bZvPhg5uWDpO3EcTWWSj8TKyM3c/ssHxQzroNJ3gc767fs7btrY395nYYAjfb5PsOb4WcnfQ3TJhE+3v54Dt67a1yQrqL+ljzeGeuBMmsupne3Mrfpf3hVzUrLetW9s6Te6Qlb2LGKsorFB/blS/h9fSQaO8vmZz/ry2Z/vXyQrwamQH6v1kUvZ7vc/tOO5XqaPzyIvYx8hkxFfopuNlUcnCa2h1vg/gu305WSk7oGf7jmSj4wC6vcn9KrUc+HBr295kRfD8eqyd2Prbs+hw9mh9zfcBD7YeN8mzpcmOii4b5x+l1RgkE0mfA77Q2vYisoLf9fLMTfLs4lpOrEzeH/GE1jm8QT2XuhgRuMCst7rtCrJs3qO+n3PI2ZqdLLXaE2uoCbRxvucP19/Xq+dxs1xkp50u9TVf0+wrOfP387SWSqnn79voeHm/1usHWSY3M1bn1vfwvkHEav3+IFnpP4C69GeX5dM4sZs687rk0iHNAKEPDyhebwJ6JmN11j3IDohmSaeu7uPQG7NZ3m52LZcuqWXzyeRKBl3V0SdKOlxf9/tt9fcryA7Nv6bnPg8dxu1dRvHd5MCRjcnGdN/LzNZ4kzLDr77uOSyYQFuJsQRaU7ebRc4K2LnPWIv6jDch65TnMIDOJnKw5C/IDuk7yE6eD5GzN3Yg6zb7dRRrYTP8TmfsurBm3fYDWoP7lsC4k5Y8q+XClYxdz5sO6leTbYHn1vKiPXiy6zrzwpI7y9HBfX3qaz2WGX53dLlvkxWXSUoWjvM+gqy7ndccX3Vb18dQe393IOvEzf5uRS6d+QuyXXgHHZaR5HV9Hnk9v59MbGxADmjbsO7zmcD/kZ3iXQwa6b3H3O51+8JW3erkPGq97vz6mX+DrD+eVI/lb9HRSg3jfLePLs1Z//ZcMgExiD7A9mf8vhp7mfp+XkorYUaW310tDT1ewuFHdT+/zYLLg3bWFhonbm9i5131s/9bOpj9vBhxv0W3K50sKu4lDG5A4Syyn+Iqsr3QzMQ9jaxrnUOr7d1RzFUZS8x9nSwTDyFXA/kI2df6ra72mWy3f4Gxvt3mmr5XLTPa7evV6rnbya1UFrK/LyPrNlfVc/kJdJCnWMgxNYxk4Zo98d9bY69Stx1DDtbpe9DguPEH8aLD+Ed2QN9FXrSbkdpr1hNzEI2o48jO6KvJhvg6ZAX/nxlrMJ5BR0st1Nd7rAnDH9G66fyA4t5DVnw3rSfISWTS4WY6SmQt5HN+Q/tzrs/rtKNrEd9vezmGTu9buJD9/Rmt9aTpeL1y/jKR1YxO2AD4Iz1rTXcQb1Gz3t5EXsA7O457Xn+7WqC/vmf7p+ngfoE9r9l7/jQ3yJ5FNmSaZVifRDYyukoYLizuzxmbBXYoeWHru1ONx54s7GzESytGb8LwXrLB2p5x8VKy0djJPU8Zu8fc8a1t27b+dhzZUfxz4JWDOJZrrCbp8Nr6uPPESn3d3vN2uXoevamWWfeRDcivdnE81RgTJc/uJEfkbUpW1G4n703y497jr4/YvTPB3k6OzjudnAW2BVnP+CBZ4R/EfWyHmkAb55hqElk30GFjcYKY84FL6+8b1ONoIDPsJoi/HDnysFk6ciD3eWt/d2QC7VdkPe4jDOBeJAuJvTu5NHVns/cniPdqFkzsN8v9rEp28sxtjrkBxmw6aZ9EtgvOI0fod7Uc2sKSDm+iLudHDl55a9129IDjtpdR3JkcbPYbOrwWMeQZfjy2BNq7yU7cjcnr/cVD+ox3IpMwg5oV/CEyYbdKLSu+T14DZ9bPoqtZwYua4TcfOKM+93DqLJclOO6kJc/I0eD/yNgS9e3j+5O1nDiQDjuaxnkPEyV3BjVAZzKWbxxqXCYpSTnBe1mOXIJ73+b7HkCM8fb3Flr1GbK+8VHyHmRdLC+7Krn0253kNXU3smP4QfLWOVew4NKVZ9JzT6XFjLuoe8ztz4BW3ep5H1HLyiaJ1gyI6iy5MsF3O4/Wqitku+R15CDgT9JBH+A4n/E59TP+izYWed34WzqqxzJxguXIerx9k45XaFhE3CPI6/0nyfZn37OfFzNuX/cMfJyf80fIutu5DLCdO857uoVaL6/73MmEkJ4YTR9RU148n6zXzaDbQeWrkv1gTyUH1f0VWc95G5nQm0/WH3tXoep6NnTv/u7F4AaKDj1ZWOO2B+MeSLZLXs5YW/d0Orxn4QKxB/Giw/pXv5T7hhhvgVkOddungIMHGPOxJAxPp8PlyRYS9zSyk6udUBpEknS8z/mTdDRdezG+30MmIe4nB3xcTZTI+mgtgAbRIf1YZr11sszDBPGfR3b23Eou3/ENagfMAGI9OpukPm4uYH9FjjJ69Ka0w4xbf59NdzcZfqzJwkdHvHSx30ycMPwKcE3PtjPJGWJ9d4KMs7+XkgmO5lgOMpH1B7ISuNIgvucaq0k6NOv/DypOc942Axiae8wtTVYKt6bbgSMLS559tJYXy5MdfPvT8cy3eg7dXfft2rptNjmg4gPktXCjLve5J/7QE2jjHFM7kp3URww4Xu++Potc5rWT2eWP8T2sTS6T3Pk1b5xYTQJtBzKhcmd7+4BjNw2KV5GdiDMGGbeeq5e2Y9ffL6BnNssQYm4zgFiLSjpcSQfLXy9G3CsYW0Zxbzro3GKSZvjx+JOUv6WDJOXj/IxfwABmztTXDnI1maYeNYeOOyBasR7LDL99p0JcJjl5xl+2hZqfFwCH1t87qaMv5D0MPLnTijW05RsnMy6TlKSc4L009ZrlB/XdTrC/q7PgvWZX6OIcYuEz/I5k7N7IPwaeX7d3Ur/hsd9j7vdk4r/TRFpPnAXO2wHGGW9pzsuBU2jNJKS7wSPjfcZX9DxnQ7Id9l06ns1fX3+8hMNnyWRA5/ddW0jcJrEzk8GuxjFKcd9JzoQfWNxW/Nlkne4O6jKL9bv+zCC/5xpnTbJv/dwBvPZMsl78JTKRdQXZz3s8OdHmNrIvrNP7YD7G/T17wHGGlixsxWwn744hZ94NNF9QSlniE3fbkhX81bq6QC/qSyI7mY6vj59MLn3TyXTthcQdesJwEXEHnciarM95usUdL5F12QDjDW3W20Lew4pkAuDVDL5T+i86EclK51mTFLfve69MEO+xJAtXotvp8QtLGP4rPUs2/v/27i/U0qoO4/jzmz81TY50UVCTgwfFiKYMKgLDGwe7sIIa+g+hIHqhA6HVZBjZRd1UlBlqZFOTiSBDBUPjFJQ1kQiDGWLJQBIjQZepOdQc0/HpYq097dmz9zlHe9da73nf7wcOnLNn9n7W++6933+/d62lbntCT88xd4/m3xH4XnU4BMEKbalxcj753n5u5vG78+OlCtCLime3K48fXnC9ztv37VQ6MLumwvtatYA2k/1GpQtMRU9g5izrB/LfRYZKWa0NFbMm2+RPSTqQfy920XRO/vuVeikXPUFWOq55SNIN+e8LlYapvKpi5gVKF9I+XShvtaLDb1TghrM15B5Rd3MEN+vhp5depOzsrvg1ruNOhqpcpR1n3KBTOKtKD78+5Kph8Uz/O6b6zMzjv1SBnjortKP48WPOqTJ8Yx9y1ahIuaAtNW5GOmt5C+Us6tE4GRb73UpzVL6odJze6c2xWvscc52da67QltPf2wrv7byhOf+oAsXJBet4X17Hm/I+4QuSLqqwjl+vNNLY3tJZc3KLFHbIPZ25lLcRX5p6rPSNMq9VGprymPL5SaGcrUojBlyhXKDL26e7lEZ7uaxUdovlXeUzVbRYmLMmvZ7PUeolvLt4Zs2VWWCFbVO6uNdpb7NVMid3u3wxZ99Y44OhERWUWq3nkeZWK2TlvGq93lr/6OyLiDuUTm5K9xytnqvKxcL8+osKhpOx04vdNab5hZ1QvtOz8uesxsn5FUrDotyvVLh8WB3M1fQy1vFOpTtriw1DOvVenh6KdOrxpYrva9UC2kx2tUJWztuudJG86AXEPv0o3WV6rwpdDF+QuUHSZ2ttp6aOa25RKqB13gNtDZk3F84bdLFDDXr4zeRXK1L25b2d046aBZYqPfxa56px8Sxv/w8oFR+uzscb+2p8nmbaUWVfr4o9/FrmqlGRstXPouUtlDWvh9/ssPrXq8Bwd/m1q8wxt8a21DjvWzQ0Z7FlXWEdH1W6Ibb093e64FDlOtyc3GqFjrHlTuVPjxxXZcQTSZeqo+G+X2L2Rz0o4+AAAAWZSURBVPJ36vyKmdWXt+FnebJd/L/nUl3LzyRs3YqIV9p+rnLmG5QuqO22/fNKmVuUPoz7JO2SdMj2rUPNzdnV1/MYc2uLiG1KXbeflnTS9t2Nm1TM1Pfn+0oXtQ7a/tbQciMilOanOmD72xGxQ2mI3Tts31cwd5+k521fFxEbbZ+KiM8rDeXx1YK5k+LovbZvj4jzlXqHHbb9jVK5LeXv7R6lObKWbf+4cN5kHd9j+86px5dsP1kyO+dsUZrfb4/t+0vnLWqD7eUW2bWNbFk3KN2dfcz24drZtl+smDc5rvm47Z8OLTNvp44q3biyPyKWJP3D9omh5EbEJyXdqjQ/7l6lgui5Sj0c3q40d+B3bP+s6+yc/z1JzyrNZfF1pbnlrlSaT/Ydkh61/XyB3Cbv7YK2VNk+ttrvtciNiPcpDbP3KqXRZK6V9Ljtayrlb1P6Dp2QdML2D2vkthIR25WKHBcoHUNWuQBVO3fq/OsG2wcjImotaws1lzcifqB0w8gJSTfZfig/Xnwd5/3B7yQdsX3LJDMiLrf965LZrcy+txXymq7jiNiodHwRtn9fOo/curlz2jHIbXNev+cpDeF7idI8fn9q26qy+vKZKm3dF+5aGUvBsGVuzq6+nseYi3Kmvj8fsn1oqLktipStCoY5e3p5L1da3ttKZo5N6+JZqwtNGL7aBbSWGh0vV8scQ7GjVfEsZzcroLXeB7UwlgJLzhxV8ay1Vjfo1M4d27FjreXN+4IjSoWdL0fEZkkvVNxGVS1k9cH0e2v7ZIW80a1joEt5O3mxpE9I+prtZ8Z0zjlkFO7WGQpKwPozlu9tiyJlq16NOXsUvWRban0BZEw9wQC8PEMvdrTufdaygNZ6H9TCWAosQClj+yxX7hXcrEdj7UJWH7QsfI9lHQOlULQbDgp3AIDONO6NXLVXY87mpobCxnYBBMD6M/RiR+veZy0LaOyDAKAfWt9Mwf6gPNYxAJyJwh0AYN2jgAYAQDlcMAUAtMa+AAAwJhTuAAAAAAAr4oIpAAAAANRB4Q4AAAAAAAAAAADogQ2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      "text/plain": [
       "<Figure size 2160x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pattern of departure time from the airports \n",
    "\n",
    "df.boxplot('CRSDepTime','Origin', rot = 40, figsize=(30,8))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describe the average delays per airport/airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UniqueCarrier\n",
      "'PA (1)'    16.223642\n",
      "AA           1.908237\n",
      "CO           4.919948\n",
      "DL           5.118715\n",
      "EA          11.728169\n",
      "NW           5.004890\n",
      "TW           5.179647\n",
      "UA           4.569664\n",
      "US           6.246798\n",
      "Name: ArrDelay, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the average delay per airline-carrier.\n",
    "carrier_group = df['ArrDelay'].groupby(df['UniqueCarrier']).mean()\n",
    "print(carrier_group)\n",
    "\n",
    "\n",
    "sns.catplot(x=\"UniqueCarrier\", y=\"ArrDelay\",\n",
    "            kind=\"bar\", data=df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dest\n",
      "MDT    21.000000\n",
      "FLL    14.638554\n",
      "MDW    11.873016\n",
      "CHS    11.830153\n",
      "PBI    11.072857\n",
      "JFK     9.690909\n",
      "MCO     9.545346\n",
      "HOU     8.954545\n",
      "BOS     8.570847\n",
      "EWR     8.528418\n",
      "MSP     8.340841\n",
      "CLE     8.035286\n",
      "RDU     7.352941\n",
      "MIA     7.196009\n",
      "BDL     7.168256\n",
      "PHL     6.934949\n",
      "SFO     6.711826\n",
      "PIT     6.639838\n",
      "TPA     6.525869\n",
      "ORD     5.786903\n",
      "MSY     5.692731\n",
      "SAV     5.617978\n",
      "RSW     5.433846\n",
      "LGA     5.377581\n",
      "DEN     5.261564\n",
      "SJU     4.901361\n",
      "DTW     4.829739\n",
      "MEM     4.674145\n",
      "SDF     4.451713\n",
      "SLC     4.170000\n",
      "IAD     4.083373\n",
      "IAH     4.073019\n",
      "ROC     3.553556\n",
      "SEA     3.325658\n",
      "ATL     3.265894\n",
      "DCA     3.000000\n",
      "ORF     2.712946\n",
      "BUF     2.539024\n",
      "HPN     2.328829\n",
      "PHX     2.186667\n",
      "DFW     2.097717\n",
      "RIC     2.000000\n",
      "JAX     1.990437\n",
      "PVD     1.922559\n",
      "SYR     1.849315\n",
      "PHF     1.416431\n",
      "STL     1.312961\n",
      "PWM     1.114379\n",
      "IND     1.016092\n",
      "LAX     0.719517\n",
      "CAE     0.692308\n",
      "MHT     0.347534\n",
      "BTV     0.217666\n",
      "BNA    -0.195440\n",
      "ISP    -2.119718\n",
      "SRQ    -2.785714\n",
      "BWI    -6.000000\n",
      "GSO    -6.012158\n",
      "CRW    -6.211009\n",
      "Name: ArrDelay, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate the average delay per airport-destination.\n",
    "airport_group = df['ArrDelay'].groupby(df['Dest']).mean().sort_values(ascending=False)\n",
    "print(airport_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify and report the most prominent rules of association between delays and point of origin AND/OR point of arrival."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reshape the dataset, in order to apply the Apriori algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_delay=df[['Origin','ArrDelay']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "origin_group = df['Origin'].groupby(df['ArrDelay']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Applying One hot Enconding\n",
    "df_onehotenc=pd.get_dummies(origin_delay,prefix=['Delay'],columns=['ArrDelay'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ORD', 'IAD', 'CLE', 'EWR', 'DTW', 'BOS', 'LGA', 'CAE', 'BDL',\n",
       "       'ISP', 'TPA', 'FLL', 'RSW', 'BUF', 'ROC', 'MCO', 'JAX', 'PBI',\n",
       "       'GSO', 'IAH', 'DEN', 'CHS', 'SFO', 'DFW', 'LAX', 'RDU', 'PIT',\n",
       "       'SLC', 'JFK', 'ATL', 'PHF', 'MSY', 'MDW', 'MIA', 'MEM', 'STL',\n",
       "       'ORF', 'SJU', 'RIC', 'DCA', 'SEA', 'MSP', 'PHL', 'MDT', 'HPN',\n",
       "       'BWI', 'SRQ', 'MHT', 'SYR', 'SDF', 'BTV', 'IND', 'BNA', 'HOU',\n",
       "       'SAV', 'PVD', 'PWM', 'PHX'], dtype=object)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "items = (df['Origin'].unique())\n",
    "items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_records=len(df_onehotenc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from apyori import apriori\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "records=[]\n",
    "for i in range(0,num_records):\n",
    "     records.append([str(df_onehotenc.values[i,j] for j in range(0,2))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "association_rules = apriori(records, min_support=0.0045, min_confidence=0.2, min_lift=3, min_length=2)\n",
    "association_results = list(association_rules)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(len(association_results))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Predict the delay given all other features and report the appropriate performance on cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform arrdelay to 0 if it is not delayed and 1 if it is.\n",
    "\n",
    "df['ArrDelay']=df['ArrDelay'].apply(lambda x: 0 if x <= 0 else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>899.0</td>\n",
       "      <td>ORD</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>357.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>341.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>581.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>902.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DayofWeek  CRSDepTime UniqueCarrier FlightNum Origin Dest  ArrDelay\n",
       "0        5.0       600.0            UA     899.0    ORD  IAD         0\n",
       "1        5.0       615.0            DL     357.0    IAD  ATL         1\n",
       "2        5.0       615.0            UA     341.0    IAD  DEN         1\n",
       "3        5.0       625.0            CO     581.0    IAD  DEN         1\n",
       "4        5.0       630.0            UA     902.0    CLE  IAD         0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform categorical data into numeric-Label Encoder\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "df['Carrier_label_enc']=LabelEncoder().fit_transform(df.UniqueCarrier)\n",
    "df['Origin_label_enc']=LabelEncoder().fit_transform(df.Origin)\n",
    "df['Dest_label_enc']=LabelEncoder().fit_transform(df.Dest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>Carrier_label_enc</th>\n",
       "      <th>Origin_label_enc</th>\n",
       "      <th>Dest_label_enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>899.0</td>\n",
       "      <td>ORD</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>DL</td>\n",
       "      <td>357.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>ATL</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>341.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.0</td>\n",
       "      <td>625.0</td>\n",
       "      <td>CO</td>\n",
       "      <td>581.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>DEN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>630.0</td>\n",
       "      <td>UA</td>\n",
       "      <td>902.0</td>\n",
       "      <td>CLE</td>\n",
       "      <td>IAD</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DayofWeek  CRSDepTime UniqueCarrier FlightNum Origin Dest  ArrDelay  \\\n",
       "0        5.0       600.0            UA     899.0    ORD  IAD         0   \n",
       "1        5.0       615.0            DL     357.0    IAD  ATL         1   \n",
       "2        5.0       615.0            UA     341.0    IAD  DEN         1   \n",
       "3        5.0       625.0            CO     581.0    IAD  DEN         1   \n",
       "4        5.0       630.0            UA     902.0    CLE  IAD         0   \n",
       "\n",
       "   Carrier_label_enc  Origin_label_enc  Dest_label_enc  \n",
       "0                  7                35              20  \n",
       "1                  3                19               0  \n",
       "2                  7                19              12  \n",
       "3                  2                19              12  \n",
       "4                  7                 9              20  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "Log_df= df[['DayofWeek','CRSDepTime','Carrier_label_enc','Origin_label_enc','Dest_label_enc','ArrDelay']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>DayofWeek</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>Carrier_label_enc</th>\n",
       "      <th>Origin_label_enc</th>\n",
       "      <th>Dest_label_enc</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>615.0</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DayofWeek  CRSDepTime  Carrier_label_enc  Origin_label_enc  Dest_label_enc  \\\n",
       "0        5.0       600.0                  7                35              20   \n",
       "1        5.0       615.0                  3                19               0   \n",
       "\n",
       "   ArrDelay  \n",
       "0         0  \n",
       "1         1  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Log_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5342997794324726\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression Classification\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "array = Log_df.values\n",
    "X = array[:,0:5]\n",
    "Y = array[:,5]\n",
    "num_folds = 10\n",
    "kfold = KFold(n_splits=10, random_state=7)\n",
    "model = LogisticRegression()\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6110963887349972\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "array = Log_df.values\n",
    "X = array[:,0:5]\n",
    "Y = array[:,5]\n",
    "num_trees = 100\n",
    "max_features = 3\n",
    "kfold = KFold(n_splits=10, random_state=7)\n",
    "model = RandomForestClassifier(n_estimators=num_trees, max_features=max_features)\n",
    "results = cross_val_score(model, X, Y, cv=kfold)\n",
    "print(results.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Identify patterns/rules regarding delays and try to explain when delays should be expected, based on these patterns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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