Aviation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aviation Business Project\n",
    "\n",
    "### STEPHEN MULINGWA\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing the Required Libraries for the analysis of the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing our dataset\n",
    "# Since datasel is really large I used argument \"low_memory = False\" inorder for pandas to read the data in chuncks of columns\n",
    "df = pd.read_csv(\"data/Aviation_Data.csv\", low_memory=False, index_col=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Investigation.Type</th>\n",
       "      <th>Accident.Number</th>\n",
       "      <th>Event.Date</th>\n",
       "      <th>Location</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Airport.Code</th>\n",
       "      <th>Airport.Name</th>\n",
       "      <th>Injury.Severity</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose.of.flight</th>\n",
       "      <th>Air.carrier</th>\n",
       "      <th>Total.Fatal.Injuries</th>\n",
       "      <th>Total.Serious.Injuries</th>\n",
       "      <th>Total.Minor.Injuries</th>\n",
       "      <th>Total.Uninjured</th>\n",
       "      <th>Weather.Condition</th>\n",
       "      <th>Broad.phase.of.flight</th>\n",
       "      <th>Report.Status</th>\n",
       "      <th>Publication.Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Event.Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20001218X45444</th>\n",
       "      <td>Accident</td>\n",
       "      <td>SEA87LA080</td>\n",
       "      <td>1948-10-24</td>\n",
       "      <td>MOOSE CREEK, ID</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fatal(2)</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNK</td>\n",
       "      <td>Cruise</td>\n",
       "      <td>Probable Cause</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20001218X45447</th>\n",
       "      <td>Accident</td>\n",
       "      <td>LAX94LA336</td>\n",
       "      <td>1962-07-19</td>\n",
       "      <td>BRIDGEPORT, CA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fatal(4)</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>UNK</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Probable Cause</td>\n",
       "      <td>19-09-1996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20061025X01555</th>\n",
       "      <td>Accident</td>\n",
       "      <td>NYC07LA005</td>\n",
       "      <td>1974-08-30</td>\n",
       "      <td>Saltville, VA</td>\n",
       "      <td>United States</td>\n",
       "      <td>36.922223</td>\n",
       "      <td>-81.878056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fatal(3)</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IMC</td>\n",
       "      <td>Cruise</td>\n",
       "      <td>Probable Cause</td>\n",
       "      <td>26-02-2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20001218X45448</th>\n",
       "      <td>Accident</td>\n",
       "      <td>LAX96LA321</td>\n",
       "      <td>1977-06-19</td>\n",
       "      <td>EUREKA, CA</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fatal(2)</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IMC</td>\n",
       "      <td>Cruise</td>\n",
       "      <td>Probable Cause</td>\n",
       "      <td>12-09-2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20041105X01764</th>\n",
       "      <td>Accident</td>\n",
       "      <td>CHI79FA064</td>\n",
       "      <td>1979-08-02</td>\n",
       "      <td>Canton, OH</td>\n",
       "      <td>United States</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fatal(1)</td>\n",
       "      <td>...</td>\n",
       "      <td>Personal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>VMC</td>\n",
       "      <td>Approach</td>\n",
       "      <td>Probable Cause</td>\n",
       "      <td>16-04-1980</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Investigation.Type Accident.Number  Event.Date  \\\n",
       "Event.Id                                                        \n",
       "20001218X45444           Accident      SEA87LA080  1948-10-24   \n",
       "20001218X45447           Accident      LAX94LA336  1962-07-19   \n",
       "20061025X01555           Accident      NYC07LA005  1974-08-30   \n",
       "20001218X45448           Accident      LAX96LA321  1977-06-19   \n",
       "20041105X01764           Accident      CHI79FA064  1979-08-02   \n",
       "\n",
       "                       Location        Country   Latitude   Longitude  \\\n",
       "Event.Id                                                                \n",
       "20001218X45444  MOOSE CREEK, ID  United States        NaN         NaN   \n",
       "20001218X45447   BRIDGEPORT, CA  United States        NaN         NaN   \n",
       "20061025X01555    Saltville, VA  United States  36.922223  -81.878056   \n",
       "20001218X45448       EUREKA, CA  United States        NaN         NaN   \n",
       "20041105X01764       Canton, OH  United States        NaN         NaN   \n",
       "\n",
       "               Airport.Code Airport.Name Injury.Severity  ...  \\\n",
       "Event.Id                                                  ...   \n",
       "20001218X45444          NaN          NaN        Fatal(2)  ...   \n",
       "20001218X45447          NaN          NaN        Fatal(4)  ...   \n",
       "20061025X01555          NaN          NaN        Fatal(3)  ...   \n",
       "20001218X45448          NaN          NaN        Fatal(2)  ...   \n",
       "20041105X01764          NaN          NaN        Fatal(1)  ...   \n",
       "\n",
       "               Purpose.of.flight Air.carrier Total.Fatal.Injuries  \\\n",
       "Event.Id                                                            \n",
       "20001218X45444          Personal         NaN                  2.0   \n",
       "20001218X45447          Personal         NaN                  4.0   \n",
       "20061025X01555          Personal         NaN                  3.0   \n",
       "20001218X45448          Personal         NaN                  2.0   \n",
       "20041105X01764          Personal         NaN                  1.0   \n",
       "\n",
       "               Total.Serious.Injuries Total.Minor.Injuries Total.Uninjured  \\\n",
       "Event.Id                                                                     \n",
       "20001218X45444                    0.0                  0.0             0.0   \n",
       "20001218X45447                    0.0                  0.0             0.0   \n",
       "20061025X01555                    NaN                  NaN             NaN   \n",
       "20001218X45448                    0.0                  0.0             0.0   \n",
       "20041105X01764                    2.0                  NaN             0.0   \n",
       "\n",
       "                Weather.Condition Broad.phase.of.flight   Report.Status  \\\n",
       "Event.Id                                                                  \n",
       "20001218X45444                UNK                Cruise  Probable Cause   \n",
       "20001218X45447                UNK               Unknown  Probable Cause   \n",
       "20061025X01555                IMC                Cruise  Probable Cause   \n",
       "20001218X45448                IMC                Cruise  Probable Cause   \n",
       "20041105X01764                VMC              Approach  Probable Cause   \n",
       "\n",
       "               Publication.Date  \n",
       "Event.Id                         \n",
       "20001218X45444              NaN  \n",
       "20001218X45447       19-09-1996  \n",
       "20061025X01555       26-02-2007  \n",
       "20001218X45448       12-09-2000  \n",
       "20041105X01764       16-04-1980  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dataframe\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "Before we begin our analysis we need to get an overview of the information and summary of the Aviation data\n",
    "     \n",
    "* Get to know the dimensions of the data\n",
    "* Understand the features in the data\n",
    "* Check for missing data\n",
    "* Check for Duplicates\n",
    "* Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 90348 entries, 20001218X45444 to 20221230106513\n",
      "Data columns (total 30 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Investigation.Type      90348 non-null  object \n",
      " 1   Accident.Number         88889 non-null  object \n",
      " 2   Event.Date              88889 non-null  object \n",
      " 3   Location                88837 non-null  object \n",
      " 4   Country                 88663 non-null  object \n",
      " 5   Latitude                34382 non-null  object \n",
      " 6   Longitude               34373 non-null  object \n",
      " 7   Airport.Code            50132 non-null  object \n",
      " 8   Airport.Name            52704 non-null  object \n",
      " 9   Injury.Severity         87889 non-null  object \n",
      " 10  Aircraft.damage         85695 non-null  object \n",
      " 11  Aircraft.Category       32287 non-null  object \n",
      " 12  Registration.Number     87507 non-null  object \n",
      " 13  Make                    88826 non-null  object \n",
      " 14  Model                   88797 non-null  object \n",
      " 15  Amateur.Built           88787 non-null  object \n",
      " 16  Number.of.Engines       82805 non-null  float64\n",
      " 17  Engine.Type             81793 non-null  object \n",
      " 18  FAR.Description         32023 non-null  object \n",
      " 19  Schedule                12582 non-null  object \n",
      " 20  Purpose.of.flight       82697 non-null  object \n",
      " 21  Air.carrier             16648 non-null  object \n",
      " 22  Total.Fatal.Injuries    77488 non-null  float64\n",
      " 23  Total.Serious.Injuries  76379 non-null  float64\n",
      " 24  Total.Minor.Injuries    76956 non-null  float64\n",
      " 25  Total.Uninjured         82977 non-null  float64\n",
      " 26  Weather.Condition       84397 non-null  object \n",
      " 27  Broad.phase.of.flight   61724 non-null  object \n",
      " 28  Report.Status           82505 non-null  object \n",
      " 29  Publication.Date        73659 non-null  object \n",
      "dtypes: float64(5), object(25)\n",
      "memory usage: 21.4+ MB\n"
     ]
    }
   ],
   "source": [
    "# Summary of the df\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90348, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the dimension of the df\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Investigation.Type         0.000000\n",
       "Accident.Number            1.614867\n",
       "Event.Date                 1.614867\n",
       "Location                   1.672422\n",
       "Country                    1.865011\n",
       "Latitude                  61.944924\n",
       "Longitude                 61.954886\n",
       "Airport.Code              44.512330\n",
       "Airport.Name              41.665560\n",
       "Injury.Severity            2.721698\n",
       "Aircraft.damage            5.150086\n",
       "Aircraft.Category         64.263736\n",
       "Registration.Number        3.144508\n",
       "Make                       1.684597\n",
       "Model                      1.716695\n",
       "Amateur.Built              1.727764\n",
       "Number.of.Engines          8.348829\n",
       "Engine.Type                9.468942\n",
       "FAR.Description           64.555939\n",
       "Schedule                  86.073848\n",
       "Purpose.of.flight          8.468367\n",
       "Air.carrier               81.573471\n",
       "Total.Fatal.Injuries      14.233851\n",
       "Total.Serious.Injuries    15.461327\n",
       "Total.Minor.Injuries      14.822686\n",
       "Total.Uninjured            8.158454\n",
       "Weather.Condition          6.586753\n",
       "Broad.phase.of.flight     31.681941\n",
       "Report.Status              8.680878\n",
       "Publication.Date          18.471909\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the percentage of missing values\n",
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copy of my Original data\n",
    "original_data = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = original_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>25-09-2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>25-09-2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>15-12-2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>15-12-2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>15-12-2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>15-12-2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NaN</th>\n",
       "      <td>20-12-2022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1390 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Investigation.Type Accident.Number Event.Date Location Country  \\\n",
       "Event.Id                                                                  \n",
       "NaN              25-09-2020             NaN        NaN      NaN     NaN   \n",
       "NaN              25-09-2020             NaN        NaN      NaN     NaN   \n",
       "NaN              25-09-2020             NaN        NaN      NaN     NaN   \n",
       "NaN              25-09-2020             NaN        NaN      NaN     NaN   \n",
       "NaN              25-09-2020             NaN        NaN      NaN     NaN   \n",
       "...                     ...             ...        ...      ...     ...   \n",
       "NaN              15-12-2022             NaN        NaN      NaN     NaN   \n",
       "NaN              15-12-2022             NaN        NaN      NaN     NaN   \n",
       "NaN              15-12-2022             NaN        NaN      NaN     NaN   \n",
       "NaN              15-12-2022             NaN        NaN      NaN     NaN   \n",
       "NaN              20-12-2022             NaN        NaN      NaN     NaN   \n",
       "\n",
       "         Latitude Longitude Airport.Code Airport.Name Injury.Severity  ...  \\\n",
       "Event.Id                                                               ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "...           ...       ...          ...          ...             ...  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "NaN           NaN       NaN          NaN          NaN             NaN  ...   \n",
       "\n",
       "         Purpose.of.flight Air.carrier Total.Fatal.Injuries  \\\n",
       "Event.Id                                                      \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "...                    ...         ...                  ...   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "NaN                    NaN         NaN                  NaN   \n",
       "\n",
       "         Total.Serious.Injuries Total.Minor.Injuries Total.Uninjured  \\\n",
       "Event.Id                                                               \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "...                         ...                  ...             ...   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "NaN                         NaN                  NaN             NaN   \n",
       "\n",
       "          Weather.Condition Broad.phase.of.flight Report.Status  \\\n",
       "Event.Id                                                          \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "...                     ...                   ...           ...   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "NaN                     NaN                   NaN           NaN   \n",
       "\n",
       "         Publication.Date  \n",
       "Event.Id                   \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "...                   ...  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "NaN                   NaN  \n",
       "\n",
       "[1390 rows x 30 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning\n",
    "### Dropping Duplicates and Columns\n",
    "* I will be dropping Duplicate rows\n",
    "* Also, I am going to drop columns that have high percentage of missing values and any column that is not relevant in the analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88958, 30)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Investigation.Type         0.000000\n",
       "Accident.Number            0.077565\n",
       "Event.Date                 0.077565\n",
       "Location                   0.136019\n",
       "Country                    0.331617\n",
       "Latitude                  61.350300\n",
       "Longitude                 61.360417\n",
       "Airport.Code              43.645316\n",
       "Airport.Name              40.754064\n",
       "Injury.Severity            1.201691\n",
       "Aircraft.damage            3.668023\n",
       "Aircraft.Category         63.705344\n",
       "Registration.Number        1.631107\n",
       "Make                       0.148385\n",
       "Model                      0.180984\n",
       "Amateur.Built              0.192226\n",
       "Number.of.Engines          6.916747\n",
       "Engine.Type                8.054363\n",
       "FAR.Description           64.002113\n",
       "Schedule                  85.856247\n",
       "Purpose.of.flight          7.038153\n",
       "Air.carrier               81.285550\n",
       "Total.Fatal.Injuries      12.893725\n",
       "Total.Serious.Injuries    14.140381\n",
       "Total.Minor.Injuries      13.491760\n",
       "Total.Uninjured            6.723398\n",
       "Weather.Condition          5.127139\n",
       "Broad.phase.of.flight     30.614447\n",
       "Report.Status              7.253985\n",
       "Publication.Date          17.198004\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the percentage of missing values\n",
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping columns that are not relevant for my analysis and with alot of missing values over 60%\n",
    "df.drop(columns=['Latitude', 'Longitude', 'Airport.Code', 'Airport.Name', 'Aircraft.Category', 'FAR.Description',\n",
    "                 'Broad.phase.of.flight','Schedule', 'Air.carrier', 'Publication.Date'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(88958, 20)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Investigation.Type         0.000000\n",
       "Accident.Number            0.077565\n",
       "Event.Date                 0.077565\n",
       "Location                   0.136019\n",
       "Country                    0.331617\n",
       "Injury.Severity            1.201691\n",
       "Aircraft.damage            3.668023\n",
       "Registration.Number        1.631107\n",
       "Make                       0.148385\n",
       "Model                      0.180984\n",
       "Amateur.Built              0.192226\n",
       "Number.of.Engines          6.916747\n",
       "Engine.Type                8.054363\n",
       "Purpose.of.flight          7.038153\n",
       "Total.Fatal.Injuries      12.893725\n",
       "Total.Serious.Injuries    14.140381\n",
       "Total.Minor.Injuries      13.491760\n",
       "Total.Uninjured            6.723398\n",
       "Weather.Condition          5.127139\n",
       "Report.Status              7.253985\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dealing with Missing Data\n",
    "#### Categorical Data\n",
    "* For the categorical data with missing values am going to drop the rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 88958 entries, 20001218X45444 to 20221230106513\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Investigation.Type      88958 non-null  object \n",
      " 1   Accident.Number         88889 non-null  object \n",
      " 2   Event.Date              88889 non-null  object \n",
      " 3   Location                88837 non-null  object \n",
      " 4   Country                 88663 non-null  object \n",
      " 5   Injury.Severity         87889 non-null  object \n",
      " 6   Aircraft.damage         85695 non-null  object \n",
      " 7   Registration.Number     87507 non-null  object \n",
      " 8   Make                    88826 non-null  object \n",
      " 9   Model                   88797 non-null  object \n",
      " 10  Amateur.Built           88787 non-null  object \n",
      " 11  Number.of.Engines       82805 non-null  float64\n",
      " 12  Engine.Type             81793 non-null  object \n",
      " 13  Purpose.of.flight       82697 non-null  object \n",
      " 14  Total.Fatal.Injuries    77488 non-null  float64\n",
      " 15  Total.Serious.Injuries  76379 non-null  float64\n",
      " 16  Total.Minor.Injuries    76956 non-null  float64\n",
      " 17  Total.Uninjured         82977 non-null  float64\n",
      " 18  Weather.Condition       84397 non-null  object \n",
      " 19  Report.Status           82505 non-null  object \n",
      "dtypes: float64(5), object(15)\n",
      "memory usage: 14.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping rows with missing values only for object columns\n",
    "df.dropna(subset=df.select_dtypes(include=['object']).columns, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75912, 20)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Investigation.Type         0.000000\n",
       "Accident.Number            0.000000\n",
       "Event.Date                 0.000000\n",
       "Location                   0.000000\n",
       "Country                    0.000000\n",
       "Injury.Severity            0.000000\n",
       "Aircraft.damage            0.000000\n",
       "Registration.Number        0.000000\n",
       "Make                       0.000000\n",
       "Model                      0.000000\n",
       "Amateur.Built              0.000000\n",
       "Number.of.Engines          1.637422\n",
       "Engine.Type                0.000000\n",
       "Purpose.of.flight          0.000000\n",
       "Total.Fatal.Injuries      12.380124\n",
       "Total.Serious.Injuries    13.489303\n",
       "Total.Minor.Injuries      12.522394\n",
       "Total.Uninjured            6.122879\n",
       "Weather.Condition          0.000000\n",
       "Report.Status              0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().mean()*100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous Data\n",
    "* For continuous data with missing values I am going to impute with the median since it represents a more typical measure of central tendency and is least affected by outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 75912 entries, 20001218X45444 to 20221109106272\n",
      "Data columns (total 20 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Investigation.Type      75912 non-null  object \n",
      " 1   Accident.Number         75912 non-null  object \n",
      " 2   Event.Date              75912 non-null  object \n",
      " 3   Location                75912 non-null  object \n",
      " 4   Country                 75912 non-null  object \n",
      " 5   Injury.Severity         75912 non-null  object \n",
      " 6   Aircraft.damage         75912 non-null  object \n",
      " 7   Registration.Number     75912 non-null  object \n",
      " 8   Make                    75912 non-null  object \n",
      " 9   Model                   75912 non-null  object \n",
      " 10  Amateur.Built           75912 non-null  object \n",
      " 11  Number.of.Engines       74669 non-null  float64\n",
      " 12  Engine.Type             75912 non-null  object \n",
      " 13  Purpose.of.flight       75912 non-null  object \n",
      " 14  Total.Fatal.Injuries    66514 non-null  float64\n",
      " 15  Total.Serious.Injuries  65672 non-null  float64\n",
      " 16  Total.Minor.Injuries    66406 non-null  float64\n",
      " 17  Total.Uninjured         71264 non-null  float64\n",
      " 18  Weather.Condition       75912 non-null  object \n",
      " 19  Report.Status           75912 non-null  object \n",
      "dtypes: float64(5), object(15)\n",
      "memory usage: 12.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Number.of.Engines</th>\n",
       "      <th>Total.Fatal.Injuries</th>\n",
       "      <th>Total.Serious.Injuries</th>\n",
       "      <th>Total.Minor.Injuries</th>\n",
       "      <th>Total.Uninjured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>74669.000000</td>\n",
       "      <td>66514.000000</td>\n",
       "      <td>65672.000000</td>\n",
       "      <td>66406.000000</td>\n",
       "      <td>71264.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.116662</td>\n",
       "      <td>0.443185</td>\n",
       "      <td>0.237773</td>\n",
       "      <td>0.325618</td>\n",
       "      <td>2.340537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.379483</td>\n",
       "      <td>3.040532</td>\n",
       "      <td>0.827569</td>\n",
       "      <td>1.452530</td>\n",
       "      <td>15.226908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>270.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>171.000000</td>\n",
       "      <td>699.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number.of.Engines  Total.Fatal.Injuries  Total.Serious.Injuries  \\\n",
       "count       74669.000000          66514.000000            65672.000000   \n",
       "mean            1.116662              0.443185                0.237773   \n",
       "std             0.379483              3.040532                0.827569   \n",
       "min             0.000000              0.000000                0.000000   \n",
       "25%             1.000000              0.000000                0.000000   \n",
       "50%             1.000000              0.000000                0.000000   \n",
       "75%             1.000000              0.000000                0.000000   \n",
       "max             8.000000            270.000000               81.000000   \n",
       "\n",
       "       Total.Minor.Injuries  Total.Uninjured  \n",
       "count          66406.000000     71264.000000  \n",
       "mean               0.325618         2.340537  \n",
       "std                1.452530        15.226908  \n",
       "min                0.000000         0.000000  \n",
       "25%                0.000000         0.000000  \n",
       "50%                0.000000         1.000000  \n",
       "75%                0.000000         2.000000  \n",
       "max              171.000000       699.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the descriptive Statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of columns to replace missing values with their median\n",
    "continuous_columns = ['Number.of.Engines', 'Total.Fatal.Injuries', 'Total.Serious.Injuries', 'Total.Minor.Injuries', 'Total.Uninjured']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Replace missing values with the median for the specified columns\n",
    "df[continuous_columns] = df[continuous_columns].fillna(df[continuous_columns].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Investigation.Type        0\n",
       "Accident.Number           0\n",
       "Event.Date                0\n",
       "Location                  0\n",
       "Country                   0\n",
       "Injury.Severity           0\n",
       "Aircraft.damage           0\n",
       "Registration.Number       0\n",
       "Make                      0\n",
       "Model                     0\n",
       "Amateur.Built             0\n",
       "Number.of.Engines         0\n",
       "Engine.Type               0\n",
       "Purpose.of.flight         0\n",
       "Total.Fatal.Injuries      0\n",
       "Total.Serious.Injuries    0\n",
       "Total.Minor.Injuries      0\n",
       "Total.Uninjured           0\n",
       "Weather.Condition         0\n",
       "Report.Status             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* My data is now clean and ready for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75912, 20)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of my clean dataset\n",
    "cleaned_df = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the cleaned DataFrame to a CSV file\n",
    "#cleaned_df.to_csv('cleaned_Aviation_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = cleaned_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Chart\n",
    "* Checking for the Distribution of the Airplane Investigation types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 5))\n",
    "plt.pie(df['Investigation.Type'].value_counts(), labels = ['Accident', 'Incident'], colors = ['red', 'blue'], autopct = '%1.2f%%')\n",
    "plt.title('Airplanes Investigation types')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the pie chart it is evident that accidents were the most common type of investigation at 98.40%. Thus, accidents are the most common problem facing the aviation industry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts\n",
    "* Here, I'm interested in knowing the Purpose of the flights that led to the accidents.\n",
    "* Also, I am investigating which Makes and Models are prone to accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aviation Accidents based on the Purpose of flights\n",
    "purpose_of_flight_10 = df['Purpose.of.flight'].value_counts().nlargest(10).index\n",
    "purpose_of_flight = df[df['Purpose.of.flight'].isin(purpose_of_flight_10)]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=purpose_of_flight, x='Purpose.of.flight', order=purpose_of_flight_10, color = 'red')\n",
    "plt.title('Top 10 Purpose of flight with the highest Number of Accidents')\n",
    "plt.ylabel('Number of Accidents')\n",
    "plt.xticks(rotation=45, ha='right') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the above graph it is evident that Personal, Instructional, and Unknown are the leading causes of accidents.\n",
    "* Thus, due to this, the company will need to restrict offering airplanes for this reasons and go for low use of flights with minimal accidents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Stinson', 'Piper', 'Cessna', ..., 'Hibbard norman e',\n",
       "       'Waszak arthur b', 'Stephen j hoffman'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Ensuring consistent unique values of Make \n",
    "df['Make_lowercase'] = df['Make'].str.lower()\n",
    "df['Make'] = df['Make_lowercase'].str.capitalize()\n",
    "\n",
    "df.drop(columns=['Make_lowercase'], inplace=True)\n",
    "df['Make'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Accidents based on the make of the Airplanes\n",
    "top_15_makes = df['Make'].value_counts().nlargest(15).index\n",
    "\n",
    "top_15_df = df[df['Make'].isin(top_15_makes)]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=top_15_df, x='Make', order=top_15_makes, color = 'blue')\n",
    "plt.title('Top 15 Airplane Makes and the Number of Accidents')\n",
    "plt.ylabel('Number of Accidents')\n",
    "plt.xticks(rotation=45, ha='right') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the makes above, Cessna, Piper, and Beech Makes of airplanes have the highest number of accidents.\n",
    "* Thus, the company will need to cross-check against the makes to avoid the airplanes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the top 10 airplane Model by accident count\n",
    "top_15_models = df['Model'].value_counts().nlargest(15).index\n",
    "\n",
    "top_15_df = df[df['Model'].isin(top_15_models)]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.countplot(data=top_15_df, x='Model', order=top_15_models, color = 'blue')\n",
    "plt.title('Top 15 Airplane Model and the Number of Accidents')\n",
    "plt.ylabel('Number of Accidents')\n",
    "plt.xticks(rotation=45, ha='right') \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* These are the aircraft models with the highest accident rates. The company should avoid purchasing these models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Weather condition\n",
    "Weather_data=df.groupby(['Weather.Condition','Model']).count()['Number.of.Engines'].sort_values(ascending=False)\n",
    "Weather_data['VMC'].head(10)\n",
    "Weather_data['IMC'].head(10)\n",
    "# create a function\n",
    "def Bar_plot(dataset,column):\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.bar(x=dataset.index,height=dataset.values)\n",
    "    plt.title(f'Bar plot of {column}')\n",
    "    plt.ylabel('Number of Accidents')\n",
    "    plt.xlabel(f'{column}')\n",
    "    plt.xticks(rotation =45);\n",
    "# Weather_data\n",
    "Bar_plot(Weather_data['IMC'].head(10),'Weather.Condition(IMC)')\n",
    "Bar_plot(Weather_data['VMC'].head(10),'Weather.Condition(VMC)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* VMC: Clear conditions, fly by sight.\n",
    "* IMC: Poor visibility, fly-by instruments.\n",
    "* From the Weather Condition plots above, it is evident that Pilots cause accidents under clear conditions when the visibility is very clear."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert Event.Date to datetime\n",
    "df['Event.Date'] = pd.to_datetime(df['Event.Date'])\n",
    "\n",
    "# Extract the year and filter the data from 1980 onwards\n",
    "df['Year'] = df['Event.Date'].dt.year\n",
    "df_filtered = df[df['Year'] >= 1982]\n",
    "\n",
    "# Check for missing data and remove\n",
    "df_filtered =df_filtered.dropna(subset=['Year'])\n",
    "\n",
    "# Get the value counts of the accidents\n",
    "Accident_year = df_filtered['Year'].value_counts().sort_index()\n",
    "Accident_year\n",
    "#Plot the year data\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(Accident_year.index,Accident_year.values, color = 'red')\n",
    "plt.title('Number of Aviation Accidents From 1980')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of Accidents')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The time series plot above shows that the number of accidents has been declining over the years.\n",
    "* Thus, air travel is becoming a safe means of transport over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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44ott23r27GlERkYa2dnZtm1FRUVGx44djWbNmtm+N+t3dO2119rt84cffjAkGS+88IJt2+233240b97c9nz9+vVlXiMHDx40AgICjMcee6zcz2w9t4888ojd9k8++cSQZHz88ceGYRhGSkqK4e3tbTz44IN247Kzs43o6Ghj5MiRdjVKMj788MNyj322ZcuWGZIMScasWbNs2yt7bZ6tqKjIKCwsNAYNGmT33VrPc8uWLY2CgoJK1da8eXNbbWc+nnrqKcMwzv2zW9nr31pT586d7T7P1KlTDUnG8OHD7d4/btw4Q5KRmZlZqfoNwzA2bdp0zr97Fy9eXOp7NwzD+OCDDwxJRps2bey2//HHH0avXr3svosbbrjByMvLK7eGrKwso3379kZsbKzddQEA8AzMmAIA1Hq+vr564YUX9NNPP5VaZlMT1uUkVu3bt5fJZLKbCePt7a1WrVqVeWfAUaNG2fV/at68uXr37m1burN3717t3r1bt9xyiyTZzYi48sorlZqaqj179tjt87rrrqtU7StWrFCHDh108cUX220fM2aMDMNw+Oyy8kRHR5eq44ILLijzOyvL8OHDS703Ly+vUkuKKqthw4YaOHCg3balS5eqY8eO6tKli925GTJkiN1yQOv5tJ5Hq1GjRlWphscff1w+Pj7y9/dXt27dlJKSonfeeUdXXnml1q1bp/T0dN1+++2lZs4kJCRo06ZNysnJUU5OjjZu3Kjrr79eDRo0sO3by8tLo0eP1qFDh0r9TJ1dd+/evdW8efNyl5gtXbpUJpNJt956q1090dHR6ty5c6XvzHj2sUeOHClvb2/bsb/++msVFRXptttuszuOv7+/+vXrV+ZxKnuNWF1xxRXq2bOnWrdurVtvvVVS1a/Nt99+WxdeeKH8/f3l7e0tHx8ffffdd0pKSip1vOHDh59zpmNZLrnkEm3atMnuMXbsWNvrZf3sVvX6v/LKK+2W31lnH1111VV246zbU1JSKl1/ea644gq1atVKjz/+uJYvX66TJ08qMTFRTz75pLy8vOxqysjI0NVXX62srCx98sknWr16taZPn661a9dq+PDhKioqKvMYeXl5GjFihA4cOKDPPvvM7roAAHgGmp8DAOqEm266Sa+++qqeeuopjRgxwiH7DA8Pt3vu6+urwMBAu14m1u1ZWVml3h8dHV3mtp9//lmSbMudJkyYoAkTJpRZw/Hjx+2eV3aZ3YkTJxQfH19qe0xMjO316vDy8lJxcXGZr1l/MTz7l+6IiIhSY/38/Mq8HXxZzn6/dZlPZd9fGWV9r0ePHtXevXvPGSJYz82JEyfk7e1dqs6yzn95Hn74Yd16660ym80KCwtTixYtbMGm9Wfl+uuvP+f709PTZTKZZBhGmZ/nXOf+XD+n5f2MHD16VIZhnPPuZuedd94531vesa3fo/XY1s/dvXv3Mt9/di+jwMDAat2d0c/PT76+vrbnVbk2//3vf+vRRx/V3/72Nz3//PNq1KiRvLy89M9//rPMYKqqS2VDQ0N10UUXnfP1svZX1eu/rL/rytuel5dXceGV4Ovrq6+++kqjR4/W4MGDJUlBQUF66aWX9Pzzz6tp06a2sa+88oq2bdumAwcO2D5z37591a5dOw0cOFCffPKJbr/9drv95+fn69prr9XatWu1dOlS9ejRwyF1AwAci2AKAFAnmEwmvfLKK7r88sv17rvvlnrdGiad3Sy8ugFNZRw5cqTMbdYAo1GjRpKkiRMnnjNMa9u2rd3zyt6BLyIiQqmpqaW2//HHH3bHrqqoqCgdPny4zNes2z3hVuznOt9nB31WZX2vjRo1UkBAgK3nUFmvSyXfdVFRkU6cOGEXTpV1/svTrFmzcwYQ1mP95z//Oecd2qKiolRYWCiz2Vylc3+un9Py7krXqFEjmUwmrVmzpsw7ypV3l7mzj3Nm+HD292itdf78+WrevHmF+6vpHSqtqnJtfvzxx+rfv7/++9//2r2enZ3t1BrL25+zrn9naNWqldavX6/Dhw8rPT1dLVu2VGZmph5++GFdeumltnHbtm1T06ZNSwVx1tBy586ddtvz8/N1zTXXaOXKlfr888/LbYQPAHAvgikAQJ1x2WWX6fLLL9dzzz2n2NhYu9eioqLk7+9v10xXKrkFubPMmTNH48ePt/3ieODAAa1bt0633XabpJJfbFu3bq2ff/5ZL730kkOPPWjQIE2ePFlbtmzRhRdeaNv+v//9TyaTSQMGDKjWfi+77DItXLhQaWlptqbcUkmD5s8++0zx8fHlBhquYp0tsn37drtwb8mSJZXex9ChQ/XSSy8pIiJCLVq0OOe4AQMGaMqUKfrkk0/00EMP2bbPnj276oWfQ58+fRQWFqZffvmlzObVVr6+vurRo4cWLlyoV199VQEBAZIki8Wijz/+WM2aNVObNm3s3vPJJ5/YLX9bt26dDhw4UG6T66FDh+rll1/W4cOHNXLkyGp/rk8++UTdunWzPZ83b56KiorUv39/SdKQIUPk7e2t33//vcpL9GqiKtemyWQqFcRt375d69evL/X3kKs46/p3pqZNm9pCyqefflpBQUG66667bK/HxMTou+++0+HDh+3CzPXr10sqCXatrDOlVqxYoYULF2rIkCEu+hQAgOogmAIA1CmvvPKKunXrpmPHjtndfcnaD+fDDz9Uy5Yt1blzZ/34448ODQ/OduzYMV177bW65557lJmZqUmTJsnf318TJ060jXnnnXd0xRVXaMiQIRozZoyaNm2q9PR0JSUlacuWLfrss8+qdexHHnlE//vf/3TVVVfpueeeU/PmzfXll19q+vTpuv/++0uFE5X1r3/9S1988YV69OihJ554Qq1bt9aRI0f03nvvadOmTQ7t8VUT3bt3V9u2bTVhwgQVFRWpYcOGWrRokdauXVvpfYwbN04LFizQpZdeqkceeUQXXHCBLBaLUlJS9M033+jRRx9Vjx49NHjwYF166aV67LHHlJOTo4suukg//PCDZs2a5bDP06BBA/3nP//R7bffrvT0dF1//fWKjIxUWlqafv75Z6Wlpdlm7EyePFmXX365BgwYoAkTJsjX11fTp0/Xzp07NWfOnFIzbH766SfdfffduuGGG3Tw4EE99dRTatq0qV0fo7P16dNH9957r+644w799NNPuvTSSxUUFKTU1FStXbtWnTp10v3331/h51q4cKG8vb11+eWXa9euXfrnP/+pzp0728Ku+Ph4Pffcc3rqqae0b98+JSQkqGHDhjp69Kh+/PFHBQUFVXjnveqq7LU5dOhQPf/885o0aZL69eunPXv26LnnnlOLFi3O2ffI2Zx1/VfF/PnzJcl2F9KffvrJ1t/pzCWpU6ZMUXR0tOLi4nT06FHNmzdPixcv1qxZs+wCqL///e/65JNPdPnll+uJJ55QbGysdu7cqRdeeEFRUVF2/cquv/56ffXVV3rqqacUERGhDRs22F4LCQlRhw4dnPrZAQBVQzAFAKhTunbtqptvvrnMwOm1116TVPKL0KlTpzRw4EAtXbq0zF4sjvDSSy9p06ZNuuOOO5SVlaWLL75Yc+fOVcuWLW1jBgwYoB9//FEvvviixo0bp4yMDEVERKhDhw41monSuHFjrVu3ThMnTtTEiROVlZWl8847T1OmTNH48eOrvd+WLVvqxx9/1LPPPqtnnnlGaWlpatCggS6++GItX768VBNmVzozcPHy8tIXX3yhBx54QH/729/k5+enm266SW+++Waphs7nEhQUpDVr1ujll1/Wu+++q+TkZAUEBCguLk6XXXaZ7efGbDZryZIlGj9+vKZMmaKCggL16dNHy5YtU7t27Rz2+W699VbFxcVpypQpuu+++5Sdna3IyEh16dJFY8aMsY3r16+fVqxYoUmTJmnMmDGyWCzq3LmzlixZUqqhvyR98MEHmjVrlm666Sbl5+drwIABmjZtWqn+Qmd755131LNnT73zzjuaPn26LBaLYmJi1KdPn1JNt89l4cKFeuaZZ/Tf//5XJpNJw4YN09SpU+36PU2cOFEdOnTQtGnTNGfOHOXn5ys6Olrdu3fX3/72t8p9edVQ2WvzqaeeUm5urj744ANNmTJFHTp00Ntvv61FixZVugm8oznr+q+KG264we75W2+9pbfeektSyQxLq7y8PD333HM6dOiQAgIC1LNnT61atUp9+/a1e3+3bt20YcMGPf/883rqqaeUlpampk2bavjw4frXv/5ltzxx6dKlkqQXX3xRL774ot1+ztU0HwDgPibjzP9lAAAAqIXCw8N155136tVXX3V3KbXGjBkzdMcdd2jTpk3lNtd2hmeeeUbPPvus0tLSPKrfEQAAcD1mTAEAgFpr+/btWrZsmTIyMtSrVy93lwMAAIAqIpgCAAC11sMPP6zdu3drwoQJ57x7GgAAADwXS/kAAAAAAADgFmZ3FwAAAAAAAID6iWAKAAAAAAAAbkEwBQAAAAAAALeg+bkDWSwW/fHHHwoODpbJZHJ3OQAAAAAAAC5nGIays7MVExMjs7n8OVEEUw70xx9/KDY21t1lAAAAAAAAuN3BgwfVrFmzcscQTDlQcHCwpJIvPiQkxM3VAAAAAAAAuF5WVpZiY2NtOUl5CKYcyLp8LyQkhGAKAAAAAADUa5Vpc0TzcwAAAAAAALiFW4Op1atXa9iwYYqJiZHJZNLixYttrxUWFurxxx9Xp06dFBQUpJiYGN122236448/7PaRn5+vBx98UI0aNVJQUJCGDx+uQ4cO2Y3JyMjQ6NGjFRoaqtDQUI0ePVonT560G5OSkqJhw4YpKChIjRo10kMPPaSCggJnfXQAAAAAAIB6z63BVE5Ojjp37qw333yz1Gu5ubnasmWL/vnPf2rLli1auHChfv31Vw0fPtxu3Lhx47Ro0SLNnTtXa9eu1alTpzR06FAVFxfbxowaNUrbtm1TYmKiEhMTtW3bNo0ePdr2enFxsa666irl5ORo7dq1mjt3rhYsWKBHH33UeR8eAAAAAACgnjMZhmG4uwipZN3hokWLdM0115xzzKZNm3TxxRfrwIEDiouLU2Zmpho3bqxZs2bpxhtvlPTXnfGWLVumIUOGKCkpSR06dNCGDRvUo0cPSdKGDRvUq1cv7d69W23bttVXX32loUOH6uDBg4qJiZEkzZ07V2PGjNGxY8cq3S8qKytLoaGhyszMpMcUAAAAAABlKC4uVmFhobvLQA34+PjIy8vrnK9XJR+pVc3PMzMzZTKZFBYWJknavHmzCgsLNXjwYNuYmJgYdezYUevWrdOQIUO0fv16hYaG2kIpSerZs6dCQ0O1bt06tW3bVuvXr1fHjh1toZQkDRkyRPn5+dq8ebMGDBhQZj35+fnKz8+3Pc/KynLwJwYAAAAAoG4wDENHjhwp1VoHtVNYWJiio6Mr1eC8PLUmmMrLy9MTTzyhUaNG2dK2I0eOyNfXVw0bNrQbGxUVpSNHjtjGREZGltpfZGSk3ZioqCi71xs2bChfX1/bmLJMnjxZzz77bI0+FwAAAAAA9YE1lIqMjFRgYGCNAw24h2EYys3N1bFjxyRJTZo0qdH+akUwVVhYqJtuukkWi0XTp0+vcLxhGHY/4GX9sFdnzNkmTpyo8ePH255nZWUpNja2wvoAAAAAAKhPiouLbaFURESEu8tBDQUEBEiSjh07psjIyHKX9VXErc3PK6OwsFAjR45UcnKyli9fbrc2MTo6WgUFBcrIyLB7z7Fjx2wzoKKjo3X06NFS+01LS7Mbc/bMqIyMDBUWFpaaSXUmPz8/hYSE2D0AAAAAAIA9a0+pwMBAN1cCR7Gey5r2C/PoYMoaSv3222/69ttvS6Wq3bp1k4+Pj5YvX27blpqaqp07d6p3796SpF69eikzM1M//vijbczGjRuVmZlpN2bnzp1KTU21jfnmm2/k5+enbt26OfMjAgAAAABQb7B8r+5w1Ll061K+U6dOae/evbbnycnJ2rZtm8LDwxUTE6Prr79eW7Zs0dKlS1VcXGyb1RQeHi5fX1+Fhobqrrvu0qOPPqqIiAiFh4drwoQJ6tSpky677DJJUvv27ZWQkKB77rlH77zzjiTp3nvv1dChQ9W2bVtJ0uDBg9WhQweNHj1a//d//6f09HRNmDBB99xzD7OgAAAAAAAAnMStM6Z++uknde3aVV27dpUkjR8/Xl27dtW//vUvHTp0SEuWLNGhQ4fUpUsXNWnSxPZYt26dbR+vv/66rrnmGo0cOVJ9+vRRYGCgvvjiC7v1jZ988ok6deqkwYMHa/Dgwbrgggs0a9Ys2+teXl768ssv5e/vrz59+mjkyJG65ppr9Oqrr7ruywAAAAAAAHVW//79NW7cuCq9x2QyafHixU6ppyIzZsxQWFiY049jMgzDcPpR6omsrCyFhoYqMzOTmVYAAAAAAPwpLy9PycnJatGihfz9/au9H4vFopSUFGVnZys4OFhxcXEym50756Z///7q0qWLpk6dard98eLFuvbaa1XZWCU9PV0+Pj4KDg6u9LGPHDmihg0bys/PryolO8SMGTM0btw4nTx5sszXyzunVclHasVd+QAAAAAAQP2WlJSkxMREZWVl2baFhIQoISFB7du3d2NllRMeHl7l90RHR9fomMXFxTKZTE4P72rCcysDAAAAAABQSSg1b948u1BKKpmZM2/ePCUlJbmpshLPPPOMunTpolmzZik+Pl6hoaG66aablJ2dbRtz9lK++Ph4vfTSS7rzzjtts7/effddu/2euZRv1apVMplMdjOYtm3bJpPJpP3790v6a/nd0qVL1aFDB/n5+enAgQMqKCjQY489pqZNmyooKEg9evTQqlWr7I41Y8YMxcXFKTAwUNdee61OnDjhyK/onAimAAAAAACASxmGoYKCgko98vLy9NVXX5W7v6+++kp5eXmV2p+zOhr9/vvvWrx4sZYuXaqlS5fq+++/18svv1zue1577TVddNFF2rp1q8aOHav7779fu3fvrlEdubm5mjx5st5//33t2rVLkZGRuuOOO/TDDz9o7ty52r59u2644QYlJCTot99+kyRt3LhRd955p8aOHatt27ZpwIABeuGFF2pUR2WxlA8AAAAAALhUYWGhJk+e7LD9ZWdn65VXXqnU2IkTJ8rX19dhx7ayWCyaMWOGrYfU6NGj9d133+nFF18853uuvPJKjR07VpL0+OOP6/XXX9eqVavUrl27atdRWFio6dOnq3PnzpJKArM5c+bo0KFDiomJkSRNmDBBiYmJ+uijj/TSSy9p2rRpGjJkiJ544glJUps2bbRu3TolJiZWu47KIpiCS7mjUR0AAAAAAM4WHx9v19i8SZMmOnbsWLnvueCCC2x/NplMio6OrvA9FfH19bXb75YtW2QYhtq0aWM3Lj8/XxEREZJKlkpee+21dq/36tWLYAp1S21vVAcAAAAAcAwfHx9NnDixUmMPHDig2bNnVzhu1KhRat68eaWOXRUhISHKzMwstf3kyZN2d5w7e78mk0kWi6VKtZT3HuukjjOXIhYWFpYaFxAQIJPJZHtusVjk5eWlzZs3y8vLy25sgwYNSu3T1Qim4BLWRnVnszaqGzlyJOEUAAAAANQTJpOp0svpWrZsqZCQkFKNz88UEhKili1bOmVFTrt27crscbVp0ya1bdvW4cc7l8aNG0uSUlNT1bBhQ0klzc8r0rVrVxUXF+vYsWPq27dvmWM6dOigDRs22G07+7mzsIYKTmexWCqc/peYmFhhkgwAAAAAqH/MZrMSEhLKHZOQkOC0NjFjx47V77//rr///e/6+eef9euvv+qtt97SBx98oH/84x9OOWZZWrVqpdjYWD3zzDP69ddf9eWXX+q1116r8H1t2rTRLbfcottuu00LFy5UcnKyNm3apFdeeUXLli2TJD300ENKTEzUlClT9Ouvv+rNN990yTI+iWAKLpCSklJusi2VzJxKSUlxUUUAAAAAgNqkffv2GjlypN3SOalkppSzV+DEx8drzZo1+v333zV48GB1795dM2bM0IwZM3TDDTc47bhn8/Hx0Zw5c7R792517txZr7zySqXvnPfRRx/ptttu06OPPqq2bdtq+PDh2rhxo2JjYyVJPXv21Pvvv6///Oc/6tKli7755hs9/fTTzvw4NibDnQsJ65isrCyFhoYqMzOz1MVSn+3YsUMLFy6scNyIESPUqVMnF1QEAAAAAHClvLw8JScnq0WLFvL396/2furTDbXy8/Pl7++v5cuX67LLLnN3OaWUd06rko/QYwpOd+ZdCRwxDgAAAABQP5nNZsXHx7u7DKfLysrSwoULZTab1a5dO3eX41QEU3C6uLi4SjWqi4uLc2FVAAAAAAB4pkmTJmn27Nl65ZVX1KxZM3eX41R1c74bPIq7G9UBAAAAAFCbvP766zp69KgmTJjg7lKcjiQALmFtVBcUFGS3PSgoyOmN6gAAAAAAgGcimILLtG/fXldccYXdtssuu4xQCgAAAADqCe6/Vnc46lwSTMGlzu4zdfz4cTdVAgAAAABwFR8fH0lSbm6umyuBo1jPpfXcVhfNz+FSmZmZkiRfX18VFBQQTAEAAABAPeDl5aWwsDAdO3ZMkhQYGCiTyeTmqlAdhmEoNzdXx44dU1hYmLy8vGq0P4IpuJQ1mDrvvPO0e/dupaWlubkiAAAAAIArREdHS5ItnELtFhYWZjunNUEwBZeyBlMtW7bU7t27lZGRoaKiInl786MIAAAAAHWZyWRSkyZNFBkZqcLCQneXgxrw8fGp8UwpK9IAuJQ1mIqNjbUt50tPT1dkZKSbKwMAAAAAuIKXl5fDQg3UfjQ/h8sUFBTYmqOFhoaqcePGksRyPgAAAAAA6imCKbiM9Y58fn5+8vf3twVTNEAHAAAAAKB+IpiCy5w8eVJSyWwpSWrUqJEkZkwBAAAAAFBfEUzBZaz9pcLCwiT9FUwxYwoAAAAAgPqJYAouYw2mQkJCJMluKZ/FYnFbXQAAAAAAwD0IpuAyZ8+YCgsLk5eXl4qLi23L/AAAAAAAQP1BMAWXsQZT1h5TZrOZ5XwAAAAAANRjBFNwmbObn0s0QAcAAAAAoD4jmIJLWCwWZWVlSfprKZ9EA3QAAAAAAOozgim4RHZ2tgzDkNlsVoMGDWzbrQ3QmTEFAAAAAED9QzAFlzizv5TJZLJtP3PGlGEYbqkNAAAAAAC4B8EUXOLsxudWERERMplMys/P16lTp9xRGgAAAAAAcBOCKbhEWY3PJcnb21sNGzaUxHI+AAAAAADqG4IpuMS5ZkxJ9JkCAAAAAKC+IpiCS5QXTHFnPgAAAAAA6ieCKbiENZgKCwsr9RrBFAAAAAAA9RPBFJzOMAyW8gEAAAAAgFIIpuB0eXl5KigokCSFhISUet06YyonJ0enT592aW0AAAAAAMB9CKbgdNY78gUFBcnHx6fU635+frbAiuV8AAAAAADUHwRTcLrylvFZWWdNsZwPAAAAAID6g2AKTlde43MrgikAAAAAAOofgik4nTWYKqu/lJW1ATpL+QAAAAAAqD8IpuB0VZkxRTAFAAAAAED9QTAFp7M2Py+vx5R1xtTJkydVWFjoirIAAAAAAICbEUzB6SrT/DwoKEgBAQGSmDUFAAAAAEB9QTAFpyoqKlJOTo6k8pfySX/NmqIBOgAAAAAA9QPBFJzKOlvKx8dH/v7+5Y6lzxQAAAAAAPULwRSc6szG5yaTqdyxBFMAAAAAANQvBFNwqso0PrdiKR8AAAAAAPULwRScqjKNz62swVR6erqKi4udWhcAAAAAAHA/gik4VVWCqZCQEPn4+MhisSg9Pd3ZpQEAAAAAADcjmIJTVSWYMplM9JkCAAAAAKAeIZiCU53Z/Lwy6DMFAAAAAED9QTAFpzEMo0ozpiTuzAcAAAAAQH1CMAWnOXXqlCwWi0wmk4KDgyv1HuuMKYIpAAAAAADqPoIpOM3JkycllTQ1N5sr96N25owpwzCcVRoAAAAAAPAABFNwmqou45Ok8PBwmc1mFRYW2t4PAAAAAADqJoIpOE1VG59LktlsVkREhCQaoAMAAAAAUNcRTMFpzlzKVxU0QAcAAAAAoH4gmILTZGVlSarajCnprwbozJgCAAAAAKBuI5iC01hnTFWlx5TEjCkAAAAAAOoLgik4TXWan0v2M6a4Mx8AAAAAAHUXwRScIi8vT/n5+ZKqHkxZm5/n5eUpJyfH4bUBAAAAAADPQDAFp7DOlgoICJCvr2+V3uvj42PrS8VyPgAAAAAA6i6CKTiFNZiqauNzKxqgAwAAAABQ9xFMwSmq2/jcigboAAAAAADUfQRTcIrqNj63ss6YIpgCAAAAAKDuIpiCU9Q0mLLOmGIpHwAAAAAAdRfBFJzCUTOmsrOzlZeX57C6AAAAAACA5yCYglPUtPm5v7+/GjRoIInlfAAAAAAA1FUEU3C44uJiZWdnS6r+jCmJPlMAAAAAANR1BFNwuKysLEmSt7e3AgMDq70f+kwBAAAAAFC3EUzB4U6ePCmpZLaUyWSq9n6swRQzpgAAAAAAqJsIpuBwNW18bmVdyseMKQAAAAAA6ia3BlOrV6/WsGHDFBMTI5PJpMWLF9u9bhiGnnnmGcXExCggIED9+/fXrl277Mbk5+frwQcfVKNGjRQUFKThw4fr0KFDdmMyMjI0evRohYaGKjQ0VKNHj7bN6rFKSUnRsGHDFBQUpEaNGumhhx5SQUGBMz52neeoYMo6Y+rkyZMqKiqqcV0AAAAAAMCzuDWYysnJUefOnfXmm2+W+fqUKVP073//W2+++aY2bdqk6OhoXX755bbG2pI0btw4LVq0SHPnztXatWt16tQpDR06VMXFxbYxo0aN0rZt25SYmKjExERt27ZNo0ePtr1eXFysq666Sjk5OVq7dq3mzp2rBQsW6NFHH3Xeh6/DzlzKVxMNGjSQn5+fDMPQiRMnHFAZAAAAAADwJN7uPPgVV1yhK664oszXDMPQ1KlT9dRTT2nEiBGSpJkzZyoqKkqzZ8/Wfffdp8zMTH3wwQeaNWuWLrvsMknSxx9/rNjYWH377bcaMmSIkpKSlJiYqA0bNqhHjx6SpPfee0+9evXSnj171LZtW33zzTf65ZdfdPDgQcXExEiSXnvtNY0ZM0YvvviiQkJCXPBt1B3W5udhYWE12o/JZFLjxo116NAhpaWlKSoqygHVAQAAAAAAT+GxPaaSk5N15MgRDR482LbNz89P/fr107p16yRJmzdvVmFhod2YmJgYdezY0TZm/fr1Cg0NtYVSktSzZ0+FhobajenYsaMtlJKkIUOGKD8/X5s3bz5njfn5+crKyrJ7wHEzpiQaoAMAAAAAUJd5bDB15MgRSSo1SyYqKsr22pEjR+Tr66uGDRuWOyYyMrLU/iMjI+3GnH2chg0bytfX1zamLJMnT7b1rQoNDVVsbGwVP2XdYxiGw3pMSTRABwAAAACgLvPYYMrKZDLZPTcMo9S2s509pqzx1RlztokTJyozM9P2OHjwYLl11Qc5OTm2/l6OWALJjCkAAAAAAOoujw2moqOjJanUjKVjx47ZZjdFR0eroKBAGRkZ5Y45evRoqf2f2bMoOjq61HEyMjJUWFhYbl8jPz8/hYSE2D3qO+tsqeDgYHl5edV4f9YZUydOnJDFYqnx/gAAAAAAgOfw2GCqRYsWio6O1vLly23bCgoK9P3336t3796SpG7dusnHx8duTGpqqnbu3Gkb06tXL2VmZurHH3+0jdm4caMyMzPtxuzcuVOpqam2Md988438/PzUrVs3p37OusYaTNW08blVaGiovL29VVxcXCqABAAAAAAAtZtb78p36tQp7d271/Y8OTlZ27ZtU3h4uOLi4jRu3Di99NJLat26tVq3bq2XXnpJgYGBGjVqlKSS0OKuu+7So48+qoiICIWHh2vChAnq1KmT7S597du3V0JCgu655x698847kqR7771XQ4cOVdu2bSVJgwcPVocOHTR69Gj93//9n9LT0zVhwgTdc889zIKqIkc2Ppcks9msRo0a6ciRIzp+/LgiIiIcsl8AAAAAAOB+bg2mfvrpJw0YMMD2fPz48ZKk22+/XTNmzNBjjz2m06dPa+zYscrIyFCPHj30zTffKDg42Pae119/Xd7e3ho5cqROnz6tQYMGacaMGXbLyD755BM99NBDtrv3DR8+XG+++abtdS8vL3355ZcaO3as+vTpo4CAAI0aNUqvvvqqs7+COseRjc+trMFUWlqaLUwEAAAAAAC1n8kwDMPdRdQVWVlZCg0NVWZmZr2daTV37lzt2bNHV155pbp37+6QfX7//fdatWqVOnfurGuuucYh+wQAAAAAAM5RlXzEY3tMoXZyxowpawP0tLQ0h+0TAAAAAAC4H8EUHMrRzc+lv4Kp48ePiwl+AAAAAADUHQRTcJiCggKdPn1akmNnTIWHh8tkMqmgoEDZ2dkO2y8AAAAAAHAvgik4jPWOfP7+/vLz83PYfr28vBQeHi6J5XwAAAAAANQlBFNwGGf0l7KizxQAAAAAAHUPwRQcxpnBVKNGjSSV9JkCAAAAAAB1A8EUHIZgCgAAAAAAVAXBFBzGGXfks2IpHwAAAAAAdQ/BFBzG2vzcmTOmcnNzlZub6/D9AwAAAAAA1yOYgsM4cymfr6+vbb8s5wMAAAAAoG4gmIJDFBcXKzs7W5Jzginpr1lTLOcDAAAAAKBuIJiCQ2RnZ8swDHl5ealBgwZOOQbBFAAAAAAAdQvBFBzCuowvJCREJpPJKcewNkBnKR8AAAAAAHUDwRQcwtr43Bl35LMimAIAAAAAoG4hmIJDOLPxuZV1KV9mZqYKCgqcdhwAAAAAAOAaBFNwCFcEU4GBgQoMDJTErCkAAAAAAOoCgik4hCuCKemv5Xw0QAcAAAAAoPYjmIJDuCqYsi7nY8YUAAAAAAC1H8EUaswwDJc0P5dogA4AAAAAQF1CMIUaO336tIqKiiRJISEhTj2WdcYUS/kAAAAAAKj9CKZQY9bZUg0aNJC3t7dTj2WdMZWenq7i4mKnHgsAAAAAADgXwRRqzFX9pSQpODhYvr6+MgxDJ06ccPrxAAAAAACA8xBMocZcGUyZTCYaoAMAAAAAUEcQTKHGrEv5XBFMSX8t56PPFAAAAAAAtRvBFGosKytLkvPvyGfFjCkAAAAAAOoGginUmLtmTBFMAQAAAABQuxFMocZc2WNKsp8xZbFYXHJMAAAAAADgeARTqJHCwkLl5uZKcl0w1bBhQ3l5eamoqMgWigEAAAAAgNqHYAo1Yg2GfH195e/v75Jjms1mRURESKIBOgAAAAAAtRnBFGrEGkyFhYXJZDK57Lj0mQIAAAAAoPYjmEKNuLrxuZW1zxQzpgAAAAAAqL0IplAjrm58bnVmA3QAAAAAAFA7EUyhRtwVTFmX8qWlpckwDJceGwAAAAAAOAbBFGrEXcFURESETCaT8vPzderUKZceGwAAAAAAOAbBFGrkzObnruTt7a2GDRtKYjkfAAAAAAC1FcEUqs1isSgrK0uS62dMSTRABwAAAACgtiOYQrWdOnVKFotFZrNZDRo0cPnxaYAOAAAAAEDtRjCFajt58qQkKSQkRGaz63+UrA3QCaYAAAAAAKidCKZQbe5qfG7FUj4AAAAAAGo3gilUm7san1tZg6lTp04pLy/PLTUAAAAAAIDqI5hCtZ25lM8d/P39FRwcLIlZUwAAAAAA1EYEU6g26x353DVjSqLPFAAAAAAAtRnBFKrNOmPKXT2mJPpMAQAAAABQmxFMoVoMw3B783Ppr2CKGVMAAAAAANQ+BFOolry8PBUUFEhybzBlXcrHjCkAAAAAAGofgilUi3W2VGBgoHx8fNxWhzWYOnnypAoLC91WBwAAAAAAqDqCKVSLNZhyZ+NzqSQYCwgIkCSdOHHCrbUAAAAAAICqIZhCtXhC43NJMplMNEAHAAAAAKCWIphCtXhC43MrGqADAAAAAFA7EUyhWjwpmKIBOgAAAAAAtZO3uwtA7eRJwRQzpgDURhaLRSkpKcrOzlZwcLDi4uJkNvPvRQAAAKhfCKZQLZ7S/Fz6a8bUiRMnZLFY+MUOgMdLSkpSYmKisrKybNtCQkKUkJCg9u3bu7EyAAAAwLX4DR5VVlRUpFOnTknyjBlToaGh8vHxkcViUXp6urvLAYByJSUlad68eXahlCRlZWVp3rx5SkpKclNlAAAAgOsRTKHKrLOlfHx8FBAQ4OZq7O/Mx3I+AJ7MYrEoMTGx3DGJiYmyWCwuqggAAABwL4IpVNmZ/aVMJpObqylhDaZogA7Ak6WkpJSaKXW2rKwspaSkuKgiAAAAwL0IplBlntT43IoZUwBqg+zsbIeOAwAAAGo7gilUmScGU9YG6MyYAuDJgoODHToOAAAAqO0IplBlnhxMHT9+XIZhuLkaAChbXFycQkJCyh3j7++vuLg4F1UEAAAAuBfBFKrs5MmTkqSwsDC31nGmhg0bymw2q7CwsML+LQDgLmazWZdffnm5Y/Ly8rRjxw4XVQQAAAC4F8EUqswTZ0x5eXkpPDxcEsv5AHg2a/+os28eERISotatW0uSPv/8cyUlJbm8NgAAAMDVvN1dAGoXwzBsM5I8KZiSSpbzHT9+XGlpaWrVqpW7ywGAUvLy8rRmzRpJ0lVXXaWIiAhlZ2crODhYcXFxMplMWrJkibZt26YFCxZo1KhROu+889xcNQAAAOA8BFOoklOnTqm4uFgmk6nCPimuxp35AHi6devW6fTp02rUqJG6du0qs7n0xOVhw4YpPz9fSUlJmjt3rkaPHq3Y2Fg3VAsAAAA4H0v5UCXWZXzBwcFl/kLlTmc2QAcAT3Pq1Clt2LBBkjRw4MBz/h1qNps1YsQInXfeeSosLNTs2bN19OhRV5YKAAAAuIxnJQvweJ7Y+NzKOmOKHlMAPNHq1atVWFiopk2bql27duWO9fb21o033qjY2Fjl5eVp1qxZOnHihIsqBQAAAFyHYApV4omNz62swdTp06eVk5Pj5moA4C8ZGRnavHmzJGnQoEGlGp+XxdfXV6NGjVJUVJRycnI0a9Ys29/BAAAAQF1BMIUq8eRgysfHxzaTy9nL+SwWi/bv368dO3Zo//79slgsTj0egNpt5cqVslgsatmypVq0aFHp9/n7++vWW29VeHi4MjMzNWvWLIJ3AAAA1Ck0P0eVeHIwJZXMmjp58qTS0tLUvHlzpxwjKSlJiYmJtrsTSiW3eU9ISFD79u2dckwAtdeRI0e0Y8cOSSWzpaqqQYMGuu222/Thhx/qxIkT+vjjj3X77bfL39/f0aUCAAAALseMKVSJpwdT1gbozuozlZSUpHnz5tmFUpKUlZWlefPmKSkpySnHBVB7fffdd5Kkjh07qkmTJtXaR2hoqEaPHq3AwEAdOXJEc+bMUWFhoSPLBAAAANyCYApV4snNz6W/+kw5YymfxWJRYmJiuWMSExNZ1gfAZv/+/dq7d6/MZrMGDBhQo301atRIo0ePlp+fn1JSUjRv3jwVFxc7qFIAAADAPQimUGl5eXnKz8+X5PkzppwRTKWkpJSaKXW2rKwspaSkOPzYAGofwzBss6UuvPBChYeH13if0dHRGjVqlHx8fLR3714tXLiQMBwAAAC1GsEUKs26jC8gIEC+vr5urqZs1hlTWVlZthDNUSp7q/bs7GyHHhdA7bRnzx4dOnRIPj4+uvTSSx2237i4ON14440ym8365ZdftHTpUhmG4bD9AwAAAK5EMIVK8/T+UlJJaBYUFCTJMbOmcnNztWXLFn388cdaunRppd4THBxc4+MCqN0sFotWrFghSerRo4fD/15o2bKlrrvuOplMJm3dulXffPMN4RQAAABqJe7Kh0qrDcGUVLKcLycnR2lpaWratGmV35+bm6vdu3frl19+0b59++x+2TObzeUumwkJCVFcXFy16gZQd2zfvl1paWny9/dXnz59nHKMDh06aNiwYVqyZIk2bNiggIAAh87MAgAAAFyBYAqVZm187unBVKNGjbR///4qzZjKzc1VUlKSfvnlFyUnJ9uFUdHR0erQoYM6dOigY8eOad68eefcT0JCgsxmJiIC9VlRUZFWrlwpSerbt6/8/f2ddqyuXbsqPz9fX3/9tVauXCk/Pz/16NHDaccDAAAAHM2jf4MuKirS008/rRYtWiggIEDnnXeennvuObsZK4Zh6JlnnlFMTIwCAgLUv39/7dq1y24/+fn5evDBB9WoUSMFBQVp+PDhOnTokN2YjIwMjR49WqGhobbbcluDGJSwNv6uDcGUJO3bt0/79+8/5wyn3Nxcbd68WbNmzdKrr76qpUuX2mZIRUdHa+DAgXrggQd03333qW/fvoqIiFD79u01cuRIhYSE2O0rICBAI0eOVPv27Z3++QB4tk2bNikrK0shISHq3r2704/Xs2dP9evXT1LJnUF//vlnpx8TAAAAcBSPnjH1yiuv6O2339bMmTN1/vnn66efftIdd9yh0NBQPfzww5KkKVOm6N///rdmzJihNm3a6IUXXtDll1+uPXv22Hp6jBs3Tl988YXmzp2riIgIPfrooxo6dKg2b94sLy8vSdKoUaN06NAhJSYmSpLuvfdejR49Wl988YV7PrwHsgZ1YWFhbq2jPElJSVq9erUkKTU1VTNnzlRISIgSEhLUvn37Ss+MioiIOOcx2rdvr7Zt2yolJUU//PCD9u7dq3bt2hFKAVBeXp7WrFkjSerXr598fHxcctx+/fopLy9PGzdu1Oeffy5fX1/+TgIAAECt4NHB1Pr163X11VfrqquukiTFx8drzpw5+umnnySVzJaaOnWqnnrqKY0YMUKSNHPmTEVFRWn27Nm67777lJmZqQ8++ECzZs3SZZddJkn6+OOPFRsbq2+//VZDhgxRUlKSEhMTtWHDBtsSiPfee0+9evXSnj171LZtWzd8es/j6T2mkpKSylxml5WVpXnz5ikqKkrHjh0rM4w6//zzq3Qrd7PZrPj4eBUUFGjv3r06cOCAQz4DgNpt/fr1On36tBo1aqQuXbq47Lgmk0lDhgxRfn6+tm3bpgULFmjUqFGKj49XSkqKsrOzFRwcrLi4OJYbAwAAwKN4dDB1ySWX6O2339avv/6qNm3a6Oeff9batWs1depUSVJycrKOHDmiwYMH297j5+enfv36ad26dbrvvvu0efNmFRYW2o2JiYlRx44dtW7dOg0ZMkTr169XaGioXV+Onj17KjQ0VOvWrTtnMJWfn6/8/Hzbc+tSt7qouLhY2dnZkjwzmLJYLLbZbudy9OhRSVKTJk1sM6OqEkaVpXnz5jKZTEpPT1dmZqZHfjcAXOPUqVNav369JGngwIEuD4BMJpOGDRum/Px8JSUlafbs2fLz81Nubq5tzJkzSAEAAABP4NH/bPr444/r5ptvVrt27eTj46OuXbtq3LhxuvnmmyVJR44ckSRFRUXZvS8qKsr22pEjR+Tr66uGDRuWOyYyMrLU8SMjI21jyjJ58mRbT6rQ0FDFxsZW/8N6OGvo5uXlpaCgIDdXU1pKSkqlgsFrr71W9957ry655JIah1JSSRAaExMjqSQoBVB/rV69WoWFhWratKnatWvnlhrMZrNGjBihqKgoFRcX24VS0l8zSJOSktxSHwAAAHA2jw6mPv30U3388ceaPXu2tmzZopkzZ+rVV1/VzJkz7caZTCa754ZhlNp2trPHlDW+ov1MnDhRmZmZtsfBgwcr87FqpTOX8VX03bqDdTZXRZxRe4sWLSRJ+/fvd/i+AdQOGRkZ2rx5syRp0KBBbv170mw2lwqkzpaYmHjOG0MAAAAAruTRwdQ//vEPPfHEE7rpppvUqVMnjR49Wo888ogmT54sqaQ/kKRSs5qOHTtmm0UVHR2tgoICZWRklDvGuszrTGlpaaVmY53Jz89PISEhdo+6ytMbn1sb3TtqXFXEx8dLUqlm6gDqj5UrV8pisahly5a2sNpdrD2lypOVlaWUlBQXVQQAAACcm0cHU7m5uaV6dHh5edn+lbdFixaKjo7W8uXLba8XFBTo+++/V+/evSVJ3bp1k4+Pj92Y1NRU7dy50zamV69eyszM1I8//mgbs3HjRmVmZtrG1HfWGVOeGr7FxcVVWFtISIji4uKccmyz2aysrKxSASiAuu/IkSPasWOHpJLZUu5W2RmklR0HAAAAOJNHNz8fNmyYXnzxRcXFxen888/X1q1b9e9//1t33nmnpJJlWePGjdNLL72k1q1bq3Xr1nrppZcUGBioUaNGSSpZenbXXXfp0UcfVUREhMLDwzVhwgR16tTJdpe+9u3bKyEhQffcc4/eeecdSdK9996roUOHcke+P1mDKU+dMWU2m5WQkFDmXfmsEhISnNKM2MfHR7GxsTpw4ICSk5Md0rsKQO2xYsUKSVLHjh3VpEkTN1fj3hmkAAAAQFV5dDD1n//8R//85z81duxYHTt2TDExMbrvvvv0r3/9yzbmscce0+nTpzV27FhlZGSoR48e+uabb+z+D/frr78ub29vjRw5UqdPn9agQYM0Y8YMeXl52cZ88skneuihh2x37xs+fLjefPNN131YD3dmjylP1b59e40cOVKJiYl2jdBdcReq+Ph4HThwQPv371e3bt2cdhwAnuXAgQP67bffZDabNWDAAHeXI+mvGaTl3RCiQYMGTplBCgAAAFSVyaApjsNkZWUpNDRUmZmZHrvkrbrefPNNnThxQrfddpvb+6dUxGKx2HqsBAcH25baOdOBAwc0Y8YMBQUF6dFHH/XIBvFlccd3BdQVhmHoww8/1KFDh3TRRRfpqquucndJNklJSeXOIA0KCtI999zj0f/YAAAAgNqrKvmIR8+YgmcwDMPjl/KdyWw22xqSu0rTpk3l7e2tnJwcHT9+XI0bN3bp8asjKSnJLbPLgLpiz549OnTokHx8fHTppZe6uxw755pB2qBBAxmGoZycHM2cOVO333474RQAAADcimAKFcrNzVVRUZEkz21+7m7e3t6Ki4vTvn37lJyc7PHB1LlmU2RlZWnevHkaOXIk4RRQDovFYust1aNHD4/s19S+fXu1bdu21KzI7OxszZw5UxkZGYRTAAAAcDvW7KBCJ0+elFTSKPfMvlywZ52ltX//frfWURGLxaLExMRyxyQmJtrufgmgtO3btystLU3+/v7q06ePu8s5J+sM0k6dOik+Pl5ms1mhoaG6/fbb1bBhQ1s4ZZ0VCwAAALgawRQqVBsan3sCa++t5ORkeXLrtpSUlHKbIkslM6dSUlJcVBFQuxQVFWnVqlWSpL59+8rf39+9BVUD4RQAAAA8BcEUKkQwVTkxMTHy9fVVXl6ejhw54u5yzik7O9uh44D6ZtOmTbYmjt27d3d3OdVGOAUAAABPQDCFClmX8hFMlc9sNqt58+aSPHs5X2V74eTl5Tm5EqD2yc/P15o1ayRJ/fr1k4+Pj5srqpmywqmKZlQCAAAAjkQwhQpZf0khmKqYtc9UcnKyewspR1xcXKWa2C9btkxfffWV8vPzXVAVUDusW7dOp0+fVqNGjdSlSxd3l+MQ1nAqLCxMGRkZmjFjBuEUAAAAXKZSwVTDhg0VHh5eqQfqHuuMqbCwMLfWURtY+0wdOHDAY5uHm81mJSQklDvGOvPrxx9/1PTp07Vnzx5XlAZ4tFOnTmn9+vWSpIEDB8psrjv/thMaGqoxY8YQTgEAAMDlvCszaOrUqbY/nzhxQi+88IKGDBmiXr16SZLWr1+vr7/+Wv/85z+dUiTcix5TlRcdHS1/f3/l5eXpjz/+ULNmzdxdUpnat2+vli1b6vfff7fbHhISooSEBLVv31779u3T0qVLlZGRoblz56pDhw5KSEio9FJAoK5ZvXq1CgsL1bRpU7Vr187d5TicNZyaMWOGbVnf7bffXqkZlgAAAEB1VSqYuv32221/vu666/Tcc8/pgQcesG176KGH9Oabb+rbb7/VI4884vgq4TYFBQU6ffq0JIKpyjCZTIqPj9fu3buVnJzsscGUYRhKS0uTVDLzIywsTMHBwYqLi7PNAjnvvPN0//336/vvv9e6dev0yy+/6Pfff9fll1+uCy+8UCaTyZ0fAXCpjIwMbd68WZI0aNCgOvvzf2Y4lZ6eTjgFAAAAp6vyOoSvv/66zGVAQ4YM0bfffuuQouA5rLOl/Pz8auUt0d3BupzPkxugp6amKisrSz4+PurVq5c6deqk+Pj4UkuTfHx8dNlll+nee+9VTEyM8vPztXTpUs2YMUPHjx93U/WA661cuVIWi0UtW7a0XeN11Zk9p6zhFMv6AAAA4CxVDqYiIiK0aNGiUtsXL16siIgIhxQFz8EyvqqzNkBPSUlRUVGRe4s5h6SkJElS69at5e1d8cTJ6Oho3XXXXRoyZIh8fHyUkpKit99+W99//72Ki4udXS7gchaLRfv379eOHTu0ZcsW7dixQ1LJbKn6ICwsjHAKAAAALlGppXxnevbZZ3XXXXdp1apVth5TGzZsUGJiot5//32HFwj3ovF51TVu3FhBQUHKycnR4cOHbY3EPYm1mXlV+uSYzWb17NlT7dq107Jly/Tbb79p1apV2rVrl4YOHaq4uDhnlQu4VFJSkhITE0sFMbGxsWrSpImbqnI9azg1c+ZMlvUBAADAaao8Y2rMmDFat26dwsLCtHDhQi1YsEChoaH64YcfNGbMGCeUCHdixlTVWftMSVJycrJ7iynDiRMnlJaWJrPZrNatW1f5/WFhYbr55pt13XXXKSgoSGlpafroo4/05ZdfKi8vzwkVA66TlJSkefPmlTk76ODBg7bZhvUFM6cAAADgbFWeMSVJPXr00CeffOLoWuCBCKaqp0WLFtq1a5dH9pmy/mLdokWLavcNM5lM6tixo1q2bKnly5dr69at+umnn7Rnzx5dccUVat++vW2sxWJRSkqKsrOzSzVYBzyJxWJRYmJiuWMSExPVtm3bevUzzMwpAAAAOFO1gqnff/9dH330kfbt26epU6cqMjJSiYmJio2N1fnnn+/oGuFGBFPVY22OfOjQIRUWFsrHx8fNFf2lOsv4ziUgIEDDhw/XBRdcoC+++ELp6emaN2+e2rVrpyuuuEKHDx8utSQqJCRECQkJduEV4AlSUlIqnA2UlZWllJQU26zI+oJwCgAAAM5S5X/y/f7779WpUydt3LhRCxYs0KlTpyRJ27dv16RJkxxeINyLYKp6GjZsqJCQEBUXF+vgwYPuLscmOztbhw4dkiS1bdvWYfuNj4/X/fffr759+8psNmv37t36z3/+U+aSqKysLM2bN6/eLYmC58vOznbouLrGGk6Fhobawqns7Gy7RvH79++XxWJx+LFdcQwAAAC4R5VnTD3xxBN64YUXNH78eAUHB9u2DxgwQNOmTXNocXAvi8ViCxVofl41JpNJLVq00M8//6zk5GSdd9557i5JkrR7925JUrNmzeyuX0fw9vbWwIEDdf7552vJkiX6448/yh1fH5dEwbNV9ppw9LVTm4SFhWnMmDGaMWOG0tPT9e6770qS7R+pJMfPiiyrGT0zLwEAAOqOKv9GuGPHDl177bWltjdu3FgnTpxwSFHwDFlZWTIMQ2azWQ0aNHB3ObWOdamPJ/WZcuQyvnOJiorSoEGDKhxnXRIFeIq4uLgKl6aFhITU+ztQWsOpwMBAnTp1yi6Ukhw7K/JczeiZeQkAAFB3VDmYCgsLU2pqaqntW7duVdOmTR1SFDzDmcv4TCaTm6upfax9pg4fPqz8/Hw3VyPl5eXZ7hLozGBKknJycio1rr4uiYJnMpvNSkhIKHdMQkICs/xUEtBV9D0kJibWaMldZZvRs6wPAACgdqvyUr5Ro0bp8ccf12effSaTySSLxaIffvhBEyZM0G233eaMGuEm9JeqmdDQUDVs2FAZGRk6cOCA2rRp49Z6fvvtN1ksFjVu3FgRERFOPRZLolBbtW/fXiNHjtSiRYtUWFho287SMXspKSmlZkqdLSsrS2+88Yb8/PwklSxxtv4jR2X+nJ+fTzN6AACAeqDKwdSLL76oMWPGqGnTpjIMQx06dFBxcbFGjRqlp59+2hk1wk0IpmouPj5eGRkZ2r9/v9uDKWt/KWfPlpL+WhJV3i+VQUFB9X5JFDxT+/bt9e233yo9PV29evVSmzZtFBcXx0ypM1R2tqP1f0eciZmXAAAAtVuVginDMPTHH3/ovffe0/PPP68tW7bIYrGoa9euat26tbNqhJucPHlSEsFUTbRo0UJbt261LaFzl8LCQv3222+SXBNMWZdEzZs375xjTp8+rV9++UUdO3Z0ej1AVeTk5Cg9PV2S1LdvXwUEBLi5Is9T2dmOgwcPVlRUlKSS/w9hGEaZfy5r29GjR7V69WqH1QIAAADPVOVgqnXr1tq1a5dat27tMXcag3MwY6rmrH2mjhw5otOnT7vtF9zk5GQVFhYqJCRETZo0cckxrUuizr6bVnBwsBo0aKDU1FQtWLBAaWlp6t+/P33M4DEOHjwoqeSmHoRSZavMrMiQkBD16NGj2jPN2rVrp23btlV4DGZeAgAA1G5VCqbMZrNat26tEydOMEOqHrAGU2FhYe4tpBZr0KCBGjVqpOPHj2v//v1u609jvXNVu3btXBoAtW/fXm3btlVKSoqys7MVHBxs+yXyu+++07p167R69WqdOHFCV199tXx8fFxWG3Au1mAqNjbWzZV4rsrMiqxpo3hXHAMAAADuV+X/NzdlyhT94x//0M6dO51RDzyEYRjMmHIQ66wpdy3ns1gs+vXXXyW5Zhnf2cxms+Lj49WpUyfFx8fLbDbLbDbr8ssv1/Dhw2U2m7Vr1y7NmDGDXjHwCCkpKZLETJwKWGdFhoSE2G0PCQnRyJEjHRLEn+sYVsy0BAAAqP2q3Pz81ltvVW5urjp37ixfX99SyxysfTlQu50+fdp2RyqCqZpp0aKFNm3apP3797vl+AcPHlRubq4CAgLUvHlzt9RwLl27dlV4eLg+/fRTW/+6m2++2WXLDYGzFRUVKTU1VRIzpirjXLMiHTmLqaxj/Prrr1q/fr2++OILNWvWTA0aNHDY8QAAAOBaVQ6mpk6d6oQy4Gmss6WCgoLk7V3lHxOcwRoGpaWl6dSpUy7/Bcq6jK9NmzYeueSlefPmuvvuuzVnzhwdP35cH330ka699lq3LXtE/fbHH3+ouLhYDRo0UMOGDd1dTq1gnRXpymM0a9ZMycnJOnLkiD7//HONGjWK2VMAAAC1VJUTh9tvv90ZdcDDcEc+xwkMDFR0dLSOHDmi/fv3u/QudIZhaPfu3ZLcs4yvssLDw3XXXXdp/vz5+v333zVv3jwNHDhQl1xyCb9swqWsy/hiY2P52fNg3t7eGjFihN59913t3btXmzZt0sUXX+zusgAAAFANVZ4+kZKSUu4DdQONzx3L+i/9ru4zdfToUWVmZsrb21stW7Z06bGryt/fX6NGjbL9crlixQotXrxYRUVFbq4M9QmNz2uPxo0b6/LLL5ckLV++XGlpaW6uCAAAANVR5RlT8fHx5f4rcnFxcY0Kgmeg8bljtWjRQhs2bHB5nynrMr5WrVrVijvemc1mXXHFFWrcuLGWLVum7du3Kz09XTfeeCM9ZOB0hmHYgikan9cO3bt312+//aa9e/dqwYIFuvvuu1l+DgAAUMtUecbU1q1btWXLFttj48aNevvtt9WmTRt99tlnzqgRbkAw5VjNmzeXyWRSenq67bt1hdqwjK8sF110kW699Vb5+/vr0KFDev/993X06FF3l4U67vjx4zp9+rS8vb0VHR3t7nJQCSaTSVdffbUCAwN19OhRrVy50t0lAQAAoIqqHEx17tzZ7nHRRRfpnnvu0auvvqo33njDGTXCDQimHMvPz08xMTGS5LJZU+np6Tp27JhMJpPatGnjkmM60nnnnae77rpL4eHhyszM1Icffqhff/3V3WWhDrPOlmrWrJm8vLzcXA0qq0GDBho+fLgkad26dS5fMg0AAICacdgtutq0aaNNmzY5andwM5qfO56r+0xZZ0vFx8crICDAJcd0tEaNGunuu+9WfHy8CgoKNGfOHK1bt06GYbi7NNRB9Jeqvdq2basLL7xQkrR48WKdPn3azRUBAACgsqocTGVlZdk9MjMztXv3bv3zn/9U69atnVEjXKywsFC5ubmSaH7uSC1atJBUMmPKFcFKbV3Gd7aAgADdeuuttl86ly9fri+++IJ+dnC4M+/Ih9pnyJAhCg8PV1ZWlr788ksCbAAAgFqiysFUWFiYGjZsaHuEh4erQ4cOWr9+vf773/86o0a4mHUZn6+vr/z9/d1cTd0RGxsrs9mszMxMZWRkOPVYp06dss3+aNu2rVOP5QpeXl4aOnSohgwZIpPJpK1bt2rWrFm2ANVisWj//v3asWOH9u/fL4vF4uaKUducOnVK6enpkgimaitfX1+NGDFCZrNZu3bt0vbt291dEgAAACqhyreuObuxqNlsVuPGjdWqVSvuhFNHnNlfqrw7MKJqfH191axZM6WkpCg5OVnh4eFOO9aePXskSTExMXVmOabJZFLPnj0VERGh+fPn68CBA3r//fd18cUXa/369crKyrKNDQkJUUJCgtq3b+/GilGbWIPcyMhIAvlarGnTpurXr59WrlypZcuWKS4uTg0bNnR3WQAAAChHlWdM9evXz+7Rt29ftWvXjlCqDqHxufNY+0w5uwF6XVnGV5bWrVvrrrvuUlhYmDIyMvT111/bhVJSyZLjefPmKSkpyU1V4v/bu/P4KMt7///vmawkmZlsJCGEIREwDCTsiKiIrYqxRUttja2W6qm1mwtoF+uvPWrr92i11Wq1i9pWsT2t0hYs2jYuVVFkh0RCCEggIQQICWSZhIRsc//+4MyUkJBMwmxJXs/HYx7CPdfMfG5ubkzeua7PNdTQX2r4uOSSSzRu3Di1t7dr9erVzKAEAAAIcV6lSWvWrNHVV1+tiIgIrVmzps+xcXFxmjx5smcHMgw9ND73n6ysLL3//vsqLy+XYRh+mZHW1tbmabA+HIMp6dSslq985St66qmn+uw1VVBQoOzsbJnNPtvnAcOUO5iy2+1BrgTnymw267Of/ax+85vf6ODBg1q3bp0uvfTSYJcFAACAs/AqmFqyZImqq6uVkpKiJUuW9Ds+LCxMjz32mO6+++5zrQ9B4J59QjDlexkZGQoPD9eJEyd07NgxjR492uefsXfvXnV1dSkpKUnJyck+f/9Qcfz48X4boDudTlVWVnpmqgG96ejo0OHDhyUxY2q4SEhI0Kc+9Sm9+uqreu+99zRhwgSNHTs22GUBAACgF15NI3C5XEpJSfH8uq/HyZMn9fzzz+uxxx7za+HwH/eMKXbk873w8HDPN77uWU2+dvoyvuHcI6ypqcmn4zByHT58WC6XS3Fxcfy7N4xMmzZNU6dOlWEYWrVqldrb24NdEgAAAHrh8/UtkZGR+tznPqcvfvGLvn5rBAg9pvwrKytLkn/6THV2dmrv3r2Shu8yPjeLxeLVuI6ODj9XgqGusrJS0qllfMM5zB1pTCaTPv3pT8tqtaqurk5vvPFGsEsCAABALwbVsfzjjz/We++9p5qamh5NRe+//35ZLBY98cQTPikQgeNyuVRRUeEJprz9xh8Dc3oDdF/3mSovL1d7e7vi4uKG/bIVu90uq9Xao/H5mV577TXt3btXCxYsoPcdekXj8+Fr1KhRWrJkiV566SVt375dkyZNGvahPQAAwFAz4GDq+eef1ze/+U0lJycrLS2t2zfVJpNJ999/v08LRGCUlpaqoKCg2zf5L7zwgq6++mo5HI4gVjb8pKenKzIyUq2trTp69KjS0tJ89t4jZRmfdKrBcV5enlauXHnWMePGjdPBgwe1e/du7d69WxMnTtSCBQtocA0PwzBofD7MZWVl6aKLLtL69eu1Zs0ajR07lh+8AAAAhJABL+X7f//v/+l//ud/VF1draKiIhUWFnoe27dv90eN8LPS0lKtXLmyx8yTpqYmrVy5UqWlpUGqbHgKCwvT+PHjJfm2z5TL5dKePXskDf9lfG4Oh0P5+fmyWq3djlutVuXn5+srX/mKbr/9dk2fPl0mk0llZWV64YUXtGLFCs/OiBjZamtrdfLkSUVERCg1NTXY5cBPPvGJTygtLU2tra36+9//zr0PAAAQQgY8Y6q+vl7XX3+9P2pBELhcLhUUFPQ5pqCgQNnZ2TKbfd6SbMTKzMzU3r17VVFRofnz5/vkPauqqnTixAlFRUWNqF3oHA6HsrOzVVlZqaamJlksFtntds/f1+TkZC1ZskQLFy7UunXrVFRUpIqKClVUVCgjI0MLFizQpEmThv0MM/TOPVtq7NixCgsLC3I18Jfw8HBdd911eu6557Rv3z5t3rxZ8+bNC3ZZAAAA0CBmTF1//fV68803/VELgqCysrLfHj1Op9PTHBi+cXoD9DP7tA2Wexnf+eefP+K+wTabzcrMzFRubq4yMzN7DVETEhJ0zTXX6K677tIFF1yg8PBwVVVV6c9//rOee+45lZaWMotiBKK/1MgxevRoXXnllZKkt956SzU1NUGuCAAAANIgZkxNnDhR//3f/62NGzcqNzdXERER3Z6/6667fFYc/K+pqcmn4+Cd1NRURUdH6+TJkzpy5Mg5Nyo3DKNbfymcnc1m09VXX60FCxZow4YN2rJli6qrq7Vy5UqNHj1aCxYs0NSpU3uEWy6X66yzsoaS4XIevnL6jnwY/ubOnau9e/eqrKxMq1at0le/+lWFhw9qHxgAAAD4yIC/GnvuuecUFxentWvXau3atd2eM5lMBFNDjLcNYGkU61vuGT67d+9WeXn5OQdTNTU1qq+vV1hYmCZOnOijKoe3uLg4XXnllbr44ou1adMmbdq0SbW1tVq1apXee+89XXLJJZo2bZrCwsJ63RzAarUqLy9vSG0OMFzOw1eam5tVX18vScrIyAhyNQgEk8mkz3zmM/r1r3+to0eP6p133tGiRYuCXRYAAMCINuAfk5eXl5/1sX//fn/UCD+y2+09GkefyWq1MpvAD9x9oHzRAN09W2rChAmKjIw85/cbSWJiYvSJT3xCy5cv1yc/+UmNGjVKdXV1WrNmjZ5++mm99tprvW4O4HQ6h9TmAGfb5GConYcvuWdLuWcwYmSIi4vTtddeK0nasGGDTzehAAAAwMCN3PUbkHRq5k5eXl6fY/Ly8kb0Uh9/cfeZqqysVFdX1zm9F8v4zl10dLQWLFig5cuXa9GiRYqLi1NjY2O/u40WFBT4rE+Yv3i7yUGon4ev0V9q5MrOztasWbMkSatXr1Zra2uQKwIAABi5vFrKd8899+ihhx5SbGys7rnnnj7HPvHEEz4pDIHjcDiUn5/PEp8AGz16tGJiYtTS0qKqqiqNHz9+UO/T0NCg6upqmUwmnX/++T6ucuSJjIzU/PnzNXfuXL399tvatGlTn+PdmwOE8k6IA9nkIJTPw9cIpka2q666ShUVFaqrq9Prr7+uz3/+8+zOCQAAEAReBVOFhYXq6Ojw/Pps+IJu6HI4HMrOzqYpcgCZTCZlZWWppKRE5eXlgw6m3LOl7Ha7YmNjfVniiBYeHu51769Q3xzA2/o2bNigiIgIpaenD/t/zzs6OnTkyBFJND4fqSIjI3Xdddfp97//vXbt2qWioiIlJCTw/0AAAIAA8yqYevfdd3v9NYYXd0NuBE5mZqZKSkpUUVEx6PdgGZ//DJfNAbyt7+OPP9bHH38sq9Wq7OxsORwOjR8/flh+c37o0CG5XC5ZLBbZbLZgl4MgGTt2rBYuXKh3331Xa9as6fYcs4YBAAACgz2SgSBy95mqqqpSR0eHIiIiBvT6EydOeBo4E0z5nntzgL6WwQ2FzQG8OY9Ro0YpMzNTZWVlcjqd2rJli7Zs2aJRo0YpOztbkydP1nnnnTfgv6Ohyn3f2O32YT87DH1LSkrq9bh7Y4D8/HzCKQAAAD8acDB14sQJ/eQnP9G///1v1dTU9GiWy858gPcSExNlsVjU1NSkgwcP6rzzzhvQ6z/++GMZhqG0tDTFx8f7p8gRzL05wMqVK886ZihsDmA2m3XppZfq9ddfP+uYa665Rg6HQx0dHdq/f792796tPXv2qLW1VUVFRSoqKlJERIQmTZqkyZMna9KkSWfdyc7lcoX8smD6S0E69Xf1zTff7HNMQUGBsrOzQ+7vMAAAwHAx4GDqq1/9qtauXaulS5dqzJgx/KQZOAfuPlM7duxQeXn5gIMplvH539k2BwgLC9PnPve5ITOTorGxUdKpuk/fBfLM5UoRERHKzs5Wdna2XC6XDhw4oN27d2v37t1yOp3atWuXdu3aJbPZrPPOO0+TJ09Wdna24uLiJEmlpaUhv5GCYRiqqqqSRDA10rExAAAAQPANOJj617/+pX/84x+6+OKL/VEPMOK4g6mB9plqb2/Xvn37JBFM+dvpmwMcPXpUBQUF6urqUmJiYrBL80pnZ6e2bdsmSfrsZz+r2NhYr2Yzmc1mZWVlKSsrS3l5eTpy5IhKS0u1e/duHTt2TGVlZSorK9Prr7+ucePGKTExUR999FGP9wm1JVG1tbU6efKkIiIilJaWFuxyEETebgwQ6hscAAAADGUDDqYSEhKGzDdjwFDg/in8oUOH1NbWpqioKK9eV1ZWpq6uLiUkJCglJcWPFUL6z+YAmZmZqqys1K5du7R582Zdc801wS6tXyUlJWppaZHVapXD4RjUkiSTyaT09HSlp6fr8ssv17Fjxzwh1eHDh3Xw4EHP8rizCZUlUe7+UhkZGUGvBcE1XDY4AAAAGMoG/BX5Qw89pPvvv18tLS3+qAcYceLj45WQkCDDMDzfMHvj9GV8LKkNrAsuuECStGPHjiHxb+HmzZslSXPmzPFZEJOcnKwFCxbotttu0/LlyzV37tx+X+NeEhVs9JeCm3tjgL4MhQ0OAAAAhrIBf4fy+OOP64033lBqaqpyc3M1a9asbg8AA+eeNVVeXu7V+K6uLn388ceSWMYXDHa7XWlpaers7FRhYWGwy+lTVVWVDh8+rLCwML/9G22z2bwOeUJhSdTpO/JhZHNvcNCXobDBAQAAwFA24KV8S5Ys8UMZwMiWlZWlwsJCr/tMVVRUqK2tTbGxscrIyPBvcejBZDLpggsu0Jo1a7RlyxbNnz8/ZL9xdc+WysnJUWxsrN8+Z6gsiWpqalJDQ4NMJhP3DiSdfYODUGvaDwAAMFwNOJh64IEH/FEHMKK5Z0wdOXJEra2tGjVqVJ/j3cv4QqFfz0iVm5urt99+W42NjdqzZ09IfvPa3NyskpISSf9Zfugv7iVRfe1wFgpLotzL+FJTU73u54bhz73BwbZt2/TPf/5T0dHRWrZsGf++AgAABABfcQEhwGKxKDk5WZJ04MCBPscahqE9e/ZIYhlfMIWHh3uWxm3atCnI1fRu27ZtcrlcysjIUHp6ul8/a6gsiXIv46O/FM5kNpuVk5MjSTp58qQ6OzuDXBEAAMDI4PV3CGazWWFhYT0eCQkJuvDCC7Vq1Sp/1gkMe972mTp06JCampoUGRmprKysAFSGs5k7d65MJpMOHDig6urqYJfTTVdXl7Zu3SrJ/7Ol3NxLos5sJh0ZGan8/PyQmFVG43P0ZdSoUZ4Zq3V1dUGuBgAAYGTweinf6tWrez3e0NCgzZs360tf+pJWrFih66+/3mfFASNJVlaWtm7d2m8w5V7GN2nSJIWHD3g1LnzIarVqypQpKikp0aZNm/SZz3wm2CV5lJaWqrm5WXFxcZoyZUrAPte9JKqyslJlZWX68MMPZTKZNGnSpIDVcDbt7e06cuSIJBqf4+wSExN16NAhHT9+XGlpacEuBwAAYNjz+rvavr7huvnmmzVlyhT97Gc/I5gCBsk9Y6q2ttYTKPTGHUyxjC80zJs3TyUlJSouLtaVV16pmJiYYJck6T9Nz2fPnq2wsLCAfrbZbFZmZqbsdruKi4vldDpVWlqq3NzcgNZxpkOHDskwDFmtVtlstqDWgtCVlJSkQ4cOMWMKAAAgQHzW7GPRokWe7esBDFxMTIxSU1Ml6ay789XW1ur48eMKCwsLiRkokDIyMjRmzBh1dXVp27ZtwS5H0qkm+gcPHpTZbNbs2bODVofZbNaMGTMkSYWFhUGrw829jI/ZUuhLYmKiJJbyAQAABIrPgqnW1lZFR0f76u2AEam/PlPu2VJZWVnsKBYiTCaT5s2bJ0naunWrurq6glzRf2ZLTZkyRRaLJai1uIOp8vJy1dfXB7UWGp/DG+5g6vjx40GuBAAAYGTwWTD1/PPPa+bMmb56O2BEcjczP9uMKZbxhaapU6cqNjZWTqfTc42C5cSJEyouLpYUuKbnfUlISPD8vS4qKgpaHS6XS1VVVZIIptC3pKQkScyYAgAACBSve0zdc889vR5vbGzU1q1btW/fPn3wwQc+KwwYicaPHy+TyaS6ujo5nc5uu5s5nU4dPnxYkpSdnR2sEtGL8PBwzZ49W++//742b96sqVOnBq2W7du3q6urS2PGjFFGRkbQ6jjdzJkzVV5erqKiIi1cuFBms89+JuK12tpatbW1KTIy0rNkFuiNe8bUiRMn1NbWxuxUAAAAP/P6u4PCwsJeH8eOHVNeXp5KSkqYMQWco+joaI0ZM0ZSz+V87pk448aNO2tjdATPnDlzZDabVVlZ6dn5LdBcLpe2bt0q6dRsKZPJFJQ6zuRwOBQdHS2n06n9+/cHpQb3Mr6MjIygBGMYOqKjoz2bGLCcDwAAwP+8njH17rvv+rMOAP8nKytLhw8fVkVFhaZPn+45zjK+0GaxWDR16lQVFxdr06ZNWrJkScBr2L17t5xOp2JiYpSTkxPwzz+b8PBw5ebmasuWLSosLNTEiRMDXoO78TnL+OCNpKQktbS0qK6uTunp6cEuBwAAYFjjx8ZAiHH34ykvL5dhGJJObS7g7jtFMBW63D2ddu7cqRMnTgT8891Nz2fNmqXwcK9/7hAQs2bNknQqPGtpaQn457tnTLEjH7xBA3QAAIDACflg6tChQ/rSl76kpKQkxcTEaMaMGd22ZDcMQw8++KDS09M1atQoXXbZZSopKen2Hm1tbbrzzjuVnJys2NhYXXvttZ4muG719fVaunSpbDabbDabli5dqoaGhkCcItDNuHHjZDab1djY6Pk7+PHHH8swDKWkpHi+YULoycjI0NixY9XV1dXt36lAOHr0qA4cOCCTyaS5c+cG9LO9kZaWpjFjxsjlcmnHjh0B/Wyn06nGxkaZTCaNHTs2oJ+Nocn97ywN0AEAAPwvpIOp+vp6XXzxxYqIiNC//vUv7dq1S48//rji4+M9Yx577DE98cQTeuaZZ7RlyxalpaXpyiuvVFNTk2fM8uXLtXr1ar388stat26dmpubtXjx4m7but94440qKipSQUGBCgoKVFRUpKVLlwbydAFJUmRkpKdptbvPFMv4hg73rKktW7Z0+zfG39yzpRwOR7em+aHE3YewsLDQMxswENzL+FJTU2lkDa+wMx8AAEDghHQw9eijj2rcuHF64YUXdMEFFygzM1OXX365JkyYIOnUbKknn3xSP/jBD3TdddcpJydHK1asUEtLi/70pz9JOrVr4O9+9zs9/vjjuuKKKzRz5kz98Y9/VHFxsd5++21JUmlpqQoKCvTb3/5W8+fP1/z58/X888/r9ddf1549e4J2/hi5MjMzJZ0Kpjo6OlRWViaJYGoomDp1quLi4tTc3KzS0tKAfGZra6tnFpI7GAtFubm5Cg8PV01NjWeHyUBwL+OjvxS8xVI+AACAwPEqmJo1a5bq6+slST/+8Y8D1h9kzZo1mjNnjq6//nqlpKRo5syZev755z3Pl5eXq7q6WosWLfIci4qK0sKFC7V+/XpJ0rZt29TR0dFtTHp6unJycjxjNmzYIJvNpnnz5nnGXHjhhbLZbJ4xvWlra5PT6ez2AHzB3WeqoqJC+/btU2dnp2w2m9LS0oJcGfoTFham2bNnS5I2bdoUkM8sLCxUZ2enUlNTQ7qHUnR0tBwOh6RTNQeKe8ZUKP/ZILS4Z0y1traqtbU1yNUAAAAMb14FU6WlpZ5Gvj/60Y/U3Nzs16Lc9u/fr1//+teaNGmS3njjDX3jG9/QXXfdpZdeekmSVF1dLenU8ozTpaamep6rrq5WZGSkEhIS+hyTkpLS4/NTUlI8Y3rzyCOPeHpS2Ww2fhoPn8nIyFBYWJiam5v15ptvSpKys7NlMpmCXBm8MWfOHJnNZlVVVenQoUN+/SyXy6UtW7ZIOjVbKtT/jriX8+3cuVMdHR1+/7z29nbPv+MEU/BWZGSk4uLiJLGcDwAAwN+82rZpxowZ+q//+i9dcsklMgxDP/vZzzxfsJ3p/vvv91lxLpdLc+bM0cMPPyzp1Dc0JSUl+vWvf60vf/nLnnFnfiNmGEa/35ydOaa38f29z3333ad77rnH83un00k4BZ/Yu3ev59fu2Yo7d+5UZmamZ8YJQldcXJxycnK0Y8cObd68WZ/97Gf99ll79+5VQ0ODoqOjlZub67fP8ZXMzEzFx8eroaFBu3bt0vTp0/36eVVVVTIMQzabLWR7byE0JSUlqbm5WcePH6dpPgAAgB95NWPqxRdfVFJSkl5//XWZTCb961//0urVq3s8Xn31VZ8WN2bMGE2ZMqXbMYfD4ekX4l7WdOasppqaGs8sqrS0NLW3t3u+uT/bmKNHj/b4/Nra2h6zsU4XFRUlq9Xa7QGcq9LSUq1cubJH4+yWlhatXLkyYH2LcG7cvZ527tzp11mm7qbnM2fOVEREhN8+x1dMJlO3Juj+5l7Gxw8NMFDszAcAABAYXgVT2dnZevnll7VlyxYZhqF///vfKiws7PHYvn27T4u7+OKLezQf//jjjzV+/HhJp/rwpKWl6a233vI8397errVr1+qiiy6SJM2ePVsRERHdxhw5ckQ7d+70jJk/f74aGxs93+BJp3rDNDY2esYAgeByuVRQUNDnmIKCArlcrgBVhMEaO3asMjIy5HK5tHXrVr98xrFjx7R//35J0ty5c/3yGf4wY8YMmUwmHThwwO/NpQmmMFgEUwAAAIEx4F35XC5Xr/2Y/OHuu+/Wxo0b9fDDD6usrEx/+tOf9Nxzz+n222+XdOon78uXL9fDDz+s1atXa+fOnbrlllsUExOjG2+8UZJks9l066236tvf/rYnUPvSl76k3NxcXXHFFZJOzcLKy8vTbbfdpo0bN2rjxo267bbbtHjxYmVnZwfkXAHp1O5h/TXRdzqdnlmDCG3uDRW2bt3aYwacL7jD9Ozs7B599EKZ1Wr17K7qz1lTLpeLxucYNHcDdHbmAwAA8K8BB1OStG/fPt1555264oordOWVV+quu+7Svn37fF2b5s6dq9WrV+vPf/6zcnJy9NBDD+nJJ5/UTTfd5Bnzve99T8uXL9e3vvUtzZkzR4cOHdKbb74pi8XiGfPzn/9cS5YsUX5+vi6++GLFxMTotddeU1hYmGfM//7v/yo3N1eLFi3SokWLNG3aNP3hD3/w+TkBfWlqavLpOASXw+GQxWLRiRMnVFJS4tP3bmtr00cffSTpP8sGhxL3cr6PPvrIbzMAa2pq1N7erqioqID9QAXDx+kzpgzDCHI1AAAAw5dXzc9P98Ybb+jaa6/VjBkzdPHFF8swDK1fv15Tp07Va6+9piuvvNKnBS5evFiLFy8+6/Mmk0kPPvigHnzwwbOOiY6O1tNPP62nn376rGMSExP1xz/+8VxKBc7Z6YGqL8YhuMLCwjRnzhy9++672rx5s6ZNm+az9y4qKlJ7e7uSk5OVlZXls/cNlOzsbMXExKi5uVl79+71y+xU98zCjIwMmc2D+jkMRjB3MHXy5Em1trYqJiYmyBUBAAAMTwP+Sv373/++7r77bm3atElPPPGEfv7zn2vTpk1avny57r33Xn/UCIwYdru93yb6VquVZUlDyOzZsxUWFqZDhw6pqqrKJ+9pGIZnGd8FF1zQ7y6koSgsLMwT1BUVFfnlM+gvhXMRERHh+feY5XwAAAD+M+BgqrS0VLfeemuP41/5yle0a9cunxQFjFRms1l5eXl9jsnLy2P2xxASGxurnJwcSac2VfCFffv2qa6uTlFRUZo+fbpP3jMY3Mv5Pv74Y7/sXEgwhXNFA3QAAAD/G/B3t6NHj+71p9tFRUX08AB8wOFwKD8/v8fMKavVqvz8fDkcjiBVhsFyN0HftWuXT/qDuWdLzZgxQ5GRkef8fsGSkpKisWPHyuVyefpl+UpjY6MaGxtlMpmUkZHh0/fGyOEOppgxBQAA4D8D7jF122236Wtf+5r279+viy66SCaTSevWrdOjjz6qb3/72/6oERhxHA6HsrOzVVlZqaamJlksFtntdmZKDVFjxoyR3W5XZWWltm7dqk984hODfq+6ujrt3btX0qkNIoa6mTNn6tChQyosLPT8P8UX3LOl0tLShnR4h+By78zHjCkAAAD/GXAw9d///d+yWCx6/PHHdd9990mS0tPT9eCDD+quu+7yeYHASGU2m5WZmRnsMuAjF1xwgSeYWrBggcLDB/zPr6T/zJaaOHGi55vmoSwnJ0dvvPGGjh8/roMHD/qsf5q78TnL+HAuWMoHAADgfwOefmEymXT33XerqqrKs1SiqqpKy5YtG5INeAEgECZPniyr1aqWlhaVlJQM6j3a29s9S6kvuOACH1YXPFFRUZo6daokqbCw0Gfv654xxUYBOBfu8Pf48eMyDCPI1QAAAAxP57QuyGKxsG09AHghLCxMc+bMkXSqCfpgvsn96KOP1NbWpsTERE2cONHXJQaNuwl6SUmJ2trazvn92tradPToUUnMmMK5SUhIkHQqFD5x4kSQqwEAABieaFgDAAEye/ZshYWF6ciRI6qqqhrQaw3D0JYtWySd6i01nGaojhs3TklJSero6Bj0bLLTVVVVyTAMxcfH99hEABiI8PBw2Ww2SSznAwAA8BeCKQAIkJiYGOXm5ko6NWtqIMrLy1VbW6uIiAjNmDHDD9UFj8lk8sya8sVyPvcyPmZLwRdOX84HAAAA3yOYAoAAmjdvniRp165dcjqdXr/OPVtq+vTpio6O9kttwTR9+nSZTCZVVVWptrb2nN6LYAq+RAN0AAAA/xpQMNXR0aFPfOIT+vjjj/1VDwAMa2lpaRo/fny3pXn9aWho0J49eyQNn6bnZ4qLi9P5558v6dxmTblcLs8ySRqfwxcIpgAAAPxrQMFURESEdu7cOax6mwBAoLlnTW3btk0dHR39jt+yZYsMw9B5552n0aNH+7u8oHEv5/voo4/U1dU1qPc4evSo2tvbFRUVNaz/rBA4LOUDAADwrwEv5fvyl7+s3/3ud/6oBQBGhOzsbNlsNrW2tmrnzp19ju3o6PDMIBqus6XcJk2apLi4OLW0tAx6Zm5lZaWkU8v4zGZWq+PcnT5jajC7aQIAAKBv4QN9QXt7u37729/qrbfe0pw5cxQbG9vt+SeeeMJnxQHAcGQ2mzV37ly9/fbb2rx5s2bMmHHWmajFxcVqbW1VfHy8Jk2aFOBKA8tsNmv69On68MMPVVhYKIfDMeD3oL8UfC0hIUEmk0kdHR1qbm6WxWIJdkkAAADDyoCDqZ07d2rWrFmS1OMn2izxAwDvzJo1S++9956qq6tVWVmp8ePH9xhjGIY2b94sSZozZ86ImAE0c+ZMffjhhyorK5PT6ZTVah3Q6wmm4GthYWGKj49XfX29jh8/TjAFAADgYwMOpt59911/1AEAI8qoUaM0bdo0bd++XZs3b+41mKqsrNTRo0cVHh7u+YHAcJeUlCS73a7KykoVFRXp0ksv9fq1jY2NcjqdMplMGjt2rB+rxEiTmJio+vp61dXVKTMzM9jlAAAADCuD/vF7WVmZ3njjDbW2tkoSfRcAYIDcPaNKS0vV2NjY43n3bKnc3FyNGjUqoLUFk7sJelFR0YD+3+LuLzVmzBhFRkb6pTaMTO4+UzRABwAA8L0BB1PHjx/X5ZdfrvPPP1+f+tSndOTIEUnSV7/6VX3729/2eYEAMFylpqYqKytLhmFoy5Yt3Z5zOp0qLS2V9J9d/EaKKVOmKDIyUvX19Tpw4IDXrzu98TngS+6d+erq6oJcCQAAwPAz4GDq7rvvVkREhCorKxUTE+M5fsMNN6igoMCnxQHAcOeeNbV9+3Z1dHR4jm/dulWGYWj8+PFKTU0NVnlBERkZqZycHEny7EjoDXd/Kbvd7pe6MHKdvjMfAAAAfGvAwdSbb76pRx99VBkZGd2OT5o0aUA/2QYASOeff77i4+PV2tqq4uJiSVJnZ6e2bdsm6T/B1Ujj7qm1a9cunTx5st/xbW1tqqmpkcSMKfje6TOmaF0AAADgWwMOpk6cONFtppTbsWPHFBUV5ZOiAGCkMJvNmjt3riRp48aNKi8v1xtvvKGWlhZZLBZNnjw5yBUGR3p6ulJSUtTZ2ekJ7PpSVVUlwzAUHx/Prmnwufj4eJnNZnV2dsrpdAa7HAAAgGFlwMHUpZdeqpdeesnze5PJJJfLpZ/+9Kf6xCc+4dPiAGAkmDlzpsLCwlRbW6uXXnpJW7dulSS1t7drz549Qa4uOEwmk2bMmCHJu+V87v5SLOODP5jNZsXHx0tiOR8AAICvDTiY+ulPf6pnn31WV199tdrb2/W9731POTk5ev/99/Xoo4/6o0YAGNYqKirU1dXV43hbW5tWrlzpaYI+0kyfPl1ms1lHjhxRdXV1n2Pd/aVYxgd/cS/nY2c+AAAA3xpwMDVlyhTt2LFDF1xwga688kqdOHFC1113nQoLCzVhwgR/1AgAw5bL5ep344iCggK5XK4AVRQ6YmJiPEsZ+5o15XK5VFVVJYkZU/AfGqADAAD4R/hgXpSWlqYf/ehHvq4FAEacysrKfnvWOJ1OVVZWKjMzMzBFhZCZM2dq165d2rFjh6688kqFh/f831Z1dbU6OjoUHR2t0aNHB6FKjAQEUwAAAP4xqGCqvr5ev/vd71RaWiqTySSHw6H/+q//8nzRBgDwTlNTk0/HDTfnnXeerFarnE6ndu/erZycnB5j3Mv4MjIyZDKZAl0iRgiW8gEAAPjHgJfyrV27VllZWfrFL36h+vp61dXV6Re/+IWysrK0du1af9QIAMOWtzvIjdSd5sxmc79N0Gl8jkBw//Ctvr5+RC6tBQAA8JcBB1O333678vPzVV5erlWrVmnVqlXav3+/vvCFL+j222/3R40AMGzZ7XZZrdY+x1it1hEduriDqf3796uhoaHbc4Zh0PgcAWGz2RQWFqaurq5+l98CAADAewMOpvbt26dvf/vbCgsL8xwLCwvTPffco3379vm0OAAY7sxms/Ly8vock5eXJ7N5wP9cDxsJCQnKysqSJBUVFXV7rrGxUU1NTTKbzRo7dmwQqsNIYTablZCQIInlfAAAAL404O90Zs2a1evW5aWlpZ6fagMAvOdwOJSfn99j5pTValV+fr4cDkeQKgsdM2fOlHQqmDp9GZV7Gd+YMWMUERERlNowctAAHQAAwPe8an6+Y8cOz6/vuusuLVu2TGVlZbrwwgslSRs3btQvf/lL/eQnP/FPlQAwzDkcDmVnZ6uyslJNTU2yWCyy2+0jeqbU6RwOh6Kjo9XY2Kjy8nJNmDBB0n+CKZbxIRDcwRQzpgAAAHzHq2BqxowZMplMMgzDc+x73/tej3E33nijbrjhBt9VBwAjiNlsVmZmZrDLCEnh4eHKzc3Vli1bVFhY6Amm3P2lRnIPLgSOe2c+ZkwBAAD4jlfBVHl5ub/rAACgTzNnztSWLVu0e/dutbS0yGw2q6amRhIzphAYLOUDAADwPa+CqfHjx/u7DgAA+jRmzBilpaWpurpaxcXFntkrCQkJiouLC3J1GAncf+fq6+vlcrlYagsAAOADXgVTZzp06JA+/PBD1dTUdGtCK53qQQUAgD/MnDlT//rXv7Rp0yalpKRIYrYUAsdqtSo8PFydnZ1qaGjwzKACAADA4A04mHrhhRf0jW98Q5GRkUpKSpLJZPI8ZzKZCKYAAH4TFRUl6dSMlfr6eknSxx9/rNLSUnYvhN+ZTCYlJCSotrZWdXV1BFMAAAA+MOA56Pfff7/uv/9+NTY2qqKiQuXl5Z7H/v37/VEjAAAqLS3Vq6++2uP4yZMntXLlSpWWlga+KIw47uV87MwHAADgGwMOplpaWvSFL3yBvgoAgIBxuVwqKCjoc0xBQUGP5eWAr9EAHQAAwLcGnC7deuut+stf/uKPWgAA6FVlZaWcTmefY5xOpyorKwNUEUYqgikAAADfGnCPqUceeUSLFy9WQUGBcnNzFRER0e35J554wmfFAQAgSU1NTT4dBwwWS/kAAAB8a8DB1MMPP6w33nhD2dnZktSj+TkAAL5msVh8Og4YLPeMqYaGBnV1dSksLCzIFQEAAAxtAw6mnnjiCf3+97/XLbfc4odyAADoyW63y2q19rmcz2q1ym63B7AqjEQWi0URERHq6OhQQ0ODZwYVAAAABmfAPaaioqJ08cUX+6MWAAB6ZTablZeX1+eYvLw8NuaA35lMJs+sKZbzAQAAnLsBfwW/bNkyPf300/6oBQCAs3I4HMrPz5fVau123Gq1Kj8/Xw6HI0iVYaShAToAAIDvDHgp3+bNm/XOO+/o9ddf19SpU3s0P1+1apXPigMA4HQOh0PZ2dmqrKxUU1OTLBaL7HY7M6UQUMyYAgAA8J0BB1Px8fG67rrr/FELAAD9MpvNyszMDHYZGMHcfaWYMQUAAHDuBhxMvfDCC/6oAwAAYEhgKR8AAIDvsPYBAABgANwzphobG9XZ2RnkagAAAIa2Ac+YysrKkslkOuvz+/fvP6eCAAAAQllsbKwiIyPV3t6u+vp6jR49OtglAQAADFkDDqaWL1/e7fcdHR0qLCxUQUGBvvvd7/qqLgAAgJBkMpmUmJio6upq1dXVEUwBAACcgwEHU8uWLev1+C9/+Utt3br1nAsCAAAIdUlJSaqurmZnPgAAgHPksx5TV199tf72t7/56u0AAABCFg3QAQAAfMNnwdRf//pXzxdpAAAAwxnBFAAAgG8MeCnfzJkzuzU/NwxD1dXVqq2t1a9+9SufFgcAABCK3DvzsZQPAADg3Aw4mFqyZEm335vNZo0ePVqXXXaZJk+e7Ku6AAAAQpZ7xpTT6VRHR4ciIiKCXBEAAMDQNOBg6oEHHvBHHQAAAENGTEyMoqKi1NbWpvr6eqWkpAS7JAAAgCHJZz2mAAAARgqTycRyPgAAAB/wOpgym80KCwvr8xEePuAJWAAAAEMSDdABAADOnddJ0urVq8/63Pr16/X000/LMAyfFAUAABDq3MEUM6YAAAAGz+tg6jOf+UyPY7t379Z9992n1157TTfddJMeeughnxYHAAAQqtxL+ZgxBQAAMHiD6jF1+PBh3XbbbZo2bZo6OztVVFSkFStWyG63+7o+AACAkMRSPgAAgHM3oGCqsbFR9957ryZOnKiSkhL9+9//1muvvaacnBx/1QcAABCS3DOmmpqa1N7eHuRqAAAAhiavg6nHHntM5513nl5//XX9+c9/1vr167VgwQJ/1gYAABCyRo0apVGjRkli1hQAAMBged1j6vvf/75GjRqliRMnasWKFVqxYkWv41atWuWz4gAAAEJZYmKiDh06pLq6OqWlpQW7HAAAgCHH62Dqy1/+skwmkz9rAQAAGFKSkpJ06NAhduYDAAAYJK+DqRdffNGPZQAAAAw9NEAHAAA4N4PalQ8AAAAEUwAAAOeKYAoAAGCQ3DvzsZQPAABgcAimAAAABsk9Y+rEiRNqa2sLcjUAAABDD8EUAADAIEVHRysmJkYSy/kAAAAGg2AKAADgHLCcDwAAYPAIpgAAAM4BDdABAAAGj2AKAADgHBBMAQAADB7BFAAAwDlgKR8AAMDgDalg6pFHHpHJZNLy5cs9xwzD0IMPPqj09HSNGjVKl112mUpKSrq9rq2tTXfeeaeSk5MVGxura6+9VlVVVd3G1NfXa+nSpbLZbLLZbFq6dKkaGhoCcFYAAGAoY8YUAADA4A2ZYGrLli167rnnNG3atG7HH3vsMT3xxBN65plntGXLFqWlpenKK69UU1OTZ8zy5cu1evVqvfzyy1q3bp2am5u1ePFidXV1ecbceOONKioqUkFBgQoKClRUVKSlS5cG7PwAAMDQ5A6mWlpadPLkySBXAwAAMLQMiWCqublZN910k55//nklJCR4jhuGoSeffFI/+MEPdN111yknJ0crVqxQS0uL/vSnP0mSGhsb9bvf/U6PP/64rrjiCs2cOVN//OMfVVxcrLfffluSVFpaqoKCAv32t7/V/PnzNX/+fD3//PN6/fXXtWfPnqCcMwAAGBqioqIUFxcnieV8AAAAAzUkgqnbb79dn/70p3XFFVd0O15eXq7q6motWrTIcywqKkoLFy7U+vXrJUnbtm1TR0dHtzHp6enKycnxjNmwYYNsNpvmzZvnGXPhhRfKZrN5xvSmra1NTqez2wMAAIw8LOcDAAAYnJAPpl5++WVt375djzzySI/nqqurJUmpqandjqempnqeq66uVmRkZLeZVr2NSUlJ6fH+KSkpnjG9eeSRRzw9qWw2m8aNGzewkwMAAMOCO5hixhQAAMDAhHQwdfDgQS1btkx//OMfFR0dfdZxJpOp2+8Nw+hx7ExnjultfH/vc99996mxsdHzOHjwYJ+fCQAAhif3znzMmAIAABiYkA6mtm3bppqaGs2ePVvh4eEKDw/X2rVr9Ytf/ELh4eGemVJnzmqqqanxPJeWlqb29nbV19f3Oebo0aM9Pr+2trbHbKzTRUVFyWq1dnsAAICRh6V8AAAAgxPSwdTll1+u4uJiFRUVeR5z5szRTTfdpKKiIp133nlKS0vTW2+95XlNe3u71q5dq4suukiSNHv2bEVERHQbc+TIEe3cudMzZv78+WpsbNTmzZs9YzZt2qTGxkbPGAAAgLNxz5hiKR8AAMDAhAe7gL5YLBbl5OR0OxYbG6ukpCTP8eXLl+vhhx/WpEmTNGnSJD388MOKiYnRjTfeKEmy2Wy69dZb9e1vf1tJSUlKTEzUd77zHeXm5nqaqTscDuXl5em2227Ts88+K0n62te+psWLFys7OzuAZwwAAIYi94ypkydPqqWlRTExMUGuCAAAYGgI6WDKG9/73vfU2tqqb33rW6qvr9e8efP05ptvymKxeMb8/Oc/V3h4uPLz89Xa2qrLL79cL774osLCwjxj/vd//1d33XWXZ/e+a6+9Vs8880zAzwcAAAw9ERERslgsampqUl1dHcEUAACAl0yGYRjBLmK4cDqdstlsamxspN8UAAAjzIoVK1RRUaElS5Zo+vTpwS4HAAAgaAaSj4R0jykAAIChggboAAAAA0cwBQAA4AMEUwAAAANHMAUAAOAD7MwHAAAwcARTAAAAPnD6jClaeAIAAHiHYAoAAMAH3MFUW1ubWlpaglwNAADA0EAwBQAA4APh4eGy2WySWM4HAADgLYIpAAAAH6EBOgAAwMAQTAEAAPiIO5hixhQAAIB3CKYAAAB8xL0zHzOmAAAAvEMwBQAA4CMs5QMAABgYgikAAAAfcc+YOn78uAzDCHI1AAAAoY9gCgAAwEcSEhJkMpnU0dGh5ubmYJcDAAAQ8gimAAAAfCQsLEw2m00Sy/kAAAC8QTAFAADgQ6cv5wMAAEDfCKYAAAB8iAboAAAA3iOYAgAA8CGCKQAAAO8RTAEAAPgQS/kAAAC8RzAFAADgQ6fPmDIMI8jVAAAAhDaCKQAAAB+Kj4+XyWRSZ2enmpqagl0OAABASCOYAgAA8KGwsDAlJCRIYjkfAABAfwimAAAAfIwG6AAAAN4hmAIAAPAxdzDFjCkAAIC+EUwBAAD4mHtnPmZMAQAA9I1gCgAAwMdYygcAAOAdgikAAAAfO33GlGEYQa4GAAAgdBFMAQAA+JjNZpPZbFZXV5caGxuDXQ4AAEDIIpgCAADwMbPZrISEBEks5wMAAOgLwRQAAIAfuJfzsTMfAADA2RFMAQAA+AEN0AEAAPpHMAUAAOAHBFMAAAD9I5gCAADwA5byAQAA9I9gCgAAwA/cM6bq6+vlcrmCXA0AAEBoIpgCAADwA5vNprCwMLlcLjU2Nga7HAAAgJBEMAUAAOAHJpPJM2uK5XwAAAC9I5gCAADwExqgAwAA9I1gCgAAwE+YMQUAANA3gikAAAA/ce/Mx4wpAACA3hFMAQAA+AlL+QAAAPpGMAUAAOAn7hlT9fX16urqCnI1AAAAoYdgCgAAwE8sFovCw8NlGIYaGhqCXQ4AAEDIIZgCAADwE5PJxHI+AACAPhBMAQAA+JF7OR878wEAAPREMAUAAOBHzJgCAAA4O4IpAAAAPyKYAgAAODuCKQAAAD9iKR8AAMDZEUwBAAD4kXvGVGNjo7q6uoJcDQAAQGghmAIAAPCjuLg4RUZGyjAM1dfXB7scAACAkEIwBQAA4Ecmk8kza4rlfAAAAN0RTAEAAPiZu88UDdABAAC6I5gCAADwM2ZMAQAA9C482AUAAAAMd+5gihlT3nG5XKqsrFRTU5MsFovsdrvMZn6eCgDAcEQwBQAA4Gcs5fNeaWmpCgoK5HQ6PcesVqvy8vLkcDiCWBkAAPAHfvQEAADgZ+4ZU42Njers7AxyNaGrtLRUK1eu7BZKSZLT6dTKlStVWloapMoAAIC/EEwBAAD4WUxMjKKioiQxa+psXC6XCgoK+hxTUFAgl8sVoIoAAEAgEEwBAAD4mclk8syaKiwsVEVFBQHLGSorK3vMlDqT0+lUZWVlgCoCAACBQI8pAAAAPystLVVtba0kaePGjdq4caNf+iYNxabhTqdTpaWl2rJli1fjt2/frtjYWCUnJ8tkMvm5OgAA4G8mwzCMYBcxXDidTtlsNjU2NspqtQa7HAAAEALcfZPOJj8/3yfh1FBqGl5fX6/S0lKVlpaqqqpqUO9hs9k0ceJETZw4UVlZWZ6lkgAAIPgGko8QTPkQwRQAADidy+XSU0891ecSNavVqmXLlp3TzKZAhV/n4vjx49q1a5dKS0t15MiRbs+NGzdOkydP1oYNG9Tc3HzW94iKilJ6eroqKyvV1dXlOW42m2W32z1BVUpKSr+zqYbi7DIAAIaKgeQjLOUDAADwE2/7Jj3zzDOKi4tTZGSkIiIiuv23v1+Hh4frn//8Z5+fUVBQoOzsbJ8EL94GOoZhqLa21hNG1dTUeJ4zmUwaP368HA6HHA6HLBaLJCkhIaHPgO0zn/mMHA6H2tvbVVFRobKyMpWVlam+vl4VFRWqqKjQ22+/LYvF4gmpzjvvPEVHR3d7n6E0uwwAgOGOGVM+xIwpAABwuuLiYq1atSrYZUiSrrjiCmVnZ8tmsykiImJQ79FfoGMYhqqrqz1h1PHjxz3jzGazsrKy5HA4NHnyZMXGxg7qM3pz/PhxT0hVUVGhzs5Oz3Mmk0njxo3zBFX19fX6y1/+ctZzDIXZZQAADHUs5QsSgikAAHC6iooKrVixot9xV1xxhRITE9Xe3q729nZ1dHR0+/WZvz/9162trd2CGG/ExMQoPj5eNptNNpvN8+v4+HjFx8f3mGEk9b9cMDs7W0ePHlVDQ4PnWFhYmCZMmCCHw6Hs7GyNGjXKq/rOZZldR0eHDhw44AmqTg/HpFNBVV9f/vpiaSUAACMdS/kAAABCgN1ul9Vq7bfH1Pz58wcdhHgbftlsNrW2tqq9vV0tLS1qaWnR4cOHex0bFRXVLbCyWq1av359n++/Z88eSVJ4eLgmTZokh8Oh888/f1BNyc1mszIzMwf8OkmKiIjwzI6STjVaLysr0759+1RWVtatN1VvnE6nKisrB/35AABgYAimAAAA/MRsNisvL6/PmUZ5eXnnNDvH2/Drrrvukslk0smTJ9XQ0KDGxkbPf0//dUtLi9ra2lRTU9OtL5Q3Fi5cqIsuukiRkZGDPh9fS0hI0Ny5czV37lx99NFHevXVV/t9TVNTk/8L8wEauAMAhgOCKQAAAD9yOBzKz8/3W7PtgYZfo0aN0qhRozRmzJhex7a3t/cIqw4cOKCDBw/2W0tSUlJIhVJnstlsXo3bsGGDzGazJk+erLCwMD9XNTg0cAcADBf0mPIhekwBAICz8ffsFn8GFd4uF7z55ptDegmcy+XSU0891e9OiW4xMTGaPn26Zs2apeTkZD9X573++n3RwB0AEGz0mAIAAAgx59I3yRvuBuP+CL+8XS5ot9vP+bP8yZvZZZ/+9KfldDpVVFSkpqYmbdiwQRs2bND48eM1a9YsTZkyReHhwfsS2uVyqaCgoM8xBQUFys7OZlkfAGBIYMaUDzFjCgAADFfDaZaON7PLXC6X9u7dq+3bt2vv3r2enfyio6M1bdo0zZ49WykpKQGvfbjMXgMADG8DyUcIpnyIYAoAAAxnw6mv0UCWVjqdThUWFqqwsFCNjY2e4xkZGZo1a5amTp3ao7eWr5ZuuhvRHz16VNXV1SovL1ddXV2/r7vuuuuUm5s74M8DAMAXCKaChGAKAAAMdyN5JziXy6V9+/Zp+/bt2rNnj2cWVVRUlHJzczVr1iyNGTNmUAGeYRiqr69XdXW1jh496gmj6uvrB1UrM6YAAMFEMBUkBFMAAAAjQ1NTk4qKilRYWNgtPEpISOgzTMrPz1dWVpaOHj3a7VFTU6OOjo5eX2OxWJSamqrU1FSlpKTozTff1IkTJ876GXFxcbr77rtHTGAIAAg9ND8HAAAA/MhisWjBggW65JJLVF5eru3bt2vXrl39znD6y1/+orP9XDgsLEwpKSmeEMr9iImJ6TYuIiKiz35fLpdLTqdT8fHxAz4vAAACjRlTPsSMKQAAgJGrvwbxp7NarT0CqKSkJK9nOfW2XDAuLk6S1NzcrPj4eN18882EUwCAoGDGFAAAABBgnZ2dXo1bvHixZs+efU6f5XA4lJ2d3aPfV3Nzs1asWKG6ujqtWLGCcAoAEPJYeA4AAAD4gMVi8WpcUlKSTz7PbDYrMzNTubm5yszMlNlsltVq1c0336zExEQ1NDRoxYoVamho8MnnAQDgDyEdTD3yyCOaO3euLBaLUlJStGTJEu3Zs6fbGMMw9OCDDyo9PV2jRo3SZZddppKSkm5j2tradOeddyo5OVmxsbG69tprVVVV1W1MfX29li5dKpvNJpvNpqVLl/I/cQAAAHjNbrf3u1zBarXKbrf7tQ7CKQDAUBLSwdTatWt1++23a+PGjXrrrbfU2dmpRYsWdduF5LHHHtMTTzyhZ555Rlu2bFFaWpquvPJKNTU1ecYsX75cq1ev1ssvv6x169apublZixcvVldXl2fMjTfeqKKiIhUUFKigoEBFRUVaunRpQM8XAAAAQ5fZbFZeXl6fY/Ly8gKyWx7hFABgqBhSzc9ra2uVkpKitWvX6tJLL5VhGEpPT9fy5ct17733Sjo1Oyo1NVWPPvqovv71r6uxsVGjR4/WH/7wB91www2SpMOHD2vcuHH65z//qauuukqlpaWaMmWKNm7cqHnz5kmSNm7cqPnz52v37t3Kzs72qj6anwMAAKC3xuRWq1V5eXlyOBwBrcXpdHp6TtlsNt1yyy30nAIA+N2wbX7e2NgoSUpMTJQklZeXq7q6WosWLfKMiYqK0sKFC7V+/Xp9/etf17Zt29TR0dFtTHp6unJycrR+/XpdddVV2rBhg2w2myeUkqQLL7xQNptN69evP2sw1dbWpra2Ns/vT//iAwAAACPT2RqTB2Km1JncM6fc4dSLL75IOAUACCkhvZTvdIZh6J577tEll1yinJwcSVJ1dbUkKTU1tdvY1NRUz3PV1dWKjIxUQkJCn2NSUlJ6fGZKSopnTG8eeeQRT08qm82mcePGDf4EAQAAMGz01pg8WKxWq2655RYlJiaqsbFRL774Isv6AAAhY8gEU3fccYd27NihP//5zz2eM5lM3X5vGEaPY2c6c0xv4/t7n/vuu0+NjY2ex8GDB/s7DQAAACDgLBaLbrnlFiUlJRFOAQBCypAIpu68806tWbNG7777rjIyMjzH09LSJKnHrKaamhrPLKq0tDS1t7ervr6+zzFHjx7t8bm1tbU9ZmOdLioqSlartdsDAAAACEUWi0U333wz4RQAIKSEdDBlGIbuuOMOrVq1Su+8846ysrK6PZ+VlaW0tDS99dZbnmPt7e1au3atLrroIknS7NmzFRER0W3MkSNHtHPnTs+Y+fPnq7GxUZs3b/aM2bRpkxobGz1jAAAAgKGut3DqzB/gAgAQSCG9K9+3vvUt/elPf9Lf//73bg3IbTabRo0aJUl69NFH9cgjj+iFF17QpEmT9PDDD+u9997Tnj17ZLFYJEnf/OY39frrr+vFF19UYmKivvOd7+j48ePatm2bwsLCJElXX321Dh8+rGeffVaS9LWvfU3jx4/Xa6+95nW97MoHAACAoaCpqUkrVqzQ8ePHZbPZdPPNN/foyQoAwGANJB8J6WDqbP2dXnjhBd1yyy2STs2q+tGPfqRnn31W9fX1mjdvnn75y196GqRL0smTJ/Xd735Xf/rTn9Ta2qrLL79cv/rVr7o1K6+rq9Ndd92lNWvWSJKuvfZaPfPMMwPasYRgCgAAAEMF4RQAwF+GTTA11BBMAQAAYCghnAIA+MNA8pGQ7jEFAAAAwH/O7Dm1YsUKek4BAAKKYAoAAAAYwdzhVHJyMg3RAQABRzAFAAAAjHAWi0Vf/vKXlZycLKfT6QmnXC6XKioqVFxcrIqKCrlcrmCXCgAYZugx5UP0mAIAAMBQ1tzcrBUrVujYsWMaNWqUwsLC1Nzc7HnearUqLy9PDocjiFUCAEIdPaYAAAAADFhcXJxuvvlmWSwWtba2dgulpFPfaKxcuVKlpaVBqhAAMNwQTAEAAADwiImJUX+LKgoKCljWBwDwCYIpAAAAAB6VlZU9Zkqdyel0qrKyMkAVAQCGM4IpAAAAAB5NTU0+HQcAQF8IpgAAAAB4WCwWr8YdOHBAbW1tfq4GADDchQe7AAAAAAChw263y2q1yul09jlu27ZtKikp0dy5czVv3jzFxsYGqEIAwHDCjCkAAAAAHmazWXl5eX2OmTNnjpKSknTy5El98MEHevLJJ/WPf/xD9fX1AapyZHK5XKqoqFBxcbEqKipoQA9gWDAZ/W25Aa85nU7ZbDY1NjbKarUGuxwAAABg0EpLS1VQUNBt5pTValVeXp4cDocMw9Du3bv14Ycf6tChQ5Ikk8mkqVOn6uKLL1ZaWlqwSh+W+rseABBKBpKPEEz5EMEUAAAAhhOXy6XKyko1NTXJYrHIbrfLbO6+6MIwDB04cEAffvihysrKPMcnTpyoiy++WOPHj5fJZAp06cNKaWmpVq5cedbn8/PzCacAhJSB5CP0mAIAAADQK7PZrMzMzD7HmEwmZWZmKjMzU9XV1frwww9VUlKisrIylZWVKSMjQxdffLGys7ODFlB5E7CFKpfLpYKCgj7HFBQUKDs7e8icEwCcjhlTPsSMKQAAAECqq6vThg0bVFhYqK6uLklScnKyLr74YuXm5iosLMwz1t+hUaCWwPnqPDo6OlRbW6ujR4/q6NGjqqio0NGjR/t93c0339xviAgAgcJSviAhmAIAAAD+o7m5WZs2bdKWLVvU1tYm6VQodOGFF2r27Nnat2+fX0OjQC2BG0z4ZRiGnE6nJ4ByP44fP67BfIu2ePFizZ49e9DnAAC+RDAVJARTAAAAQE9tbW3aunWrNm7cqObmZklSRESEOjo6zvqacw2NXC6XnnrqqW5h0ZmsVquWLVt2TjO0vAm/Jk6cqJqamh4h1MmTJ3t9zahRo5SWlqaUlBSFhYVp/fr1/dYRHh6uuXPnav78+bJYLIM+HwDwBYKpICGYAgAAAM6us7NTO3bs0Lp161RfX9/n2NjYWH3+85+XYRjq6uqSy+VSV1dXj8fZjjc0NKi0tLTfmnJycpSYmKiwsDCFh4crLCzM8+jv92azWS+99JInbOuN2WyWy+U663PJyclKTU1VSkqK0tLSlJqaqri4OE8/Lm8CttM/IywsTDNnztRFF12khISEfs8/GIZyz6/TDZfzAPyBYCpICKYAAACA/u3fv19/+MMfgl1GQMXExHiCJ/cjOTlZ4eH970fV36ys66+/XuHh4frggw9UVVUl6VRT+tzcXF1yySUaPXq0z87jXAWq55e/DZfzAPyFYCpICKYAAACA/hUXF2vVqlX9jouNjVVMTIxndtLps5XOnLl05q+bmpq0c+fOfj/D4XAoLi5OnZ2dcrlc6uzs9My6Ov3Xvf2+ra1NnZ2d/X7Gpz71Kc2dO9erP5uz8SYIMQxDBw4c0Lp167Rv375u53jJJZcoPT39nGo4V4Hq+eVvw+U8AH8aSD7SfzwPAAAAAD7kbQ+kz3/+84Peac69zKq/HlOf//znB738qqKiQitWrOh3nC9mLDkcDmVnZ/e5dMxkMikzM1OZmZk6dOiQ1q1bp927d6u0tFSlpaWaMGGCFixYoPHjx59zPQPlcrlUUFDQ55iCggJlZ2eH9HK44XIeQCghmAIAAAAQUHa7XVartd/QyG63D/ozzGaz8vLy+pzZkpeXd07hQSDO43Rms9nroG7s2LG64YYbVFNTow8//FDFxcXat2+f9u3bJ7vdrksuuUQTJ0709LJy81ffpLKysj7/nKRTMywqKysHHUb6m2EYKiwsHPLnAYQalvL5EEv5AAAAAO8EajmUv3sBDZVlXfX19frwww9VVFSkrq4uSVJaWpoWLFigyZMny2w2++TP6uTJk6qtre3x6C/MccvNzdWFF16otLS0oM84MgxDtbW1qqioUHl5uQ4cOKDW1lavXnvBBRfo8ssvV2RkpJ+rPDc0cIe/0GMqSAimAAAAAO8FqoG0v7/5HkqNsJuamrRhwwZt3bpVHR0dkqSkpCSdd9552rJly1lfd2bA1tra2msA1dTU5JM6IyIiNG7cONntdtntdmVkZCgiIsLr1w/mmhuGobq6Ok8QVVFRoRMnTnQbEx4e7lVfMffYSZMmacqUKTr//PNDLqQaSn9vMfQQTAUJwRQAAAAwMMNlxsZQO4+WlhZt2rRJmzdv1smTJ/sdHx0drdzcXB07dky1tbVqbm4+61ir1arRo0crOTlZKSkpGj16tBITE/Xcc8/1OXMqKipK48aNU1VVVY+azGazxowZI7vdrvHjx2vcuHGKiYnp9X0GErg0NDR0C6LOrC88PFx2u12ZmZnKyspSWlqann766T7PIzIyUjExMWpoaOj2PqEUUg2VmX4YugimgoRgCgAAAMBQ0tbWpjfffFPbt28f8GutVqtSUlK6BVDJycmKjo7udby3YYhhGKqpqdGBAwd08OBBHThwoNeZWKNHj/bMqLLb7YqPj+/3M6655hpFRER4gqj6+vpuz4eFhSkjI8MTRI0dO1bh4d1bM3tzHpMnT1Z1dbVKSkq0a9eubp8zkJDKH4Gny+XSU0891W9vtGXLloV0uIrQRjAVJARTAAAAAIaa4uJirVq1qt9xkyZNksPh8IRRUVFRA/6swSwfMwxDDQ0Nqqys9DyOHTvWY5zFYtHJkyc9SxS9YTKZNHbsWE8QNW7cOK+WDA7kPAzDUHV1tXbt2qWSkpIeIdXEiRM9IdXpf6b+WGpnGIb27NmjV155pd+xN998Mw3cMWgEU0FCMAUAAABgqKmoqNCKFSv6HeeroMIXs4BOnDjhmU118OBBHT58WN5+a5uYmKjs7GxlZWXJbrcPKmCTBt/Hyh1S7dq1S3V1dZ7nTg+pDMPQ6tWrz/o+vS21c7lcampqUlNTk5xOp+fh/r37v+7m9/257rrrlJub69VY4EwEU0FCMAUAAABgqBkOS7va29v1wQcfaN26df2ODZXAxTAMHT161LPc7/SQqj9RUVHKzc1Vc3OzJ4A6ceKE1+GcN6ZOnaorrrhC8fHxPntPjBwDyUfC+3wWAAA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      "text/plain": [
       "<Figure size 1200x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert Event.Date to datetime\n",
    "df['Event.Date'] = pd.to_datetime(df['Event.Date'])\n",
    "\n",
    "# Extract the year and filter the data from 1980 onwards\n",
    "df['Year'] = df['Event.Date'].dt.year\n",
    "df_filtered = df[df['Year'] >= 1982]\n",
    "\n",
    "# Group by year and sum up injuries\n",
    "yearly_data = df_filtered.groupby('Year').agg({\n",
    "    'Total.Fatal.Injuries': 'sum',\n",
    "    'Total.Serious.Injuries': 'sum',\n",
    "    'Total.Minor.Injuries': 'sum',\n",
    "    'Total.Uninjured': 'sum'\n",
    "}).reset_index()\n",
    "\n",
    "# Creating subplots\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(12, 12))\n",
    "\n",
    "# Plotting injuries on the first subplot\n",
    "ax1.plot(yearly_data['Year'], yearly_data['Total.Fatal.Injuries'], label='Fatal Injuries', color='blue', marker='o')\n",
    "ax1.plot(yearly_data['Year'], yearly_data['Total.Serious.Injuries'], label='Serious Injuries', color='orange', marker='o')\n",
    "ax1.plot(yearly_data['Year'], yearly_data['Total.Minor.Injuries'], label='Minor Injuries', color='green', marker='o')\n",
    "ax1.set_title('Number of People Involved in Accidents per Year From 1982')\n",
    "ax1.set_ylabel('Number of Injuries')\n",
    "ax1.legend()\n",
    "\n",
    "# Plotting uninjured on the second subplot\n",
    "ax2.plot(yearly_data['Year'], yearly_data['Total.Uninjured'], label='Uninjured', color='gray', marker='o')\n",
    "ax2.set_title('Number of Uninjured People per Year From 1982')\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Number of Uninjured')\n",
    "ax2.legend()\n",
    "\n",
    "\n",
    "# Tighten the layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the time series plot above, the number of injured and uninjured airplane passengers has declined over the years.\n",
    "* Thus, the aviation venture is a good venture for the business."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}