05_jupyter-labs-eda-dataviz.ipynb.jupyterlite.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import piplite\n",
    "await piplite.install(['numpy'])\n",
    "await piplite.install(['pandas'])\n",
    "await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from js import fetch\n",
    "import io\n",
    "\n",
    "URL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
    "resp = await fetch(URL)\n",
    "dataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\n",
    "df=pd.read_csv(dataset_part_2_csv)\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n",
    "sns.scatterplot(data=df, x='LaunchSite', y='FlightNumber')\n",
    "plt.xlabel(\"Launch Site\",fontsize=20)\n",
    "plt.ylabel(\"Flight Number\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.scatterplot(data=df, x='FlightNumber', y='LaunchSite', hue='Class')\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n",
    "sns.scatterplot(data=df, x='PayloadMass', y='LaunchSite')\n",
    "plt.xlabel(\"Payload Mass\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.scatterplot(data=df, x='PayloadMass', y='LaunchSite', hue='Class')\n",
    "plt.xlabel(\"Payload Mass\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "data = df.groupby('Orbit').Class.mean()\n",
    "sns.barplot(data=pd.DataFrame(data), x='Orbit', y=data.values)\n",
    "plt.xlabel(\"Orbit Type\",fontsize=10)\n",
    "plt.ylabel(\"Success rate\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n",
    "sns.scatterplot(data=df, x='Orbit', y='FlightNumber')\n",
    "plt.xlabel(\"Orbit Type\",fontsize=10)\n",
    "plt.ylabel(\"Flight Number\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.scatterplot(data=df, x='FlightNumber', y='Orbit', hue='Class')\n",
    "plt.xlabel(\"Flight Number\",fontsize=10)\n",
    "plt.ylabel(\"Orbit Type\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n",
    "sns.scatterplot(data=df, x='PayloadMass', y='Orbit')\n",
    "plt.xlabel(\"Payload\",fontsize=10)\n",
    "plt.ylabel(\"Orbit Type\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Imf973zCZILiW8zU0ayaMnJ1qH6aGDh1Keno68+fPdx0C/PLLLzl06BARERFceOGF/Otf/6Jbt24A/Prrrzz++OOsXr0ah8NBbGwsXbp04ZVXXiEmJsbHozm91IwcHp33G0v/+PP2+Ze3imHigAuoY6+4O8+W5rAjl/98/QfhfrmMrLuToOWPQG6GszM0Fq57i6LoFpgz9mBaNA4ObHD2mS0Ud7iD463u45vk49wc+gtBPz4F+ZkABIfXJ6L3TEYuK8bqb+Gpfi24+/0kdh7JBiAs0MJTfRrQ6/AsQn+Z6dxmsyvg6hchNx0+ugWO7XS2+wfBpY9Cu1sgOLLyXpyKkLYdPrkDUjc6H/v5Q6e74W8POkOVnH8KC+DAL/DpHeDY72yzhkGfZ6DF1RBk92191UFeJmz/FhY+BDnHnW222nDdGxDfFfwDfVufVAs1KnYPHDiQDRs2MGfOHLZu3cqCBQu49NJLSUtLA+DIkSP07NmTyMhIFi9eTHJyMrNmzSIuLo6srCwfV396Gdn5PPLZJrcgBfDt5sM8Pv83MnIq9/4e2fmFvPTdNpb+cZjhLQqI/Pq+P4MUOI8OvXct5qxDmObd+2eQAiguxPzzm8RueZdbmhcTvmS8K0gBkL6X2p9fz396RbBixzEenpfMoA7xrm5HbiEPzd/B1ka3OY+AAWz7Bv47CrYs+jNIgfMIzrePw87lFfNCVBbHAfi/AX8GKYCiAlg9HX75P+df1nL+yUiB/+v3Z5ACyHPAf0fAod98V1d1cnQLfDL0zyAFkHkI3hsI6Xt9VpZUL9X+yNRJ6enp/Pjjjyxfvpzu3bsD0KBBAzp37uxaZ8WKFWRkZPDWW29hsTiH3qhRI3r06OGTmsviaFY+y7ce8dj3XfJh0jLzsFfi6b6jJ/L5eH0K93WNodb6yZ5XKirAlPQeRDWGtG0luoN/eRMad/X83IJsovYupn39RNbvOc7IHk1LrPLCagevtb2DsFVTnQ07l0LXez1vb+m/oUE3CK0akxXLLG07ZOzz3LfyZbhwMNjjPfdLzWQYsOljKMzz3L/033Djh87T6uJZXiZ8/6znvuJCWPc2XPG0TpnKGdWYI1M2mw2bzcb8+fPJy/P85hIbG0thYSHz5s3DMIyz3nZeXh4Oh8NtqWyOMxx5OtOdactbZl4hBUUGzSLM+KVtKX3FQ7+X/iGfn+mc11SK8LQk6oY75zodz87HanH/dd12JIucsFNCVkGOc87DqY7thOJqfHfeI6d5jXMzoCC38mqRqqEoH/avL73/6Bbn/w9SuvwsOPJH6f2pG/UaylmpMWHKYrEwe/Zs5syZQ3h4ON26dePRRx9l48Y/T4t07dqVRx99lJtuuono6Gj69u3L1KlTS9xV9VRTpkzBbre7lvj4yj8CcKY7y57pzrTlLcTqh8VsYmdGMUURTUpfMbq58xSVJ/5B4Ff6FSEZkReQ6nAG4/Agf/IKi936G0UFE3Ri9ynbDHT+xX6q8AZgrsZ/XUaVPDLnYg0Fi+Z1nHf8AiC2ben9kY3BUvlzKauVgGCIPM3/W7VaOd+nRM6gxoQpcM6ZOnDgAAsWLKBPnz4sX76c9u3bM3v2bNc6kyZNIjU1lZkzZ9K6dWtmzpxJixYt2LRpU6nbnTBhAhkZGa4lJSWlEkbjLsoWQNfGnidQX9IsmqiQyr1MNdpmZcBFdXl3w3GOdHjI80pmP4z2t8HhZI/dORcNw2EK8/xci5VjDa5k3Z7jtK1nZ8eRknPaHuxqJ2zjO382NOgGhzd73t6lj1bfU3wA0QnOSf2edLmv9D6puUwmuOjG0k9B9fgnBOsU32lZQ6H7Pzz3mf2g8106xSdnpUaFKYDAwEAuv/xyHn/8cVauXMnQoUN54okn3NaJiopi0KBBTJs2jeTkZOLi4pg2bVqp27RarYSFhbktlS0iOIDnb7iILo3c3xwvbhJV4Xee9STEauEffZrToUEk7+4M4Xiv58D/L5foB0XAjR9RbKuNce1MqNX8zz6TCaPtEA62GsZnuwPJ+PuTYPnLrQtCanH4mrn8c3kGF9WzM21ga95bvdvVHehvZtKVDWl14FPnRFGA+hfDta9D48uct2Q4yc8fuj8MzXpVyOtQaex14bYF7keoTGZod7ve8M9n9ni45TMIjvqzzT8I+k6FuIt8Vla1EtMSBrwGASF/tgVFwJAPIaKhz8qS6sVklGXyUBX011sjePL8888zefJkjh49Wuo2+vfvT0BAAJ9++ulZ7dPhcGC328nIyKj0YHUsK5+0rDwcOQXYg/yJslmJ8MHN0046ed+r4sI86llO4J9zGD8/i/NSfVss+FkoynFgyjqMKS8D8rOdV+D5B3GMcNLyzZgL86jrfwL/7EOY/ALIC4xmd34YFouFaCvYjXQO5Vs5mmui0DATYw8iJqCAgOxUyEl3Xv4dXAtCov68507WESjMdV7iHFKD7sV04pBzbPlZzntr2Wo5/7qW81dx0f/urXbEeYVn6P9+53VJ/9krzHf+YZZ5yPlHyl/ev2qK3Nxcdu3aRaNGjQgMrNm/G6cba0V9fteY35S0tDQGDRrEsGHDaNu2LaGhoaxbt45nn32WAQMGALBw4ULmzp3LkCFDSEhIwDAMvvjiCxYtWsSsWbN8PIKzExkSQGQln9I7HXuQ//+uIrQBUUDDEuv4BYVBUMlf2qj/LU61gMYABAOt3NYMJQ6IO3UDIR7uoWMyQVicc6mJQmtX79OVUv7Mfs4jl/a6vq6k+rIEQHi8cxE5BzUmTNlsNrp06cILL7zAjh07KCgoID4+nuHDh/Poo48C0KpVK4KDgxk7diwpKSlYrVaaNWvGW2+9xa233urjEYiIiMipfvjhB6ZOncr69es5ePAg8+bN45prrvF1WW6q/Wk+X/DlaT4REZGyKtfTfDnHnaeVcx0QaHdO3ajA+5l99dVXrFixgg4dOnDdddedMUzpNJ+IiIhUXRn7nd82sXPpn21NekL/VyrsVHPfvn3p27dvhWy7vNS4q/lERESkAuQcLxmkAHYsgQX3u38lz3lGYUpERETOLOtIySB10o4lzv7zlMKUiIiInFnuGb5K7Uz9NZjClIiIiJxZ4BkmbJ+pvwZTmBIREZEzC6nlnGzuSZOezv7zlMKUiIiInFlQhPOqvVMD1cmr+Sro9giZmZkkJSWRlJQEwK5du0hKSmLv3r0Vsr9zoVsjiIiIyNmx14Xr3/7LfabCnEekKvA+U+vWraNHjx6ux2PGjAHg9ttvZ/bs2RW237JQmBIREZGzFxRRoeHpVJdeeilV/f7iOs0nIiIi4gWFKREREREvKEyJiIiIeEFhSkRERMQLClMiIiLniao+kbs8+GKMClMiIiI1nL+/PwDZ2dk+rqTi5efnA+Dn51dp+9StEURERGo4Pz8/wsPDOXz4MADBwcGYTCYfV1X+iouLOXLkCMHBwVgslRdxFKZERETOA7GxsQCuQFVTmc1m6tevX6lhUWFKRETkPGAymahTpw4xMTEUFBT4upwKExAQgNlcubOYFKZERETOI35+fpU6n+h8oAnoIiIiIl5QmBIRERHxgsJUFWcYBoXFxRQWF58X9wcREe8V59X8y99FqhLNmaqiHDkFHMzI4Xh2AbuOZrF8y2Hsgf7c3LUB9aOCiQgO8HWJIlKFFKQfwJJ9BDb8H2bHAYyGf8dodgXm6Ca+Lk2kxqtRR6aGDh2KyWTCZDIREBBA06ZNmThxIoWFhQAUFRXxwgsv0KZNGwIDA4mIiKBv376sWLHCx5W7O5FbwKfrUziQnstj8zYx4fNNLP79EB+v38eA6St4/fsdpGfn+7pMEakiCjJSsWz/BtMbf8f081uwZRGmxY9gnnUFxqHNvi5PpMarUWEKoE+fPhw8eJBt27YxduxYnnzySaZOnYphGAwZMoSJEycyevRokpOTWb58OfHx8Vx66aXMnz/f16W7HD6Rx5pdx/l28yF2HMkq0T/z+53sT8/xQWUiUhVZ8h2YFo0p2ZF1FL5+mILj+yq/KJHzSI07zWe1Wl03JrvvvvuYN28eCxYsoHHjxnz66acsWLCAfv36udZ/4403SEtL46677uLyyy8nJCSkxDbz8vLIy8tzPXY4HBU6hh+2HuHyVjE8taD0vyjn/bKf1nH2Cq1DRKqJfWuhuMhjl2nXD/gVZFZyQSLnlxp3ZOpUQUFB5Ofn88EHH5CQkOAWpE4aO3YsaWlpfPvttx63MWXKFOx2u2uJj4+v0Jqz84rw9zOTW+j5zREgM6+wQmsQkWokv+QRbDfFNfcGjSJVQY0NU4Zh8N1337F48WIuu+wytm7dSsuWLT2ue7J969atHvsnTJhARkaGa0lJSamwugEuSYhm3e7jdE+oVeo6/S6Mq9AaRKQaqZ9Yel90AsX+tsqrReQ8VOPC1MKFC7HZbAQGBtK3b18GDx7Mk08+CXDOtxawWq2EhYW5LRWpbngQx7PzuC2xIcEBJe9S27FBBM1i9OYoIk5F1giMNoNLdpjMGH3+gyWqUeUXJXIeqXFzpnr06MGMGTMICAggLi7O9a3RCQkJJCcne3zOyfaEhIRKq/N0omxW/tWvNWt3HeOt2zvy/uq9rNhxFJvVwu2JDel/URwxYYG+LlNEqghLZDxFPf6JucHFmFZPh8zDGHXbQ4/HKAyLx9/XBYrUcDUuTIWEhNC0adMS7UOGDOGmm27iiy++KDFv6rnnniMqKorLL7+8sso8o5jQQK5uG8eRE7n86+pWFBQXE2AxEx1ixWyuvG/CFpHqwS+yPkQOpbDRpZiMIgxLEJbwOAUpkUpQ48JUaYYMGcInn3zC7bffztSpU+nZsycOh4Pp06ezYMECPvnkE49X8vlarVAdgRKRs2eJaujrEkTOOzVuzlRpTCYTH3/8MY8++igvvPACzZs355JLLmHPnj0sX76ca665xtclioiISDVkMvSFb2XmcDiw2+1kZGRU+GR0ERERKR8V9fl93hyZEhEREakIClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHhBYUpERETECwpTIiIiIl5QmBIRERHxgsKUiIiIiBcUpkRERES8YPF1ASJey8uC7DQwCsESBEYxxYW5ZJtDOE4YQf5+RNusHM3MIzOvEIvZRGRIAMEBpfz652RAzjEwAUWFgAkCQiCsztnVU1wMmalQmAt+AWCLBT8LBUVFHDmRT35hMVZ/MxaziaLCIiKKjxFAPia/AArwozgvi2JLMBZ7LP4Wv7PbZ0E2ZB2luKiAQlMgmAxMGBwzwsgptuDvZyY00EJGTgFgYLX4kVtYjGEY2IP8CQ8O+N/Yj0NOOphMEBgBQXa33Thy8knLyqewyMDqb6aOPRB/v9PXeMiRS05+Ef5+ZmqFWgmw/Pk3XEFREelZ+djyj2Ax8rEEBGKy1YG/brMgGxwHoagALIHOn4PFenavi4hIJag2YSo1NZUpU6bw5Zdfsm/fPux2O02bNuWWW25h1qxZ/Pzzz6U+t3v37ixfvhyAlStX8vTTT7Nq1SpycnJo1qwZd9xxB6NHj8bvDB8KUgUd3wNLJsLm+VBcCLVbw9/HY970KbaMFDIvmcTSjDrERIbx9JfJbD+cib+fiX5t4xh7RQJ1I4L/3FZRIRzbCcd3g9kMfyyCXz90fpjb60Gvp6BJTwiOKL2erKPw2+fww7OQdQQCw+HiUeS1vZVX16Qza+UeMvMKiQ2zMvfmptTZ/TnW1S87Q0xwFP6dhkNINKx8hazuT5DXqDu28OjTvwYZ+2DpZPjtY8xFBQREJ5Bx9Vv8dCSIKUu3se94DkH+flzXvi6Xt6qNnwne+HEXP20/imFAhwYRTB3YmkbFKZgWjYe9K53bbdwD+v4HohMoLDbYk5bN91sP8/oPOznkyMNmtXBr1wbc0a0hMWGBJcvKKeCnbUeY8tUfrhpu7BzPPd2bUDsskMOOXI4c2k+9/V8R9PMLztcuKILixPsxt78VbDHOn++ySfD7584wVas5XPY4xLVz/kxERKoAk2EYhq+LOJOdO3fSrVs3wsPDeeqpp2jTpg1Wq5VNmzbxxhtvMHjwYK644goAUlJS6Ny5M9999x2tW7cGICAggMjISObNm8cNN9zAHXfcwYgRIwgPD+e7777jH//4Bz179uTjjz/GZDKdsR6Hw4HdbicjI4OwsLAKHbuchmM/zLrSGX7+ymSGwe/Cwocg+xg5dywhcdZh0rML3FZrHB3CB8O7Emv/XxA4thOObofDm2HHUtj1fcl9DngNLrzRGbZOVZgLP70Iy6eU6Mq/8FZe9hvKqysPAXBn5xjGWj4i+Jc3S26n83DIy4RfP8Rx9esYra7FHlzKkZgTB2HOADi6xdVk1O3E/FbP89AXKSVWT2wSxRWtavPUF5tdbf5+JpbeEU/8R1c4g+NfBdrhnh9IMWoxb8MBnv92a4lt9r0glinXtfnz6BZgGAb/TTrAgx8llVj/4iaRTBt0EYt/3c2gnI+xrX6+xDpGx2GY/j4e/q8/HN1Wctw3vAcNEp3BU0TkLFXU53e1mDM1YsQILBYL69at44YbbqBly5Y0btyYAQMG8OWXX3LrrbcSGxtLbGwstWrVAiAqKsrVFhkZSVZWFsOHD6d///688cYbXHTRRTRs2JC77rqLOXPm8Omnn/Lxxx/7eKRSJvt/KRmkAIxiWPkqtLsNigvx/34St7WLLLHazqNZbEl1OB8U5kLKOlg/C6KbeQ5SAN894Qwwnpw4BD+94LErYON7XNf8z0A0pJWV4KR3PG9n/Wxo1R+AsB+ewpx1yPN6AIf/cAtSAKkdxjB5+WGPq6/akUbd8CCsfznVdlWrKKJ/e6dkkALIzcD49SNO5OTz1o87PW7zq99SOZqZ516DI5fJi5I9rr9yxzEOO3JJjCnEtm66x3VM62c7T916ClLwvyN/Rz33iYhUsiofptLS0vjmm28YOXIkISEhHtc5m6NJ33zzDWlpaYwbN65EX79+/UhISODDDz/0+Ny8vDwcDofbIlXAlq9L70tZA7VbAWBJWUXHOv4eV/th2/8+kHMdYDZB+l5I2176drOOQN4Jz305x52hzBPDICj3kCvEhBSmQ3GR53WLCqAw3/lvxwGKc0vZH8COZSVLDKjFkRN5HlZ22n44k7jwINfjLnUsBO37sdT1Tdu/xVqcjSO3sNR1dh7Jcq8hr5DDp6lh0/4M7GRCYSnrGMWQsR/MpcxESN0IBVme+0REKlmVD1Pbt2/HMAyaN2/u1h4dHY3NZsNms/Hwww+fcTtbtzpPT7Rs2dJjf4sWLVzrnGrKlCnY7XbXEh8fX8ZRSIU43ZyZkOg/Q09wNOm5xR5Xq3PyFJ+fvzPE+AVAcMmjWC4mc+mTny0l5w39VbG/jfwiZx1FfqdfF7//hT+zH2b/00y2tseVaAowF2M+zd8XESEBZP4lGKXlAsFRpa5vhMRgmANK7QfcTvEBBFj88DtNEbVsVgrNZ5hEbg11zoPz2BcGpmoz5VNEargqH6ZKs3btWpKSkmjdujV5eaX/BXyqc5kiNmHCBDIyMlxLSkrJuSjiAxcMdF515slFt8CmTwHI6jiCt5NKHsUwm6Bny9rOB0EREN4AWl4NVrvzg9yT5n1Ln6cTEg11LvTcZ6/HlqxgTv76bc6wQmRjz+vWagHpewDIT+hPUVDpQYemlzsD3l9E7lpAn5aenxMc4EdEcABH/nJabu6mDA61HVHqLkwXjyIHf7o29hwyI0MCqBcR5NYWFRJA79a1S62heZ1QfjnqB7FtPO80vL7zKkhTKW9RFw4BW61SaxYRqUxVPkw1bdoUk8nEli3u80IaN25M06ZNCQoKKuWZ7hISEgBITvY8jyM5Odm1zqmsVithYWFui1QBYXEwYEbJD9xG3Z0f0rt/JLfpVWQ37uM2RwjAz2zilRvbExv2l6MjtZpD3Q7Oq9kGTAf/YPftRjeDPs+UHrRCouH6d5x1/VVQBPk3fMC0lRmupn9/n8ahK98pGcxsMdB7Eqx8heJarSjs+SQ2m/vtCdxfgzowaA6Y/7wS1Zb0Do9dYqdZjPtp8UB/M6/c2I79x93nRu07nsO+4JYUd7yr5PYvGQe1WlDXHsQ/ercgPtL9/zeb1cLbt3ck9pSr+UKsFh67siXNattK1DDrjk7UDQ+icYMG7Ov5GoTGuu8zKAJunAv2us7X03zKVbbxXaDTXRCsyeciUjVUi6v5evfuze+//86WLVtKzJu69NJLueiii3jxxRcB2L17N40aNWLDhg1cdNFFrvWysrKoX78+l156KZ999pnbNhYsWMCAAQOYO3cugwcPPmM9upqvCsnPgsxDsPsn55yl+okYRhHFKb+QWy+RPQURFAZGEBXiPBqzeucxokIC6NwokpiwQIL8T/mgzj7unPicdwKMIjiyxXnPqHqdnGEq9CzuNZWxH44kQ+omiG4OsW0oCq3LQUcum/ZnsPtoFq3j7NQLD8KWn0pYxhYCjm3BFNMSIzSOgr3rKI5qBlFN8LfXweJ3hr95CnLgRCrGnhUYmUegfleKzQEcLbaxOz+MDfsyqRsRTOu6draknsBqMVM3PIjVO9MoKjb4W7NoaocFEk6Wc6w7ljnnKjXu7jw69L97TWVkO+8xtf1wJpsPOmgaY6NtXTt1I4JLPaV3yJHL7qNZ/LL3OPUigrkoPpw69kAsfmaKig0On8jFmp1KwLEtWI9uxhzTAr86bZ1BymRyji1jn/Pnm3kYGlwM9njn6U2/0596FBE5VUV9fleLMLVjxw66detGREQETz75JG3btsVsNvPzzz8zbtw4br75Zp577jmg9DAF8OmnnzJkyBCGDRvGqFGjCAsLY8mSJYwfP163RhAREanhKurzu1rM4GzSpAkbNmxg8uTJTJgwgX379mG1WmnVqhXjxo1jxIjS53v81fXXX8+yZcuYNGkSl1xyCbm5uTRr1ozHHnuMBx988KyClIiIiMhfVYsjU1WNjkyJiIhUP+f1TTtFREREqiqFKREREREvKEyJiIiIeEFhSkRERMQLClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHhBYUpERETECwpTIiIiIl5QmBIRERHxgsXXBcgZFBXCiYNw6HdwHIA6bSG8PthifF2ZiIiI4OMjU0OHDsVkMpVY+vTpA8Cvv/5K//79iYmJITAwkIYNGzJ48GAOHz5c6jaXL1+OyWQiPT3dY//vv//OwIEDadiwISaTiRdffLECRlZOiovgwC8wIxE+HAxfPgRv9YQPBkPGfl9XJyIiIlSB03x9+vTh4MGDbsuHH37IkSNH6NmzJ5GRkSxevJjk5GRmzZpFXFwcWVlZ57y/7OxsGjduzDPPPENsbGw5jqQCOPbDe9dB3gn39gO/wNJ/Q362b+oSERERF5+f5rNarR5Dzfz588nIyOCtt97CYnGW2ahRI3r06OHV/jp16kSnTp0AeOSRR87qOXl5eeTl5bkeOxwOr2o4a4eTSwapk377FC59BAIaVk4tIiIi4tE5HZlKT0/nrbfeYsKECRw7dgyAX375hf37y+/UU2xsLIWFhcybNw/DMMptu+diypQp2O121xIfH185O3YcLL2vqACK8iunDhERESlVmcPUxo0bSUhI4D//+Q/Tpk1zzU36/PPPmTBhQpkLWLhwITabzW2ZPHkyXbt25dFHH+Wmm24iOjqavn37MnXqVA4dOlTmfXhrwoQJZGRkuJaUlJTK2XGdNqX32WIgIKRy6hAREZFSlTlMjRkzhqFDh7Jt2zYCAwNd7VdeeSU//PBDmQvo0aMHSUlJbsu9994LwKRJk0hNTWXmzJm0bt2amTNn0qJFCzZt2gRA69atXQGsb9++Zd732bJarYSFhbktlcIeD3U7ee7r+QTY6lROHSIiIlKqMs+Z+vnnn3n99ddLtNetW5fU1NQyFxASEkLTpk1L7Y+KimLQoEEMGjSIyZMn065dO6ZNm8acOXNYtGgRBQUFAAQFBZV531WeLQZumAPLJsGmj52n9my1nUGqeV8w+/z6ARERkfNemcOU1Wr1OAF769at1KpVq1yKKk1AQABNmjRxXc3XoEGDCt1flWCvC1dNg+4PQ1Ge89SerY6ClIiISBVR5jDVv39/Jk6cyMcffwyAyWRi7969PPzwwwwcOLDMBeTl5ZU4omWxWFi9ejVz585lyJAhJCQkYBgGX3zxBYsWLWLWrFln3O6mTZsIDQ11PTaZTFx44YXk5+ezefNmAPLz89m/fz9JSUnYbLbTHiHzKf9giDgPgqOIiEg1ZDLKeKlcRkYG119/PevWrePEiRPExcWRmppKYmIiixYtIiTk7CdFDx06lDlz5pRob968OYsWLeKZZ57h+++/JyUlBavVSrNmzRgxYgRDhw4tdZvLly/3ePsEPz8/CgsL2b17N40aNSrR3717d5YvX35WdTscDux2OxkZGZU3f0pERES8UlGf32UOUyf99NNPbNy4kczMTNq3b0+vXr3KraiqTmFKRESk+qlyYep8pjAlIiJS/VTU5/c5zWJesmQJV199NU2aNKFJkyZcffXVfPfdd+VWlIiIiEh1UeYw9dprr9GnTx9CQ0MZPXo0o0ePJiwsjCuvvJLp06dXRI0iIiIiVVaZT/PVq1ePRx55hFGjRrm1T58+ncmTJ5frV8pUVTrNJyIiUv1UmdN86enp9OnTp0T7FVdcQUZGRrkUJSIiIlJdlDlM9e/fn3nz5pVo/+9//8vVV19dLkWJiIiIVBdlvmlnq1atmDRpEsuXLycxMRGA1atXs2LFCsaOHcvLL7/sWveBBx4ov0pFREREqqAyz5nydMNLjxs2mdi5c+c5FVXVac6UiIhI9VNRn99lPjK1a9euctu5iIiISHVX5jlTy5Ytq4g6RERERKqlMoepPn360KRJE55++mlSUlIqoiYRERGRaqPMYWr//v2MGjWKTz/9lMaNG9O7d28+/vhj8vPzK6I+ERERkSqtzGEqOjqahx56iKSkJNasWUNCQgIjRowgLi6OBx54gF9//bUi6hQRERGpks7pu/lOat++PRMmTGDUqFFkZmbyzjvv0KFDBy655BJ+//338qpRREREpMo6pzBVUFDAp59+ypVXXkmDBg1YvHgxr776KocOHWL79u00aNCAQYMGlXetIiIiIlXOWYepyy67jPT0dO6//37q1KnDPffcQ0JCAhs2bGDVqlXcddddhISE0LBhQ6ZNm8Yff/xRkXWLiIiIVAlnfZ+p77//nvz8fDZv3swrr7zCddddh9Vq9bhudHS0bqEgIiIi54WzDlMnb5S+ZMmSM2/UYqF79+7nXpWIiIhINVGmO6Bv3ryZ1NTU067Ttm1brwoSERERqU7KFKZ69uyJp6/yM5lMGIaByWSiqKio3IoTERERqerKFKbWrFlDrVq1KqoWERERkWqnTGGqfv36xMTEVFQtcqrcDCjIBksQBIVX/P7yTkB+FvgFQHCks60gx1mHyQ9sVSBIZx2FogIIsoN/sG9ryc92vjZ+/hAS7dtaRETEZ7y6aWdlOnLkCPfddx/169fHarUSGxtL7969WbFiBQC//vor/fv3JyYmhsDAQBo2bMjgwYM5fPiw23bmzJlDp06dCA4OJjQ0lO7du7Nw4UJfDKl0uQ7YuwY+vg3evAw+ugV2r4CcjIrZX342HPwVPr8H3uwBH9wAu1fCka2w6GFnDf/XH5I+gBOHKqaGM8k8DJs+gXevgbcug4VjnPUVFlR+LUWFcHQrfDkO3urprGnjx84aRUTkvHPWR6a6d+9OQEBARdZyWgMHDiQ/P585c+bQuHFjDh06xJIlS0hLS+PIkSP07NmTq6++msWLFxMeHs7u3btZsGABWVlZrm2MGzeOV199laeffpprrrmGgoIC3nvvPQYMGMBLL73EqFGjfDY+l6JC2PoVfH73n22OAzD7R+j3Elx4I1g835LinO1dBe9fD0ax83FBDhRkwZvXO49UATj2w/z7IOFKGPAyhFTiUarsNFj8qDNMnfTrh/D7PLjzW6hTyRc9HEmGt3pBYa7zsWM/fD4cLhgIfadCSFTl1iMiIj5lMjzNKK9i0tPTiYiIYPny5R5vuTB//nwGDRpETk4OFovnfLh69WoSExN5+eWXuf/++936xo4dyyuvvMKOHTuIj48/Yz0OhwO73U5GRgZhYWHnNqjSpKfAzG7O00en8g+GkWsgvH757c9xEN7uBRn7/mxLHAmH/4AdpdwG464lUK9j+dVwJgc3wuuXeO6rfzEM+QCCIyqnlpzjMPcW2POT5/57fqz8cCciImeloj6/q8VpPpvNhs1mY/78+eTl5ZXoj42NpbCwkHnz5nm82hDgww8/xGazcc8995ToGzt2LAUFBXz22Wcen5uXl4fD4XBbKkz2Uc9BCpzzpzLL+TRb7nH3IAVQrzPsXFr6c5K/KN8azmTHaWrZu7L016si5DpKD1IA276pvFpERKRKqBZhymKxMHv2bObMmUN4eDjdunXj0UcfZePGjQB07dqVRx99lJtuuono6Gj69u3L1KlTOXToz+CxdetWmjRp4vFUZVxcHGFhYWzdutXj/qdMmYLdbnctZ3P06pyZ/c7QX6ZrBs7M5GF/RvHp92MJLN8azuR0pzVNZjCZKq8Wk+n0P6PKfm1ERMTnqkWYAuecqQMHDrBgwQL69OnD8uXLad++PbNnzwZg0qRJpKamMnPmTFq3bs3MmTNp0aIFmzZtcm3jXM9oTpgwgYyMDNeSkpJSHkPyLDgaQmNL6Yss/7lKQZEQ09K9bft30LJ/6c9pdZq+itDkstL7ml/555WHlSEoEppfXXp/s8srrxYREakSyhymJk6cSHZ2don2nJwcJk6cWC5FlSYwMJDLL7+cxx9/nJUrVzJ06FCeeOIJV39UVBSDBg1i2rRpJCcnExcXx7Rp0wBISEhg586d5Ofnl9jugQMHcDgcJCQkeNyv1WolLCzMbakwoXVg4FvOy+3/ymyB695y9pcnWy24ZiYEhPzZ9vvn0O4WCIsruX63Bz23V6TQWLjs8ZLtIbXg8olgDa28Wqw2uPxJsHm4Rcilj5b/z0dERKq8Mk9A9/Pz4+DBgyXuN5WWlkZMTEyl3gH9+eefZ/LkyRw9etRjf//+/QkICODTTz9l1apVXHzxxR4noI8ZM4ZXX321akxAByjMh/S9sOFdOJgEtS+ADreDvT74V8BppOIi5/42fQJ7VkBkE+hyr/P02o6lsPm/EBwFXe6GqGaVeyTopJzjkLYT1r7uvAVB877OpTwn45dFegps+Qq2LHIG0s73OF+3ypoILyIiZVZRn99lnoBz8mtjTvXrr78SGVkxH7JpaWkMGjSIYcOG0bZtW0JDQ1m3bh3PPvssAwYMYOHChcydO5chQ4aQkJCAYRh88cUXLFq0iFmzZgGQmJjI6NGjGT9+PPn5+W63RnjppZd48cUXK3YuVFlYAiC6KfT8l/Pye79A8DvDXCpvmP0gshFcMs55JZ+fFfz+96vR8Q5oewOY/Z11+UpQBNTrAHWmQ1G+88rGypwrdarweOg8HNrd7PvXRkREfOqsw1RERAQmkwmTyURCQoJboCoqKiIzM5N77723Qoq02Wx06dKFF154gR07dlBQUEB8fDzDhw/n0Ucf5eDBgwQHBzN27FhSUlKwWq00a9aMt956i1tvvdW1nRdffJG2bdvy2muv8c9//hM/Pz/at2/P/Pnz6devX4XU7hWzn/vptwrfn9nz/iqzhjPx8y95CtRXTKaq9dqIiIhPnPVpvjlz5mAYBsOGDePFF1/Ebre7+gICAmjYsCGJiYkVVmhVUuGn+URERKTc+fw03+233w5Ao0aNuPjii/H3ryJHB0RERER86KzClMPhcCW4du3akZOTQ05Ojsd1daRGREREzidnFaYiIiJcV/CFh4d7nIB+cmJ6ZV7NJyIiIuJrZxWmli5d6rpSb9myZRVakIiIiEh1Ui2+6Liq0QR0ERGR6sfnE9D/6vjx47z99tskJycD0KpVK+64444Ku8+UiIiISFVV5q+T+eGHH2jYsCEvv/wyx48f5/jx47z88ss0atSIH374oSJqFBEREamyynyar02bNiQmJjJjxgz8/ndX7qKiIkaMGMHKlSvdvli4ptJpPhERkeqnoj6/y3xkavv27YwdO9YVpMD5fX1jxoxh+/bt5VaYiIiISHVQ5jDVvn1711ypv0pOTubCCy8sl6JEREREqouzmoC+ceNG178feOABRo8ezfbt2+natSsAq1evZvr06TzzzDMVU6WIiIhIFXVWc6bMZjMmk4kzrXq+3LRTc6ZERESqH5/eGmHXrl3ltkMRERGRmuSswlSDBg0AKCgo4J577uHxxx+nUaNGFVqYiIiISHVQpgno/v7+fPbZZxVVi4iIiEi1U+ar+a655hrmz59fAaWIiIiIVD9l/jqZZs2aMXHiRFasWEGHDh0ICQlx63/ggQfKrTgRERGRqq7Md0A/3Vwpk8nEzp07vS6qqtPVfCIiItVPlfmiY13ZJyIiIvKnMs+ZOuno0aMcPXq0PGsRERERqXbKFKbS09MZOXIk0dHR1K5dm9q1axMdHc2oUaNIT0+voBJFREREqq6zPs137NgxEhMT2b9/PzfffDMtW7YEYPPmzcyePZslS5awcuVKIiIiKqxYERERkarmrMPUxIkTCQgIYMeOHdSuXbtE3xVXXMHEiRN54YUXyr3I80JhHmQegqw08POHkGgIjfV1VSIiInIGZ32ab/78+UybNq1EkAKIjY3l2WefZd68eeVaHMDQoUO55pprSrQvX74ck8lEenq669+eltTUVNdzjh07xoMPPkiDBg0ICAggLi6OYcOGsXfv3nKvu0yyj8HPb8P0LvDmpTCzG7zTBw4kQXGxb2sTERGR0zrrI1MHDx6kdevWpfZfcMEFbsHFF7Zs2VLiUseYmBjAGaS6du1KQEAAM2fOpHXr1uzevZt//vOfdOrUiVWrVtG4cWNflA17VsDiCe5tx3fBnKvh3hUQ0cA3dYmIiMgZnXWYio6OZvfu3dSrV89j/65du4iMjCy3ws5FTEwM4eHhHvsee+wxDhw4wPbt24mNdZ4+q1+/PosXL6ZZs2aMHDmSr776qhKr/Z/Mw7D035778k7AjqXQ8Y7KrUlERETO2lmf5uvduzePPfYY+fn5Jfry8vJ4/PHH6dOnT7kWV16Ki4uZO3cuN998sytInRQUFMSIESNYvHgxx44d8/j8vLw8HA6H21Juigrg6LbS+/etK799iYiISLkr0wT0jh07uo7itGjRAsMwSE5O5rXXXiMvL4933323QopcuHAhNpvNra2oqKjEeqceNWvQoAG///47R44cIT093XUF4qlatmyJYRhs376dzp07l+ifMmUKTz31lBcjOA0/C0Q2grQdnvvjLqyY/YqIiEi5OOswVa9ePVatWsWIESOYMGECJ7+FxmQycfnll/Pqq68SHx9fIUX26NGDGTNmuLWtWbOGW265xa3txx9/JDQ01PXY39/frb+M35zjMmHCBMaMGeN67HA4ym+sttpw6WPw2bCSff7B0OyK8tmPiIiIVIgyfZ1Mo0aN+Oqrrzh+/DjbtjlPTTVt2rTC50qFhITQtGlTt7Z9+/Z5rM/TnKlatWoRHh5OcnKyx+0nJydjMplK7OMkq9WK1Wote+Fnq8ml0OMx+GEqFP3vNGpoLAx+D8I8z1ETERGRqqHM380HEBER4fF0WFVlNpu54YYbeP/995k4caLbvKmcnBxee+01evfu7bsJ9MFRkDgK2g523mvKYoWQWhBaB0wm39QkIiIiZ+Wcv5uvKjp8+DCpqaluS0FBAQCTJ08mNjaWyy+/nK+++oqUlBR++OEHevfuTUFBAdOnT/dt8QHBzlsgxHeGOhdCWJyClIiISDVQo8JU8+bNqVOnjtuyfv16AKKioli9ejU9evTgnnvuoUmTJtxwww00adKEn3/+2Xf3mBIREZFqzWSc66zs85jD4cBut5ORkVHiJqEiIiJSNVXU53eNOjIlIiIiUtkUpkRERES8oDAlIiIi4gWFKREREREvKEyJiIiIeEFhSkRERMQLClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHhBYUqkNEWFJdsMA4o9tJ+lwqJiLwoSEZGqyOLrAkSqlLxMSN8Lv8xx/jehNzS5DKx2OLYD1s+C7DRoPRDqdwV73TNusqjYYP/xbL7+PZWfdx+neW0b17avR93wIAL9/SphUCIiUpGq7JGpoUOHYjKZuPfee0v0jRw5EpPJxNChQ93WPXXp06eP2/NWrlzJlVdeSUREBIGBgbRp04bnn3+eoqKiyhiSVHX5WbB5PsxIhDUzYcsi+GI0vHkZpG2D966DX/4P/vgSPhsGc66G9JQzbjb5oIO+L/3I5EV/8O3mQ7y6bAe9X/iB1TvTdKRKRKQGqLJhCiA+Pp65c+eSk5PjasvNzeWDDz6gfv36buv26dOHgwcPui0ffvihq3/evHl0796devXqsWzZMv744w9Gjx7N008/zZAhQzAMo9LGJVVU5mFYcH/J9qwjsPRpaHeLe/uxnbDyZSjMK3WTR07kMXruBrLy3QN7YbHB/R9s4NCJ0p8rIiLVQ5UOU+3btyc+Pp7PP//c1fb5559Tv3592rVr57au1WolNjbWbYmIiAAgKyuL4cOH079/f9544w0uuugiGjZsyF133cWcOXP49NNP+fjjj0utIy8vD4fD4bZIDZSyFoxSjhTtWg7xXUq2b3gPso6WusljWfnsOJLlse9EXiEH0nM89omISPVRpcMUwLBhw5g1a5br8TvvvMMdd9xRpm188803pKWlMW7cuBJ9/fr1IyEhwe0o1qmmTJmC3W53LfHx8WXav1QTBdml9xmG56BVmAuUflSzqPj0p/HyCnSKWUSkuqvyYeqWW27hp59+Ys+ePezZs4cVK1Zwyy23lFhv4cKF2Gw2t2Xy5MkAbN26FYCWLVt63EeLFi1c63gyYcIEMjIyXEtKypnnyUg15OnI00kxLSHDw8+92RVgDSv1aeHBAUSGBHjss5hN1I8MLmuVIiJSxVT5q/lq1arFVVddxezZszEMg6uuuoro6OgS6/Xo0YMZM2a4tUVGRro9Ptd5UVarFavVek7PlWokNBYuvBF+PeUopdkPLvsnfPO4e7t/EPR6EgJLD1O1wwKZOKA1oz7YUKJv1GVNibLp90pEpLqr8mEKnKf6Ro0aBcD06dM9rhMSEkLTpk099iUkJACQnJzMxRdfXKI/OTmZVq1alVO1Um0FR8LlE6HR3+Gn551zoep1gsseh5Ba0Gm48yq/PAc06QXd/wGRjU+7ST+zie4JtfjknkSmfvMHf6SeID4imNE9m9GxYSQh1mrxv6CIiJxGtXgn79OnD/n5+ZhMJnr37l3m519xxRVERkby3HPPlQhTCxYsYNu2bfz73/8ur3KlOrPFwEU3QdNezptzBtj+PPLU9T64YCAYRc77TllDzmqToYH+dGoUyRu3diSnoAirxUxkiI5IiYjUFNUiTPn5+ZGcnOz6tyd5eXmkpqa6tVksFqKjowkJCeH1119nyJAh3H333YwaNYqwsDCWLFnC+PHjuf7667nhhhsqfBxSjdhiSraZTBBa+5w3GR4cQPi5VyQiIlVUtQhTAGFhpc9LAfj666+pU6eOW1vz5s35448/ALj++utZtmwZkyZN4pJLLiE3N5dmzZrx2GOP8eCDD2IymSqsdhEREam5TIbuVllmDocDu91ORkbGGUOeiIiIVA0V9fld5W+NICIiIlKVKUyJiIiIeEFhSkRERMQLClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHhBYUpERETECwpTIiIiIl5QmBIRERHxgsXXBYiIiMgZ5GdBVhoYhRAQCrZavq7Ia8axXVCQA2YLRqAdc2htAA6nHSO7wMDiZyI6JIDAYJuPKz0znx+Z6tevH3369PHY9+OPP2Iymdi4cSMmk4mkpCSP682ePRuTyVRiCQwMdFsvJSWFYcOGERcXR0BAAA0aNGD06NGkpaWV97BERETKR/peWDAaXmkHL7eD/+sPu1c4A1Y1VJRxAGPHckwf3YxpRqJz+ephjKPb2XUonRve+ZVLX1xNz5fWMPHLLRxMy/B1yWfk8zB155138u2337Jv374SfbNmzaJjx46EhYWdcTthYWEcPHjQbdmzZ4+rf+fOnXTs2JFt27bx4Ycfsn37dmbOnMmSJUtITEzk2LFj5TouERERrzkOwJz+8NsnUFzobDu8GeZcBYc2+7a2c2RO343pvWvh0O/OhuJCTJvnYXr/ehpa0tidlg1AXmExH6xPZfj7SRw+dtyHFZ+Zz8PU1VdfTa1atZg9e7Zbe2ZmJp988gl33nnnWW3HZDIRGxvrttSuXdvVP3LkSAICAvjmm2/o3r079evXp2/fvnz33Xfs37+fxx57rDyHJSIi4r2DSXB8V8l2w4BvHoPs6nUgoDh9Dyx5Cozikp3Hd8GBDbwy5CK35t8OZLLnWG7lFHiOfB6mLBYLt912G7Nnz8YwDFf7J598QlFRETfeeKPX+zh27BiLFy9mxIgRBAUFufXFxsZy880389FHH7nt/6/y8vJwOBxui4iISIXburj0vn1roSC78mopB6aiQkwpa0pfYcdSujePKdG8dmfVno7j8zAFMGzYMHbs2MH333/vaps1axYDBw7Ebref1TYyMjKw2WxuS9++fQHYtm0bhmHQsmVLj89t2bIlx48f58iRIx77p0yZgt1udy3x8fFlHKGIiMg5sJ/m8yYoEkxV4mO8DAwIjiq9OzSWzLyCEs21w6wVWJP3qsRPoUWLFlx88cW88847AGzfvp0ff/zxrE/xAYSGhpKUlOS2vPXWW27rlHbk6UwmTJhARkaGa0lJSTmn7YiIiJRJqwFgMnnu6zoCbLU991VRxaF1MTre5bnTZILWA7lu+iq3Zn8/E50bV+2rF6tEmALnRPTPPvuMEydOMGvWLJo0aUL37t3P+vlms5mmTZu6LXXr1gWgadOmmEwmkpOTPT43OTmZiIgIatXy/MOyWq2EhYW5LSIiIhUuLA6ufaPkEajGPaDdLWD2801d58gvIAguuhGj0Smf7yYzxtUvURAQSuqJP+dH+fuZeOPGNtQOqdrjrDL3mbrhhhsYPXo0H3zwAf/3f//Hfffdh6m0NF5GUVFRXH755bz22ms89NBDbvOmUlNTef/997ntttvKbX8iIiLlIiAEWlwN96+H3T9BznFoeAmEx0NI1T5aUxpTREOMfi9hZB6Cnd9DUDg06k6xNZT0omA+Hd6edbuOERMaQMdG0cSGBhAQFOLrsk+ryoQpm83G4MGDmTBhAg6Hg6FDh5ZYZ8uWLSXaWrduDThP4aWmppboj4mJwWw28+qrr3LxxRfTu3dvnn76aRo1asTvv//O+PHjqVu3LpMmTSr3MYmIiHgtIBgiGzuXGsIU2QgiG0H9rq42PyAGiImEjk3q+Ky2c1FlwhQ4T/W9/fbbXHnllcTFxZXoHzJkSIm2k/OXHA4HdeqUfPEPHjxIbGwszZo1Y926dTzxxBPccMMNHDt2jNjYWK655hqeeOIJIiMjy39AIiIiUuOZjHOdlX0eczgc2O12MjIyNH9KRESkmqioz+8qMwFdREREpDpSmBIRERHxgsKUiIiIiBcUpkRERES8oDAlIiIi4gWFKREREREvKEyJiIiIeEFhSkRERMQLClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERL1h8XYCIVB8ncgo4kpnH+j3HMZugfYNIom0BhAb6+7o0ERGfqVFHpo4cOcJ9991H/fr1sVqtxMbG0rt3b1asWOFaZ+XKlVx55ZVEREQQGBhImzZteP755ykqKvJh5SJV3/HsfN74cSeXPfc94z/dyNhPNnLZc8uZs3I36dn5vi5PRMRnalSYGjhwIBs2bGDOnDls3bqVBQsWcOmll5KWlgbAvHnz6N69O/Xq1WPZsmX88ccfjB49mqeffpohQ4ZgGIaPRyBSdW0+4OCVpdvd2gwDpn2zlW2HM31UlYiI75mMGpIg0tPTiYiIYPny5XTv3r1Ef1ZWFg0aNKB79+589tlnbn1ffPEF/fv3Z+7cuQwePLjEc/Py8sjLy3M9djgcxMfHk5GRQVhYWPkPRqSKycwt5N731vHT9jSP/b1a1uaVG9sRFOBXyZWJiJw9h8OB3W4v98/vGnNkymazYbPZmD9/vlvwOembb74hLS2NcePGlejr168fCQkJfPjhhx63PWXKFOx2u2uJj48v9/pFqrK8wiIOnyj5/9VJhxy55BXqVLmInJ9qTJiyWCzMnj2bOXPmEB4eTrdu3Xj00UfZuHEjAFu3bgWgZcuWHp/fokUL1zqnmjBhAhkZGa4lJSWlYgYhUkXZrBa6NI4qtf/iJlGEWHU9i4icn2pMmALnnKkDBw6wYMEC+vTpw/Lly2nfvj2zZ892rXMuZzWtVithYWFui8j5xOrvxx0XN8RqKfmWERzgx42d6+PvV6PeTkREzlqNe/cLDAzk8ssv5/HHH2flypUMHTqUJ554goSEBACSk5M9Pi85Odm1joiUVD8ymE/vu5g2de2utnb1w/n03ouJjwz2YWUiIr5V44/Lt2rVivnz53PFFVcQGRnJc889x8UXX+y2zoIFC9i2bRv//ve/fVSlSNVn8TPTpq6dOcM6kZFdiMkE9iB/IkICfF2aiIhP1ZgwlZaWxqBBgxg2bBht27YlNDSUdevW8eyzzzJgwABCQkJ4/fXXGTJkCHfffTejRo0iLCyMJUuWMH78eK6//npuuOEGXw9DpMqLDLESGWL1dRkiIlVGjQlTNpuNLl268MILL7Bjxw4KCgqIj49n+PDhPProowBcf/31LFu2jEmTJnHJJZeQm5tLs2bNeOyxx3jwwQcxmUw+HoWIiIhUNzXmPlOVqaLuUyEiIiIVR/eZEhEREamCFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHhBYUpERETECwpTIiIiIl5QmBIRERHxgsKUiIiIiBcUpkRERES8oDAlIiIi4gWFKREREREvKEyJiIiIeEFhSkRERMQLClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4weLrAkSkmikqguwjgAlCosHs5+uKRER8qlocmUpNTWX06NE0bdqUwMBAateuTbdu3ZgxYwbZ2dkANGzYEJPJVGJ55pln3LY1Z84cOnXqRHBwMKGhoXTv3p2FCxf6Ylgi1U96CvzwLLzTG2b1gZ9egIx9vq5KRMSnqvyRqZ07d9KtWzfCw8OZPHkybdq0wWq1smnTJt544w3q1q1L//79AZg4cSLDhw93e35oaKjr3+PGjePVV1/l6aef5pprrqGgoID33nuPAQMG8NJLLzFq1KhKHZtItZKxD2ZfBel7/mxb+m9Ieh9uXwj2ur6rTUTEh6p8mBoxYgQWi4V169YREhLiam/cuDEDBgzAMAxXW2hoKLGxsR63s3r1ap577jlefvll7r//flf7pEmTyM3NZcyYMQwYMID4+PiKG4xIdVVcDL997h6kTjq2E7Z+DZ3urPy6RESqgCp9mi8tLY1vvvmGkSNHugWpvzKZTGe1rQ8//BCbzcY999xTom/s2LEUFBTw2WefeXxuXl4eDofDbRE5r+Qch00fl97/6wfOdUREzkNVOkxt374dwzBo3ry5W3t0dDQ2mw2bzcbDDz/san/44Ydd7SeXH3/8EYCtW7fSpEkTAgICSuwnLi6OsLAwtm7d6rGOKVOmYLfbXYuOXsl5x2QCP//S+/0CwKSJ6CJyfqrSYao0a9euJSkpidatW5OXl+dqHz9+PElJSW5Lx44dXf1/PSVYFhMmTCAjI8O1pKSkeD0GkWolOBI63V16f+e7ITCs8uoREalCqvScqaZNm2IymdiyZYtbe+PGjQEICgpya4+OjqZp06Yet5WQkMBPP/1Efn5+iaNTBw4cwOFwkJCQ4PG5VqsVq9V6rsMQqRkaXwr1u8Le1e7tjbpD/USflCQiUhVU6SNTUVFRXH755bz66qtkZWV5ta0hQ4aQmZnJ66+/XqJv2rRp+Pv7M3DgQK/2IVKjhdWBQbNhyIfQ7ApI6A03fQzXvQmhni/8EBE5H1TpI1MAr732Gt26daNjx448+eSTtG3bFrPZzM8//8wff/xBhw4dXOueOHGC1NRUt+cHBwcTFhZGYmIio0ePZvz48eTn57vdGuGll17ixRdf1FwokTMJrQMt6kCTy5yP/QN9W4+ISBVgMs51IlElOnjwIJMnT+bLL79k3759WK1WWrVqxaBBgxgxYgTBwcE0bNiQPXtKXrZ9zz33MHPmTNfjd955h9dee43ff/8dPz8/2rdvz/jx4+nXr99Z1+NwOLDb7WRkZBAWpnkiIiIi1UFFfX5XizBV1ShMiYiIVD8V9fldpedMiYiIiFR1ClMiIiIiXlCYEhEREfGCwpSIiIiIFxSmRERERLygMCUiIiLiBYUpERERES8oTImIiIh4QWFKRERExAtV/rv5qqKTN413OBw+rkRERETO1snP7fL+8heFqXNw4sQJAH0xsoiISDV04sQJ7HZ7uW1P3813DoqLizlw4AChoaGYTKYzru9wOIiPjyclJeW8+C6/82m8GmvNpLHWTBprzXW24zUMgxMnThAXF4fZXH4znXRk6hyYzWbq1atX5ueFhYWdF7/UJ51P49VYayaNtWbSWGuusxlveR6ROkkT0EVERES8oDAlIiIi4gWFqUpgtVp54oknsFqtvi6lUpxP49VYayaNtWbSWGsuX49XE9BFREREvKAjUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwVQmmT59Ow4YNCQwMpEuXLqxdu9bXJZ3WlClT6NSpE6GhocTExHDNNdewZcsWt3UMw+Bf//oXderUISgoiF69erFt2za3dXJzcxk5ciRRUVHYbDYGDhzIoUOH3NY5duwYN998M2FhYYSHh3PnnXeSmZlZ4WMszTPPPIPJZOLBBx90tdWkse7fv59bbrmFqKgogoKCaNOmDevWrXP115SxFhUV8fjjj9OoUSOCgoJo0qQJ//73v92+j6s6j/WHH36gX79+xMXFYTKZmD9/vlt/ZY5t7969XHXVVQQHBxMTE8P48eMpLCyslLEWFBTw8MMP06ZNG0JCQoiLi+O2227jwIEDNW6sp7r33nsxmUy8+OKLbu01aazJycn0798fu91OSEgInTp1Yu/evVVzrIZUqLlz5xoBAQHGO++8Y/z+++/G8OHDjfDwcOPQoUO+Lq1UvXv3NmbNmmX89ttvRlJSknHllVca9evXNzIzM13rPPPMM4bdbjfmz59v/Prrr0b//v2NRo0aGTk5Oa517r33XiM+Pt5YsmSJsW7dOqNr167GxRdf7LavPn36GBdeeKGxevVq48cffzSaNm1q3HjjjZU21r9au3at0bBhQ6Nt27bG6NGjXe01ZazHjh0zGjRoYAwdOtRYs2aNsXPnTmPx4sXG9u3ba9xYJ02aZERFRRkLFy40du3aZXzyySeGzWYzXnrppRox1kWLFhmPPfaY8fnnnxuAMW/ePLf+yhpbYWGhccEFFxi9evUyNmzYYCxatMiIjo42JkyYUCljTU9PN3r16mV89NFHxh9//GGsWrXK6Ny5s9GhQwe3bdSEsf7V559/blx44YVGXFyc8cILL9TIsW7fvt2IjIw0xo8fb/zyyy/G9u3bjf/+979un51VaawKUxWsc+fOxsiRI12Pi4qKjLi4OGPKlCk+rKpsDh8+bADG999/bxiGYRQXFxuxsbHG1KlTXeukp6cbVqvV+PDDD12P/f39jU8++cS1TnJysgEYq1atMgzDMDZv3mwAxs8//+xa56uvvjJMJpOxf//+yhiay4kTJ4xmzZoZ3377rdG9e3dXmKpJY3344YeNv/3tb6X216SxXnXVVcawYcPc2q677jrj5ptvNgyjZo311A+iyhzbokWLDLPZbKSmprrWmTFjhhEWFmbk5eVV+Fg9Wbt2rQEYe/bsMQyj5o113759Rt26dY3ffvvNaNCggVuYqkljHTx4sHHLLbeU+pyqNlad5qtA+fn5rF+/nl69ernazGYzvXr1YtWqVT6srGwyMjIAiIyMBGDXrl2kpqa6jctut9OlSxfXuNavX09BQYHbOi1atKB+/fqudVatWkV4eDgdO3Z0rdOrVy/MZjNr1qyp8HH91ciRI7nqqqvc6oWaNdYFCxbQsWNHBg0aRExMDO3atePNN9909deksV588cUsWbKErVu3AvDrr7/y008/0bdvX6BmjfVUlTm2VatW0aZNG2rXru1ap3fv3jgcDn7//fcKHWdpMjIyMJlMhIeHAzVrrMXFxdx6662MHz+e1q1bl+ivKWMtLi7myy+/JCEhgd69exMTE0OXLl3cTgVWtbEqTFWgo0ePUlRU5PZDAqhduzapqak+qqpsiouLefDBB+nWrRsXXHABgKv2040rNTWVgIAA1xtaaevExMS49VssFiIjIyv19Zk7dy6//PILU6ZMKdFXk8a6c+dOZsyYQbNmzVi8eDH33XcfDzzwAHPmzHHVeLLuv6qOY33kkUcYMmQILVq0wN/fn3bt2vHggw9y8803u2o8WfdfVcexnqoyx5aamupxP3+tozLl5uby8MMPc+ONN7q+7LYmjfU///kPFouFBx54wGN/TRnr4cOHyczM5JlnnqFPnz588803XHvttVx33XV8//33rjqq0lgtZRqhnHdGjhzJb7/9xk8//eTrUipESkoKo0eP5ttvvyUwMNDX5VSo4uJiOnbsyOTJkwFo164dv/32GzNnzuT222/3cXXl6+OPP+b999/ngw8+oHXr1iQlJfHggw8SFxdX48YqTgUFBdxwww0YhsGMGTN8XU65W79+PS+99BK//PILJpPJ1+VUqOLiYgAGDBjAQw89BMBFF13EypUrmTlzJt27d/dleR7pyFQFio6Oxs/Pr8TVBYcOHSI2NtZHVZ29UaNGsXDhQpYtW0a9evVc7SdrP924YmNjyc/PJz09/bTrHD582K2/sLCQY8eOVdrrs379eg4fPkz79u2xWCxYLBa+//57Xn75ZSwWi+svlJow1jp16tCqVSu3tpYtW7qujqlJP9fx48e7jk61adOGW2+9lYceesh19LEmjfVUlTm22NhYj/v5ax2V4WSQ2rNnD99++63rqNTJOmrCWH/88UcOHz5M/fr1Xe9Ve/bsYezYsTRs2NBVR00Ya3R0NBaL5YzvV1VprApTFSggIIAOHTqwZMkSV1txcTFLliwhMTHRh5WdnmEYjBo1innz5rF06VIaNWrk1t+oUSNiY2PdxuVwOFizZo1rXB06dMDf399tnS1btrB3717XOomJiaSnp7N+/XrXOkuXLqW4uJguXbpU5BBdevbsyaZNm0hKSnItHTt25OabbyYpKYnGjRvXmLF269atxC0utm7dSoMGDYCa9XPNzs7GbHZ/e/Pz83P9xVuTxnqqyhxbYmIimzZtcvvAOhlmTv0grCgng9S2bdv47rvviIqKcuuvKWO99dZb2bhxo9t7VVxcHOPHj2fx4sU1aqwBAQF06tTptO9XVW6sZz1VXc7J3LlzDavVasyePdvYvHmzcffddxvh4eFuVw5UNffdd59ht9uN5cuXGwcPHnQt2dnZrnWeeeYZIzw83Pjvf/9rbNy40RgwYIDHS6/r169vLF261Fi3bp2RmJhoJCYmuu2rT58+Rrt27Yw1a9YYP/30k9GsWTOf3RrhpL9ezWcYNWesa9euNSwWizFp0iRj27Ztxvvvv28EBwcb7733Xo0b6+23327UrVvXdWuEzz//3IiOjjb+8Y9/1IixnjhxwtiwYYOxYcMGAzCef/55Y8OGDa4r2CprbCcvK7/iiiuMpKQk4+uvvzZq1apVrpfQn26s+fn5Rv/+/Y169eoZSUlJbu9Xf70SqyaM1ZNTr+arSWP9/PPPDX9/f+ONN94wtm3bZrzyyiuGn5+f8eOPP1bJsSpMVYJXXnnFqF+/vhEQEGB07tzZWL16ta9LOi3A4zJr1izXOsXFxcbjjz9u1K5d27BarUbPnj2NLVu2uG0nJyfHGDFihBEREWEEBwcb1157rXHw4EG3ddLS0owbb7zRsNlsRlhYmHHHHXcYJ06cqIxhlurUMFWTxvrFF18YF1xwgWG1Wo0WLVoYb7zxhlt/TRmrw+EwRo8ebdSvX98IDAw0GjdubDz22GNuH7DVeazLli3z+P/o7bffXulj2717t9G3b18jKCjIiI6ONsaOHWsUFBRUylh37dpV6vvVsmXLatRYPfEUpmrSWN9++22jadOmRmBgoHHhhRca8+fPr7JjNRnGX24JLCIiIiJlojlTIiIiIl5QmBIRERHxgsKUiIiIiBcUpkRERES8oDAlIiIi4gWFKREREREvKEyJiIiIeEFhSkRERMQLClMiIsDu3bsxmUwkJSXViP2ISOVRmBKRamPo0KGYTCZMJhMBAQE0bdqUiRMnUlhY6OvSROQ8ZvF1ASIiZdGnTx9mzZpFXl4eixYtYuTIkfj7+zNhwgRflyYi5ykdmRKRasVqtRIbG0uDBg2477776NWrFwsWLOD555+nTZs2hISEEB8fz4gRI8jMzAQgKyuLsLAwPv30U7dtzZ8/n5CQEE6cOOFxX99//z2dO3fGarVSp04dHnnkEbejYF9//TV/+9vfCA8PJyoqiquvvpodO3a4bWPt2rW0a9eOwMBAOnbsyIYNG8r5FRERX1OYEpFqLSgoiPz8fMxmMy+//DK///47c+bMYenSpfzjH/8AICQkhCFDhjBr1iy3586aNYvrr7+e0NDQEtvdv38/V155JZ06deLXX39lxowZvP322zz99NOudbKyshgzZgzr1q1jyZIlmM1mrr32WoqLiwHIzMzk6quvplWrVqxfv54nn3yScePGVeCrISI+YYiIVBO33367MWDAAMMwDKO4uNj49ttvDavVaowbN67Eup988okRFRXlerxmzRrDz8/POHDggGEYhnHo0CHDYrEYy5cvNwzDMHbt2mUAxoYNGwzDMIxHH33UaN68uVFcXOzaxvTp0w2bzWYUFRV5rO/IkSMGYGzatMkwDMN4/fXXjaioKCMnJ8e1zowZM9z2IyLVn45MiUi1snDhQmw2G4GBgfTt25fBgwfz5JNP8t1339GzZ0/q1q1LaGgot956K2lpaWRnZwPQuXNnWrduzZw5cwB47733aNCgAX//+9897ic5OZnExERMJpOrrVu3bmRmZrJv3z4Atm3bxo033kjjxo0JCwujYcOGAOzdu9e1jbZt2xIYGOjaRmJiYrm/JiLiWwpTIlKt9OjRg6SkJLZt20ZOTg5z5szhyJEjXH311bRt25bPPvuM9evXM336dADy8/Ndz73rrruYPXs24DzFd8cdd7iFpbLq168fx44d480332TNmjWsWbOmxD5FpOZTmBKRaiUkJISmTZtSv359LBbnBcnr16+nuLiY5557jq5du5KQkMCBAwdKPPeWW25hz549vPzyy2zevJnbb7+91P20bNmSVatWYRiGq23FihWEhoZSr1490tLS2LJlC//85z/p2bMnLVu25Pjx4yW2sXHjRnJzc11tq1ev9vYlEJEqRmFKRKq9pk2bUlBQwCuvvMLOnTt59913mTlzZon1IiIiuO666xg/fjxXXHEF9erVK3WbI0aMICUlhfvvv58//viD//73vzzxxBOMGTMGs9lMREQEUVFRvPHGG2zfvp2lS5cyZswYt23cdNNNmEwmhg8fzubNm1m0aBHTpk0r9/GLiG8pTIlItXfhhRfy/PPP85///IcLLriA999/nylTpnhc98477yQ/P59hw4addpt169Zl0aJFrF27lgsvvJB7772XO++8k3/+858AmM1m5s6dy/r167ngggt46KGHmDp1qts2bDYbX3zxBZs2baJdu3Y89thj/Oc//ymfQYtIlWEy/noMW0Skhnv33Xd56KGHOHDgAAEBAb4uR0RqAN0BXUTOC9nZ2Rw8eJBnnnmGe+65R0FKRMqNTvOJyHnh2WefpUWLFsTGxuqrZ0SkXOk0n4iIiIgXdGRKRERExAsKUyIiIiJeUJgSERER8YLClIiIiIgXFKZEREREvKAwJSIiIuIFhSkRERERLyhMiYiIiHjh/wGPZNt9g1LCOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.scatterplot(data=df, x='PayloadMass', y='Orbit', hue='Class')\n",
    "plt.xlabel(\"Payload\",fontsize=10)\n",
    "plt.ylabel(\"Orbit Type\",fontsize=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year():\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "Extract_year()\n",
    "df['Date'] = year\n",
    "df.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ipff1glQC7DyVj8MXSwx+D0vy1aEMqDUCRoR5MdiYKYYbIiIL9vrPZ1FR24heAe6YM8p4T+z0ClBgRkQIAO3k4nqV2mj3MmdXqxsQe1y7Xtq8m+ywTuJjuCEislB7zhVi56l82EklWPFAX9jbGfctPWZ8V3i7ypFeUo2vDtnm5OJ1CZmobVSjV4A7Robpt/IzmQ7DDRGRBVLWNeI/P2l3/H5iVCh6dzD+8Ii7owNevUe7cvEney8it7zW6Pc0JzUNKqw7mglA22vT1kftyXgYboiILNCKX1JRqKxHiJczno/8+x2/DWVK/w4YGuqJukYNlv581mT3NQc/HM/G1ZpGdPR0xoTet79AIhkfww0RkYU5ll6K745pl8lYfn9fODrYmezeEokEy+7rDTupBLvPFmJfWpHJ7i2mRrUGq5uG4uaM7mT0IUBqG/52iIgsSF2jGgu3aoejpg0NQkRnL5PX0M3PDY+PCAEAvL79LOoarX9y8c5T+cgtr4W3qwwPDQoUuxz6Gww3REQW5OP4i0gvqYaPmxwLJui/47eh/CuyK3zd5bhSWoNVB9JFq8MUrt0gc9bwEJP2lNHtYbghIrIQZ/MqsOqgNkgsm9IbCicH0WpxldvjP/f0BAB8vv8SskprRKvF2PZfKEZqQSVcZHZ4bFiI2OVQKzDcEBFZAJVag1e2nIJaI2BiHz9E9RJ/Quu9ff0xIswL9SoNlljx5OKV+7W9NtOGdoTCWbxASa3HcENEZAH+dzgDZ3KVUDg54PXJvcQuB4B2cvGSyb3hYCdBfGoR9pwrFLskgzuRdRXHMspgL5Xg8ZHGWySRDIvhhojIzGWWVOP9PRcAAK/e0wM+bobZ8dsQwnxc8UTTrtivbz+L2gbrmlzcPNfmvv4dEODBDUMtBcMNEZEZEwQBC7eeRr1Kg5Fh3mb5pM78O8MQoHBEbnktPt9/SexyDOZycRV+beqNmjemk8jVkD4YboiIzFjs8WwkpJfC0UGKt/7RxyxXxXWW2WPRJO3k4lUH0pFRUi1yRYax+mA6BAGI7OGDLr5uYpdDemC4ISIyU4XKOry56zwA4IVx3dDRy1nkim4uqpcfxnRtjwa1Bou3n4UgCGKX1CZFyjpsTc4FwA0yLRHDDRGRmVq87Swq61ToG6hAdNOieeZKIpHg9cm9ILOT4uCFYsSdKRC7pDb535EMNKg1GBzcDoNDPMUuh/TEcENEZIbizuQj7mwB7E2047chhHq76OamLN1xDjUNKpEruj3KukZs+F27vQV7bSyT+f+1EBHZmIqaRry2TbtuzLwxndHD313kilrvqbFhCGznhPyKOnwcb5mTi7/7PQuV9Sp08XHFnd19xC6HbgPDDRGRmXlr13kUV9ajU3sXPHNnmNjl6MVJZofXJ2nX4fnqUDouFVWKXJF+6hrVWHNEu0Hm3DGdIZWa3wRu+nsMN0REZuTopRLE/pENAFjxgGl3/DaUyJ6+iOzhA5VGwKJtljW5+McTuSiurIe/whGT+wWIXQ7dJoYbIiIzUdugxoKmHb8fGxaMIRY8kXXxpF6Q20tx9HIpfj6VL3Y5raLWCPiyae+u2SNDIbPnR6Sl4m+OiMhMfPDbBWSV1cBf4YiX7+4mdjltEuTpjKfv0A6pvbHjHKrqzX9y8a9nC5BRUg2FkwOmDe0odjnUBgw3RERm4FROOb46pO01ePMfveHmaPkbND45uhOCvZxRVFmPD5u2jzBXgiDotlp4bFgwXOT2IldEbcFwQ0Qkska1Bi9vPgWNAEzuF4A7u/uKXZJBODrYYUnTJp9rj2YircB8JxcnpJfiZE4F5PZSzDLzNYXo7zHcEBGJ7MuD6UgtqEQ7ZwcsbtrGwFqM7eaDqF6+UGsEvLbtjNlOLl55QNtr9tDgQHi7ykWuhtqK4YaISESXi6vwUfxFAMCiST3hZYUfrIsm9YKTgx0SM8rw44lcscu5ztm8Chy8UAypBHhyFBftswYMN0REItFoBCzcchoNKg1Gd22PKf07iF2SUXTwcML8u7STi9/adR4VtY0iV9TSqqZem4l9/M16/y5qPYYbIiKRbEjMQmJmGZxldnjrH73NcsdvQ3liZCd0au+CkqoGfGBGk4uzy2qw87T2UXVutWA9GG6IiESQX1GLt39JBQC8FNUNge2su8dAZi/F0sm9AQDrEzJxNq9C5Iq0vjqUDrVGwKgu3ujdQSF2OWQgDDdERCYmCAJe++kMqupVGNDRAzMiQsQuySRGdvHGPX39oRGA1346A41G3MnFpVX1utWg2WtjXRhuiIhMbMepfPx2vggOdtodv+1saP+i1+7pCReZHZKzyrE5KUfUWtYlXEFdowZ9OigwvLOXqLWQYTHcEBGZ0NXqBry+Xbvj99N3hKGrr5vIFZmWn8IRz0V2BQC8HZeK8poGUeqoaVBhfUImAG2vjTXPd7JFDDdERCa0bOc5lFY3oKuvK/451rJ2/DaUWSNC0NXXFWXVDXh3d5ooNWxMzEZ5TSNCvJxxd28/UWog42G4ISIykQMXirE1ORcSCfD2A31tdmNGBzsplt6nnVy8ITELp3LKTXr/RrUG/zucAQCYM7qTTQ0L2grb/MsiIjKx6noV/t204/es4SEY2LGdyBWJa1gnL0zpHwChaXKx2oSTi38+mYfc8lp4u8rwwMBAk92XTIfhhojIBP77axpyy2vRwcMJL4637B2/DeXf9/SAm9weJ3MqsPF4lknuKQiCbtG+6BGhcHSwM8l9ybQYboiIjCw56yq+PpoJAHjr/j7ccbqJj5sjnh+nnVz8TlwayqqNP7l4X1oR0gor4SKzw/+FBxv9fiQOhhsiIiNqUGmwYMspCAJw/4AOGNO1vdglmZUZEcHo4e+OitpGrGha1NCYVu7X9tpMD+8IhbOD0e9H4mC4ISIyoi/2X8aFwip4ucjw2r3WteO3IdjbSbHsvl4AgNg/spGcddVo90q6chWJmWVwsJNg9shORrsPiY/hhojISC4WVuLTfdodvxdP7oV2LjKRKzJPg0M88eAg7cReY04uXnngMgBgSv8O8FM4GuUeZB4YboiIjECtEfDKllNoVAu4q7sPJvX1F7sks7ZgQne4O9rjbJ4S3/5+xeDXv1RUhT3nCgEAc8ew18baMdwQERnBNwmZSM4qh6vcHm9Y+Y7fhuDtKsdLUdqnyP77axqKK+sNev0vD2p7bcb19EWYj22tCm2LGG6IiAws52oN3mlaefeVCd3hr3ASuSLLMD08GH06KFBZp8LyX84b7LoFFXX48UQuAG6QaSsYboiIDEgQBLz64xnUNKgxNMQTjw7tKHZJFsNOKsGyKb0hkQBbk3ORmFFmkOuuOZKBRrWAoSGeGBRs24sn2gqGGyIiA/opJRcHLhRDZi/F8gf6QMql/fXSP8gDjwwJAgAs2nYGKrWmTderqG3EhmPaBQLnjeVcG1vBcENEZCClVfVY+vM5AMC/7uqCzu1dRa7IMr0c1R0ezg5ILajULX54u779/Qqq6lXo6uuKsV19DFMgmT2GGyIiA1ny8zlcrWlED393PDmavQS3q52LDK/c3R0A8OFvF1GorLut69Q1qrH2SCYAYO7ozuxFsyEMN0REBrA3tRDbT+ZBKgFWPNAHDnZ8e22LqYOD0C/IA1X1Kry58/YmF29JzkFJVT0CFI6Y3D/AwBWSOeNfHxFRG1XVq/CfH88AAJ4Y1Ql9Az3ELcgKSKUSvHGfdnLx9pN5OHq5RK/XqzUCVh/UbrUwe1Qnhk0bw982EVEbvROXiryKOnT0dMbzkV3FLsdq9AlU6Da3XLTtLBpUrZ9cHHemAJmlNVA4OegmKJPtYLghImqDPzLL8E3TirrL7+8DJ5mdyBVZlxfHd4OXiwyXiqqw5khGq14jCIJuq4WZEcHchd0GMdwQEd2mukY1Xmna8fvhwYEYEeYtdklWR+HsgAUTtJOLP46/iLzy2r99zdHLpTidWwFHBylmDg8xcoVkjhhuiIhu02f7LuFycTXau8nx6kTu+G0sDwwMxODgdqhpUOONnef+9vzmXpuHBwfBy1Vu7PLIDDHcEBHdhvP5SnyxX/shunRyLyicHUSuyHpJpRIsva83pBJg1+kCHLxQfNNzz+RW4NDFEthJJZgzio/j2yqGGyIiPak1AhZsOQWVRkBUL19M6MMdv42tZ4C7bohp8fazqFepb3jeqqYnpO7p448gT2dTlUdmhuGGiEhPa49k4GROBdwc7bH0vt5il2Mznh/XFe3d5MgoqdY95n2trNIa7DyVBwCYO4a9NraM4YaISA9ZpTX476/aHb9fndgDvu6OIldkO9wdHfDqxB4AgE/3XUJ2WU2Ln68+lA6NAIzu2h69AhRilEhmguGGiEgP7/6ahrpGDSI6eWEq108xufv6ByA81BN1jRos3fHn5OKSqnr88Ec2AGAet76weQw3REStlF3257DHa/f2hETCvYpMTSKRYNmU3rCXSrDnXCH2phYCANYdzUS9SoO+gQpEdPYSuUoSG8MNEVErfXXNsEfPAHexy7FZXX3d8PjIUADaycWlVfVYn6BdSHHemM4MncRwQ0TUGmXVDYjlsIfZePauLvB1lyO7rBZTv/wdFbWNCPV2QVQvP7FLIzPAcENE1ArrEzJR16hBnw4c9jAHrnJ7vHavduHES0VVAIA5ozrBTspeG2K4ISL6W7UNaqw7mglA+4gxhz3Mwz19/DGyacsLb1c57h/YQeSKyFyIHm4+++wzhISEwNHREeHh4UhMTLzl+fX19Xj11VcRHBwMuVyOkJAQrFmzxkTVEpEt2pyUjas1jejo6Yy7OexhNiQSCZbf3wdju7XHG1N6w9GBm5aSlqhbpcbGxiImJgYrV65EeHg4PvzwQ0RFRSEtLQ0+Pj43fM3DDz+MwsJC/O9//0NYWBjy8/Oh0WhMXDkR2QqVWoPVh7S7UT8xKhT2dqL/m5CuEeTpjK+jh4pdBpkZiSAIglg3Dw8Px5AhQ/Dpp58CADQaDYKCgjB//nwsWLDguvPj4uLwyCOPID09HZ6enrd1T6VSCYVCgYqKCri782kHIrq1Hafy8MyGE2jn7ICjC+6Ck4y9A0Ri0OfzW7R/gjQ0NCApKQmRkZF/FiOVIjIyEgkJCTd8zfbt2zF48GC888476NChA7p27YoXX3wRtbW1N71PfX09lEpliy8iotYQBAGrDmiX+Z85PITBhshCiDYsVVJSArVaDV9f3xbHfX19kZqaesPXpKen4/Dhw3B0dMSPP/6IkpIS/POf/0RpaSnWrl17w9csX74cS5YsMXj9RGT9EtJLcTq3Ao4OUsyICBG7HCJqJYsaPNZoNJBIJPjuu+8wdOhQTJw4Ee+//z7WrVt3096bhQsXoqKiQveVnZ1t4qqJyFI199pMHRwETxeZyNUQUWuJ1nPj7e0NOzs7FBYWtjheWFgIP78bP43g7++PDh06QKH4c0O0Hj16QBAE5OTkoEuXLte9Ri6XQy6XG7Z4IrJ65/OVOHChGFIJ8MQoLtpHZElE67mRyWQYNGgQ4uPjdcc0Gg3i4+MRERFxw9eMGDECeXl5qKqq0h27cOECpFIpAgMDjV4zEdmOLw9qe20m9vFHkKezyNUQkT5EHZaKiYnB6tWrsW7dOpw/fx5PPfUUqqurER0dDUA7pDRjxgzd+dOnT4eXlxeio6Nx7tw5HDx4EC+99BIef/xxODk5idUMIrIyueW12H5Su0Hm3NGdRa6GiPQl6jo3U6dORXFxMRYtWoSCggL0798fcXFxuknG+fn5yMrK0p3v6uqKPXv2YP78+Rg8eDC8vLzw8MMP44033hCrCURkhdYczoBaI2BEmBf6BCr+/gVEZFZEXedGDFznhohupaKmERFvx6OmQY11jw/FmK7txS6JiGAh69wQEZmjb49dQU2DGt393DC6i7fY5RDRbWC4ISJqUteoxtoj2q0W5o3pzA0yiSwUww0RUZOtybkoqWpABw8n3NPXX+xyiOg2MdwQEQFQawR8dUj7+PfskaFw4AaZRBaLf71ERAD2nCtEekk1FE4OmDokSOxyiKgNbjvcNDQ0IC0tDSqVypD1EBGZnCAIWHngMgDgsWHBcJGLukoGEbWR3uGmpqYGs2fPhrOzM3r16qVbh2b+/Pl4++23DV4gEZGxHc+8ipTscsjspZg5PETscoiojfQONwsXLsTJkyexf/9+ODo66o5HRkYiNjbWoMUREZnCqqZemwcHBaK9G/eiI7J0eve9/vTTT4iNjcWwYcNaPCbZq1cvXL582aDFEREZ28XCSsSnFkEiAeZwg0wiq6B3z01xcTF8fHyuO15dXc01IYjI4jRvkBnV0w+h3i4iV0NEhqB3uBk8eDB27typ+7450Hz11Vc33c2biMgcFVTU4aeUXADA3DHstSGyFnoPS7311luYMGECzp07B5VKhY8++gjnzp3D0aNHceDAAWPUSERkFGuPZKBRLWBoqCcGdGwndjlEZCB699yMHDkSKSkpUKlU6NOnD3799Vf4+PggISEBgwYNMkaNREQGp6xrxIZj2qc957HXhsiq3NZiDp07d8bq1asNXQsRkcl8fywLlfUqdPV1xdiu188jJCLLpXfPjZ2dHYqKiq47XlpaCjs7O4MURURkTPUqNdY0bZA5Z1QnSKV8GILImugdbgRBuOHx+vp6yGSyNhdERGRs21LyUKish6+7HPf17yB2OURkYK0elvr4448BaJ+O+uqrr+Dq6qr7mVqtxsGDB9G9e3fDV0hEZEAajaB7/Hv2yFDI7LnFHpG1aXW4+eCDDwA07cGycmWLISiZTIaQkBCsXLnS8BUSERnQvrQiXCqqgpvcHtOGdhS7HCIyglaHm4wM7fj0HXfcga1bt6JdOz42SUSWZ9UBba/N9GEd4eboIHI1RGQMej8ttW/fPmPUQURkdElXriIxswwOdhI8PiJU7HKIyEhu61HwnJwcbN++HVlZWWhoaGjxs/fff98ghRERGdqXB7X73/1jQAf4ujv+zdlEZKn0Djfx8fGYPHkyOnXqhNTUVPTu3RuZmZkQBAEDBw40Ro1ERG2WXlyFX88VAgCeHM1F+4ismd6PCSxcuBAvvvgiTp8+DUdHR2zZsgXZ2dkYM2YMHnroIWPUSETUZqsPZUAQgMgevgjzcRO7HCIyIr3Dzfnz5zFjxgwAgL29PWpra+Hq6oqlS5dixYoVBi+QiKitiirrsCU5BwA3yCSyBXqHGxcXF908G39/f1y+fFn3s5KSEsNVRkRkIOuOZqJBpcHAjh4YHMwnPYmsnd5zboYNG4bDhw+jR48emDhxIl544QWcPn0aW7duxbBhw4xRIxHRbauqV+GbhCsAgLljOkMi4VYLRNZO73Dz/vvvo6qqCgCwZMkSVFVVITY2Fl26dOGTUkRkdjYmZkFZp0InbxeM6+ErdjlEZAJ6h5tOnf4cr3ZxceGqxERkthrVGqw5rF2A9MnR3CCTyFYYbFOVrVu3om/fvoa6HBFRm+04lYe8ijp4u8oxZQA3yCSyFXqFm1WrVuHBBx/E9OnTcezYMQDA3r17MWDAADz22GMYMWKEUYokItKXIAi6rRaiR4TA0cHub15BRNai1eHm7bffxvz585GZmYnt27fjzjvvxFtvvYVHH30UU6dORU5ODr744gtj1kpE1GoHLhQjtaASLjI7/F94sNjlEJEJtXrOzdq1a7F69WrMnDkThw4dwpgxY3D06FFcunQJLi4uxqyRiEhvzb0204Z2hMKZG2QS2ZJW99xkZWXhzjvvBACMGjUKDg4OWLJkCYMNEZmdUznlSEgvhb1UgsdHcoNMIlvT6nBTX18PR8c/N5qTyWTw9PQ0SlFERG2x6qC212ZyvwAEeDiJXA0RmZpej4K/9tprcHZ2BgA0NDTgjTfegEKhaHEO17ohIjFdKa3GL6fzAQBPcqsFIpvU6nAzevRopKWl6b4fPnw40tPTW5zDlT+JSGxfHcqARgDGdmuP7n7uYpdDRCJodbjZv3+/EcsgImq70qp6bErKBgDMHd1Z5GqISCwGW8SPiEhs6xOuoK5Rg76BCgzrxDmBRLaK4YaIrEJNgwrrEzIBaHttOExOZLsYbojIKmz6IwdXaxrR0dMZd/f2E7scIhIRww0RWTyVWoPVh7QPOMwZ3Ql23CCTyKYx3BCRxfvlTAFyrtbCy0WGhwYFil0OEYlM73ATFxeHw4cP677/7LPP0L9/f0yfPh1Xr141aHFERH9HEASsOngZADAjghtkEtFthJuXXnoJSqUSAHD69Gm88MILmDhxIjIyMhATE2PwAomIbuXo5VKcyVXCycEOMyK4QSYR6blCMQBkZGSgZ8+eAIAtW7bg3nvvxVtvvYXk5GRMnDjR4AUSEd3KygPaXpupQ4LQzkUmcjVEZA707rmRyWSoqakBAPz2228YP348AMDT01PXo0NEZApn8ypw6GIJ7KQSzOYGmUTURO+em5EjRyImJgYjRoxAYmIiYmNjAQAXLlxAYCAn8hGR6axu2iBzYh9/BHk6i1wNEZkLvXtuPv30U9jb22Pz5s344osv0KFDBwDAL7/8grvvvtvgBRIR3UjO1Rr8fEq7Qebc0dwgk4j+pHfPTceOHbFjx47rjn/wwQcGKYiIqDX+dzgDao2AkWHe6N1BIXY5RGRG9O65SU5OxunTp3Xfb9u2DVOmTMG///1vNDQ0GLQ4IqIbuVrdgI2JTRtkjmGvDRG1pHe4mTt3Li5cuAAASE9PxyOPPAJnZ2ds2rQJL7/8ssELJCL6q29/v4LaRjV6+rtjZJi32OUQkZnRO9xcuHAB/fv3BwBs2rQJo0ePxoYNG/D1119jy5Ythq6PiKiFukY11jVvkDmmEzfIJKLr6B1uBEGARqMBoH0UvHltm6CgIJSUlBi2OiKiv9iSnIOSqgZ08HDCxD7+YpdDRGZI73AzePBgvPHGG/jmm29w4MAB3HPPPQC0i/v5+voavEAiomZqjaB7/PuJUaFwsOP2eER0Pb3fGT788EMkJyfjmWeewauvvoqwsDAAwObNmzF8+HCDF0hE1OzXswXILK2Bh7MDpg4JErscIjJTej8K3rdv3xZPSzV79913YWfHDeuIyDgEQcDKpl6bGcOC4SzT++2LiGzEbfXplpeX46uvvsLChQtRVlYGADh37hyKiooMWhwRUbPEjDKczC6H3F6KGcNDxC6HiMyY3v/0OXXqFO666y54eHggMzMTc+bMgaenJ7Zu3YqsrCysX7/eGHUSkY1b1dRr8+CgQHi7ykWuhojMmd49NzExMYiOjsbFixfh6OioOz5x4kQcPHjQoMUREQFAWkEl9qYWQSIB5ozion1EdGt6h5vjx49j7ty51x3v0KEDCgoKDFIUEdG1vmzqtZnQ2w8h3i4iV0NE5k7vcCOXy6FUKq87fuHCBbRv394gRRERNcuvqMX2k7kAgCdHdxa5GiKyBHqHm8mTJ2Pp0qVobGwEAEgkEmRlZeGVV17BAw88YPACici2rT2SiUa1gPBQT/QP8hC7HCKyAHqHm/feew9VVVXw8fFBbW0txowZg7CwMLi5ueHNN980Ro1EZKMqahux4VgWAGDeGPbaEFHr6P20lEKhwJ49e3DkyBGcPHkSVVVVGDhwICIjI41RHxHZsA3HslBVr0I3XzeM7cZhbyJqndteu3zEiBH45z//iZdffrlNweazzz5DSEgIHB0dER4ejsTExFa97siRI7C3t9dt4klE1qVepcaaIxkAgCdHc4NMImo9vcPNs88+i48//vi6459++imee+45va4VGxuLmJgYLF68GMnJyejXrx+ioqL+djHA8vJyzJgxA3fddZde9yMiy7HtRB6KK+vh5+6ISf0CxC6HiCyI3uFmy5YtGDFixHXHhw8fjs2bN+t1rffffx9z5sxBdHQ0evbsiZUrV8LZ2Rlr1qy55evmzZuH6dOnIyIiQq/7EZFl0GgErDp4GQAwe2QoZPbcIJOIWk/vd4zS0lIoFIrrjru7u6OkpKTV12loaEBSUlKLIS2pVIrIyEgkJCTc9HVr165Feno6Fi9e3Kr71NfXQ6lUtvgiIvMWn1qEy8XVcHO0xyNDuUEmEelH73ATFhaGuLi4647/8ssv6NSp9SuHlpSUQK1Ww9fXt8VxX1/fmy4GePHiRSxYsADffvst7O1bNxd6+fLlUCgUuq+gIL5REpm7VQe0vTb/NywYbo4OIldDRJZG76elYmJi8Mwzz6C4uBh33nknACA+Ph7vvfcePvzwQ0PXp6NWqzF9+nQsWbIEXbt2bfXrFi5ciJiYGN33SqWSAYfIjCVdKcMfV65CZidFNDfIJKLboHe4efzxx1FfX48333wTy5YtAwCEhITgiy++wIwZM1p9HW9vb9jZ2aGwsLDF8cLCQvj5+V13fmVlJf744w+cOHECzzzzDABAo9FAEATY29vj119/1YWta8nlcsjl3GSPyFKsOqDdauEfAzrAx93xb84mIrqe3uEGAJ566ik89dRTKC4uhpOTE1xdXfW+hkwmw6BBgxAfH48pU6YA0IaV+Ph4XXi5lru7O06fPt3i2Oeff469e/di8+bNCA0NvZ2mEJEZuVRUhT3ntf/gmTOaG2QS0e3RO9xkZGRApVKhS5cuLfaSunjxIhwcHBASEtLqa8XExGDmzJkYPHgwhg4dig8//BDV1dWIjo4GoB1Sys3Nxfr16yGVStG7d+8Wr/fx8YGjo+N1x4nIMn11KB2CAIzr6YswH/3/0UREBNzGhOJZs2bh6NGj1x0/duwYZs2apde1pk6div/+979YtGgR+vfvj5SUFMTFxekmGefn5yMrK0vfEonIAhUp67A1WbtB5rwx7LUhotsnEQRB0OcF7u7uSE5ORlhYWIvjly5dwuDBg1FeXm7I+gxOqVRCoVCgoqIC7u7uYpdDRE1WxKXii/2XMSi4HbY8NVzscojIzOjz+a13z41EIkFlZeV1xysqKqBWq/W9HBERqupV+Pb3KwCAuZxrQ0RtpHe4GT16NJYvX94iyKjVaixfvhwjR440aHFEZBs2Jmahsk6FTu1dENnD9+9fQER0C3pPKF6xYgVGjx6Nbt26YdSoUQCAQ4cOQalUYu/evQYvkIisW4NKg/8d1m6QOXd0J0il3CCTiNpG756bnj174tSpU3j44YdRVFSEyspKzJgxA6mpqXxqiYj09vPJPORX1KG9mxxTBnQQuxwisgK3tc5NQEAA3nrrLUPXQkQ2RhAEfHlQu2hf9IgQyO3tRK6IiKyB3uHm4MGDt/z56NGjb7sYIrIt+y8UI62wEi4yOzwaHix2OURkJfQON2PHjr3umETy5xg5n5giotZq3iBzenhHKJy4QSYRGYbec26uXr3a4quoqAhxcXEYMmQIfv31V2PUSERWKCW7HL+nl8FeKsHjI7l9ChEZjt49NwqF4rpj48aNg0wmQ0xMDJKSkgxSGBFZty8Pantt7uvfAf4KJ5GrISJronfPzc34+voiLS3NUJcjIiuWWVKNuDMFAIAnuWgfERmY3j03p06davG9IAjIz8/H22+/jf79+xuqLiKyYl8dTodGAO7o1h7d/NzELoeIrIze4aZ///6QSCT465ZUw4YNw5o1awxWGBFZp5Kqemz6IwcAMHdMZ5GrISJrpHe4ycjIaPG9VCpF+/bt4ejoaLCiiMh6rT+aiXqVBv2CPBAe6il2OURkhfQON8HBXIuCiG5PTYMK66/ZIPPaZSSIiAyl1ROKExISsGPHjhbH1q9fj9DQUPj4+ODJJ59EfX29wQskIuvxw/FslNc0ItjLGVG9/MQuh4isVKvDzdKlS3H27Fnd96dPn8bs2bMRGRmJBQsW4Oeff8by5cuNUiQRWT6VWoPVh7TD2nNGdYIdN8gkIiNp9bBUSkoKli1bpvt+48aNCA8Px+rVqwEAQUFBWLx4MV5//XWDF0lky05ml2P7yTxo/jKJ39IUVdYjt7wWXi4yPDgoUOxyiMiKtTrcXL16Fb6+vrrvDxw4gAkTJui+HzJkCLKzsw1bHZGNU2sEPL0hGTlXa8UuxWBmDg+BowM3yCQi42l1uPH19UVGRgaCgoLQ0NCA5ORkLFmyRPfzyspKODhwbxgiQzp4sRg5V2vh7miPxyIsfzK/u6MDZg4PEbsMIrJyrQ43EydOxIIFC7BixQr89NNPcHZ2xqhRo3Q/P3XqFDp35poVRIa04VgWAODBQUF4Kaq7yNUQEVmGVoebZcuW4f7778eYMWPg6uqKdevWQSaT6X6+Zs0ajB8/3ihFEtmi/Ipa7E0tAgBMDw8SuRoiIsvR6nDj7e2NgwcPoqKiAq6urrCzazlmvmnTJri6uhq8QCJbFXs8G2qNgKGhngjz4RYFREStZZBdwQHA05MrjRIZikqtQexx7QT9R8M7ilwNEZFlMdiu4ERkOPvTipFfUYd2zg64uzcXuyMi0gfDDZEZ2pDYPJE4EHJ7PjZNRKQPhhsiM5NbXov9adqJxNOGckiKiEhfDDdEZiY2MQsaAYjo5IVO7TlJn4hIXww3RGZEpdYg9o+micTD2GtDRHQ7GG6IzEh8ahEKlfXwdpVhfE9OJCYiuh0MN0Rm5NoViWX2/PMkIrodfPckMhPZZTU4eLEYADBtKFckJiK6XQw3RGZi4/EsCAIwqos3gr1cxC6HiMhiMdwQmYFGtQY//JEDAJjOx7+JiNqE4YbIDPx2rhDFlfVo7yZHZE9fscshIrJoDDdEZqB5ReKHBwfCwY5/lkREbcF3USKRZZZU49DFEkgkwCNDOCRFRNRWDDdEIvv+uLbXZnSX9gjydBa5GiIiy8dwQySiBpUGm5snEoez14aIyBAYbohEtPtsAUqrG+DrLsdd3X3ELoeIyCow3BCJqHlF4qmDg2DPicRERAbBd1MikaQXVyEhvRRSCTCVa9sQERkMww2RSL5vevx7bDcfdPBwErkaIiLrwXBDJIK6RjU2J3FFYiIiY2C4IRLB7rMFuFrTCH+FI8Z2ay92OUREVoXhhkgE3zVNJH5kSEdOJCYiMjC+qxKZ2KWiSiRmlMFOKsHUIUFil0NEZHUYbohMbMOxbADAnd194KdwFLkaIiLrw3BDZEJ1jWpsSeaKxERExsRwQ2RCu07no6K2ER08nDC6CycSExEZA8MNkQk1r0g8bWgQ7KQSkashIrJODDdEJpJWUIk/rlyFvVSChwdzIjERkbEw3BCZyIZjVwAAkT184ePOicRERMbCcENkArUNamw9kQuAE4mJiIyN4YbIBH4+lYfKOhU6ejpjZJi32OUQEVk1hhsiE2ieSPzI0CBIOZGYiMioGG6IjOxcnhIp2eWwl0rw0CBOJCYiMjaGGyIj25ConUgc1csP7d3kIldDRGT9GG6IjKi6XoWfTuQB4ERiIiJTYbghMqKfT+ahql6FEC9nRHTyErscIiKbwHBDZEQbEptXJO7IicRERCbCcENkJGdyK3AqpwIyOykeHBQodjlERDaD4YbISL5revz77t5+8HLlRGIiIlNhuCEygqp6FbancEViIiIxMNwQGcG2lFxUN6jRub0LwkM9xS6HiMimiB5uPvvsM4SEhMDR0RHh4eFITEy86blbt27FuHHj0L59e7i7uyMiIgK7d+82YbVEf08QBHz3+58TiSUSTiQmIjIlUcNNbGwsYmJisHjxYiQnJ6Nfv36IiopCUVHRDc8/ePAgxo0bh127diEpKQl33HEHJk2ahBMnTpi4cqKbO5lTgXP5SsjsOZGYiEgMEkEQBLFuHh4ejiFDhuDTTz8FAGg0GgQFBWH+/PlYsGBBq67Rq1cvTJ06FYsWLbrhz+vr61FfX6/7XqlUIigoCBUVFXB3d297I4j+4uXNJ/HDHzn4x4AO+GBqf7HLISKyCkqlEgqFolWf36L13DQ0NCApKQmRkZF/FiOVIjIyEgkJCa26hkajQWVlJTw9bz6nYfny5VAoFLqvoCDu7UPGo6xrxM8n8wFwIjERkVhECzclJSVQq9Xw9fVtcdzX1xcFBQWtusZ///tfVFVV4eGHH77pOQsXLkRFRYXuKzs7u011E93KTydyUduoRhcfVwwObid2OURENsle7AJu14YNG7BkyRJs27YNPj4+Nz1PLpdDLucaI2R8giBgQ9PaNtPDOZGYiEgsooUbb29v2NnZobCwsMXxwsJC+Pn53fK1GzduxBNPPIFNmza1GNYiElNyVjlSCyoht5fi/gGcSExEJBbRhqVkMhkGDRqE+Ph43TGNRoP4+HhERETc9HXff/89oqOj8f333+Oee+4xRalErdLca3Nv3wAonB1EroaIyHaJOiwVExODmTNnYvDgwRg6dCg+/PBDVFdXIzo6GoB2vkxubi7Wr18PQDsUNXPmTHz00UcIDw/Xzc1xcnKCQqEQrR1EFTWN2HEqDwAnEhMRiU3UcDN16lQUFxdj0aJFKCgoQP/+/REXF6ebZJyfn4+srCzd+V9++SVUKhWefvppPP3007rjM2fOxNdff23q8ol0tp7IQb1Kg+5+bhjY0UPscoiIbJqo69yIQZ/n5IlaQxAEjP/gIC4WVWHpfb0wIyJE7JKIiKyORaxzQ2Qt/rhyFReLquDkYIcpAzqIXQ4Rkc1juCFqo+aJxJP6+cPdkROJiYjExnBD1AZXqxuw87R2ReJHw4NFroaIiACGG6I22ZKcgwaVBr0C3NE3kE/sERGZA4YbotskCAI2JHJFYiIic8NwQ3Sbfk8vQ3pxNVxkdrivPycSExGZC4YbotvU3GszuX8HuMotdps2IiKrw3BDdBtKq+oRd6Z5IjFXJCYiMicMN0S3YXNSDhrVAvoGKtC7AycSExGZE4YbIj1pNAK+b55IPJS9NkRE5obhhkhPCemlyCytgavcHpP6BYhdDhER/QXDDZGemlcknjIgAC6cSExEZHYYboj0UFxZj91nCwAA04dyRWIiInPEcEOkh01J2VBpBPQP8kDPAO4qT0RkjhhuiFpJoxGwMTEbgHZFYiIiMk8MN0StdPhSCbLKauDmaI9JfTmRmIjIXDHcELVS80Ti+wd0gJPMTuRqiIjoZhhuiFqhUFmHPecLAQDTwzmRmIjInDHcELXCD8ezodYIGBTcDt383MQuh4iIboHhhuhvqDUCNh7XTiTmPlJEROaP4Ybobxy8UIzc8loonBwwsY+/2OUQEdHfYLgh+hvfNU0kfmBgIBwdOJGYiMjcMdwQ3UJ+RS32pjZPJA4SuRoiImoNhhuiW4g9ng2NAAwN9USYDycSExFZAoYboptQqTWI5URiIiKLw3BDdBP704qRX1GHds4OuLu3n9jlEBFRKzHcEN3EhkTtROIHBwVCbs+JxEREloLhhugGcstrsT+tCAAwbSiHpIiILAnDDdENxCZmQSMAEZ280Km9q9jlEBGRHhhuiP5CpdYg9g/tROLpnEhMRGRxGG6I/iI+tQiFynp4ucgQ1YsTiYmILA3DDdFfNK9I/ODgQMjs+SdCRGRp+M5NdI3sshoculgMAJg2hENSRESWiOGG6BrfJ2ZBEICRYd4I8XYRuxwiIroNDDdETRrVGvzwRw4ATiQmIrJkDDdETfacK0RJVT28XeUY19NX7HKIiOg2MdwQNdnQNJF46pBAONjxT4OIyFLxHZwIQGZJNQ5fKoFEAjzCicRERBaN4YYIwPfHtb02o7u0R5Cns8jVEBFRWzDckM1rUGmwmROJiYisBsMN2bzdZwtQWt0AX3c57uruI3Y5RETURgw3ZPN0E4kHB8GeE4mJiCwe38nJpqUXVyEhvRRSCTB1KIekiIisAcMN2bTvE7W9NmO7+aCDh5PI1RARkSEw3JDNqmtUY3NS00Ri9toQEVkNhhuyWXFnCnC1phH+CkeM7dZe7HKIiMhAGG7IZv25IjEnEhMRWRO+o5NNulhYicTMMu1E4iFBYpdDREQGxHBDNmlD00TiO7v7wl/BicRERNaE4YZsTl2jGluaJhI/yhWJiYisDsMN2Zydp/KhrFOhg4cTRnflRGIiImvDcEM2p3lI6pEhQbCTSkSuhoiIDI3hhmxKWkElkq5chZ1Ugoc5kZiIyCox3JBN2XDsCgBgXA9f+Lo7ilwNEREZA8MN2YzaBjW2nsgFAEznRGIiIqvFcEM24+dTeaisU6GjpzNGhnmLXQ4RERkJww3ZjOYViR8ZGgQpJxITEVkthhuyCefylEjJLoe9VIKHBnEiMRGRNWO4IZuwIVE7kTiqlx/au8lFroaIiIyJ4YasXnW9Cj+dyAPAicRERLaA4Yas3vaTeaiqVyHEyxkRnbzELoeIiIyM4YasXvNE4mlDO3IiMRGRDWC4Iat2OqcCp3MrILOT4sFBgWKXQ0REJiB6uPnss88QEhICR0dHhIeHIzEx8Zbn79+/HwMHDoRcLkdYWBi+/vpr0xRKFkk3kbi3H7xcOZGYiMgWiBpuYmNjERMTg8WLFyM5ORn9+vVDVFQUioqKbnh+RkYG7rnnHtxxxx1ISUnBc889hyeeeAK7d+82ceVkCSrrGrEtpWki8VBOJCYishUSQRAEsW4eHh6OIUOG4NNPPwUAaDQaBAUFYf78+ViwYMF157/yyivYuXMnzpw5ozv2yCOPoLy8HHFxca26p1KphEKhQEVFBdzd3Q3TEABqjYD8ilqDXY/abuepfCz/JRWd2rsgPmYMJBLOtyEislT6fH7bm6im6zQ0NCApKQkLFy7UHZNKpYiMjERCQsINX5OQkIDIyMgWx6KiovDcc8/d9D719fWor6/Xfa9UKttW+E2UVtdj5Ip9Rrk2tc30oR0ZbIiIbIho4aakpARqtRq+vr4tjvv6+iI1NfWGrykoKLjh+UqlErW1tXBycrruNcuXL8eSJUsMV/gtyO1Fn8JEfxHi5cIViYmIbIxo4cZUFi5ciJiYGN33SqUSQUGG/7DzcXNE2hsTDH5dIiIi0o9o4cbb2xt2dnYoLCxscbywsBB+fn43fI2fn98Nz3d3d79hrw0AyOVyyOV8SoaIiMhWiDaOIpPJMGjQIMTHx+uOaTQaxMfHIyIi4oaviYiIaHE+AOzZs+em5xMREZHtEXWSSExMDFavXo1169bh/PnzeOqpp1BdXY3o6GgA2iGlGTNm6M6fN28e0tPT8fLLLyM1NRWff/45fvjhBzz//PNiNYGIiIjMjKhzbqZOnYri4mIsWrQIBQUF6N+/P+Li4nSThvPz85GVlaU7PzQ0FDt37sTzzz+Pjz76CIGBgfjqq68QFRUlVhOIiIjIzIi6zo0YjLXODRERERmPPp/ffHaZiIiIrArDDREREVkVhhsiIiKyKgw3REREZFUYboiIiMiqMNwQERGRVWG4ISIiIqvCcENERERWheGGiIiIrIqo2y+IoXlBZqVSKXIlRERE1FrNn9ut2VjB5sJNZWUlACAoKEjkSoiIiEhflZWVUCgUtzzH5vaW0mg0yMvLg5ubGyQSiUGvrVQqERQUhOzsbIvet4rtMC9sh3mxlnYA1tMWtsO8GKsdgiCgsrISAQEBkEpvPavG5npupFIpAgMDjXoPd3d3i/4fsxnbYV7YDvNiLe0ArKctbId5MUY7/q7HphknFBMREZFVYbghIiIiq8JwY0ByuRyLFy+GXC4Xu5Q2YTvMC9thXqylHYD1tIXtMC/m0A6bm1BMRERE1o09N0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBzjeXLl2PIkCFwc3ODj48PpkyZgrS0tBbnCIKARYsWwd/fH05OToiMjMTFixdbnPPll19i7NixcHd3h0QiQXl5+XX3Kisrw6OPPgp3d3d4eHhg9uzZqKqqsqh2ZGZmYvbs2QgNDYWTkxM6d+6MxYsXo6GhwaLaAQCTJ09Gx44d4ejoCH9/fzz22GPIy8szSDtM3ZZm9fX16N+/PyQSCVJSUiyuHSEhIZBIJC2+3n77bYtrBwDs3LkT4eHhcHJyQrt27TBlyhSLasf+/fuv+100fx0/ftxi2gEAFy5cwH333Qdvb2+4u7tj5MiR2LdvX5vbYOp2JCcnY9y4cfDw8ICXlxeefPJJs/oMKSsrw/z589GtWzc4OTmhY8eOePbZZ1FRUdHiOsb6LGS4ucaBAwfw9NNP4/fff8eePXvQ2NiI8ePHo7q6WnfOO++8g48//hgrV67EsWPH4OLigqioKNTV1enOqampwd13341///vfN73Xo48+irNnz2LPnj3YsWMHDh48iCeffNKi2pGamgqNRoNVq1bh7Nmz+OCDD7By5cpbttsc2wEAd9xxB3744QekpaVhy5YtuHz5Mh588EGDtMPUbWn28ssvIyAgwGBtEKMdS5cuRX5+vu5r/vz5FteOLVu24LHHHkN0dDROnjyJI0eOYPr06RbVjuHDh7f4PeTn5+OJJ55AaGgoBg8ebDHtAIB7770XKpUKe/fuRVJSEvr164d7770XBQUFFtOOvLw8REZGIiwsDMeOHUNcXBzOnj2LWbNmtbkNhmpHXl4e8vLy8N///hdnzpzB119/jbi4OMyePbvFvYz2WSjQTRUVFQkAhAMHDgiCIAgajUbw8/MT3n33Xd055eXlglwuF77//vvrXr9v3z4BgHD16tUWx8+dOycAEI4fP6479ssvvwgSiUTIzc21mHbcyDvvvCOEhoYarPZrmbId27ZtEyQSidDQ0GCw+q9l7Lbs2rVL6N69u3D27FkBgHDixAljNMOo7QgODhY++OADo9T9V8ZqR2Njo9ChQwfhq6++Mmr9zUz1N9LQ0CC0b99eWLp0qUHrb2asdhQXFwsAhIMHD+qOKZVKAYCwZ88ei2nHqlWrBB8fH0GtVuuOnTp1SgAgXLx40eza0eyHH34QZDKZ0NjYKAiCcT8L2XNzC83dZ56engCAjIwMFBQUIDIyUneOQqFAeHg4EhISWn3dhIQEeHh4tPgXT2RkJKRSKY4dO2ag6v9krHbc7F7N9zE0U7WjrKwM3333HYYPHw4HB4e2FX0TxmxLYWEh5syZg2+++QbOzs6GK/oGjP07efvtt+Hl5YUBAwbg3XffhUqlMkzhf2GsdiQnJyM3NxdSqRQDBgyAv78/JkyYgDNnzhi2AU1M9Teyfft2lJaWIjo6um0F34Sx2uHl5YVu3bph/fr1qK6uhkqlwqpVq+Dj44NBgwYZthEwXjvq6+shk8labB7p5OQEADh8+LAhSm/BUO2oqKiAu7s77O2121oa87OQ4eYmNBoNnnvuOYwYMQK9e/cGAF23pa+vb4tzfX199erSLCgogI+PT4tj9vb28PT0NEjX6LWM2Y6/unTpEj755BPMnTv39gu+CVO045VXXoGLiwu8vLyQlZWFbdu2tb3wGzBmWwRBwKxZszBv3jyDDBfcirF/J88++yw2btyIffv2Ye7cuXjrrbfw8ssvG6b4axizHenp6QCA119/Hf/5z3+wY8cOtGvXDmPHjkVZWZmBWqBlyr/1//3vf4iKijLKJsTGbIdEIsFvv/2GEydOwM3NDY6Ojnj//fcRFxeHdu3aGa4RMG477rzzThQUFODdd99FQ0MDrl69igULFgAA8vPzDdQCLUO1o6SkBMuWLWsx5GTMz0KGm5t4+umncebMGWzcuFHsUtrEVO3Izc3F3XffjYceeghz5swx+PVN0Y6XXnoJJ06cwK+//go7OzvMmDEDghEW8DZmWz755BNUVlZi4cKFBr/2Xxn7dxITE4OxY8eib9++mDdvHt577z188sknqK+vN+h9jNkOjUYDAHj11VfxwAMPYNCgQVi7di0kEgk2bdpk0HuZ6m89JycHu3fvvm7uhKEYsx2CIODpp5+Gj48PDh06hMTEREyZMgWTJk0yeCgwZjt69eqFdevW4b333oOzszP8/PwQGhoKX1/fFr05hmCIdiiVStxzzz3o2bMnXn/9dcMVdwsMNzfwzDPPYMeOHdi3b1+Lf5n4+fkB0Hb7X6uwsFD3s9bw8/NDUVFRi2MqlQplZWV6XefvGLsdzfLy8nDHHXdg+PDh+PLLL9tW9A2Yqh3e3t7o2rUrxo0bh40bN2LXrl34/fff21b8Xxi7LXv37kVCQgLkcjns7e0RFhYGABg8eDBmzpxpgBZomep3cq3w8HCoVCpkZma26TrXMnY7/P39AQA9e/bUHZPL5ejUqROysrLaUnoLpvx9rF27Fl5eXpg8efLtF3wTpvj72LFjBzZu3IgRI0Zg4MCB+Pzzz+Hk5IR169YZphEwze9j+vTpKCgoQG5uLkpLS/H666+juLgYnTp1ansDmhiiHZWVlbj77rvh5uaGH3/8scVQvzE/CxluriEIAp555hn8+OOP2Lt3L0JDQ1v8PDQ0FH5+foiPj9cdUyqVOHbsGCIiIlp9n4iICJSXlyMpKUl3bO/evdBoNAgPD7eYdgDaHpuxY8fq/kVqyH81mLIdf9X8L25D9RKYqi0ff/wxTp48iZSUFKSkpGDXrl0AgNjYWLz55psW044bSUlJgVQqva4b+3aYqh2DBg2CXC5v8RhtY2MjMjMzERwcbDHtuPZ+a9euxYwZMww6H81U7aipqQGA696npFKp7m++LcT4+/D19YWrqytiY2Ph6OiIcePGtakNgOHaoVQqMX78eMhkMmzfvh2Ojo4trmPUz8I2TUe2Mk899ZSgUCiE/fv3C/n5+bqvmpoa3Tlvv/224OHhIWzbtk04deqUcN999wmhoaFCbW2t7pz8/HzhxIkTwurVq3Uz80+cOCGUlpbqzrn77ruFAQMGCMeOHRMOHz4sdOnSRZg2bZpFtSMnJ0cICwsT7rrrLiEnJ6fFvSypHb///rvwySefCCdOnBAyMzOF+Ph4Yfjw4ULnzp2Furo6i2rLX2VkZBj0aSlTtePo0aPCBx98IKSkpAiXL18Wvv32W6F9+/bCjBkzLKodgiAI//rXv4QOHToIu3fvFlJTU4XZs2cLPj4+QllZmUW1QxAE4bfffhMACOfPn29z7WK0o7i4WPDy8hLuv/9+ISUlRUhLSxNefPFFwcHBQUhJSbGYdgiCIHzyySdCUlKSkJaWJnz66aeCk5OT8NFHH7W5DYZqR0VFhRAeHi706dNHuHTpUovrqFQq3XWM9VnIcHMNADf8Wrt2re4cjUYjvPbaa4Kvr68gl8uFu+66S0hLS2txncWLF//tdUpLS4Vp06YJrq6ugru7uxAdHS1UVlZaVDvWrl1703tZUjtOnTol3HHHHYKnp6cgl8uFkJAQYd68eUJOTo5B2mHKtvyVocONqdqRlJQkhIeHCwqFQnB0dBR69OghvPXWWwYLm6b8fTQ0NAgvvPCC4OPjI7i5uQmRkZHCmTNnLK4dgiAI06ZNE4YPH26Q2sVqx/Hjx4Xx48cLnp6egpubmzBs2DBh165dFteOxx57TPD09BRkMpnQt29fYf369QZpg6Ha0fwY+42+MjIydOcZ67NQ0tQQIiIiIqvAOTdERERkVRhuiIiIyKow3BAREZFVYbghIiIiq8JwQ0RERFaF4YaIiIisCsMNERERWRWGGyIiIrIqDDdERERkVRhuiMgsCYKAyMhIREVFXfezzz//HB4eHsjJyRGhMiIydww3RGSWJBIJ1q5di2PHjmHVqlW64xkZGXj55ZfxySefIDAw0KD3bGxsNOj1iEgcDDdEZLaCgoLw0Ucf4cUXX0RGRgYEQcDs2bMxfvx4DBgwABMmTICrqyt8fX3x2GOPoaSkRPfauLg4jBw5Eh4eHvDy8sK9996Ly5cv636emZkJiUSC2NhYjBkzBo6Ojvjuu+/EaCYRGRg3ziQiszdlyhRUVFTg/vvvx7Jly3D27Fn06tULTzzxBGbMmIHa2lq88sorUKlU2Lt3LwBgy5YtkEgk6Nu3L6qqqrBo0SJkZmYiJSUFUqkUmZmZCA0NRUhICN577z0MGDAAjo6O8Pf3F7m1RNRWDDdEZPaKiorQq1cvlJWVYcuWLThz5gwOHTqE3bt3687JyclBUFAQ0tLS0LVr1+uuUVJSgvbt2+P06dPo3bu3Ltx8+OGH+Ne//mXK5hCRkXFYiojMno+PD+bOnYsePXpgypQpOHnyJPbt2wdXV1fdV/fu3QFAN/R08eJFTJs2DZ06dYK7uztCQkIAAFlZWS2uPXjwYJO2hYiMz17sAoiIWsPe3h729tq3rKqqKkyaNAkrVqy47rzmYaVJkyYhODgYq1evRkBAADQaDXr37o2GhoYW57u4uBi/eCIyKYYbIrI4AwcOxJYtWxASEqILPNcqLS1FWloaVq9ejVGjRgEADh8+bOoyiUgkHJYiIovz9NNPo6ysDNOmTcPx48dx+fJl7N69G9HR0VCr1WjXrh28vLzw5Zdf4tKlS9i7dy9iYmLELpuITIThhogsTkBAAI4cOQK1Wo3x48ejT58+eO655+Dh4QGpVAqpVIqNGzciKSkJvXv3xvPPP493331X7LKJyET4tBQRERFZFfbcEBERkVVhuCEiIiKrwnBDREREVoXhhoiIiKwKww0RERFZFYYbIiIisioMN0RERGRVGG6IiIjIqjDcEBERkVVhuCEiIiKrwnBDREREVuX/AWauyDkpXwxLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "data = df.groupby('Date').Class.mean()\n",
    "sns.lineplot(data=pd.DataFrame(data), x='Date', y=data.values)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Success Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "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>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "### TASK  7: Create dummy variables to categorical columns\n",
    "features_one_hot = pd.get_dummies(data=features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
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      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90 entries, 0 to 89\n",
      "Data columns (total 80 columns):\n",
      " #   Column                               Non-Null Count  Dtype  \n",
      "---  ------                               --------------  -----  \n",
      " 0   FlightNumber                         90 non-null     float64\n",
      " 1   PayloadMass                          90 non-null     float64\n",
      " 2   Flights                              90 non-null     float64\n",
      " 3   GridFins                             90 non-null     bool   \n",
      " 4   Reused                               90 non-null     bool   \n",
      " 5   Legs                                 90 non-null     bool   \n",
      " 6   Block                                90 non-null     float64\n",
      " 7   ReusedCount                          90 non-null     float64\n",
      " 8   Orbit_ES-L1                          90 non-null     float64\n",
      " 9   Orbit_GEO                            90 non-null     float64\n",
      " 10  Orbit_GTO                            90 non-null     float64\n",
      " 11  Orbit_HEO                            90 non-null     float64\n",
      " 12  Orbit_ISS                            90 non-null     float64\n",
      " 13  Orbit_LEO                            90 non-null     float64\n",
      " 14  Orbit_MEO                            90 non-null     float64\n",
      " 15  Orbit_PO                             90 non-null     float64\n",
      " 16  Orbit_SO                             90 non-null     float64\n",
      " 17  Orbit_SSO                            90 non-null     float64\n",
      " 18  Orbit_VLEO                           90 non-null     float64\n",
      " 19  LaunchSite_CCAFS SLC 40              90 non-null     float64\n",
      " 20  LaunchSite_KSC LC 39A                90 non-null     float64\n",
      " 21  LaunchSite_VAFB SLC 4E               90 non-null     float64\n",
      " 22  LandingPad_5e9e3032383ecb267a34e7c7  90 non-null     float64\n",
      " 23  LandingPad_5e9e3032383ecb554034e7c9  90 non-null     float64\n",
      " 24  LandingPad_5e9e3032383ecb6bb234e7ca  90 non-null     float64\n",
      " 25  LandingPad_5e9e3032383ecb761634e7cb  90 non-null     float64\n",
      " 26  LandingPad_5e9e3033383ecbb9e534e7cc  90 non-null     float64\n",
      " 27  Serial_B0003                         90 non-null     float64\n",
      " 28  Serial_B0005                         90 non-null     float64\n",
      " 29  Serial_B0007                         90 non-null     float64\n",
      " 30  Serial_B1003                         90 non-null     float64\n",
      " 31  Serial_B1004                         90 non-null     float64\n",
      " 32  Serial_B1005                         90 non-null     float64\n",
      " 33  Serial_B1006                         90 non-null     float64\n",
      " 34  Serial_B1007                         90 non-null     float64\n",
      " 35  Serial_B1008                         90 non-null     float64\n",
      " 36  Serial_B1010                         90 non-null     float64\n",
      " 37  Serial_B1011                         90 non-null     float64\n",
      " 38  Serial_B1012                         90 non-null     float64\n",
      " 39  Serial_B1013                         90 non-null     float64\n",
      " 40  Serial_B1015                         90 non-null     float64\n",
      " 41  Serial_B1016                         90 non-null     float64\n",
      " 42  Serial_B1017                         90 non-null     float64\n",
      " 43  Serial_B1018                         90 non-null     float64\n",
      " 44  Serial_B1019                         90 non-null     float64\n",
      " 45  Serial_B1020                         90 non-null     float64\n",
      " 46  Serial_B1021                         90 non-null     float64\n",
      " 47  Serial_B1022                         90 non-null     float64\n",
      " 48  Serial_B1023                         90 non-null     float64\n",
      " 49  Serial_B1025                         90 non-null     float64\n",
      " 50  Serial_B1026                         90 non-null     float64\n",
      " 51  Serial_B1028                         90 non-null     float64\n",
      " 52  Serial_B1029                         90 non-null     float64\n",
      " 53  Serial_B1030                         90 non-null     float64\n",
      " 54  Serial_B1031                         90 non-null     float64\n",
      " 55  Serial_B1032                         90 non-null     float64\n",
      " 56  Serial_B1034                         90 non-null     float64\n",
      " 57  Serial_B1035                         90 non-null     float64\n",
      " 58  Serial_B1036                         90 non-null     float64\n",
      " 59  Serial_B1037                         90 non-null     float64\n",
      " 60  Serial_B1038                         90 non-null     float64\n",
      " 61  Serial_B1039                         90 non-null     float64\n",
      " 62  Serial_B1040                         90 non-null     float64\n",
      " 63  Serial_B1041                         90 non-null     float64\n",
      " 64  Serial_B1042                         90 non-null     float64\n",
      " 65  Serial_B1043                         90 non-null     float64\n",
      " 66  Serial_B1044                         90 non-null     float64\n",
      " 67  Serial_B1045                         90 non-null     float64\n",
      " 68  Serial_B1046                         90 non-null     float64\n",
      " 69  Serial_B1047                         90 non-null     float64\n",
      " 70  Serial_B1048                         90 non-null     float64\n",
      " 71  Serial_B1049                         90 non-null     float64\n",
      " 72  Serial_B1050                         90 non-null     float64\n",
      " 73  Serial_B1051                         90 non-null     float64\n",
      " 74  Serial_B1054                         90 non-null     float64\n",
      " 75  Serial_B1056                         90 non-null     float64\n",
      " 76  Serial_B1058                         90 non-null     float64\n",
      " 77  Serial_B1059                         90 non-null     float64\n",
      " 78  Serial_B1060                         90 non-null     float64\n",
      " 79  Serial_B1062                         90 non-null     float64\n",
      "dtypes: bool(3), float64(77)\n",
      "memory usage: 54.5 KB\n"
     ]
    }
   ],
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n",
    "num_col = features_one_hot.select_dtypes(include='number').columns\n",
    "features_one_hot[num_col] = features_one_hot[num_col].astype('float64')\n",
    "features_one_hot.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n",
    "| ----------------- | ------- | -------------   | ----------------------- |\n",
    "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
   ]
  }
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