5) Exploratory_Data_Analysis-DataViz.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **70** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Falcon 9 first stage will land successfully\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing Data  Feature Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport 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\nimport 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.\nimport 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\nimport seaborn as sns",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe 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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's drill down to each site visualize its detailed launch records.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n\nsns.set_palette(\"bright\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.catplot(data=df, x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\")\nplt.ylabel(\"Launch Site\",fontsize=15)\nplt.xlabel(\"Flight Number\",fontsize=15)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x47bc0b8>",
            "image/png": 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uTLrB1kfXwgeB5u7RK0abe1Et6AulWjv//8xmg/Jd4j5npe7w4nFo62I2FsClgw/S6nQnzQ3Obd26NTt27GDfvn1kyWL/i3b16lWyZ89OWFgYRYoUoV69eixcuNDhHHfq3fHmm2/y77//MmbMGGrVqsWZM2fIlCl2Qa++ffty9epVp70fixYtokuXLty8eRMvr/jNPg8ODqZKlSocPXqUIkVc7xZos9lYuHAhHTt2dDj2ww8/0LVrVxYtWkSHDh3sjhmGETM41pXPPvuMuXPn8sYbb9CkSROuXLkS85589tlnvPbaa5w9e5aMGTMCMGrUKBYtWmTXIxQXKw3OvRkO+TvAdSc9xW3rwLod5mq495r5agLWmviqARxzsijao0FQtW/cz70VanZlH11jbqJ4Nw8v8w97YP14NkTuWL0Ngg9A8YLQuhZ4xpEvnbkZbq6S+9dOKJgb+rW5zwJ/N6/COznuf+K2nzgNEe4QHgF1n4VtB+zLn+5g3uIBIOwSnPzHDBsFqpplX7eEg04WQsxVEoa5GBSc3KKjYUpxuHrU8dgTP5ohZX6P2FtGHp7QagrUcnXP7x7XzsB7hSE60vFYqylQZ1hiW57upKkel8uXL7NkyRIGDx7sEFqAmA/eP/74g4sXLzJypPPR4HeHFsMwCAoKomfPnpQpU4YSJUowf76TaYIu5M+fn8jISBYuXEh8M2KePHnw8PBg/vz5REU56U6Nh9mzZ1O6dGmH0AJm4IkrtOzZs4e33nqLb775Bg8nI/83bNhAgwYNYkILmLfS9u3bx5UrThasAMLDwwkNDbV7JNbBk+asjQdeQTMeVmyBUZ9DaSczLW02aFTFeWgBWOp4V9G1R6ebi3zdrUpfqNz7/s/1yQYVnzAXPnt8DhSoZq4nUrQx9Fmq0JJAYbeg6TBoMgxe+AQ6vgpV+ps9b2AuENhvAjR6DoZ/BEddjHPK5A0D2kLQK/D2wHisSuzlbc4Gc3qynObvw4B1qSa0gLlw4YqpMLqP2cNUrwJ8/qJ5OyxG5lxmj8Wd0ALQYJTznsaGryV3k107vcV5aAHY8yOUfdScwtzxK2j3KYw4Gv/QAubA5hqDHMtzFIWqLm6riVNpagG6gwcPYhgGZcrEvRjRgQPm14P71QNYvnw5YWFhtGhhfnXu2bMn06dPp1evXnb1Fi9ejK9v7PLfrVq1Yt68edSqVYtXX32V7t278/TTT1OzZk0eeeQRevfuTb58zkecFyxYkGnTpjFy5EjefPNNqlevTuPGjenRowfFisVvedgDBw5QunTCB6yFh4fTrVs3Jk+eTOHChTl8+LBDnbNnz1K0qP2H7J3XcvbsWXLkcPzWOGHCBN58880Et+dutyOh30TzdsydDNi2Dnw/xpylk9Sefd8chHs37wwQfhuK+psfRmUKu35+zjjGJjrIVRKG7jd7Tq6dNhc2y5eIWzwVupoPML8Zbv4frHsXvLOafxxLtU74OdOZibPN3bLvtvuIOTW3XxtoNyr2NuC6HebA7b8+NbdMeCAZMkH5x83NIO/VYlKqXbDMzxfG9jMfMW6FmD0t2Ys4DyhFG0Gv382Vec8GQ84SUG+kGcDdJT63bDPnfLD/Dq2nmQsgbp1hjm0p2dJc+NLH9RdJcZSmelzi26ORkLtjM2bMoGvXrjG3ebp168Zff/3FoUOH7Oo1btyY4ODgmMe0adNijo0fP56zZ8/y+eefU65cOT7//HPKlCnDzp07cWXw4MGcPXuW2bNnU7t2bebNm0e5cuVYtmxZkr/Gu73yyis89NBD9OzZM1HPj+u8ISEhMY8TJ07c/0n3mPw9fLfMfpDs4vXwWjKs3L1hl2NoAXPF2+AZsP87eKKJOeuhmpN86OEBfVsl8KKeXuYspJpPJy603C0yAmY2M8dMHPzDXDTr2zZJtzNyGjbPxezxBWvhlS8cxy5dDoV3vk2ii7f5CIo3i/3ZwwtqD4Oq/Vw+JVWJCDN3456Y17zt8kFR2PG987olmsGAtfB6KDy71b2hBcC/CuRysWT1nS8DD8rDA2oNMV/viMNmz01yr8mTBqWp4FKyZElsNtt9x1mUKmX+ct6v3uXLl1m4cCGffvopXl5eeHl5UbBgQSIjIx0G6WbJkoUSJUrEPPz9/e2O58qViy5duvDee++xd+9eChQowHvvvRfn9bNmzUq7du0YP34827dvp379+rz9dvw+eEqVKhXv8SZ3W7lyJfPmzYt5vU2aNAEgd+7cjBkzBjBvf507Z7+77p2f8+d3Pnre29ubbNmy2T0S6pslCSu/1/Ww2O7+gyfNb8prgp0uAspvTrYsAnOdiuCD9mtn/fAmVL8rvOTICv8bmcT7DSXUrh/MabP3WvM23LjoWC4xXGX+6Ghz9pgzfybVMiKZskPfpTBkp7ke0AvHoPUU62witfhZc0pz1H/LBYecgPk94ZiT38XUJDrKnMXVeqr92is2m7lHk3oqU5U0dasoZ86ctGjRgk8++YTnn3/e5eDc5s2bkzt3biZNmhTn4NzZs2dTqFAhh0G3S5cu5f333+ett97CM6Ej9oCMGTNSvHhxl7OKnLHZbJQpU4b169ffvzLQvXt3nnjiCX766acEDc798ccfuXkzdhTqpk2b6NevH+vWraN4cXO58dq1a/Paa69x+/ZtMmQwt4dftmwZpUuXdnqbKKk4Gxx7b/nBk3D0LFQqAXmy/3c8DIZOg+9XmIMJc2SFq9djP6AqlTB3mi5w1757vrFjrx3ceyzQH/75EnYcgivXzJkWmRK7Q3VSObrGeXlkOJz8G0q3Tdn2WEjnRgYTZjkGhY71zVtIl50Mz7r7dydJ5Cv/4L1uKS3sMuz4zrHciDb3NypSN+XbFB97Fpo9kyEnzKBSrBk80hmMSHPqdq6k2gxNkkqa6nEB+OSTT4iKiqJmzZr8+OOPHDhwgL179zJt2jRq164NmL0jX331Fb/++ivt27dn+fLlHD16lM2bNzNy5EiefvppAKZPn07nzp0pX7683aN///5cvHiRJUvu/1V/8eLF9OzZk8WLF7N//3727dvHe++9x2+//eZ04CyYs4o6dOjA/Pnz2bNnDwcPHmT69OnMmDHD4TlHjhyxu0UVHBzMjRs3ePzxx+natSvdunXjnXfeYfPmzRw7dozFixfTtGlTVq1y3h9evHhxu9d6ZyzLQw89RN68eQEzFGXMmJH+/fuze/du5s6dy4cffsiIESOcnjOptK7lvLxNbTOcdHwVSveAFi9A4c7moFowx8XM/D12z5gr1+y/VW8/CE/f0/nVrSlkcBLr82SHVg87b0fF4uZuv3GFlrBb5vTpUZ+bM07i2sfmXtHREOlkQoJTca3YGdeaEulVRBj8OhTGZ2fU0ezUy2e/wFupAHPBuGc6On+6q/I4XfgXds2Ds65vGVvKjQuuV3oOPZWybYmvc7vMlYhD/rt1bRhwaCns/B5qPqPQkkqluenQAGfOnGH8+PEsXryYM2fOkCdPHqpVq8bw4cNp1KhRTL3NmzczYcIE1q1bR2hoKAEBATzyyCO89NJLhISEUL16dTZu3EiNGo5rJrRu3RofHx8WLFgQ53Tow4cPM3HiRNasWcOJEyfw9vamZMmSPPvss/Tt29dp+y9evMi4ceNYuXIlR48exWazERgYSJ8+fRg+fHjMTB+bi+7jdevWUa9ePaKjo/nyyy+ZMWMGu3fvxsvLi5IlS9K7d28GDhxoN6XbldWrV9O4cWO76dBgLkA3ePBgNm3aRO7cuXnuued4+eWXXZ/oHomZDn36IjR8zn6p/fw5YdU0eO97+83Y7nj3GXNcQnQcuzEDFMt0hM2fh+MXGDtge94qGPRe7KyhArlh7ptQJ5FfhI+dhUeGmj1Cd5QNNGdl5I2jo+p6GLz8OXy71Aw+TarBe89Chbj227t8GD4qG7u77x0Fa8DTGxP3AtKy2R3MLRT+Yxiw7GYXgisEUbxoFtrXNYNsZCSM/By+/Nmc7pzLD17pAcMTMgQiMsK8fbJ7XmxZqdbQ9Yf7r8mTmkXdNqf7Xney6FGDV6HZ+JRv0/38Ngw2fOj82PN7U++u0+lcmgwukvoldh2Xa2Hm2JSdh8xvwb1bQhYfyNkGbjnpvahQDHY6ToyKUdznIDNL96aO3wazIHcZc7rjf93aN27C6mBzNlGjyhDPpXic6joG5q92LLdb88KJdqPgtw32Zbn8zF2k4wo8HFgCvww294YBc7Gux76GbAUT2vS07fxeM+Q542Kn7Wv/jZcqnBd8EnpbcOWb5n5U96o91BxjkQSuXoNv/jCXDCgXaP5/4ud7v2clga1BsKi/fZemX2EzLMfVC+guP3SDnXOcH+u3Goo2TNHmSPwouIhbJOUCdCHXzeDiTOnCcOqC8/ExHkSxp0YZSma6Z9VK76ww/JC5Mm0SytzM+a2hArnN/Yqc2XMUKvRxfuydp+DlHve5aHS0uaGdd1aHjR4Fs0fqn89gyXDnx6sPhA5JvCL0B8Vjw+TdMuWAVy8/8OmPnDF7Jk/dtStAQF5YPc0cj5XsDq80l8y/dhqK1DdnRWX97/bktbNmUIi47ri2izts/Bx+ecax3DsrvHQavFMi7UlCpanBuZI++fnCw2XhHyeriLd6GPLlNG8X3atZjmWOoQUg/Bpsn53kK1l6Z3AeXLwzuH7OoTiGBsR1LIaHB+R10ZuQ3m0NMnf3vRHHvj/5KyX9dW+7GJR/+/77h8XHq1/ahxaAE+fNsV+ta0P3plDD1V5J53bBtpnmGiMlWpqLrsW1vokzxR4xH/fa+zP88Hjs7csVo+PuZTq3Cy4fAv/K5nowDyoq0lzB1zODecvUZjMXeNzyPzi91b5uk7cVWlIxBRdJE94fDC1ftO9ZKVEQRnY3g0vxgvDVYrgUYt7yeagIZNp7DkJcnNDZffoH1K2J87VhujdzLLujYnEzezgbo1NZ4wYT79ifjrc07pWjKFTq5fp4YpVuC1umO5aXSpqZXvfeVrzj4CmYNt98TH4WRtw7Lif4W3NfHuO/X7Yt06FUG+i+yOUO5/F2+yYsfNJxzNWGD+GhjuaCdHfcCjUHzN7ZEsDmAdUGQLvP7NchSIiDy8zXdu2/AXI5i5urTBesDv3WwOYvzetlymleq7i5DARRt2HvIjPw+BWGSj3NRejErXSrSNwiOfYqOnbWDCdHzkCNMvBka8gW11jHy4dhasnYP9R36/27uaplHKKjzQ+DHFljp17HJfSGOfNpTXBsWevaMO/NuMdJDJpsvq671Qi4wMrX95A5f1HIHsfyveLc/F7OV6gF8PWHMm2h8ZjkGQ8UehqmNzB7E+7IVhD6rzE/UB9QoU6x6xW5ksELjs0zQz1gzqqaXNDsabnX43MefAG2A0vgGxcrMtZ6DtrELtjJooHOd0tu/SHUfj7h175xwVwIL+Keni7ffOay/RlcLLsdfh1mNjVDyx2Zc0Hf5WYvkLiNelwkzSiSH8YNSMATchaDWs/Dhqn25SVbQYm4d0dctM5cAv7oWXPzvfZ1zUXncsSxzH+2LLDyQ/h7N+w7ARWLxW+Ruk9HmD1EXy+B0BsGX1QcSZPb07DNjjC/jZZ/3NyM0dUfYHF047zrY33+gPwVku/a2QrAs8GwY7Z5OyR3aajcK8mWfe/dEt6dHXed25GwbDP0bP5fwcm/nYcWgAO/P3hwscVzB/SoSPN9cWbbzMQFlx3fO4YWgOvnYN9iKN/Z+fPWT7EPLWBuY7B4MAxM5QvqpXEKLpK+tZ5i7gu0fTZE3jLv6VfpG+dKpTsOmTOEIv/b/zIqytz0Mfw2/DLx/pesVc58xJenJwx73HyYgwnvWnTGiDYHO/rmN1+LxE9gIzi41LE8q3/KTIH19nW+4V4SeKOPuRDjjy7WILzDrjfSO45ez7iOxVfRRuZ7e83JjpQV7lrqPzoydvfle4VfS9y1bzrf+BWAW3Ec2+fkvi7A8fXm6tNZknrVQYmvNLcAnUiClXsMui+A3r+Zs0g84xgti7mGx53Qcrff/zZvUyUrZ2MjwNy07X6L1Uisms9AnnsGLds8zOnP9/nvn9r5eMMPb8Geb82dmjM46ezwzwUta95VULA65HPSy2SzQWUX09oSwjODecsp013z9z08oek7UOiuhmTwcT6wF6C0i6mD91OiufNym4e5Mq4rXi56MD08Xe/iLSlCPS4iCeRq/IBhwNlL5s7Ryeami+my4aEQfRs83L3XgEVkyg5PrYdNX8CR1eZ03eqDIMDFssgWVLqw+ciZzVwZ+s5WBYH5Yc5YyHhvPuv2I3z3KJz/b9Vg76zQ4j0oWC1pGhTYAF48Af/+AhHXzNuxzsZntfwAZjYxb8vckbu0uYhdYhSube6MvjXIvrzBK+YAbFcq9TQHcd+rdDvwSZpxeZI4GpwrbpEcg3NTypS58OKnjuV+vnBiPmS5/4LEiffTIHMGxL0CG0L/1cl4YbGyW+Gwdgf4ZIR6Fe4zOefEP+YtlMJ1zfDiDtfPm0HjymFzrZdKPR9sVWHDMAPT3gXmmJoK3WJnDrkSHQ0/D4Kt02NnnxWoBj0Xx65LI26h4CJuYeXgEnoD6g2G3Ufsyz98HoY8lswXDzkB/6sbu7cKmB8ufVdAIcetKUTkAV06ACc3mtOhA+u7uzWCgou4iZWDC5hLqn+6CFZsNpffH9AOmqdUbgi7BJv/B2e2mdNnawxKmgW6REQsQMFF3MLqwUVERNxDs4pERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMtwaXPbs2cPw4cOpW7cupUuXZuTIkTHH1q9fz7Rp07h8+bIbWygiIiKpiZe7LvzBBx8watQoIiMjAbDZbFy8eNGuzvDhw/H29mbQoEHuaKKIiIikMm7pcfn111958cUXCQgIYMGCBZw/fx7DMOzq1KlThzx58vDTTz+5o4kiIiKSCrmlx+WDDz4gS5YsLFu2jGLFirmsV7lyZfbt25eCLRMREZHUzC09Llu2bKFWrVpxhhaA3Llzc/bs2RRqlYiIiKR2bgkuERERZM2a9b71zp8/j5eX24bhiIiISCrjluBStGhRtm/fHmediIgIduzYQalSpVKoVSIiIpLauSW4tG/fnqNHj/LBBx+4rDNp0iQuXLhAp06dUrBlIiIikprZjHun86SAK1euUKlSJU6dOkXnzp159NFH6d69O61atWLAgAEsXLiQ2bNnU7RoUbZt2xav20piLaGhofj5+RESEkK2bNnc3RwREbEItwQXgP3799O5c2d27dqFzWbDMAxsNhsAhmFQtmxZFi1aRIkSJdzRPElmCi4iIpIYbgsuANHR0fzyyy8sXbqUo0ePEh0dTaFChWjWrBmPPfYYnp6e7mqaJDMFFxERSQy3BhdJvxRcREQkMdwyOLdfv37MmDHjvvVmzpxJv379UqBFIiIiYgVuCS4zZ87kzz//vG+9v/76i6+//joFWiQiIiJW4Nbdoe8nIiJC41xEREQkRqoNLoZhsHXrVvLkyePupoiIiEgqkWLr6T/yyCN2Py9ZssSh7I7IyEgOHTrE2bNn6dWrV0o0T0RERCwgxWYVeXjEdu7cWbclLhkyZKBly5ZMnz6d3LlzJ3fzJIVpVpGIiCRGivW4HDlyBDBvARUrVozOnTszefJkp3UzZsxI7ty5yZAhQ0o1T0RERCwgxYJLkSJFYv59zJgxVKlSxa5MRERE5H60AJ24hW4ViYhIYqTaWUUiIiIi90qR4OLh4YGXlxf79+8HwNPTM94PL68Uu5slIiIiqVyKpILChQtjs9liBtsGBATE7AQtIiIiEl8a4yJuoTEuIiKSGBrjIiIiIpah4CIiIiKWkSLBJTIykvPnzxMSEuL0+MWLFxk0aBCFChXCx8eH4sWLM2rUKK5fv54SzRMRERGLSJHg8vXXX+Pv788HH3zgcCw0NJQ6derw1Vdfcfr0aSIiIjhy5AiTJ0+mefPmREVFpUQTRURExAJSJLisWbMGgP79+zscmzhxIgcPHiRTpkx8+OGH7Ny5kwULFhAYGMg///xDUFBQSjRRRERELCBFZhWVL18ewzDYvXu3wzF/f3/Onz/PhAkTGDlyZEz5vn37KFeuHI888ghLly5N7iZKCtOsIhERSYwU6XE5d+4c5cqVcyjfu3cv586dw2az8eSTT9odK126NDVq1GDHjh0p0UQRERGxgBQJLiEhIU4XnNuyZQsA5cqVI0+ePA7HixQpwpUrV5K9fSIiImINKRJccuTIwaFDhxzK169fD0CNGjWcPi8qKgpfX99kbZuIiIhYR4oEl2rVqrFt2zb++eefmLIbN27w448/YrPZaNKkidPn7d+/nwIFCqREE0VERMQCUiS4DBgwAMMwaNWqFWPGjOGjjz6iUaNGXLhwgVy5ctGxY0eH55w5c4bdu3dTtmzZlGiiiIiIWECKbLLYqVMnnnzySYKCgnj77bcBMAwDDw8PPvroIzJlyuTwnBkzZhAdHU2LFi1SookiIiJiASkSXACmT59OmzZtWLBgARcuXCAgIIB+/fpRp04dp/WPHDlChw4daNWqVUo1UURERFI57Q4tbqF1XEREJDG0yaKIiIhYhoKLiIiIWIaCi4iIlZzeBgeXQfi1xJ/j/B448AfcuJh07RJJISk2OFdERB7A1ePwfSc4ba44jndWaDYRHn42/ucIuwxzH4fDK8yfvbyh7kvQdFzSt1ckmajHRUTECn7oGhtawOxxWTwYjq+P/zl+fjo2tABEhsOat2Hn3KRrp0gyU3AREUntzu+FE387P7Y1KH7nuHkV9i54sHOIpAIKLiIiqd2tq4k7drfbNyA66sHOIZIKKLiIiKR2BapBljzOj5VsGb9zZCsIecs/2DlEUgG3Ds49cuQI69at48yZM4SHhzutY7PZGD16dAq3TEQkhURHw54fYc9C8PCECk9A6Tb2dbwyQpuPYH4P+16Too2gUs/4X6vtR/BtG7gdFluWrwLUHvYAL0AkZbll5dyIiAgGDBjA7NmzAXPfIldsNhtRUS66N8WytHJu3C6FwCcLYW0w5M0BT7WHRlXc3SpJFvN7wfZZ9mV1X4CW7znWPbcbts6Am5egWFOo0BU8MyTseleOwJbpEHICCtcxg0/GLIlvv0gKc0twGTVqFJMmTSJ79uz07NmTUqVKkTVrVpf1+/Tpk4Ktk5Sg4OLalWtQ+2k4cDK2zGaDL16C/m1cP08s6Nhf8FU9x3KbDYbuh1wlUr5NIqmcW24Vfffdd2TPnp1t27ZRpEgRdzRBJNX6bJF9aAEwDHjtS+jVHDIm8Au2pGJ3T02+m2GYxxRcRBy4ZXDu+fPnqV+/vkKLiBPrdjgvv3AV/j2eok0RIOwWvBkEFfuaj7dmwk3nQ/ISLnOuOI7lTqKLiKQtbulxUWARcc0/p/NyDw/Imz1Fm5L2HF0HGz+Fa2egSD2o9Tz45nVZ3TCg/ShYtS22bPcR+HMHLP0gCdpT4QlY9orj8v1Z/aF02yS4gEja45Yel379+rF69WouXLjgjsuLpGqDOpgh5V6P1of8cXxBl/sIngUzGsLOOXB0DawZD188DNfPu3zKyq32oeWOFVtgtZPyBMucC3r8AtkDY8tyl4Fev5nL8YukEJvNxqJFi9zdjHhxS3B56aWXaNWqFY0bN2bVqlVxzioSSW8eLgvfvg4F/1u2w9MTuj4CX73s3nZZWlQkLH3Z7EK529Wj8Pc0l0/btt/1KbfGcSxBijaE4Yfg6U3w7DYYuhf8KyfRyUVMZ8+e5bnnnqNYsWJ4e3sTEBBAu3btWLHCxTirVCxFgkuxYsXsHiVKlGDjxo3s3buXpk2bkilTJgIDAx3qFStWjOLFiyd5e9q1a0fLls4XXFq3bh02m40dO2IHGgwaNAhPT0/mzZvnUH/s2LHYbDaHx/Lly50e9/Pzo379+qxZsybONoaFhfHKK69QvHhxfHx8yJMnDw0bNuSnn36KqdOoUSOGDRsW53lWrVpF69atyZUrF5kzZ6Zs2bK88MILnDp1Ks7ngTlNvVWrVk6TuLPXbLPZmDNnzn3PK/f3RBM4PAd2fwOnFsB3YyCbZqwm3tUjcO2082PH1rl8WtECrk9ZLI5jCebhAQWrK7BIsjh69CjVqlVj5cqVTJ48mZ07d7JkyRIaN27M4MGD3d28BEuR4HL06FGnD8MwMAyDiIgIjh8/7rTOkSNHkrw9/fv3Z9myZZw8edLhWFBQENWrV6dixYqAGSDmzJnDyJEjmTFjhtPzlStXjjNnztg9GjRo4PT4hg0bKFmyJG3btiUkJMRlG59++mkWLFjARx99xL///suSJUvo3Lkzly5divfr/OKLL2jatCn58+fnxx9/ZM+ePXz++eeEhITw/vvv3/f5U6dOxWazuTweFBTk8Lo7duwY7/ZJ3Ly8oEwRyJPd3S1JAzLndr3eSVbXCaR9XShZyLG8dGFoWzuJ2iaSzJ599llsNhsbN27kscceo1SpUpQrV44RI0bw99/O98B6+eWXKVWqFJkzZ6ZYsWKMHj2a27dvxxzfvn07jRs3JmvWrGTLlo1q1aqxefNmAI4dO0a7du3IkSMHWbJkoVy5cvz2229J9npSZHBudHR0Slwm3tq2bUuePHmYOXMmr7/+ekz59evXmTdvHpMnT44pmzdvHmXLlmXUqFEUKFCAEydOEBAQYHc+Ly8v8ufP7/J6dx/Pnz8/b731FkFBQezfv58aNWo4fc7PP//Mhx9+SOvWrQEIDAykWrVq8X6NJ0+e5Pnnn+f5559nypQpMeWBgYE0aNCAq1evxvn84OBg3n//fTZv3oy/v7/TOtmzZ4/zdYukGplyQIVuEPyNfbnNBjWfcfm0DF6wbAoMmwa//LcJc/u6MPU5M1imK8fXm7tIG1FQ9jEo1tjdLZJ4uHz5MkuWLGH8+PFkyeLYbZs9e3anz8uaNSszZ86kQIEC7Ny5k4EDB5I1a1ZGjhwJQI8ePahSpQqfffYZnp6eBAcHkyGD+eVg8ODBREREsHbtWrJkycKePXvw9fVNsteU3v7XA8wg0bt3b2bOnMlrr70W06swb948oqKi6NatW0zd6dOn07NnT/z8/GjVqhUzZ858oC0IwsPDCQoKInv27JQuXdplvfz58/Pbb7/RqVOnOBfnc2XevHlERETE/JLdy9UvK5i9TN27d+eTTz5JsmASHh5ut61DaGhokpxXJN7afWr+c+f3EHXb7GlpPhECG8T5tIC88OPbEB5h5px0uY7Oyjdh1djYn//5BOoMh1ZJMbVKktPBgwcxDIMyZcok6Hl3f6kPDAzkxRdfjLn7AHD8+HFeeumlmPOWLFkypv7x48d57LHHqFChAmAOF0lK6XaTxX79+nHo0CG7sSZBQUE89thj+Pn5AXDgwAH+/vtvunbtCkDPnj0JCgpyGEy8c+dOfH19Yx41a9Z0eTxTpky89957fP/993GuGPvll1+yfv16cuXKRY0aNRg+fDh//fVXvF/fgQMHyJYtm8vekrgMHz6cOnXq0KFDhzjrdevWze51+/r6cvy484VGJkyYgJ+fX8zj3l4rSQWio8wPdKs4vgE2TIN/f3G96/HdMmaBx76GkWdh6D544RhU7hXvy3lntEBouRUCYfG/nRwvlw/D6rccy9dPgbMuFh2SVCOxk1/mzp1L3bp1yZ8/P76+vrz++ut2f99HjBjBgAEDaNq0KRMnTuTQoUMxx55//nnefvtt6taty5gxY+zGjCYFtwSXBQsWULVq1ThHMy9fvpyqVavaDUZNSmXKlKFOnTox41YOHjzIunXr6N+/f0ydGTNm0KJFC3LnNheCat26NSEhIaxcudLuXKVLlyY4ODjm8eOPP7o8vmXLFp555hm6dOkScz/QmQYNGnD48GFWrFhB586d2b17N/Xr12fcuHHxen2GYcQ5PsWVn3/+mZUrVzJ16tT71p0yZYrd6w4ODqZAAefjBV555RVCQkJiHidOnEhw2ySZ3AqBRQPh7azwpg980xou/OvuVrkWGQ6z2sH/6sBvQ2F2e/ioPIQ4jllzKnNOyF0KPNNQh3PoKZjVHt7JCRNyw1cN4NyupDn3wT/AcHG7f9+vSXMNSTYlS5bEZrPx77/x/396w4YN9OjRg9atW7N48WK2bdvGa6+9RkREREydsWPHsnv3btq0acPKlSspW7YsCxcuBGDAgAEcPnyYXr16sXPnTqpXr85HH32UdC/KcIO2bdsaOXPmNG7duuWyzq1bt4wcOXIY7du3T7Z2TJ8+3cicObMRGhpqvPrqq0bx4sWN6OhowzAMIzIy0vD39zdsNpvh6ekZ8wCM7t27x5xjzJgxRqVKlVxew9Xx0qVLGz169EhQe8eNG2dkyJDBCA8PNwzDMBo2bGgMHTrUad0PPvjAAIzTp08n6BpDhw51+po9PDyMhg0bxtQDjIULFybo3HcLCQkxACMkJCTR55AkMr2xYbyO/WNiPsMIu+LulhnGrWuGcWilYZzZEVu2ZqJje1/HMGZ1cFsz3SoqyjCmlXd8PybkNYybSfD/17Zvnb/fr2MYf3/84OeXZNeyZUujYMGCxvXr1x2OXblyxTAM+7/p7733nlGsWDG7ev379zf8/PxcXuOJJ54w2rVr5/TYqFGjjAoVKiSq7c64pcdl+/btVKpUCW9v1wsseXt7U7lyZYKDg5OtHY8//jgeHh589913fPPNN/Tr1y+ml+K3337j2rVrbNu2za5H4fvvv2fBggX3Hdx6P56enty8eTNBzylbtiyRkZHcunXrvnU7d+5MxowZmTRpktPjrto/atQoduzYYfeawexdCQoKSlB7xQJObYEjqxzLr5+D7d8mzzXDLsGfk2HBk+Y/Xd3a2PgZTC4IQY/AJxXhy9pmr8quuc7r718MEWHJ0+bU7MhKOO+kd+XGeXM8z4N6qCP4ZHcsz5AJyj/+4OeXZPfJJ58QFRVFzZo1+fHHHzlw4AB79+5l2rRp1K7tOD2uZMmSHD9+nDlz5nDo0CGmTZsW05sCcPPmTYYMGcLq1as5duwYf/31F5s2beKhhx4CYNiwYfzxxx8cOXKErVu3smrVqphjScEtfaXnz5+3my7sir+/Pxs2bEi2dvj6+tK1a1deeeUVQkND6du3b8yx6dOn06ZNGypVqmT3nLJlyzJ8+HBmz54d7/nvkZGRnD17FoBr164xd+5c9uzZw8svu15RrFGjRnTr1o3q1auTK1cu9uzZw6uvvkrjxo3txsZcuHDBIdz5+/sTEBDAlClTGDJkCKGhofTu3ZvAwEBOnjzJN998g6+vr9Mp0fnz53c6ILdw4cIULVrUruzq1asxr+uOrFmzOh25LqnU5YOuj12K41hiXToI0xuYS+7fsX4qDFgLOe9as+n4evjlWfvnnvgb5nV3fdvC/NKY1C1O/a7GsYFVXMfiy9sXui+Ced1i/7tlzg2PfQNZ8jz4+SXZFStWjK1btzJ+/HheeOEFzpw5Q548eahWrRqfffaZQ/327dszfPhwhgwZQnh4OG3atGH06NGMHTsWML94X7p0id69e3Pu3Dly585Np06dePPNNwGIiopi8ODBnDx5kmzZstGyZUu72a0PymYYKb9sbf78+SlVqhRr166Ns16DBg3Yu3dvsm4NsGHDBurUqUPr1q359Vfzfu25c+coVKgQ3333HV26dHF4zrPPPsvff//N1q1bGTt2LIsWLXLZMzR27NiY/5gAmTNnpnjx4jz77LM8/fTTLts1YcIEfvnlF/bt20dYWBgFChSgbdu2vPHGG+TKZa773qhRI6cL2Y0bNy5mRPjy5ct577332LhxIzdv3iQwMJC2bdsyYsSIeA/ctdlsLFy40G6NFlfjZyZMmMCoUaPue87Q0FD8/PwICQmJc5CyJLML+2Cai9kGjwZB1b5Je705j8Nux4UcKd8Vut61eOHC/rDV+bpJ1B4GG6Y6lpdqDb3S4ZiLc7vh4/LOj3X/CR5qnzTXiboNR9ea06EDG2pLAnEbtwSX9u3b8/vvv7N169aY6VL32rlzJ1WrVqVZs2ZJunCNpA4KLqnIvJ6wY7Z9WZ6y8Mxm83ZAUno7m+OGggDe2eD1uxZk/K4T7F3oWA+g3xpYNQaOrI4ty14EnlwJOZN22qVlzO/teGuvSH3ot9r5xlciFuaW3+hnn32WqKgo2rRpw/z58x2Oz58/nzZt2hAdHc2zzz7r5AwicTt1AQZ/ACW7Qc2n4LOFjtvUyH86zYQWkyBfBchRzOzR6L8m6UMLmAHFGR8/+59LNHdezzcfBNQyQ0qfP6DpeOg825zenF5DC0CnIHOdmsJ1oWANaPoO9F6i0CJpklt6XABeeOEFpkyZgs1mI3v27DEL1Bw+fJirV69iGAbPP/98vKblivUkZ4/LlWtQfQActR9+w9Au8MGQJL2UJNTy0bDmbcfyRm9Ak9hbqty+BV83g2N/xpZ5eMJjs6DiE8nfThFJtdwWXABmzZrFO++84zC//KGHHmLUqFH06hX/xaHEWpIzuHwwF1761LE8gxccmwf5cibp5SQhIiNgYT/Y+Z3ZBWazQYXu8OgM8Mp4T91w2PEdHFoBWXJD1f6Q3/mtZRFJP9waXO44c+ZMzIJkAQEBiVrtVawlOYNLz3Hw/XLnx5Z+AE3iv+WTJJfLh+HiPshTBnIUvX99EZH/pIqlI/39/RVWJMkUd7HZr80GRfVrljrkLJa+x6SISKJp5JakOQPbgZ+TjUgfbQDFXIQaERGxBrf1uJw/f55PP/2UtWvXcubMGbudg+9ms9nsNm8SuZ9CeWHZBzDyU1gdDL6ZoE9LmOh62RwREbEIt4xx2bt3Lw0bNuTSpUvx2rkyOtrFSpliWSm1jkt4hDkoV7NCRUTSBrf8OX/ppZe4ePEinTp1YvPmzYSGhhIdHe3yIZJY3hkVWkRE0hK3/Elft24dpUuX5ocffqBq1ar4+joZkCAiIiJpxieffEJgYCA+Pj48/PDDbNy4MVHncUtwMQyDypUru9zvRkRERJJPVBSs3mYuHbF6m/lzcpo7dy4jRoxgzJgxbN26lUqVKtGiRQvOnz+f4HO5ZYzLI488wq1bt1i/fn1KX1pSCe1VJCLiHgvWwvBpcPKu/YsL5YEpz0OnBslzzYcffpgaNWrw8ccfA+bY1YCAAJ577rl4bcx7N7f0uIwdO5ZNmzbxyy+/uOPyIiIi6dKCtfD4aPvQAub+bo+PNo8ntYiICLZs2ULTpk1jyjw8PGjatCkbNmxI8PncNh166NChdOrUie7du9OsWTMKFSqEh4tRlA0aJFMEFBERSSeiosyeFme3WQzABoz4CDrUBU/PpLvuxYsXiYqKIl++fHbl+fLlc9jyJz7cElwaNWqEzWbDMAy+/fZbZs2aFWf9qOS++SYiIpLGrdvh2NNyNwM4cd6s16hKijUrwdwSXHr37q2BuSIiIinozKWkrRdfuXPnxtPTk3PnztmVnzt3jvz58yf4fG4JLjNnznTHZUVERNIt/1xJWy++MmbMSLVq1VixYgUdO3YEzMG5K1asYMiQIQk+X6rYZFFERESSV/2K5uyhUxecj3OxYW6ZUr9i0l97xIgR9OnTh+rVq1OzZk2mTp3KjRs3ePLJJxN8LgUXERGRdMDT05zy/PhoM6TcHV7uDN744LmkHZh7R9euXblw4QJvvPEGZ8+epXLlyixZssRhwG58uGUdl379+sW7rs1mY/r06cnYGnEHreMiIuIeztZxCchrhpbkWsclKbkluLia9ny3O7OObDabZhWlQQouIiLuExVlzh46c8kc01K/YvL0tCQHt9wqWrVqldPy6OhoTpw4wdKlS5kzZw7Dhw+nXbt2Kdw6ERGRtM3TM3VPeY6LW3pc4uP777+nT58+LF++XAvQpUHqcRERkcRItcEFoEqVKuTIkYOVK1e6uymSxBRcREQkMdyyV1F8lSxZks2bN7u7GSIiIpJKpNrgEh0dzY4dO+I1kFdERETSh1SXCsLCwggODqZbt24cOHCAhg0burtJIiIikkq4ZVaRZzzmXBmGQZ48eZg8eXIKtEhERESswC3BJSAgwOUmixkzZsTf35+GDRsyePBg8ubNm8KtExERkdTKLcHl6NGj7risiIiIWFyqG+MiIiIiacvatWtp164dBQoUwGazsWjRokSfS8FFREQkvYmOgiOrYcf35j+jk3drnRs3blCpUiU++eSTBz6XW3eHDgsLY9WqVRw4cIBr167hbC08m83G6NGj3dA6ERGRNGj3AvhtKISejC3LVghafwjlOiXLJVu1akWrVq2S5FxuCy4zZ85k+PDhhIaGxpTd2VTx3p8VXERERJLA7gUwpzNwT0dB6Cmz/In5yRZekopbbhUtX76c/v37Y7PZePXVV6lduzYAX3zxBS+99BIlSpTAMAyGDBnCjBkz3NFEERGRtCU6yuxpuTe0QGzZ78OS/bbRg3JLcHn//fex2WysWrWKcePGUbJkSQAGDhzIxIkT2b17N8OGDWPGjBlUq1bNHU0UERFJW46ts7895MCAkBNmvVTMLcFl06ZN1KpVi0qVKjk97uXlxXvvvUfevHkZM2ZMCrdOREQkDbp2JmnruYlbgsv169cpXLhwzM/e3t4AXLt2LabMw8ODhx9+mHXrUnfyExERsYSs/klbz03cElzy58/P5cuXY3729zffpP3799vVu3z5Mjdv3kzRtomIiKRJReqbs4dwvnI92MAvwKyXxK5fv05wcDDBwcEAHDlyhODgYI4fP57gc7kluJQpU4YDBw7E/FynTh0Mw2DSpEkxU6LXr1/PypUrKV26tDuaKCIikrZ4eJpTngHH8PLfz62mmvWS2ObNm6lSpQpVqlQBYMSIEVSpUoU33ngjweeyGc4WT0lmH330EUOHDuXvv/+mZs2aREdHU7VqVXbu3Em+fPnw9/dn165dREZG8vXXX9OzZ8+UbqIks9DQUPz8/AgJCSFbtmzubo6ISPrhbB0XvwAztKTyqdDgpuASEhLC33//TZkyZShSpAgAp06don///ixfvpzo6Gj8/PwYOXIkr7zySko3T1KAgouIiBtFR5mzh66dMce0FKmfLD0tycEtwSUuYWFhhISEkDdvXjw9rfEmSsIpuIiISGKkur2KMmfOjL+/P56ensyYMYO33nrL3U0SERGRVCLV9bjcrXbt2mzcuJGoqNS9ip8knHpcREQkMVJdj4uIiIiIKwouIiIiYhkKLiIiImIZCi4iIiJiGQouIiIiYhkKLiIiImIZXilxES0kJyIiIkkhRYLLgywVY7O52sVSRERE0psUCS7R0dEpcRkRERFJ4zTGRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCxDwUVEREQsQ8FFRERELEPBRURERCzDy90NEBERSXKREbBnAZzbCXnKQLkukMHH3a2SJKDgIiJpy+2bsOsHuLgf8leEsp3AM4O7WyUp6cYFmNEYzu+OLVv1JvRbDX6F3NYsSRoKLiKSdoScgOmN4Mrh2LJ8FeDJlZAlt9uaJSlsxRv2oQXg8iFYOgq6zHJPmyTJaIyLiKQdS160Dy1g3ipY/ZZ72iPusXeRi/KFKdoMSR4KLiKSdvz7k/PyPfrASlc8MyasXCxFwUVE0g4PF2NZ9IGVvlTsnrBysRQFFxFJfhE3YNc82D4bwi4l33VcfmB1S75rSurTaDSUaG5fVqQeNHvHPe2RJGUzDMNwdyMk/QkNDcXPz4+QkBCyZcvm7uZIcjq4DOY+Dreumj97+UDbj6Faf8e6m76ADdMg9AQE1IZH3oSAWvG/1s2rMKsNHF8fW1ayFXT7ETJkepBXIVZ0cqM5xil3GShS192tkSSi4CJuoeCSTkTcgMmFYkPLHTYPGLYfchaPLVs3CZa+bF8vQyZ46m9zWnNCHF0Hl/ZDvopQqEaimi6p38nz8MEP8NcOKJAHBj8KTau7u1WS3HSrSESSz/7fHEMLgBENO+fG/hwZAX9Odqx3+yb89X7CrxtY3+zRUWhJs05dgFpPw4fzYPM++PlPaPkifL3EjY26fQuibruxAemDgouIJJ/I8Pgdu3EOwi46r3fvehwiwNR5cOae4VKGAaO/gsjIFG7MhX3wTSsYlxnG+cL83sk7liudU3ARkeRTqpU5psWZhzrG/nuWfJA5l/N6ecomebPShAv/mh+QU0tBUFP49xd3tyhF/e0iz566AMfPp2BDboVCUGM4sMRMTlERsP1bmNUuBRuRvii4iEjyyZzLHIhru+dPTf1RUKBK7M9eGaHui47P9/KBui/c/zon/obfhsOvz8OR1Q/UZEu4uB++rGV+QF46AIdXwOz2sGWGu1uWYgrmcV5eOusR8t7ea4aIlLDjO7h2xrH8xAb7QeKSZBRcXOjbty8dO3a0K5s/fz4+Pj68/755z/3ChQs888wzFC5cGG9vb/Lnz0+LFi3466+/7J63bds2unTpQr58+fDx8aFkyZIMHDiQ/fv3u7x+o0aNGDZsWJxtXLVqFa1btyZXrlxkzpyZsmXL8sILL3Dq1CmXzxk0aBDFixcnU6ZM5MmThw4dOvDvv//a1dm6dSvNmjUje/bs5MqVi6eeeorr1687PV+LFi3w9PRk06ZNcbZV0qDr58ywMK0c/K8ebJ3pvF61/jB0HzR525ym+swWaD7BsV6DUdD2E8hVyhyUW6wJPLkC/CvF3Y7Vb8OXtWHDVPj7I3OPmt/jEXas7M/JcCvEsXzVWIiOSvHmuMPgR8Hjrk+wQJ8jrK5Unz1ViuH7VVn4sHTKhNjLhxJ3TBJNwSWevvrqK3r06MFnn33GCy+YfxQfe+wxtm3bxtdff83+/fv5+eefadSoEZcuxd7bXLx4MbVq1SI8PJzZs2ezd+9eZs2ahZ+fH6NHj050e7744guaNm1K/vz5+fHHH9mzZw+ff/45ISEhMcHKmWrVqhEUFMTevXv5448/MAyD5s2bExVl/rE7ffo0TZs2pUSJEvzzzz8sWbKE3bt307dvX4dzHT9+nPXr1zNkyBBmzEg/3/TSveMbYMt0+OJhMyxc2APH/4KFT5p7wTiTqwQ0eg2avAUFqro+98PPwrB98EYYPLkcCteJuy1XjsLKMY7l6z+Aszvi+4qs59Rm5+UhJ8xAmQ7UrwTfvg6B+QEMfq3Qhvp+f8ZWuHQAZrV13huSlApUc33MP47fdUk0TYd2oW/fvly9epVFixYxadIkxowZw3fffcejjz4KwNWrV8mRIwerV6+mYcOGTs8RFhZGkSJFqFevHgsXOi45fvXqVbJnz+70uY0aNaJy5cpMnTrV4djJkycpXrw4zz77LFOmTEnQee+1Y8cOKlWqxMGDBylevDhffvklo0eP5syZM3j893Vm586dVKxYkQMHDlCiRImY57755pv8+++/jBkzhlq1anHmzBkyZYrfWhmaDm1BNy6atyNObHBdx8sbXjwBWVz04ye1jZ/DL884P9Z0PDR8NWXakdK+6+R83x0fP3j5nPnfIZ2IjoZLwavIs/AR5xWavwv1RyZfAyIjzBB/Nti+vHxX6Don+a6bjqnH5T5efvllxo0bx+LFi2NCC4Cvry++vr4sWrSI8HDnMyf++OMPLl68yMiRzv+niW+4uNe8efOIiIh44PPeuHGDoKAgihYtSkBAAADh4eFkzJgxJrQAMWHkzz9jv80YhkFQUBA9e/akTJkylChRgvnz5yfq9YhF/Ppc3KEFzJlC53amTHsAvLMm7pjV1RnuOG4IoObgdBVawLxdlMcrjl6m62eTtwFeGaHfSqg3EnKXhvyVoMUk6Pxt8l43HVNwicPvv//OpEmT+Omnn2jSpIndMS8vL2bOnMnXX39N9uzZqVu3Lq+++io7dsR2Tx84cACAMmXKJGm7Dhw4QLZs2fD390/U8z/99NOY4PX777+zbNkyMmY093J55JFHOHv2LJMnTyYiIoIrV64wapTZ/X/mTGyX6/LlywkLC6NFixYA9OzZk+nTp7u8Znh4OKGhoXYPsZCIMNjz4/3r2WyQvUjyt+eOMh3AJ7tjeYZM5jfetCqwPjzxY+yMq8y5oNEb5q249KhIPfDwdH4ssFHyXz9TDmjxLgz9FwYHQ72XwNPFvlnywBRc4lCxYkUCAwMZM2aM08Gpjz32GKdPn+bnn3+mZcuWrF69mqpVqzJz5kzA7JVIDoZhYLPZEv38Hj16sG3bNtasWUOpUqV4/PHHuXXrFgDlypXj66+/5v333ydz5szkz5+fokWLki9fPrtemBkzZtC1a1e8vLwA6NatG3/99ReHDjkfjDZhwgT8/PxiHnd6eMQioiPjt7BW6fb2q+EmN29f6L4IfPPHlmXOBV3ngW/elGuHO5TtCM/vhtdDzdtDTd50/eGd1vkVcj4rrXgzKN025dsjyUpjXFy4M8blww8/pHHjxhQoUIDff/+drFnj7n4eMGAAy5Yt49ixYyxcuJBOnTqxfv16ateunaDrxzXGZcqUKYwYMYLTp08nutfljoiICHLkyMFXX31Ft272G9GdO3eOLFmyYLPZyJYtG3PmzKFLly5cvnyZAgUKcPv2bbsAFRUVxauvvsr48eMdrhMeHm53Sy00NJSAgACNcbGS6Y3g6BrnxzJkgoo9oNUUM0yktKjb5gwSI8r8hp3BxdoxYi8iDLYFwaEV5rikagOsvdrwnkWwfRZE3oQy7aHKk+atHElT1ONyH0WKFGHNmjWcPXuWli1bcu3atTjrly1blhs3bgDQvHlzcufOzaRJk5zWvXr1aqLa1LlzZzJmzJgk5zUMA8MwnI7TyZcvH76+vsydOxcfHx+aNWsGwOzZsylUqBDbt28nODg45vH+++8zc+bMmBlKd/P29iZbtmx2D7GYNtMgc277Mv8qMOIovHoFOv7PPrRER0PoKfPDMSlE3YZ9v5o7TN87U8QzA5RoBiVbKrTEV8QNmNEQFg8xB/pu/tJcGybYwmMzynaEbvOh169QY5BCSxrl5e4GWEFAQACrV6+mcePGtGjRgiVLlnD79m26dOlCv379qFixIlmzZmXz5s1MmjSJDh06AJAlSxa++uorunTpQvv27Xn++ecpUaIEFy9e5IcffuD48ePMmeN61PmFCxcIDg62K/P39ycgIIApU6YwZMgQQkND6d27N4GBgZw8eZJvvvkGX19fp1OiDx8+zNy5c2nevDl58uTh5MmTTJw4kUyZMtG6deuYeh9//DF16tTB19eXZcuW8dJLLzFx4sSYQb/Tp0+nc+fOlC9f3uF9euWVV1iyZAlt2rRJ5LstqVb+iuZ6LMHfwNWjULAGlOvi/MNh+2xY/hpcPQYZs0C1geaAxcTe9z8TbK5EGnrS/NnDCx55Cxq+kthXI1tnOE6rNqJhyQtQ/vF0N8hXLMQQp/r06WN06NDBruzkyZNGyZIljVq1ahlXr141Ro0aZVStWtXw8/MzMmfObJQuXdp4/fXXjbCwMLvnbdq0yejUqZORJ08ew9vb2yhRooTx1FNPGQcOHHB5/YYNGxqAw2PcuHExdZYtW2a0aNHCyJEjh+Hj42OUKVPGePHFF43Tp087PeepU6eMVq1aGXnz5jUyZMhgFCpUyOjevbvx77//2tXr1auXkTNnTiNjxoxGxYoVjW+++Sbm2ObNmw3A2Lhxo9NrtGrVynj00Uddvq47QkJCDMAICQm5b12xmIPLDWO0zTBex/7x2/DEnS862jCmlHQ83+sYxtF1Sdv29GRWB+fv6esYxgnn/3+LpAYa4yJuoXVcUjHDgEPL4dwOc3pnyVaER3pis0HG+HSYzO4I//7kWJ7RF0adN8fD/OfKNRj/Dfz8F3hngG5N4cUn7rnOiX/MWxjO1BgE7T9PyKuTOxYNMBcSdGb4IchZLGXbIxJPulUkIrFuhcI3LWPWazlyM5BhJ4P47VxDPD1sdKwPU5+D/C72QwQg9ITz8ojrcPNKTHCJuA1NhsH2g7FVRn8FW/fD/HF3PS8qnjtMS8JUG2DeLrr3u2vxpgotkqppcK6IxFr1ZkxouRXtTZMdq1h8phHR0TZuR8K8VdDqJXPcrUuFXPSOZA+0m7a8YK19aLlj4b3lhWpBFhdTm8t0iPPlpLgd38P0hvBhGfj5abh63N0tci2gFnScbr8rd7Em0HmW+9okEg8KLiISa/e8mH+df6Ezx8IDHarsOATLXGyVA5iLb9275L/NZi7Bf9daQMEHXJ9i293HvDJCx68cB4tW7G5OeU0t1r4L87rD0bVwcR9s+sK8xXUtmVdufRBVn4QXT8JTG2D4QXN/KN987m6VSJx0q0hEnDpyy/XtgsNONiCPjDQDzaXQQJp03oj/v+/Bib/BLwBqPQ/FGtvVL17Q9bVL3HusTDsYdtBco+PWVSjRwuF8bhVxA9a+41h+7Qz88wk0Hed4LLXI4GP2vrjRucsw8jP4cY2ZcTs3gknPQJ7sbm3W/RkGhF8zt5d4gEVBJWEUXEQkVrnOsN7cuLOK71aX1aqWtv95z1Fo9zIc/a9zwdMzkFd6fMybLvY/BOjWBN7+Gk5esC+vXQ7qVXTyBL9C0MDF7tPudnE/hLvYxuJ0XN1TEhkJzUbA7iOxZd8sMW8Xbv6fXSdd6vLPp7B2gjlFP3sRaPAq1HjKPLbpC9gwzTxWuA488iYUqune9qYhqfVXQkTcofHYmG/frXP+Ru1s6x2qtK4ND5e1L+vxVmxoAYiKgre/gaWbXF/KNzOs/BDa1TU/nHwyQp+W8PPEJHgdKc2vkLm2jDPZA1O0KVazeIN9aLlj+0H4/Z+Ub0+8bJkOiwfHrit09Rj8PMhcv2jtu+b4pgt7zDB7YAnMaATndrm1yWmJelxEJJZPNhi4Hg4uxePcDn5/7AqTNkezcJ0HXp7w+CPwwj17F+48ZI57cWb2UmgexwryxQvConfMb90eHqn42/X9ZMkDlXvB1iD7ci9veHiwe9pkEftdTEIDOHACSNhuKSnjrw+cl6+bFBtm7nb7Jqz/AB6dkbztSicUXETEns0GJVtAyRZkBcZVgXEDXVcPj2PvxbiO3c0rLfwlavcZeGczpxiHXzO3Q2gxGfKVv/9z07EKccy8Lp9aZ2VfOey6PMJxQ14Azu9OvvakM2nhz4WIuFGVkhCQF06cdzzWvm7Kt8dtvLyh9VQzrNwOAx8/d7fIElrUNG89/rPHvrxuBWhSzT1tuq8C1eD4X47lBavD2e3mekX3yvNQ8rcrnbBqx6yIpBKenvDlSMh0z2zlRxtA10fc0ya38syg0JIAHh7w+2QY/rgZgAvnM1dP/nVSKp6o88hYxzFNnhnMQbh1XnCs7+UDtYenRMvSBS35L26hJf/TnjOXzDEtF0PMb8pNq6fiDx6RB3XsT/hzMpzfY94OrP9y7LTyvz8yH6EnIaAONHnLnF0kSULBRdxCwUVERBJDt4pERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMhRcRERExDIUXERERMQyFFxERETEMrzc3QBJnwzDACA0NNTNLRERsZ6sWbNis9nc3Qy3UHARt7h27RoAAQEBbm6JiIj1hISEkC1bNnc3wy1sxp2vviIpKDo6mtOnT8f7W0NoaCgBAQGcOHEi3f7Pei+9J/b0ftjT++EoLb0n6nERSWEeHh4UKlQowc/Lli2b5f/gJDW9J/b0ftjT++FI74m1aXCuiIiIWIaCi4iIiFiGgotYgre3N2PGjMHb29vdTUk19J7Y0/thT++HI70naYMG54qIiIhlqMdFRERELEPBRURERCxDwUVEREQsQ8FFLOGTTz4hMDAQHx8fHn74YTZu3OjuJqWICRMmUKNGDbJmzUrevHnp2LEj+/bts6tjGAZvvPEG/v7+ZMqUiaZNm3LgwAE3tThlTZw4EZvNxrBhw2LK0uP7cerUKXr27EmuXLnIlCkTFSpUYPPmzTHH09N7EhUVxejRoylatCiZMmWiePHijBs3jruHc6an9yNNMkRSuTlz5hgZM2Y0ZsyYYezevdsYOHCgkT17duPcuXPublqya9GihREUFGTs2rXLCA4ONlq3bm0ULlzYuH79ekydiRMnGn5+fsaiRYuM7du3G+3btzeKFi1q3Lx5040tT34bN240AgMDjYoVKxpDhw6NKU9v78fly5eNIkWKGH379jX++ecf4/Dhw8Yff/xhHDx4MKZOenpPxo8fb+TKlctYvHixceTIEWPevHmGr6+v8eGHH8bUSU/vR1qk4CKpXs2aNY3BgwfH/BwVFWUUKFDAmDBhghtb5R7nz583AGPNmjWGYRhGdHS0kT9/fmPy5Mkxda5evWp4e3sb33//vbuameyuXbtmlCxZ0li2bJnRsGHDmOCSHt+Pl19+2ahXr57L4+ntPWnTpo3Rr18/u7JOnToZPXr0MAwj/b0faZFuFUmqFhERwZYtW2jatGlMmYeHB02bNmXDhg1ubJl7hISEAJAzZ04Ajhw5wtmzZ+3eHz8/Px5++OE0/f4MHjyYNm3a2L1uSJ/vx88//0z16tXp0qULefPmpUqVKvzvf/+LOZ7e3pM6deqwYsUK9u/fD8D27dv5888/adWqFZD+3o+0SHsVSap28eJFoqKiyJcvn115vnz5+Pfff93UKveIjo5m2LBh1K1bl/LlywNw9uxZAKfvz51jac2cOXPYunUrmzZtcjiWHt+Pw4cP89lnnzFixAheffVVNm3axPPPP0/GjBnp06dPuntPRo0aRWhoKGXKlMHT05OoqCjGjx9Pjx49gPT5O5LWKLiIWMTgwYPZtWsXf/75p7ub4jYnTpxg6NChLFu2DB8fH3c3J1WIjo6mevXqvPPOOwBUqVKFXbt28fnnn9OnTx83ty7l/fDDD8yePZvvvvuOcuXKERwczLBhwyhQoEC6fD/SIt0qklQtd+7ceHp6cu7cObvyc+fOkT9/fje1KuUNGTKExYsXs2rVKrtdte+8B+nl/dmyZQvnz5+natWqeHl54eXlxZo1a5g2bRpeXl4x36LTy/sB4O/vT9myZe3KHnroIY4fPw6kv9+Rl156iVGjRvHEE09QoUIFevXqxfDhw5kwYQKQ/t6PtEjBRVK1jBkzUq1aNVasWBFTFh0dzYoVK6hdu7YbW5YyDMNgyJAhLFy4kJUrV1K0aFG740WLFiV//vx2709oaCj//PNPmnx/mjRpws6dOwkODo55VK9enR49ehAcHEyxYsXS1fsBULduXYcp8vv376dIkSJA+vsdCQsLw8PD/qPN09OT6OhoIP29H2mSu0cHi9zPnDlzDG9vb2PmzJnGnj17jKeeesrInj27cfbsWXc3Ldk988wzhp+fn7F69WrjzJkzMY+wsLCYOhMnTjSyZ89u/PTTT8aOHTuMDh06pKupnXfPKjKM9Pd+bNy40fDy8jLGjx9vHDhwwJg9e7aROXNmY9asWTF10tN70qdPH6NgwYIx06EXLFhg5M6d2xg5cmRMnfT0fqRFCi5iCR999JFRuHBhI2PGjEbNmjWNv//+291NShGA00dQUFBMnejoaGP06NFGvnz5DG9vb6NJkybGvn373NfoFHZvcEmP78cvv/xilC9f3vD29jbKlCljfPnll3bH09N7EhoaagwdOtQoXLiw4ePjYxQrVsx47bXXjPDw8Jg66en9SIu0O7SIiIhYhsa4iIiIiGUouIiIiIhlKLiIiIiIZSi4iIiIiGUouIiIiIhlKLiIiIiIZSi4iIiIiGUouIiIiIhlKLiIpFM2my3OR6NGjRzqBwYG2pUdPXrUad3ESMpzJeR6NpuNEiVKEBkZ6bTe008/jc1mY+bMmSnSroSYOXMmNpuNsWPHurspIinGy90NEBH36tOnj9PyMmXKpHBLEsdms1GkSBGOHj2a6HMcOnSIb775hn79+iVdw0QkWSi4iKRzD9KTULBgQfbu3UvmzJmTrkEpzMfHh1u3bvH222/Tu3dvvLz0Z1EkNdOtIhFJtAwZMlCmTBkKFy7s7qYkWsGCBWnfvj1HjhwhKCjI3c0RkftQcBGRRItrXMrt27d59913KV26ND4+PhQuXJgRI0Zw/fp1GjVqhM1mc3l75+bNm4waNYoiRYrg7e1NiRIlePfdd7l7T9g74zsAjh07Fuf4nPsZO3YsNpuN8ePHExEREa/nBAYGxlz/XqtXr8Zms9G3b1+78r59+2Kz2Vi9ejXLly+nQYMGZM2albx58zJw4EBCQkIAOH/+PIMGDaJgwYL4+PhQs2ZNVq9eHWd79u3bx2OPPUauXLnIkiULdevW5bfffnNZ/8SJEwwZMoTixYvj4+NDzpw5adu2LevXr4/z9Zw9e5YBAwZQqFAhvLy8mDp1apztEklq6hMVkSRnGAZdu3Zl4cKFZMmShebNm5MhQwaCgoL4888/47wdExERQfPmzdmzZw+NGjXixo0brFmzhlGjRnHt2jXefvttAEqUKEGfPn34+uuvyZIlC507d445R0LH51SpUoWOHTuycOFCZsyYwdNPP524Fx5PCxcu5JNPPqF27dq0bNmSv//+m6+++ooDBw4wf/58ateuTVRUFPXr1+fo0aP8888/tGzZkk2bNlGhQgWH8x06dIiHH36YnDlz0rx5c06fPs26deto27Yt06dP58knn7Srv2HDBtq0acOVK1coXbo0bdq04cKFC/zxxx8sWbKE2bNn07VrV4frXLhwgRo1ahAZGUm9evW4deuWpW8TikUZIpIuAUZC/gQARpEiRezKjhw5YgBGw4YN7cq//fZbAzCKFi1qnDhxIqb84sWLRuXKlWOufeTIEYdz3TlfSEhIzLFNmzYZnp6eRubMmY1r167dt13xced6xYsXNwzDMIKDgw2bzWYEBAQYt27diqk3aNAgAzCCgoLsnl+kSBGX79+qVasMwOjTp49deZ8+fQzA8PDwMBYvXhxTHhoaapQvX94AjLJlyxo9e/Y0IiIiYo6//vrrBmD07t3b7nxBQUEx71nv3r2N27dvxxz75ZdfYt6zkydPxpSHhIQY/v7+hqenpzFr1iy7823atMnIkSOH4evra5w/f97h9QDGo48+aty8edPp6xZJCbpVJJLOuZoO/SCzdD7//HMA3nrrLQoVKhRTnitXLiZPnhzncz08PPjiiy/Ili1bTFn16tVp1aoVYWFhbN68OdHtikulSpXo1KkTJ06c4H//+1+yXOOO7t2706ZNm5ifs2bNysCBAwE4efIk06ZNI0OGDDHHX3zxRWw2G2vWrHF6Pl9fX6ZOnWrXk9W2bVs6d+5MWFiY3didGTNmcObMGYYNG0aPHj3szlO9enVGjx7N9evXmTVrlsN1vL29+eijj/Dx8UncCxdJArpVJJLOuZoO7evrm6jz3b59m02bNmGz2exu39zRtGlTcubMyeXLl50+v0iRIpQuXdqhvFSpUgCcOXMmUe2Kj7Fjx7JgwQImTJjAgAEDku0Dunnz5g5lxYoVA8zwkCNHDrtjfn5+5MyZ0+Vrb968ucNzALp168bcuXNZt25dTNnSpUsB6NSpk9Nz1a9fH4CNGzc6HKtatSoFCxZ0+jyRlKLgIpLOJfXCapcuXSIiIoI8efK4/OAvXLiwy+Bydw/N3bJmzQpAeHh40jTUifLly9OlSxd++OEHvvjiC4YOHZos13H24X8nKLoKBr6+vly6dMnpsSJFijgtv7Ng4OnTp2PK7vSk1a1bN842Xrx40aHMyrPHJO1QcBGRVMXDw713sMeMGcP8+fOZOHEiTz31VKLOER0dHefxuF5jcr/+O23r3LkzWbJkcVnP2QBn3SKS1EDBRUSSVK5cuciQIQMXL17k1q1bTj/sTpw44YaWxU/ZsmXp2rUr33//PZ999pnLehkzZgTg+vXrDrfVUvr1HTt2LM7yAgUKxJQVKlSIffv2MWrUKKpVq5Yi7RNJShqcKyJJKkOGDNSsWRPDMFiwYIHD8ZUrV7q85ZHY67naZyix3njjDTw8PHj33XcJCwtzWsff3x+A/fv3OxxbtmxZkrbnfpYuXcrVq1cdyufMmQNAvXr1YsqaNWsGmFOyRaxIwUVEktyddVDeeOMNTp06FVN++fJlXnrppSS9VoECBTh37pzTD+7EKlOmDN26deP8+fPMnz/faZ2GDRsCMGHCBKKiomLKv//+e77//vska0t8XL9+nREjRtgFuN9//50ffviBTJky2a3jMmjQIPLmzcukSZP48ssvHW5rRUZG8scff7Br164Ua79IQii4iEiS69GjB48++iiHDh2iTJkydOzYkccee4wSJUrg4eFBrVq1gNjbLQ+iffv2REZGUrVqVXr27MmAAQPuO+U6Pt544w08PT25efOm0+ODBw8mT548zJ8/n7Jly9KlSxcqV65Mr169km1Qrys9evRgwYIFlCpVim7dutGoUSPatGlDVFQU06ZNsxvwnD17dn766Sf8/PwYNGgQgYGBtG7dmh49etCkSRPy5MlDy5YtOXjwYIq+BpH4UnARkSRns9mYO3cuEyZMwN/fn99//51//vmHnj17snLlSi5evIjNZnM6hTehJkyYwJAhQ4iMjGTu3LlMnz6dX3/99YHPW6pUKYd1Tu6WL18+1q5dS9u2bTlz5gy///47fn5+LFu2jPbt2z/w9ROiRIkSbNiwgYoVK/LHH3+wceNGatWqxS+//MKAAQMc6teqVYudO3cycuRIsmXLxpo1a1i0aBHHjh2jYcOGzJw5k6ZNm6boaxCJL5th3LX5h4hIMjt58iRFixalRIkS7N27193NERGLUY+LiCSLHTt2cPv2bbuyc+fO0bdvXyIjI+nZs6ebWiYiVqYeFxFJFi1btmTjxo1UrlyZfPnycebMGbZs2cL169epUaMGa9eu1bogIpJgWsdFRJJF3759MQyDnTt3sn79ejw9PSlVqhSdO3dm+PDhCi0ikijqcRERERHL0BgXERERsQwFFxEREbEMBRcRERGxDAUXERERsQwFFxEREbEMBRcRERGxDAUXERERsQwFFxEREbGM/wP/WeJxN2NBRAAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n\nsns.scatterplot(data=df, x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\")\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x3dd24c8>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "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\nsns.scatterplot(data=df, x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x3c01490>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n# HINT use groupby method on Orbit column and get the mean of Class column\n\ndf_orbitsuccess = df.groupby('Orbit').mean().reset_index()\n\nsns.barplot(data=df_orbitsuccess, x='Orbit', y='Class')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x1045a88>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's create a `bar chart` for the sucess rate of each orbit\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n# 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\nsns.scatterplot(data=df, x='FlightNumber', y='Orbit', hue='Class')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x2bd2290>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n# 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\nsns.scatterplot(data=df, x='PayloadMass', y='Orbit', hue='Class')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x41d30d0>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  6: Visualize the launch success yearly trend\n\n\nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nExtract_year()\ndf['Date'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "ename": "<class 'TypeError'>",
          "evalue": "Extract_year() missing 1 required positional argument: 'date'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[14], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# A function to Extract years from the date \u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mExtract_year\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m year\n\u001b[1;32m      4\u001b[0m df\u001b[38;5;241m.\u001b[39mhead()\n",
            "\u001b[0;31mTypeError\u001b[0m: Extract_year() missing 1 required positional argument: 'date'"
          ],
          "output_type": "error"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "## Features Engineering\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "### TASK  7: Create dummy variables to categorical columns\n\n# HINT: Use get_dummies() function on the categorical columns\n\nfeatures_one_hot = pd.get_dummies(features)\n\nfeatures_one_hot",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0              1   6104.959412        1     False   False  False    1.0   \n1              2    525.000000        1     False   False  False    1.0   \n2              3    677.000000        1     False   False  False    1.0   \n3              4    500.000000        1     False   False  False    1.0   \n4              5   3170.000000        1     False   False  False    1.0   \n..           ...           ...      ...       ...     ...    ...    ...   \n85            86  15400.000000        2      True    True   True    5.0   \n86            87  15400.000000        3      True    True   True    5.0   \n87            88  15400.000000        6      True    True   True    5.0   \n88            89  15400.000000        3      True    True   True    5.0   \n89            90   3681.000000        1      True   False   True    5.0   \n\n    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0             0            0          0  ...             0             0   \n1             0            0          0  ...             0             0   \n2             0            0          0  ...             0             0   \n3             0            0          0  ...             0             0   \n4             0            0          0  ...             0             0   \n..          ...          ...        ...  ...           ...           ...   \n85            2            0          0  ...             0             0   \n86            2            0          0  ...             0             0   \n87            5            0          0  ...             0             0   \n88            2            0          0  ...             0             0   \n89            0            0          0  ...             0             0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0              0             0             0             0             0   \n1              0             0             0             0             0   \n2              0             0             0             0             0   \n3              0             0             0             0             0   \n4              0             0             0             0             0   \n..           ...           ...           ...           ...           ...   \n85             0             0             0             0             0   \n86             0             0             0             0             1   \n87             0             1             0             0             0   \n88             0             0             0             0             0   \n89             0             0             0             0             0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0              0             0             0  \n1              0             0             0  \n2              0             0             0  \n3              0             0             0  \n4              0             0             0  \n..           ...           ...           ...  \n85             0             1             0  \n86             0             0             0  \n87             0             0             0  \n88             0             1             0  \n89             0             0             1  \n\n[90 rows x 80 columns]",
            "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>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86</td>\n      <td>15400.000000</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87</td>\n      <td>15400.000000</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88</td>\n      <td>15400.000000</td>\n      <td>6</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89</td>\n      <td>15400.000000</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90</td>\n      <td>3681.000000</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  8: Cast all numeric columns to `float64`\n\n# HINT: use astype function\n\nfeatures_one_hot = features_one_hot.astype('float64')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features_one_hot.to_csv('dataset_part\\_3.csv', index=False)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}