EDA with visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment: Exploring and Preparing\u00a0Data\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\nIn 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\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  Feature Engineering\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\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": "# andas 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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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": 2,
            "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  \\\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  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.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\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"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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SJEmS1FYj7vGPiPdGxN0RMb2W9pvA94AlwMeB6yNiRvvDlCRJkjQWrQz1eRXwo8zcXEv7OyCB9wGfAOYBb2tfeJIkSZLaoZWG/wHALY2FiJgJHA58PDP/NjPfAlwNnNDWCCVJkiSNWSsN/2lAvbf/KKre/strad8F5rQhLkmSJElt1ErDfyMws7b8MuAxYGUt7UmtbDMizo+IeyPiplra9Ii4MiJuK4/TanlnRMTtEXFrRBxbSz88Im4seR+LiCjpe0TEJSV9ZUQc0ML+SpIkSZNGKw3/7wOvjogXRMSBwO8B/52ZD9fWOQC4u4VtXggc15T2buCqzDwIuKosExGHACcBzy9lPh4Ru5UynwBOBQ4qf41tngLcl5kHAmcDH24hNkmSJGnSaKXh/xFgH+AG4Nby/B8amRHxZOAYYPVIN5iZ17Dj8CGAhcBF5flFbL9mYCHVdKGPZOYAcDuwICL2B/bOzBWZmcCnm8o0trUMeHnj1wBJkiSpn4y44Z+Z3wJ+G/gS8B/Aosz8cm2VlwBrSt5YPCMz7y6veTfw9JI+E1hfW29DSZtZnjen71AmM7cC9wODTjcaEadGxOqIWL1x48Yx7oIkSZLUW1q6gVdmfgX4yi7yrgZe2I6gdmGwnvocIn2oMjsnZp4LnAswf/78QdeRJEmSJqpWhvrsUkRMi4i92rEt4J4yfIfyeG9J3wDMrq03C7irpM8aJH2HMhExlWp4UvPQIkmSJGnSa2UGnpdHxEeaZtl5ekR8E/gpsDki/rENMV0GnFyenwwsr6WfVGbqmUd1Ee+qMhzogYg4sozff0NTmca2FgFXl+sAJEmSpL7SSo//W4Hfzcz7amlLgF+jutB2E/C2iDhxpBuMiIuBFcDBEbEhIk4BPgS8IiJuA15RlsnMm4FLgR9SDTdanJmPl02dBnyqxHEH0Lj24DxgRkTcDryTMkOQJEmS1G9ipB3gETEAfDMz/7AsP4Wqsf+tzDw2Iv4XcCPw48z8jQ7FOy7mz5+fq1ePeHIiqfdtHoDli2H9Sph9BCxcCtPndTuq9uqlfexGLF14zXWbtnD6shu4fu19HDZ3GksWHcqcGXt29DU7ppeOn07ph32U2m9SzQbZSo//09k+dh7gCODJVHPxk5kPUN3F9+B2BSepTZYvhrXXwrat1ePyxd2OqP16aR+7EUsXXvP0ZTewamAzW7clqwY2c/qyGzr+mh3TS8dPp/TDPkoaUisN/0eAp9SWf41qhpxramk/B6a3IS5J7bR+5dDLk0Ev7WM3YunCa16/9r4hlyeUXjp+OqUf9rHfbR6AC46Hs2ZUj5sHuh2RekwrDf8BoD6E5zXAbZl5Zy1tNtWFvpJ6yewjhl6eDHppH7sRSxde87C504ZcnlB66fjplH7Yx37nrzoaRisN/4uAX46IlRHxLeCXgc81rXMY1V19JfWShUth7lEwZWr1uHBptyNqv17ax27E0oXXXLLoUBbMm87UKcGCedNZsujQjr9mx/TS8dMp/bCP/c5fdTSMVi7u3Z2q8f97VBc6/CdwYmY+UvIXANcB783Mv+1MuOPDi3slSdKEc8HxVU9/w9yj4I1XdC+eyaE/L+7NzMcy8/eBacA+mbmw0egvfkx1595/bnOMkiRJGo6/6mgYU1stkJk/30X6T3F8vyRJUndMn2cPv4bUyhh/SZIkSRNUSz3+EbEX8H+BY4GZwB6DrJaZ+ew2xCZJkiSpTUbc8I+IpwH/DRxCNV//3sD9wJPYPr//XcBjbY5RkiRJ0hi1MtTnr6ga/adQXeALcDbwVOAlwPXAHcDz2hmgJEmSpLFrpeH/auCazLwga3OAZuU64HjgucBftjlGSZIkSWPUSsN/NlWvfsM2amP8M/Ne4MvASe0JTZIkSVK7tNLw3wI8Xlu+H3hm0zr3UF30K0mSJKmHtNLwX0/V69/wQ+DoiNitlvZS4CftCEySJElS+7TS8P8m8OsR0bh18SXAs4H/iojFEfHvwJGAd46QJEmSekwr8/hfRDV15yyq3v9/BX4DOAF4ZVnnWqrZfyRJkiT1kBE3/DPzeuC02vJW4Hcj4nDgQGAN8J3M3NbuICVJkiSNTUt37h1MZn4X+G4bYpEkSZLUIa2M8ZckSZI0QQ3Z4x8RbxjNRjPz06MLR5IkSVInDDfU50Igh1mnLsr6NvwlSZKkHjKSMf5bgcup5u2XJEmSNAEN1/D/JnA01ZSdTwc+CVyamb/odGCSJEmS2mfIi3sz82XAwcASqik7LwDujoh/johfGYf4JEmSJLXBsLP6ZObtmfkuYDZwIrCSaj7/70XEqog4JSL26nCckiRJksZgxNN5ZubWzPxCZh4HPBv4ILA/cC5wV0S8uEMxSpIkSRqjUc3jn5lrM/OvgVOBO4GnAvu1M7CIeEdE3BwRN0XExRHx5IiYHhFXRsRt5XFabf0zIuL2iLg1Io6tpR8eETeWvI9FRLQzTkmSJGkiaLnhHxHPioi/iogfU832MwP4N+D6dgUVETOBPwXmZ+YLgN2Ak4B3A1dl5kHAVWWZiDik5D8fOA74eETsVjb3CaovKAeVv+PaFackSZI0UYyo4R8RUyLi1RFxGbAGOAt4AHgb8KzMPDkzN7Q5tqnAUyJiKrAncBewELio5F9ENdsQJf3zmflIZg4AtwMLImJ/YO/MXJGZjfsLnIAkSZLUZ4a7c+884BTgjVTj+R+ianB/MjNXdSqozLwzIpYA64CHga9l5tci4hmZeXdZ5+6IeHopMhO4rraJDSXtsfK8OV2SJEnqK8PN4397eVwNvA+4ODMf6mxIUMbuLwTmAT8D/j0i/mCoIoOk5RDpg73mqVRDgpgzZ05L8UqSJEm9brihPkF15979gfcCt0TEumH+1rYhrt8EBjJzY2Y+BnwReAlwTxm+Q3m8t6y/gWq60YZZVEODNpTnzek7ycxzM3N+Zs7fb7+2XqcsSZKkPhIRz4yIz0fEHRHxw4i4IiKeExE3dTOu4Xr8AXZnx8bzeFgHHBkRe1IN9Xk51a8ODwEnAx8qj8vL+pcBn4uIfwSeRXUR76rMfDwiHoiII6nuP/AG4J/HdU8kSZLUN8oMkv8BXJSZJ5W0XwWe0dXAGKbhn5mjmu5zrDJzZUQso5opaCvwPar7BTwVuDQiTqH6cvDasv7NEXEp8MOy/uLMfLxs7jTgQuApwJfLnyRJktQJLwMey8x/bSRk5vcj4oDGcnn+GaBxE9y3ZOa3y4iWS4C9qdrppwHfBs4D5lMNWT8/M88eTWBRTXajuvnz5+fq1au7HYYkSZK6q+X7P0XEnwLzMvMdTekHAJdn5gvKqJZtmfmLiDiI6jra+RHxZ8CTM/MDZWr6PYHnAB/KzFeU7TwtM382mp0ZyVAfSZIkSe2zO/AvZQjQ41SNe4DvAOdHxO7Al8ovBT8Gfiki/hn4L+Bro33RrgzlkSRJkiapm4HDh1nnHcA9wKFUQ3ieBJCZ1wBHA3cCn4mIN2TmfWW9bwCLgU+NNjAb/pIkSVL7XA3sERF/3EiIiBcBc2vr7APcnZnbgP8D7FbWmwvcm5mfpBrXf1hE7AtMycwvAH8NHDbawBzqI0mSJLVJZmZE/G/gnyLi3cAvgDXA22urfRz4QkS8Fvg61cyVAMcAfx4RjwEPUs1IORO4ICIaHfZnjDY2L+4dhBf3SpIkiVFc3NvLHOojSZIk9QEb/pIkSVIfsOEvSZIk9YFdXtwbEVePcpuZmS8fZVlJkiRJHTDUrD7H7CI9GfxCh0a6VwtLkiRJPWaXQ30yc0r9D3gycBkwALwRmAc8pTy+CfgxsLysJ0lS79g8ABccD2fNqB43D/T2diWpA0Y8nWdE/A1Vg/8FmfmzQfKnAzcC52Xme9sa5ThzOk9JmmQuOB7WXrt9ee5R8MYrene7knrFmKbzPODd/zUFeB3VHP6zgfXAPwEXr/nQb20bdVARxwEfpbrx16cy80MjKdfKxb2vB74wWKMfIDM3A8uAP2hhm5Ikdd76lUMv99p2JU14pdH/BeAcYD7wjPJ4DrCs5LcsInYDlgKvAg4BXhcRh4ykbCsv+Czg0WHWeQzYv4VtSpLUebOPGHq517YraTJ4HfAKYK+m9L2AVwInjXK7C4DbM/PHmfko8Hlg4UgKttLw3wAsjIgnDZYZEXuUF72zhW1KktR5C5dWw3CmTK0eFy7t7e1Kmgzezs6N/oa9gHeMcrszqYYMNWwoacMaalafZhcB7weujoj3ANdm5uPl54aXAh8Afgl4XwvblKRxsW7TFk5fdgPXr72Pw+ZOY8miQ5kzY89uh6XxMn1eZ8bed2q76ozNA7B8cTUka/YR1Re16fO6HZUmr9ljzN+VXc2uOaxWevw/RDWrz0uArwO/iIh7gF8AV5f0/yzrSVJPOX3ZDawa2MzWbcmqgc2cvuyGbockabwtX1xdjL1ta/W4fHG3I9Lktn6M+buygR2/NMwC7hpJwRE3/DPzscw8geri3auB+4Hp5fEq4PWZeUJmbh3pNiVpvFy/9r4hlyX1AS/G1vj6J+ChXeQ9BJw9yu1+BzgoIuaVIfgnUXXOD6vlq4kz83OZ+YrM3Dczdy+Pr8zMi1vdliSNl8PmThtyWVIf8GJsja+LgSvZufH/EPA1qotyW1Y62d8CfBW4Bbg0M28eSdlRTSMkSRPNkkWHsmDedKZOCRbMm86SRYd2OyRJ482LsTWOyjz9rwFOBVYD95THU4FFY5nHPzOvyMznZOazM/MDIy034ht49RNv4CVJUvd4Mb56yJhu4NVrWurxj4j9I2JpRNweEQ9HxOOD/DnGX5IkjZoX40udMeLpPCNiJrCK6q5jNwN7AGuBR6im8ZwKfJ/qYl9JkqRR8WJ8qTNa6fF/L/BM4LjMbAyOvSAzn0vV8P8q8BTgd9sboiRJ6idejC91RisN/2OBr2Tm/2vOyMwNwGupGv7vb1NskiSpD3kxvtQZrdy595nApbXlx6ka+gBk5oMRcSWwEPjT9oQnSZL6zZwZe3Lpm1/c7TCkSaeVhv/PgSfVlu8DZjatcz+w31iDkiRJkia8M/eZArwOeDvV3XbXU93Y62LOvH/U03lGxPnAbwP3ZuYLRlqulaE+a9nx9sA3AL8REXuWAKYAr6S6jfCksG7TFk48ZwUHvucKTjxnBes2bel2SNLEsXkALjgezppRPW4e6HZE/c3/hzRqtgeG4Lll16pG/xeAc4D5VBPkzC/Ly0r+aF0IHNdqoVZe8CrgZRGxe1m+CHgW8O2I+HvgWuD5wCWtBjGYiHhaRCyLiB9FxC0R8eKImB4RV0bEbeVxWm39M8o0o7dGxLG19MMj4saS97GIGPF8rE4nJo3B8sWw9lrYtrV6XL4Y8AO0a3bx/xiO/y/J9sCQRnlu6ROvA14B7NWUvhdVZ/lJo91wZl4DbG61XCsN//OADwP7lhf8N+CjwAuAPwOOoGr0j/juYcP4KNXFxM8FDqW6JfG7gasy8yCqLyLvBoiIQ6gq7/lU334+HhG7le18guoOaQeVvxF/O3I6MWkM1q8cdNkP0C7Zxf9jOP6/JNsDQxrluaVPvJ2dG/0NewHvGMdYgBYa/pl5W2Z+ODPvrqW9A9gfeDGwf2b+fmb+YqxBRcTewNFUXzbIzEcz82dUFw5fVFa7CDihPF8IfD4zH8nMAeB2YEFE7A/snZkrsrpF8adrZYbldGLSGMw+YtBlP0C7ZBf/j+H4/5JsDwxplOeWPjF7jPltN5axRQBk5sbMXJmZ97QjoOKXgI3ABRHxvYj4VETsBTyj8cWjPD69rD+T6mKJhg0lbSY7XnPQSN9JRJwaEasjYvXGjRsBpxOTxmThUph7FEyZWj0uXAr4Ado1u/h/DMf/l2R7YEijPLf0ifVjzG+7qDrCWywUMQt4IfA0qpl8ri9z+bcnqIj5wHXAUZm5MiI+SjWr0Fsz82m19e7LzGkRsRRYUYYfERHnAVcA64C/y8zfLOm/BvxFZv7OUK8/f/78XL16dbt2R1LNuk1bOH3ZDVy/9j4OmzuNJYsOZc6MPbsdlnbB/5ekPjfia0N3cuY+r6e6kHew4T4PAady5v2fG+3mI+IA4PJWZvVpZTpPImIOcC7VhQrNeVcCf5KZa1rZ5i5sADZkZsAClmYAACAASURBVGOg2DKq8fz3RMT+mXl3GcZzb239+s8ls4C7SvqsQdIldYnzc08sff//2jxQXay4fmU1hGHhUpg+r9tRSZoYLgYWsfMFvg8BXwM+P9oNR8TFwDHAvhGxAXhfZp43XLkRD/WJiGdSzdzzSqqpPT8DfKQ8DpT0/y7rjUlm/gRYHxEHl6SXAz8ELgNOLmknA8vL88uAkyJij4iYR3UR76oyHOiBiDiyzObzhloZSZKG5owlkkarmqf/NVSTzKwG7imPpwKLxjKPf2a+LjP3z8zdM3PWSBr90FqP/19TjY9/F/CPmfl4I6PMoPMOqi8CfwW8pYXt7spbgc9GxJOAHwNvpPqicmlEnEI1jOe1AJl5c0RcSvXlYCuwuBbfaVRznT4F+HL5kyRpeM5YImksqsb958pf1414jH9ErAF+lJm7nA4zIr4CPDczD2hLdF3iGH9JElDdkGjttduX5x4Fb7yie/FIGm+jH+Pfg1qZ1eeZwHeHWee7ZT1Jk4A3b1Lfc8YSSZNIK0N97gfmDrPOnLKepHHWidlXGjdvAp64eVNfX+ip/jN9nj38kiaNVnr8/xtYFBEvGSwzIo6gGnP/3+0ITFJrOnGHVW/eJEnS5NFKw/8D5fGbEfGZiHhTRLwqIt4YERcB3yr5H2xviJJGohONdG/eJEnS5DHihn9mXk81F+n9wOuBTwKXA58C/g/VDbZOzMzhrgOQ1AGdaKR7t0r1Gq87kaTRa/nOvRGxF7AQOAzYh+qLwPeAL2XmQ22PsAuc1UcTUVfusOrNjTTOTjxnxRPXnQAsmDfd604kddKkmtWn5YZ/P7Dhr4auNKYnEqc61Dg78D1XsHXb9s+tqVOC2z94fBcjkjTJTaqGfytj/KW+04kLZicVb240epsHqi9OZ82oHjcPdDuiCcHrTiRp9HY5nWdEvGG0G83MT4+2rNRLnNVmGLOP2LHHf/YR3Ytlolm+eHvdrb22WvbXkmEtWXToTr/CSZJGZqh5/C8EWh0HFKWMDX9NCofNnbbDeGJ7F5ssXLrzGH+NjL+WjMqcGXs6pl+SRmmohv8bxy0KqUfZuzgMb240ev5aIkkaZ17cOwgv7pU0rLHOaOSMSFJn+N5Se02qi3tt+A/Chr+kYTmjkdSbfG+qvSZVw99ZfSRpNByjL/Um35vSLtnwl6TRaB6T7xh9qTf43pR2yYa/JI3GwqXVEIIpU6tHZzSSeoPvTWmXHOM/CMf4954JdwddLy6TJGkycIy/NN4m3B10Gzdn2rZ1+82ZJEmSusiGvyaECXcH3U5dXLZ5oJqx4qwZ1ePmgfZsV5IkTXojbvhHxEcj4pBOBqPxt27TFk48ZwUHvucKTjxnBes2bel2SINqvmNuz99Bt1MXl/lLgiRJGqVWevzfCtwYEddExOsj4kmdCkrjZ6IMoVmy6FAWzJvO1CnBgnnTe/8Oup26uMxp6iRJ0ihNbWHdE4FTgZcDRwEfjYgLgU9m5q0diE3jYKghNL10Qe2cGXty6Ztf3JXXHpXp8zpzw5jZR+x4YxqnqZMkSSM04h7/zFyWma8Eng18BHgUeCfww4i4OiJOjIjdOxSnOmSoITQT5deAvuI0dZKGMFGGb0rqjlFP5xkRU4ET2P4rAMBPgQuofgW4oy0RdkE/Tec5VK/+ge+5gq3bth8fU6cEt3/w+G6FKkkaxonnrGDVwOYnlhfMmz6xfi2Ves+kms6zlaE+O8jMrcAyYFlELAC+AMwE/gI4PSK+ArwvM7/blkjVEUMNoTls7rQdPkB6/oJaSepzE24GNEnjakzTeUbEr0fE54BvUjX6NwL/BPw3cDxwXUT83pijVFdMuAtqJY2eU8VOChNuBjRJ46rloT4RMR04mWqIz3OofgK5FvgE8O+Z+VhZbwHwReCBzHxeO4PutH4a6iNJQNXYr184Pveozlygro7qpUkZpEmiP4f6RMRLgTcDrwGeDDwInAN8IjNvbF4/M1dFxAXAu9oUqySpU5wqdlKYcDOgSRpXrQz1uQZ4PXAH8BbgWZn5fwdr9NfcWf5GJSJ2i4jvRcTlZXl6RFwZEbeVx2m1dc+IiNsj4taIOLaWfnhE3FjyPhYRk+qbmyS1RaduOidJ6hmtNPwvAX49M385Mz+emQ8OVyAz/zUz540+PN4G3FJbfjdwVWYeBFxVlil3FD4JeD5wHPDxiNitlPkE1bCkg8rfcWOIR5ImJ6eK7S1ecyGpA0Y9nWenRcQs4CLgA8A7M/O3I+JW4JjMvDsi9ge+kZkHR8QZAJn5d6XsV4EzgTXA1zPzuSX9daX8m4d6bcf4S5K6ymsupF4xqUaKjGlWnw77J6qpQbfV0p6RmXcDlMenl/SZwPraehtK2szyvDl9JxFxakSsjojVGzdubM8eSJI0Gl5zoV7iL1CTRsvz+EfEi4BjqRrQewyySmbmKWMJKiJ+G7g3M78bEceMpMhgcQyRvnNi5rnAuVD1+I8wVEmS2m/2ETv2+HvNhbpp+eLtx+Paa6tlf4GakFqZ1SeAC4E/oGpQNzess5Y+poY/cBTw6og4nmoGob0j4t+AeyJi/9pQn3vL+huA2bXys4C7SvqsQdIlSepdC5dWjav1K6tGv9dcqJv8BWrSaGWoz1uA/wN8BphP1cj/J+AlwHuAB4DPA7801qAy84zMnJWZB1BdtHt1Zv4BcBnVPQQoj8vL88uAkyJij4iYR3UR76oyHOiBiDiyfHF5Q62MJPWmfv9Zvd/3H2D6vKpH9b2bqsfpY5knQxojZ/2aNFpp+J8M3JqZf5iZ15e0n2XmdZn5IeBlVHP8/0a7g6z5EPCKiLgNeEVZJjNvBi4Ffgh8BVicmY+XMqcBnwJup5qK9MsdjE9Sp/VDo7Dxs/q2rdt/Vu8n/b7/Uq/pl1m/+uDzZcSz+kTEA8CnM3NxWd4G/G1mvre2zheAOZn5ok4EO16c1UfqYf0w28lZM6pGb8OUqVXPb7/o9/2X1B2Df7707aw+AdxfW34ImN60zm3Ac8calCTtUj+MNe33n9X7ff8ldUcffL600vC/kx2nwvwxcHjTOgdRfSGQpM7oh0Zhv/ysviv9vv+SuqMPPl9aGerzGeCFmfmCsvwh4M+pbpT1ReAY4KPA5Zl5QieCHS8O9ZF62OaBnWc78cJHSdJYDf75MqmG+rTS8D8B+Dvg+MwciIjpwGrgALZP5bkZeGlm/qgz4Y4PG/6SJElikt25d8Tz+Gfml4Av1ZY3R8QLgT8Gng2sobr49+52BylYt2kLpy+7gevX3sdhc6exZNGhzJmxZ7fDkiRJ0gQxoh7/iJgDvIiqZ/87mbm+04F1Uy/2+J94zgpWDWx+YnnBvOlc+uYXdzEiSZKkSa+/evwjYgnwdrbveEbE2Zn55x2NTDu4fu19Qy5LkiRJQxlyVp+I+H3gnVSN/h8Bt5bn74yI13U+vP6zbtMWTjxnBQe+5wpOPGcF6zZtAeCwudN2WK95WZIkSRrKcNN5ngJsBX4zM5+fmYcAxwLbSp7a7PRlN7BqYDNbtyWrBjZz+rIbAFiy6FAWzJvO1CnBgnnTWbLo0C5H2ufGene/Prg7oCRJ6i1DjvGPiI3A1zPzxKb0ZcAxmblvh+Prim6O8T/wPVewddv2/8nUKcHtHzy+K7FoCGO9e2w/3H1WkqSJb1KN8R+ux38a1fCeZj8Cntb+cOSQnglirHf364O7A0oahr/8SRpnwzX8pwCPDZL+GJPsG1CvcEjPBDHWu/v1wd0BJQ1j+eLql79tW6vH5Yu7HZGkSW4k8/iP7A5faos5M/Z0ms6JYOHSne/uN57l1V7eDVjd4C9/ksbZcGP8t9F6wz8zc8Q3ButFvTiPv6QO8poLdYPHnTQRTKoRLsMN9YFqh1v5G8k2Jal32POqThlqHP/CpVVjf8rU6tFf/iR12JA985lpI17S5Df7iB17Xr3mYszWbdrC6ctu4Pq193HY3GksWXQoc2bs2e2wxl9jHD9sH8ff6NWfPs8efknjyoa9pN7RrVlO7Hltu13dk6Tv+GuSpB4yocfiS5pkhuod7SR7Xtvu+rX3DbncN/w1SVIPscdfUu+wd3TS8J4kRZt/TVq3aQsnnrOCA99zBSees4J1m7a0KVBJ/cCGv6Te4f0NJg3vSVI0fk1676bqcYzTxDqEStJYONRHk4IXEo5BL81h7/0NJg3vSdIZXRlC1UvnCEljYo+/2q4bP0XbCzYGvXT30Db3jqq/9MMwmK4Moeqlc4SkMbHhr7brRiPcCwnHwHH1miT6oQOgK0OoPEdIk4ZDfSaLHvopthuN8MPmTmPVwOYdljVCzjqiSaIfOgC6MoTKc4Q0adjjP1n00E+x3fgpetJcSNiNeeydw16ThDMJdUinzhHdum+H1MciM7sdQ8+ZP39+rl69utthtOasGVWjv2HK1GqcdIcMdTGtF9qOwQXH79izNvco55fXdj30y14v8twzwXi+08QQ3Q6gnWz4D2JCNvzH+QR64jkrdhhas2DedGfwaIdx/gKnCcaGkiYTz3eaGCZVw78nh/pExOyI+HpE3BIRN0fE20r69Ii4MiJuK4/TamXOiIjbI+LWiDi2ln54RNxY8j4WEZPqH/iEcR6u0Q9jabvCeew1FC+y1GTi+U4adz3Z8Ae2An+Wmc8DjgQWR8QhwLuBqzLzIOCqskzJOwl4PnAc8PGI2K1s6xPAqcBB5e+48dyRcTPO0yBOqLG0E2kcqePtNRQbSppMPN9J425CDPWJiOXAv5S/YzLz7ojYH/hGZh4cEWcAZObflfW/CpwJrAG+npnPLemvK+XfPNTrTcihPuNsQo2ldXiEJgvH+EuTh+/niWJSjRTp+ek8I+IA4IXASuAZmXk3QGn8P72sNhO4rlZsQ0l7rDxvTtcYdWpKuY58oXB4hCaLxi97kia+xmx8sH02Pt/f6rBeHeoDQEQ8FfgC8PbM/PlQqw6SlkOkD/Zap0bE6ohYvXHjxtaDVVt05AY8Do+QJPUaO6XUBT3b8I+I3aka/Z/NzC+W5HvKEB/K470lfQMwu1Z8FnBXSZ81SPpOMvPczJyfmfP322+/9u2IWtKRi4YdRypJ6jV2SqkLerLhX2beOQ+4JTP/sZZ1GXByeX4ysLyWflJE7BER86gu4l1VhgU9EBFHlm2+oVZGPagjFw2P84XPkiQNy04pdUFPXtwbES8FvgXcCGwrye+hGud/KTAHWAe8NjM3lzJ/CbyJakagt2fml0v6fOBC4CnAl4G35jA77cW93TOhLhqWJEmT3aS6uLcnG/7dZsNfkiRJTLKGf08O9ZEkSdIkNpHusTOJ2PCXJEnS+GpMZ7pt6/bpTNVxNvwlSZI0vpzOtCts+EuSpPZzKIeG4nSmXWHDX5IktZ9DOTQUpzPtiqndDkDjYPNAdcJdv7L6Rr1wqXPZS5I6y6EcGkrjHjsaV/b49wN7XSRJ482hHFLPseHfD+x1kSSNN4dySD3HoT79YPYRVU9/fVmSpE5yKIfUc+zx7wf2ukiSJPU9e/z7gb0ukiRJfc8ef0mSJKkP2PCXJEmS+oANf0mSJKkP2PCXJEmS+oANf0mSJKkP2PCXJEmS+oDTeXbAuk1bOH3ZDVy/9j4OmzuNJYsOZc6MPbsdliRJkvqYPf4dcPqyG1g1sJmt25JVA5s5fdkN3Q5JkiRJfc6Gfwdcv/a+IZf73uYBuOB4OGtG9bh5oKvhrNu0hRPPWcGB77mCE89ZwbpNW7oajyRJUifY8B+loRqLh82dtsO6zct939BcvhjWXgvbtlaPyxd3NRx/oZEkSf3Ahv8oDdVYXLLoUBbMm87UKcGCedNZsujQEZftC+tXDr08zvyFRkPqsV+oJEkaLS/uHaWhGotzZuzJpW9+8ajK9oXZR1Q9/fXlLjps7jRWDWzeYVl6QuMXKtj+C9Ubr+huTJIkjYI9/qM03HCeTpWdFBYuhblHwZSp1ePCpV0NZ7hfaNTneuwXqknDX1IkadxFZnY7hp4zf/78XL169ZDrjGXKTqf7lCaQC47f8RequUfZ498O1qukiSG6HUA72fAfxEga/pL6xOaBanjP+pXVsLSFS2H6vG5HNfGdNaO6wL9hylR476buxSNJg5tUDX/H+EvSUKbPsye6E3rsWh9J6geO8Zckjb8eu9ZHkvpBX/T4R8RxwEeB3YBPZeaHuhySJPU3f0mRpHE36Xv8I2I3YCnwKuAQ4HURcUh3o5IkSZLG16Rv+AMLgNsz88eZ+SjweWBhl2OSJEmSxlU/NPxnAutryxtK2g4i4tSIWB0Rqzdu3DhuwUmSJEnjoR8a/oNNw7TTHKaZeW5mzs/M+fvtt984hCVJkiSNn35o+G8AZteWZwF3dSkWSZIkqSv6oeH/HeCgiJgXEU8CTgIu63JMkiRJ0ria9NN5ZubWiHgL8FWq6TzPz8ybuxyWJEmSNK4mfcMfIDOvAJwwWpIkSX2rH4b6SJIkSX3Phr8kSZLUByJzp5kt+15EbATW1pL2BX66i9V7Ka/X4jFWYzVWYzVWYzVWY53Isf40M4/bxboTT2b6N8wfsHoi5PVaPMZqrMZqrMZqrMZqrJMp1on+51AfSZIkqQ/Y8JckSZL6gA3/kTl3guR14zWNtbfyuvGaxtpbed14TWPtrbxuvKax9lZeN16zX2Kd0Ly4V5IkSeoD9vhLkiRJfcCGvyRJktQPuj2tUC//AecD9wI3NaXPBr4O3ALcDLytKf/JwCrghpL//qb83YDvAZcP8pprgBuB79M0pRTwNGAZ8KPy2i8u6QeX9Rt/PwfeXiv3jhLHTcDFwJNreW8r6TcDbx9sn4HpwJXA/cCjwA9rea8tZRPY3FTu70usm4FHmsr9DfCDEu+dVHPm3jRIfawq276llnZmKbMJeAxY01TmrSXWrcDGWvol5fU2AY8DD9fyfhW4rrbNO2p5hwIryr78FLi1/n8v9XMNsAV4sPxv3tZUP9uA1c3HTKmj20u5B5rK/k1ZfqDk30rtWKM6Dm8v9fOjWrkzgZ+Ucr+guidFvdxfl1gfKfvztqb6uRl4uJStx/qrwPVluw8DP67lHVrqb0up+1sox32pn6vK9h5oynst8MOyD/9D7f1S6ubWXWyzcfzcUPJupum9RvU+XFern/c3HT8Plf1Y21TuHbX9/2mt3CXl9R6ieh88XMv7VWBlydtS6ub9TcfPjcB/lm1c3vTeuq08/qCWVz925tN03mD7++sHwH80la2/v75WXnuH8w1weqmberlG3TTOJXfUy1G9txrH/z21cpfUyqwpj/VYG++v71O9D26t5TXq51Gqc9cPKOe+Wv08RnXs3FjLq9fPXTSdN2v18yjVMVIv26ifR8t2b2Ln8+3mUj/1co36afz/76iXK/XzGNWxc0+tXL1+tpay9Vgb9fMo1fHzP7W8Rv3cXPbzfyjn/1r93FFer55Xr5+rqH1u1OrmprI/9XL1Y+dq4HKaPnNKXH9V6ue2WtlG/dwI/AxYz46fVW8t699Pda5tlGvUz41U76Ff1PIadXNj+Z+sqeU16ubWss1G3D+n+jybDlxLda5rHD+NvNeWWLKUr5f7e6r38MNlP+rl/qas/3BJu4naZy7V5/GdbD92GuUadXML24+d7zeV/Zuy7w+X/2ej7CW1co/WXvvtpX6+X9Iax04jr1E/d5X9/yGlDcD2Y2cj1edLPa9+7NxBre3AjuedH5S46vmN4+fOst0fsXO7Y3mpn1tq5Rr1c2fZlzX1clTHzr2lfjbWyjWOnfr7spFXP++sK//Teqz1+vl5ieftTeeexrl52ni0O8fjr+sB9PIfcDRwGDs3/PcHDivP/1d5ox1Syw/gqeX57lQNgiNr+e8EPseuG/777iKei4A/Ks+fBDxtkHV2o2r0zS3LM4EB4Cll+VLgD8vzF5Q3wZ7AVOD/Ab/fvM/AR4B3l/r4GDs2pp9HdaL7HvD6pnKvLNs9GriwqdzetecfpfpCM9gXrFXA3ezc8D99sP8P8LKyHy8vebcMUkdHA58B7qmlfQ14Vcl7K/BQLe87wK+X//uZVCe2J/7vpX4+UF7v3cDZtbxG/XwbeH3zMVPqaFYp++GmsnuX1zwM+FPgPGrHGnB42e5aYG6t3JnA+xnkGC31cw1wRMmbx87H7/6lft7bVPZrtePjeOBbtbxGHT0VeFOpj5XAkWw/fp5aHv++lteon29RNW53r+U1jp+nlrqpl9u79l47HfhXmt5rVMfP/yv188xa2TNLmZ3eo2w/fqaXvGc1bTNKPP9QttMo1zh+nlrq5pu1vO8Av17Kf57qPddo9H4EeHd5fjnVh28jr1E33yh1s8N5o1E/5flVTWXr76//oPrwqjfgZwNfpWpIfYEdG/6nD3aeqtXNHiXvCwx+DvsH4MtNZb8GvKo8/yQ7fmloHDtrqI7zvxnk3LMGOAv48CDnnm9QfXDv2xRH4/hZQ3XeqpdtHD9rgDOAfx3k3PMwVcN131r6mVTHzppBXq9RP2uobgD09F2cx+8HPtSU1jh+1gAnAd8Y5PxzEXAB1fnnSVQdQY36uaj8Pz5cy2vUz08adVrLa9TNReV/VS9XP3ZWNmKh9plT6qfR+bJvrWyjfnb6rKrVz2eAPwKeTtPnWCn3VapzT6Nco24uovqs+EYtr/7eelOpmyc+A9nx/fXustzIa35/1cvV31sfbipXr58/Bc5hx8/cxntrbdnHRrkzKe+twT6va/WzR8l7Zn27Te+v99XK1d9bx5f9aeR9B3gNVRvgzaV+LgX+sOzTB0veX5X9bOQ9D/g1qvfAUfW2A9uPnZlUX4r+oSl/b7a3O/6M6txcb3fMp/qCso7q2GmUO5PqPb5Te6XUzbcaeaVe69tsvN5HqY6dRrnGsTOz1Mk1Tdv9Tnm8CfgT4G/L/+Agdj52Plz/P0zkP4f6DCEzr6H6YGxOvzszry/PGz2YM2v5mZkPlsXdy1/VaoiYBfwW8KlWYomIvakapeeV13g0M382yKovp+qtrt95eCrwlIiYStXIv6ukPw+4LjO3ZOZWqsbKrEH2eSFwUamPz1C9sRv7ektmNnpbfl4vlJlfy8ytpdy3Sz008urr/oTqW3yzs4E/puqd38ku/j+nUX2oXlXyBiv7Lap6ur++OaoT+jVl+bFa3sFUJ4y7qer/NU3/94XAv5Rj4iKqk+8twMxa/TxK1Uu0wzFT6mhDKXsd1Qmtkffz2rG2V9lG/Vg7o+xvUvWS1fMe2MUxehpwVmauLHkDTeWg+n8cA1zcVDaBx8t292F7b97MWh09SNU78rtsP+4bx8+DpX4WNvJq9dP4P+1ey2scPw+Wupldy/t5iT9rr7PDe43q+PmzXeSxi/do4/hpHFc/q5crr/cQcCJVQ6uR1zh+Hix185Na3sHANeW9P4vqy0HDQuCikrcP1RetRnyNuoHquNjhvNGon1J2P7a/r594f5W8Xy7/p7qzgSVUH6Kfacrb1XnqNOBD5bV+C1g6SLkAXlf2pV42gb3Ldl9M9UHbcDDVl1GoGi2vqeUtpDpmoOrZO6G2j/X62Umjfsriaqq6b+TVzz97UjsuirPZ3uM/Uo36abzGvc0rlPrZC/hic7hsP6/uTe1/yfaOlaOpGjWvqZ3/F1Idh0cDbwFOaORl5i1UnSb7UPWwUsv7Wtnvo6lmMJlVy2scO3tTNYBuqZctMf1zeXxgkLw9GPyz6jSqL2AvBc7LzHvr5WqfcYdQnXsaeQk8o+RdB9xVy6sfO1dSHTv1z8D68XMR1bF5R2auHeT4eXktr37sXEfVydLIqx87e1F1DtQ/c88G/qLEfTQ7fx7X1WNtnHseKXm/0ly2HD8nUn2paOTVj519KL30Je/gEv9Uql8+XsP2NsBC4N9L3heo3lt7lvq9hap3PIA96m2HprrZCsxpym/Uz9QSD+zY7jiT7W2F3ZryGuWa2yunAf/SyKN6bw5WbhFVo76RV6+b3YF7m7Z7MNX5/Drgv6g+t74J/G92PnaeOPdMeKP5ttBPf8ABDDIEpSl/HbVegJK+G9XPSw+yY0/TMqqTyDEM3ls2QDWc4rvAqbX0X6Xq/b6Q6kPgU8Beg5Q/H3hLU9rbShwbgc/W0p9H1WM7g+qNsILqhL7DPgM/a9rfxwd53W8Av7OruqL6Fr2+Ke0DVI3Hm9i55/7VwEfL8w3s3OO/hurnxEvZcQjR96l6u1dSvZnvGCSWo0vZm5rqYl2J5yfArbW8bwMLy/N3Un3YPfF/r9dPWef+5mOi1M/8YY6Z/6T6efaJvEHqqPGa9fpZA7ywllevn/OpPkAaefX6+WbZTnOsR7N9mEF9P+t1dCdwVC3v21Qnyt3Y/jP3h+vHD9vfE0/kNdXPrTS9X2rl7qf6clh/L9Xr5sZ62Ub9lLKPNuXV62dTU169fh6k6plqjucYtg/rapSr182jVB8mjbxG3SwD/rGUvbypbhrnhQfYeUjON6h69Ac9b5Sy15T6uLypfrZQDQdbWHvNRt0sY3sD4PKmuvkZVWPxt2t5jbrZRHV+evMgsRxN9aG8Q6y1+tlC9XP979XyGvUzQPVef5xy7qvVT+O8uJXaebFWP3cyyHmzVvZn5fHUpvppDLu5ofaajfoZoBoi8v1aXqN+HqUaBlbPa9RPfUhbcyxHl23uEGutfraWbd9Yy/s21XlhVfl7nHL+L/tV/2zYSu2zoeT9nOrc8r1B8lZRHbNrmvI+QHUe3EI1LOJ7tdd8NfDZUvZBqvdRI+9MqmNqC9WvTPW871P1/j5Ytn3rIPHcUuq1/nrPo/oC8wjV++qmWt5g5+YnPgPZ+dz8CDt/Pn6Dqhd6p8/O2nn5m/U8djz3fK72es3n5c/W8s5kx/PytKZYm8/Nlw0S69FUX2Lr5ZrPy5fU8hrvrbeVfd9GaQOw/b3VaB88kVd7vdvK/3Jjc17Jv4n/397ZB+lZVQf8dxCiRi2JDvkyQACDixQEJ1BgDRmghQAAEfpJREFUgqSErwokTZAwtJhQQGphJIVxqhgwoVJgUKtkpsGOBnYMaEcbDRFQmIIBKiWtiAlJ+FonS76DhAVE8rXJ7R/n3H3O3n3eDfnYJGTPb+add/c5z7n3Puc9997z3uc8961Sb+4t7NOG+uqrrs7ctyZbfV6W7bMSXXjzsmybViuzraat0032B6fnbfM66j9e/iRwNRoH3YD6T46DSt9pK6//3fra4w3Y2190E/ijK3dPA+O70e+HPg/w5+gkOsOOj6I+8B9i7wPQyejT9v8IdFDPKRp34G6J27E+1lkGumP90TzNg9BvvHOAS5z8cnQSehwdlL9dXjM7GfgDU9Bbn43seD3wnSxHv4TMBw60/8vAfyAa0O2HrgK0OdkiGwAEHfA2YdvWunPuRG/p+Wucjq6kYQPBW07WhN4yfBq9xZoDn/E19vmgfU7ja+wzopHPmI3mNvInq3cVuiJR2udldGAcX2Of280nxtfY5zR0Mijbcie6Ut6prYWNPocGFOMb2Og1Kr8vB9DXs6zGPv1qZFPQdJUuMuc/Nzn5sYV9WoHDXXu8ff4FuMfJvH1ONNuW7cn28X3b22aCXU+WNaF97BWzzRu4wJ/O40Jd4P8s8JO6ccN0nzL71MlmmH2a0VSi7DsTTNZK58B/IDo5zzDbPOhki1AfnWG2WV3T1geBX5dtNfvcYroTUD+5v/CdhVS+swANcnJwMsTZq2NcdL5zTt24acduM/vUyYaYfb7h6pyPrlQOMfs0OVn2nY+afX7oZNl3hlD5TlnfnVTPfgxwutPR1dghZp/HnKzJPuOErs6vw8Z/s0fH3IAGRB1zg8kSMKmcN0y2xewnFHOKk3/f6eZ0u9OszlVoukZuz0C79nY0LekuJ1uEprq1A39HlZrh27OVKnDOetPRFfR24KvoIlKW1Y3NHXMgncfmPlb+wMJn56Epea/WyKagc2YXmclvQAPjgXQdl1utPbkt5bjTXLTVjz2nmO3L9txptvB6fuy5GJ3zsqwJnf/fRFN51tn1XIL6jo8P2nDxgcnagDOojx2+jgbRXWILV+7Ndt4cYKLZ51CTLUPTmXJ7BqKLkI+iccjdTrYITal6FE01WlpT3wr0jtgBTi/3q/5WxmOFPPtPK+rLm6nioAj8e+uLBoG/Oc9DwHXvoIypaN7jreacrVSrKfd0ozeNKt92EO4hVjT/7oHi/LHAw8WxC9Hbqvn/iViQUVPfLcBV5TWjKzOD7e8TgI01uvOoCfyBSeg36KY6O9o5h1odOfA/Bg2SWu2VV8EG1eiOBDa4/38JjHKf3UbgICffH80vPqm4xjeoftdiGDVfbkz2CTv3OnfsBTQv/gA02HulgX3+os5nzEZPobequ/iTlfsYsLqBfbaiA/Sg7vS8faj899UG9hlWtjXbyOluaGCjI9HVwOz33n8G2/9TcfmudL4j0iFz/tO3lBX+s8jJb6zxn2Vo0FLqDkMnhNzWDv8x+e/RCfNLhX2GFn3b+4+gE62/jtz3V1A9wHmP2WK6HV9ube00LqAB5Fpqxg10NXKjnVPKbnXlbjbZbLPN61ZXQgOMOr0VTu8es829VGNYe6G3P7qitrpsq9nHj39by+ssfGcajX1nGg18p2bcLP2nk673H5M18p1v1ugNc3qNfOd2GviObw/1/uOvY5C1J9vnVDQ14QX0i26rs0/H3GB6G6j6lpddY7K+pczpLqfqW6eid55eofLVbJ+xRZ2tzj65rb9EFy5anX3Oc3ofRX1xqG+P2SaXmW1TN/8dia7cPuyOef+5FPfsVuE/19F17sy+c2Epc+dcgaZVQtdxeQt6N6lu3hqG9lnfVj93jUX7R93YfFmh531nLNBe1HUhmnaVfWci+gX8BTSVdiaV73TEB6a3msp3vGySnd/s6il1Z1L1rYnY3GivP1L5zhdr9IY5vRlmm6lYLIP6ztVO7yKz9VDfFqo5K5f7ZtnWmrEnx0Fdxp46H3g3viLHfwewPLuZ6Cr0v9bIDxKRfvb3+9FvzM+nlK5PKQ1NKQ1DH+B6NKV0idP7gIh8KP+NfrNdBJBSWgMsF5GP2+mj0Yf5PBejt2U9y4CTRKSvtXs0Lt9XRAbY+yHooFzqg67yTbK/P0uRy98IETkH+DK6grihkA13/45BOzIAKaVnU0oDUkrDzFZr0JSdNaY72OmeVZQ9Bzjd/j4MXV151cnPQHcZWFM0dxW6igW62rLJtTXbaD800JpffO7ZPjPRoLiZer5G4TPORi8DiwvZcOdrW9EczQ772PU9jt6mHe7t4/RAg//SPjPRQX19A/v8c9lWZ6OZVudibyPz+/7oKthMV9Zc4CrrE5PQyTzLEJGD0EmtU38x23wVXdV5u5ANd7oX2bEsf8bscwKaPrAC/UxPsvMGuz46Du0PuT1zgPNEpJ+IHInmK5+S24quHr2UUlrh22O2GWNlno6m1+S2DkgpXQ8cQrWalfv+XDQvdiiaM/8TinEBXd06txw3zD5HoJPdoYVseB5v0ODzSZNdYH2rX0ppf9Tvxju9wU7vW+jzMLk9c9B+OBTtd68VbT0DTREbXDPGrQIeMt3L0RSCXOcAG/v+zHznLqqxby5whY2Lk9A7Ch3jorEfutraadw0+3wFuNj8x8uGu/F2jLXnLOD/zHeORgO5FejiwimmN9jpZd/J7ZkDnC0iH3K+M9K19Qw0MH2jbKvZ52wr93R0PMxtHZDHf/ssv0s1/s9Fnylajt6Fus/J8ryxAQ3AyDKzzRfQO4UHF7LhTnej2SDLf2v+czAaFK9BUxCPN93Brq1X2rXl9sxB++NyETkbXYEfQTWPHYMGhB/w7THbNFmZE+2zym31Y/MN6AKIn8P83HUtncdCz9ler5i7Lihkfu76B2xsqJm3NgD/1GDeGmfvvq1+7rqSxmPzWYWen7v+kerzynPXMnTsm0rlO8+htjnOZFdQ+U6OD3Ia53t97OBscw1wQhlbmH1ynRdYm0cDP7W+db7JV6K+M8L0Bju9CVS+85zZ5hA0ljkW9Z0TXVsHoXPka0Wck22zzI61FG3N/jMQ9Z8fU8VB3ncmmX32Dfb0N4+9+YV++KvRVa8VwOV2fCTVNnh5i7bPOL1j0RzFhajzfq2m7FF0vU1+OHp7N28DOqWQH4fm9y1EO0J/J+uL3sY7sKaum6i2bpuF7RpgsifQwXUB2hm6XDN6++0RdFDeWMjGUeXlbkE7X5a1oIN1m+lscbLZ1p687dfa0s7uM2gv6pxFtV3chkLWB11hfN2OtxefXTP6JH95jSPR28VtaNDvZZPRCTtvDdnpczf7/MZkecu4LMv22eTkXrfFrj1RDfRZNht9wCqhX7YW43yNyg83FXXOQgOHhAYZi5ysD7pan+trKcpsRlfG665zJOpHCV2NesHJJqOrXBuw7WAxvzf7zKdz7nOWjbPr30q1ZWOWtVCtGq9HA80sy/6TA6klFH2Nqh9uKuqchQYP6033eSfrg6bErLfX0qLM+1A/6NS3zTZLqLbUa3Gy7D8vomkno6jSXHLfesnez3ey7DsbzUYPFbq5f+XPaK6T+f71c/QLe6NdxHyqT+5bC6288U6W+9YiNHXpWjqnFjUDX6gb46j61wLUF650sslU+fRrcWOf2cdvx/i8k3n7bKbqI1OcfVa5z/I1J5tNtSXjG/b3FNf2PBZvKuqcZf930TP7zHE+0FqU2YwGF13GeLPPs6b7J7T/Tin852V03OoY/6n852V3/Vnmx57ct7Is+07eLnedk3nfySlHdXPOcWZ7X2f2nxfRMXiJk2X/aXHX2FGm2edmijmOynfyM0AvOZnvW9+kmAOdfVrMBoc6mfefrcAjTpbtswCdQ+5ysmyfZ822TTX9qi863x3mjvm+9QDqj76t2T6Lrc7zizKb0dXx8hqzfRbaNZ7mZNk+6+zVEQM426yj2hwiy7Jt8l2dPzqZH3fWoHOmLzfbZy1Vny3jjpusrb7ObJ8ues42eTvPpb5MqlTGTnEOncedFabn5dk+b6Nz7wJgdIOx+cN7Khbd1a98eygIgiAIgiAIgn2YSPUJgiAIgiAIgl5ABP5BEARBEARB0AuIwD8IgiAIgiAIegER+AdBEARBEARBLyAC/yAIgiAIgiDoBUTgHwRBsJ2ISLOIJBEZ5o4Ns2PNu6D8VhFp3dly9lVEZJ6IxJZ0QRAE20kE/kEQBIAF7d29Lt3TbWzErvzSEQRBEOy77L+nGxAEQbCXcVOD47/bht5K4Cjsl1mDIAiCYG8jAv8gCAJHSmnaDuptRn85MgiCIAj2SiLVJwiCYBfQXbqNiBwpIrNFpE1E/iQiT4rIuSJyaXdpRCLSV0S+ISLLRGSjiLSIyJdFRNw509CfogeYtL3pSfl5gndSl50/ysqe1l15xbGO6xSRM0XkCRF5S0T+ICJ3i0g/O+94Ebnf7PSWiMz1z1HU1PVeEblZRJZam38vIlNFpE+D85vs+Yzldv5aEfmhiHy85tz8HMfhIvJFEVkoIutFZN42TBoEQbDXEiv+QRAEPYiINAG/Bj4MPAAsBA4HfgY82I3qAcDDwBDgF0A78NfAbcD7qFKS5gH9gMnAAmCOK2Nb6UnbW9fOMgY4D7gf+C5wCnApcJiIfAV4BHgCmAkcA5wPHCEix6SUttaU92PgBOA/gc3AWGAaMEJExqSUOh4AFpFzgJ/atf4caAGGAuOBc0XkL1NKv62p4w7gVPSzexDYshPXHwRBsEeJwD8IgsDRYCW7NaXUvINF/hsa9F+VUrrT1fNXdB/4D0ED+TNTSutN5ybgReBaEbklpbQ5pTTPVtgnA7/bwVSld1TXDpRbMgYYnVJ6zOrYD3gIOAO1xZUppXvzySIyE7gM/QJwX015RwFHp5Ta7PwpwK/QLxeXALPseH/gR8DbwKdTSktcHUcD84HvA5+qqeNTwPEppaU1siAIgncVkeoTBEHQmak1r0t3pCARORg4HV1d/ncvSyn9AvivbRRxTQ7ETecVNAA+EOiSnrKT7I66fpSDfqtjKxacA4t80G/8wN6Pa1De13PQb+VtAK63fy9z501E74pM9UG/6SwGvgccLyKfqKnj9gj6gyDYV4gV/yAIAkdKSbZ91jsmB6z/0yBV5b/R1e463kgptdQcX27v/Xe2cXugrt/UHFtl70/XyFba+9AG5T1Wc+wJNFXpeHfsZHv/ZIM7Okfa+1HAkkL2vw3qDoIgeNcRgX8QBEHPcaC9r20gb3Qc4PUGx9vt/T071KI9W1fdVqft70B2QIPyutgvpbRFRNYBA9zhj9j757fRvg/WHFuzDZ0gCIJ3DRH4B0EQ9Bxv2vvABvJGx/dm8p2LRvPHgey+3zIYCCzzB0TkPWig/6Y7nNvzyZTSwu2sI34hOAiCfYbI8Q+CIOg5nrH3k+1B1pKRu6ievNPMrrwL0IicU39wKRCRj6G59LuL02qOnYp+KXnGHXvKyYIgCHotEfgHQRD0ECml5eh2mx8D/t7LbHvJRvn920sbujJ9yC4qrzueR1fTx4pIRzqNiLwfmL4b6vfcaDv25Da8D7jV/r3bnXc3ms40VUROLAsRkf1EZFRPNjQIgmBvIFJ9giAIepar0X38Z4jIZ6j28b8A3TVnLFX6zA6RUnpLROYDp4rIveg2nFuAuTuQ2rKtujaLyB3AjcAzIvIzdC45E31Qd1V3+ruY54DFIuL38T8C3XM/7xZESmmdiHwW/e2Ep0TkEWAxavdD0Id/P4L+ZkEQBME+SwT+QRAEPUhKaYmInAzcgm7teToa/I9Dd5EZS+d89B3lc8C3gXOAiwEBVlhdu5qp6J74nweuRB+A/Q/0x7PKXXF6kgnoF5C/RX+LYKW14Tb/410AKaVHRORY4EvA2Wjazyb0i8qjwOzd1+wgCII9gxRjYxAEQbCbsNX5vwGaUkov7On2BEEQBPs2keMfBEHQg1j++KCa46OBi4AlEfQHQRAEu4NI9QmCIOhZ+gDLReRX6IOx7cDRaE78JvQZgCAIgiDocSLVJwiCoAexfeW/g+b2DwX6Aq8Cj6O56M90ox4EQRAEu4wI/IMgCIIgCIKgFxA5/kEQBEEQBEHQC4jAPwiCIAiCIAh6ARH4B0EQBEEQBEEvIAL/IAiCIAiCIOgFROAfBEEQBEEQBL2A/weysOLZ/khxWAAAAABJRU5ErkJggg==\n",
                        "text/plain": "<Figure size 762.375x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Flight number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.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": "markdown",
            "metadata": {},
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "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": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 762.375x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Flight number\",fontsize=20)\nplt.ylabel(\"Launch site\",fontsize=20)\nplt.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": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "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": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 762.375x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Pay load mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch site\",fontsize=20)\nplt.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": "markdown",
            "metadata": {},
            "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": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf_orbit = df[['Class','Orbit']].groupby('Orbit', axis=0).mean().sort_values(by='Class',ascending=False).plot.bar()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "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": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 762.375x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Flight number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.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": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "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": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 762.375x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Payload\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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": 9,
            "metadata": {},
            "outputs": [],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year"
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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\nyear = Extract_year(df)\nyear_series = pd.Series(year, name='Year')\ndf = df.merge(year_series,left_index=True, right_index=True)\ndf_year = df[['Year','Class']].groupby('Year',axis=0).mean()\nsns.lineplot(data=df_year)\nplt.xlabel(\"Year\",fontsize=20)\nplt.ylabel(\"Success rate\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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": 14,
            "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>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  \\\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  "
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "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": 15,
            "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>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  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>",
                        "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\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\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\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\n[5 rows x 80 columns]"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, prefix=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'], columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "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": 16,
            "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>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>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</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>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows \u00d7 80 columns</p>\n</div>",
                        "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n\n    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0           0.0          0.0        0.0  ...           0.0           0.0   \n1           0.0          0.0        0.0  ...           0.0           0.0   \n2           0.0          0.0        0.0  ...           0.0           0.0   \n3           0.0          0.0        0.0  ...           0.0           0.0   \n4           0.0          0.0        0.0  ...           0.0           0.0   \n..          ...          ...        ...  ...           ...           ...   \n85          2.0          0.0        0.0  ...           0.0           0.0   \n86          2.0          0.0        0.0  ...           0.0           0.0   \n87          5.0          0.0        0.0  ...           0.0           0.0   \n88          2.0          0.0        0.0  ...           0.0           0.0   \n89          0.0          0.0        0.0  ...           0.0           0.0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0            0.0           0.0           0.0           0.0           0.0   \n1            0.0           0.0           0.0           0.0           0.0   \n2            0.0           0.0           0.0           0.0           0.0   \n3            0.0           0.0           0.0           0.0           0.0   \n4            0.0           0.0           0.0           0.0           0.0   \n..           ...           ...           ...           ...           ...   \n85           0.0           0.0           0.0           0.0           0.0   \n86           0.0           0.0           0.0           0.0           1.0   \n87           0.0           1.0           0.0           0.0           0.0   \n88           0.0           0.0           0.0           0.0           0.0   \n89           0.0           0.0           0.0           0.0           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0            0.0           0.0           0.0  \n1            0.0           0.0           0.0  \n2            0.0           0.0           0.0  \n3            0.0           0.0           0.0  \n4            0.0           0.0           0.0  \n..           ...           ...           ...  \n85           0.0           1.0           0.0  \n86           0.0           0.0           0.0  \n87           0.0           0.0           0.0  \n88           0.0           1.0           0.0  \n89           0.0           0.0           1.0  \n\n[90 rows x 80 columns]"
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype('float64')"
        },
        {
            "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": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\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| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.7",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
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            "file_extension": ".py",
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