jupyter_labs_eda_dataviz.ipynb

{
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vamystry-suvam/IBM-Data-Science-Capstone/blob/main/jupyter_labs_eda_dataviz.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zYq48TfUU3g"
      },
      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
        "    </a>\n",
        "</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8S-RG28-UU3p"
      },
      "source": [
        "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C-aYtetnUU3w"
      },
      "source": [
        "## Assignment: Exploring and Preparing Data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aR90IXPFUU31"
      },
      "source": [
        "Estimated time needed: **70** minutes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mu56e-YWUU37"
      },
      "source": [
        "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
        "\n",
        "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5PAFgSn_UU4A"
      },
      "source": [
        "Falcon 9 first stage will land successfully\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B1EW1U1LUU4D"
      },
      "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": {
        "id": "VucLkUBEUU4H"
      },
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MNuv7cUUU4L"
      },
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WlXZmogQUU4R"
      },
      "source": [
        "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gc2Dds0LUU4U"
      },
      "source": [
        "## Objectives\n",
        "\n",
        "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
        "\n",
        "*   Exploratory Data Analysis\n",
        "*   Preparing Data  Feature Engineering\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TW2xLSw1UU4b"
      },
      "source": [
        "***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIG6mckeUU4f"
      },
      "source": [
        "### Import Libraries and Define Auxiliary Functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n06-EFp0UU4h"
      },
      "source": [
        "We will import the following libraries the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZKObyt2RUU4i"
      },
      "outputs": [],
      "source": [
        "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
        "import pandas as pd\n",
        "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
        "import numpy as np\n",
        "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
        "import matplotlib.pyplot as plt\n",
        "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pv5tyXBxUU4p"
      },
      "source": [
        "## Exploratory Data Analysis\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6KwpUBNUU4r"
      },
      "source": [
        "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "FBhJCBi4UU4x",
        "outputId": "446f8a65-9582-40e3-8452-28dfb8560a60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
              "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
              "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
              "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
              "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
              "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
              "\n",
              "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
              "0    None None        1     False   False  False        NaN    1.0   \n",
              "1    None None        1     False   False  False        NaN    1.0   \n",
              "2    None None        1     False   False  False        NaN    1.0   \n",
              "3  False Ocean        1     False   False  False        NaN    1.0   \n",
              "4    None None        1     False   False  False        NaN    1.0   \n",
              "\n",
              "   ReusedCount Serial   Longitude   Latitude  Class  \n",
              "0            0  B0003  -80.577366  28.561857      0  \n",
              "1            0  B0005  -80.577366  28.561857      0  \n",
              "2            0  B0007  -80.577366  28.561857      0  \n",
              "3            0  B1003 -120.610829  34.632093      0  \n",
              "4            0  B1004  -80.577366  28.561857      0  "
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              "      <th>FlightNumber</th>\n",
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              "      <th>0</th>\n",
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              "      <td>2010-06-04</td>\n",
              "      <td>Falcon 9</td>\n",
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              "      <td>LEO</td>\n",
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              "    <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",
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              "      <td>1.0</td>\n",
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              "    <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",
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              "    <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",
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            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "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",
        "\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KmHe2ygBUU4y"
      },
      "source": [
        "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
        "\n",
        "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 262
        },
        "id": "FGZWVcEnUU40",
        "outputId": "37d3ad86-fc2a-4a8a-977b-dab7e6ec173c"
      },
      "outputs": [
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          "data": {
            "text/plain": [
              "<Figure size 1842.38x360 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
        "plt.xlabel(\"Flight Number\",fontsize=20)\n",
        "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kSbO8boUU43"
      },
      "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": {
        "id": "4l2Uu6yjUU47"
      },
      "source": [
        "Next, let's drill down to each site visualize its detailed launch records.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwJoxr87UU48"
      },
      "source": [
        "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UE1PcHIaUU5A"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "zlAXpko1UU5C",
        "outputId": "753ddd16-133e-4682-b5fb-fcc554c52a3e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1842.38x360 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
        "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
        "plt.xlabel(\"Flight Number\",fontsize=20)\n",
        "plt.ylabel(\"LaunchSite\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpaVTn2WUU5E"
      },
      "source": [
        "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNae84V8UU5G"
      },
      "source": [
        "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrDOyoP8UU5I"
      },
      "source": [
        "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "9ki07CEEUU5J",
        "outputId": "58f2002c-63b7-43a0-ccfa-cd8352e28b6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
        "sns.scatterplot(x=\"PayloadMass\", y=\"LaunchSite\", data=df, alpha=0.5, hue=\"Class\", s=80)\n",
        "plt.xlabel(\"PayloadMass\",fontsize=10)\n",
        "plt.ylabel(\"LaunchSite\",fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wjkf9JyAUU5M"
      },
      "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": {
        "id": "zaTWh5NtUU5Q"
      },
      "source": [
        "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUmfMcp0UU5R"
      },
      "source": [
        "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQYPcvyxUU5T"
      },
      "source": [
        "Let's create a `bar chart` for the sucess rate of each orbit\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "efFFQ_fOUU5V",
        "outputId": "ec1b160f-a88b-4f65-e878-760e83c1a0cb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f13f57ada10>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# HINT use groupby method on Orbit column and get the mean of Class column\n",
        "df.groupby(['Orbit']).mean()['Class'].plot(kind='bar', color=[\"pink\"], alpha=0.8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQ0BEgKNUU5Z"
      },
      "source": [
        "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzSQ9IsBUU5a"
      },
      "source": [
        "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JvX3qJ7UU5d"
      },
      "source": [
        "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Gq4Xh-y9UU5j",
        "outputId": "4d0b3a78-a9c3-4eb6-dc66-eb48ebb57a0a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
        "sns.scatterplot(x=\"FlightNumber\", y=\"Orbit\", data=df, alpha=0.5, hue=\"Class\", s=80)\n",
        "plt.xlabel(\"FlightNumber\",fontsize=10)\n",
        "plt.ylabel(\"Orbit\",fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jlH2-RAUU5l"
      },
      "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": {
        "id": "Omq6fqCMUU5n"
      },
      "source": [
        "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HohLfPazUU5p"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "lvEtwNymUU5r",
        "outputId": "e5fa7095-6e39-4f5d-bc27-d36ca5c8499a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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Bid9gJyKsqimmMV5IsG7D+fZkWumQYqRyOXiD6IINhFp20RNN4CmvJ5WG8NIXc/b0SdIqhHxC0Cd4Fr2YI21pVtaU0t7WAh4/JeVVLClIQDLmfFgHCyGdGAiibhOESye8DRPi8cKCjU6hr+FUXzZwk4GZnLIlI39xCJWMnXjylT8MtS9yylkPZ8E6KJoZX04+dNOKC77Ahv1ePnTTxJO+z+fjS1/6EjfddBMrV67kda97HatXr55sqGO/75S/wwSIyAagSVVPiYgHWAvsEJEFwHxV7a9esx44Ph0xbVhUhkeE7Q3t9MSc86AlYR8bFpezsqZ40v3Xlke4dkUVhxuuoOoSH4mTW0nHekh6Q8iSa5B1ryO55+d4WvazpGoVHaUrKdjwRlpP7KWmJExHX4LKqnn4F13J833zedklzu3GnrLFLKwo4SWLw5R37IRLXgEldc7dWOAcqdRd6dxunAsVy+CyW+HYMwNHU74QzFsFi64eOemYixMpg5W3w5HfQPsJ584w8Trls5e+bGY/NzRvFWjSud24/waUQAEsWA+101pwdlLuuMI5RXXv4/s51d7HgtIwH7ppxfn2ibrlllu45ZZbshHiuOVVPRYR6VbVQhG5GfhnIOjOeg54FzAP+DrOHWNRoBl4h6qOeGUy2/VY2nvjtPXGAaGyIEBROLsffLFkipauGP5YG5FUFwUhP1JYTdoTJN58+PwDkunCBTRqOd54J8UJ5wHJnmAF6VAZpZ4+uvvi9KU8BCNFVPu68Se6nW93/Q9I9rQ4p6YLKqb/SGU48V73AckUhIqhsDrXEc1OqSR0Nw08IFk0f/Yk71iX+4Bk2kmkeXAKbK7WY8mrIxZVLXT/fQx4bJhFjgM3TGtQQ5RGApRGpu5BwqDPy8KyCBAZ1O4BQgtWAgP/852TF+VAvRPb+TlFFA166HjIQ4bekvx42j5TIALlS3Idxezn9TlHKbNRsMgeqM0TeXuNxRhjzMxkicUYY0xWWWIxxhiTVZZYjDHGZJUlFmOMmcXuvvtuqqurWbNmzbS9pyUWY4yZxe666y4ee2y4m2ynjiUWY4zJFzsegvvWwMdLnX93PDTpLq+77jrKy8uzENz45dVzLMYYM2fteAh+/DfOw6sAHSedaYC1r8tdXBNgRyzGGJMPnvjEQFLpl+hz2mcYO2IZQ1tPjFPtUZq7Y6TSSnVRkMUVEYrDOSrja8wkJXs6SJzdi7afBFWktBZf9Qr8hbkfAmVO62i4uPY8ZollBKrKgaYudjR28MTes+74YBDye1hfV8qr1y1kWXVhjqM05uJEzx4hsf0HdD77IOlENwAef4Sija8lveGNBOddkuMI57CSWuf013DtM0zenQoTkZRb4GuXiHxfRCJue62IPCoiB0XksIj8u4hM2WFDQ1svOxs7+OnO0+eTCkA0kWbr8TZ+tL2Rs53RqXp7Y6ZEav8vaP/d/eeTCkA60UvHH79FfNejaCyWw+jmuJf/T2eg2Ez+sNM+CW94wxu4+uqr2b9/P7W1tfzXf/3XpPobj7xLLECfqq5X1TVAHHiHiAjwMPCIql4CXAoU4oyAPCWONHdzqiNK3zAlieMppakjytHWnql6e2OyLtqwi54Xvj/i/O4XHiZ6ds80RmQGWfs6uP0LTlkLxPn39i9M+sL9d77zHU6fPk0ikaChoYG/+qu/yk68o8j3U2FP49RiuQGIqurXAVQ1JSLvxylR/I+q2pvNN02m0rT3JTnVNnK33bHkpEqGGjPdNNpGrHXk8kXJrrPQ0zqNEZkLrH3djLsDbDj5eMQCgIj4gFcBO4HVwJbM+araCZwAlg+z7qRq3ntE8IoQGKUcsdcjBHwjzzcm73j9iHeUs8ceD4w235hxysfEEhaRbcBmnMRx0ScEJ1vz3uMR6srCXDLKxfnSSGDU+cbkG195PZHlLxlxfmTRJjzli6Yxorkhn4opDmcq4svHxNJ/jWW9qr5HVePAHmBj5kIiUgwsAg5NRRD1VQVUFAa5dN6FyaOyMMCGRaXUloWHWdOY/OQvmU944xvxDVOG2BssIXLVWwhW1E9/YLNYKBSitbU1b5OLqtLa2kooFMpqv3lVmhgGyhMPaRPgeeALqvqAiHiBrwCdqvq3o/U3mdLE7b1xDpzpZkdDOzsbO0gk0yyuKOCaSypZV1tCYWiWlHQ1c0rfod8R3/0z+o7+AVDC9ZsIrL6d0KIXI8HgmOub8eu/YB6N5u8dpKFQiNraWvz+wZ9nkylNPCMSi9teB/wHcBnOkdbPgA+q6qj3R2aj5n13NEFXNImilIQDFATz/Z4HY0aX6u0mfu4ooPjLF+OL5FmpapNzs6bmPQzUvR+m/SRw+zSHA0BhyG9HJ2ZW8UYKCUcuz3UYZpbKx2ssxhhjZjBLLMYYY7LKEosxxpisssRijDEmqyyxGGOMySpLLMYYY7LKEosxxpisssRijDEmqyyxGGOMyaq8e/LeTIFE1Cl5emYPdJ8h4QnSFyinLR0hVbyIk8kSWnvihANellUVUlsWJhLwQdtJaD/u/LQehkAE5l0ONeugeP7g9+hpgXNHoP0EeHz0Fi+lgSq2NSUo8SVZHmyjtOsgoVgrSV+ERKiCeLCcSPUSSioX4AwH54r3QvsJ+hp3kextpbPwUk4ki2noUjRYREVJCT6vh2OtPQR9XtbXBFgkzUQ6j0AyDqV1aNkSjscK2H+mi/1NXfi8HtbWlrCqppjyAmc8rJ5Ykoa2Xg4395BMpVlYFqa2NIzGe4g2HyHdfICQVymev4yC6iVI9xloeB56mp1ysfPXQ/Vl4LOh5o3JlLOxwkRkHnAf8GKgDadaZDGQAALAEmC/u/gngf8GPga8BVCgEXi3qu4e7X2yMVbYjJbog8NPQsNmaDtG77lGp9RyoIDg6tvZeriR2KKX8XR7JVVFQXxeD6vmF3NDeQv+hj/Cqc1w7BmcXQ6Ey6H+WtjwF86HKkDbcdj7Y4h1AdDeF+fQ2W7SpfW0VL8Eb8Oz1PTsoyp+ks5zZ1GgoLgcuewWWtraqXnRHcxbtMLpK9YFB39J+5EtxJqP0Dr/Gn5xqJvuvjjJsuX09vaSKF1Cj7eIJZWFRLSXFyefx9t2mOVVhZRGAihKc8zPjqJr+OY+D/FkGgAB1teV8paX1FMQ9PHE3jMcax0o5pZKp1kcSbK+74+ca9hH2t3k8pCwNNyDv3wxnPg9pBLODH8ENt4Fy28Ef3ZHhzUm1yYzVlhOToW5oxU/AvxWVZeq6kbgTuArqroeuAU4nDF8/g+Ae4CXAOtU9VLgU8CPRMT+okfTvA9Ob4feFhIdp2jvjZNW8CV76dzxU8rLytGDj7OsMEprTxyAYLyF6LE/QscJOPY7zicVgL5zzrf2PY9Cb6tzdHHoV+eTSjyZ5mhLD/FkmpYT+1gV3QLtx6niHE1Np0ipklalr+scniNPEI4U0rT5x5xrb3f6P72TnobdxNpPEw9X8buGJJ2dnaQTUdKtR/D6A/Q1HaSts5t4Ms1a73FOHd5JNJHiaGsP8WSKnliS/SdOE9/7OJdXDvyKK/DCyXaeOnCW7SfbBiUVcEpOF53bwZEDOwi7Rd4EmO/pIHrgKZJHn4HypQMrJHph6wPQvDfb/9eMmdFydY3lBiCuql/pb1DV46r6xVHW+TucI5Red/lfAL8H3jSlkc5kyTic2g7pFHSdIZZMkzqfI4S+rnMU+1PEo33USiutPTEEpcbbRqK9kfSpbcP329sKnY3Q0QCdp6D77PlZ3bEE0USaeDJNoR/SB3/Nirr59J47jQKJZBqPCMm0kug4S1FA6G07S7T1BMS6oWkH0Wgf6c4m2kpW0nS2GY8460miB49HiPZ1U+GPIfEuIq27SaaVeDJNNJGmO5akrTdBS3eMnvazrIh0XRD+mY4om4+3DWpTVSq8vSRO7aQrmsTjnporCHjwdZ0knU6RajkMkcrBncW7oWknpJIT/J9kzOyTq8SyGtg63oXdol4FqnpkyKzNbl9mOMmocySRTkIqRso9tyNAuv8UaLQDnz+AxDpABREhEO9C0gm0r334fjXlnGKLdkKyb9CseMo57ZRWJeRJk+hqIRLwEovF3Ha3C3Ur18W7EY+Q6O1y+kz0kk4lSCcT9KV9boEkIa1O3JqMowiedIIwMTThHHX0b1t/kkm6077k4KMSgJRCZ19iSJtS4EkQj/agbj8AQU8a4j1O7On0wGmwTN3NkBy1eoMxc0pe3BUmIl8Wke0i8nyW+ptUzftZwxd0Lrh7vODx43W/hSvgca+VS7CQVDIBgUJAUVUS/gLU44NQ8fD9igd8IQgUXFAj3edxfqU8IsTSHvyRUqKJFIGA3213uwBEgEAETafxhQqceL1BxOtHvD6CknaXVEScuMXrd+IUHzECiHttw+N27PUIfq+c39a078IzpR6gYEgZBI8IfWkf/mDofD8A8bQH/OGB2L3DlE8Il9sFfGMy5Cqx7AY29E+o6j3Ay4FhC9SraifQIyJLh8za6PY1dPlJ1byfNXxBqFkLHh8UzSPg95z/YAchGCmiKx3C6/PTJFWUFQRQhLNagb94Pt4Fa4fvN1wGRTXOnVHFCyFSdn5WUchH0Och4PPQlQDv8pdx8FQLBWXOXWR+r4e0Kj6v4CuooDfpIVxcQaiizklk89cQCoXwFFVT3rWfysoK0goBrwf1hVGEUChMWypEwl9EX9kKvCLOe3o9FAR9lEYClBcECBeVcTB6YQGrqqIgV9SVDmrziNCaihCYv5qCgI/+60o9iTSp4jpEPHgr6iE65CjOH4b5a4ZPOMbMUblKLL8GQiLyzoy2yBjr3At8QUTCACJyI3AN8ODUhDhLVK2EqkuhoJJAURUlYT8CpMRP6brbaG4+S3DFjezrClPh3oZ7zlNKcPGVUFwHCzYM7i9YBDXrYeWtUFgNwUJYdiP4nG/1QZ+XxRUR/F4P5TX1HCl5MYlgBe2+SqorK/F6BAFC4ULk0lfQ1dHGvA23UFZa7vRfs45I9VKCZQsIdDdybX2EcDiC1x9AKpaSikcJz1tOIByhMOhjryyjZvFyAj4v9ZURQj4vhUEfl9ZWErrslexoHnzX44p5RVx3aRVXLCplfsngoxmvx0N31TqWLbuEvngKcE7dnUmXEa5/Ed4l10LroYwV/LD29VC1Kmv/u4yZDXJ5u3ENzu3GVwHNQA/OXWHfE5F64CequiZjeQH+J/AXQApowrmYv3O095nztxsDxHqg7SicegHtaiIhAfoiNbQmQ6TLlnI4Xs7pzhgFQR+XzS+ivqKAoqDXWefcUefD9Oxe57Tagiug7iooXeSey3J1noKWA9B6BLw+uktXcFIW8PuTcarDSVb4Wqjo2kWgr4WkN0KyaCHRQBkFNZdQWr34/KksAKKdpFuP0Hf8eVK97ZwrXcuReAnH21NoqJiq8nKCfg/7mroI+Ty8tC7IYpoo7Njv3LBQVk+68lIOx4rZ0dDBrsYOfF4PV9aXsba2lPklThLs6ItzrKWX/U2dJFLK4ooIiysi+OKd9Jw+SKppF2FvmtLaFRTOW46n+ywc+41zs0JpHdRdDfNWQ7Bgmv+HGjP1ZlXN+2yzxDKEqpMQVJ1rFv3XXVQHP6Q4dJ1+Iy0ztP/zk0P6dedrOo14xnHA3B8nDBvvSP0P7mKUbRtlGVVF4MJtTqdhPLEbM4PNqpr3Zor1f0iKc0pqoHmUD96xkskoy17Qrzs9rqTiLt/fw3DxjtT/qDEM+zYXsZ4lFWNGZX8hxhhjssoSizHGmKyyxGKMMSarLLEYY4zJKkssxhhjssoSizHGmKyyxGKMMSarLLEYY4zJKkss45VOOcPExy8cht0YY8wAe/J+LOk0nDvkFMzqPgPiheqVMG+VMwijMcaYQabsiEVEUiKyLePnI277bSLyglt/ZY+I/PUI6x8TkcohbZeJyB9EJCYiH5yq2Ac5vQ12P+IMxBjrcoZNP/EH2PUwdJ2ZlhCMMWYmmcojlj63fv15IuIH7gdepKoNIhIE6i+iz3PA3wB3ZC3K0fS0wNGnndNgQ/W1wamtsOJV0xKKMcbMFNN9jaUIJ5m1AqhqTFX3j3dlVT2rqs8Dw9SHnQJdTZAY5ZpK8wHoPTctoRhjzEwxlYklPORU2CNjh3wAAB3DSURBVOtV9RzwI+C4iHxHRN4kIlmPIWuliceqY55OOj/GGGPOm9ZTYQCq+lYRuRy4Efgg8Argrmy+sarej3PKjU2bNk284EyomIFKIMMIFrm14o0xxvTLye3GqrpTVe/DSSqvFRFvxpHNJ3IR07BKaqFo/sjzF1zhVFU0xhhz3rTebiwihcAmVX3KbVoPHFfVlPs6v/jDcOlNsO+n0JNxSk28sGAdzF8z8rrGGDNHTWViCYvItozpx4B/Bj4sIl8F+nDq3N81Sh87RCTtvn4I+CywGSgG0iLyPmCVqnZmO/jzimtg7eugo8F5jsUbcI5kiheA1z9lb2uMMTPVlCUWVfWOMOuWca5fP8Ks2gkFNBmhYgitch6KNMYYMyob0sUYY0xWWWIxxhiTVZZYjDHGZJUlFmOMMVk1rsQiIp8ZT5sxxhgz3iOWVwzTZqMvGmOMucCotxuLyDuBdwFLRWRHxqwi4JmpDMwYY8zMNNZzLA8CPwc+BXwko73LHVDSGGOMGWSsxKKqekxE7hk6Q0TKLbkYY4wZajxHLLcBW3CG+JWMeQosnaK48kes2ynqJQKRSvCHJt5XtBOiHSAeCJdDrANScfAXQEFF9mIGSCWd8c3SCWcE5kh5lvtPOIXQ0gkIFkO4NLv9G2NmrFETi6re5v67ZHrCySPJODTthIbnBxJLwTxYdJVT815k7D76JWLQtB0atziJqnghtB2BeI8z9L4/AtWroO4qCJdMPvb2k3D899B+wqkX44/A/MuhdpNbCmCS2o45/Xc0ONU1A4XOoJwLNkDQyggYM9eNe6wwEfkz4BqcI5WnVfWRyb65iHwMeCOQAtLAXwNVwP/CuWPND/y7qn7VXf7twAfc1TuBD6jq7yYbx7Aat8CR33C+FosqdDc5Ix3DxY0b1vAsHHPvdSipgwOPQW+LM0py1QqIiPN+0Q5YeZszqvJEdZ6C3T90kla/RC+cfBZinXDpq8AfnHj/7Sdh9yOQ6Btoi3c72xftdEaDtsE5jZnTxvscy38A7wB2AruAd4jIlyfzxiJyNc5ptg2quhan8FcTToGu21V1HXAF8JS7/G04iecaVb3MjedBERmlYMoE9bbCyecYtsBXOul8SCej4+ur+yw0bHFe+8POCMm9Lc60pga+9QO0HoKOk5OLvWnn4KSSqXkfdDZOvG9VOLVtcFLJdGaPk9iMMXPaeJ9juQG4SVW/rqpfxxmh+IZJvncN0KKqMQBVbQG6cI6iWt22mKrud5f/O+BD7nKo6lbgm8AFNxZMWnfz6LXuu8841xfGo6d5IAkFi5wP90zx7sHv1Xb84mLNFOuB1sMjz1eFzoaJ9x/tdE6Djdh/CrpOT7x/Y8ysMN7EcghYlDFd57ZNxi+AOhE5ICL/ISIvc+8y+xFwXES+IyJvEpH+GFfj3ESQabPbPsika95reoz56vyMq6+M5fT8f4afDwNHLxOSHjuu9BjbNlb/I5Vp7jfWvjPGzHqjJhYR+bGI/Ajngci9IvKUiDwJ7HXbJkxVu4GNwNuBZuB7InKXqr4VeDnwHPBB4GsT6Pt+Vd2kqpuqqqouPrhIOfhGuQ4RqYBw2fj76r/mkOyD0iH3QQQig+80K627uFgH9VU49vrFNRPvP1gMRaOsLwKF8ybevzFmVhjr4v3npvLN3ZLETwFPichO4C3AN1R1J7BTRL4FHMWpMrkHJxH9OqOLjcDurAdWOM8pO9ww9AAJQJy7q8Z791PhfKheDae3QawLyhc7d4jFu52+iheAx008RfOci/sTJQLz10LLQec25qFKaqF4EnXSPF5YcIVzOiydvHB+Wb1zx5sxZk4b63bj34iIF/iVqv5JNt9YRFYAaVU96DatB86IyPWq+lRGW/9Fh88CnxGRm1W1VUTW4yScq7IZlxscLHoJINC0a+AaSaAA6l4E8y6i1r3HA0uucf5t2u1cv7nsNufmABEoqHbuDitbBMv+ZPK3A5fXw2W3wNGnnZsQADw+50N/2fUQLJhc/xXLYMWr4NjT0Nc+0H/FMlh6/eSe8zHGzApj3m6sqikRSYtIiap2ZPG9C4EvikgpkMS5ZvNe4Ksi8lWgD+jBSR6o6o9EZCHwexFRnAv9f66qU3O1OFgIy290jgD6zjkPNRZUTexBw2ARXPJKqFnnPhPjdW7LjXdDMuacwiquyd5tutUrnSOf7ibneZxgkXMKy5uFStQiztFc2WLoanIelAy5p8g8I1WjNsbMJaLjuAgtIo/i3Pr7S5wPewBU9W+mLrTs2LRpk27evDnXYRhjzIwiIltUddNE1h3vV9jHgF/h3BKUxDmaMMYYYy4w1rD5PuBfgLtxrnUIzm3HXwf+fsqjM8YYM+OM9RzLvUA5sERVN6rqBpyBJ0vcecYYY8wgYyWW24C3qWpXf4OqdgLvBG6dysCMMcbMTGMlFtVhru67z5+M89FzY4wxc8lYiWWPiLx5aKOI/Dmwb5jljTHGzHFj3RV2D/CwiNzNwDhdm4Aw8JqpDMwYY8zMNNaT943AVSJyAwODPf5MVZ+Y8siMMcbMSON6jkVVf83gMbqMMcaYYWVhjI9ZKNrpFKxKRt3hVhY4oxAbY4wZkyWWoc7ugyNPDgywCFBY7Yz1NZkh7Y0xZo4Yb6GvrBKR7iHTd4nIl9zXHxeRRhHZlvFT6s67RkSeE5F97s/bsxpY+0nY/7PBSQWc8sJ7fzwwWrAxxpgR5esRy32qOqgWjFvb/kHgDlXdKiKVwOMi0qiqP83KuzbvdUYbHk60A84dc4p8GWOMGVFOjlgm6B6cImBbAVS1Bfgw8JGs9J5Kwrkx6s1Ppl68McbMEbk6YgmLyLaM6XKcWvf93u8+hAnQ5hYZWw18c0g/I9a8xyl5zKJFi8YXkXjGrlfiyVK9FGOMmcVylVj6VHV9/4SI3IXz4GW/C06FXQxVvR+4H5x6LONayeOBeauc4lUjKV860ZCMMWbOmEmnwvpr3mfKbs37ikudO8CGU3kJlI7z6McYY+awmZRYvgzc5da6R0QqgM8An83aO0TKYOWrYcF68EecUrvBIlh8tXO7sT3LYowxY8rXu8Iyr7GAcyfYMbftP0WkCKfo2OdV9cdZfefCKljxKqh7kVMv3h+BcElW38IYY2azcdW8n8ms5r0xxly8ydS8n0mnwowxxswAlliMMcZklSUWY4wxWWWJxRhjTFZZYjHGGJNVlliMMcZklSUWY4wxWWWJxRhjTFZZYjHGGJNV+Tqki8kH0Q5oOwEdJ0C8ULzAKS/Qfhw8PihfAiV14A8PWi2eTHGqPcrx1h5iyTTzioPUlUcoLwjmaEOMMdNp2hOLiCjwbVX9c3faB5wGnlXV29wh9O8FGjNWe6Oq7hGR1cAXgYU4R1sPAJ/U2T4uTS50NcHen0BPszMd64KWg1CxFKpXQ2cjNG6FykvhkldAqBiAaCLJM4da2dnYQf//ld2noDDo5ZWr57O4oiBHG2SMmS65OBXWA6wRkf6vua9gcBIB+J6qrs/42eMu/yPg06q6AlgHvAR417RFPlck43DoVwNJJZ2Ec4ch0QNNO52kE3ATRMsBaNxyftX9Td3saBhIKv26Yyme2HuGzr7ENG2EMSZXcnWN5WfAre7rNwDfGcc6bwSeUdVfAKhqL/BuslWa2AzoPAUdGbk+1gWx7oHp09sglDHic9Mu6Gsnmkixs7FjxG47+pKc6uibgoCNMfkkV4nlu8CdIhIC1gLPDpn/ehHZlvETxilBvCVzIVU9DBSKSPG0RD1XxLtB0wPTqTiQcQgSbXeutfRL9EK8h754iq7o6EckdsRizOyXk4v3qrpDROpxjlZ+Nswi31PVd2c2iMi4+59QzXszwBcaPO3xDp72hxl0rssbAG8Av89DwOshmkgzkrDfO+I8Y8zskMvbjX8EfI7xnQaDYUoTi8hSoFtVOzPbVfV+Vd2kqpuqqqqyEuycUjQfIhUD08GiwcmmeiXEewamK5ZDQSWFQR8ra0Y+eAz5PdSUhEecb4yZHXKZWL4G/JOq7hzn8t8GrhGRGwHc02NfIJuliY0jWAjLrh9IJt4glC0B8UHhPKhe5ZwOA4iUO9U23SPKVQuKqSkJXdCl1yO8dHkllUV2y7Exs13OnmNR1QacxDCc14vINRnT71LV34vInwJfFJEvA17gW8CXpjjUuanyUrg8Amd3w7ljUFDp3FbsCzm3HRdUOUcuVSucea7SSIBXXV7D4eZu9p7qJJ5KM784xMqaYhZXRHK3PcaYaWOlic3YkjHnAUmvL2PaA17/6Kul0qRUCXg9F3WNzBiTe5MpTWxP3pux+YKjT4+0mtdjv2DGzEE2VpgxxpisssRijDEmqyyxGGOMySpLLMYYY7LKEosxxpisssRijDEmqyyxGGOMySpLLMYYY7LKEosxxkxEKgnJKBdUtcsBjcVIdpwk0dXiTKdSxHo6ScRyU//IHow2xpiL0dfuVE5t2uXUKiqaD/MvdwZq9Uzvd3Xt7UVPPwuHfoX31HbUHya94lWkFmzg4T8cIJVMctkll7B8cR2llfOnLa4pTSwi8iROKeHHM9reB9wE1KnqmiHLfwN4GdBfhrBXVV/izrsD+ATgB5LAP6jqI1MZvzHGDNLbBvt+PLjCal+bk2iWvRxqN4687hTQI48hP/4AxNoAEIBDv8C7/Eb+x/X/P1/8ziM0n9zPgbpLufmGGyivrpmWuKY6vX4HuHNI253Ap0ZZ50MZte77k8o6nNotf6qqK4FXA58TkbVTEbQxxgzrzK7BSaVfOgVHfwtdZ6YtlFTDVuTJT59PKucpyMFf4Tn8BFdf90oAzpw8wK4Dh6YttqlOLD8AbhWRAIBbNXIBcPIi+/kg8C+qehTA/fdTwIeyFqkxxowm1uOc/hpJMgqdwySdKSIdJ6B1/+DGjMs9svtR1i8oPD+9f/8eutpapiW2KU0sqnoOeA54ldt0J/AQgzb/Avdm1Lr/ttt2Qb17YLPbfgERebuIbBaRzc3NzRPfAGOM6ZeKOz+jSfROTywAsc7R5/c2401Hz08moj3EY9FRVsie6bjSlHk67E7GLkWceSrsTRN5QytNbIzJukAYgiOX3gYgVDY9sQBEqkedrRXLiHkGjlgKSioIRwpHWSN7piOxPAq8XEQ2ABFVHXrkMR4X1Lt3p3dPNjhjjBkXXwgWrB95frgUShZOWzhasQStv25wY2Y9vctfz6+37hmYXHM5keLSaYltyhOLqnYDT+LUuB/raGUknwM+6l6j6b9W8/fAv046QGOMGa/qlbBwg1NBNVOwCC692Uku08RbtQKu/zt0wYYhM/zoNe+nd/5GDh/YhYiXNRuv47KlS6YttmkpTezeKvxDYKWq7nMTw0Eg8xaK9wO3Mvh2Y4AXqWpcRP4M+Cec240TwD+q6sNjvbeVJjbGZFUyAZ0NcO6Ic8G+cD6ULYaCypyEkzq1HWneC2f3QKAQFm6kr3Axv999lEAgwOKFC6iZN59gQdFF9TuZ0sRW894YY8wFJpNYbEgXY4wxWWWJxRhjTFZZYjHGGJNVlliMMcZklSUWY4wxWWWJxRhjTFZZYjHGGJNVlliMMcZklSUWY4wxWWWlic2slk4rrT1xUuk0hSE/hUH7lTdmqtlfmZm1Trf3seVEGydae0mmlMKwj7ULS1i9oJhwwH71jZkqeXkqTEQ+JiK7RWSHW/DrKhEJiMjnReSQiBwUkUdFpDbXsZr81NQR5Sc7TnHwTDexZJqUKh29CZ4+2MIfDreSTKVzHaIxs1beJRYRuRq4DdigqmuBG3FKGf8LUASsUNVLgEeAh0VERuzMzFm7T3fQHUsNO2/XqU6aOqenkp4xc1HeJRagBmhR1RiAqrYA7cBfAu9X1ZTb/nUgBtyQq0BNfuqOJjja3DPi/FRaOd1hicWYqZKPieUXQJ2IHBCR/xCRlwHLgROqOrTI87B1763m/dyWUiWdHr0cRCptp8KMmSp5l1jcipMbgbcDzcD3gOsvsg+reT+HFQb9VBcHR12msjA0TdEYM/fkXWIBUNWUqj6lqv8IvBu4HVgkIkNLoFnde3MBr0dYs7AUr2f4y28LSkMsKLXEYsxUybvEIiIrROSSjKb1wH7gm8C/iYjXXe7NQAT49fRHafLd0soCrl9RRUHQe77NI1BXHubll80jYrcbGzNl8vGvqxD4ooiUAkngEM5psS7gc8ABEUkD+4DX6GyvrWwmxOMR1taWsqg8wpnOKImUUhzyMb8kRMDnHbsDY8yE5V1iUdUtwEtGmP0e98eYcSmNBCiNBHIdhjFzSt6dCjPGGDOzWWIxxhiTVZZYjDHGZJUlFmOMMVllicUYY0xWWWIxxhiTVZZYjDHGZJUlFmOMMVllicUYY0xW5d2T98ZkVawLupoglYBQMRTVgMeGdDFmKlliMbOTKpzZDceehr52p83jg/KlsPR6KKjIZXTGzGo5ORUmIvNE5EEROSIiW0TkDyLyGhG5XkQ63Dr3/T83uuvUunXuD4rIYRH5dxGxQaDM8M4dgf2PDSQVgHQSWg7Awcch0Ze72IyZ5aY9sbg16h8BfquqS1V1I3AnUOsu8rSqrs/4+ZW7zsPAI269+0txRkH+5+mO38wA6TSc2gbpxPDz205AR+P0xmTMHJKLI5YbgLiqfqW/QVWPq+oXx1gn6ta5x617/37gbhGJTGm0ZuaJdUHnaIlDofvMtIVjzFyTi8SyGtg6yvxrh5wKW+ausyVzIVXtBE4Ay4d2YDXv5zgRkDF+tceab4yZsJz/dYnIl0Vku4g87zYNPRV2+GL7tJr3c1yoGMqWjDxfPFC8YPriMWaOyUVi2Q1s6J9Q1XuAlwOjZYA9OPXtzxORYmARToVJYwZbsA784eHnzVsNRZZYjJkquUgsvwZCIvLOjLaxrpM8AUTcOve4de//FfiGqvZOTZhmRiuphdV3OEcuHveu+kAh1L8Ulr4MfP7cxmfMLDbtz7GoqorIHcB9IvJhoBnoAf7OXeRaEdmWsconVfUHIvIa4D9E5B9wEuLPgL+fztjNDFNWD8W10NPs3GocLIJwaa6jMmbWy8kDkqp6GucW4+GUjLDOSeD2KQvKzE5eHxTX5DoKY+aUnF+8N8YYM7tYYjHGGJNVlliMMcZklSUWY4wxWSWqmusYppSINAPHR1mkEmiZpnAmwuKbHItvciy+yZnJ8S1W1Qk9YT7rE8tYRGSzqm7KdRwjsfgmx+KbHItvcuZqfHYqzBhjTFZZYjHGGJNVlljg/lwHMAaLb3Isvsmx+CZnTsY356+xGGOMyS47YjHGGJNVlliMMcZk1ZxOLCJys4jsF5FDIvKRaXzfOhF5UkT2iMhuEXmv214uIr8UkYPuv2Vuu4jIF9w4d4jIhoy+3uIuf1BE3pLFGL0i8oKI/MSdXiIiz7oxfE9EAm570J0+5M6vz+jjo277fhG5KYuxlYrID0Rkn4jsFZGr82zfvd/9/7pLRL4jIqFc7j8R+ZqInBWRXRltWdtfIrJRRHa663xBRCQL8d3r/v/dISI/FJHSjHnD7peR/p5H2veTiS9j3t+KiIpIpTudF/vPbX+Puw93i8hnM9qnfv+p6pz8AbzAYWApEAC2A6um6b1rgA3u6yLgALAK+CzwEbf9I8Bn3Ne3AD8HBHgx8KzbXg4ccf8tc1+XZSnGDwAPAj9xpx8C7nRffwV4p/v6XcBX3Nd3At9zX69y92kQWOLua2+WYvsm8Fb3dQAozZd9BywEjgLhjP12Vy73H3AdTnG9XRltWdtfwHPusuKu+6osxPdKwOe+/kxGfMPuF0b5ex5p308mPre9Dngc5wHsyjzbf38C/AoIutPV07n/pvxDNF9/gKuBxzOmPwp8NEexPAq8AtgP1LhtNcB+9/VXgTdkLL/fnf8G4KsZ7YOWm0Q8tTjF1W4AfuL+wrdk/KGf33fuH9bV7mufu5wM3Z+Zy00ythKcD24Z0p4v+24hcNL9APG5+++mXO8/oH7IB09W9pc7b19G+6DlJhrfkHmvAb7tvh52vzDC3/Nov7uTjQ/4AbAOOMZAYsmL/YeTDG4cZrlp2X9z+VRY/wdAvwa3bVq5pz6uAJ4F5qlTqwagCZjnvh4p1qnahs8DHwbS7nQF0K6qyWHe53wM7vwOd/mpim0JTnG4r4tzqu7/iEgBebLvVLUR+BxwAjiNsz+2kD/7r1+29tdC9/VUxQlwN843+YnEN9rv7oSJyJ8Cjaq6fcisfNl/l+IUTXxWRH4jIldOML4J7b+5nFhyTkQKgf8G3qeqnZnz1Pl6MO33govIbcBZVd0y3e89Tj6cw/7/rapX4FQfHXR9LFf7DsC9VvGnOAlwAVAA3JyLWMYrl/trLCLyMSAJfDvXsfQTkQhO9dr/metYRuHDOWp+MfAh4KGLvXYzGXM5sTTinCPtV+u2TQsR8eMklW+r6sNu8xkRqXHn1wBnx4h1KrbhpcCrReQY8F2c02H/DpSKSH/F0cz3OR+DO78EaJ2i2MD5xtSgqs+60z/ASTT5sO8AbgSOqmqzqiaAh3H2ab7sv37Z2l+N7uusxykidwG3AW9yk99E4mtl5H0/Uctwvjhsd/9OaoGtIjJ/AvFN1f5rAB5Wx3M4Zx8qJxDfxPbfxZ7Lmy0/OBn9CM4vSP/FqtXT9N4CPAB8fkj7vQy+oPpZ9/WtDL4g+JzbXo5zvaHM/TkKlGcxzusZuHj/fQZfwHuX+/oeBl98fsh9vZrBFwmPkL2L908DK9zXH3f3W17sO+AqYDcQcd/zm8B7cr3/uPAcfNb2FxdefL4lC/HdDOwBqoYsN+x+YZS/55H2/WTiGzLvGAPXWPJl/70D+IT7+lKc01wyXfsvKx9AM/UH5w6OAzh3Q3xsGt/3GpxTDzuAbe7PLTjnM58ADuLc0dH/iyfAl904dwKbMvq6Gzjk/vxlluO8noHEstT9Azjk/qL1320ScqcPufOXZqz/MTfm/VzknS5jxLUe2Ozuv0fcP9S82XfAPwH7gF3At9w/4pztP+A7ONd7EjjfZP8qm/sL2ORu62HgSwy5sWKC8R3C+TDs//v4ylj7hRH+nkfa95OJb8j8YwwklnzZfwHg/7r9bgVumM79Z0O6GGOMyaq5fI3FGGPMFLDEYowxJqsssRhjjMkqSyzGGGOyyhKLMcaYrLLEYuY8EUmJyDZxRiP+vvtk9cX28XER+WCW4ul2/613R879ZMa8ShFJiMiXsvFexkwFSyzGQJ+qrlfVNUAc5+GyfHEU56G7fv8D5wFMY/KWJRZjBnsaWC4it7sD+L0gIr8SkXki4nFraVQBuNOH+qf7ich6EfljRi2R/lonbxOR50Vku4j8d/+RkVvv4g9uTY5PDomnF9grIpvc6dfjjFzb/14XxOm2v8w9CtvmzisSkRoR+W3G0dm1U7IHzZxnicUYlzse0qtwnpj+HfBidQa6/C7wYVVN4zzN/CZ3lRuB7araPKSrB4C/U9W1bl//6LY/rKpXquo6YC/OE9LgjMX2v1X1cpwnqIf6LnCniNQBKeBUxrwL4nTbPwjco6rrgWuBPuCNOEOer8cZ7n3b+PeOMePnG3sRY2a9sIj0f8g+DfwXsAL4njtAYwDnlBTA13Dq53weZ4iOr2d2JCIlQKmq/sZt+ibOMBgAa9wjklKgEKcWBjiDVL7Wff0tnMJWmR4D/hdwBvjekHm1I8T5DPBvIvJtnITWICLPA19zB0B9RFUtsZgpYUcsxgxcY1mvqu9R1TjwReBL7lHEX+OM6YWqnsQZGfgG4EUM1AkZj28A73b7/Kf+Pl0jjq3kxrMF+Fuc0ZwzjRTnp4G3AmHgGRG5TFV/i1NtsBH4hoi8+SJiN2bcLLEYM7wSBoYHf8uQef8H55TY91U1lTlDVTuAtozrF38B9B+9FAGn3SOGN2Ws9gzOyMYMac/0rzin186NJ04RWaaqO1X1M8DzwGUishg4o6r/6W7DBoyZApZYjBnex4Hvi8gWnNKsmX6Ecyrr60NXcr0FuFdEduCMxPwJt/0fcCqFPoMz+nG/9wL3iMhORqjOp6q7VfWbFxHn+9wL9DtwRr39Oc5o1dtF5AWcmwD+fYT4jZkUG93YmIvk3qF1n6raXVXGDMMu3htzEUTkI8A7GfmUlTFznh2xGGOMySq7xmKMMSarLLEYY4zJKkssxhhjssoSizHGmKyyxGKMMSar/h9uPT1ZGQLekwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
        "sns.scatterplot(x=\"PayloadMass\", y=\"Orbit\", data=df, alpha=0.5, hue=\"Class\", s=80)\n",
        "plt.xlabel(\"PayloadMass\",fontsize=10)\n",
        "plt.ylabel(\"Orbit\",fontsize=10)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmpqRrvBUU5t"
      },
      "source": [
        "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
        "\n",
        "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nudz4AxkUU5x"
      },
      "source": [
        "### TASK  6: Visualize the launch success yearly trend\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zC3x67NUU5y"
      },
      "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": {
        "id": "sft_baugUU5z"
      },
      "source": [
        "The function will help you get the year from the date:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zLpcoKKVUU50"
      },
      "outputs": [],
      "source": [
        "# A function to Extract years from the date \n",
        "year=[]\n",
        "def Extract_year(date):\n",
        "    for i in df[\"Date\"]:\n",
        "        year.append(i.split(\"-\")[0])\n",
        "    return year\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "rVWBXbnSUU57",
        "outputId": "f9022f89-ba94-4d9d-b713-50fae4338596"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f13f5a233d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
        "df1 = pd.DataFrame(Extract_year(df['Date']) , columns =['year'])\n",
        "df1['Class']=df['Class']\n",
        "df1.groupby('year')['Class'].mean().plot(kind='line', color=[\"pink\"], alpha=0.8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmqeymvfUU59"
      },
      "source": [
        "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJLo_NnPUU5-"
      },
      "source": [
        "## Features Engineering\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eKTDKzfUU6C"
      },
      "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": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Jz0-iZolUU6D",
        "outputId": "616ecd13-c9f5-4cf6-eb46-e2880ab57cc8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
              "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
              "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
              "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
              "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
              "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
              "\n",
              "    Legs LandingPad  Block  ReusedCount Serial  \n",
              "0  False        NaN    1.0            0  B0003  \n",
              "1  False        NaN    1.0            0  B0005  \n",
              "2  False        NaN    1.0            0  B0007  \n",
              "3  False        NaN    1.0            0  B1003  \n",
              "4  False        NaN    1.0            0  B1004  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e1472175-a84f-4b09-9104-cb8e8084de60\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
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          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
        "features.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjBl9gruUU6F"
      },
      "source": [
        "### TASK  7: Create dummy variables to categorical columns\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W-HbhD89UU6H"
      },
      "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": null,
      "metadata": {
        "id": "Bvk-1OnSUU6J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "e0952fcd-2331-4eee-8c77-e7f383b62e2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
              "0             1  6104.959412        1     False   False  False    1.0   \n",
              "1             2   525.000000        1     False   False  False    1.0   \n",
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              "3             4   500.000000        1     False   False  False    1.0   \n",
              "4             5  3170.000000        1     False   False  False    1.0   \n",
              "\n",
              "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
              "0            0            0          0  ...             0             0   \n",
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              "\n",
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              "[5 rows x 80 columns]"
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          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# HINT: Use get_dummies() function on the categorical columns\n",
        "features_one_hot = pd.get_dummies(features, columns = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n",
        "features_one_hot.head()"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "ZEK4F5J8UU6M"
      },
      "source": [
        "### TASK  8: Cast all numeric columns to `float64`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tdh_3tkdUU6N"
      },
      "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": null,
      "metadata": {
        "id": "9cC7J6AaUU6S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "outputId": "05bbb073-33f8-4093-963d-1c46e2a44558"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n",
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              "4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n",
              "..           ...           ...      ...       ...     ...   ...    ...   \n",
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              "89          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  \\\n",
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              "..           ...           ...           ...           ...           ...   \n",
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              "..           ...           ...           ...  \n",
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              "[90 rows x 80 columns]"
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              "<p>90 rows × 80 columns</p>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3db6361d-975b-46b0-ad57-faae8f3d9054');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# HINT: use astype function\n",
        "features_one_hot.astype(float)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TeKtHrPsUU6T"
      },
      "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": {
        "id": "m7V16yUuUU6U"
      },
      "source": [
        "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N3IGh1IwUU6U"
      },
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSfMTUvUUU6W"
      },
      "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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": {
        "id": "wkyQaUpUUU6X"
      },
      "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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": {
        "id": "q4fMjB0RUU6Y"
      },
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZF7bNu_UU6b"
      },
      "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": {
        "id": "gq5vpEv2UU6c"
      },
      "source": [
        "Copyright © 2020 IBM Corporation. All rights reserved.\n"
      ]
    }
  ],
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