EDA Visualization.ipynb

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  {
   "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 Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\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": 2,
   "metadata": {},
   "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": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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",
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       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</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",
       "      <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",
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       "      <td>1.0</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
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     "execution_count": 3,
     "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",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "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": 5,
   "metadata": {
    "scrolled": true
   },
   "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\n",
    "sns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 2)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "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": 6,
   "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\n",
    "sns.catplot(y=\"PayloadMass\", x=\"LaunchSite\", hue=\"Class\", data=df, aspect = 2)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Payload\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "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": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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\n",
    "z=df.groupby(['Orbit']).mean()[['Class']]\n",
    "z.reset_index(inplace=True)\n",
    "plt.bar(z['Orbit'],z['Class'])"
   ]
  },
  {
   "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": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0522f73f10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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\n",
    "sns.catplot(y=\"FlightNumber\", x=\"Orbit\", hue=\"Class\", data=df, aspect = 2)\n"
   ]
  },
  {
   "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": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f0522f87fa0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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\n",
    "sns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of     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",
       "85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
       "88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n",
       "89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n",
       "\n",
       "        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n",
       "0     None None        1     False   False  False                       NaN   \n",
       "1     None None        1     False   False  False                       NaN   \n",
       "2     None None        1     False   False  False                       NaN   \n",
       "3   False Ocean        1     False   False  False                       NaN   \n",
       "4     None None        1     False   False  False                       NaN   \n",
       "..          ...      ...       ...     ...    ...                       ...   \n",
       "85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
       "88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n",
       "89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n",
       "\n",
       "    Block  ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0     1.0            0  B0003  -80.577366  28.561857      0  \n",
       "1     1.0            0  B0005  -80.577366  28.561857      0  \n",
       "2     1.0            0  B0007  -80.577366  28.561857      0  \n",
       "3     1.0            0  B1003 -120.610829  34.632093      0  \n",
       "4     1.0            0  B1004  -80.577366  28.561857      0  \n",
       "..    ...          ...    ...         ...        ...    ...  \n",
       "85    5.0            2  B1060  -80.603956  28.608058      1  \n",
       "86    5.0            2  B1058  -80.603956  28.608058      1  \n",
       "87    5.0            5  B1051  -80.603956  28.608058      1  \n",
       "88    5.0            2  B1060  -80.577366  28.561857      1  \n",
       "89    5.0            0  B1062  -80.577366  28.561857      1  \n",
       "\n",
       "[90 rows x 18 columns]>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe"
   ]
  },
  {
   "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": 10,
   "metadata": {},
   "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": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2010', '2012', '2013', '2013', '2013', '2014', '2014', '2014', '2014', '2014', '2014', '2015', '2015', '2015', '2015', '2015', '2015', '2016', '2016', '2016', '2016', '2016', '2016', '2016', '2016', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2017', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2018', '2019', '2019', '2019', '2019', '2019', '2019', '2019', '2019', '2019', '2019', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020', '2020']\n"
     ]
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "Extract_year(df['Date'])\n",
    "print(year)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Year']=year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "q=df.groupby(['Year']).mean()[['Class']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "q.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0519f0df40>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.plot(q['Year'],q['Class'])"
   ]
  },
  {
   "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": 48,
   "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  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "d=pd.get_dummies(features[['Orbit','LaunchSite','LandingPad','Serial']])\n",
    "features_one_hot=pd.concat([features[['FlightNumber','PayloadMass','Flights','Block','GridFins','Reused','ReusedCount','Legs']],d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "   FlightNumber  PayloadMass  Flights  Block GridFins Reused  ReusedCount  \\\n",
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  {
   "cell_type": "code",
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  {
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   "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": 55,
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>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>180 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FlightNumber  PayloadMass  Flights  Block  GridFins  Reused  ReusedCount  \\\n",
       "0            1.0  6104.959412      1.0    1.0       0.0     0.0          0.0   \n",
       "1            2.0   525.000000      1.0    1.0       0.0     0.0          0.0   \n",
       "2            3.0   677.000000      1.0    1.0       0.0     0.0          0.0   \n",
       "3            4.0   500.000000      1.0    1.0       0.0     0.0          0.0   \n",
       "4            5.0  3170.000000      1.0    1.0       0.0     0.0          0.0   \n",
       "..           ...          ...      ...    ...       ...     ...          ...   \n",
       "85           NaN          NaN      NaN    NaN       NaN     NaN          NaN   \n",
       "86           NaN          NaN      NaN    NaN       NaN     NaN          NaN   \n",
       "87           NaN          NaN      NaN    NaN       NaN     NaN          NaN   \n",
       "88           NaN          NaN      NaN    NaN       NaN     NaN          NaN   \n",
       "89           NaN          NaN      NaN    NaN       NaN     NaN          NaN   \n",
       "\n",
       "    Legs  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0    0.0          NaN        NaN  ...           NaN           NaN   \n",
       "1    0.0          NaN        NaN  ...           NaN           NaN   \n",
       "2    0.0          NaN        NaN  ...           NaN           NaN   \n",
       "3    0.0          NaN        NaN  ...           NaN           NaN   \n",
       "4    0.0          NaN        NaN  ...           NaN           NaN   \n",
       "..   ...          ...        ...  ...           ...           ...   \n",
       "85   NaN          0.0        0.0  ...           0.0           0.0   \n",
       "86   NaN          0.0        0.0  ...           0.0           0.0   \n",
       "87   NaN          0.0        0.0  ...           0.0           0.0   \n",
       "88   NaN          0.0        0.0  ...           0.0           0.0   \n",
       "89   NaN          0.0        0.0  ...           0.0           0.0   \n",
       "\n",
       "    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0            NaN           NaN           NaN           NaN           NaN   \n",
       "1            NaN           NaN           NaN           NaN           NaN   \n",
       "2            NaN           NaN           NaN           NaN           NaN   \n",
       "3            NaN           NaN           NaN           NaN           NaN   \n",
       "4            NaN           NaN           NaN           NaN           NaN   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "85           0.0           0.0           0.0           0.0           0.0   \n",
       "86           0.0           0.0           0.0           0.0           1.0   \n",
       "87           0.0           1.0           0.0           0.0           0.0   \n",
       "88           0.0           0.0           0.0           0.0           0.0   \n",
       "89           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0            NaN           NaN           NaN  \n",
       "1            NaN           NaN           NaN  \n",
       "2            NaN           NaN           NaN  \n",
       "3            NaN           NaN           NaN  \n",
       "4            NaN           NaN           NaN  \n",
       "..           ...           ...           ...  \n",
       "85           0.0           1.0           0.0  \n",
       "86           0.0           0.0           0.0  \n",
       "87           0.0           0.0           0.0  \n",
       "88           0.0           1.0           0.0  \n",
       "89           0.0           0.0           1.0  \n",
       "\n",
       "[180 rows x 80 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.astype(float)"
   ]
  },
  {
   "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": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part\\_3.csv', index=False)\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 © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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