EDA with Visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment: Exploring and Preparing\u00a0Data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **70** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Falcon 9 first stage will land successfully\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  Feature Engineering\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will import the following libraries the lab\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# andas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploratory Data Analysis\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.head(5)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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\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)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, let's drill down to each site visualize its detailed launch records.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"LaunchSite\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"LaunchSite\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f583d529710>"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": "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\n",
                        "text/plain": "<Figure size 864x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf_test = df.groupby('Orbit')['Class'].mean()\nfig = plt.figure(figsize=(12,8))\nax = fig.add_subplot(1, 1, 1)\ndf_test.plot.bar(ax=ax)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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nikmSJEmSJEkbWXBuryw4JTVIVVXuOT47DIAOtXbKliRJkiRJkiSptW2XBWdJ1gEkqWAUFcFOe2edQpIkSZIkSZKk7VoB760oSZIkSZIkSZIkaXtjwSlJkiRJkiRJkiSpYFhwSpIkSZIkSZIkSSoYFpySJEmSJEmSJEmSCoYFpyRJkiRJkiRJkqSCYcEpSZIkSZIkSZIkqWBYcEqSJEmSJEmSJEkqGBackiRJkiRJkiRJkgqGBackSZIkSZIkSZKkgmHBKUmSJEmSJEmSJKlgWHBKkiRJkiRJkiRJKhgWnJIkSZIkSZIkSZIKhgWnJEmSJEmSJEmSpIJhwSlJkiRJkiRJkiSpYFhwSpIkSZIkSZIkSSoYFpySJEmSJEmSJEmSCkZJ1gEkSZIkSZJazeqlMPa3ULkODvsOdO2TdSJJkiRJjWTBqVa1rrKKX9z/FveMn83O3Tvy4xP24ci9dso6liRJkiRpe7ByIVy+D2srKkgEHV4cA998BXYcnHUySZIkSY1QkFvURkRlREyIiDci4m8R0Tk/PiAi7o2IyRExNSKujoj2WefVRtc/M42bnp/B0tUVTJ6/nAtueYUlq9ZlHUuSJEmStD149L+5as0JjFzzJz6w5jr+Z+1nSQ98P+tUkiRJkhqpIAtOYFVKaWRKaQSwFvhqRARwN3BPSmkPYE+gK/DLDHNqEy9NW1TjeOXaSt6YvQSAyqrE6nWVWcSSJEmSJG0HXpixjKsqTmUlHVlDe66rPIF/zW6XdSxJkiRJjVSoBWd1Y4HdgY8Aq1NKNwCklCqBi4Avrl/hqeyNHNijxnH7kiL26dede8bP5oO/fIxhP3uYr/11HCvXVmSUUJIkSZK0rXqj+xGcXvwEz3T4Js93+DpfLn6AN7oeknUsSZIkSY1U0AVnRJQAxwGvA8OBcdXPp5SWAu+SK0A3vfa8iCiLiLLy8vLWiCvgK0cM5dP796ekKOi3Q0eu/uxIKqsSP/j7ayxcsZaqBA+9MZdrn3on66iSJEmSpG3MkcP68+t21zEgFtAvFvGTdrdw3J7ds44lSZIkqZFKsg7QRJ0iYkL+67HAn4GvAamOuVHXeEppDDAGoLS0tK7r1AI6tivmis+O5Len7UdRUQDw7JQFrK2sqjFv/ba1kiRJkiQ1l92n31ZrbPis24BzWj+MJEmSpCYr1IJzVUppZPWBiHgTOGWTse7AQGBqK2ZTA6wvNwH2HbADXTuUsHzNxm1pD929dxaxJEmSJEnbsuXzao+tWNj6OSRJkiRtlYLeonYTjwOdI+IcgIgoBi4Hbkwprcw0mbaoe8d2jDlnFB8YsAM7d+/A+UcM5dxDh2QdS5IkSZK0rdn3M7XHhn+q9XNIkiRJ2iqRUuHtzhoRy1NKXesYHwj8AdibXHn7IPC9lNKaLd2vtLQ0lZWVtUhWSZIkSZLURqQEt5wKUx7LHQ88CM59EIoLdYMrSZIkiah/yranIP8FX1e5mR+fCXyileNIkiRJkqRCEAFn3wXLyyFVQre+WSeSJEmS1AQFWXBKkiRJkiQ1Wdc+WSeQJEmStBW2pWdwSpIkSZIkSZIkSdrGWXBKkiRJkiRJkiRJKhgWnJIkSZIkSZIkSZIKhgWnJEmSJEmSJEmSpIJhwSlJkiRJkiRJkiSpYFhwSpIkSZIkSZIkSSoYFpySJEmSJEmSJEmSCoYFpyRJkiRJkiRJkqSCYcEpSZIkSZIkSZIkqWBYcEqSJEmSJEmSJEkqGBackiRJkiRJkiRJkgqGBackSZIkSZIkSZKkgmHBKUmSJEmSJEmSJKlgWHBKkiRJkiRJkiRJKhgWnJIkSZIkSZIkSZIKhgWnJEmS1MzWVVaxtqIq6xiSJEmSJEnbpJKsA0iS1JpWrKng6scnM27G+4wavCPf+ugedOmQ4V+H42+B1++Ebv3giO9Dr92yy7KNmrloJc9PXciwXbozov8OWcfZZqxeV8n/Pvw2T0ycx+47deXHJwxj195dso7VJlzxyNv8aew0EolzD92Vi4/bO+tIkiRJkiRJ2xQLTknSduW//vE69054D4BxM95n3tLVXH36/tmEee1vcO8FG4+nPQ3fnAAl7bPJsw169K15fO2v46ioSgB8/+N7ceFRu2+csHYlrF4M3XfJKGEbNudVeOEaqFwLB34FBh9S4/Tlj7zN9c9OA2D6wpVMX7iSRy86gojIIu2WvTcBHvoBLJwCex0Hx/0vtG+ZMvbZKQv43RNTNhz/8ampHDhkRz66z84t8nrN4W9lM7n8kUksX1PBWQcN4uLj9m69/x2rKmHGc9CxO/Tbr3VeU5IkSZIkFbw2vUVtROwcEbdGxDsRMS4ino+INyNiQkS8FRGr8l9PiIhTI+cnETE5IiZFxL8jYnjW70OS1LxmL17FXeNm8eZ7Sxp97UNvzK15/PrczcxsBW/+o+bx0tkw66Vssmyjrnps0oZyE2D0v6ewel1l7uCVm+C3e8IV+8CfPwYrFmz2Pm/OXsyXbniB464eyx+fmkpKabNztwlL34MbjodXb4M37oK/fALmvVljypNvl9c4njJ/ObPeX7XZW65cW8Gl/3yL464eyw/+/ioLl6/Z7NyUEq/PWsKs91du3fsAqKyA28+EmS/CyoUw/q/w7/+39ffdjNdm1f5z6fXZjf+zqrW8U76cH9z1GnOXrmb5mgquffod7n5lduu8+MpFcM2h8JcT4doj4M7Pt87rSpIkSZKkgtdmV3BG7sfG7wH+klI6Mz82GPhkSun3ETEEuD+lNLLaNV8HDgX2SymtjIiPAfdFxPCU0upWfxOSpJomPwqP/RxWLoCRZ8FRP4aizfyszbrVsPjd3JatRcUbhv89cT7n3VzGusrNrMirx6CenZkyf/nG416dm/ZemkOPQZsMBOwwIJMo26pVaytrHK+pqKKyKsGKhfDAd3OrEyFXfj39Gzjuslr3WPf8GPr/61JGp9XcVvkRLn3oc3TpUMLnDh7cGm8hGxMfgLUbf59QtQ7evAd23vhzY3v17cbkar+Xduzcjp26d9jsLX9+31vcUTYTgP/MWcqcJau5+UsH1Zq3YPkazr7uRSbOXUYEfOXwofzX8fs0/b0seif3wwPVTXu66ferxyG79ao1duhuvVvs9bbW+HcXs2lfP+7d9zllVCv8WfTyn6F84sbjt+6B6c/AkMNa5vXWrswV9X32yq0YlSRJkiRJBastr+D8CLA2pfTH9QMppRkppd9v4ZofAt9IKa3Mz38EeA44q0WTSpLqt7wc7jgb5r0Oy+bA2N/C+Jvqnjv5Ubhibxh9IFy9H8x9fcOpqx6btKHcBPi/J6qtyGuAS08aTo/O7QDo0bkdl56U4UL/w74NffLFTRTDh38IOw6pOeeVm+DyfeDXg+DxX7R6xEJ39iYl5Kf375975uqidzaWm+vN/0/tG8z/D+3+9X16sIyOsY4vlPyLU4rH8sR/5rVg6laweilUrtv8+W796hjrW+PwR8fvw4j+uZKod9f2/ObU/ehQUlz7urzHNvk1Gzt5QZ2/d68bO42Jc5cBkBKMefodJs9btvms9dlxMHTqWXNsl5bblnrkwB5cdsq+DOnVmf49OnHw0J6MeXoqd78yq8Vec2scMHhHNt2NdtSgHVv0NTesgF5ex++jusaaw4zn8qu1j4bLhsDvS6Hs+pZ5LUmSJEmS1OLa7ApOYDjwSkMnR0R3oEtKaeomp8ry99p0/nnAeQCDBm26gkaS1OxmvQQVmyymnzYWRp1bc6yqCv75LVj1fu54yUx46GL4wgMALF9TUWP6mopK1lZW0bHd5ouV6g7drTcv/OijTJm/nN136trg61pEt75wwfMw9zXoshO8djv8Zg8obgdHfA8GHQL3fRPIlwFjf5tbQTfi09llborFM+HJX8HCqbD3CXDI1ze/crcBKiqr+ONTU3lqUjl79e3Gt4/ek95d6145+MXDdmVgz86MnVzOsH7dOXX9qrR++0HXvrC82hbFe3689g1m1/6nyH4xlXd36trk/JlasxzuPg/efhA67gAf+x844HMbTj/8xhx+/8QU1qztxA19PszA8qdyJwYeDPudUeNW/Xt04v5vHM78pavZsUt72hVv+X/ToX26sHDFxlJ5wI6d6FBS+5rZi2tvczt78Sr22LlbY97pRiUd4NQ/5/5cWfwu7PZR+Oh/N+1eDfTZAwdx2qiBHHv107zwziIAHvvPfCoqE585cGCLvnZj7dq7C785dT8uf+Rtlq+u4MyDBvHpA/q3yGtVVSV+8cBb3PbSu3TtUMKvDjyCY+J6SPmiu3Nv2P2YFnltHr4497xdyL3ewslw/0XQoTvse2rLvKYkSZIkSWoxbbngrCEiRgOHkVvVeWBjLmXDd4Y3SimNAcYAlJaWbuMP0pKkNqDvvrlViqnaiq1dRtaet25F7e0kF07Z8OVZBw3m0vvf2nD8yf12oXvHdo2K0rFdMSP679Coa1pMRK5sm/wYPHbJxvH7L4LDv0utv8JmvlhYBWdKcMtpUJ5fHTnzBUhVudWrTXTFo5P4w5O5n2d6efr7vD13GX/76qGbnX/MsJ05ZtjONQdL2sPZd8Hjl+ZKrxGfhoO+VvviQQdDFOUy5y3oVcoFRzZ8W+Q25bnfw9u5HxZg9WK4/9uw+9HQvR/vlC/nwlvH57bwBQ7nfP5+8ncoHdANBoza7C136t6xQS/9358Yzvk3j2P24lX07NKeX316X2LTpYPAiR/oxz9ffW/DcZ9uHTh4aO1tXxtlt4/At16DijXQrmF5t9Zbc5Yyad7yGmP3vjq7zRWcAKeOGrCx/G9Bfx83ixuenQ7A6nVrOe/JYp757O30n/o36NANDrmw5baOXTyz7vGJ91twSpIkSZJUgNpywfkmcMr6g5TShRHRm9yKzFpSSksjYkVEDE0pvVPt1AHAUy0bVZJUrx6D4BNXwaP/DauXwL6nwQfPqz2vQzcYcjhMH7txbO/jN3z5xcN2pf+OnXh6Ujn79OvOZ9tgWdAkM56tPVbXFqIDGvMzPm3Agskby831/nPfVhWcD70xt8bxy9Pfp3zZGvp02/zzH+vUdwScdeeW5/TaDT79J/j3L6las5zl+57DRcf+qJGJ25Bq2z0DUFUB89+C7v14burCDeXmeg8t2InSg4c1y0uP6L8DT//gKGYsXEH/HTttdjvbjw/vy+/O2J+7xs2id9cOXHDUbs2z0jqi1cpNgJ26daC4KGr8mvbt3qnVXr8tGj/z/RrHKcGLVcP49Cl/avkXH/4pKPtz7fFeBfrDCpIkSZIkbefacsH5BPD/IuJrKaVr8mOd67nmN8DvIuK0lNKqiDia3KrP81syqCSpgQ44B0aelStVSrZQRp16Azz+c5jzKgz9MBz14xqnPz68Lx8f3nczFxeouorLfT4BPYfmtndduxI++GUYcUrteW1Zt52hpBNUVNt2tOfQrbrlwJ6dmbZgxYbjHp3b0b1TC/6TZt9TYd9TKQJaaG1Z6xl65MYVnADtu8GAUgD26Vf73Q2rY2xrFBcFQ/vUv73vJ/fbhU/ut0uzvnZr26l7R775kT24+vFJVCXYZYeOfOMj23eZduCQntz20saVlEUBpYN7buGKZnTsr6DrzjDhlvxqzioYeFBu1agkSZIkSSo4kVLb3Z01IvoBVwIHAeXACuCPKaU7ImIIcH9KaUS1+QH8DPgcUAnMBb6eUtpkuUJNpaWlqayszoWhkiS1nif+B164BopKcs/gPPQbWSdqHmU35J5/V7E6V26efddWlZz/mbOUL974MnOWrKZTu2J+fcq+nDSyZZ4ZuM2pqsoV5q/dnnsG6dGXwJAPbTh91WOT+ONTU6moTJw6agC//NS+FBfV3kZWDTdz0UreW7yK/QftSPs6njm6PUkpcfkjk7jlxRl07VjC9z62Vza/d1cuyj3nuddurf/akiRJkiQ1v+3ymzdtuuBsLRackqQ2I6XcVprbmlXvw5LZsNMwKNr6kmddZRVvz13GoF6dG/0MVm3Z6nWVVFYlunRoyxt9SJIkSZIkKW8b/GZi/fzOlSRJbcm2WG4CdNox99FM2hUXMaL/Ds12P23ULM+7lCRJkiRJklrQ9r1PliRJkiRJkiRJkqSCYsEpSZIkSZIkSZIkqVsNZJwAACAASURBVGBYcEqSJEmSJEmSJEkqGBackiRJkiRJkiRJkgqGBackSZIkSZIkSZKkgmHBKUmSJEmSJEmSJKlgWHBKkiRJkiRJkiRJKhgWnJIkSZIkSZIkSZIKhgWnJEmSJEmSJEmSpIJhwSlJkiRJkiRJkiSpYFhwSpIkSZIkSZIkSSoYFpySJEmSJEmSJEmSCoYFpyRJkiRJkiRJkqSCYcEpSZIkSZIkSZIkqWBYcEqSJEmSJEmSJEkqGBackiRJkiRJkiRJkgqGBackSZIkSZIkSZKkgmHBKUmSJEmSJEmSJKlgZFpwRkRlREyo9nFxfvzEiBgfEa9GxFsRcf5mrp8eEb03Gds7Ip6PiDUR8b3WeB+SJEmSJEmSJEmSWkdJxq+/KqU0svpARLQDxgAfTCnNiogOwJBG3HMR8E3g5GZLKUmSJEmSJEmSJKlNaItb1HYjV7wuBEgprUkpvd3Qi1NK81NKLwPrWiifWtKiaXDD8XBpr9znRdOyTiRJkiRJkiRJkqQ2JOuCs9MmW9R+NqW0CLgPmBERt0XEWRHR7Dkj4ryIKIuIsvLy8ua+vZrq3gthxrNQVZH7fO+FWSeSJEmSJEmSJElSG9LmtqgFSCl9OSL2BY4GvgccA5zbnC+cUhpDbitcSktLU3PeW1th5otbPpYkSZIkSZIkSdJ2LesVnJuVUno9pXQluXLzlIgorrbS89Ks86mFDDxoy8eSJEmSJEmSJEnarrW5gjMiukbEkdWGRgIzUkqVKaWR+Y+fZRRPLe2k0TD4Q1BUkvt80uisE0mSJEmSJEmSJKkNiZSy2501IiqB16sNPQz8ErgD2A1YBawAvpVSKqvj+ulAe6AqP3Qn8L9AGdA9P74cGJZSWrq5HKWlpamsrNbtJUmSJEmSJEmSpLYssg6QhUyfwZlSKt7MqeMbeP2QzZwa0KRAkiRJkiRJkiRJktq0NrdFrSRJkiRJkiRJkiRtTqMKzoh4IiLOqWfO2RHxxNbFkiRJkiRJkiRJkqTaGruC80hgSD1zBgMfbkoYSZIkSZIkSZIkSdqSltiithNQ0QL3lSRJkiRJkiRJkrSdK2nCNamuwYgIYBBwPDBza0JJkiRJkiRJkiRJUl3qXcEZEVURURkRlfmhS9YfV/8gt2rzHWAkcHsLZpYkSZIkSZIkSZK0nWrICs6n2bhq8wjgXWB6HfMqgYXA48B1zRFOkiRJkiRJkiRJkqqrt+BMKR25/uuIqAJuSCld2pKhJEmSJEmSJEmSJKkujX0G567A4pYIIkmSJEmSJEmSJEn1aVTBmVKa0VJBJEmSJEmSJEmSJKk+Wyw4I+Jn5J6/OTqltCh/3BAppfSLrU4nSZIkSZIkSZIkSdVESmnzJ3PP3EzAPimlSfnjhkgppeLmCNgaSktLU1lZWdYxJEmSJEmSJEmSpMaIrANkob4tao/Kf353k2NJkiRJkiRJkiRJanVbLDhTSk9t6ViSJEmSJEmSJEmSWlNR1gEkSZIkSZIkSZIkqaHq26K2ThExBPgcsD+wA7AEGA/8NaU0rbnCSZIkSZIkSZIkSVJ1jS44I+K7wC+BdtR8cOnJwE8i4kcppSuaKZ8kSZIkSZIkSZIkbdCogjMizgB+A7wP/A54EpgL9AWOAr4J/CYiZqeU7mjeqJIkSZIkSZIkSZK2d5FSavjkiDJgV+CAlNKMOs7vCowDpqaUDmy2lC2stLQ0lZWVZR1DkiRJkiRJkiRJaoyof8q2p6iR84cBd9ZVbgLkn795JzB8a4OtFxE/jog3I+K1iJgQEQdFxIkRMT4iXo2ItyLi/Grzz4uIifmPlyLisObKoq0w4zm47mi4cgQ89nOoqsw6kbbWqsXQiB+QkCRJkiRJkiRJag6NfQbnMmBxPXMWA0ubFqemiDgEOJHcitE1EdEb6AL8A/hgSmlWRHQAhuTnnwicDxyWUloQEQcA90TEB1NKc5sjk5pg9VK49bOwJv+fxTNXQNed4eCvZptLTfP+dPjbufDeeOgxGD51LQw+JOtUaikpwaJ3oFtfaN8l6zRqKRVr4JGfwJv3QI9BcOyvYOAHs04lSZIkSZIkSXVq7ArOR4CPb+5kRATwsfy85tAPWJBSWgOQUlpArmQtARbmx9aklN7Oz/8h8P38PFJKrwB/AS5spjxqivde2VhurvfOk5lEUTN46OJcuQmweAbcfR5UVWWbSS1j0TQYfRD8/gC4fG94/e9ZJ1JLGXsFvDQGVsyH2WVw2+mwbnXWqSRJkiRJkiSpTo0tOH8A7BgRt0XE4OonImIQcCvQIz+vOTwCDIyISRHxh4j4cEppEXAfMCOf46yIWP8+hpN7Bmh1ZdSxZW5+K9uyiCgrLy9vpriqU599oKhdzbG++2aTZXu2bjU8/F/wuwPgtjNh4dSm3WfuazWPl7wLq+tb2K2C9PjPYUH+50fWLIX7vwNrV2abSS1j+tiaxysXwvw3s8kiSZIkSZIkSfXYYsEZEU9U/wBuIbcF7WeAyRExJSKejYgpwJT8+JL8vK2WUloOjALOA8qBOyLi3JTSl4GPAi8B3wOu39LbAGo9KDClNCalVJpSKu3Tp09zxNXmdNsZPvk76NQTCNjrBPjQt7JOtf154hfwwmhYNBXefgBuP7Np9xl6ZM3jvh+Azj23Np3aogWTax6vWQLL52WTRS2r38iax+27Qq89sskiSZIkSZIkqU2JiL4RcXtETI2ItyLiwYjYMyLeyCpTfc/gPLKea4fmP6rbjzoKxaZKKVUCTwJPRsTrwOeBG1NKrwOvR8TNwDTgXOAtcoXoE9VucUB+XFkaeSbs+xmoWAUdumWdZvs05bGax+UTYfG7ueftNcaxv8p9nvpEbiXucZc1Tz61PXsdB/Oq/f200zDouWt2edRyjvxh7lmrkx6Gbv3ghN9Cx+5Zp5IkSZIkSZKUsfzjKf8B/CWldHp+bCSwc5a5tlhwppQau4Vts4qIvYCqlNL6ZUQjgXkRcWRK6clqYzPyX/8vcFlEHJtSWpj/BT4XOKgVY2tzikug2HIzMzvtkys11+vUE7o24c+fjjvAyX9ovlxquz78Q4giePtB6L0XfPRnWSdSS+m4A5x5O6xbBcUdoCjTv/4lSZIkSZIktR1HAetSSn9cP5BSmhARQ9Yf57++GeiSH/p6Sum5iOgH3AF0J9dJfg14DvgzUEpuweT1KaUrGxuqvhWcWesK/D4iegAV5LbB/RZwbURcC6wCVpArMUkp3RcR/YHnIiIBy4CzU0pzsggvtSnHXJp77ubc16Bzbzjp/6CkQ9ap1JYVt4Oj/iv3oe1Du05ZJ5AkSZIkSZLUtowAxtUzZz5wTEppdUTsAdxGrsA8E/hXSumXEVEMdCa3cLF/SmkEQL4DbLRGFZwRUQnckVJq4sP7GielNA44tI5Tx2/hmmuAa1oslFSoegyCr46FpXOgcy8oaZ91IkmSJEmSJEmSVPjaAf+X31m1EtgzP/4ycH1EtAPuya/8fAcYGhG/Bx4AHmnKCzZ2D7plbNwOVlIh6t7PclOSJEmSJEmSJDXEm8CoeuZcBMwD9iO3crM9QErpaeAIYDZwc0Sck1J6Pz/vSeBC4LqmhGpswTkeGNaUF5IkSZIkSZIkSZJUUJ4AOkTEV9YPRMSBwOBqc3YA5qSUqoDPAcX5eYOB+SmlP5F77uYBEdEbKEop3QX8FDigKaEaW3BeBhwfEcc05cUkSZIkSZIkSZIkFYaUUgI+BRwTEVMj4k3gEuC9atP+AHw+Il4gtz3tivz4kcCEiBgPnAJcDfQHnoyICcCNwI+akityuRo4OeIc4DTgOOAecnvnzgVq3SSldFNTAmWhtLQ0lZWVZR1DkiRJkiRJkiRJaozIOkAWGltwVpErMzf9xap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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"ay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "['2010',\n '2012',\n '2013',\n '2013',\n '2013',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020']"
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year(df['Date'])    "
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f583cac7710>"
                    },
                    "execution_count": 10,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 864x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ndf['year'] = year\ndf_test = df.groupby('year')['Class'].mean()\nfig = plt.figure(figsize=(12,8))\nax = fig.add_subplot(1, 1, 1)\ndf_test.plot(ax=ax)"
        },
        {
            "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": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  "
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "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>ES-L1</th>\n      <th>GEO</th>\n      <th>GTO</th>\n      <th>HEO</th>\n      <th>ISS</th>\n      <th>LEO</th>\n      <th>MEO</th>\n      <th>PO</th>\n      <th>SO</th>\n      <th>SSO</th>\n      <th>VLEO</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   ES-L1  GEO  GTO  HEO  ISS  LEO  MEO  PO  SO  SSO  VLEO\n0      0    0    0    0    0    1    0   0   0    0     0\n1      0    0    0    0    0    1    0   0   0    0     0\n2      0    0    0    0    1    0    0   0   0    0     0\n3      0    0    0    0    0    0    0   1   0    0     0\n4      0    0    1    0    0    0    0   0   0    0     0"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\ndummy_variable_1 = pd.get_dummies(features[\"Orbit\"])\ndummy_variable_1.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "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>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>...</th>\n      <th>GEO</th>\n      <th>GTO</th>\n      <th>HEO</th>\n      <th>ISS</th>\n      <th>LEO</th>\n      <th>MEO</th>\n      <th>PO</th>\n      <th>SO</th>\n      <th>SSO</th>\n      <th>VLEO</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</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>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</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>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 22 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass    LaunchSite  Flights  GridFins  Reused   Legs  \\\n0             1  6104.959412  CCAFS SLC 40        1     False   False  False   \n1             2   525.000000  CCAFS SLC 40        1     False   False  False   \n2             3   677.000000  CCAFS SLC 40        1     False   False  False   \n3             4   500.000000   VAFB SLC 4E        1     False   False  False   \n4             5  3170.000000  CCAFS SLC 40        1     False   False  False   \n\n  LandingPad  Block  ReusedCount  ... GEO  GTO  HEO  ISS  LEO  MEO  PO  SO  \\\n0        NaN    1.0            0  ...   0    0    0    0    1    0   0   0   \n1        NaN    1.0            0  ...   0    0    0    0    1    0   0   0   \n2        NaN    1.0            0  ...   0    0    0    1    0    0   0   0   \n3        NaN    1.0            0  ...   0    0    0    0    0    0   1   0   \n4        NaN    1.0            0  ...   0    1    0    0    0    0   0   0   \n\n   SSO  VLEO  \n0    0     0  \n1    0     0  \n2    0     0  \n3    0     0  \n4    0     0  \n\n[5 rows x 22 columns]"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot = pd.concat([features, dummy_variable_1], axis=1)\nfeatures_one_hot.drop(\"Orbit\", axis = 1, inplace=True)\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CCAFS SLC 40</th>\n      <th>KSC LC 39A</th>\n      <th>VAFB SLC 4E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   CCAFS SLC 40  KSC LC 39A  VAFB SLC 4E\n0             1           0            0\n1             1           0            0\n2             1           0            0\n3             0           0            1\n4             1           0            0"
                    },
                    "execution_count": 14,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "dummy_variable_2 = pd.get_dummies(features_one_hot[\"LaunchSite\"])\ndummy_variable_2.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>...</th>\n      <th>ISS</th>\n      <th>LEO</th>\n      <th>MEO</th>\n      <th>PO</th>\n      <th>SO</th>\n      <th>SSO</th>\n      <th>VLEO</th>\n      <th>CCAFS SLC 40</th>\n      <th>KSC LC 39A</th>\n      <th>VAFB SLC 4E</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 24 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs LandingPad  \\\n0             1  6104.959412        1     False   False  False        NaN   \n1             2   525.000000        1     False   False  False        NaN   \n2             3   677.000000        1     False   False  False        NaN   \n3             4   500.000000        1     False   False  False        NaN   \n4             5  3170.000000        1     False   False  False        NaN   \n\n   Block  ReusedCount Serial  ...  ISS  LEO  MEO  PO  SO  SSO  VLEO  \\\n0    1.0            0  B0003  ...    0    1    0   0   0    0     0   \n1    1.0            0  B0005  ...    0    1    0   0   0    0     0   \n2    1.0            0  B0007  ...    1    0    0   0   0    0     0   \n3    1.0            0  B1003  ...    0    0    0   1   0    0     0   \n4    1.0            0  B1004  ...    0    0    0   0   0    0     0   \n\n   CCAFS SLC 40  KSC LC 39A  VAFB SLC 4E  \n0             1           0            0  \n1             1           0            0  \n2             1           0            0  \n3             0           0            1  \n4             1           0            0  \n\n[5 rows x 24 columns]"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot2 = pd.concat([features_one_hot, dummy_variable_2], axis=1)\nfeatures_one_hot2.drop(\"LaunchSite\", axis = 1, inplace=True)\nfeatures_one_hot2.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>5e9e3032383ecb267a34e7c7</th>\n      <th>5e9e3032383ecb554034e7c9</th>\n      <th>5e9e3032383ecb6bb234e7ca</th>\n      <th>5e9e3032383ecb761634e7cb</th>\n      <th>5e9e3033383ecbb9e534e7cc</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   5e9e3032383ecb267a34e7c7  5e9e3032383ecb554034e7c9  \\\n0                         0                         0   \n1                         0                         0   \n2                         0                         0   \n3                         0                         0   \n4                         0                         0   \n\n   5e9e3032383ecb6bb234e7ca  5e9e3032383ecb761634e7cb  \\\n0                         0                         0   \n1                         0                         0   \n2                         0                         0   \n3                         0                         0   \n4                         0                         0   \n\n   5e9e3033383ecbb9e534e7cc  \n0                         0  \n1                         0  \n2                         0  \n3                         0  \n4                         0  "
                    },
                    "execution_count": 16,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "dummy_variable_3 = pd.get_dummies(features_one_hot2[\"LandingPad\"])\ndummy_variable_3.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>ES-L1</th>\n      <th>...</th>\n      <th>SSO</th>\n      <th>VLEO</th>\n      <th>CCAFS SLC 40</th>\n      <th>KSC LC 39A</th>\n      <th>VAFB SLC 4E</th>\n      <th>5e9e3032383ecb267a34e7c7</th>\n      <th>5e9e3032383ecb554034e7c9</th>\n      <th>5e9e3032383ecb6bb234e7ca</th>\n      <th>5e9e3032383ecb761634e7cb</th>\n      <th>5e9e3033383ecbb9e534e7cc</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 28 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount Serial  ES-L1  ...  SSO  VLEO  CCAFS SLC 40  KSC LC 39A  \\\n0            0  B0003      0  ...    0     0             1           0   \n1            0  B0005      0  ...    0     0             1           0   \n2            0  B0007      0  ...    0     0             1           0   \n3            0  B1003      0  ...    0     0             0           0   \n4            0  B1004      0  ...    0     0             1           0   \n\n   VAFB SLC 4E  5e9e3032383ecb267a34e7c7  5e9e3032383ecb554034e7c9  \\\n0            0                         0                         0   \n1            0                         0                         0   \n2            0                         0                         0   \n3            1                         0                         0   \n4            0                         0                         0   \n\n   5e9e3032383ecb6bb234e7ca  5e9e3032383ecb761634e7cb  \\\n0                         0                         0   \n1                         0                         0   \n2                         0                         0   \n3                         0                         0   \n4                         0                         0   \n\n   5e9e3033383ecbb9e534e7cc  \n0                         0  \n1                         0  \n2                         0  \n3                         0  \n4                         0  \n\n[5 rows x 28 columns]"
                    },
                    "execution_count": 17,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot3 = pd.concat([features_one_hot2, dummy_variable_3], axis=1)\nfeatures_one_hot3.drop(\"LandingPad\", axis = 1, inplace=True)\nfeatures_one_hot3.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "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>B0003</th>\n      <th>B0005</th>\n      <th>B0007</th>\n      <th>B1003</th>\n      <th>B1004</th>\n      <th>B1005</th>\n      <th>B1006</th>\n      <th>B1007</th>\n      <th>B1008</th>\n      <th>B1010</th>\n      <th>...</th>\n      <th>B1048</th>\n      <th>B1049</th>\n      <th>B1050</th>\n      <th>B1051</th>\n      <th>B1054</th>\n      <th>B1056</th>\n      <th>B1058</th>\n      <th>B1059</th>\n      <th>B1060</th>\n      <th>B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 53 columns</p>\n</div>",
                        "text/plain": "   B0003  B0005  B0007  B1003  B1004  B1005  B1006  B1007  B1008  B1010  ...  \\\n0      1      0      0      0      0      0      0      0      0      0  ...   \n1      0      1      0      0      0      0      0      0      0      0  ...   \n2      0      0      1      0      0      0      0      0      0      0  ...   \n3      0      0      0      1      0      0      0      0      0      0  ...   \n4      0      0      0      0      1      0      0      0      0      0  ...   \n\n   B1048  B1049  B1050  B1051  B1054  B1056  B1058  B1059  B1060  B1062  \n0      0      0      0      0      0      0      0      0      0      0  \n1      0      0      0      0      0      0      0      0      0      0  \n2      0      0      0      0      0      0      0      0      0      0  \n3      0      0      0      0      0      0      0      0      0      0  \n4      0      0      0      0      0      0      0      0      0      0  \n\n[5 rows x 53 columns]"
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "dummy_variable_4 = pd.get_dummies(features_one_hot2[\"Serial\"])\ndummy_variable_4.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>ES-L1</th>\n      <th>GEO</th>\n      <th>...</th>\n      <th>B1048</th>\n      <th>B1049</th>\n      <th>B1050</th>\n      <th>B1051</th>\n      <th>B1054</th>\n      <th>B1056</th>\n      <th>B1058</th>\n      <th>B1059</th>\n      <th>B1060</th>\n      <th>B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  ES-L1  GEO  ...  B1048  B1049  B1050  B1051  B1054  B1056  \\\n0            0      0    0  ...      0      0      0      0      0      0   \n1            0      0    0  ...      0      0      0      0      0      0   \n2            0      0    0  ...      0      0      0      0      0      0   \n3            0      0    0  ...      0      0      0      0      0      0   \n4            0      0    0  ...      0      0      0      0      0      0   \n\n   B1058  B1059  B1060  B1062  \n0      0      0      0      0  \n1      0      0      0      0  \n2      0      0      0      0  \n3      0      0      0      0  \n4      0      0      0      0  \n\n[5 rows x 80 columns]"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot = pd.concat([features_one_hot3, dummy_variable_4], axis=1)\nfeatures_one_hot.drop(\"Serial\", axis = 1, inplace=True)\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.drop(columns= ['GridFins', 'Reused', 'Legs'])"
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>ES-L1</th>\n      <th>GEO</th>\n      <th>GTO</th>\n      <th>HEO</th>\n      <th>ISS</th>\n      <th>...</th>\n      <th>B1048</th>\n      <th>B1049</th>\n      <th>B1050</th>\n      <th>B1051</th>\n      <th>B1054</th>\n      <th>B1056</th>\n      <th>B1058</th>\n      <th>B1059</th>\n      <th>B1060</th>\n      <th>B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 77 columns</p>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass  Flights  Block  ReusedCount  ES-L1  GEO  GTO  \\\n0             1  6104.959412        1    1.0            0      0    0    0   \n1             2   525.000000        1    1.0            0      0    0    0   \n2             3   677.000000        1    1.0            0      0    0    0   \n3             4   500.000000        1    1.0            0      0    0    0   \n4             5  3170.000000        1    1.0            0      0    0    1   \n\n   HEO  ISS  ...  B1048  B1049  B1050  B1051  B1054  B1056  B1058  B1059  \\\n0    0    0  ...      0      0      0      0      0      0      0      0   \n1    0    0  ...      0      0      0      0      0      0      0      0   \n2    0    1  ...      0      0      0      0      0      0      0      0   \n3    0    0  ...      0      0      0      0      0      0      0      0   \n4    0    0  ...      0      0      0      0      0      0      0      0   \n\n   B1060  B1062  \n0      0      0  \n1      0      0  \n2      0      0  \n3      0      0  \n4      0      0  \n\n[5 rows x 77 columns]"
                    },
                    "execution_count": 21,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>ES-L1</th>\n      <th>GEO</th>\n      <th>GTO</th>\n      <th>HEO</th>\n      <th>ISS</th>\n      <th>...</th>\n      <th>B1048</th>\n      <th>B1049</th>\n      <th>B1050</th>\n      <th>B1051</th>\n      <th>B1054</th>\n      <th>B1056</th>\n      <th>B1058</th>\n      <th>B1059</th>\n      <th>B1060</th>\n      <th>B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>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      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>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      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>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>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>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      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</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>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows \u00d7 77 columns</p>\n</div>",
                        "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  ES-L1  GEO  GTO  \\\n0            1.0   6104.959412      1.0    1.0          0.0    0.0  0.0  0.0   \n1            2.0    525.000000      1.0    1.0          0.0    0.0  0.0  0.0   \n2            3.0    677.000000      1.0    1.0          0.0    0.0  0.0  0.0   \n3            4.0    500.000000      1.0    1.0          0.0    0.0  0.0  0.0   \n4            5.0   3170.000000      1.0    1.0          0.0    0.0  0.0  1.0   \n..           ...           ...      ...    ...          ...    ...  ...  ...   \n85          86.0  15400.000000      2.0    5.0          2.0    0.0  0.0  0.0   \n86          87.0  15400.000000      3.0    5.0          2.0    0.0  0.0  0.0   \n87          88.0  15400.000000      6.0    5.0          5.0    0.0  0.0  0.0   \n88          89.0  15400.000000      3.0    5.0          2.0    0.0  0.0  0.0   \n89          90.0   3681.000000      1.0    5.0          0.0    0.0  0.0  0.0   \n\n    HEO  ISS  ...  B1048  B1049  B1050  B1051  B1054  B1056  B1058  B1059  \\\n0   0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n1   0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n2   0.0  1.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n3   0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n4   0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n..  ...  ...  ...    ...    ...    ...    ...    ...    ...    ...    ...   \n85  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n86  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    1.0    0.0   \n87  0.0  0.0  ...    0.0    0.0    0.0    1.0    0.0    0.0    0.0    0.0   \n88  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n89  0.0  0.0  ...    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n\n    B1060  B1062  \n0     0.0    0.0  \n1     0.0    0.0  \n2     0.0    0.0  \n3     0.0    0.0  \n4     0.0    0.0  \n..    ...    ...  \n85    1.0    0.0  \n86    0.0    0.0  \n87    0.0    0.0  \n88    1.0    0.0  \n89    0.0    1.0  \n\n[90 rows x 77 columns]"
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features_one_hot.astype('float64')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n| ----------------- | ------- | ------------- | ----------------------- |\n| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.7",
            "language": "python",
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        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
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            "file_extension": ".py",
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            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
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