part3-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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",
    "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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:38.942912Z",
     "start_time": "2024-09-05T03:48:38.940769Z"
    }
   },
   "source": [
    "# pandas 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"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "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"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:38.964120Z",
     "start_time": "2024-09-05T03:48:38.951775Z"
    }
   },
   "cell_type": "code",
   "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=pd.read_csv(\"dataset_part_2.csv\")\n",
    "df.head(5)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6123.547647   LEO  CCSFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCSFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCSFS 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  CCSFS 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  "
      ],
      "text/html": [
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       "</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>6123.547647</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCSFS 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>CCSFS 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>CCSFS 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>CCSFS 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>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 2
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:40.254775Z",
     "start_time": "2024-09-05T03:48:39.168019Z"
    }
   },
   "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()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2557.75x500 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 3
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:40.478019Z",
     "start_time": "2024-09-05T03:48:40.278631Z"
    }
   },
   "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(x=\"FlightNumber\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2557.75x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 4
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:40.748318Z",
     "start_time": "2024-09-05T03:48:40.528399Z"
    }
   },
   "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(x=\"PayloadMass\", y=\"LaunchSite\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Launch Site\",fontsize=20)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2557.75x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 5
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:40.848963Z",
     "start_time": "2024-09-05T03:48:40.757832Z"
    }
   },
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "viz_df = df[['Orbit', 'Class']].groupby('Orbit').mean().reset_index()\n",
    "sns.barplot(x='Orbit', y='Class', data=viz_df)\n",
    "plt.xlabel(\"Orbit\",fontsize=15)\n",
    "plt.ylabel(\"Success Rate\",fontsize=15)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 6
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.143051Z",
     "start_time": "2024-09-05T03:48:40.867110Z"
    }
   },
   "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(x=\"FlightNumber\", y=\"Orbit\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2557.75x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 7
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.413990Z",
     "start_time": "2024-09-05T03:48:41.152553Z"
    }
   },
   "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(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Pay Load Mass (kg)\",fontsize=20)\n",
    "plt.ylabel(\"Orbit\",fontsize=20)\n",
    "plt.show()\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2557.75x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "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",
    "\n",
    "\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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.424593Z",
     "start_time": "2024-09-05T03:48:41.422150Z"
    }
   },
   "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\n    "
   ],
   "outputs": [],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.511989Z",
     "start_time": "2024-09-05T03:48:41.436377Z"
    }
   },
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "df['Year'] = Extract_year(df['Date'])\n",
    "viz_df = df[['Year', 'Class']].groupby('Year').mean().reset_index()\n",
    "sns.lineplot(x='Year', y='Class', data=viz_df)\n",
    "plt.xlabel(\"Year\",fontsize=15)\n",
    "plt.ylabel(\"Success Rate\",fontsize=15)\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can observe that the success rate since 2013 kept increasing till 2017 (stable in 2014) and after 2015 it started increasing.\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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.537616Z",
     "start_time": "2024-09-05T03:48:41.531337Z"
    }
   },
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6123.547647   LEO  CCSFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCSFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCSFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCSFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>6123.547647</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCSFS 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>CCSFS 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>CCSFS 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>CCSFS 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>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.582687Z",
     "start_time": "2024-09-05T03:48:41.572477Z"
    }
   },
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n",
    "features_one_hot.head()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6123.547647        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0        False      False  ...         False         False   \n",
       "1            0        False      False  ...         False         False   \n",
       "2            0        False      False  ...         False         False   \n",
       "3            0        False      False  ...         False         False   \n",
       "4            0        False      False  ...         False         False   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0         False         False         False         False         False   \n",
       "1         False         False         False         False         False   \n",
       "2         False         False         False         False         False   \n",
       "3         False         False         False         False         False   \n",
       "4         False         False         False         False         False   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0         False         False         False  \n",
       "1         False         False         False  \n",
       "2         False         False         False  \n",
       "3         False         False         False  \n",
       "4         False         False         False  \n",
       "\n",
       "[5 rows x 80 columns]"
      ],
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1048</th>\n",
       "      <th>Serial_B1049</th>\n",
       "      <th>Serial_B1050</th>\n",
       "      <th>Serial_B1051</th>\n",
       "      <th>Serial_B1054</th>\n",
       "      <th>Serial_B1056</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6123.547647</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 80 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "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",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.612806Z",
     "start_time": "2024-09-05T03:48:41.610047Z"
    }
   },
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype('float64')"
   ],
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.713645Z",
     "start_time": "2024-09-05T03:48:41.710433Z"
    }
   },
   "cell_type": "code",
   "source": "len(features_one_hot.columns)",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "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"
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-05T03:48:41.800009Z",
     "start_time": "2024-09-05T03:48:41.790465Z"
    }
   },
   "cell_type": "code",
   "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)",
   "outputs": [],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">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/\">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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