Notebooks/5. jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\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",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "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": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <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",
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       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "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",
    "\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<Figure size 1440x1008 with 0 Axes>"
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\n",
      "text/plain": [
       "<Figure size 1842.38x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,14))\n",
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(y='LaunchSite', x='FlightNumber', hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"LaunchSite\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 9,
   "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\n",
    "sns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"PayloadMass\",fontsize=20)\n",
    "plt.ylabel(\"LaunchSite\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Orbit\n",
       "ES-L1    1.000000\n",
       "GEO      1.000000\n",
       "GTO      0.518519\n",
       "HEO      1.000000\n",
       "ISS      0.619048\n",
       "LEO      0.714286\n",
       "MEO      0.666667\n",
       "PO       0.666667\n",
       "SO       0.000000\n",
       "SSO      1.000000\n",
       "VLEO     0.857143\n",
       "Name: Class, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "df.groupby(['Orbit'])['Class'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='Orbit', y='Class', data=df)\n",
    "plt.xlabel('Orbit',fontsize=20)\n",
    "plt.ylabel(\"Class\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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": [
    "**High success rate:** We can see than ES-L1, SSO, HEO and GEO has very high success rate of 100%"
   ]
  },
  {
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x1008 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "plt.figure(figsize=(20,14))\n",
    "sns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Orbit type\",fontsize=20)\n",
    "plt.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": 23,
   "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 Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(y=\"Orbit\", x='PayloadMass', hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Orbit type\",fontsize=20)\n",
    "plt.xlabel('PayloadMass',fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['year'] = Extract_year('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Class','year']].groupby('year').mean().plot()\n",
    "plt.title('Average Success Rate per year')\n",
    "plt.ylabel('success rate')\n",
    "plt.xlabel('year');"
   ]
  },
  {
   "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])"
   ]
  },
  {
   "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": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot = features_one_hot.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90, 80)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot.shape"
   ]
  },
  {
   "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": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
   ]
  },
  {
   "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n",
    "| ----------------- | ------- | ---------- | ----------------------- |\n",
    "| 2021-10-12        | 1.1     | Lakshmi Holla     | Modified markdown |\n",
    "| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n",
    "| 2020-11-10       | 1.1    | Nayef      | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
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