notebook_3_spacex-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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 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": [
    "import piplite\n",
    "await piplite.install(['numpy'])\n",
    "await piplite.install(['pandas'])\n",
    "await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3325.000000</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>B1005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2014-04-18</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>2296.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1006</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2014-07-14</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>1316.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>True Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2014-08-05</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>4535.000000</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>B1008</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2014-09-07</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>4428.000000</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>B1011</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  \\\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",
       "5             6  2014-01-06       Falcon 9  3325.000000   GTO  CCAFS SLC 40   \n",
       "6             7  2014-04-18       Falcon 9  2296.000000   ISS  CCAFS SLC 40   \n",
       "7             8  2014-07-14       Falcon 9  1316.000000   LEO  CCAFS SLC 40   \n",
       "8             9  2014-08-05       Falcon 9  4535.000000   GTO  CCAFS SLC 40   \n",
       "9            10  2014-09-07       Falcon 9  4428.000000   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",
       "5    None None        1     False   False  False        NaN    1.0   \n",
       "6   True Ocean        1     False   False   True        NaN    1.0   \n",
       "7   True Ocean        1     False   False   True        NaN    1.0   \n",
       "8    None None        1     False   False  False        NaN    1.0   \n",
       "9    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  \n",
       "5            0  B1005  -80.577366  28.561857      0  \n",
       "6            0  B1006  -80.577366  28.561857      1  \n",
       "7            0  B1007  -80.577366  28.561857      1  \n",
       "8            0  B1008  -80.577366  28.561857      0  \n",
       "9            0  B1011  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from js import fetch\n",
    "import io\n",
    "\n",
    "URL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
    "resp = await fetch(URL)\n",
    "dataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\n",
    "df=pd.read_csv(dataset_part_2_csv)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<pyolite.display.Image at 0x47b2438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x47a3488>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n",
    "sns.catplot(x='FlightNumber', y='LaunchSite', hue='Class', data=df)\n",
    "plt.xlabel('Flight Number')\n",
    "plt.ylabel('Launch Site')\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x52576f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n",
    "sns.catplot(x='PayloadMass', y='LaunchSite', hue='Class', data=df)\n",
    "plt.xlabel('Payload Mass')\n",
    "plt.ylabel('Launch Site')\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "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": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x4796988>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
    "sns.barplot(x='Orbit',y='Class', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x3d19638>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n",
    "sns.catplot(x='FlightNumber', y='Orbit', hue='Class', data=df)\n",
    "plt.xlabel('Flight Number')\n",
    "plt.ylabel('Orbit')\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "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": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x54865c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n",
    "sns.catplot(x='PayloadMass', y='Orbit', hue='Class', data=df)\n",
    "plt.xlabel('Payload Mass')\n",
    "plt.ylabel('Orbit')\n",
    "plt.show()"
   ]
  },
  {
   "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": null,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n",
    "\n",
    "However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 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>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</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</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</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</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</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  \\\n",
       "0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013       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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year():\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n",
    "Extract_year()\n",
    "df['Date'] = year\n",
    "df.head()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<pyolite.display.Image at 0x40c71d8>"
      ]
     },
     "metadata": {},
     "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\n",
    "sns.lineplot(x='Date',y='Class', data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 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>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": 16,
     "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 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>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>Orbit_LEO</th>\n",
       "      <th>Orbit_MEO</th>\n",
       "      <th>Orbit_PO</th>\n",
       "      <th>Orbit_SO</th>\n",
       "      <th>Orbit_SSO</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>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>...</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>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>...</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>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",
       "    <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>...</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>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 72 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n",
       "0            0          0          0          0          0          1   \n",
       "1            0          0          0          0          0          1   \n",
       "2            0          0          0          0          1          0   \n",
       "3            0          0          0          0          0          0   \n",
       "4            0          0          1          0          0          0   \n",
       "\n",
       "   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0          0         0         0          0  ...             0             0   \n",
       "1          0         0         0          0  ...             0             0   \n",
       "2          0         0         0          0  ...             0             0   \n",
       "3          0         1         0          0  ...             0             0   \n",
       "4          0         0         0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 72 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features[['Orbit','LaunchSite','LandingPad','Serial']])\n",
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: use astype function"
   ]
  },
  {
   "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": [
    "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\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",
    "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
   ]
  }
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