4. Visualization.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **70** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Falcon 9 first stage will land successfully\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing Data  Feature Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "cell_type": "code",
      "source": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 4,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4
    },
    {
      "cell_type": "markdown",
      "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 2)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.xticks(['1','10','20','30','40','50','60','70','80','90'])\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1058.88x500 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 27
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's drill down to each site visualize its detailed launch records.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect=2)\nplt.xlabel(\"Flight Number\", fontsize=20)\nplt.ylabel(\"Launch Site\", fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1058.88x500 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect =2)\nplt.xlabel(\"Payload Mass (kg)\", fontsize=18)\nplt.ylabel(\"Launch Site\", fontsize=18)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1058.88x500 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's create a `bar chart` for the sucess rate of each orbit\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT use groupby method on Orbit column and get the mean of Class column\nbar_data = df.groupby(\"Orbit\")[\"Class\"].mean()\nsns.barplot(x=bar_data.index,y=bar_data.values, hue=bar_data.index)\nplt.xlabel(\"Orbit Type\", fontsize=18)\nplt.ylabel(\"Success Rate\", fontsize=18)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 20
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.scatterplot(x=\"FlightNumber\",y=\"Orbit\", hue=\"Class\", data=df)\nplt.xlabel(\"Flight Number\", fontsize=18)\nplt.ylabel(\"Orbit Type\", fontsize=18)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect=2)\nplt.xlabel(\"Payload Mass (kg)\", fontsize=20)\nplt.ylabel(\"Orbit Type\", fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1058.88x500 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  6: Visualize the launch success yearly trend",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year()\ndf['Date'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 23,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 23
    },
    {
      "cell_type": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nsns.lineplot(x=\"Date\",y=\"Class\", data=df, errorbar=None)\nplt.xlabel(\"Year\", fontsize=12)\nplt.ylabel(\"Success Rate\", fontsize=12)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 25
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Features Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ",
            "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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 26
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, columns=[\"Orbit\",\"LaunchSite\",\"LandingPad\",\"Serial\"])\nfeatures_one_hot",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0              1   6104.959412        1     False   False  False    1.0   \n1              2    525.000000        1     False   False  False    1.0   \n2              3    677.000000        1     False   False  False    1.0   \n3              4    500.000000        1     False   False  False    1.0   \n4              5   3170.000000        1     False   False  False    1.0   \n..           ...           ...      ...       ...     ...    ...    ...   \n85            86  15400.000000        2      True    True   True    5.0   \n86            87  15400.000000        3      True    True   True    5.0   \n87            88  15400.000000        6      True    True   True    5.0   \n88            89  15400.000000        3      True    True   True    5.0   \n89            90   3681.000000        1      True   False   True    5.0   \n\n    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0             0            0          0  ...             0             0   \n1             0            0          0  ...             0             0   \n2             0            0          0  ...             0             0   \n3             0            0          0  ...             0             0   \n4             0            0          0  ...             0             0   \n..          ...          ...        ...  ...           ...           ...   \n85            2            0          0  ...             0             0   \n86            2            0          0  ...             0             0   \n87            5            0          0  ...             0             0   \n88            2            0          0  ...             0             0   \n89            0            0          0  ...             0             0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0              0             0             0             0             0   \n1              0             0             0             0             0   \n2              0             0             0             0             0   \n3              0             0             0             0             0   \n4              0             0             0             0             0   \n..           ...           ...           ...           ...           ...   \n85             0             0             0             0             0   \n86             0             0             0             0             1   \n87             0             1             0             0             0   \n88             0             0             0             0             0   \n89             0             0             0             0             0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0              0             0             0  \n1              0             0             0  \n2              0             0             0  \n3              0             0             0  \n4              0             0             0  \n..           ...           ...           ...  \n85             0             1             0  \n86             0             0             0  \n87             0             0             0  \n88             0             1             0  \n89             0             0             1  \n\n[90 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>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>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86</td>\n      <td>15400.000000</td>\n      <td>2</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87</td>\n      <td>15400.000000</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</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>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88</td>\n      <td>15400.000000</td>\n      <td>6</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>5</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>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89</td>\n      <td>15400.000000</td>\n      <td>3</td>\n      <td>True</td>\n      <td>True</td>\n      <td>True</td>\n      <td>5.0</td>\n      <td>2</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>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90</td>\n      <td>3681.000000</td>\n      <td>1</td>\n      <td>True</td>\n      <td>False</td>\n      <td>True</td>\n      <td>5.0</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>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 27
    },
    {
      "cell_type": "markdown",
      "source": "### TASK  8: Cast all numeric columns to `float64`\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: use astype function\nfeatures_one_hot=features_one_hot.astype(\"float64\")\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0           1.0  6104.959412      1.0       0.0     0.0   0.0    1.0   \n1           2.0   525.000000      1.0       0.0     0.0   0.0    1.0   \n2           3.0   677.000000      1.0       0.0     0.0   0.0    1.0   \n3           4.0   500.000000      1.0       0.0     0.0   0.0    1.0   \n4           5.0  3170.000000      1.0       0.0     0.0   0.0    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0          0.0          0.0        0.0  ...           0.0           0.0   \n1          0.0          0.0        0.0  ...           0.0           0.0   \n2          0.0          0.0        0.0  ...           0.0           0.0   \n3          0.0          0.0        0.0  ...           0.0           0.0   \n4          0.0          0.0        0.0  ...           0.0           0.0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0           0.0           0.0           0.0           0.0           0.0   \n1           0.0           0.0           0.0           0.0           0.0   \n2           0.0           0.0           0.0           0.0           0.0   \n3           0.0           0.0           0.0           0.0           0.0   \n4           0.0           0.0           0.0           0.0           0.0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0           0.0           0.0           0.0  \n1           0.0           0.0           0.0  \n2           0.0           0.0           0.0  \n3           0.0           0.0           0.0  \n4           0.0           0.0           0.0  \n\n[5 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>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>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 28
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}