IBM-DS0321EN-SkillsNetwork_labs_module_2_jupyter-labs-eda-dataviz.ipynb.jupyterlite.ipynb

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  "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
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "## Exploratory Data Analysis",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 4,
      "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": {}
        }
      ]
    },
    {
      "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": "",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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\n",
      "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 = 1.5 ,height=5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 808.875x500 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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 = 1.5 ,height=6)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 958.875x600 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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\nsr_df = df.groupby('Orbit')['Class'].mean().reset_index().sort_values(by='Class', ascending=False)\nsr_df['Class'] = sr_df['Class'] * 100\n\n\nsns.barplot(data=sr_df, x='Orbit', y='Class')\nplt.xlabel('Orbit')\nplt.ylabel('Class (Success Rate (%))')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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\n",
      "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.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 1.5 ,height=6)\n\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 958.875x600 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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\n",
      "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 = 1.5 ,height=6)\n\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 958.875x600 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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\n",
      "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
      },
      "execution_count": 13,
      "outputs": [
        {
          "execution_count": 13,
          "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": {}
        }
      ]
    },
    {
      "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(data=df, x=\"Date\", y=\"Class\")\nplt.xlabel(\"Year\")\nplt.ylabel(\"Success Rate\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\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
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "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": {}
        }
      ]
    },
    {
      "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.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "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\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\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\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\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</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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.astype('float64')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "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..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \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..          ...          ...        ...  ...           ...           ...   \n85          2.0          0.0        0.0  ...           0.0           0.0   \n86          2.0          0.0        0.0  ...           0.0           0.0   \n87          5.0          0.0        0.0  ...           0.0           0.0   \n88          2.0          0.0        0.0  ...           0.0           0.0   \n89          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..           ...           ...           ...           ...           ...   \n85           0.0           0.0           0.0           0.0           0.0   \n86           0.0           0.0           0.0           0.0           1.0   \n87           0.0           1.0           0.0           0.0           0.0   \n88           0.0           0.0           0.0           0.0           0.0   \n89           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..           ...           ...           ...  \n85           0.0           1.0           0.0  \n86           0.0           0.0           0.0  \n87           0.0           0.0           0.0  \n88           0.0           1.0           0.0  \n89           0.0           0.0           1.0  \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>1.0</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    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": "code",
      "source": "features_one_hot.to_csv('dataset_part\\_3.csv', index=False)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "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": {}
    }
  ]
}