EDA with Data Visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " ## Assignment: Exploring and Preparing\u00a0Data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **70** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage. \n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Falcon 9 first stage will land successfully\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "  ## Objectives\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n- Exploratory Data Analysis\n- Preparing\u00a0Data  Feature Engineering \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "----\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will import the following libraries the lab \n\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "# andas 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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Exploratory Data Analysis \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "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>",
                        "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  "
                    },
                    "execution_count": 2,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndf.head(5)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\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"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
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                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, let's drill down to each site visualize its detailed launch records.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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um7uWuDW2TVJI0szF98Hbt3ExAFChuvRMjrkrefiPybj/JV17JZsdlQzertyVld+aOyYduzWuWvxRw/Zp+3oW15YnJE9fl8yZvmD71icl7Up/n3nOHS9k7kfeps2qn5vzR7yUvxyxiF1egEXb+sRki68l095t/Du5Ff5/ZSkya0oyZWJSM7D1trsGFlAoFpeF2zhYFtXV1aWmpia1tbWprq4udzm0oV/d9lwuvGfsAm3rrVKdfx23VTp3aFemqpYuD46dlBfempIhfbply8ErpdBK29qeeeuz+dO9Ly/Q1rlDVW464TMZ0reV79RZRj05YXJGvzY5K9eskG3X7p327Zb+RXSfer02e57/v2b7DthkQM4+eMM2rojW9Mi49/KVK0bl/emN2wu3ryrkO7usneM+N7jMlcGy65jLR83fJn2ezh2qcvtJ22S1lbqWqSoAoKUeHDspX/jLQ00Wnzl4swH59YHLzuef2ulzUjtjTgasuEKqWnMr5OGnJA/8bsG2jQ9L9vlD683Bwo29O3n8b8msumTwDskmhycdu5S7KoDl0ktvT83tT09Mh3aF7LHBylmlxwrlLmn58MwNybXHJg3zbnguJDucknz222Utq8Vefyy591fJaw8n1QOSLY5rfC9Vooa5xQz+0S3N9vWt7pSHf7RjyXMAVJS5DY2fbx+9tDG0vcKKyWdObgyDAq1q6U9ELMTEiRPzjW98I2ussUY6deqUgQMHZq+99spdd921wHGPP/54DjrooPTt2zedO3fOWmutlWOPPTYvvPBCkzF33nnntGvXLg899FCTvi9/+cspFApNHi+99NL8efbcc8/06dMnnTt3zqBBg3LIIYfk3XffXehrePnll3PooYdm5ZVXTufOnTNgwIDss88+C9RWKBRy/fXXL3SMYrGYP//5z/n0pz+dbt26pUePHtlss81y3nnnZfr06Qs9b55JkyZlwIABKRQKmTx58gJ9Y8aMyec+97mssMIKWWWVVXL66adHlpSW+P6uQ/O7QzfONmv1ziar9sh3dl4r/zh2C8HAD9ly8Eo5YqtB2WrNXq0WDJw6qz5XPPBqk/aZc+bmsgfGtcoclWCDAT1y+JaDsuOwvstEMDBJ1l25OoN7Nx9m2WejZWhL6mIxmfyara4+4lODeubBH+6QP3xhk/z6wA1y/w+2FwyEEoyfND13PfdWk/aZc+bm7yPHl6EiAGBxbTl4pfzmwA3nryDYsV1VDtp0QE7fZ+lf+T1Jpsyck2/+8/Fs+os7ss1Zd+dzv7k7tz01sfUm2PnnyWHXJhsdlmzw+eTzf0/2/n3rjb+ENcwt5o5n3so5w5/P1aNey4zZDeUuqeUeOD/5677JU/9KXhye3Pb95Ip9kvqmq8EDsGT94e6XsuM59+as25/PL295Ltv8+u5cPeq1cpe1fBi2T/Ktp5Pdf5PsfEZy4mPLTjAwSVbZJPnCVcn3xyVf+1+rBAOTpF1VISvXNL/i1ao93UgA0MS9v0oe+sMHq7nOeD+545TkiavKWxdUoGVyW+Fx48Zl6623To8ePfLrX/86G2ywQebMmZPbb789xx9/fJ577rkkyc0335wDDjggu+yyS6688soMHjw4b7/9dq655pqccsopueqqD/5SGT9+fB588MGccMIJufjii7PFFls0mXfXXXfNpZdeukBb79698/bbb2fHHXfMXnvtldtvvz09evTIK6+8khtvvHGhAb3Zs2dnp512ytChQ/Pvf/87/fv3z4QJE3LLLbektra2xT+LL33pS/n3v/+dn/zkJ/n973+f3r1754knnsh5552XQYMGZd99913k+UcffXQ22GCDvP766wu019XVZaeddsp2222XRx55JC+88EK+/OUvp2vXrvn2t5ehN/iUzd4brpy9W2NLTFpsYu2MzJjT/BfqY99pZol/lhmFQiHnH7pJjrrskUysa9xeuKqQfG3bwdlmrd5lrq6FXrwzue0HyaQXk6r2jV8g7XFOskKPcle2VOjcoZ3toStRsZg8eXUy5pqkYXYydM9k0y8n7TuWu7Klx8zaxu0CeqyadGidu/tfnzyjySpD87wxufkt2gGApc8Bmw7IPhutnNfen5GeXTumZoVlZ9uo7/3rydz6oTDga+/NyAl/fyzXH7911lulpnUmWXPHxscyZtqs+hx+ycg8+ur789vOGf5C/vGVLbJ6r6V8hecZk5O7f9m0fcLI5Klrk42+0OYlASyvXnp7Ss66/fkF2hrmFvOT65/KDkP7ZKVuncpU2XKkW+9k82PLXcVS55jPrpHTb36mSfvRn1mjDNUALMWKxeSRvzTf98hFyYaHtG09UOGWyXDg17/+9RQKhYwcOTJdu37wpdG6666bo446Kkkyffr0HHnkkdl9991z3XXXzT9m9dVXz6c//ekmq+Rdeuml2XPPPfO1r30tm2++ec4777wFxk6STp06pV+/fk3qeeCBB1JXV5e//OUvad++/fx5tt9++4W+hmeeeSYvv/xyRowYkdVWWy1Jstpqq2Xrrbdu8c/h6quvzpVXXpnrr78+++yzz/z2QYMGZe+9905dXd0iz7/wwgszefLk/PSnP82tt966QN+VV16ZmTNn5rLLLkunTp2y3nrr5YUXXsg555yTk08+udmVzmbNmpVZsz64S/bj5gda1yo9uqR7p/aZMqu+Sd/Qfrb2XtYNW7k6//3edrnn+bczefqcbLXmShmw4jJyt+E7LyT/PLQxHJUkc+sbL5zMmpp88ery1gZL0s3falwOf55X7k1evD354r+SVlo1domqn5U8fX3y+qikZmDjxc6uvVpn7Ib6ZPiPk0cvS+pnJp17NN5h3grbBazTv3s6ta/KrPq5Tfo2HNBKF+MBgDbRvl3V0h8Y+4i36mbm9qebrhJYP7eYKx9+NWfuv0EZqlp6/Om/Ly8QDEySiXUzc+qNT+fyozYvU1Ut9PqjH6xo8VGv3CccCNCGbn+66Y4BSTK7fm5GPPd2DtpsYBtXBI2O+szqaZhbzJ/veznvTJmVgT1XyDd3WCu7rtf0+jLAcq1hTjJ9UvN9U1px5X0gyTK4rfB7772X2267Lccff3yT8F6S9OjRI0ly++2359133833vve9ZseZd1zSuDXvpZdemsMOOyxDhw7NWmutlauvbnlYoV+/fqmvr891113X4m13e/funaqqqvzrX/9KQ8Mn2zrjyiuvzNprr71AMHCeQqGQmpqFX/x85plncvrpp+eKK65IVVXTPwYPPvhgPve5z6VTpw/urtpll13yxhtvZNy4cc2OeeaZZ6ampmb+Y+BAH76gLa3QsV2O+WzTu8+6d26fI7ce1PYFLadqZ8zJ8xOnLJFtkTq2r8rO6/bLwZ8auOwEA5PGcNS8YOCHvXh78t4rbV8PtIV3XlgwGDjPS3cmY+9q+3oW18za5OKdkuu+koz8c+NS/udvmrzxeOuMf88vk4f/2BgMTJKZkxvneLL0wHCPLh3z1Wa25l69V9cc/CnvTwGAJevtulmZu5CvB9+qs/Xs8GaCk0ny3xffyfTZTW92XKp0XcTK/d2WkVX9ASpE1SJuumzfbhm4IZOKduw2a+ThH+6QMafunP9+d7scuOmA1hu8oT6551fJ2eskv+ibXHlw8tbTrTc+5fXk1ckV+yZ/2TG599fJTAvxUMHad0xW3qT5vlW3bNtaYDmwzIUDX3rppRSLxQwdOnSRx7344otJ8rHHJcmdd96Z6dOnZ5dddkmSHHbYYbn44oubHHfzzTenW7du8x8HHXRQkmSLLbbIj370o3zhC19Ir169sttuu+Wss87KW281f+dSkqyyyir53e9+l5/+9KdZccUVs/322+fnP/95Xn755Y+t98Ovce21127x8fPMmjUrhx56aM4666ysuuqqzR4zceLE9O3bd4G2ec8nTmz+S7wf/vCHqa2tnf947bXXFrs2oDTf3HFIzthvvQzt1z09u3bMruv2yzXHbZnVVlq2VlpYFtU3zM3Pbngqm59xZ3Y577/Z/Iw78/sRL5a7rKVD3RsL75vyZtvVAW3ptYcW3vfqg603z6sPJHeemvz3N8nk8a037gO/T958YsG2mZOTW5q/8WaxzJ2bjLqk+b6FbSOwmE7eaa389vMbZavBK2VY/+p89XNr5F/HbZnqzsvOdoQAwLJpSN9uC90CedPVVmzjapY+7aqaD2y0KxQWGfRYKvTfIBnwqabtVR2Sjb/U9vUAy4fXRiZXHpScNST5y07JMzeWu6Klwp4b9E9z/6R06dguO6zTt2kHtLGqqkK6d+7Q7E5sJbnl24033U55o/Gm2xdvTy7dPamd0Lrz0Pbu+Gny72OTl+9OJjyS3H1GctkeyZyZ5a4MlpwdT03adVywrXOPZJvvlqMaqGjLXDhw3sp8H/dmqqUr+CXJxRdfnEMOOWT+lsCHHnpoHn744Tz//PMLHLfddttl9OjR8x+/+93v5vedccYZmThxYv74xz9m2LBh+eMf/5ihQ4dmzJgxC533+OOPz8SJE/O3v/0tW265Za655pqsu+66ueOOO1pUd7FY/ERvKn/4wx9mnXXWyWGHHbbI4z469sf97Dt16pTq6uoFHrAkzJzTkD/dOzb7X3B/Dv7Tg7ny4Vczd2G35S+Hvvjp1XLbSdvksVN2yh+/tKkthdvIb+96MZc/+Or8bSynzKrPb4a/kGtGCUpn4Kebb+/QNem7bqtMMXduMfc8/3b+cPdL+c+Tb2Z2M9uJQpvqtoitQrq30jYiN5yQXLpb8r9zkxE/T363SfL0da0z9gu3Nd8+YWQy/b3Sxm6Yncx4v/m+VtwuYJ+NVsnfj90it3zzs/nhbutkpW6dPv4kAIASde7QLt/eea0m7av36prDPr1aGSpauuy94crNtu80rG86d2jXxtV8Agf/NVl9mw+eVw9IDr486TWkfDUBlev1R5PL9kxeHJ5Me7vxM/nVX0pG/6PclZXdwJ5dcsZ+66djuw8uc3bp2C7nHbKRGwOpXFPfTh7/W9P2mZMXfiMuy4YpE5MH/9C0feKTyVP/avt6oK2s8bnk2BHJRoclq22dfPpryVfvTXo3/UwNlKZ9uQtYXEOGDEmhUMizzz6bfffdd6HHrbVW418Yzz33XLbccuHLjr733nu5/vrrM2fOnFx44YXz2xsaGnLJJZfkV7/61fy2rl27Zs0111zoWCuttFIOOuigHHTQQTnzzDOz8cYb5ze/+U0uv/zyhZ7TvXv37L333tl7773zi1/8Irvsskt+8YtfZKeddlroOR9+jc8+++zHHvdRI0aMyJgxY/KvfzW+mZgX+uvVq1d+/OMf57TTTku/fv2arBD49ttvJ0mTFQWhLc2dW8yRlz6SB1+eNL9t5Cvv5dFX3885B29UvsJY7v3toVcX2n7QZsv5NpYbH5Y8elny7oKh+2zznaRzTcnDT51VnyMuGZlHX/0gbLR6r675+7GfTv+aFUoen+XAjMmNYbUeqyVVrXTvzJo7JD0HJ++NXbC9c49k/YNKH/+lO5PH/7pg29w5yU3fTIbsknQscevxjgtZcbaqfdM7+RZXh87Jyhs3v0XxaluXNjYAwFLg8C0HZdBKXXPlw69m0tTZ2WrwSjly69VT00VY4ajPrJ7Hx0/ObR/aXnhov+45bZ/WuXFsiavunxxxU+Oq3TPrkj7rJFXLQKgRWsFzE+vyyjvTsna/7lmjd7dyl7N8+N+5SUMzW9Lf+6tko0Pbvp6lzKGbr5od1+mbEc+9lfZVVdlxWN+Frt4LFeG9V5K59c33vfN88+0sG15/bOG/t6+NbLzGwpL10l2NIdupbzVuabvlCUl3mYg20W/9ZN9mwrFAq1rmwoE9e/bMLrvskj/84Q858cQT07XrghcuJ0+enB49emTnnXdOr1698utf/zrXXdd0BZV5x1155ZUZMGBArr/++gX677rrrpx55pk544wz5q8ouDg6duyYwYMHZ9q0aS0+p1AoZOjQoXnggQdadPwXvvCFfP7zn88NN9yQffbZZ4G+YrGYurq61NQ0DV1ce+21mTFjxvznjzzySI466qjcd999GTx4cJJkyy23zI9+9KPMnj07HTs2XgAePnx4Vl555QwaNKjFrwla270vvLNAMHCefz/2er66zeCs3a97GapieTenYW7enz6n2b53pjTzBd7ypnN1ctRtyUMXNi6Jv0LPZJPDk3X2bJXhL7j7pQWCgUnyyrvT8vObn8kFX9y0VeagQs2elvznO8mYaxqDdTWrJjufnqy7X+ljV7VLDrs2ueH45NX7G9v6rZ/s9dtkhR6lj//8rc23z6xt3Gp4yI6ljb/h55PxzWx/PHTPpFMrXATa8bTkygMbVxGcZ4WejaFhAIAKsM1avbPNWr3LXcZSp0O7qvzxS5tmzITajHm9NgN7rpDPrNmr9bfcW9J6rFruCqDNTJ9dn+OvfCx3P/9OkqRQaFwF9DcHbZgO7Za5zamWLROfar79/VeSWVNb5/P5Mq5390455FP+TmY5sdLgxpt2P/x92jx9hrV9PbSemlUW3le9iD5ax6OXNd50P8+ERxp36Dn27qSbz3RAZVjmwoFJcsEFF2SrrbbK5ptvntNPPz0bbLBB6uvrc8cdd+TCCy/Ms88+m65du+Yvf/lLDjrooOy999458cQTs+aaa+bdd9/N1VdfnfHjx+ef//xnLr744hx44IFZb731FphjtdVWy/e///385z//aRK8+6ibb745//znP/P5z38+a621VorFYm666abccsstufTSS5s9Z/To0fnZz36WL33pSxk2bFg6duyYe++9N5dcckm+//3vL3DsK6+8ktGjRy/Qtuaaa+bggw/Oddddl0MPPTSnnHJKdtppp/Tu3TtjxozJueeem2984xvNrq44LwA4z7vvvpskWWedddKjR48kjcHD0047LV/+8pfzox/9KC+++GJ++ctf5qc//emy92UdFeWx8QvZhjDJ4+PfFw6kLDq0q8omq/bIY+MnN+n79BortX1BS6MuPZPtf9z4aGW3PtX8NqTDn34rDXOLaVfl361lWbFYzH9ffDcvvjUla/Xtns8OacULhzd9szEYOE/t+ORfRyc1A5MBm5U+fs/VkyNvSerebPzSbsVW3Eau3SK2yG1f4sp+SbLJEcmbTzR+MVL8/9t0D9g82ePs0sdOPtguYOSfG+967r9h8umvusgKALCcWH9ATdYfUPpK8kCjB8dOytnDn8+j499Pv+rO+fJWg/KVbdZolc/Pv7r1ufnBwCQpFpMbRr+RIX265YTtbamd+lnJE/9Mxt7VuFvAxl9KBn6qdcZeac3GIOBHdV954Sv+A5Wra69k0yOTkX9asL3LSslmR5WnJlpH/w0bV6v76M3aHbtZNXBJq5+djPhF0/ba1xr/X9v+J21fE61rwqjksSsad49aY9tkoy827m4Ey5llMhy4+uqr57HHHssZZ5yRb3/723nzzTfTu3fvbLrppgtsDbzPPvvkgQceyJlnnpkvfOELqaury8CBA7P99tvnF7/4RR599NE88cQTueiii5rM0b179+y88865+OKLPzYcOGzYsHTp0iXf/va389prr6VTp04ZMmRI/vKXv+RLX/pSs+cMGDAggwYNymmnnZZx48alUCjMf/6tb31rgWNPPvnkJufffffd2XbbbfP3v/89f/7zn3PJJZfkF7/4Rdq3b58hQ4bk8MMPzy677NKSH2ezampqcscdd+T444/PZpttlhVXXDEnn3xys7VAW+pXs/B/rPv3sH0o5fP9XYfm8EtGZlb93PltK3bpkG9sv/Dt6GkdC/uau0qYfZlXO31ODr/k4TwxoXZ+24YDe+SKozYvfZuYae823v33UcWG5JGLWyccOE91/9Yba571D0weamap/ZqBrbM1b6GQ7HlusvVJjdv/1gxMBrTySpz91k/2Pr91xwQAABZt/MPJuPsaAwbr7pd0FtJc1j31em2OuGRkZjc0fif1Zu3MnHnrc5k2uyEn77RWyeP/+7HXF9q+3IcD62cnf90/efV/H7Q9dkWy5zmtE9TZ+sTG0GFx7kfav9n4uR1Y/uz6f42rzD16eTJ9UmPQZfufLJnvH2lbh/wtufmk5LlbGr+j7rt+svtZi15VkNK993Iy7Z3m+157uG1rofU99tfkxm8kKTY+f/bG5MmrksNvFBBkuVMoFovFchdBZZq3rXFtbW2qq6vLXQ4VYsrMOdn2rHsyadqCy6av2adbhp+0TaqsEEYZvfjWlFz2wLiMmzQt665ckyO2GpRVhFaXuHOGP5/fjXipSfveG66c3x26cRkqorX85Pox+dtD45u0H7Hlajltn/WaOWMxvPVMcuGWzfetsW1y+A2ljd8WHrwgufNnH2wl0q1v8vl/tH6IDwAAliH3v/RuLvnfK3nt/enZYECPHPe5wVmzj603M3duct1XFlw9vXOP5Iv/ar1VziiLk68anX8/3jTA171z+zzy4x3TuUO7ksZf80e3pH5u08tIfas75eEf7VjS2Mu80X9Prv9a0/ZO1cm3n2ud1f2evzW558zkzScbV9vf6hvJ5seWPi4AS6cZ7ydzZiTVK5e7kuXD9PeS36yVzJ3TtG/DQ5P9/tj2NdE6Zk9PzhmazKxt2rfXb5NNv9zmJUE5LZMrBwLLr+6dO+Rvx3w6P7n+qTz66vspFJJthvTOL/dfXzCQshvSt3vO2G/9cpex3Pnatmvm0fHv5/6XJs1vG9qve36y5zplrIrW8J8n32y+fcybpYcDV1oz6dIrmf5u076BW5Q2dlvZ8uvJ+gclL9+ddOqeDN6hdbYUBgCAZdR/nnwzJ/zjscy7Hf6Ft6bm9qcm5t9f3ypD+nYvb3Hl9tS1CwYDk2Tm5OSGrycnPFKWkmgdY9+d1mz7lJn1eXfqrAxYsUtJ42+7dp/c+exbTdq3H9q3pHErwti7m2+fVZe8/miy+jalz7H2bo2PYtFqgZVmxuTkiX8kbz+T9Fo72egLSZeerTf+C8OTRy5Kpkxs3K506xOTmgGtNz6wZKywYuODttGlZ+N37E/8fcH2QrvkU8eUpyZax5tPNB8MTJKX7xEOZLkjHAjLqXl3Ub8+eUY2GtgjX/3c4KzeqxXuZGwD6/SvzrVf2yrvTp2V9lWF9OgiCFFxZk1N2nVI2ncqdyUsA1bo2C5XHrNFHn55Up5+oy6r9+qaz63VW2C4AixseetmFixYfO07Jjucktz0zQXbe6yWbP6VVpigjXTrnWxwcLmrAACApcLZw5/PR/fJmTKrPhfeMzbnHLJRWWpaajxzffPt777QuLJ632GlzzFrajLm6sbxeg1JNjgkWaFH6eOySOv0654nXpvcpL1n147p07307dJ+tPvQPDlhct6eMmt+22ordcm3dlzOtxROFh3kWqEVQ16JYGClmTw+uWS3pG7CB20PnJ8ceUuy0uDSxx91aeP2pPNMfDJ55obkK/fYfhZKMWtq8vS/k8mvJatsmgzZOamqKndVlGqPs5OqdsmTVycNsxqvEex0ejJgs3JXRikW9T6ty0ptVwcsJYQDYTl0w+jXc9JVo+d/WfrcxCm59amJuf74rZeZgGCS9OomOFY2099LGuYk3Vv5DuE3n0xu+0Hy6v1Ju07Jevsnu57pLila5NNrrJRPr+ENfSXZbb3++cfIptsK775+v9aZYNMvN37QH3VxMvXtZLWtky2+nnT15wgA+P+KxeSlu5K3nmpceXitXZN2vk6bb8b7jXfct++cDN7eDV6UVe2MOXl5ISuojZ4wuW2LWRpVLWJr2UX1tVTdG8mluyXvj/ug7X/nNgZdeq5R+vgs1DGfXT03PfFGps1uWKD9a58bnI7tSw8srNG7W+44+XP592MT8vI707JO/+rsu/HK6dLRv4fZ+EvJyIuS4oI/+6yyadKvxB0PqGwjfrFgMDBJpk5M7jotOfiK0saun53cfUbT9qkTk4cvbAy8AIvv7eeSK/ZOpn5oNd3Vtk6++K+kY2mr9FJmHbsk+/w+2eWXjSt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                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\nsns.catplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 640x480 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf.groupby('Orbit')['Class'].mean().plot.bar()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"FlightNumber\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 2558.88x500 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"PayloadMass\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "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\n\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  6: Visualize the launch success yearly trend\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend. \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "The function will help you get the year from the date:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "['2010',\n '2012',\n '2013',\n '2013',\n '2013',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2014',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2015',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2016',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2017',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2018',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2019',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020',\n '2020']"
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n\nExtract_year(df['Date'])\nyear"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 640x480 with 1 Axes>"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n\ndf['Year'] = year\ndf.groupby('Year')['Class'].mean().plot()\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Features Engineering \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\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  "
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": " ### TASK  7: Create dummy variables to categorical columns\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>Orbit_LEO</th>\n      <th>Orbit_MEO</th>\n      <th>Orbit_PO</th>\n      <th>Orbit_SO</th>\n      <th>Orbit_SSO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 72 columns</p>\n</div>",
                        "text/plain": "   Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n0            0          0          0          0          0          1   \n1            0          0          0          0          0          1   \n2            0          0          0          0          1          0   \n3            0          0          0          0          0          0   \n4            0          0          1          0          0          0   \n\n   Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0          0         0         0          0  ...             0             0   \n1          0         0         0          0  ...             0             0   \n2          0         0         0          0  ...             0             0   \n3          0         1         0          0  ...             0             0   \n4          0         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 72 columns]"
                    },
                    "execution_count": 22,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features[['Orbit', 'LaunchSite', 'LandingPad', 'Serial']])\nfeatures_one_hot.head()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "### TASK  8: Cast all numeric columns to `float64`\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>Orbit_LEO</th>\n      <th>Orbit_MEO</th>\n      <th>Orbit_PO</th>\n      <th>Orbit_SO</th>\n      <th>Orbit_SSO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</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      <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>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      <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>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      <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>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      <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>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      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<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>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      <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>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      <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>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      <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>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      <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 \u00d7 72 columns</p>\n</div>",
                        "text/plain": "    Orbit_ES-L1  Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  Orbit_LEO  \\\n0           0.0        0.0        0.0        0.0        0.0        1.0   \n1           0.0        0.0        0.0        0.0        0.0        1.0   \n2           0.0        0.0        0.0        0.0        1.0        0.0   \n3           0.0        0.0        0.0        0.0        0.0        0.0   \n4           0.0        0.0        1.0        0.0        0.0        0.0   \n..          ...        ...        ...        ...        ...        ...   \n85          0.0        0.0        0.0        0.0        0.0        0.0   \n86          0.0        0.0        0.0        0.0        0.0        0.0   \n87          0.0        0.0        0.0        0.0        0.0        0.0   \n88          0.0        0.0        0.0        0.0        0.0        0.0   \n89          0.0        0.0        0.0        0.0        0.0        0.0   \n\n    Orbit_MEO  Orbit_PO  Orbit_SO  Orbit_SSO  ...  Serial_B1048  Serial_B1049  \\\n0         0.0       0.0       0.0        0.0  ...           0.0           0.0   \n1         0.0       0.0       0.0        0.0  ...           0.0           0.0   \n2         0.0       0.0       0.0        0.0  ...           0.0           0.0   \n3         0.0       1.0       0.0        0.0  ...           0.0           0.0   \n4         0.0       0.0       0.0        0.0  ...           0.0           0.0   \n..        ...       ...       ...        ...  ...           ...           ...   \n85        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n86        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n87        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n88        0.0       0.0       0.0        0.0  ...           0.0           0.0   \n89        1.0       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 72 columns]"
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype('float64')"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range. \n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n| ----------------- | ------- | ---------- | ----------------------- |\n| 2021-10-12        | 1.1     | Lakshmi Holla     | Modified markdown |\n| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n| 2020-11-10       | 1.1    | Nayef      | updating the input data |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.10",
            "language": "python",
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        "language_info": {
            "codemirror_mode": {
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            "file_extension": ".py",
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