Week 2 - EDA with Data Visualization.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<center>\n    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n</center>\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": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n\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"
        },
        {
            "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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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "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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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\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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tWn+/lnLLafDSddnzrr3h+P/C0G3a5NIr9e7GSr3bycjhNXeCs16FD9+EQetm68g1wDdHvchjBVPlDhnYkz8duSVDB7bRdMDt2e3fgImPZc8nP539Wzv5/mafdursBZx312tU5Mu2jn73I/7xyNt8/8Ai/bdl/iy44qDl4c34/8GJd2X/pkpFr5VgrV2LXYVq8ZPbXubfz2SfIfv3M5OYOHMeX99rvRr7PjXhwyrbi5ZW8MKk2ey78ao19i+apy9cfk8BjL8Lpo3NgvnWtv+vYdQx2fW79oIDf5tNvd5OfPeADdlrw1UYP30uu6w7mDUH9W6bCy+YXXV76cLsYSgqSZKk9mbu9OWBaKXRlxuKqmN657Gq28sWw6SnYZPPFaUcdU7FnD53U+D5JuyDLAi9lmyk6SH5aNBKRxZMn3tiSmkWcDvwbkRcGxHHFE65W+BC4LKIeCgifhgRtQ5NiIi+QN+U0oQ6aqzs+1lgakppbLVdQ8hGulaakrdVP/7UiBgdEaNnzGihYK/UDdseBlV7A3WrY1vu/CN2gR1ONxBtbz6csDwQBVgyD578S/HqKbauPWC1zRociM5fvLRKIAqwaEkF245YqTWq63gmP1t1e8pzUFHR7NNOnDnv00C0UvXRum3qrfurhjckeOWW5p933kyY+Vbzz1Oqpr8KD/4KnrsMFnfeaZQXL63ghtFTqrT96+lJtfTO1u8tVF4WbLxGOxz9u2Rhw9paw1q7ZqOjv3IPfOu1bLR0OzNyxEocs/2abReIQjYKvtCGB0PvwTV2lSRJkoqqS/cVl+/q3rc4tUjNtcZWK7atXueEnQ0zYzx88Frzz6NOoX3Mn9UIEdENOAg4K6U0NyKeAfYjm24Xqk2fC5BSOjkiNgP2Ab5DNpXtCdX63BMRawMHAAcCL0ZEbUPcAki17CustRfww7y+ms5R3QrnTCldTDb9LyNHjqz3miIbnXjCnfD032Hu+7D5F2DdfYpdlVrb0kU1tLXRm84loEeXclbv34Npc5Z/z9Ye3IZvULd3w7aDtx9evj10Wyhr/ueKth4+kJV6d2PWvMWfthV1lFv/oQ1ra4yHfg2P/R4qlmYfWvnSddm0zspMehquPAQq8nUeX7oeTrpnxX6fzID7fgxTn89G7u77c+jRf8V+HVh5WdCjaxmLly3/wEGfHrX/qnr8TiN49b2P+e9L0+jXowvnHLgRQwY07IMgbWrbk+DVW7N7ALI/Aodt13bX794Hhu/QdtfrCLY/FfquCm/eCytvlP2MJEmSpPao5wDY6RvLl/Mq7w67nV3UkqQmGzii6nbXXtB75aafb9lSuP44GJ9HQ+vuC0f9B7q0k9n81C4VMxR9BTiijn2717LvAKA/MC5blpRewHyWh6I1SimNy4+5BniHaqFo3mcW8B/gPxFxB9l6pSuMWM3XEJ0XEWunlN6u47LrAGsBY/NahwIvRMR2ZCNDhxX0HQq8V9drUCP0XRX2/Vmxq1BbWnVjGLHr8ilOowy2Pbm4NRWa/Cx89G42/XLvQVnblNHw1IXZG+Xbnw4jdi5aeWVlwXmHbcY3R73IxwuXslq/HvzkMxsXrZ5257N/zdYUnfQUDBmZrSnaAnp2K+eak7bjj/e9wfSPF/G5rYbwxZHD6j+wtay5E2x5TLbGA8CQbWCbE5t+vplvLl8rAmDyM/DU32GvHzavzlLy7CXLA1HIpmee8vyKU3/fchpMeCB7PvMNWDwPPn9p29XZBsrLgm/tuz7n/vfVKtu16d6lnD8dtRXnf35zupaXFX8959qsuROc8hC8fFO2TspWx7arKWw7rY0PzR6SJElSe7f3T7LZTWa8kS3n1W/1YlckNc3zV1TdXjI/m7Vsk8Oadr7X/7s8EAV46z545WbY4qim16iSV8xQ9EHgvIg4JaV0CUBEbEsWcv4HOCciDk4p3ZnvOwCYSjZ17skppWvz9t5ka3LWuOhdRPQBRqaUHs6btgTeraHfXsDTKaX5+fS46wC1z9kGvwYujIgj85C0H3BUPrIT+DSIXaXgGhPzWmZGxO1k4esFwBrAekC1+RklNcqXrs/CnNmTsjc6h44sdkWZO78Nz+XhRbe+cMId2Sf9rjx4+WjW8f+D0x+DVTYqWpl7brgKz/xgHyZ/NJ+1B/emS3kxZ1hvZwYMz35urWCTNfpz6fHbtsq5m+Rzf4ddvw2L5sIaWzbvXLNq+NxQTW2dWXnXGtqq/Xq2bOnyQDS36NW7qPjMMnp2K2/F4treCTuvxU7rDualKXPYfq2VGLZS/Wsa9+jaAb4Hq2/eMlMCSZIkSeqchmyTPaSObO60Gtreb/r55kxZsW325BXbpAJFe8c7pZSAw4B9I2JCRLwCnAu8l1JaABwCfCMi3oyIV8lGdn4M7E/BqNCU0jzgceAztVwqgO9GxPiIGAP8jBpGiQLbAKMj4iXgKeDSlNJz+b4NImJKweMLwEXAQ8BzEfEy8AjZiNWGvv5XgOuBV4G7ga+llJY19HhJNejWC7Y7Bfb7RfsJRGdPztYJrLR4Ljx+Abx+V9XpfSuWwKu3t3191fTsVs76q/Y1EO3sBq3T/EAUshFy1afK3fDg5p+3lOxwBnTrs3x7vf1g9S2q9invssJa2S8vWYNv3zCm9esrgvVX7csR2wxtUCAqSZIkSZI6iD41jHIevN6KbQ21wUHZlNKVyrrCRrXFRFKmqGuKppTeA75Yy77XyabKrW6lGvoeXrB5ZbV9c8nWIK2vlt8Bv6uhfSJQwzAOAH6bPxokpTSi2vavgF819Hi1vZQSr7z3Mf16dGX4IN+cVRMsnscKywUv/Bj6D1mxb01tLWnRJ9naclJb6d4XjrsdHvkNzJsJW34JNj28/uM6k9W3gK89C+PvyqZWXf/Amvt99q/MuOJLrMwsJlWszI+XnMjrL7/P/MVL6dWtwy0RL3U+r9+5fE1g17qXJEmS1Bl17bFi25KFK7Y11KB14Pjbs+XJUkX2wfNVNmz6+dQp+C6aVIs585dw7GXPMG7qHACO2X44vzpssyJXpQ5nlQ1h+E4w6cnlbSNPhA0Ozj7NNP6urG2t3WHT2pZZbqb3XoSbT83WIVxjKzjiclhp7UafJqXE9Y+NI42+gmFdP2bDfb/CoA1rXgd1ybIKZn6yiNX792xu9eroVt8cjvp3sato3/oPyUa512XNHTll0JXMmPoO7zGIRBkDe3Wlm6O6pfarogIe/AU8849srRyAx/4A+/0SdvpGcWuTJEmSpLbWr6ZBIkObd87hO2QPqYEMRaVaXP3UxE8DUYB/PzOJL44cxhbDBjTo+Lc+mMtfH3yLDz9ZzOFbD+HwrZv5H3h1XMdcD6Mvh4/ezdY6XXv3rP3oa+GD16BiKazWzMB9/iy46zvwzmNZ8HnQ72Dgmtm+W07PAlHIAtI7zoLjbmv0Ja54/G12vP8YNirLllteNupm0vG3EWvtVqXfQ+M/4OwbxjLzk8VssGpfLj5uG9Yc1LtZL09qLz6at5jv3fQSj7wxgw1X68t5h2/GJmv0b5Nrf/egTTjlqgWkxcvoUhb88OCNnepaas/G/CubMr+6p/5uKCpJkiSp86lYsmLb0maMFJWawFBUqsXU2QtqbGtIKLpg8TKOuvgZZn6yCIDH35pJz67lHLhZDfOmq/R17ws7f7Pmfats1DLXuPPb8MrN2fM374GbZsHJ92dTUMx4vWrf98Y06RJvv/AAX8kDUYByKvj4iUvpVxCKLllWwdk3vMTMTxYDMH76XH5552tcclw7WeNVaqZf3PEq9746HYCxU+bw9f+8yIPf3p2IqPO459/9iN/873Xe/3ghh265Bmftsz5lZXUfU91O6wzmyXP2Zszk2Wy4Wl9W7VfDtDOS2o+JT9TcXu6fYJIkSZI6oaWLVmxbMLvNy1Dn5vACqRaHbL5Gle0Bvbqyy3qDG3TssxNnfRqIVrpj3LQWq01awTuPVN2e8ly2nmnXHjB0u6r7KkeqNlK//gNWaOveu+oIuY/mLV7h3/4b0+c26XpSe/Tcu7OqbL8zcx4zPqnhl/oC8xcv5cQrnuXZibOYNGs+f33wLa56amKTrt+/Z1d2X39lA1GpIxiydc3tu36nbeuQJEmSpPZgy2Mgypdv9x8Oa+9RtHLUORmKSrXYZb3B/PPL27DXhqtw2FZDuO7UHenXo2uDjh06cMV1FIcN7NXSJUrLrbZ51e1B60G3fMraz18K6+4LvQbDJofBwX9s0iW+eMjBPFS246fbC7sOoPuu/1elzyr9erDJGv2qtO25wSpNup7UHm09fGCV7eEr9WJw7+51HjN28hw+Xri0Stujb8xo8doktTMjvwJbHwdlXaFHf9jkcDj1Edjm+GJXJkmSJEltb/j2cOL/YJsTYJez4KR7swEdUhuKlFKxa1ADjRw5Mo0ePbrYZaiBfnv36/zjkQlUJNh49X5cc9J2DOpT9xvnUpN9OAFuPBGmjYWV1oHDL4Gh27T4ZZYuXca7z9/NyjGbfpsdBD0HrtBn8qz5nHfXa4x/fy67b7Ay3ztgQ3p0La/hbFLHM/OTRXznhrE8+sYM1l+1L789YnM2HzqgzmNmzF3ETuc/wJJly3/n+uoe6/DdAzZs5WoltQvLlkJZOdQzzbYkSZIkSW2oU/6RaijagRiKdjzvz1nIrHmL2Wj1vvWuNye1iIUfZ2uY+u9NalduGD2ZX975GnMWLGHPDVbmL0dvRd8Gzj4gSZIkSZIktbBO+QayoWgHYigqSVLHtXhpBQuWLKN/T8NQSZIkSZIkFVWnDEW7FLsASZKkzqBblzK6dXE5d0mSJEmSJKkYfGdOkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklrUtjD4iIrsDewEZAn5TSL/L2HkA/YGZKqaJFq5QkSZIkSZIkSZKkJmrUSNGIOACYCNwJ/AE4t2D3lsA04MiWKU2SJEmSJEmSJEmSmq/BoWhEjARuBRJwFvCfwv0ppaeBd4DDWrA+SZIkSZIkSZIkSWqWxowU/TEwHxiZUvoL8GYNfZ4DtmiJwiRJkiRJkiRJkiSpJTQmFN0ZuDWl9H4dfSYDqzevJEmSJEmSJEmSJElqOY0JRfsAM+vp06uR55QkSZIkSZIkSZKkVtWYAHMqsEk9fbYE3m5yNZIkSZIkSZIkSZLUwhoTiv4P2D8idqlpZ0QcCOwE3NEShUmSJEmSJEmSJElSS2hMKPprYDZwb0T8BtgYICIOzrdvAKYBF7R0kZIkSZIkSZIkSZLUVF0a2jGlNDUi9gOuB84u2HU7EMAE4PCUUn3rjkqSJEmSJEmSJElSm2lwKAqQUnohIjYADgZ2BAYBc4CngdtSSktbvkRJkiRJkiRJkiRJarpGhaIAKaVlZKNDb2/5ciRJkiRJkiRJkiSpZTV4TdGIeDAijqunz7ER8WDzy5IkSZIkSZIkSZKkltHgUBTYAxhRT581gd2bWowkSZIkSZIkSZIktbTGhKIN0RNwXVFJkiRJkiRJkiRJ7UZj1xRNNTVGRADDgYOAyc0tSpIkSZIkSZIkSZJaSp0jRSOiIiKWRcSyvOncyu3CB9no0LeBLYFRrVuyJEmSJEmSJEmSJDVcfSNFH2X56NDdgEnAxBr6LQM+BB4ALm2p4iRJkiRJkiRJkiSpueoMRVNKe1Q+j4gK4IqU0s9buyhJkiRJkiRJkiRJaimNWVN0LWB2K9UhSZIkSZIkSZIkSa2iwaFoSund1ixEkiRJkiRJkiRJklpDraFoRPyEbD3RC1NKs/LthkgppV+0SHWSJEmSJEmSJEmS1EyRUqp5R7aGaAI2Sim9kW83REoplbdUgVpu5MiRafTo0cUuQ5IkSZIkSZIkSR1XFLuAYqhr+tw986+Tqm1LkiRJkiRJkiRJUodRayiaUnqkrm1JkiRJkiRJkiRJ6gjKil2AJEmSJEmSJEmSJLWmuqbPrVNEdAXOAPYim3v4EeDClNKiFqpNkiRJkiRJkiRJkpqtzpGiEXFcREyKiL2rtZcBdwB/BD4LfAb4HfBgRDQ5aJUkSZIkSZIkSZKkllbf9Ln7An2Bh6u1H53vmw6cDBwJPAPsAJzUsiVKkiRJkiRJkiRJUtPVF4puDTyZUlpWrf1YIAHHpZQuTyndAOwHzAG+2PJlSpIkSZIkSZIkSVLT1BeKrgq8XUP7TsD0lNL9lQ0ppU+AO4FNW648SZIkSZIkSZIkSWqe+kLRfsC8woaIWJdsSt0naug/BRjQIpVJkiRJkiRJkiRJUguoLxT9CFirWtu2+dcXa+jfBfikuUVJkiRJkiRJkiRJUkupLxR9ETg4IlYvaDuKbD3RR2rovx4wrYVqkyRJkiRJkiRJkqRmqy8UvQzoBTwVERdExB3AZ4AJKaUq0+dGRBdgV2Bsq1QqSZIkSZIkSZIkSU3Qpa6dKaUbImJf4GTgzLx5DnBKDd0/AwwE7mvJAiVJkiRJkiRJkiSpOeoMRQFSSqdGxJXAzsCHwN0ppfdq6DofOAu4vUUrlCRJkiRJkiRJkqRmqDcUBUgpPQk8WU+fe4B7WqIoSZIkSZIkSZIkSWop9a0pKkmSJEmSJEmSJEkdmqGoJEmSJEmSJEmSpJJmKCpJkiRJkiRJkiSppBmKSpIkSZIkSZIkSSpphqKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSS1qXYBUiSJH1q6SIYdwN89C5sdAisvkWxK5IkSZIkSZJUAgxFJUlS+zHqGHjrvuz5Y3+AY66Hdfcpbk2SJEmSJEmSOrxGTZ8bEbtHxB0R8UFELImIZTU8lrZWsZIkqYTNfHN5IAqQlsEz/yxePZIkSZIkSZJKRoNHikbEwcCtQDkwCRgPGIBKkqSWUVbDryVlXdu+DkmSJEmSJEklpzHT554LLAEOTind2zrlSJKkTmultWDTI+DlG7Pt8u6w0zeKW5MkSZIkSZKkktCYUHRTYJSBqCRJajWHXwKbfQE+mggbHAADRxS7IkmSJEmSJEkloDGh6CfArNYqRGovxkyezY9uHceED+ax90arcN7hm9Gvh9M3SlKbKCvLwlBJkiRJkiRJakGNCUUfAHZsrUKk9mDpsgpOv+Z53v94IQB3vDSNAb268svPbcaUj+bz8PgZrDW4NzutM4iIKHK1kiRJkqR2ad5MGHcDRDlsdgT0WqnYFUmSJEmdXmNC0e8Bz0bEj4BfpZRSK9UkFc2UjxZ8GohWGj3xI56a8CHHX/Esi5dWAHDsDsP55ec2K0aJkiRJkqT27JMZcOG2sOCjbPvR38HXn4OeA4paliRJktTZldW2IyIuL3wAPwVeAX4GTIiIm6v3yR+XNbeoiHg4Ivav1nZmRPw9f75yRCyJiNOq9ZkYEeMiYkz+2CkiRkTEgnx7bEQ8GREb1HDNsoj4S0S8nJ/juYhYq+C8g2s45sCIGB0Rr0XE6xHx+zpe07YRsSwijihoW1ZQ65iI+H7jv1tqSUMG9mSVvt2rtG01fAAXPTLh00AU4D/PTOKDuQurHy5JkiRJ6uye+PPyQBRg3gfwzD+KV48kSZIkoO6RoifUsW9E/qhJAk5qWjmfuhY4CrinoO0o4Oz8+ReAp4GjgX9WO3bPlNLMyo2IGAFMSCltmW+fBvwAOL7acUcCawCbp5QqImIoMK+2AiNiU+BvwMEppdcjogtwai19y4HfVHs9AAsq61L70LW8jAuP2Zof3DyOCTM+Ye+NVuV7B2zIadc8X6VfRaJKSCpJkiRJEgAfvNqwNkmSJEltqq5QdK02q2JFNwK/jIjuKaVFebC5BvB4vv9o4NvAfyJiSEppaiPO3Q/4qIb21YFpKaUKgJTSlHrO812yaYRfz/svBf5eS99vADcB2zaiThXJtiNW4r5v7V6l7fidRvDsxFlUThq9z0arMnRgryJUJ0mSJElq1zY8BCY8UK3tM8WpRZIkSdKnag1FU0rvtmUh1a79YUQ8CxwA3EY2SvS6lFKKiGHAaimlZyPierIRnhcUHP5QRCwDFqWUts/b1omIMUBfoBewPSu6Hng8InYFHgD+lVJ6sY4yNwX+UN9riYghwGHAXqwYivbM66r065TSddWOP5V8BOrw4cPru5xayUGbrc4Np+3Ifa9OZ8Tg3hy+9ZBilyRJkiRJao96DGhYmyRJkqQ2Veuaou1A5RS65F+vLXh+ff58FNmo0UJ7ppS2LAhEIZ8+N6W0DnAmcHH1i+UjQzcAzgEqgAciYu8WeB1/Ar6XUlpWw74FeV2Vj+uqd0gpXZxSGplSGrnyyiu3QDlqqpEjVuKcgzbi6O2G071LebHLKT0LZsMb98BHRfs8hiRJkiQ139j/rNg25l9tX4ckSZJURBGxWkSMiogJEfFqRNwVEetHxMvFqqmu6XOriIgvAGcAx6aU3qth/xDgauDClNLNLVDbrcAFEbE10DOl9ELefjSwakQck2+vERHrpZTebOB5bweuqGlHSmkR8D/gfxExHfgc2ajRmrwCbAOMred6I4FREQEwGDgoIpamlG5tYL1S6Xv3Kfj3F2DxXIgyOOB82P60Ylel9ujj9+CxC2D2JNjkc7Dll4pdkSRJklRVv9VXbOs/tO3rkCRJkookslDsFuCqlNJReduWwKrFrKsxI0VPBgbUFIgC5Ot69sv7NVtK6RPgYeBy8lGiEbEB0DulNCSlNCKlNAL4NctHlDbELsCE6o0RsXVErJE/LwM2B+oasvY74AcRsX7lMRHxrRpex1oFtd4IfNVAVKrmwV9kgShAqoAHfg5LFhS3JrU/FRVw9efguUvgzXvg1jPghauLXZUkSZJU1W5nZx/2rBTlsPM3i1ePJEmS1Pb2BJaklP5R2ZBSGgNMrtyOiBER8VhEvJA/dsrbV4+IRyNiTES8HBG7RkR5RFyZb4+LiLOaUlRjQtHNgNH19BlNFia2lGuBLcimyYVslOgt1frcxIpT6Fa3Tv7NGwucR83B7SrAf/Nhuy8BS4G/Fex/KSKm5I8LUkovkU3Fe21EvAa8DNTwcdA69czrqnyc38jjpdIwb0bV7cWfwOL5xalF7df0cTBzfNW2cTcWpxZJkiSpNq/dAamClCAlIC2DN+8rdlWSJElSW9oUeL6ePh8A+6aUtgaOBP6St38JuCeltCVZRjgG2BIYklLaNKW0GbXMCFufBk+fC6yUF1iXD8mmiG0RKaVbgCjYPreGPi8BG+fPR9SwfyLQswHXuhu4u5Z9K5w3b78DuKO+cxf0P6HatgtTSgBbHA0P/Gz59nr7Q+9BxatH7VOfVbNP2Rcu0dxvSPHqkSRJkmryxv+4YMkRXLrsIMqp4PQu/+Vrr98BWx1T/7GSJElS59EV+Fs+re4yYP28/Tng8ojoCtyaUhoTEW8Da0fEX4E7gXubcsHGjBSdCaxXT5/1gNlNKUQqujlT4el/ZCPPli4udjWdyy5nwaEXwsafgz1/CEdcXuyK1B71XQ32OGf5VGT9h8HuZxe3JkmSJKmah7rvw1+WHc58ejCXXvxu6ZE81WvPYpclSZIktaVXgG3q6XMWMJ1sNOhIoBtASulRYDdgKnBNRByXUvoo7/cw8DXg0qYU1ZiRok8An42IDVNKr1ffGREbAYcC/21KIVJRTX8VLttv+bqWa+4CJ9wBEXUfp5YRAVsdmz2kuux+NmxxFMyZAkNHQnnXYlckSZIkVTFmwD7AxCptY/vtzo5FqUaSJEkqigeB8yLilJTSJQARsS3Qq6BPf2BKSqkiIo4HyvN+awJTU0qXRERvYOuIuAtYnFK6KSImAFc2pajGjBT9PVmI+nhE/F9ErB8RvfOv3wQeywv+fVMKkYrq2YuXB6IA7z4Ok58pXj2SajdgGKy5o4GoJEmS2qXt1111xbZ1VilCJZIkSVJxpJQScBiwb0RMiIhXgHOB9wq6/R04PiKeJps6d17evgcwJiJeBD4P/BkYAjwcEWPIAtFzmlJXZHU1sHPEKcCF5GltNcuAr6aUmjRkVfUbOXJkGj16dLHLKE23fwNeuLpq2wl3wohdilOPJEmSJKnDuvzxd7jksbcpi+CMPdbh2B3WLHZJkiRJUqFOOU1mo0JR+HSa3K8C2wMDyNYQfRq4KKX0WgvXpwKGoq1o2li4bH9YuiDbXmNrOPkBKGvMYGpJkiRJkiRJkqR2z1BU7ZuhaCv7cAK8fDP0HgybfxG69S52RZIkSZIkSZIkSS2tU4aiXYpdgNRuDFoHdj+72FVIkiRJkiRJkiSphTU6FI2IcmADYCA1ry1KSunRZtYlSZIkSZIkSZIkSS2iUaFoRPwYOAvoX0/XGsNSSZIkSZIkSZIkSWprDQ5FI+K7wM+AOcA1wGRgaSvVJUmSJEmSJEmSJEktojEjRU8BpgJbp5RmtFI9kiRJkiRJkiRJkkrAiO/fWQYcDZwJDCMbdPkn4NqJ5x9c0dTzRsQBwJ/JZq+9NKV0fn3HlDXi/MOAWw1EJUmSJEmSJEmSJNUlD0RvAv4JjARWzb/+E7gx399oEVEOXAgcCGwMHB0RG9d3XGMuNp1GrkEqSZIkSZIkSZIkqVM6GtgX6F2tvTewH3BUE8+7HfBWSuntlNJiYBRwaH0HNSYUvR7YNyK6N7FASZIkSZIkSZIkSZ3DmawYiFbqDZzVxPMOIZuGt9KUvK1OjQlFfwJMA26MiLUaV5skSZIkSZIkSZKkTmRYM/fXJmpoS/Ud1JjpcF8BugJrAAdFxBxgdk0XTSmt04jzSpIkSZIkSZIkSSotk8nWEa1rf1NMoWqgOhR4r76DGjNStAxYCkzKH3PIktjqjyYtiipJkiRJkiRJkiSpZPwJmFfLvnnAH5t43ueA9SJirYjoRrY26e31HdTgkaIppRFNLEySJEmSJEmSJElS53ItcASwL1XXFp0H3AuMaspJU0pLI+LrwD1AOXB5SumV+o6LlOqdYlftxMiRI9Po0aOLXYYkSZIkSZIkSZI6rprW5GwVI75/ZxnZSM6zyKa8nUw2QnTUxPMPrmirOsBQtEMxFJUkSZIkSZIkSVIztVko2p40ePrciDiuoX1TSlc3rRxJkiRJkiRJkiRJalkNDkWBK4H6hpVG3sdQVJIkSZIkSZIkSVK70JhQ9MRa2gcA25LNB3wTcGcza5IkSZIkSZIkSZKkFtPgUDSldFVd+yPiCrJA9C/NLUqSJEmSJEmSJEmSWkpZS50opfQAcDfw85Y6pyRJkiRJkiRJkiQ1V2Omz22IN4DTW/ickiRJkiRJkiRJkjqac/uXAUcDZwLDgMnAn4BrOXdORVNPGxGXA4cAH6SUNm3IMS02UjS3MZBa+JySJEmSJEmSJEmSOpIsEL0J+CcwElg1//pP4MZ8f1NdCRzQmAOaHYpGRFlErBkRvwQOBB5r7jklSZIkSZIkSZIkdWhHA/sCvau19wb2A45q6olTSo8CsxpzTIOnz42ICuoeBRrAh8DZjSlAkiRJkiRJkiRJUsk5kxUD0Uq9gbOA/7RVMY1ZU/RRag5FK4CPgGeBK1JKM1qiMEmSJEmSJEmSJEkd1rBm7m9RDQ5FU0p7tGIdkiRJkiRJkiRJkkrHZLJ1ROva32aavaaoJEmSJEmSJEmSJFXzJ2BeLfvmAX9su1IMRSVJkiRJkiRJkiS1vGuB+1gxGJ0H3AuMauqJI+Ja4Clgg4iYEhEn1XtMSjUtE1rnRbYF9geGAN1r6JJSSvVeWI03cuTINHr06GKXIUmSJEmSJEmSpI4r2uxK5/YvA44CziJbQ3Qy2QjRUZw7p6LN6qARoWhEBHAlcCzZNytR9ZtWuZ1SSuUtW6bAUFSSJEmSJEmSJEnN1nahaDvSmOlzvw58GbgGGEn2DfsTsBPwA2Au2TDXtVu2REmSJEmSJEmSJElqui6N6Hs8MD6ldAJANnCU2Smlp4GnI+Ie4GmyuYGvaOE6JUmSJEmSJEmSJKlJGjNSdAPgwWptn4aqKaUXgTuAr7ZAXZIkSZIkSZIkSZLUIhoTigYwp2B7HrBStT5vAhs2tyhJkiRJkiRJkiRJaimNCUWnAkMKtt8GtqnWZz2ysFSSJEmSJEmSJEmS2oXGhKLPUjUE/R+wXUT8OCI2iYivAYeSrSsqSZIkSZIkSZIkSe1CY0LRm4DyiFgr3/4t8C7wM+Al4K/AbOD7LVmgJEmSJEmSJEmSJDVHl4Z2TCndCtxasD0rIrYCTgHWASYCV6eUprVsiZIkSZIkSZIkSZLUdA0ORWuSUpoD/L5yOyJ6RES/lNLHza5MkiRJkiRJkiRJklpAY6bPbYiLgFktfE5JkiRJkiRJkiRJarKWDkUBohXOKUmSJEmSJEmSJElN0hqhqCRJkiRJkiRJkiS1G4aikiRJkiRJkiRJkkqaoagkSZIkSZIkSZKkkmYoKkmSJEmSJEmSJKmkGYpKkiRJkiRJkiRJKmld6toZEcvaqhBJkiRJkiRJkiRJag11hqJANOGcqSmFSJIkSZIkSZIkSVJrqDMUTSk5va4kSZIkSZIkSZKkDs3QU5IkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFJUkSZIkSZIkSZJU0gxFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSa1v8TxYtqTYVaiT6lLsAiRJUjs0Zwq8cDVULIOtjoWV1ip2RZIkSZIkSeqoli6C274OL98E3frA3j+G7U4pdlXqZAxFJUlSVfNmwsV7wLwZ2fZzl8AZT0L/oUUtS5IkSZIkSR3Uc5fBuOuz54vmwF1nwzp7waB1iluXOhWnz5UkSZl3n4JbTofrv7w8EAVYOAdeur54dUmSJEmSJKljmza2WkOC98cVpRR1Xo4UlSRJMPUFuOoQqFha8/5uvbOv82dlffqs0na1SZIkSZIkqWNbew94adTy7fLusOZORStHnZOhqCSpfXjnMXj6IoiAHb4KI3YudkWdy7gbaw9EB60Lmx8J9/wQnvlHts7oJofB4RdDede2rVOSJEmSJEkdzxZHwZwp8OLV0HMg7PUTP3SvNmcoKkkqvhnj4ZrDoGJJtv3mvdkaloPXa/1rT3gQ3nsRRuwGw7Zt/eu1V70Hr9i223dhlY1ggwNh6vPw1N+W73vl5mzdh62/3HY1qnOZ8jz895vwwauw3n5w6IXQe1Cxq5IkKbNsKYy9Ft5/KRv1sOHBxa5IkiSpfYuA3c/OHlKRuKaoJKn4Xr9jeSAKsGwxvH5n61/3wV9mYewDP4fL9oHnr2r9a7ZX25wAK2+0fHuDg2CPc2DTw6Frzyy4rm5mDW1SS6iogBtOgOnjIC2DN/4H9/6ozS6/ZFkFL02ZzZz5S+rvLEnqnO44E27/Ojx7MYz6Ejx1YbErkiRJklSPDhWKRsQnBc8Piog3I2J4RGwQEQ9HxJiIeC0iLi7ot11EPBoR4yPi9Yi4NCJ6VTvvHhFxRw3X6xoR5+fXeTkino2IA2vod1lEjI2IlyLixojok7cPjIhb8vZnI2LTascdFhEpIjZsie+PJHVYA9asoW14615z2ZIV37x64k+te832rNdKcMYTcPx/4ZQH4ehroazg14R19oKyalPlrrd/29aozmPuNJgzqWrb5Gfa5NKvvvcxu/zmQT77tyfY/tf3c8uLU9rkupKkDmTxvGyUaKHnLi1OLZIkSZIarEOFopUiYm/gr8ABKaVJwF+AP6aUtkwpbZTvIyJWBW4AvpdS2gDYCLgb6NvAS/0CWB3YNKW0KfCZWo49K6W0RUppc2AS8PW8/QfAmLz9OODP1Y47GngcOKqB9UhSadr4UFi/4DMnGxwMG3229a+bUrXtita/ZntWVg5r7QZDtllx30prwZdGwZo7wxpbw2EXw1q7tn2N6hz6rr7ihyWG79gmlz7/7teZ/vEiABYuqeDc219l0dJlbXLt1vDy1Dlc/vg7PP/urGKXUlTvz1nIdc9N4um3Pyx2KZJKQVkX6NKjalvX3sWpRZIkSVKDdbg1RSNiV+AS4KCU0oS8eXXg04/xp5TG5U+/BlyVUnoqb0/AjQ28Ti/gFGCtlNKi/PjpwPXV+6aUPs6PCaAnUPku+8bAr/M+r0fEiIhYNaU0PR9NujOwJ3A7cG6DvgGSVIrKu2aB24w3svUF2mIt0fKusMMZ8PgFy9t2/mbrX7cjW3ef7CG1trIy+OJV+Zqir8N6+8J+v2iTS0+ZNb/K9pwFS/h4wVJW7lveJtdvSdc/N5nv3vTSp9vfO2BDzthjnSJWVByjJ87i2MueYeGS7IMvx+4wnF9+brMiVyWpQ+vSHXb7Dtx/brZd1gX2+H5RS5IkSeowli7Kfn8q63h/Z6vj62ihaHfgNmCPlNLrBe1/BB6MiCeBe4ErUkqzgU2Bpi4Qty4wqTLwrE9EXAEcBLwKfDtvHgscDjweEdsBawJDgenA54C7U0pvRMSsiNg6pfRCDec9FTgVYPjwVp5KUpKKbeX1W+5c82fBo7+DD16D9faD7U+vOh0swD4/zUafTRuTjZAcvkPLXV9S86yxFZz2aJtf9qDNVudvD7316fZ2I1Zi5b7d27yO5nht2sdUpMRfH3qzSvvfH3qL03Zbm7KyKFJlxfH3hyd8GogC/OeZSXxjr/VYtV+POo6SpHrschasvSe8Pw5G7JLNqiFJkqTaLV2crcv+0nXQvS/s/VMYeWKxq1In09FC0SXAk8BJwKfDeVJKV0TEPcABwKHAaRGxRVsWllI6MSLKyabuPRK4Ajgf+HNEjAHGAS8CS/NDjgb+lD8flW+vEIqmlC4GLgYYOXJkqr5fklSLUcfApCez528/lK39tPvZK/Zbf7/sIUnAmfusR6/u5Tw8fgYbrtaXb+7dBiPXW8iSZRWcevVoHho/A4Bu5VU/CLK0ItEZf5lcsLjq9McVCRYu6bhTIktqR9bYMntIkiSpfqMvhzH/zp4v+Aju/BasvYcfLlOb6mhrilYAXwS2jYgfFO5IKb2XUro8pXQoWfC4KfAKUMPCaA3yFjA8Ihq6/igppWXAdcDn8+2PU0onppS2JFtTdGXgnYgYBOwFXBoRE4GzgSPz6XclSc318bTlgWill28qTi2SOpQu5WV8dY91uf60Hfn5oZsyqE/HGSV6zyvvfxqIAixeVnWd5ON3HEZ5JxslCvDlHdek8Lfs3ddfmTUHufafJElqRyoqYNnS+vtJUkf23otVt1MFTBtbnFrUaXW0UJSU0nzgEOCYiDgJICIOiIiu+fPVgEHAVOBvwPERsX3l8RFxbN6nIde5DPhLRHTLj109Io4t7BeZdSufA58BXs+3B1QeC5wMPJpPx3sEcHVKac2U0oiU0jDgHWCXpn1XJElV9OgP3ap9pqX/0OLUIkltZNrshSu0fb7sEc4ov41/dL2A7y+5qAhVFd9Bm63OqFN24Cs7r8XPD92Ef365qZ+ZlCRJagXPXgK/WxvOWwPu/A5UOKOFpBK11q5Vt8u7uZSV2lxHmz4XgJTSrIg4AHg0ImYCu5NNU1v5TtDZKaX3ASLiKOD3EbEK2UjTR4Gbazjt3hExpWD7C8CPgF8Cr+bnngf8pNpxAVwVEf3y52OBM/J9GwFXR8QysrVGT8rbjyabWrfQTcCXgMca+G2QpOJYugjeuAdIsN7+0LUdrsnWrRfs/yu46zuwbDH0XgX2rv6fb0kqLftvshq/v3c8i5ZmI0S7sZQzuvyXdcveyzqMHQuH/AnKO+SfAM2y/dqD2H7tQcUuQ5IkqaoPXs/+bq303CWw+haw9ZeLV5MktZYtj4HZk+CFq6HnStl7dX3rHb8mtagO9Y5ISqlPwfPJQOVk07cB36rlmKeAXWvaV9DnYaBnLbu/mz9qO7YC2LmOa6+wEFVKaY8a2v5SV42S1C4s+gQu2w8+eCXbXnlDOOk+6NGvuHXVZJvjYYODYNbb2VpPXTrOFJhSyfvo3ewPoWHbeW+2oOGDejHq1B24/ImJVKTEidN+xbpz31veoecAKCsvWn2SJEmqpvpUkgBTnzcUlVSaKpbB/FmwYHa2vXBOUctR59ShQlFJUpG9cvPyQBRgxusw7gbY9qTajymmPitnD0ntxyO/g4d+BSTouzoc/18YvMJnyNREWw0fyF+HD8w2Xj8Jbng0GzEf5bDPz8Al7CVJktqP4dtnv6elgilzR7i6lqQS9fzl2Yh4gLkL4JbTYM0dYcDwJp+yoiLx1NsfUpESO60zmPIy/+ZV3QxFJUkNt3h+DW3z2r4OSR3TJx/AI+cDKdueOw0e+Q18/tKillWyNjwIznwZpjybTcPWjD80JUmS1ApWWhs+fwk8+CtY/AmM/ApsdkSxq5Kk1jH2umoNCd68r8mDLRYuWcbRlzzNi5NmA7DpkH5cf9qO9Opm7KXa+a9DktRwmxyWBRgLZmXbPQb4B5tUyma9A8/8I5s6e+vjsk+yN8e8GVCxtGrb3Pebd07Vre+qsNFnil2FJEmSarPp57OHJHVKTR/Zede4aZ8GogAvT/2Y28a8x9Hb+YFg1c5QVJLUcH1XhVMfzhZETxVZSNJvjWJXJak1LJwDl+2bBZkAL43K1hAesnXTz7nKxrDqZjB93PK2zb7QvDolSZIkSVL7t8XRMHX08u0og/X2bfLpZs9fskLbR/MXN/l86hwMRSVJjTNwTdj7x8WuQlJre/O+5YEoZCM8X7queaFoBHz5Znj8T/DRRNj4UNjiyOZWKkmSJEmS2ruRJ8KHb8IL10DPgbDvz2DAsCaf7qDNVueP973B3EXZjFS9upXzmc0dvKG6GYpKkiRpRb0HN6ytsfqsAgec1/zzSJIkSZKkjqOsHA78TfZoAav178EtX9uZfz39LhUpccz2azJspV4tcm6VLkNRSZIkrWit3WH9A+GN/2Xbg9aDbb5S3JokSZIkSZJy667Sh3M/u0mxy1AHYigqSZKkFUXAl0bB5Odg8VwYsSuUdy12VZIkSZIkSVKTGIpKkiSpdsO2LXYFkiRJkiRJUrOVFbsASZIkSZIkSZIkSWpNhqKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSSZigqSZIkSZIkSZIkqaQZikqSJEmSJEmSJEkqaYaikiRJkiRJkiRJkkqaoagkSZIkSZIkSZKkkmYoKkmSJEmSJEmSJKmkGYpKkiRJkiRJkiRJKmmGopIkSZIkSZIkSZJKmqGoJEmSJEmSJEmSpJJmKCpJkiRJkiRJkiSppBmKSpIkSZIkSZIkSSpphqKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSSZigqSZIkSZIkSZIkqaQZikqSJEmSJEmSJEkqaYaikiRJkiRJkiRJkkqaoagkSZIkSZIkSZKkkmYoKkmSJEmSJEmSJKmkGYpKkiRJkiRJkiRJKmmGopIkSZIkSZIkSZJKmqGoJEmSJEmSJEmSpJJmKCpJkiRJkiRJkiSppBmKSpIkSZIkSZIkSSpphqKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSSZigqSZIkSZIkSZIkqaQZikqSJEmSJEmSJEkqaYaikiRJkiRJkiRJkkqaoagkSZIkSZIkSZKkkmYoKkmSJEmSJEmSJKmkGYpKkiRJkiRJkiRJKmmGopIkSZIkSZIkSZJKmqGoJEmSJEmSJEmSpJJmKCpJkiRJkiRJkiSppBmKSpIkSZIkSZIkSSpphqKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSSFimlYtegBoqIGcC7xa6jAQYDM4tdhNTOeZ9IdfMekernfSLVz/tEqpv3iFQ/7xOpft4nUt3a4z0yM6V0QLGLaGuGompxETE6pTSy2HVI7Zn3iVQ37xGpft4nUv28T6S6eY9I9fM+kernfSLVzXuk/XD6XEmSJEmSJEmSJEklzVBUkiRJkiRJkiRJUkkzFFVruLjYBUgdgPeJVDfvEal+3idS/bxPpLp5j0j18z6R6ud9ItXNe6SdcE1RSZIkSZIkSZIkSSXNkaKSJEmSJEmSJEmSSpqhqCRJkiRJkiRJkqSSZiiqFhMRB0TE+Ih4KyK+X+x6pLYUEcMi4qGIeC0iXomIb+btK0XEfRHxZv51YMEx5+T3y/iI2L+gfZuIGJfv+0tERDFek9QaIqI8Il6MiDvybe8RqUBEDIiIGyPi9fz/KTt6n0hVRcRZ+e9bL0fEtRHRw/tEnV1EXB4RH0TEywVtLXZfRET3iLgub38mIka06QuUmqmWe+R3+e9cL0XELRExoGCf94g6nZruk4J934mIFBGDC9q8T9Tp1HafRMQ38nvhlYj4bUG790k7YyiqFhER5cCFwIHAxsDREbFxcauS2tRS4NsppY2AHYCv5ffA94EHUkrrAQ/k2+T7jgI2AQ4A/p7fRwAXAacC6+WPA9ryhUit7JvAawXb3iNSVX8G7k4pbQhsQXa/eJ9IuYgYAvwfMDKltClQTnYfeJ+os7uSFf8Nt+R9cRLwUUppXeCPwG9a7ZVIreNKVrxH7gM2TSltDrwBnAPeI+rUrqSG34ciYhiwLzCpoM37RJ3VlVS7TyJiT+BQYPOU0ibA7/N275N2yFBULWU74K2U0tsppcXAKLL/EEidQkppWkrphfz5XLI3sYeQ3QdX5d2uAj6XPz8UGJVSWpRSegd4C9guIlYH+qWUnkopJeDqgmOkDi0ihgIHA5cWNHuPSLmI6AfsBlwGkFJanFKajfeJVF0XoGdEdAF6Ae/hfaJOLqX0KDCrWnNL3heF57oR2NvR1epIarpHUkr3ppSW5ptPA0Pz594j6pRq+X8JZMHMd4FU0OZ9ok6plvvkDOD8lNKivM8Hebv3STtkKKqWMgSYXLA9JW+TOp18WoOtgGeAVVNK0yALToFV8m613TND8ufV26VS8CeyP6QqCtq8R6Tl1gZmAFdENs30pRHRG+8T6VMppalkn7yeBEwD5qSU7sX7RKpJS94Xnx6Th0hzgEGtVrnU9r4C/C9/7j0i5SLis8DUlNLYaru8T6Tl1gd2zae7fSQits3bvU/aIUNRtZSaPq2QamiTSlpE9AFuAs5MKX1cV9ca2lId7VKHFhGHAB+klJ5v6CE1tHmPqNR1AbYGLkopbQXMI5/qsBbeJ+p0IlsT8VBgLWANoHdEHFvXITW0eZ+os2vKfeE9o5IVET8kWxLn35VNNXTzHlGnExG9gB8CP6lpdw1t3ifqrLoAA8mWVDsbuD4f3el90g4ZiqqlTAGGFWwPJZvGSuo0IqIrWSD675TSzXnz9HxKBPKvldMn1HbPTGH5lD2F7VJHtzPw2YiYSDbF+l4R8S+8R6RCU4ApKaVn8u0byUJS7xNpuX2Ad1JKM1JKS4CbgZ3wPpFq0pL3xafH5FNX96fmKRalDiUijgcOAY7JpzAE7xGp0jpkH0Qbm/8tPxR4ISJWw/tEKjQFuDllniWbIW0w3iftkqGoWspzwHoRsVZEdCNbQPj2ItcktZn80z+XAa+llC4o2HU7cHz+/HjgtoL2oyKie0SsRbag9rP5tFZzI2KH/JzHFRwjdVgppXNSSkNTSiPI/h/xYErpWLxHpE+llN4HJkfEBnnT3sCreJ9IhSYBO0REr/zf995ka7l7n0grasn7ovBcR5D9LueoBXVoEXEA8D3gsyml+QW7vEckIKU0LqW0SkppRP63/BRg6/zvFu8Tablbgb0AImJ9oBswE++TdqlLsQtQaUgpLY2IrwP3AOXA5SmlV4pcltSWdga+DIyLiDF52w+A88mmTDiJ7E28LwCklF6JiOvJ3uxeCnwtpbQsP+4M4EqgJ9maJpXrmkilyHtEquobwL/zD5m9DZxI9kFG7xMJSCk9ExE3Ai+Q/bt/EbgY6IP3iTqxiLgW2AMYHBFTgJ/Ssr9nXQZcExFvkY1WOKoNXpbUYmq5R84BugP3Ze9J83RK6XTvEXVWNd0nKaXLaurrfaLOqpb/n1wOXB4RLwOLgePzINP7pB0KQ2ZJkiRJkiRJkiRJpczpcyVJkiRJkiRJkiSVNENRSZIkSZIkSZIkSSXNUFSSJEmSJEmSJElSSTMUlSRJkiRJkiRJklTSDEUlSZIkSZIkSZIklTRDUUmSJEnq5CLihIhIEXFCsWsplNf0cLHraI8i4uGIGBcRZQVtrfpzjIghEbEgIn7RGueXJEmSpNZkKCpJkiRJrSgPqQofyyJiZkQ8GBHHFLu+UlLwPa6IiHXq6PdQQd8T2rDEFhERRwC7Az9NKVW01XVTSlOBfwDfjohhbXVdSZIkSWoJhqKSJEmS1DZ+lj/OBx4GdgP+FREXFLOoErQUCOCkmnZGxHpkgeLStiyqpUREAL8E3gBuKUIJvwO6AT8uwrUlSZIkqckMRSVJkiSpDaSUzs0fP0wpHQHsDyTgzIgYUdzqSsp0YDRwYkR0qWH/yWSh6R1tWlXL2QfYALgqpZTa+uIppfeA+4BjIqJ/W19fkiRJkprKUFSSJEmSiiCl9ADwOllAty1ARHwuIv4VEW9ExLyI+CQino+I/ytcOzLvOyqf/nW3ms4fEUfk+//anDojYpuIuCkiPoiIRRHxbkT8PSJWr6Hv+hFxfkSMjogZBf0vjoihtZy/W0T8OCIm5P3fiYhfRkT3ZpR9CbAacEi1a3UFjgeeBF6p4/X+OSLGRsSsiFgYEW9GxB8iYmAt9f9fRLwQER9FxPyImBgRt0XEPtX67hoR/42IKflrfT8ino6InzbitVWOgL2uoQdExMCIeCyfVvicavu2jYh7I2JuRHwcEfdHxI4RcW7+72ePGk45CugFHNWIuiVJkiSpqAxFJUmSJKl4Iv9aOeLvfGBr4Bngr8A1QB/gz8BV1Y79e/71tFrOfWr+9eImFxdxCFmA+BngfuACYDxwBjC6hhGuhwOnA5OBa/PX8CrZ6MznImJItfMHcD3wc7Lvwd/IRnB+JW9vqmuBefl1C30WWJUsNK3NKWRh33jgCrI1NKcB3wKeiIi+1fpfSfbz6QpcDfwFeBTYDDigslNEHEA2bfIuwAPAH4BbgUXAVxvyovLv117A+ymlCQ08ZjjwBLA9cFxK6dcF+3bNa90LuIvs+78AeAjYro7TPpF/3bchNUiSJElSe1DTVEKSJEmSpFaWjyLcgCwMfC5vPrh62JWPEL0COC4i/pZSegYgpfRoRLwCfD4ivplSmllwzFpk06w+mVIa18T6+pAFfl2APVJKjxXs+x5ZgHsxsF/BYdcAf0wpLap2rv2A/wE/IgtUKx0NHAo8DeyZUlqY9/8py78njZZSmhsRo4ATImJoSmlKvusU4GOywPUHtRz+a+BrKaVl1V7DScClZAHmb/K2/mQB6vPA9jUcM6hg8xSyDybvkVIaW63f4Aa+tA2AlWng1L8RsQXZ9703cFBK6f6CfWXA5UCPfN//CvadDlxU23lTSm9FxGyydXElSZIkqUNwpKgkSZIktYF8OtJzI+JXEXEjcDfZSNE/pZTeBahp9F9KqYJsJCJk65AWugjoTjYlbKFT83P/sxklHwoMAq4rDERzfwAmAvvmIxEra51aPRDN2+8lm662ev0n5l9/UBmI5v1nAb9oRu2QjQYtJxt1SkSsSTay8d8ppfm1HZRSerd6uJm7nCxQLXwNiez7vAioqOFcH9ZwngU19JtZQ7+aVH6vp9XXMQ/dK39uuxUGormdgHWBhwoD0dzFwBv1XOJ9YOWI6FFfLZIkSZLUHhiKSpIkSVLb+Gn+OIdsutLHgC+nlL5V2SEiBuVrcr6UryeaIiKRjUQEGFLtnFcDn7B8qtzKdTNPAD6ieVPQbp1/fbD6jpTSUrJpVwG2Krh2RMSx+bqUMyJiacFr2KyG+rcmCxMfr+H6DzejdvIRteOAr+SjIk8m+xu4rqlziYiuEfH1iHg8X1N0WV5/BdCv8DWklD4G/ksWMI6JiJ9ExJ4R0auGU/87//pMRPwjIo6sbZ3VOlSOPP2onn5HkE2HOwXYofrI1Fzlz22F730exD9ZzzVm5V8bOspVkiRJkorK6XMlSZIkqQ2klKKu/RExgGzK2LWAZ8kCz1nAUmAA8E2yUaGF55wbEf8CTo+IPVNKD5GN8FyNbATqQpquf/61tlGJle0DCtouAM7M990DTGX5yMgTgDVruMaslNKSGs7/fqOqrdklZGt8HkA2KvX5lNKL9RxzHXAY8DZwW15H5ejXM6n2MwCOBL4HfAn4Wd62MB8N/J2U0nSAlNLN+Rqt3yYbvXoaQEQ8D5yTUrqvAa+n8ntZ3+jMHcnWOH2abH3XmlT+fKfXsr+29ko9q9UkSZIkSe2aoagkSZIktQ8nkwWiP0spnVu4IyJ2JAtFa3IRcDpZyPYQy0eNXtzMeubkX1erZf/qhf0iYhXg/4CXgZ1SSnMLO0fE0bVcY6WI6FpDMFrbdRvjGrL1P/9JNsLz53V1joiRZIHo/WTrbC4p2FcGfLf6MSmlBcC5wLkRMYxsnc0TgGOBEcCuBX3vBO6MiN7A9sAhZGus3hERW6WUXq3n9XyQfx1UZ69svdSDyILgsoj4Sj76s9DH+ddVazlHbe2VBpEF9rPq6SdJkiRJ7YLT50qSJElS+7Bu/vWmGvbtXttBKaWXgCeAwyJie2Af4NGU0mvNrKdyROUe1XdERBdgl3zzhfzr2mR/Y95bQyA6NN9f3Qv5MbvUsG+F6zZWSmk2cCMwFJgHXFvPIZU/g9trCGm3Y/noyNquNzml9G+ydUffBHaJiBUCzJTSvJTSg/nUyecB3YAD66kNsnVZlwEb1tNvEdkUujeQrTf7r/xnVqjy57vC9z4PgHeq7eR5qDsEeCmllBpQtyRJkiQVnaGoJEmSJLUPE/OvexQ2RsRWZOuQ1uUismDtJiCAf7RAPbeSjQI8OiJ2qLbvTLKQ8/6U0qS8bWL+dZeIKK/sGBF9yKaxrWmmoivyr7+KiB4Fx6wE/KiZ9Vf6Ednoz/2rh7U1mJh/3aOwMR8Fe2H1zhGxch5EV9cb6Es2knJx3nfviKgpVK0ckTm/ntpIKc0BxgCb13Kuwr5LgKOBf+Vfr8vXm630BDAB2DMiqgeypwLr13H67YByspHJkiRJktQhOH2uJEmSJLUPVwNnA3+KiD3JRhquRzbF6s1ka1fW5gbgj2Sj92bm/ZslpfRJRHwlP/cjEXEDMAnYBtiPbK3N0wr6vx8Ro4CjgDERcS/ZupX7AgvJwrwtq13m2vx1fRZ4OSJuI1sL8wiy9VXXaYHXMSmvuyGeIwsLD4+IJ4HHyULLA4HxwHvV+g8Bno6I18hGvU4G+pH9zFYD/lIQxP4BGBERD5OFr4vJvpd7Ae8CoxpY400Fx91ZV8eU0rKIOJ7s+38ycHNEHJFSWpRSqoiIk4G7gdsj4iaykHRzsp/Z//LXXX3aXch+/pW1SJIkSVKH4EhRSZIkSWoHUkrvka0/eSfZlKZfB9YEvgp8v55jFwP/zjevTCktaqGabgN2Bu4imxL2O8BGZCNRt0kpvV3tkJPIpoPtCXwtP+YOsqlY51TrSz716heAn5L9ffp1soD0CuCLLfEaGiOltCy//kXAGmRrpO4CXEr2WqpPqTuRrPb3gT2BbwGHA+8AXyIbUVvpPLKgcROygPJ0ssD1PGDblNJHDSzzMrJA9bgGvqYKspGffyMLa2+vHGWaUnqYbGrmh4GD89fbM38tlT/bjwvPl0+teywwNqX0VANrliRJkqSiC5f/kCRJkqSOLx+BuBuwQUrpzSKXo1YUEf8kWyt0RErp/Va6xhPA9kD/lNK8gvbPALcDX04p/as1ri1JkiRJrcGRopIkSZLUwUXEdmQj/u4xEO0UfkI2WvSHzTlJRPSKiAE1tJ9ANrr33mqBaAA/A0azfGSyJEmSJHUIrikqSZIkSR1URJxBtq7liWRrP/60uBWpLaSUpkfEscAmEVGWT5HbFMOBFyPiPuAtsvcItiKbMng28O1q/VcjGyV6a3LaKUmSJEkdjNPnSpIkSVIHFRETgaFk6z+em1L6T3ErUkcSEQOB35GNMl4N6E62Pur9wK9SShOKWJ4kSZIktShDUUmSJEmSJEmSJEklzTVFJUmSJEmSJEmSJJU0Q1FJkiRJkiRJkiRJJc1QVJIkSZIkSZIkSVJJMxSVJEmSJEmSJEmSVNIMRSVJkiRJkiRJkiSVtP8H8zIEKGVYqgQAAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\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 try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "\nIn the case of launch site \"CCAFS SLC 40\", we see that the first stage is more likely to land successfully when the payload mass is high.\nIn the case of launch site \"VAFB SLC 4E\", we see that the first stage is more likely to land successfully when the payload mass is medium.\nIn the case of launch site \"KSC LC 39A\", we see that the first stage is more likely to land successfully when the payload mass is low or high but no medium."
        },
        {
            "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": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ngroupedData = df.groupby(['Orbit'])['Class'].mean().reset_index()\nsns.barplot(y=\"Class\", x=\"Orbit\", data=groupedData)\n\nplt.xlabel(\"Orbit\",fontsize=20)\nplt.ylabel(\"Success Rate\",fontsize=20)\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": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be 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(\"Flight Number\",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": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": "<Figure size 1842.38x360 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.catplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\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": 9,
            "metadata": {},
            "outputs": [],
            "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    "
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\nyear=[]\ndf2 = df.copy()\nyear = Extract_year(df2[\"Date\"])\ndf2[\"Date\"] = year\n\nsns.lineplot(data=df2, x=\"Date\", y=\"Class\")\nplt.xlabel(\"Date\",fontsize=20)\nplt.ylabel(\"Success Rate\",fontsize=20)\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": 11,
            "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": 11,
                    "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": 12,
            "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>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 \u00d7 80 columns</p>\n</div>",
                        "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]"
                    },
                    "execution_count": 12,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "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()"
        },
        {
            "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": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>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 \u00d7 80 columns</p>\n</div>",
                        "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]"
                    },
                    "execution_count": 13,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot.astype(float)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">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| 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 3.8",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
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            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
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