jupyter-labs-eda-dataviz.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": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n# Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns"
        },
        {
            "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 402.375x360 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(x='FlightNumber', y='LaunchSite', hue='Class', data=df)\nplt.title('Relationship between Flight Number and Launch Site')\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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oE3Q7Bho0qtwCXrk8tC2XZ5cj2PTDoYyeMo/Oiz+gQ/MGsNvx0LAZuMOsz6CgMbTrQ1GRk5dXi/4/iorgT61KlxU0g9/NzWw+Y/8TtpUXQn4jOPtJ6H5s9uKUrYd7aM4uaJzJVLXonypz6r1W32xaD48OhLljw3Dr7nDxO9Bk24qn7XpI6aRj+ZDfEDav21K0unVfTvj7CGYsWQu0YNCATtyyZ7OovkHnfbfUzUrCKdwEk16DVQug54nQsmPV55WXB+36wLzxJWUd9s5sHkVF8M4NIeEAFG6Ad/+opFOXff1yOMpvuA0cdDV07Bffss0yTTh1ns7p1DffvFqScAAWfwcTnq3ctHucCYdfDy06QJue8IPH4YyHoEXHkIB6n8E9m06KEk4w5PNZfD13ZZZXIsHTP4DnL4Q3r4V/DYAPbocPboOFk6o2v1P/DTvuEd536A8n3ZXZ9EWbQ8eJRGsXVy0Wyb3vPwjNwtOGhx83T5wMqxflOqp6LadHOma2I3AnMADYAEwHrnT378ysezSuO7AJmAj8wt0XRNPeBZwJdHL3oqjsQuA2YE60iAnufr6ZnQjcREiyBcBd7n5/UixtgYeBTlGd6e5+vJl1AV5z994p4v8VcAmwGSgE7nD3J9Ks66+i2Nq4++Ko7DfAxdG0l7v7W5XddmltSJEAUpWlc+g14ZWo54lQVAh5+cwbMrbMJIsSujYXFTn//mAqb3+1gK6tm3H10d3pVNUeYHPGwNT3SoY3rYX3bgrvh98K570IXQ+GmaNg8tvQZjfodTrkl3ytN24uYsR3i8jPg0O6taFB293hJx+FDhENGmYeU4OGsMdZMP6ZkrK+51Rt/ST3vnm19PDG1TB1GPQZVDPLW7cMPn8YVs4J39UmrcJ1Ol0PCe+3AjlLOmZmwIvA4+4+KCrrC7Q1s5nA68DV7v5qNO5woA2wwMzygNOAWcAhwPCEWT/r7j9PWE4B8ACwj7vPNrNGQJcUIf0JeMfd74qm27OC+H8CHB3Nd6WZtQROTVO3U1R3ZkLZ7sAgoBfQHnjXzLq7e/kXtFRk91Pg/b+Evv8QmgzWrwi/5nY+DPpdxJbuWZnIywfgtL078vL4uRSfCmzfsjH77Ryd/Fw8mfve+47bxoTBcbOW8+WcFbx91SFYumWuWRx+YTbZDnocB/kJdyIoKmdTFG2CUffB6gUw9OKS8inD4PTwe2LV+k2ccd9IvlsQumPv2bElz/3f/jQuyK9awil20l3Qtlc4ouxyMOx9QdXnJbnVqkvlyrLBHZ44FeaNC8OjHykZ16gFnP8SdIixaS9Hcnmkcziwyd3/XVzg7uMAzOxHwCfFCSca937StF8CzwKDKZ10kjUnrOeSaD4bgG9T1GsHvJ2wvAkVxP9b4HB3XxnVXwGkOwv/D+DXwMsJZacAQ6J4vjezKcA+wCcVLLd8zVrDpcNh9KNhx7zgKxj5zzDu65dDF+HDf1vl2R/avQ2PXjiAoWPm0KZpHpd1mUejeV/A9yPgvT/z9oabgF221J+8cDVTF63huwWruHvYZDYWFnHRgV3D3QaWTIWHjgy//iDswC94tSQpduwPnfaDWaNSB2MGn9xTumzic3DsX6BZa14cO2dLwgGYMHsFb3w5j9P2qsZ5IQgdMw74RfXmIbVDvwvh2zeia2QM+v8IOu9XM8uaO6Yk4UQ2eT4FVhhaIz64HQY/k3raeiSXSac38EUVxkFINM8QduJ/NbMCdy++lPxsMzsoen+Xuz9qZq8AM8xsGPAa8Exxk1yCe4BnzeznwLvAo+6esluTmTUHmrv71ArWETM7GZjj7uOTfu13ABL3prOjsuTpLwUuBejcuXNFiwu27QxH/QE2rIZbOpUeN+6ZaiUdgMN67MBhnRrAo8fB2HBuxTEM6GLzGe8lSadJQT5rNmzi50+PoSg6Ovr9S1/SZfumHDzlgZKEA+Eff8ZI6HJgGDYLv/4mPBs6Esz4OCQ3CB0c9v9ZOImfYJJ3Zu7kFey3+7asWl/2AtdUZRVaMTvsmFq0h+4Dtxz1ST3QsBlc+Bos+i7cgqY6HVUqsLSwCckdor/zDvSyqAFkXdkLq+ujOtd7zcwaAscDV7n7KjP7FDiG0BwHSc1rAO5+iZntARwF/IrQ1HVhUp23zGxnYCBwHDDWzMqcxykOA6iwr7mZNQV+F8WXah7JyszT3R8gNA/Sv3//zPq3N2gUDtvXLy8pa9Y6o1mkNeZxWFRyMt+i0H/V4DkmburKNG9Pk4J8bjx5d8bPXrEl4RT7aPJiDi5McZub5LKCJuHXKISLVye9CstnQo8ToPWucNBV8Ow5ULSZmzady8OFx8OQSbTeZhp3nt2XMxp9xvFFI1jkLXmq4AyO690us/WcMwYeO6HkWqXdToRBT1V++hkjYcStoYmz30XQT01xtVKb7jW+iOmrG9DSjXwr+WdY4gnXgu11Xo3HUBvkMul8RegIkG7coWnGDQRaAhOjI4emwFpKkk5K7j4xmuZJ4HuSkk5UZynwNPC0mb1GOF9U5ogrOoezxsx2dvdp5Sx2F6ArUHyU0xEYY2b7EI5sEg9DOgIZXjBSgfwCOPpP4YJPL8QLmrHqoOsp55LHyluTusdWp7xFDGv4Kz7tdxu7H3MRLRoXMHr60jL1erZrAW0vDCfkN68PhTv0gi6HpF9mfgPodVrpsh4D4bJRzBr/Po+8U3KguHj1Rl4c/il32J0QHZic2fRbGjT+QQYrSWi+S7w4dtJrsOBrVrToxpLVG9g54U4Lc5av47qhE/js+6Xs3bkVtx3Xjo5Pnl7S5XzuWNhmh3DuSrY6vRotLJVwAHo0XQ09zwk/ZnY7PkeRxSuXXabfAxqZ2Y+LC8xsgJkdStjxH2BmJySMGxgdrQwGLnH3Lu7ehbBTPyY6qijDzLYxs8MSivoCM1LUO6J4HlHz2S4knPhP4WbgHjNrEU3TImoK28LdJ7r7Dgmxzgb2dvf5wCvAIDNrZGZdgW7AZ+Usr2r6XQBXTuDZbrczYO2d9HlyPVcOGcumwuTWxQzteTbklZz0X+rb8K9NJ/NS4QFcUXg5uxx+AflmPDlqBh9NWcx5++1E44I8CvKNH+7bOTwyoH1fOO1+6HkyHHkD/OiNUj3PKq11N5b3OLvMYeKSRfNLDTdYM6/03Qgqo2hTmaKXvpjBPn95lyPuGMEJd3/IwlUhaV43dAIfTl7Mhs1FfDJtCUNfeKrUNU5AaKZb+j1891bZrtdSrzVqWvbn3g7bbQun3rvVJBzI4ZGOu7uZnQbcaWbXAesp6TK9LurmfKeZ3UnoMj0BuBY4Fvi/hPmsMbOPgJPSLMqAX5vZ/cA6YA0pjnKAfsC/zGwzIRk/5O6fR12me5jZ7IS6VwH3AdsAn5vZpijGOzJY/6/M7Dnga0KX659Vu+daGp8sbsK1E0vu/vzSuLkc1K0NZ/arRvt1uz3hojdgzGMs21TAhV/3Y8KG7aAQLj+yG9s3a8hp937M+Nlhx9qwQR5PXbwvvTq0oGnD6Gv35m9g1L0ArJk0jNeX9+CME04gvwoXlfbu0IKe7VrwzbyS7uFnbTcF5idVbJHhOu9zKUx6PVyfA2zseAC/+sjZHLUXfjV3Jfe8N4U/ntKbz74vfUT39qKWXJHcSW7NIrh7L8BD0+e5Q6HTPpnFJHVTin9va1HmNG69l9NzOtGJ+pTtHe4+idCUlqzMzYnc/fSEwceSxq0inAOqKJbbCNfRJJdPJ1y3k8rfolelREc7icN/Af5S2emrasrCVWXKJi8oW5axTgOg0wBaFW7mhbb3snLSi+R17Md2hx3Jp9OXbkk4EK6XeW70LG7r2icUrJgDn27puEgzX0Ozz+7i7sbduerozNvXzYynLtmXhz+axpxl6zhx3ascNeO+0pX2/3mZtvsNmwu54+3veH/SQrq3bc51x+1W+rqiLgfBpSNCz78W7fmy1UA2TxlTah5TF4Uecnt13pZR00oST8OOfWG3a+Dju6BwY7i/1tT32XLqbsNKeO/PcMErGa+v1EHt9wr36psdNWhYHuxzSW5jyoE615FAMrN242YK8vPIzzMKE87mZ/XBZG9fT8NP76M1wJxhsG4mDfe8gWPyPmeht2Kc7wpAg/yE1twNqyCpA2FL1vDGl/OqlHQAtmvWkKuO6s63U6aw+zN/Lz2y44DQlTrJ7W99y4Mffg+E7t3TFq/hjSsOLl1px97hBfTeXESjBnls2FwS+8bo/d/O6MMvnx/H59OXsVfnbbnjrD7Q5sDQy27T+rCTuSNp3dKcG5N6apfDYc7o8N1v2Tnc+WMro6RTj01esIpBD4xiyZqNAOzYojFtmjfiwgO6sF8FT/LMyIQhScPPsde093mg4QIAXig8hBvzfs5FB3YpqbPDbhR13Ie82SWnsYYUHkGnVlV/fs20Ras57+HP2LR8LqMaJV0Dm5/6YtD3Ji0sNfzNvJVcMWQsfz1tD5o1Kvvv4XiphANseVZQ5+2b8vxPDsDdS18M26QVNCEk2vyG4ainWNekBCf114o54RZOxT+2lk+HD++AE27PaVhx073X6rF/vT9lS8KB8EjoB87vxxnVOZeTSqPk594UhTsFRM7M/4DhF+xA97alH1WQd+4LfLnblTxXdAQXbLyWsS2O4NrjdqtyGHe88x1zlq9jIa14tWj/LeVuebDfZSmn2XWHss/5eXncXG59M/W93Rrm57FnizX8q+AuPmh4BX9rcD+7tSrdhSHt3Rcmv1M64UCZoz2px1bMLvt5z5+Ym1hySEc69djSNaV3cEUOy9Zsol3Lsk/IrJa2PcOvtnK0zkvxFNDGLek96I90WreJ3ZeuZbcdm5dugsvQnGUlPcWu3vRT3i3cm51tHks6HM6fe56YcprfHt+T7xas4vvFpeP7aHLqZi8z4/GWD9JqYzhC65w3giNbbkvo31KBVNdINc3SdVNS+zVM8Xyomuk7VKvpSKceO2Pv0kc0vdq3oGe7GngwWvsK7hfVuke5txZp2aSA3h1aVivhAJy4Z8mFn4Xk82rRAdxVeAaTbJe00+y0fTPevupQtm9WuvmtZ/s0VzNtWk+rRaV7tm8/74PKBdjl4HBR65YJd4UBF6evL/VLUYqLobdpW7asntORTj126l4daFyQz/8mzqPTdk24+KCd0zf9VMeAi+HLF0ruUNDzZBhwCUx8HprvCPv+JJZbx1x8UFcK8vO4a9jkLUd5ZnBh4rmkFAry87hr0F78+oXxzF2xnr06b8v1J6Q5wVvQOCSLJVNKytqmu3FFEjMY/DTM+jz0XOt6SOkbnEr91q5veJzGnOhBjJYH/S/KaUi5oCeH1iE1/rjq6igqDLd8adQ8XPSZQxs3F/Hi2NlMW7SGI3u2ZZ+ulXoEMEVFzqr1m2nZtIJEMPPTcGfrFbPCUdwPnoAdqn4uSrYi61eGW0itmAO9T6/qNVp1+smhSjp1SK1OOluboqJwoWfzra95RHKuTicdndMRqYq8PCUckSpQ0hERkdgo6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio6QjIiKxUdIREZHYKOmIiEhslHRERCQ2SjoiIhIbJR0REYmNko6IiMRGSUdERGKjpCMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISmwaZTmBmBcCRQE9gG3e/KSpvDLQAFrt7UVajFBGReiGjIx0zGwhMB14H7gBuTBjdF5gHnJ2d0EREpL6pdNIxs/7AS4ADVwFPJ45391HA98BpWYxPRETqkUyOdH4PrAX6u/vdwOQUdT4H+mQjMBERqX8ySToHAi+5+/xy6swC2lUvJBERqa8ySTrbAIsrqNM0w3mKiMhWJJMEMQfoVUGdvsC0KkcjIiL1WiZJ5w3gWDM7KNVIMzsOOAB4LRuBiYhI/ZNJ0rkZWA68bWa3ArsDmNkJ0fDzhC7Tf892kCIiUj9U+uJQd59jZscAzwHXJIx6BTBgKnC6u1d03kdERLZSGd2RwN3HmFkP4ARgf2B7YAUwCnjZ3TdnP0QREakvMr4NjrsXEo5uXsl+OCIiUp9lckeC98zs/ArqnGtm71U/LBERqY8y6UhwGNClgjo7AYdWNRgREanfsn0hZxNA53VERCSlTM/peKpCMzOgM3A84VY4IiIiZZR7pGNmRWZWaGaFUdGNxcOJL8LRzTTCHQmG1GzIIiJSV1V0pPMBJUc3hwAzCc/TSVYILAGGAQ9lKzgREalfyk067n5Y8XszKwIedfc/1XRQIiJSP2VyTqcr4TY4IiIiVZLJbXBm1GQgIiJS/6VNOmZ2A+F8zj3uvjQargx395uyEp2IiNQr5p6yF3TxORwHerr7d9FwZbi752crQCnRv39/Hz16dK7DEJHcslwHUB3lNa8dHv2dmTQsIiJSJWmTjruPKG9YREQkU9m+DY6IiEhaGT/aoJiZFQA/BY4gtDGOIHQ62JCl2EREpJ6p6DY455vZTDM7Mqk8D3gN+AdwMnAScBvwnplVOZGJiEj9VlHz2tFAc2B4UvngaNwC4BLgbOBTYD/g4uyGKCIi9UVFSWdvYGT0tNBE5xK6U5/v7o+4+/PAMYRHV/8g+2GKiEh9UFHSaUu4e3SyA4AF7v5ucYG7rwZeB3pnLzwREalPKko6LYA1iQVmtiuhye3jFPVnA9tmJTIREal3Kko6ywg3+kw0IPo7NkX9BsDq6gYlIiL1U0VJZyxwgpm1SygbRDifk+pi0W7AvCzFJiIi9UxFSedhoCnwiZn93cxeI3SPnurupZrXoq7SBwPjayRSERGp8yp6iNvzZnY0oVv0lVHxCuDHKaqfBLQC3slmgCIiUn9UeCGnu19qZo8BBxIeSf2mu89NUXUtcBXwSlYjFBGReqNSdw9w95HAyArqvAW8lY2gRESkftINP0VEJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqNn34hIsG45jPwnrFkEB/wCWnfLdURSDynpiAhs3gh39YH1y8PwmCfgR29C5/1yGpbUP2peExH4/MGShAOAw9u/y1U0Uo9llHTM7FAze83MFprZJjMrTPHaXFPBikgNWbe8bNnGtbGHIfVfpZvXzOwE4CUgH5gJfAsowdRXG9fA/C+hTQ9osm2uo5Gatt9l8NEdUJTwkOADfpG7eKTeyuSczo3AJuAEd3+7ZsKRrCoqglmfQn4BdOxf+emmfwxDBsP6FVDQFE67H3Y/uebizNT4IfDen2HDKuj/IzjyBjDLdVR1W9NWsOdgGPefMLx9d9j91JyGJPVTJs1rvYFnlXDqiI1r4OGj4NGB8NCR8MQpULipctO+9duQcAA2rYU3fg3uNRdrJpZMhZd+CitmhXMQH/0dJj6f66jqvrnjShIOwJLvQmKXOs3MdjSzIWY21cy+NrP/mVl3M/syVzFlknRWA0trKhDJsvFDYM4XJcPThrNs7MuVm3bF7NLDqxdA4cbsxVYdsz4FLypdNvOT3MRSn3z5XwAK3VjvBaFsyrs5DEiqy8wMeBEY7u67uPvuwG+BtrmMK5OkMwzYv6YCkezavHJ+mbJhn0+o3MS9zyg9vNsJ0KBRFqLKgo4DAEtRJtXSoR/vF/ZhqrdnKc15q7A/m1rtkuuopHoOBza5+7+LC9x9HDCreNjMupjZh2Y2JnodEJW3M7MPzGycmX1pZgebWb6ZPRYNTzSzq6oSVCZJ51pgFzO7PsqgUost6HwCG4p/sQIrvQn/XbdX5SY+5s9wxPXQ9VA48Eo49b6aCbIqWneDk+6CZjuE8037/Qz2HJTrqOq8mU16cmDel3TPm0N7W8qx+aMZldc312FJ9fQGvqigzkLgaHffGzgbuDsq/yHwlrv3BfoA44C+QAd37+3uewCPViWotB0JzOyRFMVfAX8EfmRm44DlKeq4u19clWASlj0cuDl6Rk9x2ZVAd3e/zMzaAHOBn7v7/Ql1pgOrgOIuOJdF9b4h9LYzYA1wkbt/m7TMPOBO4AjAgfXAD9z9+2i+/d19cdI0xwE3Ac2ieb/m7r9Ks04DgFHA2e7+QlRWCExMqDbE3W+peAtVrP2ufbis6V85cvWrbPR8HiscSNNGO1BU5OTlVfCboUFDOOSa8KqN+l0QXpI1i775gM5WWKqs8YKxOYpGYlQA/MvM+hL2m92j8s+BR8ysAHjJ3ceZ2TRgZzP7J/A6UKXz++X1XruwnHFdolcqDlQr6QDPAIMo/VC4QUDxXvAswg58MHB/6Uk5PDE5mFkXYGqUsTGz/yO0aybvtc4G2gN7unuRmXUkJKiUzKw38C9Cb75JZtYAuDRN3XzgVso+5G5dcVzZZmZceNYZDHqwQ0kfgFnLef6LWZw9oHNNLFLqsM699qfoMyPPSjqMFHSq5JGx1FZfAWdWUOcqYAHhaCaP8GMbd//AzA4BTgCeNLPb3P0JM+sDHAv8DPgB8KNMgyqvea1rFV87ZxpECi8AJ5pZI9iSONoDH0XjBwO/BDqaWYcM590CWJaivB0wzz2cpXb32e6eql6xXwN/cfdJUf3N7n5vmrq/AIYSDmVjs3TNxjKdzkZNU18QKatNl1582+8GVlkzNpPP1zucRJ9TqtRkL7XHe0AjM/txcUHU4rJTQp2WlOz3ziNch4mZ7QQsdPcHgYeBvc2sNZDn7kOB3wN7VyWotEc67j6jKjPMBndfYmafAQOBlwlHOc+6u5tZJ2BHd//MzJ4jHKH8PWHy96Nmqw3uvm9UtkvUHNgcaArsS1nPAR+Z2cGEThP/cffy2hd6A3dUtC5RUjyN0GyXfMa7SRRXsZvd/dmk6S8lOoLq3DmzI5TeHVqSZ1CUkHj27Ngyo3nI1qPnyVfDCZdD0WZ2L2ic63CkmqL95WnAnWZ2HeEoZjpwZUK1e4GhZnYW8D4lrTuHAdeY2SZCz+XzgQ7Ao9GpCIDfVCUu89py/UUSMzuX0HQ1ONox/8jdx5jZNcC27v47M9sTeNjdB0TTTCfp3Et0lPSau/eOhs8mnNMZmGKZjQjJ4QhCE+FZ7j4szXzHRPMZX8F6PA/c4e6jzOyxKJbiczqr3X2bym6T/v37++jRoytbHYDnPp/FrW9OYuX6TZy2Vwf+ctoeFOTrlnsidVid7siVyW1wzgJ+Cpzr7nNTjO8APAHc4+7/zUJsLwF/N7O9gSbuPiYqHwy0NbNzouH2ZtbN3SdXcr6vkKbXhbtvAN4A3jCzBcCphKOeVL4C+gHlJh2gPzAk6vDXGjjezDa7+0uVjLdafjCgE2f260ihu5KNiORcJnuhSwhHGGUSDoC7zyGcL7kkG4G5+2pgOPAIoWMBZtYDaObuHdy9i7t3AW4mNL9V1kHA1ORCM9vbzNpH7/OAPYHymhhvA35rZt2LpzGzq1OsR9eEWF8ALosr4RTLyzMlHKnY5Hfh/kPhrr7w0T9yHY3UU5nce20P4LUK6owGTqp6OGU8A/yXkqQymHCFbaKhwBBC1+V0is/pGLCR1IlxB+DB4s4LwGeE3mnFJphZ8aXwz7n71VE37mfMrCmh197rlVmpBMnndN509+synIdI9a2aD0N+CIUbwvC7N0LLTrBHRZ2fRDKTSdLZjop7Xy0hNCFlhbu/SEL7pbvfmKLOBGD36H2XFOOnA00qsaw3gTfTjCsz36j8NSpOxIn1L0wazq/stCI1asbHJQmn2NT3lXQk6zJpc1kMVPT82m6kvmBURGqztnuULdsxRZlINWWSdD4GTjaz3VKNNLOewCnAh9kITOqhSf+Dl34WzhdsWJ3raCRRm+4w8BZo1AIsD/Y4Kzw2QiTLKt1lOrqoaCSwAvgToSlqDqHv9nGEi4VaAge5+6c1Eu1WripdpmuNMU/CKz8vGe56KFzwSu7ikdQ2bwzNbI2a5zoSSS+jLtNdrns9j3A+/EqgE+GGn3cCz0y/5YSi9FNWIhCzgcBdhItKH6rMbbwyuk4nurL1nmgByQoJPbMeqvQMJSN1Ouk8dDTM/qx02RUToNVOqeuLSDqVTjpRwhkKHE24R2SxNYR7p51Z1cQT3d7ru2jeswn3axvs7l+XN11G/WijWyL0IVzF+gWh6/EXhETURwlH0kp+5HVeA2hY6etiRaRqBlM24RANH0Nml5sk2weY4u7T3H0joRfxKRVNlEnvNQDc/RvCvcREKu/Qa2HGJ7BxVRg+8Apotn1uYxKp/66kbMIp1oxww8+nqzjvDiQ8m4dwtJPqFmOlZJx0RKqkY3+4cgJ8PwK27wY79s51RCJbg07VHF+eVM18FZ6vyTjpRO14PYBWpD63g7t/kOl8ZSvQdDvodVquoxDZmsyi/MdTzypnXEVmUzppdSQ8v6xcGSUdM/s94XCsolsV66JHEZHcu5PwzLFUTWxrgOrc7+hzoJuZdSX0ZB5EeOJouTK54eevCU8NXQE8SciQm6sUqoiIxOEZwoPc0vVeG1LVGbv7ZjP7OeHhlPnAI+7+VUXTZXKdzmSgMbC3uy+qaqBSdXW6y7SIZEtVrtMZRGilKr5O5x/AkOpep1MVmSSd9cCD7q6eazmipCMi1PHn6WRync4C1NtNRESqIZOk8xxwdMKt/0VERDKSSdK5AZgHvBD1VhAREclIJs1lXwEFQHvCI5dXkPoxBu7uu2QhNhERqWcySTp5hC7SMxPKUp3QqtMnuUREpOZUOumke3qmiIjUYje2TPtoA25cUeUu02b2CHAisNDdK31fq4zuMi0iInVISDhDCXcl6E+4JU7/aPiFaHxVPQYMzHQiJR0Rkfqrxh5tEN1jc2mm02VyG5zzMwjmiUwDERGRrLuSmnu0QZVk0pHgMSq+bbVFdZR0RERyryYfbVAlmSSdi9KUbwsMIBymDQVer2ZMIiKSHTX5aIMqyaT32uPljTezRwkJ5+7qBiUiIllxJzX3aIMqyVpHAncfBrwJ/Clb8xQRkWp5BniHkGASVfvRBmb2DPAJ0MPMZpvZxZWZLtu9174jdMcTEZFcC9fhnAFcCowm3Lh5dDR8ZnWu03H3we7ezt0L3L2juz9cmemyfdfo3anEM7JFRCQmIbE8Tcy91NKpdtIxszxCD4gfA8cBb1R3niIiUj9lcp1OEeUfxRiwBLimukGJiEj9lMmRzgekTjpFwDLgM+BRPcpaRETSyaTL9GE1GIeIiGwFdO81ERGJjZKOiIjEJuPea2Y2ADgW6AA0SlHF3b1SFwmJiMjWJZPea0a46ee5lNzYM/EpoZ5QrqQjIiJlZNK89nPgPOBJwl0HjHBfnwOA3wKrCLdU2Dm7IYqISH2RSfPaBcC37n4hQDjwYbm7jwJGmdlbwCjCfX4ezXKcIiJSD2RypNMDeC+pbEvScvexwGvAZVmIS0RE6qFMko4BKxKG1wDbJdWZDOxW3aBERKR+yiTpzCH0WCs2DeiXVKcbZW+hLSIiAmSWdD6jdJJ5A9jHzH5vZr3M7GfAKYTzOiIiImVkknSGAvlm1jUa/hswA/gjMAH4J7AcuC6bAYqISP2Ryb3XXgJeShheamZ7ER5psAswHXjC3edlN0QREakvqvU8HXdfAdxePGxmjc2shbuvrHZkIiJS72T73mv3AUuzPE8REaknauKGn1ZxFRER2RrpLtMiIhIbJR0REYmNko6IiMRGSUdERGKjpCMiIrEp9zodMyuMKxAREan/Kro4tCrdn70qgYiISP1XbtJxdzW/iYhI1iipiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio6QjIiKxUdIREZHYKOmIiEhslHRERCQ2SjoiIhIbJR0REYmNko6IiMRGSUdERGKjpCMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio6QjIiKxUdIREZHYKOmIiEhslHRERCQ2DXIdgEidU7gZxjwOc8dC10Ngj7PALNdRidQJSjoiSTZsLuTm/03ina8XsNP2Tfn9ibvTs12Lkgr/+yV88Vh4P/ZJWDoNDrsuJ7GK1DVqXhNJcvewyTw2cjpzlq9j5NQlXPzY52wuLAojN2+EsU+VnqA4AYlIhZR0RJJ8NHlxqeG5K9YzbfGaMJCXD422KT1B45YxRSaM/Cfc1g1u7wGj/p3raKQKlHREkuzevkWp4eaNG9CxVZMwkJcPR1wPROdw8hrA4b+LN8Ct1bQR8Pb1sGYhrJ4Pb14LM0flOirJkM7piCT55TE9mLZoDZ9+v5TW2zTiL6f1pmnDhH+VAZdA10Nh7jjovB9s2ylnsW5VZn5StmzGyPAZSJ2hpCPxW7sUln0PbfeABg3jXbY7THkXlkyBbsfA9ruUqdJ6m0Y8+3/7s2LtJpo1yqdBfooGgdbdwqs+2bgGMGjYNNeRpNZxQOXKpFZT0pGasXgKjH8aCprC3hfANm1C+din4PWrYfN6aN4OznkBduwdX1yvXg5jngjv3/49nPM87HJ4yqotmxbEF1cuucObv4HRDwMG+14Kx/w511GVteuRcPj14byOAQddBV0PznVUmdm4NvxfrJwLu58C7frkOqLYmbvnOgappP79+/vo0aNzHUbFFk+BBw6FjavDcMtOcNmocD7k9u6wYWVJ3W7HhB1/HFbNhzt2AxK+8zsfDue/lNFs/jlsMk+MmkHThvlcdVR3Tt2rQ1bDjN2k/8GQwaXLzhkK3Y7KTTz12aPHw4yPw/u8BnDei+Far8zU6YvC1JFAsm/cUyUJB2DFLPj2DVi3vHTCAVg+M764ijZTKuEAFG7KaBZvTJzHHe98x6JVG5ixZC1XPzeOqYtWVzxhbTZ/YtmyBSnKpHrmjS9JOBC+j589mLt4cqROJR0zW53w/ngzm2xmnc2sh5kNN7NxZvaNmT2QUG8fM/vAzL41s0lm9pCZNU2a72Fm9lqK5RWY2S3Rcr40s8/M7LgU9R42s/FmNsHMXjCzbaLyVmb2YlT+mZn1TpruNDNzM9stG9un1ihokrqsRTvolHTSt9dp8cQE0LJjaNIoZnmw308zmsWn3y8tNVzk8HlSWZ1TpnnRYOfDchFJ/Zaf4vxlg0bxx5FjdSrpFDOzI4F/AgPdfSZwN/APd+/r7j2jcZhZW+B54Fp37wH0BN4EmldyUTcB7YDe7t4bOCnNtFe5ex933xOYCfw8Kv8tMC4qPx+4K2m6wcBHwKBKxlM37H0BtEhocurQD7ofG94Pegr2uRS23xVadYGl34dXXM54GE67Hw65Bi55F3qeGG5rU0l9OpW9JmfPjttmMcAc6LQPnHIvtOkJO/SC0x+A9nvlOqr6Z4ee0PPkkuGCZrD/z3IXT47UuY4EZnYw8CBwvLtPjYrbAbOL67h7cdvAz4DH3f2TqNyBFyq5nKbAj4Gu7r4hmn4B8FxyXXdfGU1jQBNK2nB2B26O6kwysy5m1tbdF0RHQwcChwOvADdWagPUBc3bhnM43/4vdCToPhDyo5PyzVpD297wWXQwumw6zBwJvxgL+Wm+jqvmw5wx0GFvaL5j9WLLL4A+g0qW/fCxMGsU7LhHSEZte5U7+Sl9OjBx9kqe+nQGTRrmc+WR3cpc11Mn7XVOeEnNOuByWDIVVi8I9+zbcc9cRxS7upZ0GgEvA4e5+6SE8n8A75nZSOBt4FF3Xw70Bh6v4rJ2BWYWJ5SKmNmjwPHA18Avo+LxwOnAR2a2D7AT0BFYAJwKvOnu35nZUjPb293HpJjvpcClAJ07d67iquRA4xYlO3eA1Ytg1byQcL5+uXTd5TPDzTM7pej++s4N8PHdhDyeB2c9kr0mudeuCgkHwnmNh4+BMx6CHmVaULfIyzNuOGl3fndCT/IMTDf6lMratA6e/gGsi5pjP72PZQ135MvO59B/p+1o0jA/t/HFpK41r20CRgIXJxa6+6OEprPngcOAUWYWa2Opu18EtAe+Ac6Oim8BWpnZOOAXwFiguC1nMDAkej8kGk413wfcvb+792/Tpk0NRV/DPr4b/t4T7j8Y7t03HO0ksvxwviXZgq/h47soOXAsgv9eCutXZCeuOV+UHt64Gp4ZBDM/rXDS/DxTwpHMzB1bknAiE0YM5byHP+OgW99j0vxK/b6t8+pa0ikCfgAMMLPfJo5w97nu/oi7n0LYsfcGvgL6VXFZU4DOZlbZ8z+4eyHwLHBGNLzS3S9y976EczptgO/NbHvgCOAhM5sOXAOcbfVxL7ZqAQz7IxRFvcQWfwdFhdC6RxjOawBH/j50Mkg2dVjZssKNMOJv2YltpwNTl3/zSnbmL5KodfcynQm+KQqtF0vWbOTOdybnIqrY1bWkg7uvBU4EzjGziwHMbKCZFUTvdwS2B+YA/wIuMLN9i6c3s3OjOpVZzsPA3WbWMJq2nZmdm1jPgl2L3xM6G0yKhrctnha4BPggaq47E3jC3Xdy9y7u3gn4HjioalulFls5O+qqnGDt4nDO5/8+hKsnhYv8UmmTplPf3HHZie3EO1Nf0b7tTtmZP7BszUZGfLeIxas3ZG2eUkc1ax2+c9ENYj8o3IN7N5f0ply4an2OAotXnUs6AO6+FBgIXG9mpwDHAF+a2XjgLeAad58fnfgfBNwedZn+BjgYSHUce6SZzU547Q9cDywCvjazL4GXouFEBjxuZhOBiYRODX+KxvUEvjKzScBxwBVR+WDgxaT5DAV+WJXtUau16xt6qSXa/RTIy4N2e5bcqSCVXY8KPeGSZX4xXWrN28LF70DfhN8RXQ+Bvc5NP00G3p+0kP1vGcYFj3zGATe/x+sT5mVlvlKHNdomNCcDjZo0ZRMl53FO2ztFE3M9pDsS1CF15o4EyZZND01iy2dCr1PDDTMzMfldeP/P4dYhvU6Do2/K/j3bls0It+Zp0yNrszzyjuFMXbRmy3C7lo355DdHZm3+UsesXQq37QpeuKVobJtTuL/FFRzbuy2n7VXppFOnm+HrWu81qYtadYFT76369N2OqvlbsrTKXpNasUWrSjepLV69gaIiJy+vTu8zpKqmvFsq4QDste5T/v2zqp52rpvqZPOaSK4tWb2BkVMWs2p9+tvonJ7UXHJK3w5KOFuzFu3LljXZ+h4AqCMdkQy9On4uv3x+PBs3F7FNowY8cF4/Dti1dZl6vzuhJx1bNeHT75fSt9O2XHJw1xxEK7XGTgfC9t1gSUIvtSNvzFk4uaJzOnVInT2nU48UFTn73jysVNNZr/YteP3yOnaLfcmN9Stg9KOwcg70Oh122r8qc6nTh8s60hHJwKaiIpYkdX9esHLr6OoqWdC4JRx0Za6jyCmd0xHJQKMG+Ry3R+kLWU/pW8efpyMSIx3piGTo9jP7sGubbZg4ZwX777w9Fx3YJdchidQZSjoiGWrSMJ+rju6e6zBE6iQ1r4mISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio6QjIiKxUdIREZHYKOmIiEhslHRERCQ2SjoiIhIbJR0REYmNko6IiMRGSUdERGKjpCMiIrFR0hERkdgo6YiISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYqOkIyIisVHSERGR2CjpiIhIbJR0REQkNko6IiISGyUdERGJjZKOiIjERklHRERio6QjIiKxUdIREZHYmLvnOgapJDNbBMxIM7o1sDjGcCpLcWWutsZWW+OC2htbTcS12N0HZnmesVHSqSfMbLS79891HMkUV+Zqa2y1NS6ovbHV1rhySc1rIiISGyUdERGJjZJO/fFArgNIQ3FlrrbGVlvjgtobW22NK2d0TkdERGKjIx0REYmNko6IiMRGSaeOM7OBZvatmU0xs+tiWF4nM3vfzL4xs6/M7Iqo/EYzm2Nm46LX8QnT/CaK71szOzahvJ+ZTYzG3W1mloX4pkfzHGdmo6Oy7czsHTObHP1tFWdsZtYjYbuMM7OVZnZlLraZmT1iZgvN7MuEsqxtHzNrZGbPRuWfmlmXasZ2m5lNMrMJZvaimW0blXcxs3UJ2+7fNRVbmriy9tlVZ5vVSe6uVx19AfnAVGBnoCEwHti9hpfZDtg7et8c+A7YHbgR+FWK+rtHcTUCukbx5kfjPgP2Bwx4AzguC/FNB1onlf0NuC56fx1way5iS/jM5gM75WKbAYcAewNf1sT2AS4D/h29HwQ8W83YjgEaRO9vTYitS2K9pPlkNbY0cWXts6vONquLLx3p1G37AFPcfZq7bwSGAKfU5ALdfZ67j4nerwK+ATqUM8kpwBB33+Du3wNTgH3MrB3Qwt0/8fDf9gRwag2FfQrwePT+8YTl5CK2I4Gp7p7uzhI1Gpe7fwAsTbG8bG2fxHm9ABxZ2aOxVLG5+9vuvjkaHAV0LG8eNRFbmm2WTqzbrC5S0qnbOgCzEoZnU34CyKqoGWAv4NOo6OdRM8gjCU006WLsEL1PLq8uB942sy/M7NKorK27z4OQNIEdchQbhF+yzyQM14Ztls3ts2WaKFmsALbPQowAPyIcIRTramZjzWyEmR2csPy4YsvWZ1eT26zWUdKp21L9GoqlD7yZbQMMBa5095XAfcAuQF9gHnBHBTHWVOwHuvvewHHAz8zskHLqxhqbmTUETgaej4pqyzZLpypx1NS2+x2wGXgqKpoHdHb3vYCrgafNrEWMsWXzs8vZ/3EuKOnUbbOBTgnDHYG5Nb1QMysgJJyn3P2/AO6+wN0L3b0IeJDQ9FdejLMp3VSSldjdfW70dyHwYhTHgqh5o7j5ZWEuYiMkwjHuviCKsVZsM7K7fbZMY2YNgJZUvmkqJTO7ADgROCdqmiJqvloSvf+CcO6ke1yxZfmzy/o2q82UdOq2z4FuZtY1+hU9CHilJhcYtTU/DHzj7n9PKG+XUO00oLinzyvAoKiHTlegG/BZ1Iyzysz2i+Z5PvByNWNrZmbNi98TTkJ/GcVwQVTtgoTlxBZbZDAJTWu1YZslLC9b2ydxXmcC7xUniqows4HAtcDJ7r42obyNmeVH73eOYpsWV2xZ/uyyus1qvVz3ZNCrei/geEIPsqnA72JY3kGEQ/8JwLjodTzwJDAxKn8FaJcwze+i+L4lobcV0J/wzzoV+BfRHTKqEdvOhJ5D44GvircHoX18GDA5+rtdDmJrCiwBWiaUxb7NCElvHrCJ8Av74mxuH6AxoflwCqG31s7VjG0K4XxH8XetuJfXGdFnPB4YA5xUU7GliStrn111tlldfOk2OCIiEhs1r4mISGyUdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR6QWMLPhZqaupCmY2Z/MbL2ZdUoo62JmbmaP1dAyLbp79Ic1Mf+tmZKOVEn0D5/4KjSzxWb2npmdk+v4AMzssCi24bmOJduKk1T0uqicen9IqPdYjCFmRZRofgU84O6zKqqfLR6uJfkDcJCZnRnXcrcGDXIdgNR5f4z+FgA9CHfOPdzM+rn71TmLauuxGfgx8GjyCDPLI9wkczN193/994THBNwW94Ld/WUz+wb4i5kNdV3UmBU60pFqcfcbo9fv3P1M4FjCHQuutPr+MKra4TVgfzPrlWLcsUBn4NV4Q8oOM2sJnAMMi/MoJ8njhHu6HZmj5dc7SjqSVe4+DJhEuHPuAAAzO9XM/mNm35nZGjNbHT164PLo1/gWZjYkagpKeXdoMzszGv/PbMduZu3M7B4LTx/daGaLzOy/ZtYvRd2WZnZN1Jw4O6H+K2a2XznLGBSt+zoLT6N80szaVyPsh6K/P04x7sfAOkruzJwcS3szu8HMPjaz+dE6zDWzp82sZ5ppTjazYWY2z8w2RPVHmNllSfV2NrMHLDwNc52ZLbXw1Mx/m1llb9s/mHD7oGcrWR8zy7PwVE6PPrvGCePamdmj0XYvfuroBQnNsDemmOWQ6O/FlY1ByldXD7mldiu+VXtxc8QtQBHhuTtzCHfRPQK4i5CYzkuY9l7gbOD/gA9SzLv4GTkPZDXgcHPGj4D2wHuE+211As4CTjCzM9z9tYRJegJ/iWJ8HVhGOKo4GTjOzE5y9zeTlnEV8HdgOeEhXssJRyMjCc9QqYpvoxjOM7Nr3X1DtKwdgZMICSfdvA8hPCn0fcJdw1cTblB5JnCymR3o7uMT4r8UuJ/w5NNXgcWEZ+/sCVxE+OyKb4b5OdAC+F8078aEJ2meR7jv2JJKrNtR0d+PKlGXKMH8h3BftnuAyz3cBRoz24GwnbsQttdIYMco5rfTzdPdZ5jZHOAoMzM1sWVBrm/+plfdfBESiqcoP4qQYIqAnaKyXVLUyyM0XTiwb9K4L4H1lH3sdNdovh9XMsbDovkPr0Tdt6K6v0sqP4BwTmQJsE1Cecvk+KLy4lvWf5NU3gXYQLhlfZek7TA03fYsJ97h0TS7AudG7wcnjL8uKjsw+kwceCxpHjsAzVPMuw8hAb2RVP5FtA47pJimdcL7X0TLuyJFvWZAk0qu43xgJSluahptzy3rBGwHfBh9P65NUf/hqP6tKdZ1QzTuxjRxvBiNr9FHwW8tLzWvSbWY2Y3R6y9m9gLwJuFI506PHsns7lOTp/PwC/SuaPDYpNH3EU4eX5BUfmk07/uzuAqYWUfCYxBmAn9LinMk4ahnO+D0hPIV7r44eV7uPpvwyOHdzKxzwqhzgIbAP919ekL9IuAaws6yql4gHGn9OFofAy4hJL6P003k7gs9PHI8uXw84WjvcAvPTkq0mXC35eRpymwLQtNecr017l6mPJmFR3W0BeZ7tOcvp+5OwMfAvsB57n5rinkNJhzx/TkpnvGEo87yzI/+di63llSKko5U1x+i128ITWYfEv7xt/RcM7PtzewWC4/2XV3chZfwyxnKPnL5CcIv7UsT5lEAXEjYuT6X5XXYK/r7obuX2aESdsCJ9YpjOtDMnjOzWdH5jeL1+kVUJXG99o7+jkieubtPo/QjjjPi7usJzUqHmdmuhM9hF8LDxcplZieY2avROZpNCetwEiHxt06o/hThHMtXZvaP6FxdmxSzfYXw+d1jZkPN7FIz6xUlw8oqPu+zrIJ6PYBPCM2ix7l7qvNXPYAmwIRUSZaKm++KH6jWutxaUik6pyPV4u7l7kjMbFtC+35XwrNCniD8E28GtgWuIOzcEue5ysz+A/zEzA539/eBUwht8HdGO9lsahn9nZdmfHH5tsUFZnYa4QhjPfAO4RkpawhHLIcBh1J6vYqXsSDNMuYDO2UWdikPEpLdxYRtvYEKfsGb2eWEo81lhHWYCawlNCWdSmh62rIO7v53M1sMXAZcDlwJuJmNAK5x99FRvRlmtg9wIzCQkiPEWWZ2u7vfXYn1KT4aalxurdCzbDvCs3bGpKlT0bZPV16sSVJMUg1KOlLTLiHsBP/o7jcmjjCz/QlJJ5X7gJ8QOhS8Tw11IIgUn2jfMc34dkn1AG4CNgL93f2bxMpmdj8h6aRaRlvCw8eSpVt2pbj7RDMbRUg6LYGhHj3OORULj0X+IyHZ7e3hyZaJ4/dPs5wngCeiHxMHEJ6a+SPgLTPr6eEx4UTb5OxoOX0I55V+AdxlZmvc/eEK1me5mW2k5IgnnVcJnSn+Cgwzs2NSNPWtjP62TTOPdOXFimNYWG4tqRQ1r0lN2zX6OzTFuOQd8xbuPoHQTn+ame1L2Gl9kLyDz5Kx0d+Dop1kssOjv4m/pHcFvk6RcPIIT1dNVjxtmXW28LjlTsnlVfAg0IZw7qiiprXWhCO3kSkSzjaUNAem5O7L3f1/7v5j4DHC0cbBKeptdvcvovMsg6PiUytck2Ai0M7MWlQQy83AVYTmz/fNLDmJTCIcpexp0ePMk6T6vBLtRjiCnVipqKVcSjpS06ZHfw9LLDSzvQjngcpzH2EHOpTQgeDfWY4N2HLy/x1Cj6grE8dFCe+HhCaoFxNGTQe6WcI1NtE5iz8Au6dYzFOEE/C/sISLZqMkdRvZ+V8cQjjyOIXQu608CwlNaf2iJFMcTwGhya3M+QszG5gmKe8Q/V0b1dsnxY4fSo4o1lYQW7HhhO2yT0UV3f1O4KdAL2BE4ufi7hsJ1/q0BK5PnM7M+gDnp5uvmTUC+gJj3X15JeOWcqh5TWraE4TeWXea2eHAZMK1ICcC/yVck5PO88A/CCfkF0f1q2I3S3/fsZnufgOhKe9j4DYzOwYYTcl1OkXARUknof9BSIJjzWwoIaEcSEg4rxJOxG/h7tPN7DrgjmiaZwlNbscSjjgmEK53qTJ3Xwu8VMm6RWZ2N6Fr9UQze5mQ4A8nHLW8T8kRXrEhwHoz+4iQdI1wdDOA0Cnk3ajeD4GfRed6phAS9i6EbbIBuLOSqzQU+CVhG71bQV3c/d9mtp7QPfoDMzvC3WdGo68jdLD4dfRDYiSh2fQHhGuJTiV1D8LDKPnhI9mQ6z7betXNFxlcV0LYEb9C+HW9hrCDuoSkay3STPuPqM5tVYjxsOI4y3mNS6jfgXB0NYNwvmYxYSc+IM38LyScwF4T1X0R2INwAt2Bw1JMM5jQ1LYeWEToddae6LqbDNZteLSMXStRN911Og2Aq4GvCc1P84EnCR0aHoum6ZJQ/yfROk4jHK0sJTRN/pqE630IXZfvA8ZHddYRks+jQO8MP8MxhOue8pPK0353om28iZAYd076fB+Ptvu66LO7gHAxrANXppjX06S5Nkmvqr0s2rAitZKFO0QfAvRw98k5DkdiZmaDCTv+0939xYrqV3EZfwF+Cwx097cSyncgJK6n3f2Smlj21khJR2qtqNvtp8Cb7n5cruOR+EXnyT4hdFvu69XYYZlZe3efm1S2B6GpbSPQwRO645vZnYTegN09qbOFVJ3O6UitY2Y/JTSFXERoZ/9DbiOSXHF3j+75djqhGXJONWY32symEG6ztIZwbvEEQmeFnyQlHCNcn3WeEk526UhHah0zm064h9k0wv2wns5tRFIfmNkfCB0GugDNCTdcHQXc7u7DcxXX1kZJR0REYqPrdEREJDZKOiIiEhslHRERiY2SjoiIxEZJR0REYvP/Ug5CDXxQrP8AAAAASUVORK5CYII=\n",
                        "text/plain": "<Figure size 402.375x360 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(x='PayloadMass', y='LaunchSite', hue='Class', data=df)\nplt.title('Relationship between Payload andd Launch Site')\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": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": "The Payload Mass > 7500 has 5 times more sucess rate i.e. (sucess rate ~84.25%)"
        },
        {
            "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 864x360 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\norbit_group = df.groupby('Orbit').Class.mean()\nplt.figure(figsize=(12,5))\nsns.barplot(x=orbit_group.index, y=orbit_group.values)\nplt.xlabel('Orbit', fontsize=20)\nplt.ylabel('Mean Succes', 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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WoQPbSCQWGg5H3+BdFhLq+obbMCIphksyXRfD1rxypqUNJMQnysw/OoMB0eFcP2esV3lEaAjHjE1i2MBorpvjnRI4MSacSe11k6RMhEE+qYQPuxwSfbr/EkbCET75IgYMg9R2ugEGT4YBQ73LZn8HEka0vV1zyRMgdYp32eSvt58CefQc9540d+z32+5Ka0tiBsyc712WMNIdwy6aPSqRyDDvf98bThzrfzyjsRmxEdx0kvffQVR4CDNHJnS5HW2ZnjaQ6HDvMcMbTxpLUpzP+GPKeBg63f2+5T03vtZcWCRkHNdyB6OOh9AI77Jjv98y6VrKRNcN2dyMK2FQO13VrQmPhmNu9i4LCYNxp7ZcN312y6634//XpefugtYSEnYnUeERRxzBpk2b2LZtGzU1NTz77LOcd9557W/Yjj475uKn/IfASFW90ac8E3hEVae1VmegZ0X+ak8xLy/PZuO+Mi6amcax45NJ6siA/o5PIOsRdxKbNR/SZrc7vrCnqJL31+/njdV7OGXSYMYNHsBLy7OpqWtg3tdGMDtjEJHhoZRV1fHZtnye+mwnKQMiuTQzncNHuAH9vNJqPtyUy8vL3YD+hTOHMWloB1IF526AL1+BHR/DlAtg/On+xxBKcmDzu26ANm02TLuo/eACsG+t22Z3Fky7BMaeCvGdTCuctxnWLoStS1ywHn8mJLYToBoa3D6XPeYG9DOvdoOw3Uk/XLIHNr0DX74E6UfCtLndCi6qyqpdRTyftYuc4moun53OUaOTGNBGcAEoqaxh6ZYCnv18J8MTo7k4M50Z6Qldbkd7Vu0q4oVlu8guqORSTxsTYiJarpi32d28sfV9mPVNCI2EFU9DXArMugqGZ/of0M/+ApY/DmW5kDkfMo71n9Ezd4PrbtvxiQte406HgV0fBKe6HHYuhS/+6QLFzPmeG3D8fF7PznJdxiV7YOY3IeMEiG768BbsMReAN998k1tuuYX6+nquueYabrvtthbrdHbMpc8GFxGZCexV1T2eO8ceBVYDTwNDVHW5Z/1v41Igtzoaa1PuG2OCpbNT7gf6brGO6mxw6XO3IjeTCjwsIo2XBJ8D9wKDgbtEZBju9uRc4LvBaaIxxgTW+YcPPyjBpLv6XHBp7BJT1UXAIj+r7MB998UYY0yQ9NkBfWOMMb2XBRdjjDEBZ8HFGGNMwFlwMcYYE3AWXIwxpp+75pprSE1NZerUqQGr04KLMcb0c/Pnz2fRIn8333adBRdjjOlLVj8Pd0+FBQnu5+rnu13l8ccfz6BBg7rftmb63PdcjDGm31r9PLx+k5suCqB4l3sOcNglwWuXH3blYowxfcXiXzUFlka1ld75b3oJCy7GGNNXFGd3rjyILLgcBLV1DeQUVVJc2Xoip96ssqaePUWVlFfVHZwdluS47IQHQ0Wh+8es78Z7U1Xi6vBNXNWe0v1QurdTm9TWN7CnqJLiipr2V+6Kuhr3WiqLe6Z+X6X7On0M+rXWMpd2MaNpT+qTwUVE6j1Jwr4UkRdEJMZTniYir4rIJhHZIiJ/ERE/83sfPNvyyvnZK2s46U8f8I2HP+XTrfl+M7/1VutzSrj5mRWc9KclfOuxL1idXdRzOyvdBx/fDfcfBQ/NcbnRq0t7Zl8N9S7j5CNnwn2z4a2fQEEXEpbu/BSeuADuzYSF10Huxva3qSqB5U/AQ8fB/Ue7LKTl7Ses25Ffzq9e/4pT/vwBFz/4Xz7alEu9b+rR7sjbDK/fDPceAY+fC9s/6blMYpVFkPUoPHgcPHAMfPoAlOf3zL4OJSf/3OWTaS482pV3w+WXX85RRx3Fhg0bSEtL45///Ge36oM+OOU+eOd0EZGngGXA3cBnwP2q+oiIhAIPAQWq+qO26uupKfcra+q5+dkVvLN234GyyLAQXr3hGCYOCXzO8kArKKvmkof+y+b95QfKBsVG8NoNx5CWGBP4HWb9C9641bvsipdh7MmB39eelfCPk1066UYzvgHn3ANhHfw8krcJHjoBapqODxnHwuXP+s8p0mjDInjmUu+y8x+AGZe3uklNXT3/t/BLns9q6v4ICxEWXn8MU4d3IPdOe2rK4cVvwca3msrCIuHaJZA6ufv1+1r/b3h2nnfZ3H/AtIv9r38I6+yU+6x+3o2xFGe7K5aTf35QBvM7O+V+n7xy8fERMBY3E3KVqj4C4MlIeStwTeOVzcGWU1zpFVjA5a7fklvWyha9y87CCq/AAlBQXsO2vPJWtuiGmnIXXHxtejfw+wLI2+AdWABWPwulOZ2oY5N3YAHY/jEU7Wx7u7Wvtixb9gjUt97Vta+kmpeW7/Yqq2tQNu0L0JVd8W7vwAKumy9vk//1u2v1Cy3Llj3mErWZth12Cdz6JSwocj972V1ijfp0cBGRMOBMYA0wBXcFc4CqlgA7ccHHd9trRSRLRLJyc9vvkuiKqPBQBvrJDBgX0Xa2wN4iJiKMMN+8yUBcZA/cwR4SAQmjWpb3VF9yhJ8ri9iUlilp2+Lv6iQitmW3ha9Bfl7noDEgrR/XqPAQkuNaXlENiArQexEeDVF+roDaugLrjiQ/KYaTxvnP5Gj6pL76TkaLyEogCxc8/gkI4K+Pz2+5qj6kqpmqmpmSktIjjRyWEM3t53h3KRw9JolJQ3voHzbARiXFcqNPzvVLM9MZm9qNdL+tCQuHY270PrnHpvRMlxi4nO3DZ3mXnflHGDC443WkToIJZ3uXnfLLlvnafU08G6Kb5VAPj4Ejvt3miTVlQBS/OHeKV9n0tIFMGRaALjGAhHQ4/XfeZaNPhNQp/tfvrikXQFRC0/OIOJcCuJ/q7cMTXWlfnx9zaVZ2CvBzVT2+WVk8sA1IV9WK1urryTTHlTV1fLm7hM25ZSTFRXDY8ASGDOzEp+MgK6msYc3uEnbmVzA0IYppwweSFBfZ/oZdlbMG9q2B0AgXAJLH9dy+irIhZyVUFrh89kOmQ3gnX1vpXtizwt3dljTWtTmyA8E3dwPkrHZdc0MPg8Htn8Sra+v5ck8xm/eVMTAmnMPSEhiW0M5VUmfUVLjjkbfJBfZhh0P80MDV72v/eti7GrQBhhwGg3tgbKcP2LZtGwMGDCApKQmRlj0Fwaaq5OfnU1payqhR3h+c2hpzOZSCiwBfAH9V1cc9A/oPACWq+oO26uvJ4GKMMW2pra0lOzubqqqqYDelVVFRUaSlpREe7t2l31ZwOWSmf1FVFZELgL+LyO24Lr83gZ8Ft2XGGNO68PDwFlcEh4I+GVx8r1qale8Czj3IzTHGGOOjrw7oG2OM6cUsuBhjjAk4Cy7GGGMCzoKLMcaYgLPgYowxJuAsuBhjjAk4Cy7GGGMCzoKLMcaYgLPgYowxJuAsuBhjjAm4Xj39i4gMxmWYPBIoBGqAeKAWiABGARs8q/8GeAm4DbgKN83+buAGVf3q4La8dRU1dWzPK6e2XhmVFENRVR25pdUkx0UwMim21e3yy6rZkV9BbGQoaQnR7CqqpLKmnhGDYro0S3FtfQPb88opraojLTGa1PiOzdRcWlXL9rxyRIRRSbHENs8nUp4HhdtdbpCEEVC8C6rLIXEkxKU2rVdTDvlb3IzAg8ayoyKUvLIaUuIiGNHGMcgtrWJXQSVxUWGMHhhGWPFWqK10U9zHJh1Yr6yqlu35Fagqo5JjiYtqNtleRQEUbHVT+yeNhfCm172vpIrdhZXER4eRkRRLWGgXPnuV5bpjEBHrjkHRDjfbcGIGxHUwtUNFIRRuhdAI6hJGs72kgZLKOoYnRjO4g+8TVaVQsAVEaEgYzfYyobCiliHxUQxPbDaTcl0tFGyG6jI/71OFq6O+1uWbqSyEsn1uneY5aRrq3ftZVQgD0yF+WMfa2AN2FVSwv7Sa5NgIRiTFtD7LcPO/1UFjOz8btmlXrw0unlmOFwKPqeo8T9lI4DxV/ZuIZABvqOqMZtvcABwNTFfVChE5DXhNRKaoatCnHN1XUsWf39nIc1m7SI6L4OaTx3Pn2+spqaojLjKMuy+dzskTBxPik6Br/d4Srn9qOVtyyzllUirpg2J4bOl2GhTGD47j3nkzGT+44zliyqvreObznfxh0Xpq65XhCdE8cMVMpqUltLndzoIKfvHql7y/wSVXO2vaUG47a5I7We1f59Lk7v/K5QEZMhU+/bs78QwaA5c87spK9sDiX8Oqp9G4ISw+5iluXZRPaXUd8VFh3H3pDE6amNripLA2p4TvPbmMHfkVzJsay+0J7xC27H43XfvgqTD3n5A6kezCCn79xlre/splAD15YioLzptC+qAYN839y9e6aeVFYPZ34LgfQlwKq7OL+M4Ty8gpriI8VPjpmZO4bHY6MRGd+BfZtxZemO+yXI47HZLGwGcPuDYmj4eLH2t/Wvm8TbDwOsj+nIrhx/D86N/yu/f2UlPfwNCBUTx45SwOa+d9onAHvPVj2PgWtYMm8O+ZD/LTt/dRWVvPoNgI/j5vJkeOSYKqEvj8QVjye0+gH+15n6ZBSQ68/xtY8aQLJsf/GBb/EqpLIDIeLngQJpwJdVWw6llY9GOXuXLAELjkSUg/ouPHLUA+2pjLDc+soLiyltiIUO68eDqnTxlCqG/Cu/3r4IWrIXcdSAgccwscfRPEJPqt13RNb+4WOwmoUdUHGgtUdYeq/q2NbX4M3NiYu0VV3wGWAt/o0ZZ20BfbC3guaxcAF81K4w+LXGABKKuu48ZnVrA93zttbmVNPXe9vYEtua58Rnoij3ziAgvAxn1l/OU/m6iuq+9wO9bllPCbf6+jtt5Vsruokttf/Yriyto2t1v05d4DgQXgzTU5fLAx151UPrzLBRZweeSX/s0FFnCffhf/0l2xbP8YVj0NwPbpP+DGN/MorXbHoKSqjpueWcGOfO/UO+U1ddzx73UHyuel5RKddZ87aQPs+xI+vgfqaliyIfdAYAFYvH4/767d5z59f3q/CywAqu7Ev/sLiitq+OnLa8gpdp8/auuVX72xlvU5nUghXFsF7//OBRaA9NkuuDa2MW+jW15b2XodDQ2w/DHI/hyA9eOuZcG7e6ipd3XkFFfx05fXUFzRejpkADa8eSBl8eZpN/ODN3OorHXvRUF5DTc9u4K9xZXuWLz3m6Z0zwVb4Z3b3VXMjk9cYAGYcQX85xcusID7+dK33Pu6fz28cYv7GwCX32bh99yVwUG0q6DiQGABKK+p55ZnV7LVN6V4XTV88EcXWMC9Px//GfYsP6jt7Q96c3CZAnT4HfckBotV1S0+i7I8dQXdsu2FB34PCw2hrNo7h3tVbQN7S7wvsAoravhok/tHjQwLoaSqZQD4aFMuhe2dcJrJLmx5glu5q4iC8tbraGhQ3vlqb4vyDzbud90lWxY3Fdb5OYFuXeI+KW//+EDRXkk6cNJrVF5T3+IYFJXX8MkWdwziIsNIqtjasv4t70J1KYvX72+x6J21e6GqGDa903K7nNXkl9fw1Z6SFot2FbaaX66linzY+r77PSTUBVJfW9933XKtqSl1gcEju7bl1ehXe0rIb+N9AmD9vw/8uqcunvoG75xN+0ur2V9aDUU7W2677QMXPHb+t6ksJARqfE7StRXu6sZfHfmbXPK0g2h/SVWLD0c19Q0HPjAcUFEAW95rWUHeph5sXf/Um4OLFxG5T0RWicgXnd0UP2mOReRaEckSkazc3Fw/mwXetLSmlLSqLi96cxGhIaT4jJ8MjApn5sgEAKrrGvzmTJ81MpGBUS3zq7fGXybM8YPjSIgO97O2ExIiHDcuuUX5kaOTXO719CObCsP8ZEdMO8JlaExr6i5JlhIifMY1IsNaHoP46HAOT3ddFuU1dRRFj2hZf/pREBHLMWOSWiw6blyK68oZeXTL7VImMTA6nDEpLcd6hnYmY2h0AqTNdr831LsxF19ps916rQmPhYwTDjwdHN4yuI1JiWVgG++TW+mkpjrCKvEddkiMCWdQbIT/sZHhs1zK4WEzvcvDfI5FWKTrLvOXqXJgGsS0fB96UnJcJLERoV5loSFCarzPWErUQEj/WssKEjN6rnH9VG8OLl8BB/7CVfV64GTA76ioqpYA5SIy2mfRTGCtn/UfUtVMVc1MSengQGs3fW1UEieOd/tauGI3t5wyjvBQ958fFiLcceE0RiV7n5Rio8L42ZmTSIp1wWPj3lLOOazpHzplQCQ/OG0C0T7/WG2ZPHQA3zuh6TDFR4Xx2wumkRjbdoA6d/owpgyNP/A8c2QCJ01MdYOiJ/7M9beD62457LKmDWOS4LTfQOQAGH0CjJ4DwOg1f+EPpw46cAzCQ4U/zD2sxTEYEBXO7edOIjEmHFV4JXcoVZPmNlthKMz5MYRHceqkwRyennBg0bRhAzlr2lAIi3B96/FpzQ7E+ZA+m6S4SO64cBoDIpsC9/UnjmXSkKbX2q6IWDjl5y49MLiuuqkXNS2PTYFTfuE/6DQKDYOvXetuUAAmb3+SG45q+tscEBnGHRdOa/8GjsnnwdAZAIxbfz+/ODGFxmGHyLAQ7rp4OmmJMW6d2d9p2i5mEJzxe4iKh1HHw5hTXPnq52HOTyDUE9RCw+Hcv7obIlInw4n/x4EIFhEHX/87DBjcdhsDbERSDHdePP3Ah5XQEOE3509ldLJP6qeIGDjxNohr1r7p82C4TzA13dZr0xx7BvQ/BR5V1fs9ZSOAD1U1o9mA/tRm29wEnAZcrKqVInIK8BAwRVVb7ew+mGmOiypq2Ly/jNr6BkalxFJSWcfe4ioGx0cxOiWW8FbuUMourGB7XgVxUaEMT4gmu7CSipp6MpJive/+6aCKmjo27y+juLKWEYNi2rxTrbm80mo255YRIsKYlFjvE11RtuuHj4jx3CmV7bpYksa45wd2XuDGJurrqEuewJbyKPaVVDPEcwxau0trV0EFO/LLGRAVzviEBqKLtrjumaQx7tOyR35ZNVtyy1CFMalxJDdvY/FuyN/sAmLyeK8riR355ewsqCAhOpwxqXGdG8w/cAx2eY5BnLtzqniXG8NIGgMJ6R2royTHdS2FRlI5aAKbi4SiyhpGJMYwMrlj7xNl+11Xj0D1oIlsKQ0nr6ya4YnRjEqKbbpppLrMjQdVFbsB/cSRTXVUFLhl9bWQPBYqi6A0B+KGuGMX6jk+tZVuvYp89z4nje3w4Qqk+gZla24ZOcVVpMZHMjo5joiwVj4/N3+fkidAVMdviDFN2kpz3GuDC4CIDMXdivw1IBcoBx5Q1edaCS4C/By4EqgH9uJuRV7T1n4OZnAxxphDRZ8NLgeLBRdjjOm8toJLbx5zMcYY00dZcDHGGBNwFlyMMcYEnAUXY4wxAWfBxRhjTMBZcDHGGBNwFlyMMcYEnAUXY4wxAWfBxRhjTMBZcDHGGBNwFlyMMcYEXFCDi4jUi8jKZo+feMrPEZEVnvwta0XkO61sv11Ekn3KJorIf0WkWkR+eDBeR69VVQI7lsKaF2HX5/4TWBljTA/owpziAVWpqjOaF4hIOG6a/Nmqmi0ikUBGJ+osAG4Czg9QG/ummgpY+lf48M6msjP/AJnfbpoq3RhjekinrlxE5D0R+WY761whIn7yiHbYAFzQywdQ1WpV3dDRjVV1v6p+AbSdEP5Ql7fRO7CAy49e4JsF2hhjAq+z3WJzaP8qYiRwQjvrNIr26Ra7VFULgNeAHSLyjIh8Q0QC3n0XjDTHB1VlYcuy+hqXFMoYY3pYT/SPRAN1HVy3RbcYgKp+W0SmAacAPwROBeYHqoGefTyE634jMzPz0EtqkzDS5Y2vLmkqix/usiMaY0wP68oVgd8TsTgjgbOAXd1qFaCqa1T1blxgmSsioc2ucH7V3foPeUmjYd5zkDTOPR9yGFz6BMQPDW67jDH9QrtXLiLSgHdAWSAiC9raBPhdVxskInFApqou8RTNAHaoar3nd9NRI4+Gaxa5LrKYZIhJDHaLjDH9REe6xT6kKbgcD+wEtvtZrx43CL8Y+EcH9x8tIiubPV8E/Bb4XxF5EKgEymm7S2y1JwACPA/8EcgC4oEGEbkFmKyqJa1sf2iLTXYPY4w5iNoNLqo6p/F3z0n8EVUNSLeUqoa2suisDm6f0cqitC41yBhjTEB0dkB/FFDUA+0wxhhzCOlUcFHVHT3VEGOMMYeONoOLiPwcN95yn6oWeJ53hKrqr7vdOmOMMX1Se1cuC3DB5TnctCoLOlivAhZcjDGmn2ovuJzo+bnT57kxxhjTqjaDi6p+0NZzY4wxxh/L52KMMSbgujS3mIhkAFcChwMDgWJgBfCkqm4LWOuMMcb0SZ0OLiLyA9y36MNxU700Oh/4PxH5qar+OTDNM8YY0xd1KriIyOXAnUAh8FdgCbAXGIIb7L8JuFNEdqvqc4FtqjHGmL6is1cuP8AFlpk+X6jcAHwgIo8By3DT5FtwMcaYfqqzwWUy8Fhr39RX1W0i8jzQZrbKzhCR24B5uIkxG4DvACm479GE4Lrn/qKqD3rWvxb4vmfzEuD7qvpxoNpj+qHiPbDjI9ixFNJmw6jjIGFEsFsVPHU1sDsLNrwJ4bEw/nQYdjiIQFUJ7PoMNr7tjtG4UyF1UuD2XVEIOz+FTe9AyngYezIkjw9c/X1BRQHsXAqbFkPqRBhzMiSPDXarWuhscCml/bnFinAn9W4TkaOAc3BXStUikgzEAq8As1U1W0Qi8WTHFJFzcMHnWFXNE5GZwEIRma2qewPRJtPPVJfB4gWw2nMhvuxRGHcaXPgQRPfTFAY7l8IT54N6Jkv/5B64ehEMPxzWvQ6vXte07n//BvPfCtzJb/UzsOinTc8TRsJVr0PiyMDU39upwvLH4T+/aCobNAa++Sok9K5EgJ29Ffkd4PTWFoqIAKd51guEoUCeqlYDqGoeLsCF4ab3R1WrVXWDZ/0fAz/yrIeqLgceA64PUHtMf1OwpSmwNNr0DuRtCk57gq2uFpb+rSmwANRVwYa3oGw/LP6l9/pl+2HvqsDsu2gXvO+TKqpoB+xdE5j6+4KinfDBH7zLCrbAvi+D0542dDa4/C+Q6Mlt7/VRQURGAE8DCZ71AuEdIF1ENorI30XkBFUtAF4Ddnja8Q0RaXwdU3BjPs1lecq9iMi1IpIlIlm5ubkBaq455NTXdq78kNfgruZ81ZRBQ50LNL4Cday0Huqq/TSpo1nVDwENdVBf07K8vvcdgzaDi4i81/wBPIXr9roE2CQim0XkExHZDGz2lBd71us2VS0DZgHXArnAcyIyX1W/DZwMfI67eeBfbb0M/KRmVtWHVDVTVTNTUlIC0VxzKBo0BtKP9C5LmdSUPrq/CYuEo67zLhOBiWfDgKFwzM3ey8KjYfDUwOw7Pg1mf8e7LDIeUicHpv6+YOAImHW1d1lUQmDHtQJEVFucd5sWNmV47CxtIxFYl4nIRcBVqnpus7JkYJuqDhCRj4Gfq+p7zZb/Cvc6b2+t3szMTM3Kygp0c82hIm8zrHoGNr4Fo0+Emd+ElAnBblXwVJXA1vdh6X0QEeMCyshjICwCSvfBhn9D1iMuMB91PaQfEbh9l+yBta/CyqcgdQoc+V13M0F/UpwNaxfCymdg6HSYfS0MmxGUpojIMlXN9LusreASbCIyAWhQ1U2e57/BfafmSVVd4ik7BbhHVaeKyHnA7cAZqpovIjNwXWhfU9Wc1vZjwcW0SxVqKyA8xn1SN1BbBSEhEBrhZ1kFhERAaJcmAWlfTYXbb0/V3xfUVLgryZCAf47vsLaCS29/Z+KAv4lIAlCH63q7GXhQRB4EKoFyYD6Aqr4mIsOBpSKiuMH/K9oKLMZ0iAhExAa7Fb1LeFQby2J6dt8RPVx/X9DLj0Fnv6FfDzynqvN6qD1eVHUZcLSfRWe1sc39wP091ihjjDHt6uzdYqWApTo2xhjTps4GlxW4b+kbY4wxrepscPkDcJaInNoTjTHGGHNo6OyAfiqwCHhLRBYCX+BmRfb3PZLHu906Y4wxfVJng8ujuEAiwIWeB3gHl8YvLVpwMcaYfqqzweXq9lcxxhjT33UquKjqYz3VEGOMMYeOrqQ5PgE4BhiG6/7KAT5R1Q8C3DZjjDF9VIeDiyeo3A80TqrUOAeGepavB66zIGOMMaZDwUVE5gLPeNbfAywBduECTBowB5gEvCsil6nqyz3RWGOMMX1Du8FFRIbhEm7VATcC/1DVep91QoBvAfcAj4vIp6q6J/DNNca0qr4OKgshKt5NaGhMEHXkS5S3ADHAN1T1Qd/AAqCqDar6MPANz7o3+67TWSJS5vN8vojc6/l9gYjsFpGVzR4JnmXHisjnIrLe87i2u20xptfL2wiLfgwPHgcvfwdyVge7Raaf60i32BnAZ6r6SnsrqupCEfkMOBOXcrgn3a2qdzUvEJEhuGyY56vqck+ul7dFZLeq/ruH22NMcFQWwcLrIPsL93ztKy7P/bcX97q86qb/6MiVy0hgaSfqXApkdKk13Xc98KiqLgdQ1TxcyuWfBKk9xvS8wm1NgaVR2T7I3xSc9hhDx65cwgE/SZtbVQsEIntNtIisbPZ8EC7xV6NbReQKz++FqnoiMAU3PtRclqfci6e77FqAESNGBKC5xgRJY8KoBp8e67Do4LTHGDp25ZIDTOtEnVNw8411V6Wqzmh8AD/3WX53s+Unesoap57x5W/us4dUNVNVM1NSUgLQXGOCZNBYOPIG77IJZ0PKxOC0xxg6duXyITBPRCaq6vq2VhSRScDpwFOBaFwXfAVk4n2FMwtYG5zmGHMQhEXAMTfByKNh7xpIHgdpsyEmMdgtM/1YR4LLvcBVwBsicp6q+j1RewLL67gusfsC18ROuQ/4TEReVtWVIpKESxPwqyC1x5iDIzYZJpzhHsb0Au0GF1VdJiJ3Aj8ClovIy8Bi3JcoFRgBnAJcAEQAf1LVrJ5r8gHNx1zA3SG23VP2sIgMwHWT3aOqrx+E9hhjjPEQVX9DFH5WFPk58H+4gOS7kQD1wO+ABdrRSnuJzMxMzco6GPHQGGMOHSKyTFUz/S3r8NxiqvorEXkMuAY3ceVQXFDJAT7G3QK8LQDtNcYY08d1dsr9HcAveqgtxhhjDhEduRXZGGOM6RQLLsYYYwLOgosxxpiAs+BijDEm4Cy4GGOMCTgLLsYYYwLOgosxxpiAs+BijDEm4HplcBERFZEnmj0PE5FcEXnD83y+53nzNMeTPcumiMh7IrJRRDaJyO0iIsF6LcaYJnml1Xy1u5jswopgN6V3KtrlUlSX7u/5fVUWQ84ayN8CDQ0Br75T39A/iMqBqSISraqVwKnAbp91nlNVryQWIhKNm27/e6r6jojEAC8B1xG8mZqNMcDKXYXc/OxKduRXkBATzh8uPIyTJ6USFtorP+MeXA0NsOltePV6qMiHhAyY+zCkz+6Z/eVugNduhl3/hfBoOPnncPiVEDkgYLvoze/qW8DZnt8vB57pwDbzgE9U9R0AVa0AbsDSHBsTVHml1dz0zAp25LsrlqKKWq5/ejmbc8uC3LJeIm8jPP9NF1gAirbDC1dBaSDyLvqoq4YP/ugCC0BtJSz6KexZFdDd9Obg8ixwmYhEAYcBn/ksv9SnWywalwVzWfOVVHULECci8Qel1caYFvaWVLGzoNKrrK5ByS6w7jEAinZCvU82+ZI9UOLbYRMA5bmw4c2W5fmbArqbXhtcVHU1kIG7avFzJHiueRpkT/dZa2mO8S0XkWtFJEtEsnJzcwPZdGOMj4HR4cRHt+yFT4qLDEJreqE4P6nWI+IgugeyiUYNhMFTW5YPGBrQ3fTa4OLxGnAXHesSg6Y0xweIyGigTFVLm5er6kOqmqmqmSkpft5YY0zApA+K4fcXHkZoSNO9NTeeNJbxgwPXx9+nJU+Ak25vei4hcO5fYNDowO8rcgCc/hsXvBpNOheGHR7Q3fTWAf1G/wKKVXWNiMzpwPpPAT8TkVNU9T+errK/An/swTYaYzrgtMmD+feNx7KrsILkuEjGDx5AbGRvPwUdJBExcOT3YPSJULYXEkZC8vie21/61+DaD1xXWGQ8pEyE2KSA7qJXv7Oqmg38pZXFl4rIsc2eX6eqS0Xk68DfROQ+IBR4Ari3h5tqjGlHWGgIE4fGM3GoDX/6FRELabMO3v6Sx7pHD+lwmuNDmaU5NsaYzmsrzXFvH3MxxhjTB1lwMcYYE3AWXIwxxgScBRdjjDEBZ8HFGGNMwFlwMcYYE3AWXIwxxgScBRdjjDEBZ8HFGGNMwFlwMcYYE3AWXIwxxgRc0CeuFJElwB2q+nazsluA04ARqjrVZ/1HgROAYk9Rhaoe7Vl2PvArIAKoBW5X1YU9+gKMMT2rsgj2r4XKQhg0BlImgEi7m1GUDbnr3PT1qZMgfpj/9crzYf86qCmBpPHekzmW7IXctdBQT1XyVDaWR5NTXMWwhCjGpw4gMjwUgNKqWtbvLaWwvIYxqXGMYjchBVsgKsHtuzEvS30t5K6Hoh0QNwRikiF/s1uWOgkGDm/lGBS6NlYVNR2DRqX73PGpr3FT91cVQXE2xA+H6ARXf0goJI6B8r1QngeJGW4m5JDQ9o9jFwU9uOBytVwGvN2s7DLgR8D9rWzzI1V9sXmBiEzH5X45VVW3icgo4F0R2epJPGaM6WvK8+DdX8DKJ93zsCiY9wKMPr7t7favg6cugeKd7nnyRLjsqZazAJfugzd/AOted88jYuGKV2DE1yB/i0s1vHcNtclTeHbsn1iwpABwse2350/lksx0yqvr+dM7G3j80x1EhIaw8BwIee9qlz4YYOpFcMYdEJcKa1+Dl78N2gBzfgKrnoXC7W69xNEw71nvwAFQlgtv/wzWPO+eh0fDN16CjGPcti9eA7uXudwv0y6GD/8IqnDyL+Dzh6F0D4w6ARLSYYXnOIaEwSVPwMSzOvV2dEZv6BZ7EThHRCIBRCQDGAZkd7KeHwK/U9VtAJ6fd+CClDGmL9q7pimwANRVwRu3uKDTlpXPNAUWgLz1sPGtluvtWdEUWABqyt2JvKoENr/n9g9smXwdv/6w8MBqqrDgtbVsyytnXU4xj3+6A4CLp8QxftmvmgILwJcvQs5qKNgGb9zsAsuAIe41NAYWgMKt8NXClm3MWdUUWMDV/e8fQEUhbPvQBRaAqRfCx3e7xiWNcVdIpXvcsoxjmwILQEMdvH6ju8LpIUEPLqqaD3wOnOEpugx4jtbTFQPcKSIrPY+nPGVTgGU+62V5yluwNMfG9AFl+1uWFWxxJ//W1NfDrv+2LM/2k1ajbF/Lsn1fQm055Kxo2mVDDPUN3qekmvoGCipqyC2rOVA2JQnC8te1rLN8n+vaqvYkxB2Y1tQd1tyOpX629XMMcte5Nu732Ve9py2JGZC3sVl5tZ9681yXYw8JenDxaOwaw/OzvbTGP1LVGZ7HNzxlQsuA5K8MsDTHxvQJg0a1LBs1x3UxtSY0FKbObVk+4Uw/9ftJIzzxbIgeBGNOPlA0vGEvcT5ZMxNiwhmeEM3IpJgDQ0D/2dlAxchTWtaZONqN+QxMc89zN8CwmS3Xm3qBnzb6OQbjTndtHHl0U1ltZdPYTs4ql22yUWikG3tqLqWNcagA6C3BZSFwsojMBKJVdXkX6vgK8E1aMxNY2822GWOCZcg0+Pp9Lu87wNDpcMbvIDKu7e0mnA3T57nBkZBQmP0dGD2n5XrDZsCZf3TjGADpR8EJP4awSBh1LBx1E4SEMnLVn3jgvMGkDoh0zYqP4oFvzCItMYaJQwZw9yUziI8K470tZayZdCs63HMqioiFc+6BoYe5rrCLHnVXFdWlrgvu8CvdSV9CYOZ8GHean2MwHc79i6sLYNgsOPWXLjXyiKPguB9CaDiseApO+60byC/Pg/AYmDLXHYPVz8NZdzUFn+TxcMH9EDOow29FZ/WaTJQi8jwwHlioqgs8Yy9vtHK32Bt+BvRnAC/gBvS3e7b/D3CRqq5sa9+WidKYXq5gG1SXuU/+MYkd26a2Cgq3uRN34igIi/C/nqqrv7YCEkZAVLM0zHW1bixE6yFhFHsrIb+8huS4SAbHR3lVs7OggrKqWoYOjCYxpBKKd7kAkDjK++62sv1Quted2GOT3biL4q5QwiJbb2PhdheQBqa5u8Aa1de6ZfW1kDgSqkpdV1pssrtbrWg7SKi7eirNcXeTxQ+H2KSOHcc2tJWJsjcFlwuAl4FJqrreExw2Ac07RW8Fzsb7VmSA2apaIyIXAr8EwnG3Iv9CVV9ub98WXIwxpvPaCi694VZkAFT1FdwYSePz7bgg4euFNup4GRegjDHGBFFvGXMxxhhzCLHgYowxJuAsuBhjjAk4Cy7GGGMCzoKLMcaYgLPgYowxJuAsuBhjjAk4Cy7GGGMCzoKLMcaYgLPgYowxJuAsuBhjjAm4XjO3WGeJyG3APKAeaAC+A6wA/gic6ylbC1yvqj2Xbs0Yc8gorqxhxc4iVu0qYmRSLJkZiaQlxgS7WX1SnwwuInIUcA4wU1WrRSQZiAB+BwwAxqtqvYhcDbwsIl/T3jL9szGmV2poUJ79fBd3vLX+QNmskQk8cMUsUgZEtbGl8aevdosNBfJUtRpAVfOAIuBq4FZVrfeUPwJUAycFqZ3GmD5iV0EFd/9no1fZsh1FrN9bGqQW9W19Nbi8A6SLyEYR+buInACMBXaqqm9y7Sxgim8FInKtiGSJSFZubu5BaLIxpjerqW+gqrahRXlVTX0QWtP39cngoqplwCzgWiAXeA44EZfPzZf4K1fVh1Q1U1UzU1JSerK5xpg+IC0xhjOmDPEqi48KY2xqOymVjV99cswFwNP1tQRYIiJrcAP6I0VkgKo2v46dCbwehCYaY/qQ6IhQfnrWREYkxfD6qj1MHTaQG04ey6gUCy5d0WvSHHeGiEwAGlR1k+f5b4AEoAY3oP9dz4D+N4GbgCPaGtC3NMfGmEYNDUphRQ1xkWFEhocGuzm9Wp9Ic9xJccDfRCQBqAM247rISoG7gI0i0gCsBy6wO8WMMR0VEiIkxUUGuxl9Xp8MLqq6DDi6lcU3eh7GGGOCpE8O6BtjjOndLLgYY4wJOAsuxhhjAs6CizHGmICz4GKMMSbgLLgYY4wJOAsuxhhjAs6CizHGmICz4GKMMSbgLLgYY4wJOAsuxhjTW9VWQF1NsFvRJb02uIjIYBF5WkS2isgyEfmviFwgInNEpFhEVjZ7nOLZJk1EXhWRTSKyRUT+IiIRwX4txhjTKRUFsOJp+NeZ8NyVsP0TaGiZyKw365XBRUQEWAh8qKqjVXUWcBmQ5lnlI1Wd0ezxH882LwMLVXUcMB43e/Jvg/ASjDGm69a9Bq9+D3JWwqZF8Ph5sGdFsFvVKb0yuOBy3teo6gONBaq6Q1X/1s42Var6iGf9euBW4BoRienR1hpjTKBUFMAnf/Eua6iDXZ8Gpz1d1FuDyxRgeRvLj/PpFhvj2WZZ85VUtQTYCYz1rUBErhWRLBHJys3NDWTbjTGm60LCINzP5+Hw6IPflm7orcHFi4jcJyKrROQLT5Fvt9gWQAB/ScH8lqvqQ6qaqaqZKSkpPdh6Y4zphKh4OPFnPmUDIf3I4LSni3prsrCvgLmNT1T1ehFJBtrKRey1DYCIxAPpwJaeaKQxxvSIMSfBN1+DjW9DbAqMOxUGTw52qzqlt165vAdEicj3mpW1N26yGIgRkW8CiEgo8CfgUVWt6JlmGmNMDwiPhtEnwBm/g+NuhSFTg92iTuuVwcWT8/584AQR2SYinwOPAT/2rOI75nKRZ5sLgItFZBOwEagCfuZnF8YYY3pQb+0WQ1VzcLcf+zOwlW12Aef2WKOMMcZ0SK+8cjHGGNO3WXAxxhgTcBZcjDHGBJwFF2OMMQEn7iar/k1EcoEdndgkGcjroeb0RXY8WrJj4s2OR0uHwjEZqap+v4VuwaULRCRLVTOD3Y7ewo5HS3ZMvNnxaOlQPybWLWaMMSbgLLgYY4wJOAsuXfNQsBvQy9jxaMmOiTc7Hi0d0sfExlyMMcYEnF25GGOMCTgLLsYYYwLOgksniMgZIrJBRDaLyE+C3Z5gEJF0EXlfRNaJyFcicrOnfJCIvCsimzw/E4Pd1oNJREJFZIWIvOF53t+PR4KIvCgi6z1/K0f152MiIrd6/l++FJFnRCTqUD8eFlw6yJMf5j7gTGAycLmI9K3sPYFRB/xAVScBRwLXe47DT4DFqjoOl1unvwXfm4F1zZ739+PxF2CRqk4EpuOOTb88JiIyHLgJyFTVqUAobsb3Q/p4WHDpuNnAZlXdqqo1wLPA14PcpoNOVXNUdbnn91LcSWM47lg85lntMVw+nn5BRNKAs4F/NCvuz8cjHjge+CeAqtaoahH9+Jjg0ptEi0gYLvHhHg7x42HBpeOGA7uaPc/2lPVbIpIBHA58Bgz25OBpzMWTGsSmHWz3AP8LNDQr68/HYzSQCzzi6Sr8h4jE0k+PiaruBu4CdgI5QLGqvsMhfjwsuHSc+Cnrt/dxi0gc8BJwi6qWBLs9wSIi5wD7VXVZsNvSi4QBM4H7VfVwoJxDrMunMzxjKV8HRgHDgFgRuSK4rep5Flw6LhtIb/Y8DXdp2++ISDgusDylqi97iveJyFDP8qHA/mC17yA7BjhPRLbjukpPEpEn6b/HA9z/SraqfuZ5/iIu2PTXY3IKsE1Vc1W1FngZOJpD/HhYcOm4L4BxIjJKRCJwA3KvBblNB52ICK4vfZ2q/rnZoteAqzy/XwW8erDbFgyq+lNVTVPVDNzfxHuqegX99HgAqOpeYJeITPAUnQyspf8ek53AkSIS4/n/ORk3VnlIHw/7hn4niMhZuP71UOBfqvrb4Lbo4BORY4GPgDU0jTH8DDfu8jwwAvfPdLGqFgSlkUEiInOAH6rqOSKSRD8+HiIyA3eDQwSwFbga92G2Xx4TEfklcCnubssVwLeBOA7h42HBxRhjTMBZt5gxxpiAs+BijDEm4Cy4GGOMCTgLLsYYYwLOgosxxpiAs+Bi+iQReVRE1DMFTWNZhqfs0QDUv93zxUjTBhFZ4Dnmc4LdFtO7WHAxvYbnJNXWY36w29ia7gQ2EVni2bZeRKa1sk5jMD2l24015iAIC3YDjPHjl62Ur2xnu93AJKA4oK05eEKAO4Ezgt0QY7rLgovpdVR1QRe3qwXWB7Y1B9Vm4HQROVVV3w12Y4zpDusWM4eMtrqmRGS8iLwkIoUiUi4iS0XkbBGZ31aXm2c+qDtFZKeIVIvLQvpjzxxRjessALZ5nl7Vja68n+Fm2r5TRDr0v+nZx5JWlrU5LiUiYzzZIvNFpFRE3hGRqZ71UkTkIRHJEZEqEflCRE5spy1XeabYrxSR/SLyLxEZ0sq6g0TkDnFZKitFpFhEFovIaX7WPfAeicsGu8Szvk0v0ovZlYs55InIROATYBDwb2A1LufIK8CbbWwaDryDmyb9Ldy8UOcDvweiaOq+WwIk4LJRrgIWNqtjZSeaugJ4ErgSN5HhI53YtrMycPPBrQMe9Ty/AFgiIkcBi4AS4DnccbsMeEtExqvqTj/13Qqc5ll/EXAsbj6xOSLyNVXNbVxRREbijlkGbp66RUAscA6wSES+o6oP+9nHRbguw7eABzzbm95KVe1hj17xwH1qV2CBn8d8n3Uf9ayb0awsw1P2qM+6iz3l3/MpP7PZPn3r3+4pfxOIblaeChR5HuHt7buDr3uJZ9uxuLQOlbhp62P8vN5T/ByzJa3U29YxUuA2n/Vv95QX4E7eIc2WXelZdrfPNgs85TXA4T7L7vYs+6ef19sAXOZTnoALxpW4RFqN5fM99TQAZwT779QeHXtYt5jpjX7h5zG/KxWJSDpwEm4848Hmy1T1LeA/7VRxk6pWNttmP25q9IHAhFa36iJV3YWbeXs48P1A19/MdtwVWHONKXcjgR+pavPMmk/jrtxmtFLfE6q6wqdsAe7minkiEgkgItOBE4CXVPXZ5iurS4X8C9xV4Vw/+3hVVRe1+opMr2LdYqbXUVV/WT+7aobn5399TpaNPsYlc/KnWFU3+ylvTHed2M22teYO4FvA/4rIw6q6rwf2sVJV633KGpPfbVTV0uYLVLVeRPbhkuT584FvgaoWi8hKXDCZhLsqOcqzeKBnrMpXiufnJD/LPm9l36YXsuBiDnUDPT9bO0G3deIuaqW8zvMztCsNao+qlnjyf9yL+/T/vR7YTYvbtVW1znOfQmu3ctfhxqH8ae047vX8bHwfkjw/T/U8WhPXRl2mD7BuMXOoK/H8HNzK8tbKg+1BYCPwPyLi71N8I6X1D4kJgW5UG1o7jo13ixX7/LxZVaWNx9V+6rK7w/oQCy7mUNc4DnBUK7f3Hhug/TR2MQXkakZV64Afe+r7YxurFuJuAvAiIqG0Pj7SE07w04aBnjZU4e5KA/jU8/O4g9MsEywWXMwhzTNAvgR3J9Z3mi8TkTNofbylswpxn6xHBKg+VHUh7lbdc4BjWlntc2CEn++H/B8wMlBt6YArReRwn7IFuO6wZ1S1GkBVs3Cv6UIRucZfRSIyTURSe7KxpufZmIvpD67Hfc/l7yJyFk3fc5mLu/Pr67jbXLtMVctE5DPgOBF5CtelVQ+8pqqru1H1D3Gf9se2svwu4HTgVRF5Dncb8dHAKFxQndONfXfGW8AnIvI8kIO7IjwWd1faT3zWnQe8B/xTRG7Cfd+mCHezwGHAVNzA//6D0XDTM+zKxRzyVHUt7mT1Cq475haavjT4sWe1En/bdtKVuC9pnoG7pfbXwMzuVKiqn+O+mNja8sW4L3Z+hfui41W4E/psYEd39t1JdwPX4brBbgEm4r5nc7Tn9u0DVDUbmAXchgvA3wBuwgXFnbgrzDUHp9mmp4iqjZGZ/stzlTEPmKiqG4LdHmMOFXblYg55IhLib44rETkZuBRYa4HFmMCyMRfTH0QAu0TkfdysyXXAFNz3LGpwYzLGmACybjFzyPPclnsPbhqYNCAGyAM+BH7vZ9oSY0w3WXAxxhgTcDbmYowxJuAsuBhjjAk4Cy7GGGMCzoKLMcaYgLPgYowxJuD+H4S9PAPZm4cDAAAAAElFTkSuQmCC\n",
                        "text/plain": "<Figure size 432x288 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.scatterplot(x=\"FlightNumber\", y=\"Orbit\", hue=\"Class\", data=df)\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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\n",
                        "text/plain": "<Figure size 432x288 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.scatterplot(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df)\nplt.xlabel('Payload', 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": 32,
            "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\ndf['year'] = pd.to_datetime(df['Date']).dt.year\nline_data = df.groupby('year').Class.mean()\n\nsns.lineplot(x=line_data.index, y=line_data.values, label=\"Success Rate\")\nplt.xlabel('Year', fontsize=20)\nplt.ylabel('Sucess', 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": 33,
            "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": 33,
                    "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": 47,
            "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": 47,
                    "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 = pd.concat([features, features_one_hot], axis=1)\nfeatures_one_hot.drop(['Orbit', 'LaunchSite', 'LandingPad', 'Serial'], axis=1, inplace=True)\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": 48,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "FlightNumber    float64\nPayloadMass     float64\nFlights         float64\nGridFins        float64\nReused          float64\n                 ...   \nSerial_B1056    float64\nSerial_B1058    float64\nSerial_B1059    float64\nSerial_B1060    float64\nSerial_B1062    float64\nLength: 80, dtype: object"
                    },
                    "execution_count": 48,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype('float64')\nfeatures_one_hot.dtypes"
        },
        {
            "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-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",
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        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
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            "file_extension": ".py",
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