week2_EDA_with_Data_Visualization.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n","    </a>\n","</p>\n"]},{"cell_type":"markdown","metadata":{},"source":["# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"]},{"cell_type":"markdown","metadata":{},"source":["## Assignment: Exploring and Preparing Data\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","\n","In 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","\n","Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n","\n","*   Exploratory Data Analysis\n","*   Preparing Data  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.\n","import 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\n","import 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.\n","import 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\n","import 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><div id=07423ddc-c608-4ec9-a978-eb41d11b4e51 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('07423ddc-c608-4ec9-a978-eb41d11b4e51').style.display = 'flex' </script> <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></div>"],"text/plain":["   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n","0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n","1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n","2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n","3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n","4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n","\n","       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n","0    None None        1     False   False  False        NaN    1.0   \n","1    None None        1     False   False  False        NaN    1.0   \n","2    None None        1     False   False  False        NaN    1.0   \n","3  False Ocean        1     False   False  False        NaN    1.0   \n","4    None None        1     False   False  False        NaN    1.0   \n","\n","   ReusedCount Serial   Longitude   Latitude  Class  \n","0            0  B0003  -80.577366  28.561857      0  \n","1            0  B0005  -80.577366  28.561857      0  \n","2            0  B0007  -80.577366  28.561857      0  \n","3            0  B1003 -120.610829  34.632093      0  \n","4            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","\n","df.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","\n","We 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":{"image/png":"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","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)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n","plt.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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","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\n","sns.catplot(x=\"FlightNumber\", y=\"LaunchSite\", hue='Class', data=df)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Launch site\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK 2: Visualize the relationship between Payload and Launch Site\n"]},{"cell_type":"markdown","metadata":{},"source":["We also want to observe if there is any relationship between launch sites and their payload mass.\n"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 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\n","sns.catplot(x=\"PayloadMass\", y='LaunchSite', hue='Class', data=df)\n","plt.xlabel(\"Pay load Mass (Kg)\")\n","plt.ylabel(\"Launch site\")\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"]},{"cell_type":"markdown","metadata":{},"source":["### TASK  3: Visualize the relationship between success rate of each orbit type\n"]},{"cell_type":"markdown","metadata":{},"source":["Next, we want to visually check if there are any relationship between success rate and orbit type.\n"]},{"cell_type":"markdown","metadata":{},"source":["Let's create a `bar chart` for the sucess rate of each orbit\n"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["# HINT use groupby method on Orbit column and get the mean of Class column\n","new_data = df.groupby(\"Orbit\").mean(\"Class\")"]},{"cell_type":"code","execution_count":7,"metadata":{},"outputs":[],"source":["new_data.reset_index(inplace=True)"]},{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["sns.barplot(x=\"Orbit\", y=\"Class\", data=new_data)\n","plt.xlabel(\"Orbit\", fontsize = 15)\n","plt.ylabel(\"Sucess rate\", fontsize = 15)\n","plt.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":9,"metadata":{},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAY4AAAFlCAYAAAD8hw89AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABAAElEQVR4nO3dd3gc1fX/8feRZLn3btyxsbENGCMwxfQSujGhh18oISbBIYQACZBvEtIIIdQQIJjQe+9gEzAG05GNO+4F915ldZ3fH7O22qqMtNLuSp/X8+yjvXfv3DljS3v23js7Y+6OiIhIdaXEOwAREUkuShwiIhKKEoeIiISixCEiIqEocYiISChp8Q4gHk4++WSfMGFCvMMQkcbL4h1AbTTKEcfGjRvjHYKISNJqlIlDRERqTolDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVAa5RcApRH4/itY9AF0HQL7joYUfUYSiRUlDomP5V/AVw8Gzw8dB71Hxq7vGc/Da1cWlw+6FM64N3b9izRyCfcxzMx2Rqm7xcxWmdn0Eo92kddGmdnXZjYv8hhb70FLOBsWwJNnwtw3gscTZ8CmxbHr/4t/ly5PewpytsWuf5FGLuESRyXudvfhJR5bzawb8CzwM3cfDIwCrjSz0+oqiFVbs/lqySZyCwpr3klRISz6EBZ+AIUFsQsuWcx7CwrzisuFuTDv7dj1b6llyikk+aWBRBJKsk9VjQMed/dpAO6+0cx+A9wCvBPrnT0weRF3TJxPkUO3Ns145qcj2btzq3Cd5GfD46fBqqlBudt+cNkEaBqyn+oqyIP1c6FDf2jWpm72EVabnuXr2kapq6lR18LLl4EXBeWRV9b82Letgs1LoGcGNGkeuxhFklgyJY5rzeziyPMt7n4sMBR4oky7zEh9KZEprLEAvXv3rvZOt+7K48GPF/Pd6h18tmgjRZFbtK/dnsO9HyzkXxceGO4o5rxenDQA1s6CWS9BxmXh+qmONTPgmfNg51po0hLOuh+Gjon9fsIaOgZmvRgsXgMM/AHse2YM+z8LOg2ExZOgyxAYcHzN+vnyQZj4O/BCaNkZ/t9rQaIXaeSSKXHc7e53lKkzwKO0LVfn7uOB8QAZGRnRtonqp09m8s2yLVFfW7stp7rdFMvZWr26WJj4uyBpAORnwTvXw+AzIDXO/+1p6XDxK7BuDmDBmU+x1nVo8KipnO3wwZ+CpAGQtQEm/Q0uej428YkksWRa44hmDpBRpu4gYG4sOl+5ZVeFSQPgzOE9wnc6ZDQ0a1tcTm8FQ8+uQXTVsGV56fKujZC3o272VRNdh9ZN0oiF7C1QkF26bsfq+MQikmCSacQRzf3AV2b2qrtPN7OOwD+AP8ei87bNm9CsSQo5+UV76nq1b84+XVtz8rBunJvRK3ynbXrAFZMg85FgDv6gy6B9n1iEW96QM0ufYdTvKGjevm721dC07wO9RsKKr4rr9jsvfvGIJBBzr/asTb0wsyKg5Ee7u4A2wE+BDSXqz3L3ZWZ2FHAn0Jpg6uoed3+wsn1kZGR4ZmZmteJ55NOl3PrudxQWOR1apvPk5YcwbK+2VW+YCArzYcpdwVx/t/3g2JuhRYd4R5U8dm2GT++CjQthn5PrZh1KGqukPs0v4RJHfQiTOABWb81m2cYsDuzdnubpqVVvICJSuaROHMk+VVUverRrTo92OhVTRASSf3FcRETqmRKHiIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiISixCEiIqEocYiISChKHCIiEooSh4iIhKLEISIioShxiIhIKEocIiISihKHiIiEosQhIiKhKHGIiEgoupFTgpq/dgevTFtJ66ZpXDiyN51aNa2bHeXtgs/vg9XToM8RcOhVkNoIfy0KCyBrPbTuDpbUN2eLr6VT4ON/QM5WOOgyOPgn8Y5I6kAjfIdIfHNXb2fMA5+RW1AEwItTV/C/a4+mWZM6uG3tm1fD7JeD5wsmwPbVcMpt5dutnwebF0PfUdAsSe65Xl3LPoVXroAda6DTPnDeU9BlcLyjSj471sEz50JBdlB+59fQuhsMPi2+cUnMJfxUlZntjPxMMbN/mdlsM5tlZt+YWb/Ia5dH6mZGXh9d13EVFBbx9szVPPzJEpZvyopp3y9mrtiTNABWbM5m8vwNFcZRY4X5MOe10nWzXizf7qNb4YGR8PxFcPd+sPrbmu8z0bjD61cFSQNg4wJ474b4xhQjW7Ly+GDuOlZs3lW9DRZ+AFPuglXTarbDpZ8UJ43dFkysWV+S0JJpxHE+0APY392LzKwnkBX5+TtghLtvM7NWQOe6DuZnT0/lg+/WA/DP9+fz3E9HclCfDjHpu2XT8iOLb5ZtIsXguMFdSEtNISu3gBtensGE2Wvp3rY5fzpzKCcM6Up2XiG3vvsdk+atZ2DXVvz+9CHs3blV9B2lpEHLzrBzbXFd6+6l22Rtgil3Fpdzt8HHt8OFz1V8APk58N1bwXTFkNHQqkv1D76+5WXB1uWl69Z/F59YYujzxRv5yeOZZOcXkmLw59HDuPjQPuUb5u6AjQth1kvw5QNB3Yd/hjEPwQHnh9tptFFal33DBy8JL+FHHCV0B9a4exGAu6909y1AF2AHsDNSv9Pdl9ZlIAvW7diTNADyCop49NNllW9UkAdz34SZL0HuzkqbXnxoH7q2KV7TSE0xHvl0GWOfmsqlj32Du3PfpEW8O2stRQ6rtmZzzfPfsiMnn9snzuOpL5ezamswShn7ZCbuHn1HZnDy3yE1sq/0VnDSX0u3yd0GRQWl63Ztqjj4okJ4/FR49Qp493q4/xDYHPK/Y8sy2Lm+ymYAzHsXXvsZTL4NsreG2w9A01bQ+/DSdQNODN8PUFTkfLJgA2/OWM2OnPwa9REr/5w4n+z8wiAuh39MmEdeQZnR6fz34M594eFji5MGAA6f3RO94/xs2L4m+mvd9oNjbir+fRp0Ghx0aW0Oo3KLJ8Hb18Jn/6ryb0piK5lGHC8Cn5rZkcCHwNPu/i0wA1gHLDWzD4FX3f2tshub2VhgLEDv3r1rFUhRlDfiaHV7FOTCIyfBmulBuV1v+OlkaNkxavPubZvzwa+PZtK89bw5fTUfzit+E/100UamLt/CjBVbS22TlVfIwvU7mbJwY6n6xRuyWLU1m57tW0SPbdjZ0O8oWDcbehxYfv2iQ/9g0Xz5Z8V1B15c8bEu/ghWTS0uZ2+BzEfhpL9UvM1uebvgxf8Hiz4AS4WRVwaJrSIzX4RXf1pcXvQBXPFB1fsp65xHYOLNsGZG8G9xYjViLcPduezxb/h4QTCl2Ll1U1676vCK/92Bbdn5TJi9htSUFE4Z1o2WTWP357hpZ16p8s7cAnILCklPS9kdMLxzPeTtiN5BtN/nb5+BCTdC7nbodShc8Ay07FS6zTE3BidYFOTUbqS5+COY/gw0bw+HjYP2fUu/PvtVePmy4vKCiXDZOzXfn4SSNCMOd18JDAJuAoqAD83seHcvBE4GzgEWAHeb2S1Rth/v7hnuntG5c+1msgZ3a8NR+xT3kZ6awqWH9614g3nvFCcNgK3fw7dPVbqP1s2aMHr4XnRuXf5sqpz8Ig7pV3parG3zJuzbrQ37dm9Tqr5jy3S6tG5W6b5o2Qn6H1PxoveFz8Gxv4P9z4fznoQRP664r7Kjk4rqopn2ZPDmD+CFwafgFV9X3r6kld8Ei/hhtekB5z4Ov/wWzrgXmrWpcpOyvl66eU/SANiwI5cnv1heYfvNWXmceu8UfvvKLK5/aQZn/PtTsnKr+e9UDecc1LNU+eSh3WjdrElxRWE+bF9VcQeHX126vGtz8Ok+d3tQXvFlcPZUNM3a1C5pLJ0CT40Jps++Hg+Pnhx8qChp6mOly8s/DabcpF4k04gDd88F3gPeM7N1wFnAhx7MxXwNfG1m/wMeA26py1j+++MM3p65mtVbszl5WDcGdGldceP87Ch11VuwvGhkb179dtWeaYbB3Vpz2N4dObhfezbszOWdmWvo2b45vz99CM3TU7n51MGs2LyL6Su20rVNU/7xw/2LP2XWVLO2cPRvqtd2wPHQZQisnxuU01tVf7pi8+LydZsWQa9DordvUWbEZilxO+MrK6/8m/6OnIoTwavTVrJqa/HvxZINWbw7aw3nZvSKSTxXHzeArm2aMmXhRvbt3oafjOpXukFaOgw6FeaX+JQ+4AToNRL6Hwu9Di7dfvNSKMwtXVdXa0EznwdKjHh2rIElk2HwqcV1Tcskd0uB9JZ1E4+UkzSJw8xGAGvdfbWZpQD7AzPNrAfQzd13nwoyHKj4o16MpKelcPaInlU3hOB0xEk9YMfqyMat4YALq7Xp/j3b8fbVo3hj+io6tGzKuRk9SU0xUlNSuXXMftw6Zr9S7bu3bc7r445gc1YebZs3ITWlnr+TkNoELp8IM18IFsf3O7f8NENFBp0afMLcrUkL2Pv4itsfdX3whpKzNSgffjW06V5x+zp0xIBO9O3YgmWbgg8ETVKNCw6uOAnkF5afCiooqmS6MyQz4/yDe3P+wZVMy475TzBqWDUtOM36yOugSQWj0277QatupU+kGHhSzOItpVXXquuOvA6WfFw81XbI2GDkKPXCKlw4TRBmttPdW5nZycDfgN1zN18DVwFdCUYYPYAcYAPwM3eP8vE1kJGR4ZmZmXUbeFk71gZTKwU5MPxH0HHv+t1/spj1MnzzSLBofeR10PvQytvnbIdlU4Lk1HVovYRYkU07c3n2q+/Zsiufs0fsxbC9Kh79rN2Ww6n/msLmrGAtonvbZky45ijatmhS4TZxt3Y2fPinYKp16Bg46gZIqYPvFu1cH0xP7R6B7n8+nD2+fLusTcECeYd+0DMj9nHUraT+lmnCJ466EJfEIVLGuu05vDJtJU1SUjh7xF50rKurAySjwoLghIwWHYLRTsOjxJFslDhEJM6SOnEkzVlVIiKSGJQ4REQkFCUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEBGRUJQ4REQkFCUOEREJRYlDRERCSZp7jouQsw3SmkNaerwj2WPxhp3cP2kRG3bmcvaIvRhzYDXvQy+SxJQ4GomduQXcPmEeXy7ZxH57teOmUwfTKVluVZqXBa+OhXnvQLM2cMKfIOOyeEdFTn4hF47/kvU7cgGYsnAj6ampnLZ/9zhHVoXsLbB2VnBL1ubt4x2NJKGkmKoys0Izm25ms83sJTNrEanvaWZvmNlCM1tsZveaWeJ8HE0gf3hjNk9+sZwF63byyrSVXPvC9HiHVH1f3A/z3gY8GHW8cx1sW1k3+yoqhFkvw4d/huWfV9r0m2Wb9ySN3d6dtab2MXz1ENx/KDzyA1j8Ue37K2n+BLhrCDxxRvBzwcTY9i+NQlIkDiDb3Ye7+zAgD/iZmRnwKvC6uw8E9gFaAX+LY5wJa9K89aXKUxZuJLegMLY7yd4KW1fEtk+AtTNLl70Q1n8X+/0AvH0tvPITmHInPHYKfPtMhU33ate8XF3P9uXrQpn7Brz3G9jwHaz4Ep49H3asrV2fJU24EfJ3Bc/zd8GEm2LXtzQayZI4SpoCDACOA3Lc/TEAdy8ErgUu3z0ikWIDOrcqVe7TsQVN01Jjt4NP7oA7BsI9w+Dx0yFne+z67n9s6XJ6K+h5cPW3LyqCzUuhIK/ydjnb4NunS9d9+WDFYXVuxbhj9ybFgvLgbq356VH9qx9XNIs+KF0uzIVln9auz5J2lBkRbV8du76l0UiqxGFmacApwCxgKDC15Ovuvh34niCxlN12rJllmlnmhg0bYhrXF4s3Mfr+zzjitkncMXE+RUUe0/5j4U+jh+75NNy5dVNuO3v/2HW+cRFM+gsURt6Yl02BLx+IXf8Zl8MxN0H7vtDzELjoBWjernrbrpsL9x0I/xoOdw+pfOrHUiClTDJNrXwZ8IYfDObzG4/nnV+O4r1rjqz9ulGXoVHq9q1dnyXtd26Z8jmx61sajWRZHG9uZtMjz6cAjwA/B6K9Q1u0encfD4wHyMjIiNk7+7bsfK544huy8oJpn39/tIiubZry/w7rG6tdxMTQHm355IZjWbklm+7tmtEkNYafGTYtKl+3cUHs+jeDY24MHmG99xvYsix4nrUB3vwlXDMDUqIcf9PWMPJK+Py+yH5TYNS1Ve6iW9tmdGvbLHxs0Rx0KXz/Ocx9E9KawVHXQ9coyaSmTrsT2veBFV9Dr0Pg8F/Grm9pNJIlcWS7+/CSFWY2B/hhmbo2QC9gcX0FNn3F1j1JY7dPF21MuMQBkJJi9O5YB7N4fQ6Dpm0hd1tx3aBTY7+fmtgwv3R52/fB3H7TVtHbn/RXGHBCMFLZ+9jYftqvjibN4LwnYddmSGsK6S1j239aUzjqhtj2KY1OUk1VlfEh0MLMfgxgZqnAncDj7r6rvoIY3K01absnuSOG9mhbX7tPDM3awo9fg4EnQY8D4dQ7EmcKZJ8flC73GVVx0tit/zFw2FX1nzRKatEh9klDJEbMPfHm48sys53uXu6v3cx6AQ8AgwmS4LvA9e6eW7ZtSRkZGZ6ZmRmz+F7MXMGt737H9ux8fjC0G3edN5zm6TFceJaay8sKTq1d8jF0PwBO/DO07hrvqESs6iaJKykSR6zFOnEAFBQWkVtQRMumyTL7JyJxlNSJQ+9yMZKWmkJaLBecRUQSlN7pREQkFCUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEBGRUJQ4REQkFCUOEREJRYlDRERCUeIQqaHCosZ3EzQR0I2c4m7myq08+cVy0lKMS4/oS492zUlLMVqkN8D/mtydMPcNKCqAIaOhebt4R1Qj789Zyy1vzmHdjlxOGdaN28/Zv2H+f1Vi2cYs/v3RItZtz+Gs4Xvxw4N6xjskqUeN67c9wSzesJNz//MFuQVFALw8dSXukJZqXHlUf3590qA4RxgbyzZmkbNrO4PfOAM2LQwqP74drvwYWnaqfON1c6BZW2ibGG9M27Lzueb56WTnFwLw9sw19OvUkuuq+X+1cWcur01bRZE7Y0bsRZfWzWoXUH4O/O/3MO8d6NAfTv47dNuvdn1WIa+giAsf/pI123IAmLJwI2mpxujhe5VvvGU5FORA54bxuyyBhEocZtYVuBs4FNgC5AFtgHwgHegHzI80/yvwCvA74BLAgVXAL9x9Tv1GXmzF5l38d8oStuzK59yMnhw5sHOFbd+ZuWZP0gAoiEx9FBY4/5q0iGMGd2FE7/bV3vf2nHxapqeRmlL17Yyz8wpZtH4nA7q0onl6arX3UdasldtYuz2HIwZ0LPep29257sUZvPrtKn6Y8gl3pi8sEexKmPE8HP6LCgLcCk+fDaumAgaH/jx4UwRe/GYFb85YTbe2zfjlcQPp3bFF9QNe8TUs/wx6Hgx9R4U7WGDBuh17ksZu01dsrda2W7LyOP1fn7J2e/CG+99Pl/LeNUfSqVXT0HHs8fFt8PX44Pn2VfDsBXDNDEituz/tad9v2ZM0dnt75prSicMd3vgFTH86KPc7Ci58AdJD/F9JwkqYxGFmBrwOPOHuF0Xq+gBnuvt9ZtYXeNvdh5fY5hfA4cAB7r7LzE4C3jSzoe6eU3YfdS07r5Bz/vM567bnAvDWzNU8c8VIDt87+qfqqt4wvluzvVqJY9POXMY9O40vl2ymc+um3DpmP04c0rXC9p8v2sjPn5nGtux82jRL48GLD+KIAZEY3SFvJzRtXeV+b3p1Js99vWLPsbz8s8Po26ll8X4Wb+LVb1cBkGaF5Tsoyq+4868eiiQNAIcvH4ADLuClVR34zSsz9zT7cskmJl9/TPXu9/71w/Du9cXlw8ZB12HQayR03Lvq7YF9u7ehddM0duQW7Kkb2a9DtbZ9e+bqPUkDYMOOXF7/dhVXHNm/WttHtWRy6fL2lbBpEXQZXPM+q7BXu+aYBb8qu/Vs37x0o6WfFCeNPeVn4JCf1llcUn8SaXH8OCDP3f+zu8Ldl7v7fZVs81vganffFWn/PvA58KM6jbQCny7auCdpBPHATx7/hmF/nMjf3/0O99KLqWMO3IvhvdpF7SstxRg1oIppnIg73l/Al0s2A8Gb0a9fnE52XpQ36og/vDmHbdnBm/b2nAJ+/8bs4IUVX8N9I+DvPeHBUfDMefDAYfC/P0BB8XGxbSWLZny2J2lAMAXz0CeLS+1n1ZbsPc/fKzyEVd6x+MUWHWH/84u7zM5nZ4k3Y7Z+Xz7wLct5Z9aaUlUrt2QzY+XWCo+1lCl3lS5/cT+8/nP4dwbMfLHq7XdtptWW73jo4hEM7taats2bcPGhvRl7VDWSzrZVpOZsLlfdpDoJL2sT5O2CoiJY/S1sX138Wrf9S7dt1hba9a66z1ro1aEFVx87gN0D2wFdWvHzo8v8G2xdXn7DLcvqNC6pPwkz4gCGAtOq29jM2gAt3X1xmZcyI32VbT8WGAvQu3fd/GF1bl1+BJGdXwQU8dAnSxjUrTVnjyieq2+ensprVx1O5vItpKYY363ZzhOfL6NZk1SuPm4gfTq2LNdfNHNXbytV3pFTwIotu9ina/RRw/ebd5Uqr9ycHWS5V64o/oNfNyt4AKyfG7x+0l/g43/C5FvZWjgAuKVUP1uySo8gjhncmZbpqWTlFbKdlozO+xuvj1pBzzZpcMAF0KYHhUXOja/M5NVvV5FqxuWj+nHjKYODxfOSn1ibtYP+x9BzQek3pBSD7m3LfNqtiBdVXD/pr7D/eRVv+9V4eP93UJjH4R32ZsIlr0H7PtXYp8ObV8O3T3O6N+Oh9LtZntcGCD6ljx7eI2i3cVHwibxJCzjoEmjVBfKy4OWfwIL3IK05NGsNO9eDpcBRN8CxN8Nxv4fNS2DZFGjVDc64p16mg3590iAuHNmbjTvyGNqjDSllp0cHnBgcS/7u3zWDfc+s87ikfiRS4ijFzO4HRhGMQg4OsynBekcp7j4eGA+QkZFRJ+dRDu/VjrMP3GvP9ExZU5dvKZU4AMyMg/sGUx0jerfnRyOr8WZUxhEDOjFjZXHy6NG2Gf07VZx0Th3WjdenF39qPWW/bpC9JfqnxN0WTAimdib/HbyIA20hA20lC734eM47uPSxdWndjOfHHsZDnywmO6+Qiw87mJ6DupRq8+aMVbw0dSUAhTj/+XgxxwzqzKH7nATnPAbTnoQWHWDUr6FZG8YdO4AvFm9i8YYsUlOMXx0/kB7tqpk4Dv8FvP9/0V/L3VHxdrs2B9sV5gXlzYuDf4cx/6l4m90WT4JvnwKgjWXzNtfyXsY9FPU+nFP3706bZk2CpDH+6GCKEGDq4zDuy2C6bsF7QV1BNuyMjOC8KDi5YPhF0L4vXPo25GyD9FaQUvP1qrC6t21ecdJu0x0ueQs+uwfys+HgK6D3yHqLTepWIiWOOcAPdxfcfZyZdSIYQZTj7tvNLMvM+rv7khIvjQA+rttQK3bX+cO54sj+zF29jRtemVlqHjijb/UXusP45fED2ZVXyMQ5a+nbsSX/d/q+lc7533r2fnRr25ypyzczok97rjl+IKSnBWfjrJ0VfaPOg2DHWvBgCizVnOfT/8Jjra5kbd8zGT28R9QTAfbr2ZZ/XzSiwljmrS3/hj1/7Q4O7d8Rhp0dPEro3rY5/7v2aOas3k7XNk3p0ibEWUmHXx0c4/IvgtHUvHeKX8u4vOLttq+GwtzSddWddtm8pFSxtWVzXtvv4JALiitnPFucNCBYp5j/XjDSq5DDtpVB4oBgiirR9MyA85+uup0knURKHJOAW83s5+7+YKSuqjH3P4F/mdm57p5tZicQjFKurMtAqzKkRxuG9GgDZtz5/nx25hTwo0P7cFa00xVjoFmTVG45cyi3nFluhi6qFulpwXRQWec+ESwer50FnfaBdbODT7KdB8OJf4Z2faHTINgYnNjW0XZw/fF9YcQBNY796H0689DHxW+uqSnGqIGVr+2kpBj79azhG2X/Y4JHURHMeA5WZULvw2G/cyrepssQ6Diw+FRiCKbSqmPgSZDatETiMRh8euk2TaJ8am/SHAacALNfid5vm57Q85DqxSASY1Z2wTaezKw7wem4I4ENQBbwH3d/ocRZVcNKtDfgD8D/AwqBtQSn41bwsTmQkZHhmZlRBzJSUn4O7FwL7fqAReawt68OFpm3LoehY4Lpklp6MXMFj322jPS0FMYdszcnDe1W6z5jbuv3MPk22LwUhpwJI39W/G9SlWWfBVM2BTnBdoNPK/36zvXw8HGwLXKywV4HwWUTIC0dPr0Hpj8bfN+l18hgcbxNDzjyumqfCSYJqZq/PIkpoRJHfVHikISTuyOYnmrSHAb+IEga0pAldeJIpKkqkcaraevKz+oSSSCJ9D0OERFJAkocIiISihKHiIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiISixCEiIqEocYiISChKHCIiEooSh4iIhKLEISIioShxiIhIKEocIiISihKHiIiEosQhIiKhKHGIiEgoShwiIhJKvSYOMys0s+klHjdG6k83s2/NbIaZzTWzKyvYfpmZdSpTN9jMvjCzXDO7vj6OQ0SkMavve45nu/vwkhVm1gQYDxzi7ivNrCnQN0Sfm4FfAmfFKEYREalEIkxVtSZIYJsA3D3X3edXd2N3X+/u3wD5dRSfiIiUUN+Jo3mZqarz3X0z8Caw3MyeM7MfmVnM4zKzsWaWaWaZGzZsiHX39WfzUnjsVPhzx+Dn5qXxjkhEGpn6ThzZ7j68xOMFAHe/Ajge+Bq4Hng01jt29/HunuHuGZ07d4519/XnjXGw/DMoKgh+vjEu3hGJSCOTCFNVALj7LHe/GzgR+KGZpZYYmfw53vEljBVfVV4WEalj9b04Xo6ZtQIy3H1ypGo4sNzdCyPPpaReI4ORRsmyiEg9qu/E0dzMppcoTwD+BvzGzB4CsoEs4NJK+phpZkWR5y8CtwOZQBugyMx+BQxx9+2xDT1BjL4/mJ5a8VWQNEbfH++IRKSRMXePdwz1LiMjwzMzM+Mdhog0XhbvAGojYdY4REQkOShxiIhIKEocIiISihKHiIiEosQhIiKhKHGIiEgo1U4cZvYHM+tRwWvdzewPsQtLREQSVZgRxx+BnhW81iPyuoiINHBhEocBFX1bsCewpfbhiIhIoqv0kiNmdglwSaTowINmVvZSHs2A/YD3Yx+eiIgkmqquVbWLyA2WCEYc2wjuuFdSHvAe8EBsQxMRkURUaeJw95eAlwDM7DHgL+6+pD4CExGRxFTtq+O6+2V1GYiIiCSHqtY4bgf+5e4rI88r4+7+29iFJiIiiaiqEce5wDPAysjzyjigxCEi0sBVtcbRL9pzERFpvHTJERERCSXUrWPNrAvwK+AQoDuwBviKYB1kXcyjExGRhBPmWlVHAAuBK4GNwIeRnz8DFkZeFxGRBi7MVNW/galAb3e/wN1/6e4XAH2AacB9sQjIzH5nZnPMbKaZTTezkWZ2upl9a2YzzGyumV1Zov1YM5sXeXxtZqNiEYdIQtu+BqY+DosngVd0JaAYWDMTHjsN7hwM71wP+Tl1ty9JGmGmqgYD57h7VslKd99pZncQ+aJgbZjZYcDpwAh3zzWzTkBL4DXgkMhpwU2BvpH2pxOMgEa5+0YzGwG8bmaHuPva2sYjkpBWTYXHz4D8yJ/iARfCmP/Efj+FBfDcBbB9VVD+5mFo2gpOuCX2+4qXwgKY/jSsmQH9joahZ8U7oqQQZsQxF+hWwWvdgXm1D4fuwEZ3zwVw943ADoIEtylSl+vu8yPtfwvcEGmHu08DngDGxSAWkcT02b3FSQNgxnOwOXJBhw3z4YsHYNGHtR+JbFpYnDR2WzK5dn0mmneuhbeugcxH4aVL4NN74h1ROWbWzcyeN7PFkRmXd81sHzObHa+YwiSOq4Gbzez8yKd+zKypmV0A3Aj8IgbxvA/0MrMFZvaAmR3t7puBN4HlZvacmf3IzHbHPZRg+qykzEh9KZEprUwzy9ywYUMMQpWEUlgAH/8Txh8Lr14JW1fEO6K6E226qCAX5r8HDxwGE2+Cp8+GCTfWbj/t+kCztqXruu1fuz4TSX42TH+2dF3mI/GJpQJmZgQzLpPdfW93HwLcDHSNZ1yVJg4z22Bm681sPfA6wYjjWWCXmW0juAjiM5H612objLvvBA4CxgIbgBfM7FJ3vwI4HvgauB54tLKwiXL5d3cf7+4Z7p7RuXPn2oYqiWbKHfDRX2H1NJj5PDx7frwjqjsHXwFW4k+375HQZV/49G7wwuL6b/4L2bW420F6CxgzHlp3D8r9jobjG9D92lLSoEmL0nVN28QnloodC+S7+565SHefDuz5ZGRmfc1siplNizwOj9R3N7NPImvFs83sSDNLNbPHI+VZZnZtTYKqao3jfiq+B0edcPdCYDIw2cxmEVzW/XF3nwXMMrOngKXApQTTZwcBk0p0MSJSL43JvLdLl9fPCaZvOvSPTzx1aZ+T4Cf/g7mvB6OC4T8K6gvzS7fzIigqqt2+Bp0MA+dA/i5o2rp2fSWa1CZwzI0w8eagnJIGx9wU35jKG0b5WZWy1gMnunuOmQ0EngMygIuAie7+NzNLBVoAw4G93H0YgJm1q0lQVX1z/JZI5ykE6w/bIqOCOmFmg4Aid18YqRoOrDOzY9x9com65ZHntwP/MLOT3X2TmQ0nSCgj6ypGSVAd9oa1s4rL6a2hVVxH83WrZ0bwKOnQq+DVK4rL+18ALTvWfl8pqQ0vaex22LhgJLV2JvQ5Atr3iXdENdEE+Hfk/a8Q2CdS/w3wqJk1AV539+lmtgTob2b3Ae9Qw/soVfesqhRgGXAGMKEmO6qmVsB9kSxYACwCrgEeMrOHgGwgiyA54O5vmtlewOdm5gQL6Re7+5o6jFES0Ql/hHWzYdMiaNISTrsD0lvGO6r6tf+50K43LHw/mLoaOibeESWHbsOCR2KaA5xTRZtrgXXAAQTv1TkA7v6JmR0FnAY8ZWb/dPcnzewA4AcEJxGdB1weNqhqJQ53LzCz5QRDnTrj7lOBw6O8dGol2zwIPFhnQUly6NAffpEJGxdAmx4N9xNyVXqPDB7SUEwCbjWzn7r7wwBmdjCl34vbAivdvShy19bUSLs+wCp3f9jMWgIjzOxdIM/dXzGzxcDjNQkqzPc4/gH8zsymuLtOS5LEYwadB8U7CpGYcXc3szHAPWZ2I8FoYhnBpZ92ewB4xczOBT4imJUBOAa4wczygZ3Aj4G9gMdKnJlao0Ud82qe621mLwFHEGS3qQRDo5Ibu7snxaksGRkZnpmZGe8wRKTxsngHUBthRhydgfklyp1iHIuIiCSBMLeOPaYO4xARkSRRrW+Om9lwM/tv5BvdWZHHAjN7OLJCLyIijUSVicPMbiC4jMcPgdnA+MhjdqQuM9JGREQagUqnqszsDIKzqW4HbnX37WVeb02wKn+bmc1193fqLFIREUkIVa1xXAc84e5Rr5bm7jsILnzYneAaUkocIiINXFWJ40Dg79Xo53ngxdqHIyIi0fS98Z0U4EKC73D0IrjQ4T3Ac8tuO63GFyUzs5OBewm+OPhfd7+tqm2qWuNIJbj0R1UKqtGXiIjUQCRpvAI8RHABw66Rnw8BL0deDy1y8cP7gVOAIcCFZjakqu2q2tlsgjvyVeX0SFsREYm9C4ETCe6IWlJL4CTgghr2ewiwyN2XuHsewezR6Ko2qipx/AcYZ2Y/jdxQpBwzuwK4Cl0vSkSkrvyK8kljt5YEFzqsib0ocW8PYGWkrlJVXVb9cTMbSTAcut7M3qL4kuZ9CK66uA/wkLs/WZOoRUSkSr1q+XpFog0IqrwOVZXfHHf3n5vZRILLm48DmkZeygW+AG509zdCBCoiIuGsoPLbxdb0XskrKZ10egKrq9qoupdVfx14PbKQsvsaVRsjd+sTEZG6dQ/BzE+06aos4O4a9vsNMNDM+gGrCNZKLqpqo1Ar8e5e6O7rIg8lDRGR+vEc8D+KL5m+WxbBXfyer0mn7l4A/AKYCHwHvOjuc6rartqXVW9IdFl1EYmz0JdVj5xyewHBQvju73HcDTxfm+9x1IQSh4hI/Uvq+3HoS3siIhJKwiQOM9tZpnypmf078vwWM1tlZtNLPNpFXhtlZl+b2bzIY2wcwhcRaTTC3AEw3u529ztKVphZN+BZ4Cx3n2ZmnYCJZrZKV+oVEakbCTPiqKFxwOPuPg3A3TcCvwGiXs1XRERqL5FGHM3NbHqJcgfgzRLla83s4sjzLe5+LDAUeKJMP5mR+lIiU1hjAXr37h2rmEVEGp1EShzZ7j58d8HMLiW4+uNu5aaqCM5MiHZaWLk6d99950IyMjIa36lkUr82L4U3xsGKr6DXSBh9P3ToF++oJJnd0rbCy6pzy7baXFb9UYIL1a5392HV2SbZp6rmUDq5ABwEzI1DLCLF3hgHyz+DooLg5xvj4h2RJLMgaVR4WfXI6zX1OHBymA2SPXHcD1xqZsMBzKwjxbe6FYmfFV9VXhYJp64uq467fwJsDrNNMiWOa8ucjtvX3dcAFwMPm9k84HPgUXd/K76hSqPXa2TlZZFwfkXdXFa9RhJmjcPdW5UpP04whMLdbwFuqWC7T4CD6zQ4kbBG319+jUOk5urqsuo1kjCJQ6RB6dAPLns33lFIw1FXl1WvkWSaqhIRaazuofyVcXerzWXVa0SJQ0Qk8dXJZdUBzOw5gpvyDTKzlWb2k6q2UeIQEUl0wfc0fkjwJeZMYF3k51jgnNp8j8PdL3T37u7exN17uvsjVW2jy6qLiNQ/XVZdREQaDyUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEGkEcvILmblyKztzC+IdSsOy/jtY8jEU5sc7knqlGzmJNHCZyzbz0ycz2bIrn1ZN07j3guEcv29l9wSSann7Wsh8NHjerg9c9h603Su+MdUTjThEGrg/vTWXLbuCT8Q7cwv4/euzaYxXxY6pdXOLkwbA1uXwxb/jF089S4jEYWZuZk+VKKeZ2QYzeztSvjRSnl7iMSTy2lAzm2RmC8xsoZn93syS+pLFIrG0csuuUuW123PIL1TiqJWd66pX10AlROIguIvVMDNrHimfCKwq0+YFdx9e4jE30v5N4DZ33wc4ADgcuKreIhdJcKfs171U+YR9u5Kelih/+kmqzxHQrnfpuv0viE8scZBIaxzvAacBLwMXEtwq8cgqtrkI+Mzd3wdw911m9gtgMnB/3YUqkjz+cPoQOrVM58slm9m/Z1uuOWFgvEOqPwW5sGEedNgbmraKXb9p6cGaxuf3wc71cMCFsM9Jses/wSVS4nge+ENkemp/4FFKJ47zzWxUifJhwFBgaslO3H2xmbUyszbuvn13vZmNJbjNIr17l/mkINKANWuSyq9PGhTvMOrfyqnw3AWQtR7SW8PZ42HwqbHrv21POOUfsesviSTMeNXdZwJ9CUYb70ZpUnaqKpvg9osVTdaWqnf38e6e4e4ZnTt3jmXoIpKIJt4UJA2AvB3wznVQVONbc0sJCZM4It4E7iCYpqqOOUBGyQoz6w/sdPcdMY5NRJLJlmWlyzvWQEFOXEJpaBItcTwK/NndZ1Wz/TPAKDM7ASCyWP4v4PY6ik9EksW+Z5YuDzgB0lvEJ5YGJpHWOHD3lcC9Fbxcdo3jKnf/3MxGA/eZ2f1AKvAU0HhOqBaR6H7wN2jeDpZ+At2Hw7E3xTuiBsMa4xeBMjIyPDMzM95hiEjjldTfNUu0qSoREUlwShwiIhKKEoeIiISixCEiIqEocYiISChKHCIiEooSh4iIhKLEISIioShxiIhIKEocIiISihKHiIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiISixCEiIqEocYhIfOVlQb7uBZ5MEurWsSLSiBQVwbvXwbSnICUNjrimfm7vmpcFU+6Cld9AnyNg1K8grWn52Oa/AxsXQI8R8N1bsGUZDBkNB11S9zEmOCUOEYmP2a9A5qPB86J8+Pg22PtY6H1o3e73zauDfQMs/Rh2rIEz7oHsLUFCWT83eL5qavltF38YxHrwFXUbY4Kr96kqM5tsZj8oU/crM3vXzGZHaf+4mS01s+mRx+clXjvLzGaa2Twzm2VmZ9XDIYg0fIUFkLWxbvexdmaUulnlqtZvz+HpL5czcc5aCgqLqtd3YX70+qIimPN66brZrwY/X7wEPv8XLPogetIo274Ri8eI4zngAmBiiboLgBuAByvY5gZ3f7lkhZkdANwBnOjuS82sH/A/M1vi7lF+I0WkWhb+D974BexcG0zTnP8UtO1Zuz7zc2D+u1BUCINPhfSW0P+Y4I16N0uBfkeV2mz+2h2c8+Dn7MgtAODCfrv4++BlQTzDzg6mmPKz4f3/g/kToF1vKMyDVZnQaRCMvh96HVzcYUoKtNkLtn1fXNe2Z5Akl35cvWNJbwUL3od+R0KT5jX790hy8Vgcfxk43cyaAphZX6AHsDJkP9cDt7r7UoDIz78TJCARqYmCPHjtZ0HSAFg9Dd7/feXbFBUGb6TTn4Vdm8u/nrcLHj4OXr4MXr0CHjwiaDfgeDjtTug4ELoOg3Mehc6DSm366KdL9ySNQ+w7/rT6Svjor/D6z+DZ84NGk/4K3/wXtq+E7z8PkgbAxvnw8uXBKKOkU2+HJi2D503bwMl/D5JB07YVHKAFSQ2CdgsnwrPnwr0HwKbFlf/bNFD1PuJw901m9jVwMvAGwWjjBcAr2eyfZvZ/kedz3P1HwFCCEUdJmcC4aB2Y2VhgLEDv3r1rfgAiDdnOdbCrzBTVunIzyKU9e14wvQPQvANc8QF03Lv49blvwPo5xeUtS2HmC3Doz4O1gsh6wbuz1vDMf7+kZXoaVx07gOG92pFfYmrq0rSJpFthcT9LPoK1s2HJ5Ipj2/Y9ZG2A1l2L6wadAtd9B+u/CxJW01ZB/Q/+Cm//OljDaN4hWKwvKoB9fgAtOsGKr+ClEgvjO9fBp3cFo5pGJl6L47unq3YnjsuraF9uqgowyiebaHUAuPt4YDxARkZGZUlKpPFq2zOY4tk4v7huwAkVt//+q+KkAZC9Gb56KPhUv1tBdvnt8kvXfb5oI1c9M21P+bNFG/n4N8dy8WF9eHvmGvIKiyjCyvdjKcGbf0XJreNAaNWlfH2ztuUX4Uf8GPY5GTYtgh4Hlp+GijZdl7Up+n4buHh9j+N14HgzGwE0d/dpVbSPZg6QUaZuBDC3lrGJNF5mcMGzMPAHwXrBIWPh+D9U3D5/V9V1Q86CViU+8TdrB/ufV6rJhDlrS5Wz8gr5ZMEGRvRuz9u/HMXVxw0g9fBxeFqz4kb7nAxdh8CJf4JekSTQqiv0PiyYdup9GJz3ZHBM1dWqC/Q5PPraRY8R0Hnf0nXDL6p+3w1IXEYc7r7TzCYDjxKMPmriDuAlM5vk7ssiayU3A+fEJkqRRqrTAPjRi9Vr2/fI4M10w3dBOTUdDrqsdJsWHWDsZPj26WDqZ/iPyn16792hRbmu+3QM6vbp2prrThoEDIKR+8O8d4LtB58eNGzdDX4yMTiFtmkbSEkNcbAhpKTAJW/Bl/fDtlWw3znBNFYjZO7xmbUxszHAq8C+7j4v8sa/EFhXotm1wGnA0cC2EvWHuHuemZ0N/AloAuQDf3T3Ks+Vy8jI8MzMzNgciEhjl70l+BJf1oZgJNFtv9Bd7Mor4CePZ/LFkk2YwcUj+/CXs4bVQbAJI8QwKPHELXHEkxKHSGJavGEnLdJT6d62wZ/mmtSJQ98cF5GEsXfnVvEOQapBFzkUEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEBGRUJQ4REQkFCUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJJSkSRxm9jszm2NmM81supmNNLN0M7vHzBab2UIze8PMesY7VhGRhiwp7jluZocBpwMj3D3XzDoB6cCtQGtgH3cvNLPLgFfNbKS7exxDFhFpsJJlxNEd2OjuuQDuvhHYClwGXOvuhZH6x4Bc4Lg4xSkiVfh+0y7Oe+gLBtz8Luc99AXfb9oV75AkpGRJHO8DvcxsgZk9YGZHAwOA7919e5m2mcDQsh2Y2VgzyzSzzA0bNtRDyCISzfUvz+DrpZspKHK+XrqZ61+eEe+QJKSkSBzuvhM4CBgLbABeAI4Fok1HWbR6dx/v7hnuntG5c+e6DFdEKjFt+ZZKy5L4kiJxALh7obtPdvc/Ar8gWPPoY2atyzQdAcyt9wBFpFpG9GlfaVkSX1IkDjMbZGYDS1QNB+YDTwB3mVlqpN2PgRbApHoPUkSq5Y5zDuCQfh1ISzEO6deBO845IN4hSUhJcVYV0Aq4z8zaAQXAIoJpqx3AHcACMysC5gFjdEaVSOLq3bEFL155WLzDkFqwxvgem5GR4ZmZmfEOQ0QaL4t3ALWRFFNVIiKSOJQ4REQkFCUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQlFiUNEREJR4hARkVCUOEREJBQlDhERCUWJQ0REQlHiEBGRUJQ4REQkFCUOEREJRYlDRERCUeIQEZFQlDhERCQUJQ4REQklYRKHmXU1s2fNbImZTTWzL8xsjJkdY2bbzGx6iccJkW16mtkbZrbQzBab2b1mlh7vYxERacgSInGYmQGvA5+4e393Pwi4AOgZaTLF3YeXeHwQ2eZV4HV3HwjsA7QC/haHQxARaTQSInEAxwF57v6f3RXuvtzd76timxx3fyzSvhC4FrjczFrUabQiIo1YoiSOocC0Sl4/ssxU1d6RbaaWbOTu24HvgQFlOzCzsWaWaWaZGzZsiGXsIpIMNi+Fx06FP3cMfm5eGu+IklaiJI5SzOx+M5thZt9EqspOVS0GDPBom0erd/fx7p7h7hmdO3euw+hFJCG9MQ6WfwZFBcHPN8bFO6KklSiJYw4wYnfB3ccBxwOVvcPPATJKVphZG6AXsLgOYhSRZLbiq8rLUm2JkjgmAc3M7Ocl6qpap/gQaGFmPwYws1TgTuBxd99VN2GKSNLqNbLyslRbQiQOd3fgLOBoM1tqZl8DTwC/jTQpu8ZxTmSbMcC5ZrYQWADkADfH4RBEJNGNvh/6HAEpacHP0ffHO6KkZcH7b+OSkZHhmZmZ8Q5DRBovi3cAtZEQIw4REUkeShwiIhKKEoeIiISixCEiIqEocYiISChKHCIiEooSh4iIhKLEISIioTTKLwCa2QZgeTWadgI21nE4iaIxHSs0ruPVsSaeje5+cryDqKlGmTiqy8wy3T2j6pbJrzEdKzSu49WxSqxpqkpEREJR4hARkVCUOCo3Pt4B1KPGdKzQuI5XxyoxpTUOEREJRSMOEREJRYlDRERCUeKogJmdbGbzzWyRmd0Y73hiycx6mdlHZvadmc0xs2si9R3M7H9mtjDys328Y40VM0s1s2/N7O1IuUEeq5m1M7OXzWxe5P/3sAZ8rNdGfn9nm9lzZtasoR5rolHiiCJy//L7gVOAIcCFZjYkvlHFVAFwnbvvCxwKjIsc343Ah+4+kOCe7g0pYV4DfFei3FCP9V5ggrsPBg4gOOYGd6xmthfwSyDD3YcBqcAFNMBjTURKHNEdAixy9yXungc8D4yOc0wx4+5r3H1a5PkOgjeXvQiO8YlIsycI7gOf9MysJ3Aa8N8S1Q3uWM2sDXAU8AiAu+e5+1Ya4LFGpAHNzSwNaAGspuEea0JR4ohuL2BFifLKSF2DY2Z9gQOBr4Cu7r4GguQCdIljaLF0D/AboKhEXUM81v7ABuCxyLTcf82sJQ3wWN19FXAH8D2wBtjm7u/TAI81ESlxRBftRvIN7rxlM2sFvAL8yt23xzueumBmpwPr3X1qvGOpB2nACOBBdz8QyKKBTtVE1i5GA/2AHkBLM7s4vlE1Hkoc0a0EepUo9yQYBjcYZtaEIGk84+6vRqrXmVn3yOvdgfXxii+GjgDONLNlBFOOx5nZ0zTMY10JrHT3ryLllwkSSUM81hOApe6+wd3zgVeBw2mYx5pwlDii+wYYaGb9zCydYNHtzTjHFDNmZgTz4N+5+10lXnoTuCTy/BLgjfqOLdbc/SZ37+nufQn+Hye5+8U0zGNdC6wws0GRquOBuTTAYyWYojrUzFpEfp+PJ1ira4jHmnD0zfEKmNmpBHPjqcCj7v63+EYUO2Y2CpgCzKJ43v9mgnWOF4HeBH+Y57r75rgEWQfM7Bjgenc/3cw60gCP1cyGE5wEkA4sAS4j+IDYEI/1T8D5BGcJfgtcAbSiAR5rolHiEBGRUDRVJSIioShxiIhIKEocIiISihKHiIiEosQhIiKhKHFInTOzW8zMozw+KNHGzewXJcqTzezlGuxrmZndUUWbfSIxtQsR+8Qor71sZpPDxlhTZnZMJJZh9bVPkWjS4h2ANBrbgJOj1FXkKiC/jmLZB/gj8DiwtZrbnGRmB7v7N3UUk0jSUOKQ+lLg7l9Wt7G7z63LYELaTHA5j9/RgK+2ambN3D0n3nFI4tNUlSSkaFNVZnZu5AY92ZEbUR0Ymbq5NMr215rZSjPbYmbP756Winx7/K1Is6WR7ZdVEY4DtxJc82q/SmK+xcw2RqkvOw23zMzuMLMbzWyNmW0zszstcGrk5kQ7zOz1Cm5E1MPM3jazLDP73sx+FmWfo8zsYzPbZWabzOxhM2td4vVLI3EdEvm3zgZuqOLfQQRQ4pB6ZGZpZR7RrkJc0bYZBBcpnAaMIbgm0QsVND+P4NpFY4HfAqcTvPET2f76yPOzgcMi/VXlJWABwagjFi4guO/LZcDtwK+Bu4C/AL8HfgYcDfw9yraPADMJ4n8PeDByFWAAzOwIgpsYrQXOAX4FnAo8FqWv54C3I6+/XfvDksZAU1VSXzpSfs3iROCDKG2j+S3BRewu8OA6ORMiV/j9R5S2+cBZ7l4AYMHdDS8ArnL37WY2P9LuW3dfVp2du3uRmd0GPGJmf3D3BdWMuyI5BNdRKowcy2jgamCguy+NxH0AwYX6yo4o3nP3myPPJ5pZf+D/KH7jvw343N3P372Bma0CPjSzYe4+u0Rf/3L3e2t5LNLIaMQh9WUbcHCZx1eVblHawcBbXvriahVdsfij3UkjYi7QJXKl49p4muDCeTfVsh+AyZGksdsiYNnupFGirnOUuF8rU34VOMiC+6q3IBhFvVhydAd8SpBQDyqz7Tu1PhJpdJQ4pL4UuHtmmceOENt3I7i7XUlly7ttLVPOI7g5V60SRyQZ3Q5cbGZ9atMX0WOMVhct7rL3mFhPMHvQCWhPcEXnBwgSxe5HLtCE0veZAVgXOnJp9DRVJcliLdC5TF3Zcn14lGBa6LdRXsuhzJt8BYvbtVX2dqhdCC4tvhFoRrCYfwvwbpRty96QTJfHltA04pBk8Q1wRpkF9TNr2Fde5GezsBu6ey7Bva4vB7qXeXkl0NrMSt6f/qQaRVi5sov5Y4Cp7l7o7lnAl8CgKCO8THdvUHeylPjQiEOSxT8I1kSeN7PHgH2Bn0ZeK6pwq+h2L45faWbPA7vcfVaI7R8iuPHV4cDHJeonANnAo2Z2J8H9sMudKhsDp5jZ3yL7PpvgJIPRJV7/DcFCeBHB7WN3ENzY6DTgdzFY2JdGTiMOSQrunglcSLC4+zrwQ+DnkZe3h+xrOcEpuWcDn1H8vY7qbr8LuDtK/cZIXD0jMV4MXBSm72q6guBe4q8TnGo8zt33nCjg7p8CRxFM5T1FcHy/AVagNQ2JAd0BUJKWmV1M8MbYv8zZSCJShzRVJUnDzB4E/gdsIfjE/X/AO0oaIvVLiUOSSUeC00w7ApsIvjn+m7hGJNIIaapKRERC0eK4iIiEosQhIiKhKHGIiEgoShwiIhKKEoeIiITy/wENdnGpGhj3iwAAAABJRU5ErkJggg==","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 FlightNumber and y axis to be the Orbit, and hue to be the class value\n","sns.catplot(x=\"FlightNumber\", y=\"Orbit\", hue=\"Class\", data=df)\n","plt.xlabel(\"Flight Number\", fontsize = 15)\n","plt.ylabel(\"Orbit\", fontsize = 15)\n","plt.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":"code","execution_count":10,"metadata":{},"outputs":[{"data":{"text/plain":["['FlightNumber',\n"," 'Date',\n"," 'BoosterVersion',\n"," 'PayloadMass',\n"," 'Orbit',\n"," 'LaunchSite',\n"," 'Outcome',\n"," 'Flights',\n"," 'GridFins',\n"," 'Reused',\n"," 'Legs',\n"," 'LandingPad',\n"," 'Block',\n"," 'ReusedCount',\n"," 'Serial',\n"," 'Longitude',\n"," 'Latitude',\n"," 'Class']"]},"execution_count":10,"metadata":{},"output_type":"execute_result"}],"source":["list(df.columns)"]},{"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":11,"metadata":{},"outputs":[{"data":{"image/png":"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","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 Payload and y axis to be the Orbit, and hue to be the class value\n","sns.catplot(x=\"PayloadMass\", y=\"Orbit\", hue=\"Class\", data=df)\n","plt.xlabel(\"Pay load Mass (Kg)\", fontsize = 15)\n","plt.ylabel(\"Orbit\", fontsize = 15)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n","\n","However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\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":12,"metadata":{},"outputs":[],"source":["# A function to Extract years from the date \n","year=[]\n","def Extract_year(date):\n","    for i in df[\"Date\"]:\n","        year.append(i.split(\"-\")[0])\n","    return year\n","    "]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"ename":"TypeError","evalue":"Extract_year() missing 1 required positional argument: 'date'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/var/folders/77/4jbcc_g16rl4fs9r3xtzsghw0000gn/T/ipykernel_11999/1933897256.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExtract_year\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mTypeError\u001b[0m: Extract_year() missing 1 required positional argument: 'date'"]}],"source":["# x = Extract_year()"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["   year  Class\n","0  2010      0\n","1  2012      0\n","2  2013      0\n","3  2013      0\n","4  2013      0\n","---------\n","    year  Class\n","85  2020      1\n","86  2020      1\n","87  2020      1\n","88  2020      1\n","89  2020      1\n"]}],"source":["slice_data = pd.DataFrame(columns=['year', 'Class'])\n","slice_data['year'] = pd.DatetimeIndex(df['Date']).year\n","slice_data['Class'] = df['Class']\n","print(slice_data.head())\n","print(\"---------\")\n","print(slice_data.tail())\n"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[],"source":["new_data = slice_data.groupby('year').mean().reset_index()"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=2f179417-6822-4d73-bf97-e2bc2c43524a style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('2f179417-6822-4d73-bf97-e2bc2c43524a').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>year</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>2010</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2012</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2013</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>2014</td>\n","      <td>0.333333</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>2015</td>\n","      <td>0.333333</td>\n","    </tr>\n","  </tbody>\n","</table></div>"],"text/plain":["   year     Class\n","0  2010  0.000000\n","1  2012  0.000000\n","2  2013  0.000000\n","3  2014  0.333333\n","4  2015  0.333333"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["new_data.head()"]},{"cell_type":"code","execution_count":17,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=946aacc4-2097-4f78-b543-61c4e9a8d415 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('946aacc4-2097-4f78-b543-61c4e9a8d415').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>year</th>\n","      <th>Class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>5</th>\n","      <td>2016</td>\n","      <td>0.625000</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>2017</td>\n","      <td>0.833333</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>2018</td>\n","      <td>0.611111</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>2019</td>\n","      <td>0.900000</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>2020</td>\n","      <td>0.842105</td>\n","    </tr>\n","  </tbody>\n","</table></div>"],"text/plain":["   year     Class\n","5  2016  0.625000\n","6  2017  0.833333\n","7  2018  0.611111\n","8  2019  0.900000\n","9  2020  0.842105"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["new_data.tail()"]},{"cell_type":"code","execution_count":18,"metadata":{},"outputs":[{"data":{"image/png":"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","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\n","sns.lineplot(x='year', y='Class', data = new_data)\n","plt.xticks(slice_data['year'])\n","plt.show()\n"]},{"cell_type":"markdown","metadata":{},"source":["you can observe that the sucess rate since 2013 kept increasing till 2020\n"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=d4a8fe81-4234-4c4e-a69b-5f9f75762fc7 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('d4a8fe81-4234-4c4e-a69b-5f9f75762fc7').style.display = 'flex' </script> <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></div>"],"text/plain":["   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n","0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n","1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n","2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n","3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n","4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n","\n","       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n","0    None None        1     False   False  False        NaN    1.0   \n","1    None None        1     False   False  False        NaN    1.0   \n","2    None None        1     False   False  False        NaN    1.0   \n","3  False Ocean        1     False   False  False        NaN    1.0   \n","4    None None        1     False   False  False        NaN    1.0   \n","\n","   ReusedCount Serial   Longitude   Latitude  Class  \n","0            0  B0003  -80.577366  28.561857      0  \n","1            0  B0005  -80.577366  28.561857      0  \n","2            0  B0007  -80.577366  28.561857      0  \n","3            0  B1003 -120.610829  34.632093      0  \n","4            0  B1004  -80.577366  28.561857      0  "]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"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":20,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=56566eda-cadf-4612-adbd-d40a828e80bd style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('56566eda-cadf-4612-adbd-d40a828e80bd').style.display = 'flex' </script> <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></div>"],"text/plain":["   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n","0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n","1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n","2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n","3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n","4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n","\n","    Legs LandingPad  Block  ReusedCount Serial  \n","0  False        NaN    1.0            0  B0003  \n","1  False        NaN    1.0            0  B0005  \n","2  False        NaN    1.0            0  B0007  \n","3  False        NaN    1.0            0  B1003  \n","4  False        NaN    1.0            0  B1004  "]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n","features.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":21,"metadata":{},"outputs":[],"source":["# HINT: Use get_dummies() function on the categorical columns\n","features_one_hot = pd.get_dummies(features, columns=[\"Orbit\", \"LaunchSite\", \"LandingPad\", \"Serial\"])\n"]},{"cell_type":"code","execution_count":22,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=220c251a-4581-4bc7-b410-592d426e116d style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('220c251a-4581-4bc7-b410-592d426e116d').style.display = 'flex' </script> <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></div>"],"text/plain":["   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n","0             1  6104.959412        1     False   False  False    1.0   \n","1             2   525.000000        1     False   False  False    1.0   \n","2             3   677.000000        1     False   False  False    1.0   \n","3             4   500.000000        1     False   False  False    1.0   \n","4             5  3170.000000        1     False   False  False    1.0   \n","\n","   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0            0            0          0  ...             0             0   \n","1            0            0          0  ...             0             0   \n","2            0            0          0  ...             0             0   \n","3            0            0          0  ...             0             0   \n","4            0            0          0  ...             0             0   \n","\n","   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0             0             0             0             0             0   \n","1             0             0             0             0             0   \n","2             0             0             0             0             0   \n","3             0             0             0             0             0   \n","4             0             0             0             0             0   \n","\n","   Serial_B1059  Serial_B1060  Serial_B1062  \n","0             0             0             0  \n","1             0             0             0  \n","2             0             0             0  \n","3             0             0             0  \n","4             0             0             0  \n","\n","[5 rows x 80 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["features_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":23,"metadata":{},"outputs":[],"source":["# HINT: use astype function\n","features_one_hot = features_one_hot.astype(\"float64\",)"]},{"cell_type":"code","execution_count":24,"metadata":{},"outputs":[{"data":{"text/html":["<div><div id=0f0bc669-9026-42dd-8535-1cdd18a76d4e style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('0f0bc669-9026-42dd-8535-1cdd18a76d4e').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>FlightNumber</th>\n","      <th>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_GEO</th>\n","      <th>...</th>\n","      <th>Serial_B1048</th>\n","      <th>Serial_B1049</th>\n","      <th>Serial_B1050</th>\n","      <th>Serial_B1051</th>\n","      <th>Serial_B1054</th>\n","      <th>Serial_B1056</th>\n","      <th>Serial_B1058</th>\n","      <th>Serial_B1059</th>\n","      <th>Serial_B1060</th>\n","      <th>Serial_B1062</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>6104.959412</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>525.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>677.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>500.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3170.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table></div>"],"text/plain":["   FlightNumber  PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n","0           1.0  6104.959412      1.0       0.0     0.0   0.0    1.0   \n","1           2.0   525.000000      1.0       0.0     0.0   0.0    1.0   \n","2           3.0   677.000000      1.0       0.0     0.0   0.0    1.0   \n","3           4.0   500.000000      1.0       0.0     0.0   0.0    1.0   \n","4           5.0  3170.000000      1.0       0.0     0.0   0.0    1.0   \n","\n","   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0          0.0          0.0        0.0  ...           0.0           0.0   \n","1          0.0          0.0        0.0  ...           0.0           0.0   \n","2          0.0          0.0        0.0  ...           0.0           0.0   \n","3          0.0          0.0        0.0  ...           0.0           0.0   \n","4          0.0          0.0        0.0  ...           0.0           0.0   \n","\n","   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0           0.0           0.0           0.0           0.0           0.0   \n","1           0.0           0.0           0.0           0.0           0.0   \n","2           0.0           0.0           0.0           0.0           0.0   \n","3           0.0           0.0           0.0           0.0           0.0   \n","4           0.0           0.0           0.0           0.0           0.0   \n","\n","   Serial_B1059  Serial_B1060  Serial_B1062  \n","0           0.0           0.0           0.0  \n","1           0.0           0.0           0.0  \n","2           0.0           0.0           0.0  \n","3           0.0           0.0           0.0  \n","4           0.0           0.0           0.0  \n","\n","[5 rows x 80 columns]"]},"execution_count":24,"metadata":{},"output_type":"execute_result"}],"source":["features_one_hot.head()"]},{"cell_type":"code","execution_count":25,"metadata":{},"outputs":[{"data":{"text/plain":["(90, 80)"]},"execution_count":25,"metadata":{},"output_type":"execute_result"}],"source":["features_one_hot.shape"]},{"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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"]},{"cell_type":"markdown","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","metadata":{},"source":["| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n","| ----------------- | ------- | ------------- | ----------------------- |\n","| 2021-10-12        | 1.1     | Lakshmi Holla | Modified markdown       |\n","| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n","| 2020-11-10        | 1.1     | Nayef         | updating the input data |\n"]},{"cell_type":"markdown","metadata":{},"source":["Copyright © 2020 IBM Corporation. All rights reserved.\n"]}],"metadata":{"interpreter":{"hash":"25d94ae763beb282c8936bac0f3ff970d91daf0b8f2bdf99ab1c21bc0960c6c6"},"kernelspec":{"display_name":"Python 3.9.7 ('machine_learning')","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.7"}},"nbformat":4,"nbformat_minor":4}