eda-data-visualization.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment: Exploring and Preparing Data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **70** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Falcon 9 first stage will land successfully\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing Data  Feature Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Exploratory Data Analysis\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp = await fetch(URL)\ndataset_part_2_csv = io.BytesIO((await resp.arrayBuffer()).to_py())\ndf=pd.read_csv(dataset_part_2_csv)\ndf.head(5)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "execution_count": 3,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Next, let's drill down to each site visualize its detailed launch records.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\nsns.catplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 2558.88x500 with 1 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\nsns.scatterplot(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\nsns.scatterplot(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df)\nplt.xlabel(\"Pay Load\",fontsize=20)\nplt.ylabel(\"Launch Site\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Let's create a `bar chart` for the sucess rate of each orbit\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT use groupby method on Orbit column and get the mean of Class column\norbit_success_rate = df.groupby('Orbit')['Class'].mean().reset_index()\nax=sns.barplot(y=\"Class\", x=\"Orbit\", hue=\"Class\", data=orbit_success_rate)\nplt.xlabel(\"Orbit type\",fontsize=20)\nplt.ylabel(\"Success Rate\",fontsize=20)\nplt.xticks(rotation=45)\nax.legend(bbox_to_anchor=(1, 1))\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\nsns.scatterplot(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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lIiIi5S4ssPyDlDOjR49m9OjRFbY/BatLRH5BIUnpdlIycrFYIDLYj+oh/nh5Wc5/o+lHIesY5NshMBKCq4Ov/3lvLi0rl5TMXNJz8gn19yUy2A+LBVIycknLziPY34fIID+qOXlhZuXmcyzdzvGsPAL8vIkM8iMy2Fpsudz8ApLS7aRm5uLr7UVkkB8xoedfZwwD0hMg8xgYBRAYBSE1wLuUL62MZPMc5mVDYAQExYBf4PnXJ8cGmcmQkwbWEAiKgoDw89+eMwW55mOfecw8zsAoCK1xYdu0JUBWChTkQVAkBMeCTwW8AVfE+XIiv6CQo+l2Ut34ekyy5ZCamUtuQSHhgX7EhFix+nq7sdYXJjs3n2MZuaRm5uLva75Go0KKv0adKsg3X2dZKWCxQFC0+VrJOPkeBCdfe7HgVcpjTj9qPvYFuebjHlQdfEtZn6xUc93cTPCvBsHR5vOnvOXZTx+zj9V83w0pZSfswoLT5xDKfr6k1C7ZYDVs2DBOnDjBggULipoJv/nmG44ePUp4eDht2rTh2WefpVOnTgBs3ryZZ555hjVr1mCz2YiNjeWqq67izTffJCYmxsNHU7JMez4/70hmwpdbSMs2b2uNDrby6m1taF8vAv+yvvkaBiRtg3l3Q8ouc56PP3T5B7QbZr7YyyjhRDZPzd/CT9uTAfDz9mLW8A58uuYA3/yZgGGYy3VqGMlLg9o4jNR7LMPOO8t3M2v1PvILzQVb1gxl2h1XUD8quGi545m5fLnxMFO/3052XgEAdSICeXtoW1rUCC37h1p+LhxaB1/cZ75hAfiHwY1ToUlv8A8tef1jO81zmLTN/NvbD64ZZU5B0WWrC5jhZPEEiF9A0Qlr0BX6vwVhtcu+PWeyT0D8Qvh+ItjTzXlhteHWGVCzbekD5SkF+ZC4GT4bBicOmPP8guGG5+GyW8o35Dg9X9dD/zfdd76cyLDns3x7EhPnb8GWnQ9AdIiV1267nPZ1w8schgzD4K/EdEZ++jt7j2UC4O/rxdgeTbitfRzhQRXfQnC2lAw7s1bvY/rPe8gtKASgcUwwbw9tS+Pq5wgk9nTY+SN8PRZyTpjzrrgTmvSBr8eYAR/M953+b0H9zuAX5Hp7hYWQtNV87aXuMef5BkCXCdD2znO/f504AF8+CAdODjBp8YLWQ6DHpNKHnPORlQobP4Xlk80vYgARDWDwLIi5DLxK6DKdmwn7VsCCkWcEq0gY8DbUu67k8yVlps7rwMCBA9m4cSOzZs1ix44dLFy4kK5du5KSYj4Bk5OT6d69OxEREXz//ffEx8czY8YMatasSWZmpodrf267kzMY+envRaEKIDnDzrAZ6zh0PLvsG0w7CDNvOh2qAPJzYOnzsPOHMm/uRFYu//jij6JQBXD7VXX4eM1+vt5yOlQBrNqdwiNzNpKaaQcgr6CQuWsP8OHKvUWhCmDrERt3frCWxLSconm/7U3hX19vKwpVAAdSsxjy3hoOnziP83DiAHw84HSoArPl48sHICm+5HXTDsGsm0+HKjC/Oa98Ff6YZ367LAt7Oix5GrbNx+GE7VkOX9wPmSll254rCZth4cOnQxWcPJa+kHag7NtLOwQzbz4dqgByM+Cbx+DQhguvrys5Nvj+KSfn6yfzQzPLTefLiV1JGYyevbEoVAEkp9u556O15/V6PHwim9ve+7UoVAHk5BXy7+/+4pedySWsWTEKCw2+3ZLAGz/tKgpVADuTMrj9/TUcOddrL3k7fD7sdKjyDYDm/WDeXadDFZiP2dw7IHVvyduznXzOnQpVYAaVH5+F3ctKXjcjydzHgTNG7TYKYfNs+Pk/kJdV8voXYs9y+OHp06EKzGOYebP5nlyS4/tgzu2Oz+usFHPe8X3lUNlL2yUfrE6cOMGKFSv4z3/+Q7du3ahbty5XXnklEyZMoF+/fgCsWrWKtLQ0PvjgA6644grq169Pt27dePXVV6lfv76Hj6BkmfZ8pv20y2lZfqHB//26j7yCMn6IH1wL2cedly2bDOllG9E2xcmovNc2imLxn863s37/8aKRepPT7bz3yx6nyx0+kc3eYxkAHEu389L3O5wul2HPZ+Uu56MCu1RYAJs+McOQM8smmyHLlaR41+dpxctlPodkJsPWL52XHfjVLL9QWanw07+cl+XnwJ8u9l+Sv752/WH00/OOH5zulJlshipn9q8yL9GWgwx7Hm8s3em0LL/Q4JPf9pN/RvgojbV7Ux1C2pmmLtlOUnqO07KKkpSewxtLnb8HHcvIZdsRm+uV7elmYDlT837wx//MQHM2oxBWvQG5JYS1fatOh7Sz/fSCeYnQlfQESNzivGzjx5BeTj/sm37U9WsvJw32rXS9bl62eU5KOl955/HFUly65INVcHAwwcHBLFiwALvd7nSZ2NhY8vPzmT9/PsaZ327PwW63Y7PZHKaKlpWbz/aj6S7Ltx6xkZNXtjdyDv/uuuzEAddhw4UzW9JOySsopLCEU30s3XyssvMKsOU4/1AB81sxQG5BIXtOhixnNh5wERRdyc+BI5tKqOD2kr+9Ht3quiwrxdx+WeTYnL9xnuKOYJWfY16+dOXwerOPVGkZhnkp1ZVjO8z+e+XBfo7zlVU+gS7LXsCOc7wez2xRLY0/DrkO8AdTs8nLL/17Vnmw5xeSnOH6cdx6pIQvILmZkPyX47ywuJKfh0l/lvzaK+n96/jekt+/0g67LivIg1zXj+0FKcxzbGE72+ESWndzs8xLn64c/dNcRtzmkg9WPj4+zJw5k1mzZlGtWjU6derExIkT+eOPP4qWufrqq5k4cSJ33HEHUVFR9OnTh5deeqnYaK5nmzJlCmFhYUVTXFxceR9OMQF+3jSMcn39vGn1EKw+ZexjFdvKdVloTbOvUBmEBhTvl+Pr7YWlhC5PkScHlPP38SLIz3X9G5w8dl9vL+pGuO4U3qpWWClre5KPP1S/zHV5REPwKeEX26ObuS4LCDe3XxbWEEo8YUHOB+crEx9/s0+HK7FtwLsMIyhbLFDzCtflEQ3MDrrl4Vzn6zz6CZZGgJ83DaKDXZY3rR5S5j6PLWq67stXq1oAvt4XcIOKG/j5eBFRQj+vprEl9LHyDTRfS2dKP1Ly8zCqKfiV8Nqr0dp1WVhcye9foTVdl3n5mP0Dy4OXL1Sr67q8RhvXZX6B5jlxJarphd0wI8Vc8sEKzD5WR44cYeHChfTu3Zvly5fTtm1bZs6cWbTM5MmTSUxMZPr06bRs2ZLp06fTrFkztmxx0SwMTJgwgbS0tKLp4MFzXAcvB8FWXx7u3thpmZcFhnWsh59PGZ8GdTuC1cWbeZcnzbsDyyAyyMpV9SMc5q3Zk0KP5s6307p2GFEn7/iLCbVy37XOL8fGhFhpePJDLDrEyqM3NHG6XICvN12alvEGBC9vaHe3+WbqTLeJEFDN9frVW7oOOx0fMe+KK4ugaGh6s/Oymm3PrzP82QIj4PqnnJd5+0HrwWXfZot+rkNkt6fdEwidCYqGpjc5L6vVzj3ny4kQf1/GXN/IaZm3l4W7r6mLr3fZXo8dG0a6/HIxtkfjC7vr1Q1iQvwZ2bWh07Jqgb60qlXN9cr+odB1guO8bQuhzRDnwdhigWvHmoHMlfqdXQegbhMhpIT3r9Aarr8Utb4NgsvpRqaQ6mbdnPELNm9SccU3ADqNOcf5KiGISpkpWJ3k7+/PDTfcwDPPPMPq1asZNmwYkyZNclgmMjKSQYMGMXXqVOLj46lZsyZTp051uU2r1UpoaKjD5AmNYoJ5eVAbAs948w0N8OG9u9sTF3EeL6iwOLhnEYTWOj3PyweufQya3VRyS4AT4UF+vHLb5VxZ7/QdYJ+s2c/wTvXo1tTxA+6KOtV4e2jboqEUfL29ueuaugzpEMeZN/U1iApi9gNXUeOMuwc7N47i0R6NHb7Bx4RYmf3AVdQMO48Pn7A6cMdnZuA4xTcAbn615NYsgGpxcM/XEH5GKLR4wZUPmHc8eZexFdE/FG78LzTq4Ti/dgfzriF3BZSa7aD3FMcwFBQFd35pPi/KKjQO7lrg+IHkY4Uez0Odqy+4ui75h8GNLzk/X4Nmll+gA5pUD+GlW1s7vB7DAnx5/+72xJXQqupKjbAA5jx4NbFnBChfbwujuzWie3PP37Hs7WVhwBW1GN6pHt5nvEhrhwcw94GrqVntHK+9mGbm3X6n7lzLzYDt38Et7zt+wbOGmHennt3Cdbaw2jDsa8fWJ29f6PwPaNyz5HWDq8PtcyH2rFavZn3h+mfK9+66Rj3MOp7ZKhxa03wvPtddrBEN4NaZZ52v0JPnq4TWPzkvFqMsnYYuImcOt+DMK6+8wosvvsixY677WvTr1w8/Pz8+//zzUu3TZrMRFhZGWlpahYes3PwCktPtJKXb8bJYiA6xEhNixaeM344d2BLMvjt52eY3qqDoC3pjOZ6Zy7FMO7bsPMICfIkKtmLB7OB6Iju3aKTeiKDil4fSc/JIycwlJSOXQD9vIoP9iAkp/oadlZtPSkYuyel2/Hy8iAq2Uj3UiqWMYbDIqbFhMpKhMP/keYgp/Xhe6Yknx8PJMsfCCYoB6wVcTsg6DlnJ5r/+YSfH+3HzZa28HPPuqIyj5pt8cDQE1yj5du+SFBZCRqK5zYJc88MrOKZivkVXxPly4tR4asluej0ahsFRm51jGXbs+YVEh1iJCvYj0K/yjKiTac/nWIadYxm5BPh6ERlspXppW9Pyc08+R46Cxdt8fgRGma+dzCSzv15wdXO4g9Jcji4afy7ZfD6X9f0rM9mc7OkQEHly/LNqpVv3QuRmmcebcdT8chMUbY6bV5r3r4K8k+83ZTtfOTk57N27l/r16+Pv79nWz/JW0rGW5fO78rzqPCQlJYVBgwYxfPhwWrduTUhICOvXr+e///0v/fv3B+Drr79m7ty5DBkyhCZNmmAYBosWLeLbb79lxowZHj6C0vHz8aZWeCC1wt14LT20xoUPDHmG8CA/p2PulGak3hB/X0L8fakXWfIbY6CfD4ERPufVMuCUl7f5bfF8xz0KiXXv2DeB4eZUnnz9IbyOObmDl5f5zbuk/ivlpSLOlxN+Pt7UDg+ktptejxaLhdgwf2LPp+W1ggRZfQiy+lD3HK9Rp3z8oFodczpTtThzKiuL5cKec0HR5Xa5uER+geBXD8LrlX1db9/zP19SJpd8sAoODuaqq67i1VdfZffu3eTl5REXF8cDDzzAxInmNe0WLVoQGBjI448/zsGDB7FarTRu3JgPPviAu+66y8NHICIiImf75ZdfeOmll9iwYQMJCQnMnz+fAQMGlPt+L9lLgZ7gyUuBIiIiZeXWS4HZx0/+hJTt5KX38v0Jqe+++45Vq1bRrl07brnllnMGK10KFBERkaoh7TB8Ndr8dYNTGnaHfm9CWC3X612APn360KdPn3LZdkl0V6CIiIiUn+zjxUMVwO6l5k9kufoljypKwUpERETKT2Zy8VB1yu6l7vlliEpEwUpERETKT845fs7tXOVVjIKViIiIlB//c9ysda7yKkbBSkRERMpPULTZUd2Zht09MyZYOVKwEhERkfITEG7e/Xd2uDp1V2A5DbmQkZHBpk2b2LRpEwB79+5l06ZNHDhwoFz2d4qGWxAREZHyFVYLbv3wjHGsQs2WqnIcx2r9+vV069at6O/HHnsMgHvuuYeZM2eW234VrERERKT8BYSXa5A6W9euXfHEGOi6FCgiIiLiJgpWIiIiIm6iYCUiIiLiJgpWIiIiIm6iYCUiIiIl8kQn8IrmrmNUsBIRERGnfH19AcjKyvJwTcpfbm4uAN7e3he0HQ23ICIiIk55e3tTrVo1kpKSAAgMDMRisXi4Vu5XWFhIcnIygYGB+PhcWDRSsBIRERGXYmNjAYrC1cXKy8uLOnXqXHBwVLASERERlywWCzVq1CAmJoa8vDxPV6fc+Pn54eV14T2kFKxERETknLy9vS+4/9GlQJ3XRURERNxEwUpERETETRSshMJCg8LCi3+MkvNRUFhY+c9NQSXv81BYaE7lrSC/Um+v0j2X3H2+3K2woGKeNyJupj5Wl7DkdDs7j6YzZ+0BCg2DIVfWoVlsCNEh/p6umscl2XLYlmBj3vqD+Hp7cXuHOjSqHkxUsNXTVTMV5MKJg7DlM0jYBLXaw2W3QFgd8K4kL+uMJEiKh99ngVEIbe+B6i0guLr79mHPgLRDsPH/IHUvNLweGveE8Lrnt73CQkg7CNu/hb2/QFRjuHwohMWBX+B5bTIxLYc/Dp3gi98PE2z15o6r6lI/KoiIIL/zq+OFsGeYx/f7x3B8LzTsDk16QrU6FV8XV2xH4PDv8MdcsIZAu3shoiEERXq6ZiKlYjEuheFUz2HYsGHMmjULMAdDq1OnDnfffTcTJ07Ex8eHgoIC3njjDT766CN27txJQEAAV199NU8//TSdOnUq9X5sNhthYWGkpaURGhpaXodTKsnpOUz8cgs/xDvePnttoyheHtyG6qGXbrg6asvh4dkbWbsv1WH+ja1ieb7fZUSFeDhcFRbCgdXw8d/MgHWKjz/cvRDqXOW5up2SfhQWPQI7FjvOb3g9DHgHQmIvfB952RD/Ncx/AM58GwuMgHu/h+gmZd/m0a0wow/kpJ2eZ/GCwR+bgc2nbGEoIS2b+2auY1tCusP8oVfV4fGeTYgIqsDnUl42xC+C+Q+673y5m+0IfDoYjm5xnN/+Puj2lMKVeExZPr91KfCk3r17k5CQwM6dO3n88cd57rnneOmllzAMgyFDhvD8888zZswY4uPjWb58OXFxcXTt2pUFCxZ4uurnZeOBE8VCFcDKXcf4dXeKB2pUeSzbnlQsVAF8uyWRrUfSnKxRwdIT4LNhjqEKID8HPr8XbAkeqZaDQ2uLhyqA3T/BvpXu2UfGUfjqIceQAJCVCl+PhazjZdte5jGY/3fHUAVma9uX90NGYpk2V1BQyOfrDxULVQCf/naAfccqeCTrjKPw1UgX5+tRyC7j+XK3ggLY+EnxUAWw/kM4vqfi6yRyHhSsTrJarcTGxlK3bl0eeughevTowcKFC5k3bx6ff/45//d//8f9999P/fr1adOmDe+99x79+vXj/vvvJzMz0+k27XY7NpvNYaoMMuz5zFi1z2X5zNX7OJGV67L8Ypaamcus1ftcls9YvY/s3IKKq5Azmcnm5IztMGS5KKsoOemw5h3X5b9Nd8+HeMIfrvuX7V8F2cXDcYmyUiHxD+dledlwbFeZNncsM5dPfzvgsvzT3/ZXbJ+rI5tLOF8rzeP3pMxkWP+R6/L1M4qHQpFKSMHKhYCAAHJzc5k9ezZNmjShb9++xZZ5/PHHSUlJ4YcffnC6jSlTphAWFlY0xcXFlXe1S6Wg0MCe7zocZOcWUHiJvoEVFBZiz3PdYTY7t4ACT3dAPldndU93Si7MN1vPXMnLdk+n5LxztPgUlvE8nGv5vOwybc4wDHJKeJ1l2AswqMDnkrvPl9sVlvy8sacrWEmVoGB1FsMw+PHHH/n++++5/vrr2bFjB82bN3e67Kn5O3bscFo+YcIE0tLSiqaDBw+WW73LItTfh35taros79umBmEBHuhYWwlUC/SjT6saLsv/1rYWwf4e7hweHGP2p3LGLxiCoiu2PmcLqAaX3eq6vNWt5jIXqmZb12URDcC/jPsIqAahLl4XFgvEOH8fcCUswJeeLVx31L+1XW283TDKc6nVKuF8RTYs+/lyN/9waHqT6/LL74CKPF8i50nP0pO+/vprgoOD8ff3p0+fPtx2220899xzgBm2zofVaiU0NNRhqgwsFgs9W8ZSOzygWFlMiJUBV9TC2+vi+5HN0vD19mJIhziindz9VzcykM6NPRxawLyrrsdzzst6TYYQN951dz4sFmjRz7yT7myhNc3Q5eWG0ZuDY6D9cCf794KbXi37eQipATe9Ytb/bNc8UubAGuDnw8iujQh1EsRb1gylVa2wstXvQgXHmHfYne18z5e7+QXAdY+Bv5PzEtsaalxe4VUSOR+6KxDzrsDDhw/zzjvv4OfnR82aNYt+3bp///7Ex8c7bZVavXo1nTp1Yv78+QwYMOCc+6lMdwUCHD6excdr9vPF74cxDIP+l9fi3o71qB1xfreVX0wOpmbx4co9LNqcgLeXhUHt4xh6VR1qViseRj0i6zgc2QA/TYbUPRDVCK5/xvzwcUdrkDucOABrP4A/5piXcFoNhqv+DuFuvLU/M9nsEL/iFUhPNFuxejwLUU3AL6js27NnmENE/PS82YcrrBZ0/gfUuxaCosq8ucJCgwOpWbyzfBdLth3F39eboVfVYWC72tQI88BzKePk+Vr5snnnZq120P0ZiGp63sNJuFVhoTkMxMpXYfs34BNghufL73DdmihSAcry+a1ghRmsTpw44fQOvzlz5nDHHXewcOHCYv2sBg4cyM8//8z+/fsJCjr3m3hlC1YAeQUFpGSYfXYignzx89HvQJ2Sm19IaqYdCxYigv3w9a6EDbxZqWa/FB9/87b5yqYgz7zbDiAwsszDFZRaRpLZR8gvyHmLR1llnzD7JHn7nVegOltOXgEnsnKxWCxEBVs93yKckQyFee47X+6Wmw05x83WtKBo97RwilyAsnx+V5KRBCuvIUOG8Nlnn3HPPffw0ksv0b17d2w2G2+99RYLFy7ks88+K1Woqqx8vb2JDdObljN+Pl7EeqJVoSwqY5g6k7cvhLrus+Y2wTHu3V5ANbe2/Pn7eleu51JwJbikXRK/AHMSqYIq4VfwysVisTBv3jwmTpzIq6++StOmTbnuuuvYv38/y5cvL9UlQBEREbk06FJgBaqMlwJFRESkZBp5XURERMQDFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNfDxdAal80nPyOJGVR6FhEOrvS3iQH5n2fFIzcyk0DIKtPkQGW0u9vdz8ApLTc8krKCTA15vqYf7kFRSSnG4nN78Qf18vqof6Y7FY3H4syel2MnPz8fGyEBnkR4DfhT3lbdl5nMjOBQNCA3ypFujnfEF7BmSlgFEA1lAIioLcLMg6BoX54BcMwTHO1823Q0YSFOaBbwCE1Lig82UYBkdtdnLyCvDz8SI6xA9fb29IT4TcTPD2M+vi4/wxTcmwk27Px8diISLIj0Cri3OYlQo5aWCxQEAE+Ic6Xczh+RXgS7irc3ghCvIgI9H818cfQmpAYSFkJJjn18cfQmLBy/vC9pN+FPIywcvXfIx9AziWYSejNOertHJskJ0KhgH+YRAY4fB6DPH3ISLIxesxLwcyk08+l4IgpLrz5QryIOMoFOSedb4SIT/n9DwvJ9/FDQPSE8zlvHzN8+rte2HH7CE5eQUcy7CTX2AQaPUmJsS/9CsXFJjPr4Jc8La6Pl9y0bvog1ViYiJTpkzhm2++4dChQ4SFhdGoUSPuvPNOZsyYwbp161yu26VLF5YvXw7A6tWreeGFF/j111/Jzs6mcePG3HvvvYwZMwZv7wt8c65E9h7L4IVv4vnpryQMA3o0j+HJPs149YedLN6aSEGhQYsaoTzfvyWX1QrD37fkYz9qy+GDFXv4ZM0BsvMKiAsP4N272rP4zwRmrNpHuj2f6BArj93QhN4tqxPu6gOijDLs+WzYf5x/LtzKnmOZ+Hl7MeCKmozt0YSa1QLKvL3CQoPdyRn8c9E2Vu46BsBV9SN4vn9LGsWE4O11Rsg5vg9+eA7+WgiFBVCvM9z0Mqx8Gf78wvwQi24KfV6C2u3MkHVKeiKsfhPWfwR5WRBen+TBi/h4SyYzVu0n3Z5PTIiVx3o2oWeL6q4/UE9VJTOXn/5K4qXvt5NoyyHIz5t3BzXiGq+teP/wDJzYb4a3K+6Gax+F0BpF62bl5rPlUBrPfrWV7UfT8fGycFPrGozv1ZTa4YGnd1KQD8nx8M3jcPA3M1g17A69p0BkY/Pvk/Yey+BfX29j2fZkDAOuqFON5/tfRtPqwfj5uOl1lH4U1r0Pv00He7r5ATdkNuz5GVa/DtnHITASOo+DywZBcHTZ92FPhwO/weInIGUXePuRdcNLbIm+kWcX/lXy+SqLlF2weCLsWmIGmKY3cqD720xZsocl245SUGjQsmYoz/e/jJY1Qx1fj2mH4ZeXYPNsM0xGNIDe/4Y61ziGXpfnazmsfuOM8/UPaDUQgs44X1mpsOM7WPovM1z5BcOVI+CqB82AVYUknMjmzZ928cXvh7DnF1IvMpBn+7akQ71wQvzPERQzkmDjJ6fPV1AUXDceWt1q/l8uKRbDMAxPV6K87Nmzh06dOlGtWjX++c9/0qpVK6xWK1u2bOG9997jtttuo2fPngAcPHiQK6+8kh9//JGWLVsC4OfnR0REBPPnz2fw4MHce++9jBw5kmrVqvHjjz/yj3/8g+7duzNv3rxStR7YbDbCwsJIS0sjNNT5t3lPOnQ8i/7TVpGSmVs076072vLit/EcPpHtsKyXBb4adS2taoe53F5qpp1H527m553JRfNGdG7AgdQsvvszsdjyE/s0495r65mtKRdo5c5k7vxwbbH5zWJD+L/hVxITWoZvosCB1Cxuen0F6fZ8h/mBft58+8h11IsKMmekHYKPepn/nvK36bDiZTi203GjFgvc8w3U62T+nXkMvnwAdv9UtEhap4k8c7QzC/9MLVanp25szrBOdV2er4KCQuasO8jTC/4smte6digzOxwm4rsRxVeocw3c9nHRB+fGA8e55Z3VnP0OUScikP+NuJoaYScD6rGdMP1as8XiTAHh8ODPEF4XcP78ArD6ePHNI9fSKCbE6XGUSfZx+GYc/Pn56Xnth5thduPHxZe/9nHoPB78yhi2d/0En/zt9N8hNfi993wGfrKn2PmqGxnI/x68htiwsj3nOHEA3utqtnyedGTwtwz8KoeENMdz7e1lYeHoTrSsefL1mH4UZg+GhE3Ft3vHPGjSy/x/9nEzEP/5xeny9vdBgd0MCme7bpwZSH0DzEC9/iP4bnzx5Zr3g76vQ2BE2Y7ZQ5LScxg+Yx1/HrEVK5sxrAPdmrloXQbISYcfn4P1HxQv6zwernvcPF9SpZXl8/uibqccOXIkPj4+rF+/nsGDB9O8eXMaNGhA//79+eabb7jrrruIjY0lNjaW6GjzwyQyMrJoXkREBJmZmTzwwAP069eP9957j8svv5x69epx//33M2vWLD7//HPmzZvn4SN1j6XxSQ4feg2igkhKzykWqgAKDfjv939hy85zub2jNrtDqLJY4OoGkU5DFcDrS3eSZLNfwBGYjqXb+eeibU7L/kpMZ09yZpm2l19QyLx1B4uFKoCs3AI+XrOP3PwCc8aBNY6hKiTWvPR3dqgCswXi+4mQefKDMz3BIVRhsXCszo1OQxWcOl+5TssAjqbb+e/3fznMG3tVKBGr/ul8hQO/mq0cwImsXCZ/G18sJIAZMv88nGb+kZcDq6cVD1Vgfmhv/RIKCzEMgyXbjhYLVQD2/ELeWrabrNzi57fMMpMdQxVAk96w6VPny//6JmQeLds+MpLMlqozHL/iISb/nOL0fO1POeN8lZZhQPzXDqGK6KasTQ0qFqoACgoNXlq8nfSck6/H43udhyqA7yeYl/0AMpIdQxWYoWvTbOfrrn7j9LrpCbDsBefLxS80H4sqYn9KltNQBfD819tITi/hfSkrGTZ85Lxs9Rvm80UuKRdtsEpJSWHJkiWMGjWKoKAgp8uUppVpyZIlpKSkMG7cuGJlffv2pUmTJsyZM8fpuna7HZvN5jBVVtl5+SyNd/yAaRobwu/7j7tcZ+3eVDJL+DCMT3A83hCrD0fTnXwAn5SZW0BaCUGttLLyCtiZlOGy/Nc9x8q0vQx7Pr/sdP0hsWLnMTLs+eaH4V/fOBZGN4PDv7veeMIm85IfQOKfjmXWMA7YCkqsV0nny5aThy3b8fGpGZAPtiOu63Oyrlm5BWwo4bH/6a+T5yMnDfb+7Hp7O5dAXiY5eYXFnl9n+nV3Cuk5bghWKXsc/7ZYzH5kRqHz5QtyzctZZZGbCcd2OMzKjrmc3w+6Dk/LtpfxwzUvC3YsdphVGN2SJftcn6O1+1LN5yHA/tWut52y2zwGgNTdjmUWr9Kfr5w0c3LF2ZeJSmrtHtfPgb3HMksO/RlHXZ+vfLtjOJZLwkUbrHbt2oVhGDRt2tRhflRUFMHBwQQHB/PEE0+4WPu0HTvMN9DmzZs7LW/WrFnRMmebMmUKYWFhRVNcXFwZj6Li+Hp5Fbs8lp6TT3iQ647FkUF+eJcQTiODHdfNySskxFpyXwXrOfpslYaPl4WAErZTvYyXAf18vIgs6TwEW/H19jI/xMNqOxbm2Eq+HOIfdrqDa9BZfX3yswnzL/l8WH1dv4StTvos5eMLXiV0rTzZod7bYimxU3nNU5e1fPxKPr6g6uDth4+3pcTLr5HBfuY5vFAB4Y5/G4bLTvlFfMp4ic7bB3wd+0x52W1UC3D93K5R1suApzqBnzkrN42aQa5fbxFBfqf7+p3RV64YH39z+2DeZHAmo/Dc5+vUZS3fcxxTFbkMCFA9zPUxW3288PEq4Uu4r/Mv7qfLdRnwUnPRBitX1q5dy6ZNm2jZsiV2e+kvO51PV7QJEyaQlpZWNB08eLDM26goPt5e3H1NXYd5v+1NoUsT1x177+/cgOgQ129IjaKDCT7jjqjcgkLs+QVEu7ijsH298BIDTGlFBftxx1V1nJZ5e1no1KhsnUkD/Xx4sHNDl+V/79zgdOfWy+9wLEzYCHFXmS0BznR40AwfYHZot57RzyjfTo2CBJfnq8M5zldkkB/XNXY81i932LE3HeB8Bd8AqNEagKgQK/ddW9/pYhYL3Njq5Ad3QLjZ6d2Va0aCjxk87+1Yz+ViI7o0JMINjz1htYsH1IyjEO78WIhpUXz5cwmqDm3vdpgV/eeH3Nc+0uniFgv0aVVC0HHGxw+uOqsf3N4VDGru+vX2YOcGp58rda5xfWfe5UNPd9gPq128c3VGEoTXc75u9Zanlw+MgvpdnC8XGAHVnL8GK6Mr60fi6+08PA1uX5uoEt7nCI4p4Xxdps7rl6CLNlg1atQIi8XC9u3bHeY3aNCARo0aERBQum8RTZo0ASA+Pt5peXx8fNEyZ7NarYSGhjpMlVndyEAm9GlW9HdegcG3WxKY1LcFZzdMXd8shptb1Sjxcmr1UH9m3NvBofXo7eW7+e+trQn1d2w1qR0ewMuD2rgevqAM/Hy8eeC6+rStU81hvo+XhXfvbFfmFiswO70/1KVBsfn3dqzn2IE/rDb0ffP0nXCGARtmQd83it/aX/da6HCf2QIC5t1Yd3zm0BoSu/IZPhpU1+n5mnqO8xUa4Mvkv7WibuTp7c3eeIwDV4zDiGnhuLCPv7nvYDMAeHtZGNSuNtedFUK9LPDK4MsdW2DqXA1t7ylega4TzbsCTx1uZBBP9G5abLGBV9SiY0PnoaTMQmvC0M/MIS5OWfmaeVdm4Fn7CK4Og2aV/a5AHz/oNAZqdSia5b1vOYPqpHNdI8d9eFngtbPPV2lFNITuz53+uyCXWn/NYMrNDYq9Hm9oEUPvy2JPvx5DasCQOeZQGmeqeYXZmfpUK92p59yZ52vVa3DTK8XPV0gsDJp5OogGVDOf12eHVmsI3PE5hNQs+zF7SGyolffvbl8sXLWpHcbIbo2ctv4WCYk176I8u4UuJBYGzSh7cJcq76K+K7BXr15s3bqV7du3F+tn1bVrVy6//HJee+01APbt20f9+vXZuHEjl19+edFymZmZ1KlTh65du/LFF46dPBcuXEj//v2ZO3cut9122znrU9nvCgRzjKHkdDurdqeQk1fAtY2iiAryIzO3gDV7UkjLzqNToyhqhPmXaiyr/IJCEmw5bD54gkOp2VweV42GMUHY8wv587CNvccyuKxWGI1jgokNc2+TeXJ6DgdTs1m7L5XoYCsd6oVTPdT/vC83pmXnkpyey8qdyRQacG3jKGJCrMXDTW6GeVfWvhVmH5QGXcwPsNwss+9LZjLUu9b8Rn/2WFYF+WA7DIc3mHeF1W5PYVRTjuQFnff5SkzLYU9yBpsPnaBuZBBt4sKo4WXD6/geOLgWwmpB7SvND0Ifx1aOlAw7h09k8+vuFKoF+nJ1g0iiQ6wEnj0eWFaqOVTEnmXmZaaGXc3g4u9416iz51dsqH+Jl5zLrLAQbIcgYbM5XEGNy82WloJcsx9bcjzEtITqLYpfui2LjCRzaI39q81jrduRY97RHLHlOpyvmBDr+Y+flmMzW9z2/GzeINCwG5mBtUm2+7B69zEy7AV0ahhJjWr+xYfeyLebHcwP/Gb+W+dqMwSdPZbVqfN1ZJPZ56qs58uWYPY5O/I7hDeAWldAaK0LHyOsguXmF5Bos7N+XypJ6Xba1w2nbmRQia3yRQzDvGnl6J+Q/JfZUhXTwnxtyUWhLJ/fF3Ww2r17N506dSI8PJznnnuO1q1b4+Xlxbp16xg3bhxDhw7l5ZdfBlwHK4DPP/+cIUOGMHz4cEaPHk1oaChLly5l/PjxF9VwCyIiIlJcWT6/L+oBQhs2bMjGjRt58cUXmTBhAocOHcJqtdKiRQvGjRvHyJEjS7WdW2+9lWXLljF58mSuu+46cnJyaNy4MU899RRjx44tlxHDRUREpOq5qFusKhu1WImIiFQ9GiBURERExAMUrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcxMfTFRAps/REOHEAEv+EanEQ0xxCaoKXvieIiIhnVclPomHDhmGxWIpNvXv3BmDz5s3069ePmJgY/P39qVevHrfddhtJSUkut7l8+XIsFgsnTpxwWr5161YGDhxIvXr1sFgsvPbaa+VwZHJOJw7Cx3+DD2+Abx6FT2+F6ddC4h9gGJ6unYiIXOKqZLAC6N27NwkJCQ7TnDlzSE5Opnv37kRERPD9998THx/PjBkzqFmzJpmZmee9v6ysLBo0aMC///1vYmNj3XgkUmo5NvhuPCRtc5yffRw+HQi2w56pl4iIyElV9lKg1Wp1GnAWLFhAWloaH3zwAT4+5uHVr1+fbt26XdD+OnToQIcOHQB48sknS7WO3W7HbrcX/W2z2S6oDpe8zGOwY7HrsuP7Iax2xdZJRETkDFW2xcqV2NhY8vPzmT9/PoaHLw1NmTKFsLCwoikuLs6j9any8rNLvtyXeazi6iIiIuJElQ1WX3/9NcHBwQ7Tiy++yNVXX83EiRO54447iIqKok+fPrz00kscPXq0wus4YcIE0tLSiqaDBw9WeB0uKtZQ8A9zXR7VqOLqIiIi4kSVDVbdunVj06ZNDtPf//53ACZPnkxiYiLTp0+nZcuWTJ8+nWbNmrFlyxYAWrZsWRTG+vTpU251tFqthIaGOkxyAUJiocsTzssaXG+Wi4iIeFCV7WMVFBREo0auWygiIyMZNGgQgwYN4sUXX+SKK65g6tSpzJo1i2+//Za8vDwAAgICKqrKcqG8faH1EPD2g+VTICsFfKxw+V3QeRwERnq6hiIicolzS7CqX78+Xl5efP/99yWGnTMdOHCArl27YrFY2L17tzuq4ZKfnx8NGzYsuiuwbt265bo/KUdBkdB+ODS9EXIzwdcfgmLMf0VERDzMLcFq//79WCwWcnNzS71OXl4e+/btw2KxnNc+7XY7iYmJDvN8fHxYs2YNc+fOZciQITRp0gTDMFi0aBHffvstM2bMOOd2t2zZQkhISNHfFouFNm3akJuby7Zt5m3+ubm5HD58mE2bNhEcHFzqMClu4uUNYbU8XQsREZFiquylwMWLF1OjRg2HeU2bNuXbb78lMDCQxx9/nIMHD2K1WmncuDEffPABd9111zm327lzZ4e/vb29yc/P58iRI1xxxRVF86dOncrUqVPp0qULy5cvd8sxiYiISNVmMdwwJoGXlxcWi4UtW7bQokWLUq3z+++/0759e4KCgkhPT7/QKlQJNpuNsLAw0tLS1JFdRESkiijL57fH7gr85JNPAPV3EhERkYvHeV0KvP76653Ov/feewkKCipxXbvdzp49e0hKSsJisdCzZ8/zqYKIiIhIpXNelwJPXfq70KuIDRo04NdffyU6OvqCtlNV6FKgiIhI1VOWz+/zarHq3Lmzw918P//8MxaLhXbt2pXYYmWxWPD396dGjRp07NiRIUOGnLOFS0RERKSqOK9gdfZdcF5eZletmTNnlrrzuoiIiMjFxi3DLdx9991YLBbCw8PdsTkRERGRKsktwWrmzJnu2IyIiIhIlVZlf4RZREREpLJRsBIRERFxkzJdCvT29gbMu/vy8/OLzT8fZ29LREREpKoqU7ByNW6VG34VR0RERKTKK1OwmjRpUpnmi4iIiFxK3PIjzFI6GnldRESk6qkSP8IsIiIicrFRsBIRERFxE7cMEOpMUlISW7ZsITU1FYCIiAguu+wyqlevXl67FBEREfEotwYrwzB49913efvtt9m6davTZVq0aMHIkSMZMWJE0W8MioiIiFwM3NZ5PSkpib59+7J+/XrA9RAMFosFgLZt27Jo0SJiY2PdsfsqQZ3XRUREqp6yfH67pcXKbrdz/fXXEx8fj2EYREdHM3jwYK688sqiS39Hjx5l3bp1zJs3j6SkJDZs2ECPHj3YsGEDVqvVHdUQERER8Si3tFj9+9//ZuLEiVgsFoYPH85rr71GUFCQ02WzsrJ49NFHef/997FYLEyePJknn3zyQqtQJajFSkREpOqp8OEW5s6di8Vi4YYbbuD99993GaoAAgMDeffdd+nZsyeGYTB37lx3VEFERETE49wSrHbt2gXAyJEjS73OqWV3797tjiqIyMUoIxnSEyEvx9M1kVNybOZjkn2i5OXsmWBLgKzUCqlWiQwDMpLMeufbPV2bS1P2cfP829M9XZNy55ZgdaqPVFxcXKnXObWsn5+fO6rgUnJyMg899BB16tTBarUSGxtLr169WLVqFQCbN2+mX79+xMTE4O/vT7169bjttttISkpy2M6sWbPo0KEDgYGBhISE0KVLF77++utyrbvIJSs9ETZ+Cv/XD96/Hr57AlJ2QWGBp2t26bJnwKF18Nm98H43mHsH7F1RPGDl5cDRbbBwNHxwPXxyC8QvgsxjHqk2tgRY9wHMvAk+6AFLnoXUfWbYkvKXlQo7f4DZg83nzZcPwpFNkJvp6ZqVG7cEq2bNmgFw8ODBUq9zatlT65aXgQMHsnHjRmbNmsWOHTtYuHAhXbt2JSUlheTkZLp3705ERATff/898fHxzJgxg5o1a5KZefpBHzduHCNGjOC2227jjz/+YO3atVx77bX079+fadOmlWv9RS45GcmwaAx8NRKStoHtMPw+E97tDMd2erp2l6aCfNj1gxlMdv8ItiOwfxXMuhm2fObYopiwGd69DrZ+aS53ZCP87074+T/nbuVyt/RE+HwYfDsOju2AtIOwdjq83xWO763YulyK7Bmw7kP49FY4uNZ8Pmz/1jz/+1d7unblxi2d199//31GjBhB7969+fbbb0u1zo033sj333/P22+/zYgRIy60Ck6dOHGC8PBwli9fTpcuXYqVL1iwgEGDBpGdnY2Pj/MbJNesWcM111zDG2+8wcMPP+xQ9vjjj/Pmm2+ye/fuUrXWqfO6SCkcXAcf9nBe1qQ33PIB+IdUbJ0udWmHYPq15uWcs/n4w6i1EF7XvNz2f/0gKd75dkavg6gm5VvXM+36ET4Z6Lzsinvgxv+Ab0DF1edSk7oHprV33tIcWgvuXwqhNSq+Xuehwjuv33///fTq1Yvvv/+ekSNHkpPjuj+E3W5n9OjRLF68mJ49e/Lggw+6owpOBQcHExwczIIFC7Dbi19Xj42NJT8/n/nz57scd2vOnDkEBwc7DX+PP/44eXl5fPHFF07Xtdvt2Gw2h0lEziF+oeuynd9DzokKq4qclHnMeagCyM+B9CPm/3PSXIcqgAO/ub9urhgGbJrtujx+QeXo/3UxO7bT9eV722HIvjjPf5nGsfrll19clj322GOkpqby7rvvsmDBAgYPHkyHDh2IiYnBYrEUjWP12WefkZiYSIcOHXj88cdZsWIFnTt3vuADccbHx4eZM2fywAMPMH36dNq2bUuXLl0YMmQIrVu35uqrr2bixInccccd/P3vf+fKK6/k+uuv5+677y4af2vHjh00bNjQaV+wmjVrEhoayo4dO5zuf8qUKfzzn/8sl2MTuWj5+Lsu8/KFk4MMSwXyOsdHxalyL++SlyvpsS0PJe3P20/PpfLmfY4+1JZzPF+qqDJdCvTy8ioaOd1tFbBYyM/Pd+s2z5aTk8OKFStYs2YN3333HWvXruWDDz5g2LBhAKSkpPDTTz/x22+/MX/+fFJTU/nll19o1aoVffr04ciRI2zevNnptsPCwhg6dChvv/12sTK73e7QUmaz2YiLi9OlQJGSJP4J0zs5L7v8TrhxKvjp8k2FsiWYl2fTDhUv868GD62EsDjIOg5zb4cDvxZfzuIFD2+AiAblXt0iB36Dj3o6L7v2Meg2Ebx9K64+l5rj++GtK81WzbNFN4N7FkFwTMXX6zyU66VAwzDcPpU3f39/brjhBp555hlWr17NsGHDmDRpUlF5ZGQkgwYNYurUqcTHx1OzZk2mTp0KQJMmTdizZw+5ubnFtnvkyBFsNhtNmjjvM2C1WgkNDXWYROQcQmtBx0ecz+/yD4UqTwitYfZt8znrVzK8vOGW9yH4ZD+ZwHC4+TUzbJ3txpcq/kM0sqEZxp3N73CfQlV5C64OA94p3jLoGwh/e7fKhKqyKtOlwGXLlpVXPSpUixYtWLBggdMyPz8/GjZsWHRX4JAhQ3jjjTd49913i3Venzp1Kr6+vgwc6KJzpIiUXWA4XPsoNLsZ1r4HWSnQoj806gHVSj+ki7hZ7Xbw0GrYOBuObICYFtBuGFSrA95nfJREN4URv8DWBbBnGYTVhisfgPD64BdcsXUOioIbnoPLb4ff3jVv8W89GOpdB2G1KrYulyJff2jSC/6+2rw7MHUX1LkGWt9mtnBepNz2I8yVUUpKCoMGDWL48OG0bt2akJAQ1q9fz8MPP8xNN93E3/72N+bOncuQIUNo0qQJhmGwaNEinnzySWbMmMFdd90FwNixY5k+fTqTJ09mwIAB5OXl8cknnzBlyhRee+21YoHLFd0VKFJG+blQmAd+rn/NQSpYYYF5acfbH7xL6CNjGJCXBd5Wx+DlKfl2s+5+gZ6uyaWpIB8K7OATAF5uuW+uQlX4jzAfOHAAMO/Ci4iIcMcm3SI4OJirrrqKV199ld27d5OXl0dcXBwPPPAAEydOJCEhgcDAQB5//HEOHjyI1WqlcePGfPDBB0WhCuC1116jdevWvP322zz99NN4e3vTtm1bFixYQN++fT14hCIXOR8/oHwHEZYy8vIuXdC1WCpXID77MqZULG+fyhGwK4BbWqxOdWp/8803y/SzNpcatViJiIhUPRU+jlVAgNmZtEOHDu7YnIiIiEiV5JZgVauW2QmwoEC/4yUiIiKXLrcEq549zXFCVq5c6Y7NiYiIiFRJbglWY8aMISAggKlTp3L48GF3bFJERESkynFLsGrcuDGzZ88mKyuLq6++mtmzZzsdUFNERETkYuaWuwKvv/56APbv38/evXuxWCz4+fnRuHFjwsPD8S5hrBOLxcLSpUsvtApVgu4KFBERqXoqfByr5cuXO/yGoGEY2O12/vzzT5frWCwWDMNw+28PioiIiHiKW4JV586dFZBERETkkue2FisRERGRS13V+8EeERERkUrK7T/ck5iYSEpKCjabjdDQUCIjI4mNjXX3bkREREQqHbcEq2+++YYZM2awcuVKkpOTi5VHR0dz3XXXce+993LjjTe6Y5ciIiIilc4FDbfwxx9/cNdddxXd/VfSpk51bm/VqhUff/wxrVq1Ot/dVlkabkFERKTqqZDhFr744gvuuusu7HZ7UaAKCAigTZs2VK9eneDgYDIyMjh69Ch//PEHWVlZgBnGrrzySmbPns3f/va38929iIiISKVzXsFq1apV3HnnndjtdgB69+7N2LFj6dGjB15exfvDFxYW8sMPP/Dmm2/y7bffYrfbGTp0KD/++CMdO3a8sCMQERERqSTKfCmwsLCQ1q1bs23bNqxWKx9++CF33HFHqdefO3cuw4YNIzc3l5YtW/LHH39cMmNg6VKgiIhI1VOWz+8yD7fw+eefs23bNiwWCzNmzChTqAIYMmQI//d//wfAtm3b+Oyzz8paBREREZFKqczBauHChYD5+4BDhgw5r50OHjy46PcFv/rqq/PahoiIiEhlU+ZgtW7dOiwWS5lbqs42dOhQDMNg3bp1F7QdERERkcqizMHq6NGjADRt2vSCdtysWTOH7YmIiIhUdWUOVnl5eQD4+fld0I59fX0ByM/Pv6DtiIiIiFQWZQ5WMTExAOzfv/+Cdnxq/ejo6AvajoiIiEhlUeZg1aJFCwAWLVp0QTs+1Qm+ZcuWF7QdEZGLQmEBpB2GhM1wZBOkHTLniUiVUuZgddNNN2EYBnPmzGHLli3ntdM//viDOXPmYLFYuOmmm85rG+cybNgwBgwYUGz+8uXLsVgsnDhxouj/zqbExMSidVJTUxk7dix169bFz8+PmjVrMnz4cA4cOFAudReRS0xuFuxaCu91hnc7w3tdzH93LjHLRKTKKHOwuvPOO4mMjCQ/P5++ffuye/fuMq2/Z88e+vXrR35+PhEREdx5551lrYLbbd++nYSEBIfp1CXP1NRUrr76an788UemT5/Orl27mDt3Lrt27aJDhw7s2bPHw7UXkSrv+D6YcxtkHjs9LysF5t4OqWV7jxURzypzsAoNDeXFF1/EMAwOHjzIFVdcwcsvv0x6enqJ66Wnp/PKK69wxRVXcODAASwWC5MnT64UI5DHxMQQGxvrMJ36aZ6nnnqKI0eO8OOPP9KnTx/q1KlD586d+f777/H19WXUqFEerr2IVGn5OfDrNDAKi5cZBqx6HfKyK75eInJezuu3Ah944AG2b9/OK6+8QmZmJv/4xz+YNGkS1113HW3btiU2NrboR5gTExPZuHEjv/zyC9nZ2UU/2DxmzBgefPBBtx6MuxUWFjJ37lyGDh1KbGysQ1lAQAAjR47k6aefJjU1lYiIiGLr2+32ot9TBHNIfBERB7mZcPRP1+VHt5rL+AZUXJ1E5LydV7ACmDp1Kg0aNGDcuHHk5OSQlZXFkiVLWLJkidPlTwUqq9XKSy+9xOjRo89316X29ddfExwc7DCvoKB4Z9DatWs7/F23bl22bt1KcnIyJ06coHnz5k6337x5cwzDYNeuXVx55ZXFyqdMmcI///nPCzgCEbno+QZCVFOz07ozUU3BL7Bi6yQi5+28gxXAyJEjufnmm3n11Vf5+OOPSU1NdblsREQEd999N2PGjKFu3boXsttS69atG++8847DvN9++61Yv64VK1YQEhJS9PepMbZOKePvVBeZMGECjz32WNHfNpuNuLi489qWiFykfAOg0yPw52fmpb+zXfeoGb5EpEq4oGAFUKdOHV599VVeffVVtm7dyubNmzl27Bjp6emEhIQQFRVF69atueyyy9xR3zIJCgqiUaNGDvMOHTpUbLn69etTrVq1YvOjo6OpVq0a8fHxTrcfHx+PxWIpto9TrFYrVqu17BUXkUtLRH0YNAu+Gg32k10GrCHQ9w2IaOjZuolImVxwsDpTy5YtL6pxqby8vBg8eDCffvopzz//vEM/q+zsbN5++2169erltH+ViEip+QVD0xvhodWQmWS2XAVXh5BY8PY99/oiUmmU+a7Ai1FSUhKJiYkO06mf7nnxxReJjY3lhhtu4LvvvuPgwYP88ssv9OrVi7y8PN566y0P115ELgrevlAtDmq1g9rtzf8rVIlUOQpWmD8oXaNGDYdpw4YNAERGRrJmzRq6devGiBEjaNiwIYMHD6Zhw4asW7eOBg0aeLj2IiIiUllYjPPtmS1lZrPZCAsLIy0trVKM3yUiIiLnVpbPb7VYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiHhUfmEhhmF4uhpyLoYBhfmerkXJCvPNenqQj0f3LiIil6yEtGw27D/Oos1HqBbox9Cr6lAnIpBqgX6erpqcKes4HN8HGz6CrBRoORDqXA1htTxds9NOHIS9v8BfX0NoTWg3DKrVAf+wCq/KRddiNWzYMCwWC3//+9+LlY0aNQqLxcKwYcMclj176t27t8N6q1ev5sYbbyQ8PBx/f39atWrFK6+8QkFBQUUckojIRefwiWxuf28No2dv5PutR/nfuoP0m7aKD1fuJS07z9PVk1OyT8Bv78D7XeH3/4O/voEvhsOsm80wUxmk7oWPesJXI2H7t7DuA5h+LWyaA/b0Cq/ORResAOLi4pg7dy7Z2dlF83Jycpg9ezZ16tRxWLZ3794kJCQ4THPmzCkqnz9/Pl26dKF27dosW7aMv/76izFjxvDCCy8wZMgQNV+LiJRRbn4h7/28m30pWcXK3vxpF0dOZDtZSzzCdhh+/k/x+al7YNXrkG+v+DqdyZ4BPz4HtiPFy75/EtKPVniVLspLgW3btmX37t18+eWXDB06FIAvv/ySOnXqUL9+fYdlrVYrsbGxTreTmZnJAw88QL9+/XjvvfeK5t9///1Ur16dfv36MW/ePG677Tan69vtduz20086m812oYcmIlLlpWTambf+kMvyrzcfoXmN0Aqskbj053zXZZs+gWsf9ewlwaxU+GuR8zLDgD3LIKpRhVbpomyxAhg+fDgzZswo+vujjz7i3nvvLdM2lixZQkpKCuPGjStW1rdvX5o0aeLQunW2KVOmEBYWVjTFxcWVaf8iIhcjwwB7vuuuFBn2St5B+lKSm+G6LD8H8PBVG6MACkvollNS/cvJRRus7rzzTlauXMn+/fvZv38/q1at4s477yy23Ndff01wcLDD9OKLLwKwY8cOAJo3b+50H82aNStaxpkJEyaQlpZWNB08WEmuR4uIeFCIvw/dmsW4LL+xdY0KrI2UqEU/12WNbgCrh1sW/UOhVlvX5Q2vr7i6nHRRXgoEiI6O5qabbmLmzJkYhsFNN91EVFRUseW6devGO++84zAvIiLC4e/z7UdltVqxWq3nta6IyMUqxN+XJ3o3Y9WuY+TkFTqUXd0ggvpRQR6qmRQT0RDqXQf7VjjO9w2AG/5pBhtPCoyEG6fCR72g4KybHpr3hdCKv0x50QYrMC8Hjh49GoC33nrL6TJBQUE0auT8+muTJk0AiI+Pp2PHjsXK4+PjadGihZtqKyJy6WgQFcTXD1/Hmz/tZPn2ZEL8fbi3U31ubl2DmBB/T1dPTgmpDgM/gK1fwprpYLdBw+7Q5R9m6KoMql8GD/4MyybD/tUQEA4dH4GmN0JQ8QaV8nZRB6vevXuTm5uLxWKhV69eZV6/Z8+eRERE8PLLLxcLVgsXLmTnzp3861//cld1RUQuGT7eXjSKCWbK31phy8nHywLRIVYsFounqyZnC4mFqx4yx68yCsAaBtZK1KroY4XqLeFv75rDK3j5QLDrS83lXh2P7bkCeHt7Ex8fX/R/Z+x2O4mJiQ7zfHx8iIqKIigoiHfffZchQ4bw4IMPMnr0aEJDQ1m6dCnjx4/n1ltvZfDgweV+HCIiF6tAqw+B1ov6o+jiYLGYrVeVmTXEnDzson82h4aWfP138eLF1Kjh2FGyadOm/PXXXwDceuutLFu2jMmTJ3PdddeRk5ND48aNeeqppxg7dqy+XYmIiEgRi6ERLiuMzWYjLCyMtLS0cwY+ERERqRzK8vl90Q63ICIiIlLRFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNfDxdAREREbfJsUF2KhgG+IdBYMSFbS/fDhlJUJgHvoEQElv6dQsLISPB3Ia3HwTXAG9v58vaEiAv6+Ry1cHHj+T0HDLtBfh4W4gKtuLv63zd1Ew76Tn5eFkshAf5Emz1haxUyEkDiwUCIsA/1Pl+nZ0vewZkpYBRANYwCIos/THn50LG0XOfr4IC89wU5IK3FUJqmPPTE6DgjPPl5WXOy88BL19ze96+5vnKzzbnnTxflUWVDVZ9+/YlLy+PxYsXFytbsWIFnTt3ZvPmzbRp04aNGzdy+eWXF1tu5syZ3HvvvcXmW61WcnJyiv4+ePAgkyZNYvHixRw7dowaNWowYMAAnn32WSIjy/CEExGR8nNsF3w/AXb9YAaF2h3gppchugX4+JZ9e7YEWPU6/D7LDD3V6sANz0ODbhBQreR1M4/Bn1/AL/81/+9fDTo9AlfcBcExp5fLPgF7lsMPz8CJA+AbQPqN09ngfzX//OYv9h7LxOrjxcC2tXn4+kbUqBZQtKo9r4BtCTaeXvAnW4/Y8LLA6K71eOSyPHy+Gw+H1prBqlEP6DUFoho7OV9Pwq4fzfPV9CboMQl+egH++hqMQqjRBm58GWJbga9/Kc/XTMjLdn2+MpJg48ew6g3IOQFBUXDbp3BkE6x4yTxfAeFw60eQdgiWTYb0RLCGwIDpZhj7cdLJ8xUIbe+BTmMgtMY5HtCKUWUvBd5333388MMPHDp0qFjZjBkzaN++PaGhLhL6GUJDQ0lISHCY9u/fX1S+Z88e2rdvz86dO5kzZw67du1i+vTpLF26lGuuuYbU1FS3HpeIiJyHEwfgo56wc4kZEgAOrYMPb4Dje8u+vcxjMH8E/PaOGapO7eOzYbDz+9P7cCYvB9a+B9/9w9wOmAFi6fOw/N9gTzfnGQbs/AE+u8fcNkBYHL8bjRk2awN7j2UCYM8vZPbaA9z/f+tJSj/9pX93ciaDpv/K1iM2AHy9vbitYQE+M3qZoerMfXzUE44fOOt83WCWnTqWy2+HjwdA/EIzVAEkbIYZvSFl57nP15cPnjxf2WedrzP2YU83z8HS581zAtCgK2z7ChY/cfp81WpnPn4LHzZDFUB0M7Adgs/vPX2+8rLMfX754Ol1PazKBqubb76Z6OhoZs6c6TA/IyODzz77jPvuu69U27FYLMTGxjpM1atXLyofNWoUfn5+LFmyhC5dulCnTh369OnDjz/+yOHDh3nqqafceVgiIlJWhgHxi8zLV2fLt8PKV0+Ho9KyHYG9PzsvW/KMeXnKlYyjsOo152UbZkBGsvn/9AT44WmH4uR2Y3l+WbLTVbcesbHvmHkc6Tl5vPzDdvILTwe8ga0jidz0tnnZ7GxZqbBtgXmuDMMMMllnNAzENIfj+8zjPlthPiz9l3nZ0BXbYdj3i/OyH54+fb4yks1zcKaWf4P1HzrOu3worJ7mOK/dPeZj6cy+X8w6VAJVNlj5+Phw9913M3PmTIwzvjl89tlnFBQUcPvtt1/wPlJTU/n+++8ZOXIkAQEBDmWxsbEMHTqU//3vfw77P5PdbsdmszlMIiLiZnlZsKN4t5Ai+34pORQ4k7DJdVnG0dOtTs5kpZiBzhmjEDJOtsDY00+3xpxaNaQ+u5MzXW567V4zPGbY8/ltj+MVkw6x3vgfXOm6XjuXQG6mOe343rEspgUc/M31ugdWQ26G6/Ijm12XpSeePl8Ziadbw04pyC9+vixeYD/rMfMLKXa+HCSUUIcKVGWDFcDw4cPZvXs3P/98+lvFjBkzGDhwIGFhYaXaRlpaGsHBwQ5Tnz59ANi5cyeGYdC8eXOn6zZv3pzjx4+TnOz828WUKVMICwsrmuLi4sp4hCIick5evhBcQqfywChzmbI4sx9Usf15m52rXfENcF0G4Bds/utjNbd1Bh8jF39f1x/N1UPNfk4+XhYighzrkGrH7K/kSkisWW9vv+Kdyu028zy5EhgFFhcd7+Hc58vHav7/1LGfydvJY+Nsnpd3sfPlIKiEOlSgKh2smjVrRseOHfnoo48A2LVrFytWrCj1ZUCAkJAQNm3a5DB98MEHDsu4apE6lwkTJpCWllY0HTx48Ly2IyIiJfDxg6tGuC6/dmzZ7mwDswXHWQgAaNYXgqJdrxsUZXb2dqZaHQg+uW5QNDTv71Ac/denDG7jPOD4elu4qr55HNEh/ozo3MCh/NPN6SS3Gem6Xlf93TxXPn7m/8+09xdo0sv1uh0fhpDqrsurtwC/IOdlzfudPl/B0VCtrmN56h6ofpnjvMO/Q71rHeftWQ5N+jjfh1+QWYdKoEoHKzA7sX/xxRekp6czY8YMGjZsSJcuXUq9vpeXF40aNXKYatWqBUCjRo2wWCzEx8c7XTc+Pp7w8HCio52/wKxWK6GhoQ6TiIiUg8hG0P254vMvHwp1O5V9eyE14Y55xVufoptBzxfA6iJ0gRkibp1xegiBUwLC4fa5p+f7BZl3zUWfviri99d8HmplcHltx6suvt4W3r2rHdVDrUXzerasTu+Wp1ue9hzLZE1hU/Lb3Fm8Ttc/A5ENT/8d2RC6P3v673w7bP/WPDaLxXHd5n2h2c2ujxdOnq/PnJ+vG/51OnSF1IDb5zgOg/HrNHO/Z7airf8QrhsH4fVOz9s0G9oPN7d5Jt8Ac98hNUuuYwWxGOfbHFNJZGRkUKNGDaZOncoLL7zAQw89xMSJEwHYt28f9evXL3G4hbFjx3LixAmX2+/Vqxdbt25l586dDv2sEhMTadiwIXfffTfvvPNOqepqs9kICwsjLS1NIUtExN1ybGb/pz0/mx24G3YzP8jPdyyrgnyzQ/Th9eZdaLU7mAGutGNZpR2GpG1w9E+Iamq2YoXVLh5c0hMhZbd5F1xYbajdgWTvGA4cz2HdvlRiQqy0rxtObJg/fj6Ol8JSM+wk2HJYtSuFYKs31zSMopY1G7+so2YLj7cfNOhiXio9eyyrHJvZ52nPz2awatgNAqMhN91swcpNh/pdILRWyZcYT8nPg/TDcGgDpB08eb4aFj9fhmEOo3D0T0j+y2ytqn6ZOW5WUrw5P7oZxLYBC5C8AxI2QkRDqNXWvCSZeup81YHa7c06epffCFJl+fyu8sEK4P777+fLL7/EZrNx4MABatY0U+upYDV37lyaNm3qsE7Lli359NNPGTNmDNu3by+2zZiYGLy8vNi5cycdO3akefPmvPDCC9SvX5+tW7cyfvx47HY7a9asISKidC9aBSsREZGqpyyf31V2gNAz3XfffXz44YfceOONRaHqTEOGDCk271R/J5vNRo0axQcVS0hIIDY2lsaNG7N+/XomTZrE4MGDSU1NJTY2lgEDBjBp0qRShyoRERG5+F0ULVZVhVqsREREqp6yfH5X+c7rIiIiIpWFgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiIm/h4ugIiIiKXiiRbDgdSs9iemE7t8AAaVQ+hZpg/FovF01UTN1GLFZCcnMxDDz1EnTp1sFqtxMbG0qtXL1atWlW0zOrVq7nxxhsJDw/H39+fVq1a8corr1BQUODBmouISFVx6HgWd3zwG7dO/5WnFvzJPTPWcfMbK4hPsGEYhqerJ26iYAUMHDiQjRs3MmvWLHbs2MHChQvp2rUrKSkpAMyfP58uXbpQu3Ztli1bxl9//cWYMWN44YUXGDJkiF4QIiJSIlt2Hk8v+JNdSRkO849n5XHPjHUkpuV4qGbibhbjEk8FJ06cIDw8nOXLl9OlS5di5ZmZmdStW5cuXbrwxRdfOJQtWrSIfv36MXfuXG677bZi69rtdux2e9HfNpuNuLg40tLSCA0Ndf/BiIhIpbT3WCbXv7wcV5+4n//9GtrXi6jYSkmp2Ww2wsLCSvX5fcm3WAUHBxMcHMyCBQscQtApS5YsISUlhXHjxhUr69u3L02aNGHOnDlOtz1lyhTCwsKKpri4OLfXX0REKr+cvAKXoQogNTO34ioj5eqSD1Y+Pj7MnDmTWbNmUa1aNTp16sTEiRP5448/ANixYwcAzZs3d7p+s2bNipY524QJE0hLSyuaDh48WD4HISIilVqovw/BVtf3i9WLCqrA2kh5uuSDFZh9rI4cOcLChQvp3bs3y5cvp23btsycObNomfO5Ymq1WgkNDXWYRETk0hMTamV0t0ZOy65vGkN0sLWCayTlRcHqJH9/f2644QaeeeYZVq9ezbBhw5g0aRJNmjQBID4+3ul68fHxRcuIiIg44+vtzaAOtXmuXwvCA30BsPp4cc81dZkysBXhQX4erqG4i8axcqFFixYsWLCAnj17EhERwcsvv0zHjh0dllm4cCE7d+7kX//6l4dqKSIiVUVkkJW7rq5HzxaxZOUW4O/jRXSoFauPt6erJm50yQerlJQUBg0axPDhw2ndujUhISGsX7+e//73v/Tv35+goCDeffddhgwZwoMPPsjo0aMJDQ1l6dKljB8/nltvvZXBgwd7+jBERKQK8PayULNagKerIeXokg9WwcHBXHXVVbz66qvs3r2bvLw84uLieOCBB5g4cSIAt956K8uWLWPy5Mlcd9115OTk0LhxY5566inGjh2rEXNFREQE0DhWFaos42CIiIhI5aBxrEREREQ8QMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcRMFKRERExE0UrERERETcxMfTFRAREZEqKvMYFORBQBj4Bnq6NpXCRd1ilZiYyJgxY2jUqBH+/v5Ur16dTp068c4775CVlQVAvXr1sFgsxaZ///vfDtuaNWsWHTp0IDAwkJCQELp06cLXX3/ticMSERHxrIwk2PIZfDwAPrgevn4MkndAfp6na+ZxF22L1Z49e+jUqRPVqlXjxRdfpFWrVlitVrZs2cJ7771HrVq16NevHwDPP/88DzzwgMP6ISEhRf8fN24c06ZN44UXXmDAgAHk5eXxySef0L9/f15//XVGjx5doccmIiLiMVmp8P1TsGXe6Xmb58DWL+G+H6FGa8/VrRK4aIPVyJEj8fHxYf369QQFBRXNb9CgAf3798cwjKJ5ISEhxMbGOt3OmjVrePnll3njjTd4+OGHi+ZPnjyZnJwcHnvsMfr3709cXFz5HYyIiEhlYTvsGKpOybfDt+Ph9rkQGF7x9aokLspLgSkpKSxZsoRRo0Y5hKozWSyWUm1rzpw5BAcHM2LEiGJljz/+OHl5eXzxxRdO17Xb7dhsNodJRESkStu11HXZwTWQc6LCqlIZXZTBateuXRiGQdOmTR3mR0VFERwcTHBwME888UTR/CeeeKJo/qlpxYoVAOzYsYOGDRvi5+dXbD81a9YkNDSUHTt2OK3HlClTCAsLK5rUqiUiIlWej7/rMouXOV3CLtpLgc6sXbuWwsJChg4dit1uL5o/fvx4hg0b5rBsrVq1iv5/5mXDspgwYQKPPfZY0d82m03hSkREqrZG17sua9oHAiMqri6V0EUZrBo1aoTFYmH79u0O8xs0aABAQECAw/yoqCgaNWrkdFtNmjRh5cqV5ObmFmu1OnLkCDabjSZNmjhd12q1YrVaz/cwREREKp/gWOj+LCx93nF+UDTc8C+whjhf7xJxUbbXRUZGcsMNNzBt2jQyMzMvaFtDhgwhIyODd999t1jZ1KlT8fX1ZeDAgRe0DxERkSrDPxTaD4f7f4LWQ6BBN+jzH3jgJ4hs6OnaedxF2WIF8Pbbb9OpUyfat2/Pc889R+vWrfHy8mLdunX89ddftGvXrmjZ9PR0EhMTHdYPDAwkNDSUa665hjFjxjB+/Hhyc3Mdhlt4/fXXee2113R5T0RELi0B4VC7HdSYBgW55uCgpbwp7GJnMc63A1EVkJCQwIsvvsg333zDoUOHsFqttGjRgkGDBjFy5EgCAwOpV68e+/fvL7buiBEjmD59etHfH330EW+//TZbt27F29ubtm3bMn78ePr27Vvq+thsNsLCwkhLSyM0NNQtxygiIiLlqyyf3xd1sKpsFKxERESqnrJ8fl+UfaxEREREPEHBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3ETBSkRERMRNFKxERERE3OSi/a3AyujUIPc2m83DNREREZHSOvW5XZofq1GwqkDp6ekA+tFmERGRKig9PZ2wsLASl9FvBVagwsJCjhw5QkhICJYL+BVwm81GXFwcBw8e1G8OVhJ6TCofPSaVkx6XykePybkZhkF6ejo1a9bEy6vkXlRqsapAXl5e1K5d223bCw0N1YugktFjUvnoMamc9LhUPnpMSnaulqpT1HldRERExE0UrERERETcRMGqCrJarUyaNAmr1erpqshJekwqHz0mlZMel8pHj4l7qfO6iIiIiJuoxUpERETETRSsRERERNxEwUpERETETRSsRERERNxEwaqKeeutt6hXrx7+/v5cddVVrF271tNVumRMmTKFDh06EBISQkxMDAMGDGD79u0OyxiGwbPPPkuNGjUICAigR48e7Ny500M1vvT8+9//xmKxMHbs2KJ5ekw84/Dhw9x5551ERkYSEBBAq1atWL9+fVG5HpeKVVBQwDPPPEP9+vUJCAigYcOG/Otf/3L47Ts9Ju6hYFWF/O9//+Oxxx5j0qRJ/P7777Rp04ZevXqRlJTk6apdEn7++WdGjRrFmjVr+OGHH8jLy6Nnz55kZmYWLfPf//6XN954g+nTp/Pbb78RFBREr169yMnJ8WDNLw3r1q3j3XffpXXr1g7z9ZhUvOPHj9OpUyd8fX357rvv2LZtGy+//DLh4eFFy+hxqVj/+c9/eOedd5g2bRrx8fH85z//4b///S9vvvlm0TJ6TNzEkCrjyiuvNEaNGlX0d0FBgVGzZk1jypQpHqzVpSspKckAjJ9//tkwDMMoLCw0YmNjjZdeeqlomRMnThhWq9WYM2eOp6p5SUhPTzcaN25s/PDDD0aXLl2MMWPGGIahx8RTnnjiCePaa691Wa7HpeLddNNNxvDhwx3m3XLLLcbQoUMNw9Bj4k5qsaoicnNz2bBhAz169Cia5+XlRY8ePfj11189WLNLV1paGgAREREA7N27l8TERIfHKCwsjKuuukqPUTkbNWoUN910k8O5Bz0mnrJw4ULat2/PoEGDiImJ4YorruD9998vKtfjUvE6duzI0qVL2bFjBwCbN29m5cqV9OnTB9Bj4k76EeYq4tixYxQUFFC9enWH+dWrV+evv/7yUK0uXYWFhYwdO5ZOnTpx2WWXAZCYmAjg9DE6VSbuN3fuXH7//XfWrVtXrEyPiWfs2bOHd955h8cee4yJEyeybt06HnnkEfz8/Ljnnnv0uHjAk08+ic1mo1mzZnh7e1NQUMDkyZMZOnQooNeKOylYiZyHUaNG8eeff7Jy5UpPV+WSdvDgQcaMGcMPP/yAv7+/p6sjJxUWFtK+fXtefPFFAK644gr+/PNPpk+fzj333OPh2l2a5s2bx6effsrs2bNp2bIlmzZtYuzYsdSsWVOPiZvpUmAVERUVhbe3N0ePHnWYf/ToUWJjYz1Uq0vT6NGj+frrr1m2bBm1a9cumn/qcdBjVHE2bNhAUlISbdu2xcfHBx8fH37++WfeeOMNfHx8ir596zGpWDVq1KBFixYO85o3b86BAwcAvVY8Yfz48Tz55JMMGTKEVq1acdddd/Hoo48yZcoUQI+JOylYVRF+fn60a9eOpUuXFs0rLCxk6dKlXHPNNR6s2aXDMAxGjx7N/Pnz+emnn6hfv75Def369YmNjXV4jGw2G7/99pseo3LSvXt3tmzZwqZNm4qm9u3bM3ToUDZt2kSDBg30mHhAp06dig1FsmPHDurWrQvoteIJWVlZeHk5fuR7e3tTWFgI6DFxK0/3npfSmzt3rmG1Wo2ZM2ca27ZtMx588EGjWrVqRmJioqerdkl46KGHjLCwMGP58uVGQkJC0ZSVlVW0zL///W+jWrVqxldffWX88ccfRv/+/Y369esb2dnZHqz5peXMuwINQ4+JJ6xdu9bw8fExJk+ebOzcudP49NNPjcDAQOOTTz4pWkaPS8W65557jFq1ahlff/21sXfvXuPLL780oqKijH/84x9Fy+gxcQ8FqyrmzTffNOrUqWP4+fkZV155pbFmzRpPV+mSATidZsyYUbRMYWGh8cwzzxjVq1c3rFar0b17d2P79u2eq/Ql6OxgpcfEMxYtWmRcdtllhtVqNZo1a2a89957DuV6XCqWzWYzxowZY9SpU8fw9/c3GjRoYDz11FOG3W4vWkaPiXtYDOOMYVdFRERE5Lypj5WIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIiIiImyhYiYiIiLiJgpWIADBz5kwsFgsWi4V9+/YVKx82bBgWi4V69ep5rA7iOc8991zRYyMirilYiVwkli9fXvTBV5pp5syZnq5yldG1a9ei8+bt7c3WrVtLXH7fvn1Fyz/33HMVU0kRqRQUrETkolOerWuFhYUKSyLiko+nKyAi7vfQQw8xcuTIEpepXbt2mbY5c+ZMtXKd9MUXX7B582batGnj6aqISCWjYCVyEYqJieGyyy7zdDUuOiEhIdjtdnJzc3n22Wf56quvPF0lEalkdClQRKSUIiIiuP/++wFYuHAh69ev93CNRKSyUbASkVIpbb+lRYsW0bt3b6KjowkMDKRJkyaMHz+exMREAOrVq4fFYmHYsGHn3GdhYSHvvfceHTt2JDw8nKCgIFq3bs3kyZPJysoqtvypO9dmzZoFwP79+5123L8QEydOxN/fH4Bnn332vLZR2rsfz+wE7+wy7NmPSWJiIuPGjaNJkyYEBgZSq1YtBg8eXKyz/b59+3jkkUdo0qQJAQEBVK9enaFDh7J79+5SH8OJEyeYNGkSLVu2JDg4mIiICLp168acOXNKtX5OTg7Tpk2je/fuxMbG4ufnR0xMDD169ODDDz8kPz/f5bpnP4c2bNjAsGHDqF+/PlarVXcuikfpUqCIuM2oUaN4++23Hebt3LmTqVOn8sknn/Dtt9+WeltZWVn07NmTpUuXOszfsmULW7ZsYeHChfz0008EBQW5pe6lVatWLUaMGMHrr7/Od999x6+//so111xToXVwZvPmzfTu3bsowAJkZ2fz2Wef8e2337J48WKuvfZafvrpJ2655RbS0tKKlsvJyWH27Nl89913rFixgpYtW5a4r71793LDDTc4BLHMzEyWL1/O8uXLWbBgAZ9++ik+Ps4/YjZv3kz//v3Zv3+/w/zk5GSWLl3K0qVLeffdd1m0aBHVq1cvsS7Tp0/n4YcfLjGIiVQktViJiFv897//LQpVcXFxvPXWW/z222/88ssvPPXUU6SlpXHrrbc6bWly5oEHHmDZsmXcc889fPPNN2zYsIH58+cXhZi1a9fywgsvOKwzcuRItmzZQv/+/QGoWbNmURA7c7pQEyZMIDAwEIBnnnnmgrd3obKysvjb3/5Gbm4uL774IqtWrWLNmjU899xz+Pn5kZmZyV133cWuXbsYMGAAISEhvP7666xZs4aVK1fy6KOPYrFYOH78OPfdd98593fbbbexd+9e/v73v/Pjjz+ybt06PvzwQ5o0aQLAvHnzGD9+vNN1d+3aRZcuXdi/fz+hoaFMmDCB+fPns379er7//ntGjRqFj48P69ato3///uTl5bmsx7p16xg9ejS1a9dm2rRpRcczZcqU8zuRIu5giMhFYdmyZQZgAMZDDz1kbNmyxeV09OjRYuvPmDGjaP29e/cWK7/nnnsMwKhbt26xsoSEBMPf398AjEaNGhnJycnFllm1apXh5+dXtI977rmnxDoAxscff1xsmZycHOOyyy4zACMyMtLIy8srU13PR5cuXYptb/z48UX1/Pnnnx2W37t3b1HZpEmTim3vXOfa2XZmzJhRrPzUcQJGVFSUsWvXrmLLTJs2rWiZ6Ohoo3HjxkZSUlKx5c48nt9//71Y+aRJkxwem9mzZxdbxmazGW3atDEAw8vLy9iyZUuxZTp27GgAxhVXXOH0eWIYhvHdd98ZXl5eBmC89957xcrr1q1bVI9WrVoZx48fd7odEU9Qi5XIReidd96hVatWLqezL9ddqFmzZpGTkwPAa6+9RlRUVLFlOnbsyKhRo0q9zVtuuYU777yz2Hyr1cro0aMBSElJYdu2bedZ6wvzj3/8g+DgYKBytFr961//omHDhsXmDx8+vKhPWHJyMm+88QbR0dHFlnvooYeK/r9ixYoS93XzzTdz++23F5sfEhLCe++9B5j946ZPn+5QvmLFClavXg2YzxlnzxOA3r17c+uttwKcc4iPt956i2rVqpW4jEhFUrASkQv2448/AhAVFUWfPn1cLnf33XeXeptDhw51WdauXbui/+/Zs6fU23SnqKgoHnnkEQB++eWXonPgCRaLhcGDBzstCwgIoHHjxgCEh4fTq1cvp8vVr1+fkJAQ4Nzn9N5773VZduWVVxb10Tr7nCxcuBCApk2b0qpVqxL30blzZ8C83Oeq/1RcXBzXXXddidsRqWgKViIXoUmTJmEYhsvJ3SOH//nnnwBcfvnleHm5fltp1aoVfn5+pdpms2bNXJZFREQU/T89Pb2UtXS/cePGERYWBpz/HYLuEBUV5XBOznaqRadRo0Yl3jF3arlzndMOHTqUWH7llVcCsGPHDnJzc4vmnxqeYvv27ef8yaVTrZJ5eXmkpqY63U/r1q1LrIeIJyhYicgFO378OIDTS0xn8vb2LjEAnOlU53BnzgxvBQUFpdpeeQgPD+fRRx8F4Ndff+W7777zSD1KOldw+nyVdrlzndOYmJgSy0/dyWcYRtFzAyApKanE9VxxdcNDeHj4eW1PpDxpuAURkQvw6KOP8vrrr3P8+HGeffbZEi+FXizOd5yoU4GtTZs2fPLJJ6Ver1atWk7ne3t7n1c9RMqTgpWIXLDw8HASExNJTk4ucbmCggKHFoyLQWhoKOPGjeOpp55i/fr1fPXVV+f8DcEzW9wKCwtdLpeZmem2errT0aNHiYuLK7EczAB2ZqtSZGQkABkZGfrJJblo6VKgiFywU52VN23aVGJQ2LJlC3a7vdzrU9Ejbz/yyCNFd7id6t9WklOdxIESg+aOHTvcU0E3W7duXanKGzdu7NCn7oorrgDMzvFnDmQqcjFRsBKRC9a9e3cAjh07VmI/o//7v/+rkPqcGl6gIkIcQHBwME888QRgjir+xRdflLh8/fr1i/5f0u8NlvbnYSraqZ8McmbdunVFNzP06NHDoaxfv36A2ffq9ddfL78KiniQgpWIXLB77rkHq9UKwNixYzl27FixZX799VfeeuutCqlPjRo1ALOzdEXdNThy5MiiTtv/+c9/Slz2sssuK+rEP23aNKcBcN68eXz22Wfur6gbLFy4kHnz5hWbn5GRwYgRIwDzcuep/5/Ss2fPojsGX3rpJafbONOWLVtYtGiRm2otUjEUrETkgtWsWZNJkyYB5k+WtGvXjnfeeYd169axcuVKnnnmGbp3707NmjWL7hwsz8t1HTt2BMz+S3//+99Zs2YNu3btKprKQ2BgIBMmTABwGizP5OPjUxQ6/vzzT66//nq++uorNm7cyOLFi7nvvvu4/fbbi46jsmnfvj133HEHo0aNYtmyZWzYsIEZM2bQvn17Nm7cCJi/G+lsOITZs2cTERFBQUEBt912G/369ePTTz9l7dq1bNiwge+++44XX3yRa665htatW/Pzzz9X9OGJXBB1XhcRt3jyySfZv38/7777LgcOHGDkyJEO5VFRUXz22WfccsstwOnLdeXh+uuv5+qrr2bNmjXMnj2b2bNnO5Sfqw/U+RoxYgQvvfQShw8fPueyTz/9NMuWLWPNmjWsXr2aAQMGOJR37dqVadOmVcpO3vPmzaN79+68/fbbTkfxHzhwIK+88orTdRs2bMivv/7KwIED+fPPP1m0aFGJrVKhoaFuq7dIRVCLlYi4hcViYfr06Xz11Vf07NmTiIgI/P39adSoEY888ggbN26kffv22Gw2gKKBNcuDl5cXS5Ys4emnn6ZNmzYEBwdXSId2f39/nnrqqVItGxgYyE8//cTkyZNp1aoVAQEBhIaG0qFDB6ZNm8aPP/5IUFBQOdf4/NSvX58NGzYwceJEmjdvTmBgIGFhYXTu3JlPPvmEzz//HB8f19/bmzRpwqZNm5g9ezYDBw6kTp06BAQE4OfnR40aNejatStPP/00GzZs8OjAqyLnw2KU11c3EZGzHDp0qOg2/Q8++ID77rvPwzUSEXEvtViJSIU58y63q6++2oM1EREpH2qxEhG3yMzMxGazFd2Rd7aNGzfSpUsX0tPTadeuXYnDDIiIVFXqvC4ibpGcnEzz5s0ZMGAAvXv3pmnTplitVo4cOcLixYv58MMPyc7OxmKxuOzYLCJS1anFSkTcYt++fQ4DXzrj5+fH+++/z913311BtRIRqVgKViLiFnl5ecyfP5/Fixezbt06kpOTSU1NJTAwkHr16tGjRw8efvhh6tat6+mqioiUGwUrERERETfRXYEiIiIibqJgJSIiIuImClYiIiIibqJgJSIiIuImClYiIiIibqJgJSIiIuImClYiIiIibqJgJSIiIuIm/w8+vMhPn495iwAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\nsns.scatterplot(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df)\nplt.xlabel(\"Payload\",fontsize=20)\nplt.ylabel(\"Orbit\",fontsize=20)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "### TASK  6: Visualize the launch success yearly trend\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "The function will help you get the year from the date:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year():\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\nExtract_year()\ndf['Date'] = year\ndf.head()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "execution_count": 26,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n\n\n# Calculate the success rate for each year\nsuccess_rate = df.groupby('Date')['Class'].mean()\n\n# Plot a line chart with year on the x-axis and success rate on the y-axis\nplt.figure(figsize=(10, 6))\nplt.plot(success_rate.index, success_rate.values, marker='o', linestyle='-', color='b')\nplt.xlabel(\"Year\")\nplt.ylabel(\"Success Rate\")\nplt.title(\"Success Rate Over the Years\")\nplt.grid(True)\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x600 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Features Engineering\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "### TASK  7: Create dummy variables to categorical columns\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: Use get_dummies() function on the categorical columns\n\n# Specify the columns to be one-hot encoded\ncolumns_to_encode = ['Orbit', 'LaunchSite', 'LandingPad', 'Serial']\n\n# Apply One-Hot Encoding\nfeatures_one_hot = pd.get_dummies(features, columns=columns_to_encode)\n\n# Display the first few rows of the DataFrame with one-hot encoded columns\nfeatures_one_hot.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": [
        {
          "execution_count": 32,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            0            0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>...</th>\n      <th>Serial_B1048</th>\n      <th>Serial_B1049</th>\n      <th>Serial_B1050</th>\n      <th>Serial_B1051</th>\n      <th>Serial_B1054</th>\n      <th>Serial_B1056</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>6104.959412</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>525.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>677.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>500.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>3170.000000</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 80 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "### TASK  8: Cast all numeric columns to `float64`\n",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# HINT: use astype function\nfeatures_one_hot = features_one_hot.astype('float64')",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "features_one_hot.to_csv('dataset_part_3.csv', index=False)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}