Predict Popularity from Audio Features Slides.ipynb

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    "# Source:\n",
    "# http://blog.nextgenetics.net/?e=102\n",
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   "source": [
    "# Prediction of Spotify Song Popularity from Audio Features\n",
    "\n",
    "## Riley Stange\n",
    "\n",
    "### November 2019"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "slide"
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   "source": [
    "<img src=\"http://newstoreview.com/wp-content/uploads/2018/10/Spotify-1024x1024.png\">"
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  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "notes"
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   "source": [
    "When it comes to data about music, Spotify is one of the giants. Not only do they utilize sophisticated audio analysis algorithms to recognize distinct features in a song, but they also make this data readily available to anyone with the time and knowhow. Normally, it is rather cumbersome to gather the audio features data from their database. Thankfully, however, I found a dataset on Kaggle that has over 232K songs, with a diverse selection of nearly 10K songs for each of the 26 genres represented."
   ]
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   "execution_count": 2,
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   "source": [
    "# Initialize the environment\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "#Import the data\n",
    "df = pd.read_csv(\"~/projects/spotify-song-popularity/ultimate-spotify-tracks-db.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Memory Optimization and Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "* Smaller Integers"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "* Dropping Unnecessary Columns"
   ]
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   "execution_count": 4,
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   "outputs": [],
   "source": [
    "df['duration_ms'] = df['duration_ms'].astype(\"uint32\")\n",
    "df['popularity'] = df['popularity'].astype(\"uint8\")\n",
    "df.drop([\"genre\", \"artist_name\", \"track_id\", \"track_name\"], 1, inplace=True)"
   ]
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   "source": [
    "* For Data Exploration and Cleaning, I found that the data came in nearly pristine condition, with no nulls or unusual entries. This is due to the data being gathered directly from Spotify with their API.\n",
    "* I did find that a couple of columns were using a data type much larger than what their maximum values were using. I converted the duration_ms from a 64-bit integer, to an unsigned 32-bit integer, since there will only be positive values, and they will never exceed the datatype's maximum of just over two billion. I found a similar situation with the \"popularity\" column, since popularity only ranges from 0-100, the 64-bit integer is far too large. With a range of 0-255, the unsigned 8-bit integer is a much better fit. These two changes alone decreased the memory footprint of the dataset by nearly 8%.\n",
    "* I also dropped the genre, artist_name, track_id and track_name columns, as they are irrelevant to this model."
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   "source": [
    "## Explaining Audio Features"
   ]
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   "cell_type": "markdown",
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   "source": [
    "Before going any further I will provide explanations for each of the audio features features."
   ]
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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def bighist(data, title):\n",
    "    plt.figure(figsize=(20, 17))\n",
    "    sns.distplot(data)\n",
    "    plt.title(title, fontsize=30)\n",
    "    plt.xticks(fontsize=20)\n",
    "    plt.yticks(fontsize=20)\n",
    "    plt.xlim(data.min(), data.max())\n",
    "    plt.xlabel(\"\")\n",
    "    plt.show()\n",
    "    \n",
    "bighist(df['popularity'], \"Popularity (the outcome variable)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Popularity** (the outcome variable): A score from 0-100. This metric is based on how many listens a song gets, and each listen is weighted based on how recent it is. The more recent a listen, the more it impacts a song's popularity score. This score changes on a weekly basis.\n",
    "Thankfully, the person who gathered the data was able to get something reasonably normal in distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['acousticness'], \"Acousticness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Acousticness**: A confidence measure from 0.0 to 1.0 of whether the track is acoustic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kmwIPnxnAW2YHbg/28/qhwO2s4eQCAAAIRdZaPfl2paIjvLpybqbrOECfkgITSUmxkbpnY7GOnGh2HQkAECKGO4n0n/Ivrv20tfbZAWyfHLht6Of1U8/3ubqkMeY2Y8w2Y8y248ePDy4pAACAY3sqG3XkeIuumJfJaWwIaUkxkbp1TYGSTxVJxymSAADDKJGMMd+Q9I+S9kv67AjlObWqpO3rRWvt7621y6y1yyZNmjRCHwkAADD6Ort79PQ7xzQ1OUYrCtJcxwGCSoyJ1BfWFCg1Lkr3bCpWEUUSAEx4QyqRjDFflfRzSXslXWKtrRvgW09NGiX383rSGdsBAACMC68dOq76ti59ZOE0ebgaG8JEYkykPn+Bv0i6d1Ox3jh8wnUkAIBDgy6RjDHflPTfknbLXyBVDeLtBwK3/a15NDNw29+aSQAAAGHnZGunXjl4XAuyklWQwUVoEV78E0nTlRYfpVvu3qrXDrGsBABMVIMqkYwx/0vSTyXtlL9Aqhnk520I3F5pjHnPZxtjEiWtltQmafMg9wsAABCy/v7OMRkjXXXOFNdRgCFJiI7Q5y+YroKMeH3xvu06UNXkOhIAwIEBl0jGmO/Jv5D2dkmXWWv7nWU1xkQaY+YYYwp7P2+tLZL0nKR8SV89420/kBQv6V5rLdcSBQAA48LGwye0u7JRF82arJS4KNdxgCFLiI7QPbesUHx0hL543zY1tHW5jgQAGGMDuiyIMeZzkv5Nkk/Sa5K+Yd5/Ln+xtfbuwP0sSfsklchfGPX2FUkbJf3CGHNZYLuVki6R/zS2/z3YbwIAACAUdft6dMcTe5QaF6k1MzNcxwGGLTMpRr++cYk+/fvN+ocHd+p/blomj4c1vgBgohjotWULArdeSd/sZ5tXJN0dbEfW2iJjzDL5S6kPSvqQpGOSfiHpB4NYpBsAACCk/WlziQ5WN+szK3MV6R3yRXGBkLI8P03f+8g8ff9ve/TzFw/pW1f0t9wpAGC8GVCJZK29Q9IdA92ptbZYUr9/krDWlkm6eaD7AwAACDe1zR36f88f1JqZGZo7NSn4G4AwctP5edpVXq+fv3hIC7KSdfm8TNeRAABjgD+JAQAAjIL/eu6gWte/mQkAACAASURBVDt9+v5H56mPZQCAsGaM0X9cu0DnZCXpWw/u1NETLGkKABMBJRIAAMAI213RoAe2lupzq/I1Y3Ki6zjAqIiJ9Oq3n1mqCK/RbfduU0tHt+tIAIBRRokEAAAwgqy1+v7f9ig9Pkq3Xz7TdRxgVGWnxumXn16iouPN+qe/7JK11nUkAMAookQCAAAYQY/vrNT2kpP65w/MUVJMpOs4wKi7YGaG/vmDc/T0O1X63atHXMcBAIwiSiQAAIAR0tzRrf94ep8WZifrE0uzXccBxswXL5yuDy+Yqh89s1+vHTruOg4AYJRQIgEAAIyQ379SpJqmDt1x9Xx5PCymjYnDGKMffWKhZkxO0Nf//JbK6lpdRwIAjAJKJAAAgBFworlDf3j9qD68YKqW5Ka6jgOMufjoCP3us8vk67H60p+2q73L5zoSAGCEUSIBAACMgN+8XKT2Lp/+4cpZrqMAzhRkxOtn15+rPZWN+sETe1zHAQCMMEokAACAYaqsb9N9m0v0iaXZKpyU4DoO4NRlczP1xQun689vlunlAzWu4wAARhAlEgAAwDD94sVDkpW+cdlM11GAkPCtK2ZpxuQEfeeRd9TQ1uU6DgBghFAiAQAADMOR4816eHu5bliZq+zUONdxgJAQE+nVT9Yu0vHmDv37k3tdxwEAjBBKJAAAgGH46QuHFB3h0VcvmeE6ChBSFuWk6IsXTtfD28v10v5q13EAACOAEgkAAGCI9lQ26IldlbpldYEmJUa7jgOEnNsvn6lZmYHT2lo5rQ0Awh0lEgAAwBD95LmDSoqJ0K0XTncdBQhJ0RFe/WTtuapt6eRqbQAwDlAiAQAADMG24jq9tL9GX7q4UMmxka7jACFrQXayvnpxof76VoWe38tpbQAQziiRAAAABslaqx89e0CTEqO1blW+6zhAyPvapTM1Z0qi/uXRd3SypdN1HADAEFEiAQAADNKrh07ozaN1+vqlMxQXFeE6DhDyoiI8+sknF+lkS6fu4LQ2AAhblEgAAACDYK3Vj5/dr+zUWH1qea7rOEDYmD8tWV+7dIYe31mpZ3Yfcx0HADAElEgAAACD8MzuKu2uaNQ3L5+lqAgOpYDB+OolMzRvapL+9bHdquO0NgAIOxz5AAAADFC3r0f/9dwBzZicoGsXZ7mOA4SdSK//tLaGti79n8d3u44DABgkSiQAAIABevStChUdb9G3r5wlr8e4jgOEpblTk3T7ZTP15NvH9NTbnNYGAOGEEgkAAGAAOrp9+tkLh7QgK1kfmD/FdRwgrH3pokItyErW9x7frRPNHa7jAAAGiBIJAABgAP68pVQV9W36pw/MljFMIQHDERE4ra2pvUv/+ff9ruMAAAaIEgkAACCI1s5u/feGIp03PU1rZma4jgOMC7MyE3XLBQX6y/ZybS856ToOAGAAKJEAAACC+NPmEp1o7tC3r2QKCRhJX790pjKTovX9v+2Wr8e6jgMACCLCdQAAAIBQ1trZrd+9ckRrZmZoWX6a6ziAU+u3lI74Pi+ePVkPbi3TA1tLdePKvBHfPwBg5DCJBAAAcBZ/2lyi2pZOffPyma6jAOPSwqxkrSxI04+fPaCTLZ2u4wAAzoISCQAAoB+9p5CW5jGFBIwGY4x+8LH5amrv1o+fO+A6DgDgLCiRAAAA+sEUEjA25kxJ0k3n5+nPb5bqnfIG13EAAP1gTSQAAIA+MIUEjJ31W0qVkxqnuKgIfeX+7friRYXyjMAi9jeszB2BdACAU5hEAgAA6MN9m5hCAsZSTKRXV82forKTbXqrtN51HABAHyiRAAAAztDa2a3fvcoUEjDWzs1NUW5anJ7ZU6W2Tp/rOACAM1AiAQAAnOG+TSWqYwoJGHMeY/TRRdPU2tGtF/ZXu44DADgDJRIAAEAvTCEBbmWlxGpFQZo2F9XqWEOb6zgAgF4okQAAAHphCglw74p5mYqN8uqJXcdkrXUdBwAQQIkEAAAQwBQSEBrioiJ05bwpKq5t0dvlDa7jAAACKJEAAAACmEICQsey/FRlpcTq77uPqaOLRbYBIBRQIgEAAIgpJCDUeIzR1YumqbG9WxsO1LiOAwCQFOE6AAAAQCg4NYU0f2qS1m8pdR0HgKSctDgtzUvV64dPaEVButLio1xHAoAJjUkkAAAw4Z2aQpo5OUG56fGu4wDo5Yq5mfIYoxf3VbuOAgATHiUSAACY8E5NIV02Z7LrKADOkBQbqfOnp2tnWb1qGttdxwGACY0SCQAATGinppAunDWJKSQgRK2ZNUmRER69sJ+1kQDAJUokAAAwoZ2aQrr9Mq7IBoSqhOgIrS5M1+6KBlXWt7mOAwATFiUSAACYsFo7u/X7d6/Iluo6DoCzuGDGJMVEevQCayMBgDOUSAAAYML60+YS1bZ06puXz3IdBUAQsVFeXThzkvZXNam0rtV1HACYkCiRAADAhNTW6WMKCQgz5xemKz7Kq+f3VrmOAgATEiUSAACYkO7fUqITzZ36BmshAWEjOsKri2ZPVtHxFhUdb3YdBwAmHEokAAAw4bR3+fTbV45oVWG6luenuY4DYBBWFqQpKSZCz++tlrXWdRwAmFAokQAAwISzfkupTjR3cEU2IAxFej26ZM5klda16mA100gAMJYokQAAwITin0Iq0nnT07RyerrrOACGYGleqlLjIvX8viqmkQBgDFEiAQCACeWBN0tV09TBWkhAGIvweHTZnExV1rdrT2Wj6zgAMGFQIgEAgAmjvcun37xSpBX5aTqfKSQgrC3KSVFGQrRe2FetHqaRAGBMUCIBAIAJ4+FtZapu7NDtl8+UMcZ1HADD4PUYXT53smqaOvR2eb3rOAAwIVAiAQCACaGj26dfv1ykZXmpWlXIFBIwHpyTlaypyTF6cV+NfD1MIwHAaKNEAgAAE8LD28p1rKFd37iMKSRgvPAYo8vnZqq2pVM7Sk+6jgMA4x4lEgAAGPc6u3v0m5eLtDg3RWtmZriOA2AEzZmSqOzUWL20v0bdvh7XcQBgXKNEAgAA494jO8pVUd+m25lCAsYdY4yumJephrYuvVlc5zoOAIxrlEgAAGBc6/L16FcbDmtRTooumjXJdRwAo2DGpATlp8fr1YPHWRsJAEZRhOsAAAAAg7V+S+mAt91WXKfyk226bM5k/fnNslFMBcAVY4wunJWhezeV6J2KBp2bk+I6EgCMS0wiAQCAccvXY7XhQI2yUmI1KzPRdRwAo2hWZqIyEqL0xuETspZpJAAYDZRIAABg3NpZVq+TrV26dM5k1kICxjmPMVpVmKGK+jaV1rW6jgMA4xIlEgAAGJdOTSFNS4nRnClMIQETwZLcVMVGevX64ROuowDAuESJBAAAxqVd5fWqa+nUpbMzmUICJoioCI+W56dpb2Wj6lo6XccBgHGHEgkAAIw7PdZqw/4aTU2O0dypTCEBE8n5hekyRtpUxDQSAIw0SiQAADDuvF1er9qWTl0ym7WQgIkmOTZS52Qla1vJSTW1d7mOAwDjCiUSAAAYV3qs1Uv7j2tKUozmTUtyHQeAA6sLM9TR3aOHt5W7jgIA4wolEgAAGFfeqWjQieYOXTJnsjxMIQETUk5anHLT4nTXxqPy9VjXcQBg3KBEAgAA48aptZAmJ0ZrPlNIwIS2ekaGyura9PzeatdRAGDcoEQCAADjxu6KBtU0dehSppCACW/e1CRlpcTqztePuo4CAOMGJRIAABgX/Gsh1WhSYrTOyUp2HQeAY16P0c2r8/VmcZ3eKW9wHQcAxgVKJAAAMC7srWz0TyHNZgoJgN8nl+coPsqrO99gGgkARgIlEgAACHunppAyEqK1IJspJAB+STGRWrssR0/sqlR1Y7vrOAAQ9iiRAABA2Nt3rFFVje26ZPYkppAAvMfNq/Pls1b3bip2HQUAwh4lEgAACGs2MIWUHh+lhdkpruMACDF56fG6Ym6m1m8pVVunz3UcAAhrlEgAACCs7a9q0rGGdl0yZ7K8HqaQALzf5y8o0MnWLj36VoXrKAAQ1iiRAABA2LLW6sX91UqLj9IippAA9GNFQZrmT0vSnW8clbXWdRwACFsRrgMAAAAM1YGqJlXWt+u6JVlMIQF4n/VbSt+9P29qkh7eXq4fPLFXszITh73vG1bmDnsfABBumEQCAABhyT+FVKPUuEidm5PqOg6AELcgO1mJMRF64/AJ11EAIGxRIgEAgLB0sLpZFfVtumQ2ayEBCC7C49F509N1qKZZ1Y3truMAQFiiRAIAAGHHf0W2aqXERWpxLlNIAAZmRX6aIjxGm4/Uuo4CAGGJEgkAAISdQzXNKjvZpotnMYUEYODioyN0TlaydpbVq7O7x3UcAAg7lEgAACCs+KeQapQSG6kleVyRDcDgLMtPVUd3j3ZXNriOAgBhhxIJAACElY1FtSqta9VFsycpwsOhDIDBKUiPV3p8lLYVn3QdBQDCDkdeAAAgrPxqw2ElxURoKWshARgCY4yW5aepuLZFJ5o6XMcBgLBCiQQAAMLGzrJ6bSyq1eoZGYrwchgDYGiW5KbIY6RtJXWuowBAWOHoCwAAhI1fbzis5NhIrchPcx0FQBhLjInU7ClJ2l5aL1+PdR0HAMIGJRIAAAgLh6qb9Nzean1uVb6iI72u4wAIc8vzUtXS0a39VY2uowBA2KBEAgAAYeG3rxxRbKRX61blu44CYByYmZmopJgIFtgGgEGgRAIAACGvor5Nj++s0KdW5CgtPsp1HADjgNdjtCQvVQerm9TQ1uU6DgCEBUokAAAQ8v7n1SMyRrp1zXTXUQCMI8vy0mQlbS9hGgkABoISCQAAhLTa5g49sLVU15ybpWkpsa7jABhH0uKjVDgpXttL6tRjWWAbAIKhRAIAACHtrjeK1dHdoy9dXOg6CoBxaFl+mk62dunI8RbXUQAg5FEiAQCAkNXU3qV7NhXrg/OnqHBSgus4AMaheVOTFBvp1dbiOtdRACDkUSIBAICQtX5LqZrau/VlppAAjJJIr0fn5qZo77FGtXR0u44DACGNEgkAAISk9i6f/vD6UV0wI0MLs1NcxwEwji3LS5Wvx2pnWb3rKAAQ0iiRAABASHpkR7mON3XoK0whARhlU5NjlZ0aq63FdbIssA0A/aJEAgAAIafb16PfvXJEi3JSdH5huus4ACaA5XlpqmnqUNnJNtdRACBkUSIBAICQ89Q7x1Ra16qvXFwoY4zrOAAmgIXZyYryerSNBbYBoF+USAAAIKRYa/Wbl4s0Y3KCrpib6ToOgAkiOtKrBdnJeru8QR1dPtdxACAkUSIBAICQsuFAjfZXNelLFxXK42EKCcDYWZ6Xqk5fj96paHAdBQBCEiUSAAAIKb95uUhZKbH62LnTXEcBMMHkpMVpUmK0tnJKGwD0iRIJAACEjK3FddpafFK3rilQpJfDFABjyxij5XmpKjvZpqrGdtdxACDkcHQGAABCxq83HFZ6fJSuX57rOgqACerc3FR5jdF2ppEA4H0okQAAQEg4UNWkDQeOa92qfMVGeV3HATBBJURHaO60JO0orVe3r8d1HAAIKZRIAAAgJPzx9SOKifToM+fluY4CYIJbnpeqti6f9h5rdB0FAEIKJRIAAHDueFOHHnurUmuX5ig1Psp1HAATXOHkBCXHRuqt0nrXUQAgpFAiAQAA5+7bVKyunh7dvDrfdRQAkMcYnZuTooPVTWps73IdBwBCBiUSAABwqr3Lp/s2l+iyOZmaPinBdRwAkCQtyU2VlbSTaSQAeBclEgAAcOqvOyp0srVLt64pcB0FAN41KTFauWlx2lF6UtZa13EAICRQIgEAAGd6eqz+8PoRLchK1oqCNNdxAOA9luSmqqapQxX1ba6jAEBIoEQCAADOvHywRkeOt+gLawpkjHEdBwDeY0FWsiI8RttLTrqOAgAhgRIJAAA484fXjmpqcow+tGCq6ygA8D6xUV7Nm5akt8sb1O3rcR0HAJyjRAIAAE7sqWzQxqJarVuVr0gvhyQAQtPS3FS1dfm0r6rJdRQAcI4jNgAA4MQfXzuq+CivPrUi13UUAOhX4eQEJcVEaAentAEAJRIAABh7VQ3t+tuuSn1yeY6SYyNdxwGAfnmM0eLcVB2qaVJje5frOADgFCUSAAAYc/dsKlaPtbp5VYHrKAAQ1JLcVPVYaVdZvesoAOAUJRIAABhTLR3dun9ziT4wf4py0+NcxwGAoCYlRis3LU7bS07KWus6DgA4Q4kEAADG1CM7ytXY3q0vrJnuOgoADNiS3FTVNHWoor7NdRQAcIYSCQAAjBlfj9Wdrx/V4twULc1LdR0HAAZsQVayIjxGO0pZYBvAxEWJBAAAxswL+6pVXNuqL1zAFBKA8BIb5dW8aUnaVdagbl+P6zgA4AQlEgAAGDN/fO2oslJi9YH5ma6jAMCgLclNVVuXT/uqmlxHAQAnKJEAAMCY2FVWrzeL63TLBQWK8HIIAiD8zJicoKSYCO0o4ZQ2ABMTR3AAAGBM/OH1o0qMjtAnl2W7jgIAQ+IxRotzU3Wopkk1je2u4wDAmKNEAgAAo66ivk1Pv3NMn1qRo8SYSNdxAGDIFuemqMdKj+2scB0FAMYcJRIAABh192wsliStW13gNggADNPkxBjlpMbqL9vLZa11HQcAxhQlEgAAGFUtHd3685uluuqcKcpKiXUdBwCGbUleqg5WN2t3RaPrKAAwpiJcBwAAAOPT+i2lkqRNR2rV1N6t7NS4d58DgHC2MCtFf99dpb9sL9OC7GTXcQBgzDCJBAAARk2PtdpUdELZqbHKTYtzHQcARkRslFcfmD9Fj++qVEe3z3UcABgzlEgAAGDUHKpu1onmTq0qzHAdBQBG1CeWZqu+tUsv7atxHQUAxgwlEgAAGDUbi04oMSZC52QluY4CACPqghkZykyK1l+2l7uOAgBjhhIJAACMipqmdh2qadbKgjRFeDjkADC+eD1GH1+SrZcPHldNY7vrOAAwJjiiAwAAo2JTUa28HqMVBemuowDAqFi7NFu+Hqu/7GAaCcDEQIkEAABGXENrl3aUntSi7BQlRHMxWADj0/RJCVqRn6aHt5XLWus6DgCMOkokAAAw4h7aVqYun9WqQqaQAIxvn1yeo6MnWvTm0TrXUQBg1FEiAQCAEeXrsbpnU7Hy0+M1LSXWdRwAGFUfWjBFidERenBrmesoADDqKJEAAMCIen5vtcpPtjGFBGBCiIuK0EfPnaandx9TY3uX6zgAMKookQAAwIi6642jykqJ1bxpSa6jAMCYuH5Zjtq7evS3nZWuowDAqKJEAgAAI2ZPZYO2HK3T51blyWOM6zgAMCYWZidrzpREPbSNU9oAjG+USAAAYMTc/UaxYiO9un5ZrusoADBmjDH65LIcvV3eoH3HGl3HAYBRQ4kEAABGRG1zhx7fVanrlmYpOS7SdRwAGFPXLs5SlNfDAtsAxjVKJAAAMCLWbylVZ3eP1q0qcB0FAMZcanyUrpyfqUffqlB7l891HAAYFZRIAABg2Dq7e3Tf5hJdOGuSZkxOcB0HAJy4fnmOGtq69NzeatdRAGBUUCIBAIBh+/vuY6pp6tDNq/NdRwEAZ1YXZigrJVYPcUobgHGKEgkAAAzbnW8Ua3pGvC6aOcl1FABwxuMxWrssW68fPqGyulbXcQBgxFEiAQCAYdlRelK7yuq1bnW+PB7jOg4AOLV2WY6MkR7eXu46CgCMOEokAAAwLHe9UazEmAhdtyTbdRQAcC4rJVYXzMjQX7aVyddjXccBgBFFiQQAAIasqqFdf3/nmK5flqP46AjXcQAgJFy/PEeVDe167dBx11EAYERRIgEAgCG7b3OxeqzV51blu44CACHjinmZSo2L1EPbWGAbwPhCiQQAAIakvcun9VtKdfncTOWkxbmOAwAhIzrCq2sXZ+v5vdWqbe5wHQcARgwlEgAAGJLHd1boZGuXbl5d4DoKAISc65fnqMtn9ehbFa6jAMCIYfECAAAmuPVbSgf9HmutfvnSYU1JitGR4806eqJlFJIBQPiaPSVRi3JS9NC2Mn3+ggIZw9UrAYQ/JpEAAMCgHTnRoqrGdq0qTOcfRgDQj+uX5ehgdbN2ltW7jgIAI4ISCQAADNrGolrFRXm1KCfFdRQACFkfXTRVsZFePbiVBbYBjA+USAAAYFDqWjq1/1ijVhSkKdLLoQQA9CcxJlIfXjhVT+yqVEtHt+s4ADBsHPkBAIBB2VR0QsZI5xWku44CACHv+uU5aun06al3jrmOAgDDRokEAAAGrKPLp20lJ3VOVrKSYiNdxwGAkLcsL1XTJ8XrgTcHfxEDAAg1lEgAAGDAdpSeVEd3j1YXZriOAgBhwRijG1bkakdpvfZUNriOAwDDQokEAAAGpMdabSyqVU5qrHLS4lzHAYCwsXZpjmIiPfrTZqaRAIQ3SiQAADAgh6qbVNvSqVVMIQHAoCTHReqjC6fpsbcq1Nje5ToOAAwZJRIAABiQjUW1SoqJ0DlZya6jAEDY+ez5eWrr8unRHRWuowDAkFEiAQCAoGoa23Woplkrp6fL6zGu4wBA2FmYnaKF2cm6b3OJrLWu4wDAkFAiAQCAoDb+/+zdZ3Sc52Hm/euegt57B9FIAqygJBZR1aIsFymW5BbJStF613YcJ14nefNmd1OcbMmenKxb7I3tjdayrVByElnViq1OsYi9d4DoAIkODNoAmJlnP5BSVEgKAAHcU/6/c3QgkSh/fSEGF+/neZr65XEZ3bAky3YKAESshzaWq7FnVHuaB2ynAMCcMCIBAICrmpgK6lDboNaUZigl3mM7BwAi1j2ri5Se6NVPd7faTgGAOWFEAgAAV7W/dUDTQUc3VmXbTgGAiJYY59anrivRr45fUI/PbzsHAGaNEQkAAFxRMOTozaZ+VeQkqzA90XYOAES8z20oUyDk6Gf72m2nAMCsMSIBAIArOnXep6HxaW3mFBIAzIvK3BTdVJ2jrXvbFAiGbOcAwKwwIgEAgCvada5fmUleLS9Ms50CAFHjoY3lOj/s16une2ynAMCsMCIBAIDL6hqaUEv/mDZVZstljO0cAIgaW2rzVJCWwA22AUQcRiQAAHBZu871K87t0nXlWbZTACCqeNwuPbC+TNsb+tTcN2Y7BwBmjBEJAAC8z+hkQEc6hrSuPEOJcW7bOQAQdX59fak8LqOteziNBCByMCIBAID32dPUr2DI0abKHNspABCV8tMS9OEV+fqn/R3yTwdt5wDAjDAiAQCAd5kOhrS7eUDL8lOVmxpvOwcAotZDG8s1PDGt54+et50CADPCiAQAAN7laMeQxiYD2lzNKSQAWEibKrNVlZvMDbYBRAxGJAAA8DbHcbSzsV8FaQmqyk22nQMAUc0Yo4c2lutI+5COdQzbzgGAD8SIBAAA3naud0wXfH5trs6WMcZ2DgBEvfvXlSjR69ZjnEYCEAEYkQAAwNt2NvYpOd6j1SUZtlMAICakJ3r1ibVFeuZIp4bHp23nAMBVMSIBAABJUu/IpM50j2hjRZa8bl4iAMBieWhjufzTIT15sMN2CgBcFa8QAQCAJGnXuT55XEYbKrNtpwBATFlZnK76sgw9tqdVjuPYzgGAK2JEAgAAGp8M6GDboNaUZigl3mM7BwBizkMbytXUO6Zd5/ptpwDAFfEqEQAAaG/LgKaDjjZX5dhOAYCIsHVP27x+vulgSElxbj26q0Wbq/mzGEB44iQSAAAxLhAKaXdTv6pzU1SQnmA7BwBiktft0volWXr5VLfaB8Zt5wDAZTEiAQAQ4453DsvnD2hzNfdCAgCbNlRmy2WMfvJmi+0UALgsRiQAAGKY4zja2div3JR41eSn2s4BgJiWnujVR1cW6Il97RqbDNjOAYD3YUQCACCG7WsZVOfQhG6svvi33wAAux7evEQj/oB+fqjTdgoAvA8jEgAAMeyRHU1K9LpVX5ppOwUAIGldWaZWFafr0Z3NchzHdg4AvAsjEgAAMaqtf1wvnuzWhoosxXl4SQAA4cAYo4c3L9G53jHtaOyznQMA78IrRgAAYtSPdjXL4zLaWMkNtQEgnHx8daFyUuL0o50ttlMA4F0YkQAAiEE+/7T+aV+77l5dpLREr+0cAMA7xHvcenBDuV493aPmvjHbOQDwNkYkAABi0M/2tmtsKqjP31RhOwUAcBkPbSiT1230kzdbbKcAwNtmPCIZYz5ljPk7Y8x2Y4zPGOMYYx6b7Rc0xrRc+tjL/XNhtp8PAADMTiAY0qO7WrS+Iksri9Nt5wAALiMvLUEfX1Wof97foRH/tO0cAJAkeWbxvn8qaY2kUUkdkpZfw9cdlvSty/z66DV8TgAAMAO/OtGtzqEJ/fk9dbZTAABX8dubK/T04S49eaBDv72Zk6MA7JvNiPQ1XRyPGiXdKum1a/i6Q47jfP0aPh4AAMzRIzuaVJaVpC21+bZTAABXsbY0Q/VlGfrxm636zU1L5HIZ20kAYtyML2dzHOc1x3EaHMdxFjIIAAAsnAOtAzrYNqSHNy+Rmx9GACDs/faNS9TcN6ZtZ3ttpwDArE4izad4Y8xDksokjUk6KukNx3GClnoAAIgJ/+eNZqUnevWZ60ttpwAALmPrnrZ3/XcgFFJqgkf/44VTOj/sn/PnfXBD2bWmAYC1EalA0k/f82vNxpiHHcfZZiMIAIBo19o/pl+dvKAv31al5HhbLwEAALPhcbm0oSJLL5/qUc+IX3mpCbaTAMSwGV/ONo9+JOkOXRySkiWtkvQDSUsk/asxZs2VPtAY8wVjzH5jzP7eXo5zAgAwG4/saJbX5dJvbVpiOwUAMAvrK7Lldhntbuq3nQIgxi36iOQ4zl86jvOq4zjdjuOMO45z3HGcL0n6hqRESV+/ysf+0HGc6x3HuT43N3exkgEAiHiDY1P6p/3t+sTaIuWl8bfYABBJUuI9WlOSroOtQ5qY4g4gAOyxcRLpSr5/6e0t7qGWZAAAIABJREFUVisAAIhCj+1ulX86pP9wS6XtFADAHGyqytFUMKQDbYO2UwDEsHAakXouvU22WgEAQJTxTwf14zdbdNuyXC3NT7WdAwCYg+KMRJVnJ2l3U79CPDAbgCXhNCJtuvS2yWoFAABR5ulDneobndIXbuYUEgBEshurcjQwNqUzF0ZspwCIUQsyIhljvMaY5caYqvf8+gpjTNZl3r9c0ncv/edjC9EEAEAsCoUc/cOOZq0oStOmqmzbOQCAa1BXmKb0RK92neuznQIgRs34+b7GmHsl3XvpPwsuvd1kjHn00r/3OY7zR5f+vVjSKUmtuvjUtbd8WtKfGGNek9QsaURSlaSPS0qQ9IKkv531/wUAADFg6562WX/M6Qs+NfaM6jPXl+jxve0LUAUAWCxul9HGiiz96mS3un1+5fOgBACLbMYjkqS1kn7rPb9Weekf6eJg9Ee6utckLZNUr4uXryVLGpK0Q9JPJf3UcbjAFwCA+bK9oU/piV6tKs6wnQIAmAfXL8nSK6d7tOtcv+6rL7adAyDGzHhEchzn65K+PsP3bZFkLvPr2yRtm+nXBAAAc9cxOK7mvjF9dGWB3K73fVsGAESg5HiP1pZm6HD7oO5aka+kuNmcCwCAaxNON9YGAADzaEdjn+I9Lt2w5H23IwQARLBNVdmaDjra3zJoOwVAjGFEAgAgCg2OTel457DWL8lSgtdtOwcAMI8K0xNVkZOs3U39Coa4GwiAxcOIBABAFHrryT08kQ0AotONVdkampjWqfM+2ykAYggjEgAAUWZiKqh9rYNaXZKhjKQ42zkAgAVQW5imjCSv3mzqt50CIIYwIgEAEGX2tQxoKhDSTdU5tlMAAAvEZYw2VWaruW9M54cnbOcAiBGMSAAARJFAKKRd5/pUlZusooxE2zkAgAV0fXmWvG6jXec4jQRgcTAiAQAQRY52DMvnD+jmmlzbKQCABZYY51Z9WaaOtA9pbDJgOwdADGBEAgAgSjiOox0NfcpPi1dNXortHADAIthUma1AyNG+lgHbKQBiACMSAABRorFnVBd8ft1UnStjjO0cAMAiyE9LUHVuinY39SsYcmznAIhyjEgAAESJ7Q19So33aE1Juu0UAMAiurEqWz5/QCe6hm2nAIhyjEgAAESBzsEJNfaO6sbqHHncfHsHgFiytCBVWclxepMbbANYYLzKBAAgCmxr6FW8x6UNFVm2UwAAi8xljDZVZqt1YFydgxO2cwBEMUYkAAAiXN/opE50DmtDRbYSvG7bOQAAC64rz1Scx6Vd5/pspwCIYoxIAABEuO0NfXK7jG6szradAgCwJMHr1rqyTB3tHNaIf9p2DoAoxYgEAEAE8/mndbBtUPVlmUpL8NrOAQBYtKkyW8GQo70tA7ZTAEQpRiQAACLYrsZ+hUKObqnJsZ0CALAsNzVeS/NTtLdpQIFQyHYOgCjEiAQAQITyTwe1p7lfK4rTlZ0SbzsHABAGbqzK0chkQMc7fbZTAEQhRiQAACLU3uYBTQZCurUm13YKACBMVOelKCcljhtsA1gQjEgAAESg6WBIOxv7VJ2bouLMRNs5AIAw4TJGm6py1DE4ofaBcds5AKIMIxIAABHocNuQRiYDumUpp5AAAO+2rjRD8R4Xp5EAzDtGJAAAIkzIcfRGQ6+KMxJVlZtsOwcAEGbivW5dX56pY53D8k1M284BEEUYkQAAiDAnunzqH5vSLUtzZYyxnQMACEMbK7PlONKe5gHbKQCiCCMSAAARxHEcvXG2V9nJcVpRlGY7BwAQprJT4rWsIFV7m/sVCIZs5wCIEoxIAABEkHO9Y+ocmtAtNblycQoJAHAVN1blaGwqqKOdw7ZTAEQJRiQAACLIG2d7lRrv0dqyDNspAIAwV5WbrLzUeO061yfHcWznAIgCjEgAAESIzsEJNfaOanN1jrxuvoUDAK7OGKNNVdnqGvLrQOug7RwAUYBXoAAARIhtDb2K97i0viLLdgoAIELUl2YqwevSj3a12E4BEAUYkQAAiADNfWM60TmsjZXZSvC6becAACJEnMelG5Zk6ZfHL6hraMJ2DoAIx4gEAEAE+OEbTXK7jG6syradAgCIMBsrsuU4jh7b3Wo7BUCEY0QCACDM9fj8evJAh9aVZSo1wWs7BwAQYTKT43RnXb4e39sm/3TQdg6ACMaIBABAmHtkZ7MCoZBursmxnQIAiFAPb67Q4Pi0njncaTsFQARjRAIAIIwNjk3psTdbdffqImWnxNvOAQBEqA0VWVpekKof7WyR4zi2cwBEKEYkAADC2I92tWhsKqjfvb3adgoAIIIZY/Tw5iU6fWFEe5oHbOcAiFCMSAAAhCmff1qP7mzWXSvytawg1XYOACDCfWJtsTKTvHp0Z4vtFAARihEJAIAw9dM3W+XzB/SV22tspwAAokCC160H1pfpxZMX1D4wbjsHQARiRAIAIAyNTwX0yI5m3bYsV6tK0m3nAACixEMby2WM0WO7W22nAIhAjEgAAIShrXvaNDA2pd/7EPdCAgDMn6KMRH1kZYEe39um8amA7RwAEYYRCQCAMOOfDuqHbzRpU2W2rivPsp0DAIgyD9+4RD5/QE8d6rSdAiDCMCIBABBm/vlAh3pGJjmFBABYENeVZ2plcZoe3dkix3Fs5wCIIIxIAACEkelgSN9//ZzWlWVoU1W27RwAQBQyxujhGyvU0DOqXef6becAiCCMSAAAhJGnDnWqc2hCv/ehGhljbOcAAKLU3WsKlZMSpx/tbLadAiCCMCIBABAmgiFHf//6Oa0oStNty3Jt5wAAoli8x60H15fpldM9au0fs50DIEIwIgEAECZ+cey8mvvG9JXbqzmFBABYcA9tLJfbGP3kzVbbKQAiBCMSAABhIBRy9L1XG1Wdl6K7VhTYzgEAxIC8tATdtbJATx7skH86aDsHQARgRAIAIAy8dKpbZ7pH9JXbq+VycQoJALA4Pre+TEPj0/rX4+dtpwCIAIxIAABY5jiOvvtqo8qzk3T36kLbOQCAGLKpKlsVOcnauqfNdgqACMCIBACAZdvO9upY57C+fFuVPG6+NQMAFo8xRg+sL9W+lkGd7R6xnQMgzPFKFQAAixzH0d+92qii9ATdV19iOwcAEIM+dV2p4twuTiMB+ECMSAAAWLS7aUAHWgf1pduqFOfh2zIAYPFlJcfprpUF+jk32AbwAXi1CgCARd99rUG5qfH6zPWltlMAADHswfVl8vkD+sVRbrAN4MoYkQAAsORA64B2NvbrP9xcoQSv23YOACCGbazMUmVusrbu5ZI2AFfGiAQAgCXferlB2clxemhjue0UAECMM8bowfVlOtA6qDMXuME2gMtjRAIAwIIDrQPa3tCnL95aqaQ4j+0cAAD0yXUlivO4tHVPq+0UAGGKEQkAAAs4hQQACDeZyXH62MoC/fxQpyamuME2gPdjRAIAYJFxCgkAEK4eWF+mEX9Azx/tsp0CIAwxIgEAsMg4hQQACFfrK7JUnZfCDbYBXBYjEgAAi2h/C6eQAADhyxijB9aX6VDbkE6d99nOARBmGJEAAFhE336FU0gAgPD2yXXFl26wzWkkAO/GiAQAwCLhFBIAIBJkJMXp7lWFevpQp8anArZzAIQRRiQAABbJt19pUE4Kp5AAAOHvwQ1lGpkM6Lkj3GAbwL9hRAIAYBG8fQrplipOIQEAwt515ZmqyUvhkjYA78KIBADAIvjWyxdPIX1uY5ntFAAAPpAxRg9uKNORjmEd7xy2nQMgTDAiAQCwwPa1DGhHI6eQAACR5f76EsV7XHp8L6eRAFzEiAQAwAL7NqeQAAARKD3Jq7tXF+mZw10am+QG2wAYkQAAWFCcQgIARLIHN5RpdDKgZ7nBNgAxIgEAsKA4hQQAiGTryjK0vCCVG2wDkCTxV6IAAMyjd77Ibukb047GPn10ZYGePsTf4AIAIo8xRg+sL9NfPHtCxzqGtaok3XYSAIs4iQQAwAJ59XSPkuM92lCRbTsFAIA5u7e+WHEel/7lQLvtFACWMSIBALAAWvrG1Ng7qltrchTn4dstACBypSd69eG6fD17pEtTgZDtHAAW8aoWAIAF8MrpbiXHe7SeU0gAgCjwyXUlGhyf1mtnemynALCIEQkAgHnW3Demc71jnEICAESNm2tylJMSr58f7LCdAsAiXtkCADCPHMfRSye7lRrv0YZKTiEBAKKDx+3SvWuL9OrpHg2OTdnOAWAJIxIAAPOoqW9MLf1junVZrrxuvs0CAKLHJ68r0XTQ0XNHeeIoEKt4dQsAwDx56xRSWoJHNyzJsp0DAMC8qi1MU21hmp48wCVtQKzy2A4AACBavNHQp7aBcf3amiJOIQEAwsrWPW3z8nkqcpL1wrHz+tZLZ5WXliBJenBD2bx8bgDhj1e4AADMA8dx9I2Xzioj0avrl2TazgEAYEGsKUmXy0iH2odspwCwgBEJAIB58NqZHh1pH9Lty/PkcfHtFQAQnVITvKrJS9Xh9iGFHMd2DoBFxqtcAACu0VunkMqykrSujFNIAIDoVl+WoeGJaTX1jtlOAbDIGJEAALhGL57s1vFOn37/jhq5XcZ2DgAAC6q2ME0JXpcOtQ3aTgGwyBiRAAC4BqGQo2++dFYVOcm6d22R7RwAABac1+3SquIMHe8a1uR00HYOgEXEiAQAwDX45YkLOn1hRF+9o0YensgGAIgR68oyNB10dLzLZzsFwCLi1S4AAHMUvHQKqTovRfes4RQSACB2lGUlKTs5jkvagBjDiAQAwBw9f7RLDT2j+o9buBcSACC2GGNUX5ahpr4xdQyO284BsEgYkQAAmINAMKRvv9ygZfmp+tjKQts5AAAsuvrSi08kffpQp+USAIuFEQkAgDl45nCXmvrG9LU7a+TiFBIAIAZlJsepIidZTx7slOM4tnMALAJGJAAAZmk6GNJ3Xm3QiqI03bWiwHYOAADW1JdmqLlvTIfah2ynAFgEjEgAAMzSUwc71do/rq9tWSpjOIUEAIhdK4vTleB16ckDHbZTACwCRiQAAGZhKnDxFNLqknTdUZtnOwcAAKsSvG59ZEWBnjvSpclA0HYOgAXmsR0AAIANW/e0zenj9jT3q2NwQncsz9fje9vnuQoAgMhz/7oSPX24S6+c6tHHVvGwCSCacRIJAIAZCoRCev1Mr0ozE7U0P8V2DgAAYWFzdY7y0+L184Nc0gZEO0YkAABm6EDroIYnprWlNp97IQEAcInbZXRvfbFeP9OrvtFJ2zkAFhAjEgAAMxAIXjyFVJaVpOo8TiEBAPBOn1xXokDI0bOHu2ynAFhA3BMJAIAZ2H/pFNL964o5hQQAwDu8dZ/B4oxE/cP2JiV43fPyeR/cUDYvnwfA/OEkEgAAHyAQDGnb2UunkHI5hQQAwOXUl2Woa9ivHp/fdgqABcKIBADAB3jrFNIdtXmcQgIA4ApWFqXLSDrWNWw7BcACYUQCAOAq3jqFVM4pJAAAriot0avy7CQd62BEAqIVIxIAAFfxb6eQeCIbAAAfZFVxunpGJtXNJW1AVGJEAgDgCqaDIb1+pkflWUmqyk22nQMAQNhbUXzxkrbjnZxGAqIRIxIAAFewv3VQPn+AU0gAAMxQWoJX5dnJOsaIBEQlRiQAAC5jOhjStjM9Ks/mFBIAALOxqoRL2oBoxYgEAMBl7G8ZkM8f0BZOIQEAMCsri9IuPqWN00hA1GFEAgDgPaYvPZFtSXaSKnM4hQQAwGykJni1JCeZ+yIBUYgRCQCA99h36RQS90ICAGBueEobEJ0YkQAAeId/O4WUzCkkAADmaAWXtAFRiREJAIB32NcyoBF/QHfU5nEKCQCAOXrrkrZjncNyHMd2DoB5wogEAMAlb51CqshJVlVuiu0cAAAi2qridPWOTKp7ZNJ2CoB5wogEAMAlb59CWp5nOwUAgIj39iVtHVzSBkQLRiQAAPTuU0iVnEICAOCapSZ4VXHpKW1c0gZEB0YkAADEKSQAABbCyuJ09Y5OqtvHJW1ANGBEAgDEvEAopO0NfSrPTuIUEgAA84intAHRhREJABDzDrcNaXhiWrcv4xQSAADz6a1L2nhKGxAdGJEAADEt5DjadrZXRRkJqsnjFBIAAPNtVUm6+kYndcHnt50C4BoxIgEAYtqxzmH1j03ptqV5MsbYzgEAIOqsKEqXkXScS9qAiMeIBACIWSHH0bYzvcpNjVddUZrtHAAAolJKvEcVuVzSBkQDRiQAQMw6c2FEF3x+3bY0Vy5OIQEAsGBWFaerb3SKS9qACMeIBACISY7j6PUzPcpM8mp1SYbtHAAAotpbl7TxlDYgsjEiAQBi0rneMbUPTuiWpblyuziFBADAQkqJ96gyN1nHOrikDYhkjEgAgJj0+pkepSZ4tK4s03YKAAAxYVVxhvrHuKQNiGSMSACAmHOgdVBNfWO6uTpHXjffCgEAWAx1RWlyGelYB5e0AZGKV84AgJjzv19rVKLXrRsqsmynAAAQM1LiParMSeEpbUAEY0QCAMSUk10+vXK6R5ursxXvcdvOAQAgpqwsTlf/2JTOD3NJGxCJGJEAADHle683KiXeo02VObZTAACIOW9f0sZT2oCIxIgEAIgZ53pH9cKx8/qNTeVKjOMUEgAAi+2tS9qOc0kbEJEYkQAAMeP7r59TnNulz99UYTsFAICYVVeUpv6xKfWOTNpOATBLjEgAgJjQMTiupw516oH1ZcpJibedAwBAzKotTJMknTrvs1wCYLYYkQAAMeGHbzTJGOkLt1TaTgEAIKalJ3pVnJGok4xIQMRhRAIARL2eEb+e2Neu++tLVJSRaDsHAICYV1uYqo7BCfn807ZTAMwCIxIAIOo9sr1ZgWBIX7qtynYKAACQVFeYLkfS6fMjtlMAzAIjEgAgqg2PT+ux3a36+OoiVeQk284BAACS8tPilZnk5b5IQIRhRAIARLXH9rRqbCqoL93KvZAAAAgXxhjVFabpXO+oJgNB2zkAZogRCQAQtSYDQT26q0U31+RoRVG67RwAAPAOtYVpCoQcNXSP2k4BMEOMSACAqPX0oU71jkzqi7dwLyQAAMJNeXayEr1untIGRBBGJABAVAqFHP3wjSbVFaZpc3W27RwAAPAebpfR8oJUnbkwomDIsZ0DYAYYkQAAUenV0z061zumL95aKWOM7RwAAHAZtYVpmpgOqqV/zHYKgBnw2A4AAOBqtu5pm9PH/eCNc8pI9Mo3EZjz5wAAAAurJj9FHpfRqfM+VeWm2M4B8AE4iQQAiDptA+Nq7R/X5uocuV2cQgIAIFzFe9yqzkvRqfM+OQ6XtAHhjhEJABB1tjf0KsHr0vVLMm2nAACAD1BbmKbB8Wld8PltpwD4AIxIAICo0jc6qZNdPm2oyFa8x207BwAAfIDlBakyEk9pAyIAIxIAIKrsaOyTy2W0qYonsgEAEAlSE7wqzUrSKUYkIOwxIgEAosboZEAHWwdVX5qhtASv7RwAADBDdYVp6hrya2h8ynYKgKtgRAIARI3dTf0KhBzdVJNjOwUAAMxCbWGaJHEaCQhzjEgAgKgwFQhpd1O/agtSlZeaYDsHAADMQm5qvHJS4nXq/IjtFABXwYgEAIgKB9oGNT4V1M01ubZTAADAHNQVpqqpb1QTU0HbKQCugBEJABDxgiFHOxp6VZqZqPLsJNs5AABgDuoK0xRypLPdnEYCwhUjEgAg4p3oGtbg+LRursmVMcZ2DgAAmIOSrCSlxHt0kvsiAWGLEQkAENEcx9H2hj5lJ8eprijNdg4AAJgjlzFaXpCqs90jCgRDtnMAXAYjEgAgojX3jalzaEI31eTIxSkkAAAiWl1hmiYDITX1jdlOAXAZjEgAgIi2vaFPyXFurSvLtJ0CAACuUVVeiuLcLp3ikjYgLDEiAQAiVrfPrzPdI9pUlS2vm29pAABEOq/bpZr8FJ0671Mo5NjOAfAevOIGAESs7Q198rqNNlZk204BAADzpLYwTT5/QMc6h22nAHgPRiQAQETyTUzrSPuQrivPVFK8x3YOAACYJ8vzU+Uy0ksnu22nAHgPRiQAQER6s6lfIcfR5qoc2ykAAGAeJcV7VJ6dzIgEhCFGJABAxJkKhLS3eUB1RWnKTom3nQMAAOZZXWGaznSPqLWfp7QB4YQRCQAQcQ60DmhiOqibqjmFBABANKotTJPEJW1AuGFEAgBElJDjaOe5fpVmJqo8O9l2DgAAWABZyXFaXpCql08xIgHhhBEJABBRTnb5NDA2pZtqcm2nAACABbSlNl/7WgY1ND5lOwXAJYxIAICIsqOxT5lJXq0oSrOdAgAAFtCWunwFQ45eP9NrOwXAJYxIAICI0TYwrraBcW2uzpHLGNs5AABgAa0uTlduarxe4pI2IGwwIgEAIsaOhl4leF26rjzTdgoAAFhgLpfRHcvztO1Mr6YCIds5AMSIBACIEANjUzrR5dP6JdmK97ht5wAAgEWwpTZfo5MB7W0esJ0CQIxIAIAIsbOxT8ZIm6qybacAAIBFsrk6RwleF09pA8IEIxIAIOxNTAV1oHVQa0oylJ7otZ0DAAAWSWKcWzdV5+qlk91yHMd2DhDzGJEAAGFvb3O/poIh3VSTYzsFAAAssjvr8tQ5NKHTF0ZspwAxjxEJABDWAqGQ3mzqV3VuigrTE23nAACARfah5fkyRnr5JJe0AbYxIgEAwtrRjmH5/AFOIQEAEKNyU+O1tjSD+yIBYYARCQAQthzH0Y6GPuWlxqsmL8V2DgAAsGRLbb6OdAyrx+e3nQLENEYkAEDY2tnYrws+v26qzpExxnYOAACwZEttviTpldM9lkuA2MaIBAAIW/9ne5NS4j1aW5phOwUAAFi0ND9FpVmJ3BcJsIwRCQAQls52j2jb2V5trMyWx823KwAAYpkxRltq87WjsU/jUwHbOUDM4lU5ACAs/cP2JiV4XdpYkWU7BQAAhIE7a/M1GQhpR0Of7RQgZjEiAQDCTs+IX08f6tKnritRUrzHdg4AAAgDN1RkKTXBw1PaAIsYkQAAYecnu1o1HQrp8zdV2k4BAABhwut26fZleXr1dI9CIcd2DhCTGJEAAGFldDKgn7zZorvqClSRk2w7BwAAhJE7avPUNzqlwx1DtlOAmMSIBAAIK4/vaZPPH9CXbquynQIAAMLMbUvz5HEZntIGWMKIBAAIG1OBkB7Z0axNldlaW5phOwcAAISZ9CSv1ldkcV8kwBJGJABA2Hj6cKcu+PycQgIAAFe0pTZfZ7tH1do/ZjsFiDmMSACAsBAKOfrBtnOqK0zTLTU5tnMAAECY2lKbL0l6+VSP5RIg9jAiAQDCwsununWud0xfvLVSxhjbOQAAIEyVZSdpWX4q90UCLGBEAgBY5ziO/n7bOZVmJerjqwpt5wAAgDB3R22e9rYMaHh82nYKEFMYkQAA1u1tHtChtiF94eZKedx8awIAAFe3pS5fwZCj189ySRuwmHilDgCw7vvbzik7OU6fvr7UdgoAAIgAa0sylJMSx32RgEXGiAQAsOr0BZ9eO9Or375xiRK8bts5AAAgArhcRncsz9frZ3o0FQjZzgFiBiMSAMCqH2xrUnKcW7+5aYntFAAAEEG21OVrxB/QvpYB2ylAzGBEAgBY0z4wrmePdOmB9WVKT/LazgEAABHkpuocxXtceomntAGLhhEJAGDNIzua5TLS52+usJ0CAAAiTGKcWzfX5OiV091yHMd2DhATGJEAAFYMjE3piX1t+sTaYhWmJ9rOAQAAEeiO2ny1D0zoTPeI7RQgJjAiAQCs+PGuFvmnQ/rSrZW2UwAAQITaUpsvY6RfHeeSNmAxMCIBABbd+FRAP36zRXfW5as6L9V2DgAAiFC5qfG6rixTvzpxwXYKEBMYkQAAi+6Jve0aGp/Wl26tsp0CAAAi3EdWFujkeZ/aB8ZtpwBRb8YjkjHmU8aYvzPGbDfG+IwxjjHmsbl8UWNMiTHm/xpjuowxk8aYFmPMt4wxmXP5fACAyDEdDOmRHc1avyRL15Xzxz4AALg2d60okCROIwGLYDYnkf5U0lckrZXUOdcvaIypknRA0sOS9kr6pqQmSV+V9KYxJnuunxsAEP6eO9KlzqEJ/c5tnEICAADXrjQrSXWFafrlcUYkYKHNZkT6mqSlktIk/c41fM3/LSlP0u87jnOv4zh/4jjOh3RxTFom6b9fw+cGAIQxx3H0g21NWl6QqtuW5drOAQAAUeKuFQU60DaonhG/7RQgqs14RHIc5zXHcRocx3Hm+sWMMZWSPiypRdL33vPbfyFpTNJvGGOS5/o1AADh68WT3TrTPaIv3lopY4ztHAAAECU+srJAjiO9dJKntAELabFvrP2hS29fdBwn9M7fcBxnRNJOSUmSNi5yFwBggYVCjr7x4llV5iTrntVFtnMAAEAUWZqfoiXZSfrVCUYkYCF5FvnrLbv09uwVfr9BF08qLZX0yqIUAQAWxfPHzutM94i+80C9PG4eDgoAAK5u6562Wb1/WVaSdjT06pHtzUqMc1/x/R7cUHataUDMWuxX8emX3g5f4fff+vWMy/2mMeYLxpj9xpj9vb298x4HAFgYgWBI33rprJYXpOruVYW2cwAAQBSqK0pXyJHOdPtspwBRK9z+KvitG2Rc9r5LjuP80HGc6x3HuT43lxuyAkCkeOpQp5r6xvQHdy6Vy8W9kAAAwPwryUxUWoJHJ7oYkYCFstgj0lsnjdKv8Ptp73k/AECEmwqE9O1XGrS6JF131uXbzgEAAFHKZYzqitJ0tntEU4HQB38AgFlb7BHpzKW3S6/w+zWX3l7pnkkAgAjzs/3t6hic0B9+eBlPZAMAAAuqrjBd00FHjT0jtlOAqLTYI9Jrl95+2Bjzrq9tjEmVtFnShKTdi9wFAFgA/umgvvtqg9YvydItNTm2cwAAQJSryElWotfNJW3AAlmQEckY4zURo4d5AAAgAElEQVTGLDfGVL3z1x3HOSfpRUlLJP3uez7sLyUlS/qJ4zhjC9EFAFhcj+1uVbdvUn/44aWcQgIAAAvO7TKqLUzVqQs+BUOXvdUugGvgmek7GmPulXTvpf8suPR2kzHm0Uv/3uc4zh9d+vdiSackteriYPROX5a0S9J3jDF3XHq/DZJu18XL2P7L7P4XAADhaGwyoL9//ZxursnRhsps2zkAACBGrChK18G2ITX3jak6L8V2DhBVZjwiSVor6bfe82uVl/6RLg5Gf6QP4DjOOWPM9ZL+StJHJH1M0nlJ35H0l47jDMyiCQAQph7d1aL+sSn94YeX2U4BAAAxpDovRXFul050DTMiAfNsxiOS4zhfl/T1Gb5vi6QrXrfgOE67pIdn+rUBAJFleGJaP9h2Tltq87W2NMN2DgAAiCFet0tL81N08rxP96wpkotL6oF5s9g31gYAxIBHtjfJ5w/oD+680sM4AQAAFk5dUbpG/AF1DIzbTgGiCiMSAGBe9Y9O6pEdzfr46kLVFaXZzgEAADFoeUGq3MboxHme0gbMJ0YkAMC8+sEbTZqYDuprWziFBAAA7EjwulWVl6wTXT45Dk9pA+YLIxIAYN70+Pz68a4W3Vdfwo0sAQCAVSsK0zUwNqVu36TtFCBqMCIBAObN915rVDDk6Kt31NhOAQAAMW55YaqMpBNdw7ZTgKgx46ezAQBwJVv3tGlwfEqP7W7TdeWZ2tHYJzXargIAALEsNcGr8uwknTzv0x21+bZzgKjASSQAwLx47XSPjJFuX55nOwUAAEDSxae0nR/2a2BsynYKEBUYkQAA16xraEIHWge1oSJL6Yle2zkAAACSpBWFF58UyyVtwPxgRAIAXJNQyNEzhzuVHO/Rh5ZzVBwAAISPzOQ4FaUn6ESXz3YKEBUYkQAA1+TxfW1qH5zQx1YVKDHObTsHAADgXeqK0tU+MC6ff9p2ChDxGJEAAHPWNzqpv/nlGVXmJGtNSYbtHAAAgPdZUZQmR9Kp85xGAq4VIxIAYM7++oXTGp8K6NfWFskYYzsHAADgffJS45WTEqeTXNIGXDNGJADAnOxp6teTBzv0hVsqlZeaYDsHAADgsowxWlGUrnO9oxqfDNjOASIaIxIAYNamgyH96dPHVZKZqK/cXmM7BwAA4KpWFacr5IgbbAPXyGM7AAAQeR7Z0ayGnlE98lvXczNtAAAQ9grTE5STEq8jnUO2U4CIxkkkAMCsdAyO69svN+jDdfm6ozbfdg4AAMAHMsZodUm6mnvH1OPz284BIhYjEgBgVv7quZOSpL/4tRWWSwAAAGZuVXG6HEkvHDtvOwWIWIxIAIAZe+VUt1482a2vbqlRcUai7RwAAIAZy09LUEFagp4/yogEzBUjEgBgRiamgvqLZ0+oJi9F/25zhe0cAACAWVtdkq79rYPqHJqwnQJEJEYkAMCMfPe1BnUMTui/3btScR6+fQAAgMizuiRDkvSLo12WS4DIxE8BAIAP1Ngzoh++0aRPrivRhsps2zkAAABzkpUcpzWlGXruCJe0AXPBiAQAuCrHcfRnT59QUpxH/+ljy23nAAAAXJN7VhfqWOewmvvGbKcAEYcRCQBwVc8c7tKbTf36448sU05KvO0cAACAa/Lx1YWSpOePcEkbMFuMSACAK2ofGNefP3Nc9WUZeuCGMts5AAAA16wwPVHrl2TpOe6LBMwaIxIA4LKmgyH9/hOH5DjStz9bL5fL2E4CAACYF/esKdTZ7lGduTBiOwWIKIxIAIDL+tsXz+hQ25D+5ydXqyw7yXYOAADAvPnoqkK5jPQ8p5GAWWFEAgC8z+tnevSDbU363Iayt+8bAAAAEC1yUuK1uTpHzx3pkuM4tnOAiMGIBAB4l26fX3/wT0e0vCBVf3Z3ne0cAACABXH36kK19I/reKfPdgoQMRiRAABvC4Yc/ccnDmtiKqjvPrhOCV637SQAAIAFcdeKAnndhhtsA7PAiAQAeNt3X23Um039+q/3rlR1XortHAAAgAWTkRSnW2py9fyRLoVCXNIGzAQjEgBAkrS7qV/ffuWs7q8v1qeuK7GdAwAAsODuWVOkrmG/DrYN2k4BIoLHdgAAYPFs3dN22V8fnQzou682KCs5TqtK0q/4fgAAANFkS12+4j0uPXekS9cvybKdA4Q9TiIBQIwLOY6ePNCh8amgfv2GMsV7uA8SAACIDSnxHt1Rm6dfHLugIJe0AR+IEQkAYtzOxj6d6R7Rx1YVqigj0XYOAADAorp7dZH6Rie1p6nfdgoQ9hiRACCGtQ+M61cnLmhFUZo2VHCEGwAAxJ7bl+UpOc7NU9qAGWBEAoAYNTEV1BP72pSe6NX99SUyxthOAgAAWHSJcW7dWZevfz1+QVOBkO0cIKwxIgFADAo5jn62v03DE9P69RvKlBjHfZAAAEDsumdNkYbGp7Wzsc92ChDWGJEAIAb98vgFne0e1a+tKVZpVpLtHAAAAKturslVWoJHzx3hkjbgahiRACDGHGgd0I7GPm2qzNZ67oMEAACgOI9LH11ZqBdPdss/HbSdA4QtRiQAiCGt/WN6+lCXqnNT9LFVhbZzAAAAwsbdawo1OhnQ62d6bacAYYsRCQBiRMfguB7b3aqMJK8eWF8mt4sbaQMAALxlU2W2spPj9OyRTtspQNhiRAKAGDA2GdC///F+BR1Hv7lpCTfSBgAAeA+P26W7Vxfq5VM9Gp6Ytp0DhCVGJACIcqGQo6/97LDOdo/ogRvKlJsabzsJAAAgLN23rkRTgZB+efy87RQgLDEiAUCU++bLZ/XiyW792d11qslPtZ0DAAAQttaUpKsyJ1k/P8glbcDleGwHAADeb+uetnn5PEfah/Sz/e26YUmm4tz8vQEAAMDVGGN0X32x/tdLZ9UxOK6SzCTbSUBY4ScKAIhSHYPjevJgh5ZkJ+ueNUUyhhtpAwAAfJB764slSc8c7rJcAoQfRiQAiELDE9P66e5WpSZ49OCGMnlc/HEPAAAwE6VZSbphSaaeOtQpx3Fs5wBhhZ8qACDKTAdDemx3qyYDIf3GxiVKiefKZQAAgNm4r75EjT2jOt7ps50ChBVGJACIIo7j6KlDneoamtBnry9VQXqC7SQAAICI8/FVhYpzu/TzQx22U4CwwogEAFFkR2OfDrcP6c66fNUWptnOAQAAiEjpSV59aHmenjvSpUAwZDsHCBuMSAAQJc52j+iXxy9oZXG6bl2aazsHAAAgot23rlh9o1Pa3thnOwUIG4xIABAF+kYm9cS+NhWkJ+hT60p4EhsAAMA1un1ZnjKSvHrqYKftFCBsMCIBQITzTwf1092tchmjhzaWK87DH+0AAADXKs7j0sdXFerFkxc0OhmwnQOEBX7SAIAIFnIc/Wxfu/rHJvXghjJlJsXZTgIAAIga968rln86pF8ev2A7BQgLjEgAEMFeOtmtM90junt1kSpzUmznAAAARJV1ZZkqy0rSUzylDZDEiAQAEetox5C2ne3V+iVZ2liZbTsHAAAg6hhjdG99sXad69f54QnbOYB1jEgAEIE6hyb05MEOlWcn6e41hbZzAAAAotZ99cVyHOnZw122UwDrPLYDAACzMzoZ0GO7W5UU59HnNpTL4+LvAwAAAGZq6562WX9MaWaifrSzRakJ3qu+34MbyuaaBUQEfvIAgAgSCIW0dU+rxqcCemhjuVLi+bsAAACAhVZflqkLPj+XtCHmMSIBQAT5xdHzaukf1/3rSlSckWg7BwAAICasKk6Xy0iH24ZspwBWMSIBQIQ43D6oPc0DuqUmR2tKMmznAAAAxIzkeI+W5afqSMeQQo5jOwewhhEJACJAz4hfTx/q0pLsJN1ZV2A7BwAAIOasLcuUzx/Qud5R2ymANYxIABDmpgIhPb63TV630WdvKJPbZWwnAQAAxJzlBalK8Lq4pA0xjREJAMLcc0e71OOb1GeuL1V64tWfCAIAAICF4XW7tLIoXSe6fJoKhGznAFYwIgFAGDvYNqgDrYO6bVmuavJTbecAAADEtPqyTE0FQzp5fth2CmAFIxIAhKlun1/PHO5URU6y7qjNt50DAAAQ88qzk5SR5NUhLmlDjGJEAoAw9NZ9kOI9bn32hlK5DPdBAgAAsM1ljNaWZKixZ1Qj/mnbOcCiY0QCgDDjOI6eOdyp3pGL90FKS+A+SAAAAOFibVmGHElHOrikDbGHEQkAwsw/7+/QofYhfWh5nqrzUmznAAAA4B3yUhNUnJGow22DtlOARceIBABh5PQFn/7smeOqyk3W7cvzbOcAAADgMtaWZqhr2K9un992CrCoGJEAIEyMTgb05X88qLRErz5zPfdBAgAACFdrSjPkMtLhdm6wjdjCiAQAYcBxHP2Xp46ppW9M3/n1eqVyHyQAAICwlRLvUU1eqg63DynkOLZzgEXDiAQAYeCJfe165nCXvrZlqTZVZdvOAQAAwAdYW5ah4YlpNfeN2U4BFg0jEgBY1tA9or987oRuqs7R795ebTsHAAAAM1BbkKZ4j4tL2hBTGJEAwCL/dFC/9/ghJcd59I3PrpHLxX2QAAAAIkGcx6UVRek63jms6WDIdg6wKBiRAMCiv37hlE5fGNHffnqN8lITbOcAAABgFurLMjQZCOnUeZ/tFGBRMCIBgCUvnezWj99s1edvqtDty/Ns5wAAAGCWKnKSlZbg0aE2LmlDbGBEAgALLgz79cf/ckQritL0xx9ZZjsHAAAAc+AyRmtLM9TQM6LRyYDtHGDBMSIBwCILhhx97WeH5Z8O6TsP1Cve47adBAAAgDlaW5apkCMd7eA0EqKfx3YAAMSa7287pzeb+vU3n1qtqtwU2zkAAAC4BgVpCSpMT+ApbYgJnEQCgEV0oHVQ33jprO5ZU6RPX1diOwcAAADzYG1phjoGJ3Sud9R2CrCgGJEAYJH4/NP66hOHVJieoP9+30oZY2wnAQAAYB6sKc2QkfT0oU7bKcCCYkQCgEXgOI7+88+P6fywX995oF5pCV7bSQAAAJgnaQleVeel6KlDnQqFHNs5wIJhRAKARfDPBzr0/NHz+oM7l2pdWabtHAAAAMyzty5p2986aDsFWDCMSACwwM71jurrz57QpspsfenWKts5AAAAWAB1RWlK9Lr1FJe0IYoxIgHAApoMBPX7jx9SvMelb352rdwu7oMEAAAQjeI9bn1kZYF+cbRL/umg7RxgQTAiAcAC+usXTutEl09/86k1KkhPsJ0DAACABXRffbF8/oBeO91jOwVYEIxIALBAXjh2Xo/uatG/21yhO+vybecAAABggd1Yla3c1HguaUPUYkQCgAXQ2j+m//9fjmpNaYb+5KPLbecAAABgEXjcLn1iTZFeO9OjwbEp2znAvGNEAoB55p8O6sv/eFAul9H3HqxXnIc/agEAAGLFfeuKNR109Pyx87ZTgHnHTzYAMM/+2y9O6kSXT//r02tUkplkOwcAAACLqK4wTUvzU/TUwQ7bKcC8Y0QCgHn03JEuPba7TV+4pVJbuA8SAABAzDHG6L76Eh1sG1Jr/5jtHGBeMSIBwDxp6h3Vnzx5VNeVZ+r/u2uZ7RwAAABYcm99kYwRN9hG1GFEAoB58NZ9kOI8Lv3dA/XyuvnjFQAAIFYVpidqU2W2njrUKcdxbOcA84afcgBgHnz92RM6fWFE3/jsWhVlJNrOAQAAgGX31hertX9ch9qHbKcA84YRCQCu0VOHOvTEvnb9zm1Vun1Znu0cAAAAhIGPrixQvMelpw5ySRuiByMSAFyDxp4R/eefH9f6JVn6wzuX2s4BAABAmEhN8OrDKwr03NEuTQVCtnOAecGIBABzND4V0Jf/8aCS4tz6zgP18nAfJAAAALzDffVFGhqf1utnemynAPOCn3gAYA4cx9GfP3NCDT2j+uZn16ogPcF2EgAAAMLMzTW5yk6O09OHuaQN0YERCQDm4Me7WvQvBzr0e7dX65alubZzAAAAEIa8bpfuWVOkl0/1aHhi2nYOcM08tgMAINK8fqZHf/X8SdUWpikvLUFb97TZTgIAAECYun9dsR7d1aIX/h979xkeZ3mge/x+Z0a9V6s3y5aLZMsNuUEwEDC9w+JACJAQliywOSTZkt20E7K7SU6yCQuEJKRQTDElQDDVYIyb3LvlIltWl6ze28x7PtibixCMJVvSM+X/+6ILazT6+8JRmNvP+87uOt1yTpbpHOCscBIJAEbgcGOn7lu+XQUp0bppboYclmU6CQAAAF6sKD1GeUkRemU7l7TB93ESCQCGqbV7QHf9cYtCghz67e1z9eGB46aTAAAA4EVOdUJ9YlKk3t3XoEfeP6y4iOARP++yEk4wwTtwEgkAhmFgyKN7nt6quvY+PX7bXKXHhplOAgAAgI8ozoiVJO2objNcApwdRiQAOA3btvXd1/ao9GiLfnz9DM3JjjOdBAAAAB8SFxGsnIRwba9sk23bpnOAM8aIBACn8bt1FXp2U5W+tmSirpmVbjoHAAAAPmhWZpyauvpV09ZrOgU4Y4xIAPAZPihr1ENv7NMl0yfowc8XmM4BAACAjypMj5HLYWl7FZe0wXdxY20Afu9UNzg8nYaOPv3qw3JNiA7VgrxEPbe5apTLAAAAECjCgp2akhKlXVVtuqwwVU4H7/IL38NJJAD4FN39Q3pyQ4WCnQ7dNj9bwS5+XAIAAODsFGfGqXvArcONnaZTgDPCqyIA+IQhj0fPlB5TZ9+Qbp2frdjwkb8NKwAAAPBJk1MiFR7s5JI2+CxGJAD4GI9t65VtNapo7tH1szOUGR9uOgkAAAB+wuVwqCg9RvtqO9Q36DadA4wYIxIAnGTbtv68q07bq9p04dRkzcyMNZ0EAAAAPzMrM1ZDHlt7a9tNpwAjxogEADoxIL29t0EbjzRrcX6iLihINp0EAAAAP5QZH66EiGAuaYNPYkQCAEkfHDiuNYeO65zceF1amCLL4t0yAAAAMPosy1JxZqyOHu9We++g6RxgRBiRAAS8tYeb9N7+Bs3KjNVVM9MYkAAAADCmijNjZUvawWkk+BhGJAABbdPRFq3cXafCtGhdNztDDgYkAAAAjLGEyBBlxYdre2WrbNs2nQMMGyMSgIC1vbJVr+6oUcGEKN00L1NOBwMSAAAAxkdxZqwaO/tV195nOgUYNkYkAAFpT027XtpWrdykCC0ryZLLwY9DAAAAjJ8Z6TFyWhaXtMGn8KoJQMA5UN+p5zdXKSMuXLfNz1aQkx+FAAAAGF/hIS5NTonSzqo2uT1c0gbfwCsnAAHlyPEuPVN6TBNiQnT7ghyFuJymkwAAABCgZmXGqrN/SEeOd5lOAYaFEQlAwDjW3K0nNxxTfESw7liYq7BgBiQAAACYMyUlSqFBDm3nkjb4CEYkAAGhrL5Dv1t3VNFhLt25OFcRIS7TSQAAAAhwLqdDRemx2lvbrv4ht+kc4LQYkQD4vW2VrXp64zElR4Xq7vMmKjo0yHQSAAAAIOnEJW2Dblv7ajtMpwCnxYgEwK/9ek25XtxardzECH15ca4iOYEEAAAAL5KdEK648CAuaYNPYEQC4Jds29Z/rNyvH60sU1F6zImbaAdxDyQAAAB4F8uyVJwZp/LGLnX0DprOAT4TIxIAvzPk9ugbK3bp8TVH9MUF2bp5XqZcTn7cAQAAwDvNyoqVLWlnNaeR4N14VQXAr/QOuPXVp7bqpW3V+vpFk/X9q6bLYVmmswAAAIBTSowMUWZcmLZXMiLBuzEiAfAb7T2Duu2JUr1/oFE/vKZQD1w0SRYDEgAAAHxAcVac6jv6VNfeazoFOCVGJAB+ob69Tzc+vl67qtv1yLLZunV+tukkAAAAYNhmpMfIYYnTSPBqjEgAfF5ZfYeuf2y9alp79Yc75umyolTTSQAAAMCIRIS4VDAhSjur2+SxbdM5wKdiRALg01burtN1j67XoNuj5+5eoIX5iaaTAAAAgDNSnBWnzr4hlTd2mU4BPhUjEgCf5PbY+snbZbr3mW0qSInS6/ctVlFGjOksAAAA4IxNSYlSaJBD26u4pA3eyWU6AABGqr13UF9/fofeL2vU383L1Pevnq4Ql9N0FgAAAHBWgpwOFaXHaEdVm/qH0vhvXHgdRiQAXmN5aeVpH9PY0aenNh5Ta8+Ari5OU1F6jF7aWjMOdQAAAMDYK86M0+aKVu2r7dCsrDjTOcBf4XI2AD5jX22HHvuwXH1DHt21OE8luQmyLMt0FgAAADBqshPCFRcexCVt8EqcRALg9Ty2rQ/KGrWqrFHpsWH6QkmWYsODTWcBAAAAo85hWSrOjNXqA8fV0Tuo6LAg00nAX3ASCYBX6xt065nSSq0qa9SszFjdfV4eAxIAAAD82qzMONmSdlZzGgnehZNIALxWfXufnt1Uqebufl1elKqFE7l8DQAAAP4vMSpEmXFh2l7ZpnMnJZnOAf6CEQmA17FtW6VHW7Ryd53Cgpy6Y1GuJiZFms4CAAAAxk1xVpxe31mruvZe0ynAXzAiAfAqvQNuvby9WntrOzR5QqRumJOpyBB+VAEAACCwzEiP0Ru7arWjkkva4D14ZQbAaxxr7tbzm6vU0TeoSwtTtCg/UQ4uXwMAAEAAighxqWBClHZWt8ntseV08N/FMI8bawMwzu2x9cgHh/Wbj47I4bD01fMm6txJSQxIAAAACGjFWXHq6BvS+vIm0ymAJE4iATCssaNP//j8Dq0vb9aMjBhdU5yu0CCn6SwAAADAuCkpUQoNcujlbTXcYBtegREJgDEfHGjUN17Yqe6BIf34+hkadHt49zUAAADgpCCnQ0XpMXprT71+eM2QIrhXKAzjcjYA425gyKOH3tinO36/WUlRIfrzfYt107xMBiQAAADgE4oz49Q76Nbbe+tNpwCcRAIwvo41d+u+Z7drV3W7bpufrW9fPpXL1wAAAIBTyE4IV0ZcmF7ZXqPrZmeYzkGAY0QCMG5e3VGjb7+yRw5L+tWtc7S0MMV0EgAAAODVHJala2el65EPDquho08TokNNJyGAcTkbgDHXMzCkb724Uw88t0NTUqK08oFzGZAAAACAYbp2Vro89om/lAVMYkQCMKb21XboyofXasXWat13Qb6eu3u+MuLCTWcBAAAAPiMvKVLFmbF6aWuNbNs2nYMAxogEYEzYtq2nNlTomkfXqbNvSM/cVaIHLy6Qy8mPHQAAAGCkbpyboQMNndpd0246BQGMV3MARl1bz4DueXqr/v3VvVo0MUFvPnCuFuYnms4CAAAAfNYVM9IU4nLoxa3VplMQwBiRAIyqzRUtuuwXH+n9skb92+VT9cTt85QQGWI6CwAAAPBpMWFBumR6il7dUau+QbfpHAQoRiQAo8LtsfXwqkO6+fENCnI59NLfL9SXz82Tw2GZTgMAAAD8wg1zMtTeO6hV+xtNpyBAuUwHAPB9DR19+sfndmjDkWZdXZymH15TqKjQINNZAAAAgF9ZlJ+o1JhQrdhapctnpJrOQQBiRAJwVj4oa9SDK3aqd8Ctn9wwQzfMyZBlcfoIAAAAGG1Oh6XrZqfrsdXlaujo04ToUNNJCDBczgbgjAwMefTDP+/THX/YrAnRoXr9vsW6cW4mAxIAAAAwhm6YkymPLb2yvcZ0CgIQIxKAEato6tb1j63Xb9ce1e0LsvXKvQuVnxxpOgsAAADwe7mJEZqbHacVW6pk27bpHAQYLmcDMCLfenGX/rSjRk7L0q0l2SpIidbL2/hbEAAAAGC83Dg3Q//00m7tqGrTrKw40zkIIJxEAjAs3f1D+uaKnXphS5VSY0J13wX5mpYWbToLAAAACDiXFaUqNMihFVurTacgwDAiATitPTXtuuLhtXpxW7WWFCTry4vzFBsebDoLAAAACEhRoUG6rDBVr++sVd+g23QOAggjEoBT8nhs/WbNEV376Dr1Dbr13Ffm6/PTJsjp4ObZAAAAgEk3zMlQZ9+Q3t5bbzoFAYQRCcCnauzs0+2/36SHVu7XhVMm6M0HzlVJXoLpLAAAAACS5uclKD02TC9ySRvGETfWBvA33i9r0DdX7FL3wJD+47oi/d28TFkWp48AAAAAb+FwWLp+ToYefv+Qatt6lRYbZjoJAYCTSAD+om/Qre+9tld3/mGLkqND9ef7FuuWc7IYkAAAAAAvdMPsDNm29Mp23i0Z44OTSICfWl5aOaLHN3T06fnNVarv6NOiiQm6eHqKNh1t1aajrWNUCAAAAOBsZCWEqyQ3Xiu2VOne8yfyl78Yc5xEAgKcx7a18UizHl19WJ19g7p9QbYun5GmICc/HgAAAABvd+PcTFU092jrMf7yF2OPV4lAAGvu6tfv1h7VaztrlZMQofsvnKSClGjTWQAAAACG6dLCFIUHO7ViCzfYxthjRAICkMe2te5wk375/iHVtPXqulnp+tLCHEWFBplOAwAAADACESEuXV6Uqjd216lnYMh0DvwcIxIQYBo7+vTrNUf0xu46TUyK1D9eNFlzc+K5fhoAAADwUTfMyVBX/5De2lNvOgV+jhtrAwHC7bH10aHjWlXWqBCXQzfNzdTMjBjGIwAAAMDHnZMbr6z4cL24tVrXzc4wnQM/xogEBIDatl69vK1ate19KkqP0ZUz0xQZwv/8AQAAAH9gWZZumJOhn717UFUtPcqMDzedBD/F5WyAHxtye/Tuvno9uvqwOvqG9IWSLN1yThYDEgAAAOBnrpudLsuSXt5WYzoFfoxXkoAfsm1be2ra9dbeerV0D2h2VqwuK0pVeDD/kwcAAAD8UUZcuBZOTNCL26p03wX5cji4bQVGHyeRAD+zo6pNNz2+Qcs3VSrIaemORTm6YU4mAxIAAADg526Yk6Gqll6VHm0xnQI/xatKwE9Ut/boJ28f0Ks7apUYGaxri9M1OztOTv4GAgAAAAgIS6en6juhe/X85kotmJhgOgd+iBEJ8HGdfYN6dHW5nlh7VJakf0TVHnUAACAASURBVFiSr3vOn6jXdtSaTgMAAAAwjsKCnbpuVrqe3Vyl73YPKC4i2HQS/AwjEuCjhtwePbe5Sj9/96Cauwd07ax0ffOSAqXFhplOAwAAAGDILSVZ+uOGY3p5e43uWpxrOgd+hhEJ8DFuj60399TpF+8d0qHGLp2TE6/f3zFVMzJiTacBAAAAMGxKSrSKM2P13KZK3bkoR5bF7S0wehiRAB8xMOTRK9ur9asPj+hoU7fykiL0q1vn6JLpE/g/BgAAAAB/seycLH3rpV3aeqxVc3PiTefAjzAiAV6uZ2BIz26q0m/WHFF9R5+K0mP02Bdm6+LpKdw0GwAAAAgAy0srR/T4gSGPQlwOPfTGft04N/OUj1tWknW2aQgwjEiAl2rvGdQfN1To9+uOqrVnUCW58frxDTN07qRETh4BAAAAOKVgl0MzM2O17VirrpiRprBgp+kk+AlGJMDLNHb06Ym1R/X0xmPqHnDrwinJunfJRM3J5hgqAAAAgOE5Jydem462aEdVqxZMTDSdAz/BiAR4Adu2VXq0Rc+UVuqtPXVye2xdOTNNf3/+RE1JiTadBwAAAMDHpMWGKT02TJsrWjU/L4GrGTAqGJEAg9p7BvXStmo9U3pM5ce7FR3q0m3zc3T7wmxlJ0SYzgMAAADgw87JidcrO2pU1dqrrPhw0znwA4xIwDizbVs7qtr0TGmlXt9Zq/4hj2ZlxeqnN87UFTNSFRrE9coAAAAAzt6MjBi9sbtOmytaGJEwKhiRgHHS1T+kV3fU6JmNldpX16GIYKdumJOhZSVZmp4WYzoPAAAAgJ8JCXJqZmaMdlS16fIi/sIaZ48RCRhD/UNurT5wXK/tqNV7+xvUP+TRtNRoPXRtoa4uTldkiEvLSyu1s6rddCoAAAAAPzQvJ16bK1q1o6pN8/MSTOfAxzEiAcOwvLRy2I/12LaOHO/Wzuo27a1tV9+gRxHBTs3KitWszDhlxIXJkqXXdtSOYTEAAAAASOmxYUqNCdXmihaV5MZzg22cFUYkYBTYtq3q1l7trG7T7up2dfYPKcTl0PS0aM3IiNXEpEg5HfywBgAAADC+LMvSvJx4vbazVjVtvcqI495IOHMjGpEsy8qQ9ANJSyUlSKqT9CdJ37dtu3WYz7Fa0uc+4yFhtm33jaQLMMG2bdW192l3Tbt217SrpXtALoelgpQozcyIVUFKlIKcDtOZAAAAAAJccWas3txz4gbbjEg4G8MekSzLmihpvaRkSa9KKpN0jqQHJC21LGuRbdvNI/je3z/Frw+N4DmAcfXx4WhPTbuauwfksKS8pEgtKUjW9LRoblYHAAAAwKuEBjk1IyNWO6vadVlhqkJ4zYIzNJKTSI/qxIB0v23bD//vL1qW9TNJX5f0kKR7hvtktm1/bwTfGxiWkdy7aLj+dzjac/LE0ceHo/MmJWlaWrQiQrgyFAAAAID3mpcTr63HWrWrul3zcuNN58BHDeuVr2VZeZIullQh6ZFPfPq7ku6WdJtlWQ/att09qoWAIa3dA9p8rEW7qz82HCUyHAEAAADwPZlxYUqJDtWmihZGJJyx4b4KvuDkx3ds2/Z8/BO2bXdalrVOJ0am+ZJWDecJLcu6WVKupAFJ+yW9b9t2/zB7gDFh27aONHVrQ3mz9td1SJLykiJ07snhKJLhCAAAAIAPOnGD7Ti9vqtOtW29SosNM50EHzTcV8QFJz8ePMXnD+nEiDRZwxyRJD33iX9utCzra7ZtvzjMrwdGzcCQRzur2rT+SJMaOvoVHuzUeZOTVJIbr9jwYNN5AAAAAHDWijPj9Oaeem2uaNHVxemmc+CDhjsixZz82H6Kz//vr8cO47lelfRTSdslNUvKlnS7pAclPW9Z1hW2bb/5aV9oWdbdOnHpnLKysoZXDnyG1p4BlR5p1uaKVvUOupUaE6rrZqVrZmYs76wGAAAAwK+EBTtVlB6jHVVturQw1XQOfNBoXZtjnfxon+6Btm3//BO/dEDSv1qWVSvpYUk/kvSpI5Jt27+W9GtJmjt37mm/F3Aqx5q7tfZwk/bVdsiypGmp0VowMVE5CeGyLOv0TwAAAAAAPuic3Hhtr2rTruo20ynwQcMdkf73pFHMKT4f/YnHnYnfSvq5pGLLsqJs2+48i+cCPtXAkEdv763XhiPNXLIGAAAAIOBkxYcrOSpEmytaTKfABw13RDpw8uPkU3x+0smPp7pn0mnZtt1nWVanpDhJEZIYkTCqqlp6tGJrtZq6+rVwYoIunpaiYBeXrAEAAAAIHCdusB2vN3bXaU9NuwrTT3VWBPhbw30F/cHJjxdblvVXX2NZVpSkRZJ6JW080xDLsgp0YkDqlNR0ps8DfJLbY+u9/Q16fE25Bt0e3bU4V1fMSGNAAgAAABCQZmfFKchp6emNx0ynwMcM61W0bdvlkt6RlCPpa5/49Pd14uTQk7Ztd//vL1qWNcWyrCkff6BlWXmWZf3NLeAty0qU9PuT//icbdtDw/4dAJ+hsaNPv/qwXO+XNWpmRqzuv2CSJiZFms4CAAAAAGPCgp0qzozVn3bUqL1n0HQOfMhIbqx9r6T1kn5pWdaFkvZLKpG0RCcuY/v2Jx6//+THj9+l+DxJv7Us60NJ5ZJaJGVJukwn7re0RdK3Rvh7AP6Gx7a1obxZb++tV7DLoWXnZHFMEwAAAABOmp+XoM0VrVqxtUpfPjfPdA58xLBHJNu2yy3LmivpB5KW6sTwUyfpl5K+b9v2cO7KtVXS05LmSCrWiRtyd0raLekFSY/btj0wot8B8AltPQN6cWu1jjR1a0pKlK6dla6o0CDTWQAAAADgNVJjwjQvJ05PbTymOxflyuHgXapxeiM5iSTbtqsk3THMx/7Nn0DbtndL+tJIvicwEntq2vXStmrZkq6bla452XGyLH4YAgAAAMAn3bYgR/c/u10fHjquJQXJpnPgA7izMPzG+2UNem5zpZKjQnT/BZM0NyeeAQkAAAAATmHp9BQlRoboqQ3cYBvDw4gEv7DpaIv+/ultSo0J0x2LchUfEWw6CQAAAAC8WrDLoWUlWfrgQKMqm3tM58AHMCLB5+2padddf9is9Lgw3b4wR6FBTtNJAAAAAOATlp2TJYdl6elSTiPh9BiR4NOONnXrS7/fpKhQl56+q0SRISO6zRcAAAAABLSUmFBdMn2CXthSpb5Bt+kceDlGJPisuvZe3frbUnls6akvlygtNsx0EgAAAAD4nC8uyFFbz6Be21lrOgVejhEJPqmle0C3PbFJ7b2DevLOczQxKdJ0EgAAAAD4pJLceE2eEKknN1TItm3TOfBijEjwOV39Q7rj95tU2dKj394+V4XpMaaTAAAAAMBnWZal2xbkaE9Nh7ZXtZnOgRdjRIJP6Rt06+4nt2hPbYceWTZb8/MSTCcBAAAAgM+7dla6IkNcemoDN9jGqTEiwWcMuT164LntWl/erJ/cMEOfnzbBdBIAAAAA+IXIEJdumJOhN3bVqamr33QOvBQjEnyCbdv611d26+29DfrOFdN03ewM00kAAAAA4FdunZ+tAbdHz2+uMp0CL8WIBJ/wm4+O6IUt1br/wkm6c3Gu6RwAAAAA8Dv5yZFalJ+gZzYe05DbYzoHXogRCV7vYEOnfvr2QV08bYK+ftEk0zkAAAAA4Ldum5+j2vY+rSprNJ0CL8SIBK826PbowRd2KjLUpR9dVyTLskwnAQAAAIDfumhqstJiQvXkhgrTKfBCjEjwao+tLtfumnY9dE2hEiNDTOcAAAAAgF9zOR36wvxsrTvcrMONXaZz4GVcpgMQmJaXVp72MbVtvXp09WHNzIhRa8/gsL4GAAAAAHB2bp6XqV+8d0hPbzym71013XQOvAgnkeCVhtwerdhapYgQl66cmWY6BwAAAAACRmJkiC6fkaoXt1arq3/IdA68CCMSvNKqskY1dPTrulnpCg/mwBwAAAAAjKfbFmSrq39Ir2yvMZ0CL8KIBK9T2dKjNQePa252nApSok3nAAAAAEDAmZUZq6L0GD25vkK2bZvOgZdgRIJXGRjy6MWtVYoJC9JlRammcwAAAAAgIFmWpS8tzNGhxi6tOdRkOgdeghEJXuXdffVq6hrQ9XMyFBrkNJ0DAAAAAAHryplpSo4K0RNrj5pOgZdgRILXOHK8S+vKmzU/L0ETkyJN5wAAAABAQAt2OfTFBdlac/C4DjZ0ms6BF2BEglfoH3TrpW3VSogI1tLpKaZzAAAAAACSlpVkK8Tl0O84jQQxIsFLvLmnXm09g7phToaCXfyxBAAAAABvEB8RrOtmZ+jl7TVq7uo3nQPDeLUO4w42dGpTRYsWT0pUdkKE6RwAAAAAwMfctThHA0MePVNaaToFhjEiwai+Qbde3lat5KgQXTR1gukcAAAAAMAn5CdH6fyCJD254Zj6h9ymc2AQIxKMWn3guDr6hnT97AwFOfnjCAAAAADe6K7FuWrq6tdrO2pNp8AgXrXDmOaufq0rb9LsrFhlxoebzgEAAAAAnMLi/EQVTIjSE2uPyrZt0zkwhBEJxry1t15Oy9LF03g3NgAAAADwZpZl6c7FOSqr79SG8mbTOTCEEQlGHDnepb21HfpcQZKiw4JM5wAAAAAATuPq4nQlRATribVHTafAEEYkjDu3x9Ybu+sUGx6kxfmJpnMAAAAAAMMQGuTUrfOztaqsUUeOd5nOgQEu0wEIPC9sqVJde5/+bl4mN9MGAAAAAEOWl1aO+GvCg51yOiz9y8u7dXVx+ikft6wk62zS4KV4BY9x1dE3qJ++fUA5CeEqSo8xnQMAAAAAGIGo0CAVZ8RqW2WregaGTOdgnDEiYVz9z/uH1dIzoMuL0mRZlukcAAAAAMAILcpP1KDb1uajLaZTMM4YkTBujjZ16/frjuqG2RlKjwsznQMAAAAAOAMpMaHKT4rUhiPNcnts0zkYR4xIGDc/WrlfwU6Hvrm0wHQKAAAAAOAsLMpPUEffkHbXtJtOwThiRMK4WHe4Se/ua9DXLshXclSo6RwAAAAAwFmYNCFKiZEhWne4SbbNaaRAwYiEMTfk9ugHr+9TZnyY7lyUazoHAAAAAHCWHJalRfkJqmnrVUVzj+kcjBNGJIy55zZX6UBDp/710qkKDXKazgEAAAAAjIJZmXEKC3Jq3eEm0ykYJ4xIGFPtvYP62bsHVZIbr6WFKaZzAAAAAACjJNjlUEluvPbXdai5q990DsYBIxLG1C9XHVJrz4D+/YppsizLdA4AAAAAYBTNz0uQw7K0rrzZdArGASMSxsyR41364/oK3Tw3U4XpMaZzAAAAAACjLDosSMWZsdpS0aKu/iHTORhjjEgYMz9auV+hQU49eHGB6RQAAAAAwBg5b3KS3B5b67k3kt9jRMKYWF/epPf2N+reJROVFBViOgcAAAAAMEaSokI0LS1aG482q2/QbToHY4gRCaPO47H1HyvLlBYTqjsX5ZrOAQAAAACMsfMnJ6tv0KPSoy2mUzCGGJEw6l7fVavdNe36xiUFCg1yms4BAAAAAIyx9LgwTUqO1LrDTRp0e0znYIy4TAfAey0vrRzx1wy6Pfr5eweVFhOqngH3GT0HAAAAAMD3fG5ykn679qi2HmvV7QtzTOdgDHASCaNq45FmtfUMamlhqhyWZToHAAAAADBOchMjlBkXpo8OHdcQp5H8EiMSRk3PwJA+ONCoyRMilZ8caToHAAAAADCOLMvS+QXJau0Z1Ou7ak3nYAwwImHUfFDWqP5Bj5YWpppOAQAAAAAYUJASpeSoED22ulwej206B6OMEQmjoqV7QBuPtGhOdpxSokNN5wAAAAAADHBYlj43OUkHG7q0qqzRdA5GGSMSRsXbe+vlcEgXTZ1gOgUAAAAAYNCMjFhlxIXp0dWHZducRvInjEg4a1UtPdpd067F+UmKDgsynQMAAAAAMMjpsPTV8/K0vbJNG4+0mM7BKGJEwlmxbVtv7qlTRIhL501KNJ0DAAAAAPACN87NVGJksB5dfdh0CkYRIxLOyv66TlU09+iiqckKCXKazgEAAAAAeIHQIKfuXJyrjw41aXd1u+kcjBJGJJwxt8fWW3vrlRQZornZ8aZzAAAAAABe5Nb52YoKcemxDzmN5C8YkXDGNle0qKmrX0sLU+R0WKZzAAAAAABeJDo0SLctyNabe+pVfrzLdA5GASMSzkj/oFuryhqVkxChKSlRpnMAAAAAAF7ozsW5CnY69PiH5aZTMAoYkXBG1hw6ru7+IV1amCLL4hQSAAAAAOBvJUaG6OZ5mXp5W41q23pN5+AsMSJhxNp7B7X2cJOK0mOUGR9uOgcAAAAA4MW+cm6ebEm/+eiI6RScJUYkjNi7++rl8UiXTE8xnQIAAAAA8HKZ8eG6emaanttUpeauftM5OAuMSBiRY83d2lbZpsWTEhUfEWw6BwAAAADgA+5dMlF9Q279mtNIPo0RCcPm9th6bWetYsKCtKQg2XQOAAAAAMBH5CdH6ZridP1xfYUaO/tM5+AMMSJh2DYdbVZde58uK0pVsIs/OgAAAACA4XvgwkkadNt69APeqc1XsQRgWLr6h/Tu/gblJ0WqMC3adA4AAAAAwMfkJEbohtkZWl5ayTu1+ShGJAzL23vqNThk64qZqbIsy3QOAAAAAMAH3XdhvmzZevj9w6ZTcAYYkXBalc3d2lrZqkX5CUqOCjWdAwAAAADwURlx4brlnCyt2FKlyuYe0zkYIUYkfCaPfeJm2tGhLi2Zws20AQAAAABn52tL8uV0WPrFqkOmUzBCjEj4TJuOtqj25M20Q1xO0zkAAAAAAB83ITpUX1yQrVe2V+twY5fpHIwAIxJOqat/SO/sq1deUoSK0mNM5wAAAAAA/MQ9n5uo0CCn/vu9g6ZTMAKMSDilt/fWa2DIo6tmpHEzbQAAAADAqEmIDNGdi3L151112l/XYToHw8SIhE+1rbJVW4+1alF+opKjuZk2AAAAAGB0feXcPEWFuvSzdzmN5CsYkfA33B5b33l1j6JDXbqggJtpAwAAAABGX0x4kL5ybp7e3degXdVtpnMwDC7TAfA+z26q1J6aDt08L1MhQdxMGwAAAAAwMstLK4f1uMgQl8KDnfrGip360sLc0z5+WUnW2abhLHASCX+lpXtAP3n7gBbkJWgGN9MGAAAAAIyh0CCnzpuUpIMNXapo6jadg9NgRMJf+fFbZeruH9L3r57OzbQBAAAAAGNufl6CIkNcend/g2zbNp2Dz8CIhL/YXNGi57dU6Y5FOZo8Icp0DgAAAAAgAAS7HDq/IElHm7pVfpzTSN6MEQmSpNbuAd3/7HZlxYfrgYsmm84BAAAAAASQc3LiFRMWpHf31XMayYsxIkG2besbK3aquWtAjyybrcgQ7rcOAAAAABg/LqdDSwqSVdXaq4MNnaZzcAqMSNATa49qVVmj/vWyKSrkZtoAAAAAAAPmZMcpPiJY7+5rkIfTSF6JESnA7ahq03+9VaaLp03Q7QtzTOcAAAAAAAKU02HpoqnJqm3v086qNtM5+BSMSAGsvXdQ9z27TclRofrJDTN5NzYAAAAAgFEzMmKVERemt/fWa2DIYzoHn8CIFKBs29a/vLxLdW19enjZLMWEB5lOAgAAAAAEOIdl6bLCVHX0Demjw8dN5+ATGJEC1NOllVq5u17fvKRAs7PiTOcAAAAAACBJykmMUGF6jNYcPK723kHTOfgYRqQAtLe2Xf/3z/t0fkGSvnJunukcAAAAAAD+ytLpKfLY0rv7Gkyn4GMYkQJMV/+Q7lu+XXHhQfp/N86Uw8F9kAAAAAAA3iU+IlgLJyZoe2Wratp6TefgJEakAGLbtv7tld2qaO7WL/9ulhIiQ0wnAQAAAADwqZYUJCss2KmVu+tk27bpHIgRKaCs2FKtP+2o1dcvmqySvATTOQAAAAAAnFJokFMXTZ2go03d2lfXYToHYkQKGAcbOvWd1/ZoUX6C7l2SbzoHAAAAAIDTmpcTr+SoEL25p15DHo/pnIDHiBQAmrr6de8z2xQZ4tLPby6Wk/sgAQAAAAB8gNNh6bKiVLV0D2hjebPpnIDHiOTnatt6ddPjG1Td2qOHb5mt5KhQ00kAAAAAAAzb5AlRmjwhUu8faFRL94DpnIDGiOTHjhzv0o2/2qDjHf166q4SLZjIfZAAAAAAAL7n0sJUDQx59Iv3DppOCWiMSH5qX22Hbnp8g/oG3Xr27vmalxNvOgkAAAAAgDMyITpU83Li9XRppQ43dpnOCViMSH5oS0WLbv71BgU7HXrhngUqTI8xnQQAAAAAwFm5cOoEhQc59R8r95tOCViMSH5mzcHjuu2JTUqMDNGKv1+oiUmRppMAAAAAADhrkSEu/cMF+VpV1qi1h5pM5wQkRiQ/snJ3ne7642blJEboha8uUHpsmOkkAAAAAABGze0Lc5QZH6YfvrFPbo9tOifgMCL5iRc2V+kflm/TjIxYPXf3fCVFhZhOAgAAAABgVIUGOfXPS6eqrL5TL2ypMp0TcFymA3D27n1mm1burtOk5EhdOSNNb+yqM50EAAAAAMCYuKwoRfNy4vTjt8q0dHqK4iKCTScFDE4i+bCOvkF977W9Wrm7TtPTonXb/GwFu/hXCgAAAADwX5Zl6QdXF6qjb0j/9VaZ6ZyAwkkkHzTo9mh5aaV+seqQWroHND8vXpcXpcnpsEynAQAAAAAw5qamRuvORTn6zUdHdePcDM3JjjedFBA4tuJDbNvW23vrdfHP1+i7r+1VwYQo/fm+xbpqZjoDEgAAAAAgoPzjRZOVGhOqb7+yR0Nuj+mcgMCI5CN2VLXppsc36KtPbZXTYel3X5qr5V8pUWF6jOk0AAAAAADGXUSIS9+9cprK6jv1h/UVpnMCApezebmqlh79+O0Den1nrRIjg/XQtYW6eW6mXE72PwAAAABAYLtkeoqWFCTp5+8e1OUzUpUaE2Y6ya+xRHipuvZe/Wjlfl34/z7Uu/vqdd8F+Vr9zSX6Qkk2AxIAAAAAADpxk+3vX1WoIY+tH7y+z3SO3+MkkpewbVsHGjr1zt4GvbuvQbtr2mVZ0g2zM/TgxQVKiQk1nQgAAAAAgNfJSgjXfRfk66fvHNQHBxq1pCDZdJLfYkT6FMtLK8fkeZeVZP3VPw+5PdpyrFXv7mvQO/vqVdXSK8uSZmXG6p+WTtHSwhTlJkaMSQsAAAAAAP7iK+fl6eXtNfruq3u14OsJCg1ymk7yS4xI46y7f0hrDzfp3X0NWrW/Qa09gwp2ObRoYoLuPT9fF05NVnIUp44AAAAAABiuEJdTP7y6UMt+W6pHPjisBy8uMJ3klxiRxkFzV78ONHTqzT11Kj3SogG3RzFhQbpgSrI+P22CzpucpMgQ/lUAAAAAAHCmFuYn6priND3+4RFdMytdE5MiTSf5HZaLMeD22Kpo7taB+k6V1XeqqatfkpSXFKHbF2ZryZRkzcuJVxA3yAYAAAAAYNR8+/JpWlXWqO+8ukdP31Uiy7JMJ/kVRqRR4rFt7a3t0O7qNh1q7FL/kEdOh6W8xAjNz4tXwYQo3XfhJNOZAAAAAAD4raSoEH3rkgL9+6t79drOWl1dnG46ya8wIo2Cw41demtPnWrb+xQV6lJReoympERpYnKkQlzczAsAAAAAgPGyrCRbK7ZW64dv7NeSKcmKDg0yneQ3GJHOQn1Hn97aU6eDDV2KDQ/STXMzNCMjVo5THJcbq3d9AwAAAAAgEAz3dfXi/EQ9trpcdz+5VVfNTDvt4z/5bur4dIxIZ6C9d1Dv7W/QtmOtCgly6NLCFM3PS+AeRwAAAAAAeIGMuHCV5MWr9EizZmfFKiMu3HSSX2BEGoH+QbfWHDqutYeb5PFICycmaElBssJ5ZzUAAAAAALzKxdNStLe2Qy9vq9G9SybK5eDgx9li/RgGt8fW5ooWrdrfoO4Bt2ZkxOjiaSmKjwg2nQYAAAAAAD5FaJBT1xSn66mNx7T6wHFdNHWC6SSfx4h0GgNDHj25oUJHmrqVkxChLxamKDOeY3AAAAAAAHi7qanRKs6M1eoDjZqWGq202DDTST6Ns1yfYdDt0dOlx3S0qVvXzUrXV87NZUACAAAAAMCHXDEjVeHBLr20rVpuj206x6f57Emklu6BMX23syG3R8tLK1Xe2KXrZ2dodnbcmH0vAAAAAAAwNsKDXbqmOE1Pl1bqw4ONumAKl7WdKU4ifYohj0fPbqrUgYZOXVOczoAEAAAAAIAPm5YWoxkZMfqg7Ljq2/tM5/gsRqRPcHtsPb+5SvvrO3XlzDTNy403nQQAAAAAAM7SlTPSFBrs1Ivbqris7QwxIn2Mx7a1YmuV9tZ26LKiVC3ISzCdBAAAAAAARkFEiEtXz0xTbVufPjp03HSOT2JEOslj23ppa7V2Vbdr6fQULc5PNJ0EAAAAAABGUWF6jArTY7SqrFENHVzWNlKMSDoxIP1pe422V7XpoqkTdN7kJNNJAAAAAABgDFw1M00hLgfv1nYGAn5Esm1br++s1ZZjrVpSkKwLpiSbTgIAAAAAAGMkMsSlq2amqbq1V+sON5nO8SkBPSLZtq03dtep9GiLzpuUqIumMiABAAAAAODvitJjND0tWu/tb1BjJ5e1DVdAj0gbjjRrfXmzFk1M0CXTU2RZlukkAAAAAAAwxizL0lUz0xTkdOilrVzWNlwBOyI1d/Xr7b31mjwhUpcVpTIgAQAAAAAQQKJCg3TlzDRVtfbqd2uPms7xCQE5InlsWy9vr5HDsnTtrAwGJAAAAAAAAtDMjBhNTYnST985oEMNnaZzvF5AjkibK1p0tKlblxWmKiYsyHQOAAAAAAAwwLIsXTMrXZEhLt337Hb1DbpNJ3m1gBuRWnsG9Oaeek1MitDcnDjTOQAAAAAAwKCo0CD95MYZKqvv1H++WWY6x6sF1Ihk27b+tL1GsqXruIwNAAAAAABIumDKO8P/WQAAExJJREFUBH1pYY7+sL5C75c1mM7xWgE1Im091qpDjV26pDBFcRHBpnMAAAAAAICX+OdLp2hqarS+sWKXGjv6TOd4pYAZkdp7B7VyT51yEiJUkhtvOgcAAAA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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['danceability'], \"Danceability\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Danceability**: Describes how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['energy'], \"Energy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Energy**: A perceptual measure of intensity and activity. Typically, energetic tracks feel fast, loud, and noisy. For example, death metal has high energy, while a Bach prelude scores low on the scale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['instrumentalness'], \"Instrumentalness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Instrumentalness**: Predicts whether a track contains no vocals. “Ooh” and “aah” sounds are treated as instrumental in this context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['liveness'], \"Liveness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Liveness**: Detects the presence of an audience in the recording. Higher liveness values represent an increased probability that the track was performed live. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['loudness'], \"Loudness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Loudness**: The overall loudness of a track in decibels (dB). Loudness values are averaged across the entire track. Values typical range between -60 and 0 db."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAACAOWERAAAAAHPCIgAAAADmhEUAAAAAzAmLAAAAAJgTFgEAAAAwJywCAAAAYE5YBAAAAMCcsAgAAACAOWERAAAAAHPCIgAAAADmhEUAAAAAzAmLAAAAAJgTFgEAAAAwJywCAAAAYE5YBAAAAMCcsAgAAACAOWERAAAAAHPCIgAAAADmhEUAAAAAzAmLAAAAAJgTFgEAAAAwJywCAAAAYE5YBAAAAMCcsAgAAACAOWERAAAAAHPCIgAAAADmhEUAAAAAzAmLAAAAAJgTFgEAAAAwJywCAAAAYE5YBAAAAMCcsAgAAACAOWERAAAAAHODCotqrRd9CQAAAAD3aoMKi77i+a/Od//SGy/6MgAAAADutSYXfQHn5d13b+W333x7FBcBAAAAHG4wlUW/+6d3ZlqTja2di74UAAAAgHutwYRFr7z1ziTJ5vb0gq8EAAAA4N5rMGHRTbfekURlEQAAAMBRBhEW3f7ezbz5L9+XRGURAAAAwFEGERbNqoo+4qH3y6bKIgAAAIBDDSIseuWtd+Z+65P87Q95YDZUFgEAAAAcahBh0U233pnHf+iDc9/VsZlFAAAAAEfofVj0Z3fdnbffdXeeeMODs7YyMrMIAAAA4Ai9D4teeeudSZInPfq6rE/G2ZnWbO0IjAAAAAAO0vuw6KZb78h1H7CaD/vAD8j6yjhJtKIBAAAAHKLXYVGtNa+49c484YbrUkrJ2krzcbWiAQAAABys12HRrbe/L7e/dzNPuuHBSZL1icoiAAAAgKP0Oiy6qZ1X9MQbrkuSeWXRxpbKIgAAAICD9DosesUtd+SDH3BNHvGga5Ika21l0ea2yiIAAACAg/Q2LNqZ1vzuW+7KE294cEopSZJ1lUUAAAAAR+ptWPRHf/GevPuerTzp0dfNn5vthrZpZhEAAADAgXobFt106x1Jkie0w62TZG1iNzQAAACAo/Q2LHrFLXfmhofcNw+9dn3+3KyyyG5oAAAAAAfrZVh0aXuaV7/1rsta0JI9YZEB1wAAAAAH6mVY9Prb3pW7L+3kiXta0JI9bWgGXAMAAAAcqJdh0StuuTOlJI971OVhkTY0AAAAgKP1Miy66dY78lEfdG0eeN/Vy55fX2k+7oYB1wAAAAAH6l1YdM+lnfzB2991xbyiJFmbNJVF2tAAAAAADta7sOjmt92VSzvTPGHfvKIkGY9KVsbFgGsAAACAQ/QuLLrp1jszGZV80vUPOvD19cnYzCIAAACAQ/QyLPr4Rzwg912bHPj62soom2YWAQAAAByoV2HReza28j9ue1eeeEAL2syayiIAAACAQ/UqLHrVW+7KtCZPuOHK4dYz6ysjA64BAAAADtGrsOimW+/I2mSUT3jkAw49Zm0yzqYB1wAAAAAH6lVY9Mpb78xjr39Q1lfGhx6zvjLKhsoiAAAAgAP1Jiy6432bedM735snHDGvKEnWV1QWAQAAABymN2HRK2+9M0mOHG6dJGsTlUUAAAAAh+lNWHTTrXfmfmuTfOwH3//I49ZX7IYGAAAAcJjehEWvedtf5xOvf2Am46M/UtOGprIIAAAA4CC9CYvu3trOg+6zetXjmjY0lUUAAAAAB+lNWLSzUzMelasepw0NAAAA4HC9CYu2pzWT8dXDorWVkTY0AAAAgEP0JizamR6vsmht0swsqrWew1UBAAAAdEtvwqLtac1kdPWPs77SHKO6CAAAAOBKvQmLjltZtD4ZJ0k2t4RFAAAAAPv1Jizank4zOU4bWltZtLFtyDUAAADAfr0Ji05aWWRHNAAAAIAr9SYs2j5uWLTStqGZWQQAAABwhV6ERdNpTa055m5obRuayiIAAACAK/QiLNqe1iQ51syiWWXRhgHXAAAAAFfoRVi004ZF49HVP856O+B604BrAAAAgCv0IizanjZVQsfaDW2isggAAADgML0Ii3Yri47ThmZmEQAAAMBhehEWzWcWjY9fWWQ3NAAAAIAr9SIsUlkEAAAAsBi9CItOshva2nw3NGERAAAAwH69CIt2do6/G9raZLYbmjY0AAAAgP16ERadbDe0UUpJNlUWAQAAAFyhF2HRSWYWlVKyNhllQ2URAAAAwBV6ERadZGZR0uyIprIIAAAA4Eq9CItOUlmUNDuibWypLAIAAADYrxdh0byyaHzcsGicjW2VRQAAAAD79SIs2mkHXB9nN7SkGXK9qbIIAAAA4Aq9CIu2d042s0hlEQAAAMDBehEW7dQTziyajLNhwDUAAADAFfoRFp10N7SVUTa3taEBAAAA7NeLsGj7hLuhrU3GdkMDAAAAOEAvwqKd+cyi432c9ZVRNrWhAQAAAFyhF2HRaSqLtKEBAAAAXKkXYdF8ZtH4uLuhjQy4BgAAADhAL8Ki7WlTJXTs3dBW7IYGAAAAcJBehEUn3g1tYjc0AAAAgIP0Iiw66cyi9ZVxtqc12zsCIwAAAIC9ehEW7VYWHX83tCSqiwAAAAD26UVYdJrd0JKYWwQAAACwz7HColLKg0spX1lK+S+llFtKKfeUUt5dSvmdUsqzSikHrlNKeWIp5cWllLtKKXeXUl5fSnlOKWW8yA+x07aTHXdm0ayyaENlEQAAAMBlJsc87guT/GiSv0jyW0nenuShST4/yXOTfFYp5QtrrXV2Qinlc5L8QpKNJD+b5K4k/yDJDyV5UrvmQswri8bHn1mUJJsqiwAAAAAuc9yw6M1JPjvJL9da5+U4pZRvS/J7Sb4gTXD0C+3z1yb5iSQ7SZ5ca725ff47k7wkyTNKKf+o1vozi/gQp9kNLUk2tlQWAQAAAOx1rDa0WutLaq3/bW9Q1D7/ziT/vn345D0vPSPJQ5L8zCwoao/fSPId7cOvOe1F73fimUVtZdHGtsoiAAAAgL0WMeB6q73d3vPcU9vbXz3g+N9OcneSJ5ZS1hbw/iffDW0ya0NTWQQAAACw15nColLKJMmXtQ/3BkMf0d6+ef85tdbtJH+apgXuQ8/y/jOzyqJjFhZlbT7gWmURAAAAwF5nrSz6v5J8TJIX11p/bc/z929v333IebPnH3DQi6WUry6l3FxKufn222+/6kXsTKeZjEpKOeaA64kB1wAAAAAHOXVYVEp5dpJvTPKmJF960tPb23rQi7XWH6+1PqbW+piHPOQhV11se1qPPa8oSdbbyqLNbW1oAAAAAHudKiwqpXxdkh9O8sYkT6m13rXvkFnl0P1zsGv3HXcmOzsnC4vmA65VFgEAAABc5sRhUSnlOUl+JMkfpgmK3nnAYX/c3n74AedPkjwqzUDst5z0/Q9y4sqiSTuzyIBrAAAAgMucKCwqpXxrkh9K8to0QdFfHXLoS9rbv3PAa5+a5D5Jbqq1bp7k/Q+zM62ZnKgNrZ1ZZMA1AAAAwGWOHRaVUr4zzUDr30/ytFrrHUcc/vNJ7kjyj0opj9mzxnqS72kf/ujJL/dgTWXR8XOvNZVFAAAAAAeaHOegUsqXJ/mXSXaSvDzJsw/YeeyttdbnJ0mt9T2llK9KExq9tJTyM0nuSvLZST6iff5nF/EBkt3d0I5rMh5lMipmFgEAAADsc6ywKM2MoSQZJ3nOIce8LMnzZw9qrS8qpXxakm9P8gVJ1pPckuSfJfk3tdYDd0I7jZPOLEqaVjS7oQEAAABc7lhhUa31xiQ3nnTxWusrkvzdk553UjvTmsn4ZGHR2mSksggAAABgnxPvhnZvdNrKIjOLAAAAAC7Xi7BoZ+dku6ElydrKyG5oAAAAAPv0Iiw66W5oSbI2UVkEAAAAsF8vwqKT7oaWJOsqiwAAAACu0Iuw6FQziybjbKosAgAAALhML8KinenpZhZtqCwCAAAAuEwvwqLTVhZtbAmLAAAAAPbqRVi0M62ZjE8zs0gbGgAAAMBevQiLTr8bmsoiAAAAgL16ERZNTzGzaH1llA0DrgEAAAAu04uw6FQzi1bG2TTgGgAAAOAyvQiLdqbTk++GNmkqi2qtS7oqAAAAgO7pRVh0msqitZVxkhhyDQAAALBHL8KinVPNLBIWAQAAAOzXi7Boe+c0u6E1x2/aEQ0AAABgrhdh0Vkqi+yIBgAAALCrF2HR9rRmPD5pWNRWFtkRDQAAAGCuF2HR6XZDU1kEAAAAsF8vwqLT7IY2qyzaUFkEAAAAMNeLsOhMu6GpLAIAAACY60VY1FQWnW43tA27oQEAAADM9SIsOlNl0bbKIgAAAICZzodFtdbsnGJmkcoiAAAAgCt1PizamdYkOXVlkQHXAAAAALs6HxZtt2HReHzCsGhiwDUAAADAfp0Pi05bWbS20rahqSwCAAAAmOt8WDSvLDr1bmgqiwAAAABmOh8WnbayqJSStckomyqLAAAAAOY6HxZtT5vKoJPuhpY01UVmFgEAAADs6nxYtDNvQzt5WLS+Ms7GlsoiAAAAgJnOh0XbO2cLiza3VRYBAAAAzHQ+LDrtzKKkaUNTWQQAAACwq/Nh0bY2NAAAAICF6XxYtFtZdPKPsr4y0oYGAAAAsEfnw6Kz7YamsggAAABgr86HRWeZWbS+MsrGlsoiAAAAgJnOh0XzmUXjU1QWrYyzua2yCAAAAGCm82HR2XdDU1kEAAAAMNP5sGh752y7oaksAgAAANjV+bDoTLuhTcbZVFkEAAAAMNf5sOhMu6GtjLKhsggAAABgrvNh0Zl2Q5uMs7VT52sAAAAADN3koi/gau56/6W88FVvP/T1N77j3UmSX3/jX+YN73jPsdf9ksc9MusrTVa2ub2T+6ze678KAAAAgKXrfmVRWxQ0KqfbDS2JHdEAAAAAWp0Pi6a1SYtO0YWW9ZVxkmRjy9wiAAAAgKQHYVGdhUWnmVnUhkWb2yqLAAAAAJIehEXtZmhnbENTWQQAAACQ9CEs0oYGAAAAsDCdD4t2ztCGtjbfDU0bGgAAAEDSg7Boeqbd0FQWAQAAAOzV/bCoTYvGpwiL1ldmM4tUFgEAAAAkfQiLFjCzaHNbZREAAABA0ouwqLk91cyidje0TZVFAAAAAEl6ERbNKotO04bWzixSWQQAAACQpA9hUVtadIqsaLcNTWURAAAAQJI+hEW1puS0u6HNBlyrLAIAAABIehEWnW5eUZKsjEcZj4o2NAAAAIBW98OiaT3VTmgz65ORNjQAAACAVvfDolpP1YI2s7YyVlkEAAAA0Op8WLRTTzevaEZlEQAAAMCuzodF01pPPbMoaXZE29gWFgEAAAAkfQiLpjXjM8wsWp2M7IYGAAAA0Op+WHTWNrSVcTZVFgEAAAAk6UVYdNY2NJVFAAAAADP9CIvO0Ia2NhlnU1gEAAAAkKQPYdG0ppypDW2kDQ0AAACg1f2wqCbjM84s0oYGAAAA0OhBWHTWNrRRNrZUFgEAAAAkfQmLzjTgepzNbZVFAAAAAEkfwqJpMjpzG5rKIgAAAICkB2HRziLa0LZ3Umtd3EUBAAAAdFTnw6JmZtHZKotqTbZ2hEUAAAAA3Q+LpmebWbQ2ab6CDXOLAAAAAHoQFtWcrQ1tZZwk2dgSFgEAAAD0ICw6YxtaW1m0acg1AAAAgLBova0s2tSGBgAAANCDsGiaxcwsUlkEAAAA0IOwqNYzzSxSWQQAAACwqxdh0XgBbWgqiwAAAAB6ERblTDOLdtvQVBYBAAAA9CAsqhmd4VPstqGpLAIAAADoflg0PdtuaCqLAAAAAHZ1Pyw6YxuamUUAAAAAu3oQFp11N7TmK7AbGgAAAEBfwqIzpEVrE5VFAAAAADPdD4umdkMDAAAAWJTuh0X1bAOuR6OS1cnIbmgAAAAA6XhYNK01NcnojJ9ibTJSWQQAAACQHoRFSTI+Q2VR0uyIZsA1AAAAQNfDorZz7CxtaEmzI9qmAdcAAAAAHQ+L2sqiM2yGlqTZEW1DZREAAABAT8KiM6ZF6yujbKgsAgAAAOh6WNTcnrkNbWJmEQAAAEDS9bBoOmtDO1tYtKayCAAAACBJ18OiBc0sWp+Ms7GlsggAAACg42FRc3v2mUXjbG6rLAIAAADodli0qDa0yUhlEQAAAEA6HhbtLKgNbU1lEQAAAECSjodFuzOLztqGprIIAAAAIOl8WNTcjs9YWrQ2GWfTbmgAAAAAHQ+LpgvaDW1llEs70/l6AAAAAEPV7bCobUMrZ25DG7F1yigAACAASURBVCeJuUUAAADA4HU8LGpuF7EbWhJziwAAAIDB63hY1LahnfFTqCwCAAAAaHQ7LGpLi8YL2A0tUVkEAAAA0O2waFZZdOY2tKayaGNbWAQAAAAMW8fDoub2rGHRrLJoc0sbGgAAADBsnQ6LdqYLmlk0qyzShgYAAAAMXKfDooW1oc1mFhlwDQAAAAxcx8Oi5nZRM4s2VRYBAAAAA9fxsGhWWXS2ddZXZgOuVRYBAAAAw9btsGg+s+islUVtG5rKIgAAAGDguh0WLWw3tLYNTWURAAAAMHAdD4sW1YbWfA1mFgEAAABD14uwaLygAdfa0AAAAICh63hY1NyedWbRyrhkVLShAQAAAHQ7LJoNuD5jZVEpJesrY5VFAAAAwOB1Oyxa0MyipNkRbWNLZREAAAAwbP0IixaQFq2vjLO5rbIIAAAAGLaOh0XN7QIKi9o2NJVFAAAAwLB1Oyya1oxKM3PorJo2NJVFAAAAwLB1Oyyq9czDrWfWVsZ2QwMAAAAGr+Nh0WLmFSXJusoiAAAAgG6HRTu1LmQntKSpLNpQWQQAAAAMXKfDomZm0eIqizZVFgEAAAAD1+2wqCbjRYVFZhYBAAAAdD0sqgubWWQ3NAAAAICuh0XTxc0sUlkEAAAA0PWwqC5wZtGKyiIAAACAjodFWVhYtDYZZ2NrJ7XWhawHAAAA0EUdD4tqRgv6BOsro0xrsj0VFgEAAADD1e2waLrINrRxkmhFAwAAAAat22HRQtvQmq9iY8uQawAAAGC4Oh4WLW43tLW2smhzW2URAAAAMFydDot2as1oQWnRbhuayiIAAABguDodFk2ni29DU1kEAAAADFm3w6JaM174gGuVRQAAAMBwdT4sWlBWlPVZZZHd0AAAAIAB63xYtLA2tPmAa5VFAAAAwHB1OyyaZoEDrpuvYkNlEQAAADBg3Q6Las2CsqKsTdqZRQZcAwAAAAPWg7BosZVFmwZcAwAAAAPW8bAoGS+qDW1WWaQNDQAAABiwbodF0wW2oc1mFhlwDQAAAAxYt8OiRbahtZVF2tAAAACAIet0WLRTs7CwaDQqWR2PDLgGAAAABq3TYdF0WjNa4CdYm4zMLAIAAAAGrdth0QLb0JJkbWWcTTOLAAAAgAETFu2xvqKyCAAAABi2jodFi5tZlDRtaAZcAwAAAEPW7bBowTOL1lfG2TTgGgAAABiwzoZFtdbULLayaH1lnA2VRQAAAMCAdTYsmtbmdtFtaGYWAQAAAEPW4bCoSYvGi8uK2jY0lUUAAADAcHU+LBqN7IYGAAAAsCjdDYvaAqDFtqGNs2HANQAAADBg3Q2LZpVFC21DG2XTgGsAAABgwLofFi0wLVqbjLWhAQAAAIPW4bCouV1oG9rKKBsGXAMAAAAD1t2waDprQ1vggOvJOJe2p6lt1RIAAADA0HQ3LFrKzKJxkmRTdREAAAAwUJ0Ni3aWMrOo+TrMLQIAAACGqrNh0TJmFqksAgAAAIauu2FRmxaNF9qGprIIAAAAGLbuhkV18QOu1yZNZdHGlsoiAAAAYJg6HBY1t4ucWTSrLNrcVlkEAAAADFN3w6Lp4iuLZjOLVBYBAAAAQ9XdsGjehra4Ne2GBgAAAAxdh8Oi5tZuaAAAAACL0+GwqK0sWsLMIpVFAAAAwFB1NyyaLqMNTWURAAAAMGzdDYvq4gdcr6ksAgAAAAaus2HRzmxm0ULb0Ga7oQmLAAAAgGHqbFg0ryxa4Jqz3dC0oQEAAABD1d2waLr4Ader41FKSTZVFgEAAAAD1d2waNaGtsCZRaWUrE/G2VBZBAAAAAxUh8Oixe+GljRDrlUWAQAAAEPV/bBowWnR+mScjS2VRQAAAMAwdTcsms0sWmAbWpKsr4yysa2yCAAAABim7oZF7cyi8YLDorXJOJsqiwAAAICB6nBYtJyZRSqLAAAAgCHrblg0XdLMopVxNgy4BgAAAAaqs2HRTtuGtviZRePcow0NAAAAGKjOhkWzNrQFZ0W5ZmWcjUsqiwAAAIBh6nRYVLL4yqJrVse5RxsaAAAAMFDdDYumi59XlMza0IRFAAAAwDAdOywqpTyjlPJvSykvL6W8p5RSSykvOOTY69vXD/v7mbNeeK114TuhJdrQAAAAgGGbnODY70jyt5K8L8ltSf7mMc55XZIXHfD8H57gfQ80rXXhLWhJcs3qSGURAAAAMFgnCYu+IU1IdEuST0vyW8c457W11htPcV1XtVMXP68oaSqLtqc1WzvTrIw726UHAAAAcCrHDotqrfNwqCwhpDmpaa1Lm1mUJPds7QiLAAAAgME5SWXRaTy8lPK/JnlwkjuTvLLW+vpFLDyd1oyXMbNotQmLNrZ2cu36yuLfAAAAAOBebNlh0We0f3OllJcm+fJa69vPsvB0iW1oSbJxabrwtQEAAADu7ZbVZ3V3ku9O8olJHtj+zeYcPTnJb5ZS7nvYyaWUry6l3FxKufm977rrwGOW1YZ2zZ42NAAAAIChWUpYVGv9q1rrP6+1vqbW+q7277eTPD3Jq5I8OslXHnH+j9daH1Nrfcz9HvCgA49pdkNb/LWvrwqLAAAAgOE61wnOtdbtJM9tH37qWdaaTutS2tDWJ21YdElYBAAAAAzPRWz3dXt7e2gb2nEsbWbRngHXAAAAAENzEWHR49vbt5xlkWZm0QKuZh8ziwAAAIAhW0pYVEp5XCll9YDnn5rkG9qHLzjLezQzi5Y44FobGgAAADBAk+MeWEr53CSf2z58WHv7hFLK89v7d9Rav6m9/31JPrqU8tIkt7XPfVySp7b3v7PWetNpLzpJptPltKGtrzb5mcoiAAAAYIiOHRYl+fgkX77vuQ9t/5LkbUlmYdH/l+Tzkjw2yWclWUnyl0l+LsmP1FpfftoLntlZcmWRmUUAAADAEB07LKq13pjkxmMe+7wkzzvdJR3PtNaMR0uoLNKGBgAAAAzYRQy4XojptGa8hMqilfEoK+OiDQ0AAAAYpO6GRXU5M4uSprpIWAQAAAAMUYfDopoldKElaeYWbWxNl7M4AAAAwL1Yt8OiJaVF16yODbgGAAAABqm7YdF0eW1o16yMDbgGAAAABqm7YdES29DWzCwCAAAABqrjYdGyKotGwiIAAABgkDocFmV5M4tWzCwCAAAAhqm7YdF0ibuhrZpZBAAAAAxTZ8OinSW2oa2bWQQAAAAMVGfDouXOLNKGBgAAAAxTh8OiLK8NbUUbGgAAADBM3Q2LpnV5A65Xmza0WutS1gcAAAC4t+puWLTkmUXTmlzamS5lfQAAAIB7q06GRbXWtg1teTOLkmTjkrAIAAAAGJZuhkXt7WhJV3/NahsWbZtbBAAAAAxLJ8Oi6bSJi8ZLriwy5BoAAAAYmm6GRW1p0fJmFjVfyz1bwiIAAABgWDoaFjVp0ZI2Q8v6rLJIWAQAAAAMTDfDora0aLSktGh3wLWwCAAAABiWToZFO/PKoiWFRasqiwAAAIBh6mRYVJc8s+gabWgAAADAQHUyLDq3mUXa0AAAAICB6WhY1NwubWZR24a2obIIAAAAGJhuhkXTJc8s0oYGAAAADFQnw6Kdc2tDmy7nDQAAAADupToZFk2XvBvaeFSyOhmpLAIAAAAGp6NhUXM7XlZpUZpWNDOLAAAAgKHpZlg0XW4bWiIsAgAAAIapm2HRktvQkmR9RRsaAAAAMDwdDYua29ESS4vWV8a555KwCAAAABiWjoZFy68sumZ1rLIIAAAAGJxuhkVmFgEAAAAsRTfDovOoLFpRWQQAAAAMT0fDouZ2qTOLVs0sAgAAAIank2HRzrm1oU2X9wYAAAAA90KdDIu0oQEAAAAsR0fDouZ2vOzd0LShAQAAAAPT0bCorSxa5syitrKotu8FAAAAMATdDIvOaWZRkmxum1sEAAAADEc3w6LZbmhLnVnUfDVa0QAAAIAh6WhYdD5taEmysS0sAgAAAIaj22HRMtvQVpuwSGURAAAAMCTdDIvmM4uWX1l0z5awCAAAABiOboZF5zKzqG1DExYBAAAAA9LRsOg829DshgYAAAAMRyfDop1zGHB9jTY0AAAAYIA6GRZN22KfJRYWmVkEAAAADFI3w6JaMypJWebMorYNbcNuaAAAAMCAdDgsWmZdkTY0AAAAYJi6GRZN61LnFSXCIgAAAGCYuhkW1eXuhJYka5Pmq7lHGxoAAAAwIB0Ni5bfhjYalayvjLKhsggAAAAYkM6GReMlh0VJ04qmDQ0AAAAYkm6GRdMsfWZRkqyvjFUWAQAAAIPSzbCo1qXPLEpmlUXT5b8RAAAAwL1Eh8Oi86ksMuAaAAAAGJKOhkU5l7DomlVtaAAAAMCwdDQsqhmdw5UbcA0AAAAMTTfDoqk2NAAAAIBl6GZYpA0NAAAAYCk6Ghad125oI21oAAAAwKB0MizaqTWjc0iLzCwCAAAAhqaTYdF0ej5taOurZhYBAAAAw9LNsKjWjM9jZtHKOJvb00yndenvBQAAAHBv0NmwaHQOV37NyjhJsrGtuggAAAAYhu6GRefRhtaGRVrRAAAAgKHoZlh0TjOLdiuLpkt/LwAAAIB7g26GRbXmHDZDy/qqyiIAAABgWLobFp1DWjSvLNoSFgEAAADD0NGw6Hzb0O4RFgEAAAAD0c2waHo+A66vWW2+Hm1oAAAAwFB0Myw6r5lFKosAAACAgelkWLRTY2YRAAAAwBJ0Miw6vzY0u6EBAAAAw9LNsKjWjM+hDc2AawAAAGBoOhsWnUdlkZlFAAAAwNB0NCw6n5lFa5NRSkk2tKEBAAAAA9HNsGh6PruhlVKyPhmrLAIAAAAGo3Nh0bTW1ORc2tCSZsi1sAgAAAAYis6FRbU2t+W8wqKVcTa2pufyXgAAAAAXrXNh0bRNi85jN7QkWV8ZqSwCAAAABqN7YdG0CYvOY8B10rShGXANAAAADEX3wqK2De3cZhatmFkEAAAADEfnwqKdtg3tnAqLsi4sAgAAAAakc2HRbGbRubWhrYxzjzY0AAAAYCC6FxbNZhadVxva6jgbKosAAACAgeheWGRmEQAAAMDSdDAsuoCZRdrQAAAAgIHoXlg0PeeZRavjbGxNz+W9AAAAAC5a98Kic25DW5+Mc2lnmu0dgREAAADQfx0Mi5q0aHxObWjXrDZf0ca2sAgAAADov86GRec54DqJuUUAAADAIHQwLGpuz2tm0XobFm3YEQ0AAAAYgMlFX8BJzQdcn7Gy6IWvevuxjnv9be9Kkvz879+Wh167ftXjv+RxjzzTdQEAAABcpA5WFs3CovN5v9Vx8xVtGXANAAAADEAHw6Lm9rxmFq1MZmFRPZf3AwAAALhIHQyL2sqicyotWlFZBAAAAAxI98Ki6fm2oa2Mmze6tC0sAgAAAPqve2FRXcyA6+MyswgAAAAYks6FRbPRQeffhmZmEQAAANB/nQuLzns3NDOLAAAAgCHpXljUziwan9duaO3MImERAAAAMATdC4tmbWjnFBaNRyUlySVhEQAAADAAHQyL2ja0c+pDK6VkZTLKlt3QAAAAgAHoblh0TjOLkmZukQHXAAAAwBB0LyyazsKi80uLVsfFzCIAAABgELoXFp3zzKJkVlkkLAIAAAD6r4Nh0Wxm0fm9pzY0AAAAYCi6FxZdQBvaynhkNzQAAABgEDoXFu1cQBva6sTMIgAAAGAYOhcWXdxuaMIiAAAAoP86GRaNSlLOfcC1mUUAAABA/3UvLJqebwta0oZF2yqLAAAAgP7rXlhU6wWERcWAawAAAGAQuhkWnfNVm1kEAAAADEUnw6KS829Dm9ZkZ2puEQAAANBv3QuLpsnoPLdCS7I6bt5PdREAAADQd90Li2rN+HyzoqxMmq/J3CIAAACg7zoZFl3EbmhJsr2jDQ0AAADotw6GReffhjYLi1QWAQAAAH3XubBoZ1pzzlnR7syibWERAAAA0G+dC4susg3NgGsAAACg7zoYFkVYBAAAALAk3QuLpjWjc77q3d3QDLgGAAAA+q17YdEFtKGtqiwCAAAABkJYdAyT2YBrYREAAADQcx0Mi85/ZtG8sshuaAAAAEDPdTAsuoCZRWMziwAAAIBh6F5YNK0Zn3Nl0XhUMira0AAAAID+615YdAFtaElTXSQsAgAAAPqug2FRzej8s6KsCosAAACAAehmWHQBadHKZJQtM4sAAACAnuteWDS9qDa0orIIAAAA6L3uhUUX1IZmZhEAAAAwBB0Niy5mwPWlbW1oAAAAQL91MCzKhcwsMuAaAAAAGILuhUXTi6osMrMIAAAA6L/OhUU7ZhYBAAAALE3nwqJprRfShrYyHuXSjplFAAAAQL91MCxKxtrQAAAAAJaie2HR9ILa0CajbG1PU6vqIgAAAKC/uhcW1YsZcL06HqUm2ZkKiwAAAID+6lRYVGvNtObCZhYlyZa5RQAAAECPdSssam8vaje0JLlkbhEAAADQY50Ki6ZtC9hFtKGtjJv33BYWAQAAAD3WrbCoLS26mLBIZREAAADQfx0Li9rKogvoQ1udmFkEAAAA9F+3wqJ5G9r5v/fugGuVRQAAAEB/dSos2qkXP7Noa1tYBAAAAPRXp8Ki2cyisZlFAAAAAEvRsbBoNrPo/N97tw3NzCIAAACgv7oVFrWlReUi29BUFgEAAAA91q2wqC3quYiZRasGXAMAAAAD0LGw6OJ2Q5uYWQQAAAAMQEfDovNPi8ajkvGoZGvbzCIAAACgv7oVFrVFPeOLKC1KM7dIGxoAAADQZ90Kiy6wDS1p5hYJiwAAAIA+62hYdFGVRcIiAAAAoN86FRbtzMKiC2tDG2Vrx8wiAAAAoL86FRbNZhZdXGWRmUUAAABAv3UrLLrgmUUrk1EuCYsAAACAHutUWFQvembRyMwiAAAAoN86FRZN23FBFzazaDLK1raZRf+TvTuPkus+zwP93qrqqu7qBejGShANcAdISiItKSKtzVpsxVZiKSexk4xsJZYTO+vEM46Tk2WS0cw458xMFjt2PI6dia1YsuJtvEp27MiSZW3UvlGUKO4ASAIE0I2tG+il6s4f3QBFcQNJAHVv4XnO6XO7a7n9NQ4BEi+/7/sBAAAAw6tWYVGvP9gxtLadRQAAAMCQq1VY1B/0GFrTziIAAABguNUsLFq7NgcYFuksAgAAAIZZzcKi9c6iQe0sajay0ivPLdoGAAAAGDb1CosqsLMoSVb7wiIAAABgONUrLDp7GtqgxtBaa79cK6tG0QAAAIDhVLOwaPBjaEksuQYAAACGVj3DogGNoZ0Ni1Z7xtAAAACA4VSvsOjczqLBpEVndxbpLAIAAACGVb3CokHvLFrvLFoRFgEAAABDqmZh0dmdRYP5/o+HRcbQAAAAgOFUq7CoVw52DE1nEQAAADDsahUW9ftJkUGGRXYWAQAAAMOtXmFRWQ4sKEqSkdZ6Z9GqsAgAAAAYTvULiwZYsTE0AAAAYNjVKyzqD7azqG3BNQAAADDk6hUWlYPbV5QkLTuLAAAAgCFXs7CoTGNwWVEaRZFWozCGBgAAAAyt+oVFg0yLsra3SFgEAAAADKt6hUX9wY6hJUm71cjKqp1FAAAAwHCqV1g04DG0JBlpFlnp6ywCAAAAhtN5hUVFUXxPURQ/XRTFR4qiOFEURVkUxXue5T2vLIri94uimCuKYrEoii8VRfE/FUXRfL7F9srBnoaWrI+hrQqLAAAAgOHUOs/X/S9JbklyKsmBJHuf6cVFUbw1yf+X5EySX00yl+S7k/xEklcl+d7nU2y/TEV2FhlDAwAAAIbT+Y6h/c9JbkgyleTvPNMLi6KYSvKfkvSSvK4sy79RluU/SnJrkk8k+Z6iKP7q8ym23y/THHhnUZFlC64BAACAIXVeYVFZlh8qy/KesizPp6Xme5JsSfIrZVl+5hvucSZrHUrJswROT6dflhlwVuQ0NAAAAGCoXYwF129Yv/63p3juT5MsJnllURSd53rjflV2FgmLAAAAgCF1McKiPevXr3/zE2VZriZ5IGu7kq55rjfulxn4aWhtO4sAAACAIXYxwqIN69fjT/P82cc3Pt0NiqL44aIoPlMUxWdOHps793i/Xw5+wXWryLLT0AAAAIAhdTHComdzNu152vacsix/vizLl5dl+fLJjTPnHjeGBgAAAHBxXYyw6Gzn0IaneX7qm1533vplKnAaWiOr/TL989r1DQAAAFAvFyMsunv9esM3P1EURSvJ1UlWk9z/XG/cL8s0BtEL9Q3azbUCVu0tAgAAAIbQxYhePrh+/c6neO61SbpJPl6W5dJzvXG/X4UxtLXvv2wUDQAAABhCFyMs+o0kR5L81aIoXn72waIoRpP8+PqXP/t8brx2Gtqgw6K1XzJ7iwAAAIBh1DqfFxVF8ReS/IX1L7evX7+1KIp3rX9+pCzLH0uSsixPFEXxQ1kLjf6kKIpfSTKX5C1J9qw//qvPp9i1BdfP550XzkhLWAQAAAAMr/MKi5LcmuSvf9Nj16x/JMlDSX7s7BNlWf52URTfluSfJ/lLSUaT3JvkR5P8VFk+v+3QazuLBpsWtc91FtlZBAAAAAyf8wqLyrJ8Z5J3Ppcbl2X5sSRvfu4lPb0qjKG11ncWrazqLAIAAACGz4DPFntuqrDgum1nEQAAADDE6hUWVWFnkbAIAAAAGGK1Cot6ZQa+s+hsWLRsZxEAAAAwhGoVFlVhDG3k7M4inUUAAADAEKpXWFSWaQ54DM3OIgAAAGCY1S4sGnhnUWs9LHIaGgAAADCEahYWDX5nUWv9+9tZBAAAAAyjeoVF/cGfhlYURUaahTE0AAAAYCjVJizql2XKZOBjaMnaiWjCIgAAAGAY1SYsKtenvgY9hpasLbkWFgEAAADDqDZhUX89LapOZ5GdRQAAAMDwqU9Y1D8bFg24kMTOIgAAAGBo1ScsOjuGVpHOomVhEQAAADCEahMW9c6OoVWgtajdamRpRVgEAAAADJ/ahEWP7ywacCFJxjutLC6vDroMAAAAgAuuPmHR+hxaswJjaN12M4vLvUGXAQAAAHDB1ScsqtDOom67laXVflbtLQIAAACGTI3CorM7iwZcSJLxTjNJdBcBAAAAQ6cC0cv5OTuGVpXOokRYBAAAAAyf+oRFFRpDG2+vdRYtWHINAAAADJkahUU6iwAAAAAutvqFRRWouLu+s2hhSWcRAAAAMFwqEL2cn2rtLDq74FpYBAAAAAyX2oRFvQrtLGo1Gum0GlkwhgYAAAAMmdqERY/vLBpwIevGO60sGkMDAAAAhkwNw6JqpEXddtOCawAAAGDo1Ccs6q9dGxVpLRpvt7JgZxEAAAAwZOoTFlVsDE1nEQAAADCMahgWVSMtWttZJCwCAAAAhkuNwqK1a1XG0LrtZpZ7/az0+oMuBQAAAOCCqU9Y1K/aGForSYyiAQAAAEOlPmHR+hhasyJjaN12M0mysGTJNQAAADA8ahcWVWlnUaKzCAAAABguNQqL1q5V2lmUJAvLOosAAACA4VGjsKhaO4vOdRYZQwMAAACGSH3Con61xtDGRs52FhlDAwAAAIZHfcKis2NoFQmLmo0iYyPNLBpDAwAAAIZIjcKi9c6iClXcbTctuAYAAACGSoWil2dWtTG0ZG1v0eKSsAgAAAAYHrUJi3oVG0NL1jqLnIYGAAAADJPahEVVOw0tScbbLWNoAAAAwFCpVVjUKJKiap1FS6sp14MsAAAAgLqrT1jUr9YIWpJ0O62s9sus9IRFAAAAwHCoT1hUlpULi8bbzSSxtwgAAAAYGvUKiypWbbfdShInogEAAABDo2Lxy9OrZGdRR2cRAAAAMFzqExZVcWfR2c4iYREAAAAwJOoTFq2fhlYlZ3cWLS4bQwMAAACGQ73CooqlRaPtZookC3YWAQAAAEOiRmFR9cbQGkWRsXbTGBoAAAAwNGoTFvX61VtwnSTj7VYWjKEBAAAAQ6I2YVEVdxYlSbfdzOKSziIAAABgONQoLEqaFUyLup2WBdcAAADA0KhPWFTZMbRmFuwsAgAAAIZEfcKiyo6htbK41EtZloMuBQAAAOAFq1lYVL20aLzTTK8ss7TaH3QpAAAAAC9YjcKipFHB1qJuu5Uk9hYBAAAAQ6E+YVG/mmNo4+1mkmTR3iIAAABgCNQnLKroGFq3s9ZZtLCkswgAAACovxqFRalkWKSzCAAAABgmNQqLykrvLFqwswgAAAAYArUJi3oV3Vk0OtJIo0gWl3QWAQAAAPVXm7CoqmNoRVFkrN3SWQQAAAAMhRqFRdXsLErW9hbZWQQAAAAMg5qFRdVMi7rtltPQAAAAgKFQn7CoX80F10ky3tFZBAAAAAyH+oRFFd1ZlKx3FtlZBAAAAAyBGoVF1d5ZdHp5NWVZDroUAAAAgBekXmFRRdOibqeVfpmcWekPuhQAAACAF6RGYVHSrOgY2ni7mST2FgEAAAC1V5+wqF/dMbRuu5Uk9hYBAAAAtVefsKgsK7vgeryz3lm0pLMIAAAAqLdahEVlWa6dhlbR1iKdRQAAAMCwqEdYtH6taFaUrp1FAAAAwJCoRVjU76/FRVUdQ+u0GmkWRRaWdBYBAAAA9VaPsGi9taiqYVFRFOl2mjqLAAAAgNqrSVi03llU1Tm0JOPtVhbtLAIAAABqrh5h0bkxtAEX8gy67WYWdBYBAAAANVeLsKhXVntnUZJ0O60s2lkEAAAA1FwtwqKzO4uaFQ6LxnUWAQAAAEOgJmHR2Z1FAy7kGXTbrZxe7qV3NtkCAAAAqKEKxy+Pe3xnUYU7izrNlElOnF4ZdCkAAAAAz1s9wqL1Zp0qh0XddjNJMre4POBKAAAAAJ6/moRFZ8fQqhwWtZIk8wvCIgAAAKC+6hUWVTcryvh6WDQnLAIAAABqrB5hUX/tWukxtM7aGNq8MTQAAACgxuoRFpU1WHB9dgxt0YJrAAAAoL7qFRZVuNqRZpFWo7CzCAAAAKi1Cscvj+vVARyTQAAAIABJREFUoLOoKIqMd1p2FgEAAAC1VouwqA47i5Kk227aWQQAAADUWj3CovXOoma1s6KMt3UWAQAAAPVWq7Co0ah2WtTtNC24BgAAAGqtHmFRbcbQdBYBAAAA9VaPsKgGC66TtZ1Fx0+vZLXXH3QpAAAAAM9LzcKiARfyLMbbzSTJsdNG0QAAAIB6qldYVPG0qNtpJUnmjaIBAAAANVWPsKgmO4vG2+thkSXXAAAAQE3VIyyqyRhad30MzZJrAAAAoK5qERb1arLgevzsGNqisAgAAACop1qERf21rKj6O4t0FgEAAAA1V4+wqF+PMbSRZiPddtOCawAAAKC26hEW1WQMLUmmu+3MGUMDAAAAaqomYdHatQ5h0cx4W2cRAAAAUFs1CYvWO4tqUO3G7kjmFlcGXQYAAADA81KD+KVeY2g6iwAAAIA6q0dY1E+K1CMsmu4KiwAAAID6qkdYVJa1CIqStc6ik0urWen1B10KAAAAwHNWn7CoFpUm0+PtJMm8E9EAAACAGqpFBNPv16izqLseFi1Ycg0AAADUTz3CorIe+4qSZHp8JEkyZ28RAAAAUEM1CYvKNOqRFWXGGBoAAABQY/UJi2qSFp0dQ9NZBAAAANRRPcKifn3G0Dae21kkLAIAAADqpx5hUY3G0NqtRiY7rcwZQwMAAABqqBZhUa+sz2loSbJxfERnEQAAAFBLtQiL+mVqs7MoWdtbNLe4MugyAAAAAJ6zeoRF/TLNGnUWTY+3c8wYGgAAAFBD9QiLarSzKFnvLDKGBgAAANRQfcKiGqVF0+NtO4sAAACAWqpJWJRaLbieGW9nYbmXMyu9QZcCAAAA8JzUIyzq12sMbbrbTpIcs+QaAAAAqJl6hEVlWbPOopEksbcIAAAAqJ2ahEWp186i9c6ieSeiAQAAADVTk7CoXmNoM+NrYZHOIgAAAKBu6hEW9es1hrZRZxEAAABQU/UIi2p2GtrGrp1FAAAAQD3VIizqlWWtdhaNNBuZGm05DQ0AAAConVqERWtjaIOu4rmZGW/rLAIAAABqpx5hUVmmWaMxtCSZHm/bWQQAAADUTk3ConrtLEqSTePtHD65NOgyAAAAAJ6TWoRFvX69dhYlyY6NY3nk2OlBlwEAAADwnNQiLFrp9dNu1iss2jk9lhNnVnP8tCXXAAAAQH3UJixqNWtR6jk7p7tJkgPziwOuBAAAAOD8VT6BKbO2s2ikZmHR7LmwyCgaAAAAUB+VT2DKskySWo6hJcn+OZ1FAAAAQH3UICxau460Kl/qE2zsjmSi09JZBAAAANRK5ROY9awoI43Kl/oERVFk5/SYsAgAAAColconMGfH0OrWWZRkPSwyhgYAAADUR+UTmHOdRTXbWZSsnYh2YP70ucALAAAAoOqqHxad3VlUs9PQkrXOolNLqzm2uDLoUgAAAADOS+UTmHNjaDUMi2ZnuklibxEAAABQG5VPYOo9hjaWJPYWAQAAALVR/bBoPS1q17CzaOf0WmfRfmERAAAAUBOVT2DqPIa2YWwkU6MtY2gAAABAbVQ+gXl8DK3ypT6lndPd7J/TWQQAAADUQ+UTmMdPQ6vfzqIkmZ0Z01kEAAAA1EYNwqIyRZJmo55h0c7pbg7Mnz43TgcAAABQZdUPi7I2glYUdQ2LxnJ6pZejC8uDLgUAAADgWVU/LCrrO4KWJLPrJ6IZRQMAAADqoAZhUZmRVuXLfFo7Z8aSxJJrAAAAoBYqn8KcHUOrq506iwAAAIAaqXwKU/cxtIlOK9PdkRyY11kEAAAAVF/1w6KUte4sSta6i/brLAIAAABqoPIpTFkm7ZqHRbMzYzqLAAAAgFqofAqzNoZW+TKf0c7pbg7Mn06/Xw66FAAAAIBnVPkUpkyZVo13FiXJzumxLK/2c+TU0qBLAQAAAHhG1Q+LhmEMbf1ENHuLAAAAgKqrfApTZhjG0MaSxN4iAAAAoPIqn8KUZZmR2o+hrXUWHdBZBAAAAFRc9cOi1L+zaKzdzOaJdvbP6SwCAAAAqq0WKUzdw6IkuXL9RDQAAACAKqtFCjPSqkWZz2h2eszOIgAAAKDyapHCjDTqvbMoWdtb9PCx0+n1y0GXAgAAAPC06hEWDUNn0cxYVnplHjt5ZtClAAAAADytWqQw7SHYWeRENAAAAKAOapHCDMOC653TY0niRDQAAACg0mqRwow067+z6MqNa2GRziIAAACgymoSFtWizGc0OtLM1smOziIAAACg0mqRwgxDWJQkszNdnUUAAABApdUihRmGMbRkbW/RgWM6iwAAAIDqqklYVIsyn9XO6bE8cuxMVnv9QZcCAAAA8JRqkcIMS1g0O91Nr1/m4Ikzgy4FAAAA4CnVIoVpDc0YWjdJsn/O3iIAAACgmiofFhVJGsVwhEWzM2NJkgPz9hYBAAAA1VT9sGhIgqIkuWLDWIoiTkQDAAAAKqv6YdGgC7iA2q1Gtk+NZr/OIgAAAKCiqh8WDVNalLUl1zqLAAAAgKqqQVg0XGnRzumxHJjTWQQAAABUU/XDokEXcIHtnOnm4IkzWV7tD7oUAAAAgCepflg0ZGnRzumx9Mvk4PEzgy4FAAAA4EmqHxYNWW/RzumxJLHkGgAAAKik6odFw5UVZXa6myQ5ICwCAAAAKkhYdIldsWE0zUaR/XNORAMAAACqp/ph0ZCNobWajVyxYVRnEQAAAFBJ1Q+LhisrSrK2t+jAvM4iAAAAoHqqHxYNuoCLYOd014JrAAAAoJIualhUFMWDRVGUT/Nx8DzvcTFLHIjZ6W4OnVjK0mpv0KUAAAAAPEHrEnyP40l+8ikeP3U+bx7CrCg7p8eSJA/Pn841WyYGXA0AAADA4y5FWHSsLMt3Pt83D2FWdC4sOiAsAgAAACqm+juLhrC1aHammySWXAMAAACVcyk6izpFUXx/kl1JFpJ8KcmflmV5Xgt7hjAryrap0Yw0C0uuAQAAgMq5FGHR9iTv/qbHHiiK4h1lWX742d48hFlRmo0iOzaO6SwCAAAAKudij6H9YpI3Zi0wGk/y4iQ/l+SqJH9QFMUtT/Wmoih+uCiKzxRF8ZnTCycvcomDsXN6LPvndBYBAAAA1XJRw6KyLP+3siw/WJblobIsF8uyvLMsy7+d5N8lGUvyzqd538+XZfnysixfPj45dTFLHJidG7s6iwAAAIDKuRRjaE/lPyb5h0le+2wvrNsY2ns/ue+8Xje3uJwjp5byXz7+YEaa55fZve22XS+kNAAAAIBnNajT0B5bv44/2wuH8TS0JJnujiRJ5heWB1wJAAAAwOMGFRZ96/r1/md74XBGRcl0t50kmV9cGXAlAAAAAI+7aGFRURQ3F0Ux8xSP707yH9a/fM+z3+dCV1YNj4dFOosAAACA6riYO4u+N8k/KYriQ0keSHIyybVJ/lyS0SS/n+TfPNtNhnUMbWK0lVajEBYBAAAAlXIxw6IPJdmT5FuyNnY2nuRYko8meXeSd5dlWT7bTYYzKkoaRZGN3RFjaAAAAEClXLSwqCzLDyf58MW6/zCY7rYtuAYAAAAqZVALrkmydbKTx06eSa//rA1WAAAAAJeEsGiAds50s9Irc/DEmUGXAgAAAJBEWDRQu6a7SZL9c4sDrgQAAABgjbBogDZ2RzLRaQmLAAAAgMoQFg1QURSZnelm/7ywCAAAAKgGYdGAzU6P5cip5Swurw66FAAAAABh0aDNzqztLTowf3rAlQAAAAAIiwZu58axFEn22VsEAAAAVICwaMA6I81smxq15BoAAACoBGFRBczOjGX//GL6ZTnoUgAAAIDLnLCoAmanuzmz0s/RU8uDLgUAAAC4zAmLKuDskmujaAAAAMCgCYsqYMtkJ51WI/vmhUUAAADAYAmLKqBRFJmd7uaAziIAAABgwIRFFTE7M5aDJ85kebU/6FIAAACAy5iwqCJmZ7rpl8nDx04PuhQAAADgMiYsqojZaUuuAQAAgMETFlXEeKeVmfF29ltyDQAAAAyQsKhCds10s29uMWVZDroUAAAA4DIlLKqQ2emxnDyzmuOnVwZdCgAAAHCZEhZVyOzM+t6ieUuuAQAAgMEQFlXI9g2jaTUKS64BAACAgREWVUir0ciOjWPCIgAAAGBghEUVs2umm4ePnc5qvz/oUgAAAIDLkLCoYnZOj2W1X+bg8TODLgUAAAC4DAmLKmaXJdcAAADAAAmLKmbD2EgmR1v2FgEAAAADISyqmKIoMjvdFRYBAAAAAyEsqqDZmW6OLixncWl10KUAAAAAlxlhUQXNzowlSfbP6y4CAAAALi1hUQXt3NhNkWTfnCXXAAAAwKUlLKqgdquR7RtGdRYBAAAAl5ywqKJmp7s5ML+YflkOuhQAAADgMiIsqqjZmW7OrPRz5OTSoEsBAAAALiPCooqy5BoAAAAYBGFRRW2e6GR0pGHJNQAAAHBJCYsqqlEU5/YWAQAAAFwqwqIKm53p5uDxM1la7Q26FAAAAOAyISyqsNnpbsokD88bRQMAAAAuDWFRhc1Ory+5njOKBgAAAFwawqIK63Za2TTezn6dRQAAAMAlIiyquF0z3Tx0dCH9shx0KQAAAMBlQFhUcddvm8jCcs/eIgAAAOCSEBZV3A1bJ1MkufvQyUGXAgAAAFwGhEUV1+20MjvTzd0HhUUAAADAxScsqoG92yfz8LHTeezEmUGXAgAAAAw5YVEN7Nk+mST5k7sPD7gSAAAAYNgJi2pg+9RoNoyN5I+/dmjQpQAAAABDTlhUA0VRZM+2yXz0niNZWu0NuhwAAABgiAmLamLP9sksLPfy6QfmB10KAAAAMMSERTVx7ZaJtFuNfPBrjw26FAAAAGCICYtqot1q5JXXbsqH7hYWAQAAABePsKhG3rB3ax44spD7D58adCkAAADAkBIW1cjr92xNEqNoAAAAwEUjLKqR2Zlurt86YRQNAAAAuGiERTXzhr1b86kH5nJqaXXQpQAAAABDSFhUM2/YuzUrvTIfvefwoEsBAAAAhpCwqGZetns6U6Ot/PFXjaIBAAAAF56wqGZazUZee8OWfOjuw+n3y0GXAwAAAAwZYVENvWHv1hw5tZQ7Hzk+6FIAAACAISMsqqFvu2FLiiL54NeMogEAAAAXlrCohjZNdHLr7MZ8SFgEAAAAXGDCopp6496t+eKB4zl8cmnQpQAAAABDRFhUU6/fuzVJ8qG7dRcBAAAAF46wqKZuumIq26dGjaIBAAAAF5SwqKaKosjr927JR+45kuXV/qDLAQAAAIaEsKjGXr9na04treYzD84NuhQAAABgSAiLauxV121Ou9XIB42iAQAAABeIsKjGxjut3H7NJmERAAAAcMEIi2ruDXu25P4jC3nwyMKgSwEAAACGgLCo5t6wd1uS6C4CAAAALghhUc3t2tTNtVvG86G7hUUAAADACycsGgJ/9ubt+fh9R3P/4VODLgUAAACoOWHREHjHq65Ou9nIT3zgnkGXAgAAANScsGgIbJns5B2vuiq/98VH8tVHTwy6HAAAAKDGhEVD4m+99tpMjrbyb//o64MuBQAAAKgxYdGQ2NAdyQ+/5pp84KuH8vl984MuBwAAAKgpYdEQecerr86m8bbuIgAAAOB5ExYNkYlOK3/nddfmo/ceySfuOzrocgAAAIAaEhYNme+/fXe2T43m3/zR3SnLctDlAAAAADXTGnQBnL/3fnLfeb3utmtm8jtfeCT/6+9+JXu3Tz3r6992264XWhoAAAAwJHQWDaGX757JzHg7//2uQ+nrLgIAAACeA2HREGo2irxx79Y8evxMvvLIiUGXAwAAANSIsGhI3TK7MVsnO/mA7iIAAADgORAWDalGUeTbb9yWw6eW8oV9xwZdDgAAAFATwqIhdvOOqVy5cSx//LVDWe33B10OAAAAUAPCoiFWFEW+46ZtmV9cyWcenB90OQAAAEANCIuG3PVbJ7J7UzcfuvuxrPR0FwEAAADPTFg05IqiyJtu2p6TZ1Zzx/1HB10OAAAAUHHCosvA1ZvHc/3WiXz464eztNIbdDkAAABAhQmLLhPfcdO2LC738nHdRQAAAMAzEBZdJnZOd7N3+2Q+cs/hnF7WXQQAAAA8NWHRZeTbb9yWMyv9fOy+I4MuBQAAAKgoYdFlZMfGsdy8Yyofu/dIFpdXB10OAAAAUEHCosvMG2/cluXVfj5yj+4iAAAA4MmERZeZ7VOjefHODfnEfUdzakl3EQAAAPBEwqLL0Bv2bs1Kr5+PfP3woEsBAAAAKkZYdBnaOjmaW2c35o4HjubkmZVBlwMAAABUiLDoMvWGvVvT65f5sO4iAAAA4BsIiy5TmyY6+ZZd0/nUA3N59PjpQZcDAAAAVISw6DL2hj1b0y/L/MyH7h10KQAAAEBFCIsuY9Pj7bx890x+9dP7c2B+cdDlAAAAABUgLLrMvW7PlhQp8h8+qLsIAAAAEBZd9jZ223nbbbvy6589kIeOLgy6HAAAAGDAhEXk777u2rQaRX7qj3UXAQAAwOVOWES2To3m7bfvzm99/kDuO3xq0OUAAAAAAyQsIknyt193bTqtZv79B+4ZdCkAAADAAAmLSJJsnujkHa+6Kr/7xUfyO194eNDlAAAAAAMiLOKcH/n26/OKq2fyj379S/nUA3ODLgcAAAAYAGER53Razfz821+WnTNj+eF3f8b+IgAAALgMCYt4go3ddt71A69Isyjyjl/8dI6eWhp0SQAAAMAlJCziSXZt6uY//fWX59CJM/mhX/pMzqz0Bl0SAAAAcIkIi3hKL901nZ/8K7fm8/uP5Ud/7Qvp98tBlwQAAABcAsIintZ3vfiK/NPv2pvf//LB/F9/+LVBlwMAAABcAq1BF0C1/dBrrsm+ucX83Ifvz66Zbr7vtt2DLgkAAAC4iIRFPKOiKPLO7745D8+fzr/8na9kx8axvH7P1kGXBQAAAFwkxtB4Vq1mIz/9tpdmz7bJ/P1f/lzueuTEoEsCAAAALhJhEedlotPKL/zAn8nk6Eje8a5P5cD84qBLAgAAAC4CY2jkvZ/cd96v/d6X78x/+sj9+Qs/87H88GuvzUTn6f8Retttuy5EeQAAAMAlpLOI5+SKDWN5++1X5djiSv7Lxx/M0kpv0CUBAAAAF5CwiOfs6s3jedsrduXR46fznk8+lNVef9AlAQAAABeIsIjnZe8VU/mLL92Z+w4v5Nc+sz/9shx0SQAAAMAFICzieXvprum8+UXbc+cjJ/K7X3gkpcAIAAAAas+Ca16QV1+/JQvLvXz464cz3mnmO27aPuiSAAAAgBdAWMQL9qabtmVhaTUfuvtwxjutvPLazYMuCQAAAHiehEW8YEVR5K23XpnF5V7e96VH0203c+vs9KDLAgAAAJ4HO4u4IJqNIn/lz8zmms3j+Y3PHsjdB08OuiQAAADgeRAWccGMNBv5/tt3Z/uG0fzyJx/Kb3z2wKBLAgAAAJ4jYREX1OhIMz/wyqszO9PNj/36F/OPf+OLOb3cG3RZAAAAwHmys4gLbqLTyg++6uocOnEm/+FD9+ZLB47nZ77vpbl2y8SgSwMAAACehc4iLopmo8iP/dk9+cV3/JkcOnEmb/npj+b3vvjIoMsCAAAAnoWwiIvq9Xu25v3/4DXZs30y/+N//Xz+xW/fmaVVY2kAAABQVcIiLrodG8fyq3/rW/NDr7k6777joXzPz34i++cWB10WAAAA8BSERVwSI81G/vmfuyk///aX5aGjC3nzT30kf/iVg4MuCwAAAPgmwiIuqTfdvD3v/wevydWbx/O33v3Z/L1f/lweOXZ60GUBAAAA64RFXHKzM938+t/+1vzod9yQD3z1UN74bz+c/+dP7rXLCAAAACqgKMty0DU8o2tufEn54+9636DL4CKZX1jO+7/8aO569EQ2T7Tz51+yIzdsm3zW973ttl2XoDoAAAAYTkVRfLYsy5c/1XM6ixio6fF2vv/23fmBV16Vskze9fEH8547Hsr84vKgSwMAAIDLkrCISrhh22R+5I3X5003bcs9j53MT37g6/ng1x7LSq8/6NIAAADgstIadAFwVqvZyOv2bM2tsxvz/i8/mg989VA+t28+b71lR64/j9E0AAAA4IXTWUTlbOy283237c47XnVViiS/+PEH86uf3peTZ1YGXRoAAAAMPWERlXX91sn8gzdenzfs3Zo7HzmRn/jA1/PpB+bSr/hSdgAAAKgzY2hU2kizkW+/cVtesnNDfucLj+S3vvBwPrdvPq+4eua8Tk0DAAAAnhudRdTC1snR/M1XX52/9NKdeezkUt787z+Sf/2HX8uZld6gSwMAAIChorOI2iiKIi/bPZ092ydz98GT+ZkP3Zf3fenR/B9vfVFee8OWQZcHAAAAQ0FnEbUz0Wnl3/7lW/LeH7otzaLIX/uFT+Vv/pdP597HTg66NAAAAKg9YRG19cprN+f3f+Q1+cffuSefvH8uf/YnP5J/9ltfzmMnzwy6NAAAAKgtYRG1NjrSzN993XX5k3/0urz99t35tU/vz+v+9Z/kJz/w9SwsrQ66PAAAAKgdYRFDYdNEJ+98y835wI9+W163Z0t+8gP35HX/5k/y3k/uy2qvP+jyAAAAoDaKsiwHXcMzuubGl5Q//q73DboMambf3GL+4MuP5qG5xWyZ7OQ7b96ePdsn0yiKZ3zf227bdYkqBAAAgMEpiuKzZVm+/KmecxoaQ2nXTDc//NprctejJ/KHXzmYd9/xUCZHW7l5x1RetGNDrto8/qzBEQAAAFyOhEUMraIocvOODdm7fSp3PnI8dz58PJ99aD533D+X8U4rN18xlZuvnMo1myfSbAiOAAAAIBEWcRloNorcsnNjbtm5Mcur/dx96GTufPh4vrD/WD714FzGRpq5acdUbt4xlWOLy9nYbQ+6ZAAAABgYYRGXlXarkRdfuSEvvnJDVnr93HPo1BO6jn7pEw/lyo1jufGKqdy0Yyo3XbEWIu2cHkthbA0AAIDLgLCIy9ZIs7EWCO2YymqvnwePLmbbVCd3PXoidz1yIh/82qH01/e/T462cuN6cHT7NZvyyms3ZXJ0ZLA/AAAAAFwEwiJI0mo2ct3WiSechnZ6uZe7D53MXY+cyF2PHs9dj5zIr3xqf37xYw+m2Sjy0l0b89rrt+Q1N2zJi6/cYO8RAAAAQ6Eoy3LQNTyja258Sfnj73rfoMuAJMlqv599c4u599Cp3PPYqTxy7HTKJGMjzVy3dSLXb53IdVsnnrT36BtDKAAAABi0oig+W5bly5/qOZ1F8By0Go1cs3ki12yeyJtuTk4trea+w6dyz6FTufexk/nyw8eTJBvHRrJzeiyzM93snO5mcXk13bbfbgAAAFSfv73CCzDRaZ07aa0syzx2cin3PnYq++YWc2B+MXc+ciJJ8p8/en9u2DaZW2c35tbZjblldmP2bJtMw+gaAAAAFSMsggukKIpsmxrNtqnRvGr9sVNLqzkwv5iNYyP5woHj+YM7D+ZXPr0/STLdHcmrrtuc11y/Oa++fkuu3Dg2uOIBAABgnbAILqKJTit7t0+d21lUlmUePLqYzz00n4/ddyQfuedI3velR5Mk12wZz2uu25zXXL8lt1+7KROdJ/727PXLHFtcztzCco4uLOfoqeXMLy5nY3cku2a62T0zng1dJ7QBAADwwlhwDQNUlmUOnVzKvYdO5t7Dp/LAkYWs9Mo0imTndDfNRpGFpdUsLK1mcbmXZ/vdOjXayu5N49k1083sTDe7N3Wza6abvdsns2mic0l+JgAAAJ7d/rnFvOeTD+W3P/9wmkWRbRtGs21yNNumOtk6NZrt65Mr26Y62b5hNJOjF7Y5wIJrqKiiKLJ9/Q+BV1+/JSu99dPWHlsLjvplmc0TnVy1aTzjnWbGO62Md1qZ6LQy3m6l227m2/Zsyb65xew7uph9c4t5aG4xdz16In9018Gs9B6Pl3bNdPMtu9Z2Jn3LrunceMVkOq3mAH96AACAy0u/X+aj9x7JL33iwfzx1x5Loyjyhr1bMzU6kkMnzuS+w6fysfuO5OSZ1Se8ryiS//2tL8rbb999SerUWQRDql+WOX56JUdPLeeRY6ezf34x++cWc2L9D51mo8iODaOZnelmdrqbbRtGM9Ntp91qJMm50TkAAACe2Xs/ue8Znz+93Mvn9s3njvuP5ujCcsY7rbziqum84upN2TD25I6h5dV+Tp5ZyYkzq3nRlVP55U/uy1cePp4P/djrsnVq9ILUrLMILkONosh0t53pbjvXbZ049/jx0yvZP7d4Ljz69INz+fh9R889Pznayqbxdj63bz5Xbepm16bxXLXJTiQAAIDn6uCJM7njvqP5/P75rPTK7Jrp5o03bsuLdkyl1Ww87fvarUY2TXSyaaKTt956ZW7ZuTFv+ok/zf/5376Wf/eXb73odQuL4DKzYWwkG67ckBdduSHJ2uLsQyfO5MippRxdWM7cqeUcXVjKR+45nN/47NKT3vuNAdKumW6u2jye3TPdbJnspCiKQfxIAAAAlbLS6+cDdx3KR+89kmajyC2zG3P7NZue9ynYV20ez994zdX52T+5L9932+68bPf0Ba74iYRFcJlrNors2DiWHd/0h9bbbtuV08u97JtbzINHF7Lv6Pp1bjFf2D+f93/pkfS/YYp1bKSZ3Zu6mRlvZ3SkmU6rkdGRZkZHGum0mumMNDL6DddvfG50pPGk97SbzTSbRUYaRVrNRlrNIiONtWurUQimAACASnr0+On82mf259CJpbzi6pm86cZt6XZeePzy919/XX7zcwfyzt/9Sn7n770qjcbF+zuRsAh4St88czveaeXmHRty847HO5KOLS7n6MLyekfSWmfSw/Ons9LvZ6VXZrXXz2qvzEp/7brav3A70lqNItPj7Vy3ZSLXbh3PdVsmct3WyVy7dTzbp0aFSQAAwCXVL8t85J4j+cBdh9JtN/PXv/Wq7NmCjj38AAAbVklEQVQ+ecHuP95p5Z+9+cb8yK98Ib/2mf35q6+4eHtmhUXA89JsFOdmaM9XvyzXQ6PHw6SV/vq19/h1pd9Pr1emV5bp9cv0z177Zx9bu9fJM6s5ML+Yz++fz5mV/rnvM9Fp5dot47l260Rmp7vZNNHOzPjax6bxTmbG25nujjxpRrgsy5xcWs38wnLmFpYzv7icuYWVzC8spyiSa7aM59otE9k53U3zIqb4AABAvcwtLOfXP7s/Dx1dzIt2TOWtt16Z8QvQTfTN3nLLjrznjofyf//h3fmuF1/xlMuxLwRhEXDJNIoi7VaRdp5+kdvzcTbkecnODbnvsVO57/BC7n3sVD5+79EcPPHwU76nKNZ2MM2MtzPSaGRucTnzC8vn1f3UbjVy9abxXLd14lwode2WiczOdDM12tLVBAAAl4myLPOrn96fn/rgPSmSfO/LdubW2Y0X7e8ERVHknW+5Od/90x/NT/z3r+edb7n5onwfYRFQe0VRZGp0JA8eWUyz0cgN2yZzw7a1ds9ev8zi8moWlntZWFpd+/jGz5dW0y+T3TPd3HTFVLrtZsbbrXQ769d2M+OdVvr9ModPLeXwyaVz1zvuP5rf//Kj+cZ4qVEkoyPNdNvNjI00012/x9j6x2RnJNumOtk2NZoffPXVg/kFAwAAXrDDJ5fyT3/zS/nAVx/LNZvH8z0v25mN3fZF/74379iQt922K+++46H8D6/YdUFH3c4SFgFDrdkoMjk6ksnRF96eubvTyu5N4094bLXXz9GF5Rw+uZT5xeWcXunl9HIvi8tr15NLK3ns5JksLveytNp/wnv/34/cnxu2T2bPtsns2b4WcF23dSKjI80nfe9ev8xKr5+l1X6W1++zsTuSkWc4bhMAALjwHj1+Ou/95L68546HsrDcy7/48zel02qkcQknDP7hd+zJ+770aN75u1/Je3/otgveySQsAngBWs1Gtk2NZtvU6LO+ttcvc+L0Sg6dPJNDx8/k0Mml3H3wZD5yz5H01sffiiSTo630yqTX76fXP7u36anvOTrSyHi7lfFOK+PtZrqdVsbbrbz6+k3ZPNHJ7k3dXLVpPDPjbeNxAADwPJVlmU/cfzTv/sRD+aO7DqVflnn9nq35J9+1Nzdsm3zSAUEX2/R4O//wTXvyL377zvzBnQfz5hdfcUHvLywCuESa6ye4TY+3s3f71LnHe/0yR08t5dDJpRw8fibHT6+k2SjSWv9oNoo0m0Vajca5x8tkbbxuqbd+Xc2x0yt5+NjpLCz18qf3HH7C954cbeWqTePZvambqzePZ/em8Vy9uZtdM+PZPCFIAgCAp3LyzEp+6/MP592feCj3PHYqG7sj+Zuvvjrff/vuzM50B1rb216xK+/95L78q/d/Na/fszVj7SdPKDxfwiKAAWs2imydGs3WqdG8+MoNL/h+ZVnmLbfuyGMnl/LQ0YU8eGQxDx1dyANHF/Plh4/nD+48eK6TKVnrTrpy41iunO7myo1j2Tm99rH2eTdbJztpOP0NAIDLyD2HTuaXPvFQfvNzB7Kw3MtLdm7Iv/6el+S7b9nxlGsjBqHZKPLO774pf+Xn78jPfvi+/Oh33HDB7i0sAhgyRVHk97746LmvR0ea2bN9KnvWu5l6/TLzi8s5emo5RxeWcmxxJccWl3PfY6fy2QfnsrDce8L9Wo0iWyY72T41mje/+Irs2T6ZvVdMZstER0cSAAC18mzjYifPrOQPv3Iwn9t3LM1GkZdcuSG3X7MpszPdrPTK/Obnnvq05UG57ZpNecstO/IfP3xfvvdlOy9Yt5OwCOAy02wU2TzRyeaJTpInn5ywvNrPscXlzC+u5NjptVDp0Ikzuffwqfyr3//quddtGm+vBUfbp7J3+2R2b+pm96ZxnUgAANROr1/mE/cdyR9/7bGs9sq85vrNec31WzLRqX5s8k/fvDf//a5D+fH335Wfe/vLL8g9q/9TA3BJtVuNc2Nx3+w7X7Q9Xzt4Il979GS+dvBE7j54Mu/91EM5s/L4SW+dViO7ZrrZvWltJ9LuTd3s2tTNrpluNo23Mzk6kqYwCQCAirj3sVP5vS89ksMnl3LDton8uRfvyJbJzqDLOud8lme/5vrN+cOvHMq//O07s/eKqWd9/bMRFgFw3v7bnQeTrI223To7nVtnp9Mvy8wvLOfownLm1j+OLiznyw8fz4e/fjgrvScf5TY60sjYSDPdditjI83suWIyG8ZGsmFsJJ1WIyPNxrnrSLORdquRkWaR9rnPH3+83WxkpLX23NnHOq1GNoyNGJMDAOBpzS8u5/e//Gi+8siJzIy38/bbd2fv9sla/jfkq6/bnC8dOJ7/+ul9efvtV+W6rRMv6H7CIgBekEZRZNNEJ5smnvx/X8qyzMml1cydWs784nIWl3s5vdLL6W+4Li6v5pP3z+X08mpOr/TSf3K29LyMt5vZsXEsb7p5W160Y0NedOWG7Jweq+W//AEAuHBWev386dcP58NfP5yiSL7jpm159XWbM9JsDLq0563VbOQHX311/vNH788vfeLB/LVvfWGBkbAIgIumKIpMjY5kanQkV2X8vN7TL8v0+msfq/1v/Lx/7vMnP3f2835W+2WWV/t57MRSHjl+Oj/34fuzup5AbRgbyYuunMqLdmzITTumsmWik7F2M+OdtQ6n8U4r3XYznVZDqAQAMCR6/TL3HT6VL+w/lt/+wsP5+sGTOXZ6JS++ckO+60Xbs7HbHnSJF8REp5W/8epr8gsffSC/9IkH8/bbd+f6bU/eUXo+hEUAVEqjKNJoFrlQJ5L+xZdembsPnsydjxzPnQ8fz50Pn8gvfuzBLPf6T/ueRpF022vB0XS3nc2T7XNLwbdMdtY/b5/7ema8Xev/EwUAMCzKssyjx8/ki/uP5QsHjuWL+4/lyweOnzvx9+x+zb/0sp25dssLG9WqorXA6Or8wsceyLvveCjff/vu3PA8AqOiLC9Qv/9Fcs2NLyl//F3vG3QZAAyR1X4/h08u5fRKLyur/Syt9rPS62d5df1j/fOl1X4Wlns5dWYlp5ZWc2pp9Sl3MCXJdHfkXKC0efIbwqSJzrmwactkJ5vGO2m3BEsAABfKI8dO56P3HsnH7j2ST9x3NI+dXEqSjDSL3HTFVG6Z3Zhbdm7MLbMbc8f9R9O4DDrIF5dW8wsfeyCHTi7l+2/blT3bn7z0+vtu3/3Zsiyf8vg0nUUAXHZajUau2DD2vN67tNrLqTOr58KjU0urT/j6kWOn8/VDJ3NqaTVLq0/dvTQ20sxEp5WJ0dbadf3zyU4r333L2ukbW6fWgiUnxwEAPNHx0yu54/6j+di9R/LRe47k/iMLSZLNE5288tpNednu6dwyuzE3XjGZTuuJ7eqfemBuECVfct1OKz/46qvzix97MO/55L583yt2PadT0nQWAcBFsrzaf4pAaeVJAdOppdWcWXlysNRsFNk03s7WqU62To5m62Qno+cxn9co/v/27j5Gjvq+4/j7OzO79+SzjY1NiSBADA60oUGpAhRaCkFC9I8oKIQ2JUljoihKH0SEEvWfIlWgokptg5DSRgqpQCUtqUolQApJi5rngpIUBKJBQBIHQoIBmwdz5/Pu7e7Mt3/8ZmZn78nnu1nweT8vaTU7Dzvzm9393e5+7/v7jtFIjLGBq8lFNJIoLEuMyWbClokGWycb5ZXoJhqxajWJiIjIW6LVSZmd73J4PmWu0+P+x/bRSUOmd8j4Tplt9/j5gUP86vUWTsgcOuPEKc7cOc2ZOzZx0uYxfZdZoNVJufPhZ3nxYJtrL3g751QCRsosEhEReQs0k4htSZNtU0cumthNMw61e8y2u8y0e8zOh/uz+bKnXpzhR8/26GWLg0rG4Jeiokh47ygvLdeIrQwcLbpNNhct2zoZAkxxZMSREVmYxmZEEeWyZhwRKUNKRERk5KWZs+9gi58dOMTe/YfYe2COvQcO8fMDh3jlUOeIjzfglBMmuPSdOzlz5yZO3TZBEml4/0ommjHXXXQGdz78LHf/8Hn+6PxT+fW3bTni44YaLDKzU4CbgSuB7cCLwH3ATe7++jCPLSIispE04ogTppqcsIrA0mq5O5mz6IpyxRXjWt2Uw52Udiel1U3L+VY3ZabV4+WZeQ53erS6KfPdjPXkIm8aS5geTwaG302X037QKYmMKJ8W83EckUTGeCNi29QY26eabN8UgnALU8tFRETkzZVlzux8j4OHOxw83OX1wx3eaHV5fa7DwVaXg4e7HJidZ++BQzz7ytzAMP2tkw3O3LGJy88+ibdvn2TzRIOpZsxkM+GHz77KWBzRbMRhmkSMJRGJLipy1CaaMZ+4+AzufOhZ7v7R8/z+u07mgjO2rfiYoQWLzGwX8DCwE7gfeBo4H/gMcKWZXezurw7r+CIiIqPOzIiNSt2jtQdWMnfmu1kZPGp1U1qdlG6akXlY78tMu6nT6aW0exntbkghf2V2nnYvY76blunlawlGbRpL2JYHj7ZPNdk83ugPu8uH3DXjynxsg+vjyvqkP99MIiYaMeP5baIZlwEtERGRUTLT7rLvYIt9B1u88HqLFw62y/mf7j/EbLvLSsnM442IqWbCiZvGeO/p29gxHS4AsmN6jKmxxSGJdjej3e1w1s61XfJdljbeiLnu4jP46o+e54H/e5GH9r6y4vbDzCz6IiFQdL27f6FYaGa3AjcAtwCfHuLxRUREpCaRWQiYNIeXyVNkQmXu4ZaFZWm+vNvLmOuEGk9zeT2Dubzm02yrx0tvtGl10zKTqnqrq0JjM44Yb0Rl8KgaSJpoxIxX7k80Y8aSqDI0zwbv54G81QzRM0Jm1WQzZqKZhGl+jMlmzGQjYbwZ0Ywj1WoQERlh7iGD+PB8yv7ZNgdm59k/Oz9wv5jOtLr9xw3so3+/3U05NN8bOEYjNk7eMsHbto6za8cUm8cbTI4lTDbi/HMqZAbpHy3HnvFGzJ6LTudnBw7x4JMvr7jtUApcm9k7gL3Ac8Aud88q66YJw9EM2OnucyvtSwWuRUREZL2KOk7FMLylAkppltEr6j2lTjfN6JbTjE6a0e0tmC/W9zK6WX99J9+mm775FxJpVjKoylul2DkwkP3lhB8XxfNkGOPNmMlGzNRY+MJfTivBquK7/3I/MACSSqZWtQ1FdlczjnAWvza9zMkq8xPNuBy6WAxfnGomqoUlcpzo9LJ+8L8dAhPNxEiiwazPalYo5MOsfem/G2nmmEFsxbDmiDi2ynwI1meZ0+6ltLtheHa7vIXs13YvJc0gjlhQm2+wXl8vzZhpd5lp9fJpqEE40wr1B2faXXqph79h4wmbxxvl37Tp8Ub5Ny6OjDda3YHbTCvsp5if6/Qqn1ODn1Urfe4kkZXHmx5P8gtbVLewRffi2Nha1ipssnUytHUULj1/vHN3Pvrbp7/pBa7fl08frAaK8gbNmtlDwBXAhcA3h9QGERERESB8wY9iYxUXk6vdSkP0ivsc4Tu3O/0gVa8IXGV0Us+nYV0vD3SlWTYQeCmm7W4KhEyl4nu+EYYsGiHTyR1anVB7otPrH6+zjuGCw2AGm5pJ+cOrEUfhdbZwPpHlr7uFc43McPIAmYPjA0Mo3UMGWv/1yZcV2+TrcIii/IdrEtFcMLSxCI4Z1ccV+w0Zc1meLVc9ZjGfVbYp3huxGUkczqVa1ysqfrQueP8s9RoVP567lfdHLw21zIr7ZuG/ztXsuHK+GYZmJnFEmv8oT70/TbN+UBZYuv5YFJXzkUHqodZJlmcQel5jLavsN6tuk/X7TdhuhccvuU2erVhkHDr5MTzfZvHjq9u4h/pySWTl0NVm3A9oNGMr+1Dxmhb78wX3k9jKoEfx3imCq0m+z3Y3Za6Tcni+x1ynx+FOytx8f9ruZpiF5zrJ95VEUbnv4rku3xeV93Lx3iMPGEdG2YYkCvuKo/5+kjgiy5xOmq0YpIgiaEQho7LfpsH2FZkq1eBQtYbNm8lscZC7To3YyuHM40nI/JzPA1PzvdXVA4wj62ey5pmtk80kvL5F8GrBLbLwd2l6LBkITo0lyj6VviO9F4YVLHpnPv3JMut/SggW7UbBIhERETmORWbhB/+RIkIbgBc1qNLlf9gVZ+mEH+lFoKoosD4QwEq9H9yJ+sGdMtAThSBWL83Kmlfz3azMAmjnP7ba+fDDnns/IEQ/2FMEZIrAGIQfiRHhtbF8vriyYP6ShUBTFM6qCKgVz0OaOa1Oj9nUy+yGXtovJg/9IFyxb7MF94ttbDBoZzb4JX5hgHGpgNNyr0M5b/2siOI5rmZFFMG02XZvMBBQCUYuV5OkGqAr2l0NjK3lt3j1eShfC+ufx+AyG1jeb8vgsnI+fw0Gtqm8LsVz0kzybfJjAWUWS1a8/ll/vngfVPez8HUu9lPNdixu1StZZu79rLwkohmHYa0TzYStE80yQEmRmZcHxaoBvCxfXr4fDJLy/T94HU3Pg2ndnjPvvfJcqkG0KGJxcMIsb1dMbFb2teI5aXczsqzyvLmTRGE475aJBjs3jzOeRIw1wvmNJzHNJCIyBrNAFzxfxdU+q0HhyEIgN1riua4GDYt+U5xfZLYgWNcP4iWx0YhCILoM+DpkLA7+x2ZlcHW8GYJDRyrEnLnnl4TPyowmdwYCtY3YFOCRt8SwgkXFddjeWGZ9sXzrUivN7FPAp/LZQx+58LRnamybyCg6EVi5gpmIHCvUX0U2DvVXkY1FfVZk0GnLrRhmgeuVVP/ptIi73w7c/uY1R+T4ZmaPLDcWVUSOLeqvIhuH+qvIxqI+K7J6K+fFrV2RObRlmfWbF2wnIiIiIiIiIiLHgGEFi4phY7uXWX9WPl2uppGIiIiIiIiIiLwFhhUs+nY+vcLMBo5hZtPAxUAL+MGQji8igzSsU2TjUH8V2TjUX0U2FvVZkVUyH9K1As3svwhXPLve3b9QWX4rcAPwJXf/9FAOLiIiIiIiIiIiazLMYNEu4GFgJ3A/8BRwAXAZYfjZRe7+6lAOLiIiIiIiIiIiazK0YBGAmZ0K3AxcCWwHXgTuA25y99eGdmAREREREREREVmTYdUsAsDdf+nu17n7ye7edPfT3P0zChSJrJ2ZnWJmd5jZPjObN7PnzOw2MzthlY+fMrOPmNndZva0mc2Z2ayZPWJmnzWz5rDPQWSUrLfPLrPPS8wsNTM3s7+us70io6zO/mpm55rZXWb2y3xf+83su2b2x8Nou8ioqau/mtnvmNn9+ePbZva8mX3dzK4cVttFNoKhZhaJSL2WGN75NHA+YXjnM8DFRxremX/wfQN4jVCM/mfANuD9wK/l+7/c3dtDOg2RkVFHn11in9PAE8CJwCbgFne/sc52i4yiOvurme0B/gk4DHwNeA7YCrwL2OfuH665+SIjpa7+amZ/AnwRmAPuBX4FnAJ8EJgEbnT3W4ZxDiLHOgWLRDaQOgrHm9l5wG8A97h7p7J8GvgO8B7gc+7++frPQGS0DONiD2Z2B3AV8PfALShYJFKLuvqrmV0I/A/wY+BKd39pwfqGu3drbbzIiKnpO3EDOACMAee5+zOVdecAjwEZcIK7z9d/FiLHNgWLRDYIM3sHsJfw38ld7p5V1k0TaoIZsNPd59Z4jGuBfwW+5u7vX3ejRUbYMPqsmX2AUPvvY0AC3ImCRSLrVmd/NbPvAb8LnOvuPx5ao0VGVF391cxOAl4CnnD3dy+x/gngXOBEXZhJRtFQaxaJSK3el08frH4oArj7LPAQIV32wnUco/hPZ28d+xCRoNY+a2Y7gS8D97n7v9TZUBGpp7+a2SmEQNEjwJNmdpmZfS6vCXi5mem7t8j61fX5up+QWbTbzM6qrjCz3cBZwOMKFMmo0geWyMbxznz6k2XW/zSf7l7HMT6RT/9zHfsQkaDuPns74XP7qIaticiq1NVf31vZ/lv57e8Iw0b/G3jczM5cRztFpKb+6mGIzZ8RPlsfNbN/NrO/MbO7gEeBJ4FramivyIaUvNUNEJFV25JP31hmfbF861p2bmZ/DlwJPA7csZZ9iMiA2vqsmX0C+ADwh+7+cg1tE5FBdfXXnfn0D4BXCEVyvwnsAP6KMIT0ATM7t1o3UESOSm2fr+5+j5ntA74KVK9U+DJhqPfP19pIkY1OmUUixw/Lp0ddiMzMPgjcRhi3fbUKb4q8KVbVZ83sdEL/vMfd/33IbRKRpa32MzauTD/p7ve6+4y77wU+Thiethu4ejjNFBGO4juxmX2UkPX3feAcwvC1cwhB3n8A/m1IbRQ55ilYJLJxFP8l2bLM+s0LtlsVM7uK8EG4H7jU3fUfFJF61NVn7wBawJ/W0SgRWVJd/fX1fDoPfL26Ih/ycn8+e/7RNlBESrX017wu0R2E4WYfc/en3b3l7k8TsgAfBa4xs0vX32SRjUfBIpGNo7ic53Ljr4vCfMuN317EzK4B7iGk2v5e9ZKhIrJudfXZ9xCGthwwMy9uhPR4gL/Ml923vuaKjLS6+muxn9mFhXdzRTBp4ijaJiKD6uqvVwAN4LtLFMrOgO/ls7+1lkaKbHSqWSSycXw7n15hZtESlwm9mJB98IPV7MzMrgXuAl4ALlNGkUjt6uqzdxHS4hc6C7iEUGfsUeCxdbdYZHTV1V+fINQqOtHMTlqixti78ulz62+yyMiqq7+O5dMdy6wvlqu+mIwkZRaJbBB5vYMHgdMJV26ougmYAu5y97lioZmdbWZnL9yXmX0c+ArwPHCJAkUi9aurz7r79e7+yYU3+plFD+TL/nFoJyNynKuxv/aAL+Wzf2tmUWX7c4E9QA/4j5pPQWRk1Pid+Pv59ENm9pvVFWZ2HvAhQt2jb9XXepGNw8LwaRHZCMxsF/AwYUjK/cBTwAXAZYRU24vc/dXK9g7g7lZZdhmhkF9EGKf9yyUOddDdbxvSaYiMjDr67Ar73kMIGN3i7jfW3niREVNXfzWzSUJx3AsJGX/fIWQoXE0YfvZZd791yKcjclyrsb/eAVxHyB66F/gFIQh1FdAEbnP3G4Z8OiLHJAWLRDYYMzsVuJlwmfvtwIvAfcBN7v7agm2XChbtoZ+RsJxfuPvp9bVaZHStt8+usN89KFgkUqu6+mseMPoL4MPAGUAb+F/g8+7+jWGeg8ioqKO/mpkRrlS4B3g3MA3MEAK9X3Z3XQ1NRpaCRSIiIiIiIiIiUlLNIhERERERERERKSlYJCIiIiIiIiIiJQWLRERERERERESkpGCRiIiIiIiIiIiUFCwSEREREREREZGSgkUiIiIiIiIiIlJSsEhEREREREREREoKFomIiIiIiIiISEnBIhERERERERERKSlYJCIiIiIiIiIipf8Hw3S6+/tNq5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['speechiness'], \"Speechiness\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Speechiness**: Detects the presence of spoken words in a track. The more exclusively speech-like the recording (e.g. talk show, audio book, poetry), the closer to 1.0 the attribute value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['valence'], \"Valence\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Valence**: Describes the musical positiveness conveyed by a track. Tracks with high valence sound more positive (e.g. happy, cheerful, euphoric), while tracks with low valence sound more negative (e.g. sad, depressed, angry)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['duration_ms'], \"Duration (in milliseconds)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['tempo'], \"Tempo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Tempo**: The overall estimated tempo of a track in beats per minute (BPM). In musical terminology, tempo is the speed or pace of a given piece and derives directly from the average beat duration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr_mask = np.corrcoef(corr)\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    plt.figure(figsize=(20, 15))\n",
    "    sns.heatmap(corr, mask=mask, center=0)\n",
    "    plt.title(\"Correlation Matrix\", fontsize=40)\n",
    "    plt.xticks(fontsize=30, rotation=45)\n",
    "    plt.yticks(fontsize=30, rotation=0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df[\"mode\"].mask(df[\"mode\"] == 'Minor', 0, inplace=True)\n",
    "df[\"mode\"].mask(df[\"mode\"] == 'Major', 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df['mode'] = df['mode'].astype(\"uint8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, columns=[\"time_signature\", \"key\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A lot can be said about this heatmap when it comes to how all the input features correlate with eachother, but since I only care about how they interact with the outcome variable, I will discuss their correlations.\n",
    "\n",
    "**Detractors**\n",
    "\n",
    "* **Acousticness**: With -0.38, this is the greatest detractor of popularity in this dataset. This is not too surprising, as a lot of classical and folk music have high scores in this feature, while most popular music these days have a lot of electronic instruments.\n",
    "* **Instrumentalness**: With -0.21, this is the second greatest detractor to popularity. It seems that the absence of lyrics does not help a song's popularity. Perhaps the lack of lyrics makes a song less relatable to most people?\n",
    "* **Liveness**: With -0.17, this is the third greatest detractor to popularity. I would postulate that this is due to most live versions of songs not sounding as well produced than their studio counterparts. Perhaps the crowd noise is also a turn-off for most people that just want to enjoy the music?\n",
    "* **Speechiness**: With -0.15, this is the fourth greatest detractor to popularity. This may show that many people on Spotify just want to listen to music, and not to podcasts or comedy routines as much, for example.\n",
    "\n",
    "**Contributors**\n",
    "\n",
    "* **Loudness**: At 0.36, this is the greatest contributor to popularity. It makes sense, because if a song isn't well-mastered, and thus not having the capacity for a greater perceived volume level, it may be hard for the listener to appreciate all of the nuances and elements of that song.\n",
    "* **Danceability**: At 0.26, this is the second greatest contributor to popularity. This follows intuition, as the more danceable a track is, with a beat that is steady and easier for the listener to follow, makes it more accessible to a larger audience. Also, if a track is easier to dance to, it allows for listeners to play the track while they do an athletic activity, as it helps them keep a steady pace, and also makes it more likely to be played in public places, like clubs, bars, and retail stores.\n",
    "* **Energy**: At a close third, with 0.25, I think this is due to listeners wanting to play a songs with higher energy during the day when they want to be most active.\n",
    "* **Tempo**: At a more distant 0.081, the tempo of a track plays a seemingly minor, but important role, as if it a song is too slow, a listener will be bored, and this number shows that listeners may have a slight preference towards songs that are faster paced.\n",
    "* **Valence**: At 0.06, and I think this makes sense, as the general population wants to be uplifted by their music most of the time, though at times they may want to listen to something more fitting to their current mood.\n",
    "* **Duration**: At a very minor 0.0023, which may not seem to contribute much, but listeners may grow bored if a song is too long, or end up skipping really short songs that are used as intros, outros, or interludes on an album."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Defining the Four Classes of Popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Popularity Score of:\n",
    "* **Class 0**: < 29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **Class 1**: >= 29 & < 43"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **Class 2**: >= 43 & < 55"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **Class 3**: >= 55"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "In order to make this a four-class classification model, I needed to find out how to split up the popularity score ranges. I used the 25th percentile, 50th percentile, and 75th percentile to divide the samples into four classes.\n",
    "\n",
    "* **Class 0**: Less than 29\n",
    "* **Class 1**: Greater than or equal to 29, and less than 43\n",
    "* **Class 2**: Greater than or equal to 43, and less than 55\n",
    "* **Class 3**: Greater than or equal to 55"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    232725.000000\n",
       "mean         41.127502\n",
       "std          18.189948\n",
       "min           0.000000\n",
       "25%          29.000000\n",
       "50%          43.000000\n",
       "75%          55.000000\n",
       "max         100.000000\n",
       "Name: popularity, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"popularity\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "df[\"popularity\"].mask(df['popularity'] < 29, 0, inplace=True)\n",
    "df[\"popularity\"].mask((df['popularity'] >= 29) & (df['popularity'] < 43), 1, inplace=True)\n",
    "df[\"popularity\"].mask((df['popularity'] >= 43) & (df['popularity'] < 55), 2, inplace=True)\n",
    "df[\"popularity\"].mask(df['popularity'] >= 55, 3, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    60223\n",
       "3    58432\n",
       "1    57660\n",
       "0    56410\n",
       "Name: popularity, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['popularity'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAIsxHQzrZvuQnl2fraSEyK16IjeZA3aX5KhyXZZO13ere3jS7TgAENW+/f599Y1Nr+leUAu9tD1fW33pbFAOAEtgrdW335/dkPy5CNmQfKFXqooUCludrQ+4HQUAokJd57CmguGIHsWT4qyEMsbotd3F8hjp0p0+t+MAQNSamA7pL8+36KXt+dqUn+5aDmOMvvjiJn38YFA32oZcywEA0eRG+5BudQzrVyNoQ/KFni3OVlFWik7VMZIHAItx9f6AclITtbnAvd/NFyOuSihJykhO0DPrs3WtbVDTwbDbcQAgKv3tB/fVOzqlLx8rdTuKfmHvBqUlefWNS/fcjgIAUeHb799XaqJXPxdhG5I/zOMxOllVqPNNPZqYZt8/AHiS4YkZ3Q6M6rmSHHkidHFhXtyVUJK0d1OupoJh1XWyag4ASzU5E9Jfnr+jF7fmaf+WPLfjKCslUa/tLtYPrnVogBNQAeCJRiZn9INrHfrcrnURtyH5Qq9UFWlyJqyLzT1uRwGAiHatbVBW0u6NuW5Heaq4LKE2F6QrNy1Rl1sH3I4CAFHnOx8+UGAkMu6CmvdrBzZpKhjWP1x54HYUAIho3/94dkPyX9kfmRuSP2z/ljxlpybqrVvdbkcBgIhlrdVH9we0MTdVvsxkt+M8VVyWUB5jtHdTrlp6xlg1B4AlCIbC+svzd7R/c55e3Or+XVDzyouytH9znr713n2FwtbtOAAQkeY3JK9YlxWxG5I/LNHr0bFyv842dCsYYhsNAHiUzqFJdQ9PaXdJ5N8FJcVpCSVJe0pyZSRduc/dUACwWB/c61fn0KS+9KnNEbeZ7RcPbNL9/nGdb+IkJQB4lFsdw6rrHNYX9m+MuNfwxzlZVajB8Rl9cK/f7SgAEJGu3h+Q1xjt3JDtdpRFidsSKictSdv8Gfro/oDCllVzAFiMN290KSXRo+pyn9tRfsorVUXyZSbrG5da3Y4CABHpVF23PEb6mZ3r3Y6yaIdKfUpO8OgUI3kA8FNCYauP24ZUvi5TaUkJbsdZlLgtoaTZDcoHx2fU0jPmdhQAiHihsNWbN7t0tNwfkT/kkhI8+sL+Ep1v6lFrH6/rALBQTUO39pTkKi89ye0oi5aWlKBDpT6dutUly8IxAPyE5sCIxqaCUbEh+by4LqEq12UpJdGjK63c3gsAT3P5Xr96R6f06WfWuR3lsb7wQom8xuhb73E3FAA8rHt4Ujfbh3W0wu92lCU7WVmojqHZ/ACAH7t6f1BpSV6VFmW4HWXR4rqESvR6tGtDjm51DGtiOuR2HACIaG/e7FJygkfV5ZH7BqYwK0WvVBXp7y+38boOAA+pbZjdL+9oBL+GP87xikJ5jPTWrS63owBAxJiYDqm+c1g7N+QowRM91U70JHXI3k25CoatrrcPuh0FACJWOGz15s1OHS71KSM58kbxHvbFA5s0NDGjH17rcDsKAESMsw0BFeekqqww0+0oS5abnqT9W/J0qo4SCgDm3WwfUjBstack8k87fVjcl1DFOakqykrRlVZOyQOAx7n6YEDdw1P6zLORO4o374UteSotzNA33rvH/iEAIGlyJqS3m3t1tNwfNafiLfRKVZGaukd1t5c9/wBAkj56MCBfZrKKc1LdjrIkcV9CGWO0Z1Ou2gYm1D086XYcAIhIb9zoUpLXo2NRsJeIMUZfPLBZN9uHdfUBd7kCwHstfZqYCUXlflDzTlYVSZJOMZIHAOobnVJr37j2bMyJusWFuC+hJOm5jTnyGHE3FAA8Qjhs9eaNTh0qLVBmSqLbcRbl53cXKyM5Qd9+/77bUQDAdbUNAaUkenRga77bUZatOCdVzxRnsS8UAEi6+mBQRtKujdE1iidRQkmSMpITVF6Upav3BxQKM7oBAA+71jaojqHJiD4Vb6GM5ASdqCzUmfpuBUNht+MAgGustTrbENDL2wuUkuh1O86KvFJZpI/uDyrA9AKAOGat1dX7A9rqS1dOWpLbcZaMEmrOvk25GpsOqbGLo18B4GFv3uxSotfoeEWh21GW5ERloQbHZ3SZu1wBxLHmwKjaxOLdeQAAIABJREFUBiZ0tDy6XsMfZX4k73R9t8tJAMA9l1sHNDA+o90luW5HWRZKqDk7CjOVmZzASB4APMRaqzdudOql7QXKTouOUbx5h0p9SvJ6dLqONysA4ldNQ0CSdLQ8eveDmldamKHN+Wl66xav6wDi1//4qE1JXo+q1me5HWVZKKHmeD1Gu0ty1Ng9opHJGbfjAEBEuNk+rLaBiag4FW+hjOQEvbQ9X6frujklD0DcqqkPqHJdloqyU9yOsmLGGL1SVaRLd3o1zO/rAOLQ5ExIr1/vVNX6LCUnROeINSXUQ/ZsylXYSh9zmhIASJJ+dKNTCR6jk5XROcZxorJI9/vH1dQ96nYUAFhzg+PTutzaHxUnmy7WyapCzYSsaufu8AKAeHK6rlsjk8GoHcWTKKF+gj8zRSV5abrcOsCqOYC4Z63Vmzc7dWBbflRueihJx+feeJ2u4zQlAPHnfFOPwjY2RvHm7d6Yq4KMZJ1iJA9AHPofH7VpXXaKtvrS3Y6ybJRQC+zdlKuekSnuhgIQ9+o6h9XaNx6Vo3jz/Fkpem5jjk6xLxSAOFTTEFB+epJ2bYi+I7wfx+MxOlFZqHONAU3OhNyOAwBrJjAyqQvNvXptd7E8xrgdZ9kooRZ4tjhbiV6jv7/c5nYUAHDVmze65I3iUbx5JyoLdb1tSF1DHOkNIH4EQ2Gda+zRkTK/PJ7ofbPyKK9UFWpsOqR37/S6HQUA1swPr3UqFLb6hT3FbkdZEUqoBVISvXpmfbZ+eK1DE9OsrgCIT/On4r24NU/5Gclux1mR+RKNI70BxJOP7g9qaGImpvaDmvepbQXKTE5gJA9AXDld16Wywkxt92e6HWVFKKEeYe+mXI1OBfVPtzrdjgIArmjqHlVL75g+/Uz0juLN2+6fPdL7NCN5AOJITUNACR6jgzsK3I6y6pISPDpS7tfpum6FwuzjCiD2DU3M6PK9AR2NgYUFSqhH2FyQrpK8NP39h4zkAYhPP7rRKWOkV6qK3I6yYsbM7h9y6U6vRjjSG0CcqGno1v4tecpMSXQ7iiNeqSpU39i0rrQOuB0FABx3sblHwbDVsRg4aIIS6hE8xujzezfoUkufHvSPux0HANbcmzc6tX9znnyZ0T2KN+9kVZFmQlbnm3rcjgIAjnvQP66m7tGYOhVvocOlPiV5PTp1i9NPAcS+mvqActIStbsk1+0oK0YJ9Ri/sHeDjJH+4Qp3QwGIL83dI2oOjEb1qXgL7SnJVX56EiN5AOJCTUNAknSsIroPlniSzJREvbQ9X2/VdclaRvIAxK5Q2Kq2MaDqMr+8MXDQBCXUYxTnpOrl7QX6/6608YMNQFx582aXjJFefSb6R/HmeT1GR8v9qmkIaCYUdjsOADiqpiGgrQXp2lKQ7nYUR71SVaQH/ROq7xxxOwoAOObjB4MaGJ9RdYzc3UoJ9QSf27le7YMTquscdjsKAKyZN250at+mXBVmpbgdZVWdqCzUyGRQ77f0ux0FABwzNhXUpTt9MfNm5UmOVRTKGOlUHSN5AGJXTUO3vB6jwzt8bkdZFZRQT1Bd7pcxYnwDQNxo6RlVQ9dITJyKt9DBHT6lJHp0mjcrAGLYO7d7NR0Kx8TmtU/jy0zWvk25eusWv6sDiF1n6wPatylX2WmxcdAEJdQT+DKTtackV2fq+cEGID68eXO2oImlUbx5qUlevbzdp9N13YxZA4hZNQ0BZSYnaN/mPLejrIljFYWq7xxW9/Ck21EAYNW1D06ooWtExypiZ2GBEuopjlcU6mb7sDoGJ9yOAgCOe+NGp3aX5Gh9TqrbURxxsrJQHUOTutXBmDWA2GOtVU1DQIdKfUpKiI9f8w/Njadc4PRTADFo/qCJWDrtND5+Oq3AicrZ/7PPcjcUgBjX2jemWx3D+pkYOhVvoaMVjFkDiF23OoYVGJmKi/2g5lWsy5QvM1kXmnvdjgIAq662IaCSvDRt82W4HWXVUEI9xTZfhrYUpOsUb1gAxLhYHsWbV5CRrL0luZRQAGLS2fqAjJGOlMXG5rWLYYzRwR0Feru5R6Ewo9YAYsfEdEjv3O7V0XK/jDFux1k1lFBPYYzR8Qq/3mvp08jkjNtxAMAxb97o1K4N2dqQm+Z2FEedrCpUXeew2gbG3Y4CAKuqpjGg5zbmqCAj2e0oa+pwqU8D4zO62T7kdhQAWDXv3unVVDAcU/tBSZRQi3KiskgzIavzzJoDiFEP+sd1rW1In47hUbx5Jypn7/Q6w91QAGJIz8iUrj0YjItT8RZ6eXuBjGFfKACx5WxDQGlJXu3fElsHTVBCLcKekhzlpiXyhgVAzHrr1uwo3meeif0SaktBurb7MxizBhBTahtnN6+Np/2g5uVnJOuZ9dm60EwJBSA2WGtV2xDQwR0FSk7wuh1nVVFCLUKC16Oj5YWqaQhoJhR2Ow4ArLqz9QGVFWaqJD+2R/Hmnags1Pt3+zU0zpg1gNhQUx9QUVaKKtdluR3FFYdKC/TR/UENs30GgBhQ3zmizqFJHSsvdDvKqqOEWqQTlX4NTwb14b1+t6MAwKoamZzRh/f642r1/ERloUJh+8mdAwAQzaaDYV1s7pk7ATR2Nq9dioM7fAqFrd693ed2FABYsZqG2Tv2j5TH3kETlFCLdHCHT0kJHp2p4w0LgNjyzu1eBcNWR+OohHpuQ458mcmckgcgJnxwt19j06G43A9q3p6SXKUneRnJAxATzjYEtGtDtvyZKW5HWXWUUIuUnpygl7bl63R9l6zl+FcAsaO2oUeZKQnaU5LjdpQ14/HMnnx6rjGgqWDI7TgAsCJnG7qVnODRp7YVuB3FNUkJHh3YVqALTT38rg4gqvWNTunjB4MxO6VACbUEJyqL9KB/Qk3do25HAYBVYe3sSNqhUp8SvPH1I+FEZaHGpkO6dIfRDQDR7Vxjjw5sy1dqUmxtXrtUh0sL1DYwobu9Y25HAYBlO9fYI2sVk/tBSZRQS3KsYraJPF3X5XISAFgdtzqGFRiZUnVZbK60PMmnthUoLcnLSB6AqPagf1x3e8d0uDT29g1ZqkNz/xtcbO51OQkALF9NQ0D+zGRVrY/NgyYooZagMCtFuzbm6HQ9+0IBiA3n5jbmjsc3LymJXh0u9elMfbfCYUY3AESn+T2QDsXh6/hCm/LTtSk/TRea2BcKQHSaCYV1oalHR8v98nhi86AJSqglOlHh17UHg+oennQ7CgCsWG1jj3ZuyJYvM9ntKK44UVmo7uEpXW8fcjsKACzLhaYeFeekamtButtRIsKhHT5daunTdDDsdhQAWLIP7/VrZCoYs/tBSZRQS3aiskiSdJa7oQBEuYGxaV29P6AjcTiKN+9ouV9ej2HMGkBUmgmF9e7tPh0qLZAxsblivlSHSn0anw7pcmu/21EAYMlq6gNK8nr08vbYPWiCEmqJSgsztDEvlTcsAKLeheYeha1UXRa/Ixw5aUl6fnMu+0IBiErXHgxqZCqoQzvi93V8oQPb8pXgMbrQxL5QAKJPTUNAL27LV3pygttRHEMJtUTGGJ2oKNI7d/o0NhV0Ow4ALNu5xh7lpSdp54Yct6O46kRlkZq6R9Xax2lKAKLLhaYeeczsQQuYlZGcoL2bctkXCkDUuds7ppbeMR2L4VE8iRJqWY5X+jUdDOtiMz/cAESnUNjqfFOPDpf65I3RTQ8Xa/4HfW0DY9YAosv55l49tzFH2WmJbkeJKIdKfarrHFbPyJTbUQBg0Wrmfhc9SgmFhZ7fnKeslASdruMNC4DodL1tUP1j0zoSx6N48zYXpGtrQbpqG1lYABA9Bsendb1tkFPxHmH+xFcWjAFEk5qGbu3wZ2hjXprbURxFCbUMiV6Pjpb7VdPQrWCIkzcARJ/axtkRDvYRmXW4zKf3Wvo0MR1yOwoALMrbt3tlrXSQ1/GfUrkuS/npSYzkAYgaI5Mzer+lX0crYvsuKIkSatmOVxZqYHxGH90fdDsKACzZucaAdpfkKjc9ye0oEaG6zK+pYFjvtfS5HQUAFuVCU4+yUhK0a0O221Eijsdj9PKOAl1s7lU4bN2OAwBPdbG5V8Gw1bHyQrejOI4SapkOl/qU6DU6U8+JSgCiS8/IlK63DcX1qXgL7d+Sp9REr2obGbMGEPmstbrY3KuXdxQowcuv849yaIdPfWPTquscdjsKADzV2fqAslMTtack9g8M4qfWMmWmJOrFrfk6Xdcta1lhARA9zs0VLUfKYv9238VKSfTqpe35qmkI8JoOIOLdDoyqc2iSUbwnOFg6e2LgeUbyAES4cNjqXGNAh0t9cbGwEPv/QgedrCzU3d4x3enhWG8A0eNcY4/8mcmqWp/ldpSIcqTMr7aBCV7TAUS8+WKFTckfz5+Zoop1WewLBSDiXWsbVN/YtI7FwX5QEiXUihyrmJ3XPF3HSB6A6DATCutCc4+qy/wyxrgdJ6LMnxR4jpE8ABHuYnOvtvnSVZyT6naUiHaotEBXWgc0OhV0OwoAPFZNQ0Ae8+OTPWMdJdQKrM9J1TPFWewLBSBqfNQ6oJHJoKrL4+OH3FJsyE3TDn+GzjWyag4gck3OhPT+3T5G8Rbh8A6fgmGr9+5w6ASAyHW2PqC9m3KVkxYfBwZRQq3Q8YpCfXR/QD0jU25HAYCnqm3sUYLH6KXtBW5HiUjV5X69f7dPY6yaA4hQH97r1+RMOG5WzFdi7+ZcpSZ6daGZxQUAkalraFJ1ncOqLo+PUTyJEmrFTlQWylqptoHxDQCR71xjQM9vzlNmSqLbUSLSkTKfZkJW79zudTsKADzSxeZeJXk9emFrnttRIl5yglcHtuWzLxSAiDW/DcSx8kKXk6wdSqgVqlyXpeKcVJ1iXygAEa5jcEINXSOM4j3Bvk15ykhOUC0jeQAi1IWmHu3bnKu0pAS3o0SFQzsKdK9vXPf7xt2OAgA/paYhoPXZKSotzHA7ypqhhFohY4yOV/j19u0eTUyH3I4DAI81v9dRdVn83O67VEkJHr28vUDnGgOy1rodBwB+QvfwpBq6RjgVbwnm/7c6z0gegAgzFQzpndu9OlIeXwcGUUKtguOVhZqcCettxjcARLDaxoCKc1K13R8/Ky3LcaTMp86hSTV1j7odBQB+wsXm2d81D7Ep+aJtKZg9RZCRPACR5sO7AxqbDulonC0QU0Ktghe25CszOUFnOSUPQISaX2mpLvfF1UrLchyZ+0WgtpG9/gBElgtNPSrISFZ5UabbUaKGMUaHSn26dKdPM6Gw23EA4BO1jQElJXj0qe35bkdZU5RQqyApwaNDpT6dqQ8oHGZ8A0Dk+fDugManQ4ziLUJRdooq1mVx4ASAiBIOW719u1eHdhTI42ExYSkOlxZodCqoj1oH3I4CAJ+obQzoxa35cbfHHyXUKjlW4Vfv6JSutw+5HQUAfsr8SsuBbfG10rJc1WU+XW4d0PDkjNtRAECSdKtjWP1j0+wHtQyf2l4gr8foAvtCAYgQrX1jaukZU3VZ/L2mU0KtkuoyvzxGjOQBiEi1DfG50rJc1eV+hcJWbzez1x+AyDBfoLy8o8DlJNEnKyVRuzfm6EITr+kAIsP8HffxOKVACbVKctOTtHdTrs7UM74BILLc6x1TS298rrQs1+6NOcpKSdA59oUCECHON/Woan2WCjKS3Y4SlQ6V+nSzY0h9o1NuRwEA1TT2aGtBujYXpLsdZc1RQq2iYxWFqu8cVvvghNtRAOAT80VKPK60LFeC16ODpT7VNvbIWvb6A+Cu+f2MGMVbvkOlPlkrTrMG4Lrx6aDea+n75DCceEMJtYqOVxRKkmoYyQMQQWobe7QlTldaVqK6zK+ekSnd6hh2OwqAOHfpTp+CYauDjOIt27PF2cpNS9T5RvaFAuCuS3f6NB0M62g5JRRWaJsvXZvz0xjJAxAxJqZDutTSpyOM4i3Z4bk7DhjJA+C2C009Skvyat+mPLejRC2vx+hwqU/nmnoU4jRrAC6qaQgoLcmr57fkuh3FFZRQq8gYo2MVhbp0p09jU0G34wCALrX0ajoYZhRvGXyZydq5IVu1rJoDcNnF5h4d2JqvpAR+dV+J6nK/+semdb1t0O0oAOKUtVbnGnv08vYCJSd43Y7jCn6SrbJjFX5Nh8K6yIlKACJAbUOPUhO9emErq+fLcaTUp6v3BzQ4Pu12FABx6n7fuO71jTOKtwoO7fDJY8TiAgDXNHWPqn1wQtVxOoonUUKtuuc35ykzJUFn2RcKgMustaptDOilOF5pWakj5X6FrXSBhQUALjnfPFuYsCn5yuWmJ2l3SS5j1gBcUzv3+hPPW2VQQq2yRK9HR8r8qm0MKMy8OQAXNQdG1TYwEdc/5FZq14Yc5aYl6lwDb1gAuONiU4825KZqC4dLrIrqMp+utw2pZ2TK7SgA4lBNQ0AV67K0LjvV7SiuoYRywPEKv3pHp/Ux8+YAXHRm7o7MYxXxe7vvSj28kS0LCwDW2kworHfv9OngDp+MMW7HiQnzR6Kfb2IkD8DaGpqY0ZXWAVXH+QIxJZQDjpT65fUYRvIAuOpMXbeeKY7vlZbV8MlGtu1DbkcBEGeu3h/U6FRQh0vZD2q1VK3Pkj8zWbXc4Qpgjb3d3KtQ2OpoHO8HJVFCOSI7LVH7NuXqbD0/3AC4o3d0SlcfDOp4RaHbUaLe7B0I4g0LgDV3sblHXo/RgW2UUKvFGKPqMr8uNPdoJhR2Ow6AOFLTEFB2aqKe25jjdhRXLamEMsZsMMb8N2NMhzFmyhhzzxjzZ8aY3CU8xzljjH3Cn5Sl/zMiz/GKQjV0jehB/7jbUQDEoZqGgKwVJdQqyEtP0nMbc3SO0Q0Aa+xCU4+e25ij7NREt6PElOpyn0Ymg/qodcDtKADiRDhsdb4poEOlPiV44/teoEX/640x2yRdkfTrkj6Q9KeSWiR9WdIlY0z+Eq/9h4/5E1zi80Sk+T1YGMkD4Iaz9d0qykpR1fost6PEhOoyv663Dap3lI1sAayN+THgQzvie+8QJ7y0vUAJHqPaRhYXAKyNG+1D6h2d1tFyXtOXUsH9hSS/pN+21r5mrf09a+1RzZZRZZL+aCkXttZ+5TF/YqKE2urL0NaCdJ1lfAPAGpucCelCU6+OVfjZyHaVVJf5Ze3sXQkAsBYuNvfIWukQ+0GtusyURD2/OU/nGvk9HcDaqG0MyBixsKBFllDGmK2STkq6J+m/LPj0H0gak/RFYwxnxz7kWIVf77X0aWRyxu0oAOLIpZY+TcyEdLySUbzVUrU+SwUZyayaA1gzZ+sDyk9P0s4N8b13iFOqy31q6BpRx+CE21EAxIHaxtnx6vyMZLejuG6xd0IdnXs8Za39iR38rLUjkt6RlCbpxcVe2BjzS8aY3zPG/FtjzKeNMTH3/8bxikLNhKwuNve6HQVAHDlT1620JK8ObF3qlDQex+MxOlzq04WmHgXZyBaAw2ZCYZ1rDKi6fPbEZay+6rLZrTNquRsKgMN6R6d0vW3wk9edeLfYEqps7rHpMZ9vnnssXcK1/07Sf5D0nyS9Iem+MebzS/j+iLd3U66yUxN1hn2hAKwRa63O1gd0cEeBUhK9bseJKdXlPg1NzOha26DbUQDEuMv3BjQ8GdTxCt6wOGW7P0MbclNV28AdrgCcdb5xdrz6aDmv6dLiS6jsucehx3x+/uOLuV/4+5I+J2mDpFRJ5Zoto3IkfccY8+nHfaMx5jeMMZeNMZdHBvsXFdxNCV6Pqst8OtfYo1DYuh0HQBy41TGsruFJHeNUvFV3cLtPXo/hDQsAx52t71aS16OD7B3iGGOMqsv8eud2r6aCIbfjAIhhNY0B+TKTVbmOA4OkpW1M/iTz9wk/tWmx1v6ptfZ1a227tXbSWttorf13kv63uTx//ITv/Zq1dp+1dl9mTt7qJHfYsYpC9Y9N6+p9joAF4Lwz9d0yhpUWJ2SnJWpvSS53twJwlLVWZ+q7dWBbvtKTE9yOE9Oqy32amAnpg7uRv7gNIDoFQ2FdaOrRkVKfPIxXS1p8CTV/p1P2Yz6fteDrluO/SgpKes4Yk7mC54koh8t8SvAYnaln3hyA887Ud2v3xhwVsOmhI05WFaqha0T3+8bdjgIgRt3pGdO9vnFG8dbAga0FSkrwcIcrAMd8dH9QI5NBFogfstgSqnHu8XF7Pu2Ye3zcnlFPZa2dlDQy99eYOWUvKyVR+7fk6Swr5wAc1jU0qZvtw5yK56CTlUWSpFN1XS4nARCr5n9nPMpYteNS5w7xOMfm5AAcUtMQUILH6KUdBW5HiRiLLaFq5x5PGmN+4nvm7lp6SdKEpPeWG8QYUyYpV7NFVEwdJ3esolDNgVFWzgE46mzD7BuX47xxcUxJfprKizL11i1KKADOOFsfUMW6LBXnpLodJS5Ul/nU0jume71jbkcBEIPONQb0/OY8ZaUkuh0lYiyqhLLW3pF0StJmSb+14NN/qNk7l75hrf3k1dsYU26MKX/4C40xW40xxQuf3xhTIOmv5/76d9ba4KL/BVFg/nZq9hEB4KQzdd0qyUvTDn+G21Fi2itVRbrcOqDe0Sm3owCIMQNj07rc2q8TjOKtmaPlsws3tdwNBWCVtQ9OqKFrRNXlHDLxsKVsTP6vJQUk/bkx5nvGmP9gjKmR9DuaHcP79wu+vn7uz8MOSWo1xpw1xnzNGPMfjTHflnRb0gFJlyX9H8v5h0SyTfnp2u7P+OQuBQBYbePTQb1zp0/HKvwyhk0PnXSyqlDWzpZ+ALCaahsDCltxwukaKslP01Zfumob2RcKwOqaH/WtLmNh4WGLLqHm7obaJ+nrkl7Q7Gl22yT9uaQD1tq+RTzNFUnfkuSX9Atzz/GqpBuSflvSS9bawSXkjxrHKvx6v6Vfw5MzbkcBEIMuNvdqOhjWCd64OK5yXZY25KYykgdg1Z2tnz3G+9nix50FBCdUl/n1XkufxqdjahgDgMtqG3q0ITdV25lS+AlLuRNK1toH1tpft9aus9YmWWs3WWu/bK39qXNNrbXGWmsWfOyGtfZ/ttY+a63Nt9YmWmvzrLUHrbVftdZOr/QfFKmOVxQqGLY6zyoLAAecre9WZkqCnt+S53aUmGeM0StVRXrndp9Gp3jDAmB1TAfDOt/Uo2Plfo7xXmPVZX5NB8O6dGcxa+oA8HSTMyG9c7tX1WVMKSy0pBIKy7enJFe5aYmckgdg1YXDVjUNAR0p8yvRy8v6WjhZWajpUJgTlQCsmg/u9mt0Ksgongue35KrtCSvahp4TQewOj6426+JmZCOljOKtxDvVtaI12NUXeZXbWOPgqGw23EAxJCP2wbVOzr9ySEIcN6+zXnKT0/SqVssLABYHWfqu5Wc4NHL2znGe60lJ3j18vYCnWvskbXW7TgAYkBNQ0DJCR69uDXf7SgRhxJqDR2vLNTQxIyutA64HQVADDlT1y2vx+hIKSXUWvF6jI5XFKq2IaDpIAsLAFbGWqsz9d16eXuBUpO8bseJS9XlfrUPTqg5MOp2FABRLhy2+qebXTpU6uM1/REoodbQwR0FSvQaneVWXwCr6Gx9QM9vzlV2WqLbUeLKyapCjUwF9e6dXrejAIhyTd2jahuYYBTPRUfKZo9Qr+X3dAArdPXBgLqGJ/Uzz65zO0pEooRaQ5kpiXpxa77OsC8UgFXyoH9cjd0jOs4blzX30vYCpSV5daqO13QAKzP/u+Exxqpdsy47VeVFmaplrz8AK/TGjS4leT06ymv6I1FCrbGTlYVq6RlTY9eI21EAxIAfv3GhhFprKYleHSnz6XRdt8Jh9hABsHxn67v1bHG2CrNS3I4S16rL/bp8b0DDkzNuRwEQpcJhqzdvdOpQaYGyUphSeBRKqDX26jPr5DHS69c73I4CIAacqe/WNl+6thSkux0lLr1SVaSekSldfTDodhQAUap3dPY1hDta3Vdd5lcwbPV2M2PWAJbnWtugOoYm9elnGMV7HEqoNebLTNaBbfl6/Xonp28AWJHhyRm939Kv45W8cXFLdblfiV6jU7e63I4CIErVNARkLaN4kWBPSY6yUhLYFwrAsr1xo1OJXsPv509ACeWCz+5cr7u9Y7rVMex2FABR7Hxjj4Jhy+q5i7Lm9vp761YXCwsAluVsfbfWZaeoan2W21HiXoLXo0OlPp1r6mHMGsCSWWv1xo0uvby9QNmpjOI9DiWUC16tKlKCx+iHjOQBWIGz9d3KTUvUnpJct6PEtVeqinSvb5xjvQEs2eRMSBebe3W03C9jjNtxoNmRvJ6RKdV1slgMYGmutw2pfXBCn+FUvCeihHJBbnqSXt5RoNevMZIHYHmCobBqG3tUXe6X18MbFzednLvd+q2bjOQBWJr3Wvo0Ph3ijtYIcrjMJ0mM5AFYsjdudirBY3SCUbwnooRyyed2rlf74ASb2QJYlsutAxqamNEJ3ri4zp+Vot0lOTpV1+12FABR5mx9QKmJXh3Ylu92FMwpyEjWrg3Zqm2khAKweLOjeJ16aXuBctKS3I4T0SihXHKiqlBJXo9+eI2RPABLd7a+W0lejw6W+tyOAs2O5N1on70FGwAWw1qrs/XdOrijQCmJXrfj4CHV5X5dfTCowPCk21EARIlbHcN60D+hzzxb5HaUiEcJ5ZKslEQdKfPpR9c7FWLjQwBLdKY+oBe35SsjOcHtKNCPR/I4JQ/AYtV1DqtjaJJRvAj02Z3rZK30+vVOt6MAiBI/utEpr8foZCUl1NNQQrnos7vWKzAypQ/v9bsdBUAUudMzqru9YzrOcd4RY6svQzv8GTp1i5E8AItztj4gY2bvukFk2e7PVMW6LA4RArAo1lq9eaNTn9qWr9x0RvGehhLKRccr/EpN9Op1fsABWIIzc3sPHWP1PKK8UlWkD+71a2Bs2u0oAKLA2fpu7dqQI19msttR8Ag/u2u9rt4f1IP+cbejAIhwdZ3Dutc3zql4i0QJ5aK0pAQdrfDrzRslWesCAAAgAElEQVRdCobCbscBECVev96pZ4qzVJyT6nYUPORkVaFCYauznKgE4CkCw5O61jbECUoR7LM7Z99M/oD9WwE8xZs3uuQxP96eAU9GCeWyz+1cr76xaV1q6XM7CoAocDswohvtQ3rtuWK3o2CBZ4uztT47RW+xLxSAp5gvq48xVh2xNualaU9JDocIAXii+VPxXtyar/wM7mxdDEoolx0p8ykjOYEfcAAW5R+vtstjpJ99br3bUbCAMUYnq4p0oalH49NBt+MAiGBn67tVnJOqssJMt6PgCX5213o1dI2ouXvE7SgAIlRj94haescYxVsCSiiXpSR6dbKyUP90s0vTQUbyADxeOGz1vasdOrjDJ39mittx8AgnKws1FQzrQlOv21EARKjJmZDevt2r4xV+GWPcjoMn+MzOdfIYRvIAPN4bc6N4r1RxKt5iUUJFgM/uWqfhyaAuNve4HQVABPvgXr/aByf087sZxYtU+7fkKTs1UacYyQPwGO/c7tXkTFjH2Tsk4vkzU3RgW75+eK1D1lq34wCIQG/c6NT+LXkcMrEElFAR4OXtPmWnJur1651uRwEQwb53tV1pSV6drOKNS6RK8Hp0rMKvsw0BzXDgBIBHOFMfUEZygl7Yku92FCzCz+5ar3t947rRPuR2FAARprl7RLcDo4ziLRElVARISvDo1aoinbrVpcmZkNtxAESgyZmQfnSjU69WFSktKcHtOHiCV6qKNDTx/7d33/FVlvf/x1/XycmGJJBBNhCQvZcgKiDVIg5Q3FqtrW21tnbor/PbbXe/tnWgtX5rpbZuceMGRECQvVcgIYtMyCTzXL8/zkGRMhJIcp/xfj4e53HCOXeuvMPjznXO+dzXaGH1viqno4iIn2lt8/DOtlLOH5REhFtvwwPBrOFphIcZXtmgKXki8lmvby7BGJilqXgdolc/P3HZ6HTqm9tYslNbe4vIf3t/Rxm1ja1cMU5T8fzd+WclExXu0i55IvJfPthdTkVdk3Y4DSDxMeFMG5TMa5tK8Hg0JU9EPrVo8wEm9u1NSpzWau0IFaH8xOSc3iTGRvDqRk3JE5H/9uK6IlJ6RnLOgCSno8gpREeEcf5Zyby9tVRriIjIZ7ywtojesRFMH5zidBTpgMtGp3OgppGP8zTCVUS89pTVsbO0ltkjNQqqo1SE8hPuMBezR6bx3o5S6pu0tbeIfKqqvpklO8uYMyadMJd2UgoEnx+eyoGaRjYUHHI6ioj4iUMNzbyzrZQ5Y9I1FS/AXDisD9HhYdolT0Q+sWizd/DIrBFaD6qj9AroRy4dlUZji4d3t5c6HUVE/Mjrm4pp9ViuGJvpdBRppwuH9yEq3MVzawudjiIifuLVjcU0t3m4arz68kATE+Fm5tAUFm05oE0nRATwrgc1oW8vUuM1Fa+jVITyIxP79aZPXKR2yRORz3hxfRFDUnsyLD3O6SjSTnFR4VwyMp1XNhTT0KzRrSICz68tZGhaHMPT452OIqfh8tHpVNU3s3xPhdNRRMRhe8vr2HGglou1K95pURHKj7hchktGprN0Zzk1jS1OxxERP5BXUc/6/YeYO1aL2Aaa6yZlUdfUyuu6sCAS8naX1rKxsJp52lwiYE0bnEzPKLem5IkIi7Z4N5+5eITWgzodKkL5mctGp9Hc5uHtrZqSJyKwcH0RxsCcMelOR5EOmtC3FznJsTzzcYHTUUTEYc+vK8TtMrqgEMAi3WHMGp7K21tLaWxpczqOiDjojc0ljM1OID0h2ukoAUlFKD8zJiuBzF7RvLZJV1lEQp21lpc2FDElJ5G0eL3IBRpjDNdNzGJN/kH2lNU6HUdEHNLa5mHhuiKmD04hqUek03HkDFw+Jp26plaW7CxzOoqIOCS/sp6txTVcoql4p01FKD9jjOHSUel8uLuCg/XNTscREQet23+I/MoGrtCV84B15bhM3C6j0VAiIezDPRWU1TZpQfIgMCUnkaQeEZqSJxLC3tjsnYo3S1PxTpuKUH7o0lFptHosb2494HQUEXHQwvWFRIW79CIXwJJ6RHLhsD68sK6I5lbtqCQSip5fW0ivmHAuGJLidBQ5Q+4wF5eMTOO97WXUNWnTCZFQtGhLCaMz48nsFeN0lIClIpQfGp4eR05SLK/qKotIyGpu9fDaphIuHJZKz6hwp+PIGbh2YhZV9c28u11r/YmEmuqGFt7eVsqcMRlEuPW2OxhcNjqdplYP72zTxWKRULOvop5NhdXM1lS8M6JXQz/knZKXxkd7KymrbXQ6jog4YMnOMg41tHClpuIFvPPOSiY9PoqnNSVPJOS8trmY5laPpuIFkXHZvchIiOaVDbpYLBJq/rUyn/AwwxXa6fSMqAjlpy4bnY7Hoq29RULUwvVFJMZGcO5ZSU5HkTMU5jJcPSGLZbvLKTzY4HQcEelGz68tZHCfngxPj3M6inQSl8tw6eg0lmn9VpGQUt/UynNrC7h4RBopPaOcjhPQVITyU2f16cmozHie/Cgfj8c6HUdEulH14Rbe217GZaPTCQ9TNx0Mrp7gHQXx3JpCh5OISHfZU1bH+v2HuGp8JsYYp+NIJ7psVDqtHsuiLZqSJxIqXtpQRG1jK7ec09fpKAFPn2782Jem9ie3vJ4Pdpc7HUVEutEbm0tobvNwpYb6Bo3MXjGcd1Yyz60poE0XFkRCwgvrCglzGeaMTXc6inSy4elx5CTH8srGIqejiEg3sNbyxIo8RmTEMS67l9NxAp6KUH5s9sg0UnpG8o/leU5HEZFutHB9ETnJsYzMiHc6inSi6yZmUVzdyDJdWBAJem0ey8J1RUwflKxpG0HIGMPlo9NZta+K0hqt3yoS7D7aW8Wu0jpuntJPI1s7gYpQfizC7eLmKX35YFc5u0trnY4jIt2goKqB1fuquHJshl7kgsznhvahd2wEz2iBcpGgt3xPBQdqGpmnBcmD1mWj07EWXtP6rSJBb8HKPBJiwrl8tEa2dgYVofzc9ZOyiXS7eHxFntNRRKQbvLzBO7R/zhhNxQs2EW4X88Zl8M62UirqmpyOIyJd6Pm1hcRHhzNzaIrTUaSLDEjuwfD0OF7ZqF3yRIJZ8aHDvL2tlGsnZhEVHuZ0nKCgIpSfS+wRyRVjM3hxXaF24BAJctZaFq4vYlK/3mT1jnE6jnSBaydm0eqxvLhOC5SLBKuaxhbe2nqAOWPSiXTrA0swu3x0OhsLDrG/UjufigSr/6zaj8dabjpbC5J3FhWhAsCtU/vT2OLhqY/3Ox1FRLrQ5qJqcsvruUILkgetgSk9mdC3F09/XIC1WqBcJBi9vqmEplYPV2kqXtC71Dc1Z+F6LVAuEoyaWtt4avV+Zg7powvEnUhFqAAwOLUn5w5MYsGKfFraPE7HEZEusnB9ERFhLmaPSHM6inShaydmsbe8njX5B52OIiJd4Pm1hZyV0kObS4SAjIRopg1K5l8f5dHY0uZ0HBHpZG9sLqGyvplbztEoqM6kIlSA+NK5/ThQ08iiLQecjiIiXaC1zcOrG4uZOTSF+Jhwp+NIF7pkVBo9It08vVoLlIsEm73ldazNP8hV4zO1uUSIuH3aACrqmnl+raZZiwSbJ1bkk5Mcy9QBSU5HCSoqQgWI6YNS6J8Uyz8+3Od0FBHpAq9uKqairlnTN0JATISby8ek8/rmYmoaW5yOIyKd6MV1RbgMXDFW06pDxeSc3ozOSuDvy/bS5tE0a5FgsbHgEBsKDnHz5L64XLqo0JlUhAoQLpfh1qn92FBwiHX7NYVDJJh4PJYH39/DkNSezBisnZRCwXUTs2hs8fDKBu2qJBIs2jyWF9YVcv6gZFLiopyOI93EGMMd03LIr2zgTc1YEAkaC1bmExsRxjxdIO50KkIFkHnjMukZ5dZoKJEg8+bWA+SW13PnjIG60hIiRmbEMzQtjmc+1pQ8kWCxMreSkupGjWgNQRcOS6V/UiyPLM3VphMiQaCyrolXNxUzb3wmPaO0TEZnUxEqgMRGurl+UjaLthyg6NBhp+OISCew1vLA+3vISY5l9kgtSB4qjDFcNzGLzUXVbCmqdjqOiHSCF9YVEhfl5nND+zgdRbpZmMvw1fNz2FxUzYrcSqfjiMgZevrjAppbPdw8RQuSdwUVoQLMzVP6Yq1lwco8p6OISCd4f0cZ20tquHP6QMI0CiqkzB2TQYTbxbNrNBpKJNDVNrawaEsJl49JJyo8zOk44oArxmaQ3DOSR5bmOh1FRM5Aa5uHf3+Uz9SBiQxM6el0nKCkIlSAyewVw6wRqTy1aj8Nza1OxxGRM2Ct5f7395DVO5rLx6Q7HUe6WXxMOLNHpLJwfRGHm7W1t0ggW7i+iMYWD/PGaSpeqIoKD+NLU/uzbHeFRriKBLB3t5dRXN3IzVP6OR0laKkIFYC+NLU/NY2tvLCuyOkoInIGPtxTwcaCQ9wxbSDhYeqOQ9G1E7OpbWxl0ZYSp6OIyGlqbGnjocV7mNC3F2OyEpyOIw66cXI2PSPdGg0lEsAWrMwjIyGamUO0WVBX0aeeADS+by9GZcbz+PJ9eLQVrEjAeuD9PaTGRTFvvLbyDlWTc3rTLzGGBSvztZitSIB68qN8SmuauOfzgzFG06pDWVxUODdMzuaNzSXkV9Y7HUdEOmh3aS0rciu5cXI2bl0g7jL6nw1Axhi+NLU/e8vrWbq73Ok4InIaVu2tZPW+Kr42LYdIt9YPCVXGGG47L4cNBYd4d3uZ03FEpIPqmlqZvySX885KYnJOotNxxA98eWp/3C4Xf1+21+koItJBC1bmE+F2cd3EbKejBDUVoQLU7JFppPSM5B8f7nM6ioichgcX7yGpRwTXT9KLXKi7dmIWOUmx/P7NHbS2eZyOIyId8PiH+6iqb+buiwY7HUX8REpcFFeOy+C5NYVU1DU5HUdE2qmmsYUX1hVy+eh0esdGOB0nqKkIFaAi3C5untKXZbsr2FVa63QcEemADQWHWLa7gq+cl6NdlITwMBf/7/OD2VNWxwvrCp2OIyLtVN3QwqPL9vK5oX20FpR8xlfPz6G5zcMTK/KcjiIi7fTi2kIamtu4RQuSdzkVoQLY9ZOyiXS7eHx5ntNRRKQDHnx/Nwkx4dw4ua/TUcRPzBqRypisBO57Z5d2yhMJEI8uy6WuqZW7LxrkdBTxMznJPfj8sFQWrMynvkm7WYv4O4/HsmBlPmOzExiZGe90nKCnIlQAS+wRyRVjM3hxXSEH65udjiMi7bC1uJp3t5fxpan96RHpdjqO+AljDD+8eAilNU38Y7mmWYv4u4q6Jh5fnselo9IZmhbndBzxQ1+blkP14RaeWr3f6SgicgrLcyvYW1GvUVDdREWoAHfr1P40tXr4j17gRALC/MW59Ix0c8s5/ZyOIn7m7JxEZg5J4ZElubqwIOLn5i/OpbGlje987iyno4ifGpvdi7P79+b/PtxHc6vW+xPxV9ZaHvKt1XrxyFSn44QEFaEC3ODUnpw7MIkFK/NobNEUDhF/tqeslje2lHDLOf2Ijw53Oo74oe/NGkJ9cysPLt7jdBQROYGS6sM8uSqfeeMyyUnu4XQc8WO3Tx9ASXUjr2wsdjqKiJzAq5tK+GhvFd/+3CDtWN1NVIQKAl+fMYDSmibm60OLiF+bvziXKHcYXzq3v9NRxE8NTu3JvHGZ/GtlPgVVDU7HEZHjuP+9PVhruWumRkHJyU0flMyQ1J78bWkuHo91Oo6IHKO2sYV7X9vGyIx47VjdjVSECgLnDEhi7ph0Hl6ay56yOqfjiMhx5FfW8/LGYm6anK1tX+WkvnvRIIyB+97Z5XQUETlGfmU9z60p4PpJ2WT1jnE6jvg5Ywy3TxvA7rI63t9R5nQcETnGX9/dTXldE7+aO4Iwl3E6TshQESpI/M+lw4iJcPOjhZuxVldaRPzNw0tyCXMZvnJejtNRxM+lxUdz69T+vLShiK3F1U7HEZGj/PXd3YS5DN+YMdDpKBIgLh2VRkZCNI8szXU6iogcZeeBWh5fkcd1E7MZk5XgdJyQoiJUkEjqEckPLh7C6n1VPLe20Ok4InKUokOHeWFdIddNzCIlLsrpOBIA7pg2gLiocH7/5k6no4iIz+7SWhZuKOKWc/qpL5d2c4e5+Mp5/VmTf5A1eVVOxxERvIuR/+TlLfSMcvO9zw92Ok7IUREqiFw7IYsJfXvx2ze2U6WdlUT8xqO+q59fmzbA4SQSKOJjwvnGjIF8sKuc5XsqnI4jIninyMZGuLldfbl00DUTs+gVE84jS/c6HUVEgJc3FLN6XxXfnzWEXlomo9upCBVEXC7Db64cSW1jK79+fbvTcUQEKKtt5KmPC5g3LpOMhGin40gA+cKUvmQkRPPbRdu1oK2Iw7YUVbNoywG+dG5/resnHRYT4eaL5/Tn3e2lrNCFBRFH1TS28Os3tjM6K4FrJ2Q5HSckqQgVZAb16clXz8/hhXWFrMytdDqOSMibvziX1jYPd0zXlXPpmKjwML574SC2FNXw2uYSp+OIhLT/fXsn8dHh3HaedjeV0/PV83PISYrlnuc2Un24xek4IiHrL+/spqKuiV/NGY5Li5E7QkWoIPTNC84iu3cMP164mabWNqfjiISsD3dX8M8VedxwdjZ9E2OdjiMBaO7YDIak9uRPb+2kudXjdByRkLQ2v4rFO8v52rQc4qLCnY4jASo6Ioz7rh1DaW0Tv3hlq9NxRELS9pIanliZxw2TshmVqcXInaIiVBCKjgjjV3NHsLeinoeXaCcOESdU1jXxnWc3MDClBz+ePczpOBKgwlyG7188hP1VDfx7Vb7TcURCjrWWP761k6QekXzxnH5Ox5EANyYrgTtnDOTF9UUs0ghXkW5lreUnL20hLsrN/9Ni5I5SESpITRuUzGWj05m/OJe95XVOxxEJKdZa/t/zm6huaOH+68YSHRHmdCQJYNMHJTMlJ5EH3t9DbaOmcIh0p+V7KvlobxV3zhhATITb6TgSBL55wUBGZcbzo4WbKatpdDqOSMh4cV0Ra/IP8oOLh5AQo7X9nKQiVBD7yaVDiQx38T8vbcFaLWor0l0WrMzn/R1l/HD2EIalxzkdRwKcMYYfXDyEqvpmHv1AOyuJdJeWNg9/eGsH6fFR3HB2ttNxJEiEh7m475oxNDS38f0XNuk9ukg3qD7cwm8XbWdMVgJXj9di5E5TESqIpfSM4vuzhrAit5KF64ucjiMSEraX1PDrN7ZzwZAUTd2QTjM6K4FLRqXx2LJ9HKjWlXOR7vCbN7azqbCaH84eSqRbI1ql8wxM6cEPLh7C4p3lPLW6wOk4IkHvz+/sorK+mXvnjtBi5H5ARaggd8OkbMZmJ3Dv69s5WN/sdByRoHa4uY27nlpPfHQ4f7xqFMboRU46z/d86xfc8e+1NLZo0wmRrvTiukIeX57HrVP7cdnodKfjSBC6ZUo/pg5M5N7Xt5FfWe90HJGgtbW4mgUr87jp7L6MyIh3Oo6gIlTQc7kMv7liJNWHW/jdoh1OxxEJave+vo3dZXX879WjSewR6XQcCTJ9E2P587WjWb//ED98cbOmcIh0kS1F1fzwxc2c3b83P5o91Ok4EqRcLsMfrxpNmMvw3Wc30uZRny7S2Twey09f3kqvmAjuuUiLkfsLFaFCwNC0OG47rz/PrClg1d5Kp+OIBKU3txzg36v289Xzczh/ULLTcSRIzRqRxt0XDmLh+iIeXqrdT0U6W1V9M1/711oSYyN46MZxhIfprbJ0nfSEaH41ZwRr8w/yiPp0kU73/LpC1voWI4+PCXc6jvjolTVEfGvmWWQkRPPjl7bQ3OpxOo5IUCmpPswPXtzEyIx4XWWRLveNCwZy+eh0/vjWTt7eesDpOCJBo7XNwzefWkd5XROPfGE8SRrRKt1gzph0LhmZxl/e3cXW4mqn44gEjbLaRn63aAfj+/Zi3rhMp+PIUVSEChExEW7unTuCPWV1/OntnU7HEQkabR7Lt5/eQHOrh/uvH0uEW92qdC1jDH+4ahSjMuL59jMb2FZc43QkkaDwh7d2snxPJb+eO4JRmQlOx5EQYYzh3rkj6BUTwXee2aA1/0Q6waGGZm7+v9Ucbm7TYuR+SJ+WQsiMISnceHY2j36wl7++u9vpOCJB4ZGluazaV8UvLh9O/6RYp+NIiIgKD+PvN08gLiqcryxYQ3ltk9ORRALaKxuLefSDvdwypS9XT9D23dK9esVG8PurRrGrtI7/1cVikTNS29jCLf9Yzd7yeh67ZQJD0+KcjiTHUBEqxPxqzgjmjcvkz+/u4qHFe5yOIxLQ1u0/yH3v7OLSUWlcNV7DfKV7pcRF8dgtE6isb+L2J9fS1Kqr5yKnY1txDd97fiOT+vXmfy4d5nQcCVEzBqdww9nZPPbhPj7SGq4ip+VwcxtffmINW4preOjGcUwdmOR0JDkOFaFCjMvlncYxd4x3PZG/aRFEkdNS09jCt55eT2pcFL++YiTGaJivdL8RGfHcd80Y1uYf1I55IqfhUEMzX3tyDQnRWohcnPfj2UPJ7h3D3c9upLaxxek4IgGludXDHf9ey8d5Vdx3zWguHNbH6UhyAnqlDUFhLsOfrh7NpaPS+O2iHTy2bK/TkUQCirWWn7y0heJDjdx//Rjio7Xbhjhn9sg0vvO5Qby4roi/faD+XKS92jyWbz61ntLqJh6+aRzJPbUQuTgrNtLNfdeMoaT6MHf+Zz31Ta1ORxIJCK1tHr719HqW7Cznt1eMZM6YDKcjyUmoCBWi3GEu/nLtGC4ekcq9r2/niRV5TkcSCQitbR5+9spWXt5QzLdmnsX4vr2djiTCXTMHcumoNH7/5g7e2VbqdByRgPCnt3eybHcFv5o7nLHZvZyOIwLA+L69+M0VI/lwdznXPfqR1vwTOQWPx/L9FzazaMsB/ueSoVw3KdvpSHIKKkKFMHeYi/uvH8uFw/rws1e28uRH+U5HEvFrNY0t3PrPj1mwMp+vnZ/DN2YMdDqSCODdXelPV49mZEY83356PdtLtGOeyMm8sbmEh5fkcuPZ2Vw7UR9YxL9cNymbv988gT1ldVz58HJyy+ucjiTil6y1/PzVrbywrpDvfG4Qt52X43QkaQcVoUJceJiLh24Yx8whKfzPS1t4evV+pyOJ+KX9lQ1cOX8FK3Mr+f28kfxw9lBt9yp+5ciOeT2i3Nz2xBoq6nT1XOR4NhUe4p7nNjK+by9+dtlwp+OIHNfMoX146quTaWhqY97DK1iTV+V0JBG/88e3drJgZT5fOa8/d83UxeFAoSKUEOF2Mf+mcUwblMwPF27muTUFTkcS8Ssf51Uxd/5yymub+NeXz9ZVc/FbfeKi+PvN3h3zvvj4avZXNjgdScRvWGtZsDKPqx5eSUJ0OPNvHEeEW2+FxX+NyUrgxa+fQ6+YCG54bBWLNpc4HUnEbzy0eA/zl+Ryw9nZ/Gj2UG0SFED0yisARLrD+NsXxjN1QBLfe2ETC9cXOh1JxC+8uK6QG/++ivjocF66cypTBiQ6HUnkpEZlJjD/xnHkVzYw+/5lvLC2ULvmScirPtzCHU+u46cvb2XqwEReu+s8+sRFOR1L5JT6Jsbywh3nMDw9jq//Zx3/+HCf05FEHPfEijz++NZO5o5J5945I1SACjAqQsknjkzlmNw/kbuf3cgrG4udjiTiGI/H8qe3dvLdZ71TNhZ+/Rz6J8U6HUukXS4Y0oc3v30+w9LjuPu5jXzjqfVUN2i7bwlN6/cf5JL7l/Hu9lJ+PHso/3fLRHrHRjgdS6TdesdG8J/bJnPh0D788rVt3PvaNjweXVyQ0OPxWP65fB8/e2UrFw7rwx+vHq3lMQKQilDyGdERYfzfFycwoV9vvvX0en60cDMH65udjiXSrQ43t/GNp9bx4OI9XDshiye+NImEGH1gkcCSkRDNU1+ZzPdmDeatLQeY9dcPWJlb6XQskW7j8Vge/SCXqx9ZCcBzt0/hK+fn6AOLBKToiDAevmk8t0zpy2Mf7uObT6+nsaXN6Vgi3WZ7SQ3X/G0lP391G9MHJ/PgDWMJD1M5IxC5nQ4g/icmws3jX5zIn972LvT2+qYS7rloENdPysatP3QJcmU1jdy2YA2bi6r58eyh3HZefw3xlYAV5jJ8ffpAzh2YxLee3sANj33E184fwHcvHKS1cCSoVdU3c/ezG1i8s5yLR6Tyu3mjiI8OdzqWyBkJcxl+fvlwMnpF85s3dlBe08SjN4/XhTIJanVNrfzlnV08viKPuCg3f7hqFFeNy9QFhQCmd6ByXLGRbn522XDeuOs8hqXF8ZOXt3LZg8tZtVdX0SV4bSg4xJyHlrOnrI5HvzCBr5yfowKUBIVRmQm8fte5XDcxi0eW5nLlw97zXCQYrdpbycV//YDleyr51ZzhzL9xnApQEjSMMXz1/AHcf/1YNhQc4sr5K3hve6nW/pOgY63l9U0lzPzfJTz24T6umZDF+3dP55oJWSpABTgVoeSkBqf25D9fOZv5N46j5nAL1z76EXc9tZ6S6sNORxPpFNZaVu+r4tbHVzP3oeWAd8rGhcP6OJxMpHPFRLj57ZWjeOSm8RQdPMylDyzj36vy9cFFgkabx3L/e7u5/u8fERvhZuGd5/CFKf10MUGC0uWj01nw5Uk0t3n48hNruPSBD3lzS4nWipKgsK+inpv/sZo7/7OOxNhIXvz6Ofz2ypH00np+QUHT8eSUjDHMHpnGjMEpPLw0l0eW5vLu9lLunDGQ287rT6Q7zOmIIh3m8Vje31HGw0tzWZt/kMTYCO65aBBfmNyP+BhdMZfgNWtEKmOzE7jnuY38eOEWFu8o50ezh5CT3MPpaCKnpbXNw/s7yvj7sr18nHfQu1vSFSPpEam3uRLcJucksvie6SxcX8T8xXu4/cl1DO7Tk29cMJDZI9MI02gRCTCNLW3MX5LLI0tyiXS7+Pllw7hpcl8tCRNk9Oos7RYdEcZ3LzDCeOAAABTMSURBVBzE1eMz+dVr2/jjWzt5dk0BP710GDOHatSIBIaWNg+vbCjmkaW57C6rI7NXNL+cM5yrx2cRHaGCqoSGPnFRPHHrJP6xfB9/eHMn724vZWK/Xlw9PovZo9L04V0CQuHBBp79uIBn1hRQWtNEn7hI/nDVKK4en6nRTxIywsNcXDMhiyvHZvDaphIeXLyHbz61nr+8u4tvXDCQy0al6wO8+D1rLUt2lfPzV7aSX9nAnDHp/Hj2UFLiopyOJl3ABOow/Jyho+y9/3yty9q/4ezsLms7WHywq5xfvLqV3PJ6RmXG8/nhqVw0rA8DU3rozZ/4nYbmVp75uIDHlu2j6NBhhqT25I7pA7hkZJrenJ2h/6za36Xtqz/uWmU1jby4vohn1xSwt7yemIgwLhmZxtUTspjYr5f6c/ErLb5RT0+t3s/SXeUATB+UzA1n92XG4GT1511M/b3/a/NY3txygAfe382OA7X0TYzhzukDuWJchnYSE7/i8VjWFxzkzS0HeHPrAQqqDpOTHMu9c0ZwzsAkp+P5va7uj2+c3HettXZCV7TdoSKUMSYT+CUwC0gESoCXgF9Yaw92oJ3ewE+BuUAaUAm8CfzUWlvYnjZUhPIPza0e/rMqn4UbitlYcAiAfokxXDQ8lQuH9WFcdi8NBRbHtHks20tqeGdbKQtW5nGwoYVJ/Xpzx/QBTB+crA/XnUQfSoKDtZZ1+w/y3JpCXt1YTH1zG/2TYrlqfCbzxmWSGq+rkeKcgqoGnvm4gGfXFFBW20RqXBTXTMzi2olZZCREOx0vZKi/Dxwej+Wd7aU88P5uthTVkJEQzXUTs5g8IJFRmfFaTkMc0drmYdW+Kt7ccoC3th6grLaJ8DDD1IFJXDwilbljM3RutlNIFKGMMQOAFUAK8DKwA5gEzAB2AlOttafcOs0Yk+hrZxDwPvAxMASYA5QBU6y1e0/VjopQ/qe0ppF3tpXyzrZSVuRW0NJmSYyNYObQFC4alsq5ZyURFa5ORbpOY0sbGwoOsSavitV5B1mXf5C6plYAPjc0hdunDWBCv94Opww++lASfBqaW3lj8wGeXVPA6n1VuAycPyiZy0enMyoznv5JPXSBQbpUQ3Mr20tq2VpczXvby/hgdzkGmDE4hesnZTNdo54cof4+8FhrWbKznIcW72FNvnfMQFS4i3HZvZick8jZ/XszJjtBH/ylyzS2tLF8TwVvbjnAO9tLOdTQQnR4GNMHJzNrRCozhqQQF6X1WDsqkItQHVn0YT7eAtRd1toHjjxojLkP+A7wa+D2drTzG7wFqD9ba797VDt3AX/1/ZxZHcglfqJPXBQ3Te7LTZP7UtvYwtJd5by9tZRFWw7w7JpCosPDmDowkSGpceQkx9I/KZacpB5aBFpOW3VDC2vyq/g47yAf51WxubCa5jYPAIP79GTOmHQm9e/NpP69SYvXlXKR9oqJcHPV+EyuGp9JXkU9z68t5Pm1hSzZuRHwfoAZnBrHsLQ4hqfHMSw9jiGpPYmJ0FpS0nG1jS1sK65hc1E1W4tr2FJUTW55HUc2+UqPj+KuC87iGo16EukwYwwzhqQwY0gKB+ubWbWvilX7Klm1t4o/v7sLayHS7WJsdoKvKJXI2OwEXTiWDrPWUlXfzN6KenLL6sgtryO3vJ5Veyupb26jZ5Sbzw3tw+eHpzJtULLWYg1h7RoJZYzJAXKBPGCAtdZz1HM98U7LM0CKtbb+JO3EAuWAB0iz1tYe9ZzL9zP6+X7GSUdDaSRU4Ghu9bBqXyXvbCvlw90V5Fc10HbU9rG9YyPISfIWpfonewtTOcmxpMZH0TPSrSlTIcrjsVQ1NFNyqJGS6sMcqGmkpLqRA9WNFB86TEl1IwUHG7AWwsMMIzPimdi/NxP79mZCv14kxGgL1+6iK+Ohoc1j2Xmglu0lNWwrqWFbcQ1bi6upafSONjQG+ifFMiwtjqFpcWT2iiYxNpLesREk9YigV2yE1iMJUY0tbVTWN1NR20RlfRMVtc2U1Tay40AtW4tr2Ffx6VvHPnGRjMyIZ3h6PCMy4hmREUdqXJTeC/gJ9ffBpbqhhdV5VXy0t5JV+yrZWlyDteB2GfrERZGeEEV6QjRp8dFkJESRFh9NekI06QlRxEeH6+8yBHk8ltrGVsrrmthXUU9ueR17fcWm3PI6DjW0fHJspNtF/6RYxmYnMGtEGlNyEolw631AZwmFkVAX+O7fProABWCtrTXGLAcuAiYD752knSlAtK+d2qOfsNZ6jDFvA1/FO8XvlFPyJDBEuF2cd1Yy552VDHgXFd1f1cC+8nr2VdSzt6KOveX1LN1VznNrP7skmMtAXHQ48Ufdjvw7LurIv91EucOIcLu8tzDXCb8OD3PhMt6rQsaAyxjvvzEYl/ffBt+9+ez9ke8LJdZaPPbTe4+vaO2xFmuhzVra2iwtHg+tbdZ783ho9Vha2nyPeSytbR4aWz00NLVS39xGQ3Mr9U3H3De30dDUSk1jCwdqGimtbvpkVNMR4WHeN0Vp8VGMyUrgqvGZTOzXmzFZCbqaItLFwlyGYb5RT/N8j1lrKa5uZGtR9SeFqQ0Fh3htU8lx24iPDicxNoLEHhH0jo0gsUckvWLCiXKHERUeRlS4i8hw79eRbpf3sSP34WG4w8wn/bHLGFyuo742n/3auCDsyNe+x8NcodWXH913t3k+7bc91mI9n37tsRaP57PHeaylzVqstbR5oKm1jcPNbTS2emhsaTvq5uHwUV/XNrZQWddMRV3TJ4WnWt+06GNlJEQzMiOeeeMyGJ4Rz/D0OFJ6au0xke4SHxPOhcP6cOEw7y7X1YdbWJNXxfr9hyg6dJjiQ4dZt/8gB6pLaGn77MCF6PAw0hOiSOwRSWxEGLGRbnpEuomJcNMjMoyYSDexke5PnouNcBMeZnCHubz3LhfuMIPbZQgPO/K197kwl/G+V8d7gcPg7cfh0/fm3ueOOibI+/VjB44c6aeP7uOP9OWf9O0ebz/e5rE0t3pobvPQ1OK9b2713ppaP/vvuqYWDjW0UH24hUOHW6g57Pva91hNYwvHjmFJ7hlJTlIss0emMSC5BwOSYxmQ3IP0hGhN3Zfjam8RarDvftcJnt+Ntwg1iJMXodrTDr52JEiFh7l8HVSP/3qutrGFvIoG9lbUUVbTRLWv4zv6VnTw8Cdft3q6f3fHY4tXtLdv7UBU286DO7K5ZXsPtdb707tj40yXgdgINzGRYZ/c94h0Mz67F6nx0aTFR5EaH0V6fDSp8VEkxkbg0ouZiN8wxpCREE1GQjQXDU/95PHaxhbKapuorGumqr6JirpmquqbqaxroqK+maq6ZvZV1LM2/yAHG1o+Mzq2u7iMt7Bm2t2JBwbLpx9KunsD5IgwF7GRYST1iCSxRwTD0+NI6hFJUg9vwfHI10ee1/RNEf8SHx3OzKF9mDm0z2ce93gsFXVNFPtGo3tv3pHqVfXNlNc1kV/ZQF1TKw3NbdQ3t3Z7/wMcvzjle69+bE9/3HjHefB478mP97sd+9DxZhsd72f640b1YS5DfHQ4Cb6L/71jI+ifFEvCkUEBMRH0jg2nX2IsOck9iI/W0irSMe199Y/33Vef4Pkjjyd0ZTvGmK/iHSkF0HTj5L5bTvHzTtuNXdWwSMckARVOhxDpYic9z9UfS5BQfy6h4IzOc/X3EiDUn0soGHzqQ05PZ12COlJcPtNa7knbsdY+CjwKYIxZ01VzFEX8hc5zCQU6zyUU6DyXUKDzXEKBznMJBcaYNV3VdntXBjsyQin+BM/HHXNcV7cjIiIiIiIiIiIBpL1FqJ2++xOt1XSW7/5Eaz11djsiIiIiIiIiIhJA2luEWuy7v8gY85nvMcb0BKYCh4GPTtHOR77jpvq+7+h2XHgXNz/6553Mo+04RiTQ6TyXUKDzXEKBznMJBTrPJRToPJdQ0GXnuTneyv3HPdCYt/AWie6y1j5w1OP3Ad8B/matvf2ox4cAWGt3HNPO3/AuLn6ftfbuox6/C/gr8Ja1dtZp/0YiIiIiIiIiIuJ3OlKEGgCsAFKAl4HtwNnADLzT586x1lYedbwFsNaaY9pJ9LUzCHgfWA0MBeYAZb52cs/otxIREREREREREb/S7iIUgDEmC/glMAtIBEqAl4BfWGurjjn2uEUo33O9gZ8Bc4E0oBJYBPzUWlt4Wr+JiIiIiIiIiIj4rfauCQWAtbbAWnurtTbNWhthre1rrf3WsQUo37HmeAUo33NVvu/r62snDfgp8EtjTLExpskYk2eM+YsxpldHMhpjevu+L8/XTrEx5h/GmMyOtCPSFYwxmb7z8bTPc2PMEmOMPcktqit/B5ETMcZcZYx5wBizzBhT4zsfnzzNts74b0Wkq3TWue47r0/Ulx/oiuwi7WGMSTTG3GaMWWiM2WOMOWyMqTbGfGiM+fKxa8S2oz316eJ3OvM8V38u/swY83tjzHvGmALfeV5ljFlvjPmZb6ZaR9o688+zHRkJ1VWOM9VvBzAJ71S/ncDUo6f6naSdY6f6fQwM4dOpflOstXu74ncQOZVOPM+XANOAX5zgkHutta2dkVmkI4wxG4DRQB1QiLf//be19qYOttMpfysiXaUTz/U8IAH4y3GerrPW/ukMo4qcFmPM7cDDeGc9LAb2A32AK4F44AXgatuODxLq08VfdfJ5nof6c/FTxphmYB2wDW9dJBaYDEwAioHJ1tqCdrTTOf25tdbxG/AWYIFvHvP4fb7HH2lnO3/zHX/fMY/f5Xv8Tad/V91C99aJ5/kS75+u87+TbrodffO9AJ0FGGC677x+8jTa6ZS/Fd1066pbJ57reUCe07+PbrodewMuAC4DXMc8nor3g7oF5rWzLfXpuvnlrZPPc/XnuvntDYg6weO/9p3n89vZTqf0546PhDLG5AC5eP9wB1hrPUc91xNvZdoAKdba+pO0EwuUAx4gzVpbe9RzLt/P6Of7GRoNJd2qs85z3/FLgGn2BNNdRfyBMWY63quKHRod0pl/KyLd4XTPdd/35gFYa/t1ejCRLmKM+RHeDy4PWmu/eYpj1adLQOrIee47Pg/Un0tgMcaMBjYA71prLzzFsZ3Wn3doPncXucB3//bRvwiAr5C0HIjBO1zsZKYA0cDyowtQvnY8wNu+f84448QiHddZ5/knjDHXGmN+YIz5rjHmYmNMZOfFFXFMp/+tiPi5SGPMTcaYHxljvmWMmWGMCXM6lMhJtPju2zP1X326BKqOnOdHqD+XQHOZ735TO47ttP7c3ZGEXWSw737XCZ7fDVyEd52n986wHXztiHS3zjrPj/b0Mf8uM8bcaa19/jTyifiLrvhbEfFnqcC/jnlsnzHmVmvtUicCiZyIMcYN3Oz755vt+Bb16RJwTuM8P0L9ufg1Y8w9QA+8a55NAM7FW4D6XTu+vdP6c38YCRXvu68+wfNHHk/opnZEukJnnp8v461aZ+Id/TcE+K3ve58xxlx8BjlFnKa+XELJ48BMvB9cYoGReNe37Acs8g2TF/EnvwNGAG9Ya99qx/Hq0yUQdfQ8B/XnEhjuAX4GfBtvAepN4CJrbXk7vrfT+nN/KEKdypF1b8508arOakekK7T7/LTW/tla+5q1tsha22it3Wmt/RFwN96/6d90ZVARh6kvl6Bhrf2FtfZ9a22ptbbBWrvFWns73gU+o4GfO5tQ5FPGmLvwvtfYAXyhs5r13atPF79wuue5+nMJBNbaVN+6wql4d4HMAdYbY8Z1QvPt7s/9oQh1pGIWf4Ln4445rqvbEekK3XF+PoZ33voY3+JwIoFIfbkIPOK7P9/RFCI+xpg7gb/i3d57hrW2qp3fqj5dAsYZnOcno/5c/I6vWLoQ7/S5RGBBO76t0/pzfyhC7fTdn2itprN89yeae9jZ7Yh0hS4/P621jcCRRfljT7cdEYepLxeBMt+9+nJxnDHm28CDwBa8H8wPdODb1adLQDjD8/xk1J+L37LW5uMtug43xiSd4vBO68/9oQi12Hd/kTHmM3l8ozmmAoeBj07Rzke+46YeOwrE1+5Fx/w8ke7UWef5CRljBgO98BaiKk63HRGHdfnfikgAmOK73+toCgl5xpjvA3/Gu4X3DGtt2Sm+5Vjq08XvdcJ5fjLqz8Xfpfvu205xXKf1544Xoay1ucDbeBdtu/OYp3+Bt2q8wFpbf+RBY8wQY8yQY9qpw7sbQSz/Pef2G77237LWqgOQbtdZ57kxJscYk3Fs+77K9eO+fz5tre3IdrIi3c4YE+47xwcc/fjp/K2I+LMTnevGmOHGmN7HOb4v3qvxAE92R0aR4zHG/ATvAs1rgZnW2hNe4FKfLoGqM85z9efiz3znbOpxHncZY34NpAArrLUHfY93eX9urHV+HUDfL7gC73/Ay8B24GxgBt7hXOdYayuPOt4C+BbVOrqdRF87g4D3gdXAUGAO3qGQ5/j+80S6XWec58aYL+Jd+2kpkAtUAdnAbLzzc9cAF1prD3X9byTyWcaYucBc3z9Tgc/jvfK3zPdYhbX2Ht+x/YB9QL61tt8x7XTob0Wku3XGuW6M+TnwA7xXFvfhHcU6ALgEiALeAK6w1jZ36S8jchzGmFuAf+K9Mv4Ax1/jI89a+0/f8f1Qny4BprPOc/Xn4s98U03/CHyA9/NjJdAHmIZ3YfIDeAuw23zH96OL+3O/KEIBGGOygF8Cs/AujlUCvAT84thF4U5UhPI91xvvtoNzgTS8/8mLgJ9aawu78ncQOZUzPc+NMSPx7tgxHu/QyTi8L3RbgWeBv+kFTpziexP2s5Mc8smL2cle4HzPt/tvRaS7dca5boyZBtwOjOXTLb0P4Z0O8i/gX9Zf3qRJyGnHOQ6w1Fo73Xd8P9SnS4DprPNc/bn4M2PMCOAOvNPlMoEEoB5v0eh14P6j++Hu6M/9pgglIiIiIiIiIiLBy/E1oUREREREREREJPipCCUiIiIiIiIiIl1ORSgREREREREREelyKkKJiIiIiIiIiEiXUxFKRERERERERES6nIpQIiIiIiIiIiLS5VSEEhERERERERGRLqcilIiIiIiIiIiIdDkVoUREREREREREpMupCCUiIiIiIiIiIl3u/wOCRcMIK7bydgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x1224 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bighist(df['popularity'], \"Popularity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "This method of using the quartile ranges to divide the songs into different classes has created good class balance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h2><center>Feature Importance\n",
    "\n",
    "<img src=\"https://vignette.wikia.nocookie.net/joke-battles/images/a/ac/Tree.png/revision/latest?cb=20170827155628\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Before moving forward with feature engineering, I decided to run a decision tree classifier and get it's feature importances to see if anything was worth dropping, as I also ended up creating 17 one-hot encoded features from the song's key and time signature features.\n",
    "\n",
    "\"Mode\", the already existing binary feature, and all the one-hot encoded features I made ended up only adding up to about 3% of the model's predictive power. I decided that dropping them shouldn't hurt accuracy, and will only improve runtime and memory usage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(\"popularity\", 1)\n",
    "y = df[\"popularity\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Decision Tree Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=1337, splitter='best')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.71 s\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=1337)\n",
    "%time display(dtc.fit(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>importance scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>acousticness</td>\n",
       "      <td>0.131502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>duration_ms</td>\n",
       "      <td>0.099138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>speechiness</td>\n",
       "      <td>0.097634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>danceability</td>\n",
       "      <td>0.088144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>loudness</td>\n",
       "      <td>0.085170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>valence</td>\n",
       "      <td>0.084127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>tempo</td>\n",
       "      <td>0.081976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>instrumentalness</td>\n",
       "      <td>0.080084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>liveness</td>\n",
       "      <td>0.079830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>energy</td>\n",
       "      <td>0.078725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>mode</td>\n",
       "      <td>0.010154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>key_G</td>\n",
       "      <td>0.008204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>key_A</td>\n",
       "      <td>0.007659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>key_C</td>\n",
       "      <td>0.007493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>key_D</td>\n",
       "      <td>0.007214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>key_C#</td>\n",
       "      <td>0.006291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>key_F</td>\n",
       "      <td>0.006284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>key_E</td>\n",
       "      <td>0.006155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>key_B</td>\n",
       "      <td>0.005899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>key_G#</td>\n",
       "      <td>0.005732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>key_F#</td>\n",
       "      <td>0.005169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>key_A#</td>\n",
       "      <td>0.004813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>time_signature_4/4</td>\n",
       "      <td>0.003975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>key_D#</td>\n",
       "      <td>0.003336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>time_signature_3/4</td>\n",
       "      <td>0.003113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>time_signature_5/4</td>\n",
       "      <td>0.001381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>time_signature_1/4</td>\n",
       "      <td>0.000796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>time_signature_0/4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               columns  importance scores\n",
       "0         acousticness           0.131502\n",
       "2          duration_ms           0.099138\n",
       "8          speechiness           0.097634\n",
       "1         danceability           0.088144\n",
       "6             loudness           0.085170\n",
       "10             valence           0.084127\n",
       "9                tempo           0.081976\n",
       "4     instrumentalness           0.080084\n",
       "5             liveness           0.079830\n",
       "3               energy           0.078725\n",
       "7                 mode           0.010154\n",
       "26               key_G           0.008204\n",
       "16               key_A           0.007659\n",
       "19               key_C           0.007493\n",
       "21               key_D           0.007214\n",
       "20              key_C#           0.006291\n",
       "24               key_F           0.006284\n",
       "23               key_E           0.006155\n",
       "18               key_B           0.005899\n",
       "27              key_G#           0.005732\n",
       "25              key_F#           0.005169\n",
       "17              key_A#           0.004813\n",
       "14  time_signature_4/4           0.003975\n",
       "22              key_D#           0.003336\n",
       "13  time_signature_3/4           0.003113\n",
       "15  time_signature_5/4           0.001381\n",
       "12  time_signature_1/4           0.000796\n",
       "11  time_signature_0/4           0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\n",
    "    pd.DataFrame(\n",
    "        {\"columns\": X.columns, \"importance scores\": dtc.feature_importances_,}\n",
    "    ).sort_values(by=\"importance scores\", ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "X.drop(['time_signature_0/4', 'time_signature_1/4',\n",
    "       'time_signature_3/4', 'time_signature_4/4', 'time_signature_5/4',\n",
    "       'key_A', 'key_A#', 'key_B', 'key_C', 'key_C#', 'key_D', 'key_D#',\n",
    "       'key_E', 'key_F', 'key_F#', 'key_G', 'key_G#'], 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 232725 entries, 0 to 232724\n",
      "Data columns (total 11 columns):\n",
      "acousticness        232725 non-null float64\n",
      "danceability        232725 non-null float64\n",
      "duration_ms         232725 non-null uint32\n",
      "energy              232725 non-null float64\n",
      "instrumentalness    232725 non-null float64\n",
      "liveness            232725 non-null float64\n",
      "loudness            232725 non-null float64\n",
      "mode                232725 non-null uint8\n",
      "speechiness         232725 non-null float64\n",
      "tempo               232725 non-null float64\n",
      "valence             232725 non-null float64\n",
      "dtypes: float64(9), uint32(1), uint8(1)\n",
      "memory usage: 17.1 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Steps Taken Before Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **Train Test Split**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **Scaling**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* **K-Fold Cross Validation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=10000, stratify=y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "scaler = RobustScaler()\n",
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: [ 44419  44423  44427 ... 222722 222723 222724] | test: [    0     1     2 ... 44687 44689 44694]\n",
      "Train: [     0      1      2 ... 222722 222723 222724] | test: [44419 44423 44427 ... 89349 89361 89363]\n",
      "Train: [     0      1      2 ... 222722 222723 222724] | test: [ 88807  88809  88811 ... 134023 134024 134031]\n",
      "Train: [     0      1      2 ... 222722 222723 222724] | test: [133286 133290 133295 ... 178668 178669 178672]\n",
      "Train: [     0      1      2 ... 178668 178669 178672] | test: [177831 177834 177837 ... 222722 222723 222724]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "k_fold = StratifiedKFold(n_splits=5)\n",
    "\n",
    "for train_indices, test_indices in k_fold.split(scaled_X_train, y_train):\n",
    "    print('Train: %s | test: %s' % (train_indices, test_indices))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Train Test Split**: Ended up setting aside an even 10000 samples for model validation, and used the stratify parameter to ensure the popularity score's distribution between the train and test sets stay the same.\n",
    "* **Scaling**: I used RobustScaler to scale all the features for any of the models that need things to be within the same scale.\n",
    "* **K-Fold Cross Validation**: I used Stratified K-Fold with five folds, again to maintain the same distribution between all folds for the popularity score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Setting the Baseline with a Dummy Classifier\n",
    "<img src=\"http://iconbug.com/data/ec/256/5492affab1bad37481f83894defa7a0f.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DummyClassifier(constant=None, random_state=None, strategy='stratified')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 9.95 ms\n"
     ]
    }
   ],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy = DummyClassifier()\n",
    "%time display(dummy.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: 0.2503176563026153\n"
     ]
    }
   ],
   "source": [
    "print(\"Train: {}\".format(dummy.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.2520417535640041\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(dummy, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* First let's see what a Dummy Classifier can do with the data so we know if the actual models are doing better than guessing at random.\n",
    "* As predicted, it can randomly guess correctly 25% of the time, since there are 4 classes, or a 1/4 chance.\n",
    "* As to what metrics I will focus on to assess the models, the higher the popularity range that a class represents, the more recall that I want, due to it being more important to correctly classify songs as being predicted to have a high popularity, as to not get anyone's hopes up. Precision and average weighted F1 score will be secondary metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SGD Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDClassifier(alpha=0.0001, average=False, class_weight=None,\n",
       "              early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,\n",
       "              l1_ratio=0.15, learning_rate='optimal', loss='hinge',\n",
       "              max_iter=1000, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "              power_t=0.5, random_state=None, shuffle=True, tol=0.001,\n",
       "              validation_fraction=0.1, verbose=0, warm_start=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4.23 s\n"
     ]
    }
   ],
   "source": [
    "sgdc = SGDClassifier()\n",
    "%time display(sgdc.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.386458637333034\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Score: {}\".format(sgdc.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.40      0.50      0.45      2424\n",
      "           1       0.38      0.34      0.36      2477\n",
      "           2       0.32      0.14      0.20      2588\n",
      "           3       0.40      0.56      0.47      2511\n",
      "\n",
      "    accuracy                           0.39     10000\n",
      "   macro avg       0.38      0.39      0.37     10000\n",
      "weighted avg       0.37      0.39      0.37     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = sgdc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(y_test, y_pred):\n",
    "    sns.heatmap(\n",
    "        confusion_matrix(y_test, y_pred), annot=True, cbar=False, fmt=\"d\",\n",
    "    )\n",
    "    plt.ylabel(\"True Label\")\n",
    "    plt.xlabel(\"Predicted Label\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.show()\n",
    "\n",
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.37214487092297893\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "cv = cross_val_score(sgdc, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Interesting how much it struggles with recall on class 2 (>= 50% < 75%), and overall mediocre performance, with only 13% better than a dummy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=-1, penalty='l2',\n",
       "                   random_state=None, solver='saga', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.69 s\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(\n",
    "    solver='saga', # Using this solver because it's faster and the data is scaled\n",
    "    multi_class=\"auto\",\n",
    "    n_jobs=-1\n",
    ")\n",
    "%time display(logreg.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.4143091256033225\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Score: {}\".format(logreg.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.49      0.52      0.50      2424\n",
      "           1       0.40      0.26      0.32      2477\n",
      "           2       0.35      0.25      0.29      2588\n",
      "           3       0.40      0.63      0.49      2511\n",
      "\n",
      "    accuracy                           0.41     10000\n",
      "   macro avg       0.41      0.41      0.40     10000\n",
      "weighted avg       0.41      0.41      0.40     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.41436293138484787\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(logreg, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "A little bit better than SGD, but still unacceptable. And it still struggles with class 2, though not as much as in SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 287 ms\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knc = KNeighborsClassifier()\n",
    "%time display(knc.fit(scaled_X_train, y_train))\n",
    "# When trying to get a training score to check for overfitting, doing a knc.score or a classification\n",
    "# report on the training set causes the kernel to hang indefinitely.\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.52      0.55      0.54      2424\n",
      "           1       0.39      0.35      0.37      2477\n",
      "           2       0.47      0.45      0.46      2588\n",
      "           3       0.55      0.62      0.58      2511\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = knc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.4710113000448036\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(knc, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Definitely a step in the right direction. I'll try to change the weights parameter from the default to \"distance\" and see what happens."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## KNN With weights=\"distance\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='distance')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 284 ms\n",
      "\n"
     ]
    }
   ],
   "source": [
    "knc = KNeighborsClassifier(weights=\"distance\")\n",
    "%time display(knc.fit(scaled_X_train, y_train))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.62      0.51      0.56      2424\n",
      "           1       0.49      0.40      0.44      2477\n",
      "           2       0.59      0.62      0.61      2588\n",
      "           3       0.64      0.85      0.73      2511\n",
      "\n",
      "    accuracy                           0.60     10000\n",
      "   macro avg       0.59      0.59      0.59     10000\n",
      "weighted avg       0.59      0.60      0.59     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = knc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.5754629865521703\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(knc, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "An overall 10% increase in F1 score, not bad."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=1337, splitter='best')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.87 s\n"
     ]
    }
   ],
   "source": [
    "dtc = DecisionTreeClassifier(random_state=1337)\n",
    "%time display(dtc.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.9857312829722752\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Score: {}\".format(dtc.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.59      0.52      0.55      2424\n",
      "           1       0.48      0.42      0.45      2477\n",
      "           2       0.59      0.63      0.61      2588\n",
      "           3       0.73      0.84      0.78      2511\n",
      "\n",
      "    accuracy                           0.61     10000\n",
      "   macro avg       0.60      0.60      0.60     10000\n",
      "weighted avg       0.60      0.61      0.60     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = dtc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.5754629865521703\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(knc, scaled_X_train, y_train, cv=k_fold, n_jobs=-1)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* I did a decision tree before, but I was just using it as a feature selection tool. Now I want to see how it did with train/test accuracy.\n",
    "\n",
    "* Starting to see a pattern with the classification report and confusion matrix. Interesting that the 75% and above song popularity range is easier for all the the models so far to predict. Perhaps there is less diversity, and thus less noise in the features of songs with high popularity?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "                       min_impurity_split=None, min_samples_leaf=1,\n",
       "                       min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "                       n_estimators=100, n_jobs=-1, oob_score=False,\n",
       "                       random_state=1337, verbose=0, warm_start=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 15.8 s\n"
     ]
    }
   ],
   "source": [
    "rfc = RandomForestClassifier(class_weight=\"balanced\",\n",
    "    n_estimators=100, n_jobs=-1, random_state=1337)\n",
    "\n",
    "%time display(rfc.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Score: 0.9857312829722752\n"
     ]
    }
   ],
   "source": [
    "print(\"Train Score: {}\".format(rfc.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.67      0.61      0.64      2424\n",
      "           1       0.53      0.50      0.51      2477\n",
      "           2       0.63      0.65      0.64      2588\n",
      "           3       0.76      0.86      0.81      2511\n",
      "\n",
      "    accuracy                           0.65     10000\n",
      "   macro avg       0.65      0.65      0.65     10000\n",
      "weighted avg       0.65      0.65      0.65     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = rfc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1wEEiq1ejRK8nfCsa7H3zQw79NJPw0iUpOaAH4cWLgUvlwGffsX/cF0F8VaEjLCyMl74Zwu5tcQy893mqX1GDDs/cQ0S+CNYuXsuIHsNJTUml1QO3cnWrawAIjwgntvK53FP7P8Tviw/yKwgNZcuV5tXXB1CydAlSU1MZN3oC740cS4ubm/DEUw9RueoF3NykLYsXLgMgIiKCF4f1pXqNfxEREc6ET77m9aGjgvwqTk2BnwX973yWA3sOpD3/5q0vGD94HAA3dLiR1l3uYNQzb1Lr2rqUOb8sT1zzEJVrV6Vj/wfpdUuPYJUdEuInTmb/RxMpOeCf43D49/nsGT4KUlIp+vh9FO54F3uGvkPSmg1sadsZUlIJL1GM2PFv8vfPsyAlhd2vvEXiijVYTH5iP36dhN/nkbTu7yC+stBw470t2bRmIzEFYzAzHhvchT5te7F1/Rbu7NqWa29rzNRPpjDxrS+Y+JbvTbJe40tpeV8rhf0xUlJS6N97MEsWLadAwRi+mfoxM36exaoVa3igfVcGDu6Vrv+NrZoSGZmPG65qQ3T+aH787Qu+mvA9mzZuCdIryBpN6WRDQnxC2uPomGiccwDUbVKfXydMB2DNglXEnFOAIqWKBqPEkHF4/mJS9x9I15Ywax6kpAJwZNFyIkqVAMAdPpLWblGR4DuspOzaTeKKNb4+hxJIXPc34f51vKx4meLUva4eP348BYBCRQuRlJjM1vW+0Pnz14U0aH75Ces1bHU1v078JUdrDXU7tu9iyaLlAByMP8Sa1espXbYUa1atZ92aDSf0d84RExNDeHg40dFRJCUmceBA6L+B5njgm9k9Ob3PM+FwPD2mLwO+Gcx1dzVNa//3k+14bdY7XHnL1Ywf8hEAxcoUI27LrrQ+u7fFUax0sZwuOVcpdMsNHJo5J+151CUXEvv528R+NpJd/YelvQEcFVGuNFEXVubI4hU5XWrIubfPfXww8H1cqu8Y7d+9n4iIcCpdUhmAy1tcQYmy6d8YI6MjqX1NHX7//rccrze3OLd8OS6+5EIWzlucYZ/vvprCoUOHmLNsKrP+nMzIEaPZt3d/DlaZPcEY4T+X0QIz62Rmc81s7pr4DTlYUsb6tu7Jf2/sxovt+9H07uZcWP8iAD59eSyPXH4fM7/8hRvatwDAzE5Y3z/4l5MofF9bXEoKB7+dmtZ2ZPEKNre+ny1tH6FIxzuxyHxpyyx/NKUG9ybu5TdwBw8Fo+SQUfe6euyL28e6JWvTtQ9+9GXu6d2RFye+QsLBBFKOe8O89Pr6rJi7XNM5GYgpkJ833x9Cv2deIv7AwQz71apTndSUVOpffD0N6zTn/ofbU/682BysNHsCModvZosyWgSUzmg959xIYCTAXefdEhJRefRk7P64fcyZ9AeValVhxexlactnTvyFHu89y2evfkzc1jiKl/tnRFWsTHH27Nid4zXnBgVbNiHm6svY1unk5ziS1v9NasJh8lU+n8RlqyAinFJD+hD/3U8cmjojh6sNPRfWu4hLr69PnUZ1yRcVSUyhGLoM7cqwx4fw7O1PA1DzqlqUOz99CDVseRUzvtJ0zslERETw5vtD+PKzb/nhm6mZ9m11Wwum/zST5ORk4nbtZt4fC6hR62I2/rU5h6rNnkCN8EsDdwMtT/ITF6B9nnVR+aOILhCd9rjG1bXYtPJvylQsm9anbpP6bFnr+588/8fZXNWmEQCVa1fl0IGD7PW/Ycg/8l9Rj8L33MH2Lr198/Z+EbFlINz3KxlRthT5zitP8pZtAJTo242kdX+z/8MJQak51Ix96QPub3AvDza8nyGPvszi3xYx7PEhFC5eGICIyAhufagNk8b+kLZOTKEYLmpQndmT/whW2SHtpeHPsWbVet5548NT9t28aStXXFUfgPwx+aldrwZrV68PdIlnLMMRvpl9QdppsxM551pnst1vgILOuYUn2e700ykwmAqXKELXkT0B36VsMyf+wp8/L+DxN5+i3AXlcKmOnZt3Muq/bwCw4Kd51Lq2LkN/eZMjCUd4q/vwYJYfEkoO+i/R9WoQXqQw5SePY88bH1DkXt9UTZk3XwTgyOLlxPUfRnTt6hS+9w5cUgq4VOIGDid1736ial9MoZZNSFy1jnKfvAnAnv+9S8KM2cF8aSGp1QO3Uq/xpZgZk8b8wJLf/vmwfdkNDfjzlwUcSTiSyRa8qd5ltWlzR0uWL13Fd9M/BeDl/sOJjIrkuUFPU6x4Ud77aATLlqzg7tsf4oNRH/PK/55nyszPMTPGj5vIimWrg/wqTs1cBpPMZtY4sxWdc5l/5jlDoTKlkxcMLJLxXKScvm578we7hDxlXoicr8sr/opbdOLJRL8MR/jHBrqZRQIVnHNrznJtIiKSQ045h29mNwKLgSn+57X80z0iIpKLZOWkbT/gMmAvgH9evnIgixIRkbMvK4Gf5Jzbe1yb5tdFRHKZrFyHv9zM/g2Emdn5QBfg98CWJSIiZ1tWRviPAHWBVOAL4AjweCCLEhGRs++UI3zn3EHgKTN7zvfUJZxqHRERCT1ZuUqnjpktAFYBq81snpnVCXxpIiJyNmVlSuc9oKtz7lzn3LlAN3+biIjkIlkJ/IPOuWlHnzjnpgO61Z6ISC6T2b10avgf/mFmI4CP8F2OeQcwLaP1REQkNGV20nbEcc9rHPNY1+GLiOQymd1L56qcLERERAIrS1+AYmY3ABcD0UfbnHMDA1WUiIicfacMfDN7HSgCXI3v6pw26C9tRURynaxcpdPQOdcWiHPO9cJ3I7VzA1uWiIicbVkJ/KN/WXvYzMoAh4GKAatIREQCIitz+N+bWRHgFWAhkAKMDmhVIiJy1mXlXjp9/Q/Hm9k3QH7g/EAWJSIiZ1+WrtI5yn/jtAQzWwhUCExJIiISCFmZwz+ZDL8kV0REQlN2A19/aSsikstkdi+dLzh5sBtQPGAV+U3avTTQu/CMuNRKwS4hT/mye7lgl5CnNHpxX7BL8IzM5vBfy+YyEREJQZndS2dqThYiIiKBld05fBERyWUU+CIiHpHlwDezqEAWIiIigZWVLzGvb2aLgdX+5zXN7H8Br0xERM6qrIzwhwM3AXEAzrk/gWsDWZSIiJx9WQn8MOfcX8e1pQSiGBERCZys3Etno5nVB5yZhQOPAqsCW5aIiJxtWRnhPwR0xXeztO1AA3+biIjkIlm5PfIO4M4cqEVERAIoK99p+zYnuaeOc65TQCoSEZGAyMoc/o/HPI4GbgU2BqYcEREJlKxM6Xxy7HMz+xCYErCKREQkILJza4XzgfPOdiEiIhJYWZnD38M/c/hhwG6gZyCLEhGRsy/TwDczA2oCm/1Nqc45fduViEgulOmUjj/cv3DOpfh/FPYiIrlUVubwZ5tZnYBXIiIiAZXZd9pGOOeSgYbA/Wa2FjiI7zttnXNObwIiIrlIZnP4s4E6wC05VIuIiARQZoFvAM65tTlUi4iIBFBmgV/SzLpmtNA5NyQA9YSc/73+Ak2bXcuunXFcedmN6ZY98lhH+g3oSeWK9dkdt4cqVS/gtTcGUaPmxQzoN4TXho8KUtWhLSwsjNe+Hc6ubXH0vqcPPYf3oEqNqqQkJ7Ni4UqG9RxOSnIKBQsXpNsrT1D2vHIkHklkSPchbFh5/J26vSXy+v8Qfv4luEMHODz2+bT2iJqNiKjZCFJTSVm/hKSZnxNerT756jZJ62MlYjk8biBu16Z/ttfyIcLOKZFuW14VGRXJW58PJzIyH+ER4Uz99mfefuW9tOXd+3fhpjua0ahK83TrXXfjNQx6ux/tm3Vi+aKVOV32acnspG04UBAolMGPJ4wb+zm333rvCe2xsWVodO2VbPx7c1rbnt176fnk87w2/J2cLDHXubXjLfy95p+7c0z9YhodG91Hp+sfJCo6iuZ3NQPgrkfuZO3SdTzY9CFefvxlHur7YLBKDhnJy2Zx+Mv0XzgXdm5Vwi+oyeGx/Tk8ph9J831/CJ+ycjaHxw3g8LgBHJn0Hm5/XLqwD69UC5KO5Gj9oSzxSCKdb3+Cdk060q5JRy5vVJ/qdS4C4F81qlHwnIInrBNTID93dGzD4nlLc7rcbMks8Lc65/o555472c+pNmxmF5pZYzMreFx7szOuOgfNmjmHPXv2ndA+YNAz9On1Esdeqbpr124WzF9MclJyTpaYq5QoU4L6113KDx/9kNY2Z9qctMcrF66kRNkSAFSoUoEFMxcCsHHtJkqXL02REkVytuAQk7plDRw+lK4t4pJrSJo7CVL8v3cJB05YL6LapaSsnPtPQ74oIupcT9Ls7wNZbq6TcCgBgIh8EUTki8A5R1hYGI/2eoj/9X/jhP4P9OjIh69/ROKRxJwuNVsyC3zL7kbN7DFgIr4vS1liZq2OWTwwu9sNFc1aXMfWLdtZumRFsEvJdR7q+wDvDBxFauqJf9IRHhFO49aNmTvdF0zrlq+jYfMrAahWqyqlY0tT0v9mIP8IK1qK8NjKRN3xFFFtuhJW+sQ7n4RXqUfyqn/eWPNdfjPJ83+EpNwRVDklLCyMMVPeYdKiL5n9y1yWLljO7ffcyq+TZxK3Y3e6vlWrV6F0uVLM+HFWkKo9fZkFfuMz2O79QF3n3C1AI6CXmXXxL8vwjcTMOpnZXDObeyTpxFF1KMifP5pu3TszcMDQYJeS61zWuD574/ayevGaky5/dMAjLP5jMUtm+z4efzLiUwoWLsgbP4ygVYdWrFm6lpRkfbvmCSwMomI48smLJM34nMjm96dbHFa6IiQn4uK2+LqXOJewwiVJWbswCMWGttTUVP6vyX3cVPd2Lqr1L2pfVoPGLRvx6bufp+tnZjzR92GGPfd6kCrNngxP2jrndme0LAvCnXPx/u1sMLNGwGdmdh6ZBL5zbiQwEqBYoSoh+Ve9Fc+vQIWK5/Lrb18DUC62DNN//ZLrG7Vhx45dQa4utF1c72IaNGnApdfWJzIqHzGFYnhqWA9e7PIS//d4O4oUL8xzPYen9T8Uf4jB3f65NuCD30azbeP2YJQe0lz8XlLW+MI7dfsGcA7yF4SEeADCq12abnQfXvYCrFQFou8ZABaGxRQiqk1XjkzwxHUYWRK/P575sxZQ98ralK8Yy4TfxgIQnT+aCTPHcnezTlS68HzemOAb+BUvWYxX3h9I9w7/DekTt1m5H352bDOzWs65hQDOuXgzuwl4F7gkQPvMEcuXraLaBQ3Sni9cMo3rrmnN7rg9Qawqd3j3xfd490XfVQ81GtTgtgfa8GKXl2h2ZzPqXlOXp+7qme6cSIFzCnAk4QjJSck0v6sZi/9YzKH4Qxlt3rNS1i0kvHw1UjevwoqUgvDwtLAHI7xyHY58Njitf/LiX0he/ItvaaHiRN3cWWEPFClWmOTkFOL3xxMVHUn9q+rxwYhxNK/VOq3P9NXf0+bKdgA0rf7PTPUbnw1leL83QjrsIXCBfzeQ7syl/6927zaztwK0z4B4+91XufKq+hQvXpQlK35l0MBhjPngs5P2LVWqBD/98gWFChUkNTWVBzt34PJLm3PgQPxJ+4tPlxceZfvm7Qz78lUAZnw/k7HDxlGhcgV6DO1Oakoqf63+myFPvhrkSoMvsllHws+tCtEFib73BZL++Jrkpb8R2eRuotv1gtQUEiePTusfFlsFF78Ht1+fPk+lROni9Bn2X8LCwggLM378enqump/PCgvV+6GF6pROblSvSKVgl5CnfNm9YrBLyFMavbg82CXkKbO3/JzhtHl2vgBFRERyIQW+iIhHKPBFRDxCgS8i4hEKfBERj1Dgi4h4hAJfRMQjFPgiIh6hwBcR8QgFvoiIRyjwRUQ8QoEvIuIRCnwREY9Q4IuIeIQCX0TEIxT4IiIeocAXEfEIBb6IiEco8EVEPEKBLyLiEQp8ERGPUOCLiHiEOeeCXcNJRUTGhmZhuVBMvqhgl5CnFIrMH+wS8pQNq78Odgl5Sr4SF1hGyzTCFxHxCAW+iIhHKPBFRDxCgS8i4hEKfBERj1Dgi4h4hAJfRMQjFPgiIh6hwBcR8QgFvoiIRyjwRUQ8QoEvIuIRCnwREY9Q4IuIeIQCX0TEIxT4IiIeocAXEfEIBb6IiEco8EVEPEKBLyLiEQp8EY6IA1sAAAwYSURBVBGPUOCLiHiEAv80hYWFMWf2JCZ+MRqAUe+8yuqVs5g7ZzJz50ymZs2Lg1xh6Brxxous3TCb3+d8n9Z2y63N+WPOD+w9sIbatS9Ja//3Ha2YMeubtJ+9B9ZwSY1/BaPskDX4f8/z56pfmPrbl2ltF1WvxleTxvLjzC94/6MRFCxUIG3ZI0/cx4x53/PL7G+45rorg1FySNm6fSf3PPIULdt2olW7B/jwU99xnPTTr7Rq9wCXNGzBkuWr0vpv3rqdute2ok37h2nT/mGee+l/acu+//Fnbr37IVq1e4DBI0bl+GvJKgX+aXrs0ftYsWJ1urannu5PvUubUu/Spvz559IgVRb6xo75jNa33JOubdmyVbRr+xAzZ8xO1/7pJxNpePlNNLz8Jjrd142//trE4kXLc7LckPfpR1/S7rYH0rW9PKwfA597leuvvJXvv/mRhx69F4Aq1SrRqnULrrv8Ztrd9gADX3mWsDBv//OPCA/nyUfv5+txIxk38lU+/vwb1q7/i8oXnMfQgb2oW6v6CeuUjy3LhNEjmDB6BH16PArA3n37Gfz6KEYNe4GJY98ibvcefp+7IKdfTpZ4+//4aYqNLUuL5o15992Pgl1KrvTbzDns2b03XduqlWtZs3p9puvddntLPhv/dSBLy5X++G0ee/fsS9dWqXJFfv9tLgC/Tp9Fi5ZNALihxbVM/Pw7EhOT2Pj3Zjas20jtupecsE0vKVmiGBdVqwxAgQIxXHBeebbvjKNSxQqcf965Wd7Oxi1bqVg+lmJFiwDQ4NLaTJk+MyA1n6mABb6Z1TezS/2PLzKzrmbWIlD7ywlDBj9Hz6f7k5qamq79+X5PMX/eFAa/3JfIyMggVZd3tWlzowI/i1auWE3T5tcCcFOrGygXWwaAMmVLs2XztrR+W7dso0zZ0kGpMRRt3rqd5avXUuPiaqfot43bOjxMh4efZN7CJQBUiC3H+r82snnrdpKTU/jpl1ls27EzJ8o+bQEJfDPrAwwH3jCzF4DXgIJATzN7JhD7DLQbW1zPjh27mL9gcbr2Z559gYurX02Dy2+kaLEi9Hiyc5AqzJvq1avJoYTDLF+26tSdha6P9KLDfXfx/bRPKVAwhqSkJADM7IS+zrmcLi8kHTqUwBPP9Oepxx6gYIECGfYrWbwoUz7/gM/eH8GTj3aix3MvEn/wIIXPKUSv7o/QvfcLtO/cndiypQkPD8/BV5B1EQHa7m1ALSAK2Aac65zbb2YvA38AA062kpl1AjoBWHhhwsIyPvg57Yor6tHypqY0b3Yd0dFRnHNOIUa/P5z2HR4DIDExkdGjP6HrEw8GudK8pc3tLfnsU43us2rt6vW0bdMJgAsqnUfjptcAvhH90dE+QNlyZdi+bUdQagwlScnJPP5Mf25sei1NGmV+IjsyMjLtE/zFF1ahfGxZNvy9mer/qkqjhg1o1LABAOMnfhey50cCVVWycy7FOXcIWOuc2w/gnEsAUjNayTk30jlXzzlXL5TCHuCZZwdR8YJ6VK7agHb/15lp02bSvsNjlClTKq3PzTc3Y+myFUGsMm8xM265tTkTPlPgZ1XxEsUA37Hr0v0BPnzvEwAmfz+NVq1bEBmZj/IVYjm/UgUWzFuc2abyPOccvV8YygXnlaf9na1P2X/3nr2kpKQAsHHzVv7euIXysWUBiNvjOze1b/8BPv78W9q0vCFwhZ+BQI3wE80sxh/4dY82mllhMgn83OjD0a9RomQxzIw//1xK54d7BrukkPXu+8NoeNVlFC9elOWrZjKw/zD27NnLy4P7UKJEMcZ/PorFi5Zxa6sOAFzZsD5bNm9jw4aNwS08RI1452Uuv/JSihUvwtwlU3ll0AgKFIihw313AfDdNz/yydgvAFi1Yi1ff/kD037/ipTkFJ558sRzUV6zYNFSvv5hKlUqVaRN+4cB6PJAexKTknjh1TfYvXcfnZ/sw4VVLmDkqwOYt3AJr73zIeER4YSHhdH7yUcofE4hAAYNfZOVa9YB8OA9balYIesnfXOSBWIez8yinHNHTtJeAijrnDvl0CIiMlYTjGdJTL6oYJeQpxSKzB/sEvKUDav1Ce5sylfighNP2PgFZIR/srD3t+8CdgVinyIikrnQPLMgIiJnnQJfRMQjFPgiIh6hwBcR8QgFvoiIRyjwRUQ8QoEvIuIRCnwREY9Q4IuIeIQCX0TEIxT4IiIeocAXEfEIBb6IiEco8EVEPEKBLyLiEQp8ERGPUOCLiHiEAl9ExCMU+CIiHqHAFxHxCAW+iIhHKPBFRDzCnHPBriFXM7NOzrmRwa4jr9DxPHt0LM+uvHA8NcI/c52CXUAeo+N59uhYnl25/ngq8EVEPEKBLyLiEQr8M5er5/RCkI7n2aNjeXbl+uOpk7YiIh6hEb6IiEco8EVEPEKBfwbMrJmZrTSzNWbWM9j15GZm9q6Z7TCzJcGuJbczs/JmNs3MlpvZUjPrEuyacjMzizaz2Wb2p/94PhfsmrJLc/jZZGbhwCqgCbAJmAPc5ZxbFtTCcikzuxqIBz5wzlUPdj25mZmVBco65+abWSFgHnCLfjezx8wMKOCcizezfMAMoItz7vcgl3baNMLPvvrAGufcOudcIvAx0CrINeVazrlfgN3BriMvcM5tdc7N9z8+ACwHYoNbVe7lfOL9T/P5f3LlSFmBn32xwMZjnm9C/6gkxJhZRaA28EdwK8ndzCzczBYCO4ApzrlceTwV+NlnJ2nLle/6kjeZWUFgAvC4c25/sOvJzZxzKc65WsC5QH0zy5XTjgr87NsElD/m+bnAliDVIpKOf655AjDWOfd5sOvJK5xze4HpQLMgl5ItCvzsmwNUMbPzzSwSuBP4Ksg1iRw9yTgKWO6cGxLsenI7MytpZkX8j/MD1wMrgltV9ijws8k5lww8AkzCd1LsU+fc0uBWlXuZ2UfALKCamW0ys47BrikXuxL4D3CdmS30/7QIdlG5WFlgmpktwjfQm+Kc+ybINWWLLssUEfEIjfBFRDxCgS8i4hEKfBERj1Dgi4h4hAJfRMQjFPgS0swsxX9Z4RIzG29mMWewrUZm9o3/8c2Z3eHUzIqYWeds7KOvmXXPansm24k/da/sb1+8SYEvoS7BOVfLfwfNRODBYxeaz2n/HjvnvnLODcqkSxHgtANfJJQp8CU3+RWobGYV/fd6fx2YD5Q3s6ZmNsvM5vs/CRSEtO8sWGFmM4DWRzdkZh3M7DX/49Jm9oX/fud/mtkVwCCgkv/Txcv+fk+a2RwzW3TsPdHN7Bn/9yL8CFQ7nRdkZl+a2Tz/fdY7HbdssP/1TDWzkv62Smb2g3+dX83swmwcR/EoBb7kCmYWATQHFvubquG7d35t4CDwLHC9c64OMBfoambRwNtAS+AqoEwGmx8O/OycqwnUAZYCPYG1/k8XT5pZU6AKvtti1wLqmtnVZlYX3201auN7Q7n0NF/avc65ukA94DEzK+5vLwDM97+en4E+/vaRwKP+dboDr5/m/sTDIoJdgMgp5PfflhZ8I/xRQDngr2O+gKIBcBEw03cbGSLx3abhQmC9c241gJmNAdKNov2uA+4G310RgX1mVvS4Pk39Pwv8zwviewMoBHzhnDvk38fp3k/pMTO71f+4vH+bcUAq8Im/fQzwuf9TyxXAeP/rBIg6zf2JhynwJdQl+G9Lm8YfdgePbcJ3f5O7jutXi7N3y2oDXnDOvXXcPh7P7j7MrBG+G3Fd7pw7ZGbTgegMujt8n8j3Hn88RLJKUzqSF/wOXGlmlQHMLMbMquK7o+H5ZlbJ3++uDNafCjzkXzfczM4BDuAbvR81Cbj3mHMDsWZWCvgFuNXM8vu/TrDladRdGNjjD/sL8X1SOSoMuM3/uC0ww39P+/Vmdru/BjOzmqexP/E4Bb7kes65nUAH4CP/HQ1/By50zh3GN4Xzrf+k7V8ZbKILcK2ZLcb3/a8XO+fi8E0RLTGzl51zk4FxwCx/v8+AQv6vEvwEWIjv/vO/ZlLqs/47gW4ys03AD0CEv+bn/XUfdRC42Mzm4Zty6udvbwd0NLM/8Z1r0NdqSpbpbpkiIh6hEb6IiEco8EVEPEKBLyLiEQp8ERGPUOCLiHiEAl9ExCMU+CIiHvH/5iPZNE6B/CUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.6224042567015674\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(rfc, scaled_X_train, y_train, cv=k_fold)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Now that it is apparent that decision trees do decent with this dataset, I want to see how an entire forest of trees can improve the test accuracy of the model.\n",
    "\n",
    "Very nice. About a 5% increase in test accuracy and f1-score over decision trees. Now to see if another implementation of decision trees in an ensemble, Extra Random Trees will provide any differences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Extra Random Trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight='balanced', criterion='gini',\n",
       "                     max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "                     oob_score=False, random_state=1337, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 13.1 s\n"
     ]
    }
   ],
   "source": [
    "xtc = ExtraTreesClassifier(class_weight=\"balanced\",\n",
    "    n_estimators=100, n_jobs=-1, random_state=1337) \n",
    "\n",
    "%time display(xtc.fit(scaled_X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Score: 0.9857312829722752\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Score: {}\".format(xtc.score(scaled_X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.66      0.62      0.64      2424\n",
      "           1       0.51      0.53      0.52      2477\n",
      "           2       0.66      0.64      0.65      2588\n",
      "           3       0.80      0.85      0.83      2511\n",
      "\n",
      "    accuracy                           0.66     10000\n",
      "   macro avg       0.66      0.66      0.66     10000\n",
      "weighted avg       0.66      0.66      0.66     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred = xtc.predict(scaled_X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average CV Test Score: 0.6255516724464686\n"
     ]
    }
   ],
   "source": [
    "cv = cross_val_score(xtc, scaled_X_train, y_train, cv=k_fold)\n",
    "print(\"Average CV Test Score: {}\".format(np.mean(cv)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* Overall the same in terms of average f1-score and slightly higher cross validation average accuracy, but the biggest difference is seen in the 2% increase in f1-score, and 5% increase in precision for the third class, and approximately 33% faster fitting time than the Random Forest. \n",
    "\n",
    "* This model appears to be pretty good at recognizing songs with a popularity of 55 and above. Which I think is good, as if you get such a great prediction, you want it to be more accurate than the lower predictions, as to not get your hopes up. It is also pretty good at recogning the second highest class, which was the one that the non-decision tree classifiers struggled with the most. And it is third best at predicting songs in the lowest popularity class, lower than 29. The model gets the most confused with predicting if a song has a popularity of >= 29 < 43, or class 1. Perhaps increasing the amount of training data at a later time will resolve this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Hyperparameter Tuning Methods Applied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/EpistasisLab/tpot/master/images/tpot-logo.jpg\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "<img src=\"https://miro.medium.com/max/550/1*AILbF5U9eeUg0jtG_KODtA.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* After discussing my difficulty and feeling overwhelmed about hyperparameters with my mentor, he mentioned I take a look at TPOT, which is an automated machine learning hyperparameter search tool using a method known as genetic programming, which tries out several random combinations of hyperparameters for nearly any model of choice, and then uses k-fold cross validation and compares their performance and then only the strongest make it to the next generation, where the process is repeated over however many desired generations. Depending on the models and preprocessing settings chosen, the number of hyperparameters, and the desired number of generations, this can become a very computationally intensive task, like other hyperparameter search methods. \n",
    "* This is where **Dask** comes in to help, if you're like me, and you have a couple of other machines laying around on your local network. Even on a single machine, Dask can be used to distribute the workload of tasks that normally are not able to utilize multiple CPU cores.\n",
    "* In the course of just an hour of following their easy to follow directions, I was able to get my first cluster and had all three of my systems evaluating hyperparameter settings together for models like Extra Random Trees, Random Forest and XGBoost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## TPOT Hyperparameter Search Examples\n",
    "```Python\n",
    "# Average CV score on the training set was: 0.6029363444724436\n",
    "RandomForestClassifier(\n",
    "    bootstrap=False,\n",
    "    criterion=\"entropy\",\n",
    "    max_features=0.25,\n",
    "    min_samples_leaf=3,\n",
    "    min_samples_split=7,\n",
    "    n_estimators=100,\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "```Python\n",
    "# Average CV score on the training set was: 0.5967224097593309\n",
    "exported_pipeline = ExtraTreesClassifier(\n",
    "    bootstrap=True,\n",
    "    max_features=0.6500000000000001,\n",
    "    min_samples_leaf=1,\n",
    "    min_samples_split=2,\n",
    "    n_estimators=100,\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* **Here** are some of the best models it could come up with in the limited time I had available, as, even with three Intel Core i7 machines at my disposal, it still took around 100 seconds to cross-validate and evaluate each model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## XGBoost Hyperparameter Search\n",
    "```Python\n",
    "# Average CV score on the training set was: 0.5802761430685061\n",
    "make_pipeline(\n",
    "    make_union(\n",
    "        FunctionTransformer(copy),\n",
    "        StackingEstimator(\n",
    "            estimator=make_pipeline(\n",
    "                make_union(FunctionTransformer(copy), FunctionTransformer(copy)),\n",
    "                XGBClassifier(\n",
    "                    colsample_bytree=0.7000000000000002,\n",
    "                    learning_rate=0.5,\n",
    "                    max_depth=9,\n",
    "                    min_child_weight=5,\n",
    "                    n_estimators=100,\n",
    "                    nthread=1,\n",
    "                    scale_pos_weight=0,\n",
    "                    subsample=0.9500000000000004,\n",
    "                    tree_method=\"hist\",\n",
    "                ),\n",
    "            )\n",
    "        ),\n",
    "    ),\n",
    "    StackingEstimator(\n",
    "        estimator=XGBClassifier(\n",
    "            colsample_bytree=0.6000000000000001,\n",
    "            learning_rate=1.0,\n",
    "            max_depth=9,\n",
    "            min_child_weight=7,\n",
    "            n_estimators=100,\n",
    "            nthread=1,\n",
    "            scale_pos_weight=0,\n",
    "            subsample=0.9500000000000004,\n",
    "            tree_method=\"hist\",\n",
    "        )\n",
    "    ),\n",
    "    StackingEstimator(\n",
    "        estimator=XGBClassifier(\n",
    "            colsample_bytree=0.9000000000000004,\n",
    "            learning_rate=0.001,\n",
    "            max_depth=6,\n",
    "            min_child_weight=10,\n",
    "            n_estimators=100,\n",
    "            nthread=1,\n",
    "            scale_pos_weight=0,\n",
    "            subsample=0.6500000000000001,\n",
    "            tree_method=\"hist\",\n",
    "        )\n",
    "    ),\n",
    "    XGBClassifier(\n",
    "        colsample_bytree=0.6000000000000001,\n",
    "        learning_rate=0.5,\n",
    "        max_depth=4,\n",
    "        min_child_weight=17,\n",
    "        n_estimators=100,\n",
    "        nthread=1,\n",
    "        scale_pos_weight=0,\n",
    "        subsample=0.7000000000000002,\n",
    "        tree_method=\"hist\",\n",
    "    ),\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "* This one is particularly interesting, because after 11 generations it decided that feeding the features through Function Transformer three times, and then stacking four XGBoost classifiers with varying hyperparameters was better than just one. Still, this is not as good as the previous two models when XGBoost was not the only model to be included in the search.\n",
    "* I ended up sticking with mostly default settings on Random Forest and Extra Trees, and they performed marginally better than what TPOT could find in a few days of searching. With minimal effort, and hyperparameter tweaking, I was able to get a decently performing model. With more time, and feature engineering, TPOT could come up with something that solves the overfitting problem of the best performing models so far.\n",
    "* For a more thorough hyperparameter search, I will subsample the data so that each cross validation has a large enough sample of the population, but not too large, as to slow down the process of searching for hyperparameters. I found a handy calculator at [Survey System](https://www.surveysystem.com/sscalc.htm) to help determine the optimal sample size from a total population of 50 million (the current number of songs on Spotify). For 99% confidence level, with a 1% margin of error, only a sample size of 16635 is needed. So if I'm doing a CV with 5 folds, the total size of my training data should be 83175 for the hyperparameter search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<h2><center>In Conclusion\n",
    "<img src=\"https://images-wixmp-ed30a86b8c4ca887773594c2.wixmp.com/f/74f9d312-5489-42e1-b0bb-830d2e674028/dcn9j4q-cfdd5031-e7e9-4e93-bd93-480a141183b3.png/v1/fill/w_800,h_1200,strp/small_random__trees__png_by_dreamlikestock_dcn9j4q-fullview.png?token=eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJ1cm46YXBwOjdlMGQxODg5ODIyNjQzNzNhNWYwZDQxNWVhMGQyNmUwIiwiaXNzIjoidXJuOmFwcDo3ZTBkMTg4OTgyMjY0MzczYTVmMGQ0MTVlYTBkMjZlMCIsIm9iaiI6W1t7ImhlaWdodCI6Ijw9MTIwMCIsInBhdGgiOiJcL2ZcLzc0ZjlkMzEyLTU0ODktNDJlMS1iMGJiLTgzMGQyZTY3NDAyOFwvZGNuOWo0cS1jZmRkNTAzMS1lN2U5LTRlOTMtYmQ5My00ODBhMTQxMTgzYjMucG5nIiwid2lkdGgiOiI8PTgwMCJ9XV0sImF1ZCI6WyJ1cm46c2VydmljZTppbWFnZS5vcGVyYXRpb25zIl19.gWhmmMWQJXaYBJoSXhejFSGabrtOWJUEENh0DbkjU-g\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "From the starving artist, to the executive at a record label, The insights gathered from the correlation matrix alone are helpful in understanding what adds to, and subtracts from the popularity of a song. Moreover, the unique combination of 10 song features allows for each song to have its own virtually unique fingerprint.\n",
    "\n",
    "Perhaps a much larger sample set will improve accuracy, as the model is exposed to more examples of different songs and their performance on the platform. It may also be valuable to try to take the dropped binary features (mode, key, and time signature) and do a PCA on them to reduce the dimensionality, while still retaining any information that may be helpful within the features.\n",
    "\n",
    "As for other models to try, XGBoost Classifier may be a better alternative to Extra Random Trees since it has a lot more hyperparameter options, and can theoretically evaluate different settings faster with the \"gpu_hist\" tree method, utilizing Nvidia GPU's. However, my limited experience with it yielded less-than-satisfactory results, and thus I decided not to include it in this presentation. Getting enough GPU's working together in unison, testing a wide range of different combinations of XGBoost hyperparameters and pipeline configurations could lead to greatly improved results.\n",
    "\n",
    "As for how to gather more data to expand the dataset, I would advise, once optimal hyperparameters are found on the smaller subsample, to try to gather the audio features of every song on Spotify at the end of each year, for songs that were released no sooner than a year ago, so their popularity score has stabilized, and is not biased towards fads and passing trends in music. Then, the model can be refit on the new data. Since the model only took about 10 seconds to fit on a set of 222K songs, it would only take about 30-40 minutes on my machine, and the dataset would approximately be less than 4GB. This is assuming that the fit time and dataset size would scale up in a linear fashion."
   ]
  }
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