ML0101EN-Clus-K-Means-Customer-Seg-py-v1.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
    "\n",
    "<h1><center>K-Means Clustering</center></h1>\n"
   ]
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   "source": [
    "## Introduction\n",
    "\n",
    "There are many models for **clustering** out there. In this notebook, we will be presenting the model that is considered one of the simplest models amongst them. Despite its simplicity, the **K-means** is vastly used for clustering in many data science applications, especially useful if you need to quickly discover insights from **unlabeled data**. In this notebook, you will learn how to use k-Means for customer segmentation.\n",
    "\n",
    "Some real-world applications of k-means:\n",
    "\n",
    "-   Customer segmentation\n",
    "-   Understand what the visitors of a website are trying to accomplish\n",
    "-   Pattern recognition\n",
    "-   Machine learning\n",
    "-   Data compression\n",
    "\n",
    "In this notebook we practice k-means clustering with 2 examples:\n",
    "\n",
    "-   k-means on a random generated dataset\n",
    "-   Using k-means for customer segmentation\n"
   ]
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   "source": [
    "<h1>Table of contents</h1>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "        <li><a href=\"#random_generated_dataset\">k-Means on a randomly generated dataset</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#setting_up_K_means\">Setting up K-Means</a></li>\n",
    "                <li><a href=\"#creating_visual_plot\">Creating the Visual Plot</a></li>\n",
    "            </ol>\n",
    "        <li><a href=\"#customer_segmentation_K_means\">Customer Segmentation with K-Means</a></li>\n",
    "            <ol>\n",
    "                <li><a href=\"#pre_processing\">Pre-processing</a></li>\n",
    "                <li><a href=\"#modeling\">Modeling</a></li>\n",
    "                <li><a href=\"#insights\">Insights</a></li>\n",
    "            </ol>\n",
    "    </ul>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
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   "source": [
    "### Import libraries\n",
    "\n",
    "Lets first import the required libraries.\n",
    "Also run <b> %matplotlib inline </b> since we will be plotting in this section.\n"
   ]
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   "source": [
    "import random \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.cluster import KMeans \n",
    "from sklearn.datasets.samples_generator import make_blobs \n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n",
    "Lets create our own dataset for this lab!\n"
   ]
  },
  {
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   "source": [
    "First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "np.random.seed(0)"
   ]
  },
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   "source": [
    "Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n",
    "<b> <u> Input </u> </b>\n",
    "\n",
    "<ul>\n",
    "    <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n",
    "    <ul> <li> Value will be: 5000 </li> </ul>\n",
    "    <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n",
    "    <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n",
    "    <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n",
    "    <ul> <li> Value will be: 0.9 </li> </ul>\n",
    "</ul>\n",
    "<br>\n",
    "<b> <u> Output </u> </b>\n",
    "<ul>\n",
    "    <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n",
    "    <ul> <li> The generated samples. </li> </ul> \n",
    "    <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n",
    "    <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n",
    "</ul>\n"
   ]
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   "source": [
    "X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)"
   ]
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   "source": [
    "Display the scatter plot of the randomly generated data.\n"
   ]
  },
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   "execution_count": 4,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], marker='.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"setting_up_K_means\">Setting up K-Means</h2>\n",
    "Now that we have our random data, let's set up our K-Means Clustering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "The KMeans class has many parameters that can be used, but we will be using these three:\n",
    "\n",
    "<ul>\n",
    "    <li> <b>init</b>: Initialization method of the centroids. </li>\n",
    "    <ul>\n",
    "        <li> Value will be: \"k-means++\" </li>\n",
    "        <li> k-means++: Selects initial cluster centers for k-mean clustering in a smart way to speed up convergence.</li>\n",
    "    </ul>\n",
    "    <li> <b>n_clusters</b>: The number of clusters to form as well as the number of centroids to generate. </li>\n",
    "    <ul> <li> Value will be: 4 (since we have 4 centers)</li> </ul>\n",
    "    <li> <b>n_init</b>: Number of time the k-means algorithm will be run with different centroid seeds. The final results will be the best output of n_init consecutive runs in terms of inertia. </li>\n",
    "    <ul> <li> Value will be: 12 </li> </ul>\n",
    "</ul>\n",
    "\n",
    "Initialize KMeans with these parameters, where the output parameter is called <b>k_means</b>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "k_means = KMeans(init = \"k-means++\", n_clusters = 4, n_init = 12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's fit the KMeans model with the feature matrix we created above, <b> X </b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means.fit(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now let's grab the labels for each point in the model using KMeans' <b> .labels_ </b> attribute and save it as <b> k_means_labels </b> \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, ..., 1, 0, 0], dtype=int32)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_labels = k_means.labels_\n",
    "k_means_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We will also get the coordinates of the cluster centers using KMeans' <b> .cluster_centers_ </b> and save it as <b> k_means_cluster_centers </b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.03743147, -0.99782524],\n",
       "       [ 3.97334234,  3.98758687],\n",
       "       [ 0.96900523,  0.98370298],\n",
       "       [ 1.99741008, -3.01666822]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_means_cluster_centers = k_means.cluster_centers_\n",
    "k_means_cluster_centers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"creating_visual_plot\">Creating the Visual Plot</h2>\n",
    "So now that we have the random data generated and the KMeans model initialized, let's plot them and see what it looks like!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Please read through the code and comments to understand how to plot the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ODgfawux5XOoQF6zd8/87u8GdEZKAJW5dLPQf5wJeJ7J5Q7ezpZdVrWCEe5jlKyiwbV8ux0J5HSPdpl3AxFlGTLHLQM06oHY9aOhdKCs/YssH2T5ZmwiTYaCiwTtuXLsBqGxwVg8tW88cDMMH7Ar1C06FGu3zNvNCW1iF27IXmDjDHBp3/r6rWt5lZ0PcUSBJLavqHz3MHBxtn2EVdMN2NpiSHIWix3MjOMc+mN93f4M7IyQBS9y6WOg/zoW8LjEMKqmBQpY9SNEERC44ZFl3u3dIgiwgcoFNtkX7GNG1fRZU2QAlcg6o3wqlLMSqWY9PdpUd59gCZCym7xJ55QT4gJkeKDVr2cp6u+JmQyRg5EoEqBOMrDNs/BgtD0Dx+YC6jSDTBEKbWROweg2AgE3Kp0EX/ieUtt9kdjVYbOBjWZsgZCdI/j527olhV/g7WLMPPnERyJZjlM3/1rkTmAM3ujNCNuEkbim4GzrFNnK45zTvLrW5Xjf2AYttjF9hhJcMgybOsk3EYgJuN3MZRDpBRhJIR1iVfeFZYOQwIy/b7aAYKUZ89sogjBwC7I0VLKuBxzmKs2A/S42BatYCWgJIT4ixYpo4zch+pps15Jp2Mr/vyCGgdDmTGBQf4C8Fht9lUZdTXQDprEG34i72GEz2XLFbbhd778kOJpEkh0CWwSQGMtn7RjrZd1m7ztX8vA+KPwisuBOIXWYSjXud0uhRULCCXZxm+bu9WSArYIlbC/OQD4Te2Lgz/4jxXK9r2sUaYpFO9vqme13Tcn5WZdZvBeBjGydCdzD/b/kK5p8VwxpBoPUhlvU7fJA9x70to3YDYGmg9CSU5KgrrvJ+R/etWgVl4jQQagRQyxZvhjZDqV5jux3s16y8Gxi3deCSaldzbSdo4gwUgPmFw8egNOwAgtWMwPl03+YvACu3sS9i5KBr3HqrIzFMXYSy4V8C5XXeYQpeIWfYBQFVLXbi21rmjbblGM925gX+3d4okAQscWthPvLBbJkMc71u4jSUxp0sP6FpF5SKRpZcxjdOLN/IXAmTp4HGXaCMT5AQmTrTi5vvF5NyZGqsSr3z9wElyCSAxp3MuVAecqQEEJtoa9xpDzu4dN/l7YwMl93GKkwiRqAu/Zb0BBTemPMMewDKuk+xUeDUGJMJ+LmNfWC7MhSgbhNbqZQxPJICOxeub7PXisxhV5SlyMlo2s307mXrbTlk0h7b9hfeMde8xxWbeXNAErDELYV5aYLukeH5rtWpv5OtCnJ1/qk8JIhOMdPCZ6vAB6y4CxQoZ57bxp2g4QPsvTnZuIPYq1cDVc2g9BSU2jZvEy5jMG011u/4cevvBE2dh5IKM1IlYr9SY0BZLaDF2Wt8fijqJPP3Nt5rp63xvXD3skELI84q9MmzoPqt7DmhOxxXBllQAuVAaLtzoVMngeXtrNHWtIu9N+BkDoe2sJwIIw4EK0WlzTRnFvyD6hZbYjiY1wFxo9vNCkFqwBISBVCM1ptfdyTbWraFeV6b7mVaZuyyXdkNMDJr2uWQUqDKGxW54V+K7cVkao5G2riTrYcfPQpFUYBApf3YZ1iFXrOakeGyNkCbZs2qjA5FnbIbVwo7v4zu6LtlywHugqhZy7Ioxj9g8Za+EpY6ZhmsqReoZDkUoc2AHmPH8vkdX3TGYCSfCjNCTkeA0jogMcLC3qe7gEgHe27kPBv+0GIALJbKBoKy8fPse8pYrEq2TPb92ePHGD3Kfub+3hdjQGYJIAlYQuJqYEdQev7hCzJ4H0rD3QAUpodWtdjkuBbwlzhNt8h5ID3ObuNtQlaqmz3Ta2KPm+K308V2AxX1NmnxoB0/AB9Qcxsw2QEaO5nTwFL8AWaDEz97n5FwYpgFsI8eAcrqoazcxsh4+CBABtt8QSZ7r/o72e+TZyF2tyWH2eNjJ5gmXLXaJvku+8+rAMpA8ZexinfwHecCVV4nNGSMHoFSuZIFs0fOOYFDZEsu/IJlZ0mI7/0mHU2WEoSExAJRMIJy1vjJPXZjqY15ZfWEU5mK2/gMcxq4btEFUmFGtmQBI0dsW1nGDtXZBYLCmmehzVCClXa4T5PjJwacwB/47EpTB+KD9ufwMe+vLwhUNtrNR2KeYT1h50oowIjrMzXsYL7m4YOOTNK02yOxKK0PseZaVYvr+3CGMsi95ojDs9VZYZGYdnwnedwWN77drBAkAUtILBQFprvczTQa+8CroyZHgGXrnRhK91RY/VaWWDbTzZpTyVExfUah24WzAtq0uH1XqtfYwxd2s7B5D7OcjRyCsvoxVmWbKSi1G1hVCTtoByXMWZFh0oMgztoNQM0aduyKBrv52MwaZfxCMNPt8izvBhBgEZbNe6DUrgfKV4Dig04zr3GnaxBli3eyzTKYVS10B7DyHrAqnn8h5PruvNrufAh3PvpwvudeS31ZShASEgtF1m1vjh48eph5eUXwTBmUmjUAWfn9qiPvsV1ujTuh1G+1NeLPskrQLXWUhdgIcdtn2WaMRlu6aNzJbtVnupljIVjFquiMCZSHQFMXmRfXNNm5Gilg4jSee+cy7vzyzxBY+wi2bvsInvvu/8UsZk27mAQSG2D5E3Y4PKpa7Gm4B0RIj1gVVFYHAFBKKtneuurVLDRo9DAoOcIuWmMn2eAJMuw1yzew6h8EKArTd+PDLHDo7N8D1avnTXyeFUfz0YfzPfca6suyApaQWCByqrBsH6p7fNcNV/gMrbjTThC7gz2/sokNNlQ0MlJqus9eQ2RLHSAW2l65ilWrZAFTXXYuxIyT2QuwIQnbZobYACPOkUPsmHbWxHPf+x/4P39wDN//s4ewZ2szDnWM4Mt/8/dA3UZ8/tHbodRtYp5lxc8yJeo2AVPn7dwIexT5nq+7Vtfboew1a4AMAVVNtmbN/bubWeZwWW1uWHxGFwHzKF8OlNvVb7H2Pzf4eqh8K45mQ77nXsNxZoW4JaQIbN++nU6cOLHoJyEhcTOh0C0p+/nROW1rZGq2x3UdqyiXtwPTPVAa7mZTc4lhID7ICG3bHzkSQ9tnGakGyu2wmi3str7+TkBPAGYKKFsGhE/YZOFzPLW+gC01vMKO07wXyGjYumUj/p8/3ImP3t0qzu/Xpwbxx393Cmef/yOWP7ziLtBUF5MXQEB6gl0gyGSft3kPa9SNvm9r0n5mg1u2zk5q2+DYyfh3MPYBq3zHTtgyhh+InLXHmD/DrHbVa/KukHcPunhCe7IftwPimeSytFAU5SQRbc/+uayAJSTmiwITV8XokmRqrJFWs9aZDJu+BNTf5Rx74gyLhwTsfIcmVlk27rSzgG3f7+YvMMcDZUBqBEr1KlbpNt/PdNW6TSzohixgotOl27JtyVCncaFvBHu2NnvOcc/WZlzo+TmUO/+A/WD4IJSmXYAWBawkUNli755b6eRG8HHoyQ52YahZw/Tu6tWsOZdNhjzxrHkvoE4BpTVA/V1Q2uIOcWaRL5mafXHimjc5gUKhLU6SXKQzrzaf9+9iib3DUgOWkJgv5mF5ytGFI51sNJjbrsZOMuLhZNO0m62UV/x2ZVcCwMdu6xUfew3fhBHaAqW8jlnTqlsZodW2MS35wrPMT7z6MeZGuPAse7zlASB2BQCBRg5h8zomO7hxqGMEm9tWAUZKkD3TnuuAimbm9Z3pBmDb0gjsfPn7jh5lf65sEWPOCL8PWGmQnnDuAFbcxSrnqXO2RS3ANoEEK3KqVuED7nvVtQlkN5TKZuZBduu02VumC+EG8A7LClhCYp6Yl+Upu1quv5NVfK7Nx+5KTwmUgVoeAGZ6WHVrvx9Vr2ZTZg072M43XxBK7DJbNc/zKma6oVSt8kZD+oLOn0Ob2TDFsrXA5FkobZ/GX/xnFV/+iz/D9//0AUcDfuoA/vKv/7s9OLLLGW1WiG3AiA84OcXVrUyjpYD3fXkl3LTb/px8e7JtnatuZVGZM93ONGDrQ97YTrLsBLTdbFiDLwlV/Ladr4elwvkDto1Ncb5PT+ZwPmeD7s01XiJIApaQuJbIauAo/gCorI4NEhhJKJHzoOpWoKRGkIPi89uRjzrITNva6n3sePErLI+hZi1QsZJVnROnmc2s7bPeECAobLqtcSdQuwE0fQnK8o3MM9y4HYhdwef/1W8BAP74v/4lLnS/jM3t6/CX/+1pfP7BVnsb8xeYPKL4gZleoNrlK27cybZetOxlTbmMD2jZYzfn/qetNd/P1t6nJ22yfUUQN/X9Asqdvy9kEeFYMBIsJ8ND9KuB0mo2TadOQpnqsnXxz4Ca985+UcwnGY0eZt/buk8v6eiyJGAJiWuIfMSg+AOg+q2sMgxtZjqtOiVIVzTO9BhrxhkJKOkJ5u/1BRkhWRpbE9S0G0rjTlYpli0Xmb5K/Z2egQc074FSVufox3z90OgRfP5zv43feaAFSsv9LObRrj5Z1Xw7I1+A6bqR86yKb9kLZExGvqHbGdE372Fh7Ms3Ora4aB+Uus3MEicS3lg1rKy4G+BBQfCxLIim3cxz7Bkgudcm4h3MkqbFnMq8YTurtgsF9AAFnA33LdxhsYiQGrCExHUGW0PUYU/ABaCkp9jww+A7ACxQaow1m8rqmM4brGQBOKEtLOgGxELdyQKpUy6d9RgbnGjaBfDbcRGAYwFTF5yf1d/hLOIMH4PSuANIjIJ8AUbcHd9je+ZArFlIJsuFqGgAMhojXPhY5cv1bP6Zprvt/AiNNRv5yLKhAi17PdueEelk3meymB0Pij2+7WO2t+a97Dta9ylW/ZMFpW4jGypp3gtM9+Ta/LLgzvTgmjxAN8RST1kBS9wwMDMGRlOX0FTRjoAvuNSns2hwNMj7ABCrcO3VO6jfCvIFoIw6iy6V9b/FshWgsFBy98SbkYCiR50R39AWr/YKAmIDLBs4UM5kjnQESIXZmp9gpZ2IhlwNNH6FuR1YbWhLHIeF4wAtD9rrjQ4yp0b1GlYVu/3OWsw+vk2oIksZQPNutjHD3XTky0JFJrFiryEKAtW8OvWDBt/LkREUf2D2yjcfbrDMYEnAEjcMRlOX0Bs7AUBBa9XNk+maDdFIqr+TkQT/R+/Z2QZGyJPnWKRk7XrHagYwoht+l5HbnV9lE2+ukWA+2kxjJ6GUrwA1fASK4mfhPKE7oJTWASWV7JgVDUB5PRQ9xghTCTB/bmmdne0AYPiQY+3ylbCQ94zpahbuZoE8NWuA5gfYyPNkB5BpATIm05xjA0B5PTv/nCzlZtZ0W94OTJ4RJCwajFXNnosBmToLEqpaxRwRizUMcYPtiJMShMQNg6aKdrTV7EBTxYalPpV5Ia/VzE7xYlGS97OBiqpWl4XNDhwPbWYRj0rA3q1m5/RyeWD0CGtY9b7Cjl1aC0BhFTT8UDIGULMWiq/EHuvdyHTa8jp2zNAdtoRggVLjLBYSYERaUsWkhYmzzthz024mc5QtZwRsN9sQGwCd/TvWEIwPMCtaaAvzJU9fgrK8na2iv7KfVb/N97EGXtO9rIEW6QQqm5nneXm7890AACks7pI309iXCmhT7OKA4tdAzYXFOs5iQRKwxA2DgC+I1qotN5/8kO0nrb8TGDvBSDNyHs6etgz7h998PxuYWNbGyI4sNjlWvZqNFZfXu/Tb3WwooWEHI+Wx4+x4YyfY77XtrEnGCXvsBKu8fX5WOXIfb/gYlPW/aeuohpPeNnqUeXd5toOlMl/t1AUgWAlEOkDjpxnJb/4C0HwfKNrHrHQAMPKeneXQCfgCUFY9bCe1HWIXF18pGzm+8D9Zxez+bkaPsMwHhYDkqB13aSM+CIS2sljNm2jH23whJQiJWxqLojvns5o13cc6+PYuNHF737yXWdAuPMsqYfdut81fZMMJyTBgJNjutdgAEKwCUQZK024Wwj7ynkvG2AUaegdKy157G/Iq7wSZe9Py5DnHAbFiG4gstjappIpVxPZaeiRHmI2svJ5ptKNH2LFCdwAjh6G02EHrQrcmO3x9HChbYX9e29vbvMfRmkObWbXLv5umexnpjhxiGzhcq4lQs8b2N9tr6kuXMftdoOyqJ9huhAk4DknAErccOOk2V2xy6c5Ac8Um+LMyC7LhiVUUeQznPfvbAK/9jPgwArdM1d8J5Z5vsCYX4G1iTZxh64i2fgUKH0KAAiXSySrdybP2/jWIQQulcSeYM+IKW1U01WUPcZDYLQc9AdRtZFVsaAswfIAtCYUfqFmXQ6bK5n/LJAM32Tff7+QD2+8rLjzJETb5lhz2DJmALEBPenbIAWxQgizLtY6eWeUEXE1JNN8POvO3bEKw9aNX30i7gRpxkoAlZsWH0ZnASbfEV4GminYAQEP5OkyqV9BQcZvnuTmfnw8K6FE7pDxqB9z8Zs4/ZqfSup9ZprhNiyzW0CqrZZVtoBTUfD9rjIW2MPLyBYA6V8hMw90swKauHZi6ZAfggEkHNWs8YehUs5b5hi2NkbkeY883k6zCdgenr7gLCJTl+GopWAElOeI4LBq3s4ahvZxUSY2yEWj+mWvWsEWhlc3s9UYSSuQcUFrH1s7nfCf2YInLiSGWfFr29gvRlFQY+fKw9qtdvnkDNeKkBiwxKzhZjaa6Cz7HzBgYTHTCzBhFHXO+z7/a12WDN/tCZasQ8AXRXLER09oo6sqac56b8/mbdrOV7uL3+1iDzbXJgUwNNNPn0oYPs45/w922Lst3vx1lnX6A3bZffg0w04ykR95jk19uhO5g2m1prR2AYzI/cMa084E/bW9KXsY2JfMIy6bdjPTHP8h5LkpqGNkb9raL6Uv2RcDHyBQK06aVADuvanvnXEUjAB+b1ht8h6W4KXAq7WAVULcFqGoRwfR8Lx71vsw+9+hh22+8AYDiNDKjvQCINSXtUB6l9aOO/DByyPZEL0w+uJEacbIClpgVrEJUZnUmFLKPFaqeF2o3WyybGm/2cRCAUn8lFOTKD9mfXwmUOjplw91iLZFnooqvpd/6FVFpkWmwHF/uhABE6hd7o/uYlJAMA/Ez9m25z1tVK37HlytiKl1aa/VaQAlCKa9gZCYkhb3Oap+SZazq5c27jAWMvmdb4s4yrzAUVtFOXYCiTjlRmM17nOO0fYaNKGvT7LM27gRGD9mfyc5qUKdZ7u9kB2vUTZ5hwUMf+Q9ARROrpEGs6Th62JsNPNkhch68/xMUJx/cSDrvbJAELDErsskqHwqRdCHCLIbUgVwCL/Z1+V4722MBXwC1pQ2iwm6qaEdEHUSorHXuz5+PEPgtrhJwdp6ZaWbfqmpizofWhzyHUQIloMHDWcS92+MpBmWcW/aqFpd39l52kMomFk421eUdziD7nPj48bAdes4zeHtfsR+7HYoeF7f2SukyNlbM34PI1dTbDcAHBErZlJoYpCBGsuV1ICMOGnqbuS+ifUDdZjtwfbdIO6PmvazibdoNvrdu1gGLYuWDG0jnnQ2SgCWuGoVIqqliI6qD9agKhop6fjayCbzY1+V7bTGPuX/ugx8RdQihslWza+B5CCFvMMzo+06Fl0W+gK17ctJ0EzcPLrc3RVDfL6A07HBSvWo3MGKOnLGlCztjODXmzefVpoEVdzMJgE+m1W5gVWbbZ8VYtFgauqyNyQzpSdYEm7oARY2AUuNQVj9mywE6s83V32lXsxAh6RSsAKpaocQHAfKx47k3YNjfgeIPAHUb2ZfA9+gNvpO3chUXo6yGZ1407WbVfv3W2Z+3xJAELDFvFN+YI8SNSVQF6xb0PvOpeOfz2qaKjXkfa6xYb//eBtVMoiKwDCOpi7PKHkVHUzbutJ0B94of5Wx2qNtoN95cWbhZm4GVhh1MV40PA+UN9rjwIbshGHMcCy0POM22ts8yiSGju7Y4+wD4GNm27AWp01DUCAvNWdbGrHSGvVa+bDmUiiZgeTuU0aMA12KhMElFUVgc5cq7GfleeBZK22egtH7UcYKMfWDHSSr2kk49/6aK0cOFK1de1eZpeOb7e6HQFvbd3MAyhCRgiXmjWC023/Pm46rIV/EW+/rs15oZEwCJ1+YlU/hRHaxnv5ewi8bVXAS8Bw/aFek5tlI9UOq6TSZBoGje46kAnaxbiJ+RqTFiLK+D4i9nnmN7xZFSUsv8tulxlwyxG9Bn7E0R3FK2B0gO2bvbDCiRDiZ5DB+CkhpnEkD2WnktxqbyOr7H9rzVbmCbLIYPMIIPVLLmGB8gcSN0O6umq1eLKh4ud4TAbBKD0M53FCZwN24CGUISsMS8USwp5Xve1TbS5tvw4zAyaUyqV4Tnt7Xq9pzncC3Y+zPmkphUBxAqWw0AIFgIp3rmZc1TAkGnMdb2m8x2xte2N+1ilq26LSxyMps0IuftGMoMI+fGnaCJs1Dgs3MUVrNb/Mmzzn60QBM71+Y9zJtb0Qg03gueIcF2tbUBRhIY/0AQs9K4E/CXsIpVrIRnmRMI3c4GPewlm1ACrurcZ8smAebVRZ5GWDVzRCiVjYzcq1pyKlOPfzrr9Tw3gl0A7p6bVG8gu1khSAKWmDeK1WLzPe9qK8pCr4+ogzAyKiLqUI6XFwCCvnLh+eW/zwUzYyBhTCFuTKI3dgLbQh8HAPHneV9E3IQQtcPNq1sB+NiqHyUAlK/IXacTugNKYojpyBOnoVSvhrLht4HyOlB8CApMYOSISFijigagooHppCOHmG852McSzNZ9GkhPsZjIDFjV6qmwy5wLxeYvQml9EDR2UizLVFo/ykg9NcYkEDHVtjv38+apQEWz0d4KPWuaWb4KtqqFke8spOr2Gs+2E+5GgCRgiesKNykvZMjDW5G2iteFylqhp9IIla0q8Dr2vzqvfIt579HUJUykB7A19AgARTQTK4O1yL4IFHM8T3W3rI1Vu8lRKFrMzsC1ka8CrF3HKkYevm5vPWYh60e80oK93p2a7mMkFGEDIDyA3LNBePAdUMaE0rzbscRlDzqEbvfKChmXNhyssKfaTFY12xU4mbpT4WdLBrxBFtpcsOHGnscn6hzdvCjN/SaQHjjkIIbEkiHfkEcxwxYjqYu4MHPQ87piBkbmfm8TRkYT799UsRErytdCgd92YTCCUs0kmis25vU2F/v+bBquG8rKjwChLSDT8CaqAbkhP0rACV8fZUMcdGW/nbD2WccaFj5mh90cZaRXfxfEQAUU73k03Q9l1QNAfAggkxFiRveselcCZWIQAoDQhhE+BrIsNoQxctAO5TnPnhM5x2QLWzPG6GHQTB/ISAHwsQuOe+FnPkTOg3wlLHdirq/TnUjXdF/RS1OXGrIC/pBisUaIr+UocrEacSE/cGNFG8yMiYAvkHOsQudtZSyoVtLz/LFUH0JlrUibcczoo573z7WwdaE3dgJtNTsW5G0WEKvTM2yAwl+ae1ueE/LjB/ERXTvHQYkPAgiwFUF8kaeQBVzhQIOHvDvUhI0rA4wcFmE47nOi6tXM1uZu/kU6XdrwLvZ6z+qj++1KeAsLEuKSQeMOW7u9k33e2nWuz3evp3oWCN0BhUxG9OX1roWaeYYsXFWv0vrgDV/5ckgC/pBitmZXsaRqZaxFmT4zM4YYbPAOQOTTiHMtYvn8wNXBEDoib2FF+dq8HmEuH9SWNKG6pM7zmRPGJEr9q7GybC0i6iAuzBxEW80ONFdsRHmgKuf9veeXn2gLSSOFvov6FXfBF/wq04AVP8tV+Mh/ACoavLfl2USiBBiBgcVNUvP9TI+d6XZ024a7vTKGIE0ADfcA0R5QzVqWDSwC4hl5Km2fdSb0Js96b+VdqW0en7IIB9oNRPtE0pvQX21/r7Jim2f6j38+GvvAmw4nPqpLL3bLCbMNv9wEVa8bkoA/pJitIiuWVCfVATSUr7OPt3Ab1mjqEoyMCj2VLuqikI+Uswc6qoJ1WFG+dhai3Iimig0YTXWjPFCFiDokHBD8XJorNqGurMUOgV8PAgHwoToYAr9Vz52aK9yAnFQHYJKOtBnPIX1OyBF1EMtLmzCSuoim2nb4tQQQCNqLOO9zVcb59UslUOK1b40ccqbn8umqgjS/wKrkidMsU2L0MGvKucalQQrz8vpKWNUsLGW2BstJvG4jI/bYZTsN7gILU/cHQDW32eHzLZ6qVgmU5B1AAWBrzAUkg7wLNYscfrkJIAn4Q4rZiKLY2+VQWSsi6lDBmEYrYxWs+Bzy2WiP9g7ZDbKFXBRyBzrmcmIEfAEMJjpdUZMbxbaNiDqEutImRPUx1JU1o7miHZPqFdSXrUbciGAs1QtUALWlDTnnNdvdQ6hsNRQAKTMGM2Miog7mfKb6sjWe4Y5VFRuB4QNO9kMz356Rv5mYA77dV2FhP04GhD0lx+WCuk2gwV8zvTfa74z+uqfujBQwfhJo3AX4A0ySMJJQIheYLJKOeHzAZCSgGClvA1HEYjbZSWzxooYmCj0n71bpm5Rs80ES8C2I+djI8lm6AIdgG8rX5bV+ZRMXfzzf+5oZ055Cm5ucmys2siBxxS8uCoVI0amcl4Nc772ibA1m9DBqSlYAACbVKxhOXkSobBXixiTalu1ARB0UjTj3ebnzg1sqNmNCveyRVibVQdSVtQDIoL5sDdpqVM9n8vv84s+NFW1QfH4nvL1pFxsrzg73yQOPDuomI14dNu5gAw/Ne5mVzNSh+IIAAYpr9NcDPnhRUgtasY3puHrMcUOUrwDIFD5gJXIhNxIyZxfc0q59v9EhCViiIGar9rKDzLMxW5WdfdzRVBcm0gNYV3NPXu20qaIdJb4K1JetBoFACmE0ddH1eoegq4MhVAZrwZLNnMqZOxjclbkCRTTgKgK1GE11Z8kUak4jzu0lntFHkTSnXdKKI29UB+tR4isvcKHziYk7IMueNvjOrBIEmRqbKOOaL+C5tRe6qmk4ITnlIdfPC2ctuO1hGH4XaNwB1G5wuSFKALikhHweXpc8cKOO/95IkAQsURCzyQJugs2/RULxaKmzHZcfqyoY8hCkMzq8EfVlq225YxVUM4mJ9AB4Rcyr58aKNrsxtwZN5Rswmu72VM5mRsO42o+J9AAaK9ZjNNVjV/CDWFG2FuWBavGZmGTSmnPu7O6BeYlrSlbCyOgIlbUKjTqmj2NZyUrQLN9rvok754udo5kU6WQbiJt2wbNFOfvbDwTzrnKfLWuBR22yiwD3Fe+d5ZPke99ceSCfa4FM1fWzslmf+2GG9AHfpDDiaXT+3y/BiKev2XvMtqW40AJN7uMlWIhqYwBI+GrHUn3itt59XOdYASSMKaSMKBQ4TgYgg5HURSwvbUJEHYZFBraGHkFTRTtSZhR90ZOoLWmEAr/dmGuHllHRWLEebTXb0VjRBoCgZ5gcsHn5Xkypw+iNncBYug/1ZasxoQ7AyuhotiWHisAy9j1nNHHe2f5kR6JhlTZAqClZgZFUF9iYGZNX5hMiP2dYeGgLlFg/++/ajc7wRD407WZ75prv9/5sLo+seM59c6eOFYNsP7P42SuMbG2QZYoLREFv8IcMsgK+SXHpmV/i2J98F4CC2//9b12T95hP/CMHJ83qYD1aKjd7pAp+W99Qvg7NFRtzKmcrY0Gzkmhbth1RfQwN5etQHaz3SAPNFRtxJvIGWio3I1TWghl9FOuW3QOFgJg+hpaKTZhQL2M4eRF3hh4Vt/qT6hXMaGG0LduBSfUKmirasbn2AdSVNYNAdsORNeJGU10wMirqSpkmXMjBkT2qXB2sd40pA9XBepT6K2bxNW9E2oyhPFAjJJK5v+Cj8GYveKtGQLEdFbvzJoIV08Ba9CZXQScDvCPM2fvuFhE3amUtCfgmRfuXPglAQfuXnrjm7zWfYYwmWxIIp3pQFQzZVS6/rWcNKtVMoixQCb9rA0V2U6++bI1wJjD3g2JbxYAV5WtRX7ba5SZgxFxT0gCfz4dQ2WromTQAH/wKH1VejVJ/peeCUF+2WniJ60pXwcwYMDIamis2IaqPocrWk6fUETHizKt2K2OxRh3p4jNWBZeLc22saBMab/ZdBL9INVasx4w+irJAJYr+p5iPzNy+2OpWkReB2nU3xFhufidDmQjtEQjdbi843bs4lbcbN8D3kA8K0WxqlRfbt2+nEydOXMPTkbgRwe1c2dNfhYjZef52+BQ/aoIrUR6oRsKYQlWwDmPpXgSUUjRU3JZTRbbVbPfY3pidixHxePoyqoJ1qAzUwsxo8Pn8GE11o7FiPXRLQ6m/3D6fDUgZUaStuBiyyDdVB8A+pxAcvXkDFPiEhmtmVAR95UgaU6gM1mFSHUB92RrPn0Nlq4uvYJFbOWd/r8WATN3xDoPYLbudDwEy7LwIXhEfuaqm2I1aPc4H7DMcXbLmoKIoJ4loe/bPpQYsMScKacGF8g+c57cjoJSgPFCNlDGD6mA9AAWVgeUIlbXCylhIGFMYS/WiqWKDeE1UDwvNdDTVhQszBzGpXsGK8jWY0UeRgYWEOQW3myCgBFzn0wNF8SNU1iqOaWZ0DCY6ATDXRsqYARGTPBQontfGjQiADDQrhaPjP0NUD6MqGMKkOoALMwcR1cdQEazFSOoi6kqbEVGvwMpYnu/AylhCO85GwBdEbWnDrBr7nBBa6RFGKM33s4k3RfHkRSiBkqtfQJlPw73JcCMt4nRDShC3MBYabs7hdkI4leoapIwZNFe0gwDbvXAFobLV8Pv8MDO6bQ1bDoICzUpi/TK2ery+rBUxfRwzehhGRkdDxW22Ba08a4ABqAmuFPa1raFHMKOHXRaxDTAyOnx2iE7KiCKiDbn8u5tQFaxDzJiEmTFyJv56o8cRDy5Dc8UmfCT0BCqCtRhOXbB1471YVtLgGaZortgIggXAqdoTRgSh0lViKi7f982/V96om1feRrYUMXJIjAkv+ljuTTrmezNAVsA3IRbLATHvBK8suJ0QEfWKGLEtD9ZgUr0CK6NjJNWF4eRFECzbHUGYSA9gNNUDK2OgrrQZI6kuEDII+spRU9KAutJVCJW12kE7QYTKWjGpDoiKsbFiPSqDy9FcsQm3L38QU+owLswcREQdREvFZhhWGkFfKSww50KJvxJNFe3YFvo4mio2YkYPI4MM4sYkGipuQ6isFdPaKJoq2hHTJ9BcuQkN5euEDhzTx+2s4UEEfaVQrWSOwyKiDru+mQw0KwkoCmb0UVc1n//75gE/8/l7yKnouHPBXte+mNVevuM56WO6N4msAIp5zq0IScALxPWwgRUCd0BcembfVR3nqm6Bs1BftgbT2iiMjIopdRj1ZasR9JWiqWID1tXcg3CqB72xEwinerGu5h40VWyAnlERTveiN3YCMX0CPvgAmxgJFswM+8c6mrpkE+wQVpatRV/0JLg1zecLIFS2Gptr96KutBkT6mWU+qugWSkkjRlMpAcQUQcBwLaKZZC24h7JwcjoqC9bg4g6hOqSEMoDNVDNBGpKVmA4dQE1JSuFbFLqr0apvwJ8fREIolnoWPAIPsWfQ7iFvu/5/D0Uiuu87rfYXJZIDOWVKHII90MgY1wLSAligbgeNrBCKMYBYcTTuPTML9H+pU8iWF2e9zlXdQucBb/P7wpFZxNrI6kuNFW0u7ZRMLkiYzsIZrQw1i/bicpArSC7xor1niELwFmWWVfWDB/8aKho81jTqoP1qC1phE8JIFTaCot0lPorkDAmcVfocQCKRzIIlbagxF8B9/DGhmW7UOqvEK6IpvL1eS10K8vWAmCWt2UljZhUr9ijx+QZMFlZtga1pY0AgCb7/AtJOYu17fm6gssSVavsjcizODNaH5QyRgFIAl4grqcNLBvB6vI5SX8+Fwh+Czzff9S5I8WX8vhhFTSWr2fEWbYOMX0c1cF6hMpaUVfWgpFUF5orNnkIcl3NPbZGbCGuR1EeqEZ92Woo8GFKHUZNCct3cOxePoAyttbcirDd1OPyRnPFJs/GY81KIqaPo6ViIzLIYEX5GpT5K6Eoij3IsR6Az5NPwcOEuJXucvwMWio11JY0YDx1GeWBKo/lLuALwsqYCJWuAuAreIGbb97y1ax0ulo3Q87r3XaubGtXzgLND0+AzmJCEvACUQwJLiXmc4FY6D/qwiPFyz3eXcAnmlisgdUFHwLIwERv7ARKfBWec9As5uHVrJQtQ9SI4Qm/LwC/rwSskl0FHwJImVHM6KPwKX4xSWdkNMT0CXH8UNkq2zHhQ5mvCmX+GnsIYpmd43sFdaUtCJWuggI/RlIXRT6FAgUrytYgac5gOHkReka1LxLMvpaBiZqSlXBfwLKHLXhVnxvwfslzjm4yzkfOs1XLc5L51Xph5/H6eS/QvEUhNeA5sBCtdyn1YQ5+gcgnP3Ad0chootGVb6y40OvYindHu3Q2U7Cg9LOR/ayZVbEREXUIBMtuvF2CAkUQBH89z1yotgltKHEOQAal/jLbbpaBT/Ejog6hJrgSPvihWSkoCpMWuqNH0VSxAQ3l62xXxb1ImzGEylqxuXYvQmWroEBhVS58iGhDAAilgWoMpy6AQKgvW40pbQQl/nI7sawdK8rXoCpYB91KgZBBypwRI9C1pQ1ImzGPzuv2AkfUQUykB0CwMKOP2q/bkKPh8u+ATeB5NePFWLPkQTFjyLNhvq8vYoHmrQ5ZAc+B+dzKG/E0ps72YPJ415Lpw8XArW1WB+sLB8PkeR2f4BpMdKG5YiOqgyH0RU+iuXITqkvqUBUMYUX5Wo9Vq61mB9bV3IPKYC0yIGQyLKqRQKgOhqAAGLHPqa1mB1ZXb0XaTHjWA9WWNKI8UAOARDUNm8wB2OPGA8KJ0VixHhF1CLWlTQAIk/ZGjpHURRHAU1OywnP8+rJWqFbKdnWwLcsxfRw1JSsxkupCQ/k6TKkj9gCJiYpATc4GDXcVWhGoFc1HgMVXDqcuuHTitfD7SkRFm30n4rbVFYO57mSuVgaY7+ul7DA3JAHPgfncyl965pfof+FdPPbLvyr6NdcTRjyNwX1HsPq39yJ7XLYYNFW0ozpYjyl1GEZGRVQfR03JClQtWw6yQ3eaKjbYvljyaKialcZ4uh8BpQQrytaA4GjPm2sfyKOfWvaILtNtx9P9sMjwaMtcOmiuaEfSmMrxCvMm3oid7aCn0iKngl8ANtc+gPqyVgwlzqOsaiOmtEGU+jeKhiKXTLKjNwkWRlM94Bs8zIwJzUxhXO0X59dUvh7lrm0eGWRyRrPdW5yz5YWIOggfAoD4bgtLE/lez3Et9/pJXB0kAc+B+Wi9nKz9JSU3ZOXLq/kH8X9g3b/66NwvQO4/3trSBpgZA3oqjZqSFQj4gpjRxjwhNHz4gmuocWMS1cF6ZMhCqLzVDkNfKcgoVNaKTMb0BPQkjSlUBJe7Jt1KURUMsbzfQC2WlTQgqoc9+9wAuKrHDTAzOjR7AWfajKM8UOPKqaiDD36EylZhJNWFlqpNYm1Ria8CK8pWo6ViE3Q7Vc19bAAIp3oEmbM7iIxrGIQRLK+eeSYxz46oL2v17KLzjnebGE112RGca5Ayoy7HB9OQi1lc6sYN45yQyIEk4EXEtW7MFWMtmw38ArH6N+4r+jX51upkb6ioCtahMlgLd9OtuWIjSnwVqArWIW3GkW3pMjI6KgO1qCtdBYAwrY8gVNaKwQQLWq8ILkdMH0dtSSOsjInakpUgZDCa6kZTxUakzBhqShpwT/2nUB6ohgIFZEdAcufBuNovCHFGH0VFYBlGhSTAJu8m1Svizy0Vm7Bs5UoEfGWwYCKc6gEPbq8taQKIMKldsSf0Ngp3hJWxENMnEPSVQjMTaLGHPWb0MJLmjKd6TplRBH3lnl10gLPeqa6sBSkj6qmwmfSSLU3MvU2a42qcExLXFrIJdxMhewDDTGoYe68DZrK46SJ+gaBMZs4mIW8WsUbWA55/vO7BgaQxBc1MgDKEZSUNyGQswPYAh8paMJy44HrNdtbAKmlEqGwVpvURlNkJZUlz2tVEugQFQE1wBdJWAvAR0lYC4VS3p7FFsOCz83B5M02BH6X+Snz3h0/jge0fQ2mwDHfccTue/acf2dN0G+zzWI/xVD8aK9Zjc+1eNNuWtAmV5Q9PqcPivUZSF5nWa+dLALDzI7qRNKeRNKbscHYNpYEqDKcuoqZkJWpLmjyfm0CY0UcBEHwI2J5iH8yMIfKOp9QRrK7eKrRj3txzN0nzNU0Xkt0ssfSQFfANhtmq3Gw92tJ1TBy9gNotaxGoLN7XWUxjcbaKiq9gj+ph1JQ0ACBkMhYqAstgZNKYVp3mWV3ZKlQElsHv89sxj2HUljSJZhS3oDlLO4GG8nWYVAdRX7YaPkURt/tbQ4+gOljvamwpqAmuwJQ26GmmvfziL/E3/+Vb+NP/8UVs3bkBHce68dTXf4Q11Xfic7/zOTsUCGip2gTAh7qyVaJxlzKiSBjTqLfjK91NtOpgPaqD9dDMJDQrZY8uj6MqGMJw6kJWbjDE5oyWis1IWwnPeTZXbMS42m97oL0aM3dhzFa1zmdbs8QNDCIq+tc999xDErNDj6Xo3LdeJD2WIiOlUfjQWTISatGvP/etF+kHysPU8dQLZKp6Uc89962XFnCOL5EeSxV8jmHpdCXeSYaV/xwi6SGKpIcoZSTIsHSKaRHSLZWuxM+RYekUTvaRYelkWAZdiZ8j0zLpSvwc/Xr4hxRJD5FpmRRO9pFuqWRYBmlmmhLaFJmWQeFkr/1ajSLpIftczpFhGWRaJhmWJs7NebyTdEsl3VRpw+Z19PQLf06/Hv6h+PX0C39O7ZvXk26pdHL8l3Qlfo5SRowsyxLndSXeSaZ9voalk2Fqru9BI9MyKKqOk2okybAMmlbDZFiG+L74ebu/N37uuql6zjuc7KVfD/9QfE/Zrwkne8m0zIJ/P+5zlrjxAeAE5eFUKUEUiWK9vby6HNx3FBndwMTRC7B0vej3af/SJ7Hzqa+i7d88ioGXD8393Kf/UFTEZlKDNh33nGe+857NI8wx18ohccvtK0dEHUTG1kz5bXNDxW1i4WZv7IQYpLiz7lHb1nURobIW9EVPYjR1CX4lgNJAlZ2rsAY+sKzfGT0sLGER9QrY7bsfK8vWIqaPI20mkMlYqA6G0BF5C+F0D3q7LmPrTm/luHXnBvR09bqyKFjwTgaW5/ada8KjqW4kzGkALD0N8GEk1YWKYC1m9DAAQoZMKACSRgwKfGycOnEBtSWN4nvjdxLhdA8U+NFSsQk++BAqW4W2mu2oK2vO+a559gVbbZQfi5njIbF0kBJEkSjWD8xlgjWf3YOL330Fx7/+91D8/qKac9wmtukPP42ps31Y89k9sz4/u+kX6x3CyJsncfzrfy/OM995F5I55mryWRnL00yrDNQibUbtdT6DHm9uyoii1F8FvtY9VLYKAV8Qy0oaPWPHDRVtqAouhwIfRmxZoq1GRW1Jo0ea0FNpLC9twqQ6iNqSBviVAGpKVqC6hNniQmWt9hjxBrRvWo+OY924+/7N4tw7jnVjXTt73AcfJuxs37aa7agtaWLE6POJ4Y36stXoiR5DPDiJlopNrnFt1kSECjFK3Whr4ZXBOjvMp1rYxpy19uvZRmdkxIRcbUlTXhtgMU0zKTl8SJCvLC7061aWIIq5bc/3mt6fvD2nlMDR+5O3hKRgqrqQMtwwUhoZiTSNHjhLRiLtOrcXyUikSZuOe87TSKgUPtThkUG4dNH7k7fFa/n7zSZphJO9pJkpl8zQ65EWwsk+MkzdvtU+Z8sLui0bGJTUZ7IkBZ0syxI/c45p0OXoGTIsnVJGzJYeDIppETIsw5YwDDJMdpyLU++RaiTJsiwyLIOe/acfUfPqBnr6hT+nNy//Az39wp9T8+oG+tY//F8UTvZSUp/xyBaGZZBmsO8rqo6L8zcsjQzLoIQ2lSWveD+3W2Lx/pzJA9NqmE6O/5Km1bDnNZZlef9us6QWLmlI3PxAAQlCEvBVwK33FgIjwNl1YOc4SUGKbjJ2IzEySepUjDqe/CmpUzEiIgofOku/2P21vMRpJNScc9RjSTr3rZfISKkeMtZmErNeZLi2a1lWAQ1To6n0iIdkptUwxbQIxbQIxV0ab2/0BIWTfTSthkkzUxRO9pFqpFwkpGURoSYIUDdV8mrCuuexcLKX/o+//QNav+k28vl8tHFLOz37Tz8S5yhI1dTItEzPRSGXWA0y7HM2LZMMQ7OJXrdJOVvT7SXD0mx9WM8iU8OjA3P9mIO/r5eopcb7YUAhApYSxFWgGFnC7VRIjU6gvCGUc3svjkPA5j/6F/D5fVj9G/d79F2OshW1uPjtnwtpY9NXP43J41147Jd/BX9JiXgelxPWf/Fj6H/hXQAKNn31U7jwt69g0+9/Cqs+eS+O/vG3seNvvgJAQcvj23Hs63+HDV/8GILV5bA0A+GDZ9Dw8F2ebjtbww6x7oc5FxQ7x8GH6pJ6VJeE4EzaKVDgByGDmD6OctTYkoKKUFkLeqMnUFvaiBVla5GBV+KoDtZjIj2AlWVroWc0EWhTHQyJkei0mfSMLFcHQwiVteJrv/sN/H//4P+1A3eq4bPPgSemcW/thHqZWclsv26Jr1zc+jM3xhWU+ittuUJFqLQFpagSY9nrau6BZrEIzm2hj9sBPRmx9YNZ8rrt7y8AM2OiOhgCUQZT2oj4PgFHeqgKhlxhRrkjzm5dXk653eTIx8qFft0KFXAxVa37uUNvniDLKNytdt/WT3X05a1S9ViKzj3NKmAtmpjz/bVogjqefoG0aKJgpex+3/ChDtJjKZo81UO/2P015rDQDVYF25U5q5TZn/VYijqeeoH0eKqoSiy7cjs5/kvmijBVsmz5gd3a66ISdNwOmviz29Hg/NzMqoSZVDGthimc7CXdVF2VqCYcDm4phDsY+LkZluaRFNwVMJck2Pn1ifNw5ApNfBb+/ItT74n34u/DK+iYFhHVvvu7yidBzIZCfw+yUr45AClBFIf5Wrv0WJKRVSyZ93FtOk4dT71A2nSc0hMzBaUIyzBp8lQPdTz50xwdWJ2KideZqk5Dbx4nI6WSkVKZnGCTt/e8vJq1kdJcx0oX9R30Pv/WnHY0Irdlzcjz3+c8/62ZKYppEa9u7Hoskh4my7JySI4RayeZlklJfYZMy/SQ6rQaFvIB15259nol3kmamSbdUmlaDefc4pumQWkjLo7J3ttwZATTEMcKJ3vJsixK6VEail8UcoP383hlBn5O3u9Kz9HDZ0Ohv4di/n4klh6SgLNQqNJ1iCtJvT95a85KuOOpF4RvNx+cJlh6zuMZCZXUqZggTl7ddjz1AkW7B0Vlmhqbot7n3yJTN8jQDEoMT5KhajnHswzT1nsZ8f5i99eo9ydv5/kO+GdN0lRnf0FSLwamZVJCj3q01Ozmk9vLm09zVY0EmTYxcZJ0NFW3Rqy7yE6jhB6l3ugJiqSHs55n2J5eTZyb+3XuhhdvAmZXll6fL6+chz0+YF4lX5x6j6bVMFl5GnIcXAeXFeytgUIEfMtqwIXsWYP7jmDz1z6Lyz97F+/+zn/Fzqf/sKC+a6Z1bPzyJwFA/J6NQGUpGu6/A8P7T+D8//NzVK5aifKGWlSubgRZGViqhp4fvS6sX4HKUvF+rU/sxs6n/xDrv/g4ApVluPidV3D8z/4OALD5//NZkMlef/mFX6P9S0+g938dxOrf2I1gdQWMeBqR0z2InHSiMe/7u3+PmrYWaNNxzJy/LGIzKUNQJ6MAAas+cS+u/PMRtH/5kxg/eh4tj97DPmtSQ+T0JYQ+shGB8pK8n9WtR7o3DTuaJlsvpGdUmBkNPvihWklXQth6EIDL8TOoLW102bHWQ7Gfy6fJ+NYNJzTdj1J/OYK+8qytxWy6bFIbwowWRtuy7RhJsbyJFWWrAcATUWmSDkLGk+Q2lupjNreyNRh2HZdPugHe9U4VwVpUBZdjRg97ti27ESprBVRFaOju91lMLVdqxDc2blkCzhczKUj5eUWQ32yRkl1//yqmzw/grv/0r5EanUIFlJwGG2+Gbfzyb+DxfX+N7h/+Cu1fegID/+sAWn9jNxIDY6JJlk30wepyrP7s/Tj+599D8yMf8ZyzvzSIsfc6MHH0Ao5//e8FiaZHp7Dpq5/OG40ZrC7H2HsdSAyMQ48mseH3PmE/9gkM7nsfa35zDwZ+fhDTHf0AgDWf3QNLNxG9OIDK1pUwkipQqgiPq2YmUB5YBp+PzfPw+ETWlGpFRB0So7UA7FHkMSwraUDQV4oZfRSl/goAGawoW21vybBQW9qE+rJWEPgQBJA0ZzCSvIh1y+6B06habv93HQiEtBlHS8VGJIwpkaXLG2l8tNidLNZSscmVz8vOT7WSSBjTGEv1orlyE6yMIbZgrCxb68roZb/njgQHhD+3pmSl5ztwb8kI+IKoL1uNqB5GS8UmTKiX86ajXS1kEtoNjnxlcaFfHyYJIh/m6/Xlz+e39/l043PfepF+sftrFOsfFdpqz3P7aexIp+3ZfZG0mQQZqVwJgUsEbg3YDSOhuny/Sep5bj/psSSNHe0UVjM9kSJTc/RBI6Xl6NamblC0e5AM1X7safaYoerU+5O36Px3XmESRTxFV2JeKcHtVXXf9hcao3U/ZyhxgcLJPoppEbIsS2iwbssZ10m5NGBaBo2nLrP3NVkzbio94mqqea1l/PxGEt2uYzPpIapNeCQFw/PeGrk1aMMySDdUMYIdSQ8TUW4TzG1ZywbXq7kW7W5Y8kbfYmu5UiO+MYBbVQOej6uh2ONwb222gyDfexhpjbRogobePO7y+SZp9OBZp+H39It5nRT5GoLZn0f4jG0CH3rzOB388pNkqBpFzvSQHk/lkHuhRqORUoWmzR8zEqo9TKJRz3P7SVNTomHm6JidlDJiOTpvvkGCbA9wNnlnuwgi6aGsARBNkCV/nbupdyXeaTsvNEGEfHAju2GnmWkyLZNUIymadoV065gWEe/nJlhO/Lql2kSqkWE6DgvP92u7Nwq5MRaKYht5EkuHW5aAFxpYM9txwofOFnVMTpbadJzUSJR6nttPlmGIqpg13F70VK8cpm6IgQs97hC7+zwsw3QI82k2zWakNNJmEtTx1AukRqIUOdNT4LxeEoFBkTM9ZBkmRc70kBqJiurY3TQcP3ae1EiMht44LoJ0OBllW7H4YMVQ/KIgY45CZFHIRRBJD+c06liDi1m4wsleGk/1Z1WyTsUcVcftxhyviPvsStipbnVTFQ01570dRwevgInYpJw3MIi7JTThuJhWw7M21vhx803NLQSykXfj45Yl4IWMEOeDu8plli7vMfNV2m6yHD14NmfizEioZBkm9Ty3P4fQo92DpEUTlAxHPJIBm157yfYgH6fU2JRNtjEaP3ZBvK+X4L2f3dQMipzpIVM3SJuOCwcEv1iYmkFDbx73nBP3PJuqRuPHznsqdn77zCUCTsrMC5ymcLJXPNdtDXNsY277mi6cCtxRkUuGmphQMy2TYlpEVNYJPeqyjPW5CNIQBJtd3XJCT2hTnufn/f/AZZkLJ3spoUc9P8+WLub8/2oRJAIpM9z4uGUJ+GrhJlZTMwpayQrLBS+RNh0nI5EWeu6lH70uxogTg2OsYn3yp6RFEx5rmJFSyTJNGj1wlg5++UmR/xA+1EFERNpMgtnRVI0i5/rISKRp5tIgmZpOkTM93mhLjRGKqRnU+/xbpE7FKRWeonPfepGO/8X3c6SHQheu3p+8xUjf1om1aIIiZ3poWg2LKpgTmFuGYOTm1lQ1imkRUfVyRNJDNJ667HluVB1n526TLatYh0TFzTMnvISrCymCV6iqkfDosO7q1i1TZPuTeaWrGskcy5wjhXiJPh+JS6ng1oUk4CJQqIr9xe6vUfhQB6UnZuj0X/+YYv2j9vPdlWluVUzk5DQw+cEkQ9W9r9MMRqoHz5KpeQNxTM1wTcY5Xl2u6RqqTkZKpfFjF8iyLI/n10ipbGLOliKMpCoq4vTEDA29eUKE93BdmjcUjYRKhqpTrH+U9ETK0wA0Vd0je0ye6qbz33mFdMOZRstuQKVyfL3cC2xkEZ1XGuBTbe7jcRLO1oq5JOHWZA3T8PzZ6/817MafITTmhB4Vz8/2AvPXuvMf3Ch2YtDbdJNkfKugEAFflzzgYrN0lxrZK38AYOOXP4XHfvlXmDx+EYHyUtzxJ7+N8SOdyJgmur7/Sxz/s7/DpX/Yh0A5W8Sp+Hzo++nbMOIp9P30bdTd2YbbfvtBtH5yF3wBP3x+n3hd1/d/iUBJAFf++TCmOvqRvDLmyfiNXhzApe+zc+r6/i8xdaYPl555DYpPQd9P34YCIFBeitpNa3Dh//e/kDEt9L/wLrp/8BoiH3SjpKYSm7/2m/BXlmHy5CUMvXYM6fEZjOw/icYH70LX9/8ZbzzxFwh9ZAOU0iCaHt6GNz/5n9D1/X8GMhkM/OwAMrqJWO+Q+D4oQ2j/0hPsHP/dE1i2YRWstAZfxofqYAhnI/s9ObZmxoBmJTCpDqKhfJ1Yz+P3+RHwBdBQcZuwSiWMiJOfa+f2NlasR1QPI5Nh+94IGXsdEaGpYqOdiduOCfUyrIwlMonDqV5PRnE41QsAti1sIwK+AHw+lvF7YeYgxtJ9KPNXQoEf9WWrQYBYX9RY0QYzoyKiMi/xyrI1OZ7aYvJ5myo2YmvoEbFOaSI9gNFU9yL8nytx0yIfKxf6tdAKeLEaYdca/LY72/J17mmXpvr0i5Qam3KsXk+7q9MURc700PnvvEIdT71Av9j9NdJjSQofdKIjuRvCbQNzJuBeFLrw+LHzWfGSjoMi2jcitN/sGMnwoQ5Sp2KkRRPi/Ht/8hbpiZS38rYrcaf6ddLYjIRKkTM9wlpn6s6t9LlvvchcFin2nN6fvEXH/+L7rKI2tZzq0H3LzxpguW4Pt4XM3YBTjYS4pefVojt1jFe9MS3i0nydLAmnMdgpJI+LU+95nAeO/cs90eZYz7z6sfexhSDfdJ/7ffk5ifS1RXhPiaUHllKCWKxG2PWCx3JmE9XogWzbmPOPwR2IEz50lrSZBJmaTtHuwZyLj5FgGQ7qVIwMV06wp2F34CylxqZo6M0TRMRkjINffpLChzpo8lQP6bEkRc715YmRTJJp2KPJKZdsoBlkpDXqeNrReSOne8QFgJMoH1N2+4/1WNJzQdJjKQof6hBE7Cb1saOdnvcl8mqj3p97CYc/h5OqW9/lWjGXBrJHgnnTz/0eDmmzdUf5w3I6PeeTb2Sa5RHreZ0Y+T7LXCjUMHM3J2NahDQzlbdZKHFzohABX5dJuGu9rn2x4R5Tbnp4G85/+2Vs/6t/h9A2Np7a/qVP4PJLB9D6yV0IVleg5fEd2PHkH2D9Fx/Hm5/8T7jtXz6E+h3tqLtzPdq/9ATIymD9Fx8HwEaT9VgS3T94DRt+7xPw+X3wBfxo/9ITKGtYjrX/Yi/Gj5zH1JleNH30bgBA3Z1tqN20GnV3rsP7f/pd1G5ajS1//C/wLy49i4qmOkwe70L7lz+JmfP9qGlrweWf/hrt/45N8BnxNKbO9sBMa9jwxY9DUdj5n//blxEoY2PPrU/sQmo4gtYn7s35/HXb2jB1ulf8/QWry9Fw/x2ou7MNU2d7cemZfTj+Z38HRVGQnpjBxJEL4rlmxoQTzVjn+Y6zJ7RqSxtgZgwMpy6guWKTZ2KtWUgNG5A0prGibK1ngo0vw1xZdhsCPva/dES9Ah8CAAgpM4q4MemKm8y/1p0v/uSPESxMqlfQWLEempUWr2Mxm/6Cn8UN96QcoCCiDqK5YqOYDuTPiRuT2Bp6BAr8UKBgJHXRcz7Z73k1kOPJNxDysXKhXzdrE24+wxjuQBzuXsj26PY8v1805qLdg2Qk0hQ502MH7rxNRkqlg19+0nYfMEeCoRtkqhr1/uQtz+Rc/EqYTFWnyLk+0qJJCh86KxwQXJbQogl7Gu1tGj1gP57kwyBpMVFnqt4ql4g8lerY0U4xUMFlCP4c92u47KFOxXIm6fJ/t26JxPmOc1LH7FvqfMsridxe4PyP80rZG1GpezzJvKLOnjZze5Qj6aFZt1GoRpKm1JGcc88nn/D3yk2A0z2fiUsOjgMj/3O859NJ10J2kL7h6w/cyi6I+WjQkTM97Hb/4FmKdPTRzKVBig+MCTmAiMhIpAU5qlMxil4aFL5Y9/CFkdZopnvI/nnSSTKzidRIpMkyTBp8/RiZmp4TXemOpmSZvklKjEyS4XJLhA91CFeCs2LIkXu0aIJifSPU+5O3aPBXxzzpbZZhujzAjEQTg2NkGgaNHnS2bLCLgjMBWGxSnJecdE86mLMZOd8os5d0OTFmH5u7CVJGTGjDbqLjfmJuT+PvxwdF3EjoUYrrU65z0Dznw8ee822x4NN1/M9ucvPq29kyh5O0dj19vNI3fP1xSxNwUWvYXY0wbTrObFw8qyGaENqmHktRtHvQswZI6L66Qeeedsier/yxDFNUpqxxplFicMyjnybHIl4LmmEIG1l6YoYmT3VT9NIgpcan2UqclEqDrx8jI62RnuDNwzTFbQI1UirNXGLVOW8Ujh+7kKPZHv+L79Pg68fYlNubxyk9MSNI2V3Nn/vWSzR5qoeO/8X3qee5/UIzdvIo5o6vdOfszjUF5ibCy9EzeUd2+WoknsvrXh/vzdrVyDC1LNL35hWzcWSvPuweCHHb2XLPM3sNveMvzm7ksYvG4hGg9BbfHLilCbgYhA+d9VScyZFJT7VopJn3tvcnb1F6YkZUv1o0IYjYSGuOfBFNUs+P2YTb0JsnPI6JxPAEWablrUZN07PrLdo9SOp0XDTzDE1n+b+qJjZhuN0LfKrNLUt0PPWCI3c8zS4kU539ZBkmJUe9E3amYZBlmuICwt0awlesah75gk3FeQc++Hh0Ibkne7sFD14vFFyT3XjjxMgXdRJ5CcjxFjOHRPbtP5c3ZrQxTzaE1xWRnxznc9vuNNTOFfX/3tWQqJQTbg5IAp4D2WHo7oEJEYCuGWwQQeisL5KRTHuyE7g2zLMc+K29kdbEYIOps+ELNwHGLo+SkVJdww9pGvn1KdJmvKPCXOf1Tq+9SLH+UXEBCR866yHFyJkesQnDsKWO7Ofw0CB1KsbWLJmWEwj/NLsAuSUUDlMzci4cXA7h4J/T1I0cUnQnnnn+Prj9ykzbgxemIEbuerDsapaF+/QKKYKPCXudFo69jY8u53MaFFoTNJ+qdS5CnU0nni+knHBz4JYn4PmmopmaQUNvHCc9nmIpYPbILTuWU80OvXGcTFUjQzOc0eNoUvhkTd0gLZ70+Gr5uRz88pOUGJ6k1MQMqdNxNvX29Iu2DPA2pSZmKHzoLPMV26Tf89x+UiMxUYkKgnd5htlIszPZpsdTTEZ4+kVKjUbyWsiSoxEaPXCGLv3o9RxfME9zO/+dV2jyVI8Ya+Zwtn6oIj3NuWA4Fyz3iiP3pFskPZSlh7o3HmsesnJXwmkjnmNnM7N0ZsuySDPSNJ66LBLN+PvwoB83OeeDO0MiX+U9F5xwIFbpuwnfOZYk0Q8zbnkCzk4zm4uIRXzk82+xBtmMsyxz6M3jYvhg7EinqPaMhErR7kGyTIvGj52n8995xZYMXFKBppNpMLL2EpXmyW7gFbjQpO2VRqamU/xKmAxVI8MwKDkaEZm9iZFJGj1whn6x+2sUOdPj1akPMvfE4BvH2HDIoQ4aevM4Df7qGJMT7MaekdaEDNHx5E8p1j9Kqv3+4UNnmR5eYK9dvu87Z8yZj1G7hiv4CiEip6mVMmKU0KOzNKy0WT297hVDs5FldhMtH/I1zvKfV/734PkV2ePWknBvHdzyBFxMeLobpmaIZlP8ypj3dj2lilvumUuDovkkptzsoBpuTet5br9nks49pcZJcvD1Y6RFk3b2bq49zNQN0uMpT4Tl0JvHBTnzbAcue3in6NjFhksKg68fsy8C7kwKnQ5++UmPK8IdBO+WGdx3AoUWkmY3Pt0Xg9ngOCdypQP+eG4lmls9cg14rr1rxVSf3i3Jucs1iQoTOW/6uReLFkI6pdOvXumkdEqf9WcSNx9ueQLmcGIlHSuVO2A9O40scrrH5cnlcZTslluLJkiLJkQMpGWYHhfE6IEzjMSSqneSzpM49iIZSVXEPLrlDRbEw841crqHJk91e1LL+n92gCZPdZOeYCQX7R6kSEcf6S7SHz1wloUAuXKCjSSbxnN7hkcPnBX6NBGRZVqkzsRFxeqeljM13ePsOPf0i5QYnpzVBeGWKebCbIQ5H73U7REuNh6y0HHmqlizLxxEDnkmkqmcAPp8+NUrnfSFzzxLv3rlPHV1hknXTednr55f0LlL3Bi4JQh4Pjpv9vgw99S61wZx2UGbjlP/zw7QTPcQGSmNEsOTHr+wqbHVPe4K1F1p84hIN6ESOUlrnBj1eMpjVzv/nVdYQ88wyNQNevYHP6QNq9aSDwq1t95GP/r+M45LIeW1gxkJpzk49OYJGj1wli796HUyDZOMtCqyhM89zV5/8MtPerzNXPLQpuM5lrnxYxfsLc1OpaxOx6nnuf323wMLmDddm5rns2l5tqp0trzeQkTJE9TmM9TAj5XPLleoKnVHWRIRHTnQR1/4zLN05EBfweaeG+mUTr969Twl4ip98xv76MjBflJVg7rOj5GqyhyImxm3BAHPZ+DCsxXCroqdCvgljyZ77lsvUWJ4kjXU7OxeI5F26cKsicUJlVelvCFnGQZNdfazEJu0xqpq21Y2c2nQQ/ruxhqTJViV/Tf/+o+osXwZfR130/fwEH0dd1NDsIq+gi1Mq+0b9VTH8cExMjWdht44znRbVWMyhruBaGu/RkqlWJ+zs67jyZ/S6IGzdPwvvk/nvvUSjR3pdCr7p5lezStjLi1wfzMR0eDrx0Tzkl8M3Za7+SC7gTWXnpt/omyu6jX3Od5Qd+8FgVelRw725xxnLNnvIeiO0yPzlhFU1aBfvXqeVNXwvPbIgT4pRdykuKkJuNjKdjFCf4wUs4sZquZJItMTKc9iTXe17A63cTebjLRGPT9mDgrLMJm3dypGPT/e79GKWVg7cy8YOgvO6f3JWyLU59y3XqQWVNLXcTf9QHlY/Po67qbbljc6gyMilIfZ6ExNp9TEDBvMsDOHPZ5d0xSuh1ztNy0aaHyA49zTL9oDG2wqMNu65/w9OPY9/tzZ9GLx3echQvc+uOxq1NmowZ7PEtfYKiKOQs0yd3hPoQo3lcqfQJZO6XTkICPa117OJdZs2eBqZAT+2tde7qQXnv1AShE3KW5qAl6sOMtiiNzdbBr59SnSookc4uRuBE54XG7goevxwTE2FeeKbuQExx0U7lBzy7Jo5tKgmLhzV7ITJ7tIm0mQDwp9Dw95CPh7eIh8isLOsW+E9ESaIqd7bJ+xKpqAXFbgMgMf0IgPjovA9x8oD1PPj/dTcmSSLMMkdTpOkY4+QaCmbtif423SpuPi78X7+VLev6+nHXfHQrVf90BFdjXqtqQROXYxNynna5Zlp4vxypVX2cUS5msvO+ToBpcSODFn/9nzvDkqY/drOelL3Hy4qQk4X2W7kG3H+YjcPYLMrGGONswrUdMwxK32+LEL7OfPvyVWAPGRYTakwDIX2Jhu2vNYz3P7ydR0piXbxOheyDnTNeis/HHtgOv9yVvUvvq2/BVwXSNbqmmaLKM4nqL4lTFnMadLiw4fYvkTyZFJ0YiLnMmu3FVhmxPDGwlVbNaYuTRIph3FmRPTmWedkfv7nAtussxeK59PE86nB2dXyNkr4k0+Cp0xyco4uiwnQ+e2n2mxs2mvhSrgYnGjN9ikA2PxcFMTcD4spCr2Jnc5AeruEWResfHn8IEFngnhDdV5i4yUKrRUXnGOH7tARkplnlqXt1e3CZkdK0mWZYmFnGwc2RLar6nqNHa0k0YPMOL/4d//AzUEqzwacGN5Lf3Nv/4jV25vWgyGuKtoFuSTIsu0RMOQuyz4Lrr0+AwZSVXsieNacPhQB4XthaKORs0q2vjgGEU6+thuOpd9bq7vnIhyKnT3hdRZd+9tamVDVQ3q6gx7SNLKWJQ2EpTJWKRbKqlmkjKZzJz/b2STYVdnmL75jX2LQo6FiMxd3aqqsWCyu1ZEeaNfIG4mfOgIeD56r7B7uSxV7r1r7pFh3kCa6uwnPcZ0X05mbg2VN53GjnYKTy1fkGkZJg29eZwM3vSyj+0OaO946gWa6R7yaKN6glnc3I0yUzcocprlLXwFW+i25Y3kUxRa39RK/+3zf+Rqmp2h8MGzFO0d9lTRmn0MPZESVTEfJ+buj3PfeonOf+cV6nluv8fJwT3G/DPw7RvujctaNCEGS5yqOVdu4N85H1oR++ry+LK99jFvJCMnG8MwKRFXad/Pz1EirnkeWwgR6brpucXPrm513aSOU8Ok6041PSuxun5eDJEthk7Mz3+xyLiQdCIxf3zoCHg+cJOGN34xd+OvW7vs/clbwjnQ8dQLQlIQzag3jtNM1yCpdl6DIL0oS0YT5JRIk2WaFDndw2Imn3qBtJkEGSnmTIj1jbL84Cd/SsmRSU91zyfY9Lhrw3JSJSOtkjbj5PZq0YQnZ2LyVDfz65omRc700OiBM6LS73jqBVah280/PnJsqjqde5rpurH+UbIMg8aOdgo7nJFWc2QHHlQkciPs6blsZE/+eaWRjlkvpNlOgC985lka6J/y+GaJvD5aw8gN+Mk+JidYTXOqT06yRw700QvPfiBcDEfe7aOU6zWGYQoNOJs03ecxOhz1SBqaZuQlyashO/5a6Ru+cXFLE7B7jU52UEz28+bSLk07WN095WYaLKWMywXuKEq3w8DdwDM0nfR40kvcMwkyNN3TKONJZoaqU6xvlDRbOhAj0tEE6Yk0Ja6MMXK3nRXj759nSWq2B5lvSQ4f6iA9kRJTb2NHOinaPSg+nzvrmLk3DBFraaRZGtr577zi1bZ/vN/+vC95Pi+bDPQSa/b+uny6vpFQBUllk4qum16Llou0nIpYpanI7H5jdwNtOpL0OA1EJWm/35EDfXT2g2EP4Q70RSg6nc6rAWuqQYff6aVEXKX9+y7SQH9ESBpdnWFB7tk2tsWArFpvTNzSBMzhTMEVtxmjUJXM4yi16TjTiO2Fl2KPWjwloijPPf2i8NPy7RIsi+Gs3eBKe4YWjJTmNMrsCpV7jnlYe7R7UDT13ME5XE/mQyTZkZVGUhXvy6tR52dMNoic6fEEvLu9zoNvHHOaiWLDx9u2DY6df+R0j/AFD/7qmKcxN9vdB5FDzuFDHXluq50KshCYJsyGFubyzPJqNmVXswP9ETIMU1S8HKZpigrYXbmmkhodOdDvkSSInGo9mdRI0ww6crCfdN1g79EXITWleyrp+UL6gm9O3NIEXIxjwksQhZ/rjaN8SaSkeRp5WVGUPFHM1HSKnO7xWN0Sg2NOII49BmwkVJEZYdoDG9n+3Y6nsiMpnQaiqWqC+N2PuzMsiEhU7pOnum3HhRNJ6X6+c3zdyTa2c4f5nYD7e+buCf5cd2ZwobsP5/UvORWwq5LjFWQxOieXKWa7Ddc0g8bDcVJVgzTNoERczTmmqhqkqYaofI8c6KNYNE1H3u0T1Ww28kkAYiQ5rl61THCtfcHS+XBtcEsTcDGOiXPfepHOf+cVT5ZDIbiDfbh31z0EwWGZFhmqRunJGdKidnSl5l0jz/N9nWaXVx9NjkzS5CkvaWvRBJmqJgiO/9wyTer/2QFKjUXs0HYniYyHuLszgplTQxVDH4mRSTr45SdppmuQ9ESaDFXPO2gh9F77InDuWy8W/J55RnK+48wHxeqcmmZQKqnN6rtNJjQyDJMu90Zo/76L1NUZpn0/P8dI9fyYeH5XZ5gOv9PrkRp6Lk3Maj/LJwFwyeHwO7000B+5KvvaXL7gqyVQqSFfG9zSBFzsSiJ39TcXUYgR3Cd/aleBqhPXmNJcVbetiSZSZBnMq+teI89lg+wptdEDZ+xGnSrGkcOHOshIpr2ygqp5goH0RJoMjQ1NpO0puMiZHpdM8pKQTrJdETPdg64s4dzcBu9nepuczRia/XjutJup6mJ10WJhNp0zmdDoyIE+0uzKVpy77mqa2WE3qmrQ6HDUJm2dEnHNQ16GYVIqqQupwbD9z4bBjl2sBMAJN2U3/DpODYsGX87nmoU8VZVdXLKtd27k6OXzJGOpIV8b3NIEXCy4WyJ8qMMeUPCSMCMTO0EtxRpxpp2x4PbOpidnPGQ68utTIoqSV7rhQyx9jLsi9HiSrRF68qekx5KMPHWDep5nTa9oN3NbmJru2YRhmRapUzE6/dc/dl0M0p5gHk7CRkolyzCJ5wFnOxnGjnRS5EyPk3z2/Fs53092heuWd/iUH5cZFjIsUwizkVM+l8S+n58TiWJERB2nhoWWm0xq9My3D1MqqXvIrJCdKxZNUyqheQgzu1IsdH4e4nc9N5+UMpf3mFfqs1Wongq5CClG4vpAEnABuEnCSGlCWvjF7q/Zt/iONtzxtJNvYBmmGKpwh/ho03GhAXNpwDQcgs6udDmJx6+MsUWa9pr7c996kVXArlQzI6WKRhj3+Lof1xMpVhEfPJtjOePV+kzXIPMWn2GWOO435ss7s1clEbG7A15BOwlrx4Uv2K0RF8ozzvd9zwez3RpnV31HDvSTphqUSmh05GA/q2TdtjPVoIG+SAEf8XnS7KGIL3zmWXrtFdsZ4WrCqSprqnESNYw8Tg3bwXHkQF+OW8I0TUomtLyEn4hrBatbw+CvcypU/h75XiOr2RsHkoALwDtYYd96upwJ2c0u4Tx4+sW8QwSWYVLP8/tJjURp9ACTJIbeOO5pMGVLIqZmMB02mmC711xNPj6F1vHkT5kn2E4ZM9Js4IOHxvc8t5/JD8m0xzXB5QVeAeuJFIuk5FuVUyoZdvPuF7u/5klFY35oXfiIe368X1TGfCNINlkLK5mqC0J2k+18JhhzxoOzyIQPR+TNXrC1Xi4zuAl1/76LNB1J5viIB/oidPFcmJ759mFPU250JEqmadGRA6z5li1VcKcEJ0YnhrLf45bQdafZx8/tV696CX/Wytb+Ltw/e+1lm7TTC5+kk7j2uGUJeK6KS4+lcpZNcn9s7/NvZdm9Xsqp+rK9rENvsj1yrELU2GTdLAlg2e/PN2yIqpcnpdl6MB97tiyLIp19nudapsmcDbpBybGIJwSIDXJ4Q+e1GXt4wx4W8Q6qvCjOydSMLH3cGdxwB7UTOQE97rS33L+P4ppxs/llOflEp9M5Wmo2mbmn5lJ8qCKpsWadTYZETGNlBHme1LROpmkJh8SRA32k6yaNjkSdIY1TIx59d3Q46hq86KRUUqNEQqWuzjAZhumREF57uZMG+qZoOpK0CT9/47DQZ3L/7LWXO0lTjUUbnZZYfNyyBFzoNrj3J2+Jije7UnM35PJlHMxGIm7iSYYjNNXRN6u1LVuH5cMOvNJkVjEnZ0IEodvSQe/zb3lsZo6colLP8/vJsvVfZ2Nyh8vp8aI96ZfyWMWG3jxBlmVRYnAsZwFn7uf1fhfZ49tz5f/yijHbT8sfK+QWyCYfd2WYPVZM5MqNsBthPZcmSLWlCD41d+RAH+3fd5EmJxLi+QN9EbrcG6FYNO2ZnHvt5U6Pvvvay2w8+plvH6aBvggNXp4iXXMe5+HqrHo+T8kE8wmbJjvXbH8z/+yqyp4jqmV7ko43AF97hV2Ejhzop67zY7ICvkFxyxJwPrL0Jo7lr8T4oku+/6xYeKvjs4IUC912u1f16ImUqJjdxO/Zx9bR52nCce+vNpPwDH8YaY0OfvnJ3HyHaMJp/LniI8Wo9vPe4Q1n88X8dNti8n+JKK9GyjHbbTmXHXhDLV8zrOPUsCfRTNfZoIWqGhSLpmmgz0usqsoHJxhxPvPtw5ROsUadW8Lg1eaRA/2CBJkWbFDSrqpTSY1M0yoYsuPouYxks4OFXnu505Y7vP5kN+kfOdjPpBEZU3nD45Yl4HwwVd0zoECUK1UUSyBETqQlX8rpbuSxIQynKuYEx+EJ3jEMinYPkmmYQhc2Ne8QRnpyxkOQLDlNI0PVnHVIkShNnuphP4tEaexoJ1mmJc41u6oXDcRogsaOdHo06FjfyLx02/kiu4p028d41Tcbubi3R8SiaaG/vvZKJ539YJgOv9Pr8fYSMUdEKotYf/UKGy/mWm90Oi0ed+u+sWiaRoejNB1JkqoadOn8GBmGST1d4zlWt4G+iG1fM3MqfbccUaghmH1+05HkrBesawE5mLE4kASchezKOJtk5kM64UNnqefH+12Zv4Yn79dIqRQ+yBpyfEyXY/Sgs9LHSKmuBaAvUnpixpk8y7NPzRNlad9GW6Zpr0Nik3TJcMQli8xewXJydg+EOLviXiJtJj6nJLEQuPXcsx8MiSpytn/02U0pXk2mUrqoTHmAjq57A3BM0xIVZjLBtNdkQvNM2nH3hFsKMAyT1LTTSEvZVfNrtnuh44Nhik6nRQXsrq5nplKUSmqiWtZ1dqHpOD0iSN/dECTyuh5SSZ3UlG77nPs9DciFfufFEKsczFgcSAKeA/m0zPnkRmRvsjANtlooeww31jead3RXjURp8PVjpEZiHikhX8auc87O8supzn7nPFyLPU1Vo57n9+cdA86u+vkwiaEbIrOCV8aTp1m6Go/pXCx/rzgXl32smH/07tFkIoco9v38HI2PxWmgL5J/LdAr58k0mS2t44NhJ6PBlh+ydWNuP+s4NUJqWvd6cV/ppK7OsCeBjTsmPFLBgX6PHvyrV8+TaVrU0zXuIcKBvgip6dwLnIjC1AwKj0ZpKpKgw+/0XhUxFkus0sq2OJAEfB3gJm3e+OKOAC4VcJcBkbfK5tnA3kmzFBma4QnLyQbf9cYbdD3P7Sc1EvM4K5wmo6PlmppBHU+9IAZP+Pn8YvfXWAPPTj2LnOmhyVM94liWYeTIN4uJ0eEoxWOqION84TuOy0D3NKRYYI8mNFUuTRCR5xY/Hk1TZDLhqbyPHOj3HJ+nlu3fd5E16nSDOs+OsKk5l/PBbT9zZ0q4rXP5/MCc2I+861S+XZ1hSiU1Ed7DJYsj7/ZRIqZ69GDVzqiYixgLVbqSWK8vJAFfZ3AnRax/lMaPnSdtJp5jd+PNNW06LmQDMeFmmIKsuV832j1IhmaInF6TR1fygQ97AISPB8/mwY2c6SE1EhXWM1PVyUixsWbepGTVNYvO7PkxS1jj6+zzNTA9Qy0JdUFVMvfdJpOaRxN1g1dvXefHPMMM2TBNS5CcOyktp7FmOxo4jhzoo+OHL4tUNX4h6OmaEMTIhzM0zaCpSJKi0XROdTw2EqWB/ikyTYtJB+/2kW6PIvPnqGknqOeb39jnGfiITqep5yLTlrOPPdAXyWvRyyZccRHpn5rX30O+Y0ksHJKAC2C+t9Pzac7xY/N0suwwdD2REvm8/T874BkfdofDcw2Zb8vg76/az3eT6i92f43CB8+KZLXc83lJECQfO84mVLEZxFWRs0EOjXiwej5pJNsBcjVVcvaQhBu8euNkyp0LnsftCplXmNz7q2tMs3U7DJIJjTpOjYhmmtvHy3XcZFJjK6Rcj6uqIbInnEELptlyzZhb3NyWN+aMYLqzmtY9WjP/nTcSLcuinovjzFNse5TTrso7e29dPg90qghNPfu7k6PMiwtJwAVQqNlWiJiL9be6nRa8EuV2sYNffpLOPf2iyHhwb9zwvv9LIteB7W5LehpviZFJx3UxHWch6U+zYPdikc8RMtd3UwjZBJ9PQ5/1ltjVVCvWASEm4fKsAHJ7ddMpnQydabpsOGKM1DQjSZ5w1nFqWMRPfvMb+2g8HPdMtaVTOu3fd5FmplJkGCaZpkUdp4Y90sLocNRjMUvEVVE1u5830BcRVfFA35R43ehw1GNd4/JKx+mRnC0f+bIjsi9Or7mqa34xcwZOHDljtvD7fH8HsjqeHyQBF0ChZlthYi6uAs5XWfKJspw1QHMQXU58pf3+p//6x3ZK2lkRizl5uttD5HPBSGl5ozSJvP7jxUKh5k+23SvfP2yupbqJ9oVnP6DXXB5dXk1yPZgNO1hkGAZrpNlVq9srnE7pNDgwJdwIiZjqfY4d0s4JlQ9DZA9j8PPp6nSkkX0/P0cD/RGv3c5+3q9eOU+6xiriyYmEh9SmIok5K9DZBlU837WtL/OJv+xQH95I5BeZYrRh6Y6YHyQBzxPzcUHkAw8vZ9ayXB2zmPfiCWNcCtBmEjTy61OUnpyxN1l0eMh55tKgZyzYe/zCk3juRlx2OBEfElksFPoHnk7pHk03O06RN53co8eqaghy5E2xjlMjNNAXIcuyaCqSoGe+fZiOvNtHlmVRNJom0zQ9xLR/30WPCyERV2l0eCZHAsl2WQjt+d0+SiYce9nhd3oplWRbL/hFIJXUxJAGP0/+OkPPIx+80knHD18WFjn+Xbk/J6+q9++7SKnkbHcV50Vl7r674FJJOsW2exTS273H6vSci2ziFQ9JwNcROXLDAnVQz2iwaXmSz07/9Y9p8lSPkCDcaW7FVvPOuRb2Q8/n8y5a7GSeOEVRpWWtBiJiITr7911kTTHVoGe+fdjxBSc1T/VqGKwCfu1lpv2ODkeFzeuZbx8mXTcpkVA9++XcYTv5Fmt2nGLuCF03xDiwmmaErmvsfJiNjTUCNc3IkRrEZ3/lvO2EYJoyn47jWrTYYzeVyr1o2ZVuIVLk75ednKaq7LwLpbARyYr3aiEJuEgsBpnMFtYz/3PJT47u41qGmbMAM9eR4F6YOftqpvlW/gsh7dk0xHwpZ1wTzqtTutLFdN2g8XDM45ToODVMZz8YprMfDAni6+oM5xApryQdp8QYpZJOopog+gN9wj/Moi416r44Tj2XJoiIxMZktrZoTAT08Gbd5HiCTNNyNFybOA3DpKlIguIxlVW6SZ2e+fZheu1l5qhw29+4PY+PO3d1MuJ3a8KFtPGFkKiseK8OkoCLRLFkUoio+Vhy4bAe9960+d3a53Mg6LEUnXuaTc25pYdC64GK+Wzu4Pm5Pq/z2PxIu5h83yPv9pFpj1CL99INjxvA3SQjIk8ji2nChvDSplPe7N7sUV9eZb/2cif1XBz3EO6xw5dtgmZTaT1d415fr61NCx05oQo9W00zBwQn28u9EU9VnrLzLJIJLccex4OGuF7t/j64xS7lItqB/khOwLyHkPPEdkpSvfaQBFwkiiWTQkTN4xhj/aOzvq7jyZ/m1V2LO78X81bFbutZvs8x21ZoxzKXvym42HkQs1VUbr2X+1w5WSQTGr364lnSVINmplKUiKt0+J3eHEJLp3QavDwlqsbodNr24Zri+LzZxkmU2bXYOfV0eXe/GYZJM1MptoroXVbJvuYiSt5U49GXMdsXfPCtHrG+KBFny0a7L457puIM2+frNCJV4QPOnvTjfzYMdlHpvjjuGWMe6IsUrdN6Bk2yHBYSiwtJwIuMQkRtJFSXn7dwteiugOdDbtnPdbsyTN3IkSKc95yd4N3j0vk+29U2JYuB2+XAVsVP5VZzr5yn6SkWps79sW7JYd/Pz9HFc2F7rFf3VJO8YjR007NJgofcPPPtwzQejpFhGJ7X/uqVTvYazfBU5//4g2epuek28ik+Wr2qjX7wgx+Rrpt08K0ee5dcnyBxTqacOPnP+fp6d+6Dqhqka6ZwJRDlrjFS07qofFN28y8RV1lTsUiJwX3RklXwtYUk4OuIhfpnC0kSYstEnm0a7kD0yJkeMpLpHBdE9tqgQtLJtSbY2eAmKu5scKeH5UsL49GSvOoTo8hpZ0Q55aoCO04N24lp/SJsPR5NMx017V3K6QxZOCPRk+MJSiXZeX7/ez+k5bWN9Ph9/5H+zad+QI/f9x+pbnkjfeNP/jtpNlG/8OwHnsYZl0K+8JlnqadrgnTNznhwNQH5BcDQDbrcG6FU0kuMfApvoG/Kc1EaH4vnzbOYDe6LVlfn2KwNvPn8PUpZIxeSgK8j5vLPFqpIC/28GBeDZg9iTJ7qmTVAPlsHvhbBOvMBa3Z5dU93JKQ7BH2gP/+tsqYZIljHMEzPuiFOtjxIh5OrYTgNOL6m3l3xdnWGafDylGskWiXLYn5iTTVo9ao2evy+/0hf+Myz4tfj9/1Hqq1uoSMH+5nVLGvx5sxUihIxVUgYPOOBOx10W6pIJjShCzsXF68dr9CapmzwQKFCgfduq9xibNSQbon8kAQ8B66GiPKnihX2z/KV9sVOns11+29qbOuFFk3M2dibrYKe63NdC3gGMFxkUmibcL7qStMMse5d1wwy7dv5fT8/R6kks4Nl5ygk7BFiroHO2LYudkuuCe+su8ocHZ6hZEKjwctTpCg++jef+oGHgP/Np35AiuIjNc3cGNORJBkG+51XxZzsufzhruwH+iIiknMqkhQr6N2k5m4UTkUKDwO5Ly4J21UxJ1FnjXQvBNItkR+FCDgACQDApWd+iWN/8l0ACm7/9791Va8NVJai4f47AABGPI1Lz/wS7V/6JILV5TDiaUwe78Jjv/wr+EtKPMdp/9InASho/9ITnp8Hq8tnPSd/SQB1d7YVda7Zx2p9YhdSwxG0PnHvnJ/rWuChj7Xjtg31CJb48LFPbRY/7+ocw95H2gAFePDR9QCAd9/sxnM/OAlAwW3r67D6tjooPgVjIzGMDEWxZWsT3nrtEh58bD0CQR/aNq6A36+gobkGoRWV8Pl82PtIG/x+BYpPwbbtq/Dum91Y0VAFRVFw+sQQHv7ERnSdG8OGLSuxoqESDz62AYCCvY+04cn/8hbuvX8tNt/ZiNtuW4/xyCU0rtgiznk8cglNjWtARFCg4OK5MWz9SDNOvj8IQ7fw/D+eBADcflcT7rqnBdORFM6cGMJPfvgBfD4fAkEfDMPCI5/YiP7uSfT3RPD+ewP40//zEUBRxPfw+d/bjgcfW4+erglUVJaAMoR33+zGg49tgOJTMNAbQX9PxPNdPf+PJ6H4FM93zKGmDQwPzuCee1tx8ugVbNu+CmXlwQX9fZaVBz3voaYNcW4LPeaHGvlYudCvm7UCLqaSu5om02yvvZqg9+uB+QxpXAuIppKauxMtb8jOq+edbAO7IvzmN/aJPW3ZAT5cuzVNi+IxlWJRNlgx0BcRleXFc+EcR8NrrzAppOfiOKm2Bux2RDz77D9R3fImjwZcW9NAe+/5qrC08ZhLnjfM3QY9XePCW8w/f8plZXO2L6si12GgL0Ip3rCz5YtYNE2xaDqrSo967ihSSU00FGdzQyTiao7zZDEgJQkG3MoSxNWQ3tXehmeT2PUgtfngWp5PMQ2ZYjIgNM0ZnCDyBvVwjVNkQNjbLQzDJE0zPBss+LG5ZMHsYf0iV1gE4Nje2iPv9omNyCL+0X4v07ToG3/y36m2uoUUxUdt69rpmWd+RDzljEdLmqZFM1Mpz3nwEJ4Xnv2Aei6OM1K+yFLWBq9Mi0ZfOs3Ot+PUML3w7AdivZI7BD4WTXum9ngoPNfKuQ+aXwTyIXst1NXKEG5ISYLhlibgqyGZ+aalSTiYq/rRNNbQ8nT0szIg+P622bIKxFaLtE6GPUr8mm0dyw6eISLPDrZUUqNoNCW2WPRcHGdNwHed3XK6zlYR8QpxoH9KnJe7Ko7HWKNOTRvCgeFOP3N06POePXIi8eyDYftYbDSYX3D4RUHX7YuKfQHicZZMKx4TFyQePykS0YqobHlORTI5e26JxMJQiIBvCQ14Lg11NhTSZa+HPnqz48HHNni0y2wYuoUD+3vw0Y+3Y2Wjo7WePHoFz/3gJIiAltW1ePeNbqGTfuzTjr6oaSaICJaZwYWOMOpXViGd0nD00GUce28Aj36iHWvaQmhZXQufzyfOo7l1Gd7a1yWO+cgT7QCAt/Z14fl/PIn//N8/gW07VuHzv3cPHnxsA2IzKsbCMTz6yY24eG4c6zfWY2VDFXq6JvDYb2wEZQgnDl/Bth2rMB5OIJ3WseO+NXjzny8y7VUBHnliI0Irqth5PLYeigK0tNbirX1duO/BdTh9fAiV1aU4fXwIux64Des2hKBrFi73jGEqksKWO5vw1r4uPPT4BvRcnMDDH2+HogD7f9mFY4cH8LkvfgTvvdMH07Cwti2E535wErdtqEd/96TQnh95YmPBv6uSEj+23NUEv9+3SH/7EsXgliDgq0Eh8i5EzDcrspuFi4Hshkw2Dr3d6yHW29bX4Sf/eBL/6ov34PNf2o4HH12PnkuTeOhx1lzKJvKB3giIgL7uSfzkhx8AUPDIE+346Mface+etdC0DOIxFYMD09i8tVE0gQIBv2iu7Xl4HSyLMDEWw0OPb0BtXTlaWpcxkvvERlzpn8aadctRXhHEW/u68MCj60EZwpmTw9h+32oYeganjw9h245V6OmaxKY7ViKTqcCFs2GnifjYBgCEyEQCgaAPp08MY/vuVrzzZrcgx0c/uRGJmIaKKtaYnY6kEB6JYTycwAOPtOGt1y6J5wZL/CgpC6BmWRnue3AdAGD1bXWYiqSxbXsLiFijbs26OqxeuxwE4CH7HPLB3SgrLb02lCCbcQWQrywu9OtmlSAk5sb1ag66m255swlce9xmA7/VTiX5AAXLaOAbkN2B6KmkLsLb3SvieXLar145L3y5+TzIlmV5JJLxcMwO2gmL/GDu5SUikS08OZ6wR5hz18lzuSHb35tOsc/gnqLLFxrEh1T4yiL+u2F4vzvDMGk8HGOfs4AUNJtUtFiDFbd6Mw63sgQhMTeuV0UvrGQKs0S5K+TZKubsCurUsUEkEzpu39aExqYabN7agMPv9mHDphUorwji5NFBPP+PJ5HJENra61FTW4bVa+vQd2lSWLQ+/3vbseejbWhdW4uTR6/gu08fAnwK7tm1WsgRiqJg89YGj5xy+sQQ/uw/PwLFB6xrr4emmui9NImW1ctRWgqsaKiGrpmITCRQVV2CSxcncNc9LZgYS+LRT25EeDgGAjA8OIPHfmMT0mkTw/1TOHZ4AIoCPPrERvgDPlyxf1ZbV467d7bitvUhKD4FwwPT2LZ9FU4eYbJH17kx/I9vvoXf+d/vyZEZThy5gi1bm9C8allBKShbKnJ/16dPDHn+vhaKueSoWxb5WLnQL1kB37i4WZqC8+2KZ0/KuYcy3MMF7nB2MYLs2pmm66zCfObbhz3NsclxFtoukstUZlvjuQzuZZm8OnZbvrID3tmSzoQICdJUg2amU3mrXV4xuwdAeJCQe1knn9L75jf2iXwMd2ONuxiY7a0vx7o33wGLYtcSSRQPyAr4w42raQpeC/23EHiVq2kmXn/1PPY83IZA0I9TxwbzDgC8+2Z33mGEd239VFGcoYw77mrC66+ex3M/OImv/tle3PfQbTh68DIqq0qwanUtHnxsPQYHZnDmxBDiMQ2nTw7jIztWoamlBj4fG5DIWBmcODKABx5dj0c/2Q7TJOy8bw3u2rEKAGHr3c343O9+BHseXgddNzE6FMPeR9aDCHjg0TYQAfGohpGhcdz5kRZc7o1gTVsIp44NikrytrY6HDs8gAcfWw9dM/HOG44WHFpRichECretr8NzPziJZXUV2LVnLR7+RDv2ProeB/f3IFRfwSrj5eXYcf8a+P0+PPLERpw8yqrdk+8PYteeteL75v9dLNzVajDov6rKV2IO5GPlQr9kBXzj4lpY7a4lvEEwYdq/7yJ1nR/LeV4hXTjfUIb756LStKvagf4pj62LP8edMTE57qwaeu1lNrJs6IYng5eHn/OhiNHhKDuGPWRx5F023mwY3jFjwzAd65gdCN/VGabD7/QKbThpa9qqnQHMsyRE/KVr0GSgbypHn9V1s2AYT/YizoVABu0sHLiVfcASs2MphkO4hJBKOY0zdyOrWBSzPdlNHG6ydD/Ot1zwTGAuXRx8q4cScWf1vGGwKMvREWezBk9O48MUfJjBLU1YpiWC4nmwOidFnriWr1HGJ/M6PvD6jhN2k7HYxtZ4OObxQ7u91sUie0+eRPEoRMDS9CchrHaLJT+oaQO/euU81LTh+dnrrzo/O23LAGdODiMYDMCyMui9NAnTyBR1fH6s0tIAPvapzTnSBX+OYVii8ffu/h4AQOeZUWiqJZ57+sQQtmxtwv5fdgEA7tregtKyALovTGDjHQ3oODXMcib2dcEwMjB1C8uWlcE0LPucLaSSOta01Qlr2cmjg3jwsQ22l3g9pqfSqK4uRffFCdyxrRl+vw+njg3i6MHLWLu+DgTmC977cBs0zQQA6LqJBx/fgNa1tdh4RwPOHLelkxNDKCsL2Mffjj0fXSdeU+j76r00iYc+1s5yJB5dL7zW777ZU9xfKoA9D7fZ8ktxuSMSc0MSsMSig2ua77zRLX6WTYLbtq9CTW0Ztt3TAsDxBB96u3fO4/NjnT457CFjXTdx6fwYDN0Uzzn5vk2Etq/4g6ODnvcxTQv33NuKw+/24fl/PIkD+3tQV1+Bix1j2Ly1AStWVuGeXavF4+++0Y2J8QSCJX7XOfehLlQBQ7ew9+E2fP73tuOue1rQc3ECsaiG08eHUF1TCp9fwabbV4rPsW37KphGBsuXl8PvZ24LyhBOHRsCAHzw/iCe/8EJ3H5XM4JBP7btWIXqmlJs274Kfr8PZeVB3La+Dk/95ds4dXxo1u/ru08dwpkTQ3j0kxtRVh50adnFk2kg6EfbxhUIBCVtLBrylcWFfkkJQqIYZDsU+M9mkwrcj/OoxkKaI3+uJ8P31fOkqQbt+/k5SiY0Sqdz36/j1LDQW91juvGYSmra8EgPhTYWMz2XbSl2b1fW7OfzRZzOJuVOikVZxOR4OE6m6XUjsLFozbM2iWvLPHReBPYkNbajzjVS7NaKC0kK+caRi9mEXAjZ343E3IDUgBcfN4v162YCt3l51q3PonG6Mx3cFrF8we3uhhYReRpulsW2FD/z7cPU1clWGhV6b2cIRKOBPmYL44MTnPQTCdWja7sJ0p0HnBOEo7F1SMcOX6auzjHvEMgr5wVpZhPuXN9VvgadO4ci33dViGRv9aGKhUAS8DXAjRYt+WGA2/VQ7MYHHlwz1/P5Op+O0yNERKKCTdkBNNlOgdHhaF4i5wE/3/zGPhofc5wTRw70kWVZpOssMtLZvjwm4i49UZXuhaP2eXNXBZ/k466I7M+V3RBbSEVbzGbqgtNx0hs8L0gCvga40aIlP2xwuwwW63i8Cuw4New5frYNjQ9VZFeBpmmRLkaDz3ukDD5iHI+pYuURlzvi9laKpL36x71yPhumaeUQbDZ45c9J2y0L8P+eTcYhmp1IJckuLgoRsFTTrwKL7R6Q8OKd1y/hv/6nN2ZtMBUD3qijDGHXnrU4fWII6zeuwK9/xY7/69cvIWMRKqpK8MCj6/H539uOvY+24YP3B/H+ewO40j8F07Rw9GA/DN3C27+6BADYvLUBABt2+PhntqCnawL/45tv4XJPBIriQ8/FCcRjGk4dG8JUJImHP9EOhYCd96/Bg49twD/9w3FPo5LD7/fhwcc24HdsB8XRg/0eRwnAAnl2P3gbiAinjw/BNCy8/94A3t3fg4FeNmo90DclmpHvvNENw7A8x+BDMfnCcWZ7TGLxICfhJG5YPPjYBiyrq8C2e1rw+qvnF5yklZ0/cc+u1Th55IpIEtv7MIvADK2oQtvGepHK9jv/+z3YencLDr3dg1VrliOZ0IXDgwgIraxEeUUJLCuD5aEKrN+4Al/9s71Yv2kF3vzni3jw8Q1IxDVs296CsvIgNM3EmVMjeNTOa9hyV5NwgXCoaQNX+qcwFUmhpbUWp48P4btPH8Lnv7QdH/vUZk9OQ3VNGfb/8qKYCPzc796D1WuXQ/Ep+PyXXGloBNz34DrPhJzEDYJ8ZXGhX1KCkFgKXG3TJ980HXNb9Nsr4UdEalo8qgrpwLsp+bxrhZBXekildOq5NJFzvvwY2TpyPpcIh7OOSHPcDS4pwP1dDPRFxEqi2WSaQhNycrLt+gFSA5a4WbFYemQ28XV1hunVF8+Srpk00M92rh0/fNnTrHPsZ6pYEcThdi+YpiXWF/HXJJMapRJaziYKvo7oyLt93s9o74Y7/E4vpZJz2/W4pi3WMeXY5uZeB7WQC9us9kBJ6HlRiIClBCFxw2OuYHc3smMr1bSBd97oxkOPb8Dp40Mi9Objn9mC1bfVob93ChkirF5bB103cce2ZlhWBtFpFevb6z3DDvfev9azkeOhxzeI32em0rh0fgxDV6KoqinDxz69GZaVwYVzY7h7xyrPOW7bsQoz02lsc/3cvfH5I7ta4fMrRX0XXFJ4a1+XR2bJll3yYaERkYWOXcx7SmQhHysX+iUrYIkbHdlVnRNg0yk8t3NVhd/8xj66eC5MA/0RIipcgXecGqazHwxTx+kRliPhcj24q0FdNyiZdJaOCr+yq/K82io/+/Xuyn2xXCT53ivHcSGdE3kBKUFI3ArIR0RzkW7O6+2Jt7mIq5A+6x4iOXKwX6y0v94Zu12dYU9S3LWAHMooDoUIWGGPFYft27fTiRMnrlk1LiGxECzlvjHTtHDiyBXcvbMVlCG8u78HDz2+Qfz3wx9vFzm9h9/tw0OPX79zVNMG3t3fgwcfXb+g9yzme73a97hVoCjKSSLanv1z6QOWuOmRHfRzPcC9xZpq4o1/voh33ugW+mxpaQCKjwWvExG2bV+F8+fCeOSJjYtKUtkJc9m4Wi9vMd+r+z3mOh+JXMgmnMRNhXxVGW8m8abY1Ryr2Oe5m2Zf/dO9qFlW5nnNO69fYnvnbP/utfDfXuum13ybdLIJN39IApa4oZFNfvn+kfMqjFdgxUoRxRJGvudlL+nctt3rdJgvebk/p+JT8M7rl+b8HMW8x9XIM/NxnxR7PhJeSAKWuKGRTX6z/SOfbwVWLGHkex4np3OnR/Ddp9ik2sMfb8fbr3UJspsPebnP/ba2uqI+RzHvcT2r0vl+ZglIF4TEjY35WJuupw3KbTMTu9uy7GXzGUzIsXblWMrmDteZ67gSSwfIMB6JmxHzaSTN57nFNoxEsy1r5Y+7QbVrz1qUlQexbfsqsXkj+znzOffsNUv8OO5wnWIbjjJU58aGlCAkbknMR/99/70B7Hl4Pd553dGXZ5MlOK5GE3Vrtw99rB1QFBGuU+iYS2nHk1gYZAUsccPgWtmY8h3XvSdutuc/9LF2fO5378Ght3s8lScnW8WneI7tfm2x1We+83NXurwiLi0NzHrMpbDjSVwdZAUsccPgWjWMrvRP4f33BvI6J4o5j/bNKwtWnqeODbLFmyeHsWvP2gV9hrlcFsVCuhBuPshJOIkbBtdiqorflu95eD0CQR9KSwN5H3ffts/nPNxhP3wYIfu1c0kDcprsww85CSdxwyP79noxJAleXR76dW8O+bofd9+2F5IXCh3/+X/MlSbcRDqXNJDvNXKq7NaAlCAkblgsVJLgFedDH2uf87Z8tsf5NNvVRjouRBqQU2W3BiQBS9ywWKy8Wh6Is237qpxb/Nm04GLev5jhg4UMKEg999aA1IAlPnTI1lRff/W8J5dBQuJ6o5AGLCtgiQ8dFtOPKyFxLSGbcBIfelzvabDFbqDJhtyHF5KAJT70uN4EttgDEXLA4sMLKUFIfOhxvR0Fiy15SAnlwwtJwBIfelxvAlvsWEYZ8/jhhSRgiQ89JIFJ3KiQGrCEhITEEkESsISEhMQSQRKwhISExBJBErCEhITEEkESsISEhMQSQRKwhISExBJBErCEhITEEmFeaWiKokwAGLh2pyMhISHxocQaIlqR/cN5EbCEhISExOJBShASEhISSwRJwBISEhJLBEnAEhISEksEScASEhISSwRJwBISEhJLBEnAEhISEksEScASEhISSwRJwBISEhJLBEnAEhISEkuE/z+9sTZzUPEiEQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the plot with the specified dimensions.\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "\n",
    "# Colors uses a color map, which will produce an array of colors based on\n",
    "# the number of labels there are. We use set(k_means_labels) to get the\n",
    "# unique labels.\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n",
    "\n",
    "# Create a plot\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "# For loop that plots the data points and centroids.\n",
    "# k will range from 0-3, which will match the possible clusters that each\n",
    "# data point is in.\n",
    "for k, col in zip(range(len([[4,4], [-2, -1], [2, -3], [1, 1]])), colors):\n",
    "\n",
    "    # Create a list of all data points, where the data poitns that are \n",
    "    # in the cluster (ex. cluster 0) are labeled as true, else they are\n",
    "    # labeled as false.\n",
    "    my_members = (k_means_labels == k)\n",
    "    \n",
    "    # Define the centroid, or cluster center.\n",
    "    cluster_center = k_means_cluster_centers[k]\n",
    "    \n",
    "    # Plots the datapoints with color col.\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    \n",
    "    # Plots the centroids with specified color, but with a darker outline\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "\n",
    "# Title of the plot\n",
    "ax.set_title('KMeans')\n",
    "\n",
    "# Remove x-axis ticks\n",
    "ax.set_xticks(())\n",
    "\n",
    "# Remove y-axis ticks\n",
    "ax.set_yticks(())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practice\n",
    "\n",
    "Try to cluster the above dataset into 3 clusters.  \n",
    "Notice: do not generate data again, use the same dataset as above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_means3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
    "k_means3.fit(X)\n",
    "fig = plt.figure(figsize=(6, 4))\n",
    "colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means3.labels_))))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "for k, col in zip(range(len(k_means3.cluster_centers_)), colors):\n",
    "    my_members = (k_means3.labels_ == k)\n",
    "    cluster_center = k_means3.cluster_centers_[k]\n",
    "    ax.plot(X[my_members, 0], X[my_members, 1], 'w', markerfacecolor=col, marker='.')\n",
    "    ax.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,  markeredgecolor='k', markersize=6)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h1 id=\"customer_segmentation_K_means\">Customer Segmentation with K-Means</h1>\n",
    "Imagine that you have a customer dataset, and you need to apply customer segmentation on this historical data.\n",
    "Customer segmentation is the practice of partitioning a customer base into groups of individuals that have similar characteristics. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. For example, one group might contain customers who are high-profit and low-risk, that is, more likely to purchase products, or subscribe for a service. A business task is to retaining those customers. Another group might include customers from non-profit organizations. And so on.\n",
    "\n",
    "Lets download the dataset. To download the data, we will use **`!wget`** to download it from IBM Object Storage.  \n",
    "**Did you know?** When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-03-26 20:54:32--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/Cust_Segmentation.csv\n",
      "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
      "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 33426 (33K) [text/csv]\n",
      "Saving to: ‘Cust_Segmentation.csv’\n",
      "\n",
      "Cust_Segmentation.c 100%[===================>]  32.64K  --.-KB/s    in 0.005s  \n",
      "\n",
      "2021-03-26 20:54:32 (6.96 MB/s) - ‘Cust_Segmentation.csv’ saved [33426/33426]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O Cust_Segmentation.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-Coursera/labs/Data_files/Cust_Segmentation.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Data From CSV File\n",
    "\n",
    "Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
       "      <th>Address</th>\n",
       "      <th>DebtIncomeRatio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>0.124</td>\n",
       "      <td>1.073</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA001</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>100</td>\n",
       "      <td>4.582</td>\n",
       "      <td>8.218</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA021</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>57</td>\n",
       "      <td>6.111</td>\n",
       "      <td>5.802</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NBA013</td>\n",
       "      <td>20.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.516</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA009</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NBA008</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted Address  DebtIncomeRatio  \n",
       "0        0.0  NBA001              6.3  \n",
       "1        0.0  NBA021             12.8  \n",
       "2        1.0  NBA013             20.9  \n",
       "3        0.0  NBA009              6.3  \n",
       "4        0.0  NBA008              7.2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "cust_df = pd.read_csv(\"Cust_Segmentation.csv\")\n",
    "cust_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"pre_processing\">Pre-processing</h2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "As you can see, **Address** in this dataset is a categorical variable. k-means algorithm isn't directly applicable to categorical variables because Euclidean distance function isn't really meaningful for discrete variables. So, lets drop this feature and run clustering.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
       "      <th>Card Debt</th>\n",
       "      <th>Other Debt</th>\n",
       "      <th>Defaulted</th>\n",
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       "      <td>0.516</td>\n",
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       "      <td>253</td>\n",
       "      <td>9.308</td>\n",
       "      <td>8.908</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  \n",
       "0        0.0              6.3  \n",
       "1        0.0             12.8  \n",
       "2        1.0             20.9  \n",
       "3        0.0              6.3  \n",
       "4        0.0              7.2  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = cust_df.drop('Address', axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Normalizing over the standard deviation\n",
    "\n",
    "Now let's normalize the dataset. But why do we need normalization in the first place? Normalization is a statistical method that helps mathematical-based algorithms to interpret features with different magnitudes and distributions equally. We use **StandardScaler()** to normalize our dataset.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.74291541,  0.31212243, -0.37878978, ..., -0.59048916,\n",
       "        -0.52379654, -0.57652509],\n",
       "       [ 1.48949049, -0.76634938,  2.5737211 , ...,  1.51296181,\n",
       "        -0.52379654,  0.39138677],\n",
       "       [-0.25251804,  0.31212243,  0.2117124 , ...,  0.80170393,\n",
       "         1.90913822,  1.59755385],\n",
       "       ...,\n",
       "       [-1.24795149,  2.46906604, -1.26454304, ...,  0.03863257,\n",
       "         1.90913822,  3.45892281],\n",
       "       [-0.37694723, -0.76634938,  0.50696349, ..., -0.70147601,\n",
       "        -0.52379654, -1.08281745],\n",
       "       [ 2.1116364 , -0.76634938,  1.09746566, ...,  0.16463355,\n",
       "        -0.52379654, -0.2340332 ]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "X = df.values[:,1:]\n",
    "X = np.nan_to_num(X)\n",
    "Clus_dataSet = StandardScaler().fit_transform(X)\n",
    "Clus_dataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"modeling\">Modeling</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In our example (if we didn't have access to the k-means algorithm), it would be the same as guessing that each customer group would have certain age, income, education, etc, with multiple tests and experiments. However, using the K-means clustering we can do all this process much easier.\n",
    "\n",
    "Lets apply k-means on our dataset, and take look at cluster labels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " 0 2 0 0 0 0 2 0 0 0 2 0 2 0 0 0 2 0 0 0 0 2 2 0 0 0 0 2 0 0 0 0 2 0 0 0 0\n",
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      " 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 2]\n"
     ]
    }
   ],
   "source": [
    "clusterNum = 3\n",
    "k_means = KMeans(init = \"k-means++\", n_clusters = clusterNum, n_init = 12)\n",
    "k_means.fit(X)\n",
    "labels = k_means.labels_\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<h2 id=\"insights\">Insights</h2>\n",
    "We assign the labels to each row in dataframe.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Customer Id</th>\n",
       "      <th>Age</th>\n",
       "      <th>Edu</th>\n",
       "      <th>Years Employed</th>\n",
       "      <th>Income</th>\n",
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      ],
      "text/plain": [
       "   Customer Id  Age  Edu  Years Employed  Income  Card Debt  Other Debt  \\\n",
       "0            1   41    2               6      19      0.124       1.073   \n",
       "1            2   47    1              26     100      4.582       8.218   \n",
       "2            3   33    2              10      57      6.111       5.802   \n",
       "3            4   29    2               4      19      0.681       0.516   \n",
       "4            5   47    1              31     253      9.308       8.908   \n",
       "\n",
       "   Defaulted  DebtIncomeRatio  Clus_km  \n",
       "0        0.0              6.3        0  \n",
       "1        0.0             12.8        2  \n",
       "2        1.0             20.9        0  \n",
       "3        0.0              6.3        0  \n",
       "4        0.0              7.2        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Clus_km\"] = labels\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "We can easily check the centroid values by averaging the features in each cluster.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>Edu</th>\n",
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       "      <th>Income</th>\n",
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       "      <th>DebtIncomeRatio</th>\n",
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       "      <td>410.166667</td>\n",
       "      <td>45.388889</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>19.555556</td>\n",
       "      <td>227.166667</td>\n",
       "      <td>5.678444</td>\n",
       "      <td>10.907167</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>7.322222</td>\n",
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       "      <th>2</th>\n",
       "      <td>403.780220</td>\n",
       "      <td>41.368132</td>\n",
       "      <td>1.961538</td>\n",
       "      <td>15.252747</td>\n",
       "      <td>84.076923</td>\n",
       "      <td>3.114412</td>\n",
       "      <td>5.770352</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>10.725824</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Customer Id        Age       Edu  Years Employed      Income  \\\n",
       "Clus_km                                                                 \n",
       "0         432.006154  32.967692  1.613846        6.389231   31.204615   \n",
       "1         410.166667  45.388889  2.666667       19.555556  227.166667   \n",
       "2         403.780220  41.368132  1.961538       15.252747   84.076923   \n",
       "\n",
       "         Card Debt  Other Debt  Defaulted  DebtIncomeRatio  \n",
       "Clus_km                                                     \n",
       "0         1.032711    2.108345   0.284658        10.095385  \n",
       "1         5.678444   10.907167   0.285714         7.322222  \n",
       "2         3.114412    5.770352   0.172414        10.725824  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('Clus_km').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets look at the distribution of customers based on their age and income:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oNCOQQTmzhRDfsHMp+tv3P0KIrx3j/DFCiNeFEDuEENuEEF+2txcLIV4VQuyxfxf1OudbQoi9QohdQoirByOvRqPRaN4/g416+iSw+Sj7Ntr7ByINfFVKOQNluvq8EGIm8E1guZRyCrDc/h973x3ALOAa4Od2gp9Go9FoThGDVRRjgT1H2bcfGDCGTUpZL6Vcb/8dAnYAo4CbgAftwx4Ebrb/vgmVAZ6QUtYAe4Ezq9C7RqPRjHAG66OIogb2/hiNin46LoQQ44EFwCqgQkpZD0qZCCHK7cNGASt7nXZ4gNfXaDRHwZKS/e1trDxcS1ssSoHXy9JRY5hSXILT0EWkNQMzWEXxNvA1IcQTUsqMUhBCeICv2vuPiZ2H8STwt1LKrgEqLfa3IyeeVwjxaeDTAGPHjj0eETSas4akafKHLZvY0tSI1+nE63TRHImypbGRycUl3DVvAb4zrCy2ZmgZ7FTie8AUYLcQ4gdCiM8JIX4A7La3f/dYFxBCuFBK4mEp5VP25kYhRJW9vwposrcfBnpnFY0G6vpeU0p5n5RysZRycVnZ2d0MXqPpy5/27mZLUyOj8vIp8fnxOBwU+XyMystnX3sbT+/cNtwiakY4g+1wt0kIcSnwn8A3UIrGAlYAH5RSbhrofKGWDr8Bdkgpf9Jr13PAXcCP7N/P9tr+iBDiJ0A1ShmtHozMGs3ZTCSZ5N3aQ1QEghzs7OBARzspy8JpGIwtKGBsQSEbGxq4ZnL0jC1brnn/nEjC3WrgIiGEDygC2qWUseM8/Xzg48AWIcRGe9u3UQriMSHEPcAh4Hb7tbYJIR5DFRxMA5/vrlqr0WiOTW1XJ5aU7Glrpbark4DLjdfpwpQW+9raCCWSlPr9HOrs1IpCc1ROODfeVg7HqyC6z1lB/34HgMuPcs4PgB8MTjqNRgOqFEY8neZIqJN8jwdhf/0cwiDf46EpEsbncmJKa5gl1YxkBq0ohBATgQ+hQmW9fXZLKeU9QyGYRnM6EE4m2dRQz5q6I8TTKUbnF3Du6DFMLCoeEe0wywNBQskEUsqMkuhB/d+VSFAZyDv1wmlOGwbbCvUm4HGUb6KJ3HDYwVUY1GhOYxrDYX61fg2hRJx8jxeXYbCjpZkNDfWcP2YsN0+fiTHMyqLE72dCYREHOzrI76f1dMI0qQgEqM7TikJzdAa7ovg+8AbwMSll89CLo9GcHqQti99uXEfatBiVV5DZ7nW6sKRkxaGDVOXlce7oUxeu3RAOsbGhnpRpMbuigvEFhQgh+MS8hbxXW0tnIkbA5cFpGJiWRTSVwiEEdy9YNCJWP5qRy2AVxURUCQ6tJDRnNXtaW2iNRhmdX5CzzxCCEp+f12v2s7R6NI5TkNC2q6WZ325cB7aB6Y2DNdw8fQYXjh3P2MJCPrtkKY9u2UxHIo5EGZ3yPR4+MHU6s8oqTrp8mtObwSqKneh2pxoNu1pbB+yTHHC7qQt10RGPU+I/udFEUkqe2bWDoMtDnkfZl1KmyYu7d7OoahR+l4sPTJ3OuIJCXqvZT30kRJkvwKUTJrKwqlqvJjTHZLCK4uvAvUKIVVLK/SdDII3m9KB7Xn7so042SdOkJRplVF5+ZpvL4UBKSXs8ht/lQgjBvMoq5lVWnQKJNGcag1UU30OtKHYIIfagmhb1RkopLx4KwTSakcyEwiLeOXTwqPtjqRQBt5tCb9/AwKHH7XBQ6vcTSiSyVhRCCIq8vpP++pozn8EaT01gF/Au0Gz/3/tHB2Nrzgqml5YR9HjoSuTWwZRS0hyNcMm4Caek4J4QgpumzSCcStAQDtEYDtMQCXPdlCn4T9MaTpFkkud27WDl4VoG2655KElbFs3RCPF0athkGAkMtoTHJSdJDo3mtMLjdPLJ+Qu5b90a6kNJin0+nIZBKJmkKxFnfmUV548dsOr+kDK9tIy/O+d81tfXkzJN5lRUMKGw6NgnjlDW1h/h1f17cTscTCkuOel+nv4IJ5P8et0a6sMhvE4n9yxYzLjCwlMux0jgmIrCTrA7brTvQnO2MLagkK+cez6rDh9mdd1hEsk01cF8bp0xk1ll5ack2qk3lcE8rptyZuRDjCsoJM/toSIYzJjTTjWrDtdSF+5iVF4BHfEYz+zazpeXnTcssgw3x7Oi2Mvx+eSEfZzuQKc5ayj2+bl2ylSunTJ1uEU5oxhfWMT3Lrkcp2EMW9JiylJl5aSUOAyDlHn2WtaPR1Ecq72pRqM5yTRFwry4Zxe7W1spDwS4dtIUppeVH/vE05iBwo9PBQsqq3ho8ybW1dURcLv59gVnb5zOMRWFlPLBYx2j0WhOHtFUil+sXU0inabU5yeUSHL/hnV8fuk5p7UfYqSzt62NYp+XCYWFhFNJtjU3cu6Ys7Mxmu6BqNGMcHa1NBNOJigPBHEYquqr1+kcMDxX8/7piMfxu9yU+AOU+YM0R6LDLdKwoRWFRjPCiafTOU5Cl+EgkkoOizxnC3MrKpHA4a5OWqIRzjtLVxPwPvpRaDSaU8PEomIEgqRp4rYzrjsTca6ZPGW4RTsjkFL2W8ZkTEEBX152Lgc62in2+ZlWUjoM0o0MtKLQaEY4FcEgt06fybO7diABy5IsGTWKRdWjhlu0YUOaTcjoH8BqBfe5CO/VCDE4A4mUKWTkAbDqIXAPwpH7PKvz8qnuVRrlbEUrCo3mNODcMWOZXV5BYyRMgcdLWSCQc4y0osjkWiCFcC9AGMWnXtDjRMokyBiIvEEP8AAy+ghYbSAKILEcnGPBNWtwF7G6IL0bSCFT+/pVFBqFVhQazWlCnsdz1OQzKU1k9HeQPgA4kMmVEPwywgieUhmVLBKsFhBOhJEblSXNI8jIb8GKgHMc+O9CGIPMvLaaQZSAcAIG0uo8jhKNfTCKwXs9WI0I94LBnn1WoRWFRnMmIDvBPAzGaBBCmVOsBjAmn1oxzGZ7tt8A0kK6piN8H0IYPSsgGX0apAVGFaRrkMnVCO8lg3sh9zmQeANwgvAgnIO/TyHE4F/3LEUrCo3mNEFa7WAeAaMo10wiAoBHKQzpAilBnFrbupQWMvp7sDpBVKpaDaldSPECwv/hXgfGAbdSaMIgt6PysRHe68A5Dml2IVxTEI4zO/lwuNGKQqM5DZBmIzL8c5AJQCL9H8ZwL+x1hAtcCyH2MMgkeK8Do/DUCmk1gdkMjl49L4wKSG5E+m5FCLuSrfcqiD4CZicIP8K1aNAvJYQDXHMRw1wcV8o4MvI7sEKIwD0Ix5nZ100rCo3mNEAmNygF4KhWtv34cuilKGTiLUi+Bc75IByQ3o2MPYHwf/QUSukAIe3VTLfHQNqrhh4PguGei3SUq5WHoxph5BYylDKOTLwHRimGe86pEf9EMJsgvRekCWYtaEWh0WiGDeED0moQJq5MOzZSmspeb1SCcKuNxihIbkZ6r+3XoXxSMErBMQFSO5RSQyh5vJcjRM9QI81mZGoPyAhYIXDNyPJhACp6K/YsCA/S+c1Tdw+DxTEKPJeBDIPzzC0MqRWFRnOSkVYYmdoC6X1g5CNc88ExZlC9qoVnGTK9C9L7wchD+G/ptddUdn/RazDtnsXL+FDdRgZpdanQVKM0K6pKCIH0fkApLbNFbXROVgMpdt5C7BlIrlWy4QCRRsadSO9NGJ6lPddyVCKFX83QxanvRXG8COFA+K4fbjFOOlpRaDTvEyktFXFEEowyhFHQs89sQEZ+rcxFwgckkYkV4L0cPFdllIVM70Um3lbHuecj3EsR3asDQAgvBP4aZBSEV9noM/vcSOcUMGtA2E5dKwRGEIyyob3X9EFk5DcgU+p+gp9BOCp6ZJERpGM8uJagFFUTggTgR8aeh+QaMKptRdZ90STEHscSvh4zk2Mi+G9Xz1MMTz8KTQ9aUWg07wOZPoSMPgqyDTUwSqR7KcJ3A+BCRv+o7NeO6l4nmcrH4JwKzglYye0QfcCeObsg9iwyfRD8H81adQgh7OimXITvZmT0N2DW2e4AL8L/iSyTz5Dcb+IvgKEc1mYjMvE2wn9bzwFGkfJPyCiQUvKKANJqg+SqXCUByjwliiD+J6RrFkIYyOR6iP1BnZv39WHJB9H0oBWFRnOCSKsDGbkfcKkBEJQSSK5E4kB4loJZr/IFeiMcgBuZXItwToDEKyrDuNupK4OQ2gzWFdBrtj4QwlECwa+AeVDJ4Bx3kmbiBmA38BG5fcqEoxTpvwvif1YrH98NCOHGSu5C+SyOkoVtBNWzsprAUZlJpFPXH57GRZoetKLQaE4QmVynTDCOXsXihEMphtRKpHOCGhj780UIL1gd6m+rVWUZZ/bZA6oMAcenKNRpTnBOOrq8UiJTW8HcD8ZoVeZjkOUzhPcapRzNejAKEN7cZj6Gaxq4pvV58RjHHvCFHf4LwjUPgkXKH2P0v4o6FUiZVu+TEVTmv5P1OlYXAMIYmXWltKLQaE6U9P7+TUHCYTcPFoClZviiT7c2GQHnePW3czqkd/b4F7ojhozKrFMsqwtSu8FRheHsvy6RNFuBJMJRlbsvtRGiDwM+4G0kMYTnguO9W/vWqiDvayq01SjK8qMMiFFCZiXSr+CW2m/7d4QQqrzHMCKtsPLHmA0gPBC4G+Ec2lLj0goho49Bagsgka55CP/t/YYMDydaUWhGLFKaYB5Q/zjGZzlwRwRGPqQP5W6XKpdAOEqRriW5tnmrS9VBci8GQHivRUZqlX8BCRjguzXLLm9FHofob1CKx8LyXAh5/4Bh9Ao7tdqQ4f9Ws/LgpxF9VxepbSCCqsaRFVKD0yAVBdiOdcfgZtfCNR0pPHZ0Vj/nylY7TLZQRUcl10FqI4ggwnO+MtGdYmRyjXpPHKPA6kDGnkPkfWFoXyP2hDIzpg9gzy6QMYEIjKwO1FpRaEYkqhzEI/ZMC3DNtZ27I6fXlnAvts1PfVYMskPZ2Y1KhO8DSExIrketMKQy2fg/lckNEI4SyPuKSpqz2sB9CYazx+RkpfZA5D5VksPwgmVC4k1wTIXgR3q9blqZwkhnTDhZOEZBagNIny3j3BO+96P1cDgaQniQvg9C9CHljxF5ttPbAtkCuBHe65R5LPYYJDeq46hHprYg/Z/EcE/vdat7kfHlyizkmoXwXHISHN7JXmZDp/p/CJFWGFK7QBQD+9VGUaLKnljhEeXA14pCMzKxGtUM2LBNLKktPY7OkYJjIngugsRbqNpFbjt81Y/w32EPpG6E/0NI7xUqi1d47RyKvqaoOMRfUyYpx3jopShIvIMyy9gzccMBlh+SrwE9ikI4yiH4GXUtZx8fASA8FyBlRCXEuc9FeK8a9C0rU8mjYO5HOmcqM8lx2u4N9zws4YX4n1TRQqmixHDNRnivUSswsxGSm1VxQ0yUQzusHP62opDpvcjwfWp1JDyQWIFM10Dwc0O66hTuJWoiYNUDAjy3HPOcE3uhfHCMUa8h8oCR13JVKwrNCMWFWoqbvbaNrI+rEEKVqXbNUuGcMgTOSQjXgpzZoDCKlcnnKEik6o0gu5T9P+tkD7mOYKtfE47o9nv0K69Lhe36bjjGnR0dGf+TnfRXAaktyETFoBSO4ZqGdE5VDnwSIPKyHbgyZDvyOyC1XvmAnAtU2fKMDK+p7d21rBw+VSzRPDCgM3+wCKMYgl9WlXBF4ZDXcRJGEOmaCcmVkLb7nwvAfc6IWk2A7pmtGaEIR6kqHieb1I/3arVthCGEUDkFRhBwg1F+Ql9ygQWOsWqV0rc3g+cypSysdrAkWFFlWvKdpBnuQFgtdm6EoRLuzNZBX0IIgXCUIhyjcqN8jEpAQKoe0o2QOqJWl73LY1gdR/FzRAYtyzFlNQII56STVuxP+G4F1xwQafXjmoMYjvf1GIysKZpG0wvDeznSvQzgpM6wpNmsynMblSf0OjL6BKQ2qYS51CZk8PODjo4RRjEy8FdgNSPc2dVUDWcFVsG/QOh/7DLjQfD/DYb3skHL+r5xL4Poo2CG7P8HX/l1IIQRRHqug/g/AWEgClYXwnttz0GuOapMSHepdWn7DhxjhlSWU4EwgojgZ7G8HwTAcI7MculaUWhGNCd7CW4lt6qS10KqmXLwc4NvIZrebkc1OcA8gjQPn1AYpTFAlzXDvQhKHsSyooAXwxgeY4DhXog0CpBmk1oRDHG4KIBwFCFdEyDtVKsrIz+rZLrwXIRM71PVWrtNcr6bB1040EruUBV3rQi4FyA85w1buZCRqiC6OaWKQgjxW+AGoElKOdveVgz8ERgPHAA+JKVst/d9C7gHZaj+kpTylVMpr+bkoUJfa2wzQj44Jw55uYnjIv6KHTIaBKsOmVyH8F45uGs4JqqCfyIAWCfUREfKNKS3I60YwjUN0aeXhJVugPCPIbUVjHKs4JcwPEM7mz9ehHNSbujtIJAypZ6X7FLJiY7RuRFURjF4ZitzjGyjO3QUlDmI4GftLPSIOn/QSmIzRH9vO49dEH8JaR5UbVkHEc11tnCqv5kPAP8L/F+vbd8ElkspfySE+Kb9/zeEEDOBO4BZQDXwFyHEVCmliea0QFpRwMpZFUizBRl9UEUBdc8IjQIIfKLfRLGTinCpvAbpVGGnJ/CVEP47kPGXlf3ede0JteWUsSdVwTwMZCIfgl/K2O+ljEHXDyC1HUShem6hHyAd9+bM6KWMgTRPrqkuXYtMbUK4l2QVBDyuc62o6pdt1pIZ/N3ng+/GngHaOUVVnU3tVKu03k2PbIRwYJkhMPciHCeQY5F4FUSR7VsCpF9Fg1mNIyuyboRwStevUsq3gLY+m28CHrT/fhC4udf2R6WUCSllDbAXWIrmmEgrrAZjmRo2GazEKmTXvyK7vo8Vex4p7WQimUZGH1CDs2OUKpbnqAaZQkZ+g+wv/v9k4j5XRdckXlWDhGvwTXKEEcTw344R/OwJzfKlTEBygwoJdYxWyXDpmp4DUrvtKCEJsk6Vw5ApZPLd7OtYXcjQj5GhHyHThwctx3HLG/0DxP+CjD19lPuRqueE1Z67L7kKzEP2ez9arSiS79iKQyGEG9wXAmmVcJfVyU9hWREIfQfC/4uMPJiz/5h0O8RlXJmewI62Cg/+WmcBI8FHUSGlrAeQUtYL0V3HgFHAyl7HHba35SCE+DTwaYCxY4feZnq6IK02ZOw5NRNDgPAhvZch3Oed0kQ1aYUh/oxdtsEBibfBNQ+cY9UAaDb3OCK7MQqVfT+1E+Ged8pkJbkK3AsAv8oOTm0FxyWn7vUBcIIRsAcpPyopr2dFIGVMVWU1a1W0k0iDmJYbRivDYDaCTCKtVgSjT5K4k8FqVr/7IM1mlShpNYC0kK7pCN+Heuo1pXfZiXQ2dt6DNI9kr45kG2CovBQZs0OE+9KdM3EC/VCdUyD+koogE0JdwzG6p7ijJouRoCiORn+GQtnPNqSU9wH3ASxevLjfY850pBVBhn+lbLZGpT07SkDsGaRMIk5phIypMm6FE/U2CsBe3cjcWWYPDpVUdyqRccCnnKZWB3CKVzQoMwr+u5DRh5Wy8l6l/B7d+53jkUYQnPNUuQdjNDiC4JqfOUZKiYyvgsRqwITY63bJ7qH/igvfLUrGPnWuVDb975UCE5X2274LKV5A+D+sDjLK7dVD71pGIquHB9jJbtKymyMV5shgGAGs/B+BeXDQ9aoUPrWq7VY23aG1w92Ee4QyEvIoGoUQVQD27+6R4jDQO95tNFB3imU7bZCpjXaVy/KemkLCo5b2idfUrPRUIfLBvRSsOvXjnKhyBEA5jo96nkVWl7ZTgfcqFRpr1gN+hGu4HMRjEXnfhLx/xvBekd2HwlEJvg8AjWC4gCYVpZPVM3sFRH5GJkEx8RQy8siQy6maNNWoIoZmbcakCCglbzbbK0mpfowKSG7MmEGF5zzAUMdaYRXu66jOWZ0I4cbwXojhnnFUWQz3NAzfVYi+eSfHQ3obeC5Wfcedc+xcFa/dgErTl5GwongOuAv4kf372V7bHxFC/ATlzJ4CrB4WCU8H0jv7H4SFSzlpzcaeaqUnGSGESgZzLwRMcIztcUY6J9sRRl0q7LEbKwK4Ea6jDwwnA8M9F+koV7NgR/WwVe20Uvsh9ghYESzPxQjv1VnKwvBcgEUBxB4G1zkI/0093fGkBYmX6endYAIe1RfDunnQpau7JxVC+PpsTyIjD0N6B2qOKVUehe8227TpUGHGZgjS61RJE+cCMm1ZsZVe8PN2N78mcC5FuM89/iq0Q0X3atfom0g3+BIgUiZApoe1HPrJ5lSHx/4BuAQoFUIcBv4JpSAeE0LcAxwCbgeQUm4TQjwGbAfSwOd1xNNAuMkud9GLfhrMvB+kTCFT21UpB0cZwjU/t2SFMKCfip9CuJWZJfI7NZvEiXJausH/V8PyZROOyvcd6SKtiLKlG0WDrjckpQmx34N0qRVhYjm4JvfjA+hEVY9t7xPCmYZ0u206iwJSFReUcdvvcfyKQpotyPDP1D/BL2RlJMvkFlV/SxpAh4rASqxW/ifXNDBKwRhvO6ebUX6yoMpx6GUCE45KhP/245apXzllEpl4F6w2hHs+wjnx2Cf1xn0BxF+0M8GdIJvBUZbrOzuWHFYIGf4f9ayDf4MY5PmnC6dUUUgpP3KUXZcf5fgfAD84eRKdOQj3ImRqM0i7FWU3Vlg5Dx1D46ST0rRnldsAL5BSs8Pg53LszEeV1TkW8r6mlI3VBEYxwjV7xNW3OR6kTCPjL0HyPWVtMfLB/5EBay7lYiqnqlFlz75tJ27OYXZfbvNAVvVWKZ1AWDm8pVddTxSB1YAUeRlnn7QiyORa1bjIMVaFt/ZZbUirze6/IJRDvHfpivQutRK06tRKVR4BoxqZrlG5H0IgXRNVboo01b2YUTiR8NVjoMKJ1wNeZHI1BD+PcB5/ZrYqkBiH5AqQaXBORvhvzVLyUsZVDa/0HlWaxbMsNxlTdikFLdMqMk0rCs2IxjlNleJObVaKQbjVhxgLfJ8cuqqa6f12JvLoHoVk1iMT7yJ81w58bi+E4Ud4Fg+NTO8DaUWR8RfV6sY1H+G5aFARYjK5SkV1GdV2VdcutVrK+8Zx286FcCPdS1VxOIQa8PsZXIX3GqRRinBO7dNLO4U0insieKSlookcYxAyBOSpQS/yS2WCFEHlZE6uVqsG29wmpVRhujKhlF5yA9I5pee1HGVgHQYrrlapEpTfpCjzLIm/rhzxwo+K5iqB2EtI18yhTWRLbemTDV87OEUhnOCajDQPq8mUa3qW6VbKtHof0zX29l7Pq7fyNKrBdwcQA2eu2VSa9cjEKlWB1ihFuJedlGz2k41WFGcIKnLmI8jkTHt2G7YHvguUaWWIkFZz9wv2evGgypIdoXQ7XPsbqGT8Wbv3QSHEX0AaBYg+pTSkTNkZ0YW5DXSSq0EUZ8I8MfJVsxvzABgzj1tG4bsZXDOUCcM5JcdX0p2bgNWCNIPKn5Ix57hUb20RhNRawFThn4ZLBRYAMrlVJeo5ukNmC8HszkS/RG2yGlVOiWseINXf1sU9Zjlp2kUAIyDt3hpmCrod2rJdfQ6skB3OGlfHy05U1Nvx+yEGfOYAjnHqGcs8QCKM3IKR0mpTDYFEnlox9Hr/reR2iD5gKzQXxJ5Fpg/aPU+EWkWka1SZ++7zrAZk8h2E78bMdYQQiKPkzljJjaoulnAAAft5r0H6bsLwnH/cz2IkoBXFGYQQTvWhHSDpS0qpolXSB1U/Ytf0QfUCFkaZmkhK2fMFkmFw5CZFjQSs+Jt2qQYfMvC53Cia9EE16xVeMMN2l7k+iiK5FmJ/VINK3jf6mNgMIKXMNTKZaeXZf3T30RHCAa7+FYu0wio3IbFc2f6FG+m+EAJ/hXCOQQgD6b0eIr+zX9ehVpOeD/aY86xGcr7uwmtHe/W+F3oif4TIbJNWm+ojYRSpCrbdA79RCPGnkO65SOlQ+RWU9ChOaYFsQ0pHz8dFxiC9B2lFEI4ycEzIWfEO/MxR2fCxF9TruS9TirH3+WYrMvy/dtirBO9V2aVZEq+olXe3QpZBtRq3rgBHRU+yYNbkImB/PrKxrDhgYfRaQUorArHHVSmSzPcrCDIFseeRrhmDqikmpVTfMxEclhIjWlGcZcj4KxBfDsIEKZCOMuWE6ydWvV+cE9USO70d1Xs5CUY+wnPu0MtqtatluwDhPuf4Zew+36xTHdXSR9SGyK+Rrh9mK0bXbAg/rGbDznH9JpGpBDG3yhvoG53jmgOhf1fmGCzACa6J6jkNAVJKlQltHrBzE6J2gTyp+jnn/b3KDHfPw+KvIXy/cqoHPo5wn9dzIcc44I0+Cj6aLadRBq6pEP69+j/4V2obKH+S2aJWCq7JyiYvnHaIazOk9yAcZUjHGLVyMaMopRWwVz8m4LC79f1ODbgygTTyVQnx4N3Z9ZoGeuaAMAoQgY8d/bmld9FdBwqZUs2leisKqxVELxOSEMqnIkNABcJR2c+EqAucy3q9xgFbgb8B0sJyzYHApxCumZDeq56R0WcSZkf/ydQOxCBWFTL2lDJNeq5E+AbfcOr9ohXFWYSVboDoY8oUINPqe2wdRsZeQgQ+elzXEMIBgTuRyY2qtLZjrKq6OcSOaCkTdgJhB0iQyc2Q9+XBVfeUUVRES5RMIyQZI6uXgfsqkP9uRwiNwnDldoYz3DORjr8H4c8JGcVss231dsKWtL9SVicMojiglBKZeB2sRoT3+h4ns1WvBh1RBaJBrViMIjWAWg3I1GaE5zxVVDD6CKRWARZELKRzLsKhZszCNR3pmG47b8MgfOBegnDPz8gghEA6zwXjMbXBdW7P7FXGUKsIC9K1KIe5U5m2ZAo1YSi1S327VF0qDHCNUa1KhRtpdUDk15DaY59vqFWc7ECGXZD3xczrDfjMjwMhAki7vzgy3GulZ+OcboeU2++RTKL8Q7aZzTFehXcn16MiBi1wlCPc56i3JbFKfZfSW4EidS+pHRD5GdJ7Tc91+peu/2CFgUjvUzKaNcc+9iSgFcXZRPwVsA6qmZThVV8iKwSxp5D+W477CylT2yD+HCoscy+SBHivH9oyIWazmvVZ3aXBLFV0r09UibTCqnaQc0Ku/I5RPaUwhKlm1SJ7wDAcbizXImV28Fx6VHGOWhHW3GcPmJNsxRuxZW0YnKIwWyB8HxBBko/wX2/fdps9021Rg5JwQ9pW9I4yO8QYZGK13TI1hfIv7IT4sxC4U8kvnEjXdEitBAqVM9o5NUfxCocH6Rin/jbcvbaPQYp8ZZaSEgw3WAkQXeAaC0aFUga+D0P01yrTXTjBUYzwq0Y8MrkJ0nUoJWGvRkVQrUrSO9S9OEf3es33UXrbNQvc50Byrd2jPHsiJLzXIsN7VCCCTKqIs+A9mQmPEAb4PgSuRUiz1o7Mm6F6f5tNEHvajjJzq9WoSAM+EKXqmt7r7De214okg0Q4BufQFoGPqUmBa3gCQLSiOMOQMmWXX46rL6/DNh3IOKQ2oMxFdvKbMDJ/y+RmhGdZv9fMur7ZCtE/2rNar3JwJt5EGqMQnl6ZwjKtWnuSVs7Zwc4KjUJ1D93F4kQgZ5AHVMmI1G5wL84xRQjhQwa/CPwXEFTmjf6UWcGPgSjGiayKHJX2CiUGeFDLC7dyjg8G84iaLcqEWqlhKwoR6FHoOO0B1qWUqFEARrGyXydeV05badgDuQ9S65HWLT25KWaNXc+oSF3P3J8jhpTOjHNaSmePp8U5WflQ0nuAqH1MWr2ma36maZDhnoll3QbyCfVc/Hf2VAQ2G9RzEm7bN5K0zWgudd+yAwZRn8pKblDvve/6nPdOCCfCfxvSdzPg6L+MuQiAoxSkC4QD4azucw0DXFMQrj7+j+Qa9d0x8sFRqFZPUoIjDxxFYDkgtUs9s/S+XiV1LJBNqiPiIMu0C8eoYc3R0IriDEKm99kF2bpbQkqkey7Cd5v6YgqvsjlbLahBLa0Oc0y0Sxoch6JI70UlfbnVlxs7sia9HmxFYVlp6Pq+mr1KC5xTkIU/xDjOPAtAdToLfha6/kNtCH62f/OWFVG5GDLe/3Uc1UjPjSACCNF/1rVqAjSwkpAyDjhzaicJz2XI1BZIH1KzfGeJKl/Sq9uaCiA4gkztUVE87plZM3kpLQj/FBVJ5IbEG1jJmzHcM1XpE6MErBiqhlZC/RiVgES45gLJnkz3dAMg1QCOU5kZsRWFY5yd45BSzmZXf+bGqMqTQKCUn32fwgnBe5CkIf6ykkUEwP9BhP/OnpwOswniz6vEQVIQfRSZ/y2VaOkoB3zKD2AHTSHtlxKeTITW8WCZXdDxFRUSnN4FBd/t97ij17pKqNWaY7qa8Vt1ygdzPINxei+ZWlWuBeo+ZQrcS1DZ6YVq5Z73j2rlndxknyjBNQPh++Dw9F55H5xe0mqOijRbVdw3/p7kOikhuRmJy7atCnDPVUrDaga84Bxj22eP76MghAtphSC9Vjn3cCl7b+8QxvgLkHwbVe/fULbg8G8g/yuDuifDNRNZ9D/qdY9aWiOtBpl+SqpLKZGh/7Gjngxk/r8ifNf1OcZUSVVWEzinYbj6qYia3o8M/0aZeoKfyyo3IZxjIO/vVZawbAfnTIR7UdYMVkZ+q5zMdAFOpGMmsujnGE47/0BGlLMat23CSChTjHtmj08ofL+ytVtdakA1ilTpDEeZUkSOAkitI1Naw4rYq8rizH2SWGnbulPKLJR4D+m7Mrt8hkRFGmX+6UEY+Yj8r2H5b1MmKMdEDGefgdXqVAOvEVCfP6teOZWFG+Geh3RWQNrOFheWklf4VNTSYFqZxl+1M9GTkNxkd9zLNlVJaSlFKXz9RPZ5VEmbdI393B1qpn9c2D4LUM+xd9CAeggo7ecFx2QQe1T+iqNErTJOwOdyLFSm+pvKp+M+b8gjo7SiOEOQqbWAlVWeWn1hqyC5Hum5XH15rbgyP3TH00sJsi2nT/NRX8eotpfaXWSintJbwPEx+3ImJN5TyseyW1UKA8zdSKstKyRQyrhtgy/MSU5Tzt3lEP+L+t97BcJzee4XwDlerZD6S7Yyj9jOSEOZZOIvIT2XZL2WjL0MsUftwTMPK/+bOcpCpo+owc5M247gPmGNRqF6tqYamHsPvFaqBsI/R83OLfVjbobQf0DRD+2jfOoaVo29qnDYUUoK4ai2ldFbkPiLUka+mzKJX0IIpO8WiL6E6jMt1fvqvrpngEzvUis8o1KZpawEpLfYJsdedm+Z7ClfbvXfz8RwTui3PAugBlsRUDN0aar3x14pCKMY6b8HIvdBap+9MioG11xE4Pg7y0mrC2KPoQZjF1iNyOhLEOy5hqpL9aAdCOBG+u/Kel+FEOD/uHqmMqSi6o7XJ+KaY6+q8tQ9Jt9T9+I5TykBqw3cc1T9rcQbyifomqYCAmLPqCQ/34eHOAFxuyqbjlN9dpzHb8I7HkZC9VjNUJA+iOpl0AdhgBAI2QXeDyo7sNliDwhhlWnrnKkyu48DYdUpOywmqvZQnOz4chOsQyA9apYm7Bljug5p9ZTwthIrkV3fR4Z/igx9Hyv+ep9KpC1q1mhUqJ/4q8ou31ce3+2I/G8jvNfl7IO4Wl25z7Ub4fiBZJ9DnrEd5ik1q4+/3s9NBwGHXUCun6S96BMq5Di9CyK/RaYP9br+n+zXzBTSUH+nVmQOMQwn5P+rysYWReD/RI7iFoZfrQKlBHNvZqWQ2e8Yo0JbRTmIMhX26p7dI2P6AMq8llLmQDusWfkcemHuVzkXVlO/PgxAlQJJ7eq3yZQwguC/Syk8UQS+j2flSAhnpW3br7SrChepldpxhj5LGUNG7lODLn4yPrfUCmTynZ7jkpuVj8yoVsfEHuvnakn1fbDaVd7DcSLcC5SfxQqr1Xl3/+70ftscmwTnLDtjf5SaRID6Hhij1eTFDkIYMhwVyodnlGUy5YcSrSjOFIyi/u30UqqBQfgw3DMQwS+oAUVGVeST71ZE4M7jtplKmQLhJm26icf9pFJeZfrJ9HEQanbuCKgQQ8c427Gb7rlG+jDEnlKz2tReNROPv2TbfrsxSSUl+zZH2bc5SjpF1jWULHG6jvwfrXv/g3Djo7kd/RxjbDNcCoiBZ36uQ1wm1Woj0/wm+xmqXIwHlCM4tQ4Z/l22QgO7pElVphKp7F2qWrjUM8Gix5QjyVnMG5W2P6FL+TH6mW0e3FvNXx6Ls+m93IgZKdPs2JDHwz9dwIM/XsC6t8uxzF5K0SgFZ5kaUIgpWY0K+3/7GlZErVhwohpO/aXfAVRGf48M/1LVW+oHYTWrz5dsRciO7HMT7wIpNeN2z1EDanq/GmyPA5ncrCLi3OeoAVhgK5xpEP9zL+WVRq1mhXoPZPYEQUqpVhwpO2M9+qB6r48DYeQjAnejPvMppQCEG9VPpQ38H0KQRPmKHH1PVmbQvgr6/WJUgO9W9X0+CYU1tenpNCIWjvHOM2vYvnI3/jwfy65fyOzzp6syAu4lKhqjOxGqG9mqBkxDDQjCOQbhvJNkPInT7bQducePcE4mGnWya40PwzBIpyQTZkuKCnuZLxzjOLSjlj89FCOZhItvcjDn/EmZmaVM76DpiEXzod04jA4gRH75PKqnbcpEmJiylGd+7aNmi1IeE+eN49avlWZ9YBt2PUT9jqfpaHVSXL6ZUXPclE34YI+swoP0XkyqfRPCcON0X5o7AHuvINXxJMlEB55AKU53nyQos1mZYmRcKTbjEGqF0Cus1DGBUONLdDZHqJw4HlewlwnDcz2Ef45pxm3/rVQBM+7spKnGms207W4HIbGM15h99a29zCiSVx54nT/+27NYpgC2sujq3/A3P74Lt0cpuA2vH+LPv3GTX9iKYQj+8pifts6DXP0J9TyFazYy8Qb1BySNtaWUVKUZM7Uge+VitYPwI52z7OfnsyORBjnwOCeoqB4jL7cqr1kLBOlo6iQeTZBfkoffL+2OfH0OTZsIQ2R/RtNblWlLeNTgLIqUKc3wqHIhZj04xyNcs5DJt2wTmFRl77NIqCgso0opEzNid148vuKZwjlemQNTG8ExBUiDexrCtRjhKFGZ5YPMzn9fpHdC9EHAhcz7yvsLLe4HrShOE6SUPP3Tlzmyu57iykJSyTQv/upVkJI5F85UM3fvNao0gXSgIl4Sdgz5HVkD5O71+3jyJy8wbuZo7vjmLYNSFsJRws6tlyF4CI/PxOWRrF8xjSumz1X7hYu4tYxn7m/D7XPjDQpeeTRJ1dRxlBWpUN1Qe5pHf9wKRj6FRZJQKI9EtJm7vpeizE4UbqtrZ/VyaDqoBsKWJskld3ZQPqanps/uVW/R0RgmGnIQD6UJh97OUhSxcIwXfrGeTcs7MRxOLv7IRi7+cBlOV6+Pve823vj9BlyuZgIl57D0puzyHTgqwVFCKhHCcHpwOKfQt2ZRQl7NT770PO3NHq74WIAbv9xjCzdcVTR23ESh/w9EugTegCQRL6Cg4kuZYyzL4ol7t+KSZeQXx9m/20XhhDrGTFOO4qZDLbz64JsgBIlYEl/Qy+bXt7H17R0svGIulmXx7jOrKRk7F48nDph4S/LY8tZuzrvpHPKKggjDT03NDTzx44dUMILwc8PnrmXWeb1WWI4SmuvSNOwLg5BUTiylfEZumQnhv1M5s49SIVcYRYi8L/W7D+cEWg5uYNe6iJrgOATzLiok0Eu5drWFeP2RFexevx+P38Oy6xay5Jr59ufUiZqpu5Qystp6De49JUeEkQeBL9iVboP91DvzKL9W+pCtdIxBl5oXRh7CcyF4Lszd2V3UUZrZqwppgZSIPiVH3jfCg4pAdHMyuvRpRXGa0HiwmSO76ykfW4oQApfHhRCClS+sY86FdmVOz6Xgmq6W5zKkaui4Z2XlMDQebObh7z/J7rX72bF6L9WTK7notnMHpSwaDo/l+Z/PoqA0QaTLxfjZ07niUz2KKGFdSiq9isK8LgQCjCDR1LUZZbV7U4DmOpPmwwLDWYKZNimtTrNtdZBLbEWxa+0+dq/dlzmno3kfu9bszVIUa1+XzF2SIp00cLnTrH0jzQV2KoWUkud/+SobXm/g0K5ChHBgPLUFl7eQi27rKTcihI/2jos4sL2WS+/IjRYRjgr27r2B9S/eh9NTwA1f+gie3sXlLIt3nj3A2tf9pFMWyRQsu7mZinFlmWPqjtzA629vY/eGCIXlgryyc/jkD3v8SalEil3rDlFS6qWl3knt/jCNB5sziqJuXwMOp0F7QwfxaILO1hBTFk5g99p9LLxiLumUSSwcBwG1u9owUyYV4wyklMTCcfKKVIDDtndr8eZNoLC8gHBHhE1vHmLWeT0rwa7WFE/8sorZi9XA9s6rFXzsOykKSrOjdIQRBGP6UT8fzYdbefuplXj9Hi667VyChT0rEuE+j7r9f6C0Mo5pGUgrwZGaKUwdNSHzPJ+690Va69opG11COmny+h9W4HQ5WHTlPBWOmtoKstAuP2J/YGRUmaJ6rQgO7mhl+3sHKKooZNGVxbi9vaLVhFB9URJvgBVCeM5F9DLDvV+EowTpubDHmW0ElF/FalHNnoY4J0I4J0He36rospPgo9CK4hRipk0aDjThcDqoGFc2qKiHRCyJMETWOS6Pk662cOZ/IYSqq+Prf/mciCV4/MfPkU6mAYnb6+S9Z9dQXFnE3It6CtLV1zSy8bWtREMxpi2ZzIxzpuBwqMEj1B5mx8o9eINFdLTEcbmdRDtj7N98kEnzxgOQX1LC2NkXUrOlBiEkhRUlVEzoSTCKhV2EOktxelrx+kySSYOu9gIiIW/mOa18cR3+oI9wu7q/YHEeq15cz3k3LcnI0tY8nUfujWJaFg7DycwLewavjqZODmw9hGVZdDTGMRwGTreTda9u5vxblmau0XiwmeKqQsy0iWVZdLWFyC/ODsV97N5NvPMUOBwhJiw5zMLLe2bZW97ewaoX11FSXUo8EievpJDHf/w8f/1vH8PjU+apMTMm8Id/m0VTTR3scPDRf8jt8ZyMmax61YVhOPH401nvsz/PR1FlIQXl+cQPNOMNeKmaUElBqYomcjgNYqEYm9/eQaQ9jJRQu+sIheUF+PN7BvmCsnzqDzSxe90+8gqDTJiT7etIRBNEwwEO7FsCQCTUQiI6uB7iofYwv/ra/7Fz1R4Mh8HBHUf49L/dicOpnrcw8tm79wYObnqZQCBKODyOy++6MZMI2XigmebDrUgp+ctDb+HP9zHnwhms+dMGFl05D+GagXROJdK2iQM7TFrrTaYtclJS6cNZcHfG11a3r4HH//M53F4XsUiCliNtfOBvss19wggifDcM6v4Gg/BehzTKMKPLibXvxptXhjN4o93Rb+jNUkNZJbovWlGcIsy0yVP/8xI1mw8CsOSa+Vzy4fOP+wNTOb4Mp9tJLBLHF1ADalt9B3MvPv5S1rW76gi1R4h0RknGU9ARRTgdbFi+JaMoDmyr5bH/eJZELIlhCHas2kvtriNce7fqLXVgay2WaTHznCl0tYbx5XmxTItNb2zLKArDMLjlS9exa80+UskUUxdNysgMMGHOWNzefNqb47Q1pnB53BRX5jFtsTLZRDqjSEtSOrqEcKeqiVM6qhjLsoh0RMkvUQP5h75+Mz/4yAFiXTHySvP44N/2fOnTKZOmQ83U728inTIRaZM9G2oYM7UaaUlwwOo/beCNR9/B6XLi9rp456nVrHphHbf//Y2Mmqxi6nes2s2Wt3YgEAgB//fPjzH/0tmZFdiG5VsIFgUoqS4i1BamsLyAaChK7a46Js9Xs+TS6mK+9PNP8+Zj7zBqclXWigbA5XExemolNVsOYSIoLPNQOaHHFDNhzlhKqouZff50Flw6C4nASpksvFKZ+7a/t5tIZ5RoKEakSyXJSSkprixk5fNrueLOi9V15o7j0LZaQu0RvAEPd/+/7D5iJdXFjJlazcEdyhk/buZoSqpzTU/hjgiHd9cxbuZofMHs1UZrXTsdDZ2E2yMYDkH9vkbCHZGMUgO4/GNX8t3H1tPe2Mnci6cx58Ke6KxUMk08kmDrih10tYZprWvH4XQwY9lU+wgnb788lSPbdrFrTS3xqMWeDQE8hRO59q8rKbOjQuv3N4KEoopC8k2LPev7j+A6mQhhIDzLeOmBDra+7WHC3Il8+Ov9mKlOA7SiGAQD9TU4Fkf2NlCz+SAV48qQlmTtK5tYcs2CrGU5QFNtC3V7G5i6eBL+vJ4vocfn4cbPXc1zP/sTodYQEqgcX875tyzNOr+toZ2X7l9Oe0MHCy6fw3k3LckMaumUyaHttSSiys7tcDrYvXYv/mDPIP7KA6+xfeVuIh0RLEvi8XsItYY45/pFFFUUIqUkHk2w+pWNhNvCuL1uJs0bR9Wk7GW72+tmzoX9978ePbWaD3z2Kv77c78m0hHBE/DwkW/dzMR5KnfAl+fF4TAIt4dxe5W9NdwewuFw4MvrkXXexbP4+oOfZ/WL67nkw+czce74zD5v0ENna5hIVxTLVMlRsa4YiVgCh9NB8+FW3nj0HUpHFWd8FvkleUQ6ozz7sz/xmf/4KxxOB+88s5qu1hAApmmxf9NBmg41Uzle3a+0JDVbDhGPJPDn+zm4rZaqiRV9c9UYPbmKD/39TThdzn4/P50toczfsWhCKfJez/KOb97MW4+9x7q/bGLMjNFc84lLM+atN/74Lq0NHZRWFZFOpJCWpGxUMZHOKG8+vpKLP3QeLreLrW/vYOL8CbQ3tFNUWcjG17YxZWHPSs/hdHDr393Avo2q8Nyk+RMyK4HePP3Tlzi49TCzLpjGLV/MDksuKMvHn+/D7XNhGAZFFdmrGoBQW4R5l8ziyJ4Gpi6aSLgjQmGZ8pVUTihX5ZEMgeEwcDgNQq0hZixTk4jaXXWsfGELxdULqDtkEY8m8BWNpyRdyIv3vcpd/6zyE0qqi7GkRaQzSqQzytgZuaYeM23yzE9fovFQC7f93Q2Ujy3LOWYoaG/sorUhTEFF50m5/qlAh8ceB8lEitcfXcG9n/kV9/7Nfbz+6AqSif6TkY6Gw2mQTKQ4vLueuv0NWJaF4ch+/KZp8quvPsgD332UZ3/2p5xrTJwzjg9//SamLpnEoivn8qGv30ggPzt34un/fonda/bS3tTJnx94g30bD2T2OV0O2ps6aalrpeVwG021LbTXd5CMpTL3+dbjK2lv7CQaihHtihLtjLJz9R52rFbRR+NnjaH1SCv1+xrpag3RcqSVXev2KYf6cSKEYNysMaTTJpYlMVMmE+eOzyg0l9vF9HOm0lLXTiKWJBFL0lLXwcxzp+JyZzvqVj63jp2r97LmTxuztofbIjgcBrFQT7hrqCOCaVokEyl22KYRp8uJZVmkkmmklAQK/EQ6oxzZ20AsEue959aSSqRIxVMkYymS8RTP//LPmWvOv2w27Q0dWFIS7YqSjCexTIvR07LNf9ve3cm3r/shv/ra/2FZVta+SFcs632KdsXY8vaOrGPyi/Ooq2lk15p9bFuxUykjm/2bDuByOzEcAtO0lBlNSvxBHw01jcQj6hk4XQ7yS4LMOn86heUFON2588R4OM4bf3yXN/74bua83oTaw2x/Zxe71uxl69s7iPU5pqi8gEs/egG+oJe8ogDX/fXlWe9Z/f5G/vjvzxAPJxg9tZr9mw7x6I+eIRFTJi63x8WtX7oWf56P/JIgwaIAY6eP5kJ7Fbbl7e14fG4a9jViWRYOp4Om2mZcXhfNta20HFFFJMfNHM2191xOsCjAjHOmcP2nr8i5l662MHs21NBW38HBHf3nNXS1hljx9EoO76nvd/+xOLCtliN76/H43LTVdbBz9RCHxZ4itKI4DpY//BZrXt5IQVk+BaV5rH55I6898vagrlFYnk+oNcyutXvZuWovhsPAG8iu3Lny+bUc3lNPW0MH65dvzvlwdrZ08fiPX2DX6n2seXkjL9//Ws7+955fy75NB9n+7i72bqxhxVOrMvvb6ttJxlMkoinMtEk6mSaZStN0RCWy7Vi5i3BHhHgkTjKWIp00iYVjpBIp3nlqJVJK8kvyGDNjNCAx0yZSgjfgZeqiwfVf2PLWdizTBAGWlKxfviVr//iZo3E47QgW1Gx3/Ozs7GvLstjw+hZqNh9iw2ubs/IbarYcpLOlC8NlIBwCw2lgGILOxi6O7Kkn3B7G6XZyaOdhnrz3Bf7478/w8m+W09nchRCCRDTBjpW7iXREEbJnBWA4DNb8aUPm/7kXz2T2hdOp39dI48FmErEkt331A3j92e/tk/e+yPaVu/nzA2/Q1thnZikt0kmz575Mi1goRl8Obqsl1Baibl9D1r2mUyYISXNtK+lEGjNt0lbfTjptYpqWyq8Dxs8Zy/q/bObl3yxn5fNrGT8nNx+jtb6djqZOOpo6aa1rz9n/1uPvUVRZyNLrFuDyuljz0vqs/eGOCDWbDwECy5LsXrs3a1K17tVNuNwuAgV+kvEkJdVFdLWGOLC1NnPMjHOnMv+y2cQjCWTa4oNfvSHzPMMdEdxeF3X7GsgvzqOkqggzZan32hAZn4oQgrkXzeTj372d6z99JYGC3BDfwrJ8LrrtXOZePJPpS/vpQwK89cR7vPnYezz5k+dz82eOgy1vbccX9DF10SQKywtyPuenC1pRHINYJM62d3ZRPrYUp8uJ0+WkYmwpW1fszJlNDURzbRulo4q54OZlXPjBZYAg3J6dzLR1xU4WXDqb825cQuW48owJoJsD22qp399IR3MXka4oG1/bSiLek0j07rNr6GwN0VrfTmdLF+1NHWxZsYOWOjXL6moNk0qmcXkddi6SwOEw6GzqAGDFk6tUOYheM17LlAghOLCtls6WLgAuuHkp/jwfwjBwOB1ccPPSHHOKmTZ57hd/4pEfPkksnDvo5Zfm4Xa7VJ00l5PCsuyCcEXlBRSWFWAI5cAvLM+noCTbybz2lY10NHVhmib1B1rY/t6uzGuv/fNmfHk+zKSJlBLLtEinTIJFAda8vIFRUyppPNDEO8+uIR5OIC1Ja107r//xHRKRBMVVRViWJNwZyRogUokUkc6e993hcPDZe++mpKoQp9vJVXddwryLZ2XJWV/TyLZ3dpKMJQl3RHj1/7Kzv7e8tYNgUc9A5g14OLynHtM0s46be9FMRk2pZvrSKVnPe+z0UeoesgYyQaQzSvmYUjx+Fe3z8v3LcXlcjJ81lkBBgBd6rYy6GT21ijkXzWTexTMZPTW39lFna4hUIk3trjrMlJkVTCGl5Lmf/4n2xg7OvXEJy65byPb39vL2kyszx4TbI7g8Tra9u4sNy7dweHc9UqKitmwObjvM4V0qDDxQFOC1h9/OPIsxU6uJhmJ4/R6StplNSonD6UACRRU94b6WZXF4dx3hjv6zroUQnHfjEm787NWZyLC+VE2qxOlyMmb6qBMyOeeX5imFJyWxcCznc3687Fm/nz/++zMc2jnEGd3HiVYUxyBtmySE0fMhEYZASmUy6U1bQzt7N9ZkBtTeON0OWura2LFqD9ve3U2kM4LDlW3/LR1dSrgzisvtJJVIUVje86Hvag3xp98sZ+/GGnau2sPWFTvZu7GG1x55O2PKWPvKRlKxFP58H96AF4/PQ8P+Ro7YKxOXx4nD4SARTalyQJYkFU/jDSgbcu3uOvJKApkZaDe+PB+RzmjmCzdh3jji0ThmyiQRSzBlSe5qYufqPTz4T4/x+H8+x+uPvpuzf+l1CymqLMTtc5FXHMxx8JaPK+OcDyzC6Xbg9Dg554ZFVIzPTiJ6+TfLiXRESCXTdDZ18trDapUXC8cJtYeRlsz4O5xOB/58H5GuKPU1TUxdPJlDO4+QTmRne4c7lGIoqSqiqKIAw9nnKyIhv6RnUG+pa+OpnzxPNBTDMi02v7mdFc+szhrkN72+jWQihWEIHC6Dd55anXXJDa9vZdqynhltoMCPmTZpPNCc2WZZFnmleRSU5TFqShXxXtFIl3z4PMrHlYEQWJaFZUksy6Kkqojzblqcib6KdEZxuZy43E7cXifRruzmOVJKXr7/NZ7935d5+qcv8/L9r+XMohddMZf9mw9Qv7+Rw7vrmNtLKbY3dnBkTwOFFfm0HG6hrbGD0lHFbHp9K2ZaPY9pSyfT1RYmHklkVk5CwKgpPUoplUhRXFnIpPkTmLFsCiAyfqbZF8zA6XZSPbmSSFeU2j11FFUUYCZN5l0yM2vlsP4vm3noX5/goX99nHQq+30+XhZePocv/PRubvrCNSd0/tLrFjJp/jiaDrVQPaki53N+vPzl92+xf9NB3nrivRM6//2iFcUxCBYGqJpYQXsvc0F7YyejJlcSKOjxD9RsPcTv/vFRnvnpy/z2H/5Aw4GmzL6u1hAv/Xo5sXCc2l2HqdvXQLQrxvO/+HPWsvzyj11AfkmQuv0NTFsymVnn99RfeuZ/X2bX2n0kYwkinRFioRjJRJIXf/Uqu9epiI5IZxQMSIQTyq6eSCnbeaea0VdPrsQ0TQxDKHuOAGlZmdwEr9+D052brON0OXA41QAD8MgPn1KmEkOZYh78xz9mHR+PJtj6zi5i4TjxSILtK3fT1RbKOsbjc/PFn32Ka+65jC/+76eynNTqNZ2cd+MS8kryyCsOcv5NS7Icq+te3cS6V7dgpi2QYKZM3npyJTtW7cbtc6tkLkPNGi1TqhBah4G0JAUlefgC3kyIbG+EgLwSNbuMdcXwB7N9QMJQZjAzbRLpjPDHHz1D46EW3B4X3oAHp9vB20++xzvPrMmcY5omhhA4nOo5WulsTWymLUTvAVmCIXoGR4DVL63n4X95gt1r9/Hn373OQ//yRGbfvEtmMfv86ZhpE4/PjcfnQVqSqkkVXPLhnkzz6z51BVJKWuraSMZTXP/pXq1Bgda6NlY8tZLm2laaa1tZ8dRKWuvaso6ZtmQyH/zKDUycO46PfPuDjJ3e4yS2TAspLTb8ZQvr/ryJ1S+uZ9eavVj2rB9g9gXTmb50MoXl+fiCPrxBD5d+5ALKRve0JR07czSF5QVEOiJ0NHUpM5f9ucwvyePDX7tJKdOUut90Is38y2dn3StAKpHGsiQpe1XZFzNt8vRPX+JXf/8gzYdz64iB+vwECgL9flaOB1/Ay21/9wG++pvP8tFvfzAneOV4WXjlHPwFfuZdMuvYB58EtKI4BkIIrv/0leSXBGk61ELToRbyS4Nc+6krspaiq15ch8fvoXxMKQI1kHXz1hPv0Xy4lUQ0yahJVZSPKSXcEWH/loNsfnMboJx8j/zwKTpbQvgCXvZu2M8TP36BSFeUUHuYlc+vVY7YuPrwm6ZFPJSwTRlvAjBm+ihV2slQX1ohBN6Al7Kx6ktopi3yi4NYpqSnF4Agr1gNjNOXTSERyY2b72oPUz6+NBOWemR3vbKLW+p1WnoNJrFInEd/9DT7Nx0gWBTAm+clForx++89TluDsnl3tnTxwHce5cf3/IKX73+N//7cffzqa7/Pug7A4T11dDZ30dkc4vDeHn9NR3Mnyx95m76hRZYpefn+5Zhpk6XXLVAlRpJpZZ4wJemUiS/Px9LrVPZ16Zjc0E8pZWZ2u+Lp1SRiCYJFflxeJ56AG3++n87mLrav3M22d3fReKiZ/ZsOEumKEe6MKtNgUxcrX1jbY3ITglgkQSqRJhFN4Al4iPSazc+/ZBadrb1MOFj48n1ZIbLrX9tKLBLHTCln/KY3tmb2ub1uLrx1Gb48LwVlBeSXBvEX+Fl01fysnJB5l8zin576Gp/45w/zj3/8Chfckt1/RBgG3jwfHp8bt8+NzzYv9mXxVfO5+pOX5kS1FVcV4fZ5aG/qxOl24XQ7qdvXwKT54zKRZS63i5s+fw2FZfkEC/3MvmAGS66en/0+pk3SKTWhMQwjJ5ejamIFn/nxX3Hr317P4qvn87mf3s3Vn7g0J9BhyTXz+dDf38jH/uHWnH2gJnC7Vu+lqbaVA1sP5ewH9XlIJlI5AQiDZbClcvqy7LpFfPnnf82cC/qPJDzZaEVxHBSVF/CJf72DW//uem79u+v5xL/cQVEvsxCoD0LDgUYObq+l6XArTnv2m4gl2Ll6r4pOsiThzmjGbBMsCLBh+RYiXVEe+8/nQEJJdRF5JXmUji7hyO56XvjVn2mubSHaFcMwVLatlBJpO5PdXg8HtqkP+dgZozHTJqlYilQiTTKRwmEYlI5SA6LH68qZ0RhOQUlVIQBTF0/KshV3k0qmKB9dii/oIxaOqSS43vXtJBzerQqqbVi+hcZDLUQ6o3Q2dRHpiNJQ00gynuTNx9/DNE2evPdF1r66icaDTaTiKVoOt7HxtS08/uPnMiusrtYQK55ajZkyMdNpVjy5KrMq2buhhvq9jTlffofToL6miYPbajn3A4vJKwqSiPX4cGKhOONmjmL6silEuqL9fnmlBR3NITqaO9n42hZKRxWTSqRJxdMkokk8PjcOl5O//P4ttr+3m9pddRzZV0+4I0IymqS5tpWDOw5zZHeDyilo7mT7u7uonlyB0+3EG/BQXF3E6pd7nMALr57H4d11mY6ZodYIk+aNzyo1klcYINoVJRaOE+2K4gl4smbJo6ZUEcwP0FrXSltdO0iYdd5U+uL2umhr6MDjdefsM9MmLo+TaDhOLBzH5XVlTEa9efgHT/LID57kqXtfyNpuGAbXfPJSvAGvCh5wOMgvzeeSD2XP9IUQRCNxDu06kuUP66Z2Vx2xUIwxM0YxekY1m97YnhNl6HA4uO5Tl3PX9z7ExDnjcq4BalU6ecEEiiv7z1QuLC/g/FuWMvv86Uzr48yWUrLt3Z385lsP899/cx//+8XfsuLpVSTjyX6vdaajFcVxEOmM8MRPnufh7z/Jw//6JE/81wtEOnscZAe21bJr/X5qNh9k0xvbObTzMJve2k59TSOpRBok+PN9RCMxGg40UV/TBELlC8TCCXau2kMylkRKyeqX1rP6xfVsemMbBeV5HNx+RJmUhLJTW6YytWCpAnOpZBJfwEvLkVZ2rt6D0busjCkJFAd5/Q+qpHV+aR4tR7KX2KlYGp8d535wWy2p/sJ+pVrxxKMJarbWZpK6urFMkw12NMemN7bR2dLF4T31GaXW3tRJfU0Tu9fuY9+mg9Ttrad+X2OWL6Shpommgy2ZhMRoKEZHUwemaWKmTdobOzKhro0Hmmlr7MjO3RBQMb6MpoPNdDR1svG1rUjLykROgfLRtDV0sn/TQXau2kMimsDhynZQBov81Gw+yJ71NcQiCVxuJ1b3gCyVz8ob8FC78wht9e1EOyOkeuU8SAnxUIzWhjYSsSQNNU2Y6TTFlUUEi/yUjy7F63Ozd/2Bnhc1LdweN06PC6fLgS8vO+8g0hmhfl9DT5FAwOEwOLCtJ1IoGoohhV0sWEocTkFXS7a5D+D+bzzE0//zIj/94v1Z25sPt/LID54knTLxBz34g17SiTSP/ODJnM9MY00T7U1dNNQ00ZfRU6sprioknUyTTqcZN3M0+aXZQQiWZbHxta10NnXx1hMrc67hDXjpag2x9pVNrP/zJlxeJ84+/jwzbfLw95/kvq8/xNZ3duZc43gQQnDhredwy5euy8nGX/XiOl745aukUyYV48rw5/t455k1PP3Tl3OCDIaC1vp2Wutzo8xGClpRHAdP/c9LLH94BTVbDlKz9SCvPfQ2T//0ZUB9QR//8XPU7jiiWhU7DSzTombzQR7/z+dweZ348rxse2cnhqFCNB0OlVOx/d1djJleTePBZjw+N9ve2UXToVbam7s4tLOOI3sbMAQ43U6CRUHa6jtUVrGNNCVtDZ3MvnAGm9/agWEYxMPZM55wW4i9Gw7Q3tTBH//j2ZxIK4Bnfvoy9TWNbFmxs98EK7fHTdOhFrpaQyRjSeJ9Vh3JRIqoHc7ZeLCZpoPNygViRywhJe2NHTQdaqG9oZ3OlhBWn5mqZVp2XoZtfhKCUHtE1VCzINwezRTjTKfSWKZJw/5eA5WExkMtWJYkFk6w6sV1hNojWbkqhsOgo7GT1S9voNGW0dcnD8WX58MyTdLJFEKoAIV0vMcRGu6MEmoLEywK4At6SSXSKigho0skCGUycXmceINetry1k91r9yNNaG/q5N1n15CI95hTgoVBZl8wvfs2yCsKsOCynmzlmq21NB9uywQ/CEMQ7lBRb928/ug7dDR24nQ6cLicxEIJXv718hzbvC/Ph4ScjOp3n12jMplLCxAOFVJcWF6AlPDuc2uzjr3yry5hxrLJXH5nbpZxy5G2LP9KLBQj0pHtNDcMg/ziIMIQlI0pybnGuJmjmTBvLF2tIaJdca755GU5q790Ks2uNXvZu34/h46SA3E8tNS1cWBbbZZpKRpSVZrLxpRmkl7dHheV48s4uK2WQ9sPH+1y/fLGY+/wdxd/hxfuy40yA2g61MwD332UB777KE21LSd8LycTrSiOQbgzwrvPrCEVT9JU20pTbSuJeJJ3n1lNuDPC3g011O1rpPVIG7FInGQsQTycpOlwK0f21nN4Vz2+PC+tDe0koolMQlQynqLlSCtF5QUUlOaTTKRoqm0hHomrn1CM5kPNSAn+fD9FFYU4+iTopdMmgXwfxRWFNNW24At6cwaG7vyB2l11vPPU6r4JwyCg5XAbbz+5inBHhECfLFoAp8eBZVokYwnGTB+l/BO9sNKSmecqM0e3T6CtoUM5Ny1JZ0sYiXK25xUHkZZUTuhemGlLmePsMNEVT6+ivUldwzIt2praM9FCxVVFxKPJnLDHrpYQ6VQaf76XSFeUdMrMchynk2ni0SRNh1ooKM0nWBRUU/De9+p0ECjwUzKqBIQg3BHLfqYSQq0hKsaXMX7OWAJFARzOHhORwzAQAqonVeLP99PW0E40FMNwqCLjwiEwLUldnxyZu//fx5i8YALjZ43hI9+6hYpxPf6J7jpU3WVEBCqEuTuSJ51Ks/293SRjSVKpNOlUmkQsQf3BRtoaOrJeZ9l1CygbXZLx04Bytu9Zv5/CigIQEjNlYaZU/4yi8gJ2r92XNYu+7KMX8PUHv8g51y+mL9FQlEM7jxALx4l0RDi8qy4rhBvUBOJHf/4O33r4S/ztLz+Tcw0hBAsvn8OYGaOYsmgCY6bnZlVblopEdLqdSjmfAJGuKA/96xM88sMn2f7e7sz2un0NWKaVs4oRQuD2uNizPjts/eCOw+xet++ofowXfvlnmg61qGrP/ZBMpDGTacyU2f+KfgSgFYVNt9Oq70DbXUMnEU+QiKqfZDxJpCtGtCtGV2uI9sZ24tE40Y4oka4Ykc4wiUiC9sZOOpu7aG/oZOyM0bQ2tGcyfFuPtDJl0ST2bznIjHOm2K8rbXt4inQqTTKRprA8H3+el2CRn6LKgqwS94ZhMHHeOI7srae0uoh4JJEVjw9QNaECy5J0NYdIJlK5FfLtcgm7Vu+hakJFTsgugJBQUJaHP99PSVURgYJsZSKlZOa5KkIrWBTEcBhZH/h0Kk06maZ8TCnVEyso61UBtjfF1UVMXjCBRDzJ8t+/meWDcLldvPrQmyTiSdxeFdnj9GRnFnv8brX6Kgziz/MpU12v91NaEpdL+WxmnDOFYEmQaGf26igailE+tpSWI21UjCsjryiQ9cwdToOysaV0NncxftYYJs0bT7BX9JtpWVRNqGDOhTMoqSqi6WALgTwf8XCczpYQXS0h/EEvnS3hrNctrS5i4WWzGTO9mnmXzsnaN27maEqriymqKMDpUeHERZWFzLYdmyqyy1SZ5k4HDoeBw+XETOYOXC/dv5zmQy386Xf9dPKTyjFeUlVESXVRbrVVm9a6NjYs30JHc3bioJk2efPx95i+bAol1UVUjCtj/OwxvPV4rnmpfEwpCy6fm1Wmpjduj5uiMqXM++YvSCnZvW4/tTvr1Er4ze05UXWgVgvrXt3ErrX9D+Jm2mTv+hp2rt5He2NH9s6jpExIyAqVbznSymP//ixP/OQF9m862O85V951CYVlBVz+8Yv73T9qciUf/sbNfPjrN1E96eQV9ns/aEWBmhH89h//wL2f+RX3ff337NnQU0AsvziIP89LW31Hd3Mq2uo78Od5ySsOUlxVRDKWJhlLYVkqmsgyJYlo0rZnq1m+lbYy/dgBzJRFoMBHuD1KcWUR19x9WVaEiZTKh3HTF67F6XLSdLBFJfz1KrtQOrqYhv1NxCMJ5l40EzNtZhzTgHp3JYyfPYaiqkK8AXe/YYKBAh8ut5Pzb17Sd4INqDDDqYsmZvI6eocFAzhcRsZkNXZ6NaOnVmU5QS3TonxsKaVjSsgrzuNj/3Ar42Zn9/QdNaWSj3zrFgL5floOtxLtipNf0pMElV8SJNoVo7WujVQiRVF5AWWjs6OWSqqKKakswjItllyzgILSfKTZc0OmaVIyqpil1y6guLKIqQsm5gwgsXCcc29cwvpXNzFp/ngWXjlXJXEJNUBMnjeeJVfPx+1101rXzrWfujwTNQbKyTpl4USu+2sVFTd+9lhiURXxBOr96Gzpyskyb2/s5L3n1rJz1V42v7Eta19hWQEf/MoNFFYUUlCahy/Py9WfuJRpS1SdJofTwdJrFuC2nc9m2sIwBFOXTKS4sjDrWrMvmE6gKJhZAXbLPG3xJNobOwgWBph78UzmXjSTYGGAtoYOpi+dnGX6efbnr/Dqg2/w5wfeyLp2a307kfYoY6ePYsayqcxYNpVRU6vZv+kAqWT2TPm3//gHvnXN91nxzCr6I1gUIJ0y8frcOaVG9m08wEv3vUpBeb4qHNke4bH/eC4rV6K1vp2H/uVxlj/8Nk/994u8+XhuLk88kiAaipJOJAn1ShwcNblSTXaSfTsqSlLxFFMW9uQNub0qQqw7PLo/rr7rUn62+kfc9rf9V6oVQjBu5hjGzRxzUqrKDgVnvaJob+zgyZ+8QCKSyBTse+Z/Xs7kQfiCPmZfOAMzbSFN1YfETJvMvXgmvoCXSfPG4XD1/xg9fg+TF01Qtuxk7pIy0hnLOGQnzB6bkxTkcDooHVWcSXhzuV1ZkTCBAj/pZBqH00H52DKuuftSiqt7IjwchmDC3HFce8/ljJ0+isKywhzbdPc9zr9sDnMunIEv6M35VASLAiy8an5msPAGs78QTlfPzH/Z9YuQFhi9fR0CkJKl18zH4XQwemo1//bn72Zd4z9f+x5TFqgvYFFlIU6PM6tQXmeLKrlRWF6Ax+9h9NQqJs0dj8vjAqFkmrJgAlWTVHTROR9YzLxLZuHpVUojWBjgotvOZepiNcAmEykVBWZ/N4UhKB9TSqQzSjySwOvzUDG2jCs/fjFzLpjBuR9YxKKr5+PP8+MNeGk82MyyaxcydsYoRk2rIlgcYNqSiZx3y9JMVNzURRMzfSUyj0MIPvS1m7K2WaaFaaqmNv2ZH6YumsS3HvoSH/js1Xztd5/n4tvPyxq8F1w5D5fHlTHCGE4Hi6+enzPw3PGNW/iXZ77Op/7fx7K2n3fzUgynQWtdO748H748H611yi9y7o1Lso6tHKcqGZePyy6i5/GpiUhrXTsHttWyb9MBulpCuL2uLF+RZVmseHIVDTXNvPnoO/RH06EWDIeDrrZwTjmT1S+vJ78kj0VXzGXR5XOYumQS7Q0dHN7dY847uK2WZDxF5fhyKsaWsfG1bX1fArfXRemoEoqqCrOq2/qCPi667Vyaa1syCZjdgQmTFoxnzPSeOl75JXl88vt3cPcPP8LoqcfXHe905KxXFLvX7sMyLYKFAYQQ+PN8OF0OtvWKpFh2/aKsmbiUkmXXqxaSHp+HaYsm5bTGNRyCeRfNwu12cc4Ni7Jq+XRjpk2WXbcQgA2vbSEVy7blthxu5fDuekJtYaonVyClJN1L4XS1qrLW3QPdnAtn8re//AyGHckz99LZ3PXPHyJYGCCvKMgVH78If36fxDa3gwlzxnLujYvxBX0svGIOeUW9VgwCJs4dx9Resyivv08jm173PnHuOK791GU4e9nthRCcd8tSllzTYxcPFgS45W+vxZ/n5dKPXpCpHgrgD/pYdv2CTLFCgGQsybkfWIQ/6GPy/PEYDoPqyVWMnlZFYXkB42eNoWxsKS63i3EzR+P2uLjzO7cx77JZKhPZ5+aKv7qYqz/Z0w61qKyASfPGk1ccxJ/vo6iikLEzRqsVgpSZ1UZrfQdH9tRTs7WWlK3MzVQ6U3+ofGwZWFBcUYgQRlbotGEYfOvhL+LN61FYH/zb6xk3I3tFVT6mlC/+7z381T9/mMvvvIj+qJpQwUe/dStz+ynA+N6zqzEMQX5xkLyiAL6Ah7efXJWzgnQ4HVSOL88JLS6pKuLO79zO1MUTaatvp7WujWlLJ/Px795GSVV2eOk191zG3/zkLi6+PTvLuKA0nymLJhLpiuJwOXB5XYTbwyy9bmFWwlrtziN4Ah4Vyp0y+y2xMfuCGVz8oXO54TNXZg3ioIomOt1Ou2ZUQDXHEmSFQgeLAph22ZZQWyirtEc3hWUFfOJfPsxHvnkri66al7Vv8VXzuOXL1xMo8NN4qJlkPMkld5zPjZ+7Oif5Lr8476ghuGcKZ32Z8VRSNYlpPtLKwW21jJpShcfn6TEVoCI3XC5nZinqcjuJ9QoRXXr9Ija9uY2Opp7SHUUVhSy8StmaF1w+h+0rd7Nz1Z5MNIjb6+KaT16ayWw9tOMIDrcTeg2OQhi01rUxZeFE/EE/k+aP59DOw4BSOm6PU5mVeg1MwcIATqeTZDqF1+/J+lBfe/dltNS28vh/PY9pK67RU6v51P/7WObLeP7Ny3j9j+8CKlLF5XEyZlp1JhejW/beePrYsmefP52J88ex/R1Ve6l6QgWLr5yfE1GVVxhkyTULcnJSAK78+MW88ts3CHWoVUUg38/lH7sw82wv/OAy3np8JSVVRVimpKA0n/b6dq6557KM3dsX8PLth77M1rd34PG7mXX+9KxZ+MIr57J95W7yy/KQlsQwDConlDNl4UT2LJzAwW2HKaoopK2hnXg0QTKRIh6O4y52kYglmXGOMt/c9Pmr2b+phoYDzZx/yzImL5iQdS8VY8v5p8e+ym//8VEmzx/PJ76f3Qeim3mXzO53+7FIp9Ls3VDD6GmjqN/fgLQkxdVFRLtitDV05Az0R6OkqojrP30l1/21qrR6NDOIw+HIUuy9ue5Tl1NUUcDG17fi9rhZcu18Fl4xN+uY1/7wDnMvnIEvqFYtm9/aznl9Vi1ev4cLbz2n39eYee5U3n5qFd7xHlXAMZbEMAxGT+mx709eMIFzP7CIDcu3UlxZyA19mhb1XGtav9uFEExbPIlpiyepQAIhRqxZ6FRw1iuKyQsm8N6zaziyu0GV2Nh5hDHTVVJWN9OWTiZQGMjkTgQKAlm1ec79wCKe/u8XCXdEMNMWTpeT6kmVzL1IpdsbhsFHvnkL617dxLYVu0DA1Xdfxjk39ESNVE2swB/0ZcptIFTuRWF5AcHCAJPmj2PDa1spqiikvbEDp8uJy+sm2hXL+iImIgkMhwOXW9qd7Hpwe93c/cOPYjgNnv/lnykbXcIPX/o2JVU9SmD2BdNZctV8Vjy9ilQixYTZY7nx89dkfUlmnjeVda9uJpVMYRgGk+ZnD4wAY6ZUEcj3k4glKR1VlBNdBLD46vmE26Msu35hzr68oiAzzplCzZZDalUzZxx5vWLdl12/iKpJlWx8bQstde1UjitjweVzcpyBgXx/ZvXXl4pxZXz027fy3gtraT3SzsS5Y1l63ULcHhfLrl/EnvU1xCMJJswaQ2dzF3lFqux1S10bFePLGDdTrQrKRpfyw5f+UdUEO8pgsvjqBSy6KtcUNHQIpiyaQNWEcizLIr80j/b6jhO70vuQ0e11c/Ht53Hx7ecd8zUyUUWDrMq6+Or51O9vZP/mQ3ZhSsH1n74iq86TYRhc8qHzc5L9ToT3m1V9JiBOpHTuSGbx4sVy7dq1xz6wFxtf38qzP/sT9fsbKRtdwg2fuZKl1y3M+sJsenMb//fPjyEMwce/c3tOhdC/PPQWj/3nc3S1dFFUWcgn//UOll6bPQDu23yQX/39g3j9Hr56/2ezltSdLV389Av3s+aVTaTiSYLFQa7/1BXc+d3bcDgchNrDPPDdP7L2TxtUIpslCRQGuP2rH+Cyj16YkdWyLH75lQfYuXovn733k8xYOvgm7l2tId547F0iHVGWXDs/06kts78txI8+/lP2bqihqLyAv//tZ7Ma4ACseGY17z27BpBMWzKZ6z9z5aDq5Ugp+cvv32Tj6ypXYP5lc7jizotO6axu36YDPP+LV0gl0rg8PY7iqonl3PKl6064bs/J4JUHXmfTG9syPdXb6tspHV3Mnd+5fcTNhA9ur+Wp/34RM21RUJbPHd+4+ajVW4+GlJKWI22ZKLXeHRQ1J4YQYp2UMjfmGa0oMsSjCbpausgrDvbr8D0WpmmyfvkW6vY2MGH2WOZcOKPfL2jLkVZcHleO3RXUALzulY3U7W9kxjlTmX3BDNyeHjNPMpHivefWsPKFdQQL/Vxz9+VMnDtuWAaCZDxJW0MHeUWBfmv9SzvJLp0yKR1VfEKzMuUYVQl4JdXFw3Kf8WiCvRtqaKhpwuVxMmneeKonV464WWYiluDV/3uTnav2IpGMmVbNdZ+6IlOfa6QR6YwQao9QXFmYFYarGT60otBozhJi4Rhm2iJQ4B9xKwnNyGYgRTGypkVHQQhxjRBilxBirxDim8Mtj0YzUvEFfZkIPo1mqBjxikII4QB+BlwLzAQ+IoQ4/gbNGo1Go3lfjHhFASwF9kop90spk8CjwE3HOEej0Wg0Q8TpoChGAbW9/j9sb8sghPi0EGKtEGJtc3MzGo1Goxk6TgdF0Z+xNcsDL6W8T0q5WEq5uKysrJ/DNRqNRnOinA6K4jDQu4LaaKBumGTRaDSas47TQVGsAaYIISYIIdzAHcBzwyyTRqPRnDWcFnkUQojrgHsBB/BbKeUPBji2Gei/MPzxUQqMzDZT2Wg5h5bTRU44fWTVcg4tJ1vOcVLKfm33p4WiOJUIIdYeLelkJKHlHFpOFznh9JFVyzm0DKecp4PpSaPRaDTDiFYUGo1GoxkQrShyuW+4BThOtJxDy+kiJ5w+smo5h5Zhk1P7KDQajUYzIHpFodFoNJoB0YpCo9FoNANy1ioKIcQYIcTrQogdQohtQogv29uLhRCvCiH22L+HtWv6AHJ+TwhxRAix0f65bpjl9AohVgshNtly/rO9fUQ9z2PIOqKeaTdCCIcQYoMQ4gX7/xH3TKFfOUfq8zwghNhiy7TW3jbinulR5ByWZ3rW+iiEEFVAlZRyvRAiD1gH3Ax8AmiTUv7I7n1RJKX8xgiU80NAWEr5n8MlW2+EaoAQkFKGhRAuYAXwZeBWRtDzPIas1zCCnmk3QoivAIuBfCnlDUKIf2eEPVPoV87vMTKf5wFgsZSypde2EfdMjyLn9xiGZ3rWriiklPVSyvX23yFgB6oq7U3Ag/ZhD6IG5WFjADlHFFIRtv912T+SEfY8YUBZRxxCiNHA9cD9vTaPuGd6FDlPJ0bcMx1JnLWKojdCiPHAAmAVUCGlrAc1SAPlwyhaFn3kBPiCEGKzEOK3I2Sp7BBCbASagFellCP2eR5FVhhhzxRVuubrgNVr20h8pveSKyeMvOcJalLwZyHEOiHEp+1tI/GZ9icnDMMzPesVhRAiCDwJ/K2Usmu45Tka/cj5C2ASMB+oB348fNIppJSmlHI+qsLvUiHE7GEW6agcRdYR9UyFEDcATVLKdcMpx7EYQM4R9Tx7cb6UciGqa+bnhRAXDbdAR6E/OYflmZ7VisK2Tz8JPCylfMre3Gj7Bbr9A03DJV83/ckppWy0BzsL+DWqE+CIQErZAbyBsvmPuOfZm96yjsBnej5wo22rfhS4TAjxECPvmfYr5wh8ngBIKevs303A0yi5Rtoz7VfO4XqmZ62isB2avwF2SCl/0mvXc8Bd9t93Ac+eatl6czQ5uz/UNrcAW0+1bL0RQpQJIQrtv33AFcBORtjzhKPLOtKeqZTyW1LK0VLK8ajy+q9JKe9khD3To8k50p4ngBAiYAeFIIQIAFeh5BpRz/Rocg7XM3WeihcZoZwPfBzYYtuqAb4N/Ah4TAhxD3AIuH14xMtwNDk/IoSYj7JjHgA+MxzC9aIKeFAI4UBNQB6TUr4ghHiPkfU84eiy/n6EPdOjMdI+o0fj30fg86wAnlbzL5zAI1LKPwkh1jCynunR5ByWz+hZGx6r0Wg0muPjrDU9aTQajeb40IpCo9FoNAOiFYVGo9FoBkQrCo1Go9EMiFYUGo1GoxkQrSg0Go1GMyBaUWg0J4AQokgIERdCSCHEncMtj0ZzMtGKQqM5MT4GuIEa4J5hlkWjOanohDuN5gQQQmwA2lClHu4Fpkgp9w2rUBrNSUKvKDSaQSKEWIiq3vkg8DCQAj7Zz3EOIcR3hBAHbTPVZiHEh+0uZdIuG9/7+CohxC+EEIeEEEkhRJ0Q4j4hxEgoea05i9ErCo1mkAghfoYqHFchpYwIIZ4ClgDj7Kqe3cf9Avgb4HVU9c8y4PMoc9UiYIKU8oB97FjgPZQ56zfAPmAy8FmgEdXprPOU3KBG0wetKDSaQSCE8AJ1wHNSyk/Y224CngGuk1K+bG+bhars+Yq93bK3zwE2olbzvRXFs8C5wEIp5eFer7cYWAl8X0r5vZN+gxpNP2jTk0YzOG4FiuhpmwnwIqp/wd29tt1g//7v3qsMKeUWlPLIIIQosI9/DogLIUq7f1AVQveiykxrNMPC2VxmXKM5Ee4BmoHDQojJvba/CtwuhCiVUrYAE+ztu/q5xi5U17JupqEmbfdw9Aiq/e9Lao3mfaAVhUZznAghJgCXAgLYfZTD7kRFQYnBXNr+/RDZK5XexAZxPY1mSNGKQqM5fj6JGtT/GujoZ//3USuCe1EOa1Crhb6rgWl9/t+LakTjllL+ZYhk1WiGDO3M1miOAyGEgfIXdEgp5x7lmH8CvofqYxxlcM7sF4CrgQullCv7XFcApVLK5qG+L43meNDObI3m+LgKGAM8OcAx3fvukVJuA+5DDf5/EUJ8UQjxL8AbwAb7uN6ztM+ioqneEkLcL4T4vH3Of6FCZT8/dLei0QwOvaLQaI4DIcTjwG3AXDty6WjH7UL1O64CksA/osxRFSgn9vdRK46vovIwmnqdWwp8A7gJGAvEgVrgNeBXUsrtQ39nGs2x0YpCoznFCCGeBy4D8qWU5nDLo9EcC2160mhOEkIIXz/b5qJCY1/TSkJzuqBXFBrNSUII8TfAX6ES8pqB6cCnURO086WUGwY4XaMZMWhFodGcJIQQS4F/RRUQLAZCwArgn6WU64ZRNI1mUGhFodFoNJoB0T4KjUaj0QyIVhQajUajGRCtKDQajUYzIFpRaDQajWZAtKLQaDQazYD8f2f6QXF2B7BpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "area = np.pi * ( X[:, 1])**2  \n",
    "plt.scatter(X[:, 0], X[:, 3], s=area, c=labels.astype(np.float), alpha=0.5)\n",
    "plt.xlabel('Age', fontsize=18)\n",
    "plt.ylabel('Income', fontsize=16)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D \n",
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "plt.clf()\n",
    "ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)\n",
    "\n",
    "plt.cla()\n",
    "# plt.ylabel('Age', fontsize=18)\n",
    "# plt.xlabel('Income', fontsize=16)\n",
    "# plt.zlabel('Education', fontsize=16)\n",
    "ax.set_xlabel('Education')\n",
    "ax.set_ylabel('Age')\n",
    "ax.set_zlabel('Income')\n",
    "\n",
    "ax.scatter(X[:, 1], X[:, 0], X[:, 3], c= labels.astype(np.float))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   "source": [
    "k-means will partition your customers into mutually exclusive groups, for example, into 3 clusters. The customers in each cluster are similar to each other demographically.\n",
    "Now we can create a profile for each group, considering the common characteristics of each cluster. \n",
    "For example, the 3 clusters can be:\n",
    "\n",
    "-   AFFLUENT, EDUCATED AND OLD AGED\n",
    "-   MIDDLE AGED AND MIDDLE INCOME\n",
    "-   YOUNG AND LOW INCOME\n"
   ]
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    "<h2>Want to learn more?</h2>\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
    "\n",
    "<h3>Thanks for completing this lesson!</h3>\n",
    "\n",
    "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
    "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n"
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    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description    |\n",
    "| ----------------- | ------- | ---------- | --------------------- |\n",
    "| 2020-08-04        | 0       | Nayef      | Upload file to Gitlab |\n",
    "|                   |         |            |                       |\n"
   ]
  },
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   "source": [
    "<hr>\n",
    "\n",
    "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>\n"
   ]
  }
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