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Created on Cognitive Class Labs
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"## Introduction\n",
"\n",
"There are many models for **clustering** out there. In this notebook, I 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",
"- 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",
"\n",
"In this notebook we practice k-means clustering with 2 examples:\n",
"- k-means on a random generated dataset\n",
"- Using k-means for customer segmentation"
]
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"source": [
"### Import libraries\n",
"Lets first import the required libraries.\n",
"Also run <b> %matplotlib inline </b> since we will be plotting in this section."
]
},
{
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"execution_count": 1,
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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": [
"# k-Means on a randomly generated dataset\n",
"Lets create our own dataset for this lab!\n"
]
},
{
"cell_type": "markdown",
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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>"
]
},
{
"cell_type": "code",
"execution_count": 2,
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"outputs": [],
"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",
"<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"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"outputs": [],
"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."
]
},
{
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"execution_count": 4,
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{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f5ac08af1d0>"
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"execution_count": 4,
"metadata": {},
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{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[:, 0], X[:, 1], marker='.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Setting up K-Means\n",
"Now that we have our random data, let's set up our K-Means Clustering."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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",
"<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>."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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,
"deletable": true,
"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>"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": 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=1, 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,
"deletable": true,
"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> "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We will also get the coordinates of the cluster centers using KMeans' <b> .cluster&#95;centers&#95; </b> and save it as <b> k_means_cluster_centers </b>"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Creating the Visual Plot\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!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Please read through the code and comments to understand how to plot the model."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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m4GPcH0JA2sxcjohoJ5Nwch8wfZqJKnsDaNsIdGwC3f4RxNq3Srmh2qcriTE/AcR6vOXRHZuBlp7KqKb2TeyQGHtRRrAfqUSwC9e8ycHENo6A+w4D06fYAbLzV1zR9D7Z2+KxBp3iqt4K0sfYITL4AY6we3ZxoUw+DaEv1rYcnXxtZff+IXNeKEJWePPiTn9Z72S/3Bgo1AZBlizsSAKZCxXy7NzuLdogC8hc4Mq7hWtMfIPPglp6IDJnga4dEJEER7sen+462b6yDyhbrA8TeeUH+ID5qxBtG4D0cXZmFGdkUQuYbIkAbZrJu8zl0uh7GsLnAzqHQKYJJIY5qRhfDyAgSfp10IW/ghj8INvjYHEBSvugQ9CVxvoHeO25MVczfHDyED7noVAt34jhX668KSyDh815oZJ6Cm8quBNEzSaGbM9r3Vl0y+03+Rq3qVy8xQSYnwBNn+ZJz06F3n52MWTOgYw8UMxwFH7heWD8GJOZdFMIo8BEKEcsYfwoICd6cK8Ju32lswr+rDAJatsAlHJAcdopg6bp15n8569wgi+5h/3G40eB8BqWJIQP8IeBsR9xa8/ZSwDpnPDrfpy/g8nbOrP59vG5Z86wpJK/DbIMliTI5PNmzvG97NjoSqYegPAHge6dQPYGSzru8VPp46BgjB9WS/zbPqxQEbLCmwsrkBscvbJ3T/2S6OX2S+7jBFvmHO+f3Ouq5vNzFNq1A4CPJ3IkHmP/cbSb/btO8UgQ6H8b9zoeO8LbuKeJdGwGrBKoOAORT7vacx6s6Mat6yCmXwcSvQA6eJBpYhgivl66KeQ+a58EpqSOHIq7knV7QNOnIAD2K0+cgOjZDQTjTOh29eHwR4C1T/CNGD/iKg/fUZEkZi9CbP5FINrpLe6wI+gyPyDQ2ic72m1gb7aUbzzTr+/w33a1QhGywpsLK5Ebluopsdx+069D9O7h/g/JfRCxXu7MZk/kWDPEroeZ14HefaCyzyElMnXWm1MHnUo+Mkscxe78FUAEWTLo3cPOiGiiIj2AuOKud48svnDpxmu2MDm2v4UjUCImVJf+S3oOwk70eYpPALHxfVy6XJhkWcFe2+Rr0vUhgM6tPIKqbHgkCF6LrY/zvk7PZVfrTqfPR3I/6+Xtm6R8MiPLzP2NZ/SlDrnahD6cUISs8KbCijRFd4nzSscQde3k0UouZwFFEw7xCbPo+HwFfED346BAlD2/vXtAYy/yuW3ycTemjw8ArSlQcRaiY9Cb1CsbrM1mr1f8wF07QbPnIQoTTLJE/F9hEoh0AKVF3sfnh9Bm2F/cu1d2k7Pn6u3lwg9jkSP4mdOgrh28TeKxiuuDLIhAFEjsqjz4tBlgzRZO3CX38bmBSs/lxDbuc2EsAsEWJxJnzZobGSHeJyWJI3UdFg+bva0RlIasoNAAzWjF9XVLkla2bey5Te5lLTR7Q0Z+N5nckvsqJBVo9bbG3PyLznRoMksVjbV3D+gaF2sIIYBAi/zuAxzBtw0wObYPAqU5Tn6VdQhtVibCBK+vrFf04cgawHZZtG3gXhpTr3E7T1+Iu6pZBicJAy3cRyMxDOhZPpbPX/Fllw0m/cIEE3QxA4Q7gdw4N7+fuwRkzvC2mfNcjFLKArC46xwIYujDfJ/KFkfRlsn3T5ZLI32cP3Pf93tRsLMKoAhZQeFuIFtueojAIYcfQ/Q8CUCwntraJ8lyA+APVZJ4mfNAcYpf+yVBi3jKU13nzMETftk9bT8Q65IkZjcO8gPwAW1vAWbOgCZfrUmICX+AbXfOZz9mUs6NcUP69MtApAti7RNMzmNHADJ4MgiZfK6unfznzGk4s+/yY/z95EnWlFsHJOlfkj+vA6gM4Y9wRDz6w8oDK9rpaNBIvwzRspYb1WfOVhookZRo7AeY7IXh3PdHpJRaSRYKCneIhi03l2y3eUgmqgbZq6vnKpGr89pfZieD65XeQWGCyZcsYPxlaWMryyZB+0AQnIxLDEMEW2SzomTFzwxUGhjBJyNRHVgcldfhY++xLwi09MpkJrFnWc/JvhgCGHddU89u9lWPHanIKsn9HklG9L+Nk3Wtfa77USkSIfdYKBueqdmCW4DKdqXkcXM8fPa2RlCErKBwp2hQfeZOztHka14dNj8OtG+qtN10V6117eCObPNXONmVTzvVcZTY7jg3UJpzXvdFfL0sBpHJx9QhtriNH4UYeBdH4WYBomMzR52QjYMQYudGmaUKh0g7NgNt6/nYsR6ZzExx4s1+MMxfcXmm9wMIcMvO1CGIjk1AtBu0OFpJDvbucRXGbPNW3lkGW+MSjwFrnwJH+fYNIde982rDKyHglejL9ba9n/q0kiwUFO4UVa/JNXpy+hh7iZ1GOhGItvUAWfX9suMv8Sy83j0QXTukxvwsR4puaSSS4JLnwWd5ckivlDp69/Cr/fwVdkQEWznKLptANAGavcheYNPktRoFYPp1fOWHN7Dz43+HwIZ3YMcTb8VXPv/v2dKW3MeSSfYm98+QzfLR2ier9Z52mg45o5UinQAAEWrhuX/xAW6ClD4Gyo/zQ2zyVS6EQZn3WbOZ3w5AgBCsDy+OcQOl018E4gMrJkLPSKiV6Mv1tr2P+rSKkBUU7hA1UVq1D9ZdbuyGq5kOde+UHdIe4+1bklxoEetlkkoekGObpDQC4ib2Les4miULmL0k+1rMV3oWA1y0IW1tyN5kIh0/yseUvTK+8qX/jP/zv53An/3W23BoRwpHz4zj43/4RaBzCB9+53aIzq3smRZ+7onRuRWYPS/7XsjS6ac+w1G9u0l923qgTEBrUmretn94mHsuRzpqm+eXdafhPqJrgKiMjpu1G7phj9OqNxJqKdTb9j6WXwuyLShNYNeuXXTy5Mk3cDkKCqsfjV5h+fPjy9rkyCxJj+1GjjjXbAHmrkL0PMlVfbkxYHGUCe6JX6tIEoPPMskGorL5zjaWAbp2AnoOMAtApB2YOCnJw1fx9PoCUpr4Bh8ndRgol7Bj2xD+y6f24O1P9jvr+8FPRvHrX/gJTv/Nr3H/5e7HQbOXWI4AAcVpfmCQydebOsSJv/SPpabtZ9td+0bZiW5zxb5m34PJ1zgynjwpZQ8/kDkty64/wNa++PraiSqe+7/P24So+nvZMJ8lmgcLIcSrRLRrue1UhKygsFI0qAhrRtcks8SJubYNlcq1uctA1+OVY0+f4naYgOxPkeTIs3eP7IUsfcfDH2FHBZVBWgYivo4j4dRB1mU7t3LjHrKA6XMu3ZenUUObw4Vr4zi0I+VZ46EdKVy4+vcQO3+VPxg7ApHcB5QWACsPtPTJ2X1rK30v7PLtmTP8oGhbz3p5fICTfdXkaHd0Sx0GtFkg3AZ0PQ4xuFgh0ioyJrMkH1a2Zk6VBkmJbZVOeZlzdbX9uv8Wq8y7rDRkBYWVYgUWqxpdOXOOS5ltm9fkq0xENvkk90N0P84SQWK7JDAfywDCx/vYk0IS2yCinWyFi/czwXUMshZ94Xn2Mw+8i90OF57n7/ueBrK3ABBo/CiGN7JM4cbRM+MYHlwHGAWH/Fm77gRiKfYaz18BIG1wBF6vfd70cf65pc8py8bEjwGrCNJzlTeE7sc5sp49Ky1xAZ6UEozVRLWOD/naN12TUvZDtKTYA+3WeauneDfCKvQuqwhZQWGFWJHFqjqa7trJEaFrsrQ7EhSBCKjvaWD+Kke/8nwUH+AquJ7dPDPPF4TI3gC1vaXSb2P+CkTrOm8rTF+w8nNimIs72jcAM6chBt+P3/2/NHz8d38Lf/avn65oyJ99Eb/3B/9JFrLsq5RiC+IJIYs3K32a4/2s8VLAe147Uk7ul9dpT6eWVr14P7cGnb9SqVbsf5u3TSlZssPbfi4esYeuCr+0D17lrnf+gLTNuQbHenou13NO6N6+zqsEipAVFN5IVCWEhD8AinRyYYORh8icB8X7gVCbQxbC55ctLnWQWZTa7AE+3uIt7ifRtgGIreWodPp1trUNPuttagTB1Xe9e4COzaC5yxBrhtiz3LsLyN7Ch//5zwMAfv0//B4uXPkHDG/ZiN/7j8/hw8/0y2nXH2E5RfiB+REg7vI19+7hqSB9hznJV/YBfYdksu+vpFZ9EOQLQRRnJPl+wyFyuvYtiJ2/4sgojiPCyHGfDw/xDwDhOFf7aTMQs5ekrv4BUOrw0g/JehJT+hjft43vXzVyBaAIWUHhDUU9ohD+AKhrB0eOiWHWebVZh4SdRJye5eSekYMoTrO/2BdkgrJKPFYpuR+idw9HkpE1Tk9j0bXTU4CB1CGISGdFf7bHNaVfxoc/9Av4paf7IPoOcltLGZ1yVL2dyRhgXThznqP8vsNA2WQyTmxn4k8d4ub0a4YqNryFaxCdw2zBczrYcbQsup8E7MZH8HEvi+R+9jx7Clr2SmLezRa4UrYSuffs4mi8UcMhoIFz4sCdOzjeQCgNWUHhPoPHNp2RFXoBiOIsF2OM/hCABSpMcvIq0sk6cbCFG/oktnHjHhA3uScLpM26dNoTXMiR3AfYr+9OQx8LmL1Q+azrscpg04kTEL27gVwa5AswkZ/5Es/pA3HykUzuaxHrAcolJmD4ODK29XD7muauyP4XJU5e2iXWhgb0HfZM00bmHHuvyWL7H4QsN/exzS51mO/Rxvfx2wFZEJ1DXOSSOgzMXV22u5u7J4mt6QO0KoekqghZYdXALBtIFy4jGduCgC/4oJdzz1DRMA8AII6A5agidO0A+QIQ6crgULHp57k3BAQ3aXdX5Bk5CH2hUpKc2ObVbkFA9ib3Rg5EWRYpZoDCBI9FCrbIjm+o1VAXb7GbgmNHKYkccxwN6HtGjoM6wk6Q+HqOmt1+61JWHl8SrNNLGkBqP08UcScx7eGrTk9mIcc2BYG4Hb36QaMv1cgOwh9YOjKuh1XeM1kRssKqQbpwGSPZkwAE+lsf3p62TmKqayeThk0Cnpl3YIKeOcstNDs2VaxtABPf2I+Y7HZ+kivyXCXMdik2Tb4KEe0G9bwVQvi52VDiMYhwJxBq4WPGeoBoF4SeZQIVAfYHhztlbwoAY0crVjJfiJvel01X8nE/NxhqWw+knuYS7ZkzQLkPKJusWWdvAtEuXn9NL+kUJ/HWbAFmTjmk7CQsW1OehwOZOjdGal3Hjot7VZyxymfsKclCYdUgGduCwbbdSMY2P+ilrAh1rW2ySxm3zjzIBR6t/S7LnGzAnhjmlpYiIGfTyT7FtpyQfpkTYCPf4GOHOwAIjrDhhygbQNsGCF9IliEPsc4b7eRjJh6TkoMFKkxxG0yAiTXUylLE9OlKmXZyP8sikTVMyDJ5h+xN0OkvcIJx8SZb3xLb2Bc9dxlizRagbQPo1vc4Ok4d4IRgci8n5DLngJYUe67XbKncGwAgwe097eQc31SgNMsPCzQ/Nms53KvjvFFQhKywahDwBdHfuu3hkyuq/axdO4HJk0yimfOozLkrMxGkDnIBR/sgkx9ZXNkWH+Ay6GiXS//dz0USPbuZpCdf4eNNnuQ/O7Zw0s0m8MmTHJn7/BxZ2j7iiRMQmz4odVij0p0ufZy9w3ZvCktjX+/sBSDYAmTOgKZeZ9If/giQOgBauMbWPQAYf0n2ojgH+AIQ635KdqI7yg8bX5hLpC/8FUfU7nuTfpl7VggC8mnZ3lNicRRI7OA2og/xjLyVQkkWCm9q3BPdup61LXmAHQJylpwjB6QOs+XtwvMcKbtn4w1/lIsl8hOAkePZddmbQLAVRGWI5H5uSj/+kkv22Ae6/UOIvsNy2vQ6b4Wbe5L1zNmKw6L7CRBZPGYq1MoR89iLvF1+nG1r0S7WeNMv87ESjwHjxyD6ZON5R/cm2Yx+Coh0y+uV3uLUoYpWnRjmaNi+N8m9TMLjR3lCiWuUE9rWS381a+0Ubme7XyBy1xV2q7FCz4YiZIU3HWwSTsW2unRrIBXbCn9Vz4VqeNpIOv0kznvm3wFeuxvZxRG2RatrJ8RTv81JM8CbFJs+xeObdnwCwi6KgIDInONIeOa0nF8Hp/BD9O4BOy9u8Win2UuyqISc2XzQc0DnEEe5iW3A2Is8dBV+oG1jDbmK4V9micFN/qmDlf7I8rzOgyg/zpV5+TFP0QvIAvS8ZwYfwIUbZFlOZG9FVvAIAAAgAElEQVRb8xy4kpxIHQSd+hOuYOx/+90n5lZxYk8RssKSeBSdDzYJh3wxJGNbAAA90Y2Y0W6hJ/YWz7Y1128XLugLsmn7gmzY88GaX+5KJHaQLVq2LYwsTpBFOjjyDYRBqYOcaEtsYzLzBYBOV9Ocnie5IU/nFmD2smzoA5Ya2tZ7msNT2wb2LVslJnc9y9ubeY7A3Y3kux8HApEaXy8FYxD58YqDo3cXJyDlsFdRSHPJtn3Nbet58GpLivc38hCZs0C4E6JjY517IgtdXE4PZ2iqJaeDOElOwWRsN6+/22GmqzixpzRkhSVhk1e6cKXhNmbZwGjuHMyy0dQxV7r93e5XDTt5mIisQ8AXRCo2hLlSGp2RVM22Ndef3A/R0uf68wAn7FyTLsgsgeavubTlY+wo6HlS6rr27Lzj7CQA+DX/xv8LmEUm7fGXuDLNjcRjrP2GO2RDH5P9yGVT9kd+v5xE3c6TqO2Wncn9/BCYeq1mW4TamPwNOQ1k7rJ8KPiYXCFY2xYBXldczuyL9QLwcTXh6A+5S51AJRIPtgKd24DWPqdRvz1XkEb+ga87fUz6nTcDEJXE6MIIAOIkp2wyJPrfXpErxo9KT/adyQ2rObGnImSFJcERpFjS+dDIrtYour5Te9u9ssXZyUMbBCDsb4FArVxRff0iEK7onD1POmOcPBVf6Zf51X7HJ5xIjEyD+xjbTgvA6WrGJzrA0kN+Alg8JV/jfd6oW/grvmCnLadLq41vAEQQIhpjcnMkiMOVUUihdo6K7WRg2QLSL0kL3mn2KkNwxDt7AUKbrbT+TB2qHGfwA1xSXZrja+3dA6SPymuSvSa0Oe57PHOGE38zp7iR0lt/E4glOdIGcRIzfczbG3nmjNOnwvs/QXNyw2rWiZeCImSFJVFNXvXQiLQbEWgzJA/UEnqz+9Xbd6nvAr4AOsI9TgSejG1BRhtFItK//PXXIwj7lVgEKjPjzCLbxVqT7Kzof5vnMCIQAo0eqyLy/R5PM6hcecVv7XN5d/fyQVqS3Hxt9pK3WITkmuxy6THZBN7uQTzyDfnddgh90ZECRLidy6DtcxC5koT7AfiAQJir6JzCDmLSjXaCjEXQ7X9id8fCNaBzWDag3+90c6PUYY6Ik/thz/1bsuCjWblhFevES0ERssJdoxFpJWNDiAe70BpMNLV9NaoJvdn96u3bzHfuz33wI6PdRiKybmkNvQ5B1G10k/5xJQKsImNA6qY2ibqJ3G7kLidp0LVvQfTsrnQt69jMRJ05JaUO2WO5MOntT1yaA7qfZMnArpzr2MxR6OCzThm3M4S1fZBlieIMJ9VmL0BoGVBhCmLgXVI+0Nmm17VTRrtwmsZTMAa09kMsjgLk4+O5J4TIeyD8AaBziG+CPYdw9Id1I1vn4VSVQK2L5H5+G+jasfR2qwyKkBVWjOYTfYRFYwatwc47Os9KIuKV7JuMDdX9rje2Sf45CM3MIxZox3jh4pIySdOtOHv3SOfBXuejmskXnUMykefqBVw1eVn07GZddnEMiPbI8uajMsGYrTgi+p6uJO8Gn2VJoqy7pmT7APiYfPsOg7Q5CC3DTYDaB9m6Z+jA/CUgsgYilgTWbIFIHwdsLReCJRghuP3m2ieZjC88DzH4AYj+t1ecJpOvyfaZQg491etP8kgfaxzZ2lFvnQRqvX8XSmzje/MQyRaKkBVWjGa13HrbrcS1US8ibnb/6n3NsgmAnH3rkiv8iAe7+M8QP0Tu5qHgPXhQRqxneYR9IOx6rSaHUJE65IkQK71+4XxGZomJMtoJ4Y+y51mOhBKhDvb7FqdcssV+QJ+XkzRsC9shIH9bzr4zIDJnWCIZOwpRmGLJYOJEpWdG/9tApSxXDZ75Es/J69jMkz7GXmTCD7Rwss0uaHEjsZ2j7fiAE+XD5b5wsJQk4WjvuxsTuhsPoWyhCFlhxWiWpOptd7eJuZUmEG0Y5SJmtFuO57i/dXvNNraW7P2MXRgz2k0kIgMAAIKFicLVFVkBRSBYSbQNfpBtbsm9FctX5iy7EgqTtSSSOS/bbpaZrHv3gKZPQ8An+0AMsCQwc7oyXy6Q5LWmDrE3ONYL9O6F3QODZ90NAkYemHrNIWrRuwfwhziiTe5ztqfJV5lUW9c5Q0shAq7o3SdllgB7hVEnsRZnx4Vo6WWyb+2riVw9/u2q/e2+F/xAeHJ5kl3F9rZGUISssGI0q+XW2+5uI85G+2e0URhlDRntdo2XGACCvqjjObb/XA5m2UDOmMWiMYOR7Ek8kfhZAHB+XvFDxU0QC7LZe7wfgI9HI4kAEO2uHT+UeAwid5t16OnXIeIDEJt/AYh2ghZvQ8AExl92OshRrAeI9bDOOn6UfdPBa9yhbeP7geIst8Usg6NaTwQeqTw4hj8K0f8MaPJVZ/io6H87k3xhkiUTp+puf+311olQneSlnLq9ZLe2ehFuax+T8RIk6/Y6LzVTbzVCEbLCfYWbpO+k6MQbsfY7+yUi/dALRSQi6xrsx/+r25FxM+dOFy5jungTOxLvACCc5GRLkBv8uB8KzRzPE/21D3I0nE9DlLKyB7BEvQixYyNHlHYzejlVmpvOv+yVIso6R67JA0xKGS5IsRuyeyY0j/4QVDYhUvsrFrzqwovEdq8MUXZpy8GYrLozOaqWETqZeuUNoFpisBNuieGGCTzezq74q+juTWn2D6FUYUMVhig8MNQrOmmm+GO8cBEX5o949mumgGX5c5swyiXn/MnYELqjGyDgly4PJizNzCMVG6rrrW72/FytdwVi7VuBxDaQaXg7xgG1TYtEoNKMPs1FJXTre7KD3LMVK9rECdm85ziTYNfjcAo8ILzrSB6EWPc0sHgbIJMJsqxXpA/wnD+7MAOAoy1j4gTIsrgoZPyIbDJ0nrfJnGWZQ2rOSB8DzV8DGQUAPn4AuQeo1kPmPMgX4r4Zy91Od8e95IGmh9CuNqgI+RHFvSp5fiNLp5vVmBv5kXtjgzDLJgK+QM2xGq3bKlvQrLxn+8nCNSQi/Siai5jX057z11rmLmEkexKDbbvvyFvtwBlVX+aCDn+49jW+pmmRH2SXFMs+FGJxFECARyrZg1EdGcHV7Gj0qHcGnWMbKwPjx5zmPu41UXyAbXTuZGLmnEtb3sf7e0ZFHZSR8jZujGRLDL27pfa7k6+3Y6Pr+vZ6omsHiccgyGTij3a5BpTWKfpwRcWi/5mHLjK2oQj5EcVSybNmSdYqW/ekOs4sG06hhbcgo57GXGtJq+dHjgcTOJP5PrqjG+p6lG25oSOURDzU6bnmnDGDsH8AayMbkNFGcWH+CAbbdiMVG0I00Fpzfu/66hNvIyml0b3o6n4cvuAnWUMWfu4L8dbfBGI93tf4amIRASY0cHtNSh1kPXf+SkX37XnSK3s4JAqg5ylg4SqobQP3RnYa5jOZisFnKxWEM6e9r/6urnQen7TT7Gg/sHDN6WTn6LfSXyy6n/BUJ9rXR5OvebvfOZfq0pvd8sNSxTgPYVTshiLkRxRLRWzNkuyMdhM90Y3yeHdu+0oXLsMoa9ALxaYeEvVIurrApDXYie7ohiWIcwjJ2GakC1cQDbQio912HBb2WlKxreiM9Mmm+JtAIAA+xIMJ2K/2tVV9jROaM9pNmKSjaC7WPARsgs5oo1gTTmK8cBHJji3wl3JAICgHmx5wRc719U8RCHntYuNHK9V99XRZh0Q/wlH09OvcEyN9jJN8rvJukGAvsS/EUbVjYZMark3qnUNM9NkbstvdBW4u7w+A2t4im/H3eaJeEQjVLYgBIDXqBhJD3QGlTRbjPIRQhPyIYiniaPb1OhHpR0a73bAtpVW2GkaEFTIakqXIt2XC7U4eErUFJss5PQK+AEZz51ytNYecaSQZ7TY6w0ks6JPojKSQim3BjHYLXZEBLBoZTBZGgBjQEe6pWddSbxeJyAAEgIKZhVk2kdFGa66pK7LeU2yyLjYEjL1Y6V2RsqeL1E9O1sCeniy4eVGlh4Ws4rPlhc6toNEfsF68cL1SquyuCjQKwNSrQO8+wB9gCcPIQ2QusIxSzHh8yGTkIIyCNyHptAFNyk5zi00VcTTapu7U7keEfOtBEfKbECuxrdWzkAEVwu2JbqxrNasmMvv7euc1y6askluerFOxIW6sLvzOQ6IRSVYi6zUg17m7I+sxr0+gLdQNAJjRbmEsfxGJyDosGjMYbN+NjDbqJPbc63L3T+6LDWNau+GRYma0UXRG+gCU0RVZj8E2zXNNfp/f+bk3Ngjh81ea2Sf3cRl0dbOiOvDoqG5ysqPH3t1cgJE6zNY1U4fwBQEChKtU2QO7ECTUAep+gnVgPVtxW0S7ATIdH7LIXKhtgVkzS2/p61DwQhGyQkMsFQ1WN3avxlJRePVx04VLmC7exMa2p+pqr8nYFoR8MXRFBkAgkCCkCxdd+1cIOx5MoCXYAe7cVomsbYeEO3IXEE5CLxboQLpwpUrW0GoSe24v87yeRt6cc0kxFTkkHuxCyBdt8ODzORWBQJUdzolw60sWZJa44s3WjAGPFODosqZRafoTTbg+b9wrwm1Hw9iPgN7dQMdml9siBMAlPdTzELvkhIelXHk1QRGyQkMsJSO4Cbf+lA3h0WKXOq59rNZgwkOYlVLnIXRFBqQ8sg6amcd08SbsiNmOrntjgzLRtx7J6Gaki1c8kbVZLmFKu47p4k30xjYhXbgqI/xRdEc2IBqIO9fEEkt/zdr57YK9zG2htTDKOhKRfkfjzupTaA+tBS1xX+tVBFZu7DLJqcw5nvCc3AfPlOrqux8IgkZfqpMQa9wrwm4tyg8F29d8eIkrqXfeWjmhniuCTM31WWTJbd9MUD7khxTGYhHn/p+vw1gsvmHnWGoKdKOBpLaPmGBhoTQJgBxf72ThmiMDuI9bOVYAOWMWBWMBAhWnBFDGeOEi1oSTyGhjsMjAjsQ7kIxtQcFcwLWFV9ER6oWAXyb6tqBU1tAb24TBtl3ojQ0CIOhllg+G1xzGrDaGkexJTBavoSsygGntJqyyjpSUKGKBdr7P5ZKz7mp/dEXS4UgcILSFujFeuAQug2M5ZiVN9Zdtnp7YBpG9zn/vGKoUc9RDcj/P6Usd9H62nEfX2ebA8l3VmkG1n9r5TE7VliDLdB4YDb3JjzhUhPyQ4vJffBsn/o/PAxDY/hs//4acYyXtLm3YJBoPdqGvZdgjbdgyQE90I1KxoZrI2ipbKFl5DLbvwoI+iZ7oRsSDXR4pIRUbwqnMP6KvZRiJSB/m9TQ2tj8FQUBWn0RfbCumtRsYy1/EzsQ7HWlgRruF+dIEBtt3Y0a7hWRsC4Y7nkZnJAUCyQQmJ/bShUswyho6w6wpN3KIVJdWx4NdrrJqIB7sQtgfW8JXPYSimUU00OZIKsvf4OPw9o7wRpWAkI6N/XU7njWTELvnSbOGTgl4S66r5wXeQzwskbci5IcUWz72HgACWz727jf8XCspDklKCWGicBWtwYSMgm0ZgBNemplHJNACv2tCR3WSsCuy3nE+sLtCSGsa0B3dgK7IgMutwETdFuqBz+dDIjIAvVwE4INf2KXVAwj7WzwPiK7IgONl7gyvg1k2YJRLSMW2YkGfRKvUo2e1cack247qrbLFiT/SnWtsDa5x1tobG3Q04uq3DPuh1RvbhHk9jUigBU3/KtYjN7cvN97v9LtAx8ZVUUZc3ykRcZoQOUhslwNjD9+byNyNVXAfmoEgWkrt8mLXrl108uTJN3A5CqsRtn2sujqtEVFXtt8Fn/CjLbgW0UAcOWMWrcFOTBZHEBBh9MTeUhNlDrbt8tjs2D7GxDxVvIHWYCdaAh0wyyX4fH6kC1fQG9sE3Soh7I/K9WxGwVhA0Vp0ij7qVf0BkGtKoKJXb4aAz9GAzbKGoC+KvDGLlmAnZrSb6Iqs9/yciAw0H+GiNrKuvq/NgEy94l0G8Su+7G8BMmS/CztifvmukmwPS3S5FPgajj+wZKMQ4lUi2rXcdkpDVlgWjbTkRv0bKttvQUCEEA3EUTDmEQ92ARBoCaxBItIPq2whZ8xisjCCZGyzs8+CPuForunCJVyYP4IZ7Ra6o+sxr6dRhoWcOQu3WyEgAq71XIUQfiQi/c4xzbKO0dw5AOwKKRjzIGKJREB49l00MgDKKFkFHJ/6OyzoE2gNJjCj3cSF+SNY0CcRC3ZgvHARneEUMtotWGXLcw+ssuVoz9UI+ILoCPcsqdEvC0drfZkJJnWQK/KE8PS7EIHQ3Q/0rKcBP2RYzYNN3VCSxZsYd9rs3YbbaVGJZNejYMwjFdsCAqQ74hYSkQH4fX6YZV1a0daAIFCy8tjUzqPeuyL9yOpTmNcnYJR19MTeIi1v0aqCCqAtuNaxy+1IvAPz+oTLkrYZRlmHTzYFKhgLyJRuu/zDW9Ea7ETWmIFZNmoqEkcWXsFisB2p2Fa8NfFuxIIdGCtckLrzYbSHejzFHanYEAgWgEpUnzMySITXOVV79e63fV/txN+K+oVUSxfjR52y5nteRvyIlCU/DFAR8kOIe+WwWHGHsiq4nRYZ7ZZTEhwNtmFGuwWrrGO8cAlj+YsgWNJ9QZgu3kS6cBVW2UBnOIXxwiUQygj6omgL9aAzvA6JSL9sHBREItKPGe2mE1H2xjahJbgGqdhWbF/zDGa1MVyYP4KMNoq+2DAMq4igLwwL7IwI+VuQjG3BE4mfRTI2hHl9AmWUsWjMoCf2FiQi/ZgrpZGMbUFWn0aqZSt6ohsdHTmrT8ley6MI+sLQrHyNgyOjjbnuTBklKw8IgXk97Yr2699vu2HRSv4daiI+2xmR2HbPo8F6x6t0V9O9ndYaoJltFBQh3zHuh+2sEWyHxeW/+M5dHeeuXpmr0BVZj7lSGkZZw6w2hq7IAIK+MJKxzdjY9hQmClcxkj2JicIINrY9hWRsM/SyhoniCEayJ5HVp+GDD5BESbBglvmXN124LAn3NtZGNuDawquwrXA+XwCJyACGOw6jM5zCtHYDYX8rSlYBeWMe08WbyGijACCtaWUUrUWPRGGUdXRF1iOj3UY8lEA00AbNzKEt1I2xwgW0hdY6MkvYH0fYH4M97gkEJ/lYsfwRfMJfQ8CN7vdK/h0atSe976/ktoyRu11X0qgh4EdA9rgfUJLFHeJ+2M4aoRmHhbFYxOW/+Da2fOw9CMajdbe5q1fmKvh9fleTeK6oGy9cQjK2xTWtg+WNsnQozJcmsKl9D1oCHQ759cY2eYo+gMrw0c5ICj740RMb9Fjh4sEudIR64RMBJML9sEhH2B9DzpjB44mfBiA8EkMi3IeQPwZ3Mcnm9n0I+2OO6yIZ3VTXsrc2sgEAW+zaQ72Y0W7JUmnyFLysjaxHR7gXAJCU628k/dyradr3FbaM0bpOTpxewvnR/4ySPZqEIuQ7xP20nVUjGI8u+xBYyQPDfmVe6S95bQn05Tp+XIHe6CYm0shGZPUpxINdSET60Rnpw3jhElKxrR7C3Nj2lNSYLSzqC4gG4uiKDEDAh1ltDG0h7k9RsZf5ACpLrbofEzJJaMshqdhWz0TpkpVHVp9CX2wIZZTRHV2PiL8FQghZWLIJgM/TX8NujmRb924snkJfSwkdoR5MFW4gGmj1WPwCviCssolEeB0AX8MH3kr7Td/NCKy7dUvU7O+2j1VbyWoGkj66DYHuJRQh3yGaIcUHiZU8MO70l7xxCfQaj3cY8DlJMU6IXYIPAZRhYiR7EiFfzLOGksUe4pJVkLJFm1PM4fcF4PeFwJHuOvgQQMFcwLyehk/4nUo/o1xCVp92jp+IrJOODB8ivlZE/G2yKKNd9jG+hc5wHxLhdRDwY7xw0emvISDQHVmPvDmPsfxF6GVNPjTYLleGibbQWrgfaNXFH3bUX9vw/rJnjW5yrkfWS0XTy5L73XpxV7D/igeSKgBQGvKyuBOt+EHqyzbsB0Y9ucLWIY1yyUmc1SuDbrSfWTYBVLTPyuQObhx/OvM9To7FhpDRboNgyUTeZQgIhzDs/e2eEXFJcLdzZwGUEfZHpL2tDJ/wI6PdRltwLXzwo2QVIARLEVcWjiMZ24ye6Ebp2tiLoplFItKP4Y7DSETWQUBwFAwfMqXbAAjhQBxjhQsgELoiA5gtjSPkj8qObFvQHV2P1mAndKsAQhkFc94p2e4I96BoZj06sduLnNFGMV28CYKFeT0t99tcowHb94ArBL2a870YS+VBM2XTS2Gl+zcxkFTBCxUhL4OVvPobi0XMnr6KmVcuPTB9uRm4tdF4sKtxo5s6+9kVZqO5S0jFhhAPJnBt4VWkWrYiHupEazCB7ugGjzVssG03NrY9hZZgB8oglMvcmpJAiAcTEADG5ZoG23ZjIL4DRTPnGafUEepFNNAGgJxoG5LcAcjy6JuO06M3tgkZ7TY6wkkAhBk5sWS8cNFpKNQW6vYcvyvSD80qSNcIT7HO6lNoC63FeOESeqIbMauNy4IWE7FAW82EEXeUGgt0OMlMgNt1jhUuuHTmDfD7Qk7EW/2m4rbxNYPl3nTuVjZY6f5Kplg5FCEvg5W8+l/+i2/j+gs/wru+/ftN73M/YSwWMfqdlzHwC4dRXd7bDJKxLYgHuzCrjcEoa1jQp9AW6kZr+xqQbCKUjG2WvlzyaLAlq4ip4nUERAjdkfUgVLTr4Y6n6+ivliwpZt13qngdFhkebdqWGlKxLcgbszVeZTspOC57U+iFotNnw34gDHc8ja5IP27nziPSOoTZ0ijC/iEnQWlLLNWtRgkW0oWrsCecmGUTJbOAKe26s75kdBOirmknZZRrSsndU7Kr5YiMNgofAoBzbxtLGfX2t/FGzkVUuLdQhLwMVqIV2+TtD4VWZWRsR/vP4N9i4z9/+/I7oPaXuSPcA7NsQC8U0RbqRsAXxHxp0tNUxy4GsTXYRWMG8WAXymQhEe2XzeHXOuSUiPSjXDY9DYfyxixiwTWuSrwwWoMJ7ncc6EB7qAcL+oRnHh4AV3S5GWZZR0kONC2ai4gG2lx9Njrhgx+JyDqMFy6hr3WrM+Yp5IuhOzKAvthW6LJrnPvYADBRuOqQO79hlF3FKUy4dnRt92S2e190Rfo9s/y85egm0oVLsuXoehTMBZejhDXoZgbBurFqnBkKy0IR8j3EG53oa8bKthTsB8bAew80vU+9MUTVEzxag51oCXbAncRLxYYQ8sXQGuxE0VxEtYXMKOtoCXSgM7wOAGFOH0ci0o/RHDeejwXXIKtPoSPUC6tsoiO0FoQy0oUrSMaGUDCzaAv14Kmu9yEaiENAgGTLS9vZMKVddwhyXk8jFmhH2pEQuDJwRrvl/NwX24r2tWsR8EVgwcRE4SrsRvYdoSRAhJnSLVlBOOS4L6yyhaw+jaAvjJKZQ58sPpnXJ5A35z3RdcFcQNAX9czyAyrjsDojfSgYC54InKWaailj+WndNu7GmaFwf6GSeg8RqgtCzHwJky+dgZlvrvrJfmBQubxs0tFOPnFi7GnPL7O7kCFvzKJk5kBlQnuoB+WyBUgPciLSh7HcBdc+uzghFupFIrIOc/o4IrIDW96ccyWlLkMAaAt2o2jlAB+haOUwUbjiSZQRLPhkP2A7OSfgR9jfgs//5XN4etfPIByM4LHHtuP5v/6yrPbbLNexCVOF6+iNbcJwx2GkpAVuWuP+y7PamHOu8cJF1oplfwwAsv/FFeTNOeSNWdmsvoRwoBVjhYtoC61FRyjpuW4CYV5PAyD4EJCeZh/MsuH0e57VxjEQ3+Foz3ay0J10rZeEvZPe1QqrDypCXmVYKgqu1rMtXcf08Qvo2LYBgZbmfaXNJCqXirjskfcL+gTaQj0ACOWyhVigHUa5iDmtkozrjKxDLNAOv88v21pOoCOUdJJbtuWtMgQV6IluxIw2iq7IAHxCOPLAjsQ7EA92uRJlAm3BbsyWRj3JuX/42rfxh//uc/jX//mj2LFnM86cuILPfubLWB/fiQ/90odkkyOgr3UrAB86I+ucRGDBWEDOmEOXbNfpTsrFg12IB7tQMvMoWQVZaj2F1mACY4ULVX2T4UwW6YsNo2jlPOtMxYYwpV2XHmyvRm27PJaKalcyDVvhIQIRNf3fU089RQpLQ88W6OznvkZ6tkBGoUQTR0+TkdOa3v/s575G/038FJ357AtkanpT25793NfvYI1fJz1baLiNYel0a/EcGVb9NWSKtylTvE0FI0eGpVO2lCHd0ujW4lkyLJ0m8tfIsHQyLINuLZ4l0zLp1uJZ+sHYX1KmeJtMy6SJ/DXSLY0My6CSWaRcaZZMy6CJ/Ijct0SZ4m25lrNkWAaZlkmGVXLWVvn+HOmWRrqp0ebhjfTcC79DPxj7S+e/5174HdoyvIl0S6NXp75NtxbPUsHIkmVZzrpuLZ4jU67XsHQyzJLrPpTItAxa0KZIM/JkWAbNaRNkWIZzv+x1u++bvXbd1DzrnsiP0A/G/tK5T9X7TORHyLTMhv8+7jUrrH4AOElNcKySLJpEs95iO/oc/c5xlHUD08cvwNL1ps+z5WPvwZ7PfhKD/+s7cfMfji6/7XOfciJmM19CaW7Rs856617Ko2xjuRFNziu6L4qMNoqy1Fzt1+ye2FucAaYj2ZNOYcfOzndKG9lFJCJ9uLbwKtKFy/CLAMKBVtkXYj184F7H8/qEY0HLaLfAr/t+rI1sQFafQtHMoVy2EA8mcCbzfUwUr2Lk0g3s2OONLHfs2Yyrl0ZcvTS4kVAZlud139aU04UryJlzALg7HODDeOESYsEOzOsTAAhlMiEA5I0sBHxc/p27gI5Qr3Pf7DeNieJVCPjRF9sKH3xIRNZhsG0XOiOpmntt9+7gUVD1cS/7kCisHijJokk060e2ZYX1zx7Cxc9/A6985osQfn9TyT7blrb1U+/H7OlrWP/soSW3r04iZkduY/y7r+KVz3zRWWe9dTeSRZZLGlply5Ocawl0oGguyATLzm0AACAASURBVPFHox5vcMFYQNjfiqScUZeIrEPAF0R7qNdTJt0TG0RrcA0EfBiXMsZgm4aOUK9HytALRawJJzGjjaIj1AO/CKAt1I14iG14iUi/LHvejC1bN+HMiSt48uCws/YzJ65g4xb+3gcfpmVv48G2XegIJZkofT6nmKQrMoCrCyewGJxBX2yrq7yck5LQ4JR+90otvSXYKZsTxR2bmn39PO2EZGKSK/g6Qsm6tsNmknBKonhE0UwYTUqyaOo1v94+I1/9p2WlBxsjX/2+I0GYmu5IH24YhRIZuSKlXzxNRq7oWtvXyMgVqTS36FmnkdNo4ugZj2xiSx0jX/0nZ1/7fEtJIBP5ESqZBZcsMeKRIiby18gwdflqflbKEbqUGQzK6/NVEoROlmU5n1WOadCNhVNkWDoVjKyUKgzKljJkWIaUPAwyTD7OxdmXSDPyZFkWGZZBz//1lyk10EPPvfA79N0bf07PvfA7lBrooc/9+b+nifwI5fV5j8xhWAaVDL5fC9qUs37DKpFhGZQrzVbJMd7rdksy3s9ZTpjTJujVqW/TnDbh2ceyLO+/bZU0Y0sgCg8/0KRkoQj5LuDWixuBCXFpHblynLxDkm5ydiM3PkPabJbO/NHfkjabJSKiiaOn6Vv7P12XSI2cVrNGPZuns5/7OhkFzUPOpfnckg8dWxu2LKuBBlqi2eK4h3TmtAnKljKULWVo0aURjyycpIn8NZrTJqhkFmgif400o+AipVIVMZYcQtRNjbyasu75biI/Qv/2T36VNm19C/l8PhratoWe/+svO2t0SNYskWmZnodELdEaZMg1m5ZJhlGSxK9Lkq7WhEfIsEpSX9aryNXw6Mi2/mzDPq+XuJVG/CigWUJWksVdoBkZw+2EKKSnEe1J1MgBznEIGP61/wU+vw8D7z3o0YdtRLo7cPGP/96RQrZ+8v2YeeUS3vXt34c/FHK2s+WHTR/9GVx/4UcABLZ+8n248CffwNZfeR/WvWcvjv/6H2P3H34CgEDfT+/Cic98AZs/+jMIxqOwSgYmjpxCz0897snm89h7OOOR2BkhZB8KH+KhLsRDCVQqAQUE/CCUkdWnEEWblCA0JCJ9GFk4iY5wL7ojG1CGVxKJB7swXbyJtZEN0Mslp0FPPJhwSriLZt5TYh0PJpCI9OPT//K38X//6n+VDYTi8Mk12B3hbG/vtHaDrWvSLxzyRR2pgN0etxD2t0h5Q0Mi3IcwWp0y8o1tT6FkccvRJxI/KxsOlZ2pKGwBvCLvXwBm2UQ8mABRGbOlced+AhWpojWYcDVnqi3Jduv6qgrvEUMzrE1vogi5majXve3t754ky2icDXfLALNnrtWNYvVsgc4+xxFyaSG37PlLCzk689wLVFrINYyk3eedOHqG9GyBZn5ylb61/9Ps4NANjpJl5M6RNP+sZwt05rMvkL5YaCpSq47sXp36NrsuTI0sKVewFKA7kWLFTVFyfnY7Jiqfm1WRMksbc9oETeRHSDc1V6RachwUbunEdkjYazOskkeCcEfItoTB67vmrKMib5Sca7G3vzj7knMu+zx2hJ0tZZy3Afe9qidZLIVG/w4qkn44ACVZ3BlWaiXTs3kmr2y+7veluUU689kXqDS3SMXp+YbShWWYNPOTq3Tmj/62RkfWZrPOfqam0+3vvkJGQSOjoLH8IMncuy6v5m0USq5jFZu6ByN/8/1l7W9EboucUefvZz1/L5kFypYyXt3Z9V2mOEaWZdWQHhPtOTItk/L6PJmW6SHZOW3CkRts3drWbm8tnqOSWSTd0mhOm6iRBEzToKKx6ByTz21UZAfTcI41kR8hy7KooC/Q7cWLjjzhvR6vLGGvyXuv9Bo9fSk0+ndo5t9H4cFDEfIyaBQJV4gsTyNf/f6ykfKZz77g+IbroZJUKy57PCOnkTabdYjUjn7PfPYFWrgy6kSuhclZGvmb75OpG2SUDMqNzZChlWqOZxmm1IuZiL+1/9M08tV/qnMP7GvN0+y56w1JvhmYlkk5fcGjxVYns9xe4nqarWbkyJREZZNmRZN1a8y6i/xKlNMXaGThJGWKY1XbGdJTXHLW5t7PnUCzk4rVkafXZ2xH1mMeH7IdRV+cfYnmtAmy6iT4bNg6uopw3xxolpDftBpyIzvY6HdexvCnn8WNv/sRfvRL/wF7nvtUQ33YLOoY+vh7AMD5sxqBljB6Dj6Gse+dxPn/8vdoWbcW0Z4OtAz0gqwyLK2Eq1/+n47VLNASds7X/+792PPcp7Dpoz+NQEsEF//0G3jlt74AABj+358Fmbz/jRd+gC0fezdG/vsRDLx3P4LxGIzFIjKvX0Xm1Uor0ANf+A20DfahNLeI+fM3nDahVCZoMwsAAet+bi9u/Y+XseXj78HU8fPoe+dTfK35EjKvX0birUMIREN1r9WtZ7onOVc0UR7HpJc1mOUSfPBDs/KuDmibQABuLJ5CR7jXZf/aBCG3tavd7KkklSbyfoT9UQR90aqp0Fz9NlO6jfnSBAbbd2G8wP0yuiMDAOBpyWmSDkLZ06lusnCNbXWR9RhzHdeuxAO847BiwQ60BtdgXp/wTLN2IxHpBzThaPDu89xLLVhpzA8X3rSEXK+tpkPSfyMcMlyqhealL34Tc+dv4vF/8y9QSM8iBlGTsLOTa0Mffy9++jt/gCt/+f9hy8fejZv//UX0v3c/cjcnnaRbNfEH41EMPHsQr/zOl5B6x1s9a/aHg5h86Qymj1/AK5/5okOqxfQstn7y/XVbgQbjUUy+dAa5m1PQF/LY/K9+Tn73cxj9zo+x/oOHcPPvj2DuzHUAwPpnD8HSTSxcvImW/rUw8hoQFo7HtmTmEA20w+fj+iK7XSQnufqR0W47pcAAZOn0JNpDPQj6wpjX0wj7YwDK6I4MyCkiFjrCSXRF+kGwizKAvDmP8fxFbGx/CpXE1xr5904QCEVzEX2xIeSMWaeXsJ2Ys0uh3Z3T+mJbXf2JeX2alUfOmMNkYQSplq2wyoYzJWRtZIOrRzH/WVvCHHD8wW2htZ574J4iEvAF0RUZwII+gb7YVkxrN+p2f7tbqE5vDxmaCaPpEZQs6mGlXmN7e1sOqKc7n/3c1+hb+z9N2etpR5u9+pXv0eTL56Rn+GtUms+RUaiVHGxJwa0hu2HkNJfvOE9Xv/I90rN5mjx+zrG26bkCmaWKvmgUSjW6t6kbtHBllAxNfvccf2doOo189ft0/k+/wZLGYoFuZb3Sg9sr65YJGpX9ure5nbtAE/lrlC1lyLIsR8N1W9xsndWWEkzLoKnCDT6vycm92eK4K0nntbLZ6xvPXXEdm6WKhdK0R4IwPOcukVvDNiyDdENzSsYzxTEiqk2quS1y1bD1blvLdidA7cThvdaClca8OgClITNW4ppo9ji2t7faoVDvHEaxRKWFHN3+7isun3Ge0kdOVxKIz32trlOjXoKx+nocn7Mk9NvffYWOfPyPyNBKlDl1lfTFQg3ZN0pcGgXN0cTt74ycJotbSnT1K9+jklZwEnAVHfQcFYxsjU5cr7Ch2oNcTebVLoVM8XZVQUrJIU97P3eS8NbiOensKDnEaBeSVCcAS2aRTMskzcg7ScBGune2lHHO5yZc+0GgW5ok1hIZZsXB4bm/0h3SyO1xp2g2Majw4KAIWeJOG/AsdZyJo6ebOqZNnqW5RdIyC3T1K98jyzCcqJkTeF/zRLc2TN1wCkD0xQrRu9dhGWaFQJ/jajujUKLSfI7OfPYF0jILlDl1tcG6vu40QMqcukqWYVLm1FXSMgtO9OxOQk6dOE9aJku3//EVpzGQTU7V1i+70OP24kWHnG00Io9GLoVMcawm8ccJM7aMTeRHaKpwvSrSrUTUC9qUTPTZEfM1GSlXol/d1JwEXeXcFceIHSETcSWftwGS7cYoOY6OOW1iyUSdfdx6VX13ApUYXP1QhCxxJyXP9eCOgtlC5j1mvUjcTZ7pI6drKuKMnEaWYdLVr3yvhuAXroxSaSFH+YmMR2Lg6rqvSw/0K1SYnJXkm6WpExec83oJ33vtZsmgzKmrZOoGleYWHYeF/fAwSwbd/u4rnjXZnmtTK9HUifOeiN5+3bYlBZuk2YtcpIn8iLOt24pWsam57XK644SwHRu15FhyKuhMy6RsKeNE3jl9wWVRu+YiTMMh3Oro1yb4XGnWs33d/w9cFr2J/Ajl9AXP59VSx7L/X90DSUHJEqsfipDvEdxEa5aMhta1xvLC16k0t0hGrujowZe//D+dsufc6CRHtH/0t1RayHmsaEZBI8s0Kf3iaTry8T9y+ldMHD1DRESl+Rzb37QSZc5eIyNXpPnLo2SWdMqcuupt5VligjFLBo38zfdJm12kwsQsnf3c1+iV3/2zGqmi0YNs5Kvf54eA1JlLCznKnLpKc9qEEyXbhOaWLZjs3JpsibKljBMV28gUb9NU4YZn2wVtitcuyZcj2ttORG73zPASsO5IF3YEqxm5/7+9Nw2O47rSRE8VdhIEAZAE9wUEF4kSLcqkLEqWTFoSJUtut3+8H63WvPF4aTmiXzg6Xtjdju6eeBPSzPTrN90SPT80kkceSraiJcqi/CRSNldAIgGKoECCAIlFWAoFgtiXAlBbVmbeLJz5cfPevDdrQWEhBZL3i1BILGRlZRXEL0995zvfkXRcsfoVZQ23P5pVwjqJJlj0HOlEJv5kpK6khbsXipBngVRV7ieP/AyHzjdhbHQSG//5XQx1D9rHi5VrYtWM6ORMULnCQqKb8vMMQkm25hpahhzwYxlEmNxzvMJMEya6iUTTcaTuS4zH45LnmGg6neizpQsS1XnFHBudxL4zl3kYEdO1WYOSRHQkuomh7kE0I5rUULR0U5JJxho6sfX1o2gSZ1rO3dDSEnzFzItMXMQnSwls6k48HyNlt9bMJAxR0yUWkf4s+4+J3UgkXKOOmEF+vNuLzJ4r5leIyHSiUW7iKXK+W5ApIWe99NJLGTsy3nzzzZd++tOfztjJQcIxaHvjKJTcVw5ZeQvXC9n2xlGo+/kbkF9WDGV7qUWodGcFlD+/H/qOfwFlD++AlY/dD/2nL0PxvRug7Y1jcOlvfw35K4ph1eM7oWzvDkBrCq5/VA2FG1dCz8c1sOqxr8GUQaD8L74N2QV59uvQ5+UtXwqrHtsJ1z+qhkjPCOQvL4JVjz8A+WXFsO0nz0HI1wfXj5zjx4a7hyDU0QcrvnEPXP/DOSi+ZyNk5+dCbtFiaP/1MSj52mao/49vwZRhgicrC5ZuXQfLdm8HT04WjF3ugEB9B4S7h8CK6rDu2Yeh/dfHoP4fD8E9/9efQ+6yIshdUgDnXvgniBsmLN+9FbreOQ2lX6uAyPVBKFhJfcRTJA6lD1RAwcoS2PZXz0H+8qUwerEVyh66F8DrgbbJz2EK4rA0dwUAUFtYzApCmIzB8vwNkJtVwHMdvB4vFOaUQH+UentL89ZCwPYZ53oLYNWiCijNXwMhcwTyvIXg8XhgCunuObrwdDnkePNt3/MNKMgq4javXG8BLM5ZCsOxLv7nJTnLIaD3wsqCCsj2ZoPH44H+6JfQEbwIuVkFUJK3BgA8kJ+1GLyeHFiSUwo53nxYtWgLxKdMmDAGwYhrUJq3BnKz8qX/dxZnl9jXshW8nuSbvBdnl0LZok0wpt+AwpwS6A41wBRM8c9K4c7Fyy+/PPjSSy+9Oe2BmbA2zrFCnq/G2s0G+5rutpg1HxQ02YNHUBsed6xlB8XqVcPAVR+2vn4Um179AD955GdohqI4VONEZTK3hWg7cyb0jnBdeaSu1RWn6Tg0gv4Brh27YzOHzjehPh5CIxjh19/1fhWaEU2uzO1K3amOnbQ5EtExcNXHrXyW6Xz1bv7VEeri0OgxXe9X4aV/+A2tuC0joXoUJQLaUEt0k4iWNbGhp5MIlwBYNSmmqrGqOGQEBM3YycJwGo0tXCJpG/9ccjY4djNx4s6xusn6s/yz2SDZ9KH4uuyaeLrcPLymwlcPWEiSxXw11m4VJIubTVyD1W6bmvOXQwz4GTp/DY3JCFqGicHO3oSbEYnQDAp9PIREyEmWGoDV11AbHse+M5cRkcoeNS++gkPnm3CswYdmKIqBZn+S2MwoWsQepdYEmcEgSGIGNh10dOJAo4/fEBipsrFq0f9shqLSDcoMaTh0vokTs0jywxdbpNdFlLVV+XGZgNgxjGRFfZhpzUxKcI8wsyai+BoOidP1UMnDf1qk60k24k3zmM2kTo9k72U6pGrAic3OkBFAw9KSNh8Vbk9kSsi3RLLIysuBsr07FrRcIUKULnKLCqDxv/4bbH7+27DioXupnPBXz8GNo59D4caVkJWXA4vWLof8smLY+sNnoOaH/wJTxAIAhKKKdbDs61shb1kRbP3RdyC7IA+8udlgxQzoPHQcSu4vB29ONni8Xii5bxMU7yyHLT84ANEbI6ANBGD1tx8Ej9cLeSVLIHpjBNY/9zA0/tO/0f/+s71Q8e8PwPLdWyFQ3wHrv/coTH55HXIWF4D/3SpYtmsLZOXl0BHqhg4I+wdg/bN7oWBVCR2zfq8KJpq6oWzvDijcuBKCbTdg8/Pfhqy8HP7+C1aVAiJSucaWcLLycqBwQxmU3FcOi1aXQu8fa+HSL6hsE7jSCcG2Xn6sNWUBwBQEjD5YkrNM+irPZIocbz6U5K2E/OxCmMI4DGjtUJi9TJIeSvPWQo43z95EMgkleWv4YtAcbx7o8QjErBAsyiri48GjseuACFCYUwJRaxICRh90hS7D4uxiKCvYnCAv9EfboD/6JVQs3WNHcG4DgCkYiXVDYU4JGPEYFOet4hJGXtZi8Hq8Ce/FLT9Y9qTf4uwSmEKUJBPxmIDRCxVL98CSnBWQ682HoVindD3u15wLxGtKJa8ozC8WlGTxVWMmwyFiwA9zR7g9wr7DlbzRF+zsRRKJYeCqzw4Q+hSJpmPNi6/Y7gbqeCAmQUs3sOv9KmmyL3xjCC3dxECzH41gFIfOX+MOCyZjGMGIPS33KQ5W2z+PsuGUGJ/4s3S5CkZEqZIdvtjCBzyYbMGOEZ/DZBJ9PJQw6Zf8sxUlFeczTkhVs7+CJ1sGiih6kZP/nFXSciSnKXmiWcXtnoYTPdKBWF/abR06ieK4PpBw7cnkFvZaiQl3pvSemEThODySHyNfTwveDJlC+ZZvPWAhSRZfNWaiYQeu+qg8UHMNA01+nOzoxXDPMJcPEBFJJMbJUh8PYbCjl/tyxWEQEjNwsrPPfjzqJLXZxEoiMYwTC3tP1aFlmAlRnWIUJ800jmJkYAyJ4MYYOt/EXQ/OSiZHHjKCEQz5B7Dr/SrsPVknpdPFiSV4kCmpRnqH0SIEB2ucLST0JuFMKGaahCeTlSmlnzmbp5ONXsskzIjSfW7mVtBIiGvLIvExPzOzw7HXY4MrIiJmEMPmuHANhnQ9bEw72ZYPNv3H/iySnayPu2URJ0nuVvqIlW/51kMRsoCM1t4LjTVjIkxtYyxrIhjh2qgZ0jDY2SutTeK6sUmw+aBD/mxFUpxYvHKljTgDI73Dkv4aHQ7IljdCuG0tNjqJYw2dGOzoRW1kgq4Q0nTsPVWHJGagGWHNyBiGbUIlmo6THbR6Z43HkbovEzTfS//wG+w9VUen8M5cwtjoJCdpsdpv/tWHONbgw0v/8Bv0vVfJNWcnT2P6uE4xZ3i6KTWRGK8HryYdMWarpFgusWO7I66sYQOJZbhuAnJeMx2flvVlcUBFtM8lXmeL66bi+JvdjUF6E5k/QlTe5tsDipBniKHz16SKNDowJlWTJEa9v13vV2FsdJJXx0YwwomZxAxH7ghG0fcuncDrO3NZcmRE+kcxbsXlatWypF15wc5e1CfCvDlIDJPmH+sG3xQiuiPY1J0oYzS9+oEjjxykN5bxlm6MEwujg/IEoEUIxi2L31CYG4T7mnVDkjvo1J48gMLGuVPJQ+7tHyyIPlUQj7uRx4iSLT5FlAnJ8TZTB4ZbLmByyKQxLGVbyK6L5GQ5k6/5ToOuOaP/9+ZCqkp+uD2gCHmGcIfDiwMcPBDeIHQwguu0R5BEY1L2A9OWWRYFkwJIzOCDFpZJh0FEQgxdH0Si6cIwRgwHPmtAY1IebWY6sTxddwRD3YP8hjJ0/ppEkoGrPr4phNjSiPsYFoKkj4foWior7gTkH6Q3JFFyYbAMknAjYfIJA3uflkkSSFJMdJN+H8zuZcXsQRCLEyVzVcTtapeGFXVx6YKNNctODsdOx0atkzkZUq1VmklVOx3BptOZZwolP9weUITswkxT3yyDYN/pS2iGNZpyZo8I03M51W7f6Uto6QYSgzij0sEo9+laJkEjHJV8vexaal58BSP9Y6iNTqI+EaZTeQeP2LLBp6iNTuLQ+WvU12zfBHzvVaIeCPFKlRO+4FmmI9jO5J0Z1qjscPAIaoOBpJa16GAAB6uvYsfvTiX4kllaXevrR3GswcfHsBmcrSg6T4dzbiDODUxcCSVO4gVifS49VdwobUjkJVbKMRJOsM9ZLp06Ho+jQWI4ol3niW3sdVhwkUjWySBmYCSrzKeDE3ZEvwmINwDnXIpU72QoQnbBndY2HTHzuMzDVbThNuksH+07c4kPQwzXtvBqkER0DHb2YtyK40hdK7a+ftSWGARpwTDRIpS8ZeIypOwJVqFzTdteAWUZJoZvDCHRDSSEYHQwwDOLIwNjOFh9FT955GcYuOqTde4a6s7oPV1Hh1XON2HfmUvYe7KOyg92o5DEDC5bNL3yewx1D6Juv/7Q+WtUT0+xFzDZ550wls3GvoVhD7ZyCdFpkmkkhBEzmKYBZqT1FIsrmdKRp7splwzJGnHJryv5a7D8Dfd4uCLguweKkF3IJExehGUQ3rwK3xiWv95rOv+KPtnRy5tZfArPDt5hVjjfe5XSpJ84RcdIs/dUHRrBqJ09nGhHs0yCZliTIjv7zlziZM2yKZhMIk/50ZsPkyB6T9XZNwUxU8PEmhdfkVwXYjC+KEuI3xRSLXh1N1LFm0M6OM6MRKmB/TyxUk2sLpmGPN3eukyqU3kLdeKyUsTUxM6aiOKi1lSIaSaePNqCMc1M+5jC7QdFyCngxGg61i0xcN6dthZo9AmeYBa/Sb+iG8EIGsEIj72ME0tyWQxWX6WkFtXlST8pUe0IkqjOYy1FOYQGC9FrDTT6cKyhU0pl6/5DNY41dKIZoaQX7OzFQJMfTeEmMFh9jYYaCTnJJEqnBUXP8mD1Na5vIyLGrTjqk2Fe0YrTfJZhSs6R5oNHMNI/ltZlIcoa0yEdgc5EbxU9ypnGYaY6z3QVrftGguiQaSSqJQTyJ8PJoy34g++/gyePtmJ7yxCapuU8dqx1VteusDBwVxLyTHRi97gz8/SKa5aYTGFMhLH7D9U42dmHRDMw0j8m+ZUtg646EitUsRJnkZgiwSI6SXKMKM2wJtnjWl8/ShuEhKBlEnznrd/i1nWb0Ase3La+HH/3m0OOC0KT7Wck4jQb+85cxsHqa9jxu1NoEQtJTOdZys0H6fNrXnxF8lYzicSYCCdY9EbqvrS3YDuVtD4RRt97lfbvgQbuW8Im7Jlssk5XtabLK05FnCwhbiZDFuxcyex5qapWMboTEbG22o8/+P47WFvtT9ksFBHTTDx5rBUjYR1f/uVxrK3pRl0n2N46jLqucixuZ9yVhDyTARBpa4ZdNTsV8oeSptv8qw8x0j9GG3R2djGJxARdmTbFGMGyqpU1+OKE4HhLNw3liRm06rZtbJMdvdJNQGzUURmDVuH/7d/9Da4qWIp/Bw/im7Af/w4exJU5hfhT2EG1Xv+gVD2He4fRMkzsO32J6r66QWUPsSFpa8dE0zHkd3b+Nb3yexysvoaX/uE32PyrD3G4tsWp/A9SvZtVzkyKYP5qRMTeU3W8GcpujqLFbyZwN8Sm04OTT7xNV90mHiOH3Ms3CFa11tZ0J5xnONotEXZT48CMZQddJ3jyWCvqOpGeW1vtV9LFbYo7ipAzrXznI8SIaNSeRnRDSlozI5q0qFSspsWwHrF5RWIG+t6lDo04sai3eDyEvncrJa2ZhtdTdwQxaRBQ1/tVPKSo+VdHcC0sxr+DB/EtzxP8n7+DB7G8ZJUzyMJDhqhtzzJM1EYn6aCInbkseYYti7sqErXjGG/IsYGS5oNH7AESOrXotgo6vwfHLsiOTac3888+CTGK+/Tc1aqzcYQeTxPl6OomhlTNNzGMKFUFrGnJE9Zimom1NZR4T3ycSLRumWEusgN77omPW/CDd64o6eI2xR1FyPMV35kJsYvNq4HPGtAIRhKIlLkdGAEyeYKF0Id7h+nUnhBVyQiPOTTEkPd4PI6THb18IlCsdEfr29GYjKAXPPgm7JcI+U3Yj16Ph16jfwDNSAwDjT7b56zzpiKTIZgswQZGwr0jPAD/Lc8T6Hu3EqMDYxgnFuoTYQw0+TmhWiax38enaEyE+e9Ffn+a/Ps66LhHZqsdiwMe7mpVtMAhOvY0kaSTNd/c6WmssmVVeKYEeuJjhyxFMOmBEbX7z9Jx01TO4nPZTUDh9sMdRcjJKt/ZbJNORuziyDS1ojnaMqtULUL4V/ORui/p44er+MokNuJMhyZoZgQdK45JP/O9V4mWYVIt2iZKccHpZHuvsyJJ2KHX9X4VbttQnrxCLl1Fl5RaFs1oDmsYvjHsLDoVtOyh8zQ/Izowxht7gavuyl7nNj0+TBLR+eaRyY5etOzo0YRY0iTrn8TPczqI5OksJU0eNESPT9ST3RW0KHcgIlpsdHvKwviUo+sycnRkAqrlptNuU1XImWKhN+yUw2P+cEcRcjLMpmqWk8mcQHlxZJpVdOwYNkDBMi3kkKAqJJrOtVhWkY7UfYlE06mnV/AWmzZB03NFMR6P8wWndHw6zrVjSzdx+GILDlbTG8Fv/+f/wpU5hZKGvKqgGP/bv/sbIbc4xgdVxCqbWL7dZwAAIABJREFUBhNpGLfivAHJXBxsl19sZBJJVOd79piWPHS+CYfsBa2Oxk0r3nDvMAaa/HS3n2DXm+4zR8SECl68sbLpO3eTzA1dJ9jeMiSRZnwqjjESwampOJpxHXUrilNTU9P+v+Emx/aWIXz5l8fnhSxTEZtY/eo6mTX53SziXOg3jNsJdzwhz0Qv5vYywcIl7q0TR5xZQ2q8pRvNENWNGbmJGixrYg1fbOGeXrZwNE4s7DtzCQlrotnnFgPrm179ACc7+yRt1YxQS53YeLNMgoFGmhfxU9iB5SWr0Ovx4JbV6/H/e+FvhCbcVRyquYbBrn6pyjbsc5gRjVfNbPyZuUuaf/Uhtr5+FH3vVUpOEeZxZu+BbScRN1obwQgfdHGq6kR5gn3mbIiG7/tL4guX7WpyBCUjH0IsjIR1PP5RM0bChvSz2RCTaVqSJOCufk3TwqaGfjRNp9pOS7TC45kQ23zozOz654ucU0ktCjPHHU/IM4FIInLcZOJGZVH77Hq/ijsTml79gEsQvLl1+hJOtveibudNcBIM0uQ3TlaRGMYtCwONPhqr+eoHaExGkGjU+RDyD9L85Fd+j9GBMan6ZxN2ZljYYB3VkcR0NCad3GIjGJFyMsYaOqlf2LIwcNWHg9VX+TeBplc/oBW83UxkI9KWbmLzQaoLh7oHMU4IDl9s4fY7EtMTZAoWvMRzL+zpPjfck4mylNKU9sbqdhr84PvvYE/3uOTbRZR9vIQkBha5z8kI1zCc6pSRbm21Hz945wp3SdSe86MmPIcQi2vIbhIVr2OwPyhJIIZBkpLmXMiPPVf5lhcuFCELENcOuYNv3MdNp31adtC8OIVnEZrCxuQFMXpTdDCIDUFimGiGozKRT0aQGKbUeGNJbUQ3MeQfRMOWGvhIdzCCZiSGkRvDlOxt58bIF600Kc72QLMt1EPnm9CMaHwqb7i2BYOdvfz9iVnP1B1CeIwnidG0t9bXj8ra+LuV9vv9UHq/dHJRJlr3/r9kfQES0TlpuUnGNC3ZEiaQmFMx6zgeSO93FhtyE4Go5GTglab9erXVfrx2pV8i4B5/AIMTsaQasqETvHC2CyNhHSuPt2FPd4BLIO0tQ5zs3ba5+YCqahcmFCEngTOll9nmkFRVNIvfNCbCVGO2F4jyPXRhjUdvNh88wv28bPsGzZK4ZjfMYtIQBdEMp/FmV7DM88zC64OdvbxJKAYBMT2aDbW4IzpJVOevy6pV5zEqMwSu+qTAe9Fr3Xu6zmlO8g0on9q2O3r9gUYf9yX3nqyTGn3pvp0gOmQ9dL4pyddwp8JMBaop0yGK6Ty7rNrV7Gq3pzuAhFi8ImawLItXyGJlq0UNrK3uliQMRKeaj0YNNAyCtTXdaJqEvoY/gLpmSpX2TKF8ybcnFCELyMSRIRNG6mPl+M0PeQqc1Bh0RW+yxDTLMDHQ6JOsdZHeYSfgxx5bJhGdZ15Y9gCJ2z/c9Ko7gtNpSFq6wW8E4s/FDA5E5JX9WEOn7ehwIjjF453zm062s527zL4piJ8zc2ewY8XM5FTfTpznf+hUyEKlxyrMTHRSJmuk+9puGARHhsKo6wQNg2AkrCecU9cJGjrhlXFttR9DwRjWnvPzateNZJIBH6EO63OWFW62L1k5K24OFCELyMSR0fyrI9j6+lEpiyIVxKAi5h0WhzIY4lYciW5gbGwSjaAd1SmsX2Ik2nTwA6F5Juur0YExHGuQSdwIRtDSDU547PG4ZWH3H6pRGw7YIfZO0hoLtRczkqkTROdDKJGBMax58RWcbO9FMxJDoptJBz+4XmzfFJp/dSTl58wyopOdZybIVCc1DIJa1Ejr+41GDCTEwutdAaw83obtLUN4/KNmSrKtw/z49pYhvHC2S5ImfB2jae1uySQDJlFcONuFPd2BOdnlpvMlz5VQlQZ9c6AIWUCmK5zE6nA64uAjw6/83q4SdSeeUjOEqtzWVCMaxgn1Cjvk7cgM7im6weqrduNP5+PTQ+ebkERjsgyhG1LQkRmJITHoEEfMntILXPUJssqHXGpxuy4mO3uFLOXE3An5PX2KzuYQw/554jSepZt81dN8IZ1OGo0YWFvtR8OufPm1m0ITzg7v0XWCg/1Bm8RNjIQNicwIsVCLmlyaILb/mhB67kwlA0bAmt1AbGro5w3DhPeVhkx1nd5s3FY/EQl6+wzJWWnQNweZEnLWSy+9lPEq6zfffPOln/70pxkfv1CQlZcDZXt3QFZeTspjvLnZ0PbGMaj/j2/B1h8+A5amQ/aiAuk5cYPA9T+cg8KNq2DR2hVQ9ugOWPfcw5BfVgLtvz4GZ5//r5C/rAiK79sIvt+ehLqfvwH5ZSVQtGUt5K8oho43/wjrn30YAAA82V4oWL0MCjeshIKVJbD+uw9D6dcqIH/5Utj+4nchr2QJZC8pgJ6PamCsrh0WrSqBwvJVkJWXA22//gQu/eLXkF9WDGWP3Ae5RYug7dfHYKKxC/JKCiFvaSFMmRb43jkNpTs3Q8GaZRDq6IP8FcVQ9sgOwPgULFq9DPr+dBEu/eLXULCqFCr+/QGI+AfBiuowUtsCYf8gkFAUSu4v5++/7Y2j/D1t/6vnICsvBwpWlED7m3+EkvvKYeSLVphs6QES1qDk/nIg4Ri0v/kJbP6LJyCnMH9Ov0M9RqDqRDus21AM+QU5sGX7CsjOyUr42dX6fnjj1fOwuDAXvB4PFBUXQFaWF75sGoKvP7we1m8shm88thGOf9QK9z+wBkaHwrC0ZBHkF+TAZyfb4b236mF9eQksX7EYPjvVAZsqlsGyFYsBEKCzfQzKVi0Br9cLlX9qg7df/wKKivNhy/YV0jWw6wIAICQOn53sgMNv10NRcT5s37ESipbmQyxGYMfOVdJz/J1jcOqTNrCsKdiyfUXCZ+DvGIWGuj5449XzsGRpftJj1m0ohqLifNj31BZoqOuVrjETZOdkSZ+twvzg5ZdfHnzppZfenPbATFgbb/MKOR1EfZlohjQKTSUBR1tuOujkM8SJxYc8xFAiYyLMNWQmJVjE0YDdlXDgqg8t3cDwjWG6mLSzlw9LGJMRKbWNaDpvrDGPsfhzM6LRirnmWoLFjVXzk+291Nt8lVrwmN+ZLUN1r5ZCpN8eWIXtJMhd4r5kUWNOleec7POeCdJ9lXZXhbXV3WjoBLWIgbU13bTSFW1uOsEefyCFj7kVDXtI4wfffwdPHLWdF0JTT9dpk45JBoQkcYLYDpHaan+CG8OyLIxGjKT+4UjYSFn9EsKe51Sw7DWSPUdVuwsHoCSLzCAPethfVQXng7t5xp0NB48kHWqIEwt9hytRDwRxsJpKGH2nL0kNK7eEYhmE6rjBCN1dJzQN2ZRc0yu/p55kO0WNxOgACgvR971XSeWKaExyZTA5gmnGZkSjEZxsa7WmI7GbgZ888jMp9Y36sU3uY/a9a2+bPlzFN6a4yZtb13STE7RIvjOZsEwYZ3aRCxvWSJodYWvFTJYQCbbyeBtOBKIJPuYefwDbmofw0GsXpCbf4EAQLSuOtdW0meeWNpgTgxGlE7vZLbkxTNNpHrJrO3lMvgGk0m7Fz0J87MTHNonHZj/pp3DzoQjZxnQVmRnSEpZ3Mn9u1+Eql73sw4Sq0O2l7TtD9/DRCtKgk39pEs7cr882kPCqmCXB2XoyG9OOx+MYaPFLx8YtizonTILR4YAUakQHS+QQfmPSHiaxh1fkwZkj/Josg7j0dWeQRAyuR3QCh8Q0u8TfR2bNvXR+XUZGwYlYghbrJjdxqk9jQx5Rgzb/bHJEpBotJcxW1GMmWlacOzBqq/1omhYODgSdoZGGAUkfHuwPCoMgLahFDYxEdGxvGUJCLN48ZC6JHv84TgSi9g0geSMy1XsSHzvxcQsaOpm3UW+F+YciZBupvjZ3vV/FK2J3JSc2+JJlNKQjFZGIokMBHG/yp7XSuSfe2PAFq0SpNc3JyeDB8LbU0HW4SrK1OfKLjr7DlRi3m3rORuomwUlyxJ5E1CRrWt+ZyxiPxzHSO5yw0DTx/cqfhXvcfLr8Y1ZRuv287Gep3AhuMhIrR/cYNKKQe2E31nwdo6jb0gWb6qut9mPl8TYcG43w43v8AbzeFcBQMCZN9p34uEVqFJ74mI5zH3rtAvb4A9h7fRxNw/k5C5un1XUrRiPUp2xZ9Frd/mr23nWdHsOraXvSjzUUTxylN6Xa6m5sbx1WFfIChSJkG8nIU05US16pscWhbH9cpnAvU2UkmeprurjayIxovKIWbwTSPrsmv+AtduItjcmINIxCYgbWvPhKYj5FMMLHt8W4TD5aflgeJnE2g8xM980k/xgRk2qsDOm+xjOZIho18NBrF/gxolOhqaFfSmwzTTr4oesEQ8EY9vhlotV1NshBifTQaxcwppnY3jIkSR6sGq2t7uakSLVkglG76taiBlpWPGVokKMHU9J1ByWd+LjFlkdkf7R4E6it6aZSiorlXPBQhJwGlm5KAxOIidJGpoSC6ER4siWnYmOQDoU4VTMjPAYpSIgQDHb2okUsritbhjwUEhublAiTJsMZSHTDWR8VCOJYg48+Fgji8MUWjFtxfq3uqp83JIMRHK5tkTTskH9gRrrvTOGuMkW7GqsK05GNuF0jFIxx/fbE0Ra8dqUfL5ztkrzFiIhNDf2ouYj25FE6Ds204uBEjP9c1I1DwRgO9gdxIhBFXSfY0TqMhFjoax9JsNb1+AO2Xc5K+CYgyhepGozu65sIRNPewG4G1KDI/EAR8jRwV85u0pkJCQ2dv4a+dyuFzGMi5R0TTcehGtrgY2PFDIM1zgokounCQtUjGBuddCbjkuyjk6I77a/dccuy10fRSb/oUECQUdJXuIysxQEVZ9feh2hMhqeVMGYDUQ++dqWPV5npSMDd5GLVpqaZvHJlgUCmKQf6WFacV6DRCNVuoxFDmgRk7gxROiDEQj3mNOY0u6o+Ybsjmq70Y3AixitksfqeHNdQixq8mjZNeuNpahzgNwGxwYgouyq0qIm6Zto+626poTnbzzwTolWDIvMDRcgzRDItdCa5F+5NHxahq5jcY8Mh/2DSUWM9EMTeU3WoB0KS9JAsY9i5ZmeZ6HhLt3MdwqJUSzfQd7gy6diy+1sBG24hJuGZG6xyHmuk6XEslnQ21rV0EO1qmZCAOEqN6BDH8Y+acWQ4jD3+QPI1Skdb0bKoDa7pSr+TMWHLFW7dmdndmhoGUI+ZUmV78mgLtrcMSQlzzJEhSQvV3ZKefPJYK1pWHH3tIxIx9vgDqMcSb3g8+tMgODQYxPFABC+c7ZoTUWZKtMo6Nz9QhPwVQCRx1khjjgMmLTAXA6JchbNsZHkSTkNiECn8xw22K481/HzvVaIeCEnODadp6WjBlkGw6dUP8JNHfoZD55v49XzyyM9oQ9BOdQtc9eFYg4+fK05IgtwznxjsD2I4pHNyThYm5LgYTKnBRQOIDK7JMikDESVJIByMYWAsIlXmtdXd0vlZKlvl8Tba+DMJtlwboFN9grNCtLuJmRiiVS+ZH5kRfe05pzJubxlCLWrwMCImcdSe82MkpEt6sm5nbExHlKkqYUW0txaKkL9iMKdGqHsQR+pa0ZgMJ9jrWLPOmAhzmYE16+LE4uTN/MLBzl4kBuE5xRaL6mQDKPZAChtnTucBDlz1oR4IcqubpZtINDqGzZqetPqmUaG+d2mCXN+ZSykbotKQTUSfVRXNfL/RqCFpqiJYddfeOiwNV7hhWXFOemISXEKjznZMMNRW+/HShes8NY7dGHzto5wo2bCIYRAcD0QxGIwlVM/DA0Hs6R5Hy4pTqeGcH017dJodo8ec4KGXf3lcGkAJTsTQ10a1afe5e/yBpJZANwHzm0r3+Ix+D8nOpTB7KELOEDP9+j2TZh87N0tfc4fDmxGN5xN3/6Ha3nLt5F9wx4StQbNtIuz1dft4kWQ/eeRnOFRzjSfHJV7Ph5wwA1d9SR0nfHOKULHTwRIDWdB8MinF7TCZSxXtHtoQwao7Rq7MGSH93K6gWQXKvMemQTVf0cEQjRjY1DDAm3Oij5jpwNGoQVduCT/XdcKzM5zBD6r5Ms2ZWepEix11XlDdWo+ZklbN/s0ak/F4HH1tI9TTbHukY0Jl7t77l8yDrWWgybs/O3HARenHc4ci5AyRqnmXiqgz9deKTg5WqTJ7Ws2Lr2DzQZoux2xlbCOJ/Pp2FvJE2N59F5UaeZGBMcfVMRGmofEHadB9pkjmOJnus0kFN+En0+DTfoUWmnSZOiz4pF6SlUmiVzimmUhMqgnTYY1h1GOUNFmCW1NDP4/bfPmXx3FkKCxN3cU0EyuPt+HkuIaEWGhZcWxq6JekiMH+oGRpi4R1XlWLx/X4A7xq7vGP8+cN9gclqxyTY5oaBxK2oCTb++e+WZ0Qqm92c3MGYBz5I90ygGS/A1U9zwyKkDNEquZdaqLOrEJOVnmyibeEtUnTEF9CXKf9+o3//K6dAneNx4CONXZKxD4diGYkjQ5FlP3P84VUzSS3vSzZX3SmxYrE+8E7V/CE4BFm1SbTk+nwRRwJIbQxZ1e1olc5ppnY2zPO3Q6RkC4fY4fWM4Jlwxnu4RB2Pe0tjpRy/KNm7OkOyPY++7iTR1vRNGjFPDYakUhuPBCZtkJNNzgjfda2Ps0mEiX541grb0yym04m2rJyX8wMipDniJm4LJKBhblTK1uiDprJa5khTfA20+p64LMGjI1N2ps+miSynuzolcaY5fOnnhQUG3vusCU2tDJfSPUXPqaZkibsjo9kTSxxVFrXCSdL1mRrahjAHn8A4/E4jgcieOi1C1h7zo/xeByDwRhaliURVeXxNsnlEAnrONg/mSCZuF0cXLs+58doxLGzXTjbhVqUbgVhNwUtavChEXad7HnETCI3HG3BSxeuc0se+6zE98mq7srjbahF033raOWVu/jtg0krMY1uP0ml18vnapGuRTUFM4ci5K8QCfLELHVUaZTZikvJbo3//C6ONfi4ZCGm1WVa7TvXmtqPPZP3Ox82OPEvulgh8irOtUoJkYYCVR5vo002neCh1y44vuSoIVW3hNAK+cTHVDse7A9yW9mh1y6gaVoYiejSfj4xPCjZotKmBuq+ME3Cx5f1GCV406DXQ21ztLFoGCRBmuDv/Wir7bSgmjSb3mNaNt8DOK4l3sTsSjgVSbLXcyfD6Tq97lQpc4iqIp4rFCHPEvNBLunCh2Z+LcnJUjxvnFgJC0UTHQ/iAtL0q6xm+s1gNiSeToNMluLGNOWkOqeQnmaaBEeGQpITo6mhH69d6cdrV/o4Eba3DCUQK6s0HSfGMGpRJzGOE3+1n/uXabSngZ1tI+jrGEVE5Bup6ZqnYR44xJp/YyMRtKy4owHbREqIheOBCIZDOq2EoyYeeu0CnviYOjZEux2zA7Lx7PYWeiMQNeVU2vpsSFVVxHODIuRZIlNySUXcbIw6dfiQuHduZlJAMoeDGdKw+SCd6hOlilTrlDJ5byzFLRXBJ39PMyPxTPKNa8/50bJHvvlrmURyG4hNN0SUGmNUUybcyxvT5Oxi92gyq8JPfNyCvrYRiYDrLly3CZtOzfnaR2Rfsa1tcx06onM9XI9RhwUj3+tdAalq1+w8jmjESLDjseAkpneLnwez9GkC8fZ0B3il6/6M3aSqGnO3DoqQZ4lMySUVcbP4yVD3YNrnNb3y+6S6bWbXdyRp1Sxa3ZK9j3Rbtx2LXvIm43znWaSruES9mPlsGXlEIwYeO3INDZ3g5LiGkbCOF852JRBcTDOx9/o4ryqDEzHbB2zx87PmHSNVag+j1+Rrl3fnEWLh5LhGVzedo5XuCYE4WZOORX2GbF9yTZWPr3uKhOny1s62EWlqj9g+Y6exqXMfsnsSkf2ZEHqT6Wwbkcaue/yBjHVeafCFJKbtKcwfFCHfZKQibhLRBT9x6mpSrJBnQnbuY0XXh2WSBOnCec30hC+Odyd7b3NtcmYC0UVBiIU93eOJ1d7RVpwYp+HyzJ8rShTHP2rGtuYhewzZlKpNVlES05I2bbDQnkOvXcCRoRASQqTnnjzaQp9jEKl6f/utd3DN6nL0ery4YV0FvvXW79A0Layp8tm7+Pyc1Bm5MiJlj0ej1Aki5lboOkHTsLjrARG535rbA2Mmr4w1u5kYCeu0SZmhJCHexFSVfHOhCPkrxGz9u6kkDL6FI8m2ETEgPnDVhyQaS3BZuNcspZJabjbhpoNIXMw5IaajJUtDY1GarCrko9MxZ6RaE6rEpoZ+OxGum4fPh4MxqsPG5CWnztCHM8I9NhJBLUqv8zdv/hZLilfh04/+Pf6f33sLn37077G0ZBX+8uf/goZN3B+8c0VqxDHp5Afffwd97aNoGnZGhdBUZDcEYhK83hVALSoTJZsS7PGPSzepkeFw0jyOdBBvYu0tw2kbgjP5PSoZJBGKkL9CTOffTVWxpno8E5eEYQ+GjDX40gbqu3XkmxEUNBPQ5pmsm4oRmGIofE938q/WhkF4UBAhlrSeiZEvCwZiZEuI09CLRgx5L54dGtR7fVwY4dYxHqd+ZkMnuGFdBT796N/jD77/Dv/n6Uf/HouXrMXamm5qbRMq2phGE98iIZ1LHiyjgjkpTFvaiEYMris7NxvZ/pdqrZUbLCAp1QIA0Zo3HxtHlBsjOTIl5Lti63QmIOEYtL1xFEruK0+7nTqj504BeLKzYPHaMvDmZiccP3a5DVr++/8PUyaBsr07+OPOVudi6fGS+8ohv6wYtv3kuYRry8rLgWVf3wbhrn7ILS6EguXFkF2Qm3AM27pduHGVdK6ej2vohml7U/R8fSaZoupEO5z6pA2e/+EeWFpSAPsPbIXc3GxYuaYIqit90rbmvLxs+OxUR8Jm53h8CpaVLYb2lhFYtnwx5Odnw9oNJbCkKA/2Pl4OQwMhqPu8B9549TwULc2DgkU5kJufA9fq+yEeRygoyAGv1wsV21dA0dI8eOyJLVBSughMMw5156/D4bevwNLiAlhSlAfZ2dkwNhKBl//LP8Iju34EHo+XX8eighK41PwevPY//hWysj2wdGk+5C/KhrKVSyA3PxvOf9oFGyuWwfryEqi/eAMefHg9TE0hNF7qg/WbSmFkKAzrN5ZCd9cYLCrMgwcfWgfjY1FovNwH771FPwcPALz+6nkoXJIHK9cUwf271iTdEM22YK/fVAJlq5bA2dOdsG6j/LmxDdO5uVkwNYWwuDAPvrl/M2RleRPOlynErddqc7WDTLdOJ7LFXYqOQ3+Cup+/AQAeuO///j/m9NzsxXmw8pv3AwAltY5Df4JtP/ku5CwpABKOwdildjjwp/8XsnJl4tz2k+8CgAe2/eQ56fGcJQVprykrNxtKv1aR0bW6z7X+ub2g9Qdg/XMPT/u+bgb2P7MNyrcuh5xcLzzzvXv54+0tw/D4kxUAHoB9T20BAIBzZzrhvbfqAcAD5VtKYUN5KXi8HhgeCMFAXxB27FwNVSc6YN+BLZCdQwk2K8sDK9cUwbIVi8Hr9cLjT1ZAVpYHPF4P7NqzDs6d6YQVKwvB4/FA4+U+eOLZ7dDePAxbd5TBipWLYd+BrQDggcefrIBX/nMVPPzNTXDv11ZBefkWGAl0wKoVzo1zJNABq1dtBEQED3igrXkYdn59DdR/0QvEjMPht+sBAOC+B1bDA7vXwkRAg6uX++D9314Br9cL2TleICQOTz67Hbo7x6DbF4AvPu+BX/w/TwJ4PPxzeOHHe2DfgS3gax+FRYtzAacQzp3phH0HtoLH64GergB0+wLSZ3X47XrweD3SZ8ygxwj0907C7ofXQ/3FG7BrzzrIL5jdDTi/IEd6DT1G+LXN9px3FTIpo/E2lywy+Vo+l6ZVuufOJfj+VmAmQyM3A7xJpSfulEsaGnSs1clmsAdGXv7lcb7nzh1IxLRfy4pjOKRjKEgHPXr8Af71uq15KMExceIolU58bSOo2xqy6Lh4551/w9KS1ZKGXFy0Eh/f/dfcQsdiPVneMnMz+NpHuLc5JjTmWBPQ2W6t81yKHn8ANdYAtOWOUDCGoWBM0pIH+4POCPoxOmTCGpTp3BaRsJ7gbJkPKAmDApSG7GAuJDhXjXUuwfe3AjfzejJp8GSSYWEYziAHohw8xDRSnmFhb/8gxELDINKGD3ZuwyD8n9rqbp6rzAN9bG9v7Tk/3zjN4y7t17KsOP7y5/+CxUvWosfjxYrN2/DQod8hS3FjUZqWFcfJcU26DhYq9ME7V9DXNkJJuo2myPXemOCNw1iMXm9TQz9+8M4Vvo5KDMUPBWPSVCELyWdaO/Nhs5tCMrjXaCXTm2cLNVBCoQhZwFxIZ6ZpcAoOpquODIM2yCTHgCvDgu2/S5e1wLd+xEwk9ujzCduq5g7SQURph50WNTAY1PiWD1/bCG0qnnN285kmXd3EKsie7nF+XWLVHA7Rxp8eI9zhIaa7OXnGrdIePp7odqXfPhcdZWY3IHaTME37JmPfkFh8pxY1+KSerhMet8kT3zKofFnORjSaPndFYXbIlJDvCg15Og02HVLpurdCX73dse/AVkn7dIOYcaiu9MG3v7MNylY5Wm39xRvw3lv1gAiwdkMxnDvdyXXWZ/7c0ScNwwJEhLg1BV82DcHyskKIaQZcPH8d6j7vgaee3QYbK5bB2g3F4PV6+XWsWb8Uqo6383M++dw2AACoOt4Oh9+uh//0L8/CrofWwQs/3g37DmyF0KQOw0MheOq726GteQS2bF8OZSsLwdc+Cgf+bDvgFMLlCzdg10PrYGQoArGYCQ89uhHO/LGNarcegCef2w7LVhTS6ziwBTwegLXri6HqeDs8um8zNF7qg8VL8qDxUh/s/VY5bN66DEwjDtd9wzAe0GDH11ZD1fF22P/0VvC1jcIT39kGHg9A5Z/aoe5CDzz/H74On5/1g0XisKliGbz3Vj2Ub10O3Z1jXLt+8rmyHWSBAAART0lEQVTtKX9XublZsOOB1XNq6CnMHXcFIc8Fqcg8FVHfrnA3H+cD7gaPG+c/7ZKItnxLKbz/dj38xX/YDS/8ZA/se2oL+DrGYP/TtFnlJvaergAgAvg7x+D9314BAA88+dw2+PYz2+DhxzaBYUxBOKRDb88E3LtzFW8qZWdn8WbdY09shngcYXQ4BPuf3grFpQWwdv1SSnrPbocb3ROwcXMJFCzKgarj7fCtp7YATiFcre+HPY9uAGJOQeOlPtj10DrwtY/BPfeXwdTUIvjy2pDTlDywFQAQAqMRyM7xQuPlftjzyHo4e6aTk+VT390OkZABiwppo3cioMHQQAhGhiLwrScroOpEBz82JzcLcvOzoWhpPjy6bzMAAGwoL4XxQAx27VkLiLTxt3FzKWzYVAIIAPvta0gGsfGWl3dzKEE19zJEJmU03uaShcL0uFXNRrGJlzRbQdiDlw7sq7kWZQMdNGOCbZgWA+K1qMnD7JnWzFYzHXrtAs0lthtlyTzQ8XhcklRGhkJ2cNAQz09mXmJE5NnKYyMRe+Q6mnBOJk8k25FnmkSa8ksWgsSGZtiKJ/ZvQuTPjhALR4ZC9H2mkI7SSUvzNehxtzf3QEkWCjPBrar4uXXNQy1YYgWdrqJ2V1gNdb0QjZhw367VsGp1Edy7cyVcOOeHrfesgIJFOVB/sRcOv10PU1MIFduWQ1FxPmzYVAr+jjFuCXvhx3vgsW9XwPpNxVB/8Qa8cfA8gNcDu/du4PKFx+OBe3eulOSXxst98Lf/6UnweAE2b1sOhm5BV8cYrN1QAnl5ACtWLgHTsCAwGoHCJbnQ0TYKD+xeC6PDUXjqu9thqD8ECAD9vZNw4M/ugVjMgv7ucai70AMeD8BTz22HrGwv3LAfKy4tgAe/sR7KtywDj9cD/T0TsGvPOqivpTJJe/Mw/OvLVfCXP9qdIEtcrr0BO3auhjXrlqaUjtzSkvhZMw80+33NFtPJVwo2MmFtVBXygsft0mScadfdPcknJpeJ2zLEsHo+Mi3snDNNWoEeeu2C1GwbG6Eh9jyZTac2OZYrIS4fZdWzaDFzB97TpacRHnpk6AQnJ7SU03b8MznawifmWJ6yuAKKuVFYvofYqGMuCWqz8ydYBd32wemQ6RonhcwBalLv7kKqKb9McCsm8hjYdFh8CqHqeBusXlsEiACXa3tgeVlhwnSXe5KPTYBVnaAVbNHSfJiKT8HS4gJYs24pVJ1oh7df/wLWl5fA7ofXwxfneyAwGoUsrxfu3bkKiksXwXVfAEaHoxCLEdi0uRRGhyNQvmUZFC7Jg4KCHKit7oZNFaWw9d4VAB4PECMOD3+rHLKyPLBs+WJYUpQHjz9ZAR4PwPiYBvfcvwqKlubbzToPjI9p0NUxBl97cC30Xp+A5WWF0HipD95+/Qs+bXfqj23w6LfKAacQPjtFteSlxfkQCRvwZdMweDwAr79yHjZsLoUNm0ph4+YS2Pt4OdTX3oB1G4vhw3cboWhpPjz06EbIzs6CjRWlcO1KP+zYuRquNQzAug3F/PNet6F4Rs06cdouLy8btmxfoabu5ohMJ/VUhXyH4GZY+24m5GCbIaw83obtrcMJx6XSlZMNiYiP80rUrnp7usclGxk7RszIGBtxVjOd+LiFDnKYRMogZmHwbEhjsD9Iz2EPfdSeo5GWhBApEIkQy7Gq2QH57S1DeOFsF9eWo7YmrtsZyCwLg8d9CoMvPf7xBH3XNK2U4ULuxaazgQoOmj1A+ZAVMsVXMazCJAdNcxpxYmMsU2SynVokEpE8xZ+zLSAsE5lJHTVVPoyEDS4FEEKjOwcHnM0jLBmODXew4QpRyohbcR6cz4LmGUmyRLlkjTc2Odh0RfY9R+ymZaaNspGhkOTHFr3emcK9Z1Ahc2RKyMp0qMCtffNld9NjBE4ebQU9RqTHTh1zHmu83AfhkAFX6/shJycb4vEp6OoYA4tMZXR+dq68vGx45nv3Jlip2DGExHkj8VylDwAAWq4OgqHH+bGNl/tgx87VUPmndgAAeGDPWsjLz4bOL0dh+/0roamBSgFVx9uBkCmwzDgsXZoPFonb1xwHLWrCxopSbmWrv9gL+w5stb3MW2BiPAZLluRBZ9so3L9rDWRleaGhrhcu1lyHTVtKAYH6kh9/ogIMwwIAANO0YN/TW2H9pmLYfv9KuHqJfmaNl/sgPz/bPv8eeOzbm/lzUn1eXR1jsP+ZbTQH46kt3Ot97owvs18qADz2RAU8/8Ovw2NPZJabojBzKEJWmHecPU1J6ezpTv6YmxR37VkHRcX5sGv3WgBwPMnnP+2a9vzsXI31/RI5m6YFHa3DQEyLH1P/hU2Mtq/5ysVe6XUsKw67H14PF8754fDb9VBd6YPS5YugrWkY7t25ElaUFcLuvRv4z8+d7oTRkQjk5GYJ1+yH0mWLgJhxePyJCnjhx3vggd1rwdc2CqGgAY2X+mBJUR54szxwz31l/H3s2rMOLDIFJSUFkJVF3Rw4hdBQ1wcAAFe+6IXDb12G+x5YAzk5WbDroXWwpCgPdu1ZB1lZXsgvyIHyLaXw6n/5FBou9aX9vN549TxcvdwHT313O+QX5MDOB9fMmFyzc7KgYvsKyM5RtHHTkEkZjUqyUJgB3A4I9lg6aUH8OdvkkUqzZMdKGcbHWtHQCR7/qBmjEQNjscTXa2ro53qtOFYcDumox4gkVaTaCE31YLoFWtxebdjHs8WmzqbqFgwFY3jtSh+ODIXRsmS3Ax3jNqQ1U0ybZiH8PIAoatAdf8IItKg1p5Igko1PZ7JpOhXcn43C9AClId983C5Ws9sJzFYmrbdPo5GKmRSiJS1ZkL3YIENEqYEXj9Mt0Ideu4DtLXQFVKrXdoZSDOzxUxsaG+RgN4FIRJd0cZEwCXHWRyUE+xh0fVTdhevY3jIsD6UcbeUk6ibg6T6rZA0/MUcj2WeVinTv9iGP2UAR8i3AQovSvBMguioy3YjBgnimO56tP2pqHEBE5BWuZgfquJ0Ig/3BpMTOAote/uVxHBl2nBm11X6Mx+NomjQi09luPczjPaVoTnGBq33dzLXBJg2Z68L9vtwNttlUvJls/k45vae8yTOCIuRbgIUWpXmnQXQxzNf5WJXY1NAvnd9te2NDHu4q0bLiaPJR5lZJ+mAj0eGQzldEMXkkHNK5te3lXx6nI9G2NOKGZcUTCNcN9s2AkbgoI7D/Tif7IKYnVkW684tMCVmp83PAfLsTFGScPdUB//SPp9M2rDIBa/zhFMLexzZB4+U+2LJ9BXx2kp7/s1MdMBVHWFSYC996agu88OM98PhTFXDli1744vMeuNE9DpYVh4s13UDMOHx6sgMAAO7duRIA6Mj3d76/A3zto/CvL1fBdV8APB4v+NpGIRwyoKGuD8YDUXji2W3gQYBvfHMj7DuwFf7tf12SGp8MWVle2HdgK/yl7dC4WNMtOVYAaMDQI/vKAZGugLJIHL74vAfOVfqgp4uOhvf4x3lz8+zpTiAkLp2DjaonC/tJ9zOFmweVZaGwYLHvwFZYWroIdu1eC6eOtc46Kcydn7F77waor73Bk9Ief4JGfi5bUQgV25fz1Lm//NFu2PngWjj/qQ/WbSyBaMTkDhJEgGVli6FgUS7E41NQsmwRbNm+Av76bx+HLfesgDN/bIN9T2+FSNiAXXvWQn5BDhiGBVcbBuApO29ixwOrucuEQY8RuNE9DuMBDdauL4bGS33wxsHz8MJP9sAz37tXyplYUpQPlX9q45kbz/9wN2zYVAIerwde+ImQ9oYAj+7bDPVf9MLexzbN+feicBORSRmNSrJQ+Aox1yZSsmk/6uagGQ9NDQM8FS4c1LnUIG+ibhVWLslShaaZ6OsYTbhedg63Dp3MhcLgrG8yHPeEIB2In0WPP8BXOKWTdVJN8KnJu1sHUBqywp2C+dIz3UTY3jKEx45cQ9OwsKeb7qy7dOG61Pxz7G46X6nEILojLCvO1z2x50SjBmoRI2FTB1vfVHvOL79He7fehbNdqEWntwcyTZyvr0qw6U2/Pms2N7q0dkRF8EmRKSEryUJhwWO6oHsR7phOPUbg7OlO2P/0Vmi81MdD3r/z/R2wobwUurvGYQoRNmwqBdO04P5dayAen4LghA5bti2Xhi8e/uYmaWPJ/qe38n9Pjsego3UY+m4EobAoH57583shHp+CL5uH4cGH1knXuOuhdTA5EYNdwuPiRu2v710P3ixPRp8FkyCqjrdLsoxbpkmG2UZipjp3Jq+pkB6KkBXuKLhJgWm+Hg/AE89uh7/80W5OpG5yy83NhlPHWuGLz3vgL37wdRgZDsOGTaWwobwUHn5sUwJx+dpHYe2GYvB1jME995XBrm+sh+CkDlvvWSHdGLxeAE0zoabSxzOGD79dDx6vQ1wiOc5GJ3eTK9+IYo9VJ9sEMpMbXarXMgwLzp7qgP3PbFOZx/OBTMpoVJKFwm0Ct7yRTq9N+Xx7Im86u10qfVccaqmt6UbDIE5S2y3MGG5vGZKS8G4G1JBIZoAMJQsPPTYz7NmzBy9fvnzz7g4KCrPAV7mvzbLicLn2Bjz4jfWAUwjnKn2w/+mt/L+f+M42qL9It3ZcOOeH/U/fumvUYwTOVfpmXXVn8rnO9TXuFng8nnpE3DPdccqHrHDbwx1cdCvAvM2GbsHpP7bB2dOdXALIy8sGj9cD5RWlgIiwa886aG0egief2z6vpOVO0HNjrl7iTD5X8TWmux6F6aE0ZIXbCsmqNqZdMm14LufK9DixCffXv3gcipbmS885e6qD7u2z/cM3w/97s5toM9WEVVNv7lCErLCg4SbDZH/pWZXGKrRMpYtMCSTZce6lp7v2yE6KmZKZ+D49Xg+cPdUx7fvI5DXmIufMtOmnmnpzhyJkhQUNNxmm+0s/0wotUwJJdhwjq+bGAXjjVTpJ98R3tsGnJ9o5+c2EzMRrL68ozeh9ZPIat7Jqna1rQ0FAJp0/VC4Lha8IMxkKuZWBOOIQBN99x+I/xf1+GQ5KuAc+3E6RTMKCpjuvwlcHUOFCCncCZtKYmsmxmTagePPOtSJJbHjtfWwT5BfkwK496/hmEvcxM7l291oqdh4xLCjTBqYKCbq9oCQLhbsSM9GPv/i8Bx57YgucPeXo0+lkDIa5aKqi9rv/mW0AHg8PC0p1zq/S/qcwP1AVssKCwc2yTSU7r7hnL93x+5/ZBs//cDec/9QnVaaMfD1ej3Ru8bmZVqfJrk+shFnFnJeXnfacX4X9T2F+oSpkhQWDm9WAutE9Dl983pPUmZHJdWy7tyxlZdpQ10sXmdb3w97HNs3qPUzn4sgUyuVw+0NN6iksGNyMqS/2Nf6xJ7ZAdo43IdMh2df8mVyHGF7EhiPcz51OSlDTbnc+1KSewm0H99fx+ZAwWPV5/rOupAE7yb7mp5IjUp3/8NuJUoZIrNNJCcmeo6be7k4oyUJhwWK2EgarSDNJIEv3czZtN9cIy9lICWrq7e6EImSFBYv5yutlAT+79qxLkATSacmZvH4mwxCzGZhQevDdCaUhK9xxcGuyp461SrkSCgq3GplqyKpCVrjjMJ9+YAWFWwnV1FO443Grp9XmuyGnGnx3DxQhK9zxuNWENt8DGmrg4+6BkiwU7njcasfCfEskSnK5e6AIWeGOx60mtPmOoVSxlncPFCEr3PFQhKZwu0BpyAoKCgoLBIqQFRQUFBYIFCErKCgoLBAoQlZQUFBYIFCErKCgoLBAoAhZQUFBYYFAEbKCgoLCAsGM0t48Hs8oAPTcvMtRUFBQuCOxERFXTHfQjAhZQUFBQeHmQUkWCgoKCgsEipAVFBQUFggUISsoKCgsEChCVlBQUFggUISsoKCgsECgCFlBQUFhgUARsoKCgsICgSJkBQUFhQUCRcgKCgoKCwT/G+HFhI3SI18JAAAAAElFTkSuQmCC\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",
"Try to cluster the above dataset into 3 clusters. \n",
"Notice: do not generate data again, use the same dataset as above."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# write your code here\n",
"\n",
"k_means = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"k_means.fit(X)\n",
"k_means_labels = k_means.labels_\n",
"k_means_labels\n",
"\n",
"k_means_cluster_centers = k_means.cluster_centers_\n",
"k_means_cluster_centers\n",
"\n",
"# 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": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Your answer is below:\n",
"\n",
"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",
"\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Customer Segmentation with K-Means\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)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2018-10-19 13:35:26-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv\n",
"Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 34276 (33K) [text/csv]\n",
"Saving to: ‘Cust_Segmentation.csv’\n",
"\n",
"Cust_Segmentation.c 100%[=====================>] 33.47K --.-KB/s in 0.02s \n",
"\n",
"2018-10-19 13:35:26 (1.35 MB/s) - ‘Cust_Segmentation.csv’ saved [34276/34276]\n",
"\n"
]
}
],
"source": [
"!wget -O Cust_Segmentation.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/Cust_Segmentation.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Data From CSV File \n",
"Before you can work with the data, you must use the URL to get the Cust_Segmentation.csv."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" <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",
" <td>2</td>\n",
" <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": 13,
"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": [
"### Pre-processing"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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>DebtIncomeRatio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>8.218</td>\n",
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" <th>2</th>\n",
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" <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",
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" <td>19</td>\n",
" <td>0.681</td>\n",
" <td>0.516</td>\n",
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" <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>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 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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = cust_df.drop('Address', axis=1)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Normalizing over the standard deviation\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."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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": 15,
"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": [
"### Modeling"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"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."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" 0 0 0 0 1 0 1 1 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0\n",
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" 0 0 0 0 1 0 0 1 0 1 0 0 1 2 0 1 0 0 0 0 0 0 2 1 0 0 0 0 1 0 0 1 1 0 1 0 1\n",
" 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 1 0 0 0 0\n",
" 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 0 0\n",
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" 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0\n",
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" 1 0 0 0 1 0 2 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0\n",
" 0 0 1 0 0 1 0 1 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0\n",
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" 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1]\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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Insights\n",
"We assign the labels to each row in dataframe."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"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>DebtIncomeRatio</th>\n",
" <th>Clus_km</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>41</td>\n",
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" <th>1</th>\n",
" <td>2</td>\n",
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" <td>100</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>29</td>\n",
" <td>2</td>\n",
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" <td>19</td>\n",
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" <td>0.516</td>\n",
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" <td>6.3</td>\n",
" <td>0</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>7.2</td>\n",
" <td>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 DebtIncomeRatio Clus_km \n",
"0 0.0 6.3 0 \n",
"1 0.0 12.8 1 \n",
"2 1.0 20.9 0 \n",
"3 0.0 6.3 0 \n",
"4 0.0 7.2 2 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[\"Clus_km\"] = labels\n",
"df.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We can easily check the centroid values by averaging the features in each cluster."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <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>DebtIncomeRatio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Clus_km</th>\n",
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" </tr>\n",
" <tr>\n",
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" <td>84.076923</td>\n",
" <td>3.114412</td>\n",
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" <td>10.725824</td>\n",
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" <td>10.907167</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"1 403.780220 41.368132 1.961538 15.252747 84.076923 \n",
"2 410.166667 45.388889 2.666667 19.555556 227.166667 \n",
"\n",
" Card Debt Other Debt Defaulted DebtIncomeRatio \n",
"Clus_km \n",
"0 1.032711 2.108345 0.284658 10.095385 \n",
"1 3.114412 5.770352 0.172414 10.725824 \n",
"2 5.678444 10.907167 0.285714 7.322222 "
]
},
"execution_count": 20,
"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:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"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": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f5ab40754a8>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"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"
]
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"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"
]
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"## Want to learn more?\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: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\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 [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
"\n",
"### Thanks for completing this lesson!\n",
"\n",
"Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>\n",
"\n",
"<hr>\n",
"Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
]
}
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