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Created on Cognitive Class Labs
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{
"cells": [
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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>"
]
},
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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",
"- 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"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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>"
]
},
{
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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."
]
},
{
"cell_type": "code",
"execution_count": 2,
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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"
]
},
{
"cell_type": "markdown",
"metadata": {
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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"
]
},
{
"cell_type": "markdown",
"metadata": {
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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,
"metadata": {
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},
"outputs": [],
"source": [
"np.random.seed(0)"
]
},
{
"cell_type": "markdown",
"metadata": {
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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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"metadata": {
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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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"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fd49c0ba7b8>"
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},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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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,
"deletable": true,
"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."
]
},
{
"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=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,
"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": [
"<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!"
]
},
{
"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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\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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 3.70310165, 3.69627302],\n",
" [-0.80700142, -0.17035769],\n",
" [ 2.02030226, -2.94614399]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# initialize with 3 clusters\n",
"k_means_3 = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n",
"\n",
"k_means_3.fit(X)\n",
"\n",
"#grab the labels\n",
"k_means_3_labels = k_means_3.labels_\n",
"k_means_3_labels\n",
"\n",
"# grab cluster centers\n",
"k_means_3_cluster_centers = k_means_3.cluster_centers_\n",
"k_means_3_cluster_centers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nZZOjJQdF5NvnXa9t2x5y07bPpZF1HHIGsPLp4BdP27a9siJwOwXdIrkui8Kf/VjRmg0hGxgMguH+sQ63dLd543Zy4inqqKv3CLhh3SZKne4lJ2VTw/pNPgJmslQEJQiQq/X2/fQ5irW0+326XvS6mbKOK+xqGX95NH8Wbz1F8RNddPqDwz5Sb96wnexowvMquxnHZ19r27aHso7jReFcwcfHC1+znzD1hF7wWe5cs5bsaMLzZjf9SrhMwp7vUJOBY0VrNoRsYDAIhvvHOpzjOurq6eTbH/p6UXjksn4zRRtbvcQaF3A0b9hOTjJNrW/u9vZrfWO3J5k0b6wl13Z8ESSXXWfiKXKSaUn+aS+JZvf0kRNPKRJ9bDNl4imK7G8SEsP6zV6CrvtAC2XdLDnJNCXaI5TNZr37cWz1wkh3x8ixM+Rm9ISeeKk4ybSMopXEw5Y1jnhzCz5sn6yhyzeK8M+fsmkiQ8gGBqEYjtww2MocAx3n6bKPbSYnnfFV8+kVeq7teJFhZH+Timilf5ijU52M+F6cpB1wL2yWhRhpat+xnzJ9STr1/kHqfO8ARRtbqevDJtrz46e9xCCTXdBv7EkSGaFBswujbdserxdGX6vQozOxBLVu3U26e8JJZ6hpgxhv564DniTB1Xu5iT31kss6rkfkHXX1Sr7RXkoj8bsdLRhCNjAIwdALOAov/w0eZ0fj1PneAWr61Vu+Rj3p7phwOqRtirW0k5O0teNe0PRkfxTuRbGB1UQi+5uoY2d9bsVd2lZOC2lH44o57t6mR6cfPfYCJU52eT83/Uok6/QS6IZ1m6j1jd3EZd1ieai0T2NOnOzyCDzZ2S0i/VSaGtarcnG22oURaNZxPfdG27a9lOlL+i1+gaTgcH63ow1DyAYGIRiK3DBQT4nBjvMiTxlp+qLexzZTpOGo35omS5KJVGStT/M9ku/pEySYzuSukSedEQ3rhNtBlw+69jV6ZMhRLRd8eHJGNE7RphOeFOGm/bp2+456ysQSdOr9g+TaamxcNcjEffDxl7zjWILwxiIr/fg4rkhU957ytfbk0vDI/qYcT3MQLOOYCNnA4ALEcDVmJ56mWEt7TgJKT3z1tXb6CL/veIdHrGFT+mCpsh1N0OkPDouIdj0n9eqFbNGX9DTi5o215KRtavn1DrEaSDIt/cQZ6trXSOlIlKKNreQkUpR1HGrduluuGNInPMeSnO3eOGUdh+InTlO6u08kKKUDQ7xAaslN254DRJdOIh8dVTa9aEKbESjN3EmK2UImnvQicf0lpX4fA/XCGJtyBZEhZAODUUEYEeR6bhPkpDOCJPuSosva6V6PkDKxhHBZrFPRqDelX7/Z16VNl08i+5tELwoZVTrxFNk9fdT0nLLEqYo+MR7XcTQJQETcDi/LJO1y3Qda6ODjL1Hb1j1egi9+sksQecqWLow4xY62S7ucK8g540jHSFpoxrKXhegUt5ecZJpafr3De9HwC0tPaGZiCeo9LH7WZwpuxvE9n2AvjLEsVxAZQjYwGBWEtdzMabfZl/RZujLxFDkp20viNW+s9SSFYIOfsCm9V7gRT1HThrdkhJkWq3n0Jb3ofM+Pn6b2nfU+PzOPWf8sm81S+8561dQ+bVM6EpU9KqSz4bm3KN0d88qjdQLNOi517jrgmwFkXdcnyaRO93rErEfPzRtrKeu4Htk2rNvk3auvgVI07hWnhPXCGCv2tnwwhGxgcJaRr+XmwO02ZW/iQOLOiadl8sv2ouiw/hKndh8kJ6W6s3muCJlEtGWy0O4RFXzBnhdE/gZGIvpWUonwHu+ljrp6XyKPfcGu7fii1YZ1myjV1UtZ119qnZFkrpNm1nUpearXF+mqZ5Z7r9weVJ81sP481gk4CEPIBgZnGYNNk/22N0GIp3YfyllWieGmbera10gfPbZZEOD6YHc1EVX2HlGd13zFIOs3ezLBzjVrvUj54OMv5XR6Y2SzWd99dO1rpEwsQTvXrBV69c56L5K1ownvheKTWyRJummbuj4UUsvJtz8ku7dP+ZS95agSXpEJ69miUjEt3BlJ1SLUSdpacczoNHMaWIIa/hgMIRsYnGUEo7TgH26+Hg35/sB1d8ap3Yc8CcC1Nf+yLE32km3ROKVOi6gz3a2i5XR3jJxkWrTflC+D1q2iAZAjfcV6/4qG9Zt8+nRHXQPZ0Ti1/HoHtW3bK8hSlnknO7ulvuyIiF0raWby7T7QQk7a9sbNUTY3xPd0Y80KFz/ZRa6dIddxPFfKK7d8hzrqGob5u8nt3TwYwvYdCX3aELKBwSgjbKmmfOXCTNRZx5Ul1YLU2rbtpazjUiaWFK6IRNpHsulIlDp3HSAnLSLhdCTq9bXQexYL50anV/bMyzVxVKv3yvB5h9c+L6x1sjQ63R3zouBe2ROj6bm3RG+NDds9H7Qny6x9njp21ot+Fjnl37WysGRvTvN8J2V7GrHdG9ekmOFFx/wiCFsSaiCE7TsS8oghZAODs4R8EW6hf7hMvG7apqYNb3kRotiWoPYdqh1mx856P8m2dpLe24LJOH68kyL7myh1uld0aIsmfEm5rOPm9MpgHdnu7ROrh2SkmyJte0UozRtFO87jr+3y+jB3fSg8wXoFoih6kQ3rk2lq27rHkzeyTq5EoyfsBOErr3LD2uepfUd9XmnH72LJXZ06n7Z/LmEI2cDgLOFM20Ge2n3Q02DZecHJKn858wvesk3sVuBomSNb9u6efPtDLyGnlyjvXLNWSBOyTzFHoCwLOCmb9v30ORnJbvbpvXY07nNERBtbRXSeskUT/GSaXFe0/czEk54eLBoTJejU7oOiM1xo4lOQqWtnqPdwq2iCbzua5pxLpKLvhXpBsQzi19gLX41kNL3LhpANDM4Shj4FVn/0zRu3k5PQHAha5ZrYXxVL9Da2EZGwunXsrCc7GvcIlu1lRESunVEEF+jspneDa95YS27GkR3j0l7iLn5ClUtzdHrw8Zd8x+p9lT0Pc29ceJDlaiK+JaVSad9nDes2UeJkl2d/a90qVhnRXzYDgR0Xeu8N7kwX1ImDq3jnw2h6lw0hGxiMAQT/6N10xit8CNq8GKKxzl5flOhmRBQqOrkl6eDjL/k6tnE0zOdmwnYdoSH/w3/8HtXMm09FlkU18xbQ+m//2FsPL+u6nuTgJQN7+lTCTxKm6zjUvsPvYW6XLgwn0KKTI2y9FJrHaveKhvfsKAnq7tymVF8lJdrY6iUvM/EUuXbGS1LycflIOCwSDq5deLZRKCEX/+QnP0GheOqpp37yzW9+s+D9DQwudky9egHGzZiCmodXo7i8FEUlxSidNB7HX/kN+lo64PQlUTSuFMWlpSguLwUAWEVFmHzFbPQ7WbjJND5+8mVULl0E9BNaX30PVcsWo2RCOaqW1eDI06+hZdO7uPRzn8IVX7sDLc+/jWnXV8Pu7sPkRXPw8eMvY+uRvfh/1/8D/qB7Nh7CYkyPWXjio62Y4BZh6fJlsIqLQEQYP6cK42ZOgVVSjCNPvYrffnM9pl63ELM/fwNKJ41HZ10DKq9biMpPL0LFpdNQ/dDdqPuT/46yqRMx9ar5sCwL029egmMv1mHHH/5XVEyfgqobr0TPR5+gZEIFPvn1Duz5wZOomDkNVkkJdn//cdz4j3+OipnTsOhPvgAUFeHwU6+g72g7SiZWoO3V97Dnh09i3IwpKLtkAsbPrsT0m69C39GT+GTj29j5n/4byqZOQuWna1Ayvhwzbr7Ke4Y6Pn7iJex+5AmMmzEFM26+CgBw6PEX8cHfPItFX1+FydVzzvr/g7/7u79r/8lPfvLUoDsWwtpkImQDgxGFkxIJPbu3jzrfO+Bb7Zmjud7GVq/iLrK/yfMUi0hZLMeUiasyaN22pssNC6bOpB/henrWusP79yNcTzXzFki7XMzTkf3RqSgccTOOV17NvTFc1/X1rWhYL5Z3anruLS9ZJ5Zz2is72qW9KJwLYfj8XfsaKRNPUl/rKRkh1/rGYPf00c41a6ljZ72UXPb6NPh8Sz8xwqLnfA3zzxZgJAsDg7EJXrbJjgp3ASfcuLfEzjVrqaOuQdrfpKYsm8x7yTkpI0Qajvq0W9GVLkFx6cZoWLeJiiyLnsJKHyE/hZVUBMvXdrOjrp5afr3DW2ev68NGX2/krJv1rHa8vp6bcTz3RvPGWknqteTaGeprPeVzesRbO3MaBjVv3O7ThVmm0Tu8BRd59T/HoXV3O1dNiAol5JKzHqsbGBQIpy+FI8+8hpqH70XppIpzPZwRA9/X4jX3g/r7cfjpV7Hwj+5C62vv4fIHVqCorBRHntmC3Y88AcDCLf/yPXTvawQsoHPXAez54ZOYftMSdO35GKlTvUiejGDPj34Gq7gYc++9CTV/uhqwgJqH7wER4dT7BxFrPInyaROR7oriikvnorE9iiWY6o2pEVEsnHs5bnzkW1j0jXsAAF17DqPm4dViB8vCJYvn4fDPXsHuHzwBIsJV3/sKJi+eh4//5SV88sIOTL9pCabfvASL//ReWLAwb/VNSHZ04/Ivr0C/7eDYC++i+qFVaNn0LgCgZs19aNn8Dub/h8950sK81bdg/OzpaPrFm979X/39r6CkogzTrl0oxlIKHP7Zy77tAFBcXoorvnr7kH4XR555Lec8YwqFsDaZCNlgFDDWO3YVCk5MsXvCV+asWbYyfWJ6Hj/R5WvEwx5ftULHXs+CpvofC/8wd2VLtEeEl/c5ISFEtSKOf/+3X9ClFVPoR7iensJK+hGup5nlk+nptf8fuWkR5epuCC61Tvf0+cbRunW3Z5lzeEHSeJJSp3u9ir7Y0fYcpweXX5/afTDH5kdEXu8NXUIQS1HVexHzSPWuOFc9MGAkC4PzDedbwxhGmLVNFT0kyUna1FHXEGgoJIlWVsU58TS176indCRKkX1NPoJkB4UdjQtyiycp1dXrrdThxFNkR+M+TTXruN7nHz22mf7tZz+nyydWCZfFZQvoF08/4xGuJ5loi5ymu/so2RkhJ5mWWvAeXz/mdHeMbNk2lI/PxJO0c81azZMsJAzXzngFLFwIw8+GiMhJZ0TZtNaAnl8mTnJsFHacKQwhGxiMEsKsbfqSRkHC5vXv9M5l4mdBbtyBzatiS6a96HrnmrVeEUcmlshZVZo7yoWNzUmmPZ3adRzZ/Edsb9u2l7LZLMXbTnkrfPCyTwcff0lE83LR1X0/fc6nL3PUm3Vcija2+hZy1ZsXNW+sDe377KZt2d1OvYi50X7YCtbnIwwhGxgUgJFI8gzU/0AvWhBkmcmpJPP3tnAosq+J2rbtodY3dlPbtr0U2d9EJ9/+0Ot7rBOaHY3Tvp8+R7w+XrDCTV+eia/bvLGWstksJU91e5GoHiGf2n2QnrXu8BocceN6JvTgklT8Eug+0CKr7bQiDb1Eu6ePnHTGeza+xkiRWGDRUzGe1Oleat5Y6y0fNRK/s3OR2DOEbGCQB2GdwJgsCzmWCyb0rwMdm4klfXKCaztytQ9HG48g9OYN232l016vZOlcaN7gb9ruOg4lOyM+Qm3btsdrViTGJnpdpCNRv6ND9p3QLXL6GnhitWv1uZNMe53bDj7+kq/P8qndh8hNZ3xl4SxjRBtbQ/tS6Np1sGLRF9mnbO+6wW3DwbnIVRhCNhghOER0XH69MKBHpEGyDCKspSa3keSv+f64+VgnaXsWrdxWlSKiZRmDGwY5iRRFG1t952veuF1EjBsEyXJ7S9fxN4fvfO+AWIlaa9np2hmKn+jKaSTf19rpS97xKtGtb+z2tQDlSJVtdbxCiA7HdjzZZc+Pn6am597KaZ3pPZN4OlAtpzWmt52cJKde1Ximi5mei1yFIWSDEcJxIqqVX/NhqKQ9PJIfqalm8A/SSaa9JYqCCC/trVVFEvFUjlQQbIITJGtfq0pJWNz7VxRHKJlAhxMXrTg76uq9QouGtc9TJp709YRgD7LeLCi47p5a7doR5N98wouMOeHopG0phdR7ZdCcoOMObVzIws2B+mQkzmXYmT6O8Ld7Lx3dcfLKLd+hrn2Nml9Z6OnpSJTad9SHSDBjezHTfDCEbDBCKIQ885F2vmMLIflcnK2pprBYNfhWq2AMFk2FEYTeaN6THZLJrb0FAAAgAElEQVS2JFLltNAjZI4WByJyfYofbMvpOo7vHnxSg9Z7uHmDaKfZ9WGTT5PO9CWpaQOv8bedYkdPiiIRXTrQztOwbhPFWtqpfUe9r7Aj3R2jTDxJbsYRq1lLe5yyu9l0avchb6bA1/a1BpURcJgMVOj/gbFG3IaQDUYRQyXeQiNk/35n0mWtkG2D9dkNQxhB8Dj16M7u6RO6b0/fgOcKtt7UPc1OMmwtPJEsc+2MiFI9x4JK5jnS18yRrYhoE94KIM9ad6htUoe2o3ElWXgJOdvv/oinvEhWj/pjLe1EJJrE71yzlpxkmlrf3E3pSCwn8hdR817vOoX0kh5rndwKgSFkgzEAh4h6aPj68/AiaaKB/yAHkxI+euwFOvj4S6GWtSBGiiBcW3VL04nct/Jyb9wrq2Z07Wv0Vg3RdeBT7x/0+iJnYgmKfHSUstmsb5XsyL4mL3GXiSWEBmyLXhHcajOyT/RMbvn1DmpYt8lbq4+IvGQjE7Xe6U24MmwZWWf8K4Q8tjnvc9J7eYQ968ESqP59w6PscwFDyAZnEcOLcM/edXIxEFEyUQS36YkkXuF4pCIt3ZvrH6NWjBGJ5iTy3HTGN50XenVKNJfXiG7Pj5/29mtYt4myjt/BEFwKyo7GyUmIyNV1XIo2nfAsdkxi+vPgNfB82nAyTdHGVtETOZ2hxMkuL+JmRwSDE5IDNaAnGt6LNB/GkmxhCNngLKLQyDVsv3ND0kK7zd+DmEg0gheJpJT32Uhl5J2k7a2ooTs29JUvPnrsBdHUPUAietN672e59h3/zEsmccKx+0CLJ0/wStX6GnaunaHOXQe8kmx+Lvt++pznNgkSYLIzQu079nvOja59jb6qO9dx8j7jTCzpOT+CUX5wv4H7Gg9M6DrGkmxhCNngLOJMIuThyxADHT9YNBRrafclpIYCLubwW7WGHnn5knC2o/VvSHguj8j+phwS4babYn9/m82OunplNwvRvdkbHGxn2bZtr1hD70SX77kkTnb5ZAdvfFISycRVUYdrZwqy/YU3hh/8+YUdr0sug2EsleIbQjYYozg7ETL3Dc7XG7eQCDmIoH2to66BOur8K2YMBTpBeNauncLCFtnf5BF+WO9e3SbWUddAkf1NZPf00cm3P/R7iDfW+mQHzze9fpPv2EwsSU46Q45c/kl/LrpPm4jU4qPyReZmHE8yCUtgMgbT6gfrYzxQwnQgktW9zmMFhpANzgMMl5xdIurwHTccsh0sSgtzPeRb+meoeqVrO9S1r9FLHhYyRr0ZvS1LmWNH233asZOyvX7FXOWnuyuC4/vosc2076fPUToSI0euDu3ajq/wIvhsdW3Zux85i9C7tfmWhQr4tFXXuoESpkP7ner3NFakCoYhZIPzAMPVmHOPG+ofYdj+wem0WtUi7dtHL3se7vX91XopmWjM9TP7mhYFKvI6dgo9NriGXlBGcG2h7ertLL37kdq2ssrlrhA90LNzMw7ZUVHIolc7sszCPmW9S1zQK53vmfE5IvubCn6e+u9tLEgVDEPIFz1GquT5bJZOF6oxB/fjnzPeZ8GpbL7oigsWcm1aIuk0mByRj0SGqlcGq/VYO/Y32Mk9p+fW6OlTL4x0Ri6MqnpYBI/L17ODizPSkRg1b9juGxO7TNQzTQa05aSvWET0o1BWO91fzLq3Xp2oxhnu+86nN+crxhlrUbEOQ8gXPQZKnhVKsu4g5ykUDimJYbDrFkrSPUS0N++4eMn4rn2NRBRS9BFPUeJkFzU991aeRNvQfcfBaXu+4/XCDa+yzc5ohRj5p/FeVaHWA0PvvBaUP3wkKltl8mrVvqpASaa6xDBQVzqGv/dzwtc3OTjufA6J4HV0hBHtcLXlcwlDyBc9BiK/Qkm2g4jsAc5TKI4TUdMg1x1ovGEFJgOTO/ds4K5muj9WWMyE9ctzHkTj5NhOnpUqCtOGg1PssGP9ZcQZird2quXtJXkOVXphnTtMl9VJ1M2I/sZcqpxT3i0bEHGj+OAsQ3+JOPGU92LRCzDUCyNVcLVjIVa33Ah57JJvGAwhGwyAodjWOkhEymHITa6FX6OQCHkkIno/dFlA9PJ9wSOQ1OletdKzXC2Zu6fpPtnc5kL5E01O2iYnZXuOiTBy1ZvXf/TYC5R1XN/Pru2E9jXOh6DOrY9XuCkUie776XNSL1ZSgn6dYLIu6FyJ7G/y+ZDDXC38ErJ7+gq2p10MMIRscJbBJGmTINsghiJ1OCT04ELI2pX76C+JcMLmaM2Oxn2Ng4TeuteLsHjlY46odU0zmCDSPbtZx9U0VXUcV8KJIo/cSM7ujfuIT4/4Biod1jFwP44XvGWfOFoPS1AGoUfTWdf1Srl154TeLjOsBaYv6VjXcF5FsWcThpANRgCFRrRhEfRgEoS+7TgJPbhngHFwpB5G3vpYekgl+3LHwG4GJ54mN+OoXg+BFTC4ci4IPUJu27bH1xPZK2GWiavexrbwuwnowDoGS05x8nGw/ZykHZoQG7xXhJA9GtZvotTp3oIcDsFznG9ywmjAELLBCOBMZISBGgsFzxuUN8K+1+WRGKmEnkuKoDPy82PasfpLo4f6+x3hu+2OeUkubknpz/rXDiobMIGpnsgp0Uc4mRb/htHYZjBCa964nTrfEyXPQS9wEIUmxPIdV+gqKoMhLJofqMR6rPSfGEkYQr7gMRoreQznGrolrSVwDibUgc7bQ0SHSDk89mr7szyiR8ExuX8scN643OeY/OoS0THq73fIzTjKDbB+MzmJFDVvrPVWPnaSrAOnZLJMlyUGaOfZl/T2E7a0cH/xmUBvXs9adf59czueFV7pNnJRbthLIKyMPaivX0gwhHzBYyTsaGcDuvwQjFLZaWFTuMyhR8ERuV8P5Ua6e8mfJMzIfxEiysptTOQ8DvFZf3+G+vuPU3+/aDOZ7o4R9/blZee53LhjpyiTzrdUU7C0un3Hfq/DWsPa56l9Rz3FWtrzWLQ2q65tQyjxzW/50osi/N7hcx1xhjslciNkv4VuZMd7rp9DoYRcAoPzFLMCX88mXADt8lqD/ZeZBeBSAB0AJsrv+fMuAJUA0gDGASgOucZMABEAVXL/KnkePgcBmCE/PwmgSW6bDWAygCJ5jYz8nK9RCaAcltUBoAlEwMwVn8Kuv/xnTL1qPubddxMy0QTSkRiu/PYD6NixH5XXL8KUq+fjxNa9mLf6JliWhZqHVwMAsraD46/+FpneBBZ9/QsALEy7biEql1YDloXqh+5GcVkpUFSEG9d/2zsOAI488xpaNr2L6odW4eRbv8OEeTNQMqF8kOcqUPPwvQCsnPPtfuQJABaqltegZdO7qFp+JWZ+9hrftqu//5WCrjHSKJ1UkXPt0knjcc0jv+/7bN7qW9C6ZReW/MUDKC4vHdExjIXnUBAKYW0yEfJFjjNZoqmVlJzA0WorKWcGf54vSagn9E7Kc2VJRM96hBwP/Nwrj8tf9dffH6Fs1vF1S+OVMZykTU46Q32tnV7RBu/DHdR4DTq9XWchEJF1wxlVl3EkHKzMCybzRqKM+FxHlyOBc51shJEsDEYOhRBvvv3b5dcoKZcEE7NLSjPWnRMR7Vp8jaCPORI4VzownqCmHEwQRklp0g719x8j1xYe3fad9WRHExRtbKVnrTuobeseymazXmmx5+P1VmPentPbotCqveGSRE6fC3k913ZGvIx4rJclnw8whGxQAEayFaYeyerkGywecUgl2vRjXCJKElEXCa1Zj6B5H51we0np1RnKJewkiUiaiCgRODYr//UQUZu3TWjLLu1cs1Y6DGxvuXtu3tP0q7e8JvKqmEOsZ+c9lXia2neIlZp121i+SLNQ77GOIJnrHuKRjgbPdXR5IcAQ8gWNkXJYjGRiUC+zzpCIjDlqZdLUrWnH5Xa9NJv/9WjfE+VW+jGZu/IcLE10kCDaJOX6lfXzRij3ZSKSjf39HZQ63UtOIk2ndh+ibDZLbVuF37h5w3ZqfWM3RRtbfVVtYiUQVbFmR+PU9Fxuy8tC1vIbLkabNFViMl2QpHEhyB5ngkIJ2ST1ho2hJLpGGu1Qyax5Z3CekUwMchIuA6AbwHQAljz3JIgkH495IUSiLgWgV34+AcAUAP0AYvLnLMSz5fu9CiKh1yzP0Q6RNKyU26YCOC2vnQLgADgFoEzu0yevmwSQ0MZTBZEU7IJlTUN5pQXAQtUN1WjZ9A7mrb4Z6a4o5t17M1Kd3Rg/bybgZlF14xKACMdf+g0u/8rn4PSlcOSZ11D90CoUjyvDkWe2+BJJYQk5IDxRlw98jZqH70XppArv87DE2dkEJ8mqll+Jrj0f5yTMguM8b5Jq5xiGkIeNkSLF4aAQIi3khVECMXYXQOsg+w6GYih3QyWEG+IEBNGVBsbcD+Gk6AGwCIJ8J8v9L4UgVUseC/kZAZgG4aJgF0eT/HwyBJnzGLIAygHEASyV59AdGZUQJM3nrgdQI4+ph2XNADAFRONRMXMaDj/9GtJdUaQ7e7HgqysBN4vO336ESz93LY69+BvMWbUc1N/vI535X12JWbcvBWX7sejrqwDkJ82hkOlYITZ+iVQuXYRp1y7EQM6PgV5GBgEUEkaTkSxCMBqFGWeCocgRw5UuwkqgaynXNZEh5YyIkJIcWFII6sO6ZMHJOZYhTpGQKYLyRZiObGvn14tE+uQ43MB5Mjnfq0KSvZ6GzL19mzdup/jJLjr4+EvU+uZusnsDvR/SNvUeaSUnnhq8qKTAqfyZSBNnKhsM5Xi9p8ZYWkrpXAEFShZF5/qFcP6Co8uxOsmYBaAahckRQ9lXB88S2gPnmRg4pwUR/RYBuAQiuu2EinK7AvuzB9WGiN4h94E8h+4tLoKQJzogJIo4hBzhQETgfH4LIpK2IDzQkyH80EUQkXh34JxibJbVjuJSC3Puug6pUz049M8voukXW7H8H/8Ml933GZRNrEA2ZWPmLVejaFwZrv7+V1A6qQJOXwqHn3gZE2ZXIdZ8Ai2b3sWRZ7bkPEGOJFu37ILTl8KBf3oBTl8KAHJ+BlQ0rcsVjLD9w64VNo5CMJTjSydVoGp5Dbbd+9c4/PSrw7reRYlCWJsu6gj5TMqHx2r0HIwuh3qcE/hZPw83jucItIP8ibws5Sbpgkm3Q5SblGvVjsmS8B0nSUS9e8kfJbukEnd8jEtEKfK7PvL1ygjea4KIXOrv76BsJu1FfAOtMMId5NgTzI3og1EmR5K8OkdYQ/gzWZZKx5km/oZ6vHFnKMAk9UYKQ9GKXYgIrW8Ix5wL8D3p+muhx52C0F1bISLLSRBJtjny+4kQibdLoHTbaogk3AQI/bgfIpFG8hiOoJvlvpfDn/CDHCNHhSegovnZ8ngLQpeeKq97qfyZ7+00VHUfVwNODpy/CiJqLpHbMxAJxskATsKyZsIq7UFR6Uy4qQwm18zDxPmXwiou9rRRlcxajSlXXo6mX7wptVRgyXe/jEP//H88bXXBV1eibMokTwsO6qw1D68G9RNqHr6noN/OYDrtmSb+hnr8aCcaLwQYQh4UQ3EiMGFdO4RjRhMuBNHwuC6FILJCwY6JbgiyikKQVTUEuXJicLb8WS+btiFkilIIFwTB755gYuXEYhZCWuDzdMrP9JedcEaIYxIIL6eG/CwD5bqYBPVCuEp+3yqv1w2R3OME5SWh5ywqBWC1o7hsNq7+/lfgpjJItkfQ8vzbHuEu+sYXsLj6PlQtvxKVSxehP5P1yqxrHr4HrVt2Ye4XbgIqRIIxSGCtW3ahuKIcRIQD//SC51gYqtMi3/4GYw9GQx4UQ9GKZ0FEh9YQjhlNtAM4CEHK8yDIcbAxsgPDlftOgXIpTJafxeHXkzMQJFYPQX4xCJIjeWyv/J5140qIqHm2Np4E/LpvKUT0PRvihTcb4oVwUF5zgjxuFkQ0PkuOOQH1e5klj+2T1+e+FycBzIUg4yb5fIogon4H4oXA5xQoKulEUXEXYCUAAP2Og44d+1Hz8GrZu+IenNp1ENTfj649H4P6szj+ch2aN9Tisi99FsdfeQ/vPvgoGv/1Df/TTtg48E8vwE3YuPzLn8PMz16Nxmdf92m3YVruQPrxmWrHBqOHscYY5zmYvM8WztT7zIRSOYRjIsiVX3QZZzYEUU6AIE6OuudAkDD7jfVjOFqdAGFlA0QCrhIqyh4PQeRTICLjKVBR9SwIL/FkADdASBmW3K7f3yntnnvlOfVxsHeaf54D4BaoCL0DKik5VZ4/AiF5zJL/2kE0Eb2HjqFsyiT0He/ElX/xJXS8uw8d7+xH70efYM8PnwQIuPIvHkD042OouHQq5t5zI25c/23PEpe1HRx7cSfm3L0cPQePIZvJoOnpN1Hz8L2YvGYu/FLGwA2GglGysZydPzCEfF4hqGezZj0Rhf0qh+I71sn/KvhJXJdxEhDRZBnE9J4gCOyk3N4mj9WPYV22HYLQWVLIILd7W0qeOwUR2bKG3QMhMfAkj3VldlGwzFAGQfpcIKKPow2ChMsgyLkfQmueBUG8fK125L7MxD1alugcV7m0Gp/8752Y+Zmr8fH/fBE1f7oagIUZNy2BVSTIkJws2ms/xKQFs1EyqQILvroSRRXlwpHx9KtY+Ed34cTWvbjur/+jpj0LgtVJNkyaGIh0jZZ7/sAQ8pjDQFFwUM8mqKq2oaCQROVA+5RASQaT5Wf9EBGobjcDBIGNh7Cq8TFTIAiUCZMJkMluJlTrTbagsTbPVX/NUJIGSw2ASv7NgqrQa5fHFkElEgEhUwCCsDkRmIB4yU2HIPZgAcokCD3c9l0j/kk7xs+pQjoSQ8+hYzjy8y24+j//HgBgyXceQF9LB9pe2+WR7JXf+iJann8bV37rSzj8zGsiiga81pODRbVBXdiQ7oUBQ8gjDp1QgaFFsECuHKD3DA5KIjoxDUUqKSRROdg+UQiSciD0XRuCwE7LcetR9UmoSLgJwHXyZ45eASEJ2FCJOq72S2pjsKCcHDy2mLadk4hdENH6dCjihzx2AsQzmy7/lQf2WSTHOVH7x+e2IAi7HOIlEwdQCcsqwSWL5+GSxbNA1I7P/ux7yKazAOBFv4u+vkpL6K3GsRd3YvcjT6BiVqWPfIvLS+H0pdC65T1c+a0v5u0LPFYq9gxGFoaQC0ah+i0TKkdnQ41gmVw4SpxZwL781YUgMdY+S/KMuxCtO98++vkiEOR0GiqaDL4gWuGPhLlMmiUN7mMxGyKC5ciYrXAZec0MVOP6IogkXRTCpjYRgmzr5efs8uiEn6x5O/fS4Khcf466pszkPwdiBqDfN5doM0mPA9AOyxL3WlIhfm9H9Oj3Ow9gyXcfABFh7qobsfwf/wxz7r4hJ7plsr1x/bfzkq3RhS9MGEIuGIX6kfmPmy1YQ4lg2ZY2G8rGNRCCpJmCkAuatXGGjTvfy2Wwl042cL7x8pqV8NvpOOlWrn1WKc8ZtJFdCkF6Fvy+5Snwk18GIorugupbcQmUZFEJ5aSAPB9Ht0yovL0I4iVyEIKcp8p92HXBVrhGiJfFHOQmJYFcqWM21AtHaPSL19wPwMKiP/kCKNuPftvB4Z+/hiu/eT9m3fVpFJdxTw2FQsjWSBQXJoztrWAUWl7MJFkMlRCbPeARChEIkjgJ8Yd9Eqp0mOHKf73aNk7SsXaqj3MWgOsD42ZyiWjH6mTbjnBwxM7nd8GRobLT8XUiECQIqPtPQkS1+hgnQ8wi+uF/XhF5zGTtHDYEYbIlDhBReY8c/xz5WT/Es+OkXwyqE10EIqqu1MZQAfUsk1BWON6egiBd/nkiBGGzvtwB9fu6xPccSyaUo2p5Dbbe838j8rtGHPn5Fuz5wZM4/NSrmHr1At/STWxds0qKceW3vojWLe/lLYM2uDBhIuSCETaFHyyiLIG/7eNQHA361Fm/bhaKZFgKiUMkvXjf4Dj5+oxgFK9LCvr2IDgSngMVYV6l7T9NXisOfyKMdXALgrB0cmRrHFfV6b5gQGnSBPESqoCQEljy4G38/Aj+JN8MKFueA6Vr2xAkWwSVnOTqw2AhSDnUzIUgtOd+5K4XqD9HljtcVC2/Elf95ZdRubQaU69ZAFgWFn3jHvQ7WRSVqBxBWEvLgWQLgwsPhpDPCIXIGLoTghNCwceun0efOodF5CXwSyGzIUjwWvir7pjkL4UiayabWRDk2QgxZYf8uUnuzz5cTq6xbFACpWnz+JjguARaT7pxIow9wjEIQtXbdDZBSAZMcvqzmARByDPl/qe0z9mSFiyxngQlO1TK7RVQLg8uoeaKtdNQDYmCLybW8cshXj58v+VQVZkLoSL367V7Vy/B4rIOXPG1zwEogZuwUXlDDch1cfz193HFV28HY6CWlvmq7UwV3oUFQ8g5GOoKy9xYPR/0RNdUee4geQd7BbOnNozkiwP754ukdWLjJFYSitTmQBVzsDd5Ajiq868AHYcgRP38+oyBE3fXQ3mFa+S2InlPXMjBJM9uioXwE3YwSl8KFR3rL52FEJHwNKhIlRN306GiWfYrE8TL6BQEIeuFH6xtA8r1obs9AEHGutOCx8eujYXy3jl61214PBuYicNPvyIi33XfwpLvfhk6grpwWKIv6KowbosLC4aQczCUZkIlUI3V8xG4ThZcLhx2njkQf+h6Uo4jWiYCjlxjUISTL5LWiY3dFjqpFWFwguUxAANX9/G1dJtYsBEQV8nNhD/5yJEsN/2JyWcRl8fw/etRvg1BfEx+HKmy+4ILROJQze5nyvNMkp/H4H/OjnavCXltpe8qF8dcCMK35fW4OpH15DDphx03esLuHrS/8yFm3HQVjjyzZdAIdyRWGzEY+7iICTlfJKz/MXVCTdXzYTAC52m8BeUGCEMRBImNg2qyo0e/HLkxqbE9DBAkFASTfD8EwTKplUHJDkELWyVUwQPk12IMbL3ja/G9Z+VYddfEJOQ2BeL7DGsI5CC3zJmjfJL3zuSpR6pp+Du09UF4nnWUymuzs4OPj0LJM/qYquW9TYQg40qolwwT6BSoF5Eu/RTB73Ge50XBR5+vRff+o+ipb8GeHz6JilmVPvkiiJFYbcRg7OMiJuR8drBg0qoa+SNlF4MnwrghTzdEqW45FCkQcn3D+ng4+uU/7iBx8fHcs0F/gYS1Aq2BIBEHIgrUt7GVS7ewxaBeBIWUaesEr3uogwUW7C0ugiBR/RkSgBYIeUf/3JL7dkNEtky4enRcDlXCHXxWXRBReTWUjj5dbtdbcjrI7VTHzzVYaBI2w9Cln17tGfj/f8xbfQvKKy/Bpbd+ClOvvQKVn70Wb758ELd9fhHGVYQXgwwHRmM+v3ARE3IYkertIPNJAQjsn4Do32vLz4KPVCepa6HIl/XJNPwOCR0lENFaM3IJqhjiD16fenM/iNkIbwVaIo9JQ0Sy+vk4MuyCIF5Aaa1JiBdIVn7OfTBsCIJn9yR3SeMkFzsT2EnApdOXQLyUeqFkgelQevNUqPaYbGVLQJAhJyF1kufIOQUldwQTc1VQyTg9gaoT7Gz5bOIQv6c58p7b5HPVfc56MY4+09JnC5MDz0DtWzqpApWfvRZvvdmIO+5ZitrXD+NXz34AWBZW3b8EIwWjMZ9fuIgJOczGFixiGExDngXxB1YCVQUWPIaJUe8sVgYVIfcid1rNcOFPfAXHpHdZ02UHnZDYiVGsHTMRyms8D4J0mNSCC5WyV5n9ucHS7tNQz4u3l0PptjqKoar0WBePQMgTE6AaE7GmPAsiqr1EXn+RPA/b0Cqh5JCJUAm8CVB+Z97vFMTvR2/TqT8njs7LIaLwavk598HQE4/ciW4acmdaPMuqgt+Vwuc6hXRqJo63dKKlKYL3f3MMS66dhZWragDLwm13VWMkYTTm8wsXQWEIFz4ECyzCMFDvY/08/D3k/hb80ZOO2RCEm4LfZ8xSBRdihP0q+I+9QxuTPo4SKI0aUEmxdvk5uz+4LSXfY7AApBiK2PVtTLIcqUegCiQ48uReyKmQ83Yi97nzShzdECTKpMrFHICSG9q1Y3glEF59msfFEgq7WTqgmg4xMcYgfj96j2buCse/O04C8rX1Hs8d8v5K5XldKFcG90q+FOLFAKgKvuC9i4KYd7c1YeMvfofP3bUIP/jbO3Govh3UT1h1/5JhyRXplIM3Xz6IdMrJ2TbQGnwGYw8XQYQ8FNdEoeeZFDjnYH0fOLsfgdIhg3KCHmkCImoNc2YEp9z8czVUy0o9Ck1BETZDjwpduc8EiCg0TDtloufCj6AlbBrU8kuAImu+fz1C1yNw/b+f3gNEXz0kBn+SjYtNuIIvAn9yjq2DTMbsXOFtZfJ5cK+LYHEIv5jCGgtBPiOe7VTC30T/CAS5B/9/MIS97rbPjwcsC6VlRdi+5cyline3NZ4VucNg9HEREPJgSbfhnieoL4e5NoIkHqxs40buYQnENMT0mae8rNvq49CTRtxPgSvOuHCCq9v0XzVHzxaUR7hC7nsp/Ktx8Jh4mn6JvA5rvjMhXh5ToeQRfrnwPegJPr2qUC9U0eUiG8pFwSuN8HYmR4J45lVy/Bx52xCJuTbtfJVQ5MqVk5zQ1NcVTMnr8Qss7EXLVjx+6UySx7rwz5I4as7FuIpSjzhv+/yiM5YqRuIcBmMDF4FkMZAMUQh0eULvsRA8Z1gfCO7ZwA10qiGiydNQFXGcANOXHWqFSnaVQlWScbJtEpQHukeOKwNBFkn5cw38kggn5LgCj5N0HcidnrPWq790SuTY+dgTUPLNSajVPghKChgPtRJIQu7LVYWz5Fi5pwb/nmJyXy7kKJP7cQ/mNJT2DqieFUy6/Nyug3JRMLJQVY7B3hS9UH05PoS/zwffp609p06o9p0MfSbCUbNCmLTA5HwmzoqROIfB2MBFQMhDQZjezEm5OMQfdAoiAgvuz+SrR80lUAUFbKezIP6QO+Rx/EfEf8A6sXME2yfPzU4OvS8FT8gm8N8AACAASURBVMtZI+SmPnqjIZY+HAhCmirPWQT1stD3dbVzAxs2/DuuuWYJiouLcc01K7Fhw8+0MaagKtFYs+YXBnuYHfgdDqzzVkBp6vwcJ8v7ZDsfN+6BPN94CFKeEjgfR+uceCP5HPSfmdRZM+Yy9Yh8BmkoTbgq5HdRDD+RF8M/G/oQSnufgmAQ8O62Rrz/m2M41RmHbbt5dV+DixejJFkMpRz5XCJMb54FVYk1UX7O/Q2C+3OWnbVXzvDPkF/15Yb04zqhGr3rkkQS/so93dbWCUEaTFInoIo/9Gk4T/37oAoneuDXsq+EkjH0nsKnsWHDu/ibv/kxnnnmEaxY8SnU1TXg4Yf/AcBf4cEHb5Xn5kq0fgD74XebcGQZg9+Ty5ryTKjKwGAByUIox8QUqBU/WIbRnxX3qNBL1WfK56T3/Qha8fjarMFz8yD2IvNXVz43rgYM/j8eXBpbuaoGK+6oRl1tEyqrxuP93xwzuq+BD6MUIQ/W1nGsIF+Uy0sI8TR6KpRlivdnRwFH0O1QOqY+le0NuQ4nzrrlz3OgdF19X45kuTfwSfjbZnZASR8cRRdDvEC4ZaROnrMgqs/K5bkq5M/8MjiNRx/9L3jmmUdw++3Xo7S0BLfffj2eeeYRPPro4/JeWbtl2YGPZ7RDEOI0KHklSGYcleqROlfCcdKMJRJedJU9z/PkONrkfeqR/lH4I1p+wXH1YnBmwyTdBWXB45lLsXx+XDIddFAMLo2Vl5egrrYJv3r2A9TVHsXXvr7M0311OcO2Xeza2QLXzXqfJ+I2bLsQp5DB+YxRCldHKrF2tqFXW3ESDfIrr+WmOxyYEDqhEmCToBJPuk+Vk2PsDtATPiXw94+YBL/XlbP27JqogCCGsN4Jlhy/vvQTl1gHm95UQEWRqgGO3vfi0KGjWLHiU76ntGLFp3Do0BEIkqyGck2wXhwsRWebme7J1WdNTHyAeqY805gFEdnr3meWObiEOQnlFQ5WzOnX7YVaoilsZtOL3BJv1oj1UnB9BiLI9N1tjQVV2ekJOH3f4y3dXsS85JqZuOpTs3D0SBdamiL41bMfoL+fsHDxdNQsCSuTN7hQMEqEXEiRxViCLkXwgpxXwL+Wm25TmwYVlfGUn9tQcsEBEzCXNTMZ9UOQEFuy2Dcbh9JkuQBkItSSR3MA3CSP6YMipgqoUmpAlTwH+/e2QZD2PPitaP77X7KkGnV1Dbj99uu9p1NX14AlSxZBrcisl18n4ZcrgNx+zECu3MNOhRNQlYa61MBRLC9Aqss+LOXoUgJH0JBjYqLN1/c5rLKRoFahtrX9dRvcwLYznaytIgv79rbhzntqUKL1QU6nHLQ0RfCDv70TJaVFKC628NZrh7H7t8fwg7+9E4CFFXcsREnpyExoh/ICMRhdjGVBdwQxFA3bhb9DG7fNLIJ6sXTC35eiHMpGNhf+5X9mQ1nC2OHATgtO8PEioWzJYokjCxUFcvKJk3DstWV5gv2+weiPy7uroRwYXVAN4IFcclT3/zd/8//g4YeDGvJ6PProf4e/qY5eFccI66rG1X/5SDGMNPnF4UL175ilfeXr6tefJu+biZarIadCdZTTCU63Is6AcnvoLwV+Pgq27WLFHaKScuXdi3LIziNrWFhQPQ1bX/0Yn7p+DrbXHs7dRyP0lXcLAi8pLcKqL46sxmx8y2MXFwkhD6U4JAWVIGO9kJcZ4kY7lfL7Dm2/CVCN5U9ClTvbEATL9jDWilkzLYKYKl8CQbLczJ4r+ZjMOOLmhTXDOqmFRX+zIDRt1nj1LmZz4F8IdBb8Xtx6PPjgSgA/wXe/uxaHDjViyZIr8OijP8WDD94JVS3IzzRYKh0s7dbHzStPc4FFiTYOvXRdl4/YvQD5+VT5PVfl6bJRM0Sykle3hna9qVCN7xm2dj6eYbCMwuSf21zpnTeP4P3fHMPXvr4M5eUlePPlgz6yW3HHQvT3E269cyGKii189Y8/7enIvE+Yj1j3Ko80jG957OIiIeRCNGyOvnrhb5rOzYBKtf24+EBfwYILGIK9HiJQ/lb9c+6uxhGwK8+nR2RcsTZTXtOW4+Dt4+FfoaNSnocj0SQEGXGyKxl4FnH5rwTixRKT52I5RkT7Dz64Ag8+uAqCDE/JfdgTrZeBB2cg/D0TNjeg13sZ6/q6Tu76rCYrr6ETcwkE2Z+W++pr/VkA5kMlK/meo/BX2QVnTvoLCxAvXREZp1P9eHfbSdz2+WqM06qQmdwumz9V/QwLK26/ArbtorSsGAsXT0dxSREsC6iYUJpDiGdCvsORH84m2RucGS4SH3IhxSHB4ojgop+sGUYgSFEvcGiGcpDoWf3T8vigs8KF3zbXLs+p90XgAgi9PzAv2tkPQSrcZhJQJHwaalrPboFT8hrjIUidu5iNh2pXWQRBuJygjMEf7c+S+3P/Ce7IloK/AEV3MATBkT/7gucAWIbwRWD1ZzMPap089j67UDOS4GKqHAXr54hDWPK4sIU93+ye4a/672kChFxS7E3z332LZywCwaKMcRWlWFA9Dev+vhZvv3EEZWUlqFkyA+XlJSgrK8Fl86flHDNQL4rBoMbVNPjOBmMeF0mEXAiC02vAH6FyC8ewhupcMtsFoS1zhM0tPHnqy26HEijbGaCSRqUQmrIFQdjcaD0OReCsE3Miq0k792mIlwPLGBxt10CQEKAq9FLIdRRw4o+XpTod2K73DebpPr8oOHGZgpJPgo3wZ8PvE+bonvtIBN0ZkOfg/hFMlBzhso6egXpBxaAkieDMqBpqnT0u5w5zz+TKWkOZ5l+2YBpu/MzlWHn3otDt6ZSDd7Y2YuXd+TXkQmHkhwsLFxEhD5bYC06vs8ht4ZiE+APW/9Cj8C+nlIFqZZnWtqWgms/Mgkq2LYAgFr4+E0UPlN+Zfc1MdJfCv/pI0PYW1hiH3RkzIKJBTvTxceyhTkG5GGZBWcyiUL2O+cUCiAiSbWD8ItGr79gxEdS4dUcKrw7CmjH/noKrTXNC9ZAce5X2zHlM+gvzOgiCZUsbV89Nkufj3hx6S87cSaNtuyAiLFg4DVaRmCkNJBWMqyjFF750lfdzOuVg3942LLv5MpSWFuOdrY3Y8K8fAAC+8KWrzohUjfxwYeEikSwA//S1F4O34+SojbuGQTsuBrVUEBdeMKFOg3isSaipOR/LiSyOwqfK4zvlMXqfhCr5WR8EQTM5ctP4KihHQAQiyuuHIJv9UF3e6qE6zs2CWqCTNWP2O5fIa83QxnFSPgMm3AkQRKa38wT8pcIlcrx8Dr1Yg5OiwZal+qyEZQQH4oUWbMWpLxxQBv/vlcu1+VlNhoiU+UWit6Bku6LekjM8PjnWHME7Wxvx6F9vxTtbGwHkSgUDyQ6nOvrwqaWzsX3LYdi2i5V3L8KDf7LMi6BNLwoDxkUUIYdFZgM5Li6B6vd7CXKn73GIP2Ldv8sSA0eXuu7M3df0KH0mBFGfgiBG3YtcjNzFRnmaztPxGNSy9/xy4CiYiXOGdj3Wh6+T98RJSH42CSgC5WfFxRYOlAMkDeUcyTfrCDom+OXAz11P4E0LHMel6hwl64QNKFtgcAVuRglUlB+UdvItShD0SSvMr67E/IWVmFo5AUtvECuYBKNa7lOxYFGVr3gjnXJwqKEDK+5YiN2/PQarSMgSegSt7xuMuo1n+OLCRUTIuo2KSYp7TgB+YuLvKyGIkl0IgLJA6VN0XuKei0j0ijuWMsrh1yp5LLwQJy8vr6+WoZNNUn6v97YYB0GOFVC+W92by3o2l/oGNe1ge9BqqDJhTvwxuiFIuRvihdEfOH4G/M12gsVAE6GsfgMhWLXIlXSMYOVjvqIjLrZhuUkvzgleb2ArZH+WckgxKBXofSoumz8V4ypK4ThZTZ6w8LWvL/PcGGEIepYXLKoynuGLDBcYIRdSAKIXd+i+1TBPbyVUW0YuCwYEURVDlTNPgiJ3vaiEI0JeFUMvogDU9HwWFAnrJFcEVXY8Hv7Wlvo5xsl7L4OKLHU9u1eei9ecIwhJRu+l3AxBmCxv8CrZ0+RxejVfAv7olO9ZL71mqUcv4Q6r2AvDQDZFts2FnSfMxhbs11wYODK9457FOaQYFrWWl5fgnTf9HuTDBzrxmduuACxIu1xhZdUrbr8C6/6+FnffvwQrV9VgwaIqXH7FtAGPNbgwcIER8lAKQIKLmFbDnwwLuim4LJdXmuBy3nZ5DLeDPAXhatA1UibDVoheClziPAf+fhfBwo4oVIKQifIEBMnoS8xzhzJ2WUD+zI3mOSLmpZKYtGJQibPLoSJ4ngHEIUh4MtR6gNCOCUoRbDtjy5xe9DGU303Qjxwk93zEHnaNwV/SYQTLJHzJtPGh8gRvu3nFfO88+n67drZg6Q1zcaItijvvWYwPdh3H0hvmDkjKHHXbtoubVszH9cvngvoJLY1duGz+VO+cRrq4cHGeEHKhpc+FFIAwglNV/Q+YVzzWZQpA2auaoZah13te8H5sMePVM6qgXAL9UP2LWWvW9VY+Tl8iiF8MZRDFGu2B4ziiDdNbJ0IVuegrZQCqUm86VNGGfj4byl2ib4tCvEi4gQ+/eJgwJ8sx6ks/FVqcE/w96+vp6Y4Nfk4ntf0vhbIOMvwkzeQrekMUo7y8JCcC1suhly6bkyNP3Pb5Rbhk2ngsXTYHb7x00LOv8X67drbgiXV1+MOHb8Cq+5d41Xv882AoLy/x9uNjiYC+mI1ob9pIFxcwzhNCLjS6GkwPLITY9YYy3GLThiBDtsHxumy6tawegiznQBB3KQRZ8HJHXP3GPRIc+ImRp6Q6AVdDJN+YxMI80CxpcD9l7qnBlXoVUOvrcXtOHnNGjo2f7xIoLTgLQbZMqFmoFwWPla17i5A/Gp4NVWE3WGQc9nvWGwkFZxBdgf0t7Rnx79j/ImDy1bunBSNgvRw6LBodV1GKm1fMxxsvHfTZ1xhLb5iLP3z4Bu98+WxthSTs9GP3fXACS5fNGegBGpznOE8IOSy6GkrDIEa+Ka1e+tsNf/kyF4T0QRDDePjLbzmKnQFVkMAd3Xi8ekFFFUQEzf0VuKKP15KLw9/IHVBN1QH/wqdRCCmE3RVxOT5+IbD+fUo7Vl9luQRqNWxo99IP1aeD9WmWTCZCRcL80gqWQA9EmgNBP64bynYYdGzM036+HurFGEwI5jak90qbte5p4ypKcccXavDBruO4/sZ53j5z5l0C23ZRXh7+/4tta8ECkGBEnc8rXEjCTj9Wl0dGA8bhMfo4T3zIYaXPw2l6r5fF6g3l9XMFK8J6oKb710EtcjkV4o+dK+pmQ03xS6F8wrq3mLP9vC7fHKimOO0Q0WwlBMnNhohw4xBuiX55Xe4+BwjC6ocgzX4oT7Huz9Wb1rMzhNejA9SKzDOhyDhYchyXP3fL+9XX0+Mo9Cqol4b++9K1en7mgHKYcPMgvVFRRI6rXT6bCHJ///nA/ZL598fPVmBcRSlWfXEJJkws8xFt7euH8cS6OryztdFX/sy+4zBwAchgZJXPo3zb5xd5kfSZLOl0JqXXA8GUZY8+zpMIOQxD0Yv1aDrosJgNv3OBZQkmV9ZDgy4MyGMTUJ7eJFS/B+7gpo/ThurJQFCVZkxY3JNhMtS0m0ufg+W9CXkNPQkHCFLXV3nmc/Aac6zt8suEZwPcXjToFGFnAlfGnYTyVJ/Wzs92Pd27DOS6Wq7SxtYDfwSvR75hXwF/GTb3xZgQ2MazJn62Yb0y/LjjnsWeLgyo8meWGRwni8MHOrH46pkoLRXRdr4IMm8LzkAkrEe/wS5xQ4GeZFy6bM6IRbWmLHv0cR4T8uD+UQV2P1wKFakyCRZD9dsNEjXLFXrxgK4bRyFIrR+CaDn5NAuKpJnouSlQUI5g3bMJgsxnyrHq/uCJUFVml0JFw/pnlnY+7tfASb4EhAYc1puYnwPLKax7Xy6fC/c0Zk812870FwRLA7rjI/hfqxL+kmomdAu5/mS2qwG5LTj5OlMgXkj8QuHez/qY8v8f0ftJFBVbePuNI7jt84vQdPg0Fl89E/v2tqEvZqPpSBeqa6qwb08brls+F9u3HMbKuxehpLRIeYwDJKr7ia+7YY6vA1wm44b6ms+E/PjYZTfNQ+3rh0fMt2zKskcf5zEhDwVBErkK4X+oOlHzdJd1Ub3HBcsJemk168wc7bEjQC9OqIBfD+aObFMhojyONsO8zLOhZBAg92XhQJVhc/EKIIiZPcwT5ZgnQjTQnwq1yrP+otA1de7/Wyav3SWvxdedCUGs0+En91KoykXdE6xrvAu18wP+CBdQkXCQaKvls+QXku4A4ZeGC78H2g+9n8TNt873ORni8QyW3jAX0d40Fl81Ax/sOo4Jk8rxrjzGKhLLLH3mtisAIIdEb19Vg8mXjMO1y+Zg185PsGjJdE8C+dpDn0Z3JIlY1Ma+D054uvCZkJ9+rIlqz3MQUcH/li1bRuc3HCI6Lr8Od98OIrLltgwRHZL7ONpnDhG1E1Gt/KxJO5crv/Zox3XIz0n7LB0Yg0NEx+T543IMHYF9OuQ1O7Sfg+foCWzTPyMiisnr1IYcH9Gu68p/Hdo98/F75TFd8ivfV/B8QRzXnllPyBgyeY5jOEQUo1QyQ2+8dIBSyUzePZPJDL3+4gFKJjPkOC4da4mQ47jUsO+k7zjXdSmVzNB77x71nTeZsOm9HS2Uybi+8/I+iYRNtu3QeztbKJNxxDWORiidzHjXHmh8+aCP4b0dR4d1DoPRB4C9VADHnidJvTMFT3mB/MkhFyrpNNC+VXI/TphVQ0ShJ7XP9NabHGVy9JuEvyewI68RhYi++Xp6pM3JwWZ5/go5Br2Hs56QrIJKyAV7OvN1OYnHpdL8DPSVrnWbXTuEDBOH0osJIvpmzZi7qnGfaJZr+lBYgk5Puk6EapepF6BE4E8E6hD7FZKMKi62sOzmy1BUZKG/n1BZNQHbtxxGdU2VJyPYtousS3hnayOeWF+HfXvb8JmVV2Dfnjb8499tx7TKCk9PZvC1d25vRllZCW5eMR9Zl7B9y2FUTp+AopIi7NgmIu3hJMv4/O9sbUTrJ70jnnA7WwlCg8JwkUgWhfiY2+HvuJZv37CSYZ4+MwGxLj0HQu9lV0QUqql80FrHUgHrq0zIMyBsbDwdZw1Zr8Ljz7m3Blvu9DXmWIbh6/Kq0/ySqIB4IbRAWdd4+q/f1zSoFxLk2MqQq+HO0/Ztg1o8VE/QBZGvWAcQBD0DuV5s/+8ok3HxubuqB/T93npnNcrKi5FMZFD/uxOYd/kUNB/pwp73jvuaAx1rjiByOuFJE1x5t3T5XPT2pHDZgtxy5jDJYN/eNsSiNuo/OIE5l0/BbZ9fBKJcqaMQDOZLPlOrmumdcY5RSBhN571kUYhUwdP3TAH7Eokp9V4S0/sY5coQQSnBIaIsEUVDttXKY4NTdifwryfk3GHX5X82CVkhFjhOl1qOk5IhEiHnDz6j4D1FyC+3BI9ztX1HCvl/n4m4Te/tOEp22iHbVtszGZdef/EA/fGXfklvvHSQDh/ooHTaofYTUbJth5KJDMX7bJ/U4TguJRMZT5pwHEd+Ls5dqGSQ0uQR23ao4cMTtOmXv6OGfSdz9htIakmnHUombG/sYXjjJXmPLx+kTMYdVLoJG+sbLx80UsgIA0ay0BHmYw7bpx2qNy4vE6QjCyVrTISwo82BarD+IUT0FvTylkFEm1xYAqgWklOh+mjwEk6zoKSAiLymDbWiiX7uIvl5m7x2Sp6Xe26wrMCfz4FKHAb7LztyP16Fg/3SDP26M+UzY4ueC9Unmo9z5TaWHc4EYX5lcU59ml3/uxN4Yl0dtr9+GJ80ReA4omPd4QOdWHn3InzrkRW49a6FqHv7KPqzhFhvCkRAxfhSb/HRfR+cQDrlYPuWw7As4OrrLoWbyeLjA6cBACUlJXj7jSN4Yl3doP2Q9Y5vO95qQllZCaoXT8e8BVNRXVPlO+Z4Szfe/82xvDJEWF/mIHRv8we7jg/ZR2x6M59bXCSSxUDQM/ssRUyAIOYFUE6KSvj7M/CUP9gq81IIktQlhmKo8l/dy1wtz8OLnnJBCaDcEnzdcvm53qWNvcsdEGXbgCDbPuRa3I5AOCEqIby/xch1VnA/iwr4+zy72n78tRtCfgk6IBztHobX7Ccc+aUkfZp9xxdq8K0f3IpP3zgXWbcfH7zfihtunoeFi6crm1uRhbtWL0Z/fz+aj3RhzmVTUV6uqvg+vXwu3n7ziHBeAOiL2ph3+RQsXT4Xb758ECtX1eDO1cq37LrZnDHUvn4Yd8imQkE3RmlZEa69fnaOf7ilKYIf/O2dXgVhEFfUVGHu5VNRVFTk80dzhWF5eYnPcREs4TYY+zCEnEMo7HudC6X36k3OuS3nCfi1XrbGcQP5afBXxoV5mZmU2CPMfZN1jZRbg3LxB78gONl3Gioq5UpAvoa+HmANlP0N8vteKAseF3xwWbj+TFJyjGntWrzqtE7SxfD3c56tPa+w5z2YjzzsZakKgbhYQ9dVS0uLsfSGOXj7zSO49c5qXL98LrpOJbB/b5v0DAOlpcVYdtM81NU2Y+O//Q5FRUVY9cUlONXRh/kLp+F//XwPvvrQ/9/elwbHcZ5nPj0HBiDum7hvgAAJAiTAwxRIUDhIkbKcqq2ttaTK2pKym1r92dpKvNpUtuIS15v9kcTOpsoVJ65YirVZS+ukSqZiAeJpgAfAAyBBgQAxOAYcHCTuc66e7plvf/R8Pd2NGWBwUSD5PVUqkjPdXx9Tevvt533e5z0IQArSCwtOJKdE4W6bFbdvWlFTV4iWC4MyT+vxeORgrsxMY+N3oaIqAw86n6D+bAkAAruNx42rQzheX4iTp4sBTv0ACMbdUm745OlinP5OqfxZy0WJEweRmktW82xm2Pl4CQLyWhmZUk9LO9xmIWWRyvl2NKhoi0rapoYF+E2EYqEe4BnMXU7ZPUcVGcpzU9qELsGvR9b6FOvgnzitNAOi504DpXJSiLJ5hc7q094T6n4XCSkDpuvRqSo0SNPRTXbN8ZT3fT0dlkrqJBXK+0eD0bHafAz2zwRuzPAFt6SUSNTUFcDrJahtLMKDu2MwhRuk4h+A4/WS4VJqegxEwYOMrDgYDDo0vL4HHo8X0dEm3G2zoupoNnILE9F9fxxLizwGzTMoLElCy8UB1J4qQkV1BhbmnVJm+l4VKqrS4SUECYkR4DjA0j+Lof4ZfPaP90AIULY/DUdeyfE9AKpW1Q8HKra1XvJrqeteK8btm1ZWjHvO8RIE5GCvzbShw4CVgYv+uQv+CRl6xf7KoKK9hbTVmjYnUBOh0SDbazN0QB3kkuBvRImAlEET+IPdrGZ/+jmd9ZcMKUDSSSOB2r/pEFbaik2Hg/KKa6eg94DSHcoHAr0nyqGsyn2AlQ8m5W+hlpC5nAlovTSL2sZshEeovloRjJQmQNo2aINBD4+RoKA4CToAB49mYdS6gMzsOJSW70aYSTru/TujsNvcqD6WDXAchszTCI8wghBgb0Wa3KV38EgWFuacKNmbgitNZvk8Gl4vwSd/fwcNZ0uwtyINBoMeV5ql79//wXEcOJSJjGyJcjheL5kb6fUcyirSYAzTqQIpfeC8+loxDAZOzqZfPV0kD02tbSwCAXDsRD7u3RlbcyIJw87HS1DUU2pbKZQeu7Sjixap4PuTFtN2YWXH12pFQmXhywkpiK5mhJQGybGMZqs0w46CP0h54Tc5Ipr1lGZI01Brjud9nym1xVG+a1W2jj+FxDHvhn846DgkTtuF4LrfQPfCoDneWpkw/S1moB4Ztbq5DS1enWgsxD/9w125yOVyCrjabEZlVQYGzdOw23g5WOcVJWGgfwaEACmpUZh8soT4xF24/KUZLqeAA4ezEBltQkxsOH73VT9u/M6ClN3R4F0C2los+PTjTrRcGgAIcKfNis5bo9LA0vfowFKCN9+twqPuCUlzrOfw6mvFePu9KlRWZcBkMiAyKgynv1OKMJMeouDF5S/NkhE9Afp7J8Hz0j1uuTiAO21WiIIHl780g3gJTr9RCo7jJF30j2+gq3Mcja/vQe/DCVRWZ6C4NIUV455zvAQZciA/g0Ba1kDGNNSBbS1QC89lqIt71CNZmVFTLw3lracTMDj4jY4ol5sEqWhH9cOpmvUW4c9yaTPKbkgZdRGkYO7wrU+vcx5KS0q1r0Y41NltEtbH+1KE6jUiXafLGY/WS2bU1BXAGKZHWJhBzgoDvcYrp2tkZMfh5KkiLC+50PPgqVyMy8iKw9ed40hMiUJxaQqMRj32VaThYdcTFJQkw+X0B1qO41BanopD38rG3TYrjtXmY+LJojy89I//rB4AcOxkPuZmHfjP/60WEZFhGLHMoeFsCayWOWTnJeDapUF5hl5peSrSMmNRf7YEouDFrevDqDqaDaNRD0v/jExflJbvRmJypKrAePJUEfKLEnHj6pDsi3GkJgd9dCwUgMqqDOj1um215WQWnM8YoWjjyHOvQw4ErZZVqQEO9O/VME+kVmllW3EwrTBtK1buSz+j+1EtNE8kne9qbdz0HD2+zzya7bUt2KtB295M9cpKbbYYdO+NQ5T1s20tQ8Ru59fUz1LNLtXj2m3SPg6HpBum7c4Oh5u43YJK4yuKHtL8mx5y7oMmab8veondxpNzHzSR9uu+fa8PE0EQZQ2xyyW1P7ucArEtu+RjuVwCaT7fQ2zLPOm+N04W553Sd3aeLC065RbphTkHcdh50uw7B7db0jJ3dz0hough7dcsskZa/jUE0XddvcRhdxOXw+3TWUu66O774xvWC4fSXk6IWtfMsHEgRB3yS5AhB4M2g9MWm9ZTfIqCxM0qjdrT4ed+KTdb5vuPZhrUE5h2sS361tHywkr+WgnqoJYEiR4xQa1TpvtT1UQY/NwvPb624GlS7EcLebSzj4NEYZgC7LcZVcqmWAAAIABJREFU6GWVQtXR7DUVB4Bfs0vn2l2/MihPAqk+loOpiWX87Cf+MUpXm3vlTLPh9WLUnipCZnYcwkx6lO5Lhd6gw6k3SuWRTUdrcsHzIrrujuHkqSIMPJpG0Z4kWC1zcmYLAHmFifj0o05w4FB/thgz03Y0vL4HXi9Bqy9bjkvYhaojWTKfzHGS7O7x0CwKi5Nw+cs+1DYW4Ud//TpS02LkazQY9DAY9Kg7UyxZf5alYGHRiZK9yehos+Lv/vpmyGOhtAi1I4+ZFT1jhBK1yQuZIW8HlJkoNemhHXG0o03ZtabMwu0kcCec1vwn0DHp9zYiZerKDJtm7FpjH9H39w7f/kTxb7r9qO86lhRrecj63h7Wh6fji2R5yUXarw2v6LajoNmdw+5Wmex8db6X2JZ54nIJxOUSyFdf9MoZtG3ZRZo+f0hsyzxZXnSS2RmbnP0uzjtJ+7Vh1frmngnSfs1CLjf1EatllghugfR8/UTq6lN03tHjOn1deDR7drkEuevN7RZJ+zULWZz3Z8zUrKi91aLqHnTYedmMqP2aRdq31UJsSy55bSfNzEMwKAqWCbOOvGcLsE69bwLKAlcEpCwzB36rTuWkEcDfmbcb6ikfTyGpI+h2Hvh9jUVIvPCS73OlUVE4pKx52XdsKtejRUvl+g7fcfdDUlN4IGW8BfBbghohFTWp2iQRUravLAoqoSz+rVYIDI64+AjYbDz2V6XjSrMZgtu7Yhua3Y1a59F6yV/gUk4CMZkMaDhbAuIluHVtGAajHgXFyTAYdTAY9ZiZtMlKjbZWC6qO+t9AJO8Jl1TkiwrD7vQYeAlgMhmh03F44PNKfnB3DDo9h8M1OXALHjwenMW1y1Km3nKhH0sLTpSW74ZOx2H/wQz0fv0U9WeKMWiexs9+cgOtlwZw4LAkkaupy8dnv7yHBx3jshlRWXkaRixzqPpWNsZHF+S1Wy8NYPLJEpaXJAtPJbQdg10dYzAY9ZiatKm2C6UjjxkNfQMIJWqTFzpDXo8l53q3p9vSbPQqUXPFyux2iqi5WiU/HMzHwq1Zl1pfzpPAXK+W114iajtRQbGdlrtW+mEo7UKVCGSfubEsWuYuz6/kLml2R7NQyeJSVH/vy6BpBtreapGyT17ifM990CRnnHYbT7rvPyGCIJJB85Rifz8PbLfzxOPxqL53uQTZO8PtFonbLfj8MHplzlkQpPNyuQQp0xZEKXs/L/HWLqdbxVXTP7/3e5+Q5vM90jH7pojDzvvOV8pq/W8ELpWvhZbzlfw41ubktfeOvnkw/nhrgBAzZBaQg75+Bwu8ob6uK4Owli545Pv7GJFohkDH0gZeK1kZqJUFO3eQddaC9mGxkWsNdM6Bz2XVV2hFkY5SDqsFEZdLkAtbyjVpUGr+TY9sKuR0uIngFqTXf5uLmHsmicspBc3B/mnidLhJ9/1xwvuogHMfNJGpiWU5KNFC4eWmPrIw5yCCIBJR9JDu++MqKuLp+KKiGNdDbMsumX5Qbme1zErBvNVCrJY5eb+n44u+gK32PO7ueiIHdwpzzwQ590GTKmBqH1bNvnM490GT/HBzuYQV9MdX53tkMyKVOVGQ3yDUoiCDhFAD8ktc1KMIVrwLJvUKtdinnGtHZW7KQhlt6ghHYDtJZdFxFGq7TkoXjEGS1UXDP0kkbo3z0oJabAa6JqUfRyhYzT5TQrBiEpWXSW3JUguwtthEfRsqqzMRHmGUaIFFHmMjC/jUJ3VrOFuimixNCEHjt/eAEC/6Hk7jwJEseEQvhgdnkJ0Xj76HUygsScbMtA2l5btxt82K43UFONFQiOtXpPbo/3quAYXFSWi9NCDL8kTBi5aLA6g/WyI3h3AcsLTIo6IqA8ODM3KRcW9lGqqOZvubSDjJHyMmNhx1rxXDSwhsyzxEwYsHvoaPwzU56OoYw89+fCNo4U479w9YOaePFhzf/P5BZOZITSPWIX/HIDgOeQUJcqu3tg09WMGP2XRuD1hADqqXDRak1qOvVWqEAb/uV9vtFuxY1EFNqzum+8XB/+CgZu5T8Dd/KNcJpoqg8wYL4DeZp9ty8PtxbA2CVe1rG4uQV5QkO64pTXpqG4vA6aS5d7RVel9FGk6eLsZA3zQKi5MAIq3R93AKMbEmNH67BIsLTvy/X36NsvLdOFyTg+z8eBgMHFouDMqKC4NRh/GRBdQ2FmF8ZAHlB9KxvOTCg45xeZvT3ymVh5BSFce9WyOSgiI+wue/LB3//p1RZOXGIysvHvShoNMBHEdworFQvtYHd8ewvyoDHEdgCpM4b3nQKYDE5EjsP5ihMgei3Xu1p4pgNOrQ1TEGY5geJICPhXyvwaG2sRAL807oDVLJKKcgUe4YrG0oBCEEb75zEOUH0oP6X2j1yEx9sU0IJY0mLzRlsR0IRE9sBJQymCUr9cXDxM/rrk4TrE49rKXHDgUboUoCQ1n9V3KY5p4JmQvWvi5bLbPkclMfaW+VfJB/8dM2vy7ZzsvbS/SBxPs2/0bijp+OL8qa3l/8tI243SKx2VwKVYZLHuNEOVueV+uau+9L6gu3WyDm3kmJBnBKtISbl86n+/4T4nIKxNwzSXheWEFNyNd+vtentJA4aep9TLlsSsXMzzkkNUjvpIpvN/dMrOqnrKSFlJ+beyeDeiwTwvTImwVCpCz0H374YcjB++c///mHf/iHf7htD4edARFS23AkNt5ZTq02qdMZNf5ZLyJ9a9AWauW6sb7v6XgjOgE73ncs5XVE+/ah7dXhivPR+dai/45UbBvqOWvPa224nAKuNJuRmR0Hg2IMksGoR05+AgYeTaFsfxpi4sJR21CIuMRdiI4Nxyuv5qPFp1OOiQtHTn4C7t604pVXC5CeHQedDsjJS8Dt64/xq486UVGdielJG6JjwjE/5wDH6bArKgw6DkhIjoTJZEDLhX4cPJyFfZXpaLnYj/ziZHAcB71eh7j4CBBwWJhzIiFxF76+9wSz03Zk5sQhPNyI2PgIxMaHY8y6gKVFHkUlyQAHXG3ux5ef9yAzOx77DqQjPTMWXR1jKKtIw/Iij8TkSAwPzODCv/ZBFLzwerxITIlEcmokOE6H1osDyM5NQNPnPZidsiM5JRLFe1MRHWNC7akimEx6mMKNiEuIQHZ+AvYfzEBFVQb+/m9uQhS9KPR1Iirv8ZWmPvzqo05Ex4ajsCTZf88NOiQmR8JgCP57Z2bHyb+Fwai1EmBYC+fOnXv64Ycf/nyt7RhlsQKhtgkHowFE+DnXQBQB3Y+OXFrLNF/LXysDtHI96hZHqQrtdWRBattWTtIOdkyl2VKgidDac15PE42E1ThIOsr+/T+qQcPZEuj1UqA4/UYpBEFETV0BAlldHj2eC1H0wOMhOHm6GARAflECCOHQ2T6CykOZMBh1uNIkrf/f/9cpDA/OytREXmGCPH26aE8ywiMMaGsdRmRUGPQGHfZVpONR9yQqqjJhtcwhOSUal7+UDIdmpmyorM6Ex+NFR/sIjjcU4Hh9IW5cHcTJU8WYfLqEi7/tw+K8C3v2pSIyKsznf1wHnY7Dpx93IjMnHtMTNgwPzsqGRb//Hw5JDiZegkHzNBq/vUe+Hy6ngK67Y6g4lClz4u+8f0RuLtHeYy3NsJ62aGbl+YwQShpNXirKItTX79WUCY8IIY419rMSf0PGRqR0gSgGcZXt1jpOMIme9ry3phlktcYE2vSwOO8k7deH/dv7JGpf/PPXhHcJZGHOQWzLLtLWMqSiJShNMfp4Tm7iWJx3kvZWC3Hzory+bckl0xd0kjSVlQ2ap1VSNEEQycKcQxrd1CrJ3JrP95DjVe+T9LQ8wnE6kp6WRz755P8Q27KLLC06iblngly/MiiPe7Itu4jT4SYDfVP+cVJf9BLBLRJzzwT5SiPHU14/pQzovwVBahgZ6JtStV1bLbNqCmQVpYqq8UXYjpZ4Bgow2dt2Y7W5c6vN5dPK1AhZX7DTbqvtvqP64VDONdC6WoneetbYHGhHGw2A1uE5mddU8qTzc3by1fkeWZ87NbEkf9/0+UPS93CCWC2zxOV0qyRwtDNOcIvysVwugczP2kn7NQv5xU/byNTEEhEEQbXvV+d7pH14wR8YWy3k448+IXExqeTUsT8hv//GR+TUsT8hCfFp5Id/+hPfLD6LuhvQ549BeXCqb+Z5QeVb4XIJxM2LstSOECLrrWV5oNNNHFQv7XCTtpYhYlt2kctNfSHzvMqHGJOvbS9CDciMstgwgqktDJBkasFoj0D7rfXKv9rkDKWJ+y74TeOVKgutt8VqxvGUpgjlvLcOWsP5wuIkPBldQFqG9PpNDeZr6goQGRWG2sYiDPbPQKfXwekQVFI3g4EDIcD9u2M4oXhNHzRPY29lGjpujeLQsWwcPJIFt0tEdGw4DhzKRPmBDNy4OiirOo7XFyI2fhcOHs6ElxAsL/I40SBNjC7bn4a3vvcjHN73LnYnlwEAdieXoRrfxz999nc4duQ0lhZ5LMw5Zfqh/mwJOtpG8LOf3MAP/+IM6s+UwNw7iZK9qeh7OIk7N61449/ug14PgBCkZcSA+AQ61F/j1vVhLC3ymHxqw6PupzLdcvBoFgxGPSKjTSsmUQcDpTS8XoKC4mQABNl5CZtydWPucJsDa53eFihbogNB21as1Rxr242VLc9a/+FESLxyAqR2bQf8M+0olN7EM1B7M9NzAYJ7PG8fXE4B/b2Tqjbm0n2pUgGsXDKFdzkFGMP02FuZBlO4xJ2HRxixryINJpMBuzNiMNg3jfqzxTCF6+F0CHJL9YOOcTScLQHHAWFhehiNehw4nAlCvPB6CG62WMC7ROgNOlly13ppANahWczN2BUt3B7EJ0bAGCbJ8UYez2NkZAgpicWq60lJLMbw8CBONBYhKy8eJ08V4W2fX7LD5kb5gXS8/0c1yM6LR+etEeQVJGJ4YAZFpSn49//xEIb6Z+DmvRAEL640mzE9uaxqYT5wOAvRMSakpkf7B5o2FiI5JQomkwFHa3JVgZDnRdy6NiwPe1WitrEIb79XjRMNRcjKjcNnv7y3roGogbCahzXD2mAZsoyNDt8MtO9a+l0b1LP4KDbSjEKLcHSiSASCG+oDfs/kQBl2qma/zdyT0KDyG/Zlsnq9TtVIwemkolRKajSuNJkDZl/Fe1PQ91ByRIuOMeF4fSG8XoKKqgyMjyyg58FTfPaP9/D2e1WS/3BKFLrvjUu+FlM2xMZF+LJsoKauEHo9h4V5J65fHpTn7lVUpyMmNgJzM3bk5McjPT0XU7P9coYMAFOz/cjLLYTBwGFPWQoMRg5HanKh03O4dX0Yx+sLUX0sGx3tIzhwJAui4JF9kGenbSgrT8NQ/zQyc+Lx1rtVGH08ryrO5RUkyBn3keO5QQttNFM9Xl+I8gPp8rQT5X0LjzDK8/l4XsSpN0pRdWRzb0FMn7w5sIAsYyMm7MH2NcDfMacNakrLTW3A3mgzih6StC0UaNfSjmBSYjP3JDScPF2MvKKkFSOMzD2T0qw7zj+tWQ5MPkVEdl4COB2HySdLeDK2KGXUzf2obSyEwahDQUky9HoOqekx0gQP3+gkvZ4Dp+NQWZ2J1ksDSE6NAsdx6OoYQ92ZEpgfTqKoLAXJqZEyFXK8vgB/9T+u4MgruSjdvxthJgP+559/iD/6Lx+guuwdpCQWY2q2H3cefoy//t9/CQ5A38NJlB9MR+ftUQhujxxI91akoaIqA/OzDjzoGJMDvsGogyB4UH+mBMMDMxgenF3xsAKAt9+TsuJB8zR2RYaBeIlME3A6Dtah2RXqEeWDTQuXU8D46AKqjmSpOiE3Aq0ag1EY68NLEpBDyfTWL90KbV9tUFstyG0vTxsYq53PZu5JaCBeguGBGeTkJ6g+LyxJRlfnOOrPlMDo073S7Kvm1Xz8+EdXcaQmF3kFCfjsl/fwJz9qxNXmftlv+PR3SlFcmgK3W4RtkUdcQgReeTUfHg+BKVyP8ZFFPOqewK8+6kRuQSKGh2bx6UedeOvdKiwv8RgbXUDj63swPrKAk6eLwHGQ2pR9wZ7jOHz3372J3q8n8C+f/wMutQ8ibXcOyov+DdISq2EZmEXZ/jR033+Koj3JSE2PQdn+3UjPioXVMgedT7pGs/ITjYV40DGGyupMdN4awcXf9uGP/6wO4KRuwtJ9qVLjgEGH+jPF6Ggfwd7KNLh5EW0tFjn4VlRn4LNf3pOD+In6Auh0HN5+rypo1trVMSZn0cdq89HVOb5lU0hYi/U6EUrljzz3KovNSLY2qy7Q7r/9aoX1YfvOJxQDGr/UK7gjGc9LHWvUG1lpPORySTI06phGp38Igkh4XlBN+KBr87wg/9d+bVj2VZYNfXyTRtpbJala+zULsVpmya8/uScfi04doQoOwS3KTm/y9JFWCxFFD1mYc6jOg5oK/fqTe2Swb0oyOOqTXORGR+aJw+6T2zml8+2+P05+/ck90tYyRMy9k3LX4lfne8nSolPVVSgIInn6ZFGWsQmCQNxuUVKdBOnEowZMSkOmrQLzXZYAJntTYjNBZ71ucAwUa7Xb8rxAeJdArJa5lS5jclvyOHE43LK5fCAIgijL3ARf63OzT6pm7pkgTZ8/VJ2H1TIrBzGHnSeLiw4iih7idkuBURQ9sm1ns88FzeX0a6Otw3PyeSl1ystLLuLxeIjLKemOld9bLbP+8zjfK1t9KqV+3ffGfWtJrcz0AUQfEm637yHjeyBR+06HnSfmnkn5AUXtNmXHN42mOxDsdl6W4TFsPUINyC8JZbEZKmC9bnAMFGsVeAS3B9cuD+LV14qRstvP1dLuO0KAjOw4tF4ckHlWWoQCpEIUIQQe0YtH3RNISomC08Hj1o3HuHPTioYzxT4jnTjZSAcA0rNicaXJLK9Zf1ZSStAi4g//4gwqD0nG8bWNRVhacGFyYgkNr5f43OGSkJIa5euck0zwO9qkTsCpCRucTjcOHcvBpd/2yS5w9WdLkJgcJZ1HYyE4ThrCKtMEd8cQGW1C190xHD2Rh/yiRLh5Dx4PTmJu1oGy/WlyYW6wbxp1rxWD44DLX5pxp82KN79/EDdbLBAFD3ILEvGrjzqRV5SE4YEZlewuGMLC9CirSJO7ABm+GbwkAXkzWK8b3POJ7Si+rNVue+PqkCrQ5hUm4LOPO/Hd71fJLmeD/TM4eUoqVmkDu3VoFoQAlgE6406aa/fq6WIcqckFz3uxvOTCqHUepeW75esyGPQK3bLEK09PLuHkqSLEJUQgIytWCnpnSjAyPI+c/HhE7DLiSpMZJxoKQbwEDzrHUX0sG4Lbi667Y6g8lIlB8wz27EuB17sLj76e8BclG4sAEMxO22Aw6tDVMY7qb2WhxSf1A4CG10tgW+KxKyoMADA/68DEkyVMTdhwor4AV3z8OABpKne4ATGx4fIE6uy8BMzNOlFZnQFCpMJfTn4CsnPjQQCc9J1DICh/e5Npe0ICK+6FiFDSaPLcUxYMa+FZuXkpjei1/KLSXH0t0Fdzh90tc7cOu3/CtNIg3mF3y2b2lGtuv+Z3hvvqfK88u45OgFaayXs8HtX9mZpYIuc+aJLn7tmWXLIrGyGEjFrniCh6yMyUzddybV+xJqUnAs3Ic7sFVZef1l2OdgFSikX5pyCo750giGRqYkm6ziC/7Wq//VYZ0b/sbnFglAXDevCs9KPaqrsyg14to9ZmWPfvjMJuc2NvZRp2p8WgtDwVba0WFO1JRsQuIzpvjeLTj2kXWhJi4sKRnZsAS/+MLAl7+71q1LxagKzcOHTekjrooOPUGmiOQ2l5qur+dHWM4Qc/rAenA/KLk8C7RFlLbDIByanRcPMiZqdtiIoOQ3/fNCqqMjA9aUfD6yWYGF8CATA+uoDGb++B0ylifHgOd9qs4DjJZF9v0GHE91lcQgQOHM5CXmEiOB2Hceu8pMbwGSaZH07iL89dwVvvVq2gJTraR1BWnob0zNigv+1qpkNdHWNbopJg+uQQEUrUJixDfg7wfBQZ11t1pxOgtRmW0viHEN84p64ncvbr0Mycc7ulDPQXP21TFdtmpmzkFz9tk1UeLpdAZqZssq8EHX+kzJ5pYe7p+CIRRcXYo/O9hOcFMjdrk02PeJdAFuYdAbNhmlH7PSV42RiJemlQ5QRVo1B/D2WhjqokpOnZlhXzBbUzB9dCqGOcGEIHmB/yy4b1exJTBPMm3g4YjHoUliTD4yW40tQn+zV0tFuRlBK14vhXms248K99ePOdasTGR8h+vFeapQw2JjYcXo8XsXERSM+MxZVmMz7+29vIyotH1ZEs3L5hxey0HXqdDqXluxGXsAuPB2cxPWmH0ykgNz8B05M25BUmIirahIgII9qvDSO3IAFFpckAx0HgPThyIg96PYfEpEhfJ2ABOA6Ym3Fgz77diIkN9xXrOMzNODDUP4P9BzIw+ngeSSlR6Lo7ho//9jZi4sLBAbjw2z4cO5EH4iX43QWJS46NC4dtmcej7klwHPC3f3UD2fkJyM5NQE5+PI4ez0Nn+wgyc+LwL/+3CzGx4Th0LAcGgx45BQn4+t44ysrT8PX9J8jMjpPvd2Z23LqKdUrvY5PJgMKSZOaBvEkwP+SXDhsvMn4T4n1qMk8pBbvNjZHH8yguTVFtR191DUZ1J1+gJhGl529lVQY4HQdB8ODA4SxMPl2G3qBDcWkKXE4BiwsuyYSHA/ZWpsEYpkfELiOuXR6UFR5l+3cjPTNG5UFsMEpGPDodh8goE3iXBwajDmX7d8MYpkdH2wjsdjdqGwvAu/xt0dXfysbbf1CN43UF0Bt0ePP7BzH6eB6z03a5MHe8oRAcOOj0Ugfn+z84jsqqDNy6Noyqb2Xj2qVBX2GPwzv/6ShSdkfh8pd9Mo1TdTQbnbdHA5oL8bwIUfDgxtWhNQtroY5xYtgGhJJGE0ZZvND4JsT7lHJwOPyFOGVhLFSEMp1aSRfwvLDC/5fSHIIgyp7IlOq4fmWQ2JZ5mQoQBMm68+kTaQQTPWe7jZebO2hzhZLK8IgeeWxT9/1xIooeefpz+7Vhf4FRU/Rq/k0P+fUn90j3PbXu2eYrWoZaKJuaWFLpsZVa71ChvB6G9QEhUhZMdMggZ0RblfW4nAK+Oi+5kyk/o45lgNSuu7zE40HnOIxGAzweL4b6ZyAK3pDWp2uZTIaA5063EQTPCgeyngdPwbv87mfUWe7yl2YAQEV1BkzhBgw8mkbJvlR03x+XnecEwQvR7UFsbLhsDCQKHjjsbuQUJMhSts5boz43tSrUNhZifs6J6GgTBvqmsa8yHXq9DvfvjOLW9cfILUwAgaRLPl5XAJ6X3P7cbhG1pyQntpJ9qXhwV7pnXR1jCA83yG5tNa/my/sEu19D/TM4ebpY8sFQTFppvRS6K1tNXQHefOegb2ILw3aABWSGLUfLRSkotVwckD/TBsXK6kzExIXLr9dUk3zj6tCa69O1ujrHVcHZ7RbR3zsJwS3K23TeHvXbVDYU4t6tUdVxRNGDqiNZaGu14NOPO3Ht8iASknahr3sSpeWpSE6JQtXRbPn71osDmJ6ywRimV5yzBQmJuyC4PTheV4C336tGRVUGBvumsbTIo+vuGKJjTNDpOezZ66dkKqszIQpexMdHQK+X1BzES3D/zhgA4N7tUXz6UQf2VqTDaNSj8lAmomNMqKzOhF6vQ3iEEXmFCfjxj67i/t2xVe+XZEU6hobXSxAeYUT5gfR1B1eDUY+CkmQYjCxsbBtCSaMJoywY1gGtAoJ+thq1oJ02LeuUA7xW022p+oC+hvMugTR9/pDYbTxxOlcer/v+uKwFVrYVLy+5iMspqKiKYBOhbcs8EQRpCrRyejXv217SJ08qJlX3kKVFJ/n63hiZmlgmoqhWO0ht3LxqzBSdSu10uGXlCJ2gfbmpT9UC7XIJqkkige5voPbpUCZNB4P23jCsDTAvi2eB50Nq9jyByspU4+1X4UiVnhRKSVqgGXFKTwhC/Jxo8296iMfjIeaeCfKLn7YRc480AirYsf1NKTyxWiQZGm3koA8Bm82l4sWVAVMQ/OOjVhj78NL4qDttj4m5Z1LdlHK+Vw6i2gC81r1yu0U5cKs+8513oHsVLOi+7E0eGwELyM8EWzv4k0HdrRdKsZFmenI32xpDPX/9yT3S3fWEEELkDNfhM9ShRTYaiJ6OLwYM7NSw6NwHTWRqctk/Z++ahXg8HuJ2C0RQFPXMPZOkWRlUeyZWDnD1nffT8UXC84LcaUi7DLXXpS2wbSTjXS2wrtm9x7TJ6wILyM8ELEPeTihVDFu1Hs0Su++Pq9YXBJHMTC2rAh3Pr3w1F0UPccutzL0q6oO2RC8vuYgg+D93uQSyvOSS3dTOfdAktUT7qBEtRNGzIuBqQd8MaBBX0gj076vRPoSsHlhZ0N1ahBqQGTu/KWjn2zFsJVou9OPP//TiqgWrUEALf8RLcLQmF10dYygsScbvvpLW/92Ffng9BLuiwnCioRBvv1eN4w0FuHd7FLdvWjEyPAdR9ODW9WEIbg+uftUPACgtl0ZehUcY8drvlWHQPI2/PHcFjwdnwXE6DPZNY3mJx/07Y5ibtaPuTDE4Ahx+JQe1jUX4p3+4qyp8Uuj1OtQ2FuEtn0Lj1vVhlWIFkAyGvlWbB0IIuu6OQRQ8uH3TitbLg7AOSa3hVsucXNxsuTiwYq7eauqarVbeMIQGFkkYdixqG4sQm7ALlVUZuPBF74YbErSNL1VHs9HZPuJvyKiTLD8Tk6NQUJIku8699W6VPIk6MycedptbVpAQAiSmRCJiVxg8Hi/iE3ehsCQZ7//gOAr3JOPSb/tQe6oItmUeldUZCI8wgudFPLj/BA0+v4myirQVTRwup4CR4TnMzTqQkRWHrrtj+NlPbuDtP6jG6TdKVc0Z0THhuPxln+y58eY7VcjOjQen4/D2Hyjc3ghwrDYfnbdHt2wSCMO7j9fRAAAGIElEQVQ2IZQ0mjDKguEbxGaLSIFc5CQ1h+Tx0H3/iewKt7zokqkGlYrD510RiKpwONxksH96xfnSNbQ8dCAVCkX7NYs8QUVWTyioA+W9sFpmydKiU6YngiFQQU++L0wt8UwAxiEzvCjYKj5TGwjNPRPki3/+mrh5kViHZ4nDzpO7bY9VxT+/3M0lj1SiUKojRNEjj3ui+9jtPHHY+BWTOuj4pvZWi/oaz0vBva1liDjsa8sDKScuj69aIdNbe3zWRh50q8oRWYAPiFADMqMsGHY81jK6V0Lrt+ByCmi5OICTp4rQdXdMNnl/7ffKkJ2XgOGhOXgJQXZuAtxuEfsq0+HxeLE470JhcZKq+eLIK7mqiSUnTxXJfy7MOdHfO4mxkUVExYTj9HdK4fF48ejhJA4cylSdY+WhTCzMO1Gp+Fw5Ufvg0SzZz2Kte0EpiCtNZhUtE4o/yUYtMYOtzQaabh4sIDO8UNAGBcr5chxQd6YEb71bJQdSbXALCzPgwhe9uH3Tiu9+7yCmJpeRnZuA7LwEHKnJXRG4Bs3TyMiOw2D/DPbsTUHl4SwsLrhQtCdZ9WDQ6QCHw43rlwdlj+FPP+4Ep/MHLmVw3AhPrg2u8kQUX1t1oEkg63nQBTsWz4toudCPk6eLmefxViCUNJowyoLhOUGgKSTB+Nqg+/s68taS2wXjd5VNLe3XhwnPCzK98Sw9hs09E+TcB03b2sDBmkRCA0KkLDhp29BQXV1NOjo6tu/pwMCwAXyTtpCi6EFH+wgOHM4C8RK0Xh7EyVNF8t/rXitG5y1pakdbqwUnTz27c3Q5BbReHtxw1h3Kfd3sMV4WcBzXSQipXms7pkNmeO6hNS56FqDaZt4l4uJv+9BycUCmAEwmAzgdh7yCBBBCUFmdid6HE6g/W7KlQUvroKfFZrXEodxX5THWOh+GtcE4ZIbnCoGyNspdUm54M2uFup2yCPf+Hx9HTGy4ah9qwE/1w9uh/93uItp6OWFW1Ns8WEBm2NHQBsNA/9PTLI1maKFSF6EGkEDbaYeeVlarlRTrDWbK6+R0HFou9K95HaEcYzN0znqLfqyot3mwgMywo6ENhqv9T7/eDC3UABJoOxqsHnY9wc9+LHXS1b1WjKvNZjn4rSeYKc89ryAhpOsI5RjPMmvdqGqDQYFQKn+EqSwYviGspynkWRriKJsgaBecbP+pmIwdaqOEtuFDqxQJxSxorXUZvjmAmQsxvAhYT2FqPduGWoCSi3eaEUnKgtfRmlyERxhRWZ0pTybRbrOec9eOpaLrKM2CQi1gMpOg5wuMsmB4KbEe/vj2TStq6grRcsHPT69GY1BshlNVcr8nTxcDHCebBQVbk02Ffv7BMmSGHYPtkk0FWlc5Z2+17U+eLsab71ThxtVBVWZKgy+n41RrK/cNNTsNdH7KTJhmzCaTYdU1vwn5H8PWgmXIDDsG21WAGhmew+2b1oDKjFDOo7g0JWhmev/OqDTItHMcR2tyN3QNa6k4QgVTOTz/YJ16DDsG29H1RV/ja+oKYTDqVng6BHrNX895KM2LaHOEdt+1qATW7fbig3XqMTx30L6ObwWFQbPPG78bCmiwE+g1PxgdEWz9Tz9eSWUoA+taVEKgfVjX28sJRlkw7FhslMKgGWkoDmSrfU+77TZrYbkRKoF1vb2cYAGZYcdiq/x6qcFPZXXmCkpgNS45lOOH0gyxkYYJxge/nGAcMsMLBy0ne+GLXpWvBAPDs0aoHDLLkBleOGylHpiB4VmCFfUYXng86261rS7IsQLfywMWkBleeDzrgLbVDRqs4ePlAaMsGF54PGvFwlZTJIxyeXnAAjLDC49nHdC22oaS2Vq+PGABmeGFBwtoDM8LGIfMwMDAsEPAAjIDAwPDDgELyAwMDAw7BCwgMzAwMOwQsIDMwMDAsEPAAjIDAwPDDgELyAwMDAw7BOtye+M4bhqAdftOh4GBgeGFRA4hJHmtjdYVkBkYGBgYtg+MsmBgYGDYIWABmYGBgWGHgAVkBgYGhh0CFpAZGBgYdghYQGZgYGDYIWABmYGBgWGHgAVkBgYGhh0CFpAZGBgYdghYQGZgYGDYIfj/n+KVo8KmxvgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# visualize\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_3_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(k_means_3_cluster_centers)), 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_3_labels == k)\n",
" \n",
" # Define the centroid, or cluster center.\n",
" cluster_center = k_means_3_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()"
]
},
{
"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": [
"<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)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2019-02-27 14:11:29-- 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",
"2019-02-27 14:11:29 (1.54 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": 3,
"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>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": 3,
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
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"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": 4,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" 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",
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" <tr>\n",
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" <td>12.8</td>\n",
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" <tr>\n",
" <th>2</th>\n",
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" <td>20.9</td>\n",
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" <tr>\n",
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" <td>0.516</td>\n",
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" <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": 4,
"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": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"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": 5,
"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>"
]
},
{
"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": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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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,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2 id=\"insights\">Insights</h2>\n",
"We assign the labels to each row in dataframe."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
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"outputs": [
{
"data": {
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" }\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\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",
" <th>Clus_km</th>\n",
" </tr>\n",
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" <th>3</th>\n",
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" <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>1</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 2 \n",
"2 1.0 20.9 0 \n",
"3 0.0 6.3 0 \n",
"4 0.0 7.2 1 "
]
},
"execution_count": 7,
"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": 8,
"metadata": {
"button": false,
"collapsed": false,
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},
"outputs": [
{
"data": {
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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",
" <th>Card Debt</th>\n",
" <th>Other Debt</th>\n",
" <th>Defaulted</th>\n",
" <th>DebtIncomeRatio</th>\n",
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" <tr>\n",
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" </tr>\n",
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" <td>227.166667</td>\n",
" <td>5.678444</td>\n",
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" <td>0.285714</td>\n",
" <td>7.322222</td>\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": 8,
"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": 9,
"metadata": {
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"outputs": [
{
"data": {
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\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": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f412613f358>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<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",
"\n",
"<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>"
]
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