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January 19, 2020 00:43
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"<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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"## 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" | |
] | |
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"source": [ | |
"<h1>Table of contents</h1>\n", | |
"\n", | |
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n", | |
" <ul>\n", | |
" <li><a href=\"#random_generated_dataset\">k-Means on a randomly generated dataset</a></li>\n", | |
" <ol>\n", | |
" <li><a href=\"#setting_up_K_means\">Setting up K-Means</a></li>\n", | |
" <li><a href=\"#creating_visual_plot\">Creating the Visual Plot</a></li>\n", | |
" </ol>\n", | |
" <li><a href=\"#customer_segmentation_K_means\">Customer Segmentation with K-Means</a></li>\n", | |
" <ol>\n", | |
" <li><a href=\"#pre_processing\">Pre-processing</a></li>\n", | |
" <li><a href=\"#modeling\">Modeling</a></li>\n", | |
" <li><a href=\"#insights\">Insights</a></li>\n", | |
" </ol>\n", | |
" </ul>\n", | |
"</div>\n", | |
"<br>\n", | |
"<hr>" | |
] | |
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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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{ | |
"cell_type": "code", | |
"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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{ | |
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"source": [ | |
"<h1 id=\"random_generated_dataset\">k-Means on a randomly generated dataset</h1>\n", | |
"Lets create our own dataset for this lab!\n" | |
] | |
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"source": [ | |
"First we need to set up a random seed. Use <b>numpy's random.seed()</b> function, where the seed will be set to <b>0</b>" | |
] | |
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{ | |
"cell_type": "code", | |
"execution_count": 2, | |
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"source": [ | |
"np.random.seed(0)" | |
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"source": [ | |
"Next we will be making <i> random clusters </i> of points by using the <b> make_blobs </b> class. The <b> make_blobs </b> class can take in many inputs, but we will be using these specific ones. <br> <br>\n", | |
"<b> <u> Input </u> </b>\n", | |
"<ul>\n", | |
" <li> <b>n_samples</b>: The total number of points equally divided among clusters. </li>\n", | |
" <ul> <li> Value will be: 5000 </li> </ul>\n", | |
" <li> <b>centers</b>: The number of centers to generate, or the fixed center locations. </li>\n", | |
" <ul> <li> Value will be: [[4, 4], [-2, -1], [2, -3],[1,1]] </li> </ul>\n", | |
" <li> <b>cluster_std</b>: The standard deviation of the clusters. </li>\n", | |
" <ul> <li> Value will be: 0.9 </li> </ul>\n", | |
"</ul>\n", | |
"<br>\n", | |
"<b> <u> Output </u> </b>\n", | |
"<ul>\n", | |
" <li> <b>X</b>: Array of shape [n_samples, n_features]. (Feature Matrix)</li>\n", | |
" <ul> <li> The generated samples. </li> </ul> \n", | |
" <li> <b>y</b>: Array of shape [n_samples]. (Response Vector)</li>\n", | |
" <ul> <li> The integer labels for cluster membership of each sample. </li> </ul>\n", | |
"</ul>\n" | |
] | |
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{ | |
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"source": [ | |
"X, y = make_blobs(n_samples=5000, centers=[[4,4], [-2, -1], [2, -3], [1, 1]], cluster_std=0.9)" | |
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"source": [ | |
"Display the scatter plot of the randomly generated data." | |
] | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x288 with 1 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"print(set(y))\n", | |
"plt.scatter(X[:, 0], X[:, 1], marker='x')" | |
] | |
}, | |
{ | |
"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": 31, | |
"metadata": { | |
"button": false, | |
"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": 32, | |
"metadata": { | |
"button": false, | |
"collapsed": false, | |
"deletable": true, | |
"jupyter": { | |
"outputs_hidden": false | |
}, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n", | |
" n_clusters=4, n_init=12, n_jobs=None, precompute_distances='auto',\n", | |
" random_state=None, tol=0.0001, verbose=0)" | |
] | |
}, | |
"execution_count": 32, | |
"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": 56, | |
"metadata": { | |
"button": false, | |
"collapsed": false, | |
"deletable": true, | |
"jupyter": { | |
"outputs_hidden": false | |
}, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([2, 2, 3, ..., 3, 1, 1], dtype=int32)" | |
] | |
}, | |
"execution_count": 56, | |
"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_centers_ </b> and save it as <b> k_means_cluster_centers </b>" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 35, | |
"metadata": { | |
"button": false, | |
"deletable": true, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([[ 2.00811148, -3.01440138],\n", | |
" [ 3.99211079, 3.99540917],\n", | |
" [-1.95489462, -1.03564706],\n", | |
" [ 1.01557176, 1.03442098]])" | |
] | |
}, | |
"execution_count": 35, | |
"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": 57, | |
"metadata": { | |
"button": false, | |
"collapsed": false, | |
"deletable": true, | |
"jupyter": { | |
"outputs_hidden": false | |
}, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Initialize the plot with the specified dimensions.\n", | |
"fig = plt.figure(figsize=(6, 6))\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", | |
"#print(\"colors: \\n {} \".format(colors))\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", | |
" \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": 80, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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9gDRVd0TAGXYIdFCA3qkyyOmbQGgH0LEXQgVtAEBhkt7H5d+F6Hq+QaZbO3RRBmY/pJ5mmE4WPHsf2LGPstfYHsAf9XaPJI5AdB4EFsaWbilbzPZym3ZDcABmthZLmeU0CxI1AYC6Ayzt9RBti8icBaCRDpx6wb4VdyONMpC7AiSPkC5bnKLgNfEeRPq0CrZwAo3SlJF8FhCCss1gXEkSVWDyfTrWanmzJs80P4RZcXp64/uclrKdz1O7mjQgel6yM3bMfgjE95Jea11T+y5IXxAI7YCsliDMMjD+tm3qI9tSEOOXVI/x+0DnIQBOACZv4tfpXMe+SFN50d7G/zaLBNnNNBm3mggpZcsHnzhxQl66dGkNL4dhngzK7C42dRlb6nEA5JGwMArRliHZINzlyfrolt4E5kfJqcwztnuaWtzG3ran2+jYEbKVnHgPov/TEGqaTRoVAFVnmEJokHf+DKLve51JNCUlwBeC0Hz0+oUJIJxQ9pOnAE13rCZzV1RhLezJdAGoHug+1XOsdOPUCWDqFhAfAHwhANKVAQ8BsT0kcXzwWxADr0L0veL6LCqAkNQmF9ttv4/tpOO2ghDiHSnlidqfswbMbCmW6ittqe+08yCEPwJ0HoQsTlLQ8pwjQNqnex2RpWHOjwJjl5S5zlsQup9eM95PganreW+GKITLtKcKjH6Xsu/gDrWC6NNKmggC03eUeY4kTRmCHNOEpoLvYWB2CPL6H1JrW2HKua5qic5fegzABEYvALJKrzv2Nk29BTvonKU5oOcl0nk7D9BnFXvG1t+9VIG5YdU292QbnbcjLEEwTC1CB5LPAcIPsWM/DVpYZudWdpc4rNYRKb8F6/Y62qv8e+tvtckU/RwFvul7FDwnPyB9uX2X6lFWY8k9L5H2274btodE8ghQLQLjl6iboDxDUsHAZ6hbojxD48UDr1Kr3r2vQ/R/ilri/G3UrbAwBlGeq/EN7rWn5oQeAPQuACrTlwb1TC82bjw3BEQz1DWxmATB1MEBmNn21BfmLO31DAXfhVEn2AE0ttuWUquEqKBUp2E2WD0EoQPGAuAL0jReNOPoy8ljQDTj6MMTf08ZbftupRMfASCASoEy46lrVACzTN1z16i7QAJyxwEI4YOI7CSpIXEIGLlAx/nbqI/Y+oIQutNhUSsdzD8E5u4rndk9bnwOgEnZf/IIZH4CIvsm6cRzD5ZsD2QcOAAzjJIARPsuchrLvul0KmTO0/8TgxRsUy+ozoLn6nqRpVGhSTgrY9SDLkcx5WQW2wNMvAdZyVPQjPYBXc8qeaEEsfN5oFKAXMiq1z1EwdnKghOHqM83kgYgyLtBVoFgByxFUTy+QV0gXc+RNjxywemf7nuFZIz4Pjo+Pw5M37SDrOdLJNoLtKWcYZKZu/Q5Zc4Ck1eAHfuVZPJpAD57HHm7GuusBA7ADJM+ZwcVaZQ91XrPGp++lyGNCum4riKek0GfAqau2cFOtu+i3tfj/4QKZtAgNA0ycciZZBt5DSL5LGWl2Ys0xebzQbSl6fH5LEQ0pYptLwDQVBaqslchgNHvKiniVaB9F2Xm8QNAOAkUpjxtbrJqUKC8/hU6PnMeCO1QQfYU5NyIXXSkwmMA0nZZOwSx70cp+CaOuGwzNbLNdI8jMy3BAZjZcix/qsr0TrkljwLtXh1zUetFdwadfBZiYJ6C2Qe/A9H1nL1aCNCU3+9FV0b6EdVDa/1MqraxAkT2IrWuuXuH23c5x2XOA9N34W3v8kEc+xINUgCQ0oAQPpIyhMp4k0dVm90Z6jk28qQ5mxWnr9jVaoa5B5Aj34Eoz1GgDSdsHwiaxntBrVPykS/F5AetTcYx3AXBbC0cQ/P6anxTE5/cVZoie3xLTX69T+uASjO0r236rssA55rrfCXybkifIcP1uQckB3Q9C8BHEkX6NOS9rwHZN4HKLBXRUidtFzJMvA+E4jTaO/Cq3foluo+TjCCsyTd1fLTHcTybuQsJSa1iVteC7gcgqecXJsTjm/R3f4wuOtxFgTh1CrZfhC8AqC4MOfGBx6hIVg2gNE3vT3VACD2ouiNC9IVw+Xdp6GR+mIL19a+sfFXRNoMDMLO1sLoKrIAFZ8wY0qC2q9o2KXtE9jBJDslnIY59kfTT7BuQ45caj9Fm36BgU561OxiQfQNybpikgdI0AED0vEzX4o8BepgKY8ljFKSuf4UCngq0KORotDd3HUCV2sUgIDVqfYNQWeb8KBDbQ2Y4sd1AtaR6itV1LWRd471vUJDXdDrnyAUKwkqfpuNHgbG31JfGKUijovwvLtAk3NxwY//kaA9l+anTqgNEOaElj3pfn2kISxDM1iJxWE2tuQzJlzLxcd3iy8x50lhTJyEfvQvRcx5ifqT+GGtzRaADCMQ8E24Cgs7RfUJZPX4PAGV0M6IGLkyTgrrt6TBBvb/TtyGO/QygBaind+I9uu74PtKRF0aANlpV76yhF0pCKENO3aNx4sIEEE7SY93HnSEN+32o61evT8Y6h4DSLEkn/Z+mbNY+/4uU7ddIC9T98Yr387Q6QNwj1kxDOAAzWwpr9Y4Hd1GtRpeUVcPrxZAfd4J1Rz8AARnugrBaxFInSWYQPgifa83PxHsQ+36UHMuG/9YJcpnzzou5tF8KSj4K0rNDQMczwOjr9uJMJA65FnNqVHCbuQt5/79ScGzfBaROOdpvaZq0V8uWcvoOyQ2ZszSlljwGGHmvXjx7n67PyANdx2jKDh1UaIvsdPUznyKJYsd+ek7ngVb/MajzQ3CYaQZLEMyWZ9HpN2lQgLMCRbjL47gGAGL6DkkEytwG2TcAIdTqd9U1secHgEgX5NRNpdn+EGWiPr/zWrbOexqYugHAgPzwG0BHP60bsrTh5BFHxx57ixzQJICOAZJGStMAJI0N97xIhTV/G13H3v/GNZH2BmBWSaIYfQ3w+QEo/VfoZPIuK8Cjd10ShgSmb9HP4aOCY+4a5PU/II1cbelw1ts313mFHlCudU+4+mkLwwGY2d5kL0J+8NsAYLdeeYO1Rpkf4Oid6dMqIySXMAifE+SiGbXD7ZyyaHRZQQo/aaO5K6T9Zt+kTRQQTnEsvp+CbcplOTnxPgBqHwOg1sm/Bpgl+s8XAcwKZH6CHnfbVZbnXQH5TaAwAfnoXdrEDDjbOMYuAtW8ZwsHhFSyg5JKksfqx41zVyE1HSjk1vAfaevC9wbMpmFNTLvtEeJm01smeQCnz1DVv+8Vynyv/wFlq5kX1Xiw4yCGnpfIe2H6JhBOQJrKjjL2DOBvV+t7XnVZQRadabhINzBzG+jYS+eZeBfy+h8648bBuEubBdB5BAi2k9acOKT06xdo0KI0B1ldgKi1u6zMAx39VKizNi6nTyuv4VMuOeYR9QsPfp4y7ULO0bitXt/EYQhZpSJkuJPeB7egtQwHYGbzYE+Vrd6k1ZI2iO4VPFZhzzVwIHw6pAx4tVUJYGGMRnSlSTvi0qeBUg6iLQ1pGOTnK3zqNS46rmfxfVQEO/Yltcb+sFMoK+QoU7ZHlk+p7Reveb0hANJ2pQmRe0BTc+17HAe02B4VqE8BpVmg8zB5D49coNa3nceVHJOEeO7nqLvBNIHKPBDc4e2Dnh9xjSsDsq0bYvY+eQdvww0Xy4UlCGbDY7eRJQ5RNvYEk1bL7k1Nn6rbNFynKVvbMpLHlF5bpgLawGeA8gKk5qfMMJSkW3oYwKO/p64FS0N+7n+k9xXphnjhn5GkMfJ3ADTKsiEoi5Zl1ff7InVKTH5A19iWdnU1nCGntuzrpN3mrgLBOF2r6mOWsWfIxSwUp//G3lSObOraUKX/5h4AkOTqFu9XfcYuor0euURU8qRpb9MNF8uFM2Bm41Nrst4CLW2+aOlcupruukKjwePvKmMcl2du+ixE9g0qhJVnyXXM7mYYhDArQOI4dTEsjEKUZ73+EO19FAyjPdRhoflcLWYAel5WHRJWdnvOWRmUOAzksxT8Z+7SY9KEMA0nK7e2ZOSuQhanIIRGRvGJQ0AxBwST9B7ad3sd2Wo3gzTAGlf2SDODn29pw8V23QPnhgMws/FZyS9zi5svlgoCdsbX/T2QcyM0kmvrsI7Bjd37Cjg9wckjapXQ1+zAKSoFZewD5ZDmNrEBTZbN3IPofVkF8MOAaagNHHAkDl+Q1g5FuoG2XqA8R5KFSfvhnIB8FjCK1DGROEwjzA16lAEA8X7IaIaOSZ8GoNlSi9do6HRjrdeSZhKHWttwsewvw60HB2Bmw7OiX+YmQbvuXE2CQP3OtJIakvgi6aPRHs+QgTRKztaL/Di1g0mTbs+hUaYsoVzLVP+vj0zWsWO/a3JPQLSlqEshfZrMejSdgmvmHOSdP4fo/xRlv9O3ybdB+Gj02AqInQdIZw7vpNfUI/ZQirdH+ZxtzmMNaSB9Buh+gdrUfLrzxTI3AkzfUs9VK+nLcy6LyjPLXyu0TffAueEAzGwNane6tRoMmgWB2sBsGe6kTgJZ5UbWeZR0WIBu70e+A3H0CzQePPpdNZzxI6TXLowB07fpZ0e/AIyrlUWxZ2iFUOY8MHOPLCmLU95hkGgacm6EJJj0aXrNOWtKTgLtuyE1HUJWbV9fAJQJJw6RfGH9LH3KKeBJAeQ+qPc71vwQ4SRkbLcjs4R3Ktc0y39YTc7lx9V1LD+L3a574NxwAGa2BMv9ZZZGSTmfHWusK9uB2QpcZ+u3VqjsT6ZOqtt7oaa/dgHRNB0T2kE9uqOv0XaK9j51DjXimzpJAXDHAWWUDtJwAdsbAvBBtPdQVtzzImCWyZvh6BfsTgohTWAh6wTD5FHlqKa05PY+tZPuffpS0AI0+ZY4RK9l/T91EqL4GGhLASPfcbLl5LOQD/4/iF0fp3a87uepgDh9W13HykPJdtaCOQAz25PcVRqkGL0AafX4urACut2BoSbjpHvd0Mw9av+augZE0hCeEWgdUjmFia7naOBC+MiQx917Cw3CH6XCm/A5mXP/p6mPd+LvyX/Yyp67T9BKInt10YtU+NPbXKPLtPVYdB+n9wJrVVKG/hyKU0be3kMBPtpLz4nvpzVM7dQTLd13B0In4/Xsm+rzCtrTesspjjZkG2vBHICZLUdLGVXyGAVfKxOtNZSxqAkO7kxbxnZDLIza64IoUJ92hiraUhBdx7zXoXdBlue8gbP7BABlrp4+Q4E12gNAUnAOJ4FwN8kfUzdcwxCnqEA38T5l0sq4B52HKau23mfmRdKjS7Oqpc0k6UCd1243i/bCtqhMnwHgU17GQm3OuN2Szr6sfwdgW2vB3AfMbGosT17aFqxooQdV+HSPGxj5OjToDU6fab7lQTmLIfsmMD/itXacvEy35fEDoKD2bfIWnh0iDbdaUiPGJv7Tn/w5jh45CN3vx9Ejh/GfvnlDWWFaY7+v0+r3qRtkVVnIUcYpdOe9jr1Fxbn0SSDYrqwzD0H4dAifj/p427qoz3f0NTVGfQ1C16n1bW4IgAmUpul8uWtAZQ5y/G1g/iFth06d9HwWS26YXuTfwd2P3dKm6i0KZ8DMpqFhRuWREs7Sz136rfs5gKBgpp5vjxZb7WXlubpb4Ebasq0f14zl2uPFhUl7rQ/aeyj4WgWurucg5h9SEe3u1/B/f/Wb+JVf+x18+ZdewvmjP4DXLo/ip3/lVwDhw2d/4nOk3balKKAnj0J0HlRjv0kqsqVOwLKWhPDR0s9Hf2+vCqq7pXf3F2depC+usbecnXCZ89TpEdsDZC9Sd0buGkRiEIC+PKlhscx2G8sObjgAM5uHRr+0HimhfkMxtV2p5zQYLLDby1ooJHlazSbeg4h004oeowzkLtMYL6rUHhZOOk9022GaVVoPpLTkf/nFn8KXf+klfOT5PgDAR57vw5d/6SX8/K/9Gj77Ez+hBjQyaphC0IizVWjrOU9tbJnzNBJdNUjbTZ8mn+Lk0fo3kTpp9/kKn06DJdbPrGm/rudUAe6rrpazWVrouYyC2aKF0W0sO7hhCYLZPDSQA0hKONtcJnA/x9rW4D7OvoV/e2nbxOwbtF7IWkE0fVsZ6Vyhkd/CBBWpJt6jQA04bmiWlqqRdwMETdhdv/0hzh/NeF7m/NEMrt/60HEqy75Jnr3SoELbsS9RB8SC9osAACAASURBVMb0HRpZlnC2V1hubMqYRxpFr7yiBZQsonwoOg+S/JA5Dzy+TRs/TNPlqHaGxpwtU/UGq5lqaWXcezHZYTutMuIAzGwamv3SLvbL7H6s3moSdUF90V/+9BnqSNB01wqii9TKNvh5ynrTp+39cNIoAaUZ6hZ47zcpc5x/SM+bfB+QEoN7d+O1y6Oel3nt8igGB3q9tpL+dtKE1e42jL5Gi0NzV4HqgnevXbTXo79KI0+rjowyPS+ccL5shA5oQRrYiHRDTlwhvwn1BYH8OO24E+p412qmpjv2ntQHYhv5SLAEwWxJWr9VJmnC2Vj8uqszorkeLNOnPbfy9mYMzQ9hVrxtYulzzjQcBAXVxGEgP4b/+X/95/jpf/qP8eVfehnnj2ZIA/7X38av/qNTFFx7XgSkVFN1Jxv0Ip+n95A4BFGeoX5kzQeZOQcRaAcSR2hiL/smEIrXDU0IPQAo43kZ7oYIJxzNPD8GhBK2S5qswqUha829ItJnn0xe2EbyBAdgZmuySJHHU5jLXSXP2+d+jjZNdFtFrZOQRrmpLEEFvI94f5g+AzF7Xw1UKD+F6dtU0Op6Vo00l+019fLOn+OzP/ADgPx1/Pz/9s9w/d4IBgf68Kv/6tfx2Vf2UJAuz0KEdigZoUCG8NG0MwZtlgBfGBhVu94CHZCJw1RstOwmp67aAZt24mVsUyH3+xO6Djn8mtcEaOTvnIAb3+v6fNTYdM3dg/2lt8zCWt0X5jYpzHEAZrYmrVbgM+coiER7qfc2d83xXIikFvWHaPizzgMULCsLELmrkAtZGk3ueVG99uu296/Y9yNAWwqf/eGd+OyP/QPH6KZSAIIxu8dXps9QsJwfVpuNhePvCwEsZNX7lcoAyJXFZ865OiVO25sryBUNnkWb0ih7louiMO76HE8BoE3KnoWnLfhqtMQ27YrgAMxsSVqtwLsNZwDYI7YikqI2NsusJnmscZCwPCLad5GbmFEGOg9ClGed8eTEIQhNFb3S5+jYaC8AZYKeOqk01xep0Db5PgVO6/UCHUBCFcva0pCVPHUnlKbosembQLjTMfxxySOQApgbUds13lMbLj4H0XOeRpu7T1ARMXkMyF2GHLngTOG9/WsQg/8tjStbk3W1C0+bfK7LZhvJDm44ADPbEA3InAEmr0A2WJ8j9ACNIVdVMNyxn6wda/0hAAqo6dPA/KijIVsmPKZBBTqfToG5MEGr5ecekO48aumpJrWmxfaon32V9FtrZDl51GVrqUH0vAgEYqT52j28GrWJVav0vioLELnrEN3P03aK4iR9IQx+jopqIxccqSF9Vq2tP0JfHGo9kxj4DD1n9r7jKbwITyYdWFq8WOHzNyccgJntx/yws0Zn4IcaBg1plOm4zoPA1E07kEL1/cpKXkkGZ0DNRJK6E9JnKCu1i1mnAeiU2VaLzvqhQMw5NnmEnjt9W/0spn4m1O2+TxX9AHshaOIIENzh8pVQXwqy6srAD6l3Y5K0kj5LgXTifVev8BFgYZSOLU1Rpjz/EIhkICyNu/Pgoh+nI8OchduoflmTbbY00/jfY6vCbWjM9qNmjU5DpKHGc4UTfAHKcucfAmNqTDh3FeSlMATIKgWdrmc9Y8rSVEsr731djfO+als5InEYGH2dXifWT89PDFK7mKwAlVnVS+wjHXn2Pi3TzF0FIJWV5Tkn2M0/pNdp3+XaOad6k4VQa4q+QmuNEodIhoh0A6XHFNBHLgChTpIlqgbk9D3PZueGbXpW21hhcuUtZIuNfG9hOANmtiSLtaG51+g0xXVrj8RhINJFP8+P0y16W4oeSx4lBzPr2L5XqCjm3kRsGk6mKny0kDN3BXJhDMJ/zzY5R+Y8BevcddJ5p25Q54Np2F0FMrYHYiGr1glNk2F75yHIycv0s7kHyrDdR7JJ5wF67fg+khmsLoaOASVruNrZRl2yROY8fQHM3Sc/YqNMXzz2JhCXDu7O+kOuIt4y2E6dD244ADNbkyetqluuZOlTsKbGZNVwRow1PwVmKeGYnCsZINwFWPvehI+y1MRhZ2RY+FR/rub48FpjwNE+V0/vIYjZIdKGAdJ3VWZt+zb0f5pMekKdEIWcY9LeeYTaxgAgdw0y3Ek+ElPU5SF8urKbhLLWvONqMTtDjw8r3TkQB2J7IMffJk/jmmKZ0INOH7RlpWll38yiCCllywefOHFCXrp0aQ0vh2FWB8qAL1L210SLXI6vAR37OpB6ASgXyFmslesYf4cWVQ68qrLM1+yuB6sH1+606DwI+ejvIcwyBdmBH/L008qqQdKIWh3ktMKRtkzn1UCFLOmsGIIgySJxiEakldUl2WYepQJdcAeQu06GP5bcYpTI8Ce223V+P4Tup4w4+7rXftMsA2MXgdRpoPjY9hVmACHEO1LKE7U/Zw2Y2ZK0ZHGoWshQmFzaf0BNgGHsbdpyUYNti2kUvedJHFZa8yl7+SbGLtLtvP1kUAATPohwkjLbwc97skzyeniN/uLSfKnz4qLrvKPKLP2ircUKPUCBtjRFwbQw4WjBo98FAh0AdIju553+XvUZomOgxi9DLSlVRbM6+832PfQlEK7/gtpOHg+twhIEs31Jn4VInwIKU+Tn22C3WSPrSXeQssm+QXvZ5kdoKef8CBDvh9BDkMljkCPfhcicpWCstiFbQxAozZLfbyAOxHaphZxnvEFWmeCI3DWgcxDwqUA4+YEzaGFtWQY8AxRybsQp+M0PUxEy3GWPRy/6JZUf925kdn927bvI1S13TW1CdsaaG7JNhy0WgzNgZtsi9ADddoc6nHU+7h5fgILG9a8AueuLZ9TpszSoMDdM/bOlaWpXM0rA5AcQmbN0+973CoQ/Yo9AI3cVCLbTbbyAq4vgDcdkXkqXCc4hoPAIspInGSBxBJi6TfKGFqDzukzOMTsEWcxRhl2aAiIp6nSABLpfqHs/slqFLM1CGoZt1WkNiXiPldTPDFDm3ErL2TbtdFgMzoCZ7U3yiOpioMJWnYeBlUnaPbWNEXrA5T2sPHSDcU/XgOfcicMkM3Qdowk1d28wNNKapQE5/QAIxpV8Qdco27ohxi7S7X6gnc5hLADj79jasZ1hxvZQNi5N2mw8e58WcwLk9aCgVjPTpfXCGQqpufZa06K61fRNP6Pt2emwGByAme2NFHWbLdwsK2h4bsuv2v4Sjc4t9CBkYlA5jZ2mCTXLkyFz3tnBNvEexLGf9YwXi/w4FbqED4AERr4DpM/VdG5Y76+qWsl6ALPi2aYsNNcNsNViNvEebeHwtzcfD578wGvKo7RtlhaWD0sQzLZG6H4IPUy38Or2vRlLF5HUbbk0gEg3+Q+7ioF1z3cNLYju74G92FOa1EFg+Q8LjVrJKgu0Hii2m2QM4XOKg/MPXcdcdy5pYQxy4gPqfrCLdW/XX0+0zzGanxsGgrHmhczEYeAxbeJAfozkEZYWVgRnwAwDOEMJi43CLlVEsh+H8umtIXfVfr7MnHNlzL3q+a97zm9l316Dn0NKG5ZA/pHjXhbtA9p30Z8Tg9QzLKveVfVtOx2THs/1SjICEn5gxPUemgyq2H2/4+8qc5/PP9la+m0MB2CGAVpz41rqmFqTdihtVRq21aQY/DzpyTN3aaPF3LATgNNnG+9yswx+lEsZQjtI052+TXJD5kW1JUN5HFu9wvaqemVLmbtCXwwLo5CRtGNhqYZARPfzHqP5ZtidIVbmu4Q+zjSHAzDDoF7rbTSksaQerCQAkbvu2DbOP3SMf1zFLNkx4Bn9lUo7Jm+I12hKzbr1tybtohlnrbwdXEEeEVbBrH2Xq/BnGbD3qK0dxyFnhyAnL0O0TVHAzbyonNAOqffYwGi+Fitz5sz3ieEAzDCNyC59Kw64A/U5Mu2p7ZiI9jq+ETXLRGX6rN3xIO99nV7LF2wgc+iOpGH3K6vgGumiIG95TXgKf6eUAbur1BPphvDfI8MfdR2Levw2osXOEGZpuAjHMI1InYQY+LSzDbgZ1jSdrFBmmhikVi9VaBN6AMIfof5f1xYNOfy3gJURC50MdOYe0MLMmik4ofspUMqq08WQPkOOZ/OjQERtVc6c9xT+MDsEOfshAEMV2wqUpVsubVX1c6unuEV4o/HqwQGYYRphj9Xqzbf/ApQN9n/atSX5Dcjp24vbMbqGLQDQaPCYs4K+drBBVg2aZvOMBAchfDpE5wEAVTqXNLyvE9sN0ZZxrbe/CCSPODac7tHowsSSwdN5fBF7ym200Xg1YAmCYRrg2RZsD1g02ZQc74d03faL3HUgcaS52U9tMS/cBYR2OKPDnj1t1jlOAmHLda2mQGZvKoZXv1Vr7MXRLzijylJ1PEiTOissnwrhd9lTLtLlMfEeROok5PDrJIvUdoZs09VCK4UDcA2GWUE2fwvpyH7omn+9L4fZCNQElSULdN3PU6vYaOMV97XFPLc/sRx/F3LkOzRFF++vb23LnAMmL9PmY5jA7APXAMYZyKmbtNpHCziexILWFbmRwxfszgqhh+lnS3Z5nKEin8r2RaCj7rPhabflwRJEDdn8LdydvYRs/vZ6XwqzQajTPGtusxveulvTYgOf8QRuOf6u7fHQ8HmJI2oY4gHd6qdP2Vq08Ok0Nn39K1R4y16EvP9fgdIcrQ+aHYL84LdIUsiPOzIKRP1rWUMeUccy0taO1UBIrRQh9CBEvN/xdEgcas11jmkKZ8A1pCP7AQikI/vW+1KYjUptptjISS1xWK24d61wz10Fduyj9rPMi96tx6oTgTwlXAMZmXO2Fu157WgvEO2FSJ2ijDR4BogNOPqu8JHNpS9IRupmxZONL5qp2gMhTnucO8CuJMtdjvfydoIDcA265kdflNtrmOa4A5A0Sq5psxov3dr2ruQxp/c3EHdW+SQGqSvCCk6uAC98usfisa5f2WUAhMx52oxsFIHRC+QXEe6CyI8BiRN1rXBNSZ2k86VOQl7+XYielyETh+zFm0IPuAzZWwyobEXZEJYgGOZJsDsT3rI7CQAoY/ZvUzBUCJ9OAcx9+979vMc8HfBKHku2daXPkMzRfYJkD0Cd72t0TeUZIPkshB5qKBXY53e3ogmdbC6FD6LrWXKMs9vtDOVXYdDfW+12YCvKhnAGzDBPgpWtpl6gbLHrecrwmnQmNLx9b1D8kkaJDHQaLcF0YbunTX5AskfN+ZbMTu3MVO2wi/dTUVDvIr26sgAxc6++AAdnyq4VuDjXGA7ADPMEuA1zRNfzThB1dSa0eg4PVsvX0S80CM5qQ3G0lwYvfH6g+/ji52uGdZ1qT52HxGGI8hx5Cte127UQ3JklYQmCYVYBoQc8HQSAD6LvI+St4KLlSTHbilKvlw6kQR6/tYMXTc6/2GuS3EFbOqwlod7HvK/NXQ+rCwdghllN7GWVrzd5vH5SrFGAdAJdoP4cY285Gm8L51+N6TQeMV4bWIJgmNWklWGG2sdb7BDwLgiV3s0XUNKE5Q/sWaB5prHN5XLgLoY1gQMww6wiS+mvrRbhGmLbQH4OSD4LwFfzuPIN3vejdbKBTByqt7lsgDSKrn5dl3zCI8ZrAgdghllnWi6a2TaQhxsHUSvTDXd6+4qB1jPYmu4NZ4DiLHv/rgEcgBlmk9BSdt39fGPzoFYz2NruDZYe1hQOwCuADXuYDY0dbJ0WuFaz7LqNGCw9rCncBbEC2LCHWS7LaQ970o4Dx1RHes6zkvNy29nawhnwCmDDHmbZNLqVb3Z7v1q3/S4rS5k8ZhuwL+e8bKKztnAAXgFs2MMsm0a38s1u71frtt89Jn3v6xD9n1L67qm6Q5sGWtaA1xQOwAzzFGikwTbTZVfLN8EzJh1JAUKD1Pzerc0WzQIta8BrCgdghtniCD3gBGLTaLzNuEmgZROdtWVTFuEMs4Lh+aswzMqGPB/DbEQsv4pGWi4X29aHTRmAV7sLYS27GgyzgvH8PQ7yzJqxnO4G9nTYWGxKCWK1uxDWsqshm7+FillEOV/A3dlLAAQX8JjVZTmFMi6qbSg2XQA2zApyxWFkIgfg03xLP6EF1rKrIR3Zj1zxIRKhXnDrGrMmLKdQxkW1DcWmkyCy+Vu4Pn0Bo/mb630pLaFrfnRHnrGDPE/OMRarJQcsR79lrXdjsekyYB6CYLYMLjlAZs4Bo6/xwMM2Y9NlwJs9k+RiHGPjXlSpptSexDR9KbgAt/HYdAF4s8M+EoyFRw5IHF77rcGrsBmDWV02nQQBbG43MpZQmEY8lYEHLsBtODZlALayyM3Y0sU+Esx6wVNtG49NGYC3Qha5mbN4hmFWh02pAT/tQtxaFM5YC2aeBlx429hsygz4abNakoc7601HDoCy+P2rdp0MUwdPvm1oOAC3wFKSR6tyQm0g74sesrNrliKYNYELbxsaDsAt0KxwZpgVzFemMFeZbClDbhTIN3NBkdn4cOFtY8MB+AnI5m9hojCEo4mPopWiYKNAvhUKigzDrAwOwE+AFTwFfEtmr81kCm5LY5jty6bsglgLVtLp4HRjLP09xl0PDMPUsqUD8HKC6loHyEzkIAbjLz2R1MA+EgyztdhSAbg2QC0nqKYj+zEQe2FNtFjDrGA0fwOJUG9LnQ7NAi1n0QyztdjUGnCtrlrbUbCcAtdaarHL7XRodjwX7Bhma7GpA/BSAdcdVJ909Hex5y917uUGzmbHc8GOYbYWmzoALxZwa2klC3UH0lxxGInQLgDS0+sb0CLojjzjeV6uOLzouZcbODnQMsz2YFMH4OUEqlay0PnKFCYKQwhoYfi1IArGLKbLWbvXN6BFkAj1qsm1AwCkHbAH4y+pvW+NWWkGzqY9DLN12dQBeHkItPsTAETDRw2zgrnKJI4lPgZAqKC3E2E9BqvXtzvyDIbnr+Lu7CW0+5NLTsAZpoFs/iYykYMNM3B3cKXXvIlUZC8EfHZrG0/KMczWZVMG4JVkhdn8TdydvYSB2AsNA5kV6BoF1vpCGBD1JxD1d6JRVm1dXyqy186olxpDbvcn1J+Bdn8S8WC36/W48MYwW5FNGYAbZ5OUba60EGY9vlhgBSzZ47D998WCOSDQHzuOiB7DbPlRwzHkgBZBMrQLEhLHk59CRI9B1r3e0po1yxMMs/nYlAHYCab767LNlRbCah9/ktt9d7AX0DBTHkNHIOU5xjArmC0/QiLUi8niEBKhPkyXswjrUdS2ZxtmBbniMJKh3VioTKHN3wmf5kPBmENU70TVrHAAZphNyLoNYjzJVJd7BNjKNsfyd9EfO25nrY3O/7QmydyG8T7Nh1hgJ0bzNzyvm83fwnR5DNn8LSwYj11DFndQNcue8+WKw9gRTGM0fwMRfxwz5TEYZgV5YxrtgSQeFT/k6TiG2YSsWwbs3KbTmK5P863oPO5s050FNpIp1qug1eh105EDKBizCOsx1fLWq36+j/6u9dnvJxnajdH8DfvzatPjyOZvoWIWUc4X7J+7pRGGYTY+6xaArWJWd7gfk8UHdb21rdJMWnAHZsM0MFseX1QHbkVPre1ayBUfIBnabX95NDtH49eVSnJoRzK0CzPlMfREDmK6PIYF4zFQFPZn4tN89ueVjuzHbPmR6lV+qAI3F+kYZjOybhKErvmRiRzE49LYov2zS9FcVnDazvKVx4j6OzFbfoSeyMGGAbYVnwX3MVWzjM5gxiMtNDtHox12zrG3MFeZxAdT38R0eQyxwE74tVDdZ2IV/wQ0+LUwAArQ7nMbZgXj+XuomtVlfYYMw6wP62rG49N8dhABVqbRNgt62fxNvJf7K2TzNxH1J/C4lEXU34mR/HX7/M7rGS2Z8dAxJ+xsdKo06nntdOSAOsf+Jd+Lda5UeABR/w4MxE6gI5Ba9IvAMvUJ6+0NLTDdWjFrwgyz8dlQbmgrcftqFjjdwVLTNCRDuzFeuOc5vzsLbbZp2Z1V5ivT6PDvBGDiUfFDJEJ9tsXkeP4eAGkXBytmEfnKDLL525gujcMwDXU+Qx0rkIkcQMGYgwYdPZFBSEikInubfhEs9fl0hfbgcSmLu7OXkCs+ZPtKhtngbKg2tJUsv2ymAdf26zo6qnP+VoYcCsYcOoM9mCmPIR5IoyrLyOZv20U1q2h2ffqCPeRhmBVMFh9goOMFAMDI/HW0+xMYz99DZ6gHhiwjm78J06yiJzqIkfx1O6u+N/MOuiMDS/Yyz5Ym0ObvVC1s5FnhHotOhnZ5CndPUuhkGGZt2FABeLFe3apZrZsci/o7l9X/Wnv+pXqDDbOC6XIW6cg+xALdKlAecAXCvdDgQyLUS3JCZACAt8OjJzKInuhBTBYfIBHqw2x5AjsCGQR9bTDMkufYnaFnsKv9qNJ4m1//VHEE7YEkRvM30B3utz0r6jstVqfQyTDM2rChAvBiTBaH0B3uBwCkI3vxQe6b6ArvWbN2Mm/ABzKRA2qsOIJ4YCcykQOQkHb2Gg+kIeBT1+d0LEhI5IoPsSOY9nRQzFUmEfUnPMcCAo/UNF8t7uy/I5CqyW4PqAEObzZvFTop+K+80MkwzNqwaQJwItSHXHEYmchBSEh0hfd4gs2TjOW6J82s2/TJ4hBS6vyUQQ7jYPw8Ar6wep19yBUfNuwr1jU/kqFduDvzNuLBtKePlxzVejBXmUSbPw6YAj2RQcxXcgj7Y02n+Wr7pt2Bu2yWENBCDb+MrEInwzAbj00RgK2x3a7QHmga1Q2bB6jWhywMs4L5yhRK1QV0BnvsrFTXdCRCfShVC8hEDmCy+ACdoR4IqXleJ2N1MkQGUDWrKFYXEPRFAAC6FkDEH0ci1Kf05wMARI02S4W4kfx1dIf7MVUcRX/suPKi8OIOuD7NB8OUaPcnAQj4NT9G8zfYE4JhNhkbqguiGdn8LXww9U2M5G80PWaxNrLFdqzdm30HydAuQABzlUlIUA+trvnR5idjnKCvDQICEML1OnshoYKgBCaLD1AxCxCgFrjLuW8hHkgBMDE8fxVWh8RMeQzd4X7VobEPk8UhTBSGkDdmkQz1wSf8dk8vXXPZ7sDoiQzaAdbdZjdfecy74hhmE7IpMmB39X88fw+JUJ8n07MkhEzkQMNK/2I71gJaBIDAWP5O0ww6oIUg4LM7DqjTgabrOgIpTBaH0BnqweXct9DTNoh05ADa/UlE9BhGlQ0mQNJBLNAN0zTQExmEpmlIhHahM9SDsfwdRP07lP9wvVVlRI9hojiEoC/SQDsGnkt8P6Kq08KSa2o/J4ZhNhabIgO2qv9Wu1f90MUtXJ++gNH8zYbPd3qC63esdUeeIb03PGAfY5gGhuevqkLcTYwV7mCyOIQF4zFyxQcAgIIxC10EMFMegwYfNPjQ0zaIRKgPgFTZNHk7DMReQEplzIDEePEuTFRhmAbK1QU7+Gfzd+wBCyvTTkUGcG/2HYwX7iEZ6sN4/i4AE7niMNKR/SgZ8wAoewdMjCzccBn80OdUewdgmiamiiPcH8ww68ymCMAW1uBDJnLA8/Olp9gsvVR6fmoNWSRCfZgqZZFRY8qWefuM8o+IB9JIhPrUiHAfDNPAdDmLiH8H4oE0doQyGM3fRCLUC6G2aUwUhjBfyUHXAmj3J3Bv5h1oEMgVh1Exi+r/BQR9bQ2HL6wvHQ26yqr3QwLojgwgm7+tvohuIazHMF+ZQr4yg/nKY+zrOI10ZD/a9E77fFY2PVuegGFWMJK/jlhgJ2bLE6v4r8MwzHLZFBKEha75kQj11hWclurntQYnardhZPO3kK/MoDPYg2Rol+3Lm44cQECLIBbowmz5EeKBNEby1z2SgGlWAdWG5vYizkT2Ix3Zh1Rkr5IVEmj3JxFuj6JUXUAi1IdyvkCZshSQAIrGAjKR/VioPEYs2GVfX6POjniw22UYvx8mTJSqCxjoeMFzrLvzwZJEYv4u1/ugHmWGYdaPTRWAgaW7HRo5ljWbeKtdrFk05lEw5tCmx+39b5rwIYadLs11HwCBmD+JEVc3g9W9UKzmUZUVzJTHMFEYQiqyF9n8HaQie+FH2BMAU+G9du8wAET8OzzX5962TIMn1KHh/sIZz9/DgvHYZUvZ6HORDfuOrY4ShmHWh00XgGuDaW2WWFu8uj59Acc6P46eyEGUzAIENLtQp2u6vWQzoIXREx1ENn8LIb0NpulDJnIQxeoC5itTMKWBNj2O2fIEOkMZzJYmPEG5YMwBEChV5xALdCOsR9HuT7qKe0BnsM8TAHXNj3K1iLAew0x5HB2B7rr3NBh/CZ2hDO7NvINoxw4lmeyCRBVj+TtIR/bDXw4iFtiJRl8ygLeHuCu0B+nwPrvTglcaMcz68VQD8Gr8wtfKDbUZsTdACwzGX0Is0IWJ4n0kQn118oUVEGtNzzORA+rYAwj52iAh7Z1zANDm78RMeQyJYK/LC+IE2vQ4csUHMGQZ8UDGvp5UZAACGqxeYEDAMCt4XBpFMrQbsUA3RhtsUO6JHIQJ09Z+K2YRwUob5iqTmCgMod2fRJseR7M7Auc9CvV/E9n8beUnPLzs3mmGYVaPpxqA12IjRW1GXBugLSnBuz3CeX3LtKdilJFR2m8i1IPJ4oM6CcE9pWatGrLavSzntZIxj1igG/OVHIK+CHRNt1/LNE2UzAKCWhtMaSBXemjbR6Yj+zBRGEI8kEYqshcAkIoMYMGYRnug09Z+Z8sTanHoDpfOTIMbw/NXG3656ZofO0O76zwjMpEDGIgV2cydYdaJpxqA12bFumO8buFe1KlBV2PDIw23R1g9xJQd34S1SDMR6kPQ1+bpD7Z0XqsHOBbotgt11NnwEN2RZ1AxS0pz3eHK+uk1/VoAJkyUzUJN1k3ntzwd4oE0NPjsvmCAAmks0IVc8QG6Qrs9GrSz1t775ea+6whrQY9nhE/zcebLMOvIUw3AS3UrrASrZczqcKg10YkH0gjr7YgHulGqFhDS2lCuFgAIVM0SCtV5O0i55QfyhtiFqCqMuXfODc9fflYpFQAAIABJREFUxY5ABvOVHMbzd9HfcRwLxgwSoR5UTdJmJwpD2Bl6BtPlMTsw7gztgYCA0DTkSg+RjuxThUA6f9U0oEGHpmlo0+OYLmeVtut+v+o64+7x5L0gueVFJW9Q4C0Yc3UZ72TxITKqpY1hmPVl0xXhaslEDqogS5mi1zWNss67M28j03YQYT2GoC9ka6A+LYC54qStF9Nz9mOy+MD2940HUrYBkGGWkSs+VEHTRDY/iv6O4xDwIRXeC0DDQmVKWVbus19nMP4SEqHehi1tiWCPMm+fRsQft93VZsuPVK/uI3QEUpCu4QsA2BFMY7Y8gZ2hZwBomC2Pu7J4en67P6nkDLLOnCwO2Vp1uz+JeLC77vPkwhzDPD02VR9S7USXYRqQqGK6nIU1ZJEI9eFxaUwNVQQAaIj44wjrMbXG/rbtmzBfmfKsgu8OD6BsFpS/L02h3Z65iMniEDRoyBUf4vr0BZSrC/Z5xvJ3ICCgCWp5i/h3KD8I63VuIRZIwjSriPo7MVG87/GTCPja4NfCiPoTrgGOKegigGz+FjoCKeSNGXvaL1ccRk9kEI9LY4gFuhDUI8jmb9or7q33Fg+kMFa4g8u5b6Hdn8Dj0ig6Qz22Vh31Jxp+xivZSsIwzMrYVBlwbRFPABh1baewVgq5hxCsIphhVjBXnrILXNZtuzWBVqoWENQCyBUoy7T8fnvaDiIR6rPN2E92vYqAml6ztFQJE9n8LdscPW9Mu7Lw/ar7AbbeOxh/CTtDe3B35hIybQc9q+qtwlo6sg8R/w4sVKYQ1mOezHe6nEVXaLfdx1u7ITkVGcDw/FX0RA8CEIj6O5ErPqQvCui2Vt2ItdHpGYZpxKbKgGtHjieLDzzOYm7cbmKmSQFydOEGBDQ1lqxRsA73A5ComIUG2Z9ELNBlZ6YSVeRKDyFRxb2ZdxAPpCDgWFSOF+4hone4snDSd0fzNyBhIhM5gMH4i0iEeqBrQfR3HMd0OQvDrGCqOAINGorGAiYKQ8jmb8M0K2jzd2KukoOl4c5XphAL7MR0eQxVs2rvlwv6IoCU6ArtgoAPmuYDoNV8KWkwUV3UnL3ZbjxmeRgLJYx/9zKMQhmVuQKu/ts/RWWusN6XxWwwNlQGvJT+WFvES4Z2Y6Y8bmu01IZ1QEkNTrYcD6SQjhygAKqM1xcqU4gFu+AT5KWbr8zYO9womEs78B5NfLRmqEKgOzKAiB6r0ZxpA4ZQAXEsfxcmDE+ngpWlArCLdfFAGrFAF6qgFUjHEh+D9d3o03zoDGUwnr+HgC+MWGCn/RnNlMc8U3ADsRPoiQzChKE2dDgyjTW0sdT0IAfe1cE0DExcvI4dR/rx8K/exFu/+FsABA7/wg+v96UxG4gNFYCX0yfsDho+zYfh+at23+543j1+TOuL9nWctjsCBmIF2wdBQiId2Y+KWaSCl38nytU8NKHbfcFCZcvOMs99EBComhUkQr3IFR8iEzmAYjUPXfgwXR7HjmBaeT9QcE5FBnA59y10hXcjHkgjqEXUZot9mK88xnzlMeYqk57uDQBqBb0fiVAf5itTNYbwB+DXwgjr7bBkA03TMJF/iM5gD8pmAaVq3nXexrv01qI/eztTLVVw6/f/Em//k9+B8Plw8Eufxslf/1ns/8lPrPelMRuMDRWAl6M/1k/AHbCz1InCEBKhXtUfrKGnbRBhPaYCldcHIV95jIg/jkq1iFig25MtOrvUBMrVAkK+mO2q9qgwhJ3hPZAAkqFdkDAxVSL9mILyCJKh3Sgas0pPNu01SgIaJKqYLA4hGdqFmdI4eqIHaUWR+tIAgA9y30RP2yCSoV2ebNzRnkk6iaBDmb9TZp0M7YYJA7OlCSRCuzznbbRLb6nx7q1KZa6AW7//Dez/yU/C3954EepKGPrqBQz8xMcAAPt/8hPwBf2c+TIN2VAa8HL0R7ceTDqoRDzYjXRkP/pjx5HN31YbI24p/VPaO+XcGyci/jhmy48Q8ccxX8m5vHlvqUB0B4ZZRDyQsrdQ5Ioj6A4/g5nyOAQEfJ7uilswzLIK/kBQjyJvzEJAQzpMAW5UbfboDPZgNH+TimUSKFdL6IkchJQSkEBXeLft0tYd7kdXeDcEfNgZ2gNAYLY8bm9HDvrCyBWHAZBsMZa/Y3dN+LWgCrha3S69Rp/7duiEqJYquPnlv8Bbv/hbuPV730C1tHreyH2fOIPst9/D4H//6qoGdmbrsaEy4GY0ysisoDGev2f37FpBxLFsdHqBraAyECuiL3qobnX8RPE+OoM9dVkooWG+MgVrg0Yi1IsRNT4MSEwVRzxZpNWRMFOmVrG8MY2I3gFDFjFRGGowvQakw/sQ9EVgwoQhy/BrYVvbToZ2YaHy2NZ3HxU/RCqy19NTHNDC6Artsaf0rK6JRKjX8/m1ogFbdxPNWtW2Au4sde/nvw9DX30N/T/2kZaz4sWO87eH0f9jH1nT62e2BuuSATfb0daMxTIy8mGgnlrDNOyfO8bnDwHUd1A4fydP3c5QD6ZKo/DBj3gghQ9y30Q2fwePS1nq5Q12qVHgpOt67mCukkN7IAkA9kh0d+QZ6JofHYGUvZJ+NH8DuhaEeztH1J+w+3JzpYeA2pgxWXwAqGIeSRUPKFOvTLj6j+9C0zSkI/tUZ0UfJor3IQDE/F32iiZ3l0azjLZR90epukB78LYoTpb6GUy+ewt9nzgNALj1+9+grPj3/3LR57d6HMMsxrpkwMst+iyuDVMmKaXEVGnY7m9NhnYDRWG3XLk7KJwdcnTe0fwNTBSG0B87DsMsIaS3oSu8G6nIAIrGAnYEM/ar+bWw3QPsFNb20NaL2XfQHzsOn/AjrMegQVNbkG96OjLcvhF90cOYKo5gwXiMiBF3jQ7T+x7L37GNhNwacCoyoL7ANOUPMawCPflZjCzcQNksNnCIa/b5OncLzrlu2M/N5m8iFSH/Ymtt0maiNmN1Z6k9HztuH7f/Jz8JQCxZMHOO+wHc++O/Qd8nzrDcwCybdcmAl14h5GUxbXi+ksO92XcwVrjj8UEYzd+wC2iUbXuz4+vTFzBTHgcgkY7sQ1d4D6L+HdCEDwIC7f6knQGWqgU7awck/FoQPZGDEBDoCu9GOrIXUX8CRxMfxVxlUvkJV1A28569cOnIPoT1dvi1MBKhXozn78EwDcQCO+HXQvbAhZUVU9A8gK7QM0hH9qMrvAcCPsQDKVzOfUtN8+Vw/fEFJEO7MF64Z2fmh3e8YgdWXfPb/haGadTdgdTeLXSF9rjORa9hTf0VjNkV/ZuvFY16bBv9rNWM1d8exuFf+OG6YFp7Tuu44b+8iL/7h//7hsiEud9487EuAbjVYttSUoVhVjBXmcTRxEeRdm1Edt9Sz1dyyFdmVE9sGcPzV5EI9eFY58cRC+zEaP4mpJRIBClY50ojEPCh3Z9ErjiCsB6FYZaa3MZr6Az2QYOOqlmxC3hj+bvwaQFMFIcAmJgr56hXWEpUzQq6Q/32l8B8JWefO1cctu0xhfJ3AKQatbYm/XSE9Sj6Y8dtGaNLdWO4F3n6NB2AtL945iqTGFm4gYIxW/deaN9dWO27q9SMS1tSifUFEluWfPSkLBVU3IG1Wqpg/LuXGwbb/T/5SZz89Z/FgZ/5FKqlCka+ealh4a3Z6zUL4Lt+8Bw+ceHf4cBP/+AqvNsng2WRzceGvpd0Z2aWtGAVjGqNy5u3VQlEO3ZgVN1C0/LKKXSGMvY2DAB2wSsWSEJCYmT+GpLhPQAEqrKMTOSg7RVsmAZ0TYdpmgj6wraBjrt4NVkcUsUxek42fxupyF74hA4ou8vjyU8honcg6u9EQGXFlqdvvjKNqmlgvjJl+xBba4kAoewuOz2j1qVqATtDuzFdGoMpqwj62pQc0m5/UWnQEdLbAFBvMuB8IVbNqi2XDMZf8nym1p+dz+zp9AxbQaXZEINbMhj66gVc+3d/jo9/41+gVkawMtbKXAE3fu8vcOCnPon7f/Yd7PrBs55st9nrNZMmpGli8u0b6Dw2sLpvfAW0Kp8wG4cNHYCdBZbO6KwVdANapKG2WTWrmC0/QsaVEY/n79nPGeg4gWz+tms/mrAHK6yCVyqyF70quEwWH3j0UMulrGpWASmRLdxyBdpeOzAmgr0YiJ1QGrCzaYP6iKlgF9Lb7K0biWCv671RR0PEH0c2fwtHEx+FgMDdmUsItkcwWXzg+eLwaT7yNA72QAqgWJ235QcBzeM53Bc9BGlKJbHQ5+P2LE6FB9Rn33hc+Wl7RVhB5cBP/2DDzgMrsAJUWCtkH8MXCDTtu731+9/A27/02xAQ6Di0G8N/edHTsdAsiLlfp/Z8G2XKrdk1MhsYKWXL/x0/flw+TR7MXZHfHvmP8sHcVftnlWpZPpi7KivVct3x9NgVWTLycqb4yP57pVqWYwv3pFE1mpyzIh8XxzyPzZZycqowWvOcK/Lh/HVZqZbkg7kr0qgaslwtqj9XPMcZVUNWq4bnmsvVoqxUS7JSLcvZUs5z/FRxVF3nXfu6y9WifOfRN+SDuSuyWq3az3O/T+t9Geo9jC3clXdnLsmxhXv2e6NrbPyZWZ/zO4++IR8Xx+R8eUZWqmWZK4ysyr9heTYvr/zGn8jybP6Jz3XlN/5E/l/ie+XdP/qbJ7yeP5WV+YKcfO+2rMwXZCVfknf/6FvSKJYbHLv4tVvnW433x2xdAFySDWLqhg7AiwVb5/Er9uPugFatVj1/t7ACZrlalLOlnH3cO4++4QpUJVmpVuRsKSdzhYf2cyrViiwZBZkrPJR3Zy7JXOGhrFRL8nFxTFaqFc/1WMHUqBqyUq1Iw6jIarUq58sz6mclWa4W7QBaqZbk2MLdmuu+qs5dloYdSK/ISrUkq1VD5goj0qhWZL4yLx8t3K+7BuszsYJro8+MflaRRrViB/9cYaTuM7e+pCrVyqL/ZuXZBXn53/w/sjy7IKV0guaV3/jTZf7rNzp3Xt79o7+R5dmFJw7qpcdz8vK//mNZnJqVM7eHZX58Sl23c87VvHZme9MsAG9oCWKxDRrezReW4Y7j/zBeuOdqr9pvP88qsAkI3J65iL2qF9h6zP16bXocJqLIFUeURmxivHBXeUeUEAvsRL4ybZ9vonhfadbDiOhxj3QhBTCqtGKf5oNhSpSreVu2sP0a4o52TVsyymrjMjzbLXoiBxELdGGukkPUn4CJKgSEq+UNymviACxLSgD2SLPXkF1iVNlpNrOqlKjaa5aA5uPKN7/8Dbz9y78NADjyiz+yqrqk1Tp29d/+6RPf9t/+j39lezUM/g+v4tpv/hne/uXfhtA0+5xPeu1rNerMbB02dABeDK8L2V7bN4EKUgY04d1AbKFrOuLBbhjm/9/etwbHcZ1XfjMACJAUJb5Ei5IoiuJTkqNVbMmS4iR+bNYPSl7L61q7tioVe0PTiWN5y+U45fUmP5QfW9rNUpB/xJRtRS7LWxQpklqLVswNCZJeE9SKAqglARAEBAwAggMSAxADzKOnp7tvD779cfvevrcf88CDMwDuqWKJxPT0S4XTX597vvNZcM/KXXBbw1pIWQlOnN7Fvo0rtsMaJxhH7J6j3lwDPrT8AUCYhglnrhwY9JgRiAiTL6j2K+qwpp2F5fV3wHW9R/D3AqxtuhsG0hfgQyu2wvR0Aa5mO5yUNoTl9S4xIyAgTEOWTMDKhtVgowUTxjX+MLlr+VYa17lii2eBkmYOTxrXoTBdgLponUTKzMrnhZgEt75pk6BDy4txO/c+Lf13PnRJRozbvvYZsHMm1K9sLLq9nTPhg1felohQJNdofR3s3PsMRKJRyc0w23OvJX1YoUYRVBaH/bnVEoQX7uszkXTdIKnC+yrv3xfhWq6rvVLZwasVGySHidwAWnaeH4e9rjPt9/3x3zjSg8llAlmSMLn0wa7F1Xup7MHkCvaq7x7DRp1o0md5kvVp1vQ47BpIqFyQyA2gaevCvRTljWJyD9WxeyffKaorF4iNIy3tc6KL2oaFA4dO831ZGR2THTFMdsTwyv5jRfVgpuEakxl8+6nnsOvFwz6dVzqWRZDohnQ8774quSalDyswQIgEUVNhPADFvb+uf7WPh4xPGMPQmWyBVQ3rpFdh2qYb3vBB/bDueKJ1jZsAYBpS1iggFISGiG1gT5uwrulesKYt2NB0P2SsccjZU5A04oCIPACIxkW6+8zbWVhZT8N+wAmCZ84DecTQLohAHaStBAAgZMkEPReT/pu2MddD0rjmhAFdg/poE9y1Yhu/vlXLaMRk0rgGjXXL4Yb+AbAxTV6sb9osNFr0QX20XgohCgKTgyJQBysaVnO5JsjLffXN38HJz/xA8qOW2yRAsnkYfOMMFCwb7JwJPT9+CzZ+6vdh/L0rAAAQP/4uXPnHt2DVlo1QyJuwafcToftiFWjstZPw+D98E7b92Wdg+K1zodtPmwQmLvTBPf/mMYgfPx+4r0o8tmFNHQoKDDUnQRRrUw6yQK1r2gTWtOEZAU91SdHfOmlcl8LMaTylO6K9PtogeVzvWbEL1jZuggjUgWZPQWP9KljZcDvY0zasWrbemYK8iRMQlTVsRyOlrcIRqAPdSMG6pk2gkym4veFOQB7e43qGo9EoxLVuINMG5OwUP4f7Vv0eJ3QAquluvd2A9c7wTdY+bRby/Dy8o+6DNPS6aJ3vXgbp5UFgvuMguJrnbnjmvR/Dut93/z9NdsZg6PDvoNTrePz4u7DxU78PvT9+C7Z9/bNw9ehZWL5xLdz3zFPQ/aOjsGPPblj94Gaat+vRbL0Q24WzQ6MQXVYP9z3zB6HHniY2TLT1wJoP3w+bv/SHUoux8tgqzAuCyuKwP7dCgijlfAhCxkxiyhgPtZhRW5glfW4XbO5SkI/tvoZzO5id9zkmpowElyrY9tThQJ0SbL/UImY79i5XHmDHF4+VyA1K52AQ3bed7bgfxPsUfN7yPfRvQ9AgGj/PZH7E+TNz+xlzDYy0XOBuCKLlJSmAaEbRfRSIjV3Nh7n7IHGuC23DxMvNsiMh7PXelQpyONLSjrZhYvxEW1luBtH1kOqPF/3OXNrrFBY/YCHa0MqFX0+1OPEyLy0lN5N/nsyPCNYzSkyaOYk5K4VpY9xHtoysmF3MLhBM5kck25vo4WXnIGvCsuZs2Xnu13WvxdWhxWugljNb2s4gOUmvZsf3PlgQ/Z5qZoMTz7XSB58XjBQLxMauFymJjp7tLGnn8pIm0fKYONeFRMujdmMCR1raA21iQWDH6nrxMLb/8BW83HwEraxe1netTI5un8mhbRHH8hb8HdGTPFMyFq87SHdWWDwII+CakyBmAiZbsPZZ2lLrvoY/cPtH4baGNXBd64V7b3vImXSxCbbf8aS0un/3ip3cNqaRJIhTim9rWAMICLSN+F4ndWwHj4JMWaPSfDaACGxo2gz5gsbnvhWQOG3FjotiOs8HdzIkhc67u1Zs49szG1xcYwllCOPGkNS9JsoPLPeYwSs5yCOQqFQxk9Zir9WKyQHMBbH+I9th3aM0PW7Hnt2BjgSmr+I0Qt3yRrjzsV0w0d4La35vC8ReOwG7/uILcP3kBXjw289CXWND0eOLskP8+HnYtPtJSPUM8+8Ws4aNv3cFok3LABGh9x/fgh3f2B2q3+7YsxtwGmHzsx+H3pd/zd0O6x/fAWsf2VaW7itetzGRhvzolCSnKBvb4kfd888/X/bGP/vZz57/5je/OX9nUyHsaQI5kobVjXfBsmgTbFi+BXIkDWlrDNY33QfL6pbDXSu2wzQWYFl0OUQiUUiacehLn4eV9WtgbdPdsLJ+DTREm5wg9WvQlz4PDdFGR9+thxs56qttiDZBfWQZRCECY/kB/jM2naKxfgXY0zZsWL7F2d8OiEbqIGnGYdsdj0NDtAmSZhy23vEYrGq4E+xpAhucrImb+avQVHcbRCN1sLzudkjk+3lL8l0rtsLaprudXF92Lo0QgSj0pM5CQ3Q51EcbYW3jvVAXrZOuJxqp4/cqGqmDO5bdyX9WH22C2xrWwLLoct+2lWD4rVbIDo4Cyeqw5sOuf7iusQE2PPUwRJfV078/+RDUNTbA1V+d9W2/5uEt0LRhNex0CG/4V+eg7XsvQ9P6O+Ch574E0fp6WPfoNpg2bbj6q7Nw+/Z7IVpPz7f35WN02w2rYcOTDwHa0xCpA1hxz51wx67NEP/n/wsfeurDUL+i0bd9/YpGaLhtJUyTAtxs74E7H38QGm5rguH/1Qptf/0yNK6/A9Z9dCcUdBN6Xz4Gax7ewh8Aw2+1QnYoAZG6KNz3hY9D04bVsO1rn4HWr/8DTFsENjxZ+mEmXjfJ6vDAVz8lPWC816awcPH3f//3o88///zPfB8ElcVhf6rTCRdujXLtXybqJIOJ3ACXFZhFzdvVRe1dotZKpM/E13BXQmBtxASvpjukbrrJ/A3USYa/8jOrmLeFecpI8L+bdh7H9auYMZOYMZOOfEClCM2cFKQSEzNm0pFQ/PJGOZKB2NZcqovNv//we8/g7XwrhXK2Z9voY5OSzWzg0GmfFOHVgrnc0XwER892+KQPYzKDXfveQDOl4dCbZzHZEeM/MyYzvuMnO2KBEorb0uxq2kQzSujSM5EojiqJYhEAqqkBe/275fxiIwZnQfj3282Jxfvv4HOQ25a9xCYiYybRIDmcMhJOJkM39+HmeEuxS1SyHjuIsibtkqpInuIDgXmRDaJhxkzi1XSHpFuXc39FBD0Eyr3f7gMj+N4zlK/vUvJInOvEt596ruiC2EhLO1paHgcOnZEW7mzD4try5ZeOco8w8/YS3UQzpXHSMtOajxCJbnKvrzmVRdsigddA930GiW4W9fOy6yu2uDhw6PSsWppn+32F6qOqBMx+sb2BN6UwE0eE+125uSCRG/AsQlmOG4L4zslbNYoEXXBIV1xcG8n2CrkPYvCOnxBFRwM7T7agN6L1eBbG3AW1tDEuuSlYowYjWe+CIqK34cIsswIWFzFL3/tSzQYiudmGVZYbgmgGJjtigWQtHs9LTMZkBsfOd2OhUMDcWJJXuMXOKd0fd7IljgZW5ew4YQ0cl186gm8/9RwakxlflSsvsoUv6JVCOQ8AVR3XNmqgAu4WyGx2q+3lQE4bY0QrH9eyDbyh9UsyQ7Gq0bW0BZMes3CJFbMIg2g4rl+VnA8iueeJFlqF2rzSNjkBW3YeNSuNlp0PfLix6trriJhviN1r4mt5qUrOSyZEMzBxrgvNtBZI2F5iot13OU7yVibnkQjyaKY1hyyPojmVxbefeg5jB06hkUxjuj/uO8bAodPY/sNXQjvurIyOiXNdgVX0rQjzUYFBCwNVJeBbiTC544bW7yFT9zO7YHPSTOQG0CqYDnHJ7bzelmNGeqIvmCakBflw5cQxcZ9iYptftnCT2357/RfO/uVkNOL4isVr8h5ntihX67UyeqB+i4g+rdULL5lYGR0vNx/B/M1UWRGUBWJzv3DXvjcwca5L+nziUgy79r2BrXv3YWZoFG1CeOXLPMOzuW5pXyYJ1YTZ9sUq63Kh2p0XBpYMATNCY4tyNBt3AHWSKZv4vFnCYi5Dxkw6vtwRrgGLZMg8xyJolq+JYoYvg6shE8xZKd+ioBt1SSv4gvOw8Fbp7HqT+RFBbrmMc4XRs53Yuncfjp7tRMTwV1/Rh+slF6KbmBkaRaLLREc0lr/gSgHi4pd3X8Veu9k+WAWMKFbkObQ0XZAL6OcjLe1oJNM4erbTV2mLD4XEuc6SRMc9y83FfcdBC4oKixcLnoDLXaH3yx3FtpMJVnxVD5ICGHGyz5iLwbutqx0zR4Q/rN09vokGyWFYWI94TlNGwlmYI77sYvYzuqBXXnZvuQhqGAiTE8SKjOimRJRe4vSG5QwcOoMF28b4iTa8sv8YmlNZqbqjr/tuYwfLBmYV6njbFSS6gbkbE0gMC4lFfBV5wS5ITgnbJFiwbb4fcyrrv55m9xy912ubRMontk3CO/mKhf+IC4qzCZhXWBhY8AQ8kxX6chA+JcOtiJmdzbWamQJ5W1IyWMZMYu/kO2gQzSMTuPtjXXNscoVp533nEKQxiwtjhUIh9JpKOU0qcaIgus4F3l3GF9SKv/p6JYVi/6bVZQ7HzndLbcy0I+00X8DjC14OefIW5eYjmB1OSG3MBSKTIcnTylskbUuTHwKXXzoqHRORkmyQoyKMnEXSp23ZwRX7XAbMq8W42kZNEnC5ROAdqzPTttmg47EWYbFidKtP120gVqRuhUmrT81K40i215EuCNdsvaSas1Jo2QYf+8OJlZg+BwMpWL6FNWKb9FhajzRiyXsfgiZqhH1eCnKOQ76i1XyvPhn2b6LladWaM2hlKJBosiPmafk9imZao9GRmuEuqKU1JIYpkevY+W7UxyZ5ezHVeS2+sJbsiEkZE6NnO6QJGex7k91DWCD+hUzxATJxsR+NyQzVfg0LiWFisiNGHxJB2rZgXZuLhTS1GFfbqEkCLpcIvGN1iqGYtBB0PDfn1+8RZgTIdGTX0uUd18MsbTYnXtYgImZPMBtayhzzODTsULeCWDm7DwLXNxwUPiTKE0FujEqcKPP9i63dmJDGAyEimmmN/9s2aSZD0Ks8q4gT57pw4mI/mikNR1rauWvBNkyMvX4KbdPCbHyMyxz8QZDV0UimOdEmO2JININ6g5tF/ZnuJ7jBwtGbszqaaQ0HDp3BZEcM8zdTzn5132KceE+THTFB+55tBawW42oVNUnA5RJB2HZBFa34ap8xkxJpBnW6FRxS9PqAmQtClB/KeWDIBEi43PDb679wutz8wUHF9sXmxLGOOPHYjLiZ3h3kyvBeL9WJ/WE94v0UPcylfrGLvV6X80pcILaP5G2LUDJ0FusoCZ9GwhoupEU7j3zgSCNmOoe5sSQmuwad5owjaKY0LBAbbdtGYtBBnLRyttAWKlzbJGhpsnZ8Zf+xwIcvzigSAAAgAElEQVSQ6FWmZOrowELDSPA9Oypp3wqLGzVJwLNFECGKzQ5e0rQ9i1Nyo8agR5oQ3Q0EvTJIuWCVMyNSRsbMmREEkQh7J9+RFujkrj//8M1i51iqEaaSrjmGsAq5WOXsTT9jFSDR8s7Cm1g5OvKHbtBFwIOnkegGtu7dhwMHTwuLYO5DwjYttDS6YGeblrRox8k4rWHfaydQuzEhnQ9LTjOnskgMk3qEHVmC6AYS3Qi4lrD24+CfM32ZLlIela51LipYpQfXHsIIeEGnoXkTvgrTBZgwhmFN40Y+XFIc1Jm2xiBlJcAs6PChFVukJDRvEphGkjwtbFXDesiSCR6AXh9tAHvaBs0ZiFkfpbfRnrZh1ElJYwHp65ruA7Ogw7qmTXDdGcr54Oo/5gE73hB577BROZTdPU9vcH3YYFERtzWsg5UNq8Ebau+9nyxMvhwEBZXbORO2fe2zABCBnXufgYJlw/CvzvJwc28KmJnMwrY/+wxkBq7DjZb3of1vfgosWaztey8DFqZh17e+CNf++V24+19/BHpf/jV8bN9fQl3TMhj9Pxfh0vO/hMf/x19Aw6rlULAIpHuuwcr7NsDN8z2w5vcecM4tAvf92z+A3v3H+P53fesLcLPtA0i+/4GTZgaw7qM7naD3COz69rNw230boK5pGTRtWAO9+4/B9j//PNQvd2fQeefGFUwCw2+1wn3PfDwwKD5+/F2Y7BgEgAg88NVP8W3KGTRabjqamkW3gBDEymF/aq0CFsEqQlZtel+xg2xppdqG5fwGedug2W8se1j03wYtprnj6eU2aOZVJrZbxVIPcVjAuvszFqgue5gry95ApK/fTAudKZg2y17JvY0ZzD1gZXIYe/0UmikNL71wAIkhZjm42qkxmUFiErQNU3q1T7R2Sm2+tkXdCrZhShV4Nj5Or80i3NpmprUAu1tO8hETnerBpiN1lNLCizWhuNsEN3awrr9i972c3A3ZTx1eAasq+dYCFqMEIcLrsy2+jfv67XbAydMoxCGd74//BjNm0hfK8/74b1AnGWm/bDimeExxQY4SOHVPuGTpLrJZBYOTs0vypf28Iumyc640ewOxvEU38XU56NWZvX4z61iQJ9Y2LBxpuYC2SdAmlBhpB5lV9AHg1U/F85y41I+XXzqCQ2+epQt5zVRKsAmVKbqaDyPRDSzYBUwP3nDtbrqBJG/g+HtXcOzdbky00qYT1rDhSiOUpG0rZNipYNcrlh0R7p8Oay4pj1hFqaUUlGvi1mLRE3B5i1rhlaQ3A2IgfYGTsVxNulY4tqAlV8cmJvMjgfvXSQZ7J98J1FjFhUC26FYJeXp9w3KQUPm6dTnBLyL5Jc51lpHvUHwRL3GuEwcOnUYjmZEqSEbgQWQc1OZLdAPNqSxNMdPymLwUQ9uk193VfNgXmjNw6LTnZzmMn2hDK5PDRGun9PC4sv8YJs51Iskbgd1rsl0vvIoNux/FCLFcYq3ECaFcE7cWi5KAK329rmR/rCL2jgISIXbEudWxvwIXO9/ECth/7OIyg7w9kapjVvmKpE077OamGw7R2zjRJckEM5UsBg6dxiv7jyHJm74OsmRHDC+9cIAvnNH5cjlKhEI7c1iugri4xsYciddAdEP6GSPaZEeMV+U8j/fgaSRaHicu9fuaLsTuvJlWlKUffIosFzIWJQGXZwsTbV+lyZpFPk4ZCeydfKeoG8C1q7Fc4GCyLNfCRvdZPAjd65Ao5tRgksZcoZzqmGhG2foi0U0kWp5mMOiGtH+3jZnqxD+PfBonLrrh6cxSRkwLU31xJLk8xk+2oW2YON52xaMXH8HcjQmcuNiP+Zsp6nJwdOf8zRSyWXCyC0PWaG2TuA6KqSzaJn2wyXGUYrDPXITsKI12sWBREnA5r9einFCJFlqZR9nbXedd7DOl5ohi3/c2aIgdd+LnidwgWrbBp4CwBT2m/bL0tLlo1y4HrAL0VpnFAmzS/XFfQpptmDhxUZhC4QzVvNx8RApP73rxMBKDErgoIbBFvVRfHI1kWmo5NiYzvEGDGDSYXR9Lom1aWLBtzF5LSOeeGRpFS9MlshWvi+gmkjy1qplpDRGxogGi5dxPfxayIuWFiEVJwGHwd8MNFiXU8lui/dsFEXtYrgNrjmCVanCGb3gcJfvcu2CoWWma0mbP7ZTjSiC2CYtOgmLTL4KmUXS9eDigoqSLdUQ30UzTalUfm8SJi/2YaO3Ern1vcLJmWRWJc51OpWphbjRJF9oME1P9cbQJQduwcPy9K1Izh5nJSRVw7MApqUNOdGXwRTrHzUEckrZNS2pvJnlzRrGTQW8bauFs4WJJEXD5r/t+i1ix8PJSoT1U6jB9ZO8l7ikjgYncAI5ke/k5eLVazUoXPS+xEnb3b97y8HURzAXACDNsJJBYsbqtwCwfwg3iYbANi1fLsQOn0DZMHGm5gETLCxUwm512RiL+iUv92Lp3H9oWwZGT7fwBQWWILA4cPO3mB7d2IjFoNSymvY2e7UBL03kAEtWT5UU6lhUhno+Z1jD1QZxXxLI8U3zOG7WlyfGYpfKUFWoXNUnAc72IJu+3dPUX5BxglWal+xUzeIvtg+6HlNSmmde4VEZEJfpyEObytdZrP2M5DeJxzJSGsQNU0x1puUB9u4RwO1vQYl7iXCfe+O1FqQI0Hc+umPXA0tMY8ZtpDS0tT495kBK3fjOFsYOnnLyI045n+CgPGyoQ24mxdKv4Sy8ckPy7Y+e7abed8LORlguI6FbwidZOLNgFJELspHfBj1XrQQiqdsvxCvv/fyjJohZQkwQ8G+KYC7iv6cTx75aemxa+Lzc/Qg5VDybYcrXpch5SlVrNRMzXa633FdrrPqAxjSKBtfNJFez7zPs6cOi0z4tbKBQ4sXW9eJiSXd5AYhG8+f4HmLsxQYk9b+LIyXZqcWs+zB0R7LjJ7kEce7cb0/1xLBAb9UQS8xMpni1sZXKYGRqVjhX/lzZe9RYIwfG2Hi4xsAU9t8Kl+vPAoTNO27QuVesiOYaNcpoplGRRO6hJAq4kZGfujzuz/fsX2AifkBG036DEMrYfbyhP0BihuXhIFbve4IjImVVNxb7LpgyLHWsSgRLbR8jtP3yFV4pdLx7G1r376ERjJw9C3J7JAmZaQ6IbTpiPgUYy7e+gc7zLAwepNkuTznLSTDlWXb/91HOoxceERLQcZq/RavvSCwcwfqKN6tKe6/YG1nsjKKX/P5qBZkor2UVXKZRkUTsII+CqZkHURxsCcwu8OQdzjdnsXyOTcDM/zL/LMiPItAE5O8X3e8+KXXDTuArrmjbBg6v/GNY13QsAIGQ/bIf1TfcBAsCm2x6CMX0QelKtsPX2x6Vz8uZdzPX1erMMKs0RYPkEu771xdDvejMMun90FKauDMMTL34LAAB27n0atOExuHbsHWj//k8AAODB73wJzGQWNu1+AuLH34NNu5+Ayc5BuPbrd2CycxAgEoF7PvcxePA7XwJjbBLqVy6Hm+d7YPVD90NkeR1YaR1gGiE3chPy41Ow85vPwPKNa2HT7icBIgCfOPh3sGn3E9C7/xjs+MZuSPUOw+iZSzDVOeScQwR2Pfcs3PnYTlh2x23Qu//XsOMbuyFSXwfDb56FHXt2w6otG+GuP3oEPvjp277r3vzsHznHeFK6r8s3roMHvvop6R4mL/XBzfM9TkYFwIPffraC/7vhqFu2DO588iGoW7ZsTvanMA8IYuWwP7dqEW42r9Sl9ltOFGS4LuuPa2SZEV6nhZuCJs+Hk1umvVLF3F2z1wlS7r4rjZ9k4ebu5AdXTxUnWfirwaO8mYLoBiYvD0oLcDd+exEnLsaQmBYW7AJmh8dobKTj+7VNC/PJtC/UfODQGSwQwlPMpnqHeRuylclheuA6X+Bzz+sIEt3grgcmEZhpTW6yaD6Co2c7Quxh4luEezwxQpO6KfK+alkMlld67eIE1KIEcashOgdEFPPiivCOmw8janFBzh8KJIYGyefhxWykkvnS1/1kKofLeMcXicRcjFwKdgFt0+LDNLm9y7BwpKXdGU3ven6Z1DDedgULhEoaAwdP8+8ayQyOt/UgyZv43/70P+G9K+/AaDSCO7ZvxZe+87f83PlxdINGTmp5zAyNYrKDTlD2tirzBb6prE9OIJrh04tFTdvbxh0G8SFXKndDYWFAETCGV5nFvLjyd+XqN4zkilWzdDJGeWPjwx4Ys7lWL+Sgl9LB6sUW1+TPc3yhbeDQaSdL1z/23T2OWzWyzjeR5Ccu9WPinOz5JbqB2eExTF4eRJsQKdyd6soEX/rOf8F77rsLmw//AFuu/hM2H/4B3rP5Ljxw4ABaWR0nLsbQNiy0sjpth3ZcDJamu8lsWZ02bzhBPsmOWGA78sCh0zj05llpkY9ds3if2OijsHvsbfgI+rvCwoIi4CIot6OumAe4kmOFWcvCtp3PRcnLLx3hi1yVBqsjlpYsmEQRO3AK0/3x0IU6PgfO6XzrevEwmmnabjzedoXmReQMTor5mykcOHQGU31xaeGMfZcNxNy5azs2H/4B/vb6L/if5sM/wO1bt0ij4c10jnexscW2gUOnkeRN7HvtBOZuTGDBZl5f2aGQONeJfa+dkB4iXphTWf6ZN+3NG7Yj7j/s7+L9L/YAVagNKAKeJcLIVpzAMV+YqZxQDnHTX2A2mbeyyQ5hq/rydq6uWygUMHbwlFTRibqpOzzzAp8+wUhrvK2Hk6LYiGGmspKWe/P9PuqUcEgpGo1iy9V/kgi45eo/YTQSofsX5ALmsPCOSbINk6ekBUdvHqFOi+bwMUQsyCcoylL+f1AZiZZ6gCrUBhQBzxPm2ybmft5dsUfZ20k3l1W0TFBWKHFIYTWCP7aUFlogtmfEvC2RDEsrE3NyzbSGtkknEtuEYOzgKdy89s7ACnjb5s2OHU4IYM8ZtE3ZlJPQ4ifb0MrqGHv9FB/46b0PsddP8TjNoC43GreZluIwi93TclHqAapQG1AE7GCuX+fdEfUzj32spHW6knP3ujLmclFOrIy9nld5Ozlb93LzEdo0UeR1mhKoLW0z3nYFjWSGd9NNXOznenCyI4bJSzGedGZlckgsOkHjpe/8rU8DvvveDfjLV3+Ol5vpYl42Pob6zRTvyIu9fgpJLo/p/jim++JSBSwuODL/LqvKx9+7wrvyruw/JhG1bVg4elbWsFnIT9A9df+tZIXFAEXADuai82w224fvo7zW6ZmQaDI/Ik2HnmsUa5FlC1Ne8goafVSsSuRtx4aJxLR4KzCvfMVmi2YqCQwcOo1mSsNf/uI13LlrO0ajUdy+5X78n6/90p1m3HwEC7btuBsoIYtE3rp3H822MCxJp2Zda0z7ZXGWYriPmGfB7hOzmxlTWWkAaRDCpokoLDwoAnZQiuwqJbmZxVzOpguvcq+wuPDHcnZLTW7wolg1VuwzHtjuCVAXu75Y6IyvSnyJjnovEDccZ+DQGTTTdLHLTGuYvEQdCTzZzElKY3JB1743nMU/oWtOF6YtGyYWbBvjJ9rcvIZmv2OBjjXS+f5HWi5ggdio3ZiQsx6c78YOnOIWNC/Yw6ecBc657o5TqA6WFAHfSpKrdPtq51/wqmrfGzwop5LvBZEFryaFz8Rcg9BzcBa+3n7qOdcBYFjc6WCmNCEtLccrUW+uBNNA6Zh6i/t5WTKaqB8zby4NA2qXiDl+oo23ODP9d+JiDM20xvVmGvjeIzkviJbncoaRTAcuqBHdxMzQKG/MQCxv0oXYwq2wcLGkCLjaJFcMlRD2fNjP5G61SitgsXvNdKYe53wVdanKzdtoIRMWmyhhom0Rz5gi0/2+c0wW4BO02MdADMtfATsB795AH9d+ZlCpwxmN5H0AiQ+Rya5BzI+nkDjuDK+Lgp2zmHOhCHVpYUkR8Hy1Ms8ngsi2Fh8kInlOXOwPrIy92qU3anKkpR2v7D+Gqf6RwO8wW5UYuMNkBfb97HCCBvrsewMtTecdbOKCoGhzK9gFzAyOYuvefRh7/RQW7AK/HnHhTnJ3mLQLjwYF6XyaxmT3kFQls38zy1yB2L4gHG+aWjkDNmcS5K5Qm1hSBLwQIWZEMNTig4RpnGyAZVCGgdgNl+6Py9WpM1yTLXoZkxkkuulW2Foekx0xNKaydNHN6XBLfRDni1pMLhDlBjavjcU8mlNZLovYhkW75YREM3ae6f44ZoYoMScvxSTiNKay3H7Gqmj28EkP3uAyi1cWSV6KoZFM01l3wjkzotfHkiU9v2KTSGmJIjzYXaE2oAh4njBXMkHYfm5Fo0e5kENjcs7493AZQxz7LoaRS8HkjhZdsAucuJie6hIz/TdrYuBz14TOMl5hNh/G3I0JJIYlWdhIjjZZmKksjrf1IKJ3oCZ9MBDdwMzgKBIWuN5Mq+/8REoi2UKh4C4MHqRui659bzgZEXlK9FmdB7UjUm/zSEs7krxZcgHOFoLc1SLdwoci4HnCXNvaKt3/rYR3ykWp6sutag3fv2kHm5ucFj/R5svtFY/JSNKYzKBtCpawl46gbboLd2x/NiHCecgyhm1Ywmiko2hlqRatj6d4ZSp1tjUfkWQFtuDH/msk02hpOrfiTXYPoW1YjneY3hvbJJho7cTWvfvoYp3wIAtDqUU6ZVNbOFAEPE+Ya1tbqf3Pd1h9MYQF8bBV+kqaBnhrrmnhyMl2/Hnk00K7L5Ui9MSkE7QuV8zMVsYITIuPoT6ektwY2WtjfE6bl6hsQnD0LCXDRGsnFggdQTR61m0YYdWyu1hoopXVfTpzZmiUV+vufcr5FgKZ55c1brCxRWQWxFmqTVzJErUDRcBVwlzruLVUEbPKkjU9yPGTpQdPGskMjp3vRn1sUhrM6bYIH0EzlXU9uDwGMs+bMkZOtlPpwKmCu/a9gWZKc7cxLR8hMjJkzo2CXcDY66fwyv5jtHPvHNVux85382tjjot0n0v+rJ3a+/ofZMvzt27T77Nx9nMJNYqo9qAIeB4QVI3emnFK878wV04VxUh3vK0HiZbH0bOddCy7x9EwcOiMo+d6WnkdlwPvcDPcgHZRiuCj3Z3W3aD9i7GXBUIw9vopdxErq/PWYNu03ApU8EGz82AShDmVxSv7j0nnkeyISe3DXiueOPWY5RrL91NOUAvKwnB19plXsOX4ixVuLcIIuKojiRY6gkb9zPc4pbAxTnONckYTbf7SH8PqBzfDHQ9uBlvLw0RbD6z9Vw/Ajj1PA0AEduz5PMSPvwebn/1DMCczMHjwDB859OHv/XuoX94Ig2+cASuVg1TPNQCIwLpHt8K1X78DW//0TwAAYPt//Bxk+uLwu//wX+FjzVPw8He/DNu//jlARNj5jaf5/ntfPgZt33sZPtb8Ldj17Wfh/n/3R9Dz42PQ/v2fQCQahXUf3Q5rPrwFUleuwf1f/gRseuYpmLwUA5LVIdq0DPpePQ758RTkRyf5SKKNn34UVm3ZCJFoFHbsoeOI7FweevefgB17noZINAokq0P8+HmINi4DK6NB4ncdzn0D2PVXX4Tul47Czr/8AkQbG2Djpx+FSDQKAABrH9kGW77ySdixZ7d0T8XxREHji8qBd8yUQg0jiJXD/qgKWEZ4cHttWcdmglJVlFipBY0d8m3vHaLJGjqkis+1h9Gq2eB/xKwJnj1higttchC8TQivTJlTgk3V0OJjSHSDd74RizjarJsqZqY1TPfH0UxrOHq2Ey3HHue2SR/BxLlOvPTCAWlSh7hAFztIh3qGDdy0nWkf4gIaux8zjadUqE2AkiAUZoogOUKcTuEuwhVfsWdda0Nvng3VR5lVy7YI5m+m8NILB0JDa7ySRmZoFPteO8EzGJhEEj/RhrZF+L6Nyaw0fUNsQ7YJwdyNCeovzuXlCExnZJIUfZk3PfkVNO1tsnsI9bFJaWHP61Yo5fVVWu7igSLgJYjgcUKVB+oEEUGlgyTFClXSbLluepRXj2Za440aLDgniMAYOVIS9S785dByRgPpY5MYO3AKJy5S14XYTNL+w1dw4OBpHHu3G5MdMUz3x7ntLd0f5wt7xmSG+5Bti7iuhrSGxlTW44+mxx9v63FiNfM+VwRiaa/vrdZylXti/qAIeAkiLL8g6Je92GflEkElv8CT3UMY/5c2qVGB2bvEVDEWwMMW1cRXeO7CMC1MtMrDQKkVLeG4GM44yWeW01bsWtjY3DoxQpI2mORRH0tSG1xrJ5ppTTpX8X6lPohjwfEdi6ltvInEWdALzsWonQUzVXHPHxQBL0KUclwEB3wH/7LPBREU+wWmui21d/EUMs/8NNbowNLHRFcCa2sWmzpGWtqxUChwyxojUPa9oTfPUgK3CFrZnCRZZK+N4WTXIGrXJ3xdeYhIA+Anszh2vhsLhODISTlAR2wqEXMbaNznUZ8unjjXVRMkWwy19DBYbFAEvAhRS55gxOK/wJxcp7KYHU5IXWYMdMGrgy+AeVPFxHAdFvTu7Z5L9cXR0vI43taDuRsTaFsEjckMJi+5iWaMoLuaD/McCb7o5yy2sdQyseVZBHug2CYJ1XKDGjJmf3+VRLAQoQh4EaLWHRd8YKUpp4xNXGLeWbd1GFFwNzgOCTb37XLzESlch+47x0fBu+RH3Q6Wpkve3HR/XNJpbcN0Q9edWEqRLG/89iL1DAtxlWHTomlCWrCWKz5Q5gJKIli4UARcJVSzdbja8Lcqu91jyY6YpNuyqRe2RYSMCRp2PtraybVZsQK+sv8YmmkNBw6eRpIz+FQLlnB26YUDThAQdSKw5ovstTFBB6b7Ewk04RyvYNtoae4CIbeyCeltxeSdIMKcTRUrL2SqBLSFBEXAVUKtyQReVEoIYdt7M3/dbWVS4iOKNNkXPNLS7vPMho3tYRUr+65NCKb74o6+fEH6TExk4xMsTMuXwcAqYNZy7A12Z9nDxmSG+ogFTZt24JnohZcwRV14JvkZDMWGoCrUJhQBVwm1LhNU+lrrn/Tgtt5y4vKQCiPnYkTD5AfJcmZawnh4t9IUvbVsACfbf8Eu+IZzMt8ucyJc2X9MyqwQyZNohjTFuevFw77rLhAiHcPNRQ6+Rr8z4qhPlmEoh5S9KXMKtQ9FwAqBKDbROAhhiWhMXig2ISPc4ub1Kh9FM02bI5hskOyI8QpSHD1kJDMYe/0U3793AGc+mcaU09HGZ8udbOeLd61790nbI6LbRdcsd+ex6+aLc4LuPBMbn3dqRql7VereKdQuFAEvcYT9ss72l1huAW73aavyNuV3e4l5vWLqmFj92SYRfLx0/0QznGxhC8fOd9MYyYw7Tih5eRALto0jJymJZgZHpXFB7HzpXDgzMC6SthBfkJpCZmLhCnr4VbIftSi3cKAIeIkj7Jd1rn6JxddsxNKyAyM5L2FzWUAgJaYbl0tu1Bp2hsZS6iafSOy1lXGPsKcCDvIFi+ddK1Wn8u0uHCgCXuII+2X1NhTMfoXeL00EQVyM4g6IMkJ9ZtJOPXDoNLbu3YcFYgsxkEd4YLsXrDKl4T2yNKOqToWZQBGwQknMJbnYfNx88XFFrBL2LlKFadKVtFOL+RAkb0qDQK1szklo85N2MZInjvwQRt7FUEvVs8KthSJghZKYq1famVnbjvIJyeI+2NQKcVvW/CAiKBxIrLJJXuyYO4KWM4DT20AhNVU0H+HHYTIF0fI+yaLca1fV89KFImCFW4aZ2Ku8EyIq1WGZO2LiYoxXp9zy5TR2iM4F2/K2EFM3g/gzI5nhmjYjZW+8pBcjLe18SocXSrNdulAErDBrlCJRZtFiCWSlusL8+/Z3ltEZcME6rJyMprvhPc3Bbg/mhOh77QTGDpziGRAsGJ3Z26gebeJIywUhBD4fWAFT5wd1X7BFxaDsCIWlDUXACrNGKRIdaWlHI5kJJcBKqj+308yvBXvnt7F9FoiNl5uP8InD0vw2qXPtDCV2aZIx8x9r0veKPXRYdjALCmr/4Suh2RFK/13aUASsMGuUIlFvMpn4PTZUs5yuOET/KHkRyY4Yjr3b7SaqSUHx7nggM6U55yWH5WSGRstuPAl76Mgadd6pgN2RRt7rU/rv0kYYAauhnAplo9Swx+G3zvFhmuKwyfjxd2GyYxBWP7gZRs9cLDnsk37/acBphK1/+icw/NY5PpySZPMweuYibP/zz8Oah++HSIQOzLRzJiQv9UHDqhVw49T/44M1H/7ul2H4rVZpyOfJz/9n2PKVT8L6x3fA2ke2QcOq5Xzffa/+BnbseRoaVi0HO2fAtq99FuiAUXl4JhtaitMId//JR2DtI1v5OQZdlzuodLfvM4UljCBWDvujKmCFYuA6q0VczdQ7jLNIU4WbgqYL/y5PS2bpaJam+9wQYucaC0xn2RXiAp93IbCr+bDUCh3U3ed1aVRyr5QksXQASoJQuJWQXAPNpV+/rYyOXc3UeSCO/gnaTmw7Zl1zYdawMKJzW52PSE0oIqGKCWkiMRc/t+KLlJdfKm+KdDn7U1g4CCNgJUEozAt27n0aAADWf2Q7rH3kAYBI8dfvvld/A+1//RMABHjw218EAL8kACDLIN0/Ogpt33sZ1j++Cybae6H9+z+BSDQqSQCTnTEYOvw7AIjA+sd3wLpHd0D9ykaoX74MPvjpr6XvM2lk46cfhVVb7oZU7zDc+dRDEF1WD1u+8smS8gGTJZj04T1/9vnyjevKkiS8+1NYhAhi5bA/qgJWqARsDHw5r+hWRuc5wMxeJoaoh31HbqP2Zw8nznXyKctiXKb4HfHvcvBPeMOFe3x/kltYO3alThDlG148ACVBKNxqVLry79V83e62mb2Gi3GPtkWkvOGwc2IadVhebyXXV4rAFZYOwghYSRAK84ZKV/7rGhu4kwAAoH5lI3zwytszfg2PvXYC2v/mpxCpq4OHv/tl+NDHPwxrH9la9JzqVzbCw9/9Mtg5kzsgtn/9s2DnTKhf2VjR9cWPnzAUN74AAAOuSURBVAdjIg3x4+9J16WgwBCh5FweHnvsMbxw4cI8no6Cggyqox6HHXt2cx14rr8bpDV3/+goDB3+HTz2378JU5eHYNokFT8AZnPuCosLkUjkfUR8zPtzVQEr1DRKeY/n4rtBi1079uwGQIB1j24F/cYEbNr95LwdX2HpQhGwwpJHkJQQP34e8jdTMP5ej5IPFOYNioAVljyCKtVNu5+C/OgUbHjioSqdlcJSQLTaJ6CwuECyeej+0VEg2Xy1T2VWYKQcpN0ulmtUqD4UASvMKZie2vfq8Rl9fz7Jzd23DoNvnPEdo9xjz/YaFRQYlAShMKeYbejMfHZ/iQE6xkQa8qNT0jHix98tOyhotsE6Qc4LhaUHRcAKc4rZrvzPZ2qYu+/PQ/z4e9zZ4JLhbvjEwb8r6XiYC3eDajNWAFAShEKNoZj2KmImUoW77xXwwFc/xY/BKt++V/+39PO5PLYXO/Y8DR9r/quyHjRKc168UASssCAxFzosI7ZNu5+ETxz8u7Kr7rk4drkPmrk6nkJtQkkQCgsScyFVzFQGuNXh6irMffFCtSIrLAkELXrNtlXYzpnwwStvq4U0hZIIa0VWEoTCkkDQa7xXBqhUa2VBQUoaUJgplAShsCQwHwHoShpQmC2UBKGg4ECllynMF1QamoJCCaj0MoVbDaUBKygoKFQJioAXKZR5X0Gh9qEIeJFiIZv3STbvhOXo6iGisKihNOBFioW8Qt/36m8gP54C/XoS2r//E1B5CQqLFYqAFykW8oLSjj1PQ/z4edi0+wmIRKML8iGioFAOlA1NQUFBYZ6hOuEUFBQUagyKgBUUFBSqBEXACgoKClWCImAFBQWFKkERsIKCgkKVoAhYQUFBoUpQBKygoKBQJSgCVlBQUKgSFAErKCgoVAmKgBUUFBSqBEXACvMKFYupoBAORcAK84qFHIupoDDfUGloCvOKhRyLqaAw31AErDCvWMixmAoK8w0lQSgoKChUCYqAFRQUFKoERcAKCgoKVYIiYAUFBYUqQRGwgoKCQpWgCFhBQUGhSlAErKCgoFAlKAJWUFBQqBIUASsoKChUCYqAFRQUFKoERcAKCgoKVYIiYAUFBYUqIYKI5W8cidwEgOH5Ox0FBQWFRYcJAABE/Jz3g4oIWEFBQUFh7qAkCAUFBYUqQRGwgoKCQpWgCFhBQUGhSlAErKCgoFAlKAJWUFBQqBIUASsoKChUCYqAFRQUFKoERcAKCgoKVYIiYAUFBYUq4f8DsAxBfBWGTOoAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<Figure size 432x432 with 1 Axes>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# write your code here\n", | |
"k_means = KMeans(init = \"k-means++\", n_clusters = 3, n_init = 12)\n", | |
"k_means.fit(X)\n", | |
"k_means_label = k_means.labels_\n", | |
"k_means_cluster_centers = k_means.cluster_centers_\n", | |
"\n", | |
"fig = plt.figure(figsize=(6, 6))\n", | |
"colors = plt.cm.Spectral(np.linspace(0, 1, len(set(k_means_labels))))\n", | |
"ax = fig.add_subplot(1, 1, 1)\n", | |
"\n", | |
"for k, col in zip(range(len(k_means_cluster_centers)), colors):\n", | |
" \n", | |
" my_members = (k_means_labels == k)\n", | |
" cluster_center = k_means_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", | |
"\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", | |
"-->" | |
] | |
}, | |
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"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": 81, | |
"metadata": { | |
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"text": [ | |
"--2020-01-19 00:32:28-- 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)... 67.228.254.196\n", | |
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|: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 0.03s \n", | |
"\n", | |
"2020-01-19 00:32:28 (1.04 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" | |
] | |
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} | |
}, | |
"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", | |
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"data": { | |
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"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
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" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>Customer Id</th>\n", | |
" <th>Age</th>\n", | |
" <th>Edu</th>\n", | |
" <th>Years Employed</th>\n", | |
" <th>Income</th>\n", | |
" <th>Card Debt</th>\n", | |
" <th>Other Debt</th>\n", | |
" <th>Defaulted</th>\n", | |
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" <td>8.218</td>\n", | |
" <td>0.0</td>\n", | |
" <td>NBA021</td>\n", | |
" <td>12.8</td>\n", | |
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" <td>20.9</td>\n", | |
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" <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", | |
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" <td>47</td>\n", | |
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" <td>253</td>\n", | |
" <td>9.308</td>\n", | |
" <td>8.908</td>\n", | |
" <td>0.0</td>\n", | |
" <td>NBA008</td>\n", | |
" <td>7.2</td>\n", | |
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], | |
"text/plain": [ | |
" Customer Id Age Edu Years Employed Income Card Debt Other Debt \\\n", | |
"0 1 41 2 6 19 0.124 1.073 \n", | |
"1 2 47 1 26 100 4.582 8.218 \n", | |
"2 3 33 2 10 57 6.111 5.802 \n", | |
"3 4 29 2 4 19 0.681 0.516 \n", | |
"4 5 47 1 31 253 9.308 8.908 \n", | |
"\n", | |
" Defaulted 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": 82, | |
"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" | |
] | |
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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." | |
] | |
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{ | |
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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", | |
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" <td>0.124</td>\n", | |
" <td>1.073</td>\n", | |
" <td>0.0</td>\n", | |
" <td>6.3</td>\n", | |
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" <th>1</th>\n", | |
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" <td>47</td>\n", | |
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" <td>4.582</td>\n", | |
" <td>8.218</td>\n", | |
" <td>0.0</td>\n", | |
" <td>12.8</td>\n", | |
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" <td>33</td>\n", | |
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" <td>57</td>\n", | |
" <td>6.111</td>\n", | |
" <td>5.802</td>\n", | |
" <td>1.0</td>\n", | |
" <td>20.9</td>\n", | |
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" <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>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>7.2</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
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], | |
"text/plain": [ | |
" Customer Id Age Edu Years Employed Income Card Debt Other Debt \\\n", | |
"0 1 41 2 6 19 0.124 1.073 \n", | |
"1 2 47 1 26 100 4.582 8.218 \n", | |
"2 3 33 2 10 57 6.111 5.802 \n", | |
"3 4 29 2 4 19 0.681 0.516 \n", | |
"4 5 47 1 31 253 9.308 8.908 \n", | |
"\n", | |
" Defaulted DebtIncomeRatio \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": 83, | |
"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": 84, | |
"metadata": { | |
"button": false, | |
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{ | |
"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": 84, | |
"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, | |
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} | |
}, | |
"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": 85, | |
"metadata": { | |
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"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
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" 1 1 1 1 2 1 2 2 0 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 2 1\n", | |
" 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 2 1\n", | |
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" 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 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": { | |
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} | |
}, | |
"source": [ | |
"<h2 id=\"insights\">Insights</h2>\n", | |
"We assign the labels to each row in dataframe." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 86, | |
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"</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", | |
" <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>6.3</td>\n", | |
" <td>1</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>12.8</td>\n", | |
" <td>2</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>20.9</td>\n", | |
" <td>1</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>6.3</td>\n", | |
" <td>1</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>0</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 1 \n", | |
"1 0.0 12.8 2 \n", | |
"2 1.0 20.9 1 \n", | |
"3 0.0 6.3 1 \n", | |
"4 0.0 7.2 0 " | |
] | |
}, | |
"execution_count": 86, | |
"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": 87, | |
"metadata": { | |
"button": false, | |
"collapsed": false, | |
"deletable": true, | |
"jupyter": { | |
"outputs_hidden": false | |
}, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
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"<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", | |
" <tr>\n", | |
" <th>Clus_km</th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
" <th></th>\n", | |
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" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>410.166667</td>\n", | |
" <td>45.388889</td>\n", | |
" <td>2.666667</td>\n", | |
" <td>19.555556</td>\n", | |
" <td>227.166667</td>\n", | |
" <td>5.678444</td>\n", | |
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" <td>0.285714</td>\n", | |
" <td>7.322222</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>432.006154</td>\n", | |
" <td>32.967692</td>\n", | |
" <td>1.613846</td>\n", | |
" <td>6.389231</td>\n", | |
" <td>31.204615</td>\n", | |
" <td>1.032711</td>\n", | |
" <td>2.108345</td>\n", | |
" <td>0.284658</td>\n", | |
" <td>10.095385</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>403.780220</td>\n", | |
" <td>41.368132</td>\n", | |
" <td>1.961538</td>\n", | |
" <td>15.252747</td>\n", | |
" <td>84.076923</td>\n", | |
" <td>3.114412</td>\n", | |
" <td>5.770352</td>\n", | |
" <td>0.172414</td>\n", | |
" <td>10.725824</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" Customer Id Age Edu Years Employed Income \\\n", | |
"Clus_km \n", | |
"0 410.166667 45.388889 2.666667 19.555556 227.166667 \n", | |
"1 432.006154 32.967692 1.613846 6.389231 31.204615 \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 5.678444 10.907167 0.285714 7.322222 \n", | |
"1 1.032711 2.108345 0.284658 10.095385 \n", | |
"2 3.114412 5.770352 0.172414 10.725824 " | |
] | |
}, | |
"execution_count": 87, | |
"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": 95, | |
"metadata": { | |
"button": false, | |
"collapsed": false, | |
"deletable": true, | |
"jupyter": { | |
"outputs_hidden": false | |
}, | |
"new_sheet": false, | |
"run_control": { | |
"read_only": false | |
} | |
}, | |
"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": 96, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f212ec823c8>" | |
] | |
}, | |
"execution_count": 96, | |
"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 © 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>" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python", | |
"language": "python", | |
"name": "conda-env-python-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.7" | |
}, | |
"widgets": { | |
"state": {}, | |
"version": "1.1.2" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 4 | |
} |
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