└── Hierarchical_clustering_.ipynb


/Hierarchical_clustering_.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "cells": [
  3 |     {
  4 |       "cell_type": "markdown",
  5 |       "metadata": {
  6 |         "id": "view-in-github",
  7 |         "colab_type": "text"
  8 |       },
  9 |       "source": [
 10 |         "<a href=\"https://colab.research.google.com/github/NandhanaRameshkumar/Hierarchical-clustering/blob/main/Hierarchical_clustering_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 11 |       ]
 12 |     },
 13 |     {
 14 |       "cell_type": "code",
 15 |       "execution_count": null,
 16 |       "id": "1e3de4ab",
 17 |       "metadata": {
 18 |         "id": "1e3de4ab"
 19 |       },
 20 |       "outputs": [],
 21 |       "source": [
 22 |         "import pandas as pd\n",
 23 |         "import matplotlib.pyplot as plt\n",
 24 |         "import numpy as np"
 25 |       ]
 26 |     },
 27 |     {
 28 |       "cell_type": "code",
 29 |       "execution_count": null,
 30 |       "id": "a52359d4",
 31 |       "metadata": {
 32 |         "id": "a52359d4",
 33 |         "outputId": "4a6a46fc-7264-487d-80aa-35837150b481"
 34 |       },
 35 |       "outputs": [
 36 |         {
 37 |           "data": {
 38 |             "text/html": [
 39 |               "<div>\n",
 40 |               "<style scoped>\n",
 41 |               "    .dataframe tbody tr th:only-of-type {\n",
 42 |               "        vertical-align: middle;\n",
 43 |               "    }\n",
 44 |               "\n",
 45 |               "    .dataframe tbody tr th {\n",
 46 |               "        vertical-align: top;\n",
 47 |               "    }\n",
 48 |               "\n",
 49 |               "    .dataframe thead th {\n",
 50 |               "        text-align: right;\n",
 51 |               "    }\n",
 52 |               "</style>\n",
 53 |               "<table border=\"1\" class=\"dataframe\">\n",
 54 |               "  <thead>\n",
 55 |               "    <tr style=\"text-align: right;\">\n",
 56 |               "      <th></th>\n",
 57 |               "      <th>CustomerID</th>\n",
 58 |               "      <th>Genre</th>\n",
 59 |               "      <th>Age</th>\n",
 60 |               "      <th>Annual Income (k$)</th>\n",
 61 |               "      <th>Spending Score (1-100)</th>\n",
 62 |               "    </tr>\n",
 63 |               "  </thead>\n",
 64 |               "  <tbody>\n",
 65 |               "    <tr>\n",
 66 |               "      <th>0</th>\n",
 67 |               "      <td>1</td>\n",
 68 |               "      <td>0</td>\n",
 69 |               "      <td>19</td>\n",
 70 |               "      <td>15</td>\n",
 71 |               "      <td>39</td>\n",
 72 |               "    </tr>\n",
 73 |               "    <tr>\n",
 74 |               "      <th>1</th>\n",
 75 |               "      <td>2</td>\n",
 76 |               "      <td>0</td>\n",
 77 |               "      <td>21</td>\n",
 78 |               "      <td>15</td>\n",
 79 |               "      <td>81</td>\n",
 80 |               "    </tr>\n",
 81 |               "    <tr>\n",
 82 |               "      <th>2</th>\n",
 83 |               "      <td>3</td>\n",
 84 |               "      <td>1</td>\n",
 85 |               "      <td>20</td>\n",
 86 |               "      <td>16</td>\n",
 87 |               "      <td>6</td>\n",
 88 |               "    </tr>\n",
 89 |               "    <tr>\n",
 90 |               "      <th>3</th>\n",
 91 |               "      <td>4</td>\n",
 92 |               "      <td>1</td>\n",
 93 |               "      <td>23</td>\n",
 94 |               "      <td>16</td>\n",
 95 |               "      <td>77</td>\n",
 96 |               "    </tr>\n",
 97 |               "    <tr>\n",
 98 |               "      <th>4</th>\n",
 99 |               "      <td>5</td>\n",
100 |               "      <td>1</td>\n",
101 |               "      <td>31</td>\n",
102 |               "      <td>17</td>\n",
103 |               "      <td>40</td>\n",
104 |               "    </tr>\n",
105 |               "    <tr>\n",
106 |               "      <th>...</th>\n",
107 |               "      <td>...</td>\n",
108 |               "      <td>...</td>\n",
109 |               "      <td>...</td>\n",
110 |               "      <td>...</td>\n",
111 |               "      <td>...</td>\n",
112 |               "    </tr>\n",
113 |               "    <tr>\n",
114 |               "      <th>195</th>\n",
115 |               "      <td>196</td>\n",
116 |               "      <td>1</td>\n",
117 |               "      <td>35</td>\n",
118 |               "      <td>120</td>\n",
119 |               "      <td>79</td>\n",
120 |               "    </tr>\n",
121 |               "    <tr>\n",
122 |               "      <th>196</th>\n",
123 |               "      <td>197</td>\n",
124 |               "      <td>1</td>\n",
125 |               "      <td>45</td>\n",
126 |               "      <td>126</td>\n",
127 |               "      <td>28</td>\n",
128 |               "    </tr>\n",
129 |               "    <tr>\n",
130 |               "      <th>197</th>\n",
131 |               "      <td>198</td>\n",
132 |               "      <td>0</td>\n",
133 |               "      <td>32</td>\n",
134 |               "      <td>126</td>\n",
135 |               "      <td>74</td>\n",
136 |               "    </tr>\n",
137 |               "    <tr>\n",
138 |               "      <th>198</th>\n",
139 |               "      <td>199</td>\n",
140 |               "      <td>0</td>\n",
141 |               "      <td>32</td>\n",
142 |               "      <td>137</td>\n",
143 |               "      <td>18</td>\n",
144 |               "    </tr>\n",
145 |               "    <tr>\n",
146 |               "      <th>199</th>\n",
147 |               "      <td>200</td>\n",
148 |               "      <td>0</td>\n",
149 |               "      <td>30</td>\n",
150 |               "      <td>137</td>\n",
151 |               "      <td>83</td>\n",
152 |               "    </tr>\n",
153 |               "  </tbody>\n",
154 |               "</table>\n",
155 |               "<p>200 rows × 5 columns</p>\n",
156 |               "</div>"
157 |             ],
158 |             "text/plain": [
159 |               "     CustomerID  Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
160 |               "0             1      0   19                  15                      39\n",
161 |               "1             2      0   21                  15                      81\n",
162 |               "2             3      1   20                  16                       6\n",
163 |               "3             4      1   23                  16                      77\n",
164 |               "4             5      1   31                  17                      40\n",
165 |               "..          ...    ...  ...                 ...                     ...\n",
166 |               "195         196      1   35                 120                      79\n",
167 |               "196         197      1   45                 126                      28\n",
168 |               "197         198      0   32                 126                      74\n",
169 |               "198         199      0   32                 137                      18\n",
170 |               "199         200      0   30                 137                      83\n",
171 |               "\n",
172 |               "[200 rows x 5 columns]"
173 |             ]
174 |           },
175 |           "execution_count": 3,
176 |           "metadata": {},
177 |           "output_type": "execute_result"
178 |         }
179 |       ],
180 |       "source": [
181 |         "data = pd.read_csv(\"D:\\\\Jeyashri\\\\IBM\\\\Datasets\\\\shopping-data.csv\")\n",
182 |         "data"
183 |       ]
184 |     },
185 |     {
186 |       "cell_type": "code",
187 |       "execution_count": null,
188 |       "id": "bfcd8029",
189 |       "metadata": {
190 |         "id": "bfcd8029",
191 |         "outputId": "b19792dc-c902-47d8-b466-5ffeb55ac675"
192 |       },
193 |       "outputs": [
194 |         {
195 |           "data": {
196 |             "text/plain": [
197 |               "(200, 5)"
198 |             ]
199 |           },
200 |           "execution_count": 4,
201 |           "metadata": {},
202 |           "output_type": "execute_result"
203 |         }
204 |       ],
205 |       "source": [
206 |         "data.shape"
207 |       ]
208 |     },
209 |     {
210 |       "cell_type": "code",
211 |       "execution_count": null,
212 |       "id": "6d03fe6b",
213 |       "metadata": {
214 |         "id": "6d03fe6b",
215 |         "outputId": "119172e6-cb7a-4290-b428-c17e79561926"
216 |       },
217 |       "outputs": [
218 |         {
219 |           "data": {
220 |             "text/html": [
221 |               "<div>\n",
222 |               "<style scoped>\n",
223 |               "    .dataframe tbody tr th:only-of-type {\n",
224 |               "        vertical-align: middle;\n",
225 |               "    }\n",
226 |               "\n",
227 |               "    .dataframe tbody tr th {\n",
228 |               "        vertical-align: top;\n",
229 |               "    }\n",
230 |               "\n",
231 |               "    .dataframe thead th {\n",
232 |               "        text-align: right;\n",
233 |               "    }\n",
234 |               "</style>\n",
235 |               "<table border=\"1\" class=\"dataframe\">\n",
236 |               "  <thead>\n",
237 |               "    <tr style=\"text-align: right;\">\n",
238 |               "      <th></th>\n",
239 |               "      <th>CustomerID</th>\n",
240 |               "      <th>Genre</th>\n",
241 |               "      <th>Age</th>\n",
242 |               "      <th>Annual Income (k$)</th>\n",
243 |               "      <th>Spending Score (1-100)</th>\n",
244 |               "    </tr>\n",
245 |               "  </thead>\n",
246 |               "  <tbody>\n",
247 |               "    <tr>\n",
248 |               "      <th>0</th>\n",
249 |               "      <td>1</td>\n",
250 |               "      <td>0</td>\n",
251 |               "      <td>19</td>\n",
252 |               "      <td>15</td>\n",
253 |               "      <td>39</td>\n",
254 |               "    </tr>\n",
255 |               "    <tr>\n",
256 |               "      <th>1</th>\n",
257 |               "      <td>2</td>\n",
258 |               "      <td>0</td>\n",
259 |               "      <td>21</td>\n",
260 |               "      <td>15</td>\n",
261 |               "      <td>81</td>\n",
262 |               "    </tr>\n",
263 |               "    <tr>\n",
264 |               "      <th>2</th>\n",
265 |               "      <td>3</td>\n",
266 |               "      <td>1</td>\n",
267 |               "      <td>20</td>\n",
268 |               "      <td>16</td>\n",
269 |               "      <td>6</td>\n",
270 |               "    </tr>\n",
271 |               "    <tr>\n",
272 |               "      <th>3</th>\n",
273 |               "      <td>4</td>\n",
274 |               "      <td>1</td>\n",
275 |               "      <td>23</td>\n",
276 |               "      <td>16</td>\n",
277 |               "      <td>77</td>\n",
278 |               "    </tr>\n",
279 |               "    <tr>\n",
280 |               "      <th>4</th>\n",
281 |               "      <td>5</td>\n",
282 |               "      <td>1</td>\n",
283 |               "      <td>31</td>\n",
284 |               "      <td>17</td>\n",
285 |               "      <td>40</td>\n",
286 |               "    </tr>\n",
287 |               "  </tbody>\n",
288 |               "</table>\n",
289 |               "</div>"
290 |             ],
291 |             "text/plain": [
292 |               "   CustomerID  Genre  Age  Annual Income (k$)  Spending Score (1-100)\n",
293 |               "0           1      0   19                  15                      39\n",
294 |               "1           2      0   21                  15                      81\n",
295 |               "2           3      1   20                  16                       6\n",
296 |               "3           4      1   23                  16                      77\n",
297 |               "4           5      1   31                  17                      40"
298 |             ]
299 |           },
300 |           "execution_count": 5,
301 |           "metadata": {},
302 |           "output_type": "execute_result"
303 |         }
304 |       ],
305 |       "source": [
306 |         "data.head()"
307 |       ]
308 |     },
309 |     {
310 |       "cell_type": "code",
311 |       "execution_count": null,
312 |       "id": "520f3775",
313 |       "metadata": {
314 |         "id": "520f3775",
315 |         "outputId": "13b32a82-9511-46fe-d6aa-c06b9b7342a5"
316 |       },
317 |       "outputs": [
318 |         {
319 |           "data": {
320 |             "image/png": 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\n",
321 |             "text/plain": [
322 |               "<Figure size 720x504 with 1 Axes>"
323 |             ]
324 |           },
325 |           "metadata": {
326 |             "needs_background": "light"
327 |           },
328 |           "output_type": "display_data"
329 |         }
330 |       ],
331 |       "source": [
332 |         "import scipy.cluster.hierarchy as shc\n",
333 |         "plt.figure(figsize=(10, 7))\n",
334 |         "plt.title(\"Customer Dendograms\")\n",
335 |         "dend = shc.dendrogram(shc.linkage(data, method='centroid'))"
336 |       ]
337 |     },
338 |     {
339 |       "cell_type": "code",
340 |       "execution_count": null,
341 |       "id": "fbad2837",
342 |       "metadata": {
343 |         "id": "fbad2837"
344 |       },
345 |       "outputs": [],
346 |       "source": [
347 |         "from sklearn.cluster import AgglomerativeClustering\n",
348 |         "cluster = AgglomerativeClustering(n_clusters=6, linkage='average')\n",
349 |         "cluster1=cluster.fit_predict(data)"
350 |       ]
351 |     },
352 |     {
353 |       "cell_type": "code",
354 |       "execution_count": null,
355 |       "id": "b15c9c59",
356 |       "metadata": {
357 |         "id": "b15c9c59",
358 |         "outputId": "37d62385-38fb-4663-d7fa-b46c9528111a"
359 |       },
360 |       "outputs": [
361 |         {
362 |           "data": {
363 |             "text/plain": [
364 |               "array([1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4,\n",
365 |               "       1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 4, 1, 2,\n",
366 |               "       1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
367 |               "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
368 |               "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
369 |               "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 5, 0, 2, 0, 5, 0, 5, 0,\n",
370 |               "       5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 5, 0,\n",
371 |               "       5, 0, 5, 0, 5, 0, 5, 0, 5, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0,\n",
372 |               "       3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0, 3, 0,\n",
373 |               "       3, 0], dtype=int64)"
374 |             ]
375 |           },
376 |           "execution_count": 10,
377 |           "metadata": {},
378 |           "output_type": "execute_result"
379 |         }
380 |       ],
381 |       "source": [
382 |         "cluster1"
383 |       ]
384 |     },
385 |     {
386 |       "cell_type": "code",
387 |       "execution_count": null,
388 |       "id": "dad5c1e9",
389 |       "metadata": {
390 |         "id": "dad5c1e9",
391 |         "outputId": "ef3b70cf-0402-4796-b49b-447068f8a7f8"
392 |       },
393 |       "outputs": [
394 |         {
395 |           "data": {
396 |             "text/plain": [
397 |               "<matplotlib.collections.PathCollection at 0x1d2a0956e20>"
398 |             ]
399 |           },
400 |           "execution_count": 11,
401 |           "metadata": {},
402 |           "output_type": "execute_result"
403 |         },
404 |         {
405 |           "data": {
406 |             "image/png": 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ahLV2PbAGOD4zpYqIiEhfcs7wSZwx9Cjy3CDV/zMam+/ihzuiRbQgwvAJQ7jqy5dlucredaRXBQ621m4HsNZuN8YM6mwfDszrst+WzjYRERHJAWnfZ1NrAyXhKG5Lms+XH8+Hxs/khYnryTvtbErmNRPb1syk4ydw4iVVuIGBNXCV6eUW9nX7n30OvBpjbgRuBBg1alSGyxAREZFM+8eG1/nmoifxWhMU/Wwt0ddbiYSCBIIBbv7Z9Zx9zWkDfpzqSK8K3GmMGQrQ+ba6s30LMLLLfiOAbft6AWvtXdbaKmttVWVl5RGWISIiIr3htZpNfGXB4zSn4hT+eA3hZS2Q8om3JWhtbOP/3XQXr7+wIttlZt2RBqs5wLWd718LPNyl/UpjTNgYMxaYAMzvXokiIiKSbb9Z8QpxL41blySyohUnveeAVKI9OeDWrNqXgw4FGmMeAE4HKowxW4CvA98FHjTG3ABsAj4IYK1dbox5EHgTSAM3W2u9HqpdREREeojn+zy7fQ1L67ZR1uby5sbNEIJAQwoCBlJ7z/TZubEmC5X2LQcNVtbaq/az6az97H8HcEd3ihIREZHsaUslueKZP7KptQH7ag2DfraB2FXD4KxykiMi4O0dqgIhl+POmpGFavsWrbwuIiIie/jFmy+ytrmW9vY4g+7ciJO0lMzZiRP3sQGHhsuG4Id3X6/mBhyihVE++L+XZLHqvkE3YRYREZE9zNm4nKTvEX2zddf1/oGGNCNuXUXDZYNpe08J0fwI4xelsA1JZp5zNB/6yvupGFaW3cL7AAUrERGRAW5h7RZ+/PpzrK7dyeB/NVB7fBBKA+DsuWZSoD5F5e+2AHDqB97NVxd+Liv19mUKViIiIgPYKzs38NEX/kLcSzPkO2toX9VGfssgGi8eTGxKwT6fE8kPc+51Z/Rypf2D5liJiIgMYN9Z8jRxL01obTuRt9o751NVE97Yjklbqm8Zgx9ysGEHN+QSjoY465pTOP6C47Jdep+kM1YiIiID2OrmjiUSwuvawXYM/Dkpy7CvryE+tYDE+Dwm3HI8F4yfTrIlQdV5xzB2xuhsltynKViJiIgMYBXhfLbHWkhXhMAxvD2rygDRN1spXp/k7NMmMvtj52W1zv5CQ4EiIiID2MennkTUDRI7uhAv38V/RzIIBF3O+chp2SmuH1KwEhERGcCuHj+T/5l2MvnhMHXfmkJqYgEm6BAMBxg9dQQ/fOZ2iiuKsl1mv2Gs3Xv11N5WVVVlFyxYkO0yREREBqyk51GXaKMsnEeiKYaX9ikdVJztsvokY8xCa23VvrZpjpWIiIgQcl2G5nWcmQqXFWa5mv5LQ4EiIiIiGaJgJSIiIpIhClYiIiIiGaJgJSIiIpIhClYiIiIiGaKrAkVERAaY9S11bGptZFy0lLrF2zCOYdqJkwgEFQu6S59BERGRAaItleSmFx9iUd0W8pa1UPTjNQSMQ8QN4AZcvv63/+WY06Zlu8x+TUOBIiIiA8TXFz3BgtrNpOpiFH3/LZx2D78tRXtzjJb6Vr5y8XdpbWzLdpn9moKViIjIAJD0PB7b9CZJ3yP/lca377W8J2t5/q/zeru0nKJgJSIiMgAk/TRe523s3NY0JrV3skol07Q2tPZ2aTlFwUpERGQAKAiGGVtYBkD7jEJseO8IEAgGmHnO0b1dWk5RsBIRERkg7qi6kKgbJD25gPZjCvG7hKtIfphTP/Bujjp2bBYr7P90VaCIiMgAUVU5kkfO+yh/WDWf1d8exdBFMcwzO4gEg5x3/ZmcfOnx2S6x3zPW7mv2Wu+qqqqyCxYsyHYZIiIiIgdljFlora3a1zYNBYqIiIhkiIKViIiISIYoWImIiIhkiIKViIiISIYoWImIiIhkiIKViIiISIYoWImIiIhkiIKViIiISIYoWImIiIhkiIKViIhIjkj5Hm2pJACx1hjpVJq079OaSmCtJdYWJ5lIZbnK3KZ7BYqIiPRzsXSKbyz6N3M2voH7ZgtD7t6CqY5Tf8UwWs6pILgtzuC7tuBuaMd1DO++qIrP3PUxisoLs116ztEZKxERkX7uky//nTkbl+NvaWPQd9fA5naqrxlG45ml2NYUg29/C7O2Fev5pFMe8x5bwP+edTt94X7BuUbBSkREpB/b0tbIyzs3kPDTFD9eg0n5+FGH1lPLsGGXgmfrIG0xXZ6TTnrsWFfN8pdXZa3uXKWhQBERkT6iOtbCc9vX4mIYsiZN/dpaSicPYucYB8/3GbnVoXb5DorHllM/KUTc92jb3IhJW3AguCWO8SFVGgSv42xUaGscJ7XvM1Pb1+1k+kmTe7OLOU/BSkREpA/44+rX+O6SZ3DbPMpvX4lbnSBeVcLO/x6Bu84y+CfrCa5tJzW1kO23jMLsgEG/2UJgbSuJH08BxyExIY/w2jYCNUnePkWVOCqf/NeacJJ7hivf9xl39Ogs9DS3aShQREQky9Y01/K9pc+Q8NPk370Bd1sMG3LY+d8jsCGHwkd3ElzdBr5l2ydH4Udc8l6sJ7ykkWBtioLn6zFxj6YLKrEhF5O2FM+pxsQ9Wk4tw89zsV1+44ciQaafPIXxx4zJWp9zlYKViIhIls3Z+AYp3wNrKZjXiJOGtlnF4HecZSp8rh4nZYkdXQidJ56K5tbhJDoeVPzfFkr/ugMsbPvqeCInDGboU02MntNIaTBC/XenETl9OJHCCCWDinn/Zy/imw9/MVvdzWkaChQREellrakE96x+jX9tXklkbTsN2xrwprsdGzvDlA0YrNMxnmc650tZd/cUdNNl3pSxUPJYDSWP1RDJD/Orhd9nxMRhe37QG3uwQ7KLzliJiIj0olg6xaVP/Z5fLH+JjU+/RcNnXyTx0DpMwgdjiM0oxBrIW9y860q+tlnFWBeiy1sh0NHaelIpftDs9fqFpQUMnzC0F3skXSlYiYiI9KJ/bFjG9vZmEl6Kiru34CQtkdXtFLzQgIl71F43HL/AxW1KUfL3HZiET/0VQ0mXhjBpS/ndWzBJn6ZzK0gNC+NHOn6VByNBIvlhvnz/pzBm78AlvUNDgSIiIj3Mt5YdsRYKg2Ge2byamJfCbUrjtKaBjgv4Ku7eQuGLDbSeUExs9jD+69gTaV9dTyhRSd2xeXgPn8DQhTFib9YSaiyj8YRC4n85nuHLksQW7WTw6ErO+cjplA8tzW5nBzgFKxERkR705JZVfHXhv2hraafoNxuITc6HM8rxo+4e+xkg8lYbkbfaGDVlOP/9xw/s/WLv2ccHOL5HypYjpKFAERGRHrKsbhufmfdPauNtFP58HdH5jRT9uxaTttiwQ9sJJXvNk4rkh7n8f2dnqWLpLgUrERGRHnLXyldIeGncxhR5S5pxUpbQljiVv9iI05qm7uphxI8uhKBDtChCKBLksk+9l3OvOz3bpcsR0lCgiIhID9nY2oAF3IYUNmCgc4mEgteayF/URHJUlOHDKrlzww+p3VLHiEnDyC/Ky27R0i0KViIiIj3khEGjWd1UQ2poeNdaVG8zHuRtTnLCORMpH1qqSec5QkOBIiIiPeSjk04gLxDCRAI0vm8wfnj3r13HMUTyw1x566VZrFAyTWesREREesiQvCIePvcGfvLGf3j5yiiBMRWUPlKD35DgmNOmct23rmTImEHZLlMyyFhrD75XD6uqqrILFizIdhkiIiIiB2WMWWitrdrXNg0FioiIiGSIgpWIiIhIhihYiYiIiGSIgpWIiIhIhihYiYiIiGSIgpWIiIhIhmgdKxERkQyxNgWJZ7DJxdTsKOOZvxXS0pBm1vnHcuwZ0zHGHPxFpF9TsBIREckA67di668CbzOvPBHgOx8fje+Dl3J45Ff/5rizZvD1v/0vrutmu1TpQRoKFBERyQDbdhek15Noj/G9/xlFMu6QTjpYC/G2BIvnvs4Lf52X7TKlhylYiYiIZELsESDJG/PzMfv47RpvS/D0vc/3elnSu7oVrIwxnzHGLDfGvGGMecAYEzHGlBljnjLGrO58q9t1i4hI7jMdQ3xuwMJ+7hYXCGkGTq474mBljBkO3AJUWWunAy5wJXArMNdaOwGY2/lYREQkt0U/CESYfnxbR7h6h0h+mPOvP7P365Je1d2hwAAQNcYEgDxgGzAbuKdz+z3A+7r5MURERPo8k389hI4jEIry9d9vIZrvE833CYYDhKMhzvnIaZzw3pnZLlN62BGfk7TWbjXG/BDYBMSAJ621TxpjBltrt3fus90YMyhDtYqIiPQp1qsFbz3WGcG61+Mk419iwtEtHH32mzywroyXHs+jrSnBzHOOZvSUEdkuV3rBEQerzrlTs4GxQCPwkDHmmsN4/o3AjQCjRo060jJERER6nbUetvmrEJvD+pUFfO3Dg2luDOO4EYwxfPGPn+Q9F1dx7rXZrlR6W3eGAs8G1ltra6y1KeDvwInATmPMUIDOt9X7erK19i5rbZW1tqqysrIbZYiIiPQu2/ZbiD1KMpHiCx8YSvXWIPE2S3tzjLamdu648idsX78z22VKFnQnWG0C3m2MyTMdS8meBawA5gBvZ/RrgYe7V6KIiEgf0/4nIM6CZwtJJQ2w54rqnufxxN3PZKU0ya7uzLF61RjzV2ARkAYWA3cBBcCDxpgb6AhfH8xEoSIiIu9krYXkPGz8cVoa4ckHh7P29SRVZxpOvnAnftpn7t9Hsnx+iunvdjjzshocJ8ELj45m0X/SjJ9hOO/KeiKRVl55ajTznvQZNsbw3o80UVTSyMLnR/HCo4aScrjk+lbKBtXyxmvDmTylgWAQWhoCWG/vutJJj4adTb3++ZDs69aCGtbarwNff0dzgo6zVyIiIj3GWtsxzyn+CNs3eNxy4QQSsTf5wMerOfG0Glq2O3zqvRNoaVzF6e9r4KwLtpJsMvzvpUexY9Nqjj25hQtv34hNWL54+XhWL1vLuGkxbv7KOhxj+c51I5k/dx2DRyT58cOrCYYsv7l1CI/fu57bfx/h2JNbmf7uVny79/3/IgURZp13bO9/UiTrtPK6iIj0T6mlEH8EbIw7vzycliaXksoUV3yymkjU508/HEJ9dQA3YPnEt7YSjloe+X0F29aHSafg8z/dRCRqefGxUlYvixJvN3zx5xuJ5vu8MS+f+XOLiLc7fPqHm8kr9Nm6Pszj91aQiDn8+uvDiLc5DBqe5Nwr6onk7T5tFc4LM27GKE6cPSuLnxzJFi0BKyIi/ZJNPAM2DsDiFwqxvmHWmS3YzrU5X/l3EV7a4ZgTm0inDWEs/3m4hGTCYfLMtl23nXn2HyXE210GDU9SWpkG4MXHi4m3OwRDPpOObcdxYP7cItLJjrNTG1dFuensibz/YzWcc3kDQ8cPZsF/xpKMuZx59Smcf8OZuAHdbHkgUrASEZE+x6ZWYlvuwI8v5h+/HcJffzOYvIIYn/5hPVPfVcvTD1XQ3GB43w2WYAgCQYuXNiTjzq45T8FwR8JKxs2uW8yEo35nm4PTGawi+Z1tCbMrbEXyLI4LnmfwfYOLJRTxcQIWz+sIVzs3h/nlV0YQigT56Hev4ftPXdg7nxzp0zQUKCIifYpNb8bWXwXJV/m/b5dzz/eLSCXa+fE/VzB15jbm/L6IX9xWxqP3FOJ3hpwzL2sgGPJ5+YmiXeHowg/VEY74LHmpAN/v2O/i6+oI53msezNCY20A34f3XlNHJM+jsTbImmVR0mk45/J6giGL7xlefqKIVBJOvagRs/d0KjCGUz/4nl767Ehfp2AlIiJ9im27G2yC9laHOX+oIBFzOf+quo6zTQbu/dFgEjGXHZvC/PzWESRihg9/dgfjpsVJpxy+f8t44u0OF13XwDEnt+MG4Fv/PZ62ZpcTL2jm1ItbCYUtd9w0jub6IFOqYlzyX40Ewz4/+uxYareHGTIqyfW31hAK+/z2myPYsjaP/CKfz/xoB6GIT7QAooURQtEQX/j9zZQPLc32p036CGPtfm7B3YuqqqrsggULsl2GiIhkifV2QGo5vhlEfPutRCOrWb8iwmdnH0V7q8tX7trAKRc10VTn8qF3TSWV3H1eoLAkzbtOb8ENRbjkU3eweVU9o6eUMvHobRg81q0aw9qldQwZW8z0WbUY2ti6cTxvvlpH6ZACZp7SjGOaqN4xnmUvNFBQGqXqzAQBp4aGhnEsfraFUCTA8edAKLCd1rZRLJibwBiYdf6x5BfnZ/EzJ9lgjFlora3a5zYFKxERyZaOJRO+CbGH2LIuny9dMZSPfH47Z1zWQLzN4cpjp5FKOHzoMzu44pPVBAKW90+dTqx174nhE2aO45cLvpeFXshAc6BgpaFAERHJGhv7G8T/jrVJbrt6MDXbAvzlzkGkkoaCYp9zPlhPOOrx+L3lpJMdk8sv/0Q14eieq3KG80Jc960rs9QLkd0UrEREJHva/wg2xltLozTVBbDWsHlNhC9dMZ61b0T4xLe3csn1dcRjYT77vgmsXV7ClbfUcM3naikocTDGMGh0BV+855Mcf8Fx2e6NiJZbEBGR3mP9emz7/ZBcwtJXBjN6zBZKyqGt2d11NR/AioX5fOLcSbgBy/RTpvFw0+14aY9AMIC1aa74hsMV3zC72kT6Cp2xEhGRXmHTm7E150Hrb/jTd1by1ctX8uw/wqSSMHlmO15q77UMAqEwp77/RIwxuwKUMQGMcfZoE+krFKxERKRX2JbvgW2hdrvHX+4cTCLm8MD/G0JjbRDHsdz0za2Eoz7G6bioKpIXZviEoZx3/elZrVvkcCjqi4hIj7I2jTEBSL4E+Cx5qZhA0JJKQlN9gBvPmMQFV9dx3CmtfPTrhreWv4vGWstJs2dx9odPJRwNZ7sLIodMwUpERDLOWtsxl6rtTuq2N/HzW8dzy/cSlA2CvAJvjxXM21tc/vabQfzjd0O49JYL+cI912avcJFu0lCgiIhknG2/H1q+TzpRx6cvnsD8p8M8ek858XZD1RktOO7eaygGQwEuuOHMLFQrkjkKViIiklHWWmj9ORBj3lPFtDS6eJ7hzz8fzPy5RVgLX/3dRopK0+QVQl5RlFA0xCd/8VFGTx2Z7fJFukVDgSIiclisTUPyZfDrqK0+iqUvNJBfHKHqjCQBt5qmptEU2gaMgS1rwyRiHX/De2nDHR8bw9DRCcZMiXPNbWcw5tgzSMaSzDh1KnmF0Sz3TKT7FKxEROSQ2fR6bP01YNv50w9L+cudpVQOt9xx7xoSO5I8/rdK7rp9EPfMcykbnGbMpDjhiE+sbfctaLZvDNNYV8zZ/zWL486ckcXeiGSehgJFROSQWGuxDR8Dv5YlL8JDvywhlXD49PfXM2h4nJptQe76xiCSCYf/+85Q4u2GWWc2Uz4kRSDo73odN+hSUlnEibNnZbE3Ij1DwUpERA6Ntxa8nYDlX/eWk4g5FJammfqudgJBePqh0l2LfD79UBk//txIdm4J8YO/reWUixNE8gNE8sOcfsVJ/Gzed7S4p+QkfVWLiMh+2cTz2LZ7aa5v5MXHyzjjohTRPIi1OYAhFLbYzgv84u0uXpd7I//n4VL+83Apkbwwn/jp9Xz5r2dlpQ8ivUlnrEREZJ/81p9jGz9J844XuOnUBL/+cjWpREeKOm12I5E8j7odAWq2BwE48fwmInn+3q/j+1Sdd2xvli6SNQpWIiKyF+vVQetdYGP89deVNNcHSLS7/OCWUcTbDSee38ik49qJ5Fl+cMsoYq0OU6vaePc5zbvCleMYwnkhrvnaB6kcUZ7lHon0DmPt3ou09baqqiq7YMGCbJchIpKzmmNxAq5LyBjamtopKM3HMTEAfBultaGNgpJ8HCcO+NjEPGzTFzC0cdNZE1m/YvdSCENHJzj/6joGjfBpT72XFYuKGDLKcMl1TRSV1rPw+dE8/7AhnJfHudeezqRZR2Wp1yI9wxiz0Fpbta9tmmMlIpLDlm/dyW0P/Zv1NfXkPb+JwgXbGTk2zud/spHx09v55+8quO8nQyko9vncTzYy44RWnvlHKc8/UsKtv4iRVwDF5ek9XnP7xjC///+GEYqG+L/lN3LJLYP22H787I5/IgORgpWISI6qaW7l+t8+RFsiReELm8ibv41wKMUPH1pJYYnHv/9cxj3fH0QqYfnFv1dROSzJgucK+dkXh5GMG5obXCJRn/d/rIYVC/NIxHavReUGXMYfO4YhYwYdoAKRgUdzrEREctRD898g5fngW/IXbMNJ+5x6SSPBiMVx4b6fDCYRc5l5WgtFZWkCQfjTjwaTiDlYa/jSFePZtiHMjHe3cfnN1YQiPvlFQcJ5IcYfO4Zv/P3z2e6iSJ+jM1YiIjnEplZD8hWSqUJeXdREMu1hkh4m3TGfduioJHn5HZPL66s7ruYbMiqJG+jYvnNzaNdrbdsQ5oZTJjF2SpySSodfLfo+DTvjlA4uYdTk4b3cM5H+QcFKRCQHWGuxzbdB7FE2rwnwv5eNZudRwzAnjsWGXfxIALc9xZrXo7S3OuQV+Iw8Ks6GlVHWvB7F+gawTJgRY8FzAcB0vrJh/Yoo+dvzGH7UGEZNdg9QhYhoKFBEJBcknoTYY0Ccb984jKY6l+CiWkw8Db6l+fTR+AGHeU8VU7MtSCphuPFr2whHfFYuymP1siiJmOG6W3cQjlpg9xXj4bww13/rStyAQpXIwShYiYjkANv+IBCjekuQbRvCWGtwkj6V9ywjuryG+PhSWs8bT8HoQdz24WN4Ze5RHHOyyzfvrWNyVQHf+cQ0np0zgdFTIvzw79Uce2o++SVRxkwbyefv/gSzb74g210U6Rc0FCgi0g9tb2zhd8/NZ97azRSta+QrF69gyrGQThuM2b2f25ai9F9rgbWMmDiM36/+5R6vM3MEzLxsz9eePBJ+cF6Pd0EkJylYiYj0M9sam/nAz+6lLZEk8sIm4q9u5dHaKGO+Yxg6OklpZYodm8J7PCcUCXLWNadkqWKRgUNDgSIi/cxvnp1PayKJ156icN4WnJTPM38vZfn8AuJtDl+8cxPRAo9QpOPqv2hBhFFTR/D+z1yU5cpFcp/OWImI9DOvrtmE51tCO9uwroNJe/ie4SvXjOW4U1uZeWoLV3/WECz+ADVbkkw/eTLvubhKk89FeoGClYhIP1NZmM/m+ib8/CB4u6/es9aw6D+FLH6+iFMvfw9feeCqLFYpMjBpKFBEpJ/56OmziAYDpCvySFdEse/4SR6KBnn/pzXsJ5INClYiIv3MaZPH8enzTiYvFCT+oWNIDy/CBF2iBRHyS/L4zF03MeWECdkuU2RA0lCgiEgfkkyneXr5WjbU1FOSH6UtkcT3LYXRMI1tMQqjYRIpD8/6/PaGy4gEg5R/LQ+a4rTUtzJqynACQf1oF8kWffeJiPQR1c2tXPXLB2iOJWhPpg64r2MMdz07n9kzp/LV2WdiCvOpHFHeS5WKyP5oKFBEpI/45j+foaa57aChCsC3lngqzZzFK3h13eZeqE5EDoWClYhIH2Ct5flV6/CsPfjOXcSSKR5dvKKHqhKRw6VgJSLSRxjMwXfa6zlgzOE/T0R6hoKViEgfYIzhtCljCTiHF5IiwQAXHzelh6oSkcOlYCUi0kd8dfZZDCoqJD8UPOi+rmOIBAO8f9YMZo0d0QvVicih0FWBIiK9ZFtjM/Wt7YyuKGVzXSP54TB5oQA7mloZU1FKXWs7d37kEjbWNrKhtoGygiit8SS+tRRFwzS0xSiKRkim0yTTHqdOGsuEIRXZ7paIdKFgJSLSwxrb43zmvkdYumk7FkimPYKug+dbrLWEAi6JzraA41BRmM/PP3yJQpNIP6ShQBGRHvaZ+x5h8YZtJNIeybQHQMrz8a3FAokubbFUms31TVz324dIptNZrFpEjoSClYhID9re2MKSTdtJ+f5hPS/leTy3cn0PVSUiPUVDgSIiGbalvomHF71JfVuMlOfhH+baVABpz1Lb0tYD1YlIT1KwEhHJoH8ve4sv/fXfpD0Pzz/8QPU2Y+BdY4ZnsDIR6Q0KViIiGRJLprjtb0+SSHVvblQ0GOD0KeOYNLQyQ5WJSG9RsBIRyZBFG7bhHsIq6BOHVJD2fIKuSyDg0J5IUhQJ05ZIkhcOcfnxRzN75tReqFhEMk3BSkTkHWqaW/nRv17guZXrsViCjks8lSIaCnZewWcJBwLEkikioeCuYb+A49CaSB7wtaOhIN+9/HydjRLJUQpWIiJdtCWSXH7n/dS1te81RyrWZYivLZHaq+1QlOZHmaj1qURyloKViEgXjyxeQUs80a2J5+/kGkMkFCDguNz54Ut002SRHKZgJSI5q6aljdU7ahlWUkhLPElbMsmY8lLW19ZTUZCHZ6GxLcaYylI21DRQFA3z4lsbDvss1P4EHYfbLz2beDpNWUEep00aSzioH7siuUzf4SKSc3zf8q05z/DPhcsJOA7tyRQGCLgOKc8nHHA7VkA3EHA62t6+xcyRrDm1P47jcN7RE4kewk2VRSQ3KFiJSM55YN4S5ix6k2TaI0nH7WIsHbeMgd23kMHubnv7baZEggE+MGu6QpXIAKNgJSI5508vLSaeoeE8ANcxYMEY03F/P2txHQdrLRiwlt1tWEKBAFe/+xhuOfekjNUgIv2DgpWI9HsNbTEeeu113txaTXE0zI6mloy99pDiAp78wg20J1Pkh0IkvY6zXSHXpS2ZJC8UJO35eNYSDQZoSySJBIMEXN2KVWQgUrASkX5tQ00DV/3qARKp9O4hvgxxHYfTJo/DdRwKI2EAIs7uH5tvt7nO7hBV0NkmIgOTgpWI9GvfnvMMLfEEGZxzDkAo4FIYCXPTmSdk9oVFJKcpWIlIvzZ/3eaDhqrhJUUMLysmlfYoyQvT0B4nGg6CtcSSaUrzozS0xYgEg7iOIZZMccL4kXzoxOMozY/2TkdEJCd0K1gZY0qA3wHT6bjo5r+AVcBfgDHABuBya21Ddz6OiOS2ZNrjZ0++xEOvvU57Ikk0FKI9kSTouhhjSKbT5IWDxBIpXMch4DrEU2mioeBBF/IMOg6XzZrGTWe+u5d6IyIDWXdnV/4UeMJaOxk4BlgB3ArMtdZOAOZ2PhYR2a/P//lx7n9lKa3xJL7tuK2MBZKeRyKdxtJxCxkfSPk+sVRHW3syddDXdhyHi46d0sM9EBHpcMTByhhTBJwK/B+AtTZprW0EZgP3dO52D/C+7pUoIrlsa0MTL6xaTyKdueURHGPIDwcJBwJ847KzGVFWnLHXFhE5kO4MBY4DaoDfG2OOARYCnwIGW2u3A1hrtxtjBnW/TBHJJcm0x/x1m0mk0lQ3t2bs3nmuMVz17mM44ahRpH2f9xw1ateVeyIivaE7wSoAzAQ+aa191RjzUw5j2M8YcyNwI8CoUaO6UYaI9CeLN27jE/f8k7Tnk0x7pP3MrXgeDgY4edJYTpk0JmOvKSJyOLozx2oLsMVa+2rn47/SEbR2GmOGAnS+rd7Xk621d1lrq6y1VZWVld0oQ0T6i3gqzU1/+AfNsQTtyVRGQ1XAcRhcXMBJE0Zn7DVFRA7XEQcra+0OYLMxZlJn01nAm8Ac4NrOtmuBh7tVoYjkjBff2nBI601Fgh0n04OuQ8h1AYiGOtoCjiEc6GwLBnCMIeg6nDtjAvd+7AocJzPDiiIiR6K761h9ErjPGBMC1gHX0xHWHjTG3ABsAj7YzY8hIv2UtZanl6/hHwuXE0+laYkliB3kSr5wwOWfn/4wI0qLd829stZijNn19p1tQMbmaYmIdEe3gpW1dglQtY9NZ3XndUUkN3zpwSd4+s21Bw1TXZXkRfcIVbA7NB2sTUQk27Tyuoj0iNc37+Cp5WuIpw5tGYWA4xB0He744LkKSyLSbylYiUhGJdNpjDHMW7uJlHfwmyJffNxkWuNJRpaVcNV7jmFUeUnPFyki0kMUrEQkI9ZV1/O1vz/Fko3bgI4hOv8gM9VDAZdvv/88Am53bwIhItI3KFiJSLc1xeJ86Nd/piWW4O0oZQ8hVF1w9CSFKhHJKQpWItJtDy98k2Ta4xBWUiAaDGCB40YP47ZLzujp0kREepWClYh025rquoNOUncdh0c+cy0baxsYVV7CmMrSXqpORKT36By8iHTb9OGDiQYP/HfauMpSRleUcOrksQpVIpKzFKxEpNvee+xkCiNh3P2seh4JBvjcBaf2clUiIr1PwUpEui0/HOLB/7ma82dMJC8UJC8UpCgaJhwIMHloJT+95mLdGFlEBgTNsRKRjKgsKuD7V16Y7TJERLJKZ6xEREREMkTBSkRERCRDNBQokoNiyRQt8QTl+Xk0tMcIBwOEXJemWJyy/Dxa4gmMMeSHg9S3xijNjxJLpfA8n+K8CLUt7ZTkRUh5HrFUmrK8KHVt7RREwgC0xROU5edR3x4jGgwQcF2aY3HKC/JwHf29JiIDl4KVSA5JpNLcMedZHlmyAmstnm8Bi7Udt5hxHdPRZi0YAEPAMaT9XeulA132A4zp2N0x4FmLweB03q7GQMdbYwi6DpFQkM9feCqXvmtaVvovIpJtClYiOeRrf3+Kp95YQzK9982PrbX4Xpe10W3Hf0nvneul72s/eDt7Wexe9wC01pJIeyTSHt9++BnK8/M4dfLYbvdHRKS/0Tl7kRzR2B7nyTdWk0gfeAX0nhZPpfnVM/OyWoOISLYoWInkiNqWNoJ9ZH7TtsbmbJcgIpIVfeOnsIh024iy4l3DddlkDBw9cmi2yxARyQoFK5EcEQkG+PhZJxz0nn09yQCRQIBPnnNi1moQEckmTV4XySE3nDaLoSVF3PXcfGqaW8kPB2lLpHAdQ9B1iSfT5IWDxFJpDBAOBGhPJomGQqS8NJ5vyQsFaUskiQQDeNaS9nzywyFa4wlCgY4fGam0R34kRFsiScBxcB2HpOdx9IghfOq8k5g4pCK7nwgRkSxRsBLJMRceM4kLj5mU7TJERAYkDQWKiIiIZIiClYiIiEiGKFiJiIiIZIiClYiIiEiGKFiJiIiIZIiClYiIiEiGaLkFkX6spqWNx5euor61ndL8KI1tMUoL8rjwmElUFuZnuzwRkQFHwUqkn3p59UZu+dMc0r5PyvN3tYcCLj9/8iV+9uFLOHHC6CxWKCIy8GgoUKQfSnken73/MWKp9B6hCiCZ9oil0nzugcdIeV6WKhQRGZgUrET6oWWbd+DbA99x2fMtyzbv6KWKREQENBQo0i+kPI8/vLCQB199veP+fK5DWyJ54CdZi2tM7xQoIiKAgpVIv/C5Bx7jpbc2Ek+lD/k54WCAGSOH9GBVIiLyTgpWIn3cmp11hxyqHAORYBBj4OcfvgTX0Wi/iEhvUrAS6UXJdJoV22rID4fICwXY0dTK6MpSttU3Ewy4lOdH2VzfxMjyEmpb2vB8n9c37+Ag06kIuQ63nHsSxhhK8iKcM30C+eFQ73RKRER2UbAS6SWPL1nJ7f+cC9YSS6WwtmNphETaI+AYrAXPWsKdbY4xOAbS/kFSFYAxXDZrOsXRSM93RERE9kvBSqQXrNxew1f//tRew3mJdMdyCF3D09ttvrUcSqYKBwKcN2OCQpWISB+gYCXSCx6Yt5RkOnNrSjnG4DoGxxguPm4yt11yRsZeW0REjpyClUgPSHkec5ev4aW3NhINBXnlrY0HXXfqULnG8MJXbyKZ9iiMhIkE9W0sItJX6CeySIbFU2mu/c2DrKuppz2ZyvjrDystoigSxmiNKhGRPkfBSiTD/jxvKWuq6w5rzalD4RhDKODy9UvPUqgSEemjFKxEMuzxpasOGqoigQBThg+iPZmkKBKhNZHENYZwMEBbPEFBNEwsmcK3loJwiNZEknGDyrnh1ComDa3spZ6IiMjhUrASOQwvrFrPj594kQ019USCQVK+j+/7RIJB2pNJIsEAbYmDD/9NGlbJvTdd0QsVi4hIb1KwEjlEz69cz2fuf3TX2aikl9i17e0lElLeQe7fB0SDAa484eieKVJERLJKwUrkEP3oiRe6NW8qHAgAlvOOnshFx07JXGEiItJnKFiJHKKNtY1H9LxJQyq4+Zz30NgW511jhjOmsjSzhYmISJ+hYCVyiIYUF7C5vumwnhN0Hd591CjOmnpUD1UlIiJ9iZPtAkT6i0+ec+JhL8YZdF2uOfG4HqpIRET6Gp2xEjlE7z12Mol0mp/++yXqWttxHafzfn6WkOuS8jxcx8FisRYmD63k65eexbDSomyXLiIivUTBSmQ/tje28Od5S3lrRy3F0TDtyRThgMu33n8Os8aNJBIM4PkdwSroOiTTHkHXxWLxfJ9QQN9eIiIDjX7yi+zD8q07ue6uh0h7Pklvz5snP7tyPRcdO5nbLz2bgLt7BfTwrmFCg+tolF1EZCDST3+Rffja356iPZnaK1QBxJIpHlm8glXba7JQmYiI9GUKVjIgWWvxfQuA71us3f1+PJVm9Y7aAz4/5fm8+NaGni5TRET6GQ0FyoCSTHv89MmXePDVZbQnU0SDAWKpNAHH4BiHpOcRDrh4nUFrf4KuQ3441EtVi4hIf6FgJQPKbQ89wdwV60h0rqAe63yb9i3QMez39u1pDua8GRN7pEYREem/FKxkwNjZ1MrcN9cecnB6J9cxRIIBrIUfX/1eSvOjGa5QRET6OwUryWnJdJqXV2+iPZmiJZbAGHPwJ71DKODy36fPYtrwwRgMx48fedgLhYqIyMCg3w6Ss5Zt2s7Hfv8P0r5PMp3uHO47fK4xnDxxLEePHJLhCkVEJNcoWElOSqY9PvaHf9AcT3TrdUKuy6ShlcwYMThDlYmISC5TsJKc9OraTXiHcIYqPxQklkoRdFwcx5BIp8kLhYin0oQCLpccN4XPXnDKEQ0hiojIwKNgJTmpPZkCDhys8kJB7v/EVRw1uLx3ihIRkZynBUIlJ50wbiRpzz/gPtFQkHGVZb1UkYiIDAQKVpKTSvKjfO6CU4gEA7xzEC/gOkSCAf6/D56P42iIT0REMkdDgdJvtSWSOMYQCri0JpIUhEMk0x6+tUSDQS46bgpHjxzKw4vepLE9RnFelMa2GMNLi/jgCTMYWVaS7S6IiEiO6XawMsa4wAJgq7X2ImNMGfAXYAywAbjcWtvQ3Y8j8rb1NfXc9tCTvL5lB9ba3WedbMesKmstrtNxMjYUcPnwSTP58sVn6OyUiIj0uEwMBX4KWNHl8a3AXGvtBGBu52ORjGiNJ/jQr/7Csi3b8a3FAp5vO/5Zu6st7fukfZ/2ZIp7XlzIz596Oduli4jIANCtYGWMGQG8F/hdl+bZwD2d798DvK87H0Okq8eWriSZTnOQeyTvIZ5Kc+/Li0l5R3YrGxERkUPV3aHA/wd8ASjs0jbYWrsdwFq73RgzaF9PNMbcCNwIMGrUqG6WIbliw8Zali7dRF5hmFSJS1M8wci8Auo3NROOBPjP1nW7bpx8ONK+T2s8qfv7iYhIjzriYGWMuQiottYuNMacfrjPt9beBdwFUFVVdWT3GpGcYa3lBz96nGefW0EyBNXjHawxFO5IE9nhYcOG+olBvABwBHOloqEgRdFw5gsXERHpojtnrE4CLjHGXAhEgCJjzL3ATmPM0M6zVUOB6kwUKrnt2edW8Nx/VhJPpKk7KojvQrDVJ7zTw1hoGBU44lAVCQb49Lkn7ZrQLiIi0lOO+DeNtfZL1toR1toxwJXAM9baa4A5wLWdu10LPNztKiXnPfrYEuLxFF4YvJABY4jWehgffAdSBWa/oaosP0o0GKAsP0phJEx+KERFQR55oSDjB5XxnQ+ex+UnHN3LPRIRkYGoJ9ax+i7woDHmBmAT8MEe+BjSz+2sbuKBP89j6bJNDJ7ZwsamNiAIxmDovBmNT8f7BzhJVZYf5YWv3NQrNYuIiBxMRoKVtfY54LnO9+uAszLxupKbduxo5MaP/55YLMXQszfBrB2UmHKato7CjbsYD6wLiTKHcJOP40EgZknnAV1uhhx0Hc6fMTF7HREREXkHTTqRXveHP75Ie3sSE0kw/IztuGGfQSfUkj+yDTfkUbwuhfEsiSJDssjBOlC0IY3xwHgd1znkhYIMLSnk5nNOzHJvREREdtMtbaRXJJNp6upaKS3NZ9Hi9fi+pWhEG37awQl6OAHLtI+vpHFlCY1vFXF0cTmTJ5xB80lJhtgIsa1tBPIC+INDNKYTHDNyKOfOmEAooC9hERHpO/RbSXqUtZYH/jyP++5/mUBxgjGXryIWGQXkk2wJYpzdK20YB0qnNlI2tYljS8fxgZEnZ69wERGRI6ChQOlRjz+xjHvve5lYIslRNywjb2QLw8/chhP0aN+WR7wujH3HgugBE+I95e/NTsEiIiLdoGAlPeq++18mnkhRPKGZQH4ax4XyYxoYcc5WnKDPW/ccRdvWfGzaJWQiRJw8LhtxM8Pzxme7dBERkcOmoUDpUQ0NbQCESpJdL+hj+Fk7GHJyNbGaCO8aOYuLJl1J3GtjUHgkASeYpWpFRES6R2espEcdNX4wAK2b8sHseeciN+xTNtJn2vDplIUGMyw6TqFKRET6NQUr6VEf/9iZhMMBYjvyaFxZgpfY/SXnmgAFwRKOLtEkdRERyQ0aCpQeNXXqcH7642u4+57nWftYHgXxeopnbccNWaYXn8gZgz5A0NHNkUVEJDcYa+3B9+phVVVVdsGCBdkuQ0REROSgjDELrbVV+9qmoUARERGRDFGwEhEREckQzbGSjGtqjlFd3cSQIcXEA3VYoDI8HNN1vQUREZEcpGAlGZNOe/z4/z3B3GfepGh0jNFXrCBS7BEIuOQHirhq9OcZkXdUtssUERHpMRoKlIz53f/9h2efW4HvJBl33euEyxJYN03KJmhM1XD3utuJe23ZLlNERKTHKFhJRvi+Zc6ji0kk0pQdU7/HzZV37WM9Xm98OQvViYiI9A4NBcohqa1t4am5y6mrb2HUu9I4w3dgYlHqFw+msTrF0GkeiUQKgFBhCifo7/UaKZukJd3Q26WLiIj0GgUrOahFizbwla/9DR+PsR9ayRbTTGxJhDd/NQXrr2HM7M1sLagjXDGNeE2UlvUF+CkHN7xnuAo5YcbkT8lSL0RERHqehgLlgDzP55t3PEw8kaJwSg3FRzXjhn1W3z8eL+FSMLKdipl1uCGfsZdtxAl6NK8voGVDAV5y95dX0IQYET2KsfnTs9gbERGRnqUzVnJAa9bsJJXyAKicWYsb9kk0BUnUd9yGpuyY+l3DfiUTm5l28wq2Pj2MDXNGM+2cNINmthEKucwsPZPjy8/VkgsiIpLTFKzkgAIBBzpve+R7HWeguk5Mt96eQalgZDuTrl9DyInwvuE3cUzpqb1XrIiISJZpKFAOaNy4QRQWRQGofrUSL+EQKkyTN7QdjE/twgr89N5fRtb6TCp6V2+XKyIiklUKVnJAxhi+/Y33U1gQIbGxkrqFg/BTDhOvXk+o0CNeG2Hb3OH4KYNNuwRNmKAJccXozxFx87NdvoiISK8y1u693lBvq6qqsgsWLMh2GXIAiUSKV+atoaGhndEzgiSLtxK0EZpXVtJQF2fUxAjeoC0EnRBTio4nGijIdskiIiI9whiz0Fpbta9tmmMlhyQcDnL6aV2XSji6401l1710xZ+IiAxsGgoUERERyRAFKxEREZEM0VCg7GXtumr+9cRSmttaGXdqDDN4JyXhCqrKzqE8PCTb5YmIiPRZClayh0cfW8IvfvU0nkky7X+W0+wkcJt9XAK8Uvs4V43+PJOKZma7TBERkT5JQ4GyS2trnDt/+TSJRJrBJ20nXB7fdb8/jzQpm+Cvm3+Kb70sVyoiItI3KVjJLkuXbepYaR0oP7YeN7T3Uhxpm2JnfFNvlyYiItIvaChwAGtti/Pr3zzLM8++Sf7YBgbNbCLhDQFc/KS7z+dY6xNyIr1bqIiISD+hYDVAWWv5zOfuZ9OmOiIjGhn/kVUY12L+MQhw2fHSIPKGtu8aCgQwGMpCQygPD81e4SIiIn2YhgIHqCVLN7FtWwOplMfICzbjhnwc1zL5v9/CjaapX15C7ZIy/JTB8YKEnSiFwTI+NObWbJcuIiLSZ+mMVY5qbY2z7PXNRCJBBk/0aUxXU8YINq5M4DqG5evWkkqnAcgbHN/1vMJRbVTdvpimt4qx8RBXD/kqXl4TBYESxhVMxzH7HiIUERERBauc9OhjS7jzl08TKvAZ95E3iHrtNK+qYNX9IwmEDBOuXkvat/jOeMAlXhsmWJDe9XwnYCmd2kjIiTBl6DQCTjB7nREREelHNBSYY9au3ckvfvU0yWSaYe9dRXRIG6l2l1X3jcRPugw9czP5YxspndJIuDiJcX02PTECL7nnl0LQhDm5YrZClYiIyGFQsMoxjz6+lFTKwwR8Sqc34gQttYvLsdYAMOj4WtyQxTgw7X9WUDajgZZ1hbz1p/HYlgIAom4BZw6+gjMHX57NroiIiPQ7GgrMMS0tcXzf4oZ86MhSeDEXm+544AR3X+UXzE8z8cNrsRaMNXz7mL9i8TWPSkRE5AjpjFWOOfmkiUQiQbx4gHhNGICSyU04oY5A1by2EOvv+RxjYHTBZIwxClUiIiLdoGCVY045eSKTJw0lEgmy9i/j8BIO+SPbKJ3agBPyWP/P0XgJFz/VcQbLNR1LKcwecWOWKxcREen/jLV737akt1VVVdkFCxZku4yckU57/Of5lTz3n5VEypOMPL0Wm99MfNVwNs/PI5jvMfasRmxpHcOi4zih/HyKgmXZLltERKRfMMYstNZW7XObgpWIiIjIoTtQsNJQoIiIiEiGKFiJiIiIZIiWW8gB27c3snTZJgqLwpRNbqXdNjIqbxKVkRHZLk1ERGRAUbDqx6y1/PLXc3nk0cVEypNM+OjruKs9whEXDEwrfjcfGPlJLaEgIiLSSzQU2I+9/MpqHnt8Kcmkx+grVxAoSuJGPNIkSdskbza9yoL6udkuU0REZMBQsOrH5jyymHg8RagkQXRwDOcdRzNlE8yv+3d2ihMRERmANBTYj9Q3tPG3v73GosUbqJzexvr6RsDtuE2Nb4C9l85I2URvlykiIjJgKVj1E7W1Lfz3TXfT1pag8qStFFdtpZBy6jePIl4bwYu7uOE971UTMEFmFJ+UpYpFREQGHg0F9hP33v8yra0JbCDJyPO34IZ9Bp1QS96wdpygz+r7xuMlnF23qgk5EcpCQzil8n3ZLVxERGQA0RmrPszzfJqaYxQWRJj/2lo8z6doXBvWcwAPJ2CZfvMK6t8opWFFCa3zJnHW+RPwIi2ML5jBjOKTCDjBbHdDRERkwFCw6qP+OWchd//+efxgjLGXr6E1OAwoIN0axDi751IZF8qPaaD8mEamF7+HS0dfl7WaRUREBjoNBfZBc59Zzm/ueo7W1jgTPvoGBeMaGXbGdpyQR/v2KIn6ML6353OCJsSJFRdlp2AREREBFKz6pD/e+xKJRIrCsa2ES5I4ASg7uoFhZ2zHBCxv/Wk88Z152LRLyEQJOmEuHHY9o/MnZ7t0ERGRAU1DgX1QbW0rAOGy3UslGAMjz93G0FN20r49ytTKmcyeejkxr4Wh0XGEnHC2yhUREZFOOmPVB40bVwlA25Y8cPZcmyoQ9Sgfn2LG6CkMioxgdP4UhSoREZE+QsGqD/rYR88gHA4Q25lH01vFeEmza5uDSzRQwHGlp2etPhEREdk3Bas+aPr0Efzo+1dxzNEj2f736cQXTyLql5DvFjGz9AxuPuoHhN1otssUERGRd9Acqz5g1art/Om+l9iwuZqxZzVSfOx2gkGHS794Bu+p+B+CTijbJYqIiMghULDKskWLN3DbV/9KMpli8kdXkRrXSoPvQwLm7vwzK5rn89/jv41jdHJRRESkr9Nv6yz7+Z1PkUikKRjTSuHYVtzQ7vv9pW2SHfENrG1dmsUKRURE5FApWPWwpqZ2Nm+pJ5lKUZfYTkuqgdbWOJs219HenmDjpjoACse0Ylx/r+cn/Tgb2lb0dtkiIiJyBI54KNAYMxL4IzAE8IG7rLU/NcaUAX8BxgAbgMuttQ3dL7V/aWtL8J3vPsKChespndLEqMtWEwwZ1v59JLVLyigZm2D05atww9PxEi7J5iA27UBgz3AVNGGKguVZ6oWIiIgcju6csUoDn7PWTgHeDdxsjJkK3ArMtdZOAOZ2Ph5wvvntf7Jg4Xrc0lbGXLWSYGGKNf8cRs3SEtxImqOue4NIWYIhp+zACXrULyvF+gb7jpNWjnE4uuTk7HRCREREDssRBytr7XZr7aLO91uAFcBwYDZwT+du9wDv62aN/U5NTTNLl20ilfIYcuJOjOvjJRxqF1VgUy4VVTW7Fv4cee5WKmfVYq1hxW8nkaiLYLwAQROmNDiI/xr3DaJufpZ7JCIiIociI1cFGmPGAMcBrwKDrbXboSN8GWMG7ec5NwI3AowaNSoTZfSaHTsaee4/K0kkk4w6PkG6uAantYSaJSXE29O4FY3QOV8qUp7AcSHR6mKMxQKR0iRuqCNYGRfGvX8joy7cgteYz8UTr2b6oCosPuWhoRhj9l+IiIiI9CndDlbGmALgb8CnrbXNhxoErLV3AXcBVFVV2YPs3mc8/q+l/OzOpyCQYvLHlrOiOU7zoiLe+tNRYHwmXb+GvKJ20uljAJfG1UUUHdVCqDCFE/TxUy7N6wuprKrFjewe9wtEPaJ5CSZUTKM8PCR7HRQREZEj1q2rAo0xQTpC1X3W2r93Nu80xgzt3D4UqO5eiX1HQ0MbP7vzKZLJNINP20JkcAwcy1v3jcdPOVS+q47C0a2EitKMOHcrTsij5tVBpNsCWN8wZvamjvlUr5cSbwjjpXaH0KAJc1ThMQyLjs1iD0VERKQ7unNVoAH+D1hhrf1xl01zgGuB73a+fbhbFfYhr85fi+N0hKHKd9XhBi2NK4sxdJxwq5xVixvuOAs1/MwdRCoSbHt2KCvvnshxlyUZf0IDQyqa2fb0KKr/fAIT3ltP3uQdRIJhZpWdwwkVF2StbyIiItJ93RkKPAn4MPC6MWZJZ9uX6QhUDxpjbgA2AR/sVoVZFIsluff+l3l67nIiw5soGNNE0i8GHOzbg5emyyjmOwY0y49uoPzoBkJOhI+N/w5DomNgGnBJ79QvIiIiveuIg5W19kVgfxOqzjrS1+0rPM/nU5+5l42b6oiObGDsFW/hpxzso8cCUPNaBcPO2E7R+JZdn4Xq+ZXkDY3tOmv1tqibz+DI6F7ugYiIiPQ2rby+H/NfW8fWbQ2kUh6jZ2/CDfkE89OMv2IdJuCz7YXBtG3Lw3qGiR9ZixPyqF1aRvOaIrykA74h5EQIO3lcPfqLurpPRERkANBNmLvwPJ+1a6sJhwMse3MdsVgKgLwhsV37VM6sp/ioFuqXldK6sYCrZtxIenQtgXcXU7O0iFgszegiS6pkBwWBYqYVv4ewG81Wl0RERKQXKVh1mvfqGv6/7z2KT5oRl60ilXJwQqPwky6pliDh0uSufUNFKYacXE3Eyec9Y07efTZqZJaKFxERkT5BQ4HA1m0NfONb/6SlJc6gc9ZQPLmeipl1OAEfsGx5ehheYs9PVdCEOblytob4REREZBcFKzoW/fQ8HxxL5fE1uCGLG/aZdvMK8oe3U/NaOVvmDsMmAwQIEXTCnFhxEacNuizbpYuIiEgfMiCHAj3P59X5a5k/fx355T6L160mnfZxQj7G2b1mQt6QOEd/djmp1gAhJ8ztM++jNd1EQaCYgBPMYg9ERESkLxpwwSqd9vjCl/7CypXbCQypZ+qNq0jFS3GWjMFPOiTqw0QrE3s8J1jgcVTBRAJOkJJQRZYqFxERkb5uwA0FPj13OStXbiceTzLh6rW4YZ+KmfVEKuI4QZ/1fxuDl3SwnUtROTiEnAgXDPtIdgsXERGRPm/AnbF6eu5y4vEU4bIEwcI0AE7AMv2TK9j50iBqF5ez+R/jOf59EZziZkbmTeSUykt1Y2QRERE5qJwPVm+t3sGv73qGlau2MersampjQaAQm3YwXW5H44Z8hp2xg2Fn7GBQeCSfmvS97BUtIiIi/VJOB6sNG2v59GfvIx5PMe4D6ymZWYepLKJxXR7J5hDtO6LkDW/H6TIgGjRhZpWdm72iRUREpN/K6TlWf7r3JRKJNMGCFJVVtbhhn9KpjQw6vgYT8FnzwDjSLUG8uItLkKAJM6HwWE6oOD/bpYuIiEg/lNNnrN56awfWWqKDY/hpByfoYQyMvXQTQ07ZSfOaIkLbj+Kqs2fTbhsZmTeBodGx2S5bRERE+qmcDlYjRpSxdVsD8bpw5yrqu0UrEuRX1HFs6Qymlx2fpQpFREQkl+T0UOCHP3Qi4XCAZGOYxlXFeMk9bz/jmiCnVL4vO8WJiIhIzsnpYDV16nBu/9qlDB1SzNr7J1C/uBJ8FweHyvAIrh37FQZFRmS7TBEREckRxlp78L16WFVVlV2wYEGPvb61lng8RSgUAMfi2RQhJ9JjH09ERERylzFmobW2al/bcnqO1duMMUSjoV2PXeNmsRoRERHJVTk9FCgiIiLSmxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQxSsRERERDJEwUpEREQkQ4y1Nts1YIypATb20MtXALU99Nr9gfo/cPs/kPsO6r/6P3D7P5D7Dr3T/9HW2sp9begTwaonGWMWWGursl1Htqj/A7f/A7nvoP6r/wO3/wO575D9/msoUERERCRDFKxEREREMmQgBKu7sl1Alqn/A9dA7juo/+r/wDWQ+w5Z7n/Oz7ESERER6S0D4YyViIiISK/I6WBljDnfGLPKGLPGGHNrtuvpScaYkcaYZ40xK4wxy40xn+psv90Ys9UYs6Tz34XZrrWnGGM2GGNe7+zngs62MmPMU8aY1Z1vS7NdZ08wxkzqcoyXGGOajTGfzuXjb4y52xhTbYx5o0vbfo+3MeZLnT8LVhljzstO1Zmxn77/wBiz0hizzBjzD2NMSWf7GGNMrMvXwK+zVniG7Kf/+/1az6VjD/vt/1+69H2DMWZJZ3tOHf8D/K7rM9/7OTsUaIxxgbeAc4AtwGvAVdbaN7NaWA8xxgwFhlprFxljCoGFwPuAy4FWa+0Ps1lfbzDGbACqrLW1Xdq+D9Rba7/bGa5LrbVfzFaNvaHza38rcAJwPTl6/I0xpwKtwB+ttdM72/Z5vI0xU4EHgOOBYcDTwERrrZel8rtlP30/F3jGWps2xnwPoLPvY4BH394vF+yn/7ezj6/1XDv2sO/+v2P7j4Ama+03c+34H+B33XX0ke/9XD5jdTywxlq7zlqbBP4MzM5yTT3GWrvdWruo8/0WYAUwPLtV9QmzgXs637+Hjm/AXHcWsNZa21OL7vYJ1trngfp3NO/veM8G/mytTVhr1wNr6PgZ0S/tq+/W2iettenOh/OAEb1eWC/Zz7Hfn5w69nDg/htjDB1/UD/Qq0X1kgP8rusz3/u5HKyGA5u7PN7CAAkanX+hHAe82tn0P53DA3fn6lBYJws8aYxZaIy5sbNtsLV2O3R8QwKDslZd77mSPX+oDpTjD/s/3gPt58F/Af/q8nisMWaxMeY/xphTslVUL9jX1/pAO/anADuttau7tOXk8X/H77o+872fy8HK7KMtN8c9uzDGFAB/Az5trW0GfgWMB44FtgM/yl51Pe4ka+1M4ALg5s7T5QOKMSYEXAI81Nk0kI7/gQyYnwfGmNuANHBfZ9N2YJS19jjgs8D9xpiibNXXg/b3tT5gjn2nq9jzD6ucPP77+F2331330dajxz+Xg9UWYGSXxyOAbVmqpVcYY4J0fKHdZ639O4C1dqe11rPW+sBv6eenwA/EWrut82018A86+rqzc0z+7bH56uxV2CsuABZZa3fCwDr+nfZ3vAfEzwNjzLXARcCHbOcE2s4hkLrO9xcCa4GJ2auyZxzga31AHHsAY0wAuAz4y9ttuXj89/W7jj70vZ/Lweo1YIIxZmznX/FXAnOyXFOP6RxX/z9ghbX2x13ah3bZ7VLgjXc+NxcYY/I7JzJijMkHzqWjr3OAazt3uxZ4ODsV9po9/lodKMe/i/0d7znAlcaYsDFmLDABmJ+F+nqMMeZ84IvAJdba9i7tlZ0XNGCMGUdH39dlp8qec4Cv9Zw/9l2cDay01m55uyHXjv/+ftfRl773rbU5+w+4kI4rA9cCt2W7nh7u68l0nN5cBizp/Hch8Cfg9c72OXRcTZH1enug/+OApZ3/lr99vIFyYC6wuvNtWbZr7cHPQR5QBxR3acvZ409HgNwOpOj4q/SGAx1v4LbOnwWrgAuyXX8P9H0NHXNJ3v7+/3Xnvu/v/J5YCiwCLs52/T3U//1+refSsd9f/zvb/wDc9I59c+r4H+B3XZ/53s/Z5RZEREREelsuDwWKiIiI9CoFKxEREZEMUbASERERyRAFKxEREZEMUbASERERyRAFKxEREZEMUbASERERyRAFKxEREZEM+f8BEMcLrUk1iXgAAAAASUVORK5CYII=\n",
407 |             "text/plain": [
408 |               "<Figure size 720x648 with 1 Axes>"
409 |             ]
410 |           },
411 |           "metadata": {
412 |             "needs_background": "light"
413 |           },
414 |           "output_type": "display_data"
415 |         }
416 |       ],
417 |       "source": [
418 |         "plt.figure(figsize=(10, 9))\n",
419 |         "plt.scatter(data.iloc[:,0], data.iloc[:,3], c=cluster.labels_)"
420 |       ]
421 |     },
422 |     {
423 |       "cell_type": "code",
424 |       "execution_count": null,
425 |       "id": "42a26523",
426 |       "metadata": {
427 |         "id": "42a26523"
428 |       },
429 |       "outputs": [],
430 |       "source": []
431 |     }
432 |   ],
433 |   "metadata": {
434 |     "kernelspec": {
435 |       "display_name": "Python 3 (ipykernel)",
436 |       "language": "python",
437 |       "name": "python3"
438 |     },
439 |     "language_info": {
440 |       "codemirror_mode": {
441 |         "name": "ipython",
442 |         "version": 3
443 |       },
444 |       "file_extension": ".py",
445 |       "mimetype": "text/x-python",
446 |       "name": "python",
447 |       "nbconvert_exporter": "python",
448 |       "pygments_lexer": "ipython3",
449 |       "version": "3.9.12"
450 |     },
451 |     "colab": {
452 |       "provenance": [],
453 |       "include_colab_link": true
454 |     }
455 |   },
456 |   "nbformat": 4,
457 |   "nbformat_minor": 5
458 | }


--------------------------------------------------------------------------------