└── k-means-cluster (1).ipynb
/k-means-cluster (1).ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "id": "95f5b1f4",
7 | "metadata": {},
8 | "outputs": [],
9 | "source": [
10 | "import numpy as np\n",
11 | "import pandas as pd\n",
12 | "import matplotlib.pyplot as plt\n",
13 | "from sklearn.cluster import KMeans"
14 | ]
15 | },
16 | {
17 | "cell_type": "code",
18 | "execution_count": 2,
19 | "id": "32bcf9ce",
20 | "metadata": {
21 | "scrolled": true
22 | },
23 | "outputs": [
24 | {
25 | "data": {
26 | "text/html": [
27 | "
\n",
28 | "\n",
41 | "
\n",
42 | " \n",
43 | " \n",
44 | " | \n",
45 | " Country | \n",
46 | " Latitude | \n",
47 | " Longitude | \n",
48 | " Language | \n",
49 | "
\n",
50 | " \n",
51 | " \n",
52 | " \n",
53 | " | 0 | \n",
54 | " USA | \n",
55 | " 44.97 | \n",
56 | " -103.77 | \n",
57 | " English | \n",
58 | "
\n",
59 | " \n",
60 | " | 1 | \n",
61 | " Canada | \n",
62 | " 62.40 | \n",
63 | " -96.80 | \n",
64 | " English | \n",
65 | "
\n",
66 | " \n",
67 | " | 2 | \n",
68 | " France | \n",
69 | " 46.75 | \n",
70 | " 2.40 | \n",
71 | " French | \n",
72 | "
\n",
73 | " \n",
74 | " | 3 | \n",
75 | " UK | \n",
76 | " 54.01 | \n",
77 | " -2.53 | \n",
78 | " English | \n",
79 | "
\n",
80 | " \n",
81 | " | 4 | \n",
82 | " Germany | \n",
83 | " 51.15 | \n",
84 | " 10.40 | \n",
85 | " German | \n",
86 | "
\n",
87 | " \n",
88 | " | 5 | \n",
89 | " Australia | \n",
90 | " -25.45 | \n",
91 | " 133.11 | \n",
92 | " English | \n",
93 | "
\n",
94 | " \n",
95 | "
\n",
96 | "
"
97 | ],
98 | "text/plain": [
99 | " Country Latitude Longitude Language\n",
100 | "0 USA 44.97 -103.77 English\n",
101 | "1 Canada 62.40 -96.80 English\n",
102 | "2 France 46.75 2.40 French\n",
103 | "3 UK 54.01 -2.53 English\n",
104 | "4 Germany 51.15 10.40 German\n",
105 | "5 Australia -25.45 133.11 English"
106 | ]
107 | },
108 | "execution_count": 2,
109 | "metadata": {},
110 | "output_type": "execute_result"
111 | }
112 | ],
113 | "source": [
114 | "data = pd.read_csv(\"D:\\\\Jeyashri\\\\IBM\\\\Datasets\\\\Country clusters.csv\")\n",
115 | "data"
116 | ]
117 | },
118 | {
119 | "cell_type": "code",
120 | "execution_count": 3,
121 | "id": "bab378b4",
122 | "metadata": {},
123 | "outputs": [
124 | {
125 | "data": {
126 | "image/png": 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\n",
127 | "text/plain": [
128 | ""
129 | ]
130 | },
131 | "metadata": {
132 | "needs_background": "light"
133 | },
134 | "output_type": "display_data"
135 | }
136 | ],
137 | "source": [
138 | "plt.scatter(data['Longitude'],data['Latitude'],color='orange')\n",
139 | "plt.xlim(-180,180)\n",
140 | "plt.ylim(-90,90)\n",
141 | "plt.xlabel(\"Longitude\")\n",
142 | "plt.ylabel(\"Latitude\")\n",
143 | "plt.show()\n"
144 | ]
145 | },
146 | {
147 | "cell_type": "code",
148 | "execution_count": 4,
149 | "id": "268b883c",
150 | "metadata": {},
151 | "outputs": [
152 | {
153 | "data": {
154 | "text/html": [
155 | "\n",
156 | "\n",
169 | "
\n",
170 | " \n",
171 | " \n",
172 | " | \n",
173 | " Latitude | \n",
174 | " Longitude | \n",
175 | "
\n",
176 | " \n",
177 | " \n",
178 | " \n",
179 | " | 0 | \n",
180 | " 44.97 | \n",
181 | " -103.77 | \n",
182 | "
\n",
183 | " \n",
184 | " | 1 | \n",
185 | " 62.40 | \n",
186 | " -96.80 | \n",
187 | "
\n",
188 | " \n",
189 | " | 2 | \n",
190 | " 46.75 | \n",
191 | " 2.40 | \n",
192 | "
\n",
193 | " \n",
194 | " | 3 | \n",
195 | " 54.01 | \n",
196 | " -2.53 | \n",
197 | "
\n",
198 | " \n",
199 | " | 4 | \n",
200 | " 51.15 | \n",
201 | " 10.40 | \n",
202 | "
\n",
203 | " \n",
204 | " | 5 | \n",
205 | " -25.45 | \n",
206 | " 133.11 | \n",
207 | "
\n",
208 | " \n",
209 | "
\n",
210 | "
"
211 | ],
212 | "text/plain": [
213 | " Latitude Longitude\n",
214 | "0 44.97 -103.77\n",
215 | "1 62.40 -96.80\n",
216 | "2 46.75 2.40\n",
217 | "3 54.01 -2.53\n",
218 | "4 51.15 10.40\n",
219 | "5 -25.45 133.11"
220 | ]
221 | },
222 | "execution_count": 4,
223 | "metadata": {},
224 | "output_type": "execute_result"
225 | }
226 | ],
227 | "source": [
228 | "x = data.iloc[:,1:3] # 1t for rows and second for columns\n",
229 | "x"
230 | ]
231 | },
232 | {
233 | "cell_type": "code",
234 | "execution_count": 10,
235 | "id": "b343e1cc",
236 | "metadata": {},
237 | "outputs": [
238 | {
239 | "data": {
240 | "text/plain": [
241 | "KMeans(n_clusters=3)"
242 | ]
243 | },
244 | "execution_count": 10,
245 | "metadata": {},
246 | "output_type": "execute_result"
247 | }
248 | ],
249 | "source": [
250 | "kmeans = KMeans(3)\n",
251 | "kmeans.fit(x)"
252 | ]
253 | },
254 | {
255 | "cell_type": "code",
256 | "execution_count": 11,
257 | "id": "964016f5",
258 | "metadata": {},
259 | "outputs": [
260 | {
261 | "data": {
262 | "text/plain": [
263 | "array([2, 2, 0, 0, 0, 1])"
264 | ]
265 | },
266 | "execution_count": 11,
267 | "metadata": {},
268 | "output_type": "execute_result"
269 | }
270 | ],
271 | "source": [
272 | "identified_clusters = kmeans.fit_predict(x)\n",
273 | "identified_clusters"
274 | ]
275 | },
276 | {
277 | "cell_type": "code",
278 | "execution_count": 12,
279 | "id": "0b039cf4",
280 | "metadata": {},
281 | "outputs": [],
282 | "source": [
283 | "data_with_clusters = data.copy()"
284 | ]
285 | },
286 | {
287 | "cell_type": "code",
288 | "execution_count": 13,
289 | "id": "240d3ab3",
290 | "metadata": {},
291 | "outputs": [
292 | {
293 | "data": {
294 | "text/html": [
295 | "\n",
296 | "\n",
309 | "
\n",
310 | " \n",
311 | " \n",
312 | " | \n",
313 | " Country | \n",
314 | " Latitude | \n",
315 | " Longitude | \n",
316 | " Language | \n",
317 | "
\n",
318 | " \n",
319 | " \n",
320 | " \n",
321 | " | 0 | \n",
322 | " USA | \n",
323 | " 44.97 | \n",
324 | " -103.77 | \n",
325 | " English | \n",
326 | "
\n",
327 | " \n",
328 | " | 1 | \n",
329 | " Canada | \n",
330 | " 62.40 | \n",
331 | " -96.80 | \n",
332 | " English | \n",
333 | "
\n",
334 | " \n",
335 | " | 2 | \n",
336 | " France | \n",
337 | " 46.75 | \n",
338 | " 2.40 | \n",
339 | " French | \n",
340 | "
\n",
341 | " \n",
342 | " | 3 | \n",
343 | " UK | \n",
344 | " 54.01 | \n",
345 | " -2.53 | \n",
346 | " English | \n",
347 | "
\n",
348 | " \n",
349 | " | 4 | \n",
350 | " Germany | \n",
351 | " 51.15 | \n",
352 | " 10.40 | \n",
353 | " German | \n",
354 | "
\n",
355 | " \n",
356 | " | 5 | \n",
357 | " Australia | \n",
358 | " -25.45 | \n",
359 | " 133.11 | \n",
360 | " English | \n",
361 | "
\n",
362 | " \n",
363 | "
\n",
364 | "
"
365 | ],
366 | "text/plain": [
367 | " Country Latitude Longitude Language\n",
368 | "0 USA 44.97 -103.77 English\n",
369 | "1 Canada 62.40 -96.80 English\n",
370 | "2 France 46.75 2.40 French\n",
371 | "3 UK 54.01 -2.53 English\n",
372 | "4 Germany 51.15 10.40 German\n",
373 | "5 Australia -25.45 133.11 English"
374 | ]
375 | },
376 | "execution_count": 13,
377 | "metadata": {},
378 | "output_type": "execute_result"
379 | }
380 | ],
381 | "source": [
382 | "data_with_clusters"
383 | ]
384 | },
385 | {
386 | "cell_type": "code",
387 | "execution_count": 14,
388 | "id": "28e511f5",
389 | "metadata": {},
390 | "outputs": [
391 | {
392 | "data": {
393 | "text/plain": [
394 | ""
395 | ]
396 | },
397 | "execution_count": 14,
398 | "metadata": {},
399 | "output_type": "execute_result"
400 | },
401 | {
402 | "data": {
403 | "image/png": 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bXoq3R8S91Z+ze/5cnWhdlKZfxtlOAt+IiLURsbxqm52ZWwGqx/puIHpkmmi8fbld9NSdoSLim8ApbWa9MzNvmehtbdryAO094UDrgtZhqPfTGs/7gY8Ay+jxMU9Cv4yznXMzc0tEzAJWR8T9TRd0BOnL7aKnQj4zX3MYb9sMe91bax6wpWqf16a9JxzquoiIfwK+Wk1OtC5K0y/j3E9mbqket0XEzbQORzwSEXMyc2t1mHJbo0V230Tj7cvtoh8O16wClkTEjIg4jdYHrHdVf8Y9HhGvqs6q+QNgor8Gekq1Ye/xOlofSsME62Kq65sC3wMWRsRpETGd1odtqxquqesi4tiIOH7Pc+B3af3brwKWVi9bSiHb+QFMNN5+2f730lN78gcSEa8DPgEMAf8REfdk5gWZuS4ibgLuA8aAt2Xmruptf0zr0/nnALdVPyX4m4h4Ka0/RTcBbwU4yLooRmaORcTbga8DA8ANmbmu4bKmwmzg5upM4EHgC5n5tYj4HnBTRLwZeAi4vMEaaxUR/wqcB8yMiM3Ae4AP0Wa8/bL978vLGkhSwfrhcI0k9S1DXpIKZshLUsEMeUkqmCEvSQUz5CWpYIa8JBXs/wHHLQqqydboJAAAAABJRU5ErkJggg==\n",
404 | "text/plain": [
405 | ""
406 | ]
407 | },
408 | "metadata": {
409 | "needs_background": "light"
410 | },
411 | "output_type": "display_data"
412 | }
413 | ],
414 | "source": [
415 | "data_with_clusters['C'] = identified_clusters \n",
416 | "plt.scatter(data_with_clusters['Longitude'],data_with_clusters['Latitude'],c=data_with_clusters['C'],cmap='rainbow')"
417 | ]
418 | },
419 | {
420 | "cell_type": "code",
421 | "execution_count": 36,
422 | "id": "1e41fed6",
423 | "metadata": {},
424 | "outputs": [
425 | {
426 | "data": {
427 | "text/html": [
428 | "\n",
429 | "\n",
442 | "
\n",
443 | " \n",
444 | " \n",
445 | " | \n",
446 | " Country | \n",
447 | " Latitude | \n",
448 | " Longitude | \n",
449 | " Language | \n",
450 | " C | \n",
451 | "
\n",
452 | " \n",
453 | " \n",
454 | " \n",
455 | " | 0 | \n",
456 | " USA | \n",
457 | " 44.97 | \n",
458 | " -103.77 | \n",
459 | " English | \n",
460 | " 0 | \n",
461 | "
\n",
462 | " \n",
463 | " | 1 | \n",
464 | " Canada | \n",
465 | " 62.40 | \n",
466 | " -96.80 | \n",
467 | " English | \n",
468 | " 0 | \n",
469 | "
\n",
470 | " \n",
471 | " | 2 | \n",
472 | " France | \n",
473 | " 46.75 | \n",
474 | " 2.40 | \n",
475 | " French | \n",
476 | " 1 | \n",
477 | "
\n",
478 | " \n",
479 | " | 3 | \n",
480 | " UK | \n",
481 | " 54.01 | \n",
482 | " -2.53 | \n",
483 | " English | \n",
484 | " 1 | \n",
485 | "
\n",
486 | " \n",
487 | " | 4 | \n",
488 | " Germany | \n",
489 | " 51.15 | \n",
490 | " 10.40 | \n",
491 | " German | \n",
492 | " 1 | \n",
493 | "
\n",
494 | " \n",
495 | " | 5 | \n",
496 | " Australia | \n",
497 | " -25.45 | \n",
498 | " 133.11 | \n",
499 | " English | \n",
500 | " 2 | \n",
501 | "
\n",
502 | " \n",
503 | "
\n",
504 | "
"
505 | ],
506 | "text/plain": [
507 | " Country Latitude Longitude Language C\n",
508 | "0 USA 44.97 -103.77 English 0\n",
509 | "1 Canada 62.40 -96.80 English 0\n",
510 | "2 France 46.75 2.40 French 1\n",
511 | "3 UK 54.01 -2.53 English 1\n",
512 | "4 Germany 51.15 10.40 German 1\n",
513 | "5 Australia -25.45 133.11 English 2"
514 | ]
515 | },
516 | "execution_count": 36,
517 | "metadata": {},
518 | "output_type": "execute_result"
519 | }
520 | ],
521 | "source": [
522 | "data_with_clusters"
523 | ]
524 | },
525 | {
526 | "cell_type": "code",
527 | "execution_count": 8,
528 | "id": "6241c343",
529 | "metadata": {},
530 | "outputs": [
531 | {
532 | "name": "stderr",
533 | "output_type": "stream",
534 | "text": [
535 | "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
536 | " warnings.warn(\n",
537 | "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
538 | " warnings.warn(\n",
539 | "C:\\Users\\Lenovo\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
540 | " warnings.warn(\n"
541 | ]
542 | },
543 | {
544 | "data": {
545 | "image/png": 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\n",
546 | "text/plain": [
547 | ""
548 | ]
549 | },
550 | "metadata": {
551 | "needs_background": "light"
552 | },
553 | "output_type": "display_data"
554 | }
555 | ],
556 | "source": [
557 | "individual_clustering_score=[]\n",
558 | "for i in range(1,4):\n",
559 | " kmeans=KMeans(n_clusters=i,init='random',random_state=42)\n",
560 | " kmeans.fit(x)\n",
561 | " individual_clustering_score.append(kmeans.inertia_)\n",
562 | "plt.plot(range(1,4),individual_clustering_score)\n",
563 | "plt.title(\"ELBOW\")\n",
564 | "plt.show()"
565 | ]
566 | }
567 | ],
568 | "metadata": {
569 | "kernelspec": {
570 | "display_name": "Python 3 (ipykernel)",
571 | "language": "python",
572 | "name": "python3"
573 | },
574 | "language_info": {
575 | "codemirror_mode": {
576 | "name": "ipython",
577 | "version": 3
578 | },
579 | "file_extension": ".py",
580 | "mimetype": "text/x-python",
581 | "name": "python",
582 | "nbconvert_exporter": "python",
583 | "pygments_lexer": "ipython3",
584 | "version": "3.9.12"
585 | }
586 | },
587 | "nbformat": 4,
588 | "nbformat_minor": 5
589 | }
590 |
--------------------------------------------------------------------------------