└── KMeans.ipynb


/KMeans.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyPpBSJPPlisEosrcN5cAdkP",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/NandhanaRameshkumar/KMeans/blob/main/KMeans.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "code",
 31 |       "execution_count": null,
 32 |       "metadata": {
 33 |         "id": "MfvI1jZ7PNvp"
 34 |       },
 35 |       "outputs": [],
 36 |       "source": [
 37 |         "import pandas as pd\n",
 38 |         "import numpy as np\n",
 39 |         "import matplotlib.pyplot as plt\n",
 40 |         "from sklearn.cluster import KMeans\n",
 41 |         "from sklearn.preprocessing import StandardScaler\n",
 42 |         "from sklearn.decomposition import PCA"
 43 |       ]
 44 |     },
 45 |     {
 46 |       "cell_type": "code",
 47 |       "source": [
 48 |         "data = pd.read_csv('Politics.csv')"
 49 |       ],
 50 |       "metadata": {
 51 |         "id": "PmPeRTBMUWpC"
 52 |       },
 53 |       "execution_count": null,
 54 |       "outputs": []
 55 |     },
 56 |     {
 57 |       "cell_type": "code",
 58 |       "source": [
 59 |         "print(data)"
 60 |       ],
 61 |       "metadata": {
 62 |         "colab": {
 63 |           "base_uri": "https://localhost:8080/"
 64 |         },
 65 |         "id": "oRKmfPqHmqpq",
 66 |         "outputId": "657491ef-4f8e-4d81-d44f-0d239e77f769"
 67 |       },
 68 |       "execution_count": null,
 69 |       "outputs": [
 70 |         {
 71 |           "output_type": "stream",
 72 |           "name": "stdout",
 73 |           "text": [
 74 |             "    votes,expenses,party\n",
 75 |             "0   100000,50000,Party A\n",
 76 |             "1    80000,40000,Party B\n",
 77 |             "2   120000,60000,Party A\n",
 78 |             "3    90000,45000,Party B\n",
 79 |             "4   110000,55000,Party A\n",
 80 |             "5    95000,48000,Party B\n",
 81 |             "6   130000,65000,Party A\n",
 82 |             "7    85000,42000,Party B\n",
 83 |             "8   125000,62000,Party A\n",
 84 |             "9    87000,43500,Party B\n",
 85 |             "10  105000,52000,Party A\n",
 86 |             "11   82000,41000,Party B\n",
 87 |             "12  115000,57000,Party A\n",
 88 |             "13   88000,44000,Party B\n",
 89 |             "14  135000,67000,Party A\n",
 90 |             "15   87000,43500,Party B\n",
 91 |             "16  123000,61000,Party A\n",
 92 |             "17   86000,43000,Party B\n",
 93 |             "18  118000,59000,Party A\n",
 94 |             "19   89000,44500,Party B\n"
 95 |           ]
 96 |         }
 97 |       ]
 98 |     },
 99 |     {
100 |       "cell_type": "code",
101 |       "source": [
102 |         "data = pd.DataFrame({'votes': [1, 2, 3, 4, 5], 'expenses': [100, 200, 300, 400, 500]})"
103 |       ],
104 |       "metadata": {
105 |         "id": "uAiN6OZ0VKK9"
106 |       },
107 |       "execution_count": null,
108 |       "outputs": []
109 |     },
110 |     {
111 |       "cell_type": "code",
112 |       "source": [
113 |         "print(data.head())"
114 |       ],
115 |       "metadata": {
116 |         "colab": {
117 |           "base_uri": "https://localhost:8080/"
118 |         },
119 |         "id": "jOWywDPLUicW",
120 |         "outputId": "abf52887-dc2b-4476-f4ff-d151346bc8e3"
121 |       },
122 |       "execution_count": null,
123 |       "outputs": [
124 |         {
125 |           "output_type": "stream",
126 |           "name": "stdout",
127 |           "text": [
128 |             "   votes  expenses\n",
129 |             "0      1       100\n",
130 |             "1      2       200\n",
131 |             "2      3       300\n",
132 |             "3      4       400\n",
133 |             "4      5       500\n"
134 |           ]
135 |         }
136 |       ]
137 |     },
138 |     {
139 |       "cell_type": "code",
140 |       "source": [
141 |         "features = ['votes', 'expenses']"
142 |       ],
143 |       "metadata": {
144 |         "id": "X2V1SMrKQtGY"
145 |       },
146 |       "execution_count": null,
147 |       "outputs": []
148 |     },
149 |     {
150 |       "cell_type": "code",
151 |       "source": [
152 |         "# Preprocess the data (scaling and handling missing values)\n",
153 |         "scaler = StandardScaler()\n",
154 |         "scaled_data = scaler.fit_transform(data[features])"
155 |       ],
156 |       "metadata": {
157 |         "id": "B926XIZFUsah"
158 |       },
159 |       "execution_count": null,
160 |       "outputs": []
161 |     },
162 |     {
163 |       "cell_type": "code",
164 |       "source": [
165 |         "print(scaled_data.shape)"
166 |       ],
167 |       "metadata": {
168 |         "colab": {
169 |           "base_uri": "https://localhost:8080/"
170 |         },
171 |         "id": "YLf324rSVqID",
172 |         "outputId": "ba3c41e1-9df1-41ad-c7ca-6fd411200b8a"
173 |       },
174 |       "execution_count": null,
175 |       "outputs": [
176 |         {
177 |           "output_type": "stream",
178 |           "name": "stdout",
179 |           "text": [
180 |             "(5, 2)\n"
181 |           ]
182 |         }
183 |       ]
184 |     },
185 |     {
186 |       "cell_type": "code",
187 |       "source": [
188 |         "inertia = []\n",
189 |         "for k in range(2, 5):  # Adjust the upper limit to be less than the number of samples (5)\n",
190 |         "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
191 |         "    kmeans.fit(scaled_data)\n",
192 |         "    inertia.append(kmeans.inertia_)"
193 |       ],
194 |       "metadata": {
195 |         "colab": {
196 |           "base_uri": "https://localhost:8080/"
197 |         },
198 |         "id": "5pNUgUOsVONO",
199 |         "outputId": "9cbc7ebd-3a06-45c5-cb97-16543ba3ec75"
200 |       },
201 |       "execution_count": null,
202 |       "outputs": [
203 |         {
204 |           "output_type": "stream",
205 |           "name": "stderr",
206 |           "text": [
207 |             "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
208 |             "  warnings.warn(\n",
209 |             "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
210 |             "  warnings.warn(\n",
211 |             "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
212 |             "  warnings.warn(\n"
213 |           ]
214 |         }
215 |       ]
216 |     },
217 |     {
218 |       "cell_type": "code",
219 |       "source": [
220 |         "print(type(range(1, 11)))\n",
221 |         "print(range(1, 11))\n",
222 |         "print(inertia)"
223 |       ],
224 |       "metadata": {
225 |         "colab": {
226 |           "base_uri": "https://localhost:8080/"
227 |         },
228 |         "id": "Orslk4njWWl6",
229 |         "outputId": "7faec158-85dc-46be-a10c-fdb29c596add"
230 |       },
231 |       "execution_count": null,
232 |       "outputs": [
233 |         {
234 |           "output_type": "stream",
235 |           "name": "stdout",
236 |           "text": [
237 |             "<class 'range'>\n",
238 |             "range(1, 11)\n",
239 |             "[2.499999999999999, 0.9999999999999998, 0.4999999999999999]\n"
240 |           ]
241 |         }
242 |       ]
243 |     },
244 |     {
245 |       "cell_type": "code",
246 |       "source": [
247 |         "# Plot the Elbow Method to visualize the optimal number of clusters\n",
248 |         "import matplotlib.pyplot as plt\n",
249 |         "plt.figure(figsize=(10, 6))\n",
250 |         "plt.plot(range(1, len(inertia) + 1), inertia, marker='o')\n",
251 |         "plt.xlabel('Number of Clusters')\n",
252 |         "plt.ylabel('Inertia')\n",
253 |         "plt.title('Elbow Method')\n",
254 |         "plt.xticks(range(1, len(inertia) + 1))\n",
255 |         "plt.grid(True)\n",
256 |         "plt.show()"
257 |       ],
258 |       "metadata": {
259 |         "colab": {
260 |           "base_uri": "https://localhost:8080/",
261 |           "height": 564
262 |         },
263 |         "id": "4bGoweUvWCsd",
264 |         "outputId": "3d602eb9-d13d-41af-d78a-d66ab9e01559"
265 |       },
266 |       "execution_count": null,
267 |       "outputs": [
268 |         {
269 |           "output_type": "display_data",
270 |           "data": {
271 |             "text/plain": [
272 |               "<Figure size 1000x600 with 1 Axes>"
273 |             ],
274 |             "image/png": 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\n"
275 |           },
276 |           "metadata": {}
277 |         }
278 |       ]
279 |     },
280 |     {
281 |       "cell_type": "code",
282 |       "source": [
283 |         "# Based on the Elbow Method, let's assume 2 clusters for this example\n",
284 |         "n_clusters = 2"
285 |       ],
286 |       "metadata": {
287 |         "id": "VAMSsVzqWMZ3"
288 |       },
289 |       "execution_count": null,
290 |       "outputs": []
291 |     },
292 |     {
293 |       "cell_type": "code",
294 |       "source": [
295 |         "# Apply K-means clustering\n",
296 |         "kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n",
297 |         "clusters = kmeans.fit_predict(scaled_data)"
298 |       ],
299 |       "metadata": {
300 |         "colab": {
301 |           "base_uri": "https://localhost:8080/"
302 |         },
303 |         "id": "4rduuXxJWidc",
304 |         "outputId": "abd47982-6c67-4367-a57c-2cbd6bebf1fa"
305 |       },
306 |       "execution_count": null,
307 |       "outputs": [
308 |         {
309 |           "output_type": "stream",
310 |           "name": "stderr",
311 |           "text": [
312 |             "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
313 |             "  warnings.warn(\n"
314 |           ]
315 |         }
316 |       ]
317 |     },
318 |     {
319 |       "cell_type": "code",
320 |       "source": [
321 |         "# Add the cluster labels to the original data\n",
322 |         "data['cluster'] = clusters"
323 |       ],
324 |       "metadata": {
325 |         "id": "2tCpqHg6WkP3"
326 |       },
327 |       "execution_count": null,
328 |       "outputs": []
329 |     },
330 |     {
331 |       "cell_type": "code",
332 |       "source": [
333 |         "from sklearn.decomposition import PCA"
334 |       ],
335 |       "metadata": {
336 |         "id": "4vI6JdoPWu_U"
337 |       },
338 |       "execution_count": null,
339 |       "outputs": []
340 |     },
341 |     {
342 |       "cell_type": "code",
343 |       "source": [
344 |         "# Visualize the clusters using PCA for dimensionality reduction and scatter plot\n",
345 |         "pca = PCA(n_components=2)\n",
346 |         "pca_data = pca.fit_transform(scaled_data)"
347 |       ],
348 |       "metadata": {
349 |         "id": "W0IpgfODWnBk"
350 |       },
351 |       "execution_count": null,
352 |       "outputs": []
353 |     },
354 |     {
355 |       "cell_type": "code",
356 |       "source": [
357 |         "plt.figure(figsize=(10, 6))\n",
358 |         "for cluster_label in range(n_clusters):\n",
359 |         "    plt.scatter(pca_data[clusters == cluster_label, 0], pca_data[clusters == cluster_label, 1], label=f'Cluster {cluster_label}')\n"
360 |       ],
361 |       "metadata": {
362 |         "colab": {
363 |           "base_uri": "https://localhost:8080/",
364 |           "height": 537
365 |         },
366 |         "id": "TaqKEQ_6WyTG",
367 |         "outputId": "07a8bf33-6f06-48d3-c471-c7a51ee19c27"
368 |       },
369 |       "execution_count": null,
370 |       "outputs": [
371 |         {
372 |           "output_type": "display_data",
373 |           "data": {
374 |             "text/plain": [
375 |               "<Figure size 1000x600 with 1 Axes>"
376 |             ],
377 |             "image/png": 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\n"
378 |           },
379 |           "metadata": {}
380 |         }
381 |       ]
382 |     },
383 |     {
384 |       "cell_type": "code",
385 |       "source": [
386 |         "# Visualize the clusters using PCA for dimensionality reduction and scatter plot\n",
387 |         "pca = PCA(n_components=2)\n",
388 |         "pca_data = pca.fit_transform(scaled_data)\n",
389 |         "\n",
390 |         "plt.figure(figsize=(10, 6))\n",
391 |         "for cluster_label in range(n_clusters):\n",
392 |         "    plt.scatter(pca_data[clusters == cluster_label, 0], pca_data[clusters == cluster_label, 1], label=f'Cluster {cluster_label}')\n",
393 |         "\n",
394 |         "plt.xlabel('PCA Component 1')\n",
395 |         "plt.ylabel('PCA Component 2')\n",
396 |         "plt.title('K-means Clustering')\n",
397 |         "plt.legend()\n",
398 |         "plt.grid(True)\n",
399 |         "plt.show()"
400 |       ],
401 |       "metadata": {
402 |         "colab": {
403 |           "base_uri": "https://localhost:8080/",
404 |           "height": 564
405 |         },
406 |         "id": "Hy25rJgsW3pz",
407 |         "outputId": "9ad26872-fdd6-4d46-dac5-12b92553323e"
408 |       },
409 |       "execution_count": null,
410 |       "outputs": [
411 |         {
412 |           "output_type": "display_data",
413 |           "data": {
414 |             "text/plain": [
415 |               "<Figure size 1000x600 with 1 Axes>"
416 |             ],
417 |             "image/png": 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\n"
418 |           },
419 |           "metadata": {}
420 |         }
421 |       ]
422 |     },
423 |     {
424 |       "cell_type": "code",
425 |       "source": [
426 |         "# Analyze the clusters (for demonstration purposes)\n",
427 |         "cluster_analysis = data.groupby('cluster')[features].mean()\n",
428 |         "print(cluster_analysis)"
429 |       ],
430 |       "metadata": {
431 |         "colab": {
432 |           "base_uri": "https://localhost:8080/"
433 |         },
434 |         "id": "o9aYPpi3W9nV",
435 |         "outputId": "b847a703-9bb8-4905-c401-d81da1a38d46"
436 |       },
437 |       "execution_count": null,
438 |       "outputs": [
439 |         {
440 |           "output_type": "stream",
441 |           "name": "stdout",
442 |           "text": [
443 |             "         votes  expenses\n",
444 |             "cluster                 \n",
445 |             "0          4.0     400.0\n",
446 |             "1          1.5     150.0\n"
447 |           ]
448 |         }
449 |       ]
450 |     }
451 |   ]
452 | }


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