{
"cells": [
{
"cell_type": "code",
"execution_count": 21,
"id": "ba837a2c",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.cluster import KMeans\n",
"import pandas as pd\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"from kneed import KneeLocator"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "dbef6d56",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/Users/mohi9282/Desktop/iNeuron/Machine Learning/Projects/waferFaultDetection/waferClassifier'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"os.getcwd()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e6c82a4e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>Sensor - 1</th>\n",
" <th>Sensor - 2</th>\n",
" <th>Sensor - 3</th>\n",
" <th>Sensor - 4</th>\n",
" <th>Sensor - 5</th>\n",
" <th>Sensor - 7</th>\n",
" <th>Sensor - 8</th>\n",
" <th>Sensor - 9</th>\n",
" <th>Sensor - 10</th>\n",
" <th>...</th>\n",
" <th>Sensor - 581</th>\n",
" <th>Sensor - 582</th>\n",
" <th>Sensor - 583</th>\n",
" <th>Sensor - 584</th>\n",
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" <th>Sensor - 590</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>0</td>\n",
" <td>3073.48</td>\n",
" <td>2467.18</td>\n",
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" <td>1121.1875</td>\n",
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" <td>0.004133</td>\n",
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" <td>0.0065</td>\n",
" <td>0.0022</td>\n",
" <td>1.2845</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>3027.61</td>\n",
" <td>2430.03</td>\n",
" <td>2219.7667</td>\n",
" <td>2086.4710</td>\n",
" <td>1.3381</td>\n",
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" <td>1.4090</td>\n",
" <td>0.0160</td>\n",
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" <td>0.003000</td>\n",
" <td>50.177800</td>\n",
" <td>0.4984</td>\n",
" <td>0.0130</td>\n",
" <td>0.0033</td>\n",
" <td>2.6161</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2950.97</td>\n",
" <td>2533.95</td>\n",
" <td>2249.2556</td>\n",
" <td>2065.0624</td>\n",
" <td>2.1216</td>\n",
" <td>95.7967</td>\n",
" <td>0.1222</td>\n",
" <td>1.3418</td>\n",
" <td>-0.0112</td>\n",
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" <td>0.005633</td>\n",
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" <td>0.5062</td>\n",
" <td>0.0117</td>\n",
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" <td>2.3019</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>3093.12</td>\n",
" <td>2500.90</td>\n",
" <td>2219.7667</td>\n",
" <td>2086.4710</td>\n",
" <td>1.3381</td>\n",
" <td>98.8900</td>\n",
" <td>0.1234</td>\n",
" <td>1.4117</td>\n",
" <td>0.0087</td>\n",
" <td>...</td>\n",
" <td>0.010500</td>\n",
" <td>106.558200</td>\n",
" <td>0.5038</td>\n",
" <td>0.0133</td>\n",
" <td>0.0040</td>\n",
" <td>2.6341</td>\n",
" <td>0.0292</td>\n",
" <td>0.0311</td>\n",
" <td>0.0105</td>\n",
" <td>106.5582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2988.76</td>\n",
" <td>2497.58</td>\n",
" <td>2153.9778</td>\n",
" <td>1192.6994</td>\n",
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" <td>-0.0074</td>\n",
" <td>...</td>\n",
" <td>0.007000</td>\n",
" <td>76.751000</td>\n",
" <td>0.5001</td>\n",
" <td>0.0099</td>\n",
" <td>0.0025</td>\n",
" <td>1.9807</td>\n",
" <td>0.0282</td>\n",
" <td>0.0217</td>\n",
" <td>0.0070</td>\n",
" <td>76.7510</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 475 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Sensor - 1 Sensor - 2 Sensor - 3 Sensor - 4 Sensor - 5 \\\n",
"0 0 3073.48 2467.18 2200.2000 1121.1875 1.3171 \n",
"1 1 3027.61 2430.03 2219.7667 2086.4710 1.3381 \n",
"2 2 2950.97 2533.95 2249.2556 2065.0624 2.1216 \n",
"3 3 3093.12 2500.90 2219.7667 2086.4710 1.3381 \n",
"4 4 2988.76 2497.58 2153.9778 1192.6994 1.3522 \n",
"\n",
" Sensor - 7 Sensor - 8 Sensor - 9 Sensor - 10 ... Sensor - 581 \\\n",
"0 103.8978 0.1191 1.4015 0.0095 ... 0.004133 \n",
"1 98.8900 0.1234 1.4090 0.0160 ... 0.003000 \n",
"2 95.7967 0.1222 1.3418 -0.0112 ... 0.005633 \n",
"3 98.8900 0.1234 1.4117 0.0087 ... 0.010500 \n",
"4 100.9367 0.1225 1.4590 -0.0074 ... 0.007000 \n",
"\n",
" Sensor - 582 Sensor - 583 Sensor - 584 Sensor - 585 Sensor - 586 \\\n",
"0 64.582000 0.5046 0.0065 0.0022 1.2845 \n",
"1 50.177800 0.4984 0.0130 0.0033 2.6161 \n",
"2 100.867867 0.5062 0.0117 0.0033 2.3019 \n",
"3 106.558200 0.5038 0.0133 0.0040 2.6341 \n",
"4 76.751000 0.5001 0.0099 0.0025 1.9807 \n",
"\n",
" Sensor - 587 Sensor - 588 Sensor - 589 Sensor - 590 \n",
"0 0.0267 0.0174 0.0050 65.1609 \n",
"1 0.0267 0.0174 0.0050 65.1609 \n",
"2 0.0267 0.0174 0.0050 65.1609 \n",
"3 0.0292 0.0311 0.0105 106.5582 \n",
"4 0.0282 0.0217 0.0070 76.7510 \n",
"\n",
"[5 rows x 475 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv('Data_Preprocessing/clean_X.csv')\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "3a93f98d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(837, 474)\n"
]
}
],
"source": [
"X = data.drop('Unnamed: 0', axis=1)\n",
"print(X.shape)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "a18bd0ce",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[84331035818.0085,\n",
" 44028817403.003654,\n",
" 34181715443.897434,\n",
" 27505250479.50228,\n",
" 24487238145.79915,\n",
" 21857237993.667805,\n",
" 19913967751.71556,\n",
" 18149083253.730568,\n",
" 16667110417.11506,\n",
" 15501355292.352467]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"wcss = []\n",
"for i in range(1,11):\n",
" kmeans = KMeans(n_clusters=i, init='k-means++', random_state=42)\n",
" pred = kmeans.fit(X)\n",
" wcss.append(pred.inertia_)\n",
" \n",
"wcss"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "e90ea461",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'WCSS')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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dGYzbTwGeAjCzstArS2D1tTE6u3p5rqEl6lJERAZtoMD/NNBMfBz/anc/FCw/B7hzED/fgafMbKWZ3XKiFczsFjNbYWYrmpqS4yzWmZMqycvO0LCOiCSVgQK/CHjM3e9w95f6LW8jfkB3ILPd/WLiY/63BZdnOIa7z3f3Onevq66uHnThUcrLzmT25CoW6KYoIpJEBgr8HwBVJ1g+GvjeQD/c3XcFXxuBh4FLT7fARFVfG2P73g42Nx2MuhQRkUEZKPDf4e6Lj1/o7k8C55/qG82s0MyK+54DVwNr3m6hiaZe0zNFJMkMFPjZb/M9gBHAUjN7Cfgj8Ht3H8wwUFIYXZZPzYhiFijwRSRJDHQBtE1m9m53f7z/QjN7F9Bwqm909wbggjOsL6HV18b46TMNtHV2UZI3UP8nIhKtgQL/88DvzezDwMpgWR0wk0GceJXq5tXG+NHizSzd1My73zEy6nJERE5poCGd9wCfBJYB44PHYuB8d98Ycm0J7+JxZZTkZWkcX0SSwkBb+GOADwNnAy8BzwJ7gAKgM9zSEl9WZgZzplWzcEMTvb1ORoZFXZKIyEkNdHnkL7n7LOIHYL8G7AU+Aawxs1eHob6EN682RnP7YdbsSrvbA4hIkhnsffrygRKgNHjsAp4Pq6hkcuW0aszQbB0RSXgDXQ9/vpktA35F/EDts8CHgjNjPz4cBSa6yqJcLhxbpnF8EUl4A23hjwNygTeAncAOQDd0Pc68mhgv7dhP04HDUZciInJSA43hXwNcwpsXSvsi8IKZPWVmfx92ccmi76zbxRuT4+JvIpKeBhzD97g1wOPAfxGfojkZuCPk2pLGuaNKiBXnalhHRBLaQGP4nzOzB8xsO7CE+MlWG4APAhXDUF9S6LspypKNTXT1DPa+MCIiw2ugLfwJwIPApe4+yd1vdPe73P0ld1ey9VNfG+PA4W6eWPNG1KWIiJzQKU+8cve/Hq5Ckt2V06qpPauYz/9qNa0dXdw4Y3zUJYmIHGOw8/BlAPk5mfzmMzO5clo1f/fIGr7+6Bq6NbwjIglEgT+EivOy+clNddwyZxI/X76Vj9/7Avs7uqIuS0QEUOAPucwM42vvPptv/dn5PNfQwgfvWsaWZt0VS0Sip8APyYcvGcsvP3kZew8e4bq7lrF8c0vUJYlImlPgh2jGpEoeuW02VUW53Hj389z/x21RlyQiaUyBH7LxlYU8dOssZk2p4qsPvcI/PPYqPb0edVkikoYU+MOgJC+bez5Wx8dnT+CeZa/zyZ+/wIFOHcwVkeEVeuCbWaaZvWhmvwu7rUSWlZnB1993Lt/4wHks3dTMB+96lm0th6IuS0TSyHBs4d8BrBuGdpLCRy8bzy8+cSmNBw5z3V3L+OPre6MuSUTSRKiBb2ZjiN8X96dhtpNsZk2p4pHbZlOWn81Hf/ocv1mxPeqSRCQNhL2F/13gK4BOOT3OxKpCHr51NpdNrOTLD77MPz2+TgdzRSRUoQW+mb0XaHT3lQOsd4uZrTCzFU1N6XU9+dKCbH728Uu4ccZ4frykgb/85QraD3dHXZaIpKgwt/BnA+83sy3AA8A8M/v341dy9/nBLRPrqqurQywnMWVnZvCP153HP1x7Lgs3NHH9vz3Ljn06mCsiQy+0wHf3r7r7GHefAHwEWODufxFWe8nuppkT+NnNl7CztYPrfriMlVt1MFdEhpbm4SeQOdOqefjW2RTlZnHD/Od5aNWOqEsSkRQyLIHv7ovc/b3D0VaymxIr4pHbZjN9fDl//euX+NYT6+nVwVwRGQLawk9AZQU5/OKTl3LDpWO5a9Fm/uq+lRzUwVwROUMK/ASVnZnBNz/wDv7Xe8/h6Vf38KEfLWdXa0fUZYlIElPgJzAz4xOXT+Tumy9h295DvP9fl/Hitn1RlyUiSUqBnwTqa2I8dOss8nMy+PP5z/Ho6p1RlyQiSUiBnySmjSjm0dsu58IxZdzxwGq+8/RGHcwVkdOiwE8iFYU5/PJTl/Kh6WP4/h82cfv9L9JxpCfqskQkSWRFXYCcntysTL51/flMHVHEP/3XerbtPcRPbqrjrNK8qEsTkQSnLfwkZGbcMmcyP72pjoamdq794VJe3tEadVkikuAU+EnsqrNH8NtbZ5GVkcGHf7yc37+8O+qSRCSBKfCTXO1ZJTz62dmcO6qU2/5jFd//wybcdTBXRN5KgZ8Cqopyue9Tl/HBi0bznac38rkHVrP/kO6ZKyLHUuCniLzsTP7lwxfwlWtq+N3Lu7jyzoXcu+x1unp07xkRiVPgpxAz49a5U/j97Vdw9lkl/O/HXuWa7y5h4fpGDfOIiAI/FZ0zqoT/+PRlzL9xOj29zsfvfYGb7vkjG944EHVpIhIhBX6KMjOuPvcsnvrClfzte87mpe2tvOt7S/ifD79CS/vhqMsTkQgo8FNcTlYGn7piEou/XM+NM8bzwAvbmfvtRcxfspnD3TpLVySdKPDTRHlhDn9/7Xk8+fkrqJtQzjcfX8/V/28JT6zZrfF9kTShwE8zU2LF/Ozjl/LzT1xKTmYGn/n3VXxk/nOs2bk/6tJEJGQK/DR15bRq/uuOK/jH685jU2M77/vXpXz5Ny/R2NYZdWkiEhIFfhrLyszgxhnjWfiluXz6ikk8snonc+9cxA/+sInOLo3vi6Sa0ALfzPLM7I9m9pKZrTWzvw+rLTkzpfnZfO3dZ/Pff30lc6ZW8y9Pb2TenYt4dPVOje+LpJAwt/APA/Pc/QLgQuAaM5sRYntyhsZXFvKjG6fzwC0zKC/M4Y4HVvOBu55llW6rKJISQgt8j2sPXmYHD20uJoEZkyp57LOX8+3rz2dnawcfvOtZPnf/i+zUTdRFkpqFuctuZpnASmAK8EN3/x8nWOcW4BaAcePGTd+6dWto9cjpO3i4mx8t3sz8JQ0AfPqKSfzV3MkU5ureOSKJwMxWunvdoNYdjjFaMysDHgZud/c1J1uvrq7OV6xYEXo9cvp2tnbwrSfW8+jqXVQX5/Llq2v4s+ljyMywqEsTSWunE/jDMkvH3VuBRcA1w9GeDL3RZfl87yMX8fCtsxhTns9Xfvsy7/vBUpZvbom6NBEZpDBn6VQHW/aYWT7wJ8D6sNqT4XHRuHIe+qtZfP+Gi9jf0cUNP3mOv/zlCrY0H4y6NBEZQJgDsSOBnwfj+BnAr939dyG2J8PEzHj/BaO4+pwR3L30dX648DUWrF/MzbMm8Nl5UynNz466RBE5gWEZwx8sjeEnp8a2Tu58agO/WbmD8oIcvvDOadxwyViyMnVen0jYEm4MX1JbrCSPb11/AY999nKmjSji7x5Zw7u//wxPrn2DjiM6Y1ckUWgLX4aUu/PUq3v45uPr2NpyiOxM46Jx5cycVMmsyZVcOK6M3KzMqMsUSRkJNy1zsBT4qeNIdy/Pbm5meUMLyze3sGbnfnod8rIzqBtfwczJlcycXMn5o0s19CNyBk4n8HX2jIQiJyuDuTUx5tbEANjf0cUfX98b7wQ2t/DtJzcAUJSbxSUTypk1uYqZkys5e2SJ5vaLhESBL8OiND+bd54zgneeMwKAlvbDPNewl+UNzTy7uYWFG9YdXW/GpIr4ENCUKqbGijBTByAyFBT4EonKolzec/5I3nP+SAD2tHWyfHPL0WGgJ9fuAaCqKIcZkyqP7gFMqCxQByDyNmkMXxLS9r2HWL65heUN8U5gT1v8xusjS/OYOany6DGAMeUFEVcqEi2N4UvSG1tRwNiKAj58yVjcndebD/Ls5vgB4EUbm3joxZ0AjKsoYFYQ/jMnVRIryYu4cpHEpS18STq9vc7GxgPBEFALzzW0cKCzG4DJ1YXMmlzFrMmVXDapkorCnIirFQmXpmVKWunpdV7d1cazm+MHgF/YspdDwQlfZ48s4bKJFVw8vpyLx5UxuixfxwAkpSjwJa119fTy8o5Wlm9uYdlrLby4fR+dXb0AjCjJ5eJx5fHH+DLOHVVKXrZOBJPkpcAX6aerp5f1uw+watu+o4/te+N378rJzODc0SXHdAIjS/Mjrlhk8BT4IgNoPNDJqq2tvBh0AC/v2M/h7vhewMjSvCD848NA544qJSdLZwNLYtIsHZEBxIrzuOa8s7jmvLOA+KUg1u1uY+XWeAfw4rZWfv/KbiB+1vA7Rpdy8bgypo+P7wloNpAkI23hi5zEnrZOVgUdwMqt+1izs40jPfG9gNFl+Uf3AC4eV845o0rI1jWBJAIa0hEJweHuHtbuamPV1vgewKpt+9i9vxOIXxTu/NFlXDS+7OjxgOri3IgrlnSgwBcZJrtaO+IHgrfGO4C1u/bT1RP/mxpbkc/0o8cCyqk5q1h7ATLkNIYvMkxGleUzqiyf954/CoDOrh7W7tofPxawtZVnN7fwyOpdAGRlGBOrCpk6oogpsWKmxoqYNqKYCVUFukeADAsFvsgQysvOZPr4CqaPrwDiN4TZ2drBqm2trN/dxqbGdtbtPsATa96gN9i5zswwxlcWMC1WHHQGRUyNFTOpulDnCMiQUuCLhMjMGFNewJjyAt5/waijyzu7emhoOsimxgO81tjOpj3tbGw8wNPr9tAT9AQZBuMrC4MOIL43MCVWxOTqIvJz1BHI6Qst8M1sLPAL4CygF5jv7t8Lqz2RZJKXnck5o0o4Z1TJMcsPd/ewpfkQmxoPsHFPO681HmDTnnYWrm+kO+gIzGBseQHT+g0N9e0ZFORoG05OLsz/Hd3AF919lZkVAyvN7Gl3fzXENkWSWm5WJjVnFVNzVvExy7t6etnSfJBNwd7ApqAjWLyx6ehBYoAx5flBB1B89OuUWBFFueoIJMTAd/fdwO7g+QEzWweMBhT4IqcpOzMjHuIjiuEdby7v7ull695D8U5gz4F4h9DYzrLNLRwJzhwGGFWad7QTmFRdxKTqQiZVFVJdnKuLyaWRYZmWaWYTgCXAee7edtx7twC3AIwbN2761q1bQ69HJNV19/SyfV/H0U7gtcZ2Nu6JHy843K8jKMrNYmJVIROrCplUHXytKmJidaH2CpJEQs3DN7MiYDHwDXd/6FTrah6+SLh6e51d+zt4vfkgDU0H41+bD9LQ1M7O1g76x0GsODfoBIqY1K9DGFtRoPMJEkjCzMM3s2zgt8B9A4W9iIQvI+PNWUNXTK0+5r3Orh627T1EQ1M7Dc0Heb0p3hk8ufYN9h48cnS9rAxjXEVBvz2DIiZWFTK5WkNEiS7MWToG3A2sc/fvhNWOiAyNvOxMpo0oZtqI4re813roSL9OoP3oHsLS15qPGSIqzMlkYnUwLBTsFWiIKHGE+S8wG7gReMXMVgfLvubuj4fYpoiEoKwgh4vH5XDxuPJjlvf2OrvbOo92BA3BXsGL2/fx2Mu73jJE1P9YwbiKgmBvI5/S/GztGQyDMGfpLAX0LyiSwjIyjNFl+Ywuy+fyqVXHvPfmEFFwrKApvmfw5No9xwwRQfzg8Zjy/KMdQN/zsRXxr6X52cP5a6Us7WOJSChONUS0v6OLHfsOsX1vBzv2HWLHvo7gcYjlm5s5GNyTuE9xXla8AzhJp1Ccpw5hMBT4IjLsSvOzKc0v5dxRpW95z92DDqHjLZ3C1pZDLH2t+ehN6vv/vL5OYOzRDqGAMcEego4fxOlTEJGEYmaUFeRQVpDDeaNP3CHsO9TVb8/gzU6hoekgSzY209F1bIdQXpB9wj2D0WUFjCjJTZtjCAp8EUkqZkZFYQ4VhTmcP6bsLe+7O3sPHmH7vo63dAob9xxgwfrGY2YWQfw2ltVFucRKchlRnEesJJdYcS6xkrz41+I8RpTkUl6QQ0ZG8nYMCnwRSSlmRmVRLpVFuVw49sQdQnP7EXbsO8TO1g4a2w6z50AnTcHXzU3tLG9oYX9H11u+NyvDqD6mI8hlRN/zknjHECvJpbIwl8wE7BgU+CKSVszioV1dnMtFx00z7a+zq4emA4dpPNDJnrbDNLZ1sufAYRrb4su2tRxixZa97Dv01o4hM8OoKsqJdwD9O4hj9iDyqCrKIWsYz1pW4IuInEBediZjKwoYW1FwyvUOd/d1DG92Bo1th9nT1knjgcPs2t/J6u2ttBw3FRXil7quLMxlYlUBv/nMrLB+laMU+CIiZyA3K/Po5SqC4H+tAAAFaklEQVROpaunl+b2w0f3FuIdRPzrcFHgi4gMg+zMDEaW5jOyND+yGnTJOxGRNKHAFxFJEwp8EZE0ocAXEUkTCnwRkTShwBcRSRMKfBGRNKHAFxFJE+b970EWMTNrArZGXccZqgKaoy4iQeizOJY+j2Pp83jTmXwW4929euDVEizwU4GZrXD3uqjrSAT6LI6lz+NY+jzeNFyfhYZ0RETShAJfRCRNKPCH3vyoC0gg+iyOpc/jWPo83jQsn4XG8EVE0oS28EVE0oQCX0QkTSjwh4CZjTWzhWa2zszWmtkdUdcUNTPLNLMXzex3UdcSNTMrM7MHzWx98H9kZtQ1RcnMvhD8nawxs/vNLC/qmoaTmd1jZo1mtqbfsgoze9rMNgVfT36z3TOgwB8a3cAX3f1sYAZwm5mdE3FNUbsDWBd1EQnie8AT7l4LXEAafy5mNhr4HFDn7ucBmcBHoq1q2N0LXHPcsr8B/uDuU4E/BK+HnAJ/CLj7bndfFTw/QPwPenS0VUXHzMYA7wF+GnUtUTOzEmAOcDeAux9x99Zoq4pcFpBvZllAAbAr4nqGlbsvAfYet/ha4OfB858D14XRtgJ/iJnZBOAi4PloK4nUd4GvAL1RF5IAJgFNwM+CIa6fmllh1EVFxd13AncC24DdwH53fyraqhLCCHffDfENSCAWRiMK/CFkZkXAb4HPu3tb1PVEwczeCzS6+8qoa0kQWcDFwL+5+0XAQULaXU8Gwdj0tcBEYBRQaGZ/EW1V6UOBP0TMLJt42N/n7g9FXU+EZgPvN7MtwAPAPDP792hLitQOYIe79+3xPUi8A0hXfwK87u5N7t4FPATMirimRLDHzEYCBF8bw2hEgT8EzMyIj9Guc/fvRF1PlNz9q+4+xt0nED8Yt8Dd03YLzt3fALabWU2w6Crg1QhLito2YIaZFQR/N1eRxgex+/lP4GPB848Bj4bRSFYYPzQNzQZuBF4xs9XBsq+5++MR1iSJ43bgPjPLARqAj0dcT2Tc/XkzexBYRXx224uk2SUWzOx+YC5QZWY7gK8D/wz82sw+SbxT/FAobevSCiIi6UFDOiIiaUKBLyKSJhT4IiJpQoEvIpImFPgiImlCgS8py8zOMrMHzGyzmb1qZo+b2bT+Vyk8zZ93s5mNGuo6RYaLAl9SUnBSz8PAInef7O7nAF8DRpzBj72Z+OUATqcOnesiCUP/GSVV1QNd7v6jvgXuvjq4uB0Q32Infpnezwavf0f8wl7PED9zug5w4B5ge/D6PjPrAGYC5wDfAYqAZuBmd99tZouAZ4mfkPefZraN+Mk1PcQvFjYntN9a5BQU+JKqzgPe7gXcLgRGB9drx8zK3L3VzD4LfMndVwTXTvoBcK27N5nZnwPfAD4R/Iwyd78y+P5XgD91951mVnYmv5TImVDgi7xVAzDJzH4A/B440eV7a4h3Kk/HR4/IJH653z6/6vd8GXCvmf2a+MXCRCKhwJdUtRa4foB1ujn2OFYegLvvM7MLgD8FbgM+zJtb7n0MWOvuJ7td4cG+J+7+GTO7jPhNYVab2YXu3jLo30RkiOigraSqBUCumX26b4GZXQKM77fOFuBCM8sws7HApcF6VUCGu/8W+DvevJzxAaA4eL4BqO67P62ZZZvZuScqxMwmu/vz7v6/iI/1jx2i31HktGgLX1KSu7uZfQD4rpn9DdBJPOA/32+1ZcDrwCvAGuJXcIT47Sl/ZmZ9G0RfDb7eC/yo30Hb64Hvm1kp8b+l7xLfszjet81sKvG9gj8ALw3F7yhyunS1TBGRNKEhHRGRNKHAFxFJEwp8EZE0ocAXEUkTCnwRkTShwBcRSRMKfBGRNPH/AZPTmaKmhYvMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(range(1,11), wcss)\n",
"plt.title('Elbow Plot')\n",
"plt.xlabel('Clusters')\n",
"plt.ylabel('WCSS')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "44b8474e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knee_loc = KneeLocator(range(1,11), wcss, curve='convex', direction='decreasing')\n",
"optimal_clusters = knee_loc.knee\n",
"optimal_clusters\n"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "ac186e79",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Elapsed = 0.0022 mins\n"
]
}
],
"source": [
"import time\n",
"start = time.time()\n",
"kmeans = KMeans(n_clusters=optimal_clusters, init=\"k-means++\", random_state=42)\n",
"preds = kmeans.fit_predict(X)\n",
"X['Cluster'] = preds\n",
"print(f'Elapsed = {(time.time()-start)/60:.4f} mins')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "bf71da15",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sensor - 1</th>\n",
" <th>Sensor - 2</th>\n",
" <th>Sensor - 3</th>\n",
" <th>Sensor - 4</th>\n",
" <th>Sensor - 5</th>\n",
" <th>Sensor - 7</th>\n",
" <th>Sensor - 8</th>\n",
" <th>Sensor - 9</th>\n",
" <th>Sensor - 10</th>\n",
" <th>Sensor - 11</th>\n",
" <th>...</th>\n",
" <th>Sensor - 582</th>\n",
" <th>Sensor - 583</th>\n",
" <th>Sensor - 584</th>\n",
" <th>Sensor - 585</th>\n",
" <th>Sensor - 586</th>\n",
" <th>Sensor - 587</th>\n",
" <th>Sensor - 588</th>\n",
" <th>Sensor - 589</th>\n",
" <th>Sensor - 590</th>\n",
" <th>Cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3073.48</td>\n",
" <td>2467.18</td>\n",
" <td>2200.2000</td>\n",
" <td>1121.1875</td>\n",
" <td>1.3171</td>\n",
" <td>103.8978</td>\n",
" <td>0.1191</td>\n",
" <td>1.4015</td>\n",
" <td>0.0095</td>\n",
" <td>-0.0070</td>\n",
" <td>...</td>\n",
" <td>64.582000</td>\n",
" <td>0.5046</td>\n",
" <td>0.0065</td>\n",
" <td>0.0022</td>\n",
" <td>1.2845</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3027.61</td>\n",
" <td>2430.03</td>\n",
" <td>2219.7667</td>\n",
" <td>2086.4710</td>\n",
" <td>1.3381</td>\n",
" <td>98.8900</td>\n",
" <td>0.1234</td>\n",
" <td>1.4090</td>\n",
" <td>0.0160</td>\n",
" <td>0.0018</td>\n",
" <td>...</td>\n",
" <td>50.177800</td>\n",
" <td>0.4984</td>\n",
" <td>0.0130</td>\n",
" <td>0.0033</td>\n",
" <td>2.6161</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2950.97</td>\n",
" <td>2533.95</td>\n",
" <td>2249.2556</td>\n",
" <td>2065.0624</td>\n",
" <td>2.1216</td>\n",
" <td>95.7967</td>\n",
" <td>0.1222</td>\n",
" <td>1.3418</td>\n",
" <td>-0.0112</td>\n",
" <td>0.0015</td>\n",
" <td>...</td>\n",
" <td>100.867867</td>\n",
" <td>0.5062</td>\n",
" <td>0.0117</td>\n",
" <td>0.0033</td>\n",
" <td>2.3019</td>\n",
" <td>0.0267</td>\n",
" <td>0.0174</td>\n",
" <td>0.0050</td>\n",
" <td>65.1609</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3093.12</td>\n",
" <td>2500.90</td>\n",
" <td>2219.7667</td>\n",
" <td>2086.4710</td>\n",
" <td>1.3381</td>\n",
" <td>98.8900</td>\n",
" <td>0.1234</td>\n",
" <td>1.4117</td>\n",
" <td>0.0087</td>\n",
" <td>-0.0108</td>\n",
" <td>...</td>\n",
" <td>106.558200</td>\n",
" <td>0.5038</td>\n",
" <td>0.0133</td>\n",
" <td>0.0040</td>\n",
" <td>2.6341</td>\n",
" <td>0.0292</td>\n",
" <td>0.0311</td>\n",
" <td>0.0105</td>\n",
" <td>106.5582</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2988.76</td>\n",
" <td>2497.58</td>\n",
" <td>2153.9778</td>\n",
" <td>1192.6994</td>\n",
" <td>1.3522</td>\n",
" <td>100.9367</td>\n",
" <td>0.1225</td>\n",
" <td>1.4590</td>\n",
" <td>-0.0074</td>\n",
" <td>-0.0060</td>\n",
" <td>...</td>\n",
" <td>76.751000</td>\n",
" <td>0.5001</td>\n",
" <td>0.0099</td>\n",
" <td>0.0025</td>\n",
" <td>1.9807</td>\n",
" <td>0.0282</td>\n",
" <td>0.0217</td>\n",
" <td>0.0070</td>\n",
" <td>76.7510</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2990.69</td>\n",
" <td>2428.85</td>\n",
" <td>2249.2556</td>\n",
" <td>2065.0624</td>\n",
" <td>2.1216</td>\n",
" <td>95.7967</td>\n",
" <td>0.1222</td>\n",
" <td>1.3682</td>\n",
" <td>-0.0110</td>\n",
" <td>-0.0085</td>\n",
" <td>...</td>\n",
" <td>79.617000</td>\n",
" <td>0.4964</td>\n",
" <td>0.0108</td>\n",
" <td>0.0028</td>\n",
" <td>2.1740</td>\n",
" <td>0.0229</td>\n",
" <td>0.0182</td>\n",
" <td>0.0063</td>\n",
" <td>79.6170</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>2978.77</td>\n",
" <td>2441.17</td>\n",
" <td>2254.7111</td>\n",
" <td>1981.2999</td>\n",
" <td>2.1046</td>\n",
" <td>90.9167</td>\n",
" <td>0.1224</td>\n",
" <td>1.4726</td>\n",
" <td>0.0041</td>\n",
" <td>0.0060</td>\n",
" <td>...</td>\n",
" <td>54.532300</td>\n",
" <td>0.4966</td>\n",
" <td>0.0078</td>\n",
" <td>0.0024</td>\n",
" <td>1.5646</td>\n",
" <td>0.0229</td>\n",
" <td>0.0182</td>\n",
" <td>0.0063</td>\n",
" <td>79.6170</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>3036.99</td>\n",
" <td>2448.38</td>\n",
" <td>2194.9555</td>\n",
" <td>1108.2246</td>\n",
" <td>1.2476</td>\n",
" <td>102.2822</td>\n",
" <td>0.1202</td>\n",
" <td>1.5149</td>\n",
" <td>0.0025</td>\n",
" <td>-0.0063</td>\n",
" <td>...</td>\n",
" <td>94.756367</td>\n",
" <td>0.4942</td>\n",
" <td>0.0172</td>\n",
" <td>0.0052</td>\n",
" <td>3.4764</td>\n",
" <td>0.0229</td>\n",
" <td>0.0182</td>\n",
" <td>0.0063</td>\n",
" <td>79.6170</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>3006.75</td>\n",
" <td>2517.09</td>\n",
" <td>2254.7111</td>\n",
" <td>1981.2999</td>\n",
" <td>2.1046</td>\n",
" <td>90.9167</td>\n",
" <td>0.1224</td>\n",
" <td>1.5901</td>\n",
" <td>-0.0071</td>\n",
" <td>0.0127</td>\n",
" <td>...</td>\n",
" <td>48.370800</td>\n",
" <td>0.5020</td>\n",
" <td>0.0111</td>\n",
" <td>0.0031</td>\n",
" <td>2.2182</td>\n",
" <td>0.0506</td>\n",
" <td>0.0245</td>\n",
" <td>0.0093</td>\n",
" <td>48.3708</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>2960.63</td>\n",
" <td>2570.46</td>\n",
" <td>2206.1444</td>\n",
" <td>1876.9899</td>\n",
" <td>2.0607</td>\n",
" <td>95.9511</td>\n",
" <td>0.1246</td>\n",
" <td>1.5341</td>\n",
" <td>0.0350</td>\n",
" <td>-0.0068</td>\n",
" <td>...</td>\n",
" <td>45.701900</td>\n",
" <td>0.4937</td>\n",
" <td>0.0146</td>\n",
" <td>0.0035</td>\n",
" <td>2.9664</td>\n",
" <td>0.0437</td>\n",
" <td>0.0200</td>\n",
" <td>0.0074</td>\n",
" <td>45.7019</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows × 475 columns</p>\n",
"</div>"
],
"text/plain": [
" Sensor - 1 Sensor - 2 Sensor - 3 Sensor - 4 Sensor - 5 Sensor - 7 \\\n",
"0 3073.48 2467.18 2200.2000 1121.1875 1.3171 103.8978 \n",
"1 3027.61 2430.03 2219.7667 2086.4710 1.3381 98.8900 \n",
"2 2950.97 2533.95 2249.2556 2065.0624 2.1216 95.7967 \n",
"3 3093.12 2500.90 2219.7667 2086.4710 1.3381 98.8900 \n",
"4 2988.76 2497.58 2153.9778 1192.6994 1.3522 100.9367 \n",
"5 2990.69 2428.85 2249.2556 2065.0624 2.1216 95.7967 \n",
"6 2978.77 2441.17 2254.7111 1981.2999 2.1046 90.9167 \n",
"7 3036.99 2448.38 2194.9555 1108.2246 1.2476 102.2822 \n",
"8 3006.75 2517.09 2254.7111 1981.2999 2.1046 90.9167 \n",
"9 2960.63 2570.46 2206.1444 1876.9899 2.0607 95.9511 \n",
"\n",
" Sensor - 8 Sensor - 9 Sensor - 10 Sensor - 11 ... Sensor - 582 \\\n",
"0 0.1191 1.4015 0.0095 -0.0070 ... 64.582000 \n",
"1 0.1234 1.4090 0.0160 0.0018 ... 50.177800 \n",
"2 0.1222 1.3418 -0.0112 0.0015 ... 100.867867 \n",
"3 0.1234 1.4117 0.0087 -0.0108 ... 106.558200 \n",
"4 0.1225 1.4590 -0.0074 -0.0060 ... 76.751000 \n",
"5 0.1222 1.3682 -0.0110 -0.0085 ... 79.617000 \n",
"6 0.1224 1.4726 0.0041 0.0060 ... 54.532300 \n",
"7 0.1202 1.5149 0.0025 -0.0063 ... 94.756367 \n",
"8 0.1224 1.5901 -0.0071 0.0127 ... 48.370800 \n",
"9 0.1246 1.5341 0.0350 -0.0068 ... 45.701900 \n",
"\n",
" Sensor - 583 Sensor - 584 Sensor - 585 Sensor - 586 Sensor - 587 \\\n",
"0 0.5046 0.0065 0.0022 1.2845 0.0267 \n",
"1 0.4984 0.0130 0.0033 2.6161 0.0267 \n",
"2 0.5062 0.0117 0.0033 2.3019 0.0267 \n",
"3 0.5038 0.0133 0.0040 2.6341 0.0292 \n",
"4 0.5001 0.0099 0.0025 1.9807 0.0282 \n",
"5 0.4964 0.0108 0.0028 2.1740 0.0229 \n",
"6 0.4966 0.0078 0.0024 1.5646 0.0229 \n",
"7 0.4942 0.0172 0.0052 3.4764 0.0229 \n",
"8 0.5020 0.0111 0.0031 2.2182 0.0506 \n",
"9 0.4937 0.0146 0.0035 2.9664 0.0437 \n",
"\n",
" Sensor - 588 Sensor - 589 Sensor - 590 Cluster \n",
"0 0.0174 0.0050 65.1609 0 \n",
"1 0.0174 0.0050 65.1609 0 \n",
"2 0.0174 0.0050 65.1609 0 \n",
"3 0.0311 0.0105 106.5582 0 \n",
"4 0.0217 0.0070 76.7510 2 \n",
"5 0.0182 0.0063 79.6170 0 \n",
"6 0.0182 0.0063 79.6170 0 \n",
"7 0.0182 0.0063 79.6170 1 \n",
"8 0.0245 0.0093 48.3708 1 \n",
"9 0.0200 0.0074 45.7019 0 \n",
"\n",
"[10 rows x 475 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "37f43249",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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