{ "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": [ "
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" ], "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": { "image/png": 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" ] }, "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": [ "
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