{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "13f63d5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\karampidis\\AppData\\Local\\Continuum\\anaconda3\\envs\\pr\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1038: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn import datasets\n",
    "from yellowbrick.cluster import KElbowVisualizer , SilhouetteVisualizer\n",
    "data = datasets.load_iris()\n",
    "X = data.data\n",
    "y = data.target\n",
    "\n",
    "# Instantiate the clustering model and visualizer\n",
    "model = KMeans()\n",
    "visualizer = KElbowVisualizer(model, k=(1,10), timings=False ,locate_elbow=False)\n",
    "visualizer.fit(X)        # Fit the data to the visualizer\n",
    "visualizer.show()        # Finalize and render the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56630fc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Silhouette Plot of KMeans Clustering for 150 Samples in 9 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visualizer1 = SilhouetteVisualizer(model)\n",
    "\n",
    "visualizer1.fit(X)    # Fit the data to the visualizer\n",
    "visualizer1.show()    # Draw/show/show the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73bd8251",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
