k_means_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/LondheShubham153/unsupervised_learning/blob/main/k_means_clustering_lab_6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "crqq1oQ9M-eS"
   },
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "Ej5SBoae-Vce",
    "outputId": "9ddee8d7-2660-4309-ef50-fff5351260ec"
   },
   "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>29</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x   y\n",
       "0  12  39\n",
       "1  20  36\n",
       "2  28  30\n",
       "3  18  52\n",
       "4  29  54"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zBtM0z1Z-XxC",
    "outputId": "f55cfb92-d6a2-4af6-bd43-a01c42abc788"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "4zxTCvxh-aDq"
   },
   "outputs": [],
   "source": [
    "labels = kmeans.predict(df)\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "b3K4QZEJ-c9C",
    "outputId": "8f5480ef-ac99-43fb-cecb-ac6ae7d5e4e5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "plt.scatter(df['x'], df['y'], alpha=0.8)\n",
    "\n",
    "plt.plot(centroids[0][0], centroids[0][1], 'x', markersize=20, color='red', label='New 1')  \n",
    "plt.plot(centroids[1][0], centroids[1][1], 'x', markersize=20, color='black', label='New 2')  \n",
    "\n",
    "plt.legend()\n",
    "plt.xlim(0, 80)\n",
    "plt.ylim(0, 80)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyPwtsmeTgwzpgIHvaPGohqw",
   "include_colab_link": true,
   "name": "k_means_clustering_lab_6.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
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  "latex_envs": {
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   "bibliofile": "biblio.bib",
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   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
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   "labels_anchors": false,
   "latex_user_defs": false,
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   "user_envs_cfg": false
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
   "toc_section_display": true,
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  },
  "varInspector": {
   "cols": {
    "lenName": 16,
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   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
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     "varRefreshCmd": "print(var_dic_list())"
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   "types_to_exclude": [
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