k means clustering .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd1230a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from matplotlib import pyplot as plt \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "aacae5ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {'x': [25,34,22,27,33,33,31,22,35,34,67,54,57,43,50,57,59,52,65,47,49,48,35,33,44,45,38,43,51,46],\n",
    "        'y': [79,51,53,78,59,74,73,57,69,75,51,32,40,47,53,36,35,58,59,50,25,20,14,12,20,5,29,27,8,7]\n",
    "       }\n",
    "data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6655598b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26db7cdb880>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.x,data.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5750e0db",
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4).fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b4de597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[43.2       , 16.7       ],\n",
       "       [55.1       , 46.1       ],\n",
       "       [30.83333333, 74.66666667],\n",
       "       [27.75      , 55.        ]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centroids = kmeans.cluster_centers_\n",
    "centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dbd4b7c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26db84860a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data.x,data.y,c=kmeans.labels_)\n",
    "plt.scatter(centroids[:,0],centroids[:,1],c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b24904eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27fd9a87",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data\n",
    "data1['labels'] = kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cda03d04",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = data[data['labels'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "412cc217",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>67</td>\n",
       "      <td>51</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>54</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>57</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>50</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>57</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>59</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>52</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>65</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>47</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     x   y  labels\n",
       "10  67  51       1\n",
       "11  54  32       1\n",
       "12  57  40       1\n",
       "13  43  47       1\n",
       "14  50  53       1\n",
       "15  57  36       1\n",
       "16  59  35       1\n",
       "17  52  58       1\n",
       "18  65  59       1\n",
       "19  47  50       1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9b91523",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}