ML Assignment 4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8376be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "286a8b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"k-means-data.csv\",index_col=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f4a654f1",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P3</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P4</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P5</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P6</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P7</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P8</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X     Y\n",
       "P1  0.10  0.60\n",
       "P2  0.15  0.71\n",
       "P3  0.08  0.90\n",
       "P4  0.16  0.85\n",
       "P5  0.20  0.30\n",
       "P6  0.25  0.50\n",
       "P7  0.24  0.10\n",
       "P8  0.30  0.20"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cf2a9cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:-1].values\n",
    "Y = data.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a7ba047a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18d4af0be50>"
      ]
     },
     "execution_count": 18,
     "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(X,Y,color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9f9769a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "centroids = np.array([[0.10,0.60],[0.30,0.20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dd8dd509",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1146: RuntimeWarning: Explicit initial center position passed: performing only one init in KMeans instead of n_init=10.\n",
      "  self._check_params(X)\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters = 2, init = centroids, random_state = 0)\n",
    "label = kmeans.fit_predict(data)\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cde448a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.148      0.712     ]\n",
      " [0.24666667 0.2       ]]\n"
     ]
    }
   ],
   "source": [
    "print(centroids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "41c0f09c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 0, 1, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b0ce30f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_label0 = data[label == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "191ca235",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P1</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P2</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P3</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P4</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P6</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X     Y\n",
       "P1  0.10  0.60\n",
       "P2  0.15  0.71\n",
       "P3  0.08  0.90\n",
       "P4  0.16  0.85\n",
       "P6  0.25  0.50"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_label0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ef8a1ed",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>P5</th>\n",
       "      <td>0.20</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P7</th>\n",
       "      <td>0.24</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P8</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       X    Y\n",
       "P5  0.20  0.3\n",
       "P7  0.24  0.1\n",
       "P8  0.30  0.2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_label1 = data[label == 1]\n",
    "filtered_label1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9f98d627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18d4be47c40>"
      ]
     },
     "execution_count": 55,
     "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(filtered_label0.iloc[:,0:-1],filtered_label0.iloc[:,-1],color = 'blue',s = 100, label='cluster 1')\n",
    "plt.scatter(filtered_label1.iloc[:,0:-1],filtered_label1.iloc[:,-1],color = 'yellow', s=100 , label='cluster 2')\n",
    "plt.scatter(centroids[0][0],centroids[0][1],color = 'red' , marker='^', s =150, label='centroid')\n",
    "plt.scatter(centroids[1][0],centroids[1][1],color = 'red' , marker='^', s =150,label='centroid')\n",
    "\n",
    "plt.legend(['cluster 1','cluster 2','centroid'],loc='upper right')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d51745",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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