KMeansClustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a7227cdb-4c13-4b55-a1eb-338f803a0f97",
   "metadata": {},
   "source": [
    "K-means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2c90b3bb-2a81-48c4-93ae-4a0f7c44a314",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4b46b22-17f2-4be7-8c48-a4575ceb4208",
   "metadata": {},
   "source": [
    "Question 1. Implementing K-mean Clustering, you need to run the k-means algorithm on the dataset for k=2,k=3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1140ddee-1ac9-4263-9f8b-e49b3cf79c87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# main kmean class \n",
    "\n",
    "class KMeansClustering:\n",
    "    def __init__(self, k, max_iters=100, tol=1e-4):\n",
    "        self.k = k\n",
    "        self.max_iters = max_iters\n",
    "        self.tol = tol\n",
    "        self.centroids = None\n",
    "\n",
    "    def fit(self, data):\n",
    "        np.random.seed(42)\n",
    "        self.centroids = data[np.random.choice(len(data), self.k, replace=False)]\n",
    "        \n",
    "        for _ in range(self.max_iters):\n",
    "            # calculating distances and adding value to clusters\n",
    "            distances = np.linalg.norm(data[:, np.newaxis] - self.centroids, axis=2)\n",
    "            cluster_labels = np.argmin(distances, axis=1)\n",
    "            \n",
    "            # calculating new centroid\n",
    "            new_centroids = np.array([data[cluster_labels == i].mean(axis=0) for i in range(self.k)])\n",
    "            \n",
    "            # termination\n",
    "            # if centroid don't change after iterations\n",
    "            if np.all(np.abs(new_centroids - self.centroids) < self.tol):\n",
    "                break\n",
    "            \n",
    "            self.centroids = new_centroids\n",
    "        \n",
    "        return cluster_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8897626d-3291-4ee9-b2a9-d2f0cc4d303f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>x1</th>\n",
       "      <th>x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.412</td>\n",
       "      <td>32.932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35.190</td>\n",
       "      <td>12.189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26.288</td>\n",
       "      <td>41.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.376</td>\n",
       "      <td>15.506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.116</td>\n",
       "      <td>3.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>25.893</td>\n",
       "      <td>31.515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>23.606</td>\n",
       "      <td>15.402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28.026</td>\n",
       "      <td>15.470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>26.360</td>\n",
       "      <td>34.488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23.013</td>\n",
       "      <td>36.213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27.819</td>\n",
       "      <td>41.867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>39.634</td>\n",
       "      <td>42.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>35.477</td>\n",
       "      <td>35.104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.768</td>\n",
       "      <td>5.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-0.684</td>\n",
       "      <td>21.105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.387</td>\n",
       "      <td>17.810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>32.986</td>\n",
       "      <td>3.412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>34.258</td>\n",
       "      <td>9.931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>6.313</td>\n",
       "      <td>29.426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>33.899</td>\n",
       "      <td>37.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.718</td>\n",
       "      <td>12.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21.054</td>\n",
       "      <td>5.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.267</td>\n",
       "      <td>21.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.537</td>\n",
       "      <td>38.822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.550</td>\n",
       "      <td>16.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25.712</td>\n",
       "      <td>7.409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>3.884</td>\n",
       "      <td>28.616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29.081</td>\n",
       "      <td>34.539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14.943</td>\n",
       "      <td>23.263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>32.169</td>\n",
       "      <td>45.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32.572</td>\n",
       "      <td>16.944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33.673</td>\n",
       "      <td>13.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>29.189</td>\n",
       "      <td>13.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>25.994</td>\n",
       "      <td>34.444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>16.513</td>\n",
       "      <td>23.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>23.492</td>\n",
       "      <td>11.142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>26.878</td>\n",
       "      <td>36.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>31.604</td>\n",
       "      <td>36.911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>34.078</td>\n",
       "      <td>33.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>11.286</td>\n",
       "      <td>16.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>30.150</td>\n",
       "      <td>9.642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>36.569</td>\n",
       "      <td>45.827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3.983</td>\n",
       "      <td>11.839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>12.891</td>\n",
       "      <td>23.832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>21.314</td>\n",
       "      <td>13.264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>29.101</td>\n",
       "      <td>44.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>30.671</td>\n",
       "      <td>9.294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>35.139</td>\n",
       "      <td>9.803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>35.563</td>\n",
       "      <td>42.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>35.028</td>\n",
       "      <td>15.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>9.776</td>\n",
       "      <td>16.988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>24.268</td>\n",
       "      <td>5.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>-0.360</td>\n",
       "      <td>15.319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>33.062</td>\n",
       "      <td>47.693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>21.034</td>\n",
       "      <td>37.463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>31.806</td>\n",
       "      <td>4.484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>22.493</td>\n",
       "      <td>39.466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>29.056</td>\n",
       "      <td>46.004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>29.822</td>\n",
       "      <td>13.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>35.439</td>\n",
       "      <td>14.439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        x1      x2\n",
       "0   24.412  32.932\n",
       "1   35.190  12.189\n",
       "2   26.288  41.718\n",
       "3    0.376  15.506\n",
       "4   26.116   3.963\n",
       "5   25.893  31.515\n",
       "6   23.606  15.402\n",
       "7   28.026  15.470\n",
       "8   26.360  34.488\n",
       "9   23.013  36.213\n",
       "10  27.819  41.867\n",
       "11  39.634  42.230\n",
       "12  35.477  35.104\n",
       "13  25.768   5.967\n",
       "14  -0.684  21.105\n",
       "15   3.387  17.810\n",
       "16  32.986   3.412\n",
       "17  34.258   9.931\n",
       "18   6.313  29.426\n",
       "19  33.899  37.535\n",
       "20   4.718  12.125\n",
       "21  21.054   5.067\n",
       "22   3.267  21.911\n",
       "23  24.537  38.822\n",
       "24   4.550  16.179\n",
       "25  25.712   7.409\n",
       "26   3.884  28.616\n",
       "27  29.081  34.539\n",
       "28  14.943  23.263\n",
       "29  32.169  45.421\n",
       "30  32.572  16.944\n",
       "31  33.673  13.163\n",
       "32  29.189  13.226\n",
       "33  25.994  34.444\n",
       "34  16.513  23.396\n",
       "35  23.492  11.142\n",
       "36  26.878  36.609\n",
       "37  31.604  36.911\n",
       "38  34.078  33.827\n",
       "39  11.286  16.082\n",
       "40  30.150   9.642\n",
       "41  36.569  45.827\n",
       "42   3.983  11.839\n",
       "43  12.891  23.832\n",
       "44  21.314  13.264\n",
       "45  29.101  44.781\n",
       "46  30.671   9.294\n",
       "47  35.139   9.803\n",
       "48  35.563  42.759\n",
       "49  35.028  15.779\n",
       "50   9.776  16.988\n",
       "51  24.268   5.693\n",
       "52  -0.360  15.319\n",
       "53  33.062  47.693\n",
       "54  21.034  37.463\n",
       "55  31.806   4.484\n",
       "56  22.493  39.466\n",
       "57  29.056  46.004\n",
       "58  29.822  13.830\n",
       "59  35.439  14.439"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading dataset\n",
    "df = pd.read_csv('kmeans - kmeans_blobs.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21e9ddd5-7b1d-42fc-a0b4-7951c55593b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7c15b85c-854e-438a-8203-3bdfbce709fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.62245151, 0.66665161],\n",
       "       [0.88977628, 0.19821142],\n",
       "       [0.6689816 , 0.86506628],\n",
       "       [0.02629099, 0.2731194 ],\n",
       "       [0.66471551, 0.01244326],\n",
       "       [0.65918448, 0.63465143],\n",
       "       [0.60246044, 0.27077076],\n",
       "       [0.71208889, 0.27230641],\n",
       "       [0.6707674 , 0.70179084],\n",
       "       [0.58775237, 0.7407466 ],\n",
       "       [0.70695471, 0.86843116],\n",
       "       [1.        , 0.8766288 ],\n",
       "       [0.89689469, 0.71570199],\n",
       "       [0.65608413, 0.05769969],\n",
       "       [0.        , 0.39956189],\n",
       "       [0.10097227, 0.32515074],\n",
       "       [0.83511087, 0.        ],\n",
       "       [0.86666005, 0.1472189 ],\n",
       "       [0.17354531, 0.58747544],\n",
       "       [0.85775584, 0.77060139],\n",
       "       [0.13398482, 0.19676611],\n",
       "       [0.53916365, 0.03737495],\n",
       "       [0.09799593, 0.41776383],\n",
       "       [0.62555186, 0.79966577],\n",
       "       [0.12981795, 0.28831779],\n",
       "       [0.65469517, 0.09026445],\n",
       "       [0.11329927, 0.56918317],\n",
       "       [0.73825587, 0.70294257],\n",
       "       [0.38759363, 0.44829611],\n",
       "       [0.81484697, 0.94869131],\n",
       "       [0.8248425 , 0.30559382],\n",
       "       [0.8521504 , 0.22020731],\n",
       "       [0.74093457, 0.22163004],\n",
       "       [0.66168957, 0.70079718],\n",
       "       [0.42653405, 0.45129965],\n",
       "       [0.59963292, 0.17456697],\n",
       "       [0.68361526, 0.74968948],\n",
       "       [0.80083337, 0.75650956],\n",
       "       [0.86219555, 0.68686344],\n",
       "       [0.29688973, 0.28612723],\n",
       "       [0.76477008, 0.1406924 ],\n",
       "       [0.92397936, 0.95786003],\n",
       "       [0.11575475, 0.19030736],\n",
       "       [0.33669825, 0.46114586],\n",
       "       [0.54561238, 0.2224882 ],\n",
       "       [0.73875192, 0.93423816],\n",
       "       [0.77769235, 0.1328335 ],\n",
       "       [0.88851133, 0.14432827],\n",
       "       [0.89902773, 0.88857524],\n",
       "       [0.88575822, 0.27928457],\n",
       "       [0.25943747, 0.30658748],\n",
       "       [0.6188799 , 0.05151194],\n",
       "       [0.00803611, 0.26889637],\n",
       "       [0.83699588, 1.        ],\n",
       "       [0.53866759, 0.76897541],\n",
       "       [0.80584354, 0.02420903],\n",
       "       [0.5748549 , 0.81420925],\n",
       "       [0.7376358 , 0.96185723],\n",
       "       [0.75663475, 0.23527021],\n",
       "       [0.89595218, 0.24902328]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalization\n",
    "\n",
    "df = (df - df.min()) / (df.max() - df.min())\n",
    "data = df.values\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36b95791-73d1-470e-9293-c846e5ba3f32",
   "metadata": {},
   "source": [
    "Question 2. you should have 2 plots - one for k = 2 and the other for k = 3. In the plot, you need to plot the given dataset and use different colors to show points belonging to a particular cluster. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a90fe049-5a82-4137-b343-3bc2701927eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# traning and testing\n",
    "\n",
    "kmeans2 = KMeansClustering(k=2)\n",
    "labels_k2 = kmeans2.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06a1a6fe-4910-4495-9093-add013b0cddf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for k=2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "colors = ['r', 'g', 'b', 'c', 'm', 'y']\n",
    "for i in range(2):\n",
    "    plt.scatter(data[labels_k2 == i, 0], data[labels_k2 == i, 1], c=colors[i], label=f'Cluster {i+1}')\n",
    "plt.scatter(kmeans2.centroids[:, 0], kmeans2.centroids[:, 1], c='black', marker='x', s=100, label='Centroids')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.legend()\n",
    "plt.title('K-Means Clustering (k=2)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7ed1af53-c2e0-435b-a594-00698b08112e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# traning for k = 3\n",
    "\n",
    "kmeans3 = KMeansClustering(k=3)\n",
    "labels_k3 = kmeans3.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8e8b7a0b-eb7d-4042-be07-c7212efef760",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for k = 4\n",
    "\n",
    "for i in range(3):\n",
    "    plt.scatter(data[labels_k3 == i, 0], data[labels_k3 == i, 1], c=colors[i], label=f'Cluster {i+1}')\n",
    "plt.scatter(kmeans3.centroids[:, 0], kmeans3.centroids[:, 1], c='black', marker='x', s=100, label='Centroids')\n",
    "plt.xlabel('Feature 1')\n",
    "plt.ylabel('Feature 2')\n",
    "plt.legend()\n",
    "plt.title('K-Means Clustering (k=3)')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}