sklearn-KNearestNeighbors.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q1\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "data = pd.read_csv('D:\\Bennett Courses\\SEM 3\\AI\\data_LAB7_Q1.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_x = data.iloc[:,2:18]\n",
    "data_y = data.iloc[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(data_x, data_y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model_knn = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)\n",
    "model_knn.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'M' 'B' 'M'\n",
      " 'M' 'M' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'M'\n",
      " 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'B'\n",
      " 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'M' 'B' 'B' 'M'\n",
      " 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'M'\n",
      " 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B'\n",
      " 'B' 'B' 'B' 'M' 'M' 'B']\n"
     ]
    }
   ],
   "source": [
    "y_pred = model_knn.predict(x_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[63  4]\n",
      " [ 5 42]]\n",
      "0.9210526315789473\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "ac = accuracy_score(y_test, y_pred)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(cm)\n",
    "print(ac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as mtp    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "    }\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>private</th>\n",
       "      <th>apps</th>\n",
       "      <th>accept</th>\n",
       "      <th>enroll</th>\n",
       "      <th>top10perc</th>\n",
       "      <th>top25perc</th>\n",
       "      <th>f_undergrad</th>\n",
       "      <th>p_undergrad</th>\n",
       "      <th>outstate</th>\n",
       "      <th>room_board</th>\n",
       "      <th>books</th>\n",
       "      <th>personal</th>\n",
       "      <th>phd</th>\n",
       "      <th>terminal</th>\n",
       "      <th>s_f_ratio</th>\n",
       "      <th>perc_alumni</th>\n",
       "      <th>expend</th>\n",
       "      <th>grad_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1660</td>\n",
       "      <td>1232</td>\n",
       "      <td>721</td>\n",
       "      <td>23</td>\n",
       "      <td>52</td>\n",
       "      <td>2885</td>\n",
       "      <td>537</td>\n",
       "      <td>7440</td>\n",
       "      <td>3300</td>\n",
       "      <td>450</td>\n",
       "      <td>2200</td>\n",
       "      <td>70</td>\n",
       "      <td>78</td>\n",
       "      <td>18.1</td>\n",
       "      <td>12</td>\n",
       "      <td>7041</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>2186</td>\n",
       "      <td>1924</td>\n",
       "      <td>512</td>\n",
       "      <td>16</td>\n",
       "      <td>29</td>\n",
       "      <td>2683</td>\n",
       "      <td>1227</td>\n",
       "      <td>12280</td>\n",
       "      <td>6450</td>\n",
       "      <td>750</td>\n",
       "      <td>1500</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>12.2</td>\n",
       "      <td>16</td>\n",
       "      <td>10527</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>1428</td>\n",
       "      <td>1097</td>\n",
       "      <td>336</td>\n",
       "      <td>22</td>\n",
       "      <td>50</td>\n",
       "      <td>1036</td>\n",
       "      <td>99</td>\n",
       "      <td>11250</td>\n",
       "      <td>3750</td>\n",
       "      <td>400</td>\n",
       "      <td>1165</td>\n",
       "      <td>53</td>\n",
       "      <td>66</td>\n",
       "      <td>12.9</td>\n",
       "      <td>30</td>\n",
       "      <td>8735</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>417</td>\n",
       "      <td>349</td>\n",
       "      <td>137</td>\n",
       "      <td>60</td>\n",
       "      <td>89</td>\n",
       "      <td>510</td>\n",
       "      <td>63</td>\n",
       "      <td>12960</td>\n",
       "      <td>5450</td>\n",
       "      <td>450</td>\n",
       "      <td>875</td>\n",
       "      <td>92</td>\n",
       "      <td>97</td>\n",
       "      <td>7.7</td>\n",
       "      <td>37</td>\n",
       "      <td>19016</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>193</td>\n",
       "      <td>146</td>\n",
       "      <td>55</td>\n",
       "      <td>16</td>\n",
       "      <td>44</td>\n",
       "      <td>249</td>\n",
       "      <td>869</td>\n",
       "      <td>7560</td>\n",
       "      <td>4120</td>\n",
       "      <td>800</td>\n",
       "      <td>1500</td>\n",
       "      <td>76</td>\n",
       "      <td>72</td>\n",
       "      <td>11.9</td>\n",
       "      <td>2</td>\n",
       "      <td>10922</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  private  apps  accept  enroll  top10perc  top25perc  f_undergrad  \\\n",
       "0     Yes  1660    1232     721         23         52         2885   \n",
       "1     Yes  2186    1924     512         16         29         2683   \n",
       "2     Yes  1428    1097     336         22         50         1036   \n",
       "3     Yes   417     349     137         60         89          510   \n",
       "4     Yes   193     146      55         16         44          249   \n",
       "\n",
       "   p_undergrad  outstate  room_board  books  personal  phd  terminal  \\\n",
       "0          537      7440        3300    450      2200   70        78   \n",
       "1         1227     12280        6450    750      1500   29        30   \n",
       "2           99     11250        3750    400      1165   53        66   \n",
       "3           63     12960        5450    450       875   92        97   \n",
       "4          869      7560        4120    800      1500   76        72   \n",
       "\n",
       "   s_f_ratio  perc_alumni  expend  grad_rate  \n",
       "0       18.1           12    7041         60  \n",
       "1       12.2           16   10527         56  \n",
       "2       12.9           30    8735         54  \n",
       "3        7.7           37   19016         59  \n",
       "4       11.9            2   10922         15  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('D:\\Bennett Courses\\SEM 3\\AI\\data_LAB7_Q2.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,1:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n",
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans  \n",
    "list= []\n",
    "for i in range(1, 11):  \n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', random_state= 42)  \n",
    "    kmeans.fit(x)  \n",
    "    list.append(kmeans.inertia_)  \n",
    "mtp.plot(range(1, 11), list)  \n",
    "mtp.title('The Elobw Method Graph')  \n",
    "mtp.xlabel('Number of clusters(k)')  \n",
    "mtp.ylabel('list')  \n",
    "mtp.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72040330636.73645, 48355973855.98457, 32365679994.809784, 25519092040.622574, 20748569362.254665, 17759550209.1639, 15716187649.612972, 14371763623.296766, 13235477700.634216, 12167589675.134567]\n"
     ]
    }
   ],
   "source": [
    "print(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x = sc.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\prate\\anaconda3\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1332: 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=4.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "kmeans = KMeans(n_clusters=3, init='k-means++', random_state= 42)\n",
    "y_kmeans = kmeans.fit_predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtp.scatter(x[y_kmeans == 0, 0], x[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
    "mtp.scatter(x[y_kmeans == 1, 0], x[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
    "mtp.scatter(x[y_kmeans == 2, 0], x[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
    "mtp.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n",
    "mtp.title('Clusters')\n",
    "mtp.xlabel('X_value')\n",
    "mtp.ylabel('Y_value')\n",
    "mtp.legend()\n",
    "mtp.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1ff390c49dc86344ce1616d1333123d148d99e4b2158e5b394b29cd8845a560f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}