Different types of Clustering/clustering in python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing main library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0      1            5.1           3.5            1.4           0.2   \n",
       "1      2            4.9           3.0            1.4           0.2   \n",
       "2      3            4.7           3.2            1.3           0.2   \n",
       "3      4            4.6           3.1            1.5           0.2   \n",
       "4      5            5.0           3.6            1.4           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "            Species  \n",
       "0       Iris-setosa  \n",
       "1       Iris-setosa  \n",
       "2       Iris-setosa  \n",
       "3       Iris-setosa  \n",
       "4       Iris-setosa  \n",
       "..              ...  \n",
       "145  Iris-virginica  \n",
       "146  Iris-virginica  \n",
       "147  Iris-virginica  \n",
       "148  Iris-virginica  \n",
       "149  Iris-virginica  \n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/My/Desktop/data science/clustering/Iris.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert species into numerical from by label encoder\n",
    "from sklearn import preprocessing\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "df['Species']=label_encoder.fit_transform(df['Species'])\n",
    "df\n",
    "df.isnull().sum()# missing values sort out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(['Species','Id'],axis = 1)\n",
    "y = df['Species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
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       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
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       "  </tbody>\n",
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       "<p>150 rows × 4 columns</p>\n",
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      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "0              5.1           3.5            1.4           0.2\n",
       "1              4.9           3.0            1.4           0.2\n",
       "2              4.7           3.2            1.3           0.2\n",
       "3              4.6           3.1            1.5           0.2\n",
       "4              5.0           3.6            1.4           0.2\n",
       "..             ...           ...            ...           ...\n",
       "145            6.7           3.0            5.2           2.3\n",
       "146            6.3           2.5            5.0           1.9\n",
       "147            6.5           3.0            5.2           2.0\n",
       "148            6.2           3.4            5.4           2.3\n",
       "149            5.9           3.0            5.1           1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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    "x"
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   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0\n",
       "1      0\n",
       "2      0\n",
       "3      0\n",
       "4      0\n",
       "      ..\n",
       "145    2\n",
       "146    2\n",
       "147    2\n",
       "148    2\n",
       "149    2\n",
       "Name: Species, Length: 150, dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count     150.000000    150.000000     150.000000    150.000000\n",
       "mean        5.843333      3.054000       3.758667      1.198667\n",
       "std         0.828066      0.433594       1.764420      0.763161\n",
       "min         4.300000      2.000000       1.000000      0.100000\n",
       "25%         5.100000      2.800000       1.600000      0.300000\n",
       "50%         5.800000      3.000000       4.350000      1.300000\n",
       "75%         6.400000      3.300000       5.100000      1.800000\n",
       "max         7.900000      4.400000       6.900000      2.500000"
      ]
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     "execution_count": 8,
     "metadata": {},
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   "source": [
    "x.describe()"
   ]
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   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   SepalLengthCm  150 non-null    float64\n",
      " 1   SepalWidthCm   150 non-null    float64\n",
      " 2   PetalLengthCm  150 non-null    float64\n",
      " 3   PetalWidthCm   150 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 4.8 KB\n"
     ]
    }
   ],
   "source": [
    "x.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Cqw3s+EbsVyebPeyIWp5Xlpv3xulX/39Be54YttRVT3Llswuu0l71Fm1cceexgqoWdzoRxrbSKmetcA64A7gS8AjwffrguekhKy+fc9qvzZ19x4O+lC8E//CbU+yqbu37DlRx5LVsYvUQthyzhXuft1Ij41WFss540prk5RG6Ne9T3bz8ZseO+rxf73kVP50UWfZ86KOJYtjFxmtJDdi+TAwPMh/pMRjEsjq2/c4/Uq6BLTsGv5lKnQG92tv30FefvUgbS3NFAo+4mcJWbwmImkbKcd/MfDnwHFmdsegpyYCLyXZMcmGNEpAT5wxseQs3BNntJc9p7+/wG0bXoz0uYCIFI10x/8I0A1MBf5x0ON7gY1JdUqyo1wZ5OurOLP0hd7X+OYDzwyp0PnmA8+w9NjJZdso97nAgukTqrq3r0gjqhj43X0Lxc1WzkynO5I1ccog48yQLTULd6SZu3H29hWR8DN391Is6xysl2Jlz2eSWrtHai/qJilpzZCNOnNXRI4Imwy9FvgcMBuYA3wW+BbwQ+A7yXRNsiDqJilpzZCNOnNXRI4IW9VzjrufPuj7VWb2qLtfbWZ/VelEMxtD8Z3Bi+5+XtyO1lKcqpY01lhPo19RN0lJa4Zs1Jm7InJE2MBfMLP3A7cG37930HMjTQRYQXGP3vIlGhkWJ3WR1XXc46ZhosyQjbN/LsSbhRx1P1wRKQqb6vkL4IPATmBH8PdLzKwN+GS5k4Llm99DcTOXuhQndZHGYmhp9StqGibO/rlxaCauSHyh1+MHzi/z9C8rnPo14PMU6/5LMrPlwHKArq6uMN1JVVrry2e1X1HTMHH2z41Di5uJxBe2qmca8F+AeYPPcfe/rHDOecBOd19nZu8od5y7rwJWQXHJhjD9SVNa68tnuV9RZsimmYLRTFyReMK+Ab8d6AB+BvyfQV+VvAW4wMyep1j9c5aZ3RSznzWT1vryUXVNHsc1yxYNaWOkfWrj9GtghuxFqx7l4zc9xkWrVnPbhhfp7y+UPF4pGJHsC7tI23p3Pzl2I8U7/s+OVNWT1UXa4izulfSCYJt79vHRf/v1UXvOfvcjp4XaejFsvzZs3c1Fqx496l3CzcvPKDtDVouhiaQnyUXa7jKzc9397hj9qntxUgpJL4YWZ7ZrnH7FmSGrFIxItoUN/CuAvzKzA8ABwAB391Almu7+IPBgnA7mRdRSy7Q2VYm7cqaIZFeoHL+7T3T3Jndvdff24Pu6rMvPqqillmnl0hd2tpf8LGFhZ0dV2xGR9ISt6jGKtfzHufuXzGwu0Onuv060dzmyY08fk8eN5c9OnYMFN/g/WvdC2dRNWuWMzc1NLFsymwXTJ7C9t4+ZHa0s7OzQ0scidSxsquefgAJwFsUtGPcB3wTelFC/cqezo5UPnXks193/zOFUz4qzFzCzvXxKJa1cenNzE0vmTtaqlyINIuxt2+nu/gmgD8DddwNjE+tVDh0qcDjoQzHVc939z3CodNWkiEhsYQP/wWCxNYfDE7oUkqpo597S1TM9+/pq1CMRaVRhA/9K4CfAdDP7MsVlGv42sV7l0ECVzmB5WnSsUHA29+xj9XO72Nyzj0Ihc5O4RRpG2LV6vmdm64CzKZZyLqO4EYtUyUCVzvByzjzMeE1jNVMROSLUzN2SJ5r9h7tXdVW1rM7cTUteZ7xu7tnHuSsfPmquwN1V3NdXpFElOXO3ZHujOLdq0tjwJC1pVOlk8fVKYzVTETliNIG/5klYpQiiyerrldYsZBEpqvjhrpl93cxWlvj6OjApnS6Wl8aGJ40kq6+XVvQUSddId/yVEu41T8YrRRBNVl8vbaoikq6Kgd/db0irI3EoRRBNll8vregpkp6RUj13mtkd5b7S6mQ5ShFEo9dLRGCEck4z+6NKJ7v7L6rZmTjlnHktgYxLr5dIY6l6OWe1A3sSspoiiFM2mUapZVZfLxFJT9hlmRcAfwe8ATicEHb3+Qn1q67FKZvMaqmliDSesGv1fBf4Z6AfeCdwI/C/k+pUvYtTNpnVUksRaTxhA3+bu99P8TOBLe7+RYpr80sJlcomq3mOiEgcYWfu9plZE/CMmX0SeBGYnly36lucssksl1qKSGMJe8f/KWAccDnwRuAS4EMJ9anuxSmbVKmliKQl1OqcZvY+d//3kR4brUZanTNO2aRKLUUkqjjlnGED/2PufupIj41WIwV+EZE0VL2O38zeDZwLzDazlYOeaqdY4SMiInVmpA93t1FcjO0CYN2gx/cC/z2pTomISHJGmrm7AdhgZt8Pju1y96fD/GAzawUeAo4Jzr3V3a8cZX9lkCxuqiIi2Re2nPMc4B+AscBxZnYycLW7X1DhnP3AWe6+z8xagF+a2T3u/uioeiyAZvqKSHxhyzm/CJwGvAzg7uuBeZVO8KJ9wbctwVfNd+1qFJrpKyJxhQ38/e7eG/WHm9kYM1sP7ATuc/c1JY5ZbmZrzWxtT09P1CZySzN9RSSusIH/STP7c2CMmS0Itl58ZKST3P2Qu58MzAFOM7NFJY5Z5e5L3X3ptGnTovQ91wZm+g6mmb4iEkbYwH8ZsJBi3v77QC/F2byhuPvLwIMUPyuQKtBMXxGJa6Q6/lbg48AfAk8AZ7p7qPp9M5sGHHT3l82sDXgX8JVR9lcC2qdWROIaqarnBuAg8DDwbuBEwt/pdwI3mNkYiu8sbnH3u2L2U0rQpioiEsdIgf8N7r4YwMyuB34d9ge7+0bglFH0TUREEjBSjv/gwF/CpnhERCTbRrrjX2Jme4K/G9AWfG8US/XbE+2diIhU3UhLNoxJqyMiIpKOsOWcIiLSIBT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHJGgV9EJGcU+EVEckaBX0QkZxT4RURyRoFfRCRnEgv8ZjbXzB4ws6fMbJOZrUiqLRERCa85wZ/dD3zG3R8zs4nAOjO7z91/k2CbIiIygsTu+N29290fC/6+F3gKmJ1UeyIiEk4qOX4zmwecAqwp8dxyM1trZmt7enrS6I6ISK4lHvjNbALwI+BT7r5n+PPuvsrdl7r70mnTpiXdHZHMKRSczT37WP3cLjb37KNQ8Fp3KbZGGksjSzLHj5m1UAz633P3HyfZlkg9KhScezdt59O3rKfvYIHWliauff/JnLNwJk1NVuvuRdJIY2l0SVb1GHA98JS7X5tUOyL17PmXXjkcKAH6Dhb49C3ref6lV2rcs+gaaSyNLslUz1uADwJnmdn64OvcBNsTqTs79vQdDpQD+g4W2Lm3r0Y9iq+RxtLoEkv1uPsvAb2/E6lgRnsrrS1NQwJma0sT0ye21rBX8TTSWBqdZu6K1NC8KeO59v0n09pS/FUcyIvPmzK+xj2LrpHG0ujMPTufui9dutTXrl1b626IjEqh4Dz/0ivs2NPHjPZW5k0ZX/HDzf7+Apu6e+nu7aOzo5WFnR00N9fnPVmcsUR9vWQoM1vn7kujnJNoVY9I3kStbCkUnJ8+taMhKmHijEWVQLVRn7cVIhkVtbKlkSph4oylkcZfTxT4RaooamVLI1XCxBlLI42/nijVI3Uram44Ti456jlRK1tmtLdy7JQ2zjtpNhb82Ds3vDhiJUwW8+JxxqJKoNpQ4Je6FCeXHjWXHOecgcqW4eeUq2yZ09HGJ965gCtuf/Lw8VdfuIg5HW1VG3tauiaP47KzFvCF246M5Zpli+iaPK7sOVFfL6kOVfVIXdrcs49zVz581J3i3Ze/jfnTJoz6+LjnwJG78Z17+5g+sfLd+Iatu7lo1aNHtXHz8jNYMndyVfuVtDReLzmaqnokNyrlhksFmR17+njd9Al87O3H89r+fsYd08y3Hnqu7PFx2hjQ1GTMnzYhVBDu7i3dr+29fSyZW/qcuP2K6sCBQ2zc1sv2PX10treyeFYHY8eOKXt8Gq9XXFlMjdWyXwr8Upei5oY724/h4tOP5fO3bjicUrjy/IXMnHhM1dqI49gpbSX71fUH5VM90yeW7te0CdXr14EDh7ht47ajUlDLTppVNvin0a84spoaq2W/VNUjdSnqLNGefQe46s5NQ8oGr7pzEz37DlStjThe3V8o2a9XDxTKnjOmCVacvWBIv1acvYAxVfxt3rit93DQH+jXFbc/ycZtvTXtVxxZLRmtZb90xy91qanJOGfhTF5/+dtC5Ya379lfMg2xY8/+qrURR3eZ9Mj2PeXLGbt7+7hx9RYufet8zMAdbly9hVO6JjFvanXSJdvL9GtHjfsVR1qpsahq2S8FfsmEOLnO/v4Cv3/lALv2HaClqYk5HW1l0xAz248pmYaY0V4+1TNYlBqIKGPpLJNOmtFeuQRy96sH+OYDzw45p5oloGn2K2lxU3ZJ599rWcqqVI/U3ECu89yVD3Pxt9Zw7sqHuXfT9oq7Nw3koC+5fg2f/P7j/MX1a7ht4zYOHDhU8vjFnR1cfcGiIWmIqy9YxOLOjqr2K+o5b5gxsWS/Fs6YWLaNOR1tXH3hsHNCloCG7dfiWR0l2zhpVvnXK6uLtMXpV5xrn0a/qkXlnFJzccoA1z7/ey65fs1R59x06eksnfcHJc/p6+vnie5eduzZz4z2Y1jc2UFra/k3vWmUgG7Yupsv3bWJD715Pq8d6KdtbDM3PrKZvzlvYdlyzg1bd3P5Dx8/PFHKHe7a+CIrP3BKVUtAB6p6Bu54TxqhqgeyW5oZtV9plcxW4/VSOWeGZLV8LIvi5Drj5KBbW5t503FTEu1X1HO6e/tYu6WXtVseH/J4pXLO7t4+OlpbOGHmxMMloA//rqXqJaBjx44p+59oOXFKM9P4XYnar7Ty72mUspaiwJ+ArJaPZVWcXOesjtLnzOyoXn50+oTSnwtMHV+9EtDOjrbI4yhbAjqlfKonq0sjZPV3JauvV7Uox5+ArJaPZVWcXOektpaSpYOT21qq1q/X+g+VbKOvv/TnCHHGsrCznWuWDc2lX7NsEQsrfPaw/6CXLAHdf7B82jar+fes/q5k9fWqFt3xJyCr5WNZFadscsfe/WVLB+dPL//BaBQv7H6tZBvzp45n0exJVRlLc3MTy5bMZsH0CWzv7WNmiM1L4qS50ihNjSPO70paqaEsvl7VosCfgEZ/m5iEqLnONEoHZ01qK9lG5wjppKhjaW5uYsncyWXz88PFSQ/F6Vcaov6upJkayuLrVS1K9SSg0d8mZkEar/HEY5pLpnomtlYvnRRHnPRQVkW9jllNDdUb3fEnoNHfJmZBGq/x9j3lZ6IeV8O7wDjpoayKeh2VRq0OBf6ENPLbxKxJaipKVmeiQvT0UJZF+V1RGrU66u8WQYTGn1kppemaVIdm7kpdqqeZlVJduiZDZWrmrpl9BzgP2Onui5JqR/Kp0WdWJqFRZpM30jWplSRz/P8GfAO4McE2JKeU640mqzNkpTYSy/G7+0PA75P6+ZJvyvVGozJIGazmVT1mthxYDtDV1VXj3ki9UMlsNCqDlMFqHvjdfRWwCoof7ta4O1JHlOsNT6kxGUzlnCI5oNSYDFbzO34RSZ5SYzJYYnf8ZvYDYDVwgpm9YGaXJtWWiIxsIDV2xvypzJ82QUE/xxK743f3i5P62SIiEp9y/CIiOaPALyKSMwr8IiI5o8AvIpIzmVqd08x6gC0xT58K7Kpid+pJnscO+R6/xp5fA+M/1t2nRTkxU4F/NMxsbdSlSRtFnscO+R6/xp7PscPoxq9Uj4hIzijwi4jkTCMF/lW17kAN5XnskO/xa+z5FXv8DZPjFxGRcBrpjl9EREJQ4BcRyZm6CvxmNtfMHjCzp8xsk5mtKHGMmdlKM3vWzDaa2am16Gu1hRz7O8ys18zWB19X1KKv1WZmrWb2azPbEIz9qhLHNOR1h9Djb8hrP8DMxpjZ42Z2V4nnGvbaw4hjj3Xd6209/n7gM+7+mJlNBNaZ2X3u/ptBx7wbWBB8nQ78c/BnvQszdoCH3f28GvQvSfuBs9x9n5m1AL80s3vc/dFBxzTqdYdw44fGvPYDVgBPAe0lnmvkaw+Vxw4xrntd3fG7e7e7Pxb8fS/FF2P2sMMuBG70okeBSWbWmXJXqy7k2BtScC33Bd+2BF/DqxIa8rpD6PE3LDObA7wH+HaZQxr22ocYeyx1FfgHM7N5wCnAmmFPzQa2Dvr+BRosQFYYO8CZQUrgHjNbmG7PkhO83V0P7ATuc/dcXfcQ44cGvfbA14DPA4Uyzzfytf8alccOMa57XQZ+M5sA/Aj4lLvvGf50iVMa5u5ohLE/RnHdjiXA14HbUu5eYtz9kLufDMwBTjOzRcMOaejrHmL8DXntzew8YKe7r6t0WInH6v7ahxx7rOted4E/yHH+CPieu/+4xCEvAHMHfT8H2JZG35I20tjdfc9ASsDd7wZazGxqyt1MlLu/DDwInDPsqYa97oOVG38DX/u3ABeY2fPAD4GzzOymYcc06rUfcexxr3tdBX4zM+B64Cl3v7bMYXcAHwo+6T8D6HX37tQ6mZAwYzezmcFxmNlpFK/vS+n1MhlmNs3MJgV/bwPeBfx22GENed0h3Pgb9dq7+/9y9znuPg/4APBzd79k2GENee3DjD3uda+3qp63AB8EngjynQB/BXQBuPu/AHcD5wLPAq8CH02/m4kIM/b3Av/VzPqB14APeGNMze4EbjCzMRT/Yd/i7neZ2ceh4a87hBt/o177knJ07Y9SjeuuJRtERHKmrlI9IiIyegr8IiI5o8AvIpIzCvwiIjmjwC8ikjMK/JJZZvbXwWqUG4OVB6u28FawquFdQe33LjObHDzeaWZuZm8ddGyPmU0xs2+b2RtK/KyPmNk3gr8vG3yMmT1oZiU3xDaz08zsITN72sx+G/z8cdUao0g5CvySSWZ2JnAecKq7n0Rx0tLWymdFF9Q8rwHODB56M/B48CdmdgKwy91fcvePlVgNdbhlwFH/OQxnZjOAfwf+h7ufAJwI3AtMjDMOkSgU+CWrOikG3P0A7r7L3beZ2RvN7Bdmts7M/u/AKozBnfXXzOwRM3symMU4cFf9iBXXM38kCOTD/Yog0Ad/XsvQ/wgeGdTG0uDvHzWz35nZLyhOrsPM3gxcAPx98A7l+OBnvM+K6+n/zszeFjz2CeAGd18djM/d/VZ332FmXzSzG8zsp2b2vJn9mZl91cyeMLN7rbh0h0hsCvySVT8F5gbB8p/M7I+CgPd14L3u/kbgO8CXB50z3t3fDPy34DkoLm3wdnc/BbgC+NsSbT3CkcB/GsWFrgbWfnkzxf8YDgv+s7mKYsD/Y4I7fHd/hOLyAZ9z95Pd/bnglGZ3Pw34FHBl8NgioNLiW8dTXI73QuAm4AF3X0xxduZ7KpwnMqJ6W7JBciLYdOSNwNuAdwI3A9dQDJj3BcuTjAEGr8nyg+Dch8ysPVjfZiLF5Q4WUFyxsdTd8q+BU8xsPNAStL3ZzP6QYuD/x2HHnw486O49AGZ2M/C6CsMZWFBvHTAvxPAB7nH3g2b2RDDOe4PHn4jwM0RKUuCXzHL3QxRXonwwCICfADa5+5nlTinx/Zco3i3/JyvuY/BgiXZeNbNngb+kuMwtwKMU13+ZDjwdoq1K9gd/HuLI79wm4I3A7ZXOcfeCmR0ctP5KAf3eyigp1SOZZGYnBHfpA06muOvYtOCDX8ysxYZuPHFR8PhbKa7Q2At0AC8Gz3+kQpO/opiKWR18v5rilnePllj0ag3wjqDSpwV436Dn9hLuA9pvAB8eXKlkZpeY2cwQ54qMigK/ZNUEiima35jZRop59Csorkb4FTPbAKznSG4eYLeZPQL8C3Bp8NhXgb8zs19RTJmU8ytgPkcC/2MU13V/ZPiBwZK/XwyO/RlH3iVAcd30zwUfJh8//NxBP2MHxaV2/yEo53yKYlpr+OY6IlWn1TmlIZjZg8Bn3X1trfsiknW64xcRyRnd8YuI5Izu+EVEckaBX0QkZxT4RURyRoFfRCRnFPhFRHLm/wNpHgTY54y4hgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "for i in x.columns:\n",
    "    for j in x.columns:\n",
    "        if i!=j:\n",
    "            sb.scatterplot(i,j,data=x)\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2 = x.copy()\n",
    "df_3 = x.copy()\n",
    "df_4 = x.copy()\n",
    "df_5 = x.copy()\n",
    "df_6 = x.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**DBSCAN (Density-Based Spatial Clustering of Applications with Noise)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import sklearn.utils\n",
    "db = DBSCAN().fit(df_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = db.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_2['clus'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
       "       'clus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sb.scatterplot(df_2['PetalLengthCm'],df_2['PetalWidthCm'],hue = df_2['clus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**OPTICS (Ordering Points to Identify Clustering Structure)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import OPTICS\n",
    "op = OPTICS().fit(df_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels1 = op.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_3['clus'] = labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sb.scatterplot(df_3['PetalLengthCm'],df_3['PetalWidthCm'],hue = df_3['clus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Hierarchical Clustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "agg_clustering = AgglomerativeClustering(n_clusters = 3, affinity = 'euclidean', linkage = 'ward')\n",
    "labels2 = agg_clustering.fit_predict(df_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_4['clus'] = labels2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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UlbJs5WKmTq9k0/ptbHxzM54CL8tWLmL23PQ7mO3dXc+br22ip7uHJcsWsuSYBThdzqP8bg/a19DE+tc30drcxsIl81i6fFFSLaZc00QwRj70oQ/xiU98gquvvjrXoSg1LnV2dPFf/++7PPP08wNtN336OlxuF7d/9QcDbe+56kJWrjqGz33q6wNti5ct4DNfupFr33cT0Wh8X4Gyiin85OH/Ye6Qchd7dzfwsfffSn1dIxAfQL7rZ9/gtBytLWhuauU//u1LrHt900DbV7/9GS56z7k5iSedvOwa2vPSZp647T5+9bHv8cRt97Hnpc2jvubpp5/OlCmp5W+VUnE7tu5KSgIAP/zez+nq7E5qszvsfOe/fpjUtmndVta/vmkgCUC8Vv8r/3ot5T7r39g0kAQgXhDye//9o5xtRbl1446kJABw+1d/QNO+kbfbPFry7olgz0ubWfPg37DC8bok/e09rHnwbwDMWr0ol6EpNan19QVS2iLhCCLJ30cLC310daZ+aA/d1AZI2WwG4ovPUo9rH/PtHTOVbm/m3p4+wuHcxJNO3j0RrPv98wNJ4AArHGXd758/xBlKqbEwe+7MlE1fjj1hWcoH5YY3N3PhZecktTldTuYN2RQHYPWQufgAi4+Zn7LhzPuufjflFbl5Yp8zvzZl4dt5F51F1bSRq6MeLXn3RNDfnv7x8FDtSqmxUTNrBj/65bf5/u0/YcObW3jbOadxzUevIGZitLd18uw/XmTF8Uv5xC3X4i8pxF9UyB9+8xdqZk3n5s9+lDnzZnHLbR/ngZ88ir/Iz42fvo5jVqZOxV58zELu+tk3uOOb99La0sb7PnAxl7zv/JwtMpu3cDY/+uV3uOObP2L3jjrOv/gsrrzmMtxpVkXnSt6tI3jitvvSfugXTPFzwdevHVVsu3fv5oILLhh21pCuI1D5LtAfpKenl9IpxTid8Zk84VCYzs5uiooKByqHxjdr78Dr81BYeHB3sbb9HTicDopHKM7W09VDKBSmrGLKuFhp3NfbT39fgCnlJdjtR7+UhK4jGGTZRadgdyU/CNldDpZddMqornvFFVdw0kknsWXLFqqrq7nvvvtGdT2lJitvgYfKqvKBJADxmkGVVeVJ5aNtNhsVVWVJSQCgrLx0xCQA4C/2U15ZNi6SAMS38KyoKstJEhhJ1rqGRKQGeACYCsSAe40xdww5RoA7gPOAfuBDxphXsxUTHBwQXvf75+lv76Fgip9lF50y6oHihx9+eCzCUyonjDHEQkGscAibw4nd40FsdqxQECsUQux27B4PNruD5sZWdu3Yg8PhYM78WqaUldDe1snObbuJRqPMnjtzXPV/Z1Nr8352bt+LCMyZN4vyyswrGYdDYXZu30Nr836mzqiids5MnGmK7R0N2bxrFPh3Y8yrIuIH1orI08aYjYOOOReYn/hZDdyd+N+smrV6kc4QUmqQSHcXvXt3QKKr2Dt1Bg6fn97d2zBWfMqmq7SMPpuXa95388D0zGNPWM5tX7mJ//7inbz68hsAVNdM4477vs78NIO7k8nunXu55aOfZ/vW3QAsWjqf23/wRWbNrhnx3Eg4wuO//hNf//z3iMViOBx2vvqdz3LeRWfl5Akma11DxpjGA9/ujTE9wCZgaNWoi4AHTNyLQImITMtWTEqpVFY4RF/97oEkABBoaiAa6BtIAgDhjjZCvT1Jc/Q3r9/KS8+/OpAEAOrrGvn1g38c93sCjNZfnnhmIAkAbN6wjb//9bmMzt21cy///YU7Bv4bRaMWX/nMt9mzaxLvRyAitcCxwEtDfjUDqBv0up7UZIGIXC8ia0RkTWtra9biVCofmWgUY6Xu92vS7QE8pK2iqowtm7anHLb2xdcJBkJjFuN49PILqb3Ya158I82RqdpaO7AGJVmIr5PoaO8ci9AOW9YTgYgUAr8FbjbGdA/9dZpTUqYxGWPuNcasMsasqqioyEaYSuUtm9OJzZk6lTFdm7iS2xr3tXDsqmUpx5117ukU+LxjF+Q4dNY7T09pO/PszCadTJ1eicfjTmorLimiamr5mMR2uLKaCETESTwJPGiMeSzNIfXA4A61amBfNmNSSiWzOV34Zs5GDszisdnwzZyNvcCH3Z2YxSMS39u70M+pZ65ONAnvfu95rFq9ksuvvnigb/uUt67m/He/Ixdv5ah661knc84FZw68vuDd7+CUt56Y0bmzZldz+w++RFFi9tOUshJu/8GXmF6dm57xrK0jSMwIuh9oN8bcfIhjzgc+QXzW0GrgTmPMsP8ldT8CpbLDioSJRcLY7I6BBBCLRrDCYcRmw+72ICL09fZTt6cBu93OzNkzcLvdhEIh9u5uIBqJUlM7I2XK52TV3xegbk8DADNnV+P1Hl5F0Ya6RtrbOqmonMLU6VXZCHHAcOsIsjlr6BTgA8A6EXk90XYbMBPAGHMP8BTxJLCd+PTRa7IYT1bV1dVx9dVX09TUhM1m4/rrr+emm27KdVhKZczudGEf0h1kczixOZLLN/sKC1i0dH5Sm9vtnvSzhNIp8HnTbqGZqRk105hRk/v5MVlLBMaY50g/BjD4GAPckK0YjiaHw8F3vvMdjjvuOHp6ejj++OM5++yzWbJk/G+bp9ShxCyLWCTxROCK92nHolGsUBARwebxptT1GWxffRPBQJCp0yspyNE2jCNpb+uko62T0rJippSV5jqcnMi7lcUATz7+NOec/F5W1J7BOSe/lycff3rU15w2bRrHHRcvgOX3+1m8eDENDQ2jvq5SuWIFA/Tu3UH31g10b91IsL2VaKCf/n176dmxme4dmwk27yMaSq0KGgwG+f2v/8R7zr2Wi8/6IJ/62BfYtWNvDt7F8F5bs46rL7mBd5/9Qd5/8b+x9qXMZv1MNnmXCJ58/Gm+/JnbaWxoxhhDY0MzX/7M7WOSDA7YvXs3r732GqtXZ31tnFJZYWIxAi2NRHu6E68t+uv3YAUDhDvbEwcZgq1NWH2pZZ83rd/G52/9xkBJ6H/98xXuuePnOSsFnc6++iZuvu5z7N0dn7tfv3cfN133ORoGrZPIF3mXCO781o9T5jcHAyHu/NaPx+T6vb29XHrppXzve9+jqKhoTK6p1NEWi0YId6XW+rfCqWsDIr1DZ4XD7p11KW1PP/V/tLW2j02AY6CxoZmO9q6ktu6uHk0E+eBQuwKNxW5BkUiESy+9lKuuuopLLrlk1NdTKlfEZj84dXQQmz11WDHdcWVlJSltc+bNwlc4fsYJikv8OBzJBeDsdjslpcU5iih38i4RTJ2evhjWodozZYzh2muvZfHixdxyyy2jupZSuWZzOCiYPhMG1b1xFhZh93iRQcnA5nLj8Kd+cC5etpDT33bSwGuX28V/fvGTFJeMn6fkWXNquOW2jye13fif1zF77swcRZQ7ebcxzY2fvo4vf+b2pO4hj9fNjZ++blTXff755/nFL37BsmXLWLlyJQBf//rXOe+880Z1XaVyxeErpGj+EqxgEJvdht1TgM3pxD97PtFgABHB7vHi8KZ+y6+oLOPLt/8n2zbvoKe7j9q5NcxbMDsH7+LQnE4nl17xLpYft4Smfa1UTatg/sI5OF3OkU+eZPIuEZx/8dlAfKygaV8LU6dXcuOnrxtoP1KnnnoqE22TH6WGIyI4PF4cnuRSEY4CH46CkReMlZWXUnZq2vVL44a3wMPyY5ey/NhcR5JbeZcIIJ4MRvvBr9REZlkWJhpFHI6BjVJisRjGiiJ2x7BrAyC+vkAkPpYwFmKxGG1tnZSU+JM2rOnt6cPtdo34LT0YDGJi8Q/24USjUQKBEH7/ka187u8L4HDacbnGzzaTYyEvE4FS+SzS20MosSbA4fPjLo3v4hXqbCPS043d48VTXonTl7oLWMyKEunuIri/GbHZ8FROw+nzIyMkjuFseHMzf/jNX3jx+bUcs2IR773qQqZOr+TPT/yDxx99irkLavngdZez7NjU0iyBQJCXX3iVn/7wQSKRKNd87ApOPv3EtIPSm9Zv5Rf3/ZoNb27hvIvO4oJ3n53xqt72tg6eefp5HvzZb6msKufaf7uKY09YNi53GzsSk2bP4kWLFo2bLekOxRjD5s2btdaQyplIfz99e7YTixycz2/3+nD6iwi2HJw2KQ4H/tkLUvr/Q53t9O3dmdTmn7MAZ+GRDQK3Nrdyy8e/yBtrNwy0Ta+eyue/fgsfv/rTA23eAi8P/f5u5g4ZZ3jxuTVcf9W/J7Xd8eOvceY7Tk1q27u7gasu+hhdnQenup5/8dl86Vv/gdudXAU0nYd+/hjf+OLBDRYdDjsP/PYHHLNy4vxbnvR7Fns8Htra2sZ1H70xhra2NjyewytKpdRYioUCSUkAwAr0pRxnolGsYCC5LWYR3N+ccmy4u/OI49mxbU9SEoD4Qq/WpraktkB/gG1bdqWc/+Tjf0tpe+SB36XU+t+5bXdSEgB46vd/o6GuacQY29s6uf/eR5LaolGLN17beIgzJp5J0TVUXV1NfX09433TGo/HQ3V1da7DUHnsUF046Z6mU48VxJb6kTGacQKny4mIpHyJsztTr5lunKAozSb2xSVFKWMcjjR7ATtdzpR1BOk4HHYKClL3VvB4R36SmCgmRSJwOp3Mnj2+pqYpNR7ZXB4cvkKig8pCuErKMEMSgd1bgG3IbCGx2fBWVtHT2zW4EVdRyRHHM3/RHC689Bx+/5s/D7Qdv3o5lVXJG7TUzJrBojRVPt/5rjN59BePE06UrrDb7bxv0N4IByxYNIeFS+axZePB3dSuu+H9GY0RFBX7+eR/fISbr/9/A23FJUWsPO6YzN7kBDApxgiUUpmL9PcS7evDCgVweH3YCwoQmx2rr5dIfy92txdnYSEOb+rMGmNiRPv7iXR3InY7Tn9R2uMOx56ddax95U3WvbqRBUvmsuotK5k6rZI31q7nxefXUjNrBqtPPo7aNAu9jDFsXLeV5//vZaxolJPfeiLHrFiUdhC3bk8DL//rNXZu3c0JJx3LylXHZLyKOBAIsv71zTz3zEuUVZRy0mmrJlzZ7eHGCDQRKKVUHpj0g8VKKaWO3KQYI1AqnxhjsAJ9RHp7QARnoX/U3TPpBPqDrHt9I2tfeoOKqnJWrV6RtntGTXyaCJSaYKL9ffTs3AKJbt2A2PDPXYgzg7IPh+OZvz3Pf37yKwOvp1dP5ccPfZeaWTPG9D4q97RrSKkJJtTWMpAEADAxIp1jW+e/bX8H3/363Ult++qb2LR+65jeR40PmgiUmkCMMcSi0ZT2WDQypveJhCP09qQuNAsGx88OY2rsZJQIRMQuIheKyI0icsuBn2wHp5RKJiJ4yipS2l2lZWN6n8qp5Vx1zaVJbU6Xk/mLJtaUSZWZTMcI/ggEgXVALHvhKKVG4igswlczO14bSCRe+K2gcEzvYbPZeM/7L8Ln9/HrX/6B6pppfPSmD6Zd1KUmvozWEYjIm8aY5UchnhHpOgKl4mJWFBBsWa6A2dXZjdvtwuPVOlkT2VisI/iTiLxjDGNSSo2Sze7IehKAeDkFTQKTW6ZdQy8CvxMRGxABBDDGmPGzAalSecQKh7CCQRCwu73YD7FRSixRRdRYUexuDza3Z9Tl2q1wECsYRESwebzYnbndpKW5sZXtW3cRsyzmLpzN9BlTcxrPRJRpIvgOcBKwzky0mhRKTTLRYIDeXVuJReIzhWwuD4W1c1O2lLQiEQKNdYQPTC0VwV87D2eazeYzvnegn55dWzGJmUs2jxf/rLnY3bl5Yti9Yy83Xf85dm3fC0DVtEruvv+bzJtgdYByLdOuoW3Aek0CSuVeuKNtIAkAxMJBImn2BIgF+w8mAQBj6GvYk3Tu4TDGEGxrHUgC8XsEiPR0D3NWdj3ztxcGkgBAc2MLf/jtX3IWz0SV6RNBI/CMiPwJCB1oNMZ8NytRKaXSMsYklZA+INqfOuc/7XqDcBgTs4Dh9wBOKxbD6k9z72D/4V9rjKx/Y1NK22tr1mFZ1qTZRvJoyPSJYBfwv4AL8A/6UUodRSKCq2RKSruruCSlzeZK3TjF4fMjjiNIAoDY7envfYTbVI6FM846JaXtvIvO0iRwmIZ9IhARD+A3xnx5SHsV0JX+LKVUNjmLSnAF+wm37wfAXV6J05f6YezwFuCrqaW/oQ4Ts7B7CyiYXjOqmUaukilYwUC8y0kET3kVjsLcfSdcferxfOAj7+Ghnz2GMYaL3nsuZ5ydmhzU8IZdRyAi9wJ/NsY8NqT9KuBUY8zHsxxfCl1HoBSYWAwrHO+ltbvch9yCEsAKBTGxGDanC5tj9HUmTczCCid2BHO7iU8mzJ1IJEL93kZMLMaMmdNxu3M7i2m8Gm4dwUh/K041xlw/tNEY86CI3DYm0SmlDpvYbCmzhA5lrGf0iM2e8b2PBqfTyWwtjz0qI6Xy4SYcD3uuiPxURFpEZP0hfn+GiHSJyOuJny+MFKxSk4kVDhPt7xv4dn04oqEA4Z5uwj3dREPxJwNjYkSDAaKBfmKWlWgzWKEg0f6+tIPHydcMxq8XODj4G4tGiQb6408Vid4DY1lEA/1EgwFMTCvOTAYjPRG0iMiJxpiXBzeKyAlA6wjn/hy4C3hgmGOeNcZcMGKUSk0ykZ4ueut2YaJRxOGksGY2Tn9mg66Rvl6C+5uIdHUC8X57d3klke4ugi1NgMHhL6JgWg3Rvl76G+sgFsPu9uCbOQeHtyBNPN30NewmFg4jdjveaTU4vAX01e/BCvSBCN5p1TgLiwg07SPS3QGAu7wKb0UVthwvKlOjM1Ii+A/gVyLyc2Btom0VcDVw+XAnGmP+KSK1ow1QqcnGCgXp3bMzMY0TTDRC754dFM1fgt2dOtNnqEhv90ASAAh3tmP3+gi2twPxb+3Rnm6skj76G/Yk3bd/Xx2FtfOSBoyjoSB99buJReJPJsayCHfsJ9LtiCcBAGMItjRhotGBJAAQ2t+Mo8CHO81sIjVxDNu9k3gSOJF4F9GHEj8CrDbGvDQG9z9JRN4QkT+JyNIxuJ5S414sEh5IAgeYmDXwQTwcy7KIplnAFe3txltRmXxsKJR6XF8PZsjeBbFwOOXejgIfkZ7kiYEOb0HahWuR3twtKFNjY8QpBMaYFuCLWbj3q8AsY0yviJwHPA7MT3egiFwPXA8wc6YOCqmJTewOEuW6BjUKksGMHrvdjt1bQHTIwi67t4BIT2dSmy3NegGby40MmT5qczgQux1jHUxOViiE3ePFGjReYIVDOHyFWMFA0vnpuprUxJLpxjSniMhfRWSriOwUkV0isnM0NzbGdBtjehN/fgpwikj5IY691xizyhizqqIidVMOpSYSu8dDwYyapLaC6TUZz+5xFZcm9cnb3G6chUUMngoudgd2nw9X6aB/UiL4qmelJAiHtwDvtBoGzw2xuVwUTKuBQdNS7R4vninlSQvS7N4CnDlcUKbGRqaTiu8DPkV8nMAa4diMiMhUoNkYY0TkROJJqW0srq3UeCZiw11ajsPrIxaNYHM4sXu8GVcFdRb6KZw1l2gogCDYXG6cvkL87rlYofhMHrvHi93lxj7djXtKGcaysLnch0w2rpIp2F1urEgIm8OJw1OAzemkaN4SYuEgYrNj93iwOZwUzVs0UH3U7vFicx7ZSmU1fmSaCLqMMX86nAuLyMPAGUC5iNQT715yAhhj7gEuAz4uIlEgAFyuRe1UvhCbDUeB74jPdxT4Us63OZ0pH8o2uwObb+SVvzabDVuhH+eQyjEOjwc8ycnD7nJjT1O+Qk1cI5WYOC7xx3+IyO3AYyQXnXv1UOcaY64Y7trGmLuITy9VasI6MDd/LFbsDscKhcBmwz7Ct++YZYGJJXX/xCIRYjErvrI40dVjYjGMZSEOe85XBh+KMSY+vdZmSxnXUGNrpL+93xnyevDyZAO8bWzDUWpiiEWjRHq7CTTvAwzeyuk4i4qx2cc2IUQTdX3CHW2Iw4G3choOf/HAB/oBJhaLx9PUgIlZuMurcJdMIRroJ9jcSCwSwllcijsxZhBoaSTa14uzqBhveRX2cbRSGOID06H2/YQ79mNzufFOnYGjoHDUm+qo9Ib9W2uMORNAROYYY5IGh0VEd35QeSva30vf3oP/JPrqdlE4ay6u4tIxvU+4sy2xSAyIhOnds4PC2fNxDdlcJtrfR+/u7QOvA/vq4nP/W5sG9g8I7W/B7vIQaGkcmEIabt+PFejHP3tB1p9qMmViMQItTYTb42tWY5EIPTu3UjRvsc5QypJMnwl/k6bt12MZiFITSagjdV5DsK2VsRzmigaDhNpT72P1p9b/j/b1pLSF2ltxDhkfiEUjKesIrEA/Vjg4ymjHTiwSGaisOsCYlGmrauyMNEawCFgKFIvIJYN+VQTobtYqb6UrqWBzOse06+JA3/jQD+50/eXp2mx2R8rCtUPFN67GCUQS6xqSayMNV2FVjc5I/2UXAhcAJcC7Bv0cB1yX1ciUGsdcJVNg8IenCO4pY7vGxe5y4a2cltQmDif2NLONnIVFiYVqB3kqpqUUtLO53DiHbGLjKa/K2Z7D6dhdLrzTqpPabB4Pdu0Wypph9yMYOEjkJGPMv45CPCPS/QjUeBEN9MdLNhhwFhZi9xSM+WBmLBol2t9LtL8PsTtwFPhw+grTHmsFA0T6ejExC0dBIY4CH1agP155NBLG7vXh8PkQINrXhxUKxtcbFPhGnI10tBnLItrfRzTQh83pwlHgG1fJaiI64v0IROT7JNbBi0jKdFBjzI1jEqFSE5DDW5D1wUubw4GrqARXUcmIx9o93pTZP+nWGwC4SsZ3tVCx23H6izKuyKpGZ6SuoTXEVxN7iHcHbUv8rGSMVhgrpZTKrZGmj94PICIfAs40xkQSr+8B/pr16JTKolgkTKS3h0hfL46CeM2c0a6YjfR2E+npJhaN4vQXxctIWFGivd1YwSDOQj8OXyEmZoj2dRPt74t/a/f5wWbD6usl0tuD3ePBUViEw+0h0t9HpLsDm9OF01+M3eMl2t83UAnUWVQS7wYKBoh0dxGzIrgSbWLThVhqZJlOHJ4O+IH2xOvCRJtSE5KxLPobGwh3xqdnhtvBUeincObcI55PH+ntoXfPjoEqnuGO/RTWzqN/Xx2xxP7C4c42PBVTsSJhIp3tibZ2XKXl2BwOgq1NA9ezufdTMK0maX1AsLWJwlnz6Nm55WDb/mb8s+fTs3s7JHYMC+1vobB2XkZdSkpl+jf+G8BrIvKPxOu3Al/KSkRKHQVWODSQBA6I9vZghQLYHCPX5kknGuhLKuUM8QHcA0nggOD+ZjzlVQyeFGpzOAjub046LhYKpcydjw+i9pJUxlqESG/PQBI4INC8D4fPn7QJjVLpZJQIjDE/E5E/AasTTZ8xxjQNd45S45kxh9hrdzQLwtKcm3ZWnjEwdHaRSMbnG2OG5AFJSQJAYj9hreOoRjbSBvSLEv97HPGuoLrEz/RBBemUmnDsLg+OIdMwhyvTnNE1vQUpH/B2tzdlfr+rpAwrlLyS1woFcZaUJbWJ3ZFaA0gEh9eXlDSMZeFIsyeAp2LqmNc+UpPTSH9L/p34wrGhxedAi86pCczmcOCrriXUvp9ITxeOQj+eKRWj2oTdXlBI4cy5hNpaiFlRXCVl2NxuCmfNJdjWQiwUxFlUgrO4FDEGm91BtL8XR0EhrtKyeHVRl4tIdyc2twdPWSU2t4eC6lmE2lqxOV14Kqqwu73xa7Y2gwieiiocPh/+OQsItDRjrAie8iqcQ+oRKXUowy4oE5FSY0zHIQ/IAV1QpsZazLLi5RzGaDGYZUUhFsM+KKnEEmWfBy/cOtAmdntSNVErEklpM5YFNkkqBWES3UGDSy8c6A7S2UJqqCNeUAZsEZFW4AXgeeAFY8zWsQ5QqVwa68FUu90BQy5ps9mStn08VBuQdpVv2vpCac7VejzqSIy0jqBSRBYAJyd+bhWRCuBF4HljzLeOQoxKjUtWODSwubvd48Xu9mBFwliB/oHtIh2HqPMfs6JYwSCxSAib0x3fBvIQ/fnRYAArGEBsdhxe72F1X8WiEaxAPzHLwu52Z6UMhpr4RhxJSjwBbAV+LiJzgfOAm4B3AJoIVF6KBgP07Nw6UBlUHA4Ka+fT37BnIDkgNvxz5qeUgjaxGKG2VgJNDQNt3qkz8JRXpXyjj/T10LNz68DgsN3ro3DWnIwWvsUiEfoa9gwsPAN0bYFKa6RZQyeLyK0i8lsReRn4GvGH3vcDOhKl8la4qyOpPLSJRrH6ew8mAQATI9DUEN8+chArFExKAgCBpoaUmUQxy6K/sT5phpAV6MPq78soxmigPykJAPQ37CUWiaQ/QeWtkZ4IngNeBb4LPG6MSd0RQ6k8lLI5jNjSfsBawSDELBjUxz+0zv7B9iHlu2IWsVAo5TgrEk5pS3+91PvEIuHEHgXjq9qoyq2REsF0Do4PfExEHMQTw7+Afw3dvlKpfOEqKSXS03mwwcSwe1IrkbpKpyCO5A9dm9ON2B1JH9Rid2BzJff9i8OJq6SUUFtrUnumFU/t7tTuI4e/KGlje6VghK4hY0yTMeYxY8ytxpjTgbOAzcCXiVchVSovOQuL8FROiy8gOzCXv8BHwYxZA1M3XSWleMoqUwZn7W43hbVzsSUWr9ncHgpr56X0+4sInvKqgX2QxW6noHpWfEFZBuyeAnyz5iKJ2kmOwiIKptWknYGk8ttI+xEUAydx8KngWGA78Efi00mVyks2pxNv1XTcpeWAweZyIyLY3RU4/UUYY7A7nYecz+/0+Smau5BYNIrN4Tjkt3S724OvphbP1Bnx6x9GdVSx2XAXl+Lw+jAxC5vTpXWHVFojdQ1tJz5V9AXgq8DLxhjdQVopSHzwp34wZ/phbXM4M+qmEZsdh/vIP8DtrvG9CY3KvZHWEYztJqxKpWGFQ8Qikfg348Q36/EmFo1ihYOI2LC73YjNTsyy4pVFjcHm9ui3bTVhjdQ19EeGKV9ojLlwzCNSeSXS203vnp3xgVOx4auehaukNKmUQq5ZwQC9dbsGpoa6yypxl1UQaN5HpCtegcXhL8Y3fWbaJwSlxruRuoa+fVSiUHnJCofp3bvr4OwZE6Ovbld8RW6W9wLOlDGGQFtL0vqAUFsLdk/BQBIAiPZ0Ee7uxFtRlYswlRqVkbqG/u9oBaLyj4lGkhZlHRALh2G8JAIrSrS7K6XdCvalTAGNdHXgKU+dJaTUeJfR87eIzBeR34jIRhHZeeAn28GpyU0cjrRTGW1piq7litjsKfsWANjc3pQFYE5/kSYBNSFl2hH7M+BuIAqcCTwA/CJbQan8YHe58VXPTtrMxTutBrvnyDeHGWtis8U3eBmUnJxFJTh9hdi9BwvK2T1enIn5/kpNNJluX+Q1xvyviIgxZg/wJRF5FvhiFmNTecBZVEzR/CXEImFsDid2t2fclVJ2eAvwz12MFQoiNht2twebw4F/9vx4CQkMdrd3XD3JKHU4Mk0EQYlP49gmIp8AGoDK7IWl8oWIxEs1H6Jc83hhd7lS5uPbHE5shfrhrya+TL963QwUADcCxxOvPnp1lmJS6rBEQ0Gs4MjrHK1IhGgwQCyavujbAcbEsMJhYocoDjeS+JqDcNLG8zEr0ZZmk3mlci3TJ4JaY8wrQC9wDYCIvAd46VAniMhPgQuAFmPMMWl+L8AdxPc36Ac+ZIx59fDCV/ksGg4R7e4i2NqEMTHcUypxlU7BkWYD+nBPF4GmfVihAM7CIrwVU9MOAluhIIHWZsKdbdhdHgqmV+Pw+TMaBDaxGJGeLvob6zFWNL7eYEoFsXCI/sZ6rFAAV3Ep3spp2NPEqFSuZPpE8NkM2wb7OfDOYX5/LjA/8XM98cFopTJm9fXRv29vvLRyNEqwZR+RNFM9I/199O7ZgRXog1iMSHcnfY11KeWcTSxGoLmBcHsrxGJYwX56dm3L6GkDIBqI3ycWDmEsi2BLI9H+Xnp2bR24d7ijjb59daklp5XKoZFWFp9L/Bv7DBG5c9CviojPIDokY8w/RaR2mEMuAh4w8efnF0WkRESmGWMaMwtd5btIT+qHfrizDfeUKdjsgzaJDwVhSJeM1d9HLBRK3mA+Eibc2ZF0HMZghYIZLXCLDt2jALBCgaSNZSC++MyKhHHYx/e4iMofI3UN7QPWABcCawe19wCfGuW9ZwB1g17XJ9pSEoGIXE/8qYGZM2eO8rZqskg3S8fmcIEkr01IOwtJ0mwcL7aURWKHPD9dPI7Uf04i6Tadt4+rEhpKjbQfwRvGmPuBecCvgBeNMfcn9ijoGO7cDKTrdE1b18gYc68xZpUxZlVFhdbBU3FOfzEyeMN3ETzlFdiGfHDbPV4chUVJbZ7KqdiHzFSyu1wUTK9JbvP6sGe4ytlR4EvZWN5RUIBjyJ7F3mnVWpNIjSuZDha/k3jdIRcwW0RWAl8ZZdG5emDwv7pq4k8gSmXEWeinsHYeVrAfYwwOTwHOQn/KcXa3h4JpNVjBfmKRMHa3B3uBLyVhALiKS7C5FmIFg9gcjngicGZWxtnu9uCfs4BooB9jWTi8XuxeH74aN1YgQCwaxu4eP3WUlDog00TwJeBE4BkAY8zrI/T/Z+IPwCdE5BFgNdCl4wPqcDl9hTjTzP4ZyuH14vCO3CcvNjtOnx+nLzWhZMLu9qTMCLK73Ie1oYxSR1umiSBqjOk6nDoqIvIwcAZQLiL1xFchOwGMMfcATxEfiN5OfProNZmHrZRSaqxkmgjWi8iVgF1E5hNfWPbCcCcYY64Y4fcGuCHD+yullMqSTKcufBJYCoSAh4Au4quNlVJKTXAjrSPwAB8jPmtoHXCSMebI1t0rpZQal0Z6IrgfWEU8CZyL7limlFKTzkhjBEuMMcsAROQ+4OXsh6SUUupoGumJYGAfQe0SUkqpyWmkJ4IVItKd+LMA3sRrIT7xp+jQpyqllJoIRtq8PrVQilJKqUlFK18ppVSe00SglFJ5ThOBUkrlOU0ESimV5zQRKKVUntNEoJRSeU4TgVJK5TlNBEoplec0ESilVJ7TRKCUUnlOE4FSSuU5TQRKKZXnNBEopVSe00SglFJ5ThOBUkrlOU0ESimV5zQRHAWh3gC9+7uIhiMjH6yUUkfZSFtVqlEwxtC6tZ5XH/4H3U3tTDumluWXnEbx9LJch6aUUgP0iSCLepraefaux+luagegcf1u1vzyb4T7gzmOTCmlDtJEkEXdzZ1YESuprW1nI/3tvTmKSCmlUmkiyCJXgTulzeFx4XA7cxCNUkqlp4kgi4pnlFFzwoKktpXvOZ3CiuIcRaSUUql0sDiL3D4vx77nDGrfsoRQdz+FlSWU1FTmOiyllEqiiSDLPEUFTFtam+swlFLqkLRrKIdMzOQ6BKWUym4iEJF3isgWEdkuIp9J8/szRKRLRF5P/Hwhm/GMF72tnWz+yxr+/u1fseGpl+hp7sh1SEqpPJa1riERsQM/AM4G6oFXROQPxpiNQw591hhzQbbiGG/C/UHWPvR3mjftBeLTSfe9sZPTbrgIT1FBjqNTSuWjbD4RnAhsN8bsNMaEgUeAi7J4vwmhp7lzIAkc0LGnWZ8KlFI5k81EMAOoG/S6PtE21Eki8oaI/ElElqa7kIhcLyJrRGRNa2trNmI9asQmh9WulFLZls1EkO6Tbejo6KvALGPMCuD7wOPpLmSMudcYs8oYs6qiomJsozzK/JUlzFg5N6mtYsEM/FWlOYpIKZXvsjl9tB6oGfS6Gtg3+ABjTPegPz8lIj8UkXJjzP4sxpVTTq+ble95K1OX1tK0cQ+VC6qZdsxs3IXeXIemlMpT2UwErwDzRWQ20ABcDlw5+AARmQo0G2OMiJxI/AmlLYsxjQu+siLmnraMuacty3UoSimVvURgjImKyCeAvwB24KfGmA0i8rHE7+8BLgM+LiJRIABcbozRyfVKKXUUyUT73F21apVZs2bNUbufMYaOuhY697ZidzkonVlF0dT0/fltuxrprG8lEghTPL2MsjnTiIaidOxtJtDZS9HUKZTOrCRmxejY20JvSwe+8mJKZ1Xh8adOHY3FYnTubaGjrhWnx0XprCr8lSVZfsdKqclIRNYaY1al+52WmBjB/h37+L/vPUYsGi8n7Skq4K03X5qyuUzbrkZe+tlf6G3pjDcIvOXa86hbs4WG13cMHLfyvW/FCkdZ9/jzA21zTlvGiktPw+lxJV2zdWs9/7zzdwMrkAum+HnrTe/GXzUlC+9UKZWvtMTEMKKRKJv+/MpAEgAIdvfTsmVvyrGd9a0HkwCAgQ1P/Au7MznXrnv8eSL9oaS2nc+uoyexec0BkWCY9X98MakMRX97D/u3N47iHSmlVCpNBMMwUYv+9p6U9v6OvpS2aDB1P+Jgd3/KbCArHMXmSP3PHg0lnx+LWgQ6Uu8d7OkfMW6llDocmgiG4fS6mXf68pT2qUtmprQVTZuCSPLSidq3LKFla31SW+XCagJdyYnEU+zDN2SPAnehl7lp7l02d1rG8SulVCZ0jGAEM46dRzQUYcvf1uJwO1l28amUzU79MC6bN43V176TjU++RLCrj5knLmLWWxZTc/x83nzsOboa9jPj2HksOmcVsWiMcF+Q5s11lM+dzrKLTsY3pSjlmrPespiYZbHt76/jKvSw4t2nMqV26tF420qpPKKzhjIU6OzF5rCPuPCrt7WTaCiCv3IKdpcdiPf3RwIh3H4vdkc890bDUcJ9AVw+Dw7XobeuNMYQ7OrD5rTj9umiM6XUkdFZQ2PAW1KY0XGFFSUpbU6PK2VGkMPlwOHyj3g9Ecn43kopdSQ0EWSgu7GNrsZ27A47xTPK8ZUV0dXUTk9jO7FYjKKpUyiZUU44EKK7sZ1oKIy/ogRfefq9iWNWjJ7mDvo7evAWF+KfWordYT/K70oppeI0EYxg/459vPjTP9PfFi+LVLFgBssuPpXXfvUMHbubASisLOGEq89m1/Mb2P2v+HYL7kIvp33yYqbMqkq6nokZ6l/dxss//wsxK4bYhOOvfDu1Jy3GZtdkoJQ6+nTW0DCikSg7nl03kAQAOve20rqtfiAJAPS2dFL/6nZaB80QCvUGePN3zxEJhZOu2dPaySsPPE3MigHxxLD2ob/T06T7ESilckMTwTAifUHadzUltRVM8dOxpyXl2P079lE4pJR0+66mlMVjoe5+rEg0qc3EYgS7dX2AUio3NBEMw11UQNWi5DUDva1dlM+bnnLs1KWz6GrYP6StFteQWUbekkKcBe6kNrvTjrd05IFjpZTKBk0Ew7DZbNSevIQpcxJz9wWqj59P2eypVB83f+C4ygXVTF82m7mnLRvYaaxoehlLL1iNY0iJicKKYk76yLm4fB4gvmjtLR85T4vJKaVyRtcRZKCvo4eepg7sDhtFMypwF7gJdPXSta8NEzP4p02hcEoRVtSit6WTaChMYXkx7jQVRQeu2dZNoKsPj9+bdsqpUkqNJV1HMEq+Uj++IV033uJCvMXJ8/vtDntKVdJDXrOsCF9Z6mpipZQ62vImEQS7+wj3h/AUFeAq8Bz6uN5++vb34HA5Bj7Ug70Bepo7sNmE4poKHA4H4b4w3U37wYCvqgRv4tt/2+4mYhGLgjL/QNmIjr0tRIJhPCU+iirjA8qh3gChnn5chd60exEcEI1ECbT3YHPY8JWlX5eglFKjMekTgTGGls11vPLLv9Hf1k3pzEqOf/9ZTJlZmXJs2+4m1j3+PC2b63AXell28SkUzShj+zNvUPfKFuxOBwvPPo5px8ym7tVtbP/H68QsQ+1Ji5lz6jG0725mwx//RTgQYurS2Sw970T62rt54zfPEujspXRWFSsuPQ27084rv/gb3fva8FeWsuoDZ1Exf0ZKPL2tXWx44l/sfXkLdreT5e8+hZknLsbldaUcq5RSR2rSDxb3NHfw3A//MLAWoGNvCy/+5MmU6Zqh/hAbn3yJls118de9Aepf3cq+13ew96XNmJghGoqw+a9r6ahrZctf12JFLEwsxq7nN9C6rSGeBPpDYKBp/S52PLuOLX9dS6CzN37vPc289ugzRENRuvfFt2buaengubv/QO/+zqR4jDHsfG4de17ajDGGaDDMqw//g/bduh+BUmpsTfpE0Lu/K2Xefm9LV8o+A4H2bhrX70pqm/WWJTS8sSOprWrxLJo37Um5T/2r2ygdUhm0/rXtKVNNuxr2E7OspLZIf4i+/d1JbaHeAHte2pxyn6HrGpRSarQmfSJIV7HT7nLgHNK94vS4Uvrge5o78Q9ZJNbd2JZ2q8ii6WX0tyd/mPsrSlISjrPAPVCBdIAwMJ30AIfbSdH01PsU6ACzUmqMTfpEUDStlAVnH5fUdux7z6BwyLx9X3kxKy49DbEd/E+yf3sD885YgdN7cAGYiFC1ZGbSB7LL52HW6sVJx9kcdpZeeBIO96AS0wLLLz6FSDB5tfHSC07CPzX5Q9/hcrL0guTzS2dWUj43dTGbUkqNRl6sIwj3B+lq2E+gsw9feTHF1WU4nKl7AMSiMdp2NdLd1I7L56akuhJ/ZQmt2+rpburA7rRTNK2MKbOqaNsZP87EDEXTplA+dzrte5rpaeogEgrjryyldE4VfS1d9DZ3EOwN4C8vpqi6ApfHRWfDfvrbuvGW+imZUZaURAbramyju7Edh9NBcU0FBVqSWil1BIZbR5AXiUAppfLdcIlg0ncNjSQSCmNiR5YMrYiFFU4eiA73BelPzBJSSqmJYNKvIziU3tZOdr+4iYbXtlOxoJq5py+jeHp5RudakSitW+vZ/Ne1WJEIC88+nrKFM+jY0cyO/3uDQFcfM09YSOXimUypSV2voJRS40leJoJIIMSrj/yDpg3xaaBd+9poXLeLM299DwUZVAFt29nIP7//+MDrF370JKff+G5e+NETxKLxqaGdda0sOX+1JgKl1LiXl11Dva1dA0nggL62brqb2jM6v27ttqTXYrfT3dg2kAQO2PHPN+moS927QCmlxpO8TARit4Gktme6VaRjyEb0Bit+zSHsLie2NO1KKTWe5OWnVGFlCfNOX57UVj5vOkXTUhdwpVN93Dxsgzabl5hQPKMctz958dqic1ZlPO6glFK5krfTRwNdfbRuq6dlaz1TZlVRubCGwvLMqnsaY+jY08K+9TuJRSymL5/DlNoqWrc30rq1jv6OHqYumcWU2qkZX1MppbJJ1xEopVSe03UESimlDimriUBE3ikiW0Rku4h8Js3vRUTuTPz+TRE5Lt11lFJKZU/WEoGI2IEfAOcCS4ArRGTJkMPOBeYnfq4H7s5WPEoppdLL5hPBicB2Y8xOY0wYeAS4aMgxFwEPmLgXgRIRmZbFmJRSSg2RzUQwA6gb9Lo+0Xa4xyAi14vIGhFZ09raOuaBKqVUPstmIkizZIuhU5QyOQZjzL3GmFXGmFUVFRVjEpxSSqm4bNYaqgdqBr2uBvYdwTFJ1q5du19EUveKzEw5sP8Izx2P9P2MX5PpvcDkej+T6b1A5u9n1qF+kc1E8AowX0RmAw3A5cCVQ475A/AJEXkEWA10GWOG3Z3dGHPEjwQisuZQ82gnIn0/49dkei8wud7PZHovMDbvJ2uJwBgTFZFPAH8B7MBPjTEbRORjid/fAzwFnAdsB/qBa7IVj1JKqfSyWobaGPMU8Q/7wW33DPqzAW7IZgxKKaWGl28ri+/NdQBjTN/P+DWZ3gtMrvczmd4LjMH7mXC1hpRSSo2tfHsiUEopNYQmAqWUynN5kQhE5Kci0iIi63Mdy1gQkRoR+YeIbBKRDSJyU65jOlIi4hGRl0XkjcR7+XKuYxotEbGLyGsi8kSuYxktEdktIutE5HURmfD130WkRER+IyKbE/9+Tsp1TEdKRBYm/n858NMtIjcf0bXyYYxARE4HeonXNTom1/GMVqIe0zRjzKsi4gfWAhcbYzbmOLTDJiIC+IwxvSLiBJ4DbkrUnpqQROQWYBVQZIy5INfxjIaI7AZWGWMmxQIsEbkfeNYY8xMRcQEFxpjOHIc1aokinw3AamPMYS+4zYsnAmPMP4HMdqafAIwxjcaYVxN/7gE2kaZG00SQKDjYm3jpTPxM2G8nIlINnA/8JNexqGQiUgScDtwHYIwJT4YkkPB2YMeRJAHIk0QwmYlILXAs8FKOQzliia6U14EW4GljzIR9L8D3gE8DsRzHMVYM8FcRWSsi1+c6mFGaA7QCP0t03f1ERHy5DmqMXA48fKQnayKYwESkEPgtcLMxpjvX8RwpY4xljFlJvNbUiSIyIbvvROQCoMUYszbXsYyhU4wxxxHfO+SGRDfrROUAjgPuNsYcC/QBKRtmTTSJLq4LgV8f6TU0EUxQif703wIPGmMey3U8YyHxmP4M8M7cRnLETgEuTPSrPwK8TUR+mduQRscYsy/xvy3A74jvMzJR1QP1g544f0M8MUx05wKvGmOaj/QCmggmoMQA633AJmPMd3Mdz2iISIWIlCT+7AXOAjbnNKgjZIz5rDGm2hhTS/xR/e/GmPfnOKwjJiK+xGQEEl0o7wAm7Mw7Y0wTUCciCxNNbwcm3ASLNK5gFN1CkOVaQ+OFiDwMnAGUi0g98EVjzH25jWpUTgE+AKxL9K0D3Jao7TTRTAPuT8x6sAG/MsZM+GmXk0QV8Lv49w4cwEPGmD/nNqRR+yTwYKI7ZScTvNCliBQAZwMfHdV18mH6qFJKqUPTriGllMpzmgiUUirPaSJQSqk8p4lAKaXynCYCpZTKc5oI1IQlIlai6uJ6Efl1YirdoY5dKSLnZXDNMw5UDRWRD4nIXWMZ85B71YrIlYNeH/J+IlIoIj8SkR2JKq3/FJHV2YpN5RdNBGoiCxhjViYqyoaBjw1z7EpgxERwlNUCV450UMJPiBdOnG+MWQp8CCjPTlgq32giUJPFs8C8xGrYn4rIK4nCYhclFg99BXhf4gnifSJyooi8kDjmhUGrTUckIu9P7KHweuJbuj3R3isiX0vsrfCiiFQl2ucmXr8iIl8RkQPVVr8BnJa4zqcSbdNF5M8isk1EvnXgfGA18P+MMTEAY8xOY8yTiaeKzYkCautF5EEROUtEnk9cYyKXhFBHiSYCNeGJiIN4vZV1wOeIl3Y4ATgTuJ14aesvAI8mniAeJV7G4vRE8bEvAF/P8F6LgfcRL8a2ErCAqxK/9gEvGmNWAP8Erku03wHckYhp36DLfYZ4bfyVxpj/SbStTFx/GfHEVQMsBV43xliHCGte4h7LgUXEnzJOBW4Fbsvkfan8lhclJtSk5R1UYuNZ4vWXXiBe+O3WRLsHmJnm3GLipS3mEy+17Mzwnm8HjgdeSZRe8BIvnw3x7qkD5THWEl/6D3AScHHizw8B3x7m+v9rjOkCEJGNwKwMYtpljFmXOGdD4hpGRNYR735SaliaCNREFkh8Kx+QKMh3qTFmy5D2oQOrXwX+YYx5d2JPh2cyvKcA9xtjPpvmdxFzsGaLxZH9+woN+vOBa2wAVoiI7UDX0DDnxAa9jh1hDCrPaNeQmmz+AnwykRAQkWMT7T2Af9BxxcS39oP4wGum/he4TEQqE9efIiIjfWt/Ebg08efLB7UPjSktY8wOYA3w5UHva76IXHQYcSt1SJoI1GTzVeLdPG+KyPrEa4B/AEsODBYD3wL+W0SeB+zDXO9DIlJ/4AfoBv4f8V273gSeJl5BdTg3A7eIyMuJY7sS7W8C0cTg8qcOdXLCR4CpwPZEl8+PSR5vUOqIafVRpbIssb4hkOi3vxy4whij3+bVuKH9h0pl3/HAXYlunU7gw7kNR6lk+kSglFJ5TscIlFIqz2kiUEqpPKeJQCml8pwmAqWUynOaCJRSKs/9f+SjHYDuLLMAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.scatterplot(df_4['PetalLengthCm'],df_4['PetalWidthCm'],hue = df_4['clus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Euclidean distance')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 7))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "sc.dendrogram(sc.linkage(df_4, method='ward'))\n",
    "plt.title('Dendrogram')\n",
    "plt.xlabel('Sample index')\n",
    "plt.ylabel('Euclidean distance')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Partitioning Clustering**\n",
    "\n",
    "**K-means clustering**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_range = range(2,10)\n",
    "cluster_wss = []\n",
    "from sklearn.cluster import KMeans\n",
    "for num_cluster in cluster_range:\n",
    "    cluster = KMeans(num_cluster,random_state=25)\n",
    "    cluster.fit(x)\n",
    "    cluster_wss.append(cluster.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>num_cluster</th>\n",
       "      <th>cluster_wss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>152.368706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>78.940841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>57.317873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>46.561630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>38.930963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>34.636328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>30.009659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>28.170474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_cluster  cluster_wss\n",
       "0            2   152.368706\n",
       "1            3    78.940841\n",
       "2            4    57.317873\n",
       "3            5    46.561630\n",
       "4            6    38.930963\n",
       "5            7    34.636328\n",
       "6            8    30.009659\n",
       "7            9    28.170474"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_df = pd.DataFrame({'num_cluster':cluster_range,'cluster_wss':cluster_wss})\n",
    "cluster_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f213b39d0>]"
      ]
     },
     "execution_count": 38,
     "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.xlabel('no. of cluster')\n",
    "plt.ylabel('WSS')\n",
    "plt.plot(cluster_df['num_cluster'],cluster_df['cluster_wss'],marker = '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kclusters = KMeans (n_clusters = 3,random_state = 25)\n",
    "Kclusters.fit(df_5)\n",
    "df_5['clus'] = Kclusters.predict(df_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sb.scatterplot(df_5['PetalLengthCm'],df_5['PetalWidthCm'],hue = df_5['clus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**PAM (Partitioning Around Medoids)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): ...working... done\n",
      "Solving environment: ...working... done\n",
      "\n",
      "# All requested packages already installed.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#conda install -c conda-forge scikit-learn-extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn_extra.cluster import KMedoids\n",
    "kmedoids = KMedoids(n_clusters=3, random_state=0).fit(df_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_6['clus'] = kmedoids.predict(df_6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\My\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sb.scatterplot(df_6['PetalLengthCm'],df_6['PetalWidthCm'],hue = df_6['clus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 2
}