K-means.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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>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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</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>120</th>\n",
       "      <td>121</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>123</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>125</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</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",
       "5      6            5.4           3.9            1.7           0.4   \n",
       "6      7            4.6           3.4            1.4           0.3   \n",
       "7      8            5.0           3.4            1.5           0.2   \n",
       "8      9            4.4           2.9            1.4           0.2   \n",
       "9     10            4.9           3.1            1.5           0.1   \n",
       "10    11            5.4           3.7            1.5           0.2   \n",
       "11    12            4.8           3.4            1.6           0.2   \n",
       "12    13            4.8           3.0            1.4           0.1   \n",
       "13    14            4.3           3.0            1.1           0.1   \n",
       "14    15            5.8           4.0            1.2           0.2   \n",
       "15    16            5.7           4.4            1.5           0.4   \n",
       "16    17            5.4           3.9            1.3           0.4   \n",
       "17    18            5.1           3.5            1.4           0.3   \n",
       "18    19            5.7           3.8            1.7           0.3   \n",
       "19    20            5.1           3.8            1.5           0.3   \n",
       "20    21            5.4           3.4            1.7           0.2   \n",
       "21    22            5.1           3.7            1.5           0.4   \n",
       "22    23            4.6           3.6            1.0           0.2   \n",
       "23    24            5.1           3.3            1.7           0.5   \n",
       "24    25            4.8           3.4            1.9           0.2   \n",
       "25    26            5.0           3.0            1.6           0.2   \n",
       "26    27            5.0           3.4            1.6           0.4   \n",
       "27    28            5.2           3.5            1.5           0.2   \n",
       "28    29            5.2           3.4            1.4           0.2   \n",
       "29    30            4.7           3.2            1.6           0.2   \n",
       "..   ...            ...           ...            ...           ...   \n",
       "120  121            6.9           3.2            5.7           2.3   \n",
       "121  122            5.6           2.8            4.9           2.0   \n",
       "122  123            7.7           2.8            6.7           2.0   \n",
       "123  124            6.3           2.7            4.9           1.8   \n",
       "124  125            6.7           3.3            5.7           2.1   \n",
       "125  126            7.2           3.2            6.0           1.8   \n",
       "126  127            6.2           2.8            4.8           1.8   \n",
       "127  128            6.1           3.0            4.9           1.8   \n",
       "128  129            6.4           2.8            5.6           2.1   \n",
       "129  130            7.2           3.0            5.8           1.6   \n",
       "130  131            7.4           2.8            6.1           1.9   \n",
       "131  132            7.9           3.8            6.4           2.0   \n",
       "132  133            6.4           2.8            5.6           2.2   \n",
       "133  134            6.3           2.8            5.1           1.5   \n",
       "134  135            6.1           2.6            5.6           1.4   \n",
       "135  136            7.7           3.0            6.1           2.3   \n",
       "136  137            6.3           3.4            5.6           2.4   \n",
       "137  138            6.4           3.1            5.5           1.8   \n",
       "138  139            6.0           3.0            4.8           1.8   \n",
       "139  140            6.9           3.1            5.4           2.1   \n",
       "140  141            6.7           3.1            5.6           2.4   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "142  143            5.8           2.7            5.1           1.9   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "144  145            6.7           3.3            5.7           2.5   \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",
       "5       Iris-setosa  \n",
       "6       Iris-setosa  \n",
       "7       Iris-setosa  \n",
       "8       Iris-setosa  \n",
       "9       Iris-setosa  \n",
       "10      Iris-setosa  \n",
       "11      Iris-setosa  \n",
       "12      Iris-setosa  \n",
       "13      Iris-setosa  \n",
       "14      Iris-setosa  \n",
       "15      Iris-setosa  \n",
       "16      Iris-setosa  \n",
       "17      Iris-setosa  \n",
       "18      Iris-setosa  \n",
       "19      Iris-setosa  \n",
       "20      Iris-setosa  \n",
       "21      Iris-setosa  \n",
       "22      Iris-setosa  \n",
       "23      Iris-setosa  \n",
       "24      Iris-setosa  \n",
       "25      Iris-setosa  \n",
       "26      Iris-setosa  \n",
       "27      Iris-setosa  \n",
       "28      Iris-setosa  \n",
       "29      Iris-setosa  \n",
       "..              ...  \n",
       "120  Iris-virginica  \n",
       "121  Iris-virginica  \n",
       "122  Iris-virginica  \n",
       "123  Iris-virginica  \n",
       "124  Iris-virginica  \n",
       "125  Iris-virginica  \n",
       "126  Iris-virginica  \n",
       "127  Iris-virginica  \n",
       "128  Iris-virginica  \n",
       "129  Iris-virginica  \n",
       "130  Iris-virginica  \n",
       "131  Iris-virginica  \n",
       "132  Iris-virginica  \n",
       "133  Iris-virginica  \n",
       "134  Iris-virginica  \n",
       "135  Iris-virginica  \n",
       "136  Iris-virginica  \n",
       "137  Iris-virginica  \n",
       "138  Iris-virginica  \n",
       "139  Iris-virginica  \n",
       "140  Iris-virginica  \n",
       "141  Iris-virginica  \n",
       "142  Iris-virginica  \n",
       "143  Iris-virginica  \n",
       "144  Iris-virginica  \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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Iris_data=iris.drop(['Id','Species'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "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",
       "5              5.4           3.9            1.7           0.4\n",
       "6              4.6           3.4            1.4           0.3\n",
       "7              5.0           3.4            1.5           0.2\n",
       "8              4.4           2.9            1.4           0.2\n",
       "9              4.9           3.1            1.5           0.1\n",
       "10             5.4           3.7            1.5           0.2\n",
       "11             4.8           3.4            1.6           0.2\n",
       "12             4.8           3.0            1.4           0.1\n",
       "13             4.3           3.0            1.1           0.1\n",
       "14             5.8           4.0            1.2           0.2\n",
       "15             5.7           4.4            1.5           0.4\n",
       "16             5.4           3.9            1.3           0.4\n",
       "17             5.1           3.5            1.4           0.3\n",
       "18             5.7           3.8            1.7           0.3\n",
       "19             5.1           3.8            1.5           0.3\n",
       "20             5.4           3.4            1.7           0.2\n",
       "21             5.1           3.7            1.5           0.4\n",
       "22             4.6           3.6            1.0           0.2\n",
       "23             5.1           3.3            1.7           0.5\n",
       "24             4.8           3.4            1.9           0.2\n",
       "25             5.0           3.0            1.6           0.2\n",
       "26             5.0           3.4            1.6           0.4\n",
       "27             5.2           3.5            1.5           0.2\n",
       "28             5.2           3.4            1.4           0.2\n",
       "29             4.7           3.2            1.6           0.2\n",
       "..             ...           ...            ...           ...\n",
       "120            6.9           3.2            5.7           2.3\n",
       "121            5.6           2.8            4.9           2.0\n",
       "122            7.7           2.8            6.7           2.0\n",
       "123            6.3           2.7            4.9           1.8\n",
       "124            6.7           3.3            5.7           2.1\n",
       "125            7.2           3.2            6.0           1.8\n",
       "126            6.2           2.8            4.8           1.8\n",
       "127            6.1           3.0            4.9           1.8\n",
       "128            6.4           2.8            5.6           2.1\n",
       "129            7.2           3.0            5.8           1.6\n",
       "130            7.4           2.8            6.1           1.9\n",
       "131            7.9           3.8            6.4           2.0\n",
       "132            6.4           2.8            5.6           2.2\n",
       "133            6.3           2.8            5.1           1.5\n",
       "134            6.1           2.6            5.6           1.4\n",
       "135            7.7           3.0            6.1           2.3\n",
       "136            6.3           3.4            5.6           2.4\n",
       "137            6.4           3.1            5.5           1.8\n",
       "138            6.0           3.0            4.8           1.8\n",
       "139            6.9           3.1            5.4           2.1\n",
       "140            6.7           3.1            5.6           2.4\n",
       "141            6.9           3.1            5.1           2.3\n",
       "142            5.8           2.7            5.1           1.9\n",
       "143            6.8           3.2            5.9           2.3\n",
       "144            6.7           3.3            5.7           2.5\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wcss = []\n",
    "for k in  range(1,15):\n",
    "    kmeans = KMeans(n_clusters=k)\n",
    "    kmeans.fit(Iris_data)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.title(\"WCSS /K Chart\", fontsize=18)\n",
    "plt.plot(range(1,15),wcss,\"-o\")\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"Amount of Clusters\",fontsize=14)\n",
    "plt.ylabel(\"Inertia\",fontsize=14)\n",
    "plt.xticks(range(1,20))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "kmeans_predict=kmeans.predict(Iris_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  1,  1,  1,  4, 11,  1,  4,  1,  1,  4,  4,  1,  1, 11, 11, 11,\n",
       "        4, 11,  4,  4,  4,  1,  4,  4,  1,  4,  4,  4,  1,  1,  4, 11, 11,\n",
       "        1,  1,  4,  1,  1,  4,  4,  1,  1,  4,  4,  1,  4,  1,  4,  4, 13,\n",
       "        2, 13, 12, 13,  6,  2,  5, 13,  6,  5,  2, 12,  2, 12, 13,  6, 12,\n",
       "        8, 12, 10,  2,  8,  2,  2, 13, 13, 13,  2, 12, 12, 12, 12, 10,  6,\n",
       "        2, 13,  8,  6, 12,  6,  2, 12,  5,  6,  6,  6,  2,  5,  6,  9, 10,\n",
       "        9,  3,  9,  7,  6,  7,  3,  9,  3,  3,  9, 10, 10,  3,  3,  0,  7,\n",
       "        8,  9, 10,  7, 10,  9,  7, 10, 10,  3,  7,  7,  0,  3, 10,  3,  7,\n",
       "        9,  3, 10,  9,  9,  9, 10,  9,  9,  9,  8,  3,  3, 10])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Iris_data=pd.DataFrame(Iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Iris_data[\"Label\"]=kmeans_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </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",
       "      <td>4</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",
       "      <td>1</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",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</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",
       "      <td>8</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",
       "      <td>3</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Label\n",
       "0              5.1           3.5            1.4           0.2      4\n",
       "1              4.9           3.0            1.4           0.2      1\n",
       "2              4.7           3.2            1.3           0.2      1\n",
       "3              4.6           3.1            1.5           0.2      1\n",
       "4              5.0           3.6            1.4           0.2      4\n",
       "5              5.4           3.9            1.7           0.4     11\n",
       "6              4.6           3.4            1.4           0.3      1\n",
       "7              5.0           3.4            1.5           0.2      4\n",
       "8              4.4           2.9            1.4           0.2      1\n",
       "9              4.9           3.1            1.5           0.1      1\n",
       "10             5.4           3.7            1.5           0.2      4\n",
       "11             4.8           3.4            1.6           0.2      4\n",
       "12             4.8           3.0            1.4           0.1      1\n",
       "13             4.3           3.0            1.1           0.1      1\n",
       "14             5.8           4.0            1.2           0.2     11\n",
       "15             5.7           4.4            1.5           0.4     11\n",
       "16             5.4           3.9            1.3           0.4     11\n",
       "17             5.1           3.5            1.4           0.3      4\n",
       "18             5.7           3.8            1.7           0.3     11\n",
       "19             5.1           3.8            1.5           0.3      4\n",
       "20             5.4           3.4            1.7           0.2      4\n",
       "21             5.1           3.7            1.5           0.4      4\n",
       "22             4.6           3.6            1.0           0.2      1\n",
       "23             5.1           3.3            1.7           0.5      4\n",
       "24             4.8           3.4            1.9           0.2      4\n",
       "25             5.0           3.0            1.6           0.2      1\n",
       "26             5.0           3.4            1.6           0.4      4\n",
       "27             5.2           3.5            1.5           0.2      4\n",
       "28             5.2           3.4            1.4           0.2      4\n",
       "29             4.7           3.2            1.6           0.2      1\n",
       "..             ...           ...            ...           ...    ...\n",
       "120            6.9           3.2            5.7           2.3      9\n",
       "121            5.6           2.8            4.9           2.0     10\n",
       "122            7.7           2.8            6.7           2.0      7\n",
       "123            6.3           2.7            4.9           1.8     10\n",
       "124            6.7           3.3            5.7           2.1      9\n",
       "125            7.2           3.2            6.0           1.8      7\n",
       "126            6.2           2.8            4.8           1.8     10\n",
       "127            6.1           3.0            4.9           1.8     10\n",
       "128            6.4           2.8            5.6           2.1      3\n",
       "129            7.2           3.0            5.8           1.6      7\n",
       "130            7.4           2.8            6.1           1.9      7\n",
       "131            7.9           3.8            6.4           2.0      0\n",
       "132            6.4           2.8            5.6           2.2      3\n",
       "133            6.3           2.8            5.1           1.5     10\n",
       "134            6.1           2.6            5.6           1.4      3\n",
       "135            7.7           3.0            6.1           2.3      7\n",
       "136            6.3           3.4            5.6           2.4      9\n",
       "137            6.4           3.1            5.5           1.8      3\n",
       "138            6.0           3.0            4.8           1.8     10\n",
       "139            6.9           3.1            5.4           2.1      9\n",
       "140            6.7           3.1            5.6           2.4      9\n",
       "141            6.9           3.1            5.1           2.3      9\n",
       "142            5.8           2.7            5.1           1.9     10\n",
       "143            6.8           3.2            5.9           2.3      9\n",
       "144            6.7           3.3            5.7           2.5      9\n",
       "145            6.7           3.0            5.2           2.3      9\n",
       "146            6.3           2.5            5.0           1.9      8\n",
       "147            6.5           3.0            5.2           2.0      3\n",
       "148            6.2           3.4            5.4           2.3      3\n",
       "149            5.9           3.0            5.1           1.8     10\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30,5))     \n",
    "plt.subplot(2,6,6)\n",
    "plt.title(\"Original Labels\",fontsize=16)\n",
    "plt.xlabel(\"PetalLengthCm\")\n",
    "plt.scatter(Iris_data.SepalLengthCm[Iris_data.Label == \"Lily\"],Iris_data.SepalWidthCm[Iris_data.Label == \"Lily\"])\n",
    "plt.scatter(Iris_data.SepalLengthCm[Iris_data.Label == \"Lotus\"],Iris_data.SepalWidthCm[Iris_data.Label == \"Lotus\"])\n",
    "plt.scatter(Iris_data.SepalLengthCm[Iris_data.Label == \"Hibiscus\"],Iris_data.SepalWidthCm[Iris_data.Label == \"Hibiscus\"])\n",
    "plt.subplots_adjust(top=0.9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 948.75x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=Iris_data,hue=\"Label\",palette=\"Set1\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import silhouette_score\n",
    "x = silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.744026700321914"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "silhouette_score(Iris_data,kmeans_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=Iris_data.iloc[:,:-1]\n",
    "Y=Iris_data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=knn.predict(x_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 12, 11,  7,  4,  9,  4, 13, 13,  2,  3,  2,  2, 13, 10,  1,  2,\n",
       "        6,  1,  4, 10,  6,  4,  1, 10,  1,  4,  2,  5,  4])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n           1       1.00      1.00      1.00         4\\n           2       1.00      0.83      0.91         6\\n           3       1.00      1.00      1.00         1\\n           4       0.83      1.00      0.91         5\\n           5       1.00      1.00      1.00         1\\n           6       1.00      1.00      1.00         2\\n           7       1.00      1.00      1.00         1\\n           9       1.00      1.00      1.00         1\\n          10       0.75      1.00      0.86         3\\n          11       1.00      0.50      0.67         2\\n          12       1.00      1.00      1.00         1\\n          13       1.00      1.00      1.00         3\\n\\n    accuracy                           0.93        30\\n   macro avg       0.97      0.94      0.95        30\\nweighted avg       0.95      0.93      0.93        30\\n'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 5, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0],\n",
       "       [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3]], dtype=int64)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(x_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "Iris_data[\"Label\"].replace({0: \"Hibiscus\",1:\"Lily\",2:\"Lotus\"},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </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",
       "      <td>4</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",
       "      <td>Lily</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",
       "      <td>Lily</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",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Lily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hibiscus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>9</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",
       "      <td>8</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",
       "      <td>3</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",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm     Label\n",
       "0              5.1           3.5            1.4           0.2         4\n",
       "1              4.9           3.0            1.4           0.2      Lily\n",
       "2              4.7           3.2            1.3           0.2      Lily\n",
       "3              4.6           3.1            1.5           0.2      Lily\n",
       "4              5.0           3.6            1.4           0.2         4\n",
       "5              5.4           3.9            1.7           0.4        11\n",
       "6              4.6           3.4            1.4           0.3      Lily\n",
       "7              5.0           3.4            1.5           0.2         4\n",
       "8              4.4           2.9            1.4           0.2      Lily\n",
       "9              4.9           3.1            1.5           0.1      Lily\n",
       "10             5.4           3.7            1.5           0.2         4\n",
       "11             4.8           3.4            1.6           0.2         4\n",
       "12             4.8           3.0            1.4           0.1      Lily\n",
       "13             4.3           3.0            1.1           0.1      Lily\n",
       "14             5.8           4.0            1.2           0.2        11\n",
       "15             5.7           4.4            1.5           0.4        11\n",
       "16             5.4           3.9            1.3           0.4        11\n",
       "17             5.1           3.5            1.4           0.3         4\n",
       "18             5.7           3.8            1.7           0.3        11\n",
       "19             5.1           3.8            1.5           0.3         4\n",
       "20             5.4           3.4            1.7           0.2         4\n",
       "21             5.1           3.7            1.5           0.4         4\n",
       "22             4.6           3.6            1.0           0.2      Lily\n",
       "23             5.1           3.3            1.7           0.5         4\n",
       "24             4.8           3.4            1.9           0.2         4\n",
       "25             5.0           3.0            1.6           0.2      Lily\n",
       "26             5.0           3.4            1.6           0.4         4\n",
       "27             5.2           3.5            1.5           0.2         4\n",
       "28             5.2           3.4            1.4           0.2         4\n",
       "29             4.7           3.2            1.6           0.2      Lily\n",
       "..             ...           ...            ...           ...       ...\n",
       "120            6.9           3.2            5.7           2.3         9\n",
       "121            5.6           2.8            4.9           2.0        10\n",
       "122            7.7           2.8            6.7           2.0         7\n",
       "123            6.3           2.7            4.9           1.8        10\n",
       "124            6.7           3.3            5.7           2.1         9\n",
       "125            7.2           3.2            6.0           1.8         7\n",
       "126            6.2           2.8            4.8           1.8        10\n",
       "127            6.1           3.0            4.9           1.8        10\n",
       "128            6.4           2.8            5.6           2.1         3\n",
       "129            7.2           3.0            5.8           1.6         7\n",
       "130            7.4           2.8            6.1           1.9         7\n",
       "131            7.9           3.8            6.4           2.0  Hibiscus\n",
       "132            6.4           2.8            5.6           2.2         3\n",
       "133            6.3           2.8            5.1           1.5        10\n",
       "134            6.1           2.6            5.6           1.4         3\n",
       "135            7.7           3.0            6.1           2.3         7\n",
       "136            6.3           3.4            5.6           2.4         9\n",
       "137            6.4           3.1            5.5           1.8         3\n",
       "138            6.0           3.0            4.8           1.8        10\n",
       "139            6.9           3.1            5.4           2.1         9\n",
       "140            6.7           3.1            5.6           2.4         9\n",
       "141            6.9           3.1            5.1           2.3         9\n",
       "142            5.8           2.7            5.1           1.9        10\n",
       "143            6.8           3.2            5.9           2.3         9\n",
       "144            6.7           3.3            5.7           2.5         9\n",
       "145            6.7           3.0            5.2           2.3         9\n",
       "146            6.3           2.5            5.0           1.9         8\n",
       "147            6.5           3.0            5.2           2.0         3\n",
       "148            6.2           3.4            5.4           2.3         3\n",
       "149            5.9           3.0            5.1           1.8        10\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Iris_data"
   ]
  }
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