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diff --git a/Homework/Homework#1/.ipynb_checkpoints/HW#1-checkpoint.ipynb b/Homework/Homework#1/.ipynb_checkpoints/HW#1-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length\n",
+       "0          5.1           3.5\n",
+       "1          4.9           3.0\n",
+       "2          4.7           3.2\n",
+       "3          4.6           3.1\n",
+       "4          5.0           3.6"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('data.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7a6205310>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot.scatter(x='Sepal width', y='Sepal length')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length\n",
+       "0          5.1           3.5\n",
+       "1          4.9           3.0\n",
+       "2          4.7           3.2\n",
+       "3          4.6           3.1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>-0.11757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.11757</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width       1.00000      -0.11757\n",
+       "Sepal length     -0.11757       1.00000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.166778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.166778</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width      1.000000     -0.166778\n",
+       "Sepal length    -0.166778      1.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='spearman')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.076997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.076997</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width      1.000000     -0.076997\n",
+       "Sepal length    -0.076997      1.000000"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='kendall')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Homework/Homework#1/HW#1.ipynb b/Homework/Homework#1/HW#1.ipynb
new file mode 100644
index 0000000..feca026
--- /dev/null
+++ b/Homework/Homework#1/HW#1.ipynb
@@ -0,0 +1,396 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length\n",
+       "0          5.1           3.5\n",
+       "1          4.9           3.0\n",
+       "2          4.7           3.2\n",
+       "3          4.6           3.1\n",
+       "4          5.0           3.6"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv('data.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd7a6205310>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df.plot.scatter(x='Sepal width', y='Sepal length')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.1</td>\n",
+       "      <td>3.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length\n",
+       "0          5.1           3.5\n",
+       "1          4.9           3.0\n",
+       "2          4.7           3.2\n",
+       "3          4.6           3.1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>-0.11757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.11757</td>\n",
+       "      <td>1.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width       1.00000      -0.11757\n",
+       "Sepal length     -0.11757       1.00000"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.166778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.166778</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width      1.000000     -0.166778\n",
+       "Sepal length    -0.166778      1.000000"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='spearman')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Sepal width</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.076997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sepal length</th>\n",
+       "      <td>-0.076997</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Sepal width  Sepal length\n",
+       "Sepal width      1.000000     -0.076997\n",
+       "Sepal length    -0.076997      1.000000"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.corr(method='kendall')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Homework/Homework#2/.ipynb_checkpoints/HW#2-checkpoint.ipynb b/Homework/Homework#2/.ipynb_checkpoints/HW#2-checkpoint.ipynb
new file mode 100644
index 0000000..075d72c
--- /dev/null
+++ b/Homework/Homework#2/.ipynb_checkpoints/HW#2-checkpoint.ipynb
@@ -0,0 +1,814 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "df = pd.read_csv('data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Sepal width  Sepal length\n",
+       "count   150.000000    150.000000\n",
+       "mean      5.843333      3.057333\n",
+       "std       0.828066      0.435866\n",
+       "min       4.300000      2.000000\n",
+       "25%       5.100000      2.800000\n",
+       "50%       5.800000      3.000000\n",
+       "75%       6.400000      3.300000\n",
+       "max       7.900000      4.400000"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Sepal length')"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vWU16CPjvEbGm9kpaA+R8HTg7IlZPm34tcH5E3CxpN+DfgQXRJah+LxPlGmwjhRQDmJSwLSliqNNgl2M9ddZRQjNgnWavFE2NOY6vUTrOS4hzSorLRP8A/GlELImIJcCf0UoOdVY+R9I64H7guumJoLIQuBcgIrbQSjRPqrPsunINtpFCigFMStiWFDHUGQwox3rqrCPXwEXd1Gn2SjEAU47ja5SO8xLirKtOMtgaETdNvYmIr9MqM+0pIrZGxMHAvsDhkp61M0FKOkvSpKTJjRs39vWzuQbbSCHFACYlbEuKGOo02OVYT511NKUZMEVTY47ja5SO8xLirKtOMviqpI9JOkbS8yX9PXCjpEMlHVpnJRGxCbgB+IO2jzYAiwCqy0RPBB6Y5edXVENuTixYsKDOKrep06xTSkNPr6aypmxLihjqNNjlWE+ddTSlGTBFU2OO42uUjvMS4qyrTjJ4NvBfgXcB5wO/CxwCvI8uzy+StEDS/Or1POAE4Ntts61ie2XSKcD13e4X7Ixcg22kkGIAkxK2JUUMdRrscqynzjpKaAas0+yVoqkxx/E1Ssd5CXHWVaea6NidXPY+wGWS5tBKOp+NiC9IugCYjIhVwKXApyXdAzwILN/JdXWUa7CNFFIMYFLCtqSIoc5gQDnWU2cduQYu6uaKM5f2rCZKMQBTjuNrlI7zEuKsq0410W8BfwU8JSJeJOkZwNKIuDRHgO3cdGZm1r8Ug9t8klb10Duq998BrqL1V/3IaEot8DgppYY7RRy5lpFiW0bFOG1rCnWSwd4R8VlJ50KrBFTS1l4/1CTttcAbNm3m3GvWA/jgGZI6v5Mcv7cUceRaRoptGRXjtK2p1LmB/IikJ1GNeibpSFr9ACOjSbXA46KUGu4UceRaRoptGRXjtK2p1DkzeBOtqp+nS/oGsIBW5c/IaFIt8LgopYY7RRy5ltHLOB3n47StqfQ8M4iIW4HnA88BXgs8MyJuH3RgOTWpFnhclFLDnSKOXMvoZZyO83Ha1lQ6JgNJvyfpybDtURGHAe8B3idppEaqaFIt8LgopYY7RRy5lpFiW0bFOG1rKt0uE30MeAGApKOB9wJ/DhwMrGCELhU1qRZ4XJRSw50ijlzLSLEto2KctjWVjn0Gkm6LiGdXrz8MbKxGOUPSuuqZQ9m5z8DMrH+70mcwR9Ju1SWi44Gzav6cWRIpxhHIVWueYxyKUrZ1lOr3S+lTKUG3L/UraT2k7qfAZuAmAEn7MWKlpVaeOnXi7c/f3xqx7f1sYxEMqtY8RRy95illW0epfr+UPpVSdLyBHBHvAd5MqwP5udMeIPcYWvcOzAYmxTgCuWrNc4xDUcq2jlL9fil9KqXoermnbYjKqWnfGVw4Zi0pxhHIVWueYxyKUrZ1lOr3S+lTKUWdDmSz7FKMI5Cr1jzHOBSlbOso1e+X0qdSCicDK1KKcQRy1ZrnGIeilG0dpfr9UvpUSuGqICtSinEEctWa5xiHopRtHaX6/VL6VErRczyD0rjPwMysfynGM7AxVEJtdIoYTnj/jfzb/Y9se7//b+7BdW86JnscKdZTwu/ERpfvGdgMU7XRGzZtJtheG71y7YZGxdCeCAD+7f5HOOH9N2aNI8V6Svid2GhzMrAZSqiNThFDeyLoNX1QcaRYTwm/ExttTgY2Qwm10SXEkDOOHOMZmHXjZGAzlFAbXUIMOePIMZ6BWTdOBjZDCbXRKWLY/zf36Gv6oOJIsZ4Sfic22pwMbIZlhyzkwpMPZOH8eQhYOH8eF558YNbKlRQxXPemY2Z88fdbTZRrX/RaTwm/Extt7jMwMxsD7jOwYqWomy+lNt89ANZJU44NJwMbihTPee+1DD/j34atSceG7xnYUKSomy+lNt89ANZJk44NJwMbihR186XU5rsHwDpp0rHhZGBDkaJuvpTafPcAWCdNOjacDGwoUtTNl1Kb7x4A66RJx4ZvINtQpHjOe69l+Bn/NmxNOjbcZ2BmNgZ69RkM7DKRpEWSbpB0p6Q7JJ0zyzzHSHpI0rrq3zsHFY+ZmXU2yMtEW4A3R8StkvYE1ki6LiLubJvvpog4cYBxjJQcjVq5pGgYK2VbUjhv5fqOw1rmMkr70/ozsGQQEfcB91WvfyHpLmAh0J4MrKYcjVq5pGgYK2VbUjhv5Xouv+VH295vjdj2PldCGKX9af3LUk0kaQlwCLB6lo+XSrpN0pckPTNHPE2Vo1ErlxQNY6VsSwpXrr63r+mDMEr70/o38GoiSY8H/hl4Q0T8vO3jW4GnRsTDkl4MrAT2n2UZZwFnASxevHjAEZcrR6NWLikaxkrZlhS2dijk6DR9EEZpf1r/BnpmIGkurURwRURc0/55RPw8Ih6uXn8RmCtp71nmWxERExExsWDBgkGGXLQcjVq5pGgYK2VbUpgj9TV9EEZpf1r/BllNJOBS4K6IeH+HeZ5czYekw6t4HhhUTE2Xo1ErlxQNY6VsSwqnHbGor+mDMEr70/o3yMtERwGvBNZLWldNezuwGCAiPgqcApwtaQuwGVgeTWt8yChHo1YuKRrGStmWFKZuEg+zmmiU9qf1z01nZmZjwIPbjJhRqgMvoa7ezFqcDBpklOrAS6irN7Pt/NTSBhmlOvAS6urNbDsngwYZpTrwEurqzWw7J4MGGaU68BLq6s1sOyeDBhmlOvAS6urNbDvfQG6QUaoDL6Gu3sy2c5+BmdkYcJ9BIk2q729KrE2JMxfvDxsmJ4MamlTf35RYmxJnLt4fNmy+gVxDk+r7mxJrU+LMxfvDhs3JoIYm1fc3JdamxJmL94cNm5NBDU2q729KrE2JMxfvDxs2J4MamlTf35RYmxJnLt4fNmy+gVxDk+r7mxJrU+LMxfvDhs19BmZmY8B9Bma7KMW4C+4hsNI5GZh1kWLcBfcQWBP4BrJZFynGXXAPgTWBk4FZFynGXXAPgTWBk4FZFynGXXAPgTWBk4FZFynGXXAPgTWBbyCbdZFi3AX3EFgTuM/AzGwM9Ooz8GUiMzNzMjAzMycDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMwYYDKQtEjSDZLulHSHpHNmmUeSPijpHkm3Szp0UPGYmVlng3wcxRbgzRFxq6Q9gTWSrouIO6fN8yJg/+rfEcBHqv/aLvBAKmbWr4GdGUTEfRFxa/X6F8BdQPs30kuBT0XLLcB8SfsMKqZxMDWQyoZNmwm2D6Sycu2GYYdmZgXLcs9A0hLgEGB120cLgemjhPyYmQnD+uCBVMxsZww8GUh6PPDPwBsi4uc7uYyzJE1Kmty4cWPaAEeMB1Ixs50x0GQgaS6tRHBFRFwzyywbgOkPht+3mraDiFgRERMRMbFgwYLBBDsiPJCKme2MQVYTCbgUuCsi3t9htlXAH1dVRUcCD0XEfYOKaRx4IBUz2xmDrCY6CnglsF7Sumra24HFABHxUeCLwIuBe4BfAq8eYDxjwQOpmNnO8OA2ZmZjwIPbmJlZT04GZmbmZGBmZk4GZmaGk4GZmdHAaiJJG4EfDjGEvYGfDnH9/WhKrI4zrabECc2JdRTifGpEdOzabVwyGDZJk93Ks0rSlFgdZ1pNiROaE+s4xOnLRGZm5mRgZmZOBjtjxbAD6ENTYnWcaTUlTmhOrCMfp+8ZmJmZzwzMzMzJoCtJcyStlfSFWT47Q9JGSeuqf38ypBh/IGl9FcOMJ/hVjwf/oKR7JN0u6dBhxFnF0ivWYyQ9NG2fvnNIcc6XdLWkb0u6S9LSts+L2Kc14ixlfx4wLYZ1kn4u6Q1t8wx9n9aMs5R9+kZJd0j6lqQrJT2u7fPHSrqq2p+rq9EmuxrkI6xHwTm0xm5+QofPr4qI12WMp5NjI6JTbfGLgP2rf0cAH6n+OyzdYgW4KSJOzBbN7P4W+HJEnCLpvwC7t31eyj7tFScUsD8j4m7gYGj9gUVrAKvPtc029H1aM04Y8j6VtBB4PfCMiNgs6bPAcuCT02Z7DfCziNhP0nLgIuDUbsv1mUEHkvYFXgJcMuxYdtFLgU9Fyy3AfEn7DDuoUkl6InA0rYGZiIj/jIhNbbMNfZ/WjLNExwPfjYj2xtGh79M2neIsxW7APEm70foj4Cdtn78UuKx6fTVwfDXgWEdOBp19AHgr8Osu87ysOqW9WtKiLvMNUgBfkbRG0lmzfL4QuHfa+x9X04ahV6wASyXdJulLkp6ZM7jK04CNwCeqS4SXSNqjbZ4S9mmdOGH4+7PdcuDKWaaXsE+n6xQnDHmfRsQG4G+AHwH30Roh8itts23bnxGxBXgIeFK35ToZzELSicD9EbGmy2yfB5ZExEHAdWzPwrk9NyIOpXWa/WeSjh5SHHX0ivVWWi3zzwY+BKzMHSCtv7gOBT4SEYcAjwBvG0IcvdSJs4T9uU11Kesk4J+GGUcvPeIc+j6V9Bu0/vJ/GvAUYA9Jp+/qcp0MZncUcJKkHwCfAY6TdPn0GSLigYh4tHp7CXBY3hC3xbGh+u/9tK5vHt42ywZg+lnLvtW07HrFGhE/j4iHq9dfBOZK2jtzmD8GfhwRq6v3V9P60p2uhH3aM85C9ud0LwJujYj/N8tnJezTKR3jLGSfvgD4fkRsjIhfAdcAz2mbZ9v+rC4lPRF4oNtCnQxmERHnRsS+EbGE1uni9RGxQ+Ztu555Eq0bzVlJ2kPSnlOvgd8HvtU22yrgj6tqjSNpnVLelznUWrFKevLUdU1Jh9M6PrsewKlFxL8D90o6oJp0PHBn22xD36d14ixhf7Y5jc6XXoa+T6fpGGch+/RHwJGSdq9iOZ6Z3z+rgFdVr0+h9R3WtanM1UR9kHQBMBkRq4DXSzoJ2AI8CJwxhJB+C/hcdWzuBvxjRHxZ0v8AiIiPAl8EXgzcA/wSePUQ4qwb6ynA2ZK2AJuB5b0O4AH5c+CK6nLB94BXF7pPe8VZyv6c+gPgBOC106YVt09rxDn0fRoRqyVdTeuS1RZgLbCi7fvpUuDTku6h9f20vNdy3YFsZma+TGRmZk4GZmaGk4GZmeFkYGZmOBmYmRlOBjbCJL2jerLj7dUTJpM++EytJ1jOeKJtjZ97SlUaONtnN0qaqF6/fdr0JZLae0jMknEysJGk1uOcTwQOrR4Z8gJ2fPbN0ETETyLilBqzvr33LGZpOBnYqNoH+OnUI0Mi4qcR8RMASYdJ+mr1wLxrp7rJq7/K/7Y6i/hW1WGKpMMl3Vw9EO6b07p+ZyXpXyQdVL1eq+qZ95IukHTm9L/yJc2T9Bm1xiP4HDCvmv5eWk+lXCfpimrRcyR9vDrb+Yqkeal3mo0vJwMbVV8BFkn6jqS/l/R8AElzaT1g7JSIOAz4B+A9035u94g4GPjT6jOAbwPPqx4I907gr3qs+ybgeWo9ZnoLrWddATwP+FrbvGcDv4yI3wXeRfWMq4h4G7A5Ig6OiFdU8+4PfDginglsAl5Wc1+Y9eTHUdhIioiHJR1G6wv4WOAqSW8DJoFnAddVj8aYQ+sxwFOurH7+a5KeIGk+sCdwmaT9aT2Ge26P1d9Ea/CR7wP/ApwgaXfgaRFxt3Ycdepo4IPVOm+XdHuX5X4/ItZVr9cAS7rMa9YXJwMbWRGxFbgRuFHSeloP7loD3BERSzv92Czv/xK4ISL+sPoiv7HHqv8VmKD1vKDrgL2BM6t174pHp73eSnVJySwFXyaykaTWeLb7T5t0MPBD4G5gQXWDGUlzteMAJadW059L68mZD9F6/O/U45TP6LXuiPhPWjer/wi4mdaZwl8w8xIR1bSXV+t8FnDQtM9+VV3WMhs4JwMbVY+ndWnnzurSyzOA86sv6lOAiyTdBqxjx2fB/4ektcBHaY0jC/DXwIXV9Lpn0zfRGiBpc/V63+q/7T4CPF7SXcAF7Hj2sAK4fdoNZLOB8VNLzSqSbgT+IiImhx2LWW4+MzAzM58ZmJmZzwzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzMwP+P0B830uCS2uHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#1. Find the elbow(Elbow Technique)\n",
+    "plt.scatter(df['Sepal width'], df['Sepal length'])\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KMeans(n_clusters=3)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km = KMeans(n_clusters=3)\n",
+    "km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted = km.fit_predict(df[['Sepal width', 'Sepal length']])\n",
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</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>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster\n",
+       "0          5.1           3.5        2\n",
+       "1          4.9           3.0        2\n",
+       "2          4.7           3.2        2\n",
+       "3          4.6           3.1        2\n",
+       "4          5.0           3.6        2"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['cluster'] = y_predicted\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9ca52984f0>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df1=df[df.cluster==0]\n",
+    "df2=df[df.cluster==1]\n",
+    "df3=df[df.cluster==2]\n",
+    "plt.scatter(df1['Sepal width'], df1['Sepal length'], color='green', label='1')\n",
+    "plt.scatter(df2['Sepal width'], df2['Sepal length'], color='red', label='2')\n",
+    "plt.scatter(df3['Sepal width'], df3['Sepal length'], color='yellow', label='3')\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster\n",
+       "0     0.222222      0.625000        2\n",
+       "1     0.166667      0.416667        2\n",
+       "2     0.111111      0.500000        2\n",
+       "3     0.083333      0.458333        2\n",
+       "4     0.194444      0.666667        2"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler=MinMaxScaler()\n",
+    "scaler.fit(df[['Sepal length']])\n",
+    "df['Sepal length'] = scaler.transform(df['Sepal length'].values.reshape(-1, 1))\n",
+    "scaler.fit(df[['Sepal width']])\n",
+    "df['Sepal width'] = scaler.transform(df['Sepal width'].values.reshape(-1, 1))\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['cluster1'] = df['cluster']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>cluster1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster  cluster1\n",
+       "0     0.222222      0.625000        2         2\n",
+       "1     0.166667      0.416667        2         2\n",
+       "2     0.111111      0.500000        2         2\n",
+       "3     0.083333      0.458333        2         2\n",
+       "4     0.194444      0.666667        2         2"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
+       "       2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
+       "       2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km = KMeans(n_clusters=3)\n",
+    "y_predicted = km.fit_predict(df[['Sepal width', 'Sepal length']])\n",
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.208333</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>0.527778</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sepal width  Sepal length  cluster\n",
+       "0       0.222222      0.625000        1\n",
+       "1       0.166667      0.416667        1\n",
+       "2       0.111111      0.500000        1\n",
+       "3       0.083333      0.458333        1\n",
+       "4       0.194444      0.666667        1\n",
+       "..           ...           ...      ...\n",
+       "145     0.666667      0.416667        2\n",
+       "146     0.555556      0.208333        0\n",
+       "147     0.611111      0.416667        2\n",
+       "148     0.527778      0.583333        2\n",
+       "149     0.444444      0.416667        0\n",
+       "\n",
+       "[150 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['cluster'] = y_predicted\n",
+    "df.drop('cluster1', axis='columns', inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.40483539, 0.28009259],\n",
+       "       [0.19897959, 0.60459184],\n",
+       "       [0.69562648, 0.45390071]])"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9ca5a9b3a0>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df1=df[df.cluster==0]\n",
+    "df2=df[df.cluster==1]\n",
+    "df3=df[df.cluster==2]\n",
+    "plt.scatter(df1['Sepal width'], df1['Sepal length'], color='pink', label='1')\n",
+    "plt.scatter(df2['Sepal width'], df2['Sepal length'], color='red', label='2')\n",
+    "plt.scatter(df3['Sepal width'], df3['Sepal length'], color='yellow', label='3')\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')\n",
+    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], color='blue', marker='*', label='centroid')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##elbow plot method\n",
+    "k_rng = range(1,10)\n",
+    "sse = []\n",
+    "for k in k_rng:\n",
+    "    km = KMeans(n_clusters=k)\n",
+    "    km.fit(df[['Sepal width', 'Sepal length']])\n",
+    "    sse.append(km.inertia_) ##SSE\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'SSE')"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(k_rng, sse)\n",
+    "plt.xlabel('K')\n",
+    "plt.ylabel('SSE')\n",
+    "##답은 3개의 K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/Homework/Homework#2/HW#2.ipynb b/Homework/Homework#2/HW#2.ipynb
new file mode 100644
index 0000000..075d72c
--- /dev/null
+++ b/Homework/Homework#2/HW#2.ipynb
@@ -0,0 +1,814 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from matplotlib import pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "df = pd.read_csv('data.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>150.000000</td>\n",
+       "      <td>150.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.843333</td>\n",
+       "      <td>3.057333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.828066</td>\n",
+       "      <td>0.435866</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>4.300000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.100000</td>\n",
+       "      <td>2.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5.800000</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.400000</td>\n",
+       "      <td>3.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.900000</td>\n",
+       "      <td>4.400000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Sepal width  Sepal length\n",
+       "count   150.000000    150.000000\n",
+       "mean      5.843333      3.057333\n",
+       "std       0.828066      0.435866\n",
+       "min       4.300000      2.000000\n",
+       "25%       5.100000      2.800000\n",
+       "50%       5.800000      3.000000\n",
+       "75%       6.400000      3.300000\n",
+       "max       7.900000      4.400000"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Sepal length')"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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vWU16CPjvEbGm9kpaA+R8HTg7IlZPm34tcH5E3CxpN+DfgQXRJah+LxPlGmwjhRQDmJSwLSliqNNgl2M9ddZRQjNgnWavFE2NOY6vUTrOS4hzSorLRP8A/GlELImIJcCf0UoOdVY+R9I64H7guumJoLIQuBcgIrbQSjRPqrPsunINtpFCigFMStiWFDHUGQwox3rqrCPXwEXd1Gn2SjEAU47ja5SO8xLirKtOMtgaETdNvYmIr9MqM+0pIrZGxMHAvsDhkp61M0FKOkvSpKTJjRs39vWzuQbbSCHFACYlbEuKGOo02OVYT511NKUZMEVTY47ja5SO8xLirKtOMviqpI9JOkbS8yX9PXCjpEMlHVpnJRGxCbgB+IO2jzYAiwCqy0RPBB6Y5edXVENuTixYsKDOKrep06xTSkNPr6aypmxLihjqNNjlWE+ddTSlGTBFU2OO42uUjvMS4qyrTjJ4NvBfgXcB5wO/CxwCvI8uzy+StEDS/Or1POAE4Ntts61ie2XSKcD13e4X7Ixcg22kkGIAkxK2JUUMdRrscqynzjpKaAas0+yVoqkxx/E1Ssd5CXHWVaea6NidXPY+wGWS5tBKOp+NiC9IugCYjIhVwKXApyXdAzwILN/JdXWUa7CNFFIMYFLCtqSIoc5gQDnWU2cduQYu6uaKM5f2rCZKMQBTjuNrlI7zEuKsq0410W8BfwU8JSJeJOkZwNKIuDRHgO3cdGZm1r8Ug9t8klb10Duq998BrqL1V/3IaEot8DgppYY7RRy5lpFiW0bFOG1rCnWSwd4R8VlJ50KrBFTS1l4/1CTttcAbNm3m3GvWA/jgGZI6v5Mcv7cUceRaRoptGRXjtK2p1LmB/IikJ1GNeibpSFr9ACOjSbXA46KUGu4UceRaRoptGRXjtK2p1DkzeBOtqp+nS/oGsIBW5c/IaFIt8LgopYY7RRy5ltHLOB3n47StqfQ8M4iIW4HnA88BXgs8MyJuH3RgOTWpFnhclFLDnSKOXMvoZZyO83Ha1lQ6JgNJvyfpybDtURGHAe8B3idppEaqaFIt8LgopYY7RRy5lpFiW0bFOG1rKt0uE30MeAGApKOB9wJ/DhwMrGCELhU1qRZ4XJRSw50ijlzLSLEto2KctjWVjn0Gkm6LiGdXrz8MbKxGOUPSuuqZQ9m5z8DMrH+70mcwR9Ju1SWi44Gzav6cWRIpxhHIVWueYxyKUrZ1lOr3S+lTKUG3L/UraT2k7qfAZuAmAEn7MWKlpVaeOnXi7c/f3xqx7f1sYxEMqtY8RRy95illW0epfr+UPpVSdLyBHBHvAd5MqwP5udMeIPcYWvcOzAYmxTgCuWrNc4xDUcq2jlL9fil9KqXoermnbYjKqWnfGVw4Zi0pxhHIVWueYxyKUrZ1lOr3S+lTKUWdDmSz7FKMI5Cr1jzHOBSlbOso1e+X0qdSCicDK1KKcQRy1ZrnGIeilG0dpfr9UvpUSuGqICtSinEEctWa5xiHopRtHaX6/VL6VErRczyD0rjPwMysfynGM7AxVEJtdIoYTnj/jfzb/Y9se7//b+7BdW86JnscKdZTwu/ERpfvGdgMU7XRGzZtJtheG71y7YZGxdCeCAD+7f5HOOH9N2aNI8V6Svid2GhzMrAZSqiNThFDeyLoNX1QcaRYTwm/ExttTgY2Qwm10SXEkDOOHOMZmHXjZGAzlFAbXUIMOePIMZ6BWTdOBjZDCbXRKWLY/zf36Gv6oOJIsZ4Sfic22pwMbIZlhyzkwpMPZOH8eQhYOH8eF558YNbKlRQxXPemY2Z88fdbTZRrX/RaTwm/Extt7jMwMxsD7jOwYqWomy+lNt89ANZJU44NJwMbihTPee+1DD/j34atSceG7xnYUKSomy+lNt89ANZJk44NJwMbihR186XU5rsHwDpp0rHhZGBDkaJuvpTafPcAWCdNOjacDGwoUtTNl1Kb7x4A66RJx4ZvINtQpHjOe69l+Bn/NmxNOjbcZ2BmNgZ69RkM7DKRpEWSbpB0p6Q7JJ0zyzzHSHpI0rrq3zsHFY+ZmXU2yMtEW4A3R8StkvYE1ki6LiLubJvvpog4cYBxjJQcjVq5pGgYK2VbUjhv5fqOw1rmMkr70/ozsGQQEfcB91WvfyHpLmAh0J4MrKYcjVq5pGgYK2VbUjhv5Xouv+VH295vjdj2PldCGKX9af3LUk0kaQlwCLB6lo+XSrpN0pckPTNHPE2Vo1ErlxQNY6VsSwpXrr63r+mDMEr70/o38GoiSY8H/hl4Q0T8vO3jW4GnRsTDkl4MrAT2n2UZZwFnASxevHjAEZcrR6NWLikaxkrZlhS2dijk6DR9EEZpf1r/BnpmIGkurURwRURc0/55RPw8Ih6uXn8RmCtp71nmWxERExExsWDBgkGGXLQcjVq5pGgYK2VbUpgj9TV9EEZpf1r/BllNJOBS4K6IeH+HeZ5czYekw6t4HhhUTE2Xo1ErlxQNY6VsSwqnHbGor+mDMEr70/o3yMtERwGvBNZLWldNezuwGCAiPgqcApwtaQuwGVgeTWt8yChHo1YuKRrGStmWFKZuEg+zmmiU9qf1z01nZmZjwIPbjJhRqgMvoa7ezFqcDBpklOrAS6irN7Pt/NTSBhmlOvAS6urNbDsngwYZpTrwEurqzWw7J4MGGaU68BLq6s1sOyeDBhmlOvAS6urNbDvfQG6QUaoDL6Gu3sy2c5+BmdkYcJ9BIk2q729KrE2JMxfvDxsmJ4MamlTf35RYmxJnLt4fNmy+gVxDk+r7mxJrU+LMxfvDhs3JoIYm1fc3JdamxJmL94cNm5NBDU2q729KrE2JMxfvDxs2J4MamlTf35RYmxJnLt4fNmy+gVxDk+r7mxJrU+LMxfvDhs19BmZmY8B9Bma7KMW4C+4hsNI5GZh1kWLcBfcQWBP4BrJZFynGXXAPgTWBk4FZFynGXXAPgTWBk4FZFynGXXAPgTWBk4FZFynGXXAPgTWBbyCbdZFi3AX3EFgTuM/AzGwM9Ooz8GUiMzNzMjAzMycDMzPDycDMzHAyMDMznAzMzAwnAzMzw8nAzMwYYDKQtEjSDZLulHSHpHNmmUeSPijpHkm3Szp0UPGYmVlng3wcxRbgzRFxq6Q9gTWSrouIO6fN8yJg/+rfEcBHqv/aLvBAKmbWr4GdGUTEfRFxa/X6F8BdQPs30kuBT0XLLcB8SfsMKqZxMDWQyoZNmwm2D6Sycu2GYYdmZgXLcs9A0hLgEGB120cLgemjhPyYmQnD+uCBVMxsZww8GUh6PPDPwBsi4uc7uYyzJE1Kmty4cWPaAEeMB1Ixs50x0GQgaS6tRHBFRFwzyywbgOkPht+3mraDiFgRERMRMbFgwYLBBDsiPJCKme2MQVYTCbgUuCsi3t9htlXAH1dVRUcCD0XEfYOKaRx4IBUz2xmDrCY6CnglsF7Sumra24HFABHxUeCLwIuBe4BfAq8eYDxjwQOpmNnO8OA2ZmZjwIPbmJlZT04GZmbmZGBmZk4GZmaGk4GZmdHAaiJJG4EfDjGEvYGfDnH9/WhKrI4zrabECc2JdRTifGpEdOzabVwyGDZJk93Ks0rSlFgdZ1pNiROaE+s4xOnLRGZm5mRgZmZOBjtjxbAD6ENTYnWcaTUlTmhOrCMfp+8ZmJmZzwzMzMzJoCtJcyStlfSFWT47Q9JGSeuqf38ypBh/IGl9FcOMJ/hVjwf/oKR7JN0u6dBhxFnF0ivWYyQ9NG2fvnNIcc6XdLWkb0u6S9LSts+L2Kc14ixlfx4wLYZ1kn4u6Q1t8wx9n9aMs5R9+kZJd0j6lqQrJT2u7fPHSrqq2p+rq9EmuxrkI6xHwTm0xm5+QofPr4qI12WMp5NjI6JTbfGLgP2rf0cAH6n+OyzdYgW4KSJOzBbN7P4W+HJEnCLpvwC7t31eyj7tFScUsD8j4m7gYGj9gUVrAKvPtc029H1aM04Y8j6VtBB4PfCMiNgs6bPAcuCT02Z7DfCziNhP0nLgIuDUbsv1mUEHkvYFXgJcMuxYdtFLgU9Fyy3AfEn7DDuoUkl6InA0rYGZiIj/jIhNbbMNfZ/WjLNExwPfjYj2xtGh79M2neIsxW7APEm70foj4Cdtn78UuKx6fTVwfDXgWEdOBp19AHgr8Osu87ysOqW9WtKiLvMNUgBfkbRG0lmzfL4QuHfa+x9X04ahV6wASyXdJulLkp6ZM7jK04CNwCeqS4SXSNqjbZ4S9mmdOGH4+7PdcuDKWaaXsE+n6xQnDHmfRsQG4G+AHwH30Roh8itts23bnxGxBXgIeFK35ToZzELSicD9EbGmy2yfB5ZExEHAdWzPwrk9NyIOpXWa/WeSjh5SHHX0ivVWWi3zzwY+BKzMHSCtv7gOBT4SEYcAjwBvG0IcvdSJs4T9uU11Kesk4J+GGUcvPeIc+j6V9Bu0/vJ/GvAUYA9Jp+/qcp0MZncUcJKkHwCfAY6TdPn0GSLigYh4tHp7CXBY3hC3xbGh+u/9tK5vHt42ywZg+lnLvtW07HrFGhE/j4iHq9dfBOZK2jtzmD8GfhwRq6v3V9P60p2uhH3aM85C9ud0LwJujYj/N8tnJezTKR3jLGSfvgD4fkRsjIhfAdcAz2mbZ9v+rC4lPRF4oNtCnQxmERHnRsS+EbGE1uni9RGxQ+Ztu555Eq0bzVlJ2kPSnlOvgd8HvtU22yrgj6tqjSNpnVLelznUWrFKevLUdU1Jh9M6PrsewKlFxL8D90o6oJp0PHBn22xD36d14ixhf7Y5jc6XXoa+T6fpGGch+/RHwJGSdq9iOZ6Z3z+rgFdVr0+h9R3WtanM1UR9kHQBMBkRq4DXSzoJ2AI8CJwxhJB+C/hcdWzuBvxjRHxZ0v8AiIiPAl8EXgzcA/wSePUQ4qwb6ynA2ZK2AJuB5b0O4AH5c+CK6nLB94BXF7pPe8VZyv6c+gPgBOC106YVt09rxDn0fRoRqyVdTeuS1RZgLbCi7fvpUuDTku6h9f20vNdy3YFsZma+TGRmZk4GZmaGk4GZmeFkYGZmOBmYmRlOBjbCJL2jerLj7dUTJpM++EytJ1jOeKJtjZ97SlUaONtnN0qaqF6/fdr0JZLae0jMknEysJGk1uOcTwQOrR4Z8gJ2fPbN0ETETyLilBqzvr33LGZpOBnYqNoH+OnUI0Mi4qcR8RMASYdJ+mr1wLxrp7rJq7/K/7Y6i/hW1WGKpMMl3Vw9EO6b07p+ZyXpXyQdVL1eq+qZ95IukHTm9L/yJc2T9Bm1xiP4HDCvmv5eWk+lXCfpimrRcyR9vDrb+Yqkeal3mo0vJwMbVV8BFkn6jqS/l/R8AElzaT1g7JSIOAz4B+A9035u94g4GPjT6jOAbwPPqx4I907gr3qs+ybgeWo9ZnoLrWddATwP+FrbvGcDv4yI3wXeRfWMq4h4G7A5Ig6OiFdU8+4PfDginglsAl5Wc1+Y9eTHUdhIioiHJR1G6wv4WOAqSW8DJoFnAddVj8aYQ+sxwFOurH7+a5KeIGk+sCdwmaT9aT2Ge26P1d9Ea/CR7wP/ApwgaXfgaRFxt3Ycdepo4IPVOm+XdHuX5X4/ItZVr9cAS7rMa9YXJwMbWRGxFbgRuFHSeloP7loD3BERSzv92Czv/xK4ISL+sPoiv7HHqv8VmKD1vKDrgL2BM6t174pHp73eSnVJySwFXyaykaTWeLb7T5t0MPBD4G5gQXWDGUlzteMAJadW059L68mZD9F6/O/U45TP6LXuiPhPWjer/wi4mdaZwl8w8xIR1bSXV+t8FnDQtM9+VV3WMhs4JwMbVY+ndWnnzurSyzOA86sv6lOAiyTdBqxjx2fB/4ektcBHaY0jC/DXwIXV9Lpn0zfRGiBpc/V63+q/7T4CPF7SXcAF7Hj2sAK4fdoNZLOB8VNLzSqSbgT+IiImhx2LWW4+MzAzM58ZmJmZzwzMzAwnAzMzw8nAzMxwMjAzM5wMzMwMJwMzMwP+P0B830uCS2uHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#1. Find the elbow(Elbow Technique)\n",
+    "plt.scatter(df['Sepal width'], df['Sepal length'])\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KMeans(n_clusters=3)"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km = KMeans(n_clusters=3)\n",
+    "km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,\n",
+       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_predicted = km.fit_predict(df[['Sepal width', 'Sepal length']])\n",
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</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>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>4.7</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.6</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster\n",
+       "0          5.1           3.5        2\n",
+       "1          4.9           3.0        2\n",
+       "2          4.7           3.2        2\n",
+       "3          4.6           3.1        2\n",
+       "4          5.0           3.6        2"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['cluster'] = y_predicted\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9ca52984f0>"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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LgDcBm4CnD7Qhfm5WDVQr5e1AFhEprxAdyH8OvMDdX+vur4sfhTQEklZSjoAWlSmlpUuhvz9axrG/P3rdizFIIdIsbvMLorUINHKoEpIWjdGiMqW0dClcWbPo0L59B16vWtX4M90YgxQmzW2ibwAnA2uoaRDc/f3ZhtaYbhMlGaZ1jkDS+1KI/v7oy7deXx/szSk3owwxSGaSbhOluTK4jqkT1UmpJeUIaFGZUmr0JdyqvFtjkMIkNgbu/qV4CupBd38gh5ikI4M0/st/MOX7Uoi+vuZ/lfdSDFKYxA5kM3sdsAG4OX69wMx0pVBaSTkCWlSmlEab5GA0K+/WGKQwaUYTXQqcAjwG4O4bgOdlGJN0ZIRobsEhohlDh+LXIynfl0KsWgVjYwf+Cu/ri17n2XFbhhikMKkmqnP3U81svbu/OC7b6O4n5RJhHXUgi4i0L0SewSYzewvQZ2bHm9k/Ay1XOaumEGPvk+rIa45/5RG0I8h6BXlIygHIaz2EpP3ktXaD1n8Iq1V6cnzVMHlD+SfA2vj5rKTPZfXIZjqKEGsRJNWR1xz/IY6ldwRZryAPY2NT1zqYfIzFvz95rYeQtJ+81m7Q+g9to9PpKMomm9tEw3Q+9j6pjrzm+E+KQ2oFWa8gD0k5AHmth5C0n7zWbtD6D22b9hrIZnY90LSl8K6am2gGjQ/VODAdU6d1WIvPhmyQQxxL75ixfAbe4HwZxtOXlOh8WYvfH/foVkmj/8tm8HTA40jaT5o4QsSa1/F2kU6Szv4pg3hKKsTY+6Q6+mh+ZRCS8gjaMTh7sOGVQVvrFeQhKQdgcLDxX8qh10NI2k+aOELEmtfx9pCmHcju/v1WjzyDzF6IsfdJdeQ1x7/yCNoRZL2CPCTlAOS1HkLSfvJau0HrP4TXqkOhjI/s1jMIsRZBUh15zfEf4lh6R5D1CvIwNube1xd1lvb1Heg8npTXeghJ+8lr7Qat/9AW1IEsIiIh8gwktW7KVZDSyWNc/VlnRZ2wk4+zziqmDsld0w7kso4mKq8Q6wQk1bEUqJlvnn01rzVlQFebmIj6B3bHvxtbtx7oLxgJNJXIWWfBmjVTy9asicq/97386pBCtBpa+vJWH/SCOpHLe5tomO7JVZDSyWNcfdLw1bzqkExMe2hpUV/21RVinYCkOprNK6/55rvetia/G83KRdqUZgrr483sWjPbbGa/mHzkEVy1NBvf3G6uQqvyZjkJmm++6zUbP69x9RJImg7kLxDdmN4LnAl8GVidZVDV1E25ClI6eYyrX7KkvfKs6pBitBp3GvcnrIt/3ltfVsQjuzyDELopV0FKJ49x9UuWTJ38bcmSYuqQ4Og0z8DMfgicDlwL3Ao8DPy9u78gwzaqqfJ2IIuIlFeIPIMPEN2reD+wCPhL4MIUO55lZj82s3vMbJOZLW+wzTPM7Boze9DM7jaz4RTxiIhIYImNgbv/xN0fB34HvN/d3+Dud6Wo+yngle5+MrAAeJWZnVq3zTuA37r784HPAJe1F35aaZLByrIgTFJSWTWOJcSCMUtvXEr/in5sudG/op+lNx6cYJfHftLsI02smUuT7JWUuJa0gE6aOkLIa4GcTlUlzjRa3UOKbyEtBu4lGui+BbgHWJT0ubo6BoCfAi+pK/9X4LT4eT/wKHHuQ7NH+30GaRZ7KcuCMEkL4FTjWEIsGDN2w9iUz08+xm440EeSx37S7CNNrJmrv0/f6H590oIwSQvopKkjhLwWyOmVOGME6DPYCLzH3e+IX58OrPIUayCbWR+wDng+cIW7//e69+8DXuXu2+PX/x43GI82q7P9PoNhkpPB0myTh6SksmGqcCwhFozpX9HPPj/4XPRZH3sv3pvbftLsI02smUuT7JWUuJa0gE6aOkLIa4GcTlUlzliIPoN9kw0BgLv/GynTXd19n7svAOYCp5jZiWk+V8/MRs1srZmt3bFjR5ufTpMMFiJhLISkpLJqHMu2XY331ay8kUZfrvXleewnzT7SxFoKSYlrjRqC+vI8kt/S7KMMSXhViTOlNI3B983sf5jZK8zs5Wa2CrjdzBaa2cI0O3H3x4DbgFfVvfUwcCyAmfUDs4GdDT4/7u6L3X3xnDlz0uyyRppksBAJYyEkJZVV41iaLQzTzoIxfdb4XNSW57GfNPtIE2spJCWu9TWJt7Y8j+S3NPsoQxJeVeJMKU1jcDLwJ8AlwKXACcCLgU/RYjU0M5tjZofHz58JnA38rG6z6zgwMul84FZPum/VtjTJYGVZECYpqawaxxJiwZjRRY3PRW15HvtJs480sWYuTbJXUuJa0gI6aeoIIa8FcjpVlTjTatWh0MkDOAlYD2wE7gMujstXAOfGz2cBXwceBH4MPC+p3uklnaVJBivLgjBJSWXVOJYQC8aM3TDmfcv7nEvxvuV9DTtk89hPmn2kiTVzaZK9khLXkhbQSVNHCHktkNOpqsTpYTqQ/xj4W+C57v5qM5tHNALo6iwapyRKOhMRaV+IDuQvEg0BfW78+ufABzsPrWyKH5svU6UZ3x8izyCPOFLFmTQePcR49aqMeQ+hl441hFaXDfFVw0/in+tryjYkfS6rRzZzExU/Nl+mSjO+P0SeQR5xpIozaTx6iPHqJRrznrleOtaUCHCb6HbgPOAWd18YZxFf5u4tF7/JSja3iYYpemy+TJVmfH+IPIM84kgVZ9J49BDj1Us05j1zvXSsKU17cZsaHyYa9fOfzexOYA7RyJ8uUvzYfJkqzfj+EHkGecSRKs6k8eghxqtXaMx7x3rpWANJMzfRT4GXAy8F3gXMd/eNWQeWr+LH5stUacb3h8gzyCOOVHEmjUcPMV69QmPeO9ZLxxpI08bAzP7UzJ4D4O57iWYsXQl8ysyenVN8OSl+bL5MlWZ8f4g8gzziSBVn0nj0EOPVqzTmvVO9dKyhNOtMIJpY7tnx8zOAXxH1HXwSuLZVR0SWj+wWtyl+bL5MlWZ8f4g8gzziSBVn0nj0EOPVSzLmPRe9dKwpMN0OZDO7x6PppzGzK4Ad7n5p/HqDR3MO5U55BiIi7eskz6Avni8IYAnRKmeT0nQ8i3QkxDoCeeQhhIojcZuktQbyGlffTeP3y7I2Qxk0u2QAlgF3Av+HaFqJyauI5wN3trrcyPJR7jWQJZQQ6wjkkYcQKo7EbZLWGshrXH03jd8vy9oMOaGTPIM4p+Bo4Lvu/oe47E+AQzwaZZQ73SbqDSHWEcgjDyFUHInbJK01kNe4+m4av1+WtRly0lGegTdY3tLdfx4iMJFWQqwjkEceQqg4ErdJWmsgr3H13TR+vyxrM5REmrmJRHIXYh2BPPIQQsWRuE3SWgN5javvpvH7ZVmboSTUGEgphVhHII88hFBxJG6TtNZAXuPqu2n8flnWZiiLVh0KZXyoA7l3hFhHII88hFBxJG6TtNZAXuPqu2n8flnWZsgBnU5UVzbqQBYRaV+I9QykB+U1Pj/rGOZfMR9bbvsf86+YX0gcQfZTlfHqUkm6MpCDTNw7wej1o+zes3t/2cDMAcZfN87Ii0YqE8P8K+az+dHNB5XPO3Iem96zKbc4guxnYiLqH9h94H0GBmB8HEby+TeRaku6MlBjIAfJa3x+1jHYcmv6nl+S7vc+r3ORuJ8SjVeXatJtImlbXuPzyx5DnnEk7qdC49WlmtQYyEHyGp9f9hjyjCNxPxUary7VpMZADpLX+PysY5h35Ly2yrOKI8h+qjReXSpJjYEcZORFI4y/bpyh2UMYxtDsoVw7j0PFsOk9mw764m+n8zhUHEH2MzISdRYPDUWzlg4NqfNYglIHsohID1AHspRWiPH7SXWUJkdAeldF8kO0SI0Uon5c/dZdWxm9PppnJ+0tmKQ6Quwjr2ORLlWfH7J164H5pEp2i0+3iaQQIcbvJ9VRmhwB6V0lyg/RbSIppRDj95PqKE2OgPSuCuWHqDGQQoQYv59UR2lyBKR3VSg/RI2BFCLE+P2kOkqTIyC9q0L5IWoMpBAhxu8n1VGaHAHpXRXKD1EHsohIDyisA9nMjjWz28xss5ltMrMPNNjmFWa2y8w2xI+Ls4pHRESay/I20V7gb9x9HnAq8B4zazQpzB3uviB+rMgwnq6QR6JWXkIkjJXlWEJYeuNS+lf0Y8uN/hX9LL1xaf5BVCRBSsLLLOnM3R8BHomf/97M7geOAQ5ebURSySNRKy8hEsbKciwhLL1xKVeuvXL/632+b//rVa9dlU8QFUqQkvBy6TMws2HgB8CJ7v67mvJXAN8AtgO/Aj7i7i1nEevlPoM8ErXyEiJhrCzHEkL/in72+b6Dyvusj70X780niBIlSEl4SX0GmU9HYWaHEH3hf7C2IYj9FBhy98fN7DXAt4HjG9QxCowCDJZwfG5e8kjUykuIhLGyHEsIjRqCVuWZqFCClISX6dBSM5tJ1BBMuPs3699399+5++Px85uAmWZ2ZIPtxt19sbsvnjNnTpYhl1oeiVp5CZEwVpZjCaHP+toqz0SFEqQkvCxHExlwNXC/u3+6yTbPibfDzE6J49mZVUxVl0eiVl5CJIyV5VhCGF002lZ5JiqUICUZcPdMHsDpgAMbgQ3x4zXAu4F3x9u8F9gE3APcBbw0qd5FixZ5L1u9cbUPfWbI7VLzoc8M+eqNqwupI4SkONLEWZZjCWHshjHvW97nXIr3Le/zsRvG8g9i9Wr3oSF3s+jn6uqeT5kKWOstvluVdCYi0gM0a2mX0bh6EcmCFrepEI2rF5Gs6MqgQpatWba/IZi0e89ulq1ZVlBE0ze+brytchHJlhqDCtG4ehHJihqDCtG4ehHJihqDCtG4ehHJihqDCummRVRWvXYVY4vH9l8J9FkfY4vH1HksUhDlGYiI9ADlGQQzAQwTnbLh+HU5VSUXoSpx5kXnQ4qkPINUJogmTZ0c1rk1fg1Qrls0VclFqEqcedH5kKLpNlEqw0QNQL0hYEuukSSpyhz/VYkzLzofkjXdJgqi2Tj+8o3vr0ouQlXizIvOhxRNjUEqzcbxl298f1VyEaoSZ150PqRoagxSWQnUzfPOQFxeLlXJRahKnHnR+ZCiqTFIZQQYJ+ojsPjnOGXrPIbq5CJUJc686HxI0dSBLCLSA9SBLNKhEOsuKIdAyk55BiIthFh3QTkEUgW6MhBpIcS6C920DoV0LzUGIi2EWHdBOQRSBWoMRFoIse6CcgikCtQYiLQQYt0F5RBIFagxEGkhxLoLyiGQKlCegYhID1CegYiIJFJjICIiagxERESNgYiIoMZARERQYyAiIqgxEBER1BiIiAgZNgZmdqyZ3WZmm81sk5l9oME2ZmafM7MHzWyjmS3MKh4REWkuyyuDvcDfuPs84FTgPWY2r26bVwPHx49R4EqkY1pIRUTaldniNu7+CPBI/Pz3ZnY/cAywuWaz1wNf9mhOjLvM7HAzOzr+rEyDFlIRkenIpc/AzIaBFwN31711DPBQzevtcZlMkxZSEZHpyLwxMLNDgG8AH3T3302zjlEzW2tma3fs2BE2wC6jhVREZDoybQzMbCZRQzDh7t9ssMnDwLE1r+fGZVO4+7i7L3b3xXPmzMkm2C6hhVREZDqyHE1kwNXA/e7+6SabXQf8VTyq6FRgl/oLOqOFVERkOjLrQAZeBvwlcK+ZbYjLPgEMArj754GbgNcADwK7gbdlGE9PmOwkXrZmGdt2bWNw9iArl6xU57GItKTFbUREeoAWtxERkURqDERERI2BiIioMRAREdQYiIgIFRxNZGY7gK0FhnAk8GiB+29HVWJVnGFVJU6oTqzdEOeQuzfN2q1cY1A0M1vbanhWmVQlVsUZVlXihOrE2gtx6jaRiIioMRARETUG0zFedABtqEqsijOsqsQJ1Ym16+NUn4GIiOjKQERE1Bi0ZGZ9ZrbezG5o8N5FZrbDzDbEj3cWFOMWM7s3juGgGfzi6cE/Z2YPmtlGM1tYRJxxLEmxvsLMdtWc04sLivNwM7vWzH5mZveb2Wl175finKaIsyzn8wU1MWwws9+Z2Qfrtin8nKaMsyzn9ENmtsnM7jOzr5rZrLr3n2Fm18Tn8+54tcmWspzCuht8ALgfOKzJ+9e4+3tzjKeZM9292djiVwPHx4+XAFKI+VsAAAYoSURBVFfGP4vSKlaAO9z9nNyiaeyzwM3ufr6Z/SdgoO79spzTpDihBOfT3R8AFkD0BxbRAlbfqtus8HOaMk4o+Jya2THA+4F57v6Emf0LcAHwxZrN3gH81t2fb2YXAJcBb2pVr64MmjCzucBrgauKjqVDrwe+7JG7gMPN7OiigyorM5sNnEG0MBPu/h/u/ljdZoWf05RxltES4N/dvT5xtPBzWqdZnGXRDzzTzPqJ/gj4Vd37rwe+FD+/FlgSLzjWlBqD5i4HPgo83WKb8+JL2mvN7NgW22XJge+a2TozG23w/jHAQzWvt8dlRUiKFeA0M7vHzL5jZvPzDC52HLAD+EJ8i/AqM3tW3TZlOKdp4oTiz2e9C4CvNigvwzmt1SxOKPicuvvDwD8B24BHiFaI/G7dZvvPp7vvBXYBR7SqV41BA2Z2DvBrd1/XYrPrgWF3Pwm4hQOtcN5Od/eFRJfZ7zGzMwqKI42kWH9KlDJ/MvDPwLfzDpDoL66FwJXu/mLgD8DHCogjSZo4y3A+94tvZZ0LfL3IOJIkxFn4OTWzPyL6y/844LnAs8zsrZ3Wq8agsZcB55rZFuBrwCvNbHXtBu6+092fil9eBSzKN8T9cTwc//w10f3NU+o2eRiovWqZG5flLilWd/+duz8eP78JmGlmR+Yc5nZgu7vfHb++luhLt1YZzmlinCU5n7VeDfzU3f9fg/fKcE4nNY2zJOf0LOCX7r7D3fcA3wReWrfN/vMZ30qaDexsVakagwbc/ePuPtfdh4kuF2919yktb939zHOJOppzZWbPMrNDJ58D/wW4r26z64C/ikdrnEp0SflIzqGmitXMnjN5X9PMTiH6/Wz5Cxyau/9f4CEze0FctATYXLdZ4ec0TZxlOJ913kzzWy+Fn9MaTeMsyTndBpxqZgNxLEs4+PvnOuDC+Pn5RN9hLZPKNJqoDWa2Aljr7tcB7zezc4G9wG+AiwoI6Y+Bb8W/m/3A/3b3m83s3QDu/nngJuA1wIPAbuBtBcSZNtbzgTEz2ws8AVyQ9AuckfcBE/Htgl8AbyvpOU2Ksyznc/IPgLOBd9WUle6cpoiz8HPq7neb2bVEt6z2AuuB8brvp6uBr5jZg0TfTxck1asMZBER0W0iERFRYyAiIqgxEBER1BiIiAhqDEREBDUG0sXMbFk8s+PGeIbJoBOfWTSD5UEz2qb43HPjoYGN3rvdzBbHzz9RUz5sZvU5JCLBqDGQrmTRdM7nAAvjKUPOYurcN4Vx91+5+/kpNv1E8iYiYagxkG51NPDo5JQh7v6ou/8KwMwWmdn34wnz/nUymzz+q/yz8VXEfXGGKWZ2ipn9KJ4Q7oc1Wb8NmdmNZnZS/Hy9xXPem9kKM/vr2r/yzeyZZvY1i9Yj+BbwzLj874lmpdxgZhNx1X1m9j/jq53vmtkzQ5806V1qDKRbfRc41sx+bmarzOzlAGY2k2iCsfPdfRHwv4CVNZ8bcPcFwNL4PYCfAX8WTwh3MfC3Cfu+A/gzi6aZ3ks01xXAnwE/qNt2DNjt7icAlxDPceXuHwOecPcF7j4Sb3s8cIW7zwceA85LeS5EEmk6CulK7v64mS0i+gI+E7jGzD4GrAVOBG6Jp8boI5oGeNJX48//wMwOM7PDgUOBL5nZ8UTTcM9M2P0dRIuP/BK4ETjbzAaA49z9AZu66tQZwOfifW40s40t6v2lu2+In68DhltsK9IWNQbStdx9H3A7cLuZ3Us0cdc6YJO7n9bsYw1efxK4zd3/a/xFfnvCrn8CLCaaL+gW4Ejgr+N9d+Kpmuf7iG8piYSg20TSlSxaz/b4mqIFwFbgAWBO3MGMmc20qQuUvCkuP51o5sxdRNP/Tk6nfFHSvt39P4g6q/8C+BHRlcJHOPgWEXHZW+J9ngicVPPenvi2lkjm1BhItzqE6NbO5vjWyzzg0viL+nzgMjO7B9jA1LngnzSz9cDnidaRBfgH4O/i8rRX03cQLZD0RPx8bvyz3pXAIWZ2P7CCqVcP48DGmg5kkcxo1lKRmJndDnzE3dcWHYtI3nRlICIiujIQERFdGYiICGoMREQENQYiIoIaAxERQY2BiIigxkBERID/D8qq47wWZ4/nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df1=df[df.cluster==0]\n",
+    "df2=df[df.cluster==1]\n",
+    "df3=df[df.cluster==2]\n",
+    "plt.scatter(df1['Sepal width'], df1['Sepal length'], color='green', label='1')\n",
+    "plt.scatter(df2['Sepal width'], df2['Sepal length'], color='red', label='2')\n",
+    "plt.scatter(df3['Sepal width'], df3['Sepal length'], color='yellow', label='3')\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster\n",
+       "0     0.222222      0.625000        2\n",
+       "1     0.166667      0.416667        2\n",
+       "2     0.111111      0.500000        2\n",
+       "3     0.083333      0.458333        2\n",
+       "4     0.194444      0.666667        2"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaler=MinMaxScaler()\n",
+    "scaler.fit(df[['Sepal length']])\n",
+    "df['Sepal length'] = scaler.transform(df['Sepal length'].values.reshape(-1, 1))\n",
+    "scaler.fit(df[['Sepal width']])\n",
+    "df['Sepal width'] = scaler.transform(df['Sepal width'].values.reshape(-1, 1))\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['cluster1'] = df['cluster']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "      <th>cluster1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Sepal width  Sepal length  cluster  cluster1\n",
+       "0     0.222222      0.625000        2         2\n",
+       "1     0.166667      0.416667        2         2\n",
+       "2     0.111111      0.500000        2         2\n",
+       "3     0.083333      0.458333        2         2\n",
+       "4     0.194444      0.666667        2         2"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 2, 2, 2, 0, 2, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 2,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 2, 2, 0,\n",
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
+       "       2, 0, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
+       "       2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km = KMeans(n_clusters=3)\n",
+    "y_predicted = km.fit_predict(df[['Sepal width', 'Sepal length']])\n",
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "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>Sepal width</th>\n",
+       "      <th>Sepal length</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.625000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.111111</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.083333</td>\n",
+       "      <td>0.458333</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.194444</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>145</th>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>146</th>\n",
+       "      <td>0.555556</td>\n",
+       "      <td>0.208333</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>147</th>\n",
+       "      <td>0.611111</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>0.527778</td>\n",
+       "      <td>0.583333</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>149</th>\n",
+       "      <td>0.444444</td>\n",
+       "      <td>0.416667</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>150 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sepal width  Sepal length  cluster\n",
+       "0       0.222222      0.625000        1\n",
+       "1       0.166667      0.416667        1\n",
+       "2       0.111111      0.500000        1\n",
+       "3       0.083333      0.458333        1\n",
+       "4       0.194444      0.666667        1\n",
+       "..           ...           ...      ...\n",
+       "145     0.666667      0.416667        2\n",
+       "146     0.555556      0.208333        0\n",
+       "147     0.611111      0.416667        2\n",
+       "148     0.527778      0.583333        2\n",
+       "149     0.444444      0.416667        0\n",
+       "\n",
+       "[150 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['cluster'] = y_predicted\n",
+    "df.drop('cluster1', axis='columns', inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.40483539, 0.28009259],\n",
+       "       [0.19897959, 0.60459184],\n",
+       "       [0.69562648, 0.45390071]])"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "km.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f9ca5a9b3a0>"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df1=df[df.cluster==0]\n",
+    "df2=df[df.cluster==1]\n",
+    "df3=df[df.cluster==2]\n",
+    "plt.scatter(df1['Sepal width'], df1['Sepal length'], color='pink', label='1')\n",
+    "plt.scatter(df2['Sepal width'], df2['Sepal length'], color='red', label='2')\n",
+    "plt.scatter(df3['Sepal width'], df3['Sepal length'], color='yellow', label='3')\n",
+    "plt.xlabel('Sepal width')\n",
+    "plt.ylabel('Sepal length')\n",
+    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], color='blue', marker='*', label='centroid')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##elbow plot method\n",
+    "k_rng = range(1,10)\n",
+    "sse = []\n",
+    "for k in k_rng:\n",
+    "    km = KMeans(n_clusters=k)\n",
+    "    km.fit(df[['Sepal width', 'Sepal length']])\n",
+    "    sse.append(km.inertia_) ##SSE\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'SSE')"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+d28VOekprH5wE61dfUFHEpEJKqhVPZ8H1prZNmAJ8LWAckwoJXkZ/Mu9VTS09fC7D21hYHAo6EgiMgEFUvzuvjUyfr/I3e90d91/8AJVVkzir+9cwGsHmvnG03uCjiMiE5CuqT8B/dp7prGzro37XzvEVWV5fLwyFHQkEZlAdMmGCerPbruKa2dN5suPbmdbrU6DEJELp+KfoFKTk/ineyopyknnt360icaTumG7iFwYFf8EVpiTzppPV3Giq4/P/XgzfQOa7BWR0an4J7iry/L521WLqT58gr94YmfQcURkAtDkbhz42OIydtW3890Nb3F1WR6funZ60JFEJIZpjz9O/MGvzOXGuUX87yd28sahlqDjiEgMU/HHieQk49ufWErF5Cw+t3YTda3dQUcSkRil4o8j+ZmprPl0FT39Q6z+UTU9/bphu4icS8UfZ+YU5/IPv76EnXXtfHn9Nt2wXUTOoeKPQzdfVcJ9N1/Jz7bWcf+rh4KOIyIxRsUfp373g3P4yIKpfP3p3byyL3FvZCMi51Lxxykz41u/upgrS3L5/MNbOHy8M+hIIhIjVPxxLDs9hTX3LsMMPvtgNR29A0FHEpEYoOKPcxWFWfzj3ZUcaOzg9x/RDdtFRMWfEK6/YgpfuXU+z+48xv978UDQcUQkYCr+BPGZ62fy8aXl/P0L+3huZ0PQcUQkQCr+BGFmfO3jC1kUyudLP93K/mMng44kIgFR8SeQjNTwDdsz01L47IPVtHX1Bx1JRAKg4k8wpfmZfO/eSo62dvOFn2xhUJO9IglHxZ+AqqZP5q/uWMDL+5r45rO6YbtIotH1+BPU3ddUsLOuje+9fJCrSvO4Y0l50JFEZJxojz+B/fltV3PNjMn88fpt7DjaFnQcERknKv4ElpaSxD9/qpLJWWn81o820dzRG3QkERkHKv4ENyUnne/du4zmjl4+t3Yz/YO6YbtIvAus+M0s2cy2mNmTQWWQsIWhfL65ahFvHGrhTx/bTnuPlnmKxLMgJ3e/COwG8gLMIBF3LClnT8NJvrvhLf7jzXpuX1zGJ6+tYFGoIOhoIjLGAil+MwsBHwX+BrgviAxyrj++ZR4fWTCVhzbW8PjWOn5afYSF5fncs7yC2xeXkZ2uRWAi8cCCuDWfma0Dvg7kAn/g7reN8J7VwGqAioqKqsOHD49vyATX3tPP41uOsnZjDXsaTpKTnsJdS8u5Z3kF80t1kCYyEZjZJndfds7z4138ZnYbcKu7f87MbuQ8xX+6ZcuWeXV19bjkkzO5O5trTrB2Yw1Pbqunb2CIyooC7lk+ndsWlZKRmhx0RBE5j1gq/q8D9wIDQAbhMf5H3f1T5/sZFX9saO3qY92mWh56o4aDTZ3kZ6aysjLEPcsrmFOcE3Q8ETlLzBT/GR+uPf4Jyd15/WALazce5tmdDfQPOstnTuaT107nw1eXkJ6iowCRWHC+4tdsnVw0M+O62YVcN7uQ5o5e/r26lofeOMwXHt5CYXYaq5aFuOeaCqYXZgcdVURGEOge/4XSHn/sGxpyXj3QzEMbD/PC7kYGh5z3XzGFTy6v4Kb5JaQm61xBkfEWk0M9F0rFP7E0tPXw018e4Se/rKG+rYfi3HQ+8Z5p/Po1FZQXZAYdTyRhqPhl3A0MDrFhbxNrNx5mw74mDFgxt5h7lldw49xikpMs6IgicU1j/DLuUpKTuPmqEm6+qoQjLV2Ro4Aj/HxPNeUFmeGjgPdMozgvI+ioIglFe/wyrvoHh3h+1zEe2ljDaweaSU4yPjS/hE9eW8H7Zk8hSUcBImNGe/wSE1KTk7h1YSm3LizlUHMnP3mjhkeqj/DMzgamF2Zx9zUV/GpViMKc9KCjisQt7fFL4HoHBnlmRwNrN9bwxqEW0pKTuGXBVO5ZXsHymZMx01GAyKXQ5K5MCPuPnWTtxhoe3VxLe88As4uyuWf5dFZWllOQlRZ0PJEJRcUvE0p33yBPbqtj7cYath5pJTXZuGbmZFbMLebGucXMLsrWkYDIKFT8MmHtrGvj8a11bNjbyL5jHQBMm5zJirnFrJhbzLWzCslM02UiRM6m4pe4UHuiiw17m9iwt5H/OnCc7v5B0lOSuHZWISvmFrFiXrEuFSESoeKXuNPTP8gbh1p4aW8jG/Y2cai5E4BZU7K5cW4xK+YVcc3MybponCQsFb/EvUPNnWyIbAR+cfA4fQNDZKYm8745U1gxr4gb5xbrkhGSULSOX+LezCnZzJwyk99830y6+wb5xcFmXtrTxIt7Gnlh9zEArizJOTVBvGzGJF08ThKS9vgl7rk7bzV18NKeJl7a28gv326hf9DJTU/h+iumRDYERbp0hMQdDfWIRJzs6ee/Dhxnw95GXtrbyLH2XgCuLssLrxSaV8SSaZN0ETmZ8FT8IiNwd3bXn2TDvkY27GliU80JBoecgqxUbriiiBXzirjhiiJdQkImJBW/yAVo6+rn1QNNvLSniZf3NdLc0YcZLAoVhJeLzi1mYXm+LiYnE4KKX+QiDQ05O+raTs0NvFnbijsUZqfxgchG4IYrisjPSg06qsiIVPwil+l4Ry+v7A8fDbyyv4nWrn7MYE5RDkumFbC0YhJLphVwZUkOKVotJDFAxS8yhgaHnK1HWnltfzNbj5xg65FWTnT1A5CVlszC8nyWVBSwdNokllYUUKIVQxIAreMXGUPJSUbV9ElUTZ8EhCeJDx/vYuuRVrYeaWVLzQm+/9oh+gcPAlCWn8GSioJTRwYLyvJ1fSEJjIpfZAyYGTOmZDNjSjZ3Li0HwpeU2FnXfmpDsPVIK09tbwDCG475pbksmVbAkshRwczCbE0ay7jQUI/IOGo62Rs5KjjBlppWttW20dE7AEBeRgpLIvMES6eFjw4mZeseBHLpNMYvEoMGh5wDjR2n5gm21LSy79hJhiL/LGcUZp2aNF5aUcC8qXmkpWjiWC6Mil9kgujoHWBbbXiuYGtNK1uOtNJ0Mnx2cVpKEgvK8s7YGJQXZOqmNDKimCl+M5sGPAiUAA6scfdvv9vPqPglkbk7dW094XmCyIZgx9E2egeGAJiSk87S4YnjaQUsmlZATrqm7yS2VvUMAL/v7pvNLBfYZGbPu/uuALKIxDwzo7wgk/KCTG5bVAZA38AQexrazzgqeH7Xscj7YXZRDhWTsygryKAs8rNlkT8luek6zyDBjXvxu3s9UB95fNLMdgPlgIpf5AKlpSSxKFTAolABn74u/NyJzj621oY3BDvr2qlr7WZzzQlaI+cXDEsymJqXcWpDEN4wnPl9XkaKho/iWKDHg2Y2A1gKbAwyh0g8mJSdduo+xKfr7B2gvq2bo6091LV2U9fazdHI161HWnl6Rz39g2cO+eakp5w6WnjniCGDsvzw91PzM3QvgwkssOI3sxxgPfB77t4+wuurgdUAFRUV45xOJH5kp6cwpziXOcW5I74+NOQ0d/RGNgY9Z2wY6tq62VbbRktn3xk/YwYluRkjDiWVFWRQXpBJfmaqjhpiVCCreswsFXgSeNbd/26092tyVyRY3X2D1LV1n3bE8M7RQ3gD0UNfZLJ5WFZa8jsbg/x3NhDTC7OoKMyiKCddG4Yoi5nJXQv/l34A2H0hpS8iwctMS2Z2UQ6zi3JGfN3dOd7Zd94Nw666dpo7es/4may0ZComZzGjMJvphVlMP/U1i9L8TN0IJ4qCGOp5H3AvsN3Mtkae+4q7PxVAFhEZA2bGlJx0puSksyhUMOJ7evoHqWvt5nBLFzXHu3j7eCc1x7vY33iSF/c00jf4zhFDWnISocmZzCjMjmwc3tkwhCZl6SS2yxTEqp7XAG3KRRJMRmoys4pymDXCUcPgkNPQ3sPh5k4Ot7yzUXj7eBcbDx6ns2/w1HuTDMoiQ0bTC7OZPvnMo4WsNJ3DMBr9DYlI4JKT3jlX4b1nvebuNHf0UdPSydvNXRxu6eLw8U4OH+/i6e31py6HPaw4N/3MjcKU8NcZhdm6aU6Eil9EYpqZUZSbTlFuOlXTJ5/zelt3PzXHuzjcEt4YvB05anh1fxPr2s+cV8jPTGVGYRYVhdnhr5OzmBHZMEzJSU+Yq6Oq+EVkQsvPTGVhKJ+FofxzXuvuG6TmtCOE4Y3Dm0da+c9tdacuhgeQmmyU5IXPVSgtyGBqfvjx8NfSggwmZ6XFxcZBxS8icSszLZm5U3OZO/Xccxj6B4c4eqI7PJ/Q0kVdaw8Nbd2R6yK10tDWc8aEM4QnnafmD28UMigtyKQ0P4PS/OGvGUzOTov5ZaoqfhFJSKnJSadunjOSoaHwEtWGth7q2rqpb+2mvr2H+tYe6tu6qT58gmPbzz3rOS0l6dRGYPiIoTRyLsPw0UNBVrAnt6n4RURGkJT0ztzCSMNIEDnrubP31Magvq2H+rbwOQwNbT1sPNRCQ3sPg0NnbhwyUpNOHSWcPpR0+tFDNM98VvGLiFyipCSjODeD4twMFk8b+fyFwcglMYY3BnVtPacdPXTzi7eOc6y9h7O2DWSmJlNakMHX7lrItbMKxzS3il9EJIqSk8KTxiV5Ged9z8DgEE0dveEjhjOOHropiMISVBW/iEjAUpKHh34yYRyuSanznkVEEoyKX0Qkwaj4RUQSjIpfRCTBqPhFRBKMil9EJMGo+EVEEoyKX0QkwQRys/WLZWZNwOFL/PEpQPMYxhkrynVxlOviKNfFidVccHnZprt70dlPTojivxxmVj3SXeaDplwXR7kujnJdnFjNBdHJpqEeEZEEo+IXEUkwiVD8a4IOcB7KdXGU6+Io18WJ1VwQhWxxP8YvIiJnSoQ9fhEROY2KX0QkwcRt8ZvZ982s0cx2BJ3ldGY2zcxeMrNdZrbTzL4YdCYAM8swszfM7M1Irr8MOtPpzCzZzLaY2ZNBZxlmZm+b2XYz22pm1UHnGWZmBWa2zsz2mNluM7suBjLNjfw9Df9pN7PfCzoXgJl9KfL//A4ze9jMzn+rrHFkZl+MZNo51n9XcTvGb2Y3AB3Ag+6+IOg8w8ysFCh1981mlgtsAu50910B5zIg2907zCwVeA34oru/HmSuYWZ2H7AMyHP324LOA+HiB5a5e0yd+GNmPwRedff7zSwNyHL31qBzDTOzZOAosNzdL/XEzLHKUk74//Wr3L3bzB4BnnL3HwScawHwE+AaoA94Bvhtdz8wFr8/bvf43f0VoCXoHGdz93p33xx5fBLYDZQHmwo8rCPybWrkT0zsFZhZCPgocH/QWWKdmeUDNwAPALh7XyyVfsRNwFtBl/5pUoBMM0sBsoC6gPMAzAc2unuXuw8ALwMfH6tfHrfFPxGY2QxgKbAx2CRhkeGUrUAj8Ly7x0Qu4B+APwKGgg5yFgeeM7NNZrY66DARM4Em4N8iQ2P3m1l20KHO8gng4aBDALj7UeBbQA1QD7S5+3PBpgJgB/B+Mys0syzgVmDaWP1yFX9AzCwHWA/8nru3B50HwN0H3X0JEAKuiRxuBsrMbgMa3X1T0FlGcL27VwIfAX4nMrwYtBSgEviuuy8FOoEvBxvpHZGhp9uBfw86C4CZTQLuILzBLAOyzexTwaYCd98N/B/gOcLDPFuBwbH6/Sr+AETG0NcDa9390aDznC0yNPAScEvQWYD3AbdHxtN/AnzQzH4cbKSwyN4i7t4IPEZ4PDZotUDtaUdr6whvCGLFR4DN7n4s6CARNwOH3L3J3fuBR4H3BpwJAHd/wN2r3P0G4ASwb6x+t4p/nEUmUR8Adrv73wWdZ5iZFZlZQeRxJvAhYE+wqcDd/8TdQ+4+g/AQwYvuHvgemZllRybniQyl/Arhw/NAuXsDcMTM5kaeugkIdOHAWe4mRoZ5ImqAa80sK/Jv8ybC826BM7PiyNcKwuP7D43V704Zq18Ua8zsYeBGYIqZ1QJ/4e4PBJsKCO/B3gtsj4ynA3zF3Z8KMBNAKfDDyIqLJOARd4+ZpZMxqAR4LNwVpAAPufszwUY65fPA2siwykHgNwPOA5zaQH4I+K2gswxz941mtg7YDAwAW4idyzesN7NCoB/4nbGcpI/b5ZwiIjIyDfWIiCQYFb+ISIJR8YuIJBgVv4hIglHxi4gkGBW/yCUws47THt9qZvvMbHqQmUQuVNyu4xcZD2Z2E8sXlZYAAACwSURBVPAd4MMxdNExkXel4he5RJFr8/wrcKu7vxV0HpELpRO4RC6BmfUDJ4Eb3X1b0HlELobG+EUuTT/w38Bngg4icrFU/CKXZgj4NcKXr/5K0GFELobG+EUukbt3mdlHgVfN7FiMXARQZFQqfpHL4O4tZnYL8IqZNbn7E0FnEhmNJndFRBKMxvhFRBKMil9EJMGo+EVEEoyKX0Qkwaj4RUQSjIpfRCTBqPhFRBLM/wdfV69NobDt3QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(k_rng, sse)\n",
+    "plt.xlabel('K')\n",
+    "plt.ylabel('SSE')\n",
+    "##답은 3개의 K"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}