Iris- predict the cluster .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GRIP INTERSHIP - Nov20"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <p style=\"font-family: Arial; font-size:1.4em;color:Blue;\"> Gargi Singh </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK-2 \n",
    "\n",
    "## <p style=\"font-family: Arial; font-size:1.4em;color:Darkgreen;\"><u>Prediction using Unsupervised ML</u></p>\n",
    "## <p style=\"font-family: Arial; font-size:1.4em;color:Darkgreen;\"><u> From the given ‘Iris’ dataset, predict the optimum number of clusters and represent it visually.</u></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Supress Warnings\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#To perform K-means clustering\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading and understanding the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the data\n",
    "df=pd.read_csv(\"iris-sparkintern.csv\",sep=\",\", encoding=\"ISO-8859-1\", header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 6)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the shape of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we have 150 rows and 6 columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             150 non-null    int64  \n",
      " 1   SepalLengthCm  150 non-null    float64\n",
      " 2   SepalWidthCm   150 non-null    float64\n",
      " 3   PetalLengthCm  150 non-null    float64\n",
      " 4   PetalWidthCm   150 non-null    float64\n",
      " 5   Species        150 non-null    object \n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# checking the overall information \n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the dataset from statistical point of view\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA CLEANING "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "SepalLengthCm    0\n",
       "SepalWidthCm     0\n",
       "PetalLengthCm    0\n",
       "PetalWidthCm     0\n",
       "Species          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the null values \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is no null values . the dataset looks very clean we can proceed with the same dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Univariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i+KQD8H4qmnr04JoqRYS5ddv5hbppft57iqGjXMZoUmqs/mHRZC3IT9Gb5c36w6oqdQ8Mj3NqAKGCcgghxWetVmw/qNTYCC0qTnM6DvyE22V04/xcuYz0180N8ll78m8CAP+VLanumNv1R+4D4JB9jd16aG21UmMjdduiQuWnxX6o7wt3u/SRudlaNi9HDR19+sOqKg0Ms2A1gNFHOYSQsqW2XU3dg7psxgSFufnxx/9JionQFWdMVHVrr9ZXtjodBwBG4kTXnZyw9TbG3GaMKTPGlDU3N49xLCA07T3UpYfX1Sg9PlKfO69AcZGnvrLH3LxkffKsfLX0DOqR9bXy+vggC8Do4t0xQsaw16dXdjcpJzlaM7JY1A/vNW9Ssooz4vTSzka19Q45HQcATle9pNxjbudIOnCiA62191lrS621penp6eMSDggl2+s79af1tcpMOFwMxZ5GMXRUUUacrpubo4rmHj27tUGWSWcAo4hyCCFj7f5WdfYPa+kZE1iEGidkjNF1c7NljPTUlnpOugAEquWSbjWHnSWp01p70OlQQKjp7B/WHY9sUlxkmD5zToFiIka+F9C8SclaUpKuspp2vbGPaT8Ao4dyCCGhf8ir1/c1qSQzXoVpcU7HgR9LionQ0jMmqLK5V49sqHU6DgC8hzHmUUlrJZUYY+qNMZ8zxtxujLn9yCErJFVKqpD0e0l3OBQVCFnWWv3rk9t0sGNAN8/PHdHE0PEunpap2TmJ+vuuRu1r7B615wUQ2tjKHiHhjX1NGhz26dIZmU5HQQBYkJ+i7Q2d+q8Ve3TxtExlJkQ5HQkA3mGtvfkkj1tJXxynOABO4ME11frbzkP61hVTFRcZPqrPbYzRR8/M0cHOAT21uV5fuWiKoiPco/oaAEIPk0MIel39w1qzv1VzcpM0MTHa6TgIAMYYXTcnW0Nen37wwm6n4wAAgADydl2Hfrhity6elqEvnF84Jq8R7nZp2bxc9Qx69Py2Ey4pBgCnhHIIQe+t8mb5rNVF05gawoeXGhep2y+YrOVvH9Ca/S1OxwEAAAFg0OPVV5/YqvS4SP142ewxXecyOzlaF0zJ0Ja6Du0+2DVmrwMgNFAOIaj1Dnq0obpNs3OSlBIb4XQcBJg7Fk9Wbkq07np2p4Y8PqfjAAAAP3fv65WqbO7VDz86U0kxY3/uuWRquiYmRunpLQ1qZ6dVACNAOYSgtnp/izxeqwumsD0vTl1UuFvfvXqGKpp6dP/qKqfjAAAAP1bV0qt7Xq/QVbMmanFJxri8ZpjLpevn5ah/yKu7lu8cl9cEEJwohxC0Boa9WlfZqulZCcpgQWGcpoumZeriaZn65avlOtDR73QcAADgh6y1+vYz2xXpdumuq6aP62tPTIzW4pJ0Pcel8ABGgHIIQWtdZasGhn1aMk6f3CB4fefq6fJZqx+uYHFqAADwXs9sbdDqilZ94/KpjnwouWhKunKSo/W953bJ4+VSeACnjnIIQWnI49PqihZNyYxTVhI7lGFkclNidNuiyXp+20Ftqml3Og4AAPAjHX1D+s/nd2tObpI+viDPkQzhbpe+feU07TnUrUc31DqSAUBgoxxCUNpY3abeIS9TQxg1/7CoUBnxkfr+87tkrXU6DgAA8BO/fLVC7X1D+sF1Z8jlGrvdyU7mshkTdM7kVP347/tYnBrAKaMcQtDx+qxWV7QoPzVGk1JjnY6DIBEbGaZ/uaxEW+s69Ny2g07HAQAAfqCmtVcPr6vWDaW5mpGV6GgWY4y+c/UM9Qx69NOX9zmaBUDgGVE5ZIxZaozZa4ypMMZ88wSP326M2W6M2WqMWWWMGd/V2RCSdh3sUkf/sM4vZocyjK6PnZmj6RMT9KMX92hg2Ot0HAAA4LC7/7ZXYS6XvnbJFKejSJJKJsTrEwvz9Of1Ndp9sMvpOAACyGmXQ8YYt6R7JF0uabqkm09Q/jxirZ1prZ0j6W5JPz3tpMCHtLqiRSmxESqZEO90FAQZt8vo21dOU0NHP1vbAwAQ4jbXtuuF7Qd126JCv9oZ96uXTFFCdLj++8U9TkcBEEBGMjm0QFKFtbbSWjsk6TFJ1x57gLX22Lo6VhILdWBM1bX1qbatT+dMTpXLOHfNN4LXOUVpunhapn6zcr9aegadjgMAABxgrdUPX9it9PhI3bao0Ok475IUE6F/vGCy3tjXrHWVrU7HARAgRlIOZUuqO+Z2/ZH73sUY80VjzH4dnhz68gheDzipNftbFBnm0ry8ZKejIIj92xVT1T/s1a9fq3A6CgAAcMBLOxtVVtOur148RbGRYU7HeY9PnZOvzIRI3f23PWykAeBDGUk5dKKxjPf85rHW3mOtnSzpXyV9+4RPZMxtxpgyY0xZc3PzCCIhlHX2D2t7Q6dKJyUrMtztdBwEscnpcbqhNEd/Xl+jurY+p+MAAIBx5PH6dPff9qgo4/D5gD+KCnfrny6eos21HXp1d5PTcQAEgJGUQ/WSco+5nSPpwAcc/5ikj5zoAWvtfdbaUmttaXo6iwjj9KyvbJW10tmT05yOghDw5YuK5TJGP3uF3UAAAAglT21uUGVLr75xWYnC3P67+fOyeTkqSIvV/7y0V14f00MAPthIfpttlFRsjCkwxkRIuknS8mMPMMYUH3PzSknlI3g94H0NeXzaUN2maRMTlBIb4XQchICJidH69Dn5enpLg/Ye6nY6DgAAGAdDHp9+8Wq5Zuck6pLpmU7H+UBhbpf++dIp2tvYreVvNzgdB4CfO+1yyFrrkXSnpJck7Zb0hLV2pzHme8aYa44cdqcxZqcxZqukr0n61IgTAyfwdn2H+oa8Oqco1ekoCCH/uHiy4iLD9D8v7XU6CgAAGAePl9WpoaNfX7u0RCYANj+54oyJmpGVoJ++vE9DHp/TcQD4sRHNQVprV1hrp1hrJ1trf3DkvrustcuPfP0Va+0Ma+0ca+0Sa+3O0QgNHMtaq/WVrZqQEKWC1Fin4yCEJMVE6PYLJuuV3Y3aVNPmdBwAADCGBoa9+vVr5SqdlKxFxYGxjIHLZfSNpVNV19avxzbWOh0HgB/zv6X1gVP0dn2nDnQO6JrZWQHxCQ6Cy2fOzdeDa6r1o7/t1eO3ncXPIAAAAeKR9adWlqyuaFFj16CunpWlRzfUnfwb/MSi4jQtLEjRL1+t0PXzchQTwVtAAO/lvyuoAR/Sn9bVKMLt0pzcJKejIATFRITpziVF2lDVprX7W52OAwAAxsCQx6fX9zWrMD1WhelxTsc5JcYcnh5q6RnUA6urnY4DwE9RDiGgdfYN67m3D2hObpKi2L4eDrlxfq4mJETp56+Uy1p2AwEAINisq2xV76BHl0zz70Wo38+8Scm6eFqm7n1jvzr6hpyOA8APUQ4hoD25uV6DHp8WFKQ4HQUhLCrcrTuWTNaG6jatYXoIAICgMuTx6c3yZhVnxGlSAK9v+fXLStQz6NFv39jvdBQAfohyCAHLWqs/r6/R3LwkZSVFOx0HIe7/pof2MT0EAEAQWV/Vqr4hry6amuF0lBEpmRCv6+Zm68HV1TrUOeB0HAB+hnIIAWttZasqm3v18YWTnI4CKDLMrS8umayN1e1aXcH0EAAAwWDI49Nb5S0qSo9TXgBPDR311YunyGetfvFqudNRAPgZyiEErD+vr1VidLiumjXR6SiAJOmG+bmamMj0EAAAwWJjdZt6Bj1aEuBTQ0flpsTolgV5eqKsTtUtvU7HAeBHKIcQkJq7B/XSjkO6fl4OC1HDb0SGuXXHkiKV1TA9BGBsGWOWGmP2GmMqjDHfPMHjecaYlcaYLcaYbcaYK5zICQSyYa9Pb5U3qyAtVgVpgT81dNSdFxYrwu3ST1/e53QUAH6EcggB6anN9fL4rG5ekOt0FOBdbijN0cTEKP2M6SEAY8QY45Z0j6TLJU2XdLMxZvpxh31b0hPW2rmSbpL0m/FNCQS+TTXt6hrw6MIgmRo6Kj0+Up89L1/L3z6gXQe6nI4DwE9QDiHgWGv1eFmdSiclqygj3uk4wLscnR7aVNOuVRUtTscBEJwWSKqw1lZaa4ckPSbp2uOOsZISjnydKOnAOOYDAp7H69Mb+5o1KSVGhUE0NXTUbYsmKyEqTD/++16nowDwE5RDCDhlNe2qbO7VDfOZGoJ/uqE0R1mJUfr5K+VMDwEYC9mS6o65XX/kvmN9V9InjDH1klZI+tL4RAOCw+baDnX2D+vCqRkyxjgdZ9QlRofr9sWT9dqeJpVVtzkdB4AfoBxCwHl8Y53iIsN05UwWooZ/OnZ66K1ypocAjLoTvVM9vom+WdKD1tocSVdIetgY857zPmPMbcaYMmNMWXNz8xhEBQKP12f1xr4m5SZHqygjzuk4Y+Yz5xQoPT5Sd7+0lw+zAFAOIbB0DwzrhW0HdfXsLMVGhjkdB3hfN5TmHpkeYu0hAKOuXtKx47M5eu9lY5+T9IQkWWvXSoqSlHb8E1lr77PWllprS9PT08coLhBYtta1q71vWEuCdGroqOgIt758YZE2VLXp9X2Uw0CooxxCQHnu7YPqH/bqRi4pg5+LCHPpixcWaXNth95kegjA6NooqdgYU2CMidDhBaeXH3dMraSLJMkYM02HyyHe/QEn4fVZrdzbrKykKJVkBv/aljfOz9Ok1Bj96MU98vr4MAsIZZRDCCiPb6zV1Anxmp2T6HQU4KSWzctVdlI000MARpW11iPpTkkvSdqtw7uS7TTGfM8Yc82Rw/5Z0heMMW9LelTSpy2/iICT2lbfobbeIV1YkhnUU0NHRYS59PXLSrTnULee2lzvdBwADqIcQsDYfbBLb9d36sb5uSHxlzUCX0SYS19cUqQtTA8BGGXW2hXW2inW2snW2h8cue8ua+3yI1/vstaea62dba2dY639u7OJAf/ns1Yr9zZpYmKUpk0M/qmho66cOVGzcxL1k7/v08Cw1+k4ABxCOYSA8fjGOkW4XfrInOM3ZAH818fmZSsrMUq/epWdywAA8Gfb6zvV0jOkJSXBvdbQ8Ywx+rcrpulQ14DuX13ldBwADqEcQkAY9Hj1zNYGXTIjU8mxEU7HAT60yDC3bl88WWU17Vpb2ep0HAAAcAJHp4Yy4iM1PSvB6Tjj7qzCVF08LUO/Xblfbb1DTscB4ADKIQSElXua1NE3rGXzcpyOApyyG0pzlREfqV+9WuF0FAAAcAI7GjrV1D2oC6dmyBVCU0PH+telU9U75NGvXit3OgoAB7AXOALCk5vqlZkQqfOL2WYX4+eR9bWj9lzz81P0wvaD+uELu5WfFnvaz3PLwrxRywQAAA5PDb225/DU0BnZobvpSXFmvG6cn6eH19boE2dN0uT0OKcjARhHTA7B7zV3D2rl3mZdNzdHbldofpKDwDc/P0WxkWFaubfJ6SgAAOAYR6eGloTw1NBRX7tkiqLC3frP53c5HQXAOKMcgt97dmuDvD6r6+exEDUCV0SYS+cXpam8qUd1bX1OxwEAAPq/qaH0+EjNDOGpoaPS4yP15YuKtHJvs1bu4QMtIJRQDsGvWWv1l7J6zclNUlFG6GwpiuC0sDBFMRFuvcbJFgAAfmHnga7Daw2VMDV01KfPKVBhWqy+//wuDXl8TscBME4oh+DXdh7o0t7Gbl3PQtQIApFhbp1XlKa9jd1qaO93Og4AACHt8NRQo9LjIjUzh6mhoyLCXPqPq6arsqVXf1xT7XQcAOOEcgh+7clN9YoIc+nqWVlORwFGxVmFqYoKd7H2EAAADtt5oEuNXaG9Q9n7WTI1Q0tK0vXLV8vV3D3odBwA44ByCH5r0OPVM1sbdOn0TCXGhDsdBxgVUeFunTs5TbsOdulgJ9NDAAA4wedjauhk/uOq6RrwePWjv+1xOgqAcUA5BL+1ck+TOvqGuaQMQeecyWmKDHNp5d5mp6MAABCSXtp5SI1d7FD2QQrT4/S58wr15KZ6ra9sdToOgDFGOQS/9eSmemUmROr84nSnowCjKjrCrbMLU7WzoVONXQNOxwEAIKT4fFa/eLVcaXGRmsXU0Af6ykXFykmO1ree3q5Bj9fpOADGEOUQ/FJz96BW7m3WdXNz5HbxaQ6Cz7lFaQp3u/Q6aw8BADCu/r7rkPYc6taFU9OZGjqJ6Ai3vkeTyOQAACAASURBVP+RM7S/uVf3vVHpdBwAY4hyCH7p2a0N8vqsrp+X7XQUYEzERoZpYWGKttV3qoWFHgEAGBc+n9XPXylXYVqsZuUkOR0nICwpydCVMyfqVysrVN3S63QcAGOEcgh+x1qrv5TVa05ukooy4p2OA4yZ84rSFOY2en0f00MAAIyHo1NDX7qoiKmhU3DX1dMV6XbpP57dIWut03EAjAHKIfidnQe6tLexm4WoEfTio8K1ID9FW+s61NY75HQcAACC2uG1hipUmBarq2dlOR0noGQmROlfLivRW+UtenpLg9NxAIwByiH4nSc31SsizMVf2ggJ5xcfXu/gDaaHAAAYUy/uOKTdB7t054VFCnPzNuhUfeKsSSqdlKzvLt+pJjbUAIIOvxXhVwY9Xj2ztUGXTs9UYky403GAMZcQHa7S/GRtrulQRx/TQwAAjAWP16efvLxXxRlxunYOa1qeDrfL6O7rZ2nQ49O3nt7O5WVAkKEcgl9ZuadJHX3DXFKGkLKoOF2S9Ma+ZoeTAAAQnJ7a0qDK5l7986Ul7IQ7AoXpcfr6ZSV6ZXeTntnK5WVAMKEcgl95clO9MhMidf6RN8tAKEiKidCZk5JVVtOuzv5hp+MAABBUBj1e/eKVcs3KSdRlMzKdjhPwPnNuwZHLy3ZxeRkQRCiH4Deauwe1cm+zrpubwyc6CDkXTEmXtVZvlTM9BADAaHp0fa0aOvr19ctKZNihbMSOXl42MOzVvz3F5WVAsAhzOgBw1LNbG+T1WV0/j+vAEXpSYiM0NzdZG6radMGUdMVHseYWAAAj1Tfk0a9XVuiswhSdV5TmdJyg8Mj6WknSxdMy9cL2g/qnx7dqYUHqqL/OLQvzRv05Abw/JofgF6y1+ktZvebkJqkoI97pOIAjFpeky+uzWlXe4nQUAACCwgOrq9XSM8TU0Bg4e3KqijPitGL7QTVyeRkQ8CiH4Bd2HujS3sZuFqJGSEuNi9Ts3CStq2pVz6DH6TgAAAS0tt4h3fv6fl00NUPzJqU4HSfouIzR9fNyFOF26YmyOnm8PqcjARgByiH4hSc31SsizKWrZ2U5HQVw1OKSdHm8VqsrmB4CAGAkfvlquXqHPPrm5VOdjhK04qPC9bF5OTrYOaCXdh5yOg6AEaAcguMGPV49s7VBl07PVGIM66wgtGXER+mM7EStq2xV3xDTQwBOzBiz1Biz1xhTYYz55vscc4MxZpcxZqcx5pHxzgg4qbqlV39aV6Mb5+eqOJMlC8bS1AkJOqswVav3t2pfY7fTcQCcphGVQyc7MTHGfO3ISck2Y8yrxphJI3k9BKeVe5rU0TfMJWXAEUtKMjTo8WnN/lanowDwQ8YYt6R7JF0uabqkm40x0487pljSv0k611o7Q9I/jXtQwEF3v7RHEWEuffXiKU5HCQmXnzFBmQmRenJTPZfGAwHqtMuhD3NiImmLpFJr7SxJT0q6+3RfD8HryU31ykyI1PnF6U5HAfzChMQoTZ+YoDX7WzQw7HU6DgD/s0BShbW20lo7JOkxSdced8wXJN1jrW2XJGtt0zhnBByzqaZdK7Yf0hfOL1RGQpTTcUJCuNulG+fnaWDYq79uqmd7eyAAjWRy6KQnJtbaldbaviM310liNATv0tw9qJV7m3Xd3By5XewgARx14dQMDQz7tGY/aw8BeI9sSXXH3K4/ct+xpkiaYoxZbYxZZ4xZOm7pAAdZa/VfK3YrPT5Sty0qdDpOSJmQEKXLz5igvY3dWlvJ9DMQaEZSDn2YE5NjfU7SiyN4PQShZ7c2yOuzun7eB/3oAKEnKyla0ybEa1VFi/qHmB4C8C4n+jTl+I/pwyQVS1os6WZJ/2uMSXrPExlzmzGmzBhT1tzcPOpBgfH20s5DKqtp11cvnqLYyDCn44ScswpTVZIZr7/tOKRDnWxvDwSSkZRDH+bE5PCBxnxCUqmk/3mfxzkxCUHWWj1RVqc5uUkqymChQOB4F0/P1MCwT29V8HsRwLvUS8o95naOpAMnOOZZa+2wtbZK0l4dLovexVp7n7W21Fpbmp7O5d0IbAPDXv3nC7tVkhmvG0q5YMEJxhh9bF6OosLdemxjrYbZ3h4IGCMphz7MiYmMMRdL+ndJ11hrB0/0RJyYhKatdR3a19ijG+fnnvxgIARNTIzWzOxEraloZXFHAMfaKKnYGFNgjImQdJOk5ccd84ykJZJkjEnT4cvMKsc1JTDO7nuzUvXt/frONdMV5mZTZqfERYZp2bwcNXUP6oXtB52OA+BDGslvzZOemBhj5kr6nQ4XQyyEiHd5oqxO0eFuXTVrotNRAL910bQMDXt9enMf00MADrPWeiTdKeklSbslPWGt3WmM+Z4x5pojh70kqdUYs0vSSklft9ayCAiC1oGOfv3m9QpdMXOCzpmc5nSckFecGa9FxWnaUNWm7Q2dTscB8CGc9oW41lqPMeboiYlb0v1HT0wklVlrl+vwZWRxkv5ijJGkWmvtNe/7pAgZfUMePff2QV0xc6Lio8KdjgP4rYz4KM3NS9a6yladW8TJLoDDrLUrJK047r67jvnaSvrakT9A0Pvhit2yVvrWFdOcjoIjLpk+QZUtvXp6S71ykqKVHBvhdCQAH2BE85bW2hXW2inW2snW2h8cue+uI8WQrLUXW2szrbVzjvyhGIIkacX2Q+oZ9HBJGfAhXDg1Q9ZKr+9lABMAgOOtr2zV89sO6vYLJisnOcbpODjC7TK6aX6erJUe21grr4/t7QF/xsW4cMQTG+tUmBar+fnJTkcB/F5KbIRK85O1sbpNta19TscBAMBveLw+ffe5XcpOitbtF0x2Og6OkxIboevmZquuvV+v7G50Og6AD0A5hHFX2dyjDdVtWlaaqyOXGwI4iSUlGXK7jH7y8l6nowAA4DceXFOt3Qe79O0rpyk6wu10HJzArJwkzc9P1pv7mlXe1O10HADvg3II4+6Jsnq5XUYfOzPb6ShAwEiIDtc5k9P07NYD2sHCjgAAqKGjXz99eZ8unJqhpWdMcDoOPsCVM7OUHh+pv5TVq3tg2Ok4AE6AcgjjyuP16a+b67WkJF0ZCVFOxwECyqLidCXFhOtHf9vjdBQAABxlrdV3nt0ha6X/d80MptH9XESYSzctyNPAsFdPbqqXz7L+EOBvKIcwrl7f26zm7kHdUMpC1MCpio5w684lRXqrvEVvlbO1PQAgdL20s1Gv7G7SVy8pVm4Ki1AHggkJUbpqVpbKm3q0qrzF6TgAjkM5hHH1eFmd0uIitWRqhtNRgID0ybMnKTspWv/94h752PUDABCCugeG9d3lOzVtYoI+c26B03FwCubnJ+uM7ET9fdch1baxyQbgTyiHMG6augf02p4mfWxetsLd/OgBpyMyzK1/uWyKdh7o0vK3DzgdBwCAcffjl/aqsXtAP7zuDM4pA4wxRtfNyVZidLge31ir/iGv05EAHMFvU4ybpzc3yOuzXFIGjNC1s7M1fWKC/uelvRoY5qQKABA61u5v1R/X1uhTZ+drbl6y03FwGqIj3Lpxfp46+4f19NYGWdYfAvwC5RDGhbVWj5fVqXRSsianxzkdBwhoLpfRv185TQ0d/frDqiqn4wAAMC56Bz36+pNvKz81Rv+6dKrTcTACeSkxumT6BO1o6NTG6nan4wAQ5RDGyaaadlU29+qG+UwNAaPh3KI0XTo9U/esrFBj14DTcQAAGHP/9eJuNXT068fLZis6wu10HIzQ+cVpKsqI0/PbDnAuA/gByiGMi8c31ik2wq0rZ050OgoQNP79ymnyeC1b2wMAgt6q8hb9aV2tPn9egUrzU5yOg1HgMkbL5uUoKtytRzfUasjjczoSENLCnA6A4Ncz6NEL2w/qmtlZio3kRw4YLZNSY/WZ8/L1uzcqdevZ+ZqTm+R0JAAARuyR9bXvuj0w7NUvXi1XWlykcpJj3vM4Ald8VLiWlebogdXVenHHQV07J9vpSEDIYnIIY+6FbQfUN+TlkjJgDNy5pEhpcZH63nM7WdARABB0rLV6dmuDugeGdf28HHYnC0LFGfE6vyhN66vatOtAl9NxgJDFb1eMucc21qkoI05zmWoARl18VLi+cVmJNtd26NmtbG0PAAguW+o69HZ9py6cmqG8lBin42CMXDIjU1lJUfrr5np19g87HQcISVzjg1NyqmO8Bzv7taW2Q1fMnKhHN9SNUSogtF0/L0d/Wl+jH6zYrSVTM5QYHe50JAAARqylZ1DL3z6g/NRYLS7JcDoOxlCYy6WbSvP0q5Xl+ktZnT57XoHTkYCQw+QQxtSGqjaFuYzOzGNqCBgrLpfRDz4yU609g/rJ3/c6HQcAgBHz+Hx6fGOd3MbohtIcuYxxOhLGWFp8pK6elaXKll69ta/Z6ThAyKEcwpgZHPZqS12HZuUkKiaCITVgLM3MSdStZ+fr4XU1eruuw+k4AACMyMs7G9XQ0a+PnpmtpJgIp+NgnMyblKyZ2Yl6eXejtnI+A4wr3rFjzGyt79CQx6cFBalORwFCwj9fOkUrth/Ut57erme/eK7CWLQTABCAdh3o0lsVLVpQkKIZWYlOxwkYwbCLmzFGH5mTrbq2Pn3lsS164cvnK47djoFxwTsHjAlrrTZUtWliYpRyk6OdjgOEhPiocH3n6hnaeaBLD6+rcToOAACnrKqlV3/ZVKfspGhdOXOi03HggOgIt24ozVVdW5/uenaH03GAkEE5hDFR196vg50DWlCQIsM14sC4uWLmBF0wJV0/+fs+HejodzoOAAAfWt+QR7c/vEkuY3TLwjy2rQ9h+Wmx+tKFxXpqc4Oe3drgdBwgJPAbF2NiQ1WrIsJcmpPDQtTAeDLG6PvXniGftfrmU9tlrXU6EgAAJ2Wt1bee2q59Td26aX6ukllnKOR96cIizZuUrG8/vUN1bX1OxwGCHuUQRl3fkEfb6js1JzdJkeFup+MAIScvNUbfvHyq3tzXrMc31jkdBwCAk/rjmmo9s/WAvnrxFBVnxjsdB34gzO3Sz2+cIxnpK49tkcfrczoSENQohzDqNte0y+OzWliQ4nQUIGR9YuEknV2Yqv98Ybfq2/m0DQDgv94qb9b3X9iti6dl6M4lRU7HgR/JTYnRD66bqc21Hfrlq+VOxwGCGuUQRpXPWq2ratOk1BhNTGQhasApLpfR3dfPkrVW3/wrl5cBAPzT/uYe3fHnzSrOiNPPb5orl4u1KvFu18zO0vXzcvTrlRVaX9nqdBwgaFEOYVTtPdSttt4hnTM5zekoQMjLTYnRt66cplUVLfpzEGxvCwAILp19w/r8H8sU4Xbp97eWsmU53td3r5mhvJQYffXxrersG3Y6DhCUKIcwqtbub1VCVJimT0xwOgoASbcsyNP5xWn6wQu7VdHU7XQcAKPEGLPUGLPXGFNhjPnmBxx3vTHGGmNKxzMfcDLDXp+++Mhm1bf36d5PzlNuSozTkeDH4iLD9Mub56qpe1DffGobE9HAGKAcwqhp7BpQRXOPzipMlZuRYMAvGGP042WzFRPh1p2PbNHAsNfpSABGyBjjlnSPpMslTZd0szFm+gmOi5f0ZUnrxzch8MGstfr3p7drVUWLfnjdTM3PZ51KnNysnCR9/bISvbjjkB5aW+N0HCDoUA5h1KytbFWYy6iUv+ABv5KZEKWf3DBbew516/vP73I6DoCRWyCpwlpbaa0dkvSYpGtPcNz3Jd0taWA8wwEn84tXy/VEWb2+fFGxlpXmOh0HAeQL5xfqoqkZ+s8XdmlLbbvTcYCgQjmEUdE/5NWW2nbNzknienHADy0uydA/LCrUn9fXasX2g07HATAy2ZLqjrldf+S+dxhj5krKtdY+P57BgJN5YmOdfv5Kua6fl6OvXlzsdBwEGJfL6Cc3zFZGfJTufGSL2nuHnI4EBA3exWNUbKpp07DX6uzJqU5HAfA+/uWyEq2vatO//nWbZmYnsr4DELhOdO32OwtwGGNckn4m6dMnfSJjbpN0myTl5eWNUjwEo0dGYWODfY3demhttYoy4jQ7J0mPbqg76fcAx0uKidBvPn6mlt27Vl97Yqv+8Kn57HIHjAImhzBiPmu1trJV+akxykpi+3rAX4W7XfrVzXNlJH3hoTL1DnqcjgTg9NRLOvZanBxJB465HS/pDEmvG2OqJZ0lafmJFqW21t5nrS211pamp6ePYWSEurq2Pj2yvlaZCVG6ZUEe61NiRGbnJuk/rpqmlXub9ZvXK5yOAwQFyiGM2K4DXWrvG9bZbF8P+L3clBj96pYzta+xW//yl7fl87HbBxCANkoqNsYUGGMiJN0kafnRB621ndbaNGttvrU2X9I6SddYa8uciYtQd6hrQA+uqVZcVJg+fU6+osLdTkdCEPjEWZP0kTlZ+snL+/Tyrkan4wABj3III2Kt1VvlzUqJjdCMLLavBwLBBVPS9a0rpunFHYf0q9f4tA0INNZaj6Q7Jb0kabekJ6y1O40x3zPGXONsOuDd2nqH9MDqKoW5jT57boHio8KdjoQgYYzRf39slmZmJ+qfHtuivYe6nY4EBDTKIYxIbVuf6tr7dW5RmlyG8WAgUHzuvAJ99Mxs/eyVffrbjkNOxwFwiqy1K6y1U6y1k621Pzhy313W2uUnOHYxU0NwQvfAsO5fXSWP1+oz5xYoJTbC6UgIMlHhbt33yVLFRobp8w9tZIFqYAQohzAib5a3KDrcrXl5yU5HAXAKjDH64XUzNSc3SV97Yqu21nU4HQkAEER6Bj36w6oqdQ8M61NnT9KEhCinIyFITUiM0u8+OU+NXYO648+bNez1OR0JCEiUQzhtzd2D2nOwS2cVpioijB8lINBEhbt1363zlBYXqc88sEEVTT1ORwIABIG+QY/uX1Wl9r4hfersfOWlxjodCUFubl6y/vujM7W2slXfeHIbayoCp4F39Dhtqypa5HYZnVWY4nQUAKcpIz5KD39ugdwul279w3od7Ox3OhIAIID1D3l1/+oqtfQM6pNn5aswPc7pSAgRHz0zR1+/rERPb2nQD1bslrUURMCpoBzCaekZ9GhLbbvm5iWzsCAQ4CalxurBz8xX94BHn/zDBq7XBwCcloHhw8VQY/egPnHWJBVlUAxhfN2xeLI+fU6+/rCqSve+Uel0HCCgUA7htKzd3yqvz+q8IravB4LBGdmJuu/WUtW29enW+zeoo4+CCADw4Q0Me/XA6iod6hzQxxfkaUpmvNOREIKMMbrrqum6ZnaWfvS3PXp8Y63TkYCAQTmEUzYw7NXayhZNm5ig9PhIp+MAGCVnT07VvZ84U3sbu3Xz79ertWfQ6UgAgAAw6PHqj2uq1dDRr5sX5GrqxASnIyGEuVxGP142W4umpOubT23XoxsoiIAPg3IIp2xdZasGhn1aUpLhdBQAo+zCqZn631tLVdnco5vuW6em7gGnIwEA/NiQx6eH1taorr1PN87P0/SsRKcjAYoIc+m+T87TBVPS9W9PbdcDq6ucjgT4PcohnJJBj1erKlpUkhmv7ORop+MAGAOLpqTrwc8sUENHv2763To1dLBINQDgvQY9Xj20tlrVLb1aVpqrmdkUQ/AfUeFu/e6T83TZjEz9v+d26TevVzgdCfBrYSP5ZmPMUkm/kOSW9L/W2v8+7vFFkn4uaZakm6y1T47k9eC8DVVt6hvyaslUpoaAYHb25FQ99NkF+swDG/WRe1br/k/N18wcTvoBAIcNDB++lKyuvU/LSnM1OyfJ6UgIMo+sH53Lwc4rStehzgHd/be9WlPRqqVnTJDLmFF57lNxy8K8cX9N4FSc9uSQMcYt6R5Jl0uaLulmY8z04w6rlfRpSY+c7uvAfwwMe/VWeYuK0uOUlxLjdBwAY6w0P0V/veMcRbhduuF3a/XyrkanIwEA/EDfkEd/WFWluvY+3TQ/T3NyKYbgv9wuo2WluTq7MFWrKlr00Npq9Q95nY4F+J2RTA4tkFRhra2UJGPMY5KulbTr6AHW2uojj/lG8DrwE49uqFXPoIepIcBBo/Up2qm49exJenhdjW57qExXzJyocyanyhz3iRufhgFAaOgZ9OiB1VVq6h7UxxdO0jQWn0YAcBmjq2dnaUJClJa/fUC/faNCnzwrn811gGOMZM2hbEl1x9yuP3IfgtCgx6vfvVGp/NRYFaTFOh0HwDiKjwrX588r1LSJCXph+0E9XlanQQ+fuAFAqOkaGNbv36pUS8+gbj2bYgiBZ35Bij57XoH6hrz6zesV2lrXLmut07EAvzCScuhEF2qe1v9ZxpjbjDFlxpiy5ubmEUTCWHl8Y50OdQ3oQqaGgJAUEebSLQvzdOn0TG2v79RvXt+vpi52MgOAUNHRN6Tfv1mpzv5hfeqcfBVnxDsdCTgtBWmx+uKSImUmROmJsno9trFOfUMep2MBjhtJOVQvKfeY2zmSDpzOE1lr77PWllprS9PT00cQCWOhb8ijX75aoYUFKZqcztQQEKpcxmhxScYxn7jt1+ZaPnEDgGBX29qn+96qVM+gR589J1+FaXFORwJGJDkmQrctKtSl0zO180CnfvFqufYc6nI6FuCokZRDGyUVG2MKjDERkm6StHx0YsGfPLC6Wi09g/rG0qnvWWcEQOiZnB6nLy0pUlZStJ7cVK9HNtSqrXfI6VgAgDGwr7Fby363RoPDPn3+vELlpfJBIYLD0Q+97lhcpOhwtx5aW6OH11ZzToOQddrlkLXWI+lOSS9J2i3pCWvtTmPM94wx10iSMWa+MaZe0jJJvzPG7ByN0Bg/HX1DuveN/bp4WqbmTUp2Og4AP5EQHa7Pn1+gpTMmaM/Bbl36szf16m52MwOAYLK5tl3L7l0rn5W+cH6hspOjnY4EjLqspGjdeWGRls6YoP3Nvfr5K/v08q5G1ldEyBnJbmWy1q6QtOK4++465uuNOny5GQLUb9/Yr55Bj75+WYnTUQD4GZcxWjQlXcWZcXp5V6M+98cyXTc3W9++cppS49j9AwAC2Rv7mnX7w5uUHh+pP31uoVZVtDgdCRgzYS6XFk1J1+zcJL2446BW7m3ShqpWXTAlXQsLUxXuHskFN0Bg4Kcc7+tQ54AeXF2t6+Zkq2QCiw4COLGJidF69s5z9aULi/T8tgO6+Kdv6K+b6lmLCAAC1HNvH9Dn/7hR+WmxevIfz1ZeaozTkYBxkRgdrpvm5+n2RYWamBitFTsO6ccv7dXa/S3yeH1OxwPG1IgmhxDcfvlauXzW6quXTHE6CgA/99dNDZqYGK07Fhfp6S0N+ue/vK17Xq/Q1bOylJkQ5UimWxbmOfK6ABDIHl5Xo7ue3aHSScn630/NV2J0uNORgHGXlxqrz55XoMqWHr2yq0nPbTuoN8tbtLgkXfMmJSvMxYwFgg8/1Tih8sZuPb6xTrcsyFNuCp8WAfhwMhOidNuiQl0zO0sHOvr1q9fK9cK2A+of4rp9APBn1lr96tVy/cczO3RhSYYe+uxCiiGEvMK0OH3h/AJ99twCJUSF6dmtB/Szl/dpU027vD4mpBFcmBzCe1hr9b3ndyk2wq2vXMzUEIBT4zJGZxWmamZ2ol7e1ag1+1u1ta5Dl82YoDMnJcvFrocAMGoeWV874ufwWasV2w9qzf5Wzc1N0uKSDD29pWEU0gGBzxijoow4TU6frH2NPf+fvTuPj7K89///+mTf94SE7GwioKyyCoKK26lYW+uu9dQWa91ae3pOe/r9tdb29LT1tLWtWrXW1i7uW2lFrRsuIPu+EyCQkEAgCVkIZL1+f8yAkQYIkOTOZN7PxyOPzNxzz8w7NzfJdX/mWnh7wx5eWl7K+5v3cuGZGYzITlTbRvoEFYfkX7y9oYIPt+zj+5cPIyU2wus4IhKgYiPD+OzobMYXpvD3VWW8vGIXi7ZXcfnZWVoKWUSkl2htc7y0vJSVJfuZMjCVS8/K0oWuSAfMjDMy4xnSL4715bW8tX4Pzy4pIXPTXmYO68fQzHhM/3ckgKk4JJ/S2NLKj15bz6CMOG6cmO91HBHpA/onRTN72gBWldbwxtpyHv1gG6Nzk7h4eCYJGrIgIuKZppY2nlm8k0176rhoWD/OG5Kui1uREzAzhvdP5MysBFaX1vDOhj38eeEOcpKjmTmsH4PS4/T/SAKSikPyKX+YX8yOygae+tJ4LdkoIl3GzBiVm8SZWfHM27SXj4r2sa6slulnpDNlUJp+34iI9LCDTa38aWExOysbuGJUfyYUpnodSSSghPjbNmdlJ7JiZzXvbqzgD/OLGZAWy7+dnUVWYrTXEUVOiopDckRF3SF+884WLjwzg/OGpHsdR0T6oMiwUC4ensm4/GReX7ubf67fw9Id1Vw2IpMzsxL0SZuISA+oOdjMUwuK2VvXyDXn5HJ2TpLXkUQCVmiIMa4ghVG5SSwuruLdjRU89G4REwakcOGZ/YiJ0CW3BAZ9VCtH/GTuRppa2/juvw3zOoqI9HGpcZHcODGfL00pJCzE+MuinTw5fzu7aw95HU0kIJjZJWa2ycyKzOzbHTx+r5mtN7PVZvaOmWmsuACwp/YQj76/laqGJm6enK/CkEgXCQsNYfLANO6dOYSJA1JZvL2Kn/9zM0uKq3BOK5tJ76fikADwwea9vLxiF7dNG0hhmiaKFZGeMSgjjrvOH8zlZ2dRtt/Xe/FvK3fR0NTidTSRXsvMQoGHgUuBYcB1Znb0JzsrgHHOubOBF4Gf9WxK6Y227avnsQ+20tbmmD11AIMz4r2OJNLnxESEcfnI/tw5YzCZiVG8smIXT3y0ne37DngdTeS4VBwSGppa+O6raxiQFsud5w/yOo6IBJnQEGPSwDS+OXMIEwaksqS4il++tZnlO6r1SZtIx8YDRc65bc65JuBZ4Ir2Ozjn3nPONfjvLgRyejij9DKrS/fzh/nFxEeG89XpA+mfpPlQRLpTZmIUXz63kCtHZ1Nec5CLH/yAR+YV0dLa5nU0qHC2+gAAIABJREFUkQ5pAKTwy7c2U1J1kOdmTyQqPNTrOCISpGIiw5g1sj/nFCTzt5VlvLi8lKU7qrliVH/6JUR5HU+kN8kGStrdLwUmHGf/W4HXuzWR9Grzi/Yxd005eSkx3DQpX3OgiPQQM+OcghTOyIxnVcl+fvbGJt5ev4cHrxlNXmqM1/FEPkU9h4LcmtIafv/Rdq4bn8eEAVqlQkS8l5UYzexpA7hyVDZ7ag/xm3e38Mba3TS16JM2Eb+OZm7vsJudmd0IjAMeOMbjs81sqZkt3bt3bxdGlN6gzTleW13Ga2vKGdY/gS+dW6jCkIgHEqLC+e2NY/nVtaPYUlHPpb/6gBeWlqiHtPQqKg4FsZbWNr798mrS4iL59qVDvY4jInJEiBnnFKbwjZlDGJWbzAdb9vLgO5vZUF7rdTSR3qAUyG13PwcoO3onM7sQ+C4wyznX2NELOeced86Nc86NS0/XSqV9SXNrG88tKWH+1komDUzluvF5hIeq6S/ipStGZfPG16cxIjuRb724mjueXs7+hiavY4kAKg4Ftd+8W8S6slruv2I4idHhXscREfkXcZFhXDU2h69MHUBEaAh/XriDvy7aQd2hZq+jiXhpCTDYzArNLAK4FpjTfgczGw08hq8wVOFBRvHQwaZW/jC/mDW7arh0RCafOSuLEOuow5mI9LTspGie/spE/uuSoby1fg+XPPgh84v2eR1LRMWhYLW0uIrfvLuFz43J5pIRWV7HERE5rsK0WO46fzAXD+vHpt11PPj2FlaWaMJqCU7OuRbgTuBNYAPwvHNunZndb2az/Ls9AMQBL5jZSjObc4yXkz5mf0MTj32wlZKqBq4el8vUwemYCkMivUpoiHH79IG88rUpxESGcsMTi/if19bT2NLqdTQJYhp0HIRqDzXz9edWkp0czQ9mDfc6johIp4SGGOedkcGZ/RN4efkunl9ayurSGj47KpsE9X6UIOOcmwvMPWrb99rdvrDHQ4nnymsO8tSCYhpb2rhlSgED0+O8jiQixzEiO5HX7prKj+du4Hcfbmd+USW/uX60/u+KJ9RzKAh979W1lNcc4sFrRhMfpQsqEQksGfFRzJ42gMvOymLr3noefGczy7TsvYgEuY27a3nsg20A3DZtoC4uRQJEdEQoP/zsCJ64eRzlNQe5/DcfabJq8YSKQ0Hm1RW7eHVlGXefP5ix+clexxEROSUhZpw7KI27zh9MZkIULy0v5amPizWpo4gEHeccH23Zy58/3kFaXAS3Tx9EZmKU17FE5CRdOKwfr98zjbNzfJNV3/PsSs2xKD1KxaEgsmVPHd99ZQ1j85O5Y8ZAr+OIiJy2tLhIvjx1AJ85O4vt+w7wq3e2qBeRiASNppY2Xlmxi7lrdzOsfwKzpw7UIiMiASwzMYq/fnki35w5hNfWlHPZrz9kZcl+r2NJkFBxKEjUHGxm9p+XER0RykPXjyZMS5mKSB8RYsbkgWncc8EQshJ9vYj+snAHFXWHvI4mItJtqg80cfOTi1i6o5rpQ9K5bnweEWFq34kEutAQ464LBvPc7Im0tcFVv13Ao+9vpa1NH3xJ99JfkCDQ2ua459kVlFQ18MgNY8lKjPY6kohIl0uJjeDLU31zEW2pqOfiX37Aa6vLvY4lItLliirqufKR+SzfsZ8vjM3houGZWqpepI8ZV5DC3LunMnNYP37y+kZuenIRZfsPeh1L+jAVh4LAL97axLxNe/n+rOGML0zxOo6ISLc5PBfRnTMGkZcSwx1PL+euZ1ZoLiIR6TM+2rKPKx+ZT31jC8/MnsjoPM0hKdJXJcaE88gNY/jfz53Fip37ufjBD3hlRamGz0u3UHGoj/v7qjIefm8r156Ty40T8ryOIyLSIzISonjp9sl8c+YQXl9TzsxffsC7G/d4HUtE5JQ553j8g63c/OQi+idG8+odU7S4iEgQMDOuG5/H6/dMZUi/eL7x3CrueHo5lfWNXkeTPkbFoT7soy37uPf5lZxTkMwPrhiOqbuxiASRsNAQ7rpgMH+7cwqpsRF86Y9L+c8XV2nlDxEJOPWNLdz59Ap+PHcjFw/P5KWvTSYnOcbrWCLSg/JTY3n+tkn81yVDeWv9Hi78xfu8vFy9iKTrqDjUR60q2c/sPy9lYHocT9x8DpFhoV5HEhHxxPD+ifztzil8bfpAXlxWyiUPfsiCon1exxIR6ZSiino++/B8Xl9bzncuHcojN4whLjLM61gi4oHQEOP26QN57e6pFKbFcu/zq7j5ycWUVDV4HU36ABWH+qCiinr+/Y9LSImN4KkvjScxRkuaikhwiwwL5T8vGcqLt08mMiyE659YxH1z1nGwqdXraCIiHXLO8cLSEi7/zUdUHWjiL7dO4LbzBqonuIgwpF88L351MvdfMZzlO6q58Bfv88u3NqtdI6dFxaE+ZmdlA198cjEhBn+5dQL9EqK8jiQi0muMyUvmtbuncsvkAv64oJjLfv0hy3ZUex1LRORT6htbuPf5VXzrxdWMzE1k7t1TmTwozetYItKLhIQYN08q4K17z2PmsH786p0tXPDzecxZVaahZnJK1Ce1D9myp44bf7+IxpY2/nLrBArSYr2OJCLS60RHhHLfrOFcNLwf33phNV94dAGzpw3kGzMHawiuiHhuxc5qvvHcSnZWNXDvzCHcMWMQoSHqLSQS6J5etLPbXnvywDSyEqN5bXUZdz+zgt9/tJ17Zw5h2uA09TaUTlPPoT5i7a4arnl8IW0Onps9iRHZiV5HEhHp1SYPTOONr0/lC2NzefT9rVz2qw9ZvL3K61giEqQaW1r56Rsb+fxvF9Dc6nh29iTuvmCwCkMi0imFabF8bcYgfnbV2eyra+SLTy7mC49+zIKifepJJJ2i4lAfsKS4iuseX0h0eCgv3DaJMzLjvY4kIhIQ4qPC+elVZ/PUl8bT2NLG1Y99zHdeXkPNQa1oJiI9Z+2uGmb9Zj6/nbeVL4zN5Y2vT2V8YYrXsUQkwISYcfW4XN77j+n88LMjKKlu4PonFvHZh+czZ1UZLa1tXkeUXkzDygLc80tL+H+vrCUnOZq/fHkC/ZOivY4kIhJwzhuSzj+/MY1fvrWZ33+0nbc37OG/LxvKZ0dlqzu2iHSbukPN/OKtzTy1oJj0+Ej+cMs5zBia4XUsEQlwEWEh3DQxny+MzeGFZaU8+dF27n5mBT9NiuamSfl8bkw2GfGam1Y+TcWhANXc2sb/vLaBPy4oZsqgVB66bgzJsRFexxIRCVgxEWF899+GMWtkNv/vb2v5xnOreHrRTu6bNZzh/TVUV0S6jnOOf6wu54f/WM/e+kZumJDHty4aqhVmRaRLRYWHctPEfG4Yn8e7Gyv43Yfb+MnrG3ngzU2cPzSDq8flMv2MdMJDNaBIVBwKSHvrGrnrmeUs3FbFrecW8p1LhxKm/9AiIl3irJxEXrl9Mi8sK+Gnb2zi8t98xLXj8/j6hYP1KZuInLYVO6v539c3snh7FWdlJ/K7m8cxMjfJ61gi0oeFhBgXDuvHhcP6UVRRzwvLSnhp2S7eWr+HxOhwLhrWj8vOzmLKwDQiwnRdGaxUHAowb6wt579fWUt9Yws//8JIPj82x+tIIiJ9TkiIcc05eVwyPItfvr2ZvyzcwasrdjF72gC+MnUAsZH68ykiJ6d43wEeeHMTr60pJy0ugh9+dgTXj8/ThNMi0qU6sypafkos91wwmM176li7q4Y5q8p4YVkpUeEhDEqPY0i/eIb0iych+ti9Ga+fkNeVsaUXUOs2QNQ0NHPf39fxyopdjMhO4BdXj2JIP008LSLSnRJjwrlv1nC+OLmAB97cyINvb+EvC3fytekDuX5CHlHhoV5HFJFerqiinkff38qrK3YRHhrCPRcM5ivTBhCnIrOIeCg0xDgzK4EzsxJoaW2jqKKe9eW1voJRWS0AGfGR5KfGkp8aQ0FqLMkx4ZqLsQ/TX6Verq3N8erKXfz0jY3sq2/ingsGc+f5gzQuVESkBxWmxfLIDWNZtqOaB97cyP3/WM8j87Zy27QB3DAxj5gI/TkVkU9bXbqfR9/fyutrdxMZFsKNE/P52vSBZCRoeKqI9C5hoSEMzUpgaFYCzjl21x5i8556tu+rZ82u/SwprgIgPjKM/NQY8lNjOTMrnqGZCURH6IOyvkKt2V5saXEV9/9jPatLaxiZ4xuTfnaOxqSLiHhlbH4yz86exKJtlfz63S38z9wNPDKviOsn5HHTxAIyE3XRJxLMDjW38trqcv60cAerSvYTHxnG16YP5EtTCkmNi/Q6nojICZkZWYnRZCVGc96QdNqco6K2kR1VB9hR2cCOygOsLavltTXlmEFhaixnZiUwNDPe9z0rnuykaPUwCkAqDvVCy3ZU8+j7W3lr/R4yE6L45TUjuWJkNiEaky4i0itMGJDKXweksmxHFY+9v41H5m3lsfe3cdlZWdw0KZ9x+clqFIkECeccq0treGXFLv62chfVDc0MSI/l+5cP4/Njc0iI0gpkIhK4QszITIwiMzGKCYWpANQcbKYwLZaNu2vZUF7L2rIaXltTfuQ58ZFhDEiPpSAtlsJ2XwVpsfqd2IupONRLtLU55m2u4NF521hcXEVSTDj3zhzCl6cWariCiEgvNTY/hcdvTmFnZQNPfVzM80tKmLOqjMK0WK4am8PnxmSTlRjtdUwR6WLOOTburuPNdbuZs7KMbfsOEBEawgVnZnDjxHwmD0xVgVhE+qzE6HAuGZHJJSMyj2yrb2xh0+46NpTXsml3HcWVB1haXM2cVWU498lzU2MjjhSKDheN8lJiyE2JIfE4E2BL9zPX/l+qFxg3bpxbunSp1zF6zI7KA7y0rJSXlu9i1/6D9E+M4tapA7j2nNxeuRpOZ2a/FxHpDbxYReNAYwuvr93NC0tLWLS9CjMYl5/MxcMzuXh4JrkpMT2eqbcys2XOuXFe55BPBFsb7GQdbGpl6Y4q3t1YwVvr91BafRCACYUpXDk6m0tHZJEY88mFjdpMIiLQ3NpG1YEmKusb2VffxL76RioP+L7XHWr51L5R4SGkxEaQHBNBSqzv63NjcshLiSE7KZqIMM27e7qO1/7qfdWHPs45x9a99by9oYK31+9h6Y5qzODcQWn85yVncOmILJ30IiIBKjYyjKvG5nDV2Bx2VB7g1RVlvLFuNz96bQM/em0DZ2YlMG1wGucOTuOcghStdibSizU0tbCqpIZlO6qYX1TJsh3VNLW2EREWwrmD0rhjxiAuODODjHjNNSYicizhoSH0S4iiXweT8Te2tFJZ30R1QxNVB3xf1Q1N7KltZOPuOlrbHH9bWQaAGWQlRJGbEnOkp9Hh77kp0aTHRarH5mk6reKQmV0C/AoIBZ5wzv3kqMcjgT8BY4FK4BrnXPHpvGegcc5RWn2QpTuqWFJczUdb9rGzqgGAYVkJfOviMzTsQESkD8pPjeWeCwdzz4WDKd53gDfW7ea9jRU8OX87j32wjciwEEbmJDE6L4nRecmMzksiI14NG+kctcG61qHmVjbtrmNdWS3rympYWbL/yIUJ+NpsX5ycz5RBaYwvTNGQfxGRLhAZFkr/pGj6J/3rtXCbc9QdamFsfjI7qxoo8X/trGrggy172VPb+Kn9o8NDyU2JJjc5poMCUrR+b3fCKR8hMwsFHgZmAqXAEjOb45xb3263W4Fq59wgM7sW+ClwzekE7s0amlrYWdXA5j31bCz3Tc61vrz2yIkbHxnGOYUpzJ42gAvOzFBBSEQkSBSkxfLV8wby1fMGcqCxhcXbq/ioaB/LdlTz5PztNH+wDYCU2AiGZvqWhh3cL4781BgKUmPJTIjSogRyhNpgp6a5tY2SqgZ2VDZQXHmA4n0HKPbfLqlqwF8HIi4yjLNzErn9vIGMyU9idG4yybER3oYXEQkyIWYkRoczvjCF8YUp//L4oeZWSqsb/IWjg+ysajhSRFq4rZIDTa2f2j8tLsLXyyjZVyzKTIwmMyGKzIQo+iVGkhYbGfRtrdMpn40Hipxz2wDM7FngCqB9w+QK4D7/7ReBh8zMXG+b6OgYnHM0tbZxsKmVmoPN7Gs/TrL+k3GTe2oPsaOqgb11n1Qvw0KMQRlxTBqQypj8ZMblp3BGZjyhQX7CiYgEu9jIMGYMzWDG0AzA17hZV1bLqpL9bNpdx8Y9dTyzeCcHmz9p1ESEhdAvIZL0uEgy4qPIOHw7wXc/MSac+MgwYiPDiIsKIzYiTH9v+rY+3wbrjJbWNg40tlLX2Ex9Ywt1h1qorG9kb53/q77J/72RfXWN7K49dKQnEPiKQAVpMYzITuSKkf0Z1j+BYVmJ5CRHB/0FgohIbxcVHsqgjHgGZcT/y2POOaoONFFSffBfeh0t31nNa2vKP/X3AHzX7xnxkfRLjCItLpKUmAiSYyNIjgknOTbiyP3EaF97Kzay77W3Tqc4lA2UtLtfCkw41j7OuRYzqwFSgX2n8b6npKiijoff20pzaxstrY6WNkdLWxutbe7ItkMtrTQ0ttLQ1MqBphYONrXS0nbsNlRidDipcRFkxEcy44x08lN9M60PTI9jUEac5g4SEZETigoPZWx+MmPzk49sa21z7K49xA5/z4YdlQfYU3uIirpGtu6t5+NtldQcbD7u68ZEhBIXGUZMRCgRYSFEhIUQHhpCRKjvdkSo735ICJgZIWaEmO+TOvN//9zobCYPSuvuQyAnL6DaYB9u2cvHWytpdY62Nkeb853jzjnfNudbtbXV/1hLWxtNLb6vxsPfW9tobG6lqaWNusYW6g+1fKqAejQz34o4aXGRpMdHMjAtlv5J0RSkxVKQGkNBWiypsREaxiki0geZGalxkaTGRTIqN+lfHm9pbWNffRO7aw+xu+YQe2oPsbv2EHtqfN9LqhpYXbqf6gPNNLW2Hfe9osJD/O2tT9pc4aEhhIfakXZXeGgI4WG+bYfvh4ZYuzaXL/Ph9pcBX546gJQe7rV6OsWhjv6aHl1J6cw+mNlsYLb/br2ZbTqNXMEsDQ8afX2UjmXX0vHsOjqWnXTDiXfRsTyBn5/c7id7PPNP7uWlnd7WBuuV/5eKvQ7QS49LL6Fjc2w6Nh3TcTm2Pn1sOtGeO5aAPi7/1X0vfcz21+kUh0qB3Hb3c4CyY+xTamZhQCJQdfQLOeceBx4/jSwCmNlSLQvcNXQsu5aOZ9fRsew6OpZdS8ezR/WqNpj+7Tum43JsOjbHpmPTMR2XY9Ox6ZiOy8k7nXFPS4DBZlZoZhHAtcCco/aZA3zRf/sq4N2+NNZdRERExANqg4mIiEiXOuWeQ/7x63cCb+JbRvVJ59w6M7sfWOqcmwP8HvizmRXh+7Tq2q4ILSIiIhKs1AYTERGRrnY6w8pwzs0F5h617Xvtbh8CvnA67yEnRUPzuo6OZdfS8ew6OpZdR8eya+l49qBe1gbTv33HdFyOTcfm2HRsOqbjcmw6Nh3TcTlJph7GIiIiIiIiIiLBS2uti4iIiIiIiIgEMRWH+ggzCzWzFWb2D6+zBDozKzazNWa20syWep0nkJlZkpm9aGYbzWyDmU3yOlOgMrMz/Ofk4a9aM/u617kClZl9w8zWmdlaM3vGzKK8zhSozOwe/3Fcp3Oy7zKzJ82swszWHuPx6WZW0+531Pc62q+vMbNcM3vP/zdunZnd08E+Zma/NrMiM1ttZmO8yNrTOnlsgu68MbMoM1tsZqv8x+UHHewTaWbP+c+ZRWZW0PNJe14nj80tZra33TnzZS+yeuF413vBes4cdoJjE7TnzMk6rTmHpFe5B9gAJHgdpI+Y4Zzb53WIPuBXwBvOuav8K+rEeB0oUDnnNgGjwPcHENgFvOJpqABlZtnA3cAw59xBM3se32S9f/Q0WAAysxHAV4DxQBPwhpm95pzb4m0y6QZ/BB4C/nScfT50zn2mZ+L0Gi3AN51zy80sHlhmZm8559a32+dSYLD/awLwW//3vq4zxwaC77xpBM53ztWbWTjwkZm97pxb2G6fW4Fq59wgM7sW+ClwjRdhe1hnjg3Ac865Oz3I57XjXe8F6zlz2ImuhYP1nDkp6jnUB5hZDvBvwBNeZxE5zMwSgGn4VszBOdfknNvvbao+4wJgq3Nuh9dBAlgYEG1mYfiKlmUe5wlUZwILnXMNzrkW4H3gSo8zSTdwzn2Ab9Uzacc5V+6cW+6/XYfv4iT7qN2uAP7kfBYCSWaW1cNRe1wnj03Q8Z8H9f674f6voyeBvQJ4yn/7ReACM7MeiuiZTh6boNSJ672gPGdA18JdScWhvuFB4D+BNq+D9BEO+KeZLTOz2V6HCWADgL3AH/zdPJ8ws1ivQ/UR1wLPeB0iUDnndgH/B+wEyoEa59w/vU0VsNYC08ws1cxigMuAXI8ziXcm+YeDvG5mw70O09P8wzhGA4uOeigbKGl3v5QgK5Ic59hAEJ43/iEwK4EK4C3n3DHPGX/hvQZI7dmU3ujEsQH4vH+I5otmFix/c050vRe05wyduxYOxnPmpKk4FODM7DNAhXNumddZ+pApzrkx+LqB32Fm07wOFKDCgDHAb51zo4EDwLe9jRT4/MPzZgEveJ0lUJlZMr5P2AqB/kCsmd3obarA5JzbgK/r+lvAG8AqfENJJPgsB/KdcyOB3wCvepynR5lZHPAS8HXnXO3RD3fwlKDpDXGCYxOU541zrtU5NwrIAcb7h+i2F7TnTCeOzd+BAufc2cDbfNJbps/q5PVeUJ4znTw2QXfOnCoVhwLfFGCWmRUDzwLnm9lfvI0U2JxzZf7vFfjmdBnvbaKAVQqUtvvE50V8xSI5PZcCy51ze7wOEsAuBLY75/Y655qBl4HJHmcKWM653zvnxjjnpuEbdqT5hoKQc6728HAQ59xcINzM0jyO1SP8c6O8BPzVOfdyB7uU8ukedTkEyVDWEx2bYD5vAPzD7ecBlxz10JFzxj/8OZEgG9Z5rGPjnKt0zjX67/4OGNvD0bzQmeu9YD1nTnhsgvScOSUqDgU459x3nHM5zrkCfENN3nXO6RPwU2Rmsf5JE/EPgboI37AJOUnOud1AiZmd4d90AXD0JJRy8q5DQ8pO105gopnF+MfjX4BvLgw5BWaW4f+eB3wOnZ9BycwyD89vYWbj8bUxK71N1f38P/PvgQ3OuV8cY7c5wM3mMxHfUNbyHgvpkc4cm2A8b8ws3cyS/Lej8X1gsfGo3eYAX/Tfvgpf+z4YeoGc8NgcNV/XLILg73cnr/eC8pzpzLEJxnPmVGm1MpFP6we84m+nhAFPO+fe8DZSQLsL+Kt/KNQ24N89zhPQ/HO6zARu8zpLIHPOLTKzF/ENZ2gBVgCPe5sqoL1kZqlAM3CHc67a60DS9czsGWA6kGZmpcD38U0Wi3PuUXwXI7ebWQtwELg2GC5M8H1qfROwxj9PCsB/A3lw5NjMxTcfVxHQQPD8LezMsQnG8yYLeMq/8mgI8Lxz7h9mdj+w1Dk3B19R7c9mVoSv98e13sXtUZ05Nneb2Sx8f7+rgFs8S+sxnTPHpnPm1Fjf//0rIiIiIiIiIiLHomFlIiIiIiIiIiJBTMUhEREREREREZEgpuKQiIiIiIiIiEgQU3FIRERERERERCSIqTgkIiIiIiIiIhLEVBwSCSJm9l0zW2dmq81spZlN6MLXnm5m//DfvsXMHuqq1+7gvZLM7GsdvXcH+4ab2U/MbIuZrTWzxWZ2aXdlExERETmenmiPmc8+M0v2b88yM2dm57bbd6+ZpZrZV83s5g5eq8DM1vpvjzKzy9o9dp+Z/ccxMmSa2bNmttXM1pvZXDMb0lU/o4h0jzCvA4hIzzCzScBngDHOuUYzSwMiPI51qpKArwGPdGLfHwJZwAj/z90POK87w4mIiIh0pKfaY845Z2aLgEnAXGAysML//SMzOwPY55yrBB7txEuOAsb5X+uYzMyAV4CnnHPX+reNAvoBm0/xxxGRHqCeQyLBIwtfI6ARwDm3zzlXZmZjzex9M1tmZm+aWRaAmc0zswfNbIG/x814//bx/m0r/N/P6GwAM7vIzD42s+Vm9oKZxfm3F5vZD/zb15jZUP/2dDN7y7/9MTPb4W9E/QQY6P+07QH/y8eZ2YtmttHM/ur/xCwG+ApwV7ufe49z7nn/69eb2U/9P/vb/p9tnpltM7NZXXHQRURERNrpyfbYfHzFIPzff4GvWHT4/gL/ax3pBeTPscrMPgbu8G+LAO4HrvG3va7xv8awdu2mu/3bZgDNzrkjBSfn3Ern3If+Xk3vm9nzZrbZfD27bzBfr+41Zjbw9A+viJwqFYdEgsc/gVz/H+NHzOw8MwsHfgNc5ZwbCzwJ/E+758Q65ybj66XzpH/bRmCac2408D3gx515c39R5/8BFzrnxgBLgXvb7bLPv/23wOFuyt8H3vVvfwXI82//NrDVOTfKOfct/7bRwNeBYcAAYAowCNjpnKs9RqxYYJ7/Z68DfgTMBK7E1wgSERER6Uo92R5bwCfFofHAq0Cu//5kfMWjo/0BuNs5d7iIhHOuyf8ez/nbXs/5HxoKXOx/7e/7f44RwLLj/PwjgXuAs4CbgCHOufHAE8Bdx3meiHQzDSsTCRLOuXozGwtMxfepznP4iiEjgLd8vYAJBcrbPe0Z/3M/MLMEM0sC4oGnzGww4IDwTkaYiK9wM9//XhHAx+0ef9n/fRnwOf/tc/EVanDOvWFm1cd5/cXOuVIAM1sJFACrT5CpCXjDf3sN0OicazazNf7ni4iIiHSZHm6PLQZGm1ksEO5/721mNghfcejn7Xc2s0QgyTn3vn/Tn4HjzdP4mr8HVKOZVeAbOnYiS5xz5f7324qvWAa+dtiMTjxfRLqJikMiQcQ51wrMA+b5CyB3AOvafzp09FM6uP9D4D3n3JVmVuB/vc4w4C3n3HXHeLzR/72VT343WSdfu/3z279GEZBnZvHOuboOntPsnDv8M7aUdu7lAAAgAElEQVQdfg3nXJuZ6fejiIiIdLmeao855xrMrAj4ErDcv3khcBmQAWw66inWwXsdT0dtr3XAVZ18Tlu7+23o2lTEUxpWJhIkzOwM/6dLh40CNgDp5psc8fDKXsPb7XONf/u5QI1zrgZIBHb5H7/lJCIsBKb4P63CzGLsxCtXfARc7d//IiDZv70O3ydmx+WcawB+D/zaP17+8GodN55EbhEREZEu4UF7bD6+YfeHe2t/jG9Y18J2H5AB4JzbD9TYJyua3dDu4U61vYB3gUgz+8rhDWZ2jplpMRCRXk7FIZHgEYev+/F6M1uNb4jX9/B9uvNTM1sFrOSTsekA1Wa2AN8qFrf6t/0M+F8zm4+v2/Ox3GJmpYe/gEh8jZdn/O+/EN9Y9eP5AXCRmS3H1625HKjzr6wx3z8x4wPHfQXfPEd7gfXmW471Vf99ERERkZ7W0+2x+fjmYjxcHFoO5OCfjLoD/w487J+Q+mC77e/hm4C6/YTU/8JfcLoSmGm+pezXAfcBZcfJKCK9gB1VMBYRAXyrYwD/4Zxb6mGGSKDVOdfi/zTtt865UV7lEREREelJvaE9JiLBQeM6RaQ3ywOeN7MQfJNHf+UE+4uIiIiIiMhJUs8hEREREREREZEgpjmHRERERERERESCmIpDIiIiIiIiIiJBTMUhEREREREREZEgpuKQiIiIiIiIiEgQU3FIRERERERERCSIqTgkIiIiIiIiIhLEVBwSEREREREREQliKg6JiIiIiIiIiAQxFYdERERERERERIKYikMiIiIiIiIiIkFMxSERERERERERkSCm4pCIiIiIiIiISBBTcUhEREREREREJIipOCQiIiIiIiIiEsRUHBIRERERERERCWIqDomIiIiIiIiIBDEVh0REREREREREgpiKQyIiIiIiIiIiQSzM6wBHS0tLcwUFBV7HEBERkW60bNmyfc65dK9zyCfUBhMREenbjtf+6nXFoYKCApYuXep1DBEREelGZrbD6wyBzMwuAX4FhAJPOOd+ctTjvwRm+O/GABnOuaTjvabaYCIiIn3b8dpfva44JCIiIiLHZmahwMPATKAUWGJmc5xz6w/v45z7Rrv97wJG93hQERERCRiac0hEREQksIwHipxz25xzTcCzwBXH2f864JkeSSYiIiIBScUhERERkcCSDZS0u1/q3/YvzCwfKATe7YFcIiIiEqBUHBIREREJLNbBNneMfa8FXnTOtXb4QmazzWypmS3du3dvlwUUERGRwKLikIiIiEhgKQVy293PAcqOse+1HGdImXPucefcOOfcuPR0LR4nIiISrFQcEhEREQksS4DBZlZoZhH4CkBzjt7JzM4AkoGPezifiIiIBBgVh0REREQCiHOuBbgTeBPYADzvnFtnZveb2ax2u14HPOucO9aQMxERERFAS9mLiIiIBBzn3Fxg7lHbvnfU/ft6MpOIiIgELvUcEhEREREREREJYuo5JD3i6UU7vY7Qba6fkOd1BBEREZET6ovtMbXDRES6hnoOiYiIiIiIiIgEMRWHRERERERERESCmIpDIiIiIiIiIiJBTMUhEREREREREZEgpuKQiIiIiIiIiEgQU3FIRERERERERCSIqTgkIiIiIiIiIhLEVBwSEREREREREQliKg6JiIiIiIiIiAQxFYdERERERERERIKYikMiIiIiIiIiIkFMxSERERERERERkSCm4pCIiIiIiIiISBBTcUhEREREREREJIiFeR1ARKQnPb1op9cRus31E/K8jiAiIiIiIgFIPYdERERERERERIKYikMiIiIiIiIiIkFMxSERERERERERkSCm4pCIiIiIiIiISBBTcUhEREREREREJIipOCQiIiIiIiIiEsRUHBIRERERERERCWIqDomIiIiIiIiIBLFOFYfM7BIz22RmRWb27Q4ev9fM1pvZajN7x8zy2z3WamYr/V9zujK8iIiISDA6UdvMv8/V/vbZOjN7uqczioiISOAIO9EOZhYKPAzMBEqBJWY2xzm3vt1uK4BxzrkGM7sd+Blwjf+xg865UV2cW0RERCQodaZtZmaDge8AU5xz1WaW4U1aERERCQSd6Tk0Hihyzm1zzjUBzwJXtN/BOfeec67Bf3chkNO1MUVERETE74RtM+ArwMPOuWoA51xFD2cUERGRANKZ4lA2UNLufql/27HcCrze7n6UmS01s4Vm9tlTyCgiIiIin+hM22wIMMTM5vvbYJf0WDoREREJOCccVgZYB9tchzua3QiMA85rtznPOVdmZgOAd81sjXNu61HPmw3MBsjLy+tUcBEREZEg1Zm2WRgwGJiOr0f3h2Y2wjm3/1MvpDaYiIiI0LmeQ6VAbrv7OUDZ0TuZ2YXAd4FZzrnGw9udc2X+79uAecDoo5/rnHvcOTfOOTcuPT39pH4AERERkSDTmbZZKfA351yzc247sAlfsehT1AYTERER6FxxaAkw2MwKzSwCuBb41KpjZjYaeAxfYaii3fZkM4v0304DpgDtJ7IWERERkZNzwrYZ8CowA460wYYA23o0pYiIiASMEw4rc861mNmdwJtAKPCkc26dmd0PLHXOzQEeAOKAF8wMYKdzbhZwJvCYmbXhK0T95KhVzkREeqXaQ82U7z9Ic6ujubWNljZHckwEeSkxRIR1pq4uItI9Otk2exO4yMzWA63At5xzld6lFhERkd6sM3MO4ZybC8w9atv32t2+8BjPWwCcdToBRUR6yv6GJtaV1bK2rIadlQ0dTq4WakZOSjQD0+MYX5hCQlR4j+cUEelE28wB9/q/RERERI6rU8UhEZG+7FBzK29v2MPHWytxQGZCFOefmcGg9Dgiw0IJDzVCQ4yKuka27T3A9n31vLexgg+37GXywDSmDU4nOiLU6x9DRERERETklKg4JCJByznH2rJaXltdRt2hFsYXpjBlUBppcZEd7p8UE8GQfvEAVNY38vaGPXyweS+Ltldy/tB+TBmYin9orYiIiIiISMBQcUhEglJTSxsvLCthXVkt/ROjuGFCPrkpMZ1+fmpcJNeck8e0IQd5c91u5q4pp3jfAa4am0NUuHoRiYiIiIhI4NCsqiISdBoaW3hy/nbWl9VyyfBMbp8+6KQKQ+1lJUbzxUkFXHZWFht31/LIvK1U1B7q4sQiIiIiIiLdR8UhEQkq+xuaeOzDbZTtP8h14/OYNiSd0JDTGwpmZpw7KI0vTSnkYHMrj7y/lc176roosYiIiIiISPdScUhEgsbWvfU8+v5Wag82c8uUAkZkJ3bp6w9Ij+POGYNIjY3gLwt3sHVvfZe+voiIiIiISHfQnEMiEhQq6xu55Q+LaXUwe9oAshKju+V9EqPDuXVKIb/7aBt/+riYL00pJD81tlveS0RERPqepxft9DpCl7t+Qp7XEUTkBNRzSET6vMaWVm778zIqahu5eWJ+txWGDouJDONLUwpJjA7njwuKKa1u6Nb3ExEREREROR0qDolIn+ac49svrWHpjmp+fvXIU554+mTFR4Vz67kDiIkI5Q/zi9lX39gj7ysiIiIiInKyVBwSkT7toXeLeGXFLr45cwifObt/j753YrSvQGQGf120g8aW1h59fxERERERkc5QcUhE+qwFRfv4+VubuXJ0NneeP8iTDCmxEVxzTi4VtY28vHwXzjlPcoiIiIiIiByLikMi0ifVHGzmmy+sYkB6LD++8izMTm+5+tMxOCOei4ZnsmZXDfOL9nmWQ0REREREpCMqDolIn3TfnHVU1DXyy6tHER0R6nUcpg1OY3j/BN5Yt1tL3IuIiIiISK+i4pCI9DmvrS7nlRW7uHPGIEbmJnkdBwAz46oxOaTGRfL8khIamlq8jiQiIiIiIgKoOCQifUxF7SG+++oazs5J9GyeoWOJDA/lmnG5HGhq4e+ryryOIyIiIiIiAqg4JCJ9zH+/spaDTa384upRhIf2vl9x/ZOiOX9oBqtKa1izq8brOCIiIiIiIioOiUjf8d7GCt7esIevXziEQRlxXsc5pvOGZJCdFM3fVu6ivlHDy0RERERExFsqDolIn9DY0soP/r6OAWmx3Hpuoddxjis0xLhqbA5NLW28ukLL24uIiIiIiLdUHBKRPuGJD7dTXNnA92cNJyKs9/9q65cQxcxh/VhfXstqDS8TEREREREP9f4rKBGREyjbf5CH3i3iomH9OG9IutdxOm3KoDSyk6KZu6acQ82tXscREREREZEgpeKQiAS8H8/dQJtz/H+fGeZ1lJMSYsYVo/pTf6iFdzbs8TqOiIiIiIgEKRWHRCSgLdpWyT9Wl3P79IHkpsR4Heek5STHcE5BCh9vq2R3zSGv44iIiIiISBBScUhEApZzjp+9uYnMhCi+et5Ar+OcsouG9SMqPJQ5qzQ5tYiIiIiI9DwVh0QkYL23qYJlO6q5+4LBRIWHeh3nlMVEhnHx8EyKKxtYWbLf6zgiIiIiIhJkVBwSkYDU1uZ44M3N5KfG8IVxOV7HOW1j85PJTY5m7trdmpxaRERERER6lIpDIhKQ/rGmnA3ltdw7cwjhoYH/qyzEjMtH9udAYwsfbtnrdRwREREREQkigX9FJSJBp7m1jV/8cxNDM+O5/Oz+XsfpMjnJMZyVnchHRfuoPdjsdRwR6cXM7BIz22RmRWb27Q4ev8XM9prZSv/Xl73IKSIiIoFBxSERCTgvLSuluLKB/7joDEJCzOs4Xeri4Zm0tcHbWtpeRI7BzEKBh4FLgWHAdWY2rINdn3POjfJ/PdGjIUVERCSgqDgkIgGlqaWNX7+zhdF5SVxwZobXcbpcSmwEEweksGxHNbtrtbS9iHRoPFDknNvmnGsCngWu8DiTiIiIBDAVh0QkoLyyopSymkN8/cIhmPWtXkOHzTgjg8jwEN5cu9vrKCLSO2UDJe3ul/q3He3zZrbazF40s9yeiSYiIiKBSMUhEQkYrW2O387bylnZiUwbnOZ1nG4TExnG9CEZbNpTx9a99V7HEZHep6PKuDvq/t+BAufc2cDbwFMdvpDZbDNbamZL9+7VZPgiIiLBSsUhEQkYc9eUU1zZwB0zBvbZXkOHTRqYSmJ0OG+u241zR1/ziUiQKwXa9wTKAcra7+Ccq3TONfrv/g4Y29ELOeced86Nc86NS09P75awIiIi0vt1qjjUiRUx7jWz9f6uy++YWX67x75oZlv8X1/syvAiEjycczz8XhED02O5aFim13G6XXhoCOcPzaC0+iCbdtd5HUdEepclwGAzKzSzCOBaYE77Hcwsq93dWcCGHswnIiIiAeaExaFOroixAhjn77r8IvAz/3NTgO8DE/BNnvh9M0vuuvgiEize21TBxt11fG36oD63QtmxjMlLJiU2grc37FHvIRE5wjnXAtwJvImv6PO8c26dmd1vZrP8u91tZuvMbBVwN3CLN2lFREQkEHSm59AJV8Rwzr3nnGvw312Ir3szwMXAW865KudcNfAWcEnXRBeRYOGc46F3i8hOimbWqP5ex+kxoSHG+WdkUFZziA3ltV7HEZFexDk31zk3xDk30Dn3P/5t33POzfHf/o5zbrhzbqRzboZzbqO3iUVERKQ360xxqLMrYhx2K/D6KT5XRORfLNpexfKd+7ntvAGEhwbXVGkjc5NIjY3g7Q0VtKn3kIiIiIiIdIPOXGV1ZkUM345mNwLjgAdO5rlaKUNEjuex97eSFhfB1eOCbyXm0BDjgjMz2F17iHVl6j0kIiIiIiJdrzPFoROuiAFgZhcC3wVmtVsdo1PP1UoZInIsRRX1vLdpLzdNLCAqPNTrOJ44OyeJ9PhI3t6wR72HREREpFdraWujvrGF/Q1NNLW0eR1HRDoprBP7HFkRA9iFb0WM69vvYGajgceAS5xzFe0eehP4cbtJqC8CvnPaqUUkaDw5fzsRYSHcMDHP6yieCTHjgqEZPLukhDW7ahiZk+R1JBEREQlyrW2OXfsPsrOqgZKqBkqrG6g71EJL26c/yIoIDeG37xeRnxLLqNwkRuclMSo3idS4SI+Si0hHTlgccs61mNnhFTFCgScPr4gBLPVPfPgAEAe8YGYAO51zs5xzVWb2Q3wFJoD7nXNV3fKTiEifU32giZeXl3LlqGzSgrwBMSI7kfSNFczbVMFZ2YmEWHCs2CYiIiK9y776RpYWV7Fs534ONLYAkBQTTm5yDEkx4USFhxIVFkJYSAgNTS3UN7aQFh9JUUU9v31/K63+4tHZOYlcMiKTy0ZkUZAW6+WPJCJ0rucQzrm5wNyjtn2v3e0Lj/PcJ4EnTzWgiASvpxfv5FBzG7dOLfQ6iudCzJg+JJ0XlpWysbyOYf0TvI4kIiIiQWRn5QH+uWEP2/YeIMTgjMwERuUmkZ8aQ0JU+HGfe/0EXw/whqYW1u6qZUlxFf9cv4efvbGJn72xieH9E7hxYj5XjOpPTESnLlFFpIvpf56I9EpNLW08taCYqYPTGNIv3us4vcLZOUm8s7GCeZsrODMrHlPvIREREelmNQebeXPdblaW7Cc+KoyLhvVjTF4yCdHHLwh1JCYijPGFKYwvTOGOGYMorW7gjbW7eWn5Lr7z8hp+PHcDV4/L5ZbJBeSmxHTDTyMix6LikIj0Sv9YXUZFXSM/u+psr6P0GqEhxrTB6by6chdFe+sZnKGimYiIiHSPNuf4aMs+3tm4B+dg+hnpnDcknciwrlsgJCc5hi9PHcCt5xaydEc1Ty0o5qkFxfxxQTGfH5PNnTMGk5eqIpFIT1BxSEQ69PSinZ69t3OOh94rIiM+kl3VBz3N0tuMyUvi3Y17eG/jXhWHREREpFs0NLbw/LISNu+pZ1hWApedlUVKbES3vZ+ZcU5BCucUpLC75hCPvr+Vpxfv5KXlu/j8mGzuuXAI2UnR3fb+ItK5pexFRHpUcWUD5TWHmDIwTUOnjhIWGsLUwekUVx6geN8Br+OIiIhIH1NS1cBD7xWxde8BrhjVnxsm5HVrYehomYlR3DdrOB/+5wxunpTP31aWcf7/zeOBNzdS758AW0S6nopDErBqDjbT1NLmdQzpBh9vqyQ6PJSRuVqyvSPnFKQQGxHKvM0VXkcRERGRPmT5zmoe/2AbGNw2bQATClM9+6CuX0IU3798OO/+x3QuHZHJw+9tZfoD83h28U7a/CueiUjX0bAyCRitbY7t+w6waXctG3fXUXmgiRDz/eHITY6hMD1WS3z3ATUHm1lfVsPkgWlEhKl+3ZGIsBCmDErjn+v3UF5zkKxEdbMWERGR07NwWyVzVpUxID2W68fn9ZpVw7KTonnw2tH8+5RCfvTaer798hqeXVLCjz47ghHZiV7HE+kzdOUlAaHqQBMPv1fEk/O3s2h7FalxEVx2VhbThqQTGxnG6l37eW5JCX+Yv52ag81ex5XTsKS4CudgQmGK11F6tQmFqUSEhvDRln1eRxEREZEA9+GWvcxZVcbQzHi+OKmg1xSG2huZm8Tzt03il9eMpLS6gVkPfcQP/r6OukNq+4t0hd73v17kKFsq6nh2cQkOx9XjchmWlfAvPUranGPZjmr+sbqMX7+zhStHZ+uThADU0tbGku1VDO4XR2pcpNdxerXoiFDGFSSzcFslFw3PJPEUlpMVEREReWfjHt7ZUMFZ2YlcPS6X0JDe2wvfzLhydA7nn9GP//vnJv64oJg31+7mZ1eN5NzBaV7HEwlo6jkkvZZzjg827+WP84tJiA7jjumDGJWb1OFQoxD/Cgd3zRhMSmwETy/eydw15Tin8ciBZH1ZLXWNLUwakOp1lIAwZWAazsGCreo9JCIiIidvftE+3tlQwZi8JK45p3cXhtpLjAnnh58dwUu3TyYqIpQbf7+I776yhgOasFrklKk4JL3W+5v38sa63QzPTuSr5w3sVE+StPhIvnreQCYUpvBR0T4WbK3sgaTSVT7eVklKbASD+2mJ9s5Ijo1gRHYii7dXcai51es4IiIiEkDW7qph7ppyhvdP4HNjcgJy3s4xecnMvXsqs6cN4OnFO7n4wQ/4WO1/kVOi4pD0Slsq6nhr/R7OzknkunNyiQwL7fRzQ0OMy0f2Z3j/BOauKWdDeW03JpWuUl5zkB2VDUwoTAnIxolXpg5Oo7GljaXFVV5HERERkQCxo/IAzy8tITclhqvH5QZ02ysqPJT/vuxMXrhtEmEhxnW/W8h9c9bR0KReRCInQ8Uh6XWqG5p4bkkJGQmRfG50ziktnxlixhfG5tI/KZrnlpRQtv9gNySVrrRwWxVhIcbY/GSvowSUnOQYCtNimb+1kubWNq/jiIiISC+3r66RP328g8TocG6amE94aN+4JBxXkMLr90zj36cU8McFxVz6qw9Zog/PRDqtb/wmkD6jubWNpxftpLXNccOE/NNayjwiLISbJuUTHRHKnz4u5v9n787jq66v/I+/Pvfe3Oz7vpKFAAkEAoSwIyouuID7XtfWbk6n05nOONNtaseZjp3WtqN2tHVrK1oVF1QUdwUEkkAgEEIgJCH7erPv997P7w/T/hBBA9zke5fzfDzyMLn3e+99+0hIvvd8P59zemWSgdsaHnOwt76LeSkRbjkdw92tzI6hZ2iMN8qajY4ihBBCCDc2PObgjztrMSm4fVk6wf7edd4VaDXzk8tn89zdS3BqzfWP7uA37x7B4ZQ+pEJ8GSkOCbfyelkzjd1DXLswlRgXTKsKC/Dj1qXTGBpzsGlvkwsSismwr6GbMYemUMbXn5EZ8aHEhvrz2MfV0oRdCCGEECelteal0kZsA6PctHiaV0+GXZIZzZt/v4r1+ck8+O5hbv7DTlp7h42OJYRbk+KQcBvVHf0U19pYlR1DblKYy543MTyQ82bFc7C5l/KmHpc9r3Cd4lobCWEBpEQGGh3FI5mUYuX0GA4290oTdiGEEC7l1JpjnQNUNPdyuLWP6vZ+mrqH5GKEB9pR3cmBxh4uyE0gIybY6DiTLsTfwq+um8cvrpnLvvoe1v5mKx9UthkdSwi35V3rCIXHcjg1r+9rJiLQj/Nmxbv8+VdMj2FffTev7WsiKzaEAL+JN7gWk6uxa4im7mEun5d0Rv2lxKfyUyP4+EgHj31czfLpMUbHEUII4eG6B0fZXdfFnmNddA1+fmt+bKg/SzOjmZ8WcVqDQ4Qx6m2DvLm/hVkJoazM9p3zBKUU1xakMj8tkns27OGOJ4u5e1Um/3ThzLNqXyGEN5LikHALxbU2WnqHubEwbVJ+UZtNiivnJ/N/Hx3l7YOtrJuX5PLXEGemqNaGn1mRnxJhdBSPZjGbuGN5Or/YUsmhll5mJbhu9Z0QQgjfMeZw8npZMyW1NjQwPTaEC3ITiA3xx+50YndqugZG2VVjY9O+JraUt7AsK4bzZsVhNslFHnc0OGJnQ1EdYYEWrl3o2ZPJztT0uBBe+fZy/uONgzz2cTW7amw8dON8UqOCjI4mhNuQ4pAw3OConXcOtpIRE8wcF24nO1FqVBBLMqPZWd1JfmoEafLHwHAjYw72NXSTlxxBoFWuOp6tmxen8dD7Vfz+4xp+ed08o+MIIYTwMN2Do2woqqOha4hlWdEsz4ohMtj6+QNjYeG0SOq7hthW1cEHlW0csw1w46I0r2tw7Om01ryyt5H+YTtfPyfTp8+3AvzM/McVeSzPiuGfN5ZxyW+38psb8idl14IQnkjW0gnDvVvRyvCYg8vmJk76tqILc+MJC/TjldJGnLJX3nBlDT2M2p0Upsv4eleICLJy/aJUNu1rpKVHmi4KIYSYuOr2fh7+oIr2vhFuXpzGZXOTTl4YGqeUIi0qiJsK07h6QQp1nYM8/EEVjd1DU5hafJl9DT0caOrl/Jw4UiLlwijA2rxENn9nJWlRQdz1dAn/+94RnDLNTAgpDgljtfQMs6vaxuLMKBLDJ78Zsb+fmUvyEmnpHWZfffekv574YkW1NuLD/GVJrwvdtSIDh1Pz1Ce1RkcRQkwipdTFSqlKpVSVUureLzjuGqWUVkoVTGU+4VkONffyxPYagqwWvrk6i9lJ4af1+IXTIrl7VSYaePSjo1Q0905OUHFaeobG2LSvkbSoIFZmxxodx62kRgWx8ZvLuCI/mV++c5hvPrOb/hG70bGEMJQUh4Sh3j7YQoCfmTU5U7ecc3ZSGEnhAbx3qA2HXCUwTFP3EI3dQyxKj5JG1C6UGhXE2rxEntl1TE5yhPBSSikz8DCwFsgFblRK5Z7kuFDgO8CuqU0oPElzzxDPFdeTEB7AN1dnERcacEbPkxIZxLfPnU58WADPFddRZxt0cVJxOrTWvFzagMOpuWZhivSDOokAPzO/um4eP7w0h3cr2rjy4e3UdAwYHUsIw0hxSBimuWeIQy19LJ8eTZB16vanm5RiTW48toFRdh/rmrLXFZ9VXGvDYlLMT5UtZa72tZWZ9A3beaGk3ugoQojJUQhUaa2rtdajwHPA+pMc9zPgAUD2mYqT6h0e4487jhHgZ+LWJelnPc01xN/CbcvSCQ3w4487aunoG3FNUHHaimptHG7t5+I5icSE+Bsdx20ppfjqykz+eGchHf0jrHtom4y7Fz5LikPCMB8dbsdqMbE0c+rHac6MDyUtKogPKtsYczin/PV93ajdyd76bvKSw326MeJkyU+NYOG0SJ7cXiur44TwTsnA8dXfhvHb/kYpNR9I1Vq/PpXBhOcYtTv5045jDI7auXVpOmGBfi553hB/C3csS0cBT35SQ9/wmEueV0xc9+Aob+5vYXpsCIszooyO4xGWT49h0z0rSIkM4s6ninn4gyq09CcVPkbGCQhDdPSPsL+hh5XZMYYUB5RSXJAbz+PbaiiqsbF8+tQXqHxZWUM3I3YnhXLCMmnuWpHBt57Zw7sVrVw0O8HoOEII1zrZ/pC/vYtRSpmAB4Hbv/SJlLobuBsgLS3NRfGEu9Na81JpA03dQ9yyZBpJEa7t+xgd4s9ty9L5/dZq/rjjGHevyqa+Ll8AACAASURBVMTPLNekp4LWmk37mtBorlyQ7DZj6zfsqjM6woRcX5DKS6UN/GJLJVvKW7h6Qcopf3ZvWiy/M4V3kd/SwhAfH27HbFKGFmWyYkPIjA3mw8PtjNpl9dBUKq61ERfqT5o0op40F+bGkxwRyBPbaoyOIoRwvQYg9bivU4Cm474OBeYAHyqlaoElwKaTNaXWWj+mtS7QWhfExkrDWl9xoKmXsoYezs+JJycxbFJeIyUyiBsWpdHYPcRbB1om5TXE5x1o6uVQSx9rcuKJDDr1tDlxclaLiesLUrkwN56yhh6e2FbDgPRwFD5CikNiynUPjlJa101BeiShAa5ZwnymLsyJZ2DEzo6jHYbm8CXNPUPUd0kj6slmMZu4fVk6u2psHGjsMTqOEMK1ioFspVSGUsoK3ABs+uudWuserXWM1jpda50O7ATWaa1LjIkr3MngiJ1N+5pIjgjknBmTWxDMSQxjWVY0O6o7qWyRCWaTbXjMwetlTSSFB7AsS1bFnymlFKtnxnHDolQau4f43UdHaZf+WcIHSHFITLmtVR1otFuM1EyLDiY7LoTtRzul99AU+Vsj6rQIo6N4vesLUwm2mmX1kBBeRmttB+4BtgAVwPNa63Kl1H1KqXXGphPu7o39zQyN2rlqQfKUTLC6aHYCCWEBvLinUaZoTrIt5S30D9u5Yv7UfG+93dyUCL66IoORMQf/99FRjnXKJDPh3aQ4JKZU/4idklob+amRbrPUdWV2LP0jdvbVdxsdxeuN2p2U1nUzJzl8SifU+aqwAD+uLUjltbIm2nplWJEQ3kRrvVlrPUNrnaW1vn/8th9rrTed5NjVsmpIABxu7aO0vptzZsSSGO7aPkOn4mc2cd2iVEbGHGzc3SBNfidJXecARTU2lmZFkxIp2/ZdJS06mG+unk6Q1czj22qoaJYVcMJ7SXFITKmSWhtjDs3KbPdZ6poVG0xieABbqzpwygnLpNrf2MOI3cmidGlEPVXuWJ6O3an5085jRkcRQghhoP4RO6+UNhIb6s+5M+Om9LUTwgK4eE4Cla197KqxTelr+wKn1ry6r4mwQD8uyIk3Oo7XiQq28vVzskgID+DPO49RXCs/w8I7SXFITBmn1hTV2MiMDSY+LMDoOH+jlGJldiztfSMcbukzOo5XK661ERPiT3q0XNGaKtOig1mTE88zu+oYHnMYHUcIIYRBfvveEXqGxrh6fjIWAyaHLc2MJjsuhLfKW+gZkvH2rlRUY6O5Z5hL8hLx95v6KcC+IMTfwl0rMsiOD+Hl0kY+qGyTVXDC60hxSEyZypY+uofGWJIRbXSUz8lLDic80I+tVdKYerK09A5TZxukMD1SGlFPsTuXZ2AbGOWV0kajowghhDBAY/cQT31Sy/y0SNKigw3JoJRifX4yTqfmjbKmL3+AmJCBETvvHGwlMzaYOUmTM3lOfMrfYuYrS9KZnxrBOwdb+flbh6RAJLyKFIfElNlZ3UlYgGXSRqaeDbNJsTwrmpqOARq6Bo2O45WKa22YTYr5aZFGR/E5SzKjyE0M44ntNXISI4QQPujBdw4DsCZnareTnSgq2Mp5s+I40NRLpazWdom3D7YyYndw+dwkufg2BcwmxdULU1icEcWjH1Xzw1cO4HTKuZXwDlIcElOio3+EI239FGZEue30hEXpUQT4mdh6RFYPudqYw0lpXRezk8II9pdG1FNNKcVdKzI43NrPNlkdJ4QQPuVQSy8b9zRw+7J0ItxgGMiK7BhiQ/zZtK9RJsWepcauIUpqbSzNjHarlg3ezqQU6+Yl8Y1zsnhmVx3/+MI+7PKzLLzAhIpDSqmLlVKVSqkqpdS9J7l/lVJqj1LKrpS65oT7HEqpveMfn5ugIXzDrupOTAoK3LgRsb+fmcL0KA409mAbGDU6jlc50NjD8JiTQjf+/nu7y+YlEhPiz+My1l4IIXzKA29VEuJv4Vurs4yOAoDFZGJ9fhJdg2N8UNlmdByP5dSaTfsaCfa3cL40oZ5ySinuXTuL7180k5dLG/n7v+yVApHweF9aHFJKmYGHgbVALnCjUir3hMPqgNuBDSd5iiGtdf74x7qzzCs80NCog911XcxOCicswM/oOF9oaVYMSsGumk6jo3iVolob0cFWMmKM6XMgPt0nf+vSaXxY2U5VmyzlF0IIX7CzupP3D7XxrdXT3WLV0F9lxoYwPzWCrYc7aOsbNjqOR9pX30191xAXz04gQJpQG+bb507n3y6ZxRtlzVIgEh5vIiuHCoEqrXW11noUeA5Yf/wBWutarXUZIP8axOds2tfI8JiTJZnu14j6ROGBfuQkhlFS2yVLnV2ktXeYY52DFGZEyV54g928OA2rxcST22uNjiKEEGKSaa35+ZuHSAgL4I7l6UbH+Zy1eYlYzIo397cYHcXjjNqdbClvISUykPy0CKPj+Ly7V2VJgUh4hYkUh5KB+uO+bhi/baIClFIlSqmdSqkrTiud8Ap/3llHfJjnjC9fkhnN0JiDsoZuo6N4hZJaG2YljajdQXSIP1fmJ7NxTwNdsnVSCCG82oeH29lb381312S75cqSEH8L586Mo7K1jyOyovW0bD3STu+wnUvmJGKSC29u4fgC0Xf/sheHNKkWHmgixaGT/cY5nZ/2NK11AXAT8Gul1Oc2PCul7h4vIJW0t7efxlMLd1fR3Mv+xh4WpXvOqpHMmGDiQv3ZWW2TyU5naczhZE9dN7lJYYRII2q3cOeKDIbHnGwoqjM6ihBCiEn0uw+OkhgewFULUoyOckrLsqKJDPLjzf0tOOWca0J6hsb4+Eg7c5LDSZft+m7l7lVZ/OvaWbxe1sy9G8tkipnwOBMpDjUAqcd9nQI0TfQFtNZN4/+tBj4E5p/kmMe01gVa64LY2NiJPrXwAC+UNGA1m8hP8Zwlr0oplmRG09g9REPXkNFxPFp5Uy9DYw4KM6QRtbuYmRDKyuwY/rijVrZOCiGElyqptVFUa+NrKzOxWtx3OLHFbOLiOYm09A6zu7bL6Dge4Z2DLTg1XDw7wego4iS+fk4W3zk/mxd2N3Df6wflQrPwKBP5a1EMZCulMpRSVuAGYEJTx5RSkUop//HPY4DlwMEzDSs8y6jdySt7G1mTG0eQh60amZ8agb/FxM5qaUx9NopqbERJI2q3c+fyDFp7R9i8v9noKEIIISbBIx8eJTLIjxsKU7/8YIPNSQpjWlQQb1e0MjLmMDqOW2vsGmJPXTfLs6KJCnafBuPis/5hTTZ3rcjgqU9qefCdw0bHEWLCvrQ4pLW2A/cAW4AK4HmtdblS6j6l1DoApdQipVQDcC3wqFKqfPzhOUCJUmof8AHwc621FId8xPuH2rANjHLtQvc/MTmRv5+Z+WmRlDX20D9iNzqOR2rrHaa2c4DC9CjZD+9mzpkRS2ZsMI9vq5ErWkII4WUqmnt5/1AbdyzPIMjq/hfnlFJcOjeRgRE7Hx2W9hKnorXmjf1NBFvNrJ4ZZ3Qc8QWUUvzw0hxuWJTKb9+v4vcfVxsdSYgJmdA6U631Zq31DK11ltb6/vHbfqy13jT+ebHWOkVrHay1jtZazx6//ROtdZ7Wet74fx+fvP8V4W5eKKknLtSfldkxRkc5I0syonA4NSW1NqOjeKTi8UbUC6ZJI2p3YzIp7lyeQVlDD7uPyTJ+IYTwJr/78CjBVjO3LU03OsqEpUQGkZ8awbaqDroHZWDCyZQ39VLbOcia3Hi3bDAuPkspxf1X5nHp3ETu31zBq3sbjY4kxJdy303IwqO19Q3z4eF2rlqQgsXsmT9mcWEBZMYGU1RjkyaJp0kaUbu/qxYkEx7ox+PbaoyOIoQQwkWOdQ7welkTNy+ZRniQn9FxTssFufFo4N2KNqOjuB27w8lb5S3EhfpTME36OHoKs0nxq+vmsSQzin96YR/bjnQYHUmIL+SZ79qF23t5TyMOp+baAvedkDERSzKi6R4a43CrjFg9Hfsbexgac7BYGlG7rSCrhZsWp7GlvIV626DRcYQQQrjAYx9XYzGZuGtFhtFRTltkkJWlmdGU1nXR0jNsdBy3sqO6E9vAKJfmJWI2yVZ9T+JvMfPYrQVkxYbw9T+VcKCxx+hIQpySFIeEy2mteWF3AwvSIsiKDTE6zlnJSQwj1N9CUY1sLTsdRTU2YkL8pRG1m7t16TRMSvH0J7VGRxFCCHGWugdH2bingSvnJxMfFmB0nDOyemYs/n4mtpS3GB3FbfSP2Hn/UBsz40PJjg81Oo44A2EBfjx1RyERQVZuf7KYhi65KCfckxSHhMvtre+mqq2faws8rxH1icwmxcL0SCpb+mQP/AS19AxTZxukMD0SJY2o3VpieCCX5CXyl+J6abwuhBAe7rnieobHnNyxIt3oKGcsyGph9Yw4Klv7ONreb3Qct/BeRStjDidr58joek+WEB7A03cuYtTu4K6nSugbHjM6khCfI8Uh4XIv7WnE32LisrmJRkdxiUXpn26NKq6Vxr0TUVTbicWkWJAmjag9wV0rMugbsfNCSb3RUYQQQpwhu8PJn3YcY2lmNLMSwoyOc1aWZkUTHujHWwdafL7nY2vvMMW1Ngozoojz0NVg4v+bHhfK725ZyNH2fu7ZUIrd4TQ6khCfIcUh4VJjDidv7G9mTW48oQGe1QjxVCKDrMyID6XkmA2H07dPUr7MqN1JaV03c5LDCZJG1B5hXmoEBdMieXJ7rfx8CyGEh3q3opXG7iFuX55udJSz5mc2cUFOPI3dQz7dn0Vrzeb9zVgtJs6fFW90HOEiy6fH8LMr5vDR4XZ+9vpBo+MI8RlSHBIute1IB7aBUa7ITzY6ikstzoiib9hORXOv0VHcWllDNyN2J4Xp0ojak9y5IoM62yDvVrQaHUUIIcQZeGJ7LSmRgazJ8Y4iQn5aBAlhAbx9sBW70zdXV1S29nGkrZ/zZsUTLBfcvMqNhWl8bWUGT+84Jn0fhVuR4pBwqVf2NhIe6Mc5M2KNjuJSMxJCCQ/0o6hWGlN/kaJaG3Gh/kyLDjI6ijgNF+bGkxwRyBMy1l4IITxOeVMPRTU2blua7jWTrExKcdHsBGwDoz45FMTudLJ5fzMxIVaWZMoFN29079oc1uTEcd/rB/nkqIy4F+5BikPCZQZH7bxd3soleYlYLd71o2VSikXpkVS19dPZP2J0HLfU1D1EQ9cQhRlR0ojaw1jMJm5fls6uGptPL+EXQghP9PQntQT6mbnOCwaBHG9GfAiZMcG8f6iN4TGH0XGm1M5qGx39o1ySl4jF5F3n1OJTZpPiwevzyYgJ5tvP7KHeJhPMhPFkjaJwmXcOtjI05mB9fpLRUSZFwbQo3j/URnGtjYvneEezbVcqqrHhZ1bMT5VG1J7o+sJUfv3uYZ7YVsOvrs83Oo4QQogJ6Owf4ZW9TVxXkEJ4kHf0evwrpRQXz0ngkQ+PsvVIOxfk+sa0rs7+Ed4/1Ep2XAgzZXS9W9uwq+6sn2Pd3CQe+aiK6x7dwddXZRl+gf2mxWmGvr4wlpSihcu8ureJxPAAr+03ExboR05iGCXHumS6wAlGxhzsbegmLzmCQKvZ6DjiDIQF+HFtQSqvlTXR1jtsdBwhhBAT8HxJA6N2J7ctTTc6yqRIiQwiLzmcbVUd9PrI6O9fvXOYUbuTS/ISZSW2D4gJ9ef6gjRaeoZ5qbQB7eMT+oSxpDgkXMI2MMrHh9tZNy8Jk5fsdz+ZwvQoBkcdlDdJY+rj7W3oZtTuZHGGdxYGfcUdy9OxOzV/2nnM6ChCCCG+hNaavxTXUZgRRbYXrzC5MDcepxPeq2gzOsqkq2ju5dmiOhZnRBMvo+t9xsyEUC7MjaesoYdtVdJ/SBhHikPCJd7Y34zdqVnvZVPKTpQVF0JUsJVdPtgc8VS01hTX2EgMDyAlMtDoOOIsTIsOZk1OPM/sqvO5/g5CeBql1MVKqUqlVJVS6t6T3P8NpdR+pdRepdQ2pVSuETnF5NlR3Ult5yA3FnpXr6ETRYf4U5gRxe5jNq9e2aq15mevHyQs0I/zc+KMjiOm2KoZscxOCmNLeQu1HQNGxxE+SopDwiU27W0kOy6EnETvvXIFnzamLkyPorZzwKtPUE5HY/cQTT3DLEqXRtTe4K4VGdgGRnmltNHoKEKIU1BKmYGHgbVALnDjSYo/G7TWeVrrfOAB4FdTHFNMsmeL6gkP9GOtD/RBPHdWHH5mE1sOthodZdK8fbCVT4528g9rZhBklbawvkYpxdULUogMsvJscR19PrKNUrgXKQ6Js9bQNUhxbRdXzE/2ieLAgmmRmJWSsfbjdtXYsJpN5KdGGB1FuMDijChyE8N4YnuN7HsXwn0VAlVa62qt9SjwHLD++AO01sfvfw4G5B+0F7ENjLLlQAtXzk8mwM/7e/2F+FtYNSOWiuZejnV636qKEbuD/9xcQXZcCDdLQ2CfFeBn5qbFaQyNOvhLST1OOQ8TU0yKQ+KsbdrXBMC6ed45pexEIf4WZieHsaeui1G7bzemHhp1UNbQzdyUcJ84OfUFSinuWpHB4dZ+2fcuhPtKBuqP+7ph/LbPUEp9Wyl1lE9XDn1nirKJKfDSngZGHU5uLPSdQsLyrBhCAyy8eaDF6y5ePLm9lmOdg/zoslwsZnl75ssSwwNZn59MdfsA71Z470o54Z7kt484a6+WNrFwWiSpUUFGR5kyhRlRDI852d/YY3QUQ+0+ZmPMoVmSGW10FOFCl81LJDbUn8e31RgdRQhxcidbpvu5d8ta64e11lnAvwA/POkTKXW3UqpEKVXS3t7u4phiMmitebaojgVpEcxM8O7t/MezWkysmRVPnW2Qg83eMxikvW+Eh96v4rxZcayaEWt0HOEGFk6LpGBaJB9WtnOktc/oOMKHSHFInJVDLb1UtvaxPt83Vg39VUZ0MLEh/hTVdBodxTBOrdlZYyM9OoikCGlE7U38LWa+smQaH1a2U9UmJyVCuKEG4PguxClA0xcc/xxwxcnu0Fo/prUu0FoXxMbKG1NPUHKsi6PtA9zgQ6uG/mrBtEhiQ/zZUt6Kw+kdq4f+Z0slw2MOfnBpjtFRhBu5fF4ScaH+PL+7QfoPiSkjxSFxVl4pbcJsUlya5/3NEI+nlKIwI4r6riHKm3xz9VBlSx+2gVGWZsUYHUVMgpsXp2G1mHhye63RUYQQn1cMZCulMpRSVuAGYNPxByilso/78lLgyBTmE5Po2V11hPpbuGyub517AZhNiotmJ9DRP0LJMc/v/bj7mI2/lNRzx/J0smJDjI4j3Iif2cSNhWmM2h28sLtB+g+JKSHFIXHGnE7Na/uaWJkdQ3SIv9FxptyCtEgsJsWGXXVGRzHEjupOwgIs5CaGGR1FTILoEH+ump/Mxj0NdA2MGh1HCHEcrbUduAfYAlQAz2uty5VS9yml1o0fdo9SqlwptRf4HnCbQXGFC/UMjvHG/mbWz0/y2YlWOYmhTIsO4t2KNobHHEbHOWNjDif/9tIBksID+O6aGUbHEW4oPiyAS/OSqGrrZ+sR6QMpJp8Uh8QZKznWRWP3EFfkf64Hpk8ItJqZmxLOK6WN9I/YjY4zpdp6h6lq62dJZjRmk/dPqPNVdyzPYHjMyYYi3yyACuHOtNabtdYztNZZWuv7x2/7sdZ60/jnf6+1nq21ztdan6u1Ljc2sXCFV/Y2MmJ3csMi39tS9ldKfbpifXDEznse3LD3iW01VLb28e/rZhPs75uFPvHlFqVHMic5nHcOtlDnhZP6hHuR4pA4Y6/ubSTQz8wFufFGRzFMYUY0A6MOXt3baHSUKbWjuhOLSVGQHmV0FDGJZiaEsjI7hj/uqGXM4duT+YQQwmh/bUQ9NyWcOcnhRscxVEpkEIvSo9hR3Ulli+f1xmvoGuTX7x5hTU48F85OMDqOcGNKKa7MTyY80I/ndzcw4sGr5YT7k+KQOCOjdidv7G/mgtx4n77akRoZSE5iGM/srPO6saqnMjzmoLTu0/H1IT78vfcVd67IoLV3hM37m42OIoQQPm1vfTeHWvp8etXQ8S7MjcffYuYnmw541DmY1pqfvFqOUvDT9bONjiM8QKDVzLULU+kaGOUNOR8Tk0iKQ+KMbD3STvfgmM9NKTuRUoqbF6dxsLmXfQ2+0Zh697EuRh1OlmZKI2pfcE52LJmxwTy+rcajTr6FEMLbPFdUT5DVzDofP/f6qyB/CxfOjmdntY3XyjznDfOW8hbeO9TGP6yZQbJMexUTlB4TzMrsWEqOdVHR3Gt0HOGlpDgkzsire5uIDPJj1QwZe7s+P4kgq5lndh4zOsqkczg1nxztYFpUEMmRckLjC0wmxZ3LMyhr6GH3sS6j4wghhE/qGx5j074m1s1LklW7x1mUHsWc5DDuf+MgAx7Q/7Gzf4QfvHyA2Ulh3L483eg4wsOsyYkjMTyAl3yw36mYGlIcEqdtYMTOOwdbWZuXiJ9ZfoRCA/xYn5/Ma2VN9AyOGR1nUpU39dA1OMbKbFk15EuuXpBCeKAfj2+rMTqKEEL4pE37mhgac3BDoWwpO55JKX66bg6tvSP88u3DRsf5QlprfvDyAfqG7fzqunw5hxanzWI2cW1BKsNjDl4pbZQV3cLl5LeSOG3vHGxlaMzhs1PKTubmxWkMjzl5qbTB6CiTRmvNtqoOooOtzJLx9T4l0GrmpsVpbClvod42aHQcIYTwOc8W1ZGTGMa8FN9uRH0yC6dFcsuSNJ78pIaSWpvRcU5p074m3ipv4XsXzmBmQqjRcYSHSggL4MLceA4297KnTlZ0C9eS4pA4ba/ubSQ5IpCCaZFGR3Ebc5LDmZcawZ92HsPp9M4qfk3nAA1dQ6zIjsGkZHy9r7l16TRMSvH0J7VGRxFCCJ9yoLGHA4293FiYipK/vyf1r2tzSI4I5PsvljE06n7TnFp7h/nxq+UsSIvgayszjY4jPNzy6TFkxATzelkztoFRo+MILyLFIXFaOvtH+PhIB5fPS8JkkhOU492xLJ3q9gE+OtJudJRJse1IB0FWMwvSpCjoixLDA7kkL5G/FNfTO+zd2yeFEMKdPFtUR4CfifWyYvuUgv0tPHD1XGo6BviftyuNjvMZWmvu3VjGiN3B/1w7D7OcP4uzZFKKaxamAPDi7nqcsr1MuIgUh8Rp2XygBYdT+/yUspO5JC+RuFB/nvDCvixtvcMcauljaWa07JH3YXevyqRvxM4zO+uMjiKEED5hYMTOq3ubuDQvifBAP6PjuLVl02O4ZUkaT2x3r+1lf9hawweV7dx78SwyY0OMjiO8RGSQlcvnJVHbOci2Ix1GxxFeQt7lidOyaW8jM+JDmCV7pT/HajFx27J0th7p4HBrn9FxXGpbVQcWk2JxZrTRUYSB5iSHs2pGLI9vq2F4zP2W7QshhLd5o6yZ/hE7NxamGh3FIxy/vcwdppftqu7k528dYu2cBG5blm50HOFl5qdGMDspjHcqWmnuGTI6jvACUhwSE9bQNUhxbRfr85Nlz/sp3FSYhr/F5FWrh/qGxyit72bhtEgZnyv4xjmZdPSPsHGP9zZfF0IId/FscR3T40JYKH0eJyTY38IvrpnHsc4B/mVjmaHTnNp6h7nn2VKmRQXxwDVz5dxZuJxSiivykwn0M/NCSQN2p9PoSMLDTag4pJS6WClVqZSqUkrde5L7Vyml9iil7Eqpa0647zal1JHxj9tcFVxMvdf2NQOwbp5sKTuVyGArVy1I4aXSRjr7R4yO4xJbj3TgdGpWTJfx9QKWZkYzLzWCRz+qxu6QkxAhhJgsh1p6Ka3r5sbCNCksnIalWdF8/6JZvF7WzOMGXawbczi5Z0Mp/cN2fnfLQkIDZEugmBzB/haump9MS+8wH1Z6Z99TMXW+tDiklDIDDwNrgVzgRqVU7gmH1QG3AxtOeGwU8BNgMVAI/EQpJZc+PNSrextZkBZBalSQ0VHc2p3L0xm1O9mwy/P7svSP2NlV08m81AiiQ/yNjiPcgFKKb56TRZ1tkDcPtBgdRwghvNZzRfVYzSaumi+NqE/XN87JZO2cBP7rzUPsONo5pa+tteb+NyooqrXxX1flydh6MelmJYaRnxrBh5Vtsr1MnJWJrBwqBKq01tVa61HgOWD98QdorWu11mXAiZeRLwLe0VrbtNZdwDvAxS7ILaZYZUsfh1r6ZFLGBGTHh7JqRix/3HmMUbtnr6zYdqQDu0Ozemas0VGEG7kwN57M2GB+9+FRQ5fsCyGEtxoec/DSngYunpNAZLDV6DgeRynFL66dR3p0EPds2DOlb5gfer+Kpz6p5a4VGVwhhT0xRS7LSyTIamHj7gYcTjk3E2dmIsWhZKD+uK8bxm+biLN5rHAjr+5txGxSXJKXaHQUj3DXigza+0bYtK/J6ChnbGDEzs7qTvJSwokLDTA6jnAjJpPiG+dkcbC5l49lQoYQQrjc5v3N9A7bubEwzegoHivE38KjXylgxO7krqdK6B4cnfTXfPqTWn75zmGuWpDMDy7JmfTXE+KvgvwtrJuXRFPPMFuPyPYycWYmUhw62SbniZYjJ/RYpdTdSqkSpVRJe7v8MLsbrTWv7m1i+fQYYkNla9FErMqOIScxjEc+rPLY6v32qg7GHE7OnRlndBThhq7ITyYxPICHP6gyOooQQnid54rqyYgJZklmlNFRPNr0uBAeuXkBVe393PyHXZNaIHq5tIGfbCrngtx4Hrh6LiaT9IkSU2tOcjhzksN571Abrb3DRscRHmgixaEG4Pj5mSnARJdDTOixWuvHtNYFWuuC2FjZvuJu9tR10dg9xBX50oh6opRS3HPudKrbB3jzQLPRcU5b9+AoO6o7mZMcTnyYrBoSn2e1mLh7VSZFNTZ2VU9tPwchhPBmVW19FNXauH5RqjSidoFVM2J57CsLOdLWzy2PT06B6IWSev7phTKWZUXzvzfOx2KWgdDCGOvmJeFvMbFxTwNO2fovfdlRNQAAIABJREFUTtNEfnMVA9lKqQyllBW4Adg0weffAlyolIocb0R94fhtwoO8urcJf4uJC2cnGB3Fo1w8J4Gs2GAeer8Kp4etHnpiWw0jdlk1JL7YjYVpxIRYeUhWDwkhhMs8V1SPn1lxzcIUo6N4jdUz43jsKws53PJpgcg24JoCkd3h5GevH+T7L5axJDOKx24tIMDP7JLnFuJMhPhbuHxuEg1dQ2yvkq3/4vR8aXFIa20H7uHTok4F8LzWulwpdZ9Sah2AUmqRUqoBuBZ4VClVPv5YG/AzPi0wFQP3jd8mPMSYw8kbZc2syY0nxN9idByPYjYpvn3udA619PFuRavRcSass3+EJ7bXMjspjIRwWTUkTi3Az8zXVmay9UgHpXVdRscRQgiPN2J3sHFPAxfkxhMjU0JdavXMOB69dSGHW/u5+Ncf8/Hhs2tl0TM4xh1PFfP4thpuX5bO03cUyrmycAtzU8LJSQzjnYOtdPSNGB1HeJAJrXnUWm/WWs/QWmdpre8fv+3HWutN458Xa61TtNbBWutorfXs4x77hNZ6+vjHk5PzvyEmy/aqDjoHRlk/T7aUnYl185JIiwrioQ+qPGaq02/fO8LQmIMLcuKNjiI8wC1LphER5MdD78vqISGEOFtbylvpGhyTRtST5NyZcbzyreVEBPlx6xNF/PS1cobHHKf1HE6nZtO+Ji79363srO7kv6/O49/XzZatZMJtKKVYn5+ExazYWCrby8TEyW8x8YU27W0iLMDCOTLK/IxYzCa+tTqLsoYej5jqVN3ezzO76rhhUSpx0mtITECwv4W7lmfw3qE2DjT2GB1HCCE82nNFdaRGBbI8K8boKF4rNymMTfes4PZl6Ty5vZZLfrOVP+6opWdo7Esfu+1IB+se3sZ3ni0lxN/Cc3cv4fpFUsgT7icswI/L8pI41jnITukNKSZI1j6KUxoadbClvIXL5yXhb5H902fqqgUp/Pa9I/zve0dYlR3j1s0l//utQ/hbTHx3zQzeOeg5W+GEsW5bns5jW6t56P0q/u8rC42OI4QQHqm2Y4BPjnbyTxfOkElXkyzAz8y/r5vN6pmx/PLtw/z41XL+c3MFl+QlsjwrhqhgK5HBVgL8TOxv6KG41kZxbRc1HQMkRwTy4PXzWD8vWb5Pwq3NT4ugrLGbLeUtzEoIIyrYanQk4eakOCRO6b1DrQyMOlgnU8rOitVi4purs/jRq+W8f6iN8910u1ZxrY0t5a1874IZxIZKnwMxcWEBfty+LJ3/fb+KypY+ZiaEGh1JCCE8znPF9ZhNimsLUr/8YOESq2fGsXpmHPsbeni2uI5Ne5t4aU/j546LCPKjYFoUd67I4LqCFLloKjyCUoor8pP5zXtHeLm0gTuXZ7j1RWphPCkOiVN6dW8T8WH+LM6INjqKx7uhMI0nt9fyn5srWDUjFj8325euteY/N1cQH+bPV1dmGB1HnKENu+oMe+3wQD/8LSb+8fm93LR4mkuf+6bFsmRfCOHdRu1OXtxdz3mz4oiXbd1TLi8lnLyUPH58WS4tPcN0DY7SNThK37Cd3MQwsmJDZJWQ8EgRQVYump3Apn1N7D7WRUF6lNGRhBtzr3eowm10DYzyYWUbl89Nwix/DM+an9nEvWtncbR9gOeK642O8zlv7G+mtK6bf7xgJkFWqRmL0xdktbAsK4YDTb00dQ8ZHUcIITzKexWtdPSPcmOhrBoyUoCfmfSYYOanRXLerHjW5yeTHR8qhSHh0QozokiPDmbzgWZ6J9BbS/guKQ6Jk9q0r4kxh+aqBSlGR/EaF+TGszgjil+/c5i+Yff5xdw7PMZ9rx0kNzGMqxfK91ucuRXTYwjwM/HeoTajowghhEd5triexPAAzpkRZ3QUIYSXMSnFVfOTsTs+nbTnKROUxdST4pA4qY17GshJDCM3KczoKF5DKcUPLs2hc2CURz48anScv/nvNw/R0T/Cz6/Ok1Vi4qwEWs2smB5DRXMvjV2yekiIyaSUulgpVamUqlJK3XuS+7+nlDqolCpTSr2nlHLtfk/hMnWdg2w90s51Banyd1gIMSliQv1ZkxPPweZeDjT1Gh1HuCkpDonPOdLaR1lDD9fIKhKXm5sSwZXzk3l8Ww0NXYNGx6G41sYzu+q4Y3kGc1MijI4jvMCyrBgC/cy8WyHT7oSYLEopM/AwsBbIBW5USuWecFgpUKC1ngu8CDwwtSnFRG0oqsOkFDcWSn81IcTkWT49hqSIADbta2Jw1G50HOGGpDgkPufFPQ1YTIr1MqVsUnz/opko4P43KgzNMWJ3cO/GMpIjAvneBTMMzSK8R4CfmZXZMVS29lFvM74AKoSXKgSqtNbVWutR4Dlg/fEHaK0/0Fr/9R/hTkCu+LihEbuD50vqWZMTR0K4NKIWQkwes0lx9YIUhkbtbN7fbHQc4YakOCQ+w+5w8vKeRlbPjCUmRMaZT4akiEC+c342bx5o4bV9TYbleOSDoxxtH+D+K+cQ7C9NqIXrLM2MJshq5h1ZPSTEZEkGjp9u0DB+26ncBbw5qYnEGXnrQAu2gVFuWSK7/oQQky8xPJBVM2LZU9fN4dY+o+MINyPFIfEZ26o6aOsb4WppRD2pvr4qk3mpEfzo1QO09Q1P+esfaOzhkQ+rWJ+fxOqZ0vxSuJa/n5lzZsRS1dbP0fZ+o+MI4Y1O1pjmpB1GlVK3AAXAL05x/91KqRKlVEl7e7sLI4qJ+PPOY0yLDmJ5VozRUYQQPuLcmXHEhvjzSmkjI2MOo+MINyLFIfEZG/c0EhHkx3k5UjCYTBaziV9eO5fBUQc/ePnAlE4N6Bkc45vP7CYmxJ+fXD57yl5X+JYlmdGEB/qxpbxFpmII4XoNwPEzz1OAzy1FVUqtAX4ArNNaj5zsibTWj2mtC7TWBbGxsZMSVpzcoZZeimu7uHlxmoxKF0JMGT+ziasWJNMzNMbbB2WVt/j/pDgk/qZnaIwt5S2sm5eEv8VsdByvNz0ulO9fOJN3DrbycmnjlLym06n53vN7aekZ5uGbFxAVbJ2S1xW+x89s4vxZcTR0DVEuUzGEcLViIFsplaGUsgI3AJuOP0ApNR94lE8LQ20GZBRfYsOuOqwWE9cuTP3yg4UQwoWmRQezJCuandWdHOscMDqOcBNSHBJ/80ZZM6N2p2wpm0J3rsigYFokP9lUPiXTy3730VHeO9TGDy/NZUFa5KS/nvBt89MiiQ315+2DrTicsnpICFfRWtuBe4AtQAXwvNa6XCl1n1Jq3fhhvwBCgBeUUnuVUptO8XTCAAMjdl7a08hleYlEyoUaIYQBLsyNJzzIj417GhlzOI2OI9yAFIfE3zxfUk92XAhzU8KNjuIzzCbF/1w7DzTc+VQxPUNjk/Za26s6+OXblVw+L4lbl0rjSzH5zCbFRbnxdPSPsKeuy+g4QngVrfVmrfUMrXWW1vr+8dt+rLXeNP75Gq11vNY6f/xj3Rc/o5hKr+5ton/Ezs3SiFoIYRB/i5kr85Pp6B/hg0OywFRIcUiMq2juZW99NzcUpqGU7HufSukxwTz6lYXUdAzwjT/tZsTu+sZwe+u7+cafd5MZG8LPr8qT77GYMjmJYaRFBfFeRatclRJCCEBrzdOf1JKbGMaCtAij4wghfFh2fCgL0iL5+Eg7Td1DRscRBpPikADguaI6rGYTV83/okm4YrIsmx7DA9fMZUd1J//yYplLG/jure/mK3/YRUSQH0/dsUjG1osppZTiotkJ9A7b+aSqw+g4QghhuB1HO6ls7eP25elysUYIYbhL8hIIslp4aU8DdrmQ59OkOCQYGnXwUmkja/MSZN+7ga6cn8L3L5rJK3ub+Pmbh1xSICqt6+Irf9hFZLCV5+5eSkpkkAuSCnF6MmKCyUkI5cPD7fQNT97WSSGE8ARPflJLVLCVdfOSjI4ihBAEWS2sm5dEU88wv99aY3QcYSApDgk272+mb9jODYvSjI7i8761OoubF6fx6MfVfPPPe87qjfRHh9u59fGi8cLQEpIjAl2YVIjTs3ZOImMOJ+9WyMhUIYTvqrcN8m5FKzcVphHgJ5NhhRDuYU5yOLOTwnjw3cNUt/cbHUcYRIpDgmeL6siICWZJZpTRUXyeUor/uGIOP7w0h3cqWln/0HYOt/ad1nP0DY9x78YybnuiiITwAJ67ewlJUhgSBosJ9WdpZjQltV0098iediGEb3r6k1pMSnGLNKIWQriZdfOSCLCYuHfjfpwyZdYnSXHIxx1p7aPkWBc3LEqVfe9uQinFV1dmsuGri+kdtrP+oe088mEVnf0jX/g4p1PzQWUbFz34Mc+X1PP1czJ57e9WSGFIuI3zZsUT4Gdm8/5ml/bVEkIITzAwYucvJfWsnZNAQniA0XGEEOIzQgP8+NFluRTV2nimqM7oOMIA0pnWxz1bVI+fWXH1whSjo4gTLM6MZvN3VvD9F8t44K1Kfv3OES6ek8C1BSnEhwUQ6GcmwM9MVVs/bx1oZkt5Ky29w2TFBvPiN5exIC3S6P8FIT4j0Grm/Jw4Xi9r5lBLHzmJYUZHEkKIKfNSaSN9w3buWJ5hdBQhhDipaxamsGlfEz/fXMF5s+KkLYWPkeKQDxsec/BSaQMX5iYQE+JvdBxxEnFhATx9ZyFVbX08s6uOF3c3sGlf0+eO87eYOGdGLP+SN5O1cxKlj4FwW4szotlVbePNA81kx4dgMckCViGE93M6NU9tr2FuSriMrxdCuC2lFP95ZR4X/fpjfvDyfp68fZHsLvEhUhzyYZv3N9M9OMaNhdKI2t1NjwvlJ5fP5p8vmsWumk4GRhwMjX36ERNsZdWMWBlRLzyC2aS4JC+Rp3fUsr2qk3NmxBodSQghJt1Hh9s52j7Ar66bJ2+0hBBuLTUqiO9fNJOfvnaQl0sbuWqB7DDxFfJu0kdprXlyey1ZscEsnx5tdBwxQYFWM6tnxhkdQ4izMjMhlJzEMN4/1Mq8lHAigqxGRxJCiEn1fx8dJTE8gMvmyvh6IYT7u3VpOq+XNfPT1w6yLCtG+qT5CFnP76P21HWzv7GH25dnyBUsIcSUuywvEYA39jcbnEQIISZXaV0Xu2ps3LUiA6tFTr2FEO7PbFL8z7XzGLU7+eeNZTJIxEfIXygf9dQntYQGWLhqfrLRUYQQPigy2MrqmXGUN/VyuLXP6DhCCDFpHvu4mrAACzfINn4hhAfJiAnm3y7N4ePD7fx5l0wv8wVSHPJBLT3DvLm/mesLUqVPjRDCMCunxxATYuW1fU2MOZxGxxFCCJerbu/nrfIWvrJ0GiFyziWE8DC3LE5j1YxY/vONCmo6BoyOIyaZFId80DO7juHQmluXphsdRQjhwyxmE5fPTaJzYJStR9qNjiOEEC73+601+JlN3L5MxtcLITyPUooHrp6Ln1nxj8/vxS4X87yaFId8zPCYgw276jh/Vjxp0UFGxxFC+Ljs+FDyksP5oLKdtr5ho+MIIYTLtPUNs3FPA9csTCE21N/oOEIIcUYSwgP42RVz2FPXzaMfVxsdR0wiKQ75mNf2NdE5MMody9ONjiKEEABcNjcRq9nES3sacUrDQyGEl3hqey1jDidfW5lpdBQhhDgr6+YlcencRH797mHKm3qMjiMmiRSHfMhfx9fPiA9hWZaMrxdCuIfQAD8uzUukzjbIrupOo+MIIcRZ6x4c5Y87jrF2TgIZMcFGxxFCiLOilOI/1s8hIsjK9/6yj+Exh9GRxCSQ4pAP+fhIBwebe7lrhYyvF0K4l/lpEWTHhbClvJWuwVGj4wghxFn5w9Ya+kfs/N152UZHEUIIl4gMtvLANXOpbO3jwXcOGx1HTIIJFYeUUhcrpSqVUlVKqXtPcr+/Uuov4/fvUkqlj9+erpQaUkrtHf/4P9fGF6fj4Q+qSAwP4Mr5KUZHEUKIz1BKcUV+MgCvlDaiZXuZEMJDdQ2M8uT2Gi7NSyQnMczoOEII4TLnzozjpsVpPLa1mqIam9FxhIt9aXFIKWUGHgbWArnAjUqp3BMOuwvo0lpPBx4E/vu4+45qrfPHP77hotziNJXU2iiqsfG1lZlYLbJgTAjhfiKDrVw4O54jbf3sPtZldBwhhDgjf9hWzeCYg79fI6uGhBDe5weX5JAaGcT3nt9L7/CY0XGEC02kSlAIVGmtq7XWo8BzwPoTjlkPPD3++YvA+Ur2LbmVRz48SlSwlRsKU42OIoQQp7QkM5r06GBe39+MbUC2lwkhPIttYJSnttdyaV4iM+JDjY4jhBAuF+xv4cHr82nuGebfXtovq729yESKQ8lA/XFfN4zfdtJjtNZ2oAf4a8fjDKVUqVLqI6XUyrPMK85AeVMP7x9q445l6QRZLUbHEUKIUzIpxbUFKSjghZJ6mV4mhPAov986vmrofFk1JITwXgunRfK9C2bwelkzz5fUf/kDhEeYSHHoZCuATjxbP9UxzUCa1no+8D1gg1Lqc5uvlVJ3K6VKlFIl7e3tE4gkTsfvPjxKiL+FW5emGx1FCCG+VGSQlXXzkjhmG+Tjw/I3QQjhGTr7R3j6k1oun5tEtqwaEkJ4uW+ek8WK6TH8ZFM5R1r7jI4jXGAixaEG4Pi9SClA06mOUUpZgHDAprUe0Vp3AmitdwNHgRknvoDW+jGtdYHWuiA2Nvb0/y/EKdV0DLB5fzO3LJlGeJCf0XGEEGJC8lMjyEsO592KVvY39BgdRwghvtTDHxxleMzBd2TVkBDCB5hMil9dP48Qfwv3bCiV8fZeYCLFoWIgWymVoZSyAjcAm044ZhNw2/jn1wDva621Uip2vKE1SqlMIBuodk10MREPvV+FxWzizhXpRkcRQogJU0qxPj+JEH8L3/1LKYOjdqMjCSHEKdV2DPCnnbVcvyiV6XEhRscRQogpERcawC+vy6eytY9/31RudBxxlr60AY3W2q6UugfYApiBJ7TW5Uqp+4ASrfUm4HHgT0qpKsDGpwUkgFXAfUopO+AAvqG1lpl3U6SypY+XShv42spM4kIDjI4jhBCnJchq4dqCVJ7YXsMPXz7AL6+bh8w6EOJTSqmLgd/w6bnZH7TWPz/h/lXAr4G5wA1a6xenPqXv+Pmbh7CaTfzDBZ9bIC8m2YZddUZHEMKnnTMjlm+tzuKRD4+yYFok1xXIACRPNaHuxFrrzcDmE2778XGfDwPXnuRxG4GNZ5lRnKFfbDlEiL+Fb63OMjqKEEKckazYEL57/gwefPcwizKiuLEwzehIQhhufFX2w8AFfLq1v1gptUlrffC4w+qA24F/mvqEvqWoxsZb5S384wUz5GKcEMInfe+CGeyt7+ZHrxxgdlIYs5PCjY4kzsBEtpUJD1Rca+Pdija+cU4WEUFWo+MIIcQZ+7vzprMy+9OGhwcapf+QEEAhUKW1rtZajwLPAeuPP0BrXau1LgOcRgT0FU6n5v43DpIQFsBXV2YaHUcIIQxhMZv47Y3ziQyy8o0/76ZncMzoSOIMSHHIC2mt+e83DxEb6s8dy9ONjiOEEGfFZFL8+vp8ooKsfHvDHnqH5YRD+Lxk4PjZwQ3jt4kp9lpZE/saevj+RTMJtJqNjiOEEIaJCfHnkVsW0NIzzD88vxen88QB58LdSXHIC71/qI2SY138/fnZBFkntHNQCCHcWnSIPw/dNJ+GriG+95d9csIhfN3Jmm+d0T8KpdTdSqkSpVRJe3v7WcbyLUOjDh54q5I5yWFcOV9qc0IIsSAtkh9flsv7h9p48N3DRscRp0mKQ17G4dQ88FYl6dFBXL9ImoEJIbxHQXoUP7o0h3crWvnF25VGxxHCSA3A8X/kU4CmM3kirfVjWusCrXVBbGysS8L5il+/d5jG7iF+dGkuJpM0yxdCCIBblkzj+oJU/vf9Kl7d22h0HHEapDjkZTYU1VHZ2sc/XTQTP7N8e4UQ3uW2ZenctDiN3314lJf2NBgdRwijFAPZSqkMpZSVT6fEbjI4k0852NTLH7bWcH1BKoszo42OI4QQbkMpxc+umENhRhTff7GM0rouoyOJCZLqgRdp7xvhgbcOsSwrmkvzEo2OI4QQLqeU4qfrZrM0M5p7N+5nj5xwCB+ktbYD9wBbgArgea11uVLqPqXUOgCl1CKlVAOfTpN9VClVblxi7+Jwav715f1EBPrxr5fMMjqOEEK4HavFxP/dspD4MH/u/tNumrqHjI4kJkCKQ17kv96sYHjMwX3r56CULG8WQngnP7OJR25eQGJEAHf/cTf1tkGjIwkx5bTWm7XWM7TWWVrr+8dv+7HWetP458Va6xStdbDWOlprPdvYxN7jzzuPsa++mx9dlisTYYUQ4hSigq08ftsihkYdfPXpEvpkoIjbk+KQl9hZ3clLexq5e1Um0+NCjI4jhBCTKjLYyuO3FTBqd3DbE0V09o8YHUkI4QNaeob5xZZKVmbHsD4/yeg4Qgjh1mbEh/LQTfOpbO3jm3/ew6jdaXQk8QWkOOQFRu1OfvTKAVIiA7nn3Gyj4wghxJSYHhfKE7cvoqlniDueKqZ/xG50JCGEF9Na84OX9zPmcPIfV8gqbSGEmIjVM+P476vnsq3q/7V33/FV1/cex1+fnOwNYSWssGQPWYIg7hYVtRV7wSqoVbFFpWqrvXrvba221tZbR69oVaBSBw5cuIrWgoAM2RtkQ5ghEAhJyDrf+8f5QSMyIiT55STv5+ORR874jTfnwOFzPr/v7/vbyy/f0hVnazI1h2qB8bM2sW7PIX57VWfiogN+xxERqTa9M+vz7PU9WbnjILe/vICi0jK/I4lILfXK3C18vmYP9w/uQMu0BL/jiIiEjWt7NeP+we2ZsnQHj3682u84cgJqDoW5NbsO8uQ/v+Z7nRpzccfGfscREal2F3VozJ+GduPL9Tnc88YSSss0ZFlEKtfaXXn87qPVXNC+IT8ZkOl3HBGRsPOz89tw07mZjJu1ibHT1vsdR44j0u8AcvoOl5Rx9+tLSI6N5NFruvodR0TEN0N7NSO3sIRHPlyFsYSnhvcgKqDjHyJy5g6XlDFm0mKSYiN5/NruOp1MROQ0mBm/HtKJ/QXFPD51LdGBCG4b1NrvWFKOmkNh7PGpa1mzK4+/3dSHBokxfscREfHVLQNb4Zzjdx+tpizo+Mt1ZxMdqQaRiJyZxz5Zw9rdoXqrYZLqLRGR0xURYfz5R90pDTp+//FqAhHGTwa28juWeNQcClMz12UzftYmbuzfkgs7NPI7johIjXDrea0xMx75cBV3vLaIsT/uqQaRiJy2f6zYxUuzN3PzgEzVWyIilSAyEMFTw3oQDDoe/nAVkQFjZP9Mv2MJmnMoLO3PL+YXby6lXaNEHri8o99xRERqlFsGtuKhKzvx2ard3DJRVzETkdOzcscB7nljCT2ap/KrwR38jiMiUmtEBSJ4evjZXNqpMb9+fyXPf7HB70iCmkNhp6QsyB2vLSK3oISnhvcgNkpXJxMROdZNA1rxx6Fdmb0hh2HPz2FP3mG/I4lIGNmTd5jbJi4gNT6KF0b2Ur0lIlLJoiMjGPvjngzpls4fPlnDHz5ZjXO6zL2f1BwKI845fjNlJbM35PDY0K50zkjxO5KISI01rE8Lxo3szcbsfK55djYbsg/5HUlEwsDhkjJuf3kh+wtKeHFkbxolxfodSUSkVoqODI0guqFfC57/YiO/enuZrjrrIzWHwsjE2Zt5bd5WfnZBG67p2czvOCIiNd6FHRrxxu39OFxSxtDnZjNr3V6/I4lIDRYMOn719jIWb83lyWHd6dJUB+JERKpSIMJ45OoujLm4HW8uyOKnryzUlAA+UXMoTExfu4eHP1zF9zo15r7vtfc7johI2OjWLJV3fjaARkkxjJwwj79+sUHDlkXkW5xz/Nd7K3h/yQ7u+357BndJ9zuSiEidYGbce+lZPHJ1Z6atzWbos7PZtq/A71h1jppDYWDJtlzuem0x7Zsk8+SwHkREmN+RRETCSou0eN4dPYDLuqbz2CdrGP3qIh2VEpGjjpy6P+mrrYy+oA2jL2jjdyQRkTpnRP9MJt7cl10HD3PVM7OYsyHH70h1ippDNdySbbmMGD+PegnRTLipNwkxkX5HEhEJSwkxkTxz3dn81+UdmbpyF1f93yyWbsv1O5aI+Mw5xyMfrubvc7YwalBr7vt+e8x0IE5ExA8D2zXgvTsGkJYYw4jx8xg3cyPBoEZ8Vwc1h2qwo42h+GheH9WP9JQ4vyOJiIQ1M+O2Qa159dZ/z0P0l8/XafJDkTqqLOh4aMpKJny5iZsHZPLAZR3UGBIR8VmrBgm8M/pcLurQiN99tJqbX5pPdl6R37FqPTWHaqjFW/d/ozGUkarGkIhIZenfJo1P7h7EkG7pPPHZ1/zo+Tms36OrmYnUJflFpYz6+wImztnCbee14tdDOqkxJCJSQyTHRvH8iF48cnVn5mzM4bKnZzLj62y/Y9VqOkepBpqydAf3vbWUxsmxagyFgdfmbfU7goichpS4KJ4afjYXd2zMf7+3gsuensGoQa2588J2xEUH/I4nIlVo54FCbnlpAWt35/HID7owol9LvyOJiMgxzIwR/TPp06o+d722mJETvmJY7+Y8eHlHUuKj/I5X62jkUA0SDDoen7qGMZMW071ZKu+MPleNIRGRKnZl9wz+ee/5XNktg7HTNnDpk1/w+erduqKZSC01b2MOPxj7JVv3FTD+xt5qDImI1HAdmiQz5c6B3D6oNZMXZXHxE1/wwdIdqtUqmZpDNcSBwhJuf2UhY6dt4Lq+zXnl1nNokBjjdywRkTqhYVIMTwzrweuj+hEbFeCWiQu47sW5LNq63+9oIlJJikuD/Okfaxj+4lziogK89dP+XNC+kd+xRESkAuKiAzxweUfev2MA6Smx3DUpNJJo1Y6DfkerNdQcqgGmrd3D95+cwb/W7OGhKzvx6A+7Eh2pt0ZEpLr1a53Gx2PO47dXdWb9nkNc8+wIl2SAAAATSUlEQVRsbn95AWt35fkdTUTOwIbsQwx9bjbPTt/Af/RqzkdjzqNjerLfsURE5Dvq0jSFd0efy6+HdGJZ1gGu+L+Z/OLNpew8UOh3tLCnOYd8dKCwhN9/tIo3F2TRrlEiz4/oRffmqX7HEhGp06IjI7jx3Eyu7dWM8bM28cKMjUxdOYML2zfk9vPbcE6r+pq0ViRM5B0u4Zlp6/nbrM3ExwT46w29GNylid+xRETkDEQGIvjJwFYM7dmMsdPX89KXm/lw2Q6G92nOree1pnn9eL8jhiU1h3xQUhbkzQXbeOqf68g5VMToC9rw80vaEROpCVBFRGqKhJhIxlzcjhH9WvLK3C28NHszw1+YS/dmKYzon8kVXdM1cbVIDVUWdLy9MIs/TV3L3kNFXNurGfcPbk+jpFi/o4mISCVJiY/iwcs7MqJfS/7y+TpenbeVV+Zt5aruGYwa1FojRL8jNYeqUTDo+Gj5Tv786Vo25xTQu2U9xo3srdFCIiI1WL2EaO66uB23DWrN5IVZTJi1iV++tZTfTlnJ1WdnMKx3C7o0TdZoIpEaoKi0jHcWbefFGRvZuDefni1SGX+jai0Rkdqsef14Hv9Rd+659CzGz9rEpK+28u7i7fRskcp1fVswpFuGDuhVgJpD1SDvcAlvL8zi5blb2JCdT/vGSYy/sTcXdWikLxMiImEiNirADf1acv05Lfhq0z5en7+NtxZk8crcrbRMi+fyrulc0TWdzhlqFIlUtz0HDzN5URZ/+3Iz2XlFdM5IZuyPe3J51yb69ygiUkdkpMbxP0M6ceeFbZm8MItJ87dy3+RlPPzBKi7r2oTLu6YzoG0DogKa3/d41ByqIsGgY+HW/by/ZDvvLtpOfnEZ3Zun8vTwHgzplkEgQoWKiEg4MjPOaZ3GOa3TeOjKznyyYicfLd/JCzM28tz0DWSkxDLorIYMOqshA9o2ICUuyu/IIrVSYXEZn67axTuLtjNzXTZBBwPbNuDJ/+jBgLZpagqJiNRR9RKiuW1Qa249rxVfbdrHG/O38fHyXby5IIuUuCgu6diYCzs0ZECbBtRLiPY7bo2h5lAlKiguZcHm/Xy2ajf/WLmL7LwioiMjuLJbBiP7t9SQZhGRWiYlPorhfVswvG8L9uUX89mqXUxfm81Hy3by+vxtRBh0aJJMn8x69M6sz9ktUmmaGqcvrSKnwTnH5pwCpq/dw/S12czdmENRaZCmqXGMvqAtP+zZlDYNE/2OKSIiNUT5A3qHS8qYtW4vHy/fyWerdvH2oizMoGvTFPq3SaNni3r0bFGPhkkxfsf2TYWaQ2Y2GHgaCADjnHOPHfN8DPB3oBeQAwxzzm32nnsAuAUoA8Y456ZWWnofOefYnlvIqh0HWZqVy9yN+1i6LZfSoCMuKsCFHRoyuEs6F3VoRGKMenAiIrVd/YRohvVpwbA+LSgpC7JkWy6z1u1lwZZ9vLUwi4lztgCQGh9Fp/RkOqUn07ZRIi3TEshsEE/jpFgiNKpUKuhMarNwsS+/mLW78lialcvirftZtDWX7LwiAFo3TOD6c1pyaafGnNOqvv7tiIjIScVGBbikU2Mu6dSY0rIgy7YfYObXe5m5LpsJszbxfNlGAJrXj6Nzegod0pPo0CSZ9k2SaFYvrk6cinbKroWZBYCxwKVAFjDfzKY451aVW+wWYL9zrq2ZDQf+CAwzs07AcKAzkAH808zOcs6VVfYfpCocLikjJ7+YvXlF7DxQyOacArbk5LMxO581u/I4UFgCQGSE0a1ZCqMGtaZf6zT6ZNbXhFciInVYVCCCPpn16ZNZH4DSsiCrd4a+5K7ccZBVOw7w8twtFJUGj64TExlBy7T4ULMoLZ6M1DjSEmNokBhNw8QY0hJjSI2L0pdgOaParPrTHl9pWZADhSXsLyhhf0Ex2/cXsj23kKz9BWzam8+63YfIyS8+unxmWjwD2zagZ8t6nN+uIS3SdJliERE5PZGBiKMjhX5+STsOl5SxYvsBFm/NZfG2/azemcfUVbtwLrR8IMJoVi+OlmkJNE2No2FSDI2SYr7xu2FSTNhffbwiQ1r6AuudcxsBzOx14GqgfAFyNfCQd3sy8IyFxsxfDbzunCsCNpnZem97cyonfsXtyy9mweZ9FJaUUVBcRmFxmXe79Oj9/QXF7D1UTM6hInIOFZNXVPqt7aQlRB+deLRTRjKdM5Lp0CSJ+GiNDhIRkeOLDETQtVkKXZulHH2sLOjYkVvIlpwCNufksyUnny05BWzJKWDG19nfaBwdEYgw6idEk5YQTUJMJIneT0JMgMSYKBJjAiTERBITGUFUZATRgQiiIyNC973bgSOntB39FbpxzMOYGS3T4mmcrEt/10CnXZs5d6TUrT7vLs7ivcU7yC0oJrewhP35xRw8/O0aC6BBYjTN68dzScfGtGucSLvGSXTJSCYtse4O8xcRkaoVGxWgd2Z9ensH9SA0ZczXuw/x9e48th6t1QpYteMAOfnFHO9/06SYSBKO1mWRxEf/+358dCSREUYgwogwIxABERFGwEKPBZ2jNOgoKwv9HnNxO+pX83xIFeloNAW2lbufBZxzomWcc6VmdgBI8x6fe8y6TU877RlYtzuPUS8v/NbjgQgjPipAXHSAevHRpCVG07VZKg0So2ngHbFNS4ihSUosLdPiSYrVxKIiInLmAhFG8/rxNK8fz8B2Db7xXDDoOFBYwt5DRWR7Byz2lvu9L7+Y/OJScguKydpfQH5RGYeKSskvLj1usXK6Hrm6MyP6Z1beBqWynElttrdaEpaTd7iU/QXFpMZHk9kggXrx0aTERVEvPop6CdHUi48mIzWOpqlxGnktIiI1Qnx0JD2ap9LjOPMGl5YFyckvJjuviD15h0O/DxaRk19MQXHp0bqsoLiU7bmFRx8rCwYpCzqCLnSQsMw5gt7vCDMiI+xoA+nmAZk1sjl0vPHrx5aeJ1qmIutiZqOAUd7dQ2a2tgK56qoG+FDY1WF6vauXXu/qFRav9/V+B6hcYfGaHzHyjzCy6jbfsuo2XeudSW32zYVUg9UGYfW5It+g9y481dr3rZbVXMcKq/ct87dVtukT1l8VaQ5lAc3L3W8G7DjBMllmFgmkAPsquC7OuReAFyqQpc4zswXOud5+56gr9HpXL73e1Uuvd/XTay6V5Exqs29QDRb+9LkSvvTehSe9b+FJ79upVWTK7flAOzNrZWbRhCaYnnLMMlOAG73b1wL/8s5pnwIMN7MYM2sFtAO+qpzoIiIiInXSmdRmIiIiIt9yypFD3nnqdwJTCV0udYJzbqWZPQwscM5NAcYDL3sTTu8jVKTgLfcmoQkSS4E7wuVKZSIiIiI10ZnUZiIiIiLHYzqIFF7MbJQ3BFyqgV7v6qXXu3rp9a5+es1FpLLpcyV86b0LT3rfwpPet1NTc0hEREREREREpA6ryJxDIiIiIiIiIiJSS6k5FEbMbLCZrTWz9Wb2n37nqc3MrLmZTTOz1Wa20sx+7nemusDMAma22Mw+9DtLbWdmqWY22czWeH/P+/udqTYzs3u8z5IVZjbJzGL9ziQi4U+1YXgyswlmtsfMVvidRSpO3w/Ck5nFmtlXZrbUe9+q7iLxYU6nlYUJMwsAXwOXEro87XzgOufcKl+D1VJmlg6kO+cWmVkSsBD4gV7vqmVm9wK9gWTn3BC/89RmZjYRmOmcG+dd7SjeOZfrd67ayMyaArOATs65Qu9CDR87517yN5mIhDPVhuHLzAYBh4C/O+e6+J1HKkbfD8KTmRmQ4Jw7ZGZRhGqynzvn5vocrcbRyKHw0RdY75zb6JwrBl4HrvY5U63lnNvpnFvk3c4DVgNN/U1Vu5lZM+AKYJzfWWo7M0sGBhG6mhHOuWI1hqpcJBBnZpFAPLDD5zwiEv5UG4Yp59wMQlcRlDCi7wfhyYUc8u5GeT8aIXMcag6Fj6bAtnL3s9CHUbUws0zgbGCev0lqvaeA+4Gg30HqgNZANvA37zS+cWaW4Heo2so5tx34X2ArsBM44Jz71N9UIlILqDYU8Ym+H4QXb+qKJcAe4DPnnN6341BzKHzYcR5Tx7OKmVki8DZwt3PuoN95aiszGwLscc4t9DtLHREJ9ASec86dDeQDmquiiphZPUJH81sBGUCCmd3gbyoRqQVUG4r4QN8Pwo9zrsw51wNoBvQ1M53OeRxqDoWPLKB5ufvN0GkJVco7J/Vt4FXn3Dt+56nlBgBXmdlmQsPiLzKzV/yNVKtlAVnljppMJtQskqpxCbDJOZftnCsB3gHO9TmTiIQ/1YYi1UzfD8KbN43CdGCwz1FqJDWHwsd8oJ2ZtfImjx0OTPE5U63lTVw2HljtnHvC7zy1nXPuAedcM+dcJqG/2/9yzmlkRRVxzu0CtplZe++hiwFNplh1tgL9zCze+2y5mNA8BSIiZ0K1oUg10veD8GRmDc0s1bsdR+ig3Rp/U9VMag6FCedcKXAnMJXQl4o3nXMr/U1Vqw0ARhAawbLE+7nc71Ailegu4FUzWwb0AB71OU+t5Y3QmgwsApYT+r/3BV9DiUjYU20YvsxsEjAHaG9mWWZ2i9+ZpEL0/SA8pQPTvJp3PqE5hz70OVONpEvZi4iIiIiIiIjUYRo5JCIiIiIiIiJSh6k5JCIiIiIiIiJSh6k5JCIiIiIiIiJSh6k5JCIiIiIiIiJSh6k5JCIiIiIiIiJSh6k5JFLHmFmZd+nNFWb2lpnFn2L5Byu43c1m1sC7fagysp5kXzeZWcbx9n2cZS8zswVmttrM1pjZ/1ZlNhEREZGTqepazMyeNLO7yz0+1czGlbv/ZzO718wyzGzyCbY13cx6H7t/M8s0sxUnyfBLr95aYWZLzWxkRbKLiP/UHBKpewqdcz2cc12AYuCnp1i+QgVJNbsJyDjVQmbWBXgGuME51xHoAmys2mgiIiIiJ1XVtdhs4FwAM4sAGgCdyz1/LvClc26Hc+7aCmyvos2pnwKXAn29P9sgwL5LcBHxj5pDInXbTKAtgJndYGZfeUeynjezgJk9BsR5j73qLfeemS00s5VmNqqiOzKzhmb2tpnN934GeI8/ZGYTvCNUG81sTLl1/sc7+vSZmU3yjkZdC/QGXvVyxXmL32Vmi8xsuZl18B67H/i9c24NgHOu1Dn3rLftl8zsOTOb5u33fC/HajN76UxeVBEREZEKqopa7Eu85hChptAKIM/M6plZDNARWFx+FJCZxZnZ62a2zMzeAOK8x7+1fyBgZi96+/+0XC32IDDaOXcQwDl3wDk30dvOZjN71MzmeCO6e3ojmjZ4TSUR8ZmaQyJ1lJlFApcBy82sIzAMGOCc6wGUAdc75/6Tfx/dut5b9SfOuV6EGjRjzCytgrt8GnjSOdcHGAqMK/dcB+D7QF/gN2YW5Q1lHgqcDVzj7Q/n3GRggZevh3Ou0NvGXudcT+A54JfeY12AhSfJVA+4CLgH+AB4klAR1dXMelTwzyUiIiLynVVVLeac2wGUmlkLQk2iOcA8oL+3zjLnXPExcX4GFDjnugG/B3p52zre/tsBY51znYFcYKiZJQFJzrkNJ/kjb3PO9SfUEHsJuBboBzxc0ddMRKpOpN8BRKTaxZnZEu/2TGA8MIpQETDfzCB0tGjPCdYfY2Y/9G43J1Qg5FRgv5cAnbztAyR7hQTAR865IqDIzPYAjYGBwPtHmj9m9sEptv+O93shoWZSRXzgnHNmthzY7Zxb7u1rJZAJLDnZyiIiIiKnoTpqsSOjh84FngCaercPEDrt7FiDgL8AOOeWmdmyk+Tf5Jw7kn8hoZrJAHeSdQCmeL+XA4nOuTxCI5oOm1mqcy73FOuLSBVSc0ik7in0jkgdZaEqZKJz7oGTrWhmFxBq8vR3zhWY2XQgtoL7jfDWKyz/oFcAFZV7qIzQZ9N3PUf9yDaOrA+wklChtfQU6wSPyRBEn48iIiJSNaqjFjsy71BXQqeVbQN+ARwEJpxg86dq7hxxbN0W55w7aGb5ZtbaOXei+R1Vd4nUYDqtTEQAPgeuNbNGAGZW38xaes+VmFmUdzsF2O8VIx0IDQWuqE+BO4/cqcBpW7OAK80s1swSgSvKPZcHJB1/tW94HHjQzM7y9hlhZvd+h8wiIiIi1aGya7EvgSHAPudcmXNuH5BK6NSyOcdZfgZwvbfvLkC3cs+V3//J/AEYa2bJ3naSv8v8lCLiLzWHRATn3Crgv4FPvWHEnwHp3tMvAMu8SQj/AUR6yzwCzD3BJuPNLKvcz73AGKC3N9HhKk5xZQ7n3HxCw4+XEjplbAGhodAQOk/9r8dMSH28bSwD7gYmmdlqQkfO0k+0vIiIiIgfqqAWW07oKmVzj3nsgHNu73GWfw5I9LZ7P/BVuefK7/9kngOmETo1bgXwBVBwinVEpIYw5yo6elBEpHqZWaJz7pCZxRM6ojXKObfI71wiIiIiIiK1ic7tFJGa7AUz60ToXPqJagyJiIiIiIhUPo0cEhERERERERGpwzTnkIiIiIiIiIhIHabmkIiIiIiIiIhIHabmkIiIiIiIiIhIHabmkIiIiIiIiIhIHabmkIiIiIiIiIhIHabmkIiIiIiIiIhIHfb/QPBNOTWS2YgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the dataset and analysing it\n",
    "fig,ax=plt.subplots(2,2,figsize=(20,10))\n",
    "plt1=sns.distplot(df[\"SepalLengthCm\"],ax=ax[0,0])\n",
    "plt2=sns.distplot(df[\"SepalWidthCm\"],ax=ax[0,1])\n",
    "plt3=sns.distplot(df[\"PetalLengthCm\"],ax=ax[1,0])\n",
    "plt4=sns.distplot(df[\"PetalWidthCm\"],ax=ax[1,1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Sepal Length and Sepal width has the data distribution looks normal so we might not be able to draw some references but the Petal length and Petal width has the variation in the data distribution hence it might be helping us in drawing some references "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#closing checking the variables \n",
    "fig=plt.figure(figsize=(15,8))\n",
    "sns.countplot(df[\"SepalLengthCm\"])\n",
    "plt.title(\"Sepal length \")\n",
    "plt.xlabel(\" Sepal length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#closing checking the variables \n",
    "fig=plt.figure(figsize=(10,8))\n",
    "sns.countplot(df[\"SepalWidthCm\"])\n",
    "plt.title(\"Sepal width \")\n",
    "plt.xlabel(\" Sepal width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can notice that most of the flower Sepal width fall in the range  2.5 to 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#closing checking the variables \n",
    "fig=plt.figure(figsize=(10,8))\n",
    "sns.countplot(df[\"PetalLengthCm\"])\n",
    "plt.title(\"Petal Length \")\n",
    "plt.xlabel(\"Petal Length\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#closing checking the variables \n",
    "fig=plt.figure(figsize=(10,8))\n",
    "sns.countplot(df[\"PetalWidthCm\"])\n",
    "plt.title(\"Petal Width\")\n",
    "plt.xlabel(\"Petal Width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- many flower have the petal width as 0.2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bivariate analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the scatter plot of the features with the hue as species \n",
    "fig = plt.figure(figsize = (10,8))\n",
    "sns.scatterplot(x=\"SepalLengthCm\",y=\"SepalWidthCm\",data=df,hue=\"Species\")\n",
    "plt.xlabel('Sepal length')\n",
    "plt.ylabel(\"Sepal width\")\n",
    "plt.title(\"Sepal length vs Sepal width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the scatter plot of the features with the hue as species \n",
    "fig = plt.figure(figsize = (8,8))\n",
    "sns.scatterplot(x=\"PetalLengthCm\",y=\"PetalWidthCm\",data=df,hue=\"Species\")\n",
    "plt.xlabel('Petal length')\n",
    "plt.ylabel(\"Petal width\")\n",
    "plt.title(\"Petal length vs Petal width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can notice we have the datset with good variance .It is definitlely going to give us good clusters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1003.5x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking thre pairplot of the dataset\n",
    "sns.pairplot(df,hue=\"Species\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the correaltion of the variables \n",
    "plt.figure(figsize=(10,10))\n",
    "sns.heatmap(df.corr(),annot=True,cmap=\"RdYlBu\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The variables are highly correlated "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers Treatment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,2, figsize = (20,10))\n",
    "plt1 = sns.boxplot(df['SepalLengthCm'], ax = axs[0,0])\n",
    "plt2 = sns.boxplot(df['SepalWidthCm'], ax = axs[0,1])\n",
    "plt3 = sns.boxplot(df['PetalLengthCm'], ax = axs[1,0])\n",
    "plt1 = sns.boxplot(df['PetalWidthCm'], ax = axs[1,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We see some outliers in the sepal Width we can cap them instead of dropping the outliers\n",
    "- We can perform the soft range that is 1-99\n",
    "- **But we have very less data we shall not touch the outliers and proceed ** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hopkins Statistics:\n",
    "The Hopkins statistic, is a statistic which gives a value which indicates the cluster tendency, in other words: how well the data can be clustered.\n",
    "\n",
    "- If the value is between {0.01, ...,0.3}, the data is regularly spaced.\n",
    "\n",
    "- If the value is around 0.5, it is random.\n",
    "\n",
    "- If the value is between {0.7, ..., 0.99}, it has a high tendency to cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from random import sample\n",
    "from numpy.random import uniform\n",
    "import numpy as np\n",
    "from math import isnan\n",
    " \n",
    "def hopkins(X):\n",
    "    d = X.shape[1]\n",
    "    #d = len(vars) # columns\n",
    "    n = len(X) # rows\n",
    "    m = int(0.1 * n) \n",
    "    nbrs = NearestNeighbors(n_neighbors=1).fit(X.values)\n",
    " \n",
    "    rand_X = sample(range(0, n, 1), m)\n",
    " \n",
    "    ujd = []\n",
    "    wjd = []\n",
    "    for j in range(0, m):\n",
    "        u_dist, _ = nbrs.kneighbors(uniform(np.amin(X,axis=0),np.amax(X,axis=0),d).reshape(1, -1), 2, return_distance=True)\n",
    "        ujd.append(u_dist[0][1])\n",
    "        w_dist, _ = nbrs.kneighbors(X.iloc[rand_X[j]].values.reshape(1, -1), 2, return_distance=True)\n",
    "        wjd.append(w_dist[0][1])\n",
    " \n",
    "    H = sum(ujd) / (sum(ujd) + sum(wjd))\n",
    "    if isnan(H):\n",
    "        print(ujd, wjd)\n",
    "        H = 0\n",
    " \n",
    "    return H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7174316893779327"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the hopkins on my dataset after removing the Species feature\n",
    "hopkins(df.drop(\"Species\",axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2. Rescaling\n",
    "rfm_df=df[[\"SepalWidthCm\",\"SepalLengthCm\",\"PetalWidthCm\",\"PetalLengthCm\"]]\n",
    "\n",
    "#instantiate\n",
    "scaler=StandardScaler()\n",
    "\n",
    "#fit_transform\n",
    "rfm_scaled=scaler.fit_transform(rfm_df)\n",
    "rfm_scaled.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        -1.34127240e+00],\n",
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       "        -1.34127240e+00],\n",
       "       [ 3.37848329e-01, -1.38535265e+00, -1.31297673e+00,\n",
       "        -1.39813811e+00],\n",
       "       [ 1.06445364e-01, -1.50652052e+00, -1.31297673e+00,\n",
       "        -1.28440670e+00],\n",
       "       [ 1.26346019e+00, -1.02184904e+00, -1.31297673e+00,\n",
       "        -1.34127240e+00],\n",
       "       [ 1.95766909e+00, -5.37177559e-01, -1.05003079e+00,\n",
       "        -1.17067529e+00],\n",
       "       [ 8.00654259e-01, -1.50652052e+00, -1.18150376e+00,\n",
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       "       [ 8.00654259e-01, -1.02184904e+00, -1.31297673e+00,\n",
       "        -1.28440670e+00],\n",
       "       [-3.56360566e-01, -1.74885626e+00, -1.31297673e+00,\n",
       "        -1.34127240e+00],\n",
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       "       [ 1.49486315e+00, -5.37177559e-01, -1.31297673e+00,\n",
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       "       [-1.24957601e-01, -1.26418478e+00, -1.44444970e+00,\n",
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       "       [-1.24957601e-01, -1.87002413e+00, -1.44444970e+00,\n",
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       "        -1.39813811e+00],\n",
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       "       [ 8.00654259e-01, -5.37177559e-01, -1.05003079e+00,\n",
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       "       [ 2.42047502e+00, -7.79513300e-01, -1.44444970e+00,\n",
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       "       [ 2.65187798e+00, -4.16009689e-01, -1.31297673e+00,\n",
       "        -1.34127240e+00],\n",
       "       [ 1.06445364e-01, -1.14301691e+00, -1.44444970e+00,\n",
       "        -1.28440670e+00],\n",
       "       [ 3.37848329e-01, -1.02184904e+00, -1.31297673e+00,\n",
       "        -1.45500381e+00],\n",
       "       [ 1.03205722e+00, -4.16009689e-01, -1.31297673e+00,\n",
       "        -1.39813811e+00],\n",
       "       [ 1.06445364e-01, -1.14301691e+00, -1.44444970e+00,\n",
       "        -1.28440670e+00],\n",
       "       [-1.24957601e-01, -1.74885626e+00, -1.31297673e+00,\n",
       "        -1.39813811e+00],\n",
       "       [ 8.00654259e-01, -9.00681170e-01, -1.31297673e+00,\n",
       "        -1.28440670e+00],\n",
       "       [ 1.03205722e+00, -1.02184904e+00, -1.18150376e+00,\n",
       "        -1.39813811e+00],\n",
       "       [-1.74477836e+00, -1.62768839e+00, -1.18150376e+00,\n",
       "        -1.39813811e+00],\n",
       "       [ 3.37848329e-01, -1.74885626e+00, -1.31297673e+00,\n",
       "        -1.39813811e+00],\n",
       "       [ 1.03205722e+00, -1.02184904e+00, -7.87084847e-01,\n",
       "        -1.22754100e+00],\n",
       "       [ 1.72626612e+00, -9.00681170e-01, -1.05003079e+00,\n",
       "        -1.05694388e+00],\n",
       "       [-1.24957601e-01, -1.26418478e+00, -1.18150376e+00,\n",
       "        -1.34127240e+00],\n",
       "       [ 1.72626612e+00, -9.00681170e-01, -1.31297673e+00,\n",
       "        -1.22754100e+00],\n",
       "       [ 3.37848329e-01, -1.50652052e+00, -1.31297673e+00,\n",
       "        -1.34127240e+00],\n",
       "       [ 1.49486315e+00, -6.58345429e-01, -1.31297673e+00,\n",
       "        -1.28440670e+00],\n",
       "       [ 5.69251294e-01, -1.02184904e+00, -1.31297673e+00,\n",
       "        -1.34127240e+00],\n",
       "       [ 3.37848329e-01,  1.40150837e+00,  2.64698913e-01,\n",
       "         5.35295827e-01],\n",
       "       [ 3.37848329e-01,  6.74501145e-01,  3.96171883e-01,\n",
       "         4.21564419e-01],\n",
       "       [ 1.06445364e-01,  1.28034050e+00,  3.96171883e-01,\n",
       "         6.49027235e-01],\n",
       "       [-1.74477836e+00, -4.16009689e-01,  1.33225943e-01,\n",
       "         1.37235899e-01],\n",
       "       [-5.87763531e-01,  7.95669016e-01,  3.96171883e-01,\n",
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       "       [-5.87763531e-01, -1.73673948e-01,  1.33225943e-01,\n",
       "         4.21564419e-01],\n",
       "       [ 5.69251294e-01,  5.53333275e-01,  5.27644853e-01,\n",
       "         5.35295827e-01],\n",
       "       [-1.51337539e+00, -1.14301691e+00, -2.61192967e-01,\n",
       "        -2.60824029e-01],\n",
       "       [-3.56360566e-01,  9.16836886e-01,  1.33225943e-01,\n",
       "         4.78430123e-01],\n",
       "       [-8.19166497e-01, -7.79513300e-01,  2.64698913e-01,\n",
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       "       [ 1.06445364e-01,  1.03800476e+00,  2.64698913e-01,\n",
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       "       [-1.97618132e+00,  4.32165405e-01,  3.96171883e-01,\n",
       "         4.21564419e-01],\n",
       "       [-1.28197243e+00, -2.94841818e-01, -1.29719997e-01,\n",
       "         8.03701950e-02],\n",
       "       [ 3.37848329e-01,  6.86617933e-02,  7.90590793e-01,\n",
       "         5.92161531e-01],\n",
       "       [-5.87763531e-01,  3.10997534e-01,  1.33225943e-01,\n",
       "         1.37235899e-01],\n",
       "       [-1.28197243e+00,  5.53333275e-01,  3.96171883e-01,\n",
       "         6.49027235e-01],\n",
       "       [-5.87763531e-01,  3.10997534e-01,  1.75297293e-03,\n",
       "         5.35295827e-01],\n",
       "       [-3.56360566e-01,  6.74501145e-01,  1.33225943e-01,\n",
       "         3.07833011e-01],\n",
       "       [-1.24957601e-01,  9.16836886e-01,  2.64698913e-01,\n",
       "         3.64698715e-01],\n",
       "       [-5.87763531e-01,  1.15917263e+00,  2.64698913e-01,\n",
       "         5.92161531e-01],\n",
       "       [-1.24957601e-01,  1.03800476e+00,  6.59117823e-01,\n",
       "         7.05892939e-01],\n",
       "       [-3.56360566e-01,  1.89829664e-01,  3.96171883e-01,\n",
       "         4.21564419e-01],\n",
       "       [-1.05056946e+00, -1.73673948e-01, -2.61192967e-01,\n",
       "        -1.47092621e-01],\n",
       "       [-1.51337539e+00, -4.16009689e-01, -1.29719997e-01,\n",
       "         2.35044910e-02],\n",
       "       [-1.51337539e+00, -4.16009689e-01, -2.61192967e-01,\n",
       "        -3.33612130e-02],\n",
       "       [-8.19166497e-01, -5.25060772e-02,  1.75297293e-03,\n",
       "         8.03701950e-02],\n",
       "       [-8.19166497e-01,  1.89829664e-01,  5.27644853e-01,\n",
       "         7.62758643e-01],\n",
       "       [-1.24957601e-01, -5.37177559e-01,  3.96171883e-01,\n",
       "         4.21564419e-01],\n",
       "       [ 8.00654259e-01,  1.89829664e-01,  5.27644853e-01,\n",
       "         4.21564419e-01],\n",
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       "         5.35295827e-01],\n",
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       "         3.64698715e-01],\n",
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       "         1.94101603e-01],\n",
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       "         1.37235899e-01],\n",
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       "         3.64698715e-01],\n",
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       "         4.78430123e-01],\n",
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       "       [-5.87763531e-01, -1.73673948e-01,  1.33225943e-01,\n",
       "         1.94101603e-01],\n",
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       "         1.27454998e+00],\n",
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       "         7.62758643e-01],\n",
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       "         1.44514709e+00],\n",
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       "         1.16081857e+00],\n",
       "       [ 1.26346019e+00,  1.64384411e+00,  1.71090158e+00,\n",
       "         1.33141568e+00],\n",
       "       [ 3.37848329e-01,  7.95669016e-01,  1.05353673e+00,\n",
       "         7.62758643e-01],\n",
       "       [-8.19166497e-01,  6.74501145e-01,  9.22063763e-01,\n",
       "         8.76490051e-01],\n",
       "       [-1.24957601e-01,  1.15917263e+00,  1.18500970e+00,\n",
       "         9.90221459e-01],\n",
       "       [-1.28197243e+00, -1.73673948e-01,  1.05353673e+00,\n",
       "         7.05892939e-01],\n",
       "       [-5.87763531e-01, -5.25060772e-02,  1.57942861e+00,\n",
       "         7.62758643e-01],\n",
       "       [ 3.37848329e-01,  6.74501145e-01,  1.44795564e+00,\n",
       "         8.76490051e-01],\n",
       "       [-1.24957601e-01,  7.95669016e-01,  7.90590793e-01,\n",
       "         9.90221459e-01],\n",
       "       [ 1.72626612e+00,  2.24968346e+00,  1.31648267e+00,\n",
       "         1.67260991e+00],\n",
       "       [-1.05056946e+00,  2.24968346e+00,  1.44795564e+00,\n",
       "         1.78634131e+00],\n",
       "       [-1.97618132e+00,  1.89829664e-01,  3.96171883e-01,\n",
       "         7.05892939e-01],\n",
       "       [ 3.37848329e-01,  1.28034050e+00,  1.44795564e+00,\n",
       "         1.10395287e+00],\n",
       "       [-5.87763531e-01, -2.94841818e-01,  1.05353673e+00,\n",
       "         6.49027235e-01],\n",
       "       [-5.87763531e-01,  2.24968346e+00,  1.05353673e+00,\n",
       "         1.67260991e+00],\n",
       "       [-8.19166497e-01,  5.53333275e-01,  7.90590793e-01,\n",
       "         6.49027235e-01],\n",
       "       [ 5.69251294e-01,  1.03800476e+00,  1.18500970e+00,\n",
       "         1.10395287e+00],\n",
       "       [ 3.37848329e-01,  1.64384411e+00,  7.90590793e-01,\n",
       "         1.27454998e+00],\n",
       "       [-5.87763531e-01,  4.32165405e-01,  7.90590793e-01,\n",
       "         5.92161531e-01],\n",
       "       [-1.24957601e-01,  3.10997534e-01,  7.90590793e-01,\n",
       "         6.49027235e-01],\n",
       "       [-5.87763531e-01,  6.74501145e-01,  1.18500970e+00,\n",
       "         1.04708716e+00],\n",
       "       [-1.24957601e-01,  1.64384411e+00,  5.27644853e-01,\n",
       "         1.16081857e+00],\n",
       "       [-5.87763531e-01,  1.88617985e+00,  9.22063763e-01,\n",
       "         1.33141568e+00],\n",
       "       [ 1.72626612e+00,  2.49201920e+00,  1.05353673e+00,\n",
       "         1.50201279e+00],\n",
       "       [-5.87763531e-01,  6.74501145e-01,  1.31648267e+00,\n",
       "         1.04708716e+00],\n",
       "       [-5.87763531e-01,  5.53333275e-01,  3.96171883e-01,\n",
       "         7.62758643e-01],\n",
       "       [-1.05056946e+00,  3.10997534e-01,  2.64698913e-01,\n",
       "         1.04708716e+00],\n",
       "       [-1.24957601e-01,  2.24968346e+00,  1.44795564e+00,\n",
       "         1.33141568e+00],\n",
       "       [ 8.00654259e-01,  5.53333275e-01,  1.57942861e+00,\n",
       "         1.04708716e+00],\n",
       "       [ 1.06445364e-01,  6.74501145e-01,  7.90590793e-01,\n",
       "         9.90221459e-01],\n",
       "       [-1.24957601e-01,  1.89829664e-01,  7.90590793e-01,\n",
       "         5.92161531e-01],\n",
       "       [ 1.06445364e-01,  1.28034050e+00,  1.18500970e+00,\n",
       "         9.33355755e-01],\n",
       "       [ 1.06445364e-01,  1.03800476e+00,  1.57942861e+00,\n",
       "         1.04708716e+00],\n",
       "       [ 1.06445364e-01,  1.28034050e+00,  1.44795564e+00,\n",
       "         7.62758643e-01],\n",
       "       [-8.19166497e-01, -5.25060772e-02,  9.22063763e-01,\n",
       "         7.62758643e-01],\n",
       "       [ 3.37848329e-01,  1.15917263e+00,  1.44795564e+00,\n",
       "         1.21768427e+00],\n",
       "       [ 5.69251294e-01,  1.03800476e+00,  1.71090158e+00,\n",
       "         1.10395287e+00],\n",
       "       [-1.24957601e-01,  1.03800476e+00,  1.44795564e+00,\n",
       "         8.19624347e-01],\n",
       "       [-1.28197243e+00,  5.53333275e-01,  9.22063763e-01,\n",
       "         7.05892939e-01],\n",
       "       [-1.24957601e-01,  7.95669016e-01,  1.05353673e+00,\n",
       "         8.19624347e-01],\n",
       "       [ 8.00654259e-01,  4.32165405e-01,  1.44795564e+00,\n",
       "         9.33355755e-01],\n",
       "       [-1.24957601e-01,  6.86617933e-02,  7.90590793e-01,\n",
       "         7.62758643e-01]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#since its scaled the result is the numpy array\n",
    "rfm_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.032057</td>\n",
       "      <td>-0.900681</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.385353</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.398138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.506521</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.284407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.021849</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>-0.124958</td>\n",
       "      <td>1.038005</td>\n",
       "      <td>1.447956</td>\n",
       "      <td>0.819624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>-1.281972</td>\n",
       "      <td>0.553333</td>\n",
       "      <td>0.922064</td>\n",
       "      <td>0.705893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>-0.124958</td>\n",
       "      <td>0.795669</td>\n",
       "      <td>1.053537</td>\n",
       "      <td>0.819624</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.800654</td>\n",
       "      <td>0.432165</td>\n",
       "      <td>1.447956</td>\n",
       "      <td>0.933356</td>\n",
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       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>-0.124958</td>\n",
       "      <td>0.068662</td>\n",
       "      <td>0.790591</td>\n",
       "      <td>0.762759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3\n",
       "0    1.032057 -0.900681 -1.312977 -1.341272\n",
       "1   -0.124958 -1.143017 -1.312977 -1.341272\n",
       "2    0.337848 -1.385353 -1.312977 -1.398138\n",
       "3    0.106445 -1.506521 -1.312977 -1.284407\n",
       "4    1.263460 -1.021849 -1.312977 -1.341272\n",
       "..        ...       ...       ...       ...\n",
       "145 -0.124958  1.038005  1.447956  0.819624\n",
       "146 -1.281972  0.553333  0.922064  0.705893\n",
       "147 -0.124958  0.795669  1.053537  0.819624\n",
       "148  0.800654  0.432165  1.447956  0.933356\n",
       "149 -0.124958  0.068662  0.790591  0.762759\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#converting to dataframe\n",
    "rfm_scaled=pd.DataFrame(rfm_scaled)\n",
    "rfm_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.032057</td>\n",
       "      <td>-0.900681</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.124958</td>\n",
       "      <td>-1.143017</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.337848</td>\n",
       "      <td>-1.385353</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.398138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.106445</td>\n",
       "      <td>-1.506521</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.284407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.263460</td>\n",
       "      <td>-1.021849</td>\n",
       "      <td>-1.312977</td>\n",
       "      <td>-1.341272</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SepalWidthCm  SepalLengthCm  PetalWidthCm  PetalLengthCm\n",
       "0      1.032057      -0.900681     -1.312977      -1.341272\n",
       "1     -0.124958      -1.143017     -1.312977      -1.341272\n",
       "2      0.337848      -1.385353     -1.312977      -1.398138\n",
       "3      0.106445      -1.506521     -1.312977      -1.284407\n",
       "4      1.263460      -1.021849     -1.312977      -1.341272"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying the feature name\n",
    "rfm_scaled.columns=[\"SepalWidthCm\",\"SepalLengthCm\",\"PetalWidthCm\",\"PetalLengthCm\"]\n",
    "rfm_scaled.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding the optimal number of clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[223.73200573676345,\n",
       " 141.15417813388652,\n",
       " 114.36281520484567,\n",
       " 91.12699991626465,\n",
       " 80.8137969399017,\n",
       " 72.54899742311954,\n",
       " 63.33628304292468]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#elbow-curve/ssd\n",
    "ssd=[]\n",
    "range_n_clusters=[2,3,4,5,6,7,8]\n",
    "for num_clusters in range_n_clusters:\n",
    "    kmeans=KMeans(n_clusters=num_clusters,max_iter=50)\n",
    "    kmeans.fit(rfm_scaled)\n",
    "    \n",
    "    ssd.append(kmeans.inertia_)\n",
    "ssd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21a621887c8>]"
      ]
     },
     "execution_count": 71,
     "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": [
    "#plot the ssd\n",
    "plt.plot(ssd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference** :\n",
    "\n",
    "Looking at the above elbow curve it looks good to proceed with 3 clusters.\n",
    "Lets check the other technique to find the optimal cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Silhouette Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Silhouette score=p-q/max(p,q)\n",
    "\n",
    "p is the mean distance to the point in the nearest cluster that a data point is not a part of.\n",
    "\n",
    "q is the mean intra-cluster distance to all the points in its own cluster.\n",
    "\n",
    "**The value of the silhouette score range lies between -1 to 1.**\n",
    "- A score closer to 1 indicates that the datapoint is very similar to other data points in the cluster\n",
    "- A score closer to -1 indicates the datapoints is not similar to the data in its cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For n_clusters=2,the silhouetee score is 0.580184463257396\n",
      "For n_clusters=3,the silhouetee score is 0.4589717867018717\n",
      "For n_clusters=4,the silhouetee score is 0.38912159486904757\n",
      "For n_clusters=5,the silhouetee score is 0.34805236242232845\n",
      "For n_clusters=6,the silhouetee score is 0.3408911516034541\n",
      "For n_clusters=7,the silhouetee score is 0.32869217573090237\n",
      "For n_clusters=8,the silhouetee score is 0.343298692177904\n"
     ]
    }
   ],
   "source": [
    "#silhoette analysis\n",
    "range_n_clusters=[2,3,4,5,6,7,8]\n",
    "for num_clusters in range_n_clusters:\n",
    "    #initialising k means\n",
    "    kmeans=KMeans(n_clusters=num_clusters,max_iter=50)\n",
    "    kmeans.fit(rfm_scaled)\n",
    "    \n",
    "    \n",
    "    cluster_labels=kmeans.labels_\n",
    "    \n",
    "    #silhouette score\n",
    "    silhouette_avg=silhouette_score(rfm_scaled,cluster_labels)\n",
    "    print(\"For n_clusters={0},the silhouetee score is {1}\".format(num_clusters,silhouette_avg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#plot the silhoutte score\n",
    "ss=[]\n",
    "for k in range(2,11):\n",
    "    kmeans=KMeans(n_clusters=k,random_state=100).fit(rfm_scaled)\n",
    "    ss.append([k,silhouette_score(rfm_scaled,kmeans.labels_)])\n",
    "temp=pd.DataFrame(ss)\n",
    "plt.plot(temp[0],temp[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We will go ahead with the k=3 as shown int the elbow curve and also k=3 in the silhouette score confirms the good cluster "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=50,\n",
       "       n_clusters=3, n_init=10, n_jobs=None, precompute_distances='auto',\n",
       "       random_state=100, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#final model with k=3\n",
    "kmeans=KMeans(n_clusters=3,max_iter=50,random_state=100)\n",
    "kmeans.fit(rfm_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0,\n",
       "       2, 2, 2, 2, 0, 2, 2, 2, 2, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 0, 0, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 0, 0, 0, 0, 2, 0, 0, 0,\n",
       "       0, 0, 0, 2, 2, 0, 0, 0, 0, 2, 0, 2, 0, 2, 0, 0, 2, 0, 0, 0, 0, 0,\n",
       "       0, 2, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 2])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make the copy of the original data \n",
    "df_new=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species  \\\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa   \n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa   \n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa   \n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa   \n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa   \n",
       "\n",
       "   label  \n",
       "0      1  \n",
       "1      1  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding  the cluster labels in our  dataset\n",
    "\n",
    "df_new['label']=kmeans.labels_\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    53\n",
       "1    50\n",
       "0    47\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check the counts in each clusters \n",
    "df_new.label.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing the clusters formed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets plot the cluster plot to check the clusters formed\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x='SepalLengthCm',y='SepalWidthCm',hue='label',data=df_new,palette='Set1')\n",
    "plt.title(\"Sepal length vs Sepal Width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets plot the cluster plot to check the clusters formed\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.scatterplot(x='PetalLengthCm',y='PetalWidthCm',hue='label',data=df_new,palette='Set1')\n",
    "plt.title(\"Petal length vs Petal Width\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>54</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>56</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>58</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>59</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>60</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>62</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.9</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>68</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>72</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89</td>\n",
       "      <td>5.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>91</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>92</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>93</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>94</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>95</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>96</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>97</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>99</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>100</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>102</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>107</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>114</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>115</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>120</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>124</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>127</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>134</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>135</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>139</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>143</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>147</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>150</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "53    54            5.5           2.3            4.0           1.3   \n",
       "54    55            6.5           2.8            4.6           1.5   \n",
       "55    56            5.7           2.8            4.5           1.3   \n",
       "57    58            4.9           2.4            3.3           1.0   \n",
       "58    59            6.6           2.9            4.6           1.3   \n",
       "59    60            5.2           2.7            3.9           1.4   \n",
       "60    61            5.0           2.0            3.5           1.0   \n",
       "61    62            5.9           3.0            4.2           1.5   \n",
       "62    63            6.0           2.2            4.0           1.0   \n",
       "63    64            6.1           2.9            4.7           1.4   \n",
       "64    65            5.6           2.9            3.6           1.3   \n",
       "66    67            5.6           3.0            4.5           1.5   \n",
       "67    68            5.8           2.7            4.1           1.0   \n",
       "68    69            6.2           2.2            4.5           1.5   \n",
       "69    70            5.6           2.5            3.9           1.1   \n",
       "71    72            6.1           2.8            4.0           1.3   \n",
       "72    73            6.3           2.5            4.9           1.5   \n",
       "73    74            6.1           2.8            4.7           1.2   \n",
       "74    75            6.4           2.9            4.3           1.3   \n",
       "78    79            6.0           2.9            4.5           1.5   \n",
       "79    80            5.7           2.6            3.5           1.0   \n",
       "80    81            5.5           2.4            3.8           1.1   \n",
       "81    82            5.5           2.4            3.7           1.0   \n",
       "82    83            5.8           2.7            3.9           1.2   \n",
       "83    84            6.0           2.7            5.1           1.6   \n",
       "84    85            5.4           3.0            4.5           1.5   \n",
       "87    88            6.3           2.3            4.4           1.3   \n",
       "88    89            5.6           3.0            4.1           1.3   \n",
       "89    90            5.5           2.5            4.0           1.3   \n",
       "90    91            5.5           2.6            4.4           1.2   \n",
       "91    92            6.1           3.0            4.6           1.4   \n",
       "92    93            5.8           2.6            4.0           1.2   \n",
       "93    94            5.0           2.3            3.3           1.0   \n",
       "94    95            5.6           2.7            4.2           1.3   \n",
       "95    96            5.7           3.0            4.2           1.2   \n",
       "96    97            5.7           2.9            4.2           1.3   \n",
       "97    98            6.2           2.9            4.3           1.3   \n",
       "98    99            5.1           2.5            3.0           1.1   \n",
       "99   100            5.7           2.8            4.1           1.3   \n",
       "101  102            5.8           2.7            5.1           1.9   \n",
       "106  107            4.9           2.5            4.5           1.7   \n",
       "113  114            5.7           2.5            5.0           2.0   \n",
       "114  115            5.8           2.8            5.1           2.4   \n",
       "119  120            6.0           2.2            5.0           1.5   \n",
       "121  122            5.6           2.8            4.9           2.0   \n",
       "123  124            6.3           2.7            4.9           1.8   \n",
       "126  127            6.2           2.8            4.8           1.8   \n",
       "133  134            6.3           2.8            5.1           1.5   \n",
       "134  135            6.1           2.6            5.6           1.4   \n",
       "138  139            6.0           3.0            4.8           1.8   \n",
       "142  143            5.8           2.7            5.1           1.9   \n",
       "146  147            6.3           2.5            5.0           1.9   \n",
       "149  150            5.9           3.0            5.1           1.8   \n",
       "\n",
       "             Species  label  \n",
       "53   Iris-versicolor      2  \n",
       "54   Iris-versicolor      2  \n",
       "55   Iris-versicolor      2  \n",
       "57   Iris-versicolor      2  \n",
       "58   Iris-versicolor      2  \n",
       "59   Iris-versicolor      2  \n",
       "60   Iris-versicolor      2  \n",
       "61   Iris-versicolor      2  \n",
       "62   Iris-versicolor      2  \n",
       "63   Iris-versicolor      2  \n",
       "64   Iris-versicolor      2  \n",
       "66   Iris-versicolor      2  \n",
       "67   Iris-versicolor      2  \n",
       "68   Iris-versicolor      2  \n",
       "69   Iris-versicolor      2  \n",
       "71   Iris-versicolor      2  \n",
       "72   Iris-versicolor      2  \n",
       "73   Iris-versicolor      2  \n",
       "74   Iris-versicolor      2  \n",
       "78   Iris-versicolor      2  \n",
       "79   Iris-versicolor      2  \n",
       "80   Iris-versicolor      2  \n",
       "81   Iris-versicolor      2  \n",
       "82   Iris-versicolor      2  \n",
       "83   Iris-versicolor      2  \n",
       "84   Iris-versicolor      2  \n",
       "87   Iris-versicolor      2  \n",
       "88   Iris-versicolor      2  \n",
       "89   Iris-versicolor      2  \n",
       "90   Iris-versicolor      2  \n",
       "91   Iris-versicolor      2  \n",
       "92   Iris-versicolor      2  \n",
       "93   Iris-versicolor      2  \n",
       "94   Iris-versicolor      2  \n",
       "95   Iris-versicolor      2  \n",
       "96   Iris-versicolor      2  \n",
       "97   Iris-versicolor      2  \n",
       "98   Iris-versicolor      2  \n",
       "99   Iris-versicolor      2  \n",
       "101   Iris-virginica      2  \n",
       "106   Iris-virginica      2  \n",
       "113   Iris-virginica      2  \n",
       "114   Iris-virginica      2  \n",
       "119   Iris-virginica      2  \n",
       "121   Iris-virginica      2  \n",
       "123   Iris-virginica      2  \n",
       "126   Iris-virginica      2  \n",
       "133   Iris-virginica      2  \n",
       "134   Iris-virginica      2  \n",
       "138   Iris-virginica      2  \n",
       "142   Iris-virginica      2  \n",
       "146   Iris-virginica      2  \n",
       "149   Iris-virginica      2  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will filter the dataset based on the clusters \n",
    "df_new[df_new[\"label\"]==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>52</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>53</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>57</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71</td>\n",
       "      <td>5.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>6.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.7</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>101</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>103</td>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>104</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>105</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>106</td>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>108</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>109</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>110</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>111</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>112</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>113</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>116</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>117</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>118</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>119</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>121</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>123</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>125</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>126</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>128</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>129</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>130</td>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>133</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>136</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>137</td>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>138</td>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>140</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>141</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>142</td>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>144</td>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>145</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>146</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>148</td>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>149</td>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "50    51            7.0           3.2            4.7           1.4   \n",
       "51    52            6.4           3.2            4.5           1.5   \n",
       "52    53            6.9           3.1            4.9           1.5   \n",
       "56    57            6.3           3.3            4.7           1.6   \n",
       "65    66            6.7           3.1            4.4           1.4   \n",
       "70    71            5.9           3.2            4.8           1.8   \n",
       "75    76            6.6           3.0            4.4           1.4   \n",
       "76    77            6.8           2.8            4.8           1.4   \n",
       "77    78            6.7           3.0            5.0           1.7   \n",
       "85    86            6.0           3.4            4.5           1.6   \n",
       "86    87            6.7           3.1            4.7           1.5   \n",
       "100  101            6.3           3.3            6.0           2.5   \n",
       "102  103            7.1           3.0            5.9           2.1   \n",
       "103  104            6.3           2.9            5.6           1.8   \n",
       "104  105            6.5           3.0            5.8           2.2   \n",
       "105  106            7.6           3.0            6.6           2.1   \n",
       "107  108            7.3           2.9            6.3           1.8   \n",
       "108  109            6.7           2.5            5.8           1.8   \n",
       "109  110            7.2           3.6            6.1           2.5   \n",
       "110  111            6.5           3.2            5.1           2.0   \n",
       "111  112            6.4           2.7            5.3           1.9   \n",
       "112  113            6.8           3.0            5.5           2.1   \n",
       "115  116            6.4           3.2            5.3           2.3   \n",
       "116  117            6.5           3.0            5.5           1.8   \n",
       "117  118            7.7           3.8            6.7           2.2   \n",
       "118  119            7.7           2.6            6.9           2.3   \n",
       "120  121            6.9           3.2            5.7           2.3   \n",
       "122  123            7.7           2.8            6.7           2.0   \n",
       "124  125            6.7           3.3            5.7           2.1   \n",
       "125  126            7.2           3.2            6.0           1.8   \n",
       "127  128            6.1           3.0            4.9           1.8   \n",
       "128  129            6.4           2.8            5.6           2.1   \n",
       "129  130            7.2           3.0            5.8           1.6   \n",
       "130  131            7.4           2.8            6.1           1.9   \n",
       "131  132            7.9           3.8            6.4           2.0   \n",
       "132  133            6.4           2.8            5.6           2.2   \n",
       "135  136            7.7           3.0            6.1           2.3   \n",
       "136  137            6.3           3.4            5.6           2.4   \n",
       "137  138            6.4           3.1            5.5           1.8   \n",
       "139  140            6.9           3.1            5.4           2.1   \n",
       "140  141            6.7           3.1            5.6           2.4   \n",
       "141  142            6.9           3.1            5.1           2.3   \n",
       "143  144            6.8           3.2            5.9           2.3   \n",
       "144  145            6.7           3.3            5.7           2.5   \n",
       "145  146            6.7           3.0            5.2           2.3   \n",
       "147  148            6.5           3.0            5.2           2.0   \n",
       "148  149            6.2           3.4            5.4           2.3   \n",
       "\n",
       "             Species  label  \n",
       "50   Iris-versicolor      0  \n",
       "51   Iris-versicolor      0  \n",
       "52   Iris-versicolor      0  \n",
       "56   Iris-versicolor      0  \n",
       "65   Iris-versicolor      0  \n",
       "70   Iris-versicolor      0  \n",
       "75   Iris-versicolor      0  \n",
       "76   Iris-versicolor      0  \n",
       "77   Iris-versicolor      0  \n",
       "85   Iris-versicolor      0  \n",
       "86   Iris-versicolor      0  \n",
       "100   Iris-virginica      0  \n",
       "102   Iris-virginica      0  \n",
       "103   Iris-virginica      0  \n",
       "104   Iris-virginica      0  \n",
       "105   Iris-virginica      0  \n",
       "107   Iris-virginica      0  \n",
       "108   Iris-virginica      0  \n",
       "109   Iris-virginica      0  \n",
       "110   Iris-virginica      0  \n",
       "111   Iris-virginica      0  \n",
       "112   Iris-virginica      0  \n",
       "115   Iris-virginica      0  \n",
       "116   Iris-virginica      0  \n",
       "117   Iris-virginica      0  \n",
       "118   Iris-virginica      0  \n",
       "120   Iris-virginica      0  \n",
       "122   Iris-virginica      0  \n",
       "124   Iris-virginica      0  \n",
       "125   Iris-virginica      0  \n",
       "127   Iris-virginica      0  \n",
       "128   Iris-virginica      0  \n",
       "129   Iris-virginica      0  \n",
       "130   Iris-virginica      0  \n",
       "131   Iris-virginica      0  \n",
       "132   Iris-virginica      0  \n",
       "135   Iris-virginica      0  \n",
       "136   Iris-virginica      0  \n",
       "137   Iris-virginica      0  \n",
       "139   Iris-virginica      0  \n",
       "140   Iris-virginica      0  \n",
       "141   Iris-virginica      0  \n",
       "143   Iris-virginica      0  \n",
       "144   Iris-virginica      0  \n",
       "145   Iris-virginica      0  \n",
       "147   Iris-virginica      0  \n",
       "148   Iris-virginica      0  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will filter the dataset based on the clusters \n",
    "df_new[df_new[\"label\"]==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>5.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>5.5</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>5.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species  \\\n",
       "0    1            5.1           3.5            1.4           0.2  Iris-setosa   \n",
       "1    2            4.9           3.0            1.4           0.2  Iris-setosa   \n",
       "2    3            4.7           3.2            1.3           0.2  Iris-setosa   \n",
       "3    4            4.6           3.1            1.5           0.2  Iris-setosa   \n",
       "4    5            5.0           3.6            1.4           0.2  Iris-setosa   \n",
       "5    6            5.4           3.9            1.7           0.4  Iris-setosa   \n",
       "6    7            4.6           3.4            1.4           0.3  Iris-setosa   \n",
       "7    8            5.0           3.4            1.5           0.2  Iris-setosa   \n",
       "8    9            4.4           2.9            1.4           0.2  Iris-setosa   \n",
       "9   10            4.9           3.1            1.5           0.1  Iris-setosa   \n",
       "10  11            5.4           3.7            1.5           0.2  Iris-setosa   \n",
       "11  12            4.8           3.4            1.6           0.2  Iris-setosa   \n",
       "12  13            4.8           3.0            1.4           0.1  Iris-setosa   \n",
       "13  14            4.3           3.0            1.1           0.1  Iris-setosa   \n",
       "14  15            5.8           4.0            1.2           0.2  Iris-setosa   \n",
       "15  16            5.7           4.4            1.5           0.4  Iris-setosa   \n",
       "16  17            5.4           3.9            1.3           0.4  Iris-setosa   \n",
       "17  18            5.1           3.5            1.4           0.3  Iris-setosa   \n",
       "18  19            5.7           3.8            1.7           0.3  Iris-setosa   \n",
       "19  20            5.1           3.8            1.5           0.3  Iris-setosa   \n",
       "20  21            5.4           3.4            1.7           0.2  Iris-setosa   \n",
       "21  22            5.1           3.7            1.5           0.4  Iris-setosa   \n",
       "22  23            4.6           3.6            1.0           0.2  Iris-setosa   \n",
       "23  24            5.1           3.3            1.7           0.5  Iris-setosa   \n",
       "24  25            4.8           3.4            1.9           0.2  Iris-setosa   \n",
       "25  26            5.0           3.0            1.6           0.2  Iris-setosa   \n",
       "26  27            5.0           3.4            1.6           0.4  Iris-setosa   \n",
       "27  28            5.2           3.5            1.5           0.2  Iris-setosa   \n",
       "28  29            5.2           3.4            1.4           0.2  Iris-setosa   \n",
       "29  30            4.7           3.2            1.6           0.2  Iris-setosa   \n",
       "30  31            4.8           3.1            1.6           0.2  Iris-setosa   \n",
       "31  32            5.4           3.4            1.5           0.4  Iris-setosa   \n",
       "32  33            5.2           4.1            1.5           0.1  Iris-setosa   \n",
       "33  34            5.5           4.2            1.4           0.2  Iris-setosa   \n",
       "34  35            4.9           3.1            1.5           0.1  Iris-setosa   \n",
       "35  36            5.0           3.2            1.2           0.2  Iris-setosa   \n",
       "36  37            5.5           3.5            1.3           0.2  Iris-setosa   \n",
       "37  38            4.9           3.1            1.5           0.1  Iris-setosa   \n",
       "38  39            4.4           3.0            1.3           0.2  Iris-setosa   \n",
       "39  40            5.1           3.4            1.5           0.2  Iris-setosa   \n",
       "40  41            5.0           3.5            1.3           0.3  Iris-setosa   \n",
       "41  42            4.5           2.3            1.3           0.3  Iris-setosa   \n",
       "42  43            4.4           3.2            1.3           0.2  Iris-setosa   \n",
       "43  44            5.0           3.5            1.6           0.6  Iris-setosa   \n",
       "44  45            5.1           3.8            1.9           0.4  Iris-setosa   \n",
       "45  46            4.8           3.0            1.4           0.3  Iris-setosa   \n",
       "46  47            5.1           3.8            1.6           0.2  Iris-setosa   \n",
       "47  48            4.6           3.2            1.4           0.2  Iris-setosa   \n",
       "48  49            5.3           3.7            1.5           0.2  Iris-setosa   \n",
       "49  50            5.0           3.3            1.4           0.2  Iris-setosa   \n",
       "\n",
       "    label  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  \n",
       "5       1  \n",
       "6       1  \n",
       "7       1  \n",
       "8       1  \n",
       "9       1  \n",
       "10      1  \n",
       "11      1  \n",
       "12      1  \n",
       "13      1  \n",
       "14      1  \n",
       "15      1  \n",
       "16      1  \n",
       "17      1  \n",
       "18      1  \n",
       "19      1  \n",
       "20      1  \n",
       "21      1  \n",
       "22      1  \n",
       "23      1  \n",
       "24      1  \n",
       "25      1  \n",
       "26      1  \n",
       "27      1  \n",
       "28      1  \n",
       "29      1  \n",
       "30      1  \n",
       "31      1  \n",
       "32      1  \n",
       "33      1  \n",
       "34      1  \n",
       "35      1  \n",
       "36      1  \n",
       "37      1  \n",
       "38      1  \n",
       "39      1  \n",
       "40      1  \n",
       "41      1  \n",
       "42      1  \n",
       "43      1  \n",
       "44      1  \n",
       "45      1  \n",
       "46      1  \n",
       "47      1  \n",
       "48      1  \n",
       "49      1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will filter the dataset based on the clusters \n",
    "df_new[df_new[\"label\"]==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### With the help of  K-mean clustering found the best 3 clusters from the dataset .\n",
    "#### Used  two techiniques to get the optimal clusters \n",
    "- Elbow curve \n",
    "- Silhouette Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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