Python-Data-Machine-Learning/iris/iris_classification_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-11-25T18:46:42.789023Z",
     "start_time": "2024-11-25T18:46:42.780719Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris"
   ],
   "outputs": [],
   "execution_count": 83
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:50:28.730129Z",
     "start_time": "2024-11-25T18:50:28.717075Z"
    }
   },
   "cell_type": "code",
   "source": [
    "iris_data = load_iris()\n",
    "\n",
    "iris = pd.DataFrame(columns= iris_data.feature_names, data= iris_data.data)\n",
    "target_df = pd.DataFrame(columns= ['species'], data= iris_data.target)\n",
    "iris = pd.concat([iris, target_df], axis= 1)\n",
    "\n",
    "iris.head()"
   ],
   "id": "f1735038002194a9",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   species  \n",
       "0        0  \n",
       "1        0  \n",
       "2        0  \n",
       "3        0  \n",
       "4        0  "
      ],
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:50:21.262654Z",
     "start_time": "2024-11-25T18:50:21.242783Z"
    }
   },
   "cell_type": "code",
   "source": [
    "iris.info()\n",
    "iris.describe()"
   ],
   "id": "f8ca5caf66f9df7c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   sepal length (cm)  150 non-null    float64\n",
      " 1   sepal width (cm)   150 non-null    float64\n",
      " 2   petal length (cm)  150 non-null    float64\n",
      " 3   petal width (cm)   150 non-null    float64\n",
      " 4   species            150 non-null    int64  \n",
      "dtypes: float64(4), int64(1)\n",
      "memory usage: 6.0 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "       sepal length (cm)  sepal width (cm)  petal length (cm)  \\\n",
       "count         150.000000        150.000000         150.000000   \n",
       "mean            5.843333          3.057333           3.758000   \n",
       "std             0.828066          0.435866           1.765298   \n",
       "min             4.300000          2.000000           1.000000   \n",
       "25%             5.100000          2.800000           1.600000   \n",
       "50%             5.800000          3.000000           4.350000   \n",
       "75%             6.400000          3.300000           5.100000   \n",
       "max             7.900000          4.400000           6.900000   \n",
       "\n",
       "       petal width (cm)     species  \n",
       "count        150.000000  150.000000  \n",
       "mean           1.199333    1.000000  \n",
       "std            0.762238    0.819232  \n",
       "min            0.100000    0.000000  \n",
       "25%            0.300000    0.000000  \n",
       "50%            1.300000    1.000000  \n",
       "75%            1.800000    2.000000  \n",
       "max            2.500000    2.000000  "
      ],
      "text/html": [
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>species</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>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "      <td>0.819232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:50:43.876639Z",
     "start_time": "2024-11-25T18:50:43.756388Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig = iris[iris[\"species\"] == 0].plot(kind='scatter',x='sepal length (cm)',y='sepal width (cm)',color='orange', label='Setosa')\n",
    "iris[iris[\"species\"] == 1].plot(kind='scatter',x='sepal length (cm)',y='sepal width (cm)',color='blue', label='versicolor',ax=fig)\n",
    "iris[iris[\"species\"] == 2].plot(kind='scatter',x='sepal length (cm)',y='sepal width (cm)',color='green', label='virginica', ax=fig)\n",
    "fig.set_xlabel(\"Sepal Length\")\n",
    "fig.set_ylabel(\"Sepal Width\")\n",
    "fig.set_title(\"Sepal Length VS Width\")\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,6)\n",
    "plt.show()"
   ],
   "id": "557644b20b2540ed",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 101
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:51:35.015610Z",
     "start_time": "2024-11-25T18:51:34.903590Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig = iris[iris[\"species\"] == 0].plot.scatter(x='petal length (cm)',y='petal width (cm)',color='orange', label='Setosa')\n",
    "iris[iris[\"species\"] == 1].plot.scatter(x='petal length (cm)',y='petal width (cm)',color='blue', label='versicolor',ax=fig)\n",
    "iris[iris[\"species\"] == 2].plot.scatter(x='petal length (cm)',y='petal width (cm)',color='green', label='virginica', ax=fig)\n",
    "fig.set_xlabel(\"Petal Length\")\n",
    "fig.set_ylabel(\"Petal Width\")\n",
    "fig.set_title(\" Petal Length VS Width\")\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,6)\n",
    "plt.show()\n"
   ],
   "id": "e4fbbc234b398961",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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t2k8//aR//OMf+vbbb1VRUaETTzxRl112ma688som7/cBAKAhFEkAAAAAYIctwAEAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7LSZ5yQVFpbK0/v4mUxSVFSoV8QC1yK3vo38+i5y69vIr+8it77L6NzWzt+UNlMk2WzymjeRN8UC1yK3vo38+i5y69vIr+8it77L07lluR0AAAAA2KFIAgAAAAA7FEkAAAAAYKfN3JPUGJvNpurqKsOPYzJJBw4cUFVVJetnj4LZ7C8/P+p6AAAAGKvNF0nV1VUqLNwrm63GLcezWPxUU+OeY/mi4OAO6tgxUiaTydOhAAAAwEe16SLJZrOppMQiPz8/hYUdI5PJ+KsUZrNJViuXkZxls9lUWXlQZWVFkqSwsCgPRwQAAABf1aaLpJoaq6qqDigsLFqBge3cckx/fz9VV3Ml6WgEBgZJksrKihQaGsHSOwAAABiiTf+WWbvszWxu07Viq1JbKFmt1R6OBAAAAL6qTRdJtbi/pfUgVwAAADAaRRIAAAAA2KFIaqMOHjyo33/f5+kwAAAAAK/DzTit1J9//qlFixbqq6/W6c8/SxQSEqJBg87QTTdN07HHdmry82+55UZddNElGjt2vBuiBQAAAFoPriS5iLk8S4EFn8pcnu2W4z300L0qKSnWP//5uj777Av961//VlVVpWbMuEXV1U1valBcXOSGKAEAAIDWhytJLWSqsih0+xQFFabVtR2MGqHSPotlC4gw7Ljbtn2re+55QFFR0ZKkyMgo3XbbXXr55RdUWlqqoKBAvfzyC/rii0xVVlZqwICBuv32uxUZGaUZM27Rvn179fTTj2nHjh91552z9d1332jRooXKyclSaGhHnXPOGE2ePEWBgYEqKPhDjz02Vz/++L3atWun3r1P1Z13zlZ0dLTKy8v0wgv/0DffbFFBwR/q0CFUF110ia655nrDXjsAAABgJI9cSdqxY4euu+46JSQkaMiQIZo1a5YsFkuDY2+44Qb16dNH8fHxdf8yMzPdHPGRhW6fosDCDIe2wMIMhW43tkgYOXK0nn76MT399ONKS1ujvXt/U1RUtObMeVgRERH6+9//pt27d+nVV9/Qf//7vtq376D77pspm82mZ599UZ06ddbdd9+rO++crZ078zVjxi0aNmy4PvxwjZ599kV98UWmFi5cIEl6+eUXdOyxx+rDDz/Vm2++rYqK/Vq69DVJ0ksvvaA9e/bon/9cojVr1umOO+7WokULtXv3LkNfPwAAcJ+c4iyl/fKpcovds2KmKTnFWfo462PlNDMeb4sf3s/tV5IOHDigG264QZdeeqlSU1NVXl6u2bNn67777tPLL79cb/z333+vV199VQkJCe4OtUnm8iyHK0i1TLIqqDBN5vJsWUO6G3Ls2bPvV//+A5WW9qmeemqeysrKdMIJXTRlylQNGjRYGRlp+ve/lysiIlKSdPvtd2n06GH6+ecd6tWrt8Ncn376ibp1665LL71cktSlS4xSUm7R/ffP1m233amgoCB9++1WffbZag0cmKD585+ve5DrlCk3yWw2KyQkRL//vq/uOUYFBX+oS5cYQ147AABwj6IDFqWsmaL0XX/9vpMcM0KpoxYrvJ1xK2ZcFY+3xY/Ww+1F0p49e9SrVy/dcsstMpvNCgwM1KRJkzRr1qx6Y3ft2qWSkhKdcsop7g6zWcwVeU305xpWJPn5+Wn06LEaPXqsbDab8vPztHr1Ks2d+6CmTr1FknTTTdc6xmP212+//VqvSLJYCnX88Sc4tB133Ak6ePCgioosuuOOmVqyZLH+8583NG/ew+revYfuuGOm+vaNV1GRRc89N18//7xDxx9/vHr2PJSr2gf1AgCA1itlzRRl7s5waMvcnaGpa67XsvErvD4eb4sfrYfbi6S4uDi98sorDm2rV6/WqaeeWm/s9u3bFRISohkzZmj79u2Kjo7Wtddeq4kTJzp93IaeQdrS55Jag2Ob6I9r2QGOYMOG9br//ll6552V6tgxTCaTSbGxcUpJuVUbN36tqqoqSdKbby6vu2dJkvLycusVQ5J03HHH6/PP0x3afv11twIDA9WxY5h+/nmHzj//Yk2ZMlVFRUV67bV/as6cmVq58jM98MA9GjIkUfPnPy9/f3+VlBTrww+N/6ZjMrU8f65UG4s3xQTXIb++i9z6NvLbMjnFWQ5XYGpZbVal70pTbkm2uoUb88dgV8TjbfGjeYx+3zZ3Xo9u3GCz2fSPf/xD6enpWrp0ab3+yspK9evXTzNmzFCPHj20YcMGTZ8+XSEhIRozZoxTx4qKCq3XduDAAVksfjKbTfL3P4rbs8J6qip6pPwL02WyWf96XSazqqOSZQo7ucETfFTHsjNgwABFRkbpscf+pqlTp6lr1646eLBS69Z9rt27d2nkyFHaseMHLVgwX7Nm3auQkBC98cbrev31xXrnnQ8UEhKsoKAgVVSUy9/fT6NHj9Hrr7+qd955SxdddIn27durRYte1OjRYxQcHKQ33lisgIAAPfDAw4qICFP79u0VFhYuf38/lZeXKTi4nYKCAlRcXKznnnv60DmwWVv8OhtSU2OSn5+fIiJC1K5dO5fP31INfZ3Bd5Bf30VufRv5PTqbihp/nqLFtleDo+PdFI3z8Xhb/HCOp9+3JpvNZvPEgcvKynTvvffqhx9+0EsvvaSePXs26/MeeeQRFRYWasGCBU4dr7CwVIe/0qqqShUU/KaoqOMUEBDo1Hy1TFVFCt1+fbN3t/P391N1dcuXohUUFGjx4lRt3Pi1ioosCggI0Kmnnq7rr79Jp556mv7880+9/PLz+vrrr1ReXqbY2G66+ebb1LdvP0nSv/+9RIsXL1JiYrIefHCuvvvuW6WmvqDc3GwFBQVp5MhzddNNNysoqJ0KCgo0f/7j+u67b1RVVaVevXprxoyZiovrrg0b1mvBgvn6/fffFRoaqpEjz9HmzRs1atQYXX75VS1+nYerqqpUYeFvio4++pwZwWQ69GZu6OsMrR/59V3k1reR35bJKc7SGW8OOGL/11dudfuVJGfi8bb40TxGv29r529ynCeKpJ07d+rGG2/U8ccfr/nz5ysyMrLBccuXL6931WjOnDmqrKzUU0895dQxCwoaLpIKC1tWJNUyl2cfugcpOK7R+5BcVSS1Va7MmSuZTFJ0dGiDX2do/civ7yK3vo38ttykDy9U5u4MWe1WzJhNZiV2SfLIPT3OxuNt8aNpRr9va+dvitu3AC8pKdHkyZPVv39/vfrqq0cskKRDV5vmzp2rH3/8UTU1NcrIyNDKlSs1adIkN0bcPNaQ7qqMPsewjRoAAADcLXXUYiV2SXJoS+ySpNRRi1tFPN4WP1oPt9+T9O6772rPnj36+OOP9cknnzj0ffPNN4qPj9cjjzyiCRMmaPLkydq/f79uvfVWFRYWKiYmRk888YQGDhzo7rABAADanPB2EVo2foVyi7OVV5Kr2LA4xXlwiVpdPCXZstj2KtLUWXFhR47H2+JH6+Gxe5Lczejlds3FcruWYbkdPIH8+i5y69vIr+8it76rzS63AwAAAABvRpEEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkNuuqqS/Xppx+3aI7fftujoUMH6rff9rgoKgAAAMB4bn+YLFqHpUv/6+kQAAAAAI+gSHKRnByT8vP9FBtbo7g4455qNnfug7JarXr44Xl1bQ8+eK/CwsJ12WVX6rnn5uuHH7apXbtgnXPOGF1//U0KCAjQqlUf6p13/quOHTvqp59+0J133qNjjz1Wzz//rH79dZfCwsJ11lln65Zbbpe/v78mThyv66+/SWPHjldFRYVeemmB1q79TNXVVTrttNN19933qnPn41RSUqyXX35RX32Vqerqap16ah9Nn36nYmJOrBf73r2/aeHCBdq6dbP8/Pw0YMAg3XLLHYqOjtbWrZv16KMP6fTT++nrr7/UVVddq6uuutaw8wgAgL2c4izll+QpNixOceHdPR2O4dJ3fqat+zZrQKcEJZ043NPhAF6H5XYtVFQkTZoUrDPP7KDLL2+vM87ooEmTglVcbMzxJky4UOvWZai8vEySVFpaqi++yNSoUaN1++03Ky6um959d5UWLnxFmzdv1KuvptZ97s8//6RzzhmjDz74VMOGJWnu3Ac1ceIkffJJhv7xj4VKT/9MX3zxeb1jPvPME/rppx/16qtv6IMPPlVkZJQeeug+SdKcObO0Z89uLV78pt599yOdeGJX3X77zXXx1aqurtYdd9wiPz8/vfXWCr355nLZbDbNnj1D1dXVkqTff9+nrl1jtXLlZ7rookuMOYEAANgpOmDRpA8v1Jn/HqDLP5qoM/7dX5M+vFDFB4o8HZoh8kpy1evVrpq08iI9senvunTlBer1alf9UpLv6dAAr0KR1EIpKcHKzDQ7tGVmmjV1arAhx+vbN16dOnVWevpnkqTPPlutk046SX/88Yeqqqo0deotCgoKUqdOnXXjjTfr3XffrvvcgIAAjR49VoGBgQoKaqegoCCtXbtGX365TmFhYXr33Y+UlDTC4XhVVVVKS/tUN954szp16qzAwEBNn36nZsyYqV9/3a1vv92qO+6YqaioaAUFtdPNN0+X1Vqt9eu/dJjnu+++0W+//aqZM+9Vhw4dFBoaqlmz7lN29v9px46f6sadd94E+fv7q337EEPOHwAA9lLWTFHm7gyHtszdGZq65nrPBGSwMcuHy3LQ4tBmOWjR6OVJngkI8FIUSS2Qk2NSerq/rFaTQ7vVeqg9N9d0hM9smXHjLtAnn6ySJK1a9aHGjbtAe/fuUXFxkcaMSda55ybp3HOT9MADs1VdXaWiokPfDCMjo+Tn91fKn3vuJUVHH6P58x/XmDHDdc89d+n33/c5HOvPP0tUWVmpzp0717WFhoaqV69T6uY9/vgT6vrMZrOOPbazfvvtN4d5LJZChYWFKySkQ11b+/Yh6tgxTHv3/rWxQ3T0MS09PQAANEtOcZbSd6XJarM6tFttVqXvSlNucbaHIjNG+s7P6hVItSwHLcrYudbNEQHeiyKpBfLzGz99eXnGnN4xY8bpxx+/16ZNG5STk61Ro87VMcd00gkndNEnn2TU/VuxYpWWLFmm8PAISZLJ9FfRdvDgQeXn5+quu+7Ru+9+pDfe+K/Ky8u0YMEzDseKiIhUYGCg9u3bW9dWVGTR888/q86dj5ck/frr7ro+q9Wqffv2Kjo62mGe4447XiUlxQ7L8MrKylRSUqyoqL/G2scIAICR8kvyGu3PK8l1UyTusXXf5kb7t+zb6KZIAO9HkdQCXbvWNNofG9t4/9GKiIjQWWedrSeeeFRJScPVsWNHDRkyVPv379e//71ElZWVKi0t1dy5D+nBB+9tsPAwmUx6+OE5euutpaqurlZUVJT8/f0VHh7uMM7Pz0+jR5+nV19dpIKCP3Tw4EEtWvSSfvhhu6Kjo3XmmUP03HNPq7CwQAcPHtBLLz2vmhqrhgw522GeXr1OUdeucXrqqcdUVlamsrIyPf30YzrhhC7q06evIecJAIDGdA2LbbQ/NizOTZG4R/9OAxvtH9ApwU2RAN6PIqkFunWzKTm5Wmaz4252ZvOhdiN3uZsw4ULt3fubxo07X5IUEtJB//jHQm3dulkXXTRWl156vvz8THriiWca/PzAwEA9/vgzWrfuc5133ghNnDhBUVHRSkmZXm/s9Okz1KvXKbrxxsm64IIxKikp1qOPPiFJeuCBv+n440/Q9ddfpfHjRysnJ0vPPfeyOnYMc5jD399fTz75rKxWqy6//CJNnDhO1dVVevbZhfL3Z5NFAID7dQvvoeSYETKbHO8tNpvMSo4Z4XO73CWfOFKRQZEN9kUGRbLLHWDHZLPZjPtN3osUFJTq8FdaVVWpwsLfFBV1nAICAo9q3uJiaerUYKWn//WLfnJytVJTK3TYRRlJkr+/n6qrjbnC1Ba4ImdGMJmk6OjQBr/O0PqRX99Fbn1bc/JbfKBIU9dcr/RdaXVtyTEjlDpqscLbRbgpUvf5pSRfo5cnOdybFBkUqdUTM3RSWFfPBeYk3ru+y+jc1s7fFP6E30Lh4dKyZRXKzTUpL8/45yQBAADXCW8XoWXjVyi3OFt5Jbk+/5ykk8K6aseUfGXsXKst+zbynCTgCCiSXCQuzqa4OGvTAwEAgNeJC+/u08XR4ZJOHE5xBDSCe5IAAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAAAAAOxQJAEAAACAHYokAAAAALDDc5IAAECbl1OcpfySvGY/TNbbxhvN6Hha+/w4stZ67imSfMTevXt19dWX6I033lbnzp2d/vynnvq7JGnmzPuaHHvXXbepb99+uuaa650+DgAA3qTogEUpa6YofVdaXVtyzAiljlqs8HYRXj/eaEbH09rnx5G19nNvstlsNk8H4Q4FBaU6/JVWVVWqsPA3RUUdp4CAwBbN39wq2d/fT9XVNS06Vlvmypy5kskkRUeHNvh1htaP/PoucuvbmpPfSR9eqMzdGbLarHVtZpNZiV2StGz8Cq8fbzSj4zna+Zv73vW289mWGJ3bo1U7f1O4ktRC7q6S5859UFarVQ8/PK+u7cEH71VFxX6tX/+l3n77Ax133PEaOnSgJk6cpDVrPtGpp56uJ598Vp99tlqvvpqqoiKLTjmljzp16qzq6irNmfOw5s17WJI0Z87DevXVVOXl5SgwMEhffbVOwcHtNXr0WKWk3CpJuvXWmxQfP0BTpkxVdXW1XnvtFa1a9aHKysrUo8fJuuOOmerR42QVFPyhBQue0U8//SCLpVCRkdGaPPl6jRt3vsvPCwAAzsopznL4+V3LarMqfVeacouzHf7w6W3jjWZ0PK19fhyZL5x7Nm5ooZQ1U5S5O8OhLXN3hqauMWYp2oQJF2rdugyVl5dJkkpLS/XFF5m64Yab64399dfdeuedj/TAA3/T999v07x5D+uWW+7QypWf6fzzL9KqVR8c8Tiff56uQYMG66OP0jRr1n16883X9f332+uNe/31V7VmzSeaP/95ffzxWsXHD9Ds2TNktVr1+ONzFRDgrzfe+K8+/TRTF198iZ599knt37/fVacDAICjll+S12h/XkmuV483mtHxtPb5cWS+cO65ktQCnqiS+/aNV6dOnZWe/pnGjbtAn322WieddJI6duxYb+yoUeeqXbt2ateunT766AMlJiZr6NBESdKwYclKTEw+4nFiYk7UmDHjJElnnjlUUVHR2rXrF512Wh+HcZ988pGuuupaxcbGSZImT56iM84YIpvNptmz71dISIj8/f21b99etW8fooMHD+rPP/9U+/btXXVKAAA4Kl3DYhvtjw2L8+rxRjM6ntY+P47MF849V5JawFNV8rhxF+iTT1ZJklat+lDjxl3Q4Ljo6GPq/n/fvn067rjjHfqPP/6EIx4jMjLK4WN/f381dPtaYWGBOnc+ru7jgIAAnXZaH/n7+2vPnl91xx23aPz4c/Tww3P03XdbJUk2G/dkAQA8r1t4DyXHjJDZZHZoN5vMSo4ZUe8Pnd423mhGx9Pa58eR+cK5p0hqAU9VyWPGjNOPP36vTZs2KCcnW6NGndvk53Tu3Fl79/7m0Hb4x0fj2GM7ad++vXUfV1dXa8GC+dq3b69mzbpDo0eP1apVaVq06DVdcskVLT4eAACulDpqsRK7JDm0JXZJUuqoxa1ivNGMjqe1z48ja+3nnuV2LVBbJR9p5w6jquSIiAidddbZeuKJR5WUNFwdO3asu0fpSCZMuFDTpt2oDRvWa+DABG3c+LUyM9M1cuToFsUydux4/fvfb6hfv/46/vgTtGTJYmVmZuj666fqwIEDateunUwmk/bu3auXXlogSaqqqmrRMQEAcJXwdhFaNn6FcouzlVeS2+Qutd423mhGx9Pa58eRtfZzT5HUQqmjFmvqmusd7k1yR5U8YcKFyshI0333PdSs8b16naKZM+/V008/rpKSYvXt208DBgySv39Ai+K44oprVF1drbvumq4//yxRr16n6umnF6hDhw66776H9MorL+sf/3haERERGj/+QuXl5So3N1snnnhSi44LAIArxYV3d+oXOG8bbzSj42nt8+PIWuu55zlJLnrmTnOrZE89J2nnzl9ks9l00kld69rmzJmpE0/sqqlTb3F7PEeL5yTBE8iv7yK3vo38+i5y67u85TlJ3JPkInHh3TXipHO8tlLOy8vVbbel6Ndfd0uStm7drA0b1uuss4Z6ODIAAADAu7Dcro0YNixZ+fm5mj59qkpL/1Tnzsdp1qz71adPX0+HBgAAAHgViqQ2ZPLkKZo8eYqnwwAAAAC8GsvtAAAAAMAORRIAAAAA2KFIktRGNvjzCTab+3cGBAAAQNvSpu9JMpv9JZlUVlaiDh3CZDKZDD9mTY1JVitFmbNsNpus1mqVlhbLZPJr8fOdAAAAgCNp00WSn5+fIiKOUVHRH7JYKtx2zJoaroYcrcDAdurYMdItBS0AAL4qpzhL+SV5TT7f0Vvnh2uRr/radJEkSUFBwTr22C6yWqsNP5bJJEVEhKioqJwHnx0FPz8/+fmZKZAAADhKRQcsSlkzRem70urakmNGKHXUYoW3i/D6+eFa5OvITLY2ckOONzyRmadD+y5y69vIr+8it76N/NY36cMLlbk7Q1abta7NbDIrsUuSlo1f4fXz1yK3ruGufDnD6NzWzt8UNm4AAABoA3KKs5S+K83hF2JJstqsSt+VptzibK+eH65FvhpHkQQAANAG5JfkNdqfV5Lr1fPDtchX4yiSAAAA2oCuYbGN9seGxXn1/HAt8tU4iiQAAIA2oFt4DyXHjJDZZHZoN5vMSo4Z0eJdzYyeH65FvhpHkQQAANBGpI5arMQuSQ5tiV2SlDpqcauYH65Fvo6szW8BDgAA0FaEt4vQsvErlFucrbySXJc/F8fo+eFa5OvIKJIAAADamLjw7ob+Mmz0/HAt8lUfy+0AAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAAAAAOxQJAEAAACAHYokAAAAALDDc5IAAAC8TE5xlvJL8pr9cE9vG+8so+dvazifLUeRBAAA4CWKDliUsmaK0nel1bUlx4xQ6qjFCm8X4fXjnWX0/G0N59N1WG4HAADgJVLWTFHm7gyHtszdGZq65vpWMd5ZRs/f1nA+XYciCQAAwAvkFGcpfVearDarQ7vVZlX6rjTlFmd79XhnGT1/W8P5dC2KJAAAAC+QX5LXaH9eSa5Xj3eW0fO3NZxP16JIAgAA8AJdw2Ib7Y8Ni/Pq8c4yev62hvPpWhRJAAAAXqBbeA8lx4yQ2WR2aDebzEqOGVFvlzJvG+8so+dvazifrkWRBAAA4CVSRy1WYpckh7bELklKHbW4VYx3ltHztzWcT9cx2Ww2m6eDcIeCglJ5+pWaTFJ0dKhXxALXIre+jfz6LnLr21pzfnOLs5VXktvs59x423hnOTt/a86tOxidLyMZndva+ZscR5HkPryhfRe59W3k13eRW99Gfn0XufVd3lIksdwOAAAAAOxQJAEAAACAHYokAAAAALBDkQQAAAAAdiiSAAAAAMAORRIAAAAA2KFIAgAAAAA7HimSduzYoeuuu04JCQkaMmSIZs2aJYvF0uDYzz//XOPHj1e/fv00ZswYpaenuzlaAAAAAG2J24ukAwcO6IYbblB8fLy++OILrVy5UsXFxbrvvvvqjc3Pz9f06dN1++23a/PmzZo+fbruuOMO7du3z91hAwAAD8spzlLaL58qtzi72eM/zvpYOc0YfzRzOzMesOdtXz/eFo838Hf3Affs2aNevXrplltukdlsVmBgoCZNmqRZs2bVG7tixQoNHDhQI0eOlCSNHTtW7777rpYtW6bbbrvN3aEDAAAPKDpgUcqaKUrflVbXlhwzQqmjFiu8XUSLxhs5N3A4b/v68bZ4vInbryTFxcXplVdekdlsrmtbvXq1Tj311Hpjs7OzdfLJJzu0de/eXTt27DA8TgAA4B1S1kxR5u4Mh7bM3Rmauub6Fo83cm7gcN729eNt8XgTt19Jsmez2fSPf/xD6enpWrp0ab3+8vJyBQcHO7S1a9dO+/fvd/pYJtNRh+kytTF4QyxwLXLr28iv7yK33i+nOMvhr9y1rDar0nelKbckW93Cux/VeCPnhrFa43vX275+vC2eWkbntrnzeqxIKisr07333qsffvhBS5cuVc+ePeuNCQ4O1oEDBxzaDhw4oJCQEKePFxUVetSxupo3xQLXIre+jfz6LnLrvTYVNX4fssW2V4Oj449qvJFzwz1a03vX275+vC2ew3k6tx4pknbu3Kkbb7xRxx9/vJYvX67IyMgGx5188sn64YcfHNqys7N12mmnOX3MwsJS2WxHFa7LmEyHEu4NscC1yK1vI7++i9x6v0hTpyb6O6ugoPSoxhs5N4zVGt+73vb1423x1DI6t7XzN8XtRVJJSYkmT56sM844Q/PmzZOf35Fvi5owYYL+9a9/adWqVTrnnHP06aefauPGjZozZ47Tx7XZ5DVvIm+KBa5Fbn0b+fVd5NZ7xYX1UHLMCGXuzpDVZq1rN5vMSuySpLiw7g65c2a8kXPDPVrTe9fbvn68LZ7DeTq3bt+44d1339WePXv08ccfa8CAAYqPj6/7J0nx8fH64IMPJEndunXTiy++qNTUVA0aNEgLFy7U888/r9jYWHeHDQAAPCR11GIldklyaEvskqTUUYtbPN7IuYHDedvXj7fF401MNltrqb9bpqDA85djTSYpOjrUK2KBa5Fb30Z+fRe5bV1yi7OVV5Kr2LA4xTXjhvLckmxZbHsVaeqsuLDGxzs9t5Pj4Vqt/b3rbV8/3hSP0bmtnb/JcRRJ7tPa39A4MnLr28iv7yK3vo38+i5y67u8pUhy+3I7AAAAAPBmFEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADAjr+nAwAAADBCTnGWNhXta9bDZNN3fqat+zZrQKcEJZ043JBY8kvyvOJhnW0R5x/OokgCAAA+peiARSlrpih9V1pdW3LMCKWOWqzwdhEOY/NKcjVm+XBZDlrq2iKDIrV6YoZOCuvq1ljgepx/HC2W2wEAAJ+SsmaKMndnOLRl7s7Q1DXX1xt7eIEkSZaDFo1enuT2WOB6nH8cLYokAADgM3KKs5S+K01Wm9Wh3WqzKn1XmnKLs+va0nd+Vq9AqmU5aFHGzrVuiwWux/lHS1AkAQAAn5Ffktdof15Jbt3/b923udGxW/ZtdFsscD3OP1qCIgkAAPiMrmGxjfbHhsXV/X//TgMbHTugU4LbYoHrcf7REhRJAADAZ3QL76HkmBEym8wO7WaTWckxIxx2Nks+caQigyIbnCcyKLLFu9w5Ewtcj/OPlqBIAgAAPiV11GIldklyaEvskqTUUYvrjV09MaNeoVS7u527Y4Hrcf5xtEw2m83m6SDcoaCgVJ5+pSaTFB0d6hWxwLXIrW8jv76L3Pq23JJsWWx7m/WcpIyda7Vl30bDnpOUW5ytvJJcntPjIs6+dzn/rYfR35dr529yHEWS+/DD2HeRW99Gfn0XufVt5Nd3kVvf5S1FEsvtAAAAAMAORRIAAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHb8PR0AAABom97ckKYv87fo7NiBujxheJPj03d+pq37NmtApwQlndj0eGfkFGcpvyRPsWFxigvv7tK53cHZc2P063XH/JuK9inS1FlxYZ7PV2v/+kF9FEkAAMCtvt2Zq7HvjlB1YKEkaXmhdNdXUVo9MV19unStNz6vJFdjlg+X5aClri0yKFKrJ2bopLD6451RdMCilDVTlL4rra4tOWaEUkctVni7iBbN7Q7OnhujX29rn7+1xwPXYbkdAABwq7HvjlB1QKFDW3VAoUYvT25w/OFFgCRZDlo0enlSi2NJWTNFmbszHNoyd2do6prrWzy3Ozh7box+va19/tYeD1yHIgkAALjNmxvSDl1BMh3WYZKqAwv1n41rHZrTd35WrwioZTloUcbOtQ32NUdOcZbSd6XJarM6tFttVqXvSlNucfZRz+0Ozp4bo19va5+/tccD16JIAgAAbvNl/pZG+9flbXb4eOu+zUcYeciWfRuPOpb8krxG+/NKco96bndw9twY/Xpb+/zO8rZ44FoUSQAAwG2GdB3QaP/ZsQMdPu7faeARRh4yoFPCUcfSNSy20f7YsLijntsdnD03Rr/e1j6/s7wtHrgWRRIAAHCbKwePkH9llGQ7rMMm+VdG1dvlLvnEkYoMimxwrsigyBbtctctvIeSY0bIbDI7tJtNZiXHjPD6XcqcPTdGv97WPn9rjweuRZEEAADcavXEdPlXRTm0+Vcd2t2u4fEZ9YqB2h3cWip11GIldklyaEvskqTUUYtbPLc7OHtujH69rX3+1h4PXMdks9kO/1uOTyooKJWnX6nJJEVHh3pFLHAtcuvbyK/vIree9Z+Na7Uub3Ozn5OUsXOttuzb2OxnATmT39zibOWV5Lba59w4e26Mfr2Gz1+SLYttr9c8J6m1f/14E6O/L9fO3+Q4iiT34Yex7yK3vo38+i5y69vIr+8it77LW4okltsBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGDH39MBAAAA98gpzlJ+SV6beeBlTo5JmzZJkZEmxcXxMB0AzUeRBACAjys6YFHKmilK35VW15YcM0KpoxYrvF2EByMzRlGRlJISrPT02l9zOig5uVqpqRUKD/dkZABaC5bbAQDg41LWTFHm7gyHtszdGZq65nrPBGSwlJRgZWaaHdoyM82aOjXYQxEBaG0okgAA8GE5xVlK35Umq83q0G61WZW+K025xdkeiswYOTkmpaf7y2o1ObRbrYfac3NNR/hMAPgLRRIAAD4svySv0f68klw3ReIe+fmN/2qTl8evPgCaxncKAAB8WNew2Eb7Y8Pi3BSJe3TtWtNof2xs4/0AIFEkAQDg07qF91ByzAiZTY736JhNZiXHjPC5Xe66dbMpOblaZrPjbnZm86F2drkD0BwUSQAA+LjUUYuV2CXJoS2xS5JSRy32TEAGS02tUGKi4z1YiYlWpaZWeCgiAK0NW4ADAODjwttFaNn4FcotzlZeSa7PPycpPFxatqxCubkmWSwdFBlZxhUkAE6hSAIAoI2IC+/u08XR4bp1s2nwYKmgwCYbNRIAJ7DcDgAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHh8kCANBG5OSYlJ/vp9jYGsXFuf7pqkbPb6TWHLs3yinOUn5JnmLD4trUA4zhOyiSAADwcUVFUkpKsNLT//qxn5xcrdTUCoWHe//8RmrNsXujogMWpayZovRdaXVtyTEjlDpqscLbRXgwMsA5LLcDAMDHpaQEKzPT7NCWmWnW1KnBrWJ+I7Xm2L1Rypopytyd4dCWuTtDU9dc75mAgKNEkQQAgA/LyTEpPd1fVqvJod1qPdSem2s6wmd6x/xGas2xe6Oc4iyl70qT1WZ1aLfarErflabc4mwPRQY4jyIJAAAflp/f+I/6vLyW/Spg9PxGas2xe6P8krxG+/NKct0UCdByvPsBAPBhXbvWNNofG9t4v6fnN1Jrjt0bdQ2LbbQ/NizOTZEALUeRBACAD+vWzabk5GqZzY47tpnNh9pbupOb0fMbqTXH7o26hfdQcswImU2O93iZTWYlx4xglzu0KhRJAAD4uNTUCiUmOt4nkphoVWpqRauY30itOXZvlDpqsRK7JDm0JXZJUuqoxZ4JCDhKJpvN1ib+TFJQUCpPv1KTSYqODvWKWOBa5Na3kV/f1dZym5trUl6ecc8CMnp+ZzmTX2+LvbXLLc5WXkmuYc9Jamvv3bbE6NzWzt8UnpMEAEAbERdnU1yctemBXjq/kVpz7N4oLrw7y+vQqrHcDgAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOx4tkiwWi0aNGqUNGzYcccwNN9ygPn36KD4+vu5fZmamG6MEAODo5OSYlJZmVm6uydOhSJLuvz9ASUnBeuihgGaNdzZ+o19verqf5s8PUEZG8359ySnO0sdZHyunOLtZY9N++VS5zRgLwPd57GGyW7Zs0T333KOdO3c2Ou7777/Xq6++qoSEBDdFBgBAyxQVSSkpwUpP/+vHbHJytVJTKxQe7v54PvpIuu66DpIOFS8//uivl14K0tKlZTrnnPrjnY3f6NeblyeNGRMii+Wv4igyskarV5frpJMaiOeARSlrpih9V9pf8cSMUOqoxQpvF3HUYwG0HR65krRixQrdfffdmjFjRqPjdu3apZKSEp1yyiluigwAgJZLSQlWZqbZoS0z06ypU4M9Es+hAqm+q65quN3Z+I1+vYcKJMerUxaLSaNHhzQcz5opytyd4RjP7gxNXXN9i8YCaDs8ciVp6NChGj9+vPz9/RstlLZv366QkBDNmDFD27dvV3R0tK699lpNnDjR6WOavGClQ20M3hALXIvc+jby67uMyG1Ojsnhikotq/VQe26uSd262Vx3wCbcf3+Aaq8gOTrU9tBDAfrb36rqWp2N3+jXm57u53AFyT5+i8WkjAw/JSfX/BVPcZbDVaG6eGxWpe9KU25JtrqFd3d6LLwL35d9l9G5be68HimSjjnmmGaNq6ysVL9+/TRjxgz16NFDGzZs0PTp0xUSEqIxY8Y4dcyoqNCjCdUQ3hQLXIvc+jby67tcmdtNmxrvt1g6aPBglx2uSevXN97/5ZftFB3dru5jZ+M3+vX+9FNT/SG65BK7eIr2NR6Pba8GR8c7PRbeie/LvsvTuXW6SPruu++0c+dOWa1Wh/YLLrjAVTE5zGk/79ChQ3XBBRfo448/drpIKiwslc19f7hrkMl0KOHeEAtci9z6NvLru4zIbWSkSVLDy9gO9ZepoMB9X0hnnhmgbdvaHbF/yJADKij460qSs/Eb/Xp79/aT1PCyukP95Soo+OtKUqSpU6PzRZo6q6Cg1Omx8C58X/ZdRue2dv6mOFUkPfvss1q0aJGio6MVEPDXzjgmk8mQImn58uX1rhpVVlYqKCjI6blsNnnNm8ibYoFrkVvfRn59lytzGxdnU3JytTIzzbJa/1rXYTbblJhoVVycza1fR3PnVik1tfbnpv06k0NBPPJIlUM8zsZv9OtNSqpRZGTN/+5Jcow/MtKmpKQax3jCeig5ZoQyd2fIavvrD7pmk1mJXZIUF9a9brwzY+Gd+L7suzydW6c2bvjvf/+r1157TevWrdPatWvr/qWl1V/P6wplZWWaO3eufvzxR9XU1CgjI0MrV67UpEmTDDkeAACukJpaocRExxUXiYlWpaZWeCSepUvLnGp3Nn6jX+/q1eWKjHT8bSky0qbVq8sbjmfUYiV2SXKMp0uSUkctbtFYAG2HyWZrfo02dOhQffHFFy4NoGfPnlqyZIkG/2/Bcnx8vB555BFNmDBBNptNL730kpYvX67CwkLFxMTo1ltv1bnnnuv0cQoKPH851mSSoqNDvSIWuBa59W3k13cZndvcXJPy8vwUG1ujuDjPf/E89FCAPv/cX8OGVeuRR6qaHO9s/Ea/3owMP23ZYtaAAVYlJdU0OT63JFsW215FmjorLqzxDRhyi7OVV5Kr2LA4xbFZg9fj+7LvMjq3tfM3Oc6ZIumBBx7Q4MGDNW7cuBYF5wne8CbiDe27yK1vI7++i9z6NvLru8it7/KWIqlZ9yRdffXVMplMKi8v1zvvvKNFixYp/LCnwy1ZsuSoAgUAAAAAb9KsImmw3d6dycnJhgUDAAAAAJ7WrCLp1ltvlaQjbr29bNky10YFAAAAAB7SZJFUUVGhoqIiSdJ9992nfv36yf42ptLSUj3++OPsOAcAAADAJzRZJJWVlem8887TgQMHJEnDhw+v67PZbDKZTBo5cqRxEQIAAACAGzVZJB1zzDH67LPPVFFRofHjx2vlypUO/UFBQYqOjjYsQAAAAABwp2bdkxQVFSVJ2rJli/z8nHr+LAAAAAC0Kk5tAd4YtgAHAAAA4AuadVlo8ODBSkhI0PHHH68ff/xRvXv31ujRo9W3b1/9/PPPio2NNTpOAACalJNjUlqaWbm5jf9hz1ekp/tp/vwAZWQYs8rD2fPpbDxtLV/eJKc4S2m/fKrc4mxPhwJ4Jae2AL/iiiu0aNEi9e/fv65v9OjReuCBB4yJDgCAZigqklJSgpWe/tePteTkaqWmVuiwZ5/7hLw8acyYEFksfxUjkZE1Wr26XCed1PL5nT2fzsbT1vLlTYoOWJSyZorSd6XVtSXHjFDqqMUKbxfhwcgA7+LUn55++ukn9e3b16GtZ8+eys/Pd2VMAAA4JSUlWJmZZoe2zEyzpk4N9lBExjpUkDhefbFYTBo9OsQl8zt7Pp2Np63ly5ukrJmizN0ZDm2ZuzM0dc31ngkI8FJOFUndunXTa6+95tD28ssvq1evXq6MCQCAZsvJMSk93V9Wq+Mv6VbroXZfW8qVnu73vys2h78ukywWvxYvvXP2fDobT1vLlzfJKc5S+q40WW1Wh3arzar0XWksvQPsOPWd9L777lNqaqqSkpJ02WWXKTExUcuWLdPDDz9sUHgAADQuP7/xH2V5eb61K+vWreZG+7dsaby/Kc6eT2fjaWv58ib5JXmN9ueV5LopEsD7NeuepFr9+/fX6tWrlZGRod9//12dO3fW8OHDFRoaalR8AAA0qmvXmkb7Y2Mb729t+ve3Nto/YEDj/U1x9nw6G09by5c36RrW+EZbsWFxbooE8H5OFUmSFBERoQsvvNCIWAAAcFq3bjYlJ1crM9PssITLbLYpMdGquDibB6NzveTkGkVG1vzvHiD7pWk2RUbalJTUsiLD2fPpbDxtLV/epFt4DyXHjFDm7gyHJXdmk1mJXZIUF97dg9EB3qVZ17Rrd7Pr1auXevfu3eA/AAA8JTW1QomJjlcsEhOtSk2t8FBExlq9ulyRkY7FRGSkTatXl7tkfmfPp7PxtLV8eZPUUYuV2CXJoS2xS5JSRy32TECAlzLZbLYm/2SzefNmDRw4UBs2bDjiQ2UTEhJcHpwrFRSUqulXaiyTSYqODvWKWOBa5Na3kd/WIzfXpLw8P8XG1jTrikRrz21Ghp+2bDFrwABri68gNcTZ8+lsPM7O76zWnl8j5RZnK68kV7Fhca3yChK59V1G57Z2/qY0a7nd2rVrJUmDBg2Snx83VAIAvFNcnE1xcS27J6c1SUqqMaQ4quXs+XQ2nraWL28SF969VRZHgLs0q0j69ttvtWTJEoWEhGjYsGFKTk7W2WefrQ4dOhgdHwAAAAC4VbOKpH//+9/av3+/Nm7cqK+++koLFy7UrFmzNGjQICUnJys5OVldunQxOlYAAAAAMFyzd7dr3769kpKSlJSUJEn6448/9NFHH+lf//qX/v73v+unn34yKkYAAAAAcBuntwDftm2b0tLSlJmZqaysLPXr109XX321EbEBAAAAgNs1q0j68ssvtWbNGqWlpam8vFxDhw7V5MmTlZSUpPDwcINDBAAAAAD3aVaRNGXKFPXv31/z5s3TGWecocDAQKPjAgAAAACPaNZ+3hdffLF++eUXzZkzR/PmzdO6detUVVVldGwAAAAA4HbNupI0b9482Ww2bd26VZ999pkeffRRFRQUaOjQoRoxYoSSkpLUsWNHo2MFAHiZnByT8vONexio0d5806zNm6VBg8y6/PKmn9eTnu6nrVub/7BUo8c7e/6NHg8AvsJksx3ds2yzsrKUnp6ut99+W7/99pu+//57V8fmUt7wRGaeDu27yK1vI7/1FRVJKSnBSk//629tycnVSk2tUGu4VfXbb6WxYzuoutpU1+bvb9Pq1WXq06f++Lw8acyYEFksfy3AiIys0erV5TrpJPePd/b8Gz3eW/He9V3k1ncZndva+Zsc52yRlJOTo6+//lobNmzQ5s2b1a5dOw0bNkwPPfTQUQfrDt7wJuIN7bvIrW8jv/VNmhSszEyzrNa/igyz2abERKuWLavwYGTNc/zxHVRdLUkmu1ab/P2lPXvK6o3v1StEFoup3vjISJt27Ch3+3hnz7/R470V713fRW59l7cUSc1abrd8+fK6wqioqEj9+vXTsGHDNH36dPXo0aPFwQIAWo+cHJPDFYZaVuuh9txck1cvzXrzTbPDFaS/mFRdLf3nP45L79LT/Ryu8NiPt1hMysjwc1gaZ/R4Z8+/0eMBwBc1a+OG+fPny8/PT/fee6+++uorLV26VDfeeCMFEgC0Qfn5jf/oyMtr1o8Wj/nyy8b/PrhunWP/1q3mRsdv2eLYb/R4Z8+/0eMBwBc160rSV199JZOpob+6AQDamq5dG99QIDa26Q0HPGnIkGotX37kR1mcfXa1w8f9+ze+ocOAAY79Ro939vwbPR4AfFGz/hxEgQQAqNWtm03JydUymx2XXJnNh9q9fSnWlVda5e9vk3R4nDb5+9vq7XKXnFyjyMiaBsdHRtbU24XO6PHOnn+jxwOAL+KaOQDAaampFUpMdCwmEhOtSk1tHTf1r15dJv/D1lL4+x9qb3h8uSIjHYuDyEibVq+uv6mCO8Y7e/6NHg8AvuaotwBvbbxh9xN2YvFd5Na3kd8jy801KS+v9T5H5z//MWvTpvYaNGh/s56TlJHhpy1bmv8cI6PHO3v+jR7vbXjv+i5y67u8ZXc7iiQ34g3tu8itbyO/vovc+jby67vIre/yliKpWRs3DB8+vMn7ktLS0poXGQAAAAB4sWYVSdOnTzc6DgAAAADwCs0qki688MJG+6urqxvtBwAAAIDWollFUq2dO3fqxRdf1L59+1RTc+iG0qqqKuXl5enrr782JEAAAAAAcCentgCfM2eOfv31V4WGhqq6ulonn3yysrKydNVVVxkVHwAAAAC4lVNF0vfff68XX3xR06ZNU2hoqO6//34988wzWr9+vVHxAQAAAIBbOVUkBQcHKywsTCeeeKL+7//+T5KUmJio3NxcQ4IDAAAAAHdzqkg68cQT9fnnnyskJEQ1NTXatWuX9u3bx8YNAAAAAHyGUxs33HTTTbrtttu0cuVKTZo0SZdddpnMZrOGDx9uVHwAADRbTo5J+fl+io2tUVxc008hzMkxadMmKTLS1KzxRsfjbfO39ngA4Gg5VSQNHz5cn376qSIjIzVt2jR17dpVZWVlTW4RDgCAkYqKpJSUYKWn//VjLTm5WqmpFQoPb874Do2ONzoeb5u/tccDAC3l1HK7m2++WZ06dVJAQIAkaezYsbr00kt13XXXGRIcAADNkZISrMxMs0NbZqZZU6cGu2S80fF42/ytPR4AaKkmryTt3r1b7733niTpiy++0AsvvODQX1ZWpp9//tmQ4AAAaEpOjsnhCkYtq/VQe26u41I6Z8cbHY+3zd/a4wEAV2iySDr++OOVlZUli8Uiq9WqDRs2OPQHBQXpoYceMixAAAAak5/f+KKIvDw/xcVZj3q80fF42/zO8rZ4AMAVmiyS/Pz89Nxzz0mS7r//fj366KOGBwUAQHN17VrTaH9srGO/s+ONjsfb5neWt8UDAK7g1D1Jjz76qCorK7VmzRq99tprqqio0I4dO4yKDQCAJnXrZlNycrXMZsclXWbzofbDl3o5O97oeLxt/tYeDwC4glNF0s6dOzV27Fg9+uijeu6557R3715dfPHFSk9PNyo+AACalJpaocRExyVdiYlWpaZWuGS80fF42/ytPR4AaCmTzWZr9p94pk6dqr59++rmm29WQkKCNm3apBUrVmjJkiVasWKFkXG2WEFBqZr/So1hMknR0aFeEQtci9z6NvLbeuTmmpSX1/zn9OTmmmSxdFBkZJkhVzycjcfb5m/t8fDe9V3k1ncZndva+Zvi1HOSvv32Wz3//PMymUwymUySpPPPP1/z5s07uigBAHChuDibU5sEdOtm0+DBUkGBzZAfxs7G423zO8vb4gGAo+XUcrvQ0FAVFBQ4tP3xxx8KCwtzaVAAAAAA4ClOFUnjx4/Xrbfeqi+//FI1NTXatm2b7r77bp133nlGxQcAAAAAbuXUcrtp06bpwIEDuvXWW1VRUaFrrrlGEydO1K233mpUfAAAAADgVk4VSQEBAZo9e7Zmz54ti8WiiIiIunuTAAAAAMAXNLtIeuGFF/TDDz9o6NChuvLKKxUZGWlkXAAAAADgEc26J+nJJ5/Uv//9bwUEBGjBggVatGiR0XEBAAAAgEc0q0hauXKlXn/9dS1YsEALFizQhx9+aHRcAAAAAOARzVpuV1paqh49ekiSBgwYoH379hkaFAB4k5wckzZtkiIjTV7xgMy2JCfHpPz85j+c1NnxAAA0pFlFkp/fXxec/P2d2usBAFqtoiIpJSVY6em13/c6KDm5WqmpFQoP92Rkvq/+uVej597Z8QAANKZZy+1sRjyGHAC8XEpKsDIzzQ5tmZlmTZ0a7KGI2g5nzz25AgC4UrMuC1VXV+u9996r+7iqqsrhY0m64IILXBgWAHhWTo7J4apELav1UHtuLkvvjOLsuSdXAABXa1aRFB0drQULFtR9HBER4fCxyWSiSALgU/LzG7/Qnpfnp7g4q5uiaVucPffkCgDgas0qktauXWt0HADgVbp2rWm0Pza28X4cPWfPPbkCALhas+5JAoC2pls3m5KTq2U2Oy7TMpsPtbN8yzjOnntyBQBwNYokADiC1NQKJSY6LtNKTLQqNbXCQxG1Hc6ee3IFAHAl9vMGgCMID5eWLatQbq5JFksHRUaWcVXCTezPfV5e0889cnY8AACNoUgCgCZ062bT4MFSQYFNPBHBveLibE5tuuDseAAAGsJyOwAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7Hi0SLJYLBo1apQ2bNhwxDGff/65xo8fr379+mnMmDFKT093Y4QAAAAA2hqPFUlbtmzRpEmTtHPnziOOyc/P1/Tp03X77bdr8+bNmj59uu644w7t27fPjZECgHdJT/fT/PkByshw/bdwI+eWpJwck9LSzMrNNRkyv7Nyckz6+OND/23ueG+KHwBgDH9PHHTFihVasGCBZs6cqRkzZjQ6buDAgRo5cqQkaezYsXr33Xe1bNky3Xbbbe4KFwC8Ql6eNGZMiCyWvwqYyMgarV5drpNO8t65JamoSEpJCVZ6+l8/dpKTq5WaWqHw8JbP3/J4OjQaj7fFDwAwlkeuJA0dOlRr1qzR2LFjGx2XnZ2tk08+2aGte/fu2rFjh5HhAYBXOlTEOF7BsFhMGj06xKvnlg4VGJmZZoe2zEyzpk4Ndsn8RsfjbfEDAIzlkStJxxxzTLPGlZeXKzjY8QdQu3bttH//fqePafKClRG1MXhDLHAtcuvbvCG/6el+Dld5/mKSxWJSRoafkpNrvG5u6dASNfsrMLWs1kPtubkmdetmO+r5jY7H2+JH83nDexfGILe+y+jcNndejxRJzRUcHKwDBw44tB04cEAhIc7/ZTMqKtRVYbWYN8UC1yK3vs2T+f3pp6b6Q3TJJd43tyRt2tR4v8XSQYMHH/38znI2Hm+LH87je7PvIre+y9O59eoi6eSTT9YPP/zg0Jadna3TTjvN6bkKC0tl8/Af+kymQwn3hljgWuTWt3lDfnv39pN05D8Q9e5droKCo7vaY+TckhQZaZLUoZH+MhUUuO/EOhuPt8WP5vOG9y6MQW59l9G5rZ2/KV5dJE2YMEH/+te/tGrVKp1zzjn69NNPtXHjRs2ZM8fpuWw2ec2byJtigWuRW9/myfwmJdUoMrLmf/cN2a8VsCky0qakpJqjjs3IuSUpLs6m5ORqZWaaZbX+Nb/ZbFNiolVxcTa3nldn4/G2+OE8vjf7LnLruzydW697mGx8fLw++OADSVK3bt304osvKjU1VYMGDdLChQv1/PPPKzY21sNRAoD7rV5drshIx58YkZE2rV5d7tVzS1JqaoUSE60ObYmJVqWmVrhkfqPj8bb4AQDGMtlsbaP+Lijw/OVYk0mKjg71iljgWuTWt3lbfjMy/LRli1kDBliVlHT0y+DcPbck5eaalJfnp9jYGsXFef5k5uaaZLF0UGRkWbPi8bb40Thve+/Cdcit7zI6t7XzNzmOIsl9eEP7LnLr28iv7yK3vo38+i5y67u8pUjyuuV2AAAAAOBJFEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7FAkAQAAAIAdiiQAAAAAsEORBAAAAAB2KJIAAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAAAAAOxQJAEAAACAHYokAAAAALBDkQQAAAAAdiiSAAAAAMAORRIAAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7FAkAQAAAIAdiiQAAAAAsEORBAAAAAB2KJIAAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAAAAAOxQJAEAAACAHYokAAAAALBDkQQAAAAAdiiSAAAAAMAORRIAAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7FAkAQAAAIAdf08HANQyl2fJXJEna3CcrCHdPR0OAAAA2iiKJHicqcqi0O1TFFSYVtd2MGqESvssli0gwoORAQAAoC1iuR08LnT7FAUWZji0BRZmKHT79Z4JCAAAAG0aRRI8ylyepaDCNJlkdWg3yaqgwjSZy7M9FBkAAADaKookeJS5Iq+J/lw3RQIAAAAcQpEEj7IGxzbRH+emSAAAAIBDKJLgUdaQHjoYNUI2mR3abTLrYNQIdrkDAACA21EkweNK+yxWZVSSQ1tlVJJK+yz2TEAAAABo09gCHB5nC4jQn/1XyFyeLXNFLs9JAgAAgEdRJMFrWEO6UxwBAADA41huBwAAAAB2KJIAAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAAAAAOxQJAEAAACAHY8USYWFhZo2bZoGDhyowYMHa968eaqurm5w7A033KA+ffooPj6+7l9mZqabI4a3MZdnKbDgU5nLsz0dCgAAAHyMRx4me8cdd6hTp05at26dCgoKdPPNN+u1117TDTfcUG/s999/r1dffVUJCQkeiBTexlRlUej2KQoqTKtrOxg1QqV9FssWEOHByAAAAOAr3H4l6ZdfftHGjRs1c+ZMBQcHKyYmRtOmTdObb75Zb+yuXbtUUlKiU045xd1hwkuFbp+iwMIMh7bAwgyFbr/eMwEBAADA57j9SlJWVpbCw8PVqVOnurZu3bppz549+vPPP9WxY8e69u3btyskJEQzZszQ9u3bFR0drWuvvVYTJ050+rgmk0vCb5HaGLwhltbIrzzL4QpSLZOsCipMk3l/tmpCunsgMnLr68iv7yK3vo38+i5y67uMzm1z53V7kVReXq7g4GCHttqP9+/f71AkVVZWql+/fpoxY4Z69OihDRs2aPr06QoJCdGYMWOcOm5UVGjLg3cRb4qlVanc12h3ZMBeKTreTcE0jNz6NvLru8itbyO/vovc+i5P59btRVL79u1VUVHh0Fb7cUhIiEP7BRdcoAsuuKDu46FDh+qCCy7Qxx9/7HSRVFhYKpvt6GJ2FZPpUMK9IZbWyK+qkyIb6bdUdVZNQanb4rFHbn0b+fVd5Na3kV/fRW59l9G5rZ2/KW4vknr06KHi4mIVFBQoOjpakpSTk6POnTsrNNQx4OXLl9e7alRZWamgoCCnj2uzyWveRN4US2tibd9DB6NGKLAwQyZZ69ptMqsyKknW9t0lD59XcuvbyK/vIre+jfz6LnLruzydW7dv3NC1a1cNGDBAf//731VWVqZdu3Zp4cKFDd5nVFZWprlz5+rHH39UTU2NMjIytHLlSk2aNMndYcNLlPZZrMqoJIe2yqgklfZZ7JmAAAAA4HM8sgX4ggUL9Le//U0jRoyQn5+fLrjgAk2bNk2SFB8fr0ceeUQTJkzQ5MmTtX//ft16660qLCxUTEyMnnjiCQ0cONATYcML2AIi9Gf/FTKXZ8tckStrcJysHtqsAQAAAL7JZLO1jYuUBQWeX7NqMknR0aFeEQtci9z6NvLru8itbyO/vovc+i6jc1s7f1PcvtwOAAAAALwZRRIAAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHb8PR0APMdcniVzRZ6swXGyhnR3+fxBu5cosGidKiOG6WCXq1waj9GxGz0/AAAAvBdFUhtkqrIodPsUBRWm1bUdjBqh0j6LZQuIaPH85pJvFLFphEy2aklSu73LZNtxm4oSMmTteHqL4jE6dqPnBwAAgPdjuV0bFLp9igILMxzaAgszFLr9epfMH7FphPS/AqmOrVoRG5NaHI/RsRs9PwAAALwfRVIbYy7PUlBhmkyyOrSbZFVQYZrM5dktmj9o9xKZbNUyHdZukmSyVSto99Kjjsfo2I2eHwAAAK0DRVIbY67Ia6I/t0XzBxata6L/86OOx+jYjZ4fAAAArQNFUhtjDY5toj+uRfNXRpzdRP+wo47H6NiNnh8AAACtA0VSG2MN6aGDUSNkk9mh3SazDkaNaPFObge7XCObyV+2w9ptkmwm/3q73DkTj9GxGz0/AAAAWgeKpDaotM9iVUYlObRVRiWptM9il8xflJAhmQ7bONHkf6i9hfEYHbvR8wMAAMD7sQV4G2QLiNCf/VfIXJ4tc0Wuy58FZO14ugpGWhS0e6kCiz5v8jlJzsRjdOxGzw8AAADvR5HUhllDuhtaABzsclWzHiJ7NPEYHbvR8wMAAMB7sdwOAAAAAOxQJAEAAACAHYokAAAAALBDkQQAAAAAdiiSAAAAAMAORRIAAAAA2KFIAgAAAAA7PCepDQso+EwBJZtVFZagqujhTY4P2r1EgUXrmnw4bC1zeZbMFXmGPJDVyLkBAADQtlEktUF++3MVvnG4zFWWujZrQKSKEzJU075rvfHmkm8UsWmETLZqSVK7vctk23GbihIyZO14er3xpiqLQrdPUVBhWl3bwagRKu2zWLaAiBbFbuTcAAAAgMRyuzYpfONw+dkVSJLkV2VR+MakBsdHbBoh/a9AqmOrVsQRxodun6LAwgyHtsDCDIVuv/4oI3bP3AAAAIBEkdTmBBR8JnOVRabD2k2SzFUWBRSsdWgP2r1EJlt1g+NNtmoF7V7q0G4uz1JQYZpMsh423qqgwjSZy7OPOnYj5wYAAABqUSS1MQElm5vo3+jwcWDRukbHBxZ97vCxuSKv0fHmitxG+xv/XOPmBgAAAGpRJLUxVWEDm+hPcPi4MuLsRsdXRgxz+NgaHNvoeGtwXKP9jX+ucXMDAAAAtSiS2piq6JGyBkTKdli7TYc2bzh8l7uDXa6RzeTf4Hibyb/eLnfWkB46GDVCNpkPG2/WwagRLdqJzsi5AQAAgFoUSW1QcUKGagIiHdpq/re7XUOKEjIk02EbIZr8D7U3oLTPYlVGJTm0VUYlqbTP4qML2E1zAwAAABJbgLdJNe27ypKUr4CCtQoo2djkc5KsHU9XwUiLgnYvVWDR500+J8kWEKE/+6+QuTxb5opclz7LyMi5AQAAAIkiqU2rih7erIfI1jrY5apmPUS2ljWku2EFjJFzAwAAoG1juR0AAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkWSG/mVZ0l7PpZfebYh8wcUfKb2OY8roGBts8YH5zylsM3jFJz7TLPGh26aoMi1MQrdfGGzxnfYeoUiM7qrwzdXN2t8+5/uUfj6s9R+x5wmx5rLsxRY8KnMBp1Lo+cHAACA9zLZbDabp4Nwh4KCUnnqlZqqLArdPkVBhWl1bQejRqi0z2LZAiJaPL/f/lyFbxwuc5Wlrs0aEKnihAzVtO9ab7x/4ecK3zpeJrs2m6TiAatUHTm03vigvAUKzb6/3vg/ezyuyq7T6o/f+apCf55Rf3yvBaqMubbe+IB9KxW27Yp640v6LlPVsWMcxhp9Lo92fpNJio4O9ejXGYxDfn0XufVt5Nd3kVvfZXRua+dvCleS3CB0+xQFFmY4tAUWZih0+/UumT9843D52RVIkuRXZVH4xqSGx28d33D7lrENtodm399ge8esexoe//OMhsfvuK3B9rBtVzTc/t2k+nMbfC6Nnh8AAADejyLJYObyLAUVpskkq0O7SVYFFaa1eDlXQMFnMldZHK7CHJpfMldZ6i29C855Sqb/9R8+3iTVW3oXumlCo+MPX3rXYesVjY4/fOld+5/uaXS8/dI7o8+l0fMDAACgdaBIMpi5Iq+J/twWzR9QsrmJ/o0OHwcWfd7o+ECLY1EVUPpN4/P/6Xj8wD83HmHk//pL1jt+XJzZ+Pii9Lr/N/pcGj0/AAAAWgeKJINZg2Ob6I9r0fxVYQOb6E9w+LgyYlij4ysjhzt+fmh84/N3dDx+ZceEI4z8X3/YmY4fhyc2Pj4iue7/jT6XRs8PAACA1oEiyWDWkB46GDVCNpkd2m0y62DUCFlDurdo/qrokbIGROrw+9psOrR5Q1W0Y9FT0W2mbP/rP3y8TVJF3J0O7aWDPmh0fOnAFQ7tZf3/3ej4svg3HNr393680fH7e82razP6XBo9PwAAAFoHiiQ3KO2zWJVRSQ5tlVFJKu2z2CXzFydkqCYg0qGt5n+72zU4fsAqp9r/7PG4c+29FjjVXtJ3WbPbjT6XRs8PAAAA78cW4G5k3p+tyIC9slR1lrW9669KBBSsVUDJRlWFJdS7gtSQ4NxnFGhZq8rI4fWuIDUkdPOFCvhzs6o6Dqx3BakhHb65WoEl61UZdma9K0gNab9jjgKL0lUZkexwBakh5vJsmStyZQ2OM+QKj7PzsxWpbyO/vovc+jby67vIre/yli3AKZLciDe07yK3vo38+i5y69vIr+8it77LW4okltsBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGDH39MB4MjM5VkyV+QZ9sBUZwUUfKaAks3Nflits+MBAAAAb0CR5IVMVRaFbp+ioMK0uraDUSNU2mexbAERbo/Hb3+uwjcOl7nKUtdmDYhUcUKGatp3bfF4AAAAwJuw3M4LhW6fosDCDIe2wMIMhW6/3iPxhG8cLj+7gkeS/KosCt+Y5JLxAAAAgDehSPIy5vIsBRWmySSrQ7tJVgUVpslcnu3WeAIKPpO5yiLTYe0mSeYqiwIK1rZoPAAAAOBtKJK8jLkir4n+XDdFckhAyeYm+je2aDwAAADgbSiSvIw1OLaJ/jg3RXJIVdjAJvoTWjQeAAAA8DYUSV7GGtJDB6NGyCazQ7tNZh2MGuH2Xe6qokfKGhAp22HtNh3ajOHwXeucHQ8AAAB4G4okL1TaZ7Eqo5Ic2iqjklTaZ7FH4ilOyFBNQKRDW83/dqtzxXgAAADAm7AFuBeyBUToz/4rZC7Plrki1+PPSapp31WWpHwFFKxVQMnGJp975Ox4AAAAwJtQJHkxa0h3r3iIbK2q6OFOFTvOjgcAAAC8AcvtAAAAAMAORRIAAAAA2KFIAgAAAAA7FEkAAAAAYIciCQAAAADsUCQBAAAAgB2KJAAAAACw45EiqbCwUNOmTdPAgQM1ePBgzZs3T9XV1Q2O/fzzzzV+/Hj169dPY8aMUXp6upujBQAAANCWeKRIuuOOO9S+fXutW7dOy5cv1/r16/Xaa6/VG5efn6/p06fr9ttv1+bNmzV9+nTdcccd2rdvn/uDBgAAANAmuL1I+uWXX7Rx40bNnDlTwcHBiomJ0bRp0/Tmm2/WG7tixQoNHDhQI0eOlL+/v8aOHatBgwZp2bJl7g4bAAAAQBvh9iIpKytL4eHh6tSpU11bt27dtGfPHv35558OY7Ozs3XyySc7tHXv3l07duxwS6wAAAAA2h5/dx+wvLxcwcHBDm21H+/fv18dO3ZsdGy7du20f/9+p49rMh1FsC5WG4M3xALXIre+jfz6LnLr28iv7yK3vsvo3DZ3XrcXSe3bt1dFRYVDW+3HISEhDu3BwcE6cOCAQ9uBAwfqjWuOqKhQpz/HKN4UC1yL3Po28uu7yK1vI7++i9z6Lk/n1u1FUo8ePVRcXKyCggJFR0dLknJyctS5c2eFhjqejJNPPlk//PCDQ1t2drZOO+00p49bWFgqm+3o43YFk+lQwr0hFrgWufVt5Nd3kVvfRn59F7n1XUbntnb+prj9nqSuXbtqwIAB+vvf/66ysjLt2rVLCxcu1MSJE+uNnTBhgjZu3KhVq1apurpaq1at0saNG3X++ee7O2wAAAAAbYTJZnN//V1QUKC//e1v2rBhg/z8/HTBBRfo7rvvltlsVnx8vB555BFNmDBBkrRu3To9/fTT2rlzp0444QTNnDlTw4YNc3fIAAAAANoIjxRJAAAAAOCtPPIwWQAAAADwVhRJAAAAAGCHIgkAAAAA7FAkAQAAAIAdiiQAAAAAsEORBAAAAAB2KJIAAAAAwA5FEgAAAADYoUhyM4vFolGjRmnDhg2eDgUusmPHDl133XVKSEjQkCFDNGvWLFksFk+HBRdYv369LrnkEvXv319DhgzR3LlzdeDAAU+HBReyWq26+uqrdc8993g6FLjQqlWrdMoppyg+Pr7u38yZMz0dFlykuLhYs2bN0uDBgzVo0CBNmzZNv//+u6fDQgt98MEHDu/Z+Ph4nXbaaTrttNM8Eg9Fkhtt2bJFkyZN0s6dOz0dClzkwIEDuuGGGxQfH68vvvhCK1euVHFxse677z5Ph4YWslgsmjp1qi6//HJt3rxZK1as0MaNG7Vo0SJPhwYXeuGFF7R582ZPhwEX2759u84//3x98803df+eeuopT4cFF5k+fbr279+vNWvWKD09XWazWQ888ICnw0ILTZgwweE9+8knnyg8PFzz5s3zSDz+HjlqG7RixQotWLBAM2fO1IwZMzwdDlxkz5496tWrl2655RaZzWYFBgZq0qRJmjVrlqdDQwtFRkbqq6++UocOHWSz2VRcXKyDBw8qMjLS06HBRdavX69PP/1U55xzjqdDgYtt375dY8aM8XQYMMD333+v7777ru77syTNnTtXf/zxh4cjgyvZbDbNnDlTSUlJOv/88z0SA1eS3GTo0KFas2aNxo4d6+lQ4EJxcXF65ZVXZDab69pWr16tU0891YNRwVVqfwAPGzZM48eP1zHHHKOLLrrIw1HBFQoLCzVnzhzNnz9fwcHBng4HLlRTU6MffvhBGRkZSk5OVmJioh544AGVlJR4OjS4wLZt29S9e3f997//1ahRozR06FA98cQTOuaYYzwdGlzo/fffV3Z2tkeXQlMkuckxxxwjf38u3Pkym82mZ599Vunp6ZozZ46nw4ELffrpp8rMzJSfn59uu+02T4eDFqqpqdHMmTN13XXXqVevXp4OBy5msVh0yimnaPTo0Vq1apXeeust5efnc0+SjygpKdHPP/+s/Px8rVixQu+995727dun2bNnezo0uEhNTY1eeuklpaSk1P2x0hP4rR1wgbKyMt1777364YcftHTpUvXs2dPTIcGF2rVrp3bt2mnmzJm65JJLVFJSorCwME+HhaOUmpqqwMBAXX311Z4OBQaIjo7Wm2++WfdxcHCwZs6cqUsvvVRlZWUe/aULLRcYGChJmjNnjoKCgtShQwfdcccduvTSS1VeXq6QkBAPR4iW2rBhg37//XdNnDjRo3FwJQlooZ07d+riiy9WWVmZli9fToHkI7Zu3apzzz1XlZWVdW2VlZUKCAhgeVYr9/7772vjxo0aOHCgBg4cqJUrV2rlypUaOHCgp0ODC+zYsUNPP/20bDZbXVtlZaX8/PzqfsFG69W9e3fV1NSoqqqqrq2mpkaSHHKO1mv16tUaNWqU2rdv79E4KJKAFigpKdHkyZPVv39/vfrqq9zU70N69uypAwcOaP78+aqsrNSvv/6qJ554QhMnTuQXrVbuk08+0datW7V582Zt3rxZ48aN07hx49jlzkeEh4frzTff1CuvvKLq6mrt2bNHTz31lC688ELeuz7grLPOUkxMjO677z6Vl5fLYrHo2Wef1ciRI7lK6CO2bNmiQYMGeToMiiSgJd59913t2bNHH3/8sQYMGOCwtz9at5CQEL3yyivKysrSkCFDdPXVV+uss85ie3fAy3Xu3FmpqalKS0tTQkKCLr74YvXp00cPPvigp0ODCwQEBOiNN96Q2WzW6NGjNXr0aHXu3Fl///vfPR0aXGT37t069thjPR2GTDauTQIAAABAHa4kAQAAAIAdiiQAAAAAsEORBAAAAAB2KJIAAAAAwA5FEgAAAADYoUgCAAAAADsUSQAAAABghyIJAOB1rFardu3a5ekwWiw/P9/TIQAAjgJFEgDApYYPH64+ffooPj5e8fHx6tevn4YOHaonnnhCNTU1zZpjxowZeu+995o19t1339Xw4cMb7NuwYYN69uzZ3NBd6scff9S4cePqPr7nnnt0zz33eCQWAIBz/D0dAADA9zzyyCO66KKL6j7++eefde211yo4OFi33XZbk59fVFRkZHhuUVpaqqqqKk+HAQA4ClxJAgAYrmfPnho0aJB+/PFHSVJlZaWee+45jRgxQgkJCbrxxhv1yy+/SJLmzJmjzZs3KzU1VSkpKZKktWvX6rLLLtOZZ56pvn376qqrrnLJUrbG4qiN+4033tDo0aMVHx+vyy67TD///HNd/1dffaULLrhA/fv312WXXaannnpKV199tXbt2qUbb7xRkhQfH69vvvlGklRYWKjbbrtNgwcP1tChQ7V06dIWvwYAgOtRJAEADFVVVaUNGzbo66+/1pAhQyRJzz77rDIyMvTaa69p3bp16tu3r66//nodPHhQ8+bN08CBAzV16lS9/PLL2rt3r26//XbddNNNWr9+vTIyMmSz2fTiiy+2OLbG4qj10UcfaenSpcrMzFRwcLCefPJJSdLu3buVkpKiyy+/XBs3btTdd9+tZcuWSZJiYmL0z3/+U5L0zTffKD4+XpL09ddf67LLLtPXX3+tu+66S48++qj27dvX4tcBAHAtiiQAgMs98sgjGjhwoAYOHKgzzzxTc+fO1XXXXaerrrpKNptNb731lu68807FxMQoKChIt9xyi6qqqpSRkVFvrsjISH300UcaPny4ysrKtHfvXkVERLS4uGhuHFdffbWOOeYYhYaGasyYMXVXsD788EP17t1bkyZNkr+/vwYOHKhLL7200WMOGTJEZ511lkwmk8477zzZbDaf2KACAHwN9yQBAFzuoYcecrgnyV5hYaH279+v22+/XX5+f/2trqqqSr/++mu98QEBAVq5cqXeeustmUwmnXzyySorK5O/f8t+hFkslmbFER0dXff//v7+stlskqTffvtNJ5xwgsOcMTEx2r59+xGPGR4eXvf/gYGBkg7t5AcA8C4USQAAt4qIiFBQUJAWL16sfv361bXn5uaqU6dO9cZ//PHHWrp0qf7zn//opJNOkiTNnTtX//d//+fWOA53wgknKD093aFtz549LYoJAOAdWG4HAHArPz8/TZw4UfPnz9fevXtVU1OjFStWaNy4cXWbJgQGBqq0tFTSoV3i/Pz81K5dO9lsNmVmZuq9995zaue4vXv3Ovz7/fffmxVHY84//3z99NNPeu+992S1WvXdd9/pv//9b11/UFBQXfwAgNaFK0kAALebPXu2nn/+eV1xxRUqLi5WTEyMFixYoFNOOUWSdMEFF+jhhx/W999/r9dee01btmzReeedJ7PZrLi4OE2ePFlvvvmmKisrm3W8YcOGOXwcHR2tL7/8ssk4GtO5c2ctWLBATz75pB555BH17t1bQ4cOrdu+/OSTT9aAAQN09tln67nnnnPyDAEAPMlkq11cDQAAmu23335TUVGRQ0H1+OOP648//tD8+fM9GBkAoKVYbgcAwFEoKirSFVdcoe+//16StGPHDn3wwQdKTk72cGQAgJbiShIAAEfp7bff1j//+U/98ccfio6O1pVXXqlrr73W02EBAFqIIgkAAAAA7LDcDgAAAADsUCQBAAAAgB2KJAAAAACwQ5EEAAAAAHYokgAAAADADkUSAAAAANihSAIAAAAAOxRJAAAAAGCHIgkAAAAA7Pw/vSfUrDn+6gsAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 103
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "As we can see, the petal feature is more useful than the sepal feature for classification of the iris flowers.",
   "id": "2ed5b861490f960f"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:51:38.637760Z",
     "start_time": "2024-11-25T18:51:38.380692Z"
    }
   },
   "cell_type": "code",
   "source": [
    "iris.hist(edgecolor='black', linewidth=1.2)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(12,6)\n",
    "plt.show()"
   ],
   "id": "550fa32af5ff1a49",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x600 with 6 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:52:05.843332Z",
     "start_time": "2024-11-25T18:52:05.426686Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.subplot(2,2,1)\n",
    "sns.violinplot(x='species',y='petal length (cm)',data=iris)\n",
    "plt.subplot(2,2,2)\n",
    "sns.violinplot(x='species',y='petal width (cm)',data=iris)\n",
    "plt.subplot(2,2,3)\n",
    "sns.violinplot(x='species',y='sepal length (cm)',data=iris)\n",
    "plt.subplot(2,2,4)\n",
    "sns.violinplot(x='species',y='sepal width (cm)',data=iris)"
   ],
   "id": "bb3d2dbd8ed800a2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='species', ylabel='sepal width (cm)'>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 106
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "The problem is a classification problem. Thus we will be using the classification algorithms to train our model.",
   "id": "c7385388e8a59590"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:52:09.209747Z",
     "start_time": "2024-11-25T18:52:09.205564Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split"
   ],
   "id": "dbe0ffbd3c6b9ff4",
   "outputs": [],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:52:10.709934Z",
     "start_time": "2024-11-25T18:52:10.704211Z"
    }
   },
   "cell_type": "code",
   "source": "iris.shape",
   "id": "78a46ede30b23d3c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T18:52:11.625142Z",
     "start_time": "2024-11-25T18:52:11.512618Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(7,4))\n",
    "sns.heatmap(iris.corr(), annot = True)\n",
    "plt.show()"
   ],
   "id": "5cb4dc7569a7f772",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 700x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 109
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "We observe that the Sepal Width and Length are not correlated while the Petal Width and Length are highly correlated\n",
    "\n",
    "We will use all the features for training the algorithm and check the accuracy.\n",
    "\n",
    "Then we will use 1 Petal Feature and 1 Sepal Feature to check the accuracy of the algorithm as we are using only 2 features that are not correlated. Thus, we can have a variance in the dataset which may help in better accuracy. We will check it later."
   ],
   "id": "a8e89e9a36b6210c"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:22:40.599087Z",
     "start_time": "2024-11-25T19:22:40.586663Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train, test = train_test_split(iris, test_size=0.3)\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ],
   "id": "4b05e1d5166d04cb",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(105, 5)\n",
      "(45, 5)\n"
     ]
    }
   ],
   "execution_count": 145
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:22:42.059105Z",
     "start_time": "2024-11-25T19:22:42.054391Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_X = train[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n",
    "train_y = train.species\n",
    "test_X = test[['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']]\n",
    "test_y = test.species"
   ],
   "id": "d4dba8b5164e8cae",
   "outputs": [],
   "execution_count": 146
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:22:57.040719Z",
     "start_time": "2024-11-25T19:22:57.033503Z"
    }
   },
   "cell_type": "code",
   "source": "train_X.head()",
   "id": "a26e17458d13d478",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "111                6.4               2.7                5.3               1.9\n",
       "129                7.2               3.0                5.8               1.6\n",
       "113                5.7               2.5                5.0               2.0\n",
       "136                6.3               3.4                5.6               2.4\n",
       "19                 5.1               3.8                1.5               0.3"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.3</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 148
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:22:57.492247Z",
     "start_time": "2024-11-25T19:22:57.489271Z"
    }
   },
   "cell_type": "code",
   "source": "train_y.head()",
   "id": "72e3c49eebf7802a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "111    2\n",
       "129    2\n",
       "113    2\n",
       "136    2\n",
       "19     0\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 149
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:22:58.007668Z",
     "start_time": "2024-11-25T19:22:58.000484Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = svm.SVC()\n",
    "model.fit(train_X,train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of the SVM is:', metrics.accuracy_score(prediction, test_y))"
   ],
   "id": "8385725c964235f4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM is: 1.0\n"
     ]
    }
   ],
   "execution_count": 150
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:06.479606Z",
     "start_time": "2024-11-25T19:23:06.461377Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_X,train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of the Logistic Regression is', metrics.accuracy_score(prediction, test_y))"
   ],
   "id": "b32c445130bf452e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression is 0.9777777777777777\n"
     ]
    }
   ],
   "execution_count": 151
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:07.398280Z",
     "start_time": "2024-11-25T19:23:07.390791Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_X,train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of the Decision Tree is',metrics.accuracy_score(prediction, test_y))"
   ],
   "id": "8870aaf92dacde77",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree is 0.9555555555555556\n"
     ]
    }
   ],
   "execution_count": 152
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:08.629708Z",
     "start_time": "2024-11-25T19:23:08.619387Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(train_X,train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of the KNN is', metrics.accuracy_score(prediction,test_y))"
   ],
   "id": "dbc1771235bc7a56",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN is 0.9555555555555556\n"
     ]
    }
   ],
   "execution_count": 153
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:18.415199Z",
     "start_time": "2024-11-25T19:23:18.313591Z"
    }
   },
   "cell_type": "code",
   "source": [
    "a_index = list(range(1,11))\n",
    "a = []\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "for i in list(range(1,11)):\n",
    "    model = KNeighborsClassifier(n_neighbors=i)\n",
    "    model.fit(train_X,train_y)\n",
    "    prediction = model.predict(test_X)\n",
    "    a.append(pd.Series(metrics.accuracy_score(prediction, test_y)))\n",
    "plt.plot(a_index, a)\n",
    "plt.xticks(x)"
   ],
   "id": "463d92c2f0c2ec62",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x1765c2600>,\n",
       "  <matplotlib.axis.XTick at 0x17673fda0>,\n",
       "  <matplotlib.axis.XTick at 0x16c7f4bf0>,\n",
       "  <matplotlib.axis.XTick at 0x1765264b0>,\n",
       "  <matplotlib.axis.XTick at 0x1765e9fa0>,\n",
       "  <matplotlib.axis.XTick at 0x1477e52b0>,\n",
       "  <matplotlib.axis.XTick at 0x1477e7da0>,\n",
       "  <matplotlib.axis.XTick at 0x16c7785c0>,\n",
       "  <matplotlib.axis.XTick at 0x1477e5fd0>,\n",
       "  <matplotlib.axis.XTick at 0x1477e4d70>],\n",
       " [Text(1, 0, '1'),\n",
       "  Text(2, 0, '2'),\n",
       "  Text(3, 0, '3'),\n",
       "  Text(4, 0, '4'),\n",
       "  Text(5, 0, '5'),\n",
       "  Text(6, 0, '6'),\n",
       "  Text(7, 0, '7'),\n",
       "  Text(8, 0, '8'),\n",
       "  Text(9, 0, '9'),\n",
       "  Text(10, 0, '10')])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 154
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "### We used all the features of iris in above models. Now we will use Petals and Sepals Seperately",
   "id": "cceec82f8019eeef"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:39.668564Z",
     "start_time": "2024-11-25T19:23:39.664003Z"
    }
   },
   "cell_type": "code",
   "source": [
    "petal = iris[['petal length (cm)', 'petal width (cm)', 'species']]\n",
    "sepal = iris[['sepal length (cm)', 'sepal width (cm)', 'species']]"
   ],
   "id": "9b088d596655fe48",
   "outputs": [],
   "execution_count": 155
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:40.278456Z",
     "start_time": "2024-11-25T19:23:40.270346Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_p, test_p = train_test_split(petal, test_size = 0.3, random_state = 0)\n",
    "train_x_p = train_p[['petal length (cm)', 'petal width (cm)']]\n",
    "train_y_p = train_p.species\n",
    "test_x_p = test_p[['petal length (cm)', 'petal width (cm)']]\n",
    "test_y_p = test_p.species\n",
    "\n",
    "train_s, test_s = train_test_split(sepal, test_size = 0.3, random_state = 0)\n",
    "train_x_s = train_s[['sepal length (cm)', 'sepal width (cm)']]\n",
    "train_y_s = train_s.species\n",
    "test_x_s = test_s[['sepal length (cm)', 'sepal width (cm)']]\n",
    "test_y_s = test_s.species"
   ],
   "id": "2e8c1859b79b3875",
   "outputs": [],
   "execution_count": 156
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:49.826510Z",
     "start_time": "2024-11-25T19:23:49.814831Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = svm.SVC()\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the SVM using Petals is:', metrics.accuracy_score(prediction, test_y_p))\n",
    "\n",
    "model = svm.SVC()\n",
    "model.fit(train_x_s,train_y_s)\n",
    "prediction = model.predict(test_x_s)\n",
    "print('The accuracy of the SVM using Sepal is:', metrics.accuracy_score(prediction, test_y_s))"
   ],
   "id": "e8f296e63bc0f601",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM using Petals is: 0.9777777777777777\n",
      "The accuracy of the SVM using Sepal is: 0.8\n"
     ]
    }
   ],
   "execution_count": 157
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:51.631035Z",
     "start_time": "2024-11-25T19:23:51.613887Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the Logistic Regression using Petals is:', metrics.accuracy_score(prediction, test_y_p))\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(train_x_s,train_y_s)\n",
    "prediction = model.predict(test_x_s)\n",
    "print('The accuracy of the Logistic Regression using Sepals is:', metrics.accuracy_score(prediction, test_y_s))"
   ],
   "id": "e264772b6b1248e4",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Logistic Regression using Petals is: 0.9777777777777777\n",
      "The accuracy of the Logistic Regression using Sepals is: 0.8222222222222222\n"
     ]
    }
   ],
   "execution_count": 158
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:53.664331Z",
     "start_time": "2024-11-25T19:23:53.654733Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the Decision Tree using Petals is:', metrics.accuracy_score(prediction, test_y_p))\n",
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_x_s,train_y_s)\n",
    "prediction = model.predict(test_x_s)\n",
    "print('The accuracy of the Decision Tree using Sepals is:', metrics.accuracy_score(prediction, test_y_s))"
   ],
   "id": "417ec57d4dc07caa",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the Decision Tree using Petals is: 0.9555555555555556\n",
      "The accuracy of the Decision Tree using Sepals is: 0.6444444444444445\n"
     ]
    }
   ],
   "execution_count": 159
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-11-25T19:23:55.734286Z",
     "start_time": "2024-11-25T19:23:55.720104Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(train_x_p, train_y_p)\n",
    "prediction = model.predict(test_x_p)\n",
    "print('The accuracy of the KNN using Petals is:', metrics.accuracy_score(prediction, test_y_p))\n",
    "\n",
    "model = KNeighborsClassifier(n_neighbors=3)\n",
    "model.fit(train_x_s,train_y_s)\n",
    "prediction = model.predict(test_x_s)\n",
    "print('The accuracy of the KNN using Sepals is:', metrics.accuracy_score(prediction, test_y_s))"
   ],
   "id": "9ddd96c1c4610780",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the KNN using Petals is: 0.9777777777777777\n",
      "The accuracy of the KNN using Sepals is: 0.7333333333333333\n"
     ]
    }
   ],
   "execution_count": 160
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "Observations:\n",
    "- Using Petals over Sepal for training the data gives a much better accuracy.\n",
    "- This was expected as we saw in the heatmap above that the correlation between the Sepal Width and Length was very low whereas the correlation between Petal Width and Length was very high."
   ],
   "id": "4454fa82f0917ab1"
  }
 ],
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    "name": "ipython",
    "version": 2
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   "file_extension": ".py",
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