IRIS FLOWER CLASSIFICATION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#loading the dataset\n",
    "df = pd.read_csv('Iris.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display stats about data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "# to basic info about datatype\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to display no. of samples on each class\n",
    "df['species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sepal_length    0\n",
       "sepal_width     0\n",
       "petal_length    0\n",
       "petal_width     0\n",
       "species         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preprocessing the dataset\n",
    "# check for null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Exploratory Data Analysis\n",
    "# histograms\n",
    "df['sepal_length'].hist()\n",
    "df['sepal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['petal_length'].hist()\n",
    "df['petal_width'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scatterplot\n",
    "colors = ['red', 'orange', 'blue']\n",
    "species = ['Iris-virginica','Iris-versicolor','Iris-setosa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18a011ebfd0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['species'] == species[i]]\n",
    "    plt.scatter(x['petal_length'], x['petal_width'], c = colors[i], label=species[i])\n",
    "plt.xlabel(\"petal_length\")\n",
    "plt.ylabel(\"petal_width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18a014c6690>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['species'] == species[i]]\n",
    "    plt.scatter(x['sepal_length'], x['sepal_width'], c = colors[i], label=species[i])\n",
    "plt.xlabel(\"sepal Length\")\n",
    "plt.ylabel(\"sepal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18a0181a3d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['species'] == species[i]]\n",
    "    plt.scatter(x['sepal_length'], x['petal_length'], c = colors[i], label=species[i])\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Petal Length\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x18a016c6f50>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAj4AAAGwCAYAAACpYG+ZAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAABVG0lEQVR4nO3deXgUVdo28LtpyEYW1iyYQFijgMgSlKAhBBAUvjERkFWGjNeoiMgSCYjODKjzvlHwBWQ0Ko6KoqJCAuIQHVCTAAIjIAFljRhMDAnIYoc9JDnfHzXd0kmnU52c7q7uun/X1ZeTqtPVT52usR+rzjmPQQghQERERKQDTdwdABEREZGrMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG03dHYCrVVdX4+TJkwgKCoLBYHB3OERERKSCEAIXLlxAu3bt0KRJw+/b6C7xOXnyJKKiotwdBhERETVAcXExIiMjG/x+3SU+QUFBAJSOCw4OdnM0REREpEZ5eTmioqIsv+MNpbvEx/x4Kzg4mIkPERGRh2nsMBUObiYiIiLdYOJDREREusHEh4iIiHSDiQ8RERHpBhMfIiIi0g0mPkRERKQbTHyIiIhIN5j4EBERkW4w8SEiIiLd0N3KzURE9aqoADIygOPHgc6dgenTAR8fd0clV1UVsG0bUFoKREQA8fGA0ejuqIiczq13fNLT09G/f38EBQUhNDQUycnJOHr0qN335ObmwmAw1HodOXLERVETkVebNw8ICADmzAFeeUX5Z0CAst1bZGUB0dFAYiIwaZLyz+hoZTuRl3Nr4pOXl4fHH38cu3btwpYtW1BZWYnhw4fj0qVL9b736NGjKC0ttby6du3qgoiJyKvNmwcsWaLcDblRVZWy3RuSn6wsYOxY4JdfrLeXlCjbmfyQlzMIIYS7gzD79ddfERoairy8PAwaNMhmm9zcXCQmJuL8+fNo0aKFw59RXl6OkJAQmEwmFiklot9VVCh3dmomPTcyGoHLlz33sVdVlXJnp2bSY2YwAJGRQGEhH3uR5sj6/dbU4GaTyQQAaNWqVb1t+/Tpg4iICAwdOhQ5OTl1trt27RrKy8utXkREtWRk2E96AGV/RoZr4nGGbdvqTnoAQAiguFhpR+SlNJP4CCGQmpqKu+66Cz179qyzXUREBFauXInMzExkZWUhJiYGQ4cOxdatW222T09PR0hIiOUVFRXlrFMgIk92/LjcdlpUWiq3HZEH0sysrhkzZuDAgQPYvn273XYxMTGIiYmx/B0XF4fi4mK89NJLNh+PLViwAKmpqZa/y8vLmfwQUW2dO8ttp0UREXLbEXkgTdzxeeKJJ7Bx40bk5OQgMjLS4fcPGDAABQUFNvf5+voiODjY6kVEVMv06fWPazEalXaeKj5eGcNjMNjebzAAUVFKOyIv5dbERwiBGTNmICsrC19//TU6duzYoOPs27cPEfwvFCJqDB8f4Ia7wzalpnruwGZASdxefln53zWTH/Pfy5dzYDN5Nbc+6nr88cfx4Ycf4tNPP0VQUBDKysoAACEhIfD39wegPKoqKSnBe++9BwBYvnw5oqOj0aNHD1RUVOD9999HZmYmMjMz3XYeROQlFi9W/rl0qfVAZ6NRSXrM+z3Z6NHAunXArFnWA50jI5WkZ/Rot4VG5Apunc5uqON26zvvvIOUlBQAQEpKCk6cOIHc3FwAwOLFi7Fy5UqUlJTA398fPXr0wIIFCzBy5EhVn8np7ERUL67cTKQ5sn6/NbWOjysw8SEiIvI8XrmODxEREZEzMfEhIiIi3dDMOj5EZIMexmF4+zl6+/kReRgmPkRalZVle+bNyy97z8wbbz9Hbz8/Ig/ER11EWqSHCtrefo7efn5EHoqzuoi0Rg8VtL39HL39/IjcgLO6iLyVHipoe/s5evv5EXkwJj5EWqOHCtrefo7efn5EHoyJD5HW6KGCtrefo7efH5EHY+JDpDV6qKDt7efo7edH5MGY+BBpjR4qaHv7OXr7+RF5MCY+RFpkrqB9003W2yMjle3esAaMt5+jt58fkYfidHYiLdPDqr/efo7efn5ELsLq7A3ExIeIiMjzcB0fIiIiIgcx8SEiIiLdYJFSIjOOxVCvogLIyACOHwc6dwamTwd8fBp2LFn9fuUKkJYGFBQAXbsCS5YA/v4Ni0kmXldE2iJ0xmQyCQDCZDK5OxTSksxMISIjhVCKCSivyEhlO1lLSxPCaLTuK6NR2e4oWf2elGR9DPMrKcnxmGTidUUkjazfbz7qImIVbfXmzVPupFRVWW+vqlK2z5un/liy+j05Gfj0U9v7Pv1U2e8OvK6INImzukjfWEVbvYoKICCgdtJzI6MRuHy5/sdesvr9yhUlpvpcvuzax168roik46wuIhlYRVu9jAz7SQ+g7M/IqP9Ysvo9La3+z3KknSy8rog0i4kP6RuraKt3/Li8drL6vaBA3XHUtpOF1xWRZjHxIX1jFW31OneW105Wv3ftqu44atvJwuuKSLM4xof0zTwWo6REefxQE8di/M4ZY3wa2+9aH+PD64pIGo7xIZKBVbTV8/EBUlPtt0lNVbeej6x+9/cHkpLst0lKcv16PryuiDSLiQ8Rq2irt3ixMlC45g+20ahsX7xY/bFk9fuGDXUnP0lJyn534HVFpEl81EVkxhV21ePKzerxuiKSgtXZG4iJDxERkefhGB8iIiIiBzHxISIiIt1gdXYiLdPq+BCtxkWejdcVuQATHyKtysoCZs2yLn0QGalMk3bnjCCtxkWejdcVuQgfdRFpkVYre2s1LvJsvK7IhTiri0hrtFrZW6txkWfjdUUqcVYXkbfSamVvrcZFno3XFbkYEx8irdFqZW+txkWejdcVuRgTHyKt0Wplb63GRZ6N1xW5GBMfIq2Jj1fGNNQsbmlmMABRUUo7xkWejtcVuRgTHyKt0Wplb63GRZ6N1xW5GBMfIi3SamVvrcZFno3XFbkQp7MTaZlWV7LValzk2XhdkR2szt5ATHyIiIg8D9fxISIiInIQEx8iIiLSDRYpJdIyrY55uHIFSEsDCgqArl2BJUsAf/+GHUuL56jFmIhICt7xIdKqrCylhlFiIjBpkvLP6Gj3F2xMTgYCAoBXXwU2b1b+GRCgbHeUFs9RizERkTRMfIi0SKvVqpOTgU8/tb3v008dS360eI5ajImIpOKsLiKt0Wq16itXlDs79bl8uf7HXlo8Ry3GREQWnNVF5K20Wq06LU1eOy2eoxZjIiLpmPgQaY1Wq1UXFMhrp8Vz1GJMRCQdEx8irdFqtequXeW10+I5ajEmIpKOY3yItMY81qSkRHm8UpM3jfHR0jlqMSYisuAYHyJvpdVq1f7+QFKS/TZJSerW89HiOWoxJiKSjokPkRZptVr1hg11Jz9JScp+tbR4jlqMiYik4qMuIi3T6grCXLmZiFyM1dkbiIkPERGR5+EYHyIiIiIHMfEhIiIi3WB1dvJs3j4Wo6ICyMgAjh8HOncGpk8HfHzcHZU2+12LMZFj+B2SKwg3+t///V8RGxsrAgMDRdu2bUVSUpI4cuRIve/Lzc0Vffv2Fb6+vqJjx47itddeU/2ZJpNJABAmk6kxoZMWZGYKERkphLLqivKKjFS2e4O0NCGMRuvzMxqV7e6kxX7XYkzkGH6HVA9Zv99uTXxGjBgh3nnnHfHDDz+I/Px8MWrUKNG+fXtx8eLFOt/z008/iYCAADFr1ixx6NAh8eabb4pmzZqJdevWqfpMJj5eIjNTCIPB+l+SgLLNYPD8f1mmpdU+txtf7kp+tNjvWoyJHMPvkFSQ9futqVldv/76K0JDQ5GXl4dBgwbZbDN//nxs3LgRhw8ftmybNm0a9u/fj507d9b7GZzV5QW8vYp2RYWyQnJVVd1tjEZlhWRXPvbSYr9rMSZyDL9DUskrZ3WZTCYAQKtWrepss3PnTgwfPtxq24gRI7Bnzx5cv369Vvtr166hvLzc6kUezturaGdk2E96AGV/RoZr4jHTYr9rMSZyDL9DcjHNJD5CCKSmpuKuu+5Cz54962xXVlaGsLAwq21hYWGorKzEmTNnarVPT09HSEiI5RUVFSU9dnIxb6+iffy43HayaLHftRgTOYbfIbmYZhKfGTNm4MCBA1izZk29bQ016uiYn9bV3A4ACxYsgMlksryKi4vlBEzu4+1VtDt3lttOFi32uxZjIsfwOyQX00Ti88QTT2Djxo3IyclBZGSk3bbh4eEoKyuz2nb69Gk0bdoUrVu3rtXe19cXwcHBVi/ycPHxyjN/G4kuAGV7VJTSzhNNn17/WAajUWnnSlrsdy3GRI7hd0gu5tbERwiBGTNmICsrC19//TU6duxY73vi4uKwZcsWq22bN29GbGwsmjVr5qxQSUu8vYq2jw+Qmmq/TWqq69fz0WK/azEmcgy/Q3K1xk8wa7jHHntMhISEiNzcXFFaWmp5Xb582dLmqaeeElOmTLH8bZ7OPmfOHHHo0CHx1ltvcTq7Xtla9yMqynumvnrSOj7u7nctxkSO4XdI9fCK6ey2xuQAwDvvvIOUlBQAQEpKCk6cOIHc3FzL/ry8PMyZMwcHDx5Eu3btMH/+fEybNk3VZ3I6u5fx9pVeuXKzZ8dEjuF3SHawOnsDMfEhIiLyPF65jg8RERGRMzHxISIiIt1gdXbybFocE3DlCpCWBhQUAF27AkuWAP7+DTuWFs+PiMiD8Y4Pea6sLKXGT2IiMGmS8s/oaGW7uyQnK3W2Xn0V2LxZ+WdAgLLdUVo8PyIiD8fEhzxTVhYwdmztGj8lJcp2dyQHycnAp5/a3vfpp44lP1o8PyIiL8BZXeR5tFjN+coV5c5OfS5frv+xlxbPj4jIzTiri/RLi9Wc09LktdPi+REReQkmPuR5tFjNuaBAXjstnh8RkZdg4kOeR4vVnLt2lddOi+dHROQlOMaHPI95DExJifLYpyZvGeOjpfMjInIzjvEh/dJiNWd/fyApyX6bpCR16/lo8fyIiLwEEx/yTKNHA+vWATfdZL09MlLZPnq062PasKHu5CcpSdmvlhbPj4jIC/BRF3k2La5szJWbiYikY3X2BmLiQ0RE5Hk4xoeIiIjIQUx8iIiISDdYnZ08mxbHwMiMSeaxKiqAjAzg+HGgc2dg+nTAx6dhx7p4EZgy5fdjrV4NBAY27FiyzvF6BfB1BnD2ONC6MzBkOtCsgedHRN5L6IzJZBIAhMlkcnco1FiZmUJERgqhrHajvCIjle3eEJPMY6WlCWE0Wh/LaFS2O6p/f+vjmF/9+zt+LFnn+EmaEK8ahfgAv79eNSrbicgryPr95uBm8kzm6uU1L1/zOjfumPItMyaZx5o3T5lZVpe0NGDxYnXHuv12YPfuuvf37w98+626Y8k6x7XzgIr/nt+Nyx5V//dvnzTgAZXnR0SaxVldDcTExwtosXq5zJhkHquiQllRuqqq7jZGo7KidH2PvS5eBIKC7LcBgAsX6n/sJescr1cAbwYAIVXWSY9ZNYByI/DwZT72IvJwnNVF+qXF6uUyY5J5rIwM+0kPoOzPyKj/WFOm1N9GbTtZ5/h1BtCijqQHUP4N16JKaUdEBCY+5Im0WL1cZkwyj3X8uLpjqWkn81iyzvGsypjUtiMir8fEhzyPFquXy4xJ5rE6d1Z3LDXtZB5L1jm2VhmT2nZE5PU4xoc8jxarl8uMSeaxtD7Gp7HnaB7jE1xl+z/jOMaHyGtwjA/plxarl8uMSeaxfHyA1FT7bVJT1a3nExiozNqyp39/dev5yDrHZj5A21RljE91jX3mWV1tU5n0EJEFEx/yTFqsXi4zJpnHWrxYmbJeM4kwGh2byg4oU9XrSn4cmcoOyDvHBxYrU9bLa5xfuZFT2YmoFj7qIs/GlZvV48rNROTBuI5PAzHxISIi8jwc40NERETkICY+REREpBuszk6eTYtjfPSgugr4dRtwpRTwjwDaxgNN2O9EpH1MfMhzZWUBs2ZZlz6IjFSmSbtjVpdeFGcBe2cBl2/o94BIoN/LQBT7nYi0jY+6yDOZK3vXrPdUUqJsz8pyT1zerjgL2DbWOukBgMslyvZi9jsRaRsTH/I8VVXKnR5bExLN22bPrr84Jzmmukq50wNbE0H/u23vbKUdEZFGMfEhz6PF6ux68Ou22nd6rAjgcrHSjohIo5j4kOfRYnV2Pbiisj/VtiMicgMmPuR5tFidXQ/8Vfan2nZERG7AxIc8T3y8MnurZnFLM4MBiIpS2pE8beOV2Vuoo99hAAKilHZERBrFxIc8jxars+tBE6MyZR1A7eTnv3/3W871fIhI05j4kGfSYnV2PYgaDcSvAwJq9HtApLKd6/gQkcaxSCl5Nq7c7B5cuZmIXEzW7zdXbibPZjQCgwe7Owr9aWIEwga7OwoiIofxURcRERHpBhMfIiIi0g0+6iKSTQ/jX7x9bJXM71AP1wORB2HiQySTHiqXZ2UptdJuLBsSGaksMeANs+lkfod6uB6IPAwfdRHJoofK5VlZwNixtWullZQo27M8/Bxlfod6uB6IPBATHyIZ9FC5vKpKudNjawUM87bZs5V2nkjmd6iH64HIQzHxIZJBD5XLt22rfafnRkIAxcVKO08k8zvUw/VA5KGY+BDJoIfK5Wqr3attpzUyv0M9XA9EHoqJD5EMeqhcrrbavdp2WiPzO9TD9UDkoZj4EMmgh8rl8fHK7K2ahWHNDAYgKkpp54lkfod6uB6IPBQTHyIZ9FC53GhUpqwDtZMf89/Ll3vuej4yv0M9XA9EHqpBRUqrq6vx448/4vTp06iurrbaN2jQIGnBOQOLlJJT2Vy3JUr5kfOWdVtsreMTFaUkPV67jk8Dv0M9XA9ELiLr99vhxGfXrl2YNGkSfv75Z9R8q8FgQJXGp7Iy8SGn08NKvVy52T3HItIxtyU+vXv3Rrdu3fDss88iIiIChhq3vENCQhocjCsw8SEiIvI8sn6/HS5ZUVBQgHXr1qFLly4N/lAiItKHqqoqXL9+3d1hkIfw8fFBkybOHX7scOJzxx134Mcff2TiQ0REdRJCoKysDL/99pu7QyEP0qRJE3Ts2BE+Pj5O+wxVic+BAwcs//uJJ57Ak08+ibKyMtx6661o1qyZVdtevXrJjZAa53oF8HUGcPY40LozMGQ60Mx5F5TLefv4CZnfn8y+qrgC7E8DLhQAQV2B25YAPv4NO1ZlBfBjBnDhOBDUGegyHWjagHP09mvBw5iTntDQUAQEBNQaFkFUU3V1NU6ePInS0lK0b9/eadeMqjE+TZo0gcFgqDWY2XKQ/+5zdHDz1q1bsWTJEuzduxelpaVYv349kpOT62yfm5uLxMTEWtsPHz6Mm2++WdVn6mqMz9p5wK9LgRY3fCe/GYG2qcADi90XlyzeXvla5vcns6/ykoGST2tvvykJSNjg2LH2zQOOLAXEDedoMAI3pwJ9HDhHb78WPExVVRWOHTuG0NBQtG7d2t3hkAcxmUw4efIkunTpUuvGikvH+BQWFjb4A+y5dOkSbrvtNvzpT3/CmDFjVL/v6NGjVifdtm1bZ4Tn2dbOAyqWADXHmgdXKdvXwrOTH3Pl65pFIM2Vr+PXefYPnszvT2Zf1ZX0AMr2vGT1yc++ecDhJbW3i6rft6tJfrz9WvBA5jE9AQEBbo6EPI35EVdVVVWtxEcWVYlPhw4dLP9769atGDhwIJo2tX5rZWUlduzYYdW2Pvfeey/uvfde1e3NQkND0aJFC4ffpxvXK5Q7BSGovXZaEwDVUPZf/7tnPvaqt/K1Qal8fVOSZz7qkPn9yeyriit1Jz1mJZ8q7ep77FVZodzpsefIUuDWv9t/7OXt14KH4+MtcpQrrhmHh04nJibi3LlztbabTCabj6GcoU+fPoiIiMDQoUORk5Njt+21a9dQXl5u9fJ6X2coj0fqun6aQNn/dYYro5LH2ytfy/z+ZPbV/rT626ht92OG9eMtm6FVKe3s8fZrgYikczjxMY/lqens2bNo3ry5lKDqEhERgZUrVyIzMxNZWVmIiYnB0KFDsXXr1jrfk56ejpCQEMsrKirKqTFqwtnjcttpjbdXvpb5/cnsqwsF6o6lpt0FledYXztvvxaISDrV09lH/3cpeoPBgJSUFPj6+lr2VVVV4cCBAxg4cKD8CG8QExODmJgYy99xcXEoLi7GSy+9VGepjAULFiA1NdXyd3l5ufcnP607A2dVtvNE3l75Wub3J7OvgroCZZvVtau3jcprr7523n4tkCYZDIZ6J+M0lHkSz/nz5xs9pMPROFetWoXZs2d7/RIEqu/4mO+YCCEQFBRkdRclPDwcjzzyCN5//31nxmrTgAEDUFBQ939h+vr6Ijg42Orl9YZMV2b/VNexvxrK/iHTXRmVPN5e+Vrm9yezr26zMRC5oe26TFdmb9ljMCrt7PH2a4GU8ii5ucCaNco/nVwWKSUlpd5EobS0tEHjU9UYOHAgSktLpVRBcDTO8ePH49ixY43+XK1TfcfnnXfeAQBER0dj7ty5Tn+spda+ffsQEcH/mrPSzEeZ8lyxRPmRvDG9rYbyG9E21TMHNgO/V77eNhbKydw4sNULKl/L/P5k9pWPvzJI2N4A55uS1K3n09RHmbJua1aX2c2p9a/n4+3Xgt7ZKogbGQm8/LJbCuJWVFTAx8cH4eHhTvuM+o5fVVUFg8GganVjR+P09/eHv38D1+PyIA6P8Vm4cKG0pOfixYvIz89Hfn4+AGXafH5+PoqKigAoj6n++Mc/WtovX74cGzZsQEFBAQ4ePIgFCxYgMzMTM2bMkBKPV3lgMeCTBpTX+Bd+uVHZ7slT2QFlenL8OiDgJuvtAZHeMX1Z5vcns68SNijJjS2OruPTZzFwS1rtOz8Go7Jd7To+3n4t6FVWFjB2rHXSAwAlJcr2rCynhzB48GDMmDEDqampaNOmDe6++24AyiOkDRs2AFCSoRkzZiAiIgJ+fn6Ijo5Genq6zeMdPXoUBoMBR44csdq+dOlSREdHQwiB3NxcGAwGy+OmVatWoUWLFvjXv/6F7t27w9fXFz///DNKS0sxatQo+Pv7o2PHjvjwww8RHR2N5cuXW457Y5wnTpyAwWBAVlYWEhMTERAQgNtuuw07d+60tDd/1o02btyI2NhY+Pn5oU2bNpZhLwDw/vvvIzY2FkFBQQgPD8ekSZNw+vTpBvS0a6m649OnTx/VU8y+++471R++Z88eq5lg5rE4U6dOxapVq1BaWmpJggDlAps7dy5KSkrg7++PHj16YNOmTRg5cqTqz9SVBxYrU55vXPn3AS9auTlqtPJj662r9cr8/mT2VcIGeSs391msTFlv7MrN3n4t6E1VlXKnx9aiuUIABgMwezaQlAQYnfsdv/vuu3jsscfwzTff2FzEd8WKFdi4cSM++eQTtG/fHsXFxSguLrZ5rJiYGPTr1w8ffPABnn/+ecv2Dz/8EJMmTarzd/by5ctIT0/HP//5T7Ru3RqhoaFITk7GmTNnkJubi2bNmiE1NVVV0vHMM8/gpZdeQteuXfHMM89g4sSJ+PHHH2stUQMAmzZtwujRo/HMM89g9erVqKiowKZNmyz7Kyoq8PzzzyMmJganT5/GnDlzkJKSguzs7HrjcCuhwqJFiyyvp556SgQHB4sBAwaIOXPmiDlz5oi4uDgRHBwsnnrqKTWHcyuTySQACJPJ5O5QiIi80pUrV8ShQ4fElStXGnaAnBwhlBTH/isnR2bYQgghpk6dKpKSkoQQQiQkJIjevXvXagNArF+/XgghxBNPPCGGDBkiqqurVR1/6dKlolOnTpa/jx49KgCIgwcPCiGEyMnJEQDE+fPnhRBCvPPOOwKAyM/Pt7zn8OHDAoDYvXu3ZVtBQYEAIJYtW2YzzsLCQgFA/POf/7TsP3jwoAAgDh8+bPmskJAQy/64uDgxefJkVeclhBDffvutACAuXLig+j012bt2ZP1+q3rUtXDhQsvr119/xcyZM7Fz504sXboUS5cuxY4dOzB79mycOnXKGbkZERHpSanK5QfUtmuE2NhYu/tTUlKQn5+PmJgYzJw5E5s3/z7zcdq0aQgMDLS8AGDChAn4+eefsWvXLgDABx98gN69e6N79+51foaPj49VHcyjR4+iadOm6Nu3r2Vbly5d0LJly3rP58bjmMfH1nWnKD8/H0OHDq3zWPv27UNSUhI6dOiAoKAgDB48GACsntRokcNjfNauXWs17sbswQcfRGZmppSgiIhIx9ROWHHBxJb6xrT27dsXhYWFeP7553HlyhWMGzcOY8eOBQA899xzlnGs5rGsERERSExMxIcffggAWLNmDR588EG7n+Hv72/1GEzUUTezru03urEMhPmY1dW2p5DaG+h86dIlDB8+HIGBgXj//fexe/durF+/HoDyCEzLVM/qMvP398f27dvRtav1Wh3bt2+Hn5+ftMCIXK6qCti2TfmvyIgIID6+YeMHZFYJ1+qxZFVUlxkXq7N7j/h4ZfZWSYntcT4Gg7I/XhvLFAQHB2P8+PEYP348xo4di3vuuQfnzp1DaGgoQkNDa7WfPHky5s+fj4kTJ+L48eOYMGGCQ5938803o7KyEvv27UO/fv0AAD/++KP09Xd69eqFr776Cn/6059q7Tty5AjOnDmDF154wbI23p49e6R+vrM4nPjMnj0bjz32GPbu3YsBAwYAAHbt2oW3334bf/vb36QHSOQSsqbNyqwSrtVj2aqovm+u4xXVZcbF6uzexWhU/r83dqyS5NyY/JjvfCxf7vSBzWosW7YMERER6N27N5o0aYK1a9ciPDzc7uKDo0ePxmOPPYbHHnsMiYmJuOmmm+psa8vNN9+MYcOG4ZFHHsFrr72GZs2a4cknn6x1Z6ixFi5ciKFDh6Jz586YMGECKisr8fnnn2PevHlo3749fHx88I9//APTpk3DDz/8YDVgW8scftT11FNP4b333sO+ffswc+ZMzJw5E/v27cOqVavw1FNPOSNGIueSNW3WXCW8Zu0oc5XwYgem32r1WOaK6jXrbJkrqu+b5/q4ZJ4facfo0cC6dUDNpCAyUtnuhnV8bAkMDMSLL76I2NhY9O/fHydOnEB2drbddXaCg4Pxhz/8Afv378fkyZMb9LnvvfcewsLCMGjQINx///14+OGHERQUJPXJy+DBg7F27Vps3LgRvXv3xpAhQ/Cf//wHANC2bVusWrUKa9euRffu3fHCCy/gpZdekvbZzmQQah4KepHy8nKEhITAZDLpYxVnsq+qCoiOrp30mJlvqRcW2v+vy+oqYGO0nYKZBuUOxH2F9T9+0eqxKiuAtQH2i4sajMADl+t/7CUrLpnnR9JcvXoVhYWF6NixY+N/iGU9gvZyv/zyC6KiovDll1/aHZCsdfauHVm/3w7f8SHyKtu21Z30AMot9uJipZ09MquEa/VYsiqqy4yL1dm9n9EIDB4MTJyo/JNJDwDg66+/xsaNG1FYWIgdO3ZgwoQJiI6OrrNuJf1O1RifVq1a4dixY2jTpg1atmxp9xniuXPnpAVH5HSyps3KrBKu1WPJqqiu9vPUtGN1dtKp69ev4+mnn8ZPP/2EoKAgDBw4EB988IHVrC2yTVXis2zZMgQFBVn+t8zBU0RuJWvarMwq4Vo9lqyK6mo/T007VmcnnRoxYgRGjBjh7jA8kqrEZ+rUqbh8+TIAZbEmIq8ha9qsuUr45RJYF8q0HEjZr6ZKuFaP1WW6MnurvjE+9VVUlxmXzPMjIl1QPcanRYsWiI+Px9/+9jfk5ubi2rVrzoyLyDXM02aB36fJmjkybdZcJVx5Y42dDlYJ1+qxzBXV7VFTUV1mXDLPj4h0QXXi89ZbbyEmJgYffvghhgwZgpYtW2LIkCF4/vnnsX37dly/ft2ZcRI5j6xpszKrhGv1WLIqqsuMi9XZicgBDZrO/ssvv+Drr79GXl4ecnJy8PPPP8Pf3x933nkn/v3vfzsjTmk4nZ3qxJWb1ePKzWSH1OnspCuumM7e6HV8CgoK8N5772HFihW4ePEiqqrqme7qZkx8iIici4kPNZQrEh+HS1b89NNPyMnJQW5uLnJzc2EymTBw4EDMnz8fCQkJDQ6EiIiIyNlUj/GZOnUq2rdvj379+mH9+vW49dZbsW7dOpw/fx5ffPEFnn76adx5553OjJWIiMjtDAYDNmzY4O4wHBIdHY3ly5dr9niupPqOz+rVq9G+fXs8/fTTGDp0KPr06cP1fPRE5pgOmbQ4rkNmTBVXgP1pwIUCIKgrcNsSwMe/Yce6agK2jgIuFQHN2wODNgF+IQ07lhb7nbyTi6+1lJQU/Pbbb3YTm9LSUrRs2dJpMTjD7t270bx5c3eHoQmqE59Dhw5ZHm8tXboUV69exV133YWEhAQMHjwYffv2tVuUjTyYzGrcMmmxIrfMmPKSgZJPf/+7bDNQ8CpwUxKQsMGxY23sAly8YUXlK8VAVgsgsDNw34+OHUuL/U7eSWPXWkVFBXx8fBAeHu7yz7bHHJc9bdu2dVE06ly/ft1tq0yrzlRuvvlmTJs2DR999BFKS0vxzTffYOTIkfj222/xhz/8Aa1atcL/+3//z5mxkjvIrMYtkxYrcsuMqWbSc6OST5X9atVMem508biyXy0t9jt5Jw1ca4MHD8aMGTOQmpqKNm3a4O677wZg/airoqICM2bMQEREBPz8/BAdHY309HSbxzt69CgMBgOOHDlitX3p0qWIjo6Gea7RoUOHMHLkSAQGBiIsLAxTpkzBmTNn6o1r0aJFaN++PXx9fdGuXTvMnDnT8p6aj6Z+++03PPLIIwgLC4Ofnx969uyJf/3rX5b9mZmZ6NGjB3x9fREdHY3/+7//s9tXRUVFSEpKQmBgIIKDgzFu3DicOnXKsn/RokXo3bs33n77bXTq1Am+vr5wV430Bt+i6d69O+6//36MHj0a9913H4QQ+Pzzz2XGRu5WWaHc6bHnyFKlnStVVyn/FWhzpd7/bts7W2nniTFVXKk76TEr+VRpV5+rprqTHrOLx5V29dFiv5N30tC19u6776Jp06b45ptv8MYbb9Tav2LFCmzcuBGffPIJjh49ivfffx/R0dE2jxUTE4N+/frhgw8+sNr+4YcfYtKkSTAYDCgtLUVCQgJ69+6NPXv24IsvvsCpU6cwbtw4u3GtW7cOy5YtwxtvvIGCggJs2LABt956q804qqurce+992LHjh14//33cejQIbzwwgsw/nf5jr1792LcuHGYMGECvv/+eyxatAh//etfsWrVKpvHE0IgOTkZ586dQ15eHrZs2YLjx49j/PjxVu1+/PFHfPLJJ8jMzER+fr7NY7mCQ7O6Tp8+jdzcXMusrmPHjsHHxwe333475syZg8TERGfFSe7gSDXum2e7JCQAjlXkDhvseTHtT1P3mfvTgP6v2G+zdZS6Y20dBQzfbr+NFvudvJOGrrUuXbpg8eK6H+kXFRWha9euuOuuu2AwGNChQwe7x5s8eTJeeeUVPP/88wCAY8eOYe/evXjvvfcAAK+99hr69u2L//3f/7W85+2330ZUVBSOHTuGbt262YwrOzsb4eHhGDZsGJo1a4b27dvj9ttvtxnDl19+iW+//RaHDx+2HK9Tp06W/UuXLsXQoUPx17/+FQDQrVs3HDp0CEuWLLFZturLL7/EgQMHUFhYiKioKADKuOAePXpg9+7d6N+/PwDl7tjq1avd/thN9R2f7t27IyIiAlOmTMH333+PMWPGYPPmzTh//jzy8vKwaNEiTmf3NjKrccukxYrcUqugF6g7lpp2l4rUHUtNOy32O3knDV1rsbGxdvenpKQgPz8fMTExmDlzJjZv3mzZN23aNAQGBlpeADBhwgT8/PPP2LVrFwDggw8+QO/evdG9e3cAyt2WnJwcq/fdfPPNAIDjx3//d23NuB544AFcuXIFnTp1wsMPP4z169ejsrLSZsz5+fmIjIy0JD01HT58uNYs7TvvvBMFBQU21+o7fPgwoqKiLEkPoOQMLVq0wOHDhy3bOnTo4PakB3Dgjk9SUhISExNx1113ISAgwJkxkVbIrMYtkxYrckutgt5VGcispl19mrdXBjKraVcfLfY7eScNXWv1zYTq27cvCgsL8fnnn+PLL7/EuHHjMGzYMKxbtw7PPfcc5s6da9U+IiICiYmJ+PDDDzFgwACsWbMGjz76qGV/dXU1/vCHP+DFF1+s9VkREb+fb824oqKicPToUWzZsgVffvklpk+fjiVLliAvL6/WIGJ/f/szQ4UQtWZt2xuPY6u9re1amVWm+o5Peno6hg8fzqRHT7pMr12TqSa11bhlMlfkrlWU0swABES5tiK3zJhuW6LuM9W0G7RJ3bHUtNNiv5N38rBrLTg4GOPHj8ebb76Jjz/+GJmZmTh37hxCQ0PRpUsXy8ts8uTJ+Pjjj7Fz504cP34cEyZMsOzr27cvDh48iOjoaKv3dunSpd7Ewd/fH/fddx9WrFiB3Nxc7Ny5E99//32tdr169cIvv/yCY8eO2TxO9+7dsX279aPvHTt2oFu3bpZxQDXbFxUVobj49//IOnToEEwmE2655Ra7MbsD559T3WRW45ZJixW5Zcbk469MWbfnpiR16/n4hShT1u0J7KxuPR8t9jt5Jw+61pYtW4aPPvoIR44cwbFjx7B27VqEh4ejRYsWdb5n9OjRKC8vx2OPPYbExETcdEOB5Mcffxznzp3DxIkT8e233+Knn37C5s2b8dBDD9ktCbVq1Sq89dZb+OGHH/DTTz9h9erV8Pf3tznmKCEhAYMGDcKYMWOwZcsWyx2rL774AgDw5JNP4quvvsLzzz+PY8eO4d1338Urr7xS6+6V2bBhw9CrVy9MnjwZ3333Hb799lv88Y9/REJCQr2PCt2BiQ/ZJ7Mat0xarMgtM6aEDXUnP46u43Pfj3UnP46u46PFfifv5CHXWmBgIF588UXExsaif//+OHHiBLKzs+2uaxccHIw//OEP2L9/PyZPnmy1r127dvjmm29QVVWFESNGoGfPnpg1axZCQkLsHrNFixZ48803ceedd6JXr1746quv8Nlnn6F169Y222dmZqJ///6YOHEiunfvjnnz5lkSq759++KTTz7BRx99hJ49e+Jvf/sbnnvuOZsDm4Hfp/e3bNkSgwYNwrBhw9CpUyd8/PHH9fSeezS6SKmnYZHSBuLKze6JiSs3kweSWqSU15queER1dk/DxIeIyLlYnZ0aSjPV2Q8cOKD6gL169WpwMERERETOpCrx6d27NwwGQ53T2cz7DAaD3cFXRERERO6kKvEpLCx0dhxE7idrHJPMMQlaHd+g1biIiOqhKvGpbwluIo8nqwK9zGrSGqtMrfm4iIhUcKhW140OHTqEoqIiVFRYF6i87777Gh0UkUuZK9DXZK5AD6hLfszVpGsWVjRXk3ZkCq7MY8mk1biIiFRyOPH56aefcP/99+P777+3GvdjXpaaY3zIo6itQH/r3+0/9qq3mrRBqSZ9U1L9j4RkHksmrcZFROQAhxcwnDVrFjp27IhTp04hICAABw8exNatWxEbG4vc3FwnhEjkRI5UoLfHkWrS9ZF5LJm0GhcRkQMcvuOzc+dOfP3112jbti2aNGmCJk2a4K677kJ6ejpmzpyJffv2OSNOIueQVYFeZjVpDVWmbtDnsTo7EWmYw3d8qqqqEBgYCABo06YNTp48CUAZAH306FG50RE5m6wK9DKrSWuoMnWDPo/V2cnLmUs0kGdyOPHp2bOnZUHDO+64A4sXL8Y333yD5557Dp06dZIeIJFTyapAL7OatFYrU2s1LvJqVVVAbi6wZo3yT2cPI01JSUFycrLdNqWlpbj33nudG0gdFi1ahN69e7vls72Fw4nPX/7yF1RXVwMA/v73v+Pnn39GfHw8srOz8fLLL9fzbiKNkVWBXmY1aa1WptZqXOS1srKA6GggMRGYNEn5Z3S0st0dzLOYw8PD4evr654gqNEcTnxGjBiB0aOV6aqdOnXCoUOHcObMGZw+fRpDhw6VHiCR08mqQC+zmrRWK1NrNS7yOllZwNixwC81xtOXlCjbXZH8DB48GDNmzEBqairatGmDu+++G4D1o66KigrMmDEDERER8PPzQ3R0NNLT0+s8Zn3tTSYTHnnkEYSGhiI4OBhDhgzB/v37AQCrVq3Cs88+i/3798NgMMBgMGDVqlUAgKKiIiQlJSEwMBDBwcEYN24cTp06ZTnu/v37kZiYiKCgIAQHB6Nfv37Ys2cPAODs2bOYOHEiIiMjERAQgFtvvRVr1qyR2ZWa4vDg5oceeggvv/wygoKCLNtatWqFS5cu4YknnsDbb78tNUAil+izWJmy3tiVm6NGK9O5ZaxqLPNYMmk1LvIaVVXArFmArSpJQgAGAzB7NpCUBBidfNm9++67eOyxx/DNN9/YLNu0YsUKbNy4EZ988gnat2+P4uJiFBcX13k8e+2FEBg1ahRatWqF7OxshISE4I033sDQoUNx7NgxjB8/Hj/88AO++OILfPnllwCAkJAQCCGQnJyM5s2bIy8vD5WVlZg+fTrGjx9vmW09efJk9OnTB6+99hqMRiPy8/PRrFkzAEph0H79+mH+/PkIDg7Gpk2bMGXKFHTq1Al33HGH5B7VAOGgJk2aiFOnTtXa/uuvvwqj0ejo4VzOZDIJAMJkMrk7FCIir3TlyhVx6NAhceXKlQa9PydHCCXFsf/KyZEathBCiKlTp4qkpCQhhBAJCQmid+/etdoAEOvXrxdCCPHEE0+IIUOGiOrqalXHt9f+q6++EsHBweLq1atW2zt37izeeOMNIYQQCxcuFLfddpvV/s2bNwuj0SiKioos2w4ePCgAiG+//VYIIURQUJBYtWqVqhiFEGLkyJHiySefVN1eFnvXjqzfb9WPusrLy2EymSCEwIULF1BeXm55nT9/HtnZ2QgNDXVWfkZERDpRqnJFBLXtGiM2Ntbu/pSUFOTn5yMmJgYzZ87E5s2bLfumTZuGwMBAy6u+9nv37sXFixfRunVrq/cVFhbi+PG6l9Q4fPgwoqKiEBUVZdnWvXt3tGjRAocPHwYApKam4s9//jOGDRuGF154wep4VVVV+J//+R/06tXL8tmbN29GUVGRY53lIVQ/6mrRooXlmWK3bt1q7TcYDHj22WelBkdERPoToXJFBLXtGqN58+Z29/ft2xeFhYX4/PPP8eWXX2LcuHEYNmwY1q1bh+eeew5z585V3b66uhoRERE2FwNu0aJFnTEIISzVE+ravmjRIkyaNAmbNm3C559/joULF+Kjjz7C/fffj//7v//DsmXLsHz5ctx6661o3rw5Zs+eXasklbdQnfjk5ORACIEhQ4YgMzMTrVq1suzz8fFBhw4d0K5dO6cESV5GZmVvWRXViUgz4uOByEhlILOtcT4Gg7I/XiMrJwQHB2P8+PEYP348xo4di3vuuQfnzp1DaGiozSchdbXv27cvysrK0LRpU0RHR9v8LB8fn1qlobp3746ioiIUFxdb7vocOnQIJpMJt9xyi6Vdt27d0K1bN8yZMwcTJ07EO++8g/vvvx/btm1DUlISHnzwQQBAdXU1CgoKrN7rTVQnPgkJCQCAwsJCtG/f3mZ2SVQvmZW9ZVVUJyJNMRqBl19WZm8ZDNbJj/mnZ/ly5w9sVmPZsmWIiIhA79690aRJE6xduxbh4eF13qGx137YsGGIi4tDcnIyXnzxRcTExODkyZPIzs5GcnIyYmNjER0djcLCQuTn5yMyMhJBQUEYNmwYevXqhcmTJ2P58uWWwc0JCQmIjY3FlStXkJaWhrFjx6Jjx4745ZdfsHv3bowZMwYA0KVLF2RmZmLHjh1o2bIlli5dirKyMq9NfByezt6hQwds374dDz74IAYOHIiSkhIAwOrVq7F9+3bpAZIXMVf2rlnvyVzZu9iB+anmiuo162yZK6rvm9f4eInIbUaPBtatA26qsXJCZKSyfbRGVk4IDAzEiy++iNjYWPTv3x8nTpxAdnY2mjSx/fNqr73BYEB2djYGDRqEhx56CN26dcOECRNw4sQJhIWFAQDGjBmDe+65B4mJiWjbti3WrFljmV7fsmVLDBo0CMOGDUOnTp3w8ccfAwCMRiPOnj2LP/7xj+jWrRvGjRuHe++91zI85a9//Sv69u2LESNGYPDgwQgPD693EUdPZhDC1o3EumVmZmLKlCmYPHkyVq9ejUOHDqFTp07IyMjAv/71L2RnZzsrVinKy8sREhICk8mE4OBgd4ejH9VVwMZoO0UuDcqdn/sK63/sVVkBrA2wX1zUYAQeuMzHXkRucPXqVRQWFqJjx47w8/Nr1LGqqoBt25SBzBERyuMtLdzpIeewd+3I+v12+I7P3//+d7z++ut48803LWsAAMDAgQPx3XffNTgQ8nIyK3vLqqhORJpnNAKDBwMTJyr/ZNJDjeVw4nP06FEMGjSo1vbg4GD89ttvMmIibySzsresiupERKQ7Dic+ERER+PHHH2tt3759O4uUUt1kVvaWVVGdiIh0x+HE59FHH8WsWbPwn//8BwaDASdPnsQHH3yAuXPnYvr0eipYk37JrOwtq6I6ERHpjsO1uubNmweTyYTExERcvXoVgwYNgq+vL+bOnYsZM2Y4I0byBubK3tvGQkl+bhxT72Blb3NF9cNL6m6jpqI6ETlVdXW1u0MgD+PgfKsGcXhWl9nly5dx6NAhVFdXo3v37pbluLWOs7rczOY6PlFK0iNjHR+Dkev4ELmZeQE8o9GItm3bwsfHh2u/Ub2EEPj1119x+fJldO3aFcYaI9ll/X6rTnwuX76MtLQ0bNiwAdevX8ewYcOwYsUKtGnTpsEf7g5MfDSAKzcTeb2KigqUlpbi8uXL7g6FPIjBYEBkZKTNmykuT3zS0tKQkZGByZMnw8/PD2vWrMHgwYOxdu3aBn+4OzDxISJyDSEEKisra5VYIKpLs2bNat3pMZP1+616jE9WVhbeeustTJgwAQDw4IMP4s4770RVVVWdQRIRkX4ZDAY0a9bMas03IndTPauruLgY8TdUhLv99tvRtGlTnDx50imBEREREcmm+o5PVVUVfHysx040bdoUlZWV0oPySDLHrWgxLj2My9FiXxERkVSqEx8hBFJSUuDr62vZdvXqVUybNg3Nmze3bMvKUl9ocuvWrViyZAn27t2L0tJSrF+/vt7CaHl5eUhNTcXBgwfRrl07zJs3D9OmTVP9mU4hs+K4FuPSQ0V1LfYVERFJp/pR19SpUxEaGoqQkBDL68EHH0S7du2stjni0qVLuO222/DKK6+oal9YWIiRI0ciPj4e+/btw9NPP42ZM2ciMzPToc+VSmbFcS3GpYeK6lrsKyIicooGr+Mjm8FgqPeOz/z587Fx40YcPnzYsm3atGnYv38/du7cqepzpM7qkllxXCZZcemhoroW+4qIiGpxW3V2d9q5cyeGDx9utW3EiBHYs2cPrl+/bvM9165dQ3l5udVLGpkVx2WSFZceKqprsa+IiMhpPCrxKSsrQ1hYmNW2sLAwVFZW4syZMzbfk56ebvUoLioqSl5AMiuOyyQrLj1UVNdiXxERkdN4VOIDoNay5+YndXUth75gwQKYTCbLq7i4WF4wMiuOyyQrLj1UVNdiXxERkdN4VOITHh6OsrIyq22nT59G06ZN0bp1a5vv8fX1RXBwsNVLGpkVx2WSFZceKqprsa+IiMhpPCrxiYuLw5YtW6y2bd68GbGxse5ZGdRccRxA7R88ByuOyyQrLpnnZ66obo87Kqprsa+IiMhp3Jr4XLx4Efn5+cjPzwegTFfPz89HUVERAOUx1R//+EdL+2nTpuHnn39GamoqDh8+jLfffhtvvfUW5s6d647wFVGjgfh1QMBN1tsDIpXt7lq7RVZcMs+vz2LglrTad34MRmW7u9bx0WJfERGRU7h1Ontubi4SExNrbZ86dSpWrVqFlJQUnDhxArm5uZZ9eXl5mDNnjmUBw/nz5zu0gKHTipRqdbVeLa5GzJWbiYjIQS6vzu4tWJ2diIjI8+hyHR8iIiKixmDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG0x8iIiISDeY+BAREZFuMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG0x8iIiISDeY+BAREZFuMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG0x8iIiISDeY+BAREZFuMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG0x8iIiISDeY+BAREZFuMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6QYTHyIiItINJj5ERESkG0x8iIiISDeY+BAREZFuMPEhIiIi3WDiQ0RERLrBxIeIiIh0g4kPERER6UZTdwdARETqVFUB27YBpaVARAQQHw8Yje6OisizMPEhIvIAWVnArFnAL7/8vi0yEnj5ZWD0aPfFReRp+KiLiEjjsrKAsWOtkx4AKClRtmdluScuIk/ExIeISMOqqpQ7PULU3mfeNnu20o6I6sfEh4hIw7Ztq32n50ZCAMXFSjsiqh8THyIiDSstlduOSO+Y+BARaVhEhNx2RHrHxIeISMPi45XZWwaD7f0GAxAVpbQjovox8SEi0jCjUZmyDtROfsx/L1/O9XyI1GLiQ0SkcaNHA+vWATfdZL09MlLZznV8iNTjAoZERB5g9GggKYkrNxM1FhMfIiIPYTQCgwe7Owoiz8ZHXURERKQbTHyIiIhIN/ioi4ioBlZBJ/Jebr/jk5GRgY4dO8LPzw/9+vXDNjvrrufm5sJgMNR6HTlyxIURE5E3y8oCoqOBxERg0iTln9HRLARK5C3cmvh8/PHHmD17Np555hns27cP8fHxuPfee1FUVGT3fUePHkVpaanl1bVrVxdFTETejFXQibyfQQhbNX9d44477kDfvn3x2muvWbbdcsstSE5ORnp6eq32ubm5SExMxPnz59GiRYsGfWZ5eTlCQkJgMpkQHBzc0NCJyMtUVSl3duoqCGowKOvmFBbysReRO8j6/XbbHZ+Kigrs3bsXw4cPt9o+fPhw7Nixw+57+/Tpg4iICAwdOhQ5OTl22167dg3l5eVWLyKimlgFnUgf3Jb4nDlzBlVVVQgLC7PaHhYWhrKyMpvviYiIwMqVK5GZmYmsrCzExMRg6NCh2Lp1a52fk56ejpCQEMsrKipK6nkQkXdgFXQifXD7rC5DjeIzQoha28xiYmIQExNj+TsuLg7FxcV46aWXMGjQIJvvWbBgAVJTUy1/l5eXM/kholpYBZ1IH9x2x6dNmzYwGo217u6cPn261l0gewYMGICCgoI69/v6+iI4ONjqRURUE6ugE+mD2xIfHx8f9OvXD1u2bLHavmXLFgwcOFD1cfbt24cI/icYETUSq6AT6YNbH3WlpqZiypQpiI2NRVxcHFauXImioiJMmzYNgPKYqqSkBO+99x4AYPny5YiOjkaPHj1QUVGB999/H5mZmcjMzHTnaRCRlzBXQZ81y3qgc2SkkvSwCjqR53Nr4jN+/HicPXsWzz33HEpLS9GzZ09kZ2ejQ4cOAIDS0lKrNX0qKiowd+5clJSUwN/fHz169MCmTZswcuRId50CEXkZVkEn8m5uXcfHHbiODxERkefx+HV8iIiIiFyNiQ8RERHphtvX8SEi8mYVFUBGBnD8ONC5MzB9OuDj4+6o5JFZyV7msby937VK5nfoNEJnTCaTACBMJpO7QyEiL5eWJoTRKIRS8EJ5GY3Kdm+QmSlEZKT1+UVGKtvdeSxv73etkvkd2iLr95uPuoiInGDePGDJEuW/gG9UVaVsnzfPPXHJIrOSvcxjeXu/a5XM79DZOKuLiEiyigogIKD2j++NjEbg8mXPfPwis5K9zGN5e79rlczv0B7O6iIi0qiMDPs/voCyPyPDNfHIJrOSvcxjeXu/a5XM79AVmPgQEUl2/Ljcdlojs5K9zGN5e79rlczv0BWY+BARSda5s9x2WiOzkr3MY3l7v2uVzO/QFTjGh4hIMm8fa2Ie01FSojzGqKkhY3xkHMvb+12rZH6H9nCMDxGRRvn4AKmp9tukpnruj6/MSvYyj+Xt/a5VMr9DV2DiQ0TkBIsXA2lptf9lbzQq2xcvdk9cspgr2d90k/X2yEhluyOV7GUey9v7XatkfofOxkddRERO5O0rCHPlZrqRM1dulvX7zcSHiIiINI9jfIiIiIgcxMSHiIiIdIPV2UkVj6i4Sy7D60E9jltRz9vPjzSikcVSPQ6rszvO2RV3ybPwelCPFcfV8/bzo8ZjdXZyCU+quEvOx+tBPVYcV8/bz4+0hbO6qE6uqrhLnoHXg3qsOK6et58fycNZXeR0nlZxl5yL14N6rDiunrefH2kPEx+qk6dV3CXn4vWgHiuOq+ft50faw8SH6uRpFXfJuXg9qMeK4+p5+/mR9nCMD9XJVRV3yTPwelCPFcfV8/bzI3k4xoecztMq7pJz8XpQjxXH1fP28yPtYeJDdnlSxV1yPl4P6rHiuHrefn6kLXzURapwpV66Ea8H9bhys3refn7UOKzO3kBMfIiIiDwPx/gQEREROYiJDxEREekGq7MT6YTMsSZXriiDTgsKgK5dlXpK/v4NO5ascR0mEzBqFFBUBLRvD2zaBISENCwmmX117hyQkACcPAm0awfk5QGtWjXsWFrsd5njcrQ6dkyLcWkxJo/RyGKpHofV2UmPZFYJT0qyPo75lZTk+LFkVeTu3Nl2TJ07Ox6TzL4KC7MdV1iY48fSYr/LrKgus99l0mJcWozJFWT9fjPxIfJymZlCGAy1fzANBuXlyL8s6/rxbciPcFqa/WOp/fGsK+lpSPIjs6/qSnoakvxosd9lHUcIuf0ukxbj0mJMriLr95uzuoi8mMwq4VeuKCvs1ufy5fofv8harddkAlq0qD+m336r/7GXzL46dw5o3br+uM6erf+xlxb7XeZqyzL7XSYtxqXFmFyJs7qIqF4yq4Snpan7TDXtZFXkHjVKXUxq2snsq4QEdXGpaafFfpdZUV1mv8ukxbi0GJMnYuJD5MVkVgkvKFB3LDXtZFXkLipSdxw17WT21cmT6o6lpp0W+11mRXWZ/S6TFuPSYkyeiIkPkReTWSW8a1d1x1LTTlZF7vbt1R1HTTuZfdWunbpjqWmnxX6XWVFdZr/LpMW4tBiTJ+IYHyIvJrNKuBbHmjhjjI+MvuIYH8fH+Mjod5m0GJcWY3IljvEhonrJrBLu7w8kJdlvk5Skbl0ZWRW5Q0LU3Z1Qs56PzL5q1QoIC7PfJixM3Xo+Wux3mRXVZfa7TFqMS4sxeSQJM8w8Cqezkx7ZWvcjKorr+Ngis6+4jo96MvtdJi3GpcWYXIHT2RuIj7pIr7hys3pcudn1xwG0uxqxFuPSYkzOxursDcTEh4iIyPNwjA8RERGRg5j4EBERkW6wOjsROUzmuA6Z41aItEyP43K0iGN8iMgh8+YBS5dar+FiNCrTlxcvduxYycnAp5/W3p6UBGzY0JgoibQlKwuYNcu65ERkpDI9ffRo98XlSTjGh4hcbt485Y5MzYXrqqqU7fPmqT9WXUkPoGxPTm5olETakpUFjB1bu85WSYmyPSvLPXHpFe/4EJEqMlfrlbkaMZGW6b2iuky840NELiWzIrfMiuNEWsaK6trDxIeIVJFZkVtmxXEiLWNFde1h4kNEqsisyC2z4jiRlrGiuvZwjA8RqcIxPkSO03tFdZk4xoeIXEpmRW6ZFceJtIwV1bWHiQ8RqbZ4sTLguOa/pI1GZbsj6/hs2FB38sN1fMibjB4NrFsH3HST9fbISGU71/FxLT7qIiKHceVmIsdx5ebGYXX2BmLiQ0RE5Hk4xoeIiIjIQUx8iIiISDeY+BAREZFuuD3xycjIQMeOHeHn54d+/fphWz3rdufl5aFfv37w8/NDp06d8Prrr7soUiIiIvJ0bk18Pv74Y8yePRvPPPMM9u3bh/j4eNx7770oKiqy2b6wsBAjR45EfHw89u3bh6effhozZ85EZmamiyMnIiIiT+TWWV133HEH+vbti9dee82y7ZZbbkFycjLS09NrtZ8/fz42btyIw4cPW7ZNmzYN+/fvx86dO1V9Jmd1EREReR6Pn9VVUVGBvXv3Yvjw4Vbbhw8fjh07dth8z86dO2u1HzFiBPbs2YPr16/bfM+1a9dQXl5u9SIiIiJ9clvic+bMGVRVVSEsLMxqe1hYGMrKymy+p6yszGb7yspKnDlzxuZ70tPTERISYnlFRUXJOQEiIiLyOG4f3GyoUbxECFFrW33tbW03W7BgAUwmk+VVXFzcyIiJiIjIUzV11we3adMGRqOx1t2d06dP17qrYxYeHm6zfdOmTdG6dWub7/H19YWvr6/lb3OixEdeREREnsP8u93YocluS3x8fHzQr18/bNmyBffff79l+5YtW5BUR+XCuLg4fPbZZ1bbNm/ejNjYWDRr1kzV5164cAEA+MiLiIjIA124cAEhISENfr9bZ3V9/PHHmDJlCl5//XXExcVh5cqVePPNN3Hw4EF06NABCxYsQElJCd577z0AynT2nj174tFHH8XDDz+MnTt3Ytq0aVizZg3GjBmj6jOrq6tx8uRJBAUF2X2k1hDl5eWIiopCcXExZ4y5EPvdPdjv7sF+dw/2u3vc2O9BQUG4cOEC2rVrhyZNGj5Sx213fABg/PjxOHv2LJ577jmUlpaiZ8+eyM7ORocOHQAApaWlVmv6dOzYEdnZ2ZgzZw5effVVtGvXDitWrFCd9ABAkyZNEBkZKf1cbhQcHMz/Y7gB+9092O/uwX53D/a7e5j7vTF3esx0V53dmbhGkHuw392D/e4e7Hf3YL+7hzP63e2zuoiIiIhchYmPRL6+vli4cKHVLDJyPva7e7Df3YP97h7sd/dwRr/zURcRERHpBu/4EBERkW4w8SEiIiLdYOJDREREusHEh4iIiHSDiY9K6enp6N+/P4KCghAaGork5GQcPXq03vfl5eWhX79+8PPzQ6dOnfD666+7IFrv0ZB+z83NhcFgqPU6cuSIi6L2fK+99hp69eplWTQsLi4On3/+ud338FpvPEf7nde6fOnp6TAYDJg9e7bddrze5VLT77KudyY+KuXl5eHxxx/Hrl27sGXLFlRWVmL48OG4dOlSne8pLCzEyJEjER8fj3379uHpp5/GzJkzkZmZ6cLIPVtD+t3s6NGjKC0ttby6du3qgoi9Q2RkJF544QXs2bMHe/bswZAhQ5CUlISDBw/abM9rXQ5H+92M17ocu3fvxsqVK9GrVy+77Xi9y6W2380afb0LapDTp08LACIvL6/ONvPmzRM333yz1bZHH31UDBgwwNnheS01/Z6TkyMAiPPnz7suMB1o2bKl+Oc//2lzH69157HX77zW5blw4YLo2rWr2LJli0hISBCzZs2qsy2vd3kc6XdZ1zvv+DSQyWQCALRq1arONjt37sTw4cOtto0YMQJ79uzB9evXnRqft1LT72Z9+vRBREQEhg4dipycHGeH5rWqqqrw0Ucf4dKlS4iLi7PZhte6fGr63YzXeuM9/vjjGDVqFIYNG1ZvW17v8jjS72aNvd7dWqTUUwkhkJqairvuugs9e/ass11ZWRnCwsKstoWFhaGyshJnzpxBRESEs0P1Kmr7PSIiAitXrkS/fv1w7do1rF69GkOHDkVubi4GDRrkwog92/fff4+4uDhcvXoVgYGBWL9+Pbp3726zLa91eRzpd17rcnz00Uf47rvvsHv3blXteb3L4Wi/y7remfg0wIwZM3DgwAFs37693rYGg8Hqb/HfhbJrbqf6qe33mJgYxMTEWP6Oi4tDcXExXnrpJf4YOCAmJgb5+fn47bffkJmZialTpyIvL6/OH2Fe63I40u+81huvuLgYs2bNwubNm+Hn56f6fbzeG6ch/S7reuejLgc98cQT2LhxI3JychAZGWm3bXh4OMrKyqy2nT59Gk2bNkXr1q2dGabXcaTfbRkwYAAKCgqcEJn38vHxQZcuXRAbG4v09HTcdtttePnll2225bUujyP9bguvdcfs3bsXp0+fRr9+/dC0aVM0bdoUeXl5WLFiBZo2bYqqqqpa7+H13ngN6XdbGnK9846PSkIIPPHEE1i/fj1yc3PRsWPHet8TFxeHzz77zGrb5s2bERsbi2bNmjkrVK/SkH63Zd++fbz93EhCCFy7ds3mPl7rzmOv323hte6YoUOH4vvvv7fa9qc//Qk333wz5s+fD6PRWOs9vN4bryH9bkuDrvdGDY3Wkccee0yEhISI3NxcUVpaanldvnzZ0uapp54SU6ZMsfz9008/iYCAADFnzhxx6NAh8dZbb4lmzZqJdevWueMUPFJD+n3ZsmVi/fr14tixY+KHH34QTz31lAAgMjMz3XEKHmnBggVi69atorCwUBw4cEA8/fTTokmTJmLz5s1CCF7rzuJov/Nad46as4t4vbtGff0u63rnHR+VXnvtNQDA4MGDrba/8847SElJAQCUlpaiqKjIsq9jx47Izs7GnDlz8Oqrr6Jdu3ZYsWIFxowZ46qwPV5D+r2iogJz585FSUkJ/P390aNHD2zatAkjR450Vdge79SpU5gyZQpKS0sREhKCXr164YsvvsDdd98NgNe6szja77zWXYPXu3s463o3CPHfEVlEREREXo6Dm4mIiEg3mPgQERGRbjDxISIiIt1g4kNERES6wcSHiIiIdIOJDxEREekGEx8iIiLSDSY+REREpBtMfIjIYxkMBmzYsEHqMXNzc2EwGPDbb7/V2WbVqlVo0aJFvcdyRnxE1DhMfIjIIadPn8ajjz6K9u3bw9fXF+Hh4RgxYgR27tzp7tCsvP766wgKCkJlZaVl28WLF9GsWTPEx8dbtd22bRsMBgOOHTuGgQMHWkpGqLVo0SL07t1bVuhE5ESs1UVEDhkzZgyuX7+Od999F506dcKpU6fw1Vdf4dy5c+4OzUpiYiIuXryIPXv2YMCAAQCUBCc8PBy7d+/G5cuXERAQAEC5y9OuXTt069YNABAeHu62uInIuXjHh4hU++2337B9+3a8+OKLSExMRIcOHXD77bdjwYIFGDVqlKWdyWTCI488gtDQUAQHB2PIkCHYv3+/Zb/5Dskbb7yBqKgoBAQE4IEHHrB6vLR7927cfffdaNOmDUJCQpCQkIDvvvtOdawxMTFo164dcnNzLdtyc3ORlJSEzp07Y8eOHVbbExMTLf+75qOuVatWoX379ggICMD999+Ps2fPWu179tlnsX//fhgMBhgMBqxatcqy/8yZM7j//vsREBCArl27YuPGjarPgYjkY+JDRKoFBgYiMDAQGzZswLVr12y2EUJg1KhRKCsrQ3Z2Nvbu3Yu+ffti6NChVneFfvzxR3zyySf47LPP8MUXXyA/Px+PP/64Zf+FCxcwdepUbNu2Dbt27ULXrl0xcuRIXLhwQXW8gwcPRk5OjuXvnJwcDB48GAkJCZbtFRUV2LlzpyXxqek///kPHnroIUyfPh35+flITEzE3//+d8v+8ePH48knn0SPHj1QWlqK0tJSjB8/3rL/2Wefxbhx43DgwAGMHDkSkydP1tzdMSJdEUREDli3bp1o2bKl8PPzEwMHDhQLFiwQ+/fvt+z/6quvRHBwsLh69arV+zp37izeeOMNIYQQCxcuFEajURQXF1v2f/7556JJkyaitLTU5udWVlaKoKAg8dlnn1m2ARDr16+vM9aVK1eK5s2bi+vXr4vy8nLRtGlTcerUKfHRRx+JgQMHCiGEyMvLEwDE8ePHhRBC5OTkCADi/PnzQgghJk6cKO655x6r444fP16EhIRY/l64cKG47bbban0+APGXv/zF8vfFixeFwWAQn3/+eZ0xE5Fz8Y4PETlkzJgxOHnyJDZu3IgRI0YgNzcXffv2tTze2bt3Ly5evIjWrVtb7hAFBgaisLAQx48ftxynffv2iIyMtPwdFxeH6upqHD16FIAyiHratGno1q0bQkJCEBISgosXL6KoqEh1rImJibh06RJ2796Nbdu2oVu3bggNDUVCQgJ2796NS5cuITc3F+3bt0enTp1sHuPw4cOIi4uz2lbzb3t69epl+d/NmzdHUFAQTp8+rfr9RCQXBzcTkcP8/Pxw99134+6778bf/vY3/PnPf8bChQuRkpKC6upqREREWI2tMbM3BdxgMFj9MyUlBb/++iuWL1+ODh06wNfXF3FxcaioqFAdZ5cuXRAZGYmcnBycP38eCQkJAJTByx07dsQ333yDnJwcDBkypM5jCCFUf54tzZo1s/rbYDCgurq6UcckooZj4kNEjda9e3fLejV9+/ZFWVkZmjZtiujo6DrfU1RUhJMnT6Jdu3YAgJ07d6JJkyaWmVXbtm1DRkYGRo4cCQAoLi7GmTNnHI4tMTERubm5OH/+PNLS0izbExIS8O9//xu7du3Cn/70J7vntmvXLqttNf/28fFBVVWVw7ERkevxURcRqXb27FkMGTIE77//Pg4cOIDCwkKsXbsWixcvRlJSEgBg2LBhiIuLQ3JyMv7973/jxIkT2LFjB/7yl79gz549lmP5+flh6tSp2L9/P7Zt24aZM2di3LhxlqnkXbp0werVq3H48GH85z//weTJk+Hv7+9wzImJidi+fTvy8/Mtd3wAJfF58803cfXq1ToHNgPAzJkz8cUXX2Dx4sU4duwYXnnlFXzxxRdWbaKjo1FYWIj8/HycOXOmzoHfROR+THyISLXAwEDccccdWLZsGQYNGoSePXvir3/9Kx5++GG88sorAJRHOdnZ2Rg0aBAeeughdOvWDRMmTMCJEycQFhZmOVaXLl0wevRojBw5EsOHD0fPnj2RkZFh2f/222/j/Pnz6NOnD6ZMmYKZM2ciNDTU4ZgTExNx5coVdOnSxerzExIScOHCBXTu3BlRUVF1vn/AgAH45z//iX/84x/o3bs3Nm/ejL/85S9WbcaMGYN77rkHiYmJaNu2LdasWeNwnETkGgbR2AfYREQOWrRoETZs2ID8/Hx3h0JEOsM7PkRERKQbTHyIiIhIN/ioi4iIiHSDd3yIiIhIN5j4EBERkW4w8SEiIiLdYOJDREREusHEh4iIiHSDiQ8RERHpBhMfIiIi0g0mPkRERKQb/x/VxweAZumOZgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(3):\n",
    "    x = df[df['species'] == species[i]]\n",
    "    plt.scatter(x['sepal_width'], x['petal_width'], c = colors[i], label=species[i])\n",
    "plt.xlabel(\"Sepal Width\")\n",
    "plt.ylabel(\"Petal Width\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width\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"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# delete a column\n",
    "df = df.drop(columns = ['species'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.109369      0.871754     0.817954\n",
       "sepal_width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "petal_length      0.871754    -0.420516      1.000000     0.962757\n",
       "petal_width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "fig, ax = plt.subplots(figsize=(5,4))\n",
    "sns.heatmap(corr, annot=True, ax=ax, cmap = 'coolwarm')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</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>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Iris.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</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>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  species\n",
       "0           5.1          3.5           1.4          0.2        0\n",
       "1           4.9          3.0           1.4          0.2        0\n",
       "2           4.7          3.2           1.3          0.2        0\n",
       "3           4.6          3.1           1.5          0.2        0\n",
       "4           5.0          3.6           1.4          0.2        0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['species'] = le.fit_transform(df['species'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# train - 70\n",
    "# test - 30\n",
    "X = df.drop(columns=['species'])\n",
    "Y = df['species']\n",
    "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# logistic regression \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "# model training\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  93.33333333333333\n"
     ]
    }
   ],
   "source": [
    "# print metric to get performance\n",
    "print(\"Accuracy: \",model.score(x_test, y_test) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# knn - k-nearest neighbours\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "model = KNeighborsClassifier()\n",
    "model.fit(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  95.55555555555556\n"
     ]
    }
   ],
   "source": [
    "# print metric to get performance\n",
    "print(\"Accuracy: \",model.score(x_test, y_test) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# decision tree\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "0.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}