IrisDataSet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = pd.read_csv('iris.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris.info()#checking how many values, and if some values are inconsistent like are they null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>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>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  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = iris[iris.species == 'setosa'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='setosa', color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1 = iris[iris.species == 'versicolor'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='versicolor', color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = iris[iris.species == 'virginica'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='virginica', color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sepal length vs sepal width\n",
    "fig = iris[iris.species == 'setosa'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='setosa', color='green')\n",
    "iris[iris.species == 'versicolor'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='versicolor', color='blue', ax=fig)\n",
    "iris[iris.species == 'virginica'].plot(kind = 'scatter', x = 'sepal_length', y = 'sepal_width', label='virginica', color='orange', ax=fig)\n",
    "fig.set_xlabel('Sepal Length')\n",
    "fig.set_ylabel('Sepal Width')\n",
    "fig.set_title('Sepal Length vs Sepal Width')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#petal length vs petal width\n",
    "fig = iris[iris.species == 'setosa'].plot(kind = 'scatter', x = 'petal_length', y = 'petal_width', label='setosa', color='green')\n",
    "iris[iris.species == 'versicolor'].plot(kind = 'scatter', x = 'petal_length', y = 'petal_width', label='versicolor', color='blue', ax=fig)\n",
    "iris[iris.species == 'virginica'].plot(kind = 'scatter', x = 'petal_length', y = 'petal_width', label='virginica', color='orange', ax=fig)\n",
    "fig.set_xlabel('Petal Length')\n",
    "fig.set_ylabel('Petal Width')\n",
    "fig.set_title('Petal Length vs Petal Width')\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(10,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsUAAAF1CAYAAAAA6ZfwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3X2YZGV54P/vTc8o2JJhgLGZF8IwG1djQnyZCWrYxIlgRxMiyQZdTOJiVjNmiausMxcQd3+rycZdkquNmDVqRjG2iUEQNaKricbQsu4mJIBEHDEjDoMyPQ0oQzFTJEN3z/37o05j0fRLdVedeun6fq6rr66q8/LczzmnT919znOeJzITSZIkqZ8d1+kAJEmSpE4zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpVleIiA9FxO8uMs/2iLi3XTHNKvttEfFnnShbklaaRs75S1jXr0TE5xeYPhYRr2tHLOptJsUqRUTsj4jzOh3HcnQy+ZakXtTJc35mfiQzhxuZNyJeExFfLjsm9SaTYkmSJPU9k2ItqPjv/7ci4usRcSgi/iQiji+mnR8Rt0fEQxHx/yLix4rP/xT4QeDTEXEkIi4rPv9YRExERCUiboqIH2kytg0R8fGIeCAi7o6IN9ZNe1tEXBcRH46IwxGxJyK21U1/XkR8pZj2sYi4NiJ+NyIGgc8BG4rYj0TEhmKxJ823PklaCbrpnB8RX4qIXype/5uIyIj42eL9eRFxe/H6cVd/I+IlEfGNotx3A1F8/sPA+4AXFnE+VFfc2oj438X5/eaI+FfL24LqZSbFasSvAD8D/CvgXwP/NSKeB3wQeD1wCvDHwA0R8eTMfDXwbeDnM/Opmfn7xXo+BzwdeBpwG/CR5QYUEccBnwb+EdgInAtcGhE/Uzfby4GPAicBNwDvLpZ9EvBJ4EPAycA1wC8CZGYVeBkwXsT+1MwcX2h9krTCdMs5/0vA9uL1TwH7gBfVvf/S7AUi4lTg48B/BU4FvgWcA5CZdwK/AfxtEedJdYu+CvhtYC1wF/D2JcaqFcCkWI14d2Z+JzMfpHaieBXw68AfZ+bNmTmdmaPAUeAF860kMz+YmYcz8yjwNuDZEbFmmTH9OLAuM38nMx/NzH3A+4GL6ub5cmZ+NjOngT8Fnl18/gJgFfCHmTmZmZ8A/r6BMudbnyStJN1yzv8Sj0+C/2fd+xcxR1IM/Czw9cy8PjMngauAiQbK+kRm/n1mTlFL3p+zhDi1QpgUqxHfqXt9D7ABOAPYWdxGe6i4DXV6Me0JImIgIq6MiG9FxMPA/mLSqcuM6QxqTRzqy38LMFQ3T/2J8BHg+IhYVcR4IDNznjrOZ771SdJK0i3n/L8F/nVEDFFLUj8MnF5cDT4buGmOZTbUx1+c55dzfn/qEuLUCuEXuhpxet3rHwTGqZ1k3p6Z891iylnvfxm4ADiP2slxDXCIoq3XMnwHuDszn76MZQ8CGyMi6hLj06ndZoMnxi5J/aQrzvmZ+UhE3Aq8CfhaZj4aEf8PeDPwrcz87hyLHayPPyJiVn08v2teXilWI34zIjZFxMnUrsZeS62pwm9ExPOjZjAifi4iTiyWuQ/YUreOE6ndavse8BTgfzQZ098DD0fE5RFxQnFV4kcj4scbWPZvgWngDRGxKiIuoHbVYcZ9wClNNO2QpF7WTef8LwFv4PtNJcZmvZ/tfwM/EhH/triT90bgtLrp9wGbimdLpMcxKVYj/hz4PLWHHPYBv5uZt1BrY/Zuav/93wW8pm6Z/0nt4YyHImIXtdte9wAHgK8Df9dMQEW73p+ndkvtbuC7wAeoXY1YbNlHgX8LvBZ4CPhV4DPUTuBk5jeoPXy3r4h/ztuDkrRCddM5/0vUEuyb5nn/OMXV41cAV1JLyJ8O/N+6Wf4G2ANMRMRcV5rVx+LxzSqlx4uI/cDrMvOvOx1LmSLiZuB9mfknnY5FkjqlX8750ly8Uqy+FBEviojTiuYTFwM/Bvxlp+OSJEmdYVKsrhIRb4nvD5pR//O5Fhf1DGp9HFeAncCFmXmwxWVIkhbQxnO+tCibT0iSJKnveaVYkiRJfc+kWJIkSX2vrYN3nHrqqbl58+YlLVOtVhkcHCwnoJIYc3sYc/l6LV5oLOZbb731u5m5rk0h9ZX683wvHj+Nsm69ybr1puXWbann+rYmxZs3b+aWW25Z0jJjY2Ns3769nIBKYsztYczl67V4obGYI+Ke9kTTf+rP8714/DTKuvUm69abllu3pZ7rbT4hSZKkvmdSLEmSpL5nUixJkqS+19Y2xcsxPT3N+Ph4W8oaHBxkzZo1bSlLkvR9lUqFarVaejme5yXNp6uT4kqlwh13fI3zzjuvLeWtWXsy99y9zxOmpL4UEQPALcCBzDw/Is4EPgqcDNwGvDozH211uZVKhTPO3ELl0IOtXvUTeJ6XNJ+uToqr1SrT01OcdvFVDAyuLbWs6eohJkYvpVqterKU1K/eBNwJ/EDx/veAd2bmRyPifcBrgfe2utBqtUrl0IOln+s9z0taSFcnxTMGBtey6sRTOh2GJK1YEbEJ+Dng7cCbIyKAFwO/XMwyCryNEpLiGZ7rJXWSD9pJkgCuAi4DjhXvTwEeysyp4v29wMZOBCZJ7dATV4olSeWJiPOB+zPz1ojYPvPxHLPmPMvvAHYADA0NMTY2BsCRI0cee72QyclJRkZGWL1ukBiYWnT+5crpQSa3jLBnzx727t3b1LoarVsvsm69ybo1z6RYknQO8PKI+FngeGptiq8CToqIVcXV4k3AnF0BZeZuYDfAtm3bcmbkqUZHoRofH2d4eJiNl4yW2nxi6nCFA+/ZxYEDB9iwYUNT63L0sN5k3XpTu+pm8wlJ6nOZ+VuZuSkzNwMXAX+Tmb8C3AhcWMx2MfCpDoUoSaUzKZYkzedyag/d3UWtjfHVHY5Hkkpj8wlJ0mMycwwYK17vA87uZDyS1C5eKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS31s0KY6I4yPi7yPiHyNiT0T8dvH5mRFxc0R8MyKujYgnlR+uJEmS1HqNXCk+Crw4M58NPAd4aUS8APg94J2Z+XTgEPDa8sKUJEmSyrNoUpw1R4q3q4ufBF4MXF98Pgr8QikRSpIkSSVb1chMETEA3Ar8EPBHwLeAhzJzqpjlXmDjPMvuAHYADA0NMTY21nBwk5OTbNq0icvWrSIGphZfoAk5PcjklhH27NnD3r17m1rXkSNHllTPbmDM7dFrMfdavNCbMUt6vEqlQrVabfl6JycnGR8ff+z94OAga9asaXk56k0NJcWZOQ08JyJOAj4J/PBcs82z7G5gN8C2bdty+/btDQc3Pj7ONddcwzv3rWPViac0vNxyTB2ucOA9uzhw4AAbNmxoal1jY2MspZ7dwJjbo9di7rV4oTdjlvR9lUqFM87cQuXQgy1f98jICMPDw4+9X7P2ZO65e5+JsYAGk+IZmflQRIwBLwBOiohVxdXiTcD4ggtLkiQtolqtUjn0IKddfBUDg2tbuu7V6wbZeMkoANPVQ0yMXkq1WjUpFtBAUhwR64DJIiE+ATiP2kN2NwIXAh8FLgY+VWagUruVcftu9q078PadJM1lYHBty+8Sx8BU6Xee1bsauVK8Hhgt2hUfB1yXmZ+JiK8DH42I3wW+AlxdYpxSW5V1+272rTvw9p0kSd1g0aQ4M78KPHeOz/cBZ5cRlNRpZd2+q791B96+kySpWyypTbHUb1p9+85bd5IkdSeHeZYkOXqppL5nUixJAkcvldTnTIolSY5eKqnvmRRLkoDa6KURcTtwP/AFljB6qST1Oh+0kyQByx+9NCJ2ADsAhoaGHhtmu9EhtycnJxkZGWH1ukFiYGrR+ZcrpweZ3DLCnj172Lt3b1PrWsnDiXe6bmUeD0MnwM6zauts5fHQDTq938rUrrqZFEuSHmepo5dm5m5gN8C2bdtyZpjtRofcHh8fZ3h4mI2XjJbaO8vU4QoH3rOLAwcOsGHDhqbWtZKHE+903co8HnaeNcU77qilPq08HrpBp/dbmdpVN5tPSJKIiHXFFWLqRi+9k++PXgqOXippBfNKsXrOzPDLcw2Z3CoTExOlrFfqYo5eKqmvmRSrp9QPvzzXkMmSlsfRSyX1O5Ni9ZT64ZdXr9v0uCGTW+nRB/bzwMfeWsq6JUlS9zEpVk8aGFxLDKwq7aGc6eqhUtYrSZK6kw/aSZIkqe+ZFEuSJKnvmRRLkiSp75kUS5Ikqe+ZFEuSJKnvmRRLkiSp75kUS5Ikqe+ZFEuSJKnvmRRLkiSp75kUS5Ikqe+ZFEuSJKnvmRRLkiSp75kUS5Ikqe+ZFEuSJKnvmRRLkiSp761abIaIOB34MHAacAzYnZnvioiTgWuBzcB+4JWZeai8UCVJknpTpVKhWq2Wtv7JyUnGx8cZHBxkzZo1pZWzki2aFANTwM7MvC0iTgRujYgvAK8BvpiZV0bEFcAVwOXlhSpJktR7KpUKZ5y5hcqhB0srY2RkhOHhYdasPZl77t5nYrwMiybFmXkQOFi8PhwRdwIbgQuA7cVso8AYJsWSJEmPU61WqRx6kNMuvoqBwbWllLF63SCnXXwVE6OXUq1WTYqXoZErxY+JiM3Ac4GbgaEiYSYzD0bE0+ZZZgewA2BoaIixsbGGy5ucnGTTpk1ctm4VMTC1lFCXLKcHmdwywp49e9i7d29T6zpy5MiS6tkNeiXmyclJRkZGWL1ukKETYOdZ5RwX+cz1TJ5VK6eVx97smFt53JWhV46Ler0Ys6T+MDC4llUnnlLKumNgqrSEu180nBRHxFOBjwOXZubDEdHQcpm5G9gNsG3btty+fXvDwY2Pj3PNNdfwzn3rSjuIZkwdrnDgPbs4cOAAGzZsaGpdY2NjLKWe3aBXYh4fH2d4eJiNl4xy+U+s4R13LOn/uoYdndjPxOguNl4y2tJjb+dZU4+LuZXHXRl65bio14sxS5I6r6HeJyJiNbWE+COZ+Yni4/siYn0xfT1wfzkhSpIkSeVaNCmO2iXhq4E7M/MP6ibdAFxcvL4Y+FTrw5MkSZLK18iV4nOAVwMvjojbi5+fBa4EXhIR3wReUryXJPWgiDg9Im6MiDsjYk9EvKn4/OSI+EJEfLP4baNFSStSI71PfBmYrwHxua0NR5LUIXa/KamvOaKdJInMPJiZtxWvDwP13W+OFrONAr/QmQglqVzlPLovaUkmJibaUo4jHakRy+l+U5J6nUmx1EHHjj4Cxw2wdevWtpTnSEdazHK635yvP/pG+4yu73+8zD7p+70/+kZ1um5lHg/1fcW3s5/4dhzjQyfAZc/v7r7vl6tdx6RJsdRBOXUUjk2XOsrRjOnqIUc60oIW6n6zuEo8Z/eb8/VH32if0fX9j5fZJ32/90ffqE7Xrczjob6v+Hb2E9+OY3znWVP8/s3Vru77frnadUyaFEtdoMxRjqRGNND95pXY/aakFcykWJIE3+9+846IuL347C3UkuHrIuK1wLeBV3QoPkkqlUmxJMnuNyX1PbtkkyRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfMymWJElS31vV6QC6zcTERNPrmJycZHx8fN7pg4ODrFmzpulyJEmS1BomxYVjRx+B4wbYunVr0+saGRlheHh43ulr1p7MPXfvMzGWJEk9q1KpUK1WSy9nenq69DLApPgxOXUUjk1z2sVXMTC4tql1rV43yMZLRuecNl09xMTopVSrVZNiSZLUkyqVCmecuYXKoQdLL+ud77yKbdu2lZ43mRTPMjC4llUnntLUOmJgqul1SJIkdatqtUrl0IMtuZi4kOnqIaanp9pyMdGkWJIkScvSiouJi3ug5PXX2PuEJEmS+p5JsSRJkvqezSekPrOUbgcX615wPnY7KEnqNSbFUp9YTreDi3UvOB+7HZQk9ZpFk+KI+CBwPnB/Zv5o8dnJwLXAZmA/8MrMPFRemJKatZxuBxfqXnA+djsoSepFjVwp/hDwbuDDdZ9dAXwxM6+MiCuK95e3PjxJrbaUJ4XtXlCS1C8WfdAuM28CZvfMfAEwc/loFPiFFsclSZIktc1y2xQPZeZBgMw8GBFPm2/GiNgB7AAYGhpibGys4UImJyfZtGkTl61bRQxMLTPUxuQz1zN51gir1w02XdbQCbDzrLnXkdODTG4ZYc+ePezdu7epclrpyJEjS9o3nTI5OcnISG0/LbSdm9XK46He7JjLKmcuyylrOdu408d4rxzL3camcpL6XekP2mXmbmA3wLZt23L79u0NLzs+Ps4111zDO/etK/0W7tGJ/UyM7mLjJaNNl7XzrCneccfcm3bqcIUD79nFgQMH2LBhQ1PltNLY2BhL2TedMj4+zvDwMBsvGeXyn1gz73ZuViuPh3qzj42yypnLcspa6FieT6eP8V45lrvQh7CpnKQ+ttx+iu+LiPUAxe/7WxeSJKndbConqd8tNym+Abi4eH0x8KnWhCNJ6iKPayoHzNtUTpJ6XSNdsl0DbAdOjYh7gbcCVwLXRcRrgW8DrygzSElS95rv2ZFG23fXPytQZtv6VrZ3X8lt1ztdtzKPh/rnJNr5/EM7jvGhE+Cy56+sOkFtPw09+cltqdOiSXFmvmqeSee2OBZJUne5LyLWFw9Uz9tUbr5nRxpt313/rECZbetb2d59Jbdd73Tdyjwe6p+TaOfzD+04xneeNcXv31xdUXWC2n76z1se4Cd/8idLr5Mj2qklKpUK1Wq19HKWMkSxpKbNNJW7EpvKST2jHd+VK/H72KRYTatUKpxx5hYqh2Y/oyOpV9hUTup9x44+AscNsHXr1k6H0pNMitW0arVK5dCDSxo+eLkefWA/D3zsraWWIfUjm8pJvS+njsKxab+Pl8mkWC2zlOGDl2u66rgBkprTitu+k5OTjI+PLzjP4OAga9asabqsbtKOpnIr8bZ8u/l9vDwmxZKkvtDKW8sjIyMMDw8vOM+atSdzz937VkxibFM5rXQmxZKkvtDKW8ur1w2y8ZLReadPVw8xMXop1Wp1xSTF7WoqtxJvy6s3mBRLkvpKK24tx8BU6benG9XqJg3zNQ2ZadZQ9q35lXhbXr3BpFiSpB5VRpOGRpqGSCuRSbEkST2qjCYN8zUNsVmDVjqTYkmSelwrmzTM1zTEZg1a6UyKJUlS33L0N80wKZZUinZ9CazEvmAllc/R3zSbSbGklmr3F81K6wtWUns4+ptmMymW1FLt/KJZiX3BSmovR3/TDJNiSaVoxxeNJEmtclynA5AkSZI6zaRYkiRJfc+kWJIkSX3PNsUrXKVSoVqtLjrffGPdN8L+FyVJUq8zKV7BKpUKZ5y5hcqhBxed17HuJUlSPzMpXsGq1SqVQw821DXWfGPdN8L+FyVJUq8zKe4DjXSNNd9Y942w/0VJktTrfNBOkiRJfc+kWJIkSX3PpFiSJEl9z6RYkiRJfc8H7SRJKknZ/bjbT7zUOk0lxRHxUuBdwADwgcy8siVRSZK6huf6pTt29BE4boCtW7d2OhRJDVp2UhwRA8AfAS8B7gX+ISJuyMyvtyo4SVJnea5fnpw6CsemG+onvhn2Ey+1TjNXis8G7srMfQAR8VHgAqDlJ8p29IM7/UilZWXl9CBThytzl1Osvx23vGbKaKROC8W8mFZuu6WU1UzMSymnlWbH3Klt16jlbOO21sk+stthxZzr23Wery+rXVq57earW7v+tsssp75u3X7+XaqcHlxxdWrH+utFZi5vwYgLgZdm5uuK968Gnp+Zb5g13w5gR/H2GcA/LbGoU4HvLivIzjHm9jDm8vVavNBYzGdk5rp2BNPrGjnXL3Ce78Xjp1HWrTdZt9603Lot6VzfzJXimOOzJ2TYmbkb2L3sQiJuycxty12+E4y5PYy5fL0WL/RmzF1u0XP9fOf5lbwvrFtvsm69qV11a6ZLtnuB0+vebwLGmwtHktRlPNdL6gvNJMX/ADw9Is6MiCcBFwE3tCYsSVKX8FwvqS8su/lEZk5FxBuAv6LWTc8HM3NPyyL7vmU3veggY24PYy5fr8ULvRlz12ryXL+S94V1603WrTe1pW7LftBOkiRJWikc5lmSJEl9z6RYkiRJfa9rk+KI+GBE3B8RX+t0LI2KiNMj4saIuDMi9kTEmzod02Ii4viI+PuI+Mci5t/udEyNiIiBiPhKRHym07E0IiL2R8QdEXF7RNzS6XgaEREnRcT1EfGN4ph+YadjWkhEPKPYvjM/D0fEpZ2Oa6WLiJdGxD9FxF0RccUc058cEdcW02+OiM3tj3J5GqjbayLigbpj7nWdiHM5FvuOjZo/LOr+1Yh4XrtjXK4G6rY9Iip1++2/tTvG5Wokz+jVfddg3crdd5nZlT/ATwHPA77W6ViWEPN64HnF6xOBvcCzOh3XIjEH8NTi9WrgZuAFnY6rgbjfDPw58JlOx9JgvPuBUzsdxxJjHgVeV7x+EnBSp2NaQuwDwAS1jts7Hs9K/Sm287eALcUx8o+zz3nAJcD7itcXAdd2Ou4W1u01wLs7Hesy67fgdyzws8Dniu+IFwA3dzrmFtZte698d8wR+6J5Rq/uuwbrVuq+69orxZl5E/Bgp+NYisw8mJm3Fa8PA3cCGzsb1cKy5kjxdnXx09VPX0bEJuDngA90OpaVKiJ+gNoXy9UAmfloZj7U2aiW5FzgW5l5T6cDWeEeGwI6Mx8FZoaArncBtX+wAK4Hzo2IuQYE6TaN1K1nNfAdewHw4eI74u+AkyJifXuia04v5g+NajDP6Ml91w05VNcmxb2uuEX4XGpXXrta0RThduB+4AuZ2e0xXwVcBhzrdCBLkMDnI+LWYkjcbrcFeAD4k6KZygciYrDTQS3BRcA1nQ6iD2wEvlP3/l6e+CX22DyZOQVUgFPaEl1zGqkbwC8Vt6ivj4jT55jeqxqtf696YdFs8HMR8SOdDmY5Fsgzen7fLZJDlbbvTIpLEBFPBT4OXJqZD3c6nsVk5nRmPofaSFVnR8SPdjqm+UTE+cD9mXlrp2NZonMy83nAy4DfjIif6nRAi1hF7fbjezPzuUAVeEKbym5UDDDxcuBjnY6lDyw6BHSD83SjRuL+NLA5M38M+Gu+f0V8JejV/daI26g1rXo28L+Av+hwPEu2SJ7R0/tukbqVuu9MilssIlZT25kfycxPdDqepShuj48BL+1wKAs5B3h5ROyndjvzxRHxZ50NaXGZOV78vh/4JLVbs93sXuDeursG11NLknvBy4DbMvO+TgfSBxoZAvqxeSJiFbCG3ri1vWjdMvN7mXm0ePt+YGubYmuHFTu8d2Y+PNNsMDM/C6yOiFM7HFbDGsgzenbfLVa3svedSXELFe3krgbuzMw/6HQ8jYiIdRFxUvH6BOA84BudjWp+mflbmbkpMzdTu0X+N5n5qx0Oa0ERMRgRJ868BoaBru5VJTMngO9ExDOKj84Fvt7BkJbiVdh0ol0aGQL6BuDi4vWF1P5me+Gq1aJ1m9VO8+XU2kCuFDcA/77oyeAFQCUzD3Y6qFaIiNNm2rVHxNnUcqHvdTaqxjSYZ/TkvmukbmXvu2UP81y2iLiG2lOGp0bEvcBbM/Pqzka1qHOAVwN3FG10Ad5S/DfTrdYDoxExQO3gui4ze6Kbsx4yBHyy+DteBfx5Zv5lZ0NqyH8CPlIkBPuAX+twPIuKiKcALwFe3+lY+kHOMwR0RPwOcEtm3kDtS+5PI+IualeIL+pcxI1rsG5vjIiXA1PU6vaajgW8RHN9x1J70JrMfB/wWWq9GNwFPEIP/P3PaKBuFwL/MSKmgH8GLuqRf9RgnjwD+EHo+X3XSN1K3XcO8yxJkqS+Z/MJSZIk9T2TYkmSJPU9k2JJkiT1PZNiSZIk9T2TYkmSJPU9k2JJkiT1PZNiSZIk9T2TYkmSJPU9k2JJkiT1PZNiSZIk9T2TYkmSJPU9k2J1lYjIiPihReb5UET8brtimlX2/og4rxNlS9JK08g5fwnr+lxEXDzPtM1FWavaEYt6k0mxNI9OJt+SpKXJzJdl5mgj80bEWES8ruyY1FtMiiVJktT3TIq1oIi4PCIORMThiPiniDg3Io6LiCsi4lsR8b2IuC4iTi7mn7lFtSMixiPiYETsrFvf2RHxtxHxUDHt3RHxpCZjPD8ibi/W+f8i4sfqpu2PiF0R8dWIqETEtRFxfN30y4o4xiPidTO3zyJiB/ArwGURcSQiPl1X5HPmW58k9bJuO+dHxJnFsscV7z8QEffXTf+ziLi0eP3Y1d+IGIiIkYj4bkTsA36ubpm3Az8JvLs4v7+7rsjzIuKbEXEoIv4oImJ5W1K9yKRY84qIZwBvAH48M08EfgbYD7wR+AXgRcAG4BDwR7MW/2ng6cAwcEVdO9xp4D8DpwIvBM4FLmkixucBHwReD5wC/DFwQ0Q8uW62VwIvBc4Efgx4TbHsS4E3A+cBP1TUB4DM3A18BPj9zHxqZv78YuuTpF7Wjef8zLwbeBh4bvHRTwJHIuKHi/c/BXxpjkV/HTi/WG4bcGHdOv8L8H+ANxTn9zfULXc+8OPAs6md63+m0VjV+0yKtZBp4MnAsyJidWbuz8xvUUtA/0tm3puZR4G3ARfOeoDhtzOzmpl3AH8CvAogM2/NzL/LzKnM3E8tiX0Ry/frwB9n5s2ZOV20JzsKvKBunj/MzPHMfBD4NPCc4vNXAn+SmXsy8xHgtxssc771SVIv69Zz/peAF0XEacX764v3ZwI/APzjHMu8ErgqM79TnKv/Z4NlXZmZD2Xmt4Eb8fzeV0yKNa/MvAu4lNoJ8P6I+GhEbADOAD5Z3NJ6CLiT2sl0qG7x79S9vofa1QUi4l9HxGciYiIiHgb+B7UrCMt1BrBzJpYintNnyitM1L1+BHhq8XrDrDjrXy9kvvVJUs/q4nP+l4Dt1K4K3wSMUUusXwT8n8wtc/9VAAAd9klEQVQ8Nscys8/v9zRYluf3PmZSrAVl5p9n5r+hdlJM4PeonWhelpkn1f0cn5kH6hY9ve71DwLjxev3At8Anp6ZPwC8BWimzdZ3gLfPiuUpmXlNA8seBDbNEzPU6itJfaNLz/lfotZsYnvx+svAOdSS4rmaTkDt/D47pnqe3/UEJsWaV0Q8IyJeXLTP/Rfgn6ldHXgf8PaIOKOYb11EXDBr8f8vIp4SET8C/BpwbfH5idTahx2JiGcC/7HJMN8P/EZEPD9qBiPi5yLixAaWvQ74tYj44Yh4CvDfZk2/D9jSZHyS1BO69Zyfmd8sYvlV4KbMfJja+fmXmD8pvg54Y0Rsioi1wBWzpnt+1xOYFGshTwauBL5L7ZbS06j9l/8u4Abg8xFxGPg74Pmzlv0ScBfwRWAkMz9ffL4L+GXgMLWE9lqakJm3UGtX/G5qD3/cRYMPvmXm54A/pNZu7C7gb4tJR4vfV1NrW/dQRPxFM3FKUg/o5nP+l4DvFW19Z94H8JV55n8/8FfU2hvfBnxi1vR3UWsXfSgi/nCZMWmFiUzvIKh1ImIzcDewOjOnOhvN0hRPM38NeHKvxS5JndDL53xpNq8Uq69FxC9GxJOK22u/B3zaE7v6VdG361ci4jPF+zMj4uai39Zrl9K/rCT1GpNidaWI2FN0qj7751daXNTrgQeAb1FrO9dsG2epl72JWs8CM34PeGdmPp1a86TXdiQqrXhtPOdL87L5hCSJiNgEjAJvpzaozc9T+4fxtMyciogXAm/LTAczkLQieaVYkgRwFXAZMNPn6ynAQ3XNie4FNnYiMElqh1WLz9I6p556am7evLlt5VWrVQYHB9tWXiO6LaZuiweMqVHdFlO3xQPzx3Trrbd+NzPXdSCkrhQR5wP3Z+atEbF95uM5Zp3z1mJE7AB2AJxwwglbTz99dpffizt27BjHHbcyr9NYt95k3XpTfd327t27tHN9ZrbtZ+vWrdlON954Y1vLa0S3xdRt8WQaU6O6LaZuiydz/piAW7KN575u/6E2BO69wH5qXXE9AnyEWtdcq4p5Xgj81WLrWu55vhuPn1axbr3JuvWm+rot9Vy/Mv9NkCQ1LDN/KzM3ZeZm4CLgbzLzV6j14X1hMdvFwKc6FKIklc6kWJI0n8uBN0fEXdTaGF/d4XgkqTRtbVMsSepumTkGjBWv9wFndzIeSWoXrxRLkiSp73mleIWrVCpUq9V5p09OTjI+Pt50OYODg6xZs6bp9UiSutfs75RWfYfM5neKOsGkeAWrVCqcceYWKocenHeekZERhoeHmy5rzdqTuefufZ7EJGmFmus7pVXfIbP5naJOMClewarVKpVDD3LaxVcxMLh2znlWrxtk4yWjTZUzXT3ExOilVKtVT2CStELN9Z3Siu+Q2fxOUaeYFPeBgcG1rDrxlDmnxcDUvNMkSZqt/jvF7xCtJD5oJ0mSpL7nlWL1nMUeHmwVH/SQJKl/mBSrpzTy8GCr+KCHJEn9Y9GkOCJOBz4MnAYcA3Zn5rsi4m3ArwMPFLO+JTM/W1agEjT28GAr+KCHJEn9pZErxVPAzsy8LSJOBG6NiC8U096ZmSPlhSfNbaGHByVJkpZq0aQ4Mw8CB4vXhyPiTmBj2YFJkiRJ7bKk3iciYjPwXODm4qM3RMRXI+KDEVHevWxJkiSpRA0/aBcRTwU+DlyamQ9HxHuB/w5k8fsdwH+YY7kdwA6AoaEhxsbGWhB2Y44cOdLW8hrRzpgmJycZGRlh9bpBYmBqznmGToCdZ809rVE5PcjklhH27NnD3r17m1oXLLyNGqlTK8yuU78fS43otnigO2OSJHWnhpLiiFhNLSH+SGZ+AiAz76ub/n7gM3Mtm5m7gd0A27Zty+3btzcZcuPGxsZoZ3mNaGdM4+PjDA8Ps/GS0Xnb3+48a4p33NFcJyRThysceM8uDhw4wIYNG5paFyy8jRqpUyvMrlO/H0uN6LZ4oDtjkiR1p0WbT0REAFcDd2bmH9R9vr5utl8Evtb68CRJkqTyNXKJ8Bzg1cAdEXF78dlbgFdFxHOoNZ/YD7y+lAglSZKkkjXS+8SXgZhjkn0SS5IkaUVYUu8TkiRJ0kpkUixJkqS+Z1IsSZKkvmdSLEmSpL7XXAe1K0ylUqFarZZaxuTkJJVKhTVr1pRajiRJkhpnUlyoVCqcceYWKoceLLWckZERXvHvLuKeu/eZGEuSJHUJk+JCtVqlcuhBTrv4KgYG15ZWzupTjqdy6EGq1apJsSRJUpcwKZ5lYHBtqcMHc9y/lLduSZIkLYsP2kmSJKnveaVYkiR1nYmJidLLGBwctCmjHmNSLEkiIo4HbgKeTO274frMfGtEnAl8FDgZuA14dWY+2rlItdIdO/oIHDfA1q1bSy9rzdqTffBdjzEpliQBHAVenJlHImI18OWI+BzwZuCdmfnRiHgf8FrgvZ0MVCtbTh2FY9OlP/g+XT3ExOilPviux5gUS5LIzASOFG9XFz8JvBj45eLzUeBtmBSrDUp/8F2axaRYkgRARAwAtwI/BPwR8C3gocycKma5F9g4x3I7gB0AQ0NDjI2NLbnsI0eOLGu5XrBS6jY5OcnIyAir1w0SA7VDYugE2HnW1CJLLk0+cz2TZz2+nDLk9CCTW0bYs2cPe/fufcL0lbLf5mLd5mZSLEkCIDOngedExEnAJ4Efnmu2OZbbDewG2LZtW27fvn3JZY+NjbGc5XrBSqnb+Pg4w8PDbLxk9LEruDvPmuIdd7Q2lTg6sZ+J0V2PK6cMU4crHHjPLg4cOMCGDRueMH2l7Le5WLe52SWbJOlxMvMhYAx4AXBSRMxkPZuA8U7FJUllWjQpjojTI+LGiLgzIvZExJuKz0+OiC9ExDeL3+W1hpcklSoi1hVXiImIE4DzgDuBG4ELi9kuBj7VmQglqVyNXCmeAnZm5g9Tu2rwmxHxLOAK4IuZ+XTgi8V7SVJvWg/cGBFfBf4B+EJmfga4HHhzRNwFnAJc3cEYJak0izYEysyDwMHi9eGIuJPagxYXANuL2Uap3Wq7vJQoJUmlysyvAs+d4/N9wNntj0iS2mtJbYojYjO1k+bNwFCRMM8kzk9rdXCSJElSOzT8yGhEPBX4OHBpZj4cEY0u13RXPcu1lG455upqpgxDxw8wMjJ/FzCt1EidWtGdzmLd2izVQvutXftpdp26sfuaboup2+KB7oxJktSdGkqKi9GNPg58JDM/UXx8X0Ssz8yDEbEeuH+uZVvRVc9yLaVbjrm6minDm5/1L+zaNX8XMK3USJ1a0Z3OYt3aLNVC+61d+2l2nbqx+5pui6nb4oHujEmS1J0a6X0iqD1YcWdm/kHdpBuoPYkMPpEsSZKkHtbIJcJzgFcDd0TE7cVnbwGuBK6LiNcC3wZeUU6IkiRJUrka6X3iy8B8DYjPbW04kiRJUvs5op0kSZL6nkmxJEmS+p5JsSRJkvqeSbEkSZL6XnMd1Eor3MTEBFAbNGR8fLyUMgYHB1mzZk0p65YkSY0xKZbmcOzoI3DcAFu3bgVgZGSE4eHhUspas/Zk7rl7n4mxJEkdZFIszSGnjsKxaU67+CoGBteyet0gGy8ZbXk509VDTIxeSrVaNSmWJKmDTIqlBQwMrmXViacQA1OlDistSZI6ywftJEmS1Pe8UixJUg+rVCpUq9XSy5l58FhaqUyKJUnqUZVKhTPO3ELl0IOdDkXqeSbFkiT1qGq1SuXQg489FFymRx/YzwMfe2upZUidZFIsSVKPm3kouEzT1UOlrl/qNB+0kyRJUt/zSrFaplUPYSw0epwPekiSpDKYFKtps0d/a1aZo8dJkiTNZdGkOCI+CJwP3J+ZP1p89jbg14EHitnekpmfLStIdbfZo781a6HR43zQQ1KvmOkqbaG7X83y7pnUOo1cKf4Q8G7gw7M+f2dmjrQ8IvWsVj3osdDocT7oIakX1HeV5t0vqTcsmhRn5k0Rsbn8UCRJWhnqu0pbvW7TvHe/muXdM6l1mmlT/IaI+PfALcDOzPQSniRJdQYG1xIDq0rrLs27Z1LrLDcpfi/w34Esfr8D+A9zzRgRO4AdAENDQ4yNjS2zyKU7cuRIw+VNTk4yMjLC6nWDxMBUaTENHT/AyMgIe/bsYe/evaWVA43VaegE2HlWc/XNZ65n8qzWbbuFYmp1WfOZXU4rttOc5UwPMrllecfDUo7vdui2eKA7Y+pWEXE6tWZypwHHgN2Z+a6IOBm4FtgM7Ade6UUQSSvRspLizLxv5nVEvB/4zALz7gZ2A2zbti23b9++nCKXZWxsjEbLGx8fZ3h4mI2XjJbaAfqbn/Uv7Nq1iwMHDrBhw4bSyoHG6rTzrCnecUdznZAcndjPxOiulm27hWJqdVnzmV1OK7bTXKYOVzjwnuUdD0s5vtuh2+KB7oypi01Ru+t3W0ScCNwaEV8AXgN8MTOvjIgrgCuAyzsYpySVYlmDd0TE+rq3vwh8rTXhSJI6ITMPZuZtxevDwJ3ARuACYKZB7CjwC52JUJLK1UiXbNcA24FTI+Je4K3A9oh4DrXmE/uB15cYoySpjYqHq58L3AwMZeZBqCXOEfG0DoYmSaVppPeJV83x8dUlxNJX2tG3pP1XSlqqiHgq8HHg0sx8OCIaWabpZ0dWWvvv+mc6ynomAdr3nMV8ZZVRt7Y9O7LIMx0r7ZisZ93m5oh27ZbHWjr6myS1SkSsppYQfyQzP1F8fF9ErC+uEq8H7p+9XCueHVlp7b/rn+m4/CfWlPJMArTvOYv5yirjeYt21WmxZzpW2jFZz7rNzaS43TJbOvrbQuy/UlKjonZJ+Grgzsz8g7pJNwAXA1cWvz/VgfAkqXQmxR3SqtHfFmL/lZKW4Bzg1cAdEXF78dlbqCXD10XEa4FvA6/oUHySVCqTYkkSmfllYL4GxOe2MxZJ6oRldckmSZIkrSQmxZIkSep7JsWSJEnqeybFkiRJ6nsmxZIkSep7JsWSJEnqeybFkiRJ6nsmxZIkSep7JsWSJEnqeybFkiRJ6nsmxZIkSep7JsWSJEnqe4smxRHxwYi4PyK+VvfZyRHxhYj4ZvF7bblhSpIkSeVp5Erxh4CXzvrsCuCLmfl04IvFe0mSJKknLZoUZ+ZNwIOzPr4AGC1ejwK/0OK4JEmSpLZZbpviocw8CFD8flrrQpIkSZLaa1XZBUTEDmAHwNDQEGNjY2UX+ZgjR440XN7k5CQjIyOsXjdIDEyVFtPQ8avbUg5APnM9k2ctXNbQCbDzrObiaKScpVgoplaXNZ/Z5bRiO81ZzvQgk1tG2LNnD3v37l3Ssks5vtuh2+KB7oxJktSdlpsU3xcR6zPzYESsB+6fb8bM3A3sBti2bVtu3759mUUu3djYGI2WNz4+zvDwMBsvGWXViaeUFtObn1ll165dpZcDcHRiPxOjC5e186wp3nFHc/8bNVLOUiwUU6vLms/sclqxneYydbjCgffs4sCBA2zYsGFJyy7l+G6HbosHujMmSVJ3Wm7ziRuAi4vXFwOfak04kiRJUvs10iXbNcDfAs+IiHsj4rXAlcBLIuKbwEuK95IkSVJPWvR+cGa+ap5J57Y4ljlVKhWq1eqylp2cnGR8fLyheScmJpZVhiRJknpf6Q/aNaNSqXDGmVuoHJrdI1xjRkZGGB4ebnFUkiRJWmm6OimuVqtUDj3IaRdfxcDg0gfNW71ukI2XjC4+I/DoA/t54GNvXXIZkiRJ6n1dnRTPGBhcu6yeBmJgquHlpquHlrx+SZIkrQzL7X1CkiRJWjFMiiVJktT3eqL5hCRJUi9rpjetpRgcHGTNmjWll7MSmRRLkoiIDwLnA/dn5o8Wn50MXAtsBvYDr8xMH8CQlqjZ3rSWYs3ak7nn7n0mxstgUixJAvgQ8G7gw3WfXQF8MTOvjIgriveXdyA2qac125tWo6arh5gYvZRqtWpSvAwmxZIkMvOmiNg86+MLgO3F61FgDJNiadmW25uW2sOkWJI0n6HMPAiQmQcj4mlzzRQRO4AdAENDQ4yNjS25oCNHjixruW41OTnJyMgIq9cNMnQC7DxrqpRy8pnrmTyrVk4MlFPGQmWVUbd21SmnB5ncMsKePXvYu3fvE6a38pisPx46WacZK+3vrV4zdTMpliQ1JTN3A7sBtm3bltu3b1/yOsbGxljOct1qfHyc4eFhNl4yyuU/sYZ33FHO1+3Rif1MjO5i4yWjpV+BnKusnWdNtbxu7arT1OEKB96ziwMHDrBhw4YnTG/lMVl/PHSyTjNW2t9bvWbqZpdskqT53BcR6wGK3/d3OB5JKo1XiiVJ87kBuBi4svj9qc6GI7XexMTEnJ9PTk4yPj5eahnqLibFkiQi4hpqD9WdGhH3Am+llgxfFxGvBb4NvKJzEUqtdezoI3DcAFu3bp1z+sjICMPDw22OSp1kUix1geVcRVjOVYx2dererk7qwY7qWyUzXzXPpHPbGojUJjl1FI5Nz9tN2up1g2y8ZLQlZT36wH4e+NhbW7IulcekWOqgxa5ULGQ5VzHa0al7OzupBzuql9Sc+bpJi4Gplj0UN111zJte0FRSHBH7gcPANDCVmdtaEZTULxa7UrGQpV7FaFen7u3qpB7sqF6S1DqtuFL805n53RasR+pby+nQvZVXMcpgJ/WSpF5il2ySJEnqe81eKU7g8xGRwB8XHbhL6mJldQ008+CfXQ+pm7XrIVD/DqTe02xSfE5mjhdDf34hIr6RmTfVz9DM8J/NDou4lOEn2zWs5NDxq9sy1CM0VqdWDNHZ6m23UExtG/5zVjllDdPaTH2WGlNOncDklhFuvPHGpYbZkE2bNnHNNdcAtO8Yb+Mwrep97X4IVFJvaSopzszx4vf9EfFJ4GzgplnzLHv4z2aHRVzK8JPtGlbyzc+ssmtX54bknK0VQ3S2etstFFO79tPscsoYynSucpZiqTHNlFXWA3CXrVvFO/ete6zroXYc4+0cplW9r50PgdoFl9R7lv0tHxGDwHGZebh4PQz8Tssik1SKsh6Am3nwz66H1O3a8RCofwdS72nm0tcQ8MmImFnPn2fmX7YkKkmSJKmNlp0UZ+Y+4NktjEWSJEnqCLtkkyRJUt8zKZYkSVLfMymWJElS3zMpliRJUt8zKZYkSVLfa/1oBJIkLdH09DTj4+OlluHQy5IWYlIsSeqoSqXCHXd8jfPOO6/ToUjqYybFkqSOqlarTE9PlT78skMvS1qISbEkqSuUPfyyQy9LWogP2kmSJKnvmRRLkiSp75kUS5Ikqe/ZpliSJGkFWaz7wcnJyZZ0gTg1NcWqVeWnkoODg6xZs6b0ckyKJUmSVoBjRx+B4wbYunXrgvONjIwwPDzcdHkxsIqcnmp6PYtZs/Zk7rl7X+mJsUmxJEnSCpBTR+HY9KLdG65eN8jGS0abKmumi8Oyu1Kcrh5iYvRSqtWqSbEkSZIat1j3hjEw1XT3hzNdHJbdlWI7NfWgXUS8NCL+KSLuiogrWhWUJKl7eK6X1A+WnRRHxADwR8DLgGcBr4qIZ7UqMElS53mul9QvmrlSfDZwV2buy8xHgY8CF7QmLElSl/BcL6kvNNOmeCPwnbr39wLPby6cuS13aM6cHmTqcKWxMh6pNFVWo/LY6raUA43VaSnbqJlylmKhmNq1n2aX04rt1Eg5S7HUmMredjPxtGsftasMdf+5vuH1t/PYrCurrPPH7HLKNldZZdStU+f52VpZt26p04xu/O6ft5w2nucjM5e3YMQrgJ/JzNcV718NnJ2Z/2nWfDuAHcXbZwD/tPxwl+xU4LttLK8R3RZTt8UDxtSoboup2+KB+WM6IzPXtTuYXtTIub5F5/luPH5axbr1JuvWm+rrtqRzfTNXiu8FTq97vwl4Qk/Qmbkb2N1EOcsWEbdk5rZOlD2fboup2+IBY2pUt8XUbfFAd8bUgxY917fiPL+S95V1603WrTc1U7dm2hT/A/D0iDgzIp4EXATc0MT6JEndx3O9pL6w7CvFmTkVEW8A/goYAD6YmXtaFpkkqeM810vqF00N3pGZnwU+26JYytCRZhuL6LaYui0eMKZGdVtM3RYPdGdMPadN5/qVvK+sW2+ybr1p2XVb9oN2kiRJ0krR1Ih2kiRJ0kqwIpLiiBiIiK9ExGfmmPaaiHggIm4vfl7Xppj2R8QdRZm3zDE9IuIPi2FTvxoRz+twPNsjolK3nf5bmfEUZZ4UEddHxDci4s6IeOGs6W3dRg3G1NbtFBHPqCvr9oh4OCIunTVP27ZTg/F04lj6zxGxJyK+FhHXRMTxs6Y/OSKuLbbRzRGxueyY9HgRcXpE3Fj8Xe2JiDfNMU/b/+ZbpcH6tf1voxUi4viI+PuI+Meibr89xzw9+TfWYN06kke0SiycI/XkfpuxSN2WvN+aalPcRd4E3An8wDzTr83MN7Qxnhk/nZnz9QP4MuDpxc/zgfdSUof4DcYD8H8y8/ySY6j3LuAvM/PCqD3V/pRZ0zuxjRaLCdq4nTLzn4DnwGPD7R4APjlrtrZtpwbjgTZuo4jYCLwReFZm/nNEXEeth4QP1c32WuBQZv5QRFwE/B7w79oRnx4zBezMzNsi4kTg1oj4QmZ+vW6eTvzNt0oj9YP2n2db4Sjw4sw8EhGrgS9HxOcy8+/q5unVv7FG6gadyyNaYaEcqVf324yW5n89f6U4IjYBPwd8oNOxLNEFwIez5u+AkyJifaeDapeI+AHgp4CrATLz0cx8aNZsbd1GDcbUSecC38rMe2Z93qljab54OmEVcEJErKL2j8zsPtMvAEaL19cD50ZEtDG+vpeZBzPztuL1YWpfZBtnzdaz58UG69eTiv1xpHi7uviZ/UBST/6NNVi3ntVAjtST+w3Kyf96PikGrgIuA44tMM8vFbfiro+I0xeYr5US+HxE3Bq10Z5mm2vo1DJPoIvFA/DC4hbS5yLiR0qMBWAL8ADwJ8Wtjw9ExOCsedq9jRqJCdq7nepdBFwzx+ft3k6LxQNt3EaZeQAYAb4NHAQqmfn5WbM9to0ycwqoAKeUGZfmV9yifS5w86xJnTqWW2qB+kHnzh9NKW5T3w7cD3whM+fdd732N9ZA3aAzeUQrLJYj9ex+o4T8r6eT4og4H7g/M29dYLZPA5sz88eAv+b7/xGV7ZzMfB6124G/GRE/NWv6XP+Jlfnf6WLx3EZtOMRnA/8L+IsSY4Halb3nAe/NzOcCVeCKWfO0exs1ElO7txMARVOOlwMfm2vyHJ+VeqVjkXjauo0iYi21qx1nAhuAwYj41dmzzbHoirka1Esi4qnAx4FLM/Ph2ZPnWKSn9tMi9evI+aMVMnM6M59DbUTDsyPiR2fN0rP7roG6dSqPaEqDOVJP7rey8r+eToqBc4CXR8R+4KPAiyPiz+pnyMzvZebR4u37ga3tCCwzx4vf91Nrc3n2rFkaGia7XfFk5sMzt5CKPklXR8SpZcVDrf731v1Hfj21hHT2PG3bRo3E1IHtNONlwG2Zed8c09q9nRaMpwPb6Dzg7sx8IDMngU8APzFrnse2UdHEYg3wYIkxaQ5Fm82PAx/JzE/MMUsnjuWWWax+HTx/tEzRpGwMeOmsST3/NzZf3TqVR7TAojkSvbvfSsn/ejopzszfysxNmbmZ2q3cv8nMx10hmtUe7eXU2nmVKiIGiwctKG6/DwNfmzXbDcC/j5oXULvle7BT8UTEaTPtiCLibGrHxvfKiAcgMyeA70TEM4qPzgVmP5DStm3UaEzt3k51XsX8TRXaup0Wi6cD2+jbwAsi4ilFuefyxL/zG4CLi9cXUjtXdP3VkJWk2DdXA3dm5h/MM1snjuWWaKR+HTx/NCUi1kXEScXrE6j9I/qNWbP15N9YI3XrRB7RCo3kSPTofisr/1spvU88TkT8DnBLZt4AvDEiXk7tyeAHgde0IYQh4JPFuW8V8OeZ+ZcR8RsAmfk+aqND/SxwF/AI8GsdjudC4D9GxBTwz8BFbfjD+E/AR4pb8fuAX+vgNmo0prZvp4h4CvAS4PV1n3VsOzUQT1u3UWbeHBHXU7s1PQV8Bdg96zxwNfCnEXEXtfPARWXFo3mdA7wauKNovwnwFuAHoaN/863SSP06cZ5thfXAaNR6nDkOuC4zP7NC/sYaqVsn8ojSrJD9Nqdm95sj2kmSJKnv9XTzCUmSJKkVTIolSZLU90yKJUmS1PdMiiVJktT3TIolSZLU90yKJUmS1PdMiiVJktT3TIolSZLU9/5/ST5IwX2NJIIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#length and width distribution\n",
    "iris.hist(edgecolor='black', linewidth=1.2)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(12,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b99b622d30>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAD9CAYAAAD+pHP5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3Xd8FOXWwPHfMRTpIZTQpVsApUSUDkpXaaLXhqIoAgJevGC7XOViQZQXREEBLygi4hWU3lGKdAIiTQXkUiK9g0Qg2fP+sUPYhJRNssls4vnymQ8zzzwzc2aS7NnnmWdnRVUxxhhj3HKd2wEYY4z5a7NEZIwxxlWWiIwxxrjKEpExxhhXWSIyxhjjKktExhhjXGWJyBhj/kJEZIKIHBWRbUmsFxH5QER2i8gWEants+4JEdnlTE8EKiZLRMYY89fyGdA6mfVtgCrO1B34GEBEwoDXgTuAusDrIlI4EAFZIjLGmL8QVV0BnEymSnvgc/VaC4SKSEmgFbBYVU+q6ilgMcknNL/lCMRO/oouH99jj6QAHqnTz+0QgsakqY+5HULQ0LMn3A4hqORp3kPSs31qXm9yFav0LN6WzBXjVHVcKg5XGjjgsxzllCVVnm6WiIwxJhtxkk5qEk9CiSVNTaY83axrzhhjgp0n1v8p/aKAsj7LZYCDyZSnmyUiY4wJdrEx/k/pNwt43Bk9dydwRlUPAQuBliJS2Bmk0NIpSzfrmjPGmCCn6gnYvkRkCtAUKCoiUXhHwuX0HkfHAPOAtsBu4ALwpLPupIi8AWxwdjVYVZMb9OA3S0TGGBPsPIFLRKr6cArrFXguiXUTgAkBC8ZhicgYY4JdAFtEwcgSkTHGBLvADEIIWpaIjDEm2FmLyBhjjJs0MKPhgpYlImOMCXYBHKwQjCwRGWNMsLOuOWOMMa6ywQrGGGNcZS0iY4wxrrJ7RMYYY1xlo+aMMca4SdXuERljjHGT3SMyxhjjKrtHZIwxxlXWIjLGGOOq2MtuR5ChLBEZY0yws645Y4wxrrKuORNsBr49nBWr1hNWOJQZX4xxO5xM8eSgZ6jdrA4Xoy8yuv9I/rdtzzV1Hh7wGI07NSN/oXx0ueWhuPKb695C19ef5oabyvN+n2Gsnbc6M0MPqFVbdjJ00jw8Hg8dm9ah231N4q0/dPw0A8d9w7kLf+LxeHj+wZY0qnkjc1dtZuK8lXH1dh44wldv9OKmG0pm9ikEzKrte3l32jLvtWhQnada1o23/tDJs/zr84Wci76Ix6P0bd+QRtUrsObnfXwwcyWXY2PJGRJCv46NqHtjOZfOwk/WIjLBpkPbFjxyfztefWOY26FkilrN6lCyQkn6NOlBlVpVeebNnrzaYcA19SKXrGf+xLl8uOzjeOXHDx5n9D9G0q57x8wKOUPEejy8PXE2Y196kvCwgjzy2hia1r6ZSqWLx9X5ZOYyWtWtzoPN7+C334/Se9jnzK95I/c0qMk9DWoCsOvAYZ4fMTlLJ6FYj4chX3/PmD6dCA8twKPvfkmTGpWoVLJIXJ1PFqyjZe2qPNj4Nn47dILeH81gfvVuFM6fh5E92lM8ND+7Dx6n56hvWfx2dxfPxg/ZPBFd5+bBRaSpiMxJZn1XERmVAcftKiKlfJb3ikjRQB8no0TUrEGhggXcDiPT3N6iLsu/WQrArh93kq9gPkKLF76m3q4fd3L66Klryo9FHWX/L/vQLP7HvO23KMqGF6FM8TBy5shB6ztrsGzjz/ErCZz/8yIA5y/8SbHQa39P5q/ZQpt6t2ZGyBlm297DlC0WSpmioeTMEUKrOjeybMtv8eoIwh9/XgLgfPRFihXKB8BNZYtTPDQ/AJVKFuFSTCyXLgf3kwtUY/2esqK/aouoK7ANOOhyHMYPYSWKcOLg8bjlE4ePExZeJNGkk50dPXWWEmGF4paLhxVk629R8er07HQ3PYZ+xpRFa4m+eIlxLz95zX4WrtvK+39/LMPjzUhHT5+nROGrSTY8ND9b9x6OV6fHPXfSc9S3TFm+meiLlxnb9/5r9rPkx13cVKYYuXIG+UthNn/ET4otIhHJJyJzReQnEdkmIn8TkToislxENorIQhEp6dRdJiLvi8hqp25dp7yuU/aj8/+NqQ1URIqJyDcissGZGjjlg0RkgnPsPSLS12ebf4nILyKyWESmiEh/EekMRACTRWSziORxqvcRkU0islVEbkptfCbjiMi1haqZH4jLEjvlhNdm/pottGtUi8UfvMjo/o/zzzHT8Pi0BLfsPsD1uXJRpWx4RoeboRL76Se8Fgsif6XdHdVY9NYzjOrVgYETF+DxXN1y98HjjJy5koEPN8/gaAPA4/F/yoL86ZprDRxU1dtUtTqwAPgQ6KyqdYAJwFs+9fOpan2gl7MO4BegsarWAl4D3k5DrCOBEap6O3A/8B+fdTcBrYC6wOsiklNEIpx6tYBOeJMPqjoNiAQeVdWaqhrt7OO4qtYGPgb6JxaAiHQXkUgRifzP51PScArGX60eb8t780bw3rwRnDxykiKlrvacFilRlJNHT7oYnTvCwwpy+OSZuOWjJ89SPEHX2/TlG2l1R3UAbqtSjouXYzh17kLc+oVrt9KmXo3MCTgDhYfm5/Cpc3HLR06fj+t6u2L66m20rFMVgNsqluLi5RhO/+H9cz9y6hwvfDKbNx5vRdlioZkXeFqpx/8pC/InEW0FmovIUBFpBJQFqgOLRWQzMBAo41N/CoCqrgAKikgoUAiYKiLbgBFAtTTE2hwY5RxzlrPvK3+Fc1X1oqoeB44C4UBDYKaqRqvqOWB2Cvv/1vl/I1A+sQqqOk5VI1Q14unHH07DKRh/Lfx8HgPa9mNA235sWLSWJvc3A6BKrapcOPfHX65bDqBaxdLsP3yCqKMnuRwTw4K1W2lSO37jvWSRQqzb7h1RuOf3o1y6HENYQe8LtMfjYdH6bbS+M2vfHwKodkMJ9h89xe/Hz3A5JpaFG3+lSY2K8eqUDCvIul/2A7Dn8AkuxcRSOH8ezl74kz4fz6Bvu4bUqlTajfBTL5u3iFLsGFXVnSJSB2gLDAEWA9tVtV5SmySy/AawVFU7ikh5YFkaYr0OqOfTggHimuMXfYpi8Z5XIv05ybqyjyvbB60Br7/Dhh+3cPr0We7u8Bi9unXh/vtauR1Whtn0/UZqNYvgwxVjuBR9kdH9P4xb9968EQxo2w+Ax155gobtG5MrT27GrB3Pd18tZur7X1Hp1soMGPcK+Qrlp07z23mw38O80KKPW6eTZjlCQnjl8Xvp+d5EPB4PHRrXoXKZcEZ/s4RqFUrTtPbN/OORNgweP4MvFqxGBAZ37xTXZbXx172EhxWkTPEwl88k/XKEXMfLD95Fz9Hf4vEo7etVo3Kponw0ZzW3lAun6a2VeKFTYwZ/uZjJSzcBwr+7tEJE+O/yn9h/7DTj5q9j3Px1AIzp04mwAnndPankZNGWjr9EU+hrd0aXnVTVP0WkA9AdqAp0UdU1IpITqKqq20VkGfCLqvYQkYbAx6paQ0SmA1+o6jciMgjoqqrlRaQp0F9V703i2F2BCFXtLSJfAj+q6nvOupqqutnZ33lVHeaUbwPuBYoBY4H6eBPLRuATVR0mIrOB4aq61Nlmr3Oc406X3jBVbZrcdbl8fM9f7yZFIh6p08/tEILGpKlZewBAIOnZE26HEFTyNO+R2jfG8UTP/8Dv15s8bfqm61hu8Oedfw3gPRHxAJeBnkAM8IGIFHL28T6w3al/SkRWAwWBp5yyd4GJIvIC8H0aY+0LjBaRLc4xVwA9kqqsqhtEZBbwE7AP732hKx3snwFjRCQaSKplZ4wxwSGbj5pLsUWUqp15W0T9VTUyYDtNBxHJr6rnRSQv3sTVXVU3BWLf1iLyshbRVdYiuspaRPGlu0U0a5j/LaJ2/bNliygrGycitwDXAxMDlYSMMSZTZfN7RAFNRCndV0mKiDwJPJ+geJWqPpfOeB5Jz/bGGBMUAjwaTkRa4/1ITAjwH1V9J8H6EUAzZzEvUFxVQ511sXhHUwPsV9V26Y0nKFpEqvop8KnbcRhjTFAKYItIREKA0UALIArYICKzVHVH3OFU+/nU74P385hXRKtqzYAFRJAkImOMMcmICehghbrAblXdAyAiXwHtgR1J1H8YeD2QASTk6kNPjTHG+EHV/yllpYEDPstRTtk1ROQGoALxRztf7zxhZq3zkZ50sxaRMcYEu1TcIxKR7ng/73nFOFUd51slkc2SymAPAdM0/mO9y6nqQRGpCHwvIltV9bcktveLJSJjjAl2qUhETtIZl0yVKLyParuiDEl/E8FDQLxBY6p60Pl/j/ORnVpAuhKRdc0ZY0ywC+xDTzcAVUSkgojkwptsZiWs5HxLQmFgjU9ZYRHJ7cwXBRqQ9L0lv1mLyBhjgl0Ah2+raoyI9AYW4h2+PcF5RNtgIFJVrySlh4GvNP5TD24GxjpP2rkOeMd3tF1aWSIyxphgFxvYb15V1XnAvARlryVYHpTIdqvxPvYtoCwRGWNMsMuiX+/gL0tExhgT7OwRP8YYY9yknuz9jGVLRMYYE+ysa84YY4yrrGvOGGOMq2ICO2ou2FgiMsaYYGddc8YYY1wVwG/SDkaWiIwxJthZi8gYY4yrbPi2ScwjdfqlXOkv4MuNI9wOIWjkKdXI7RCCRsHced0OIaicPNcjfTsI8CN+go0lImOMCXJqXXPGGGNcZV1zxhhjXGUfaDXGGOMqaxEZY4xxld0jMsYY4yobNWeMMcZV1jVnjDHGTTZ82xhjjLusRWSMMcZVloiMMca4yj5HZIwxxk0aY4nIGGOMm6xrzhhjjKts1JwxxhhXWYvIGGOMqywRGWOMcZPGWtecMcYYN1mLyBhjjJs0myei69wOwBhjTAo86v/kBxFpLSK/ishuEXk5kfVdReSYiGx2pqd91j0hIruc6YlAnJ61iIwxJtgF8BaRiIQAo4EWQBSwQURmqeqOBFX/q6q9E2wbBrwORAAKbHS2PZWemKxFZIwxQU496vfkh7rAblXdo6qXgK+A9n6G0gpYrKonneSzGGidppPyYYnIGGOCXYz6PYlIdxGJ9Jm6J9hbaeCAz3KUU5bQ/SKyRUSmiUjZVG6bKpaIgtiTg57hw+VjGLZgJBWqV0y0zsMDHuPjNeOZtOOreOU3172FoXOH89Vv33Jn2/qZEa4rBr49nMb3PESHx3q4HUqmGTF8ML/sWMmmjYupVbN6snWnf/spm3/8Lm556JCBbNu6nE0bFzNt6n8oVKhgRoeboYa8+y8iNy/hhzWzufW2WxKtM/Xb8axYPYvV6+fxf+8P5rrrrr7sPfNsF9ZtWsjq9fMY9MaLmRV2qqWmRaSq41Q1wmcal2B3ktghEizPBsqr6q3AEmBiKrZNNUtEQapWszqUrFCSPk16MPaV0TzzZs9E60UuWc8r7ftfU3784HFG/2MkK2euyOhQXdWhbQvGDH/T7TAyTZvWd1GlcgVuuqUhPXu+xOhRQ5Ks26FDG86f/yNe2ZLvVnBbzbuoXacFu3bt4eWXeiexdfBr3rIJlSrdQETN5vTr+y/+b8TgROs99cTzNK7fjvp121K0aBgdOrYBoGGjO2hzz900uvM+6tdty6iR/8nM8FPHk4opZVFAWZ/lMsBB3wqqekJVLzqLnwB1/N02LYIqEYlIUxGZk47tI0TkgyTW7RWRoiISKiK9AnXMjHJ7i7os/2YpALt+3Em+gvkILV74mnq7ftzJ6aPX3ic8FnWU/b/sy/bf7BhRswaFChZwO4xMc999rZg0eRoA69ZvolBoIUqUKH5NvXz58tLv+e68PWRkvPLFS1YQGxsLwNp1myhdumTGB51B2t7TnK+mzAAgcsNmCoYWIDy82DX1zp07D0COHDnImSsnqt438E89/Qgjh4/j0qVLABw/fjKTIk+9AN8j2gBUEZEKIpILeAiY5VtBRHx/MdoBPzvzC4GWIlJYRAoDLZ2ydAmqRJReqhqpqn1TqBYK9EqhjuvCShThxMHjccsnDh8nLLyIixGZYFC6VAmiDlx9A/p71CFKlypxTb3Bg15k+PtjuXAhOsl9Pdn1IRYsXJohcWaGkqXC+f33Q3HLB38/TMlS4YnWnTZ9Ajv3rOX8uT+YOWMBAJUqV6Be/QgWfz+N2fMnU6t2jUyJO00C2CJS1RigN94E8jPwtapuF5HBItLOqdZXRLaLyE9AX6Crs+1J4A28yWwDMNgpS5dUJyIRyScic0XkJxHZJiJ/E5E6IrJcRDaKyMIr2VRElonI+yKy2qlb1ymv65T96Px/o5/H3uq0aERETojI4075JBFp7tu6EZEiIrLIOcZYrvZtvgNUcsbGv+eU5XduyP0iIpNFJLF+0Hg3Afec35vaS5cqiYag2ftDbSZlif1eaILfi9tuq0alyuWZOXNBkvt55eW+xMTE8OWX3wY8xsziz7W4onPHp7i5Sn1y585F4yb1AMiRI4RCoYVocVdnXh84lAkTRya6bTDQGP8nv/anOk9Vq6pqJVV9yyl7TVVnOfOvqGo1Vb1NVZup6i8+205Q1crO9Gkgzi8tLaLWwEEnwOrAAuBDoLOq1gEmAG/51M+nqvXxtkImOGW/AI1VtRbwGvC2n8deBTQAqgF7gEZO+Z3A2gR1XwdWOseYBZRzyl8GflPVmqo6wCmrBfwduAWo6BzjGr43ASvmL+9nyP5r9Xhb3ps3gvfmjeDkkZMUKVU0bl2REkU5eTR4uw5MxunZ4wkiNywicsMiDh46TJmypeLWlS5TkoOHjsSrf+cddahdqwa7d65l+dIZVK1Ske8WT41b36XLA9zTtjldHs9694e6PfMoy1fNYvmqWRw+dCRe12Kp0iU4fOhokttevHiJ+fO+o809dwPeFtScWd5epU0bt+DxKEWKhmXsCaSRevyfsqK0JKKtQHMRGSoijfDeuKoOLBaRzcBAvDewrpgCoKorgIIiEgoUAqaKyDZgBN7E4o8fgMbO9DFQQ0RKAydV9XyCuo2BL5xjzwWS+8DVelWNUlUPsBko72c8AbXw83kMaNuPAW37sWHRWprc3wyAKrWqcuHcH4neCzLZ38djJhJxe0sibm/JrFkL6fJoZwDuqFubs2fOcvhw/BffseM+p1z5OlSueidNmnVg56493N3iAQBatWzKgP696NCpK9HRf2b6uaTX+E8m06RBO5o0aMfcOUt46OEOAETcXpOzZ85x5MixePXz5csbd98oJCSEFi2bsGvnHgDmzlkS1zqqVLk8uXLl5ESw3icK7GCFoJPqRKSqO/GOoNgKDAHuB7Y7LYyaqlpDVVv6bpJwF3j7GJc6Lar7gOv9PPwKvK2gRsAy4BjQGW+CSjRcP/d70Wc+liB44sSm7zdyZP8RPlwxhh7vPMcnA8fGrXtv3oi4+cdeeYIxa8eTK09uxqwdzwN/fwiASrdWZsza8dx5TwO6v92T4Ys/zPRzyAwDXn+HR5/tx979Udzd4TG+mZ3u+6ZBbd7879jzv/38+vMqxox5l959Xo1bF7lhUYrbj3z/TQrkz8+C+V8RuWERo0e9k5HhZqjFC5exd+8BNv70He9/+CYDXhgUt275Ku+997x58zD5v2P4Yc1sflgzm2PHTvDp+CkATJ40jRvKl2XVurn859P36fVsMA/fzt4tIkmqTzXJDURK4W2B/CkiHYDuQFWgi6quEZGcQFXn5tcy4BdV7SEiDYGPVbWGiEwHvlDVb0RkENBVVcuLSFOgv6rem8zxdwJnVTVCRF7Ce9Ott6rO9N3eGT13VFXfFJE2wDygGN7ktElVb3D2F++YIjIKiFTVz5K7Dg/c0N5u2ABfbhyRcqW/iDylGqVc6S+iYO68bocQVE6e25XofWd/Hb27id+vN8W/W56uY7khLe/8awDviYgHuAz0BGKAD0SkkLPP94HtTv1TIrIaKAg85ZS9C0wUkReA71N5/HVAiDP/A95W2cpE6v0bmCIim4DlwH7wjo8XkVVOt+B8YG4qj2+MMZkqq7Z0/JXqFlGqdu5tEfVX1cgMO4hLrEXkZS2iq6xFdJW1iOJLb4vocOOmfr/elFix7C/RIjLGGJOJ1JPlckuqZGgiUtWmadlORJ4Enk9QvEpVn0t3UMYYk8Vk9665oGwROR+SCsgHpYwxJqtTtRaRMcYYF1mLyBhjjKvsHpExxhhXeWItERljjHGRtYiMMca4Krs/eN8SkTHGBDlrERljjHGVDd82xhjjqlgbrGCMMcZN1iIyxhjjKrtHZIwxxlU2as4YY4yrrEVkjDHGVR67R2SMMcZNHmsRGWOMcZO1iIwxxrjKhm8bY4xxlY2aM4maNPUxt0MICnlKNXI7hKARffAHt0MIGhp9zu0QshXrmjPGGOMq65ozxhjjqthsnoiuczsAY4wxyfOo+D35Q0Rai8ivIrJbRF5OZP0LIrJDRLaIyHcicoPPulgR2exMswJxftYiMsaYIBfIrjkRCQFGAy2AKGCDiMxS1R0+1X4EIlT1goj0BN4F/uasi1bVmgELCGsRGWNM0POkYvJDXWC3qu5R1UvAV0B73wqqulRVLziLa4Ey6T6JZFgiMsaYIKeI35OIdBeRSJ+pe4LdlQYO+CxHOWVJ6QbM91m+3tnvWhHpEIjzs645Y4wJcjGp6JpT1XHAuGSqJLazRD+pJCKPARFAE5/icqp6UEQqAt+LyFZV/c3vABNhLSJjjAlyqWkR+SEKKOuzXAY4mLCSiDQH/gm0U9WLcbGoHnT+3wMsA2ql/cy8LBEZY0yQC/A9og1AFRGpICK5gIeAeKPfRKQWMBZvEjrqU15YRHI780WBBoDvIIc0sa45Y4wJcn62dPzbl2qMiPQGFgIhwARV3S4ig4FIVZ0FvAfkB6aKCMB+VW0H3AyMFREP3obMOwlG26WJJSJjjAlyfrZ0/Kaq84B5Ccpe85lvnsR2q4EaAQ7HEpExxgS7QCeiYGOJyBhjglysZO9H/FgiMsaYIOcJ4D2iYGSJyBhjglw2/zoiS0TGGBPs7B6RMcYYV3nsHpExxhg3WdecMcYYV8Vk7waRJSJjjAl2NmrOGGOMq6xrzhhjjKs82btBZIkoWK3aspOhk+bh8Xjo2LQO3e5rEm/9oeOnGTjuG85d+BOPx8PzD7akUc0bmbtqMxPnrYyrt/PAEb56oxc33VAys08hoEYMH0yb1ndxITqabt368ePmbUnWnf7tp1SoUI6ate4GYOiQgdxzbwsuXbrEnj376Pb0C5w5czazQs80A98ezopV6wkrHMqML8a4HU6GW7l+M0M/+pRYj4dObe7m6Yfjf0fbwSPHeG3Yx5w8fZZCBfIz5JU+lChWBIBDR47z+vAxHD52AgE+evsVSpco7sJZ+Ce7D9+2r4EIQrEeD29PnM1HAx5n+tC+LFizld9+Pxqvziczl9GqbnW+fvM5hj73N96eOBuAexrU5Ou3evP1W715q0dnShUNzfJJqE3ru6hSuQI33dKQnj1fYvSoIUnW7dChDefP/xGvbMl3K7it5l3UrtOCXbv28PJLvTM6ZFd0aNuCMcPfdDuMTBEb6+GtD8fz0duvMnP8COYvXcVv+6Li1Rk2dhL3tWjMt58Mo0eXzowc/2XculeHjqLrg+2YNWEEU0YPISy0UGafQqrEiv9TVpQhiUhEuopIKT/qfSYinZNZv0xEIgIcW6iI9PJZbioicwJ5jPTa9lsUZcOLUKZ4GDlz5KD1nTVYtvHn+JUEzv/p/a6q8xf+pFhogWv2M3/NFtrUuzUzQs5Q993XikmTpwGwbv0mCoUWokQi717z5ctLv+e78/aQkfHKFy9ZQWxsLABr122idOmsnZiTElGzBoUKXvt7kB1t/XU35UqVoGypcHLmzEGbpvVZumpDvDp79kVxRy3vg6Lr1qzG0tWRAPy2L4rY2Fjq1/H+beTNcz15rs+duSeQSgH+PqKgk1Etoq5AionIJaFArxRruejoqbOUCLv6Dq14WEGOnIrfldSz093MXfUTLfq+y3PDPuflx++9Zj8L122l9Z1ZPxGVLlWCqANXv0Dy96hDlC5V4pp6gwe9yPD3x3LhQnSS+3qy60MsWLg0Q+I0mefo8ZOUKF4kbjm8WBGOnDgZr07Vijew5Id1AHy3cj1/XIjm9Jlz7I06SIH8+fj7oGE88OyL/N/YScTGBvdLuCUiQETKi8gvIjJRRLaIyDQRySsidURkuYhsFJGFIlLSaeFEAJNFZLOI5BGR10Rkg4hsE5FxIqn/mLCItBSRNSKySUSmikh+p3yviPzbKd8qIjc55cVEZLFTPlZE9jnfKPgOUMmJ7T1n9/mdc/pFRCYnFZ+IdBeRSBGJHD99SWpPwW+ayBCZhCHNX7OFdo1qsfiDFxnd/3H+OWYaHs/VX8Mtuw9wfa5cVCkbnmFxZpbEfhya4CLddls1KlUuz8yZC5Lczysv9yUmJoYvv/w24DGazJXw5w8gCYY493+2C5FbdvDAsy8SuWUHxYuGERISQmysh01bf+Yf3bsw5aMhRB06wsxFyzIp8rRR8X/KilLTIroRGKeqtwJngeeAD4HOqloHmAC8parTgEjgUVWtqarRwChVvV1VqwN5gGvfvifDSSADgeaqWtvZ/ws+VY475R8D/Z2y14HvnfLpQDmn/GXgNye2AU5ZLeDvwC1ARbxff3sNVR2nqhGqGtGtY6LfGxUQ4WEFOXzyTNzy0ZNnKZ6g62368o20uqM6ALdVKcfFyzGcOnchbv3CtVtpUy/g31+VaXr2eILIDYuI3LCIg4cOU6bs1QZ26TIlOXjoSLz6d95Rh9q1arB751qWL51B1SoV+W7x1Lj1Xbo8wD1tm9Pl8ex5f+ivJrxYEQ4fPRG3fOTYCYoXKRyvTvGiYbw/qD9Tx75L36ceBqBA/ryEFw3jpsoVKFsqnBwhIdzVoC47du3J1PhTy1pEVx1Q1VXO/BdAK6A6sFhENuNNFGWS2LaZiKwTka3AXUC1VMZ5J94ksco51hPADT7rr7zF3QiUd+YbAl8BqOoC4FQy+1+vqlGq6gE2++zDFdUqlmb/4RNEHT3J5ZgYFqzdSpPaN8WrU7JIIdZt9/7x7Pn9KJcuxxBWMB8AHo+HReu3ZelIn1nKAAAXv0lEQVRuuY/HTCTi9pZE3N6SWbMW0uVR763EO+rW5uyZsxw+HH/wxthxn1OufB0qV72TJs06sHPXHu5u8QAArVo2ZUD/XnTo1JXo6D8z/VxM4FW/sRL7fj9E1KGjXL4cw/xlq2laP/7t5FNnzsb1EvxnynQ6tm7mbFuZs+f/4ORpb3f3us3bqHRDUi9dwSG7J6LUDN9O2BY+B2xX1XrJbSQi1wMfARGqekBEBgHXpypKEGCxqj6cxPqLzv+xXD2n1DRSL/rM++7DFTlCQnjl8Xvp+d5EPB4PHRrXoXKZcEZ/s4RqFUrTtPbN/OORNgweP4MvFqxGBAZ37xTXhbXx172EhxWkTPEwN08jYObN/47Wre/i159XcSE6mqefvtoYjtywiIjbWya7/cj33yR37twsmP8VAOvWbeK53i9naMxuGPD6O2z4cQunT5/l7g6P0atbF+6/r5XbYWWIHCEhvNrnKXq8/BaxHg8dWzejcvmyjPrsv1SrWolm9SPY8NMORo7/EkGoc+vN/LNPNwBCQq7jH8924ekBg1FVbqlakc5tM66HIxCy6mg4f0lifa3XVBIpD/wPqK+qa0TkE2A38AzQxSnLCVRV1e0iMhsYrqpLRSQU+BVvKyMEWAtMU9VBIvIZMMfpzkvsuMvwdrXtw9vauUtVd4tIXqCMqu4Ukb14k9xxZ4TdMFVtKiKjgf2qOlREWgILgWJ4E+omVb3BOUZToL+q3ussjwIiVfWz5K7Jn+unZvcPO/slf8O/ux1C0Ig++IPbIQQNjT7ndghBJVfZ29KVSkaUe8zv15t++7/IcmkrNV1zPwNPiMgWIAzn/hAwVER+wtulVd+p+xkwxulGuwh8AmwFZgAbSCVVPYZ3JN4U5/hrgZuS3Qj+DbQUkU1AG+AQcE5VT+Dt4tvmM1jBGGOCVnbvmktNi2iOM9ggSxCR3ECsqsaISD3gY1WtGaj9W4vIy1pEV1mL6CprEcWX3hbRsFS0iPpnwRZRdn7ETzngaxG5DriEtxvRGGOyHHvWHKCqe/GOkMsQIjIdqJCg+CVVXZjWfarqLrzDso0xJkuLdTuADBYULSJV7eh2DMYYE6w82fyLIIIiERljjElaVh2E4C9LRMYYE+Syd3vIEpExxgQ9axEZY4xxVXYfNWdfjGeMMUEuFvV78oeItBaRX0Vkt4hc87wrEcktIv911q9zPkt6Zd0rTvmvIhKQZ0hZIjLGmCAXyCcriEgIMBrvE2duAR4WkVsSVOsGnFLVysAIYKiz7S3AQ3gfXN0a+MjZX7pYIjLGmCDnQf2e/FAX2K2qe1T1Et5vKWifoE57YKIzPw242/metvbAV6p6UVX/h/eZo3XTe36WiIwxJshpKiY/lAYO+CxHOWWJ1lHVGOAMUMTPbVPNEpExxgS51HTN+X6TtDN1T7C7xIY+JMxhSdXxZ9tUs1FzxhgT5FLzZAVVHQeMS6ZKFFDWZ7kMcDCJOlEikgMoBJz0c9tUsxaRMcYEudhUTH7YAFQRkQoikgvv4INZCerMwvtN2OD9up/v1ftVDbOAh5xRdRWAKsD6NJ+Yw1pExhgT5DSAz1ZwvhqnN94vCw0BJjhfaDoY75eCzgLGA5NEZDfeltBDzrbbReRrYAcQAzynqul+JqslImOMCXKBfrKCqs4D5iUoe81n/k/ggSS2fQt4K5DxWCIyxpggZ0/fNsYY46rsnYYsERljTNCLyeapyBKRMcYEuUAOVghGlojSSM+ecDuEoFAwd163QwgaGn3O7RCChuQp4HYI2Yp9DYQxxhhXWYvIGGOMq6xFZIwxxlUetRaRMcYYF/n7hXdZlSUiY4wJcnaPyBhjjKvsHpExxhhX2SN+jDHGuMq65owxxrjKuuaMMca4KlazdyqyRGSMMUEue6chS0TGGBP07B6RMcYYV9moOWOMMa5Se8SPMcYYN9kjfowxxrjKuuaMMca4yrrmjDHGuMpaRMYYY1xlw7eNMca4yr4YzxhjjKts1JwxxhhX2T0i44pV2/fy7rRleDweOjaozlMt68Zbf+jkWf71+ULORV/E41H6tm9Io+oVWPPzPj6YuZLLsbHkDAmhX8dG1L2xnEtnEThD3v0XLVo2ITo6mud6vMSWn3ZcU2fqt+MJL1GMHDlysGZ1JANeGITH431K1zPPduHpZx8jNiaWRQuXMehf72b2KQTEyvWbGfrRp8R6PHRqczdPP9wh3vqDR47x2rCPOXn6LIUK5GfIK30oUawIAIeOHOf14WM4fOwEAnz09iuULlHchbPIeAPfHs6KVesJKxzKjC/GuB1OutmoOZPpYj0ehnz9PWP6dCI8tACPvvslTWpUolLJInF1Plmwjpa1q/Jg49v47dAJen80g/nVu1E4fx5G9mhP8dD87D54nJ6jvmXx291dPJv0a96yCZUq3UBEzeZE3F6T/xsxmBZ3db6m3lNPPM+5c+cBmPjFKDp0bMO338ylYaM7aHPP3TS68z4uXbpE0aJhmX0KAREb6+GtD8czbuhAShQrwkPPvUKz+hFUuqFMXJ1hYydxX4vGtG/ZlHU/bmPk+C8Z8nIfAF4dOopnHu1E/Tq3ciH6T0TErVPJcB3atuCR+9vx6hvD3A4lILJ7i+i6zDqQiHQVkVJ+1PtMRK59lfH/OINFpHki5U1FZI7PfP1AHTPQtu09TNlioZQpGkrOHCG0qnMjy7b8Fq+OIPzx5yUAzkdfpFihfADcVLY4xUPzA1CpZBEuxcRy6XJM5p5AgLW9pzlfTZkBQOSGzRQMLUB4eLFr6l1JQjly5CBnrpxx7yKfevoRRg4fx6VL3ut1/PjJTIo8sLb+uptypUpQtlQ4OXPmoE3T+ixdtSFenT37orijVg0A6tasxtLVkQD8ti+K2NhY6te5FYC8ea4nz/W5M/cEMlFEzRoUKljA7TACRlPxLyvKtEQEdAVSTETppaqvqeqSFKo1BeqnUMc1R0+fp0Thq39E4aH5OXr6fLw6Pe65k7kbfqblPz+h90czePnBZtfsZ8mPu7ipTDFy5czaDd+SpcL5/fdDccsHfz9MyVLhidadNn0CO/es5fy5P5g5YwEAlSpXoF79CBZ/P43Z8ydTq3aNTIk70I4eP0mJ4ldbxeHFinDkRPykWrXiDSz5YR0A361czx8Xojl95hx7ow5SIH8+/j5oGA88+yL/N3YSsbHZ/csFsg9V9XtKDxEJE5HFIrLL+b9wInVqisgaEdkuIltE5G8+6z4Tkf+JyGZnqunPcdOciESkvIj8IiITnWCmiUheEakjIstFZKOILBSRkk5rIwKY7ASXR0ReE5ENIrJNRMaJH/0EIlJXRL515tuLSLSI5BKR60Vkj8+F6OzMt3ZiXAl0uhI30APo58TSyNl9YxFZLSJ73G4dJfarlPDyLIj8lXZ3VGPRW88wqlcHBk5cgMdzdcvdB48zcuZKBj58TeMwy0nsVyOpP7jOHZ/i5ir1yZ07F42b1AMgR44QCoUWosVdnXl94FAmTByZofFmlMTOWYh/bfo/24XILTt44NkXidyyg+JFwwgJCSE21sOmrT/zj+5dmPLREKIOHWHmomWZFLlJr1j1+D2l08vAd6paBfjOWU7oAvC4qlYDWgPvi0ioz/oBqlrTmTb7c9D0tohuBMap6q3AWeA54EOgs6rWASYAb6nqNCASeNQJLhoYpaq3q2p1IA9wrx/H2wTUcuYbAduA24E7gHW+FUXkeuAT4D6nbgkAVd0LjAFGOLH84GxSEmjoxPFOYgcXke4iEikikePn/pBYlYAID83P4VPn4paPnD4f1/V2xfTV22hZpyoAt1UsxcXLMZz+I9pb/9Q5XvhkNm883oqyxULJiro98yjLV81i+apZHD50hNKlS8atK1W6BIcPHU1y24sXLzF/3ne0ueduwNuCmjNrIQCbNm7B41GKZMH7ROHFinD46Im45SPHTlC8SPw3rMWLhvH+oP5MHfsufZ96GIAC+fMSXjSMmypXoGypcHKEhHBXg7rs2LUnU+M3aedB/Z7SqT0w0ZmfCHRIWEFVd6rqLmf+IHAUuLavPBXSm4gOqOoqZ/4LoBVQHVgsIpuBgUCZJLZtJiLrRGQrcBdQLaWDqWoMsFtEbgbqAsOBxngTTcLMcBPwP1Xdpd63kl+ksPsZqupR1R1Aov0+qjpOVSNUNaLbPY0SqxIQ1W4owf6jp/j9+Bkux8SycOOvNKlRMV6dkmEFWffLfgD2HD7BpZhYCufPw9kLf9Ln4xn0bdeQWpVKZ1iMGW38J5Np0qAdTRq0Y+6cJTzkjA6LuL0mZ8+c48iRY/Hq58uXN+6+UUhICC1aNmHXTu8L7dw5S+JaR5UqlydXrpycyIL3iarfWIl9vx8i6tBRLl+OYf6y1TStHxGvzqkzZ+NGCv5nynQ6tm7mbFuZs+f/4OTpswCs27wt3iAHE9xSc4/I9w2zM6VmtFK4qh4CcP5PdliliNQFcgG+N7HfcnrJRoiIXzci03vzIGH6PQdsV9V6yW3ktFY+AiJU9YCIDAKu9/OYPwBtgMvAEuAzIATo70d8ybnoG2Iqtgu4HCHX8fKDd9Fz9Ld4PEr7etWoXKooH81ZzS3lwml6ayVe6NSYwV8uZvLSTYDw7y6tEBH+u/wn9h87zbj56xg339tIHNOnE2EF8rp5SumyeOEyWrRswsafviM6OprePa/2FixfNYsmDdqRN28eJv93DLlz5yIkJIQVy9fw6fgpAEyeNI0PPxrCqnVzuXTpMr2efdGtU0mXHCEhvNrnKXq8/BaxHg8dWzejcvmyjPrsv1SrWolm9SPY8NMORo7/EkGoc+vN/LNPNwBCQq7jH8924ekBg1FVbqlakc5ts363bVIGvP4OG37cwunTZ7m7w2P06taF++9r5XZYaZaaJyuo6jhgXFLrRWQJTg9RAv9MTUwiUhKYBDyhGtcn+ApwGG9yGge8BAxOcV9pvbnl3Gv5H1BfVdeIyCfAbuAZoItTlhOoqqrbRWQ2MFxVlzr9ib8C5fEmkbXANFUdJCKfAXOc7rzEjtsU+Bz4XFUHishavBe1gqrqle2daSfQTFV/E5EpQAFVvVdE/gEUVNXXnX3GO6aInFfV/Mmdf/SSMVlzeEqAle74f26HEDQO70j0V/YvSfJknxFrgZCzaMV0vbmtFn6H368324+sS/OxRORXoKmqHnISzTJVvTGRegWBZcAQVZ2axL6aAv1VNcXbLuntmvsZeEJEtgBhOPeHgKEi8hOwmauj0z4Dxjhddhfx3r/ZCswANuC/dXi7zlY4y1uALZogo6rqn0B3YK4zWGGfz+rZQMcEgxWMMSYoZeJghVnAE878E8DMhBVEJBcwHW9jYGqCdSWd/wXv/aVt/hw0vS2iOc5gg78caxF5WYvoKmsRXWUtovjS2yKqWizC79ebncci09MiKgJ8DZQD9gMPqOpJEYkAeqjq0yLyGPApsN1n066qullEvsc7cEHwNkR6qGr8z54kImt/wMQYY/4CMuuDqqp6Arg7kfJI4Gln/guSGPylqnel5bhpTkTOMOgMaw2JyHSgQoLil1R1YUYd0xhjgpF9DYRLVLWj2zEYY0wwyKqP7vFX0CYiY4wxXpr+QQhBzRKRMcYEuQCMhgtqloiMMSbIZfevgbBEZIwxQc6+GM8YY4yrbNScMcYYV9moOWOMMa6yrjljjDGuslFzxhhjXGX3iIwxxrjKuuaMMca4yj5HZIwxxlXWIjLGGOMqG6xgjDHGVTZYwRhjjKusa84YY4yr7MkKxhhjXGUtImOMMa7K7olIsvsJZmci0l1Vx7kdRzCwa3GVXYur7FpkDde5HYBJl+5uBxBE7FpcZdfiKrsWWYAlImOMMa6yRGSMMcZVloiyNuv7vsquxVV2La6ya5EF2GAFY4wxrrIWkTHGGFdZIjLGGOMqS0TGGGNcZYkoiIlIUxGZk8z6riIyKgOO21VESvks7xWRooE+TlqkdE382D5CRD5IYt1eESkqIqEi0itQx0xFbPGuezL1PhORzsmsXyYiEQGOLUtfEz+2HywizRMpjztPZ75+oI5prrJEZBLTFUjxjz8rUtVIVe2bQrVQoFcKdTJCV4L3umfra6Kqr6nqkhSqNQXqp1DHpIElonQSkXwiMldEfhKRbSLyNxGpIyLLRWSjiCwUkZJO3WUi8r6IrHbq1nXK6zplPzr/35iGOIqJyDcissGZGjjlg0RkgnPsPSLS12ebf4nILyKyWESmiEh/5x1eBDBZRDaLSB6neh8R2SQiW0XkpmC9Jk58oeJ1QkQed8oniUjzBO9wi4jIIucYYwFxdvMOUMk5//ecsvwiMs25XpNFRK49+jWxlHfqTxSRLc72eRO7FolddxF5zflZbhORcf4cM5EYWorIGudnN1VE8jvle0Xk3wl/ps7v0WKnfKyI7BNvazjLXhPnd+lbZ769iESLSC4RuV5E9jjlca0bEWntxLgS6HQlbqAH0M+JpZGz+8bO7+cesdZR2qmqTemYgPuBT3yWCwGrgWLO8t+ACc78sit1gcbANme+IJDDmW8OfOPMNwXmJHPsrsAoZ/5LoKEzXw742Zkf5MSTGygKnABy4v0D3wzkAQoAu4D+PnFG+BxnL9DHme8F/CeIr8kY4B6gOrDBZ9+7gPy+2wMfAK858/cA6lyj8lfi8DnmGaAM3jdva65c6xSuQ3lnnw2c5QnAgBSuhe91D/OZnwTc58x/BnRO5rjLnJ9vUWAFkM8pf8nnfBP9mQKjgFec+dbZ4Zrgfbjz/5z5Yc7vRQOgCTDFd3vgeuAAUAXvG5OvfX5fBuH8jfhsM9U5/1uA3ZnxmpMdJ3v6dvptBYaJyFBgDnAK74vgYufNWghwyKf+FABVXSEiBUUkFG8imCgiVfD+keZMQxzNgVt83iAWFJECzvxcVb0IXBSRo0A40BCYqarRACIyO4X9f+v8vxHnXWIy3LwmP+BNaPuAj4HuIlIaOKmq5xO8gW585VxUda6InEpmv+tVNQpARDbjfUFd6Uc8B1R1lTP/BfAqyV8LX81E5EUgLxAGbAdS+jn5uhPvC+Qq51i58CaMKxL7mTYEOgKo6oLscE1UNUZEdovIzUBdYDjen30I3t8XXzfhTVq7nPP6guSfVzdDVT3ADhEJTy4OkzRLROmkqjtFpA7QFhgCLAa2q2q9pDZJZPkNYKmqdnS6AJalIZTrgHpXEssVzh/2RZ+iWLw/99R281zZx5Xtk+TyNVkBPIe3VfhPvC+qnbn2BSepYyclsWvoj4T7P0fy1wIAEbke+Ahva+CAiAzC+249NQRYrKoPJ7E+sZ9pan4vstI1+QFoA1wGluBtzYQA/f2ILzm+1yDVXafGy+4RpZN4R/RcUNUv8Db77wCKiUg9Z31OEanms8nfnPKGwBlVPYO36+p3Z33XNIayCOjtE1fNFOqvBO5z+snz4+2auuIc3hZJmrh5TVT1AN6upCqqugfvefYn8US0AnjUOXYboLBTnq7zT6DclfMGHgbWkvS18D3ulRfY487PJy33H9YCDUSksnOsvCJSNYVtVgIPOvVbkn2uyQrg78AaVT0GFMHb+tmeoN4vQAURqeQT3xWBvAbGhyWi9KsBrHe6Jv4JvIb3D2SoiPyE9z6M70ibUyKyGu+9jG5O2bvAEBFZhfddWlr0BSKcG8A78N5YTZKqbgBmAT/h7aKJxNvnD953i2Mk/mCF1HD7mqwDdjrzPwClSbzL6N94bzZvAloC+wFU9QTe7qxtcvXGfFr9DDwhIlvwdiV9SNLX4jOc6473nfYneLs5Z+C9r5EqzgtuV2CKc/y1eF98k/NvoKVzTdrg7SI7lw2uyTq8XdIrnOUtwBZ1bvZcoap/4u2Km+sMVtjns3o20DHBYAUTAPasuUwkIsvw3uyMdDsWABHJ79w3yYv3D7S7qm7K5BiWEUTXJJCcLsU5qlrd5VD8JiK5gVjnvko94GNVTal1nZr9lyeLXROT8ewe0V/bOBG5BW+Xx8TMTkImKJUDvhaR64BLwDMux2P+AqxFlAWIyJPA8wmKV6nqc27EEwzsmoCITAcqJCh+SVUXuhFPMLBrkjVZIjLGGOMqG6xgjDHGVZaIjDHGuMoSkTHGGFdZIjLGGOOq/wc10vGKJYaYWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(iris.corr(), annot = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#steps to follow when applying classification or regression algorithm \n",
    "# 1. split data set into training and test data set. test dataset is generally smaller because we want more data to train for getting accurate answers\n",
    "# 2. select any algorithm based on the problem -> classification or regression\n",
    "# 3. pass the training set to the algorithm to train it. .fit() method does this\n",
    "# 4. pass the test data to training algorithm to predict outcome. .predit() used.\n",
    "# 5. calculate accuracy by passing the predicted outcome and the actual output. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mitra\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#importing necessary packages\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import svm\n",
    "from sklearn import metrics\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(iris, test_size = 0.3) # training set= 70%, test set = 30%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(105, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45, 5)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] #training features\n",
    "train_Y = train[['species']]\n",
    "test_X = test[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] #test features\n",
    "test_Y = test[['species']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>120</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "120           6.9          3.2           5.7          2.3\n",
       "37            4.9          3.1           1.5          0.1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       species\n",
       "120  virginica\n",
       "37      setosa"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_Y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "107           7.3          2.9           6.3          1.8\n",
       "8             4.4          2.9           1.4          0.2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        species\n",
       "107   virginica\n",
       "8        setosa\n",
       "29       setosa\n",
       "92   versicolor\n",
       "126   virginica"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of svm:  1.0\n"
     ]
    }
   ],
   "source": [
    "#SVM Model\n",
    "model = svm.SVC()\n",
    "model.fit(train_X, train_Y.values.ravel()) #train the data, .fit\n",
    "prediction = model.predict(test_X) #pass test data to predict outcome\n",
    "print(\"Accuracy of svm: \", metrics.accuracy_score(prediction, test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression:  0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "#logistic regression\n",
    "model = LogisticRegression()\n",
    "model.fit(train_X, train_Y.values.ravel())\n",
    "prediction = model.predict(test_X)\n",
    "print(\"Accuracy of logistic regression: \", metrics.accuracy_score(prediction, test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of decision tree classification  0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "#decision tree\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_X, train_Y.values.ravel())\n",
    "prediction = model.predict(test_X)\n",
    "print(\"Accuracy of decision tree classification \", metrics.accuracy_score(prediction, test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of knn with 3 neighbors 0.9555555555555556\n"
     ]
    }
   ],
   "source": [
    "#knn\n",
    "model = KNeighborsClassifier(n_neighbors = 3)#this examines 3 neighbors when putting i the element into a class\n",
    "model.fit(train_X, train_Y.values.ravel())\n",
    "prediction = model.predict(test_X)\n",
    "print(\"Accuracy of knn with 3 neighbors\", metrics.accuracy_score(prediction, test_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now for different values of k 1,10\n",
    "a = pd.Series()\n",
    "for i in range(1, 11):\n",
    "    model = KNeighborsClassifier(n_neighbors = i)\n",
    "    model.fit(train_X, train_Y.values.ravel())\n",
    "    prediction = model.predict(test_X)\n",
    "    a = a.append(pd.Series(metrics.accuracy_score(prediction, test_Y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b99b416630>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_index = list(range(1,11))\n",
    "plt.plot(a_index,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now instead of using all features in training data set, use petals and sepals separately\n",
    "petals = iris[['petal_length', 'petal_width', 'species']]\n",
    "sepals = iris[['sepal_length', 'sepal_width', 'species']]\n",
    "train_p, test_p = train_test_split(petals, test_size=0.3)\n",
    "train_p_X = train_p[['petal_length', 'petal_width']]\n",
    "train_p_Y = train_p[['species']]\n",
    "test_p_X = test_p[['petal_length', 'petal_width']]\n",
    "test_p_Y = test_p[['species']]\n",
    "train_s, test_s = train_test_split(sepals, test_size=0.3)\n",
    "train_s_X = train_s[['sepal_length', 'sepal_width']]\n",
    "train_s_Y = train_s[['species']]\n",
    "test_s_X = test_s[['sepal_length', 'sepal_width']]\n",
    "test_s_Y = test_s[['species']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        species\n",
       "129   virginica\n",
       "2        setosa\n",
       "54   versicolor\n",
       "10       setosa\n",
       "77   versicolor"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_p_Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of SVM using Petal features:  0.9333333333333333\n",
      "Accuracy of SVM using Sepal features:  0.8\n"
     ]
    }
   ],
   "source": [
    "#SVM Model\n",
    "model = svm.SVC()\n",
    "model.fit(train_p_X, train_p_Y.values.ravel())\n",
    "prediction = model.predict(test_p_X)\n",
    "print(\"Accuracy of SVM using Petal features: \", metrics.accuracy_score(prediction, test_p_Y))\n",
    "\n",
    "model = svm.SVC()\n",
    "model.fit(train_s_X, train_s_Y.values.ravel())\n",
    "prediction = model.predict(test_s_X)\n",
    "print(\"Accuracy of SVM using Sepal features: \", metrics.accuracy_score(prediction, test_s_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression using Petals:  0.8888888888888888\n",
      "Accuracy of logistic regressionusing Sepals:  0.6\n"
     ]
    }
   ],
   "source": [
    "#Logistic Regression\n",
    "model = LogisticRegression()\n",
    "model.fit(train_p_X, train_p_Y.values.ravel())\n",
    "prediction = model.predict(test_p_X)\n",
    "print(\"Accuracy of logistic regression using Petals: \", metrics.accuracy_score(prediction, test_p_Y))\n",
    "\n",
    "#Logistic Regression\n",
    "model = LogisticRegression()\n",
    "model.fit(train_s_X, train_s_Y.values.ravel())\n",
    "prediction = model.predict(test_s_X)\n",
    "print(\"Accuracy of logistic regressionusing Sepals: \", metrics.accuracy_score(prediction, test_s_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of decision tree classification using Petals:  0.9555555555555556\n",
      "Accuracy of decision tree classification using Sepals:  0.6\n"
     ]
    }
   ],
   "source": [
    "#Decision Tree:\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_p_X, train_p_Y.values.ravel())\n",
    "prediction = model.predict(test_p_X)\n",
    "print(\"Accuracy of decision tree classification using Petals: \", metrics.accuracy_score(prediction, test_p_Y))\n",
    "\n",
    "#Decision Tree:\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_s_X, train_s_Y.values.ravel())\n",
    "prediction = model.predict(test_s_X)\n",
    "print(\"Accuracy of decision tree classification using Sepals: \", metrics.accuracy_score(prediction, test_s_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of KNN using Petals:  0.9555555555555556\n",
      "Accuracy of KNN using Sepals:  0.7111111111111111\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "model = KNeighborsClassifier(n_neighbors = 3)\n",
    "model.fit(train_p_X, train_p_Y.values.ravel())\n",
    "prediction = model.predict(test_p_X)\n",
    "print(\"Accuracy of KNN using Petals: \", metrics.accuracy_score(prediction, test_p_Y))\n",
    "\n",
    "#KNN\n",
    "model = KNeighborsClassifier(n_neighbors = 3)\n",
    "model.fit(train_s_X, train_s_Y.values.ravel())\n",
    "prediction = model.predict(test_s_X)\n",
    "print(\"Accuracy of KNN using Sepals: \", metrics.accuracy_score(prediction, test_s_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#observation: Using Petals over sepals for training the data gives better accuracy This is as per expectation in the heatmap. \n",
    "#sepal_width correlation with sepal_length is -0.11 \n",
    "#petal_width correlation with petal_length is 0.96. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}