7.4_unsupervised_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5173c854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "119e7226",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris=load_iris()\n",
    "x=iris.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "93ef30cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan=DBSCAN(eps=.5,min_samples=5,metric='l2')\n",
    "dbscan.fit(x)\n",
    "y=dbscan.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "ca3067a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
       "        0,  0,  0,  0,  0,  0,  0, -1,  0,  0,  0,  0,  0,  0,  0,  0,  1,\n",
       "        1,  1,  1,  1,  1,  1, -1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,\n",
       "       -1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,\n",
       "        1,  1, -1,  1,  1,  1,  1,  1, -1,  1,  1,  1,  1, -1,  1,  1,  1,\n",
       "        1,  1,  1, -1, -1,  1, -1, -1,  1,  1,  1,  1,  1,  1,  1, -1, -1,\n",
       "        1,  1,  1, -1,  1,  1,  1,  1,  1,  1,  1,  1, -1,  1,  1, -1, -1,\n",
       "        1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1,  1])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d1df88aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x71b06b3e4e00>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAGdCAYAAADAAnMpAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABTxElEQVR4nO3dd5wV1f3/8deZe7c36tKbDUQQFFHBKBYsSIjEiGA0gIVY0Fh+JgZjNMnXiMYSY6IiNqLGoFjAroiiETECigE7NpCyFGF7u3fO74+7LCzcubt7d+/Olvfz8biPB3vPnDOfGXTnw8yc8zHWWouIiIiITxy/AxAREZG2TcmIiIiI+ErJiIiIiPhKyYiIiIj4SsmIiIiI+ErJiIiIiPhKyYiIiIj4SsmIiIiI+CrodwB14bouGzZsICsrC2OM3+GIiIhIHVhrKSwspHv37jiO9/2PFpGMbNiwgV69evkdhoiIiMRh3bp19OzZ07O9RSQjWVlZQORgsrOzfY5GRERE6qKgoIBevXpVX8e9tIhkZOejmezsbCUjIiIiLUxtr1joBVYRERHxlZIRERER8ZWSEREREfGVkhERERHxlZIRERER8ZWSEREREfGVkhERERHxlZIRERER8ZWSERERaXasdX0Z21qL6yZu3xJdvZKRmTNnMnz4cLKyssjNzWX8+PF8/vnnMfvMmTMHY0yNT2pqaoOCFhGR1se6BbiFt+HmHY7NG4C7eSS26O9Yt6ThY4e34RbchJs3rGrsY7BFs7G2AoDV73zKtWNv4pTkSZySPImrj/8Dy1/7qMH7lbqp13Lwb731FtOnT2f48OGEQiGuvfZaTjrpJD755BMyMjI8+2VnZ9dIWlR5V0REdmfdAuy2iRD+FghHvnS3YovuhrLF0PExjEmLb+zwFuy2CeDm7Tb2JmzR7VCxhLcXTePPZ92FMQY3HLkrsuo/n/LR4o+5cvZFnHrBCQ09PKlFvZKRV155pcbPc+bMITc3lxUrVnDMMcd49jPG0LVr1/giFBGRVs8W3wfhb4A9H5G4EPoYih+FzF/GN3bRnTUTkV0tlOe/x1+nVWBdi8Xu2mtVUvL3Sx/gR6cfTnaH2IXepGEa9M5Ifn4+AB06dIi5XVFREX369KFXr16cdtppfPzxxzG3Ly8vp6CgoMZHRERaJ2stlDzJ3onITi629Ik4x66A0gXsnYhELH01h+KCkGf/cGWYxXPfjWvfUndxJyOu63LFFVdw1FFHMWjQIM/t+vfvz0MPPcSCBQt47LHHcF2XkSNH8v3333v2mTlzJjk5OdWfXr16xRumiIg0eyGw+bE3CW+Ob2i3AKjwbN66KQknYD3bA0GHreu3xbdvqbO4k5Hp06ezevVq5s6dG3O7ESNGMHnyZIYOHcqoUaN45pln6Ny5M/fdd59nnxkzZpCfn1/9WbduXbxhiohIM2dMEpj2sbaAQLf4BneyAe9JE7k9KnHD3u8xhkMuub07x7dvqbO4kpFLL72UF154gTfffJOePXvWq29SUhKHHHIIa9as8dwmJSWF7OzsGh8REWnF0s8i1iXJpE+Ka1hjkiHtdCAQtf3IE/PJap+M17yKpJQgx04cGde+pe7qlYxYa7n00kt59tlneeONN+jXr1+9dxgOh1m1ahXdusWZ5YqISKtjMi6AYH/2viwZSBoG6WfHP3bWryDQc4+xDWBIzjmOX8+5HCfgEAjuancCDsYYrpx9EZntvGeLSuMw1lrvh2V7uOSSS3j88cdZsGAB/fv3r/4+JyeHtLTIlKvJkyfTo0cPZs6cCcCf/vQnjjzySPbbbz927NjBrbfeyvz581mxYgUDBw6s034LCgrIyckhPz9fd0lERFop6xZDySPYkifA3QqBrpj0syD9HIxJaeDY+djih6H0aXC3Q6AXJv1sSJ+EMUE+X/4V825bwH9f+hDrWg45YRATf30ag350YCMdXdtU1+t3vZIRr/VBHn74YaZOnQrAscceS9++fZkzZw4AV155Jc888wybNm2iffv2DBs2jBtvvJFDDjmk0Q9GREREmo+EJCN+UTIiIiLS8tT1+q3aNCIiIuIrJSMiIiLiKyUjIiKtWCKr3zaE64ZUHVeq1as2jYiINH/WVkDxP7Elj4G7EWsyIe0MTMaFmEBH3+JyXRcK/w9KnwLKI9853SHrWpy0k3yLS/ynOyMiIq2ItZXY7b/EFt0G7saqL4ug5FHsttOx8S6r3hi2T4LSf7EzEQHA3QD5l+KWPOlbWOI/JSMiIq1J6XyoeBfYc6JkGNzN2MI7mz4mwC19GSpXem9Q8Ec9tmnDlIyIiLQikeq2XrVWwlD2HNaWe7QnULF3PbKISihf0CShSPOjZEREpDUJb2LvuyK7qwC3lgq5ieD+UPs2oa8TH4c0S0pGRERak0APYv9qTwGnXRMFsxunDpVvg/1r30ZaJSUjIiKtSKS6rde7FwFIOz1SybapZU6vZYNkSDm1SUKR5kfJiIhIa5I6DlKOZ2dV2l0cCPTEZF3uS1hO6vGQPMJ7g5ybcBxdktoq/c2LiLQixgQx7f6Byb4eAvsCyeDkQsZFmI7zME4H32JzOvwTMi4Ek7nry8A+0H4OTtpPfItL/KdCeSIiIpIQKpQnIiIiLYKSEREREfGVkhERERHxlZIREZFWLFbVXmttLe0usV4rTGRF4IaMXVvcsfvaNrcsfXM4ZlXtFRFpZWqr2mtD32GLZkHZC0A5NrAfJmMKpE3AGAdb9jq2eDZUfgQEsCnHYjIvxiQNxrolUPIgtuTf4G7FmnaQfiYm45cYp2ETDKxbENlvyZNgd2CdTpj0syD9fIyTXssxWyidhy2eA+E1QAo2dWwk7mCfWve9+p1PeXzms6x47SOstRx8zEAm/fanHHbSkAYdU3P2zeq1/PumZ/jPM/8lXBli36H9mPD/xnHcWT/CGK+SAomh2TQiIq1IpGrvNKhYSs1l4QORKb45t8COS8CWAuGqNhPZNm0CBPpB0V+I3Dh3d/XFQLu/Q9G9EFpNzYXVHAj0xXR8AuPkxBe3W4DdNhHC3+4WV9XYwYMwHR/DmDSPY7bYgt9D6ZO7jmVn3CYN02EuJukAz32//dRSbpz0V4wxuOHIcTkBBzfscuXsizj1ghPiOqbm7JP3vuDXx/+RUCiEG4ocs3EM1rVMumY85888u1H2o9k0IiJtUS1Ve9lx1R6JCLu2LZ1XlYhAzWQjHPnkXw2hVey9wqsL4W+xtRbD82aL74PwN3vEVTV26GMoftS7c+WyqkQEah53GGwptuAGz67lpeXcMW0W1rXViQhQ/ee/X/oABT8U1utYmjtrLbeffw+hisrqRATAupFzN/eW+Xyz6rsmjUnJiIhIK1Jr1V67lb0v+DvFujVvwRbjXYTPhZIn43pXw1obeTTjuYy9W3VcHv1LniJy9yaaMFSuwIbWRm19d8FyivNLPMcOV4ZZPPddz/aW6IsVX7P20/W4bvS/q0DQ4dWH32zSmJSMiIi0JrVW7Y2lgU/tbQFQGUfHENhaKgmHN3u3uRvxTrB2bhO9/9b1P+AEvC+FgaDD1vXbYo/dwmxbH7uCsutatm6oQ5XlRqRkRESkNam1am8sDXxp0bSPqwifMUlg2sfaAgLdvJsDPfG+M1LF6Rr169zenWo8ntlTOOSS27sOFYdbkNzenWK2O44ht1fsbRqbkhERkVak1qq9The8L9wW74TEAZMVuz19Ut0D3VP6WcS6JJkYY5u0CXjfGQlA8pGYYM+orSPGDSOrfQZek0eSUoIcO3Gk575bon2H9mWfg/t43hEKh1xOPu/4Jo1JyYiISGtSS9Ve2v29KqkI1GwDSJ+Myfp91Xe7tweAILS7C5IOY++ExIHgAZiMaXGHbTIugGB/9r4sGUgaBunesztM8qGQPmVXLLvHbbIw2X/w7Jucmsxv/nkZTsAhENzV1wk4GGO4cvZFZLbLqO/hNGvGGK5+6BJS0pL3OmaAqX+aRJ8DoydvCYtJU3tFRFoXa0NQ+gS2+F8QXgtOu6p1RqZinHbY8EZs8YNQ+hzYEkgagEmfCqljMcZgy5dgix+AihVgApAyGpMxDZN0ANaWQ8lj2JK5EN4ITidM+sRIIuM07KJt3WIoeQRb8gS4WyHQtWqdkXMwJqWWY7ZQ9iK2ZA5UfgYmHdJ+gsk4DxPoXuu+P1/+FfNuW8B/X/oQ61oOOWEQE399GoN+dGCDjqk5W79mI/NufY635i2loqyC/ofvxxlXjmPkacMbbR91vX4rGREREZGE0DojIiIi0iIoGRERERFfKRkRERERXykZERGRJtfQyrqJrBjcErlu/OezOVDVXhERaRKRGS8vRGbyhD4FgtjUkzEZF8UsZFfdv3IVtuheKF8MhLFJQyKzfFJPTHTozdaS+e/zxF8W8Nl/v8AJBjhy7DDOuvZ0+h+2r9+h1Ytm04iISJNwC2+H4vvYq7IuQUyHRzHJQz372vIlkWrEWHYtcBapLGyyfhNZp6SNefLWBdx/zWM4jqmuM7NzfZQbX5jBYScN8TlCzaYREZFmxFZ+VpWIwF6VdanE5s/wfMxgbRibfw3V1YOrRR7V2MJbseH1jR90M5b33RYe+O2/AGoUvHPDLm7Y5bbz7iYcrqVeTzOiZERERBLOlj6D9zL0LoS/gtDq6M0V71UVuvO6kW+gdH6DY2xJFj7yFsaJvoa9tZZtG7az8g2P89kMKRkREZHEc/OotSpwOK9+31dzsLVu07psXf+DZzKy+zYthZIRERFJPKc7tVYF9lq2PVbFXgDcOi353prk9u6EdWMnd7VV521OlIyIiEjCmfSf4V1Z14kUyQt61IFJPgKcbsS8ZKWNb1iALcxJU0aBxzs2xjHk9u7EkGMPauKo4qdkREREEs4E98NkXlH1056VdVMxOTdjTPQ7J8Y4mHa3EVmNYs9qwmCyrsMEujZ+0M1Ypx4dufjOc4Fd1XYBAkGHYHKQax65DMdpOZd4Te0VEZEmY8texxY/DJX/A5MCqWMwGRdggn1q71v5RaSacPlCsCFIPizSN+WoJoi8eVqx8CPm3fYcq/7zKcGkIEf99HAmXP0T+g3q7XdogKr2ioiIiM+0zoiIiIi0CEpGRERExFdKRkRERMRXSkZERCQusSrn1lZZt7aqvQ2pytuQisDNlbUW1229lYpVtVdEROrMuiVQ8iC25N/gbsWadpB+JibjlxgnG1v5Jbb4Xih7FQhhgwMwGedD6rjIADGq9lq3AFs8G0qeBLsD63TCpJ8F6edjnPTYcVkLpfOwxXMgvAZIwaaOxWReXKeZOs3V+jUb+fdNz/DmE+9SUVpBn4E9Of2KHzPm/OM9p0K3RJpNIyIidWJtKXbbL6pqyOz+r3QHAn0h6zrYcTEQYtcCZ1UVejOmRbbzqNpL+1lQcCOEv6Xm4mgOBA/CdHwMY9I84rLYgt9D6ZN7j23SMB3mYpIOaPDxN7VvVq/lih9dR3lJOeFQ5HwbE1nr7NRpJ3DFrAubfUKi2TQiItK4ih+D0CpqJiJEfg5/C/lXAZXUTCaqEoPi+2NW7WXHlRD+mr1XaXUh9DEUP+odV+WyqkQkyti2FFtwQ62H1hzddcn9lBXvSkRg16KrL92/iP+99YlPkTU+JSMiIlIntvQJvIvduWB3sHeispPBuzbNzr7eY0f27RFXyVN4VwQOQ+UKbGitZ//maOPXeax+5zPccPTzGQg6vDrnzSaOKnGUjIiISN2ENzegs6XWqr3x7tvdiHfdm53bNCT2prdtQ+yKu+GQy+a1W5somsRTMiIiInUT6EatlXc9xbozUoe+sSr3BnrifWekitOyatd07hW74m4g6NCtX24TRZN4SkZERKROTPqkGK0OmI54X1Zi3RnZ2dc7WYm1b5M2Ae87IwFIPhIT7OnZvznq0qczQ48bVKMI3u7CIZdTzj+hiaNKHCUjIiJSN+lnQ9Jh7J00OBA8ANr/A0wqNe9SRC4zJvPymFV7aXcXBAew92XJQNKwyL49mORDIX2Kx9hZmOw/1OXomp3L751GRk46TnDXMTlO5Nz/7IqxHDSyv1+hNTpN7RURkTqzthxKHsOWzIXwRnA6YdInQvpkjJOBDX0XWUek7CWwZZB0MCbjPEzq6Ej/GFV7rVsMJY9gS54AdysEulatM3IOxqTUEpeFshexJXOg8jMw6ZD2k8i+A92b4Mwkxua1W5h3+/O88fh/KC0uZ9+D+3D6FT/m2Ikjm/20XlDVXhEREfGZ1hkRERGRFkHJiIiIiPhKyYiIiIj4SsmIiEgzFql+672gV0Oq39ZWWVcal+smrppwIsduCvVKRmbOnMnw4cPJysoiNzeX8ePH8/nnn9fab968eQwYMIDU1FQGDx7MSy+9FHfAIiJtgQ1vws3/A3bzIdi8A3G3jMYWP4K1oUh7+RLcHyZj8wZi8wbi/nA+tmJZpM1WYIvux908Cps3ADfvUNyCm7DhbZH20He4O2Zg8w6OtG85FVvyhBKTBFn+2kdcffwfOCV5EqckT+LasTex+p1PG2XsJfPf51cjf8cpSRMZk3oWfzj9Vj5f/lWjjN2U6jWb5pRTTmHSpEkMHz6cUCjEtddey+rVq/nkk0/IyMiI2ufdd9/lmGOOYebMmfz4xz/m8ccf55ZbbuGDDz5g0KBBddqvZtOISFtiQ+uwP0wAN5+a1W+BlBMh+Wgo/D2Rf0/uTCACkT/n3A6lT0HFUmouMhYAJxdyboEdl4AtZa/KumkTMNk3togpoy3FSw8s4q+/nIUTcKrrzDgBB2st1829kmPOGBH32E/euoD7r3kMxzG4rq0e2xjDjS/M4LCThjTKMTREk0zt3bJlC7m5ubz11lscc8wxUbeZOHEixcXFvPDCC9XfHXnkkQwdOpRZs2bVaT9KRkSkLXG3T4fyN4i5qmjUNgMkA+Xe/Ux7sNs9xzbtH8GkHFnPiCWagm2FTOzxS0IVoajt6dlpPLnxflLSYq+hEk3ed1v4xT7Toz6aMcbQoVs7/vXdvQQCtSyTn2BNMrU3Pz8fgA4dOnhus3TpUkaPHl3ju5NPPpmlS5d69ikvL6egoKDGR0SkLbDuDihfhHciYmK0WbwTESL97NYY/QPY0qfrFKfUbvET7xKu9H7fp6SglHcXLI9r7IWPvIVxot/BstaybcN2Vr6xOq6x/RB3MuK6LldccQVHHXVUzMctmzZtokuXLjW+69KlC5s2bfLsM3PmTHJycqo/vXr1ijdMEZGWxd3Krkcv0STyJcVwVQVcaQxb128jEPS+zDoBh63rY1fn9R77B89kZPdtWoq4k5Hp06ezevVq5s6d25jxADBjxgzy8/OrP+vWrWv0fYiINEtOZ2L/aq7L+xzxvvMRgID+8ddYcnt3JhzyTizdsEuXPrGr83qP3Qnrxk5Mc3vHN7Yf4kpGLr30Ul544QXefPNNevaMXQmxa9eu5OXl1fguLy+Prl29yzmnpKSQnZ1d4yMi0hYYJwdSTqFmsbndWSDo1RtIw/vuSQCcLjHGDmPSzqhzrBLbsRNHkpQS/e/KGMhsn8GR4w6La+yTpowCj1c+jWPI7d2JIcceFNfYfqhXMmKt5dJLL+XZZ5/ljTfeoF+/frX2GTFiBIsWLarx3cKFCxkxIv43iEVEWjOT/VtwOlEzaai625F6GmTfXPXz7u0BwIF2t0HK8VXtu98hcSDQE9r9HUwW0Srrkj4ZkzyskY+m7cpsl8FV91+MMQYnsOtyGwg6OAGHa/55GckpSXGN3alHRy6+81yAvcYOJge55pHLcJyWs5RYvWbTXHLJJTz++OMsWLCA/v13lS7OyckhLS0NgMmTJ9OjRw9mzpwJRKb2jho1iptvvpmxY8cyd+5cbrrpJk3tFRGJwYa3YksehtJnwC2E4D6Y9F9A2s8wxsFWLI9Uxy1fAsaB5FGYzAswSYMja5GUPoEt/heE14LTDtLOwGRMxTjtsOGNkb6lz4EtgaQBmPSpkDpW03oTYNV/PuXJ2xbw4aLVGMdwxKmHMOHq0+h/2L4NHnvFwo+Yd9tzrPrPpwSTghz108OZcPVP6DeodyNE3nAJmdrr9R/pww8/zNSpUwE49thj6du3L3PmzKlunzdvHtdddx3ffvst+++/P3/5y1849dRT67pbJSMiIiItUJOsM9JUlIyIiIi0PE2yzoiIiIhIQykZEREREV8pGRERacFqq9rrutGXIm+MsVsjVTL2h9dkdRERacZs+RJs8X1Q8T5gsMkjMZkXYZKH47pFsOMKqHgHcCPruQYHQs6tOEn71z522evY4tlQ+REQwKYci8m8GJM0OKHH5CdbsRxbNAsqlgAuNulwTOaFmJQf+R1am6AXWEVEWhhbMg9b8DuiVu3NvgUK/ww2P0pPBzo8g5M80Hvs4gexhbdEGdtg2s9ulRdnW/YKdscV1Kz7Ezl+k30jJv1M32Jr6TSbRkSkFbLuDuzmo4DKKK07F0KL8WjG6YuT+1r0scMbsFuOI/oKrgacTpjOb2OMv5VgG5O1ZZHzaQs9tkjC5C7BOO2aMqxWQ7NpRERao7KX8E42bIy2Ku63uK5HJfTS+XjXtbHgboEK74rrLVLZ6zESEYAQlL3YZOG0VUpGRERaEBvOw7u2TB2Fvo8xdi2XhbB3xfUWya3tmANV50USScmIiEgLYgLd2PVeQ5yC0ZcKj4xdy0ySQPeG7bu5CXQn9jGHMa3tmJshJSMiIi1J6lgg2aPRALUUXgvsh+NkRm9LGx+jowNON0g+otYQW5SU48Hk4P14KhlS616+ROKjZEREpAUxThYm5yY8q/bm3A6mk0fvILT/h/fYga6YrOt2G2/3sYOYdre2qpdXAYxJweT8hcjlcM9jNpicGzGOJk4kmpIREZEWxqSNw3T4F6QcC6SCSYeUkzAdn8RJOwU6v1H1r/mdd0kcSBoGnV7GCe4Te+yMczDtH4bkI6vGzoDUH2M6PoNJPjyxB+YTk3ocpuOTkHJS5FySCimjMB3+hUk7ze/w2gRN7RUREZGE0NReERERaRGUjIiIiIivlIyIiIiIr5SMiIj4zA1X4Iajrx3iui6uWxH32H5VoI3E7b0abEMqAltrcd34jytR5yRS8beBa8C0UaraKyLiAzdcCtsnQuizXd+ZbMi+BSftBNyK/0H+DAh/GWkjGFkTI+c2HCc15tjWVkDxP7Elj4G7EWsyIe0MTMaFmEDHxB5X6etQeCO4G6riToG0n0HW9RhjoOwFbPGDEPoUCGJTT8ZkXIRJOqDWsT9f/hX/vukZ3ntxBW4ozIAjD2Dir0/jqPG1z/Kx4W2RKselT4MtxDpdMennQMZUjPFat6VubHgjtug+KJsPtgQb6I1JnwzpP8cYXWbrQrNpRESamBuugC3DgPLoG6RfDCWziFqwzuRC58U4TvSLnLWV2O3TqmrI7N4/AE4upuM8TCC3gUcQnVvyJBRcF70xaUhkunDxfUTWSNkZW9UaJh0exSQP9Rx7xcKP+N3YmZG7IuHInQ3HMbiuZdot53Dmr72n4NrwFuy2CVVLv+9+58JA8pGY9g9gTC2LxXmNHVqH/WECuPm7jV21gFrKSZh2f8OYtvsQQrNpRESaq/wr8ExEAEruJXrlXMBuhqLbvfuWzoeKd6P0D4O7GVt4Zz0CrTvXdaHgj94bVH5UlYiwR2xhoBKbP8PzsU04HObWc+/GDbvViUhkn5HtH5jxL/K+2+K5a1t0Z5REpCqOiqVQ+ox33LWwhTP3SESqxsVC+atQvjDusdsSJSMiIk2t4q2G9Y9x8bSlT+C9tHkYyp7D2hiJULzKnwcq4+zsQvgrCK2O2rryjdVs27DdM1kxxrDwkejn1NoKKF2Adz0fgy15Mo6YwbrbofyNGGM72JKn4hq7rVEyIiLS5OK9aFexRd5t4U143lUBoKLqX/KNLPRVw8fwqI67df0PMbsZx3hv4xYAsV4AtuDGWYnY3UbsIntu9bszEpuSERGRJhff+wnVTIx35wI9iP2rPQWcdg3bfzTB2l9ArZVHddzc3l61diKsa+nSp3P0RicbiPXCrwOBnnWLb6+unYl9rgMQ6BXf2G2MkhERkaaWclLD+qdP8Gwy6ZPw/td6ANJOb/DskahSTgVS4uzsQLA/BA+M2jrk2IPI7d0J43g8frKWEycfE7XJmGRIO52aRfB252LSJ9Y/ZMA4OZBycoyxw5i0M+Mau61RMiIi0tSybwPSvdszfoPnr2enJ2Rc4d03dVxkCjCGmu+ORO4AmKzL6xttnTiOAzkzvTdIPhKTecWuWKoFwKRicm6OTP31GPuaRy4jmBwkENzV1wlE/nzJ386jUw/vKcsm61dVdz9232/V+Uk5HlJ/EuvQYjLZM8DpSM2EpOo4UsdDynFxj92WaGqviIgP3HAFbJ8CoQ+ofsfD6QQ5t+OkjMCt/AIKroXKVVXtKZFKvNl/9pzWu5O1ISh9Alv8LwivjTyWSTsDkzEVk4hHNLsfV/m7UPB/kRdSAUwmpJ+Nk/X/IrGVvY4tfhgq/wcmBVLHYDIuwAT71Dr2N6vXMu+251jy7PuEKkMMPvpAJlz9E4adOKTWvtbNj+y39Glwt0OgFyb9bEif1OC1QGx4K7bkISh9FtxCCO4TWWck7fQ2Pa0X6n79VjIiIiIiCaF1RkRERKRFUDIiIiIivlIyIiIiIr5SMiIiUgeuG3+V2URqSPVbkeZC5QRFRDxYa3nloTd4+q8v8N0n35OclsxxE0dy1rWn02O/bv7GVr4kUoW24n3AYJNHYjIvwiQP9zUukXhoNo2ISBTWWu68eDYvzX4dY2Dnb8pA0CElPYU7//N/9Btc+3TUhMRWMg9b8DsiN7d3LnAWAFxMuzsxqWN8iUtkT5pNIyLSAKv+8ykvzX4d2JWIAIRDLmXF5fztkvt9icu6O7AFf6j6afeVViPF2mz+77C2tImjEmkYJSMiIlG88vAbNVb73J0bdvl4yeds+CrOAmsNUfYSEPJotJEiemWLmjIikQZTMiIiEsWWddsIh2JVZK29mmwi2HAe3rVQiLS50avfijRXSkZERKLo2qez552RnWqrJpsIJtCNnY9kogt7Vr8Vaa6UjIiIRDHmghM874w4AYchxx5E1765TRwVkDoW8Kq6a8BkVxXKE2k5lIyIiEQxcER/fnbFWACc3UrXO0GHjJx0Lr93mi9xGScLk3MTkcqwuz+uCQAOJucvGJPiS2wi8dI6IyIiHi68fQr9D9+fZ/72Il999C1pGSkcf9bRTLh6HLm9O/sWl0kbB4Fu2OIHoXwJGAeSR2EyL8AkDfYtLpF4aZ0RERERSQitMyIiIiItgpIRERER8ZWSEREREfGVkhERkTpoSHVcay3Wxl5ALVH82m8i+Xk+JTE0m0ZExIO1FkqfwhY/DOE1QAo2dSwm82JMsPYiebZiObZoFlQsAVxs0nBM5oWYlKMTHHcFFP8TW/IYuBuxJhPSzsBkXIgJdEzovhPJVq7CFt0L5YuBMDZpCCZjGib1RL9DkwbSbBoRkSistdiC66H0CSJreuz8VRkAk4bp8G9MUn/v/mWvYndcXtV354qpkSq7Jvv/MOkTExR3JXb7NKhYulvMVXE7uZiO8zABHxZrayBbviRyXFj2Op9Zv8FkXOBfcOJJs2lERBqicnlVIgI1L+phsCXYghs8u1pbhs2/lkhV3d2Xbo88WrAFf8K62xs54Cql86HiXWrGTCQOdzO28M7E7DeBrA1j868hci6jnM/CW7Hh9X6EJo1EyYiISBS25Cm8C9K5UPkBNrQ2enPZIrCFMUYPQemLDYwwOlt9JyeaMJQ9h7XlCdl3wlS8B+5m9k6wdjKRJExaLCUjIiLRuBuJXZAO7+q4bh6xf70GsO6mOAOrRXgT3hdtgApw8xOz70QJ11aF2KmqZiwtlZIREZFoAj3xvjNSxenm0bc7Ox8hRBfGBHrEGVgtAj2I/as9BZx2idl3ogQ8znM1F6NKxS2akhERkShM2gS874wEIPkITLBn9OaU48Hk4P24JLmq+m7jM+mT8E6EApB2OsZ4Vf1tppKPqEr8Ylyy0sY3VTSSAEpGRESiMMmHQPrUqp92/1UZAJOFyf6jd1+TjMn5S1W/PSvrGkzOjRgnQTMDU8dFkiEMNZMhBwI9MVmXJ2a/CWSMg2l3G5HVKPY8n2CyrsMEuvoRmjQSTe0VEfFgrYWyF7El/4TKT8GkQ9o4TMb5dXosYCtXY4segIrFYF1IGYHJuACTPDzBcYeg9Als8b8gvDbyWCbtDEzGVExLe0SzG1v5Bbb4AShfCDYEyYdFzmfKUX6HJh7qev1WMiIiIiIJoXVGREREpEVQMiIiIiK+UjIiIiIivlIyIiKtRkMq69YmVFFCuLIy5r692+KvMuu6Lq5bEXO/sY5Z1W2lJah3MvL2228zbtw4unfvjjGG+fPnx9x+8eLFGGP2+mzalKDVB0WkTbHWYktfwN06Hpt3IDZvEO6OK7GVXzR47HBlJWuXjqJ87QE4PwyFrQex7X8Dyfvir5F9h7fhFtyEmzcMmzcAd/Mx2KLZkaq5RKr2uj9cgM0biM07EHfbOdjy/9Rp327F/3C3jIXNA2DzINxNA3G3X4rrlkXGLl+C+8PkqrEH4v5wPrZiWdU5qcAW3Y+7eVQkrrxDcQtuwoa3NficiCRCsL4diouLGTJkCOeddx6nn356nft9/vnnNd6kzc1teVUjRaT5sUV/heJZ7KqsWwllr2DLXocOj0TWC4lDuLKSHz45hJ79Kth548EYaNc5BNzLhlUb6NplWdXS71WLo7mbsEW3Q8U72LSJkP//qFG1t3I5dvv7UEvVXrf8fdj+C2ou6x6C8tdgy2jczMug8PfsrFoLQMW72B/ewebcDqVP1azaa4ug5FFs2avQQqv2SutW72RkzJgxjBkzpt47ys3NpV27dvXuJyLixVZ+XpWIwF6VdbHY/BnQ6WWM8VoJ1duGj35Jj56ROxy7dzcGrIWcjOfAddh7lVYbKexW8SF7r4S6q2ovqSdhnPbRd77jSjzry9jNULizYvDu44cBA/kzgGiF8HZV7TXtboo+tohPmuydkaFDh9KtWzdOPPFElixZEnPb8vJyCgoKanxERPZkS58hZmXd8NcQWhXX2Okp7+P1KoYxkJZu2boxVpITqzKud9VeN/Qd2C21ROf1Hoit2m8rq9orrV7Ck5Fu3boxa9Ysnn76aZ5++ml69erFscceywcffODZZ+bMmeTk5FR/evXqlegwRaQlcvOIXaGWOlR8jS4lLUxtN1TKy+L9FRqjam/oqzjH3F0rq9orrV69H9PUV//+/enfv3/1zyNHjuSrr77ir3/9K48++mjUPjNmzOCqq66q/rmgoEAJiYjsLdAd77sAO7eJrzpueUmAtAw3ZkKSmu5VSK82Mar2BvePc8zd7Xx/JpoWWLVXWj1fpvYefvjhrFmzxrM9JSWF7OzsGh8RkT2ZtJ/hXVnXgeABEDwwrrFLQ0d7JiLWQkmRQ8cusUZII56qvU6wV1WF2li8Hk0ZIBXvRKSFVu2VVs+XZGTlypV061bb/2wiIrGZ4L6YzCurftqzsm4KJufmuF5eBeh56Cy2bEgFqPHuiLWAhaLKn0Og1x77raqUm3IctLuDuKv2tvs7nr+enZ6QfXPVvvYc24Gc21td1V5p/er9mKaoqKjGXY1vvvmGlStX0qFDB3r37s2MGTNYv349jzzyCAB33nkn/fr146CDDqKsrIwHHniAN954g9dee63xjkJE2iyTeTEED8AWPwSV/wOTDKljItVcg30bNHanwSv4fvlYOnX9jpRUi7XwQ14KTs4VdD3wfKybjy2eE5lK626HQC9M+s8hfRLGJGE7zouraq+TfDBux+eg4FqoXEXkTkcKpJ4K2X/GcYLYYA9s8YNQvgSMA8mjMJkXYJIGY1OPa5VVe6X1qnfV3sWLF3Pcccft9f2UKVOYM2cOU6dO5dtvv2Xx4sUA/OUvf2H27NmsX7+e9PR0Dj74YK6//vqoY3hR1V4REZGWp67X73onI35QMiIiItLy1PX6rdo0IiIi4islIyIiIuIrJSMi0qQSWVm3ISLVcUNx949VHbe2sZvrOUkk1217xyzeEr7omYgIgC17HVt8P1SuBALYlGMxmRdhkg72NS63dAEU/KV6CXaXdEg/Gyf717X2teFt2OL7oPRpsIVYpysm/RzImIoxybjFj0HR38Fuj4xtsiDjApzMi7E2DCX/wpY8EpnxYtKxqadFzkmgdS59YK3lzX+/w7zbn+erld8QSApy9OlHcNa1p9NvUG+/wxMf6QVWEUk4W/wgtvAWalSZ3bnmRvv7MClH+xKXWzQbim6L3ph8DE6HBzz72vBW7LYzalbtBcBA8hEQGAClc6J3TvkJUAHlr1BztdQAmGxMx3mYYOu7OD947ePMvflZjGOwbuSYnaBDMBjk1jduYOCRB/gcoTQ2vcAqIs2CDW/EFt5a9dOeVWbD2PzfYm38j0fi5boVUHSH9wYVb+OWL/dstkV3RklEoLpqr1ciAlD+XFUiUrV9tTDYAmzhzJixt0Rf/+875t78LEB1IgLghlxCFZXcfv49emzThikZEZHEKp0fo9GCuwUqljZVNLuUzMG7+m2V4r9F/draCihdgPdS9A0RhvI3sO4PCRjbP6/NeZNAMPolx3Utaz9dzxcrvm7iqKS5UDIiIgllw3nU+qsm7FHBNpHC39Vhmy3Rv3cLgfJGDacmC+FtCRy/6W3d8AOuG/vOx7b1rSsBk7pTMiIiCRV5GbOWOxCB7k0SS8197luHbTxeJHWyiBSka4hYNXMcCHRu4PjNS26vTjhO7DpBuX06NVE00twoGRGRxEobH6PRAacrJB/ZVNHskj4Z7+q3VaqL8NVkTDKkn157/5hiVNZNOanV1ZA5+bzjCYeiJ6VOwGGfg/uw75C+TRuUNBtKRkQkoUygCyb791U/7VllNojJuRVjGnJRj4/jBCHrWu8NUk7GSfaedmwyL49dtTf9Eu+xUydC6k9367NTAJyOmOwZtR9AC9PnwJ5M/dMkIJJ87BQIOqSkJXP1Q5fEXWFZWj5N7RWRJmHLl0SqzFYsBxOAlNGRCrZJ/X2Nyy19HQpvBndt5AuTAxnn42ReVGvf2qr2uqULoPAOcDdWjd0RMi/ByfhFZJG00mcj64yEvgKTBek/xaSfi2llj2h29+6CZTz11+f5/P01JKcmM2rCCCb8+if02K91rq3S1qlQnoiIiPhK64yIiIhIi6BkRERERHylZERERER8pWRERPbiZxVZN1zh3VZL9VvXDeG6sarnxqqca2P2re2cxKraKyKxqWqviFSLzHi5DyreBww2eQQm82JM8vCE7tcNfQ/bfwHh9ZGfAZzO0O5+nOSBVTNebgR3Q1V7CqT9DLKujwxQeGNkRgtlkXanG2T9DiftJNzQesi/Eio/AiwuBpKHQ87fcAId+WLFVzx+0zO898IK3FCYAYfvz5m/OY0f/fSISPJR+hS2+GEIrwFSsKljI+ck2KfWqr0iUjeaTSMiANiSp7AF17J3ZV0Xk/NXTNqpCdmvG94AW47Hc5XW9Euh5B/R25IOjsRY+WH09syroOjvQGWUxlQ+WnU31/74blzX4oYj+3ccg+taLrj5bCZcuAJKn2Dvyrpp0O4eyP+tZ9Ve0/4BJSTS5mlqr4jUmXXzsZuPAqI9IjFg0jG572JMWqPv291yCoSbvkCatfDEP3oy55ZONarI7mQMzHnvE7r2ipbIOGDag92BV7E8k/0nTPqkRo1ZpKXR1F4Rqbuyl4h+9wDAgi2GstcTs28fEpGdRv1kc9REBMA48Pq8jh49XbDb8K7aa7AlTzZGiCJtgpIREamqrBtrSfZA1eOI1sMY6NjFKwGLtG/bFO9rdRZcHyoRi7RQSkZEpKqyrte/8om0OT5U1k0ga2HLhqSY7bk9vWffxGYg0DPOviJtj5IREYHUUwGvly1NpG5K6gmJ2XfggMSMC9QsQre3t57vivEqa28No8/Y5tEzAE4u3neTLCZN74uI1JWSERHBOFmYnJlELt57VtZ1MDl/wZiUxOy8wyN4X9QNZP7Gu2/yEZA80rs983dAavSRTQZDTp1JMDlIILjrV+HOirIX//VcOu939s5vd+sZiCRn7f4Ru2pv2k+84xKRGpSMiAgAJu3HmA6PQ8qxQGpk+mrKiZiOT2ASdVcEcAIdoPMSCOxXsyHQCzq8gpN5AbSfA4F9dws2E9J/idPhUZwOcyDjosh31X33gfYP42ROhk6LIPkodiU8wcgx5r7F4KOHc8+yWzjh7GNIz04jOTWJoccdxMxXrmP8ZWMwWTMwOXdA0mAgGUw7SP85ptN8nOShmI7zIONicLpE2gP7YLKuw7T7B8ZoGSeRutLUXhEREUkITe0VERGRFkHJiIiIiPhKyYiIiIj4SsmIiDSpRFa/bUi1YWttwirvJnJskdZAr3uLSJOwZa9ji++HypVAAJtyLCbzIkzSwVi3BEoewpb8G9wtWNMO0idgMn6JcXJij2stlL2ILX4AQp8CQWzqSZiMizFJta9hYitXY4vuhfI3gTA26WBMxjRM6kkNP+aK5diiWVCxBHCxScMxmRdiUo5u8NgirYlm04hIwtniB7GFt7B3RWAD7e6C4vugchU1K/c6EOiD6fhkzITELbwDimexV2VdgpgOj2CSD/GOq3wJdvu0qn47V6CNxGgyf43JnFb/g905dtmr2B2XV8W1x9jZ/4dJnxj32CIthWbTiEizYMMbsYW3Vv20e7IRjnzyfw2V/9ujrWrb8NrInQWvsSs/r0pEYFcisnPsSmz+DM/HNtaGsfm/3RXH7vsFbNFt2ND3sQ/OKy5bhs2/tmqsKGMX/Anrbo9rbJHWSMmIiCRW6fwYjVUVgfG6QRuG0nneCUXpM3iv3upGKgKHVkVvrnivqvif174dKJvvFXhsZYvAFsbYIASlL8Y3tkgrpGRERBIqUhG4Ab9qbAHgUV03ZjJRJexRbbjWKsSmKvY4uLUdcwCrqr4i1ZSMiEhCRSoCN2AmiWkHeFTXDXSntmJ4BHpE/77WKsQuJhBnpeJAd2IfcxjjFZdIG6RkREQSK218jEYnUnTOM6FwIH0SxkRvN2k/o+Y7GXv0DR4AwQOjNycfXpWQxPg1mPZT77ZYUo4Hk4P3cSVD6tj4xhZphZSMiEhCmUAXTPbvq37asyJwEHL+BknD2fvC7UBwf0zGL73HDu6Lybxy1/a7j21SMDk3eycyxsG0uzUSw15xgcm6FhPoWsvRecRlkjE5f6mKac+xDSbnRoyjmYEiO2lqr4g0CVu+BFv8IFQsBxOAlNGYjAswSf2xtgJKHousMxLeCE4nTPqZkD4Z42TWPnbZImzxQ5FZOSYZUsdExg72rb1v5ZeRuMpfA1sJycMifVN+1PBjrlyNLXoAKhaDdSFlRGTs5OENHlukJajr9VvJiIiIiCSE1hkRERGRFkHJiIiIiPhKyYiIiIj4SsmISAJZa3HdtlWttbYKtQ2prCsirZOq9ookwJcffM3jf36apS+swA2F6T98P878zXiOPv0Iv0NLGBtaG6l+W/YiUIYN7IvJmAJpZ2KME7Nqr4i0bZpNI9LIPli0it+d+mdc1+KGI3cIHMfgupbzb/o5k34b50JazZit/BL7wySwJexahKyqim7aBAj0g6Kd627UrNpr2t+HSTnah6hFJNE0tVfEB+FwmHP6TWfbhh+wbpT/tQw8suYfdOvXpemDSyB329lQ+QHeq6FWJSbRvnc6YTq/hTG6USvS2mhqr4gPPlr8CVu/3xY9EQEcx+H1R95u4qgSy4a+h8pl1D8RIfK9uwUqliYmOBFpEZSMiDSibet/iNlujGHr+m1NFE0TcTfXskEdbr6GVcFWpC1TMiLSiHJ7d4rZbq0lt3fnJoqmiQS61bJBLVV1oarKrYi0VUpGRBrR4GMOpEvfzhjH4wJsLSdOGdW0QSWYCXSD5JHULAi3O0vMqrxOV0g+MjHBiUiLoGREpBE5jsNvH7mMYHKQQHDX/15OIPLni+6YSm6v2HdPWiKT/Qcw2dRMSKqOP+0XmOzrq77bu2qvybkVY7wSGRFpCzSbRiQBvv14HfNue453nv0voYoQg340gDP+308YfvJQv0NLGBveGKmcW7ogMsU32B+TMRVSf4wxJmbVXhFpnTS1V0RERHylqb0iIiLSIigZEREREV8pGRERERFfKRkRaaFc1yUUCsXdNxyqiHvfsaryNlTsir+xKwKLSMtU72Tk7bffZty4cXTv3h1jDPPnz6+1z+LFizn00ENJSUlhv/32Y86cOXGEKiIAz937KmfknsfJwYmMST6L8e2n8K+bnq5T323ff8rK5ydS+s1AzNZBbFwxhJUv/YZwZXmtfa2twBbdj7t5FDZvAG7eobgFN2HDWxt6SFi3BFv0D9zNR1WNfThu4a1YNz/SXvkl7o6rsHmDIu1bT8OWPkcLeP9eROqg3slIcXExQ4YM4e67767T9t988w1jx47luOOOY+XKlVxxxRVccMEFvPrqq/UOVqStm/2bR/n79AfI31pY/V1xfglzrpvLzb+4K2bfzd99hMk/g4GHfEhaRuTuQudupQwaOp/PFo2LeafE2krs9guxRbeBu7HqyyIoeRS77WfYcG1LwnuzthS7fQq26B+ROjUAdgcUP4jddiZu+RLstp9B2ctAZaQ99Dk2/+pIPCLS4jVoaq8xhmeffZbx48d7bnPNNdfw4osvsnr16urvJk2axI4dO3jllVfqtB9N7RWBoh1F/LTDuTG3mfP5XfTYP/ry7KtfPpkDBn9D0KM47qefXspBx/0qapsteQpbcK3HXgOQOh6n3cyYsXmxxQ9gC28leg0bJ7KYmi0Aoj+eMR0XYJIOjGvfIpJYzWZq79KlSxk9enSN704++WSWLvWu0lleXk5BQUGNj0hb9+ifan8U8+DvHo/6feEPGxhwsHciEg5BoPIZz3Ft6ZN4/7oIQ9nzWFtWa3xRxy55Au9iem7kLolHIgIBbKl33CLSMiQ8Gdm0aRNdunSp8V2XLl0oKCigtLQ0ap+ZM2eSk5NT/enVq1eiwxRp9jZ+XXtl2y3ror+/Ubj1O5wYK64HgpCZk++9QXgj3gkBQAW4MfrHUmvV35idVfFXpBVolrNpZsyYQX5+fvVn3bp1fock4rueB9Re2bZr39yo32d37kc4xsSbcAgK89t7bxDoSexfFyngtKs1vqicbtSpsm/0zqr4K9IKJDwZ6dq1K3l5eTW+y8vLIzs7m7S0tKh9UlJSyM7OrvERaevOvu5ntV6zz7vp51G/z2zflc/+t59nQhIIAqkTPMc16ROJ9aiEtJ9iTErs4DzHnhSrFZyOxHpEZNLOiGu/ItJ8JDwZGTFiBIsWLarx3cKFCxkxYkSidy3SqmRkp/OL670ThrG/HE23fl0827sPuZ38bck1EhI3DNbCxysOoP9R07x3njoOUk6o+mH3jMiBQA9M1hV1Ooao0n8OScPZO9NyIHgA5PwdTBrRKgKbzF9hkvaPf98i0izUOxkpKipi5cqVrFy5EohM3V25ciVr164FIo9YJk+eXL39RRddxNdff81vfvMbPvvsM+655x6efPJJrrzyysY5ApE2ZPINZ3LNI5fSuVfH6u/a5eZw6V3nccWsC2P27djjQIJdn+fjD0eyY1sSFeWG9d9m8/GqczjolPk4AY+3WwFjAph2f8dk3wCBfYFkcHIh4yJMx6cwToe4j8mYZEyHhzBZ10CgT9XY3SOJRod/46Qchuk4H9ImRGbWkAxJh2La3Y3JvDTu/YpI81Hvqb2LFy/muOOO2+v7KVOmMGfOHKZOncq3337L4sWLa/S58sor+eSTT+jZsye///3vmTp1ap33qam9IiIiLU9dr98NWmekqSgZERERaXmazTojIiIiIrEoGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRkRERERXykZEREREV8pGRERERFfKRlpgay1uNb6HYaIiEijCPodgNTd6s153LPsv7z+zVeEXZeDu3TlwmGHc8p++/sdmoiISNx0Z6SFWLLuO05/8nEWfr2GkOtigVWb87jkpee4Z9l//Q5PREQkbkpGWoCw6/Kbha8Sdi3h3R7P7HxUc/vSd1iXn+9XeCIiIg2iZKQF+O/679lYVIgl+nsijjE8/enHTRyViIhI41Ay0gLkFRXFbDfGsKmosImiERERaVxKRlqA7llZMdtda+mRnd1E0YiIiDQuJSMtwPAePemZnY1jTNR2ay2nH3hQE0clIiLSOJSMtACOMdx+0hiSnACB3RKSnX++7pjj6JGlOyMiItIyKRlpIYZ378lzk85h/ICBZCYnkxIIcmTPXsw57WecO/RQv8MTERGJm7G2+S/lWVBQQE5ODvn5+WTr3QgREZEWoa7Xb90ZEREREV8pGRERERFfKRkRERERXykZaYVca2kBrwKJiIgAcSYjd999N3379iU1NZUjjjiC999/33PbOXPmYIyp8UlNTY07YInOWsuTH6/i5MfmsN/f72DgPXfx64Wv8M2O7X6HJiIiElO9k5EnnniCq666ihtuuIEPPviAIUOGcPLJJ7N582bPPtnZ2WzcuLH689133zUoaKnJWssNixfx20WvseaHbQCUh0PM/+wTfvLvx/h06xafIxQREfFW72TkjjvuYNq0aZx77rkMHDiQWbNmkZ6ezkMPPeTZxxhD165dqz9dunRpUNBS0/KN63ls1UcANUrpha2lLFTJ795Y6E9gIiIidVCvZKSiooIVK1YwevToXQM4DqNHj2bp0qWe/YqKiujTpw+9evXitNNO4+OPY1eYLS8vp6CgoMZHvD31ycc1VmbdXdhaVm7aqMc1IiLSbNUrGdm6dSvhcHivOxtdunRh06ZNUfv079+fhx56iAULFvDYY4/hui4jR47k+++/99zPzJkzycnJqf706tWrPmG2ORuLCgnX8sJqbZV/RURE/JLw2TQjRoxg8uTJDB06lFGjRvHMM8/QuXNn7rvvPs8+M2bMID8/v/qzbt26RIfZovXMyva8M7JTbZV/RURE/FKvZKRTp04EAgHy8vJqfJ+Xl0fXrl3rNEZSUhKHHHIIa9as8dwmJSWF7OzsGh/xduZBgz3vjASM4fDuPemd065pgxIREamjeiUjycnJDBs2jEWLFlV/57ouixYtYsSIEXUaIxwOs2rVKrp161a/SMXT0K7dmHboYUCkwu9OAWPISE7mz8eP9uoqIiLiu2B9O1x11VVMmTKFww47jMMPP5w777yT4uJizj33XAAmT55Mjx49mDlzJgB/+tOfOPLII9lvv/3YsWMHt956K9999x0XXHBB4x5JG/fbo45hcG4XHl75AZ9s2UxaMImf9B/AtEOH00N3lkREpBmrdzIyceJEtmzZwvXXX8+mTZsYOnQor7zySvVLrWvXrsVxdt1w2b59O9OmTWPTpk20b9+eYcOG8e677zJw4MDGOwrBGMOPDxjAjw8Y4HcoIiIi9WJsC1g3vK4liEVERKT5qOv1W7VpRERExFdKRkRERMRXSkYSxFqL24AnYBXhcNxjN6Rqb0PjFhERqa96v8AqsX25bRv3LP8vL6/5gopwmAM7dWbaoYdxWv8DMbUsTPb51q1MXfA0ecW7VkvtnpnFv06fQJ927Vm9OY97lv2X17/5irDrcnCXrlw47HBO2W9/rLXM+2Q1D364gi9/2EZKIMiPD+jPJcOPoF+79rXGvXzDeu5Z9l/+s/ZbXGs5vEdPLhp2OKP69mvwOREREYlFL7A2oo/yNnHW009QGQ5XL0LmYHCxTDv0MGb8aJRn38+3bmXM4/+M2maA204cwzWLXsVau2tsY3Ct5eoRP2JTUSGPrfoIw65ieQFjSA0m8eSESRzYqbPnvl/96kumv/Q8BvYa+8bjRvPzwUPqeypERETqfP1WMtJIrLWc+vgjfPnDNs/HHM9NOodBudErFo948L4ad0T2FDAG14Klfn9dAWMY3KUrz5z586jt5aEQRzwwi8KK8qgjJzkO7553IR3T0+u1XxEREc2maWKfbd3C59u2eiYiAWN46pPVnv1jJSIQuWPhlYiYqo9Xv1hVexd98zUFHokIQMh1ef6Lz2LGJiIi0hBKRhrJplqSCddaz21ivaxaV7XdL/Gq2ptXXFRjCfk9BRyn1mMTERFpCCUjjaR7VuzHR44x9PDYJjkQaPD+Y78a6121t3tWVszZM2HX9YxbRESkMSgZaST9O3ZiUOdcz7sMYWuZMHCQZ//umdGThZ2Cxol5B8Mrnaitau9xffehfWqqZzKTFAgw7oD+MWMTERFpCCUjjeiW0SeTFkwisFvSsDOBuOzwIxkQY0bLv06f4JkQOMDfT/0xSU6gxtg7/3zdMcfFXbU3ORDg9pNOJeA4e41tgJuOP5F2qWme/UVERBpKs2ka2Xc7dnD/h8t58YvPKAuFGJzblfMPHcbJ++5fa991+flMmf8U3+bvqP5u33btefT0CXTNzOLLbduY/cEyXv3qSyrDLod17860Q4dzTJ++WGt58cvP467au3pzHrNXLOONb7/GWsuInr355bDhHN6jZ0NOh4iItGGa2isiIiK+0tReERERaRGUjIiIiIivlIyIiIiIr5SMJIjruoRiLGZWEQ4TjtHekMq5DanaKyIi0tRUtbeRvf3uKu76/WPs+M83mJDF9M7mhEtG8+urJ2Kt5fzn5/Oftd9WrwuSGghy1YijuODQwygoL2f2imX8e/X/2F5WSqf0dH4+aAjTDj2MjOTkmPttaNVeERERv2g2TSN6/tX/cudP7sCEXYwb+c4aMBZ6TzqEz0Z3YmNRYdS+vzh4KO99v46vtv9Q466IYwwDO3Vm7hmTSE9KitrXWssNixfFXbVXREQkETSbpom5rstdv7wPE9qViEAkEQFY8+ZnnokIwKP/W8maKBV/XWv5ZOsWHl65wrPv8o3reWzVR0DNlVjD1lIWquR3byys9/GIiIg0FSUjjeSNt1fCusLq5GNPBUd2Bjf2TSivVtda5q5e5dnvqU8+rrF66u5qq9orIiLiNyUjjeSbbzbFbA9lx37nozabY1TO3VhUSLiWp21eVXtFRET8pmSkkey3f4+Y7cEd5Q0av1uMQno9s7I974zs5FW1V0RExG9KRhrJcT8agumbg/XICbLf2wJO7ITBq9Vg+PngIZ79zjxosOedkdqq9oqIiPhNyUgj+s1Dl2CTA9jdzurO5GTguCH0jZEQXHjocA7qnFuj6i5EEpFDu3Vj8pChnn2Hdu0Wd9VeERERv2lqbyNb9uEX3PmHf7Fp0ReYijDBfTvw41+dwqUXjwfgspef55U1X1bfychKSuZ3x4zizIMOpqSykjkrP+Dfq//HlpJiumZm8vNBQ5g8ZCipwejTendqaNVeERGRxqaqvSIiIuIrrTMiIiIiLYKSEREREfGVkhERERHxVZtORqy1DaqOG3Jd77ZQiLJQyLN9R1kZZWVlnu0F5d7rklRWVlJSWenZ3pCqvQ09JyIiIvXVJqv2rs3fwT3L/stzX3xGWSjEvu07cN4hw5h40OC9ptbu6YeSEi575QXe+/57LBYDHNylK3edMpZeOe247d13mLXi/RoX9P3ad+Cls35BKBTikAdmUe6Ga4w5uHMXFpx1DvM//YSrFr681z4f+PFpHL/Pfkx/aQEvr1lTo61Pdg5vTr2gwVV7l29Yzz3L/st/1n6Lay2H9+jJRcMOZ1TffrX2FRERaYg2N5tmzQ/bOOPJf1NcWVE9vXZnpdszBh7ELSecjPFISHaUlfKjh++Pelci6Dic2G8/Xv7qi6h904JBSmPcKclJTiG/wvtuSN+cHL7Nz4/alhoIcsbAg+Ku2vvqV18y/aXnMVB9ThxjcK3lxuNGx1xwTURExItm03i4YfGiGokI7Lp4P/XJxyxZt9az768XvuL5eCTkup6JCBAzEQFiJiKAZyICUBYOxV21tzwU4pqFr2KtrXFOdt7Z+eNbb7CtpCRmbCIiIg3RppKR9YUFLP1+Xcyl05/6ZLVn/7e++zZBkSVObVV7F33zNQUV5Z4Vg0Ouy/NffJa4AEVEpM1rU8nI5loq14atZX1hgWd7rBdWmzuvqr15xUUx35MJOA6bYlQMFhERaag2lYx0jVH5FiJ3Rnpl53i2JzmBxg6pyXhV7e2elRVz9kzYdemRpVVvRUQkcdpUMtItK4uje/ch4HEnIGwtEw8a7Nn/hH32SVRoCVNb1d7j+u5D+9RUz4rBSYEA4w7on7D4RERE2lQyAvDHY08gJyW1RkKy8zHFLw4eyuE9enr2veWEk8hOTonalhwIcOaBB3n2zUxK9rzgA+SmZ8SMe2DHTjHHjrdqb3IgwO0nnUrAcWqck4AxGOCm40+kXWpazNhEREQaos1N7QXYWFjIAx8uZ/5nn1JSWcEBHTtx7tBhnNZ/gOe03p0Ky8v4fwtfYfG33xByXRxjOKJHT+446VS6ZGZy/4pl3Lb0HSp3e79kSJeuPDvxbAAG33MXxaGaM3JG9uzJY6dP5O1vv+G8557F3e11UscY5v50Aof17MW1i15j7seravQd2KkzL/x8coOr9q7enMfsFct449uvsdYyomdvfjlseMzkTEREJBZV7RURERFfaZ0RERERaRGUjIiIiIivlIyIiIiIr5SMxNCQ6re1Cbkubqyqvy14gTUREZH6aJNVe2uz6OuvmLXifT7YuAHHGI7ruw/Thx/BkK7dGjSu67r8338W88THqyirqlXTLTOL6445ljH7HcD6ggIuf+VFPty0AUukgN/w7j35+5ixdM7IbOhhiYiINEuaTbOHBz9cwZ//s7i6ai1Qvf7G/eN+yrF9+8U99pnz5rJ84/qobVcdeRT/eP89KtzwXm2pwSBvT51Gp/T0uPctIiLS1DSbJg4bCwuZ+c5bADWWSA9bi2stv174CpXhvZOFunj96688ExGAv763JGoiAlAWCnHlqy/GtV8REZHmTsnIbuZ//olnmwW2lZbwzrrv4hr7H+8vjdle2+2p975fF9d+RUREmjslI7vJK4pdwXbnNvHYVloaV7+dws3/aZqIiEhclIzspntWdswKtkDcFWw7N/B9j6DRX5WIiLROusLt5qcDBnoWs3OMoUtGBiN79Y5r7CuOHBmzPfb9GDi6d5+49isiItLcKRnZTeeMDP547AkAe1WwDRjDHVXVbeNxTJ9+MROK644+ltRg9JnWGUlJ3H7ymLj2KyIi0txpam8US9Z9x/0rlvP+hu8JGMOJ++zHtGHDObBT5waPfcfSd/jnRx9SWFEBwD7t2nP9qOM4pk8/tpaU8P9ee4l3160lbC1Bx+HoXn24/eQxtEtNa/C+RUREmpKq9oqIiIivtM6IiIiItAhKRkRERMRXSkZERETEV0pGRERExFdxJSN33303ffv2JTU1lSOOOIL3338/5vbz5s1jwIABpKamMnjwYF566aW4ghUREZHWp97JyBNPPMFVV13FDTfcwAcffMCQIUM4+eST2bx5c9Tt3333Xc466yzOP/98PvzwQ8aPH8/48eNZvXp1g4MXERGRlq/eU3uPOOIIhg8fzj/+8Q8AXNelV69eXHbZZfz2t7/da/uJEydSXFzMCy+8UP3dkUceydChQ5k1a1ad9qmpvSIiIi1PQqb2VlRUsGLFCkaPHr1rAMdh9OjRLF0avSrt0qVLa2wPcPLJJ3tuD1BeXk5BQUGNj4iIiLRO9UpGtm7dSjgcpkuXLjW+79KlC5s2bYraZ9OmTfXaHmDmzJnk5ORUf3r16lWfMEVERKQFaZazaWbMmEF+fn71Z926dX6HJCIiIgkSvTKbh06dOhEIBMjLy6vxfV5eHl27do3ap2vXrvXaHiAlJYWUlJTqn3e+1qLHNSIiIi3Hzut2ba+n1isZSU5OZtiwYSxatIjx48cDkRdYFy1axKWXXhq1z4gRI1i0aBFXXHFF9XcLFy5kxIgRdd5vYWEhgB7XiIiItECFhYXk5OR4ttcrGQG46qqrmDJlCocddhiHH344d955J8XFxZx77rkATJ48mR49ejBz5kwALr/8ckaNGsXtt9/O2LFjmTt3LsuXL2f27Nl13mf37t1Zt24dWVlZGGPqG7KngoICevXqxbp16zRLp450zupH56v+dM7qR+erfnS+6q8h58xaS2FhId27d4+5Xb2TkYkTJ7Jlyxauv/56Nm3axNChQ3nllVeqX1Jdu3YtjrPrVZSRI0fy+OOPc91113Httdey//77M3/+fAYNGlTnfTqOQ8+ePesbap1lZ2frP8p60jmrH52v+tM5qx+dr/rR+aq/eM9ZrDsiO9V7nZHWROuX1J/OWf3ofNWfzln96HzVj85X/TXFOWuWs2lERESk7WjTyUhKSgo33HBDjZk7EpvOWf3ofNWfzln96HzVj85X/TXFOWvTj2lERETEf236zoiIiIj4T8mIiIiI+ErJiIiIiPhKyYiIiIj4qk0mI2+//Tbjxo2je/fuGGOYP3++3yE1azNnzmT48OFkZWWRm5vL+PHj+fzzz/0Oq1m79957Ofjgg6sXCRoxYgQvv/yy32G1GDfffDPGmBplJKSmP/zhDxhjanwGDBjgd1jN2vr16znnnHPo2LEjaWlpDB48mOXLl/sdVrPVt2/fvf4bM8Ywffr0Rt9Xm0xGiouLGTJkCHfffbffobQIb731FtOnT+e9995j4cKFVFZWctJJJ1FcXOx3aM1Wz549ufnmm1mxYgXLly/n+OOP57TTTuPjjz/2O7Rmb9myZdx3330cfPDBfofS7B100EFs3Lix+vPOO+/4HVKztX37do466iiSkpJ4+eWX+eSTT7j99ttp376936E1W8uWLavx39fChQsBmDBhQqPvq97LwbcGY8aMYcyYMX6H0WK88sorNX6eM2cOubm5rFixgmOOOcanqJq3cePG1fj5z3/+M/feey/vvfceBx10kE9RNX9FRUWcffbZ3H///dx4441+h9PsBYPBmBXQZZdbbrmFXr168fDDD1d/169fPx8jav46d+5c4+ebb76Zfffdl1GjRjX6vtrknRFpmPz8fAA6dOjgcyQtQzgcZu7cuRQXF9erWnVbNH36dMaOHcvo0aP9DqVF+PLLL+nevTv77LMPZ599NmvXrvU7pGbrueee47DDDmPChAnk5uZyyCGHcP/99/sdVotRUVHBY489xnnnndeoBWt3apN3RiR+rutyxRVXcNRRR9Wr2GFbtGrVKkaMGEFZWRmZmZk8++yzDBw40O+wmq25c+fywQcfsGzZMr9DaRGOOOII5syZQ//+/dm4cSN//OMfOfroo1m9ejVZWVl+h9fsfP3119x7771cddVVXHvttSxbtoxf/epXJCcnM2XKFL/Da/bmz5/Pjh07mDp1akLGVzIi9TJ9+nRWr16tZ9N10L9/f1auXEl+fj5PPfUUU6ZM4a233lJCEsW6deu4/PLLWbhwIampqX6H0yLs/qj54IMP5ogjjqBPnz48+eSTnH/++T5G1jy5rsthhx3GTTfdBMAhhxzC6tWrmTVrlpKROnjwwQcZM2YM3bt3T8j4ekwjdXbppZfywgsv8Oabb9KzZ0+/w2n2kpOT2W+//Rg2bBgzZ85kyJAh/O1vf/M7rGZpxYoVbN68mUMPPZRgMEgwGOStt97irrvuIhgMEg6H/Q6x2WvXrh0HHHAAa9as8TuUZqlbt257/UPgwAMP1KOtOvjuu+94/fXXueCCCxK2D90ZkVpZa7nssst49tlnWbx4sV76ipPrupSXl/sdRrN0wgknsGrVqhrfnXvuuQwYMIBrrrmGQCDgU2QtR1FREV999RW/+MUv/A6lWTrqqKP2WpLgiy++oE+fPj5F1HI8/PDD5ObmMnbs2ITto00mI0VFRTX+9fDNN9+wcuVKOnToQO/evX2MrHmaPn06jz/+OAsWLCArK4tNmzYBkJOTQ1pams/RNU8zZsxgzJgx9O7dm8LCQh5//HEWL17Mq6++6ndozVJWVtZe7yBlZGTQsWNHvZvk4eqrr2bcuHH06dOHDRs2cMMNNxAIBDjrrLP8Dq1ZuvLKKxk5ciQ33XQTZ555Ju+//z6zZ89m9uzZfofWrLmuy8MPP8yUKVMIBhOYMtg26M0337TAXp8pU6b4HVqzFO1cAfbhhx/2O7Rm67zzzrN9+vSxycnJtnPnzvaEE06wr732mt9htSijRo2yl19+ud9hNFsTJ0603bp1s8nJybZHjx524sSJds2aNX6H1aw9//zzdtCgQTYlJcUOGDDAzp492++Qmr1XX33VAvbzzz9P6H6MtdYmLtURERERiU0vsIqIiIivlIyIiIiIr5SMiIiIiK+UjIiIiIivlIyIiIiIr5SMiIiIiK+UjIiIiIivlIyIiIiIr5SMiIiIiK+UjIiIiIivlIyIiIiIr5SMiIiIiK/+P//sMCUtPRCiAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,2],x[:,3],c=y,s=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fc1d1610",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "08604c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5cfbbd4e",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3\n",
       "0    5.1  3.5  1.4  0.2\n",
       "1    4.9  3.0  1.4  0.2\n",
       "2    4.7  3.2  1.3  0.2\n",
       "3    4.6  3.1  1.5  0.2\n",
       "4    5.0  3.6  1.4  0.2\n",
       "..   ...  ...  ...  ...\n",
       "145  6.7  3.0  5.2  2.3\n",
       "146  6.3  2.5  5.0  1.9\n",
       "147  6.5  3.0  5.2  2.0\n",
       "148  6.2  3.4  5.4  2.3\n",
       "149  5.9  3.0  5.1  1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "0867785c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target']=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b8301956",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>y</th>\n",
       "      <th>target</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0    1    2    3  y  target\n",
       "0    5.1  3.5  1.4  0.2  0       0\n",
       "1    4.9  3.0  1.4  0.2  0       0\n",
       "2    4.7  3.2  1.3  0.2  0       0\n",
       "3    4.6  3.1  1.5  0.2  0       0\n",
       "4    5.0  3.6  1.4  0.2  0       0\n",
       "..   ...  ...  ...  ... ..     ...\n",
       "145  6.7  3.0  5.2  2.3  1       1\n",
       "146  6.3  2.5  5.0  1.9  1       1\n",
       "147  6.5  3.0  5.2  2.0  1       1\n",
       "148  6.2  3.4  5.4  2.3  1       1\n",
       "149  5.9  3.0  5.1  1.8  1       1\n",
       "\n",
       "[150 rows x 6 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "52c4f0e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target\n",
       " 1    84\n",
       " 0    49\n",
       "-1    17\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(df.target).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "8aaa8ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x71b06291e600>"
      ]
     },
     "execution_count": 112,
     "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": [
    "plt.scatter(df[0],df[2],c=df.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "32599f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5.1\n",
       "1      4.9\n",
       "2      4.7\n",
       "3      4.6\n",
       "4      5.0\n",
       "      ... \n",
       "145    6.7\n",
       "146    6.3\n",
       "147    6.5\n",
       "148    6.2\n",
       "149    5.9\n",
       "Name: 0, Length: 150, dtype: float64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'==-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86428078",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Machine_learning_Puyan_Rajiyan",
   "language": "python",
   "name": "machine_learning_puyan_rajiyan"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}