iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic Statistical Manipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ishmum/Datasets\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "IPython.notebook.set_autosave_interval(60000)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autosaving every 60 seconds\n"
     ]
    }
   ],
   "source": [
    "%cd /home/ishmum/Datasets/\n",
    "\n",
    "%autosave 60\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv('iris.data', header=None, names=['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width', 'Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal Length</th>\n",
       "      <th>Sepal Width</th>\n",
       "      <th>Petal Length</th>\n",
       "      <th>Petal Width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Sepal Length  Sepal Width  Petal Length  Petal Width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.054000      3.758667     1.198667\n",
       "std        0.828066     0.433594      1.764420     0.763161\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f19f1b5e550>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f19efb3ba20>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f19efaeff28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f19efaad518>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5o6cTuKQJwA3AWRHxStHxDEXSUcCaiFhcdCwjsCmwN/CNiNgLWA/0TDuvjW1VyB89m8AlbUb24V8dETcWHQ+ApOWSDmmy+gDg45KWk/3UPkjSd7oWXCLpKkkX5Nx8JbAyIgZrJ9eTJXTLuOt9nWG+AyM91o8kndxkXb+kkNS0s2Jav2uTdaXLH430ZAKXJLJ22aURcXGD9R+W9BNJL0t6UdK/Svpg9yN9hynAVRHRT9ZF+46I+EQnTyjpFEmLWt0/Ip4DVkjaPS06GCjdhZ4Clbbrfdm+A5L2l/RquvA7uOxbTZZ9EyAiDo+IBTmPPyDptJzbDpk/yqQnEzhZbfYkslrsg+lxBICkbYFbgP8D7EDWJnk+8HpRwVbc54CrJT0E7Al8qeB4SiMiNgCDXe+XAteVYbiJdnwHJF0D3APsLmmlpFNHGdZ9ZPmo9hfcR8h+5dUuOxC4a5TnGk7T/FE6ETGmHmS36K0dZptPkX3hXiL78u1csy6AzwNPkY0V/L+BTdK6dwN38Pb4z1cDE2v2XQ4c0uScVwEXNFn3HuA2sqv+jwPH1e13KXAr8CrZxZZ316z/aNrnZeAfgDuB04D3Ar8B3iS7HWxtnuP5Uf1Hib8DtwNz0vPfS8c/v25ZAFPT6wHgtPR8HHBROudTwOlp202Bv03l/DeprH+95n18BngCWJvKvYr++4zk0as18KH8G/CmpAWSDpe0fe1KSUcDXwT+IzAZuBu4pu4Yf0r2Jdib7LawTw3uDvwdsCNZgpwGnDeaYNNdHbcB3yUrwMcD/yBpes1mx5MV9O2BZWQFFkmTyNqlzwXeRZbIPwQQEUvJCu89ETEhIiYOdzzrGWX9DtxFVsMm/bsoPWqX/TIiVjbY9y+Ao8juGJkJHDu4IiL+R3oPZ6SyXjsBx1HAB4H3AccBf5Iz1lIYcwk8sqvJHyb73/dbwPOSbpbUlzb5DPB3EbE0sp/AXwL2lLRzzWEujIgXI+JXZLf+nZCOvSwibouI1yPieeBi4I9HGfJRwPKIuDIiNkTEA2QXV/6sZpubIuJnKd6ryZoyAI4AlkTEjWnd14Dncpyz2fGsB5T4O3An8OHUBv0RsqR7D7BfzbI7m+x7HHBJRKyIiBfJ/hPJY15ErE3vYyEVK+tjLoFDVvuMiFMiYiqwB1lt4ZK0emfgq5LWSlpL1mwh3nn/bu29vU+n/ZHUJ+laSc9IegX4Dtm0TKOxM7DvYDwpphOB36/ZpjYp/xqYkJ7vWBtrZL8bG9Ve6jU7nvWIkn4HfkpW1vYgq23fHRHr0rkGlzVr/35HWU8x5VHpsj4mE3itiHiMrN13sDvwCuDTETGx5rFVRPykZrfaW8P+AHg2Pf8SWa1mRkRsC3yCrOCPxgrgzrp4JkTEZ3Psu4rs1jXgravrU2vW9944CjZiZfkORMRvyO7c+RhZV/bH0qq707L30TyBr2oQ0zsOnyeGqhlzCVzSeyTNSd3WkTSN7OffT9Mm3wTOlfRHaf12kv6s7jB/KWn7tO+ZwPfS8m3ILpK8LGkn4C9HGN44SVvWPDYnu1vgP0g6SdJm6fFBSe/NcbxbgRmSjkn3w57OO2vuq4Gp6Tw2RpT8O3BXOl7tfxaL0rJVEfFkk/2uAz4vaWpq06/vULYa+MMRxlJ6Yy6Bk91ZsS9wr6T1ZIX2EWAOQETcBFwIXJt+Aj4CHF53jO+TDXDzIFmSvDwtP5/sos7LaflIOwDMBV6redwR2VgMHyW7sPgs2U++C4EthjtYRLxA1lb+92R3BUwnu11r8HaxO4AlwHOSyjL7tnVemb8Dd5JdrK/tn7AoLbt7iP2+RXa3zC+A+xuc96vAsZJekvS1EcZUWj05GmEnSQpgt4hYVnQsIyVpE7I28BMjYmHR8Vg1Vfk70GvGYg18TJH0J8rG7N6C7NYw8fZPZTOrMCfw3rc/8CRZB4ePkY1r/FqxIZlZO7gJxcysolwDNzOrqKZDLXbCpEmTor+/v5unfIf169czfny55htwTMOrjWfx4sUvRMTkgkPKbagyX7bPuZ383tqrabnv5sArH/jAB6JICxcuLPT8jTim4dXGA9wXJRhEKO9jqDJfts+5nfze2qtZuXcTiplZRTmBm5lVlBO4mVlFdfUiZlX1z711xPssn3dkByIxK7fB78qcGRs4Jef3xt+V1rkGbmZWUU7gZmYV5SaUknFzjZnl5Rq4mVlFOYGbmVWUE7iZWUU5gZuZVZQTuJlZRTmBm5lVlBO4mVlFOYGbmVWUE7iZWUVVuifmSHstzpmxgVmdCcXMrOtcAzczqygncDOziqp0E4pZJ0jaErgL2ILsO3J9RPy1pF2Aa4F3AYuBkyLijeIi7Q0ewK11roGbbex14KCIeD+wJ3CYpP2AC4GvRMSuwEvAqQXGaOYEblYvTQS+Lr3cLD0COAi4Pi1fABxTQHhmb3ETilkDksaRNZPsClwKPAmsjYgNaZOVwE5N9p0NzAbo6+tjYGCg4TnWrVvXdF1VzZmRfTx9W739vBOK/NzK9HcbNoFLmgZ8G+gjq4XMj4ivStoB+B7QDywHjouIlzoXqln3RMSbwJ6SJgI3Ae8Zwb7zgfkAM2fOjFmzZjXcbmBggGbrquqUmjkxv/xw5+qHy0+c1bFjD6dMf7c8TSgbgDkRMR3YDzhd0nRgLnB7ROwG3J5em/WUiFgLLAT2ByZKGsxKU4FnCgvMjBwJPCJWRcT96fmrwFKyn45Hk7UDgtsDrYdImpxq3kjaCjiUrNwvBI5Nm50MfL+YCM0yI/qNI6kf2Au4F+iLiFVp1XNkTSyN9snVHtiKkbax9W3VWttZK215ec9T357WyXPlVaY2PigkninAgtQOvglwXUTcIulR4FpJFwAPAJd3MyizerkTuKQJwA3AWRHxiqS31kVESIpG++VtD2zFKS10pT+uhfOP9DyQv42uvj2tk+fKq0xtfND9eCLiIbKKSv3yp4B9uhaI2TBy3UYoaTOy5H11RNyYFq+WNCWtnwKs6UyIZmbWyLAJXFlV+3JgaURcXLPqZrJ2QHB7oJlZ1+VpQjkAOAl4WNKDadkXgXnAdZJOBZ4GjutMiGZm1siwCTwiFgFqsvrg9oZjZmZ5uSu9mVlFuSt9D/BobmZjk2vgZmYV5QRuZlZRbkIxs4200ixn3ecauJlZRTmBm5lVlJtQOiTvT9A5Mza0NP6JmZlr4GZmFeUauFmP8wXJ3uUauFkdSdMkLZT0qKQlks5My3eQdJukJ9K/2xcdq41tTuBmG/M0glYJTuBmdTyNoFWF28DNhtDJaQS7NVVcK9P0jVbfVp09b5FT/pVpysExl8B9Qcfy6vQ0gt2aKq6I21TnzNjAlx/uXHpp9zSCI1GmKQfdhGLWgKcRtCpwAjer42kErSrGXBOKWQ6eRtAqwQncrI6nEbSqcBOKmVlFOYGbmVWUE7iZWUU5gZuZVZQTuJlZRQ2bwCVdIWmNpEdqlnlUNjOzguWpgV8FHFa3zKOymZkVbNgEHhF3AS/WLfaobGZmBWu1I0+uUdkg/8hsrRjpaGedHiGtFUXFNNTfoUyjrUH54jEri1H3xBxqVLa0PtfIbK0Y6ShrnR4hrRVFxTTUaG5lGm0NyhePWVm0eheKR2UzMytYqwnco7KZmRVs2N/ukq4BZgGTJK0E/po2j8rmSRbMzEZu2AQeESc0WeVR2czMCuSemGZmFeUEbtaAeyBbFZTrnjqz8rgK+Drw7Zplgz2Q50mam15/oYDYrAWtXmtbPu/INkfSPq6BmzXgHshWBa6Bm+WXqwdy3t7H3ephWkRP3073MG7lc2s1nvpzlalnsBO4WQuG6oGct/dxt3qYjrTHcjt0uofxUD2Jm2n1c6g/V5l6BjuBm+W3WtKUiFjlHsjFct+RjNvAzfJzD2QrFdfAx6ihajBzZmxo+HOz1avxrdSWir7y340eyGaj5QRu1oB7IFsVuAnFzKyiXAO33HzhqHj+G1gt18DNzCrKCdzMrKKcwM3MKsoJ3MysopzAzcwqygnczKyifBuhWUEefublQgaast7hGriZWUU5gZuZVZQTuJlZRTmBm5lVlC9impkNoX78mWbDLY9WK0MouwZuZlZRo0rgkg6T9LikZZLmtisos7JymbcyaTmBSxoHXAocDkwHTpA0vV2BmZWNy7yVzWhq4PsAyyLiqYh4A7gWOLo9YZmVksu8lcpoLmLuBKyoeb0S2Ld+I0mzgdnp5TpJj4/inKPyeZgEvFDU+RtxTI3pwne8rI1n564H87Z2l/nCP+dOKUMZ6pROvbe6Ml+vYbnv+F0oETEfmN/p8+Qh6b6ImFl0HLUc0/DKFs9w8pb5qr2vkfB7647RNKE8A0yreT01LTPrVS7zViqjSeA/B3aTtIukzYHjgZvbE5ZZKbnMW6m03IQSERsknQH8CzAOuCIilrQtss4oRVNOHcc0vFLE04EyX4r31SF+b12giCg6BjMza4F7YpqZVZQTuJlZRY2ZBC5puaSHJT0o6b4SxDNR0vWSHpO0VNL+Bceze/psBh+vSDqryJhSXP9N0hJJj0i6RtKWRcc0GpKmSVoo6dH0vs4sOqZ2kbSlpJ9J+kV6b+cXHVO7SRon6QFJtxQdC4yhNnBJy4GZEVGKzgWSFgB3R8Rl6Y6GrSNibdFxwVtdxp8B9o2IpwuMYydgETA9Il6TdB3ww4i4qqiYRkvSFGBKRNwvaRtgMXBMRDxacGijJknA+IhYJ2kzsr/dmRHx04JDaxtJZwMzgW0j4qii4xkzNfAykbQdcCBwOUBEvFGW5J0cDDxZZPKusSmwlaRNga2BZwuOZ1QiYlVE3J+evwosJevhWXmRWZdebpYePVNDlDQVOBK4rOhYBo2lBB7AjyUtTl2di7QL8DxwZfo5dpmk8QXHVOt44Jqig4iIZ4CLgF8Bq4CXI+LHxUbVPpL6gb2Ae4uNpH1SE8ODwBrgtojomfcGXAKcA/yu6EAGjaUE/uGI2JtsJLnTJR1YYCybAnsD34iIvYD1QCmGJk3NOR8H/qkEsWxPNljULsCOwHhJnyg2qvaQNAG4ATgrIl4pOp52iYg3I2JPsl6q+0jao+iY2kHSUcCaiFhcdCy1xkwCT7U5ImINcBPZyHJFWQmsrKmdXE+W0MvgcOD+iFhddCDAIcAvI+L5iPgtcCPwoYJjGrXUPnwDcHVE3Fh0PJ2QmgQXAocVHUubHAB8PF1LuxY4SNJ3ig1pjCRwSePTBSNSU8VHgUeKiicingNWSNo9LToYKMtFrBMoQfNJ8itgP0lbpwtkB5O1GVdWeh+XA0sj4uKi42knSZMlTUzPtwIOBR4rNqr2iIhzI2JqRPSTNTHeERGF/xocK3Ni9gE3Zd8dNgW+GxH/t9iQ+BxwdWqyeAr4ZMHxDP7ndijw6aJjAYiIeyVdD9wPbAAeoETdmFt0AHAS8HBqKwb4YkT8sMCY2mUKsCDdxbQJcF1ElOJ2u141Zm4jNDPrNWOiCcXMrBc5gZuZVZQTuJlZRTmBm5lVlBO4mVlFOYGbmVWUE7iZWUX9f4eJtDPu44DpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19efa01e10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H500ADwAXSzoqaWP9ZZmZ2YyOY9QRMboQhZiZWXse+jAzy5yD2swscw5qM7PMOajNzDLnoDYzy5yD2swscw5qM7PMOajNzDLnoDYzy5yD2swscw5qM7PMOajNzDKX7MIBZmap9W+5O11ju+ff1qrlyxIUMncOajPL0pFb3p6srf4tdydtb6F56MPMLHMOajOzzFUKaklvk/SIpL+RtKXuoszM7KSOY9SSlgAfBd4KHAW+LulzEXG47uLMzE5HUvVlt3VepupV4hdalS3qK4C/iYhHI+JZ4HbgnfWWZWbWWURUujUajUrL5arKUR8XAX/bcv8o8LpTF5K0CdgE0NfXR7PZTFFfbaamprKvsZe4P9Nyf6bV6/2Z7PC8iNgObAcYGhqK4eHhVE3XotlsknuNvcT9mZb7M61e788qQx/fBV7Zcn9NOc/MzBZAlaD+OvAaST8u6aXAu4DP1VuWmZnN6Dj0ERHPSfo3wBeAJcAnIuJQ7ZWZmRlQcYw6Ij4PfL7mWszMrA1/M9HMLHMOajOzzKmOg7wlPQl8J3nDaV0APNXtIhYR92da7s+0eqE/10bEhe0eqCWoe4GkfREx1O06Fgv3Z1ruz7R6vT899GFmljkHtZlZ5s7moN7e7QIWGfdnWu7PtHq6P8/aMWozs15xNm9Rm5n1BAe1mVnmuhrUkrZKOiTpYUkPSXrRea7n2f6wpF1V5yd+7Q+2TPdLOljn653y2t3q129KurycXippStK7Wx7fL+m1kt4x2yXdJE2VP/slXdcy/3pJH0n5e3Qiabrsv4OSPi3p3A7Lf/B0j7csd0TSBVXnpyLplyVd0nK/Kakrh6wtZN9KukHSH7fc/7ikL7Xc3yzpP5XT98/S7m2Srimn39ta78w6W6euBbWk1wO/CLw2In4aeAsvvEBBr6u0YqXW5X69D7iynL4M+PbMfUkrgFcDByLicxFxS4e2+oHrOixTt+MRcXlErAeeBf5Vh+W78p7PwS8Dl3RcamEsZN+2rpdQrJuryssMUj52P0BEXEln7wVO+48ltW5uUb8CeCoifggQEU9FxBMAkgYl/WW5BfYFSa8o5zcl3dryn/iKcv4Vkh4ot+jul3TxmRTU4XW3SfqapG9LemM5/1xJn5J0WNJdkh6UNCTpFmB5Wecny+aXSPrTckt3j6Tl8+q92XWzX+/n5B/ElcCfAJeX968A9kfEdOvWsYrT5z4g6VuS/qClrVuAN5Y1va+ct1rSbkl/LenD8+qlufsK8BNlze8u14WHyq2zJe3ec0l/Ufb1IRVXQJozSSskfaJ8vW9Kemc5/3pJd7brD0kby/X0a+U69xFJVwLvAP6wrPHV5eK/fup63QV19+1DwE9KWi5pFXC8nHdp+fiVFGHe+olOZb89omLr+8fK+e8BVgMNSY2ZF5A0JumApK9K6kvTLS2qXnMs9Q04r+ysbwMfA36unL+M4g/+wvL+tRSnVgVoAn9aTr8JOFhOvwxYWk6/BfhsOT0M7Grz2i+aX+F1/2M5/QvAl8rp9wMfL6fXA88BQ+X9qZa2+8vHLi/vfwp49yLs17XAo+X0BPBTQANYCWwFPlQ+dj3wkXL6c8BvldO/M9Nvp75G+ZxHgVXAORSnKHhlzevoTC1Lgf8O/GtgAPgfwLLysY+11D91yvNfXv5cDhwEfrS8fwS4oM3rvWg+8O9n1hXg/PJ9XTFbf1CEyBHg5eV7/pWWvr4NuKal7SZt1uuFuHWhbxsU6/bPU2wEbAR+m+JSg4+3qetXgS9SnNp5NfCDmb479TWAAH6pnP4w8Hup+yvZpbjmKiKmJA0CbwRGgDtUjFvuowi9L6q4wvAS4HstT50on/9lSS+TdD5FEOyQ9BqKTlt2BiVd3OF17yx/7qcIXoCrgFvLeg5Kevg07T8WEQ+1aSOpbvZrRHxH0ksl/ROKkH6E4sITr6PYavnPbZ72BuDXyuk/A053reh7IuIYgKTDFP8Y6hzWWS5p5j37CjBOcV3QQeDrZT8uB74/y/PfI+lXyulXAq8B/s8ca7gaeIek95f3zwFeVU63648LgL+MiL8v538a+MnTtN9uvV4IC923M5/2lgMPAH9NMZzyZPnYqd4ETETENPCEpL2naftZYGafzX7gradZ9ox0LagByk5oAk1J3wL+BcUveigiXj/b09rc/xDQiIhfkdRftjlX6vC6Pyx/TnNm/fbDlulpihWmFl3u1/uBXwe+FxEh6asUYXwFxR9Ildeezal9WPf6ezwiLm+doSJBdkTE757uiZKGKT6FvD4i/p+kJkXIzpWAX4uIR05p/3Wk6Y/5rtdnaqH79j6KcfBzgI9SBPQlzB7Uc3Eiys1paurHbu5MvLjcUptxOcXHt0eAC1XsFEPSMknrWpa7tpx/FXCs3KJYxcnrOF5/hiV1et127gP+ebn8JZwc8wI4IelMtuznJYN+vZ9iZ8tMKD8A/BbwdzNbf6e4j+LybgC/0TL/aYot+tzcA1wjaWbM8uWS1paPtb7nq4B/KIPkp4CfPcPX+wKwuQwxJP1Mh+W/DvycpB+RtJSTn1Yg3z6dUWffPlAud2FEfL8M1ieBd1KOT5/iy8C15Rj5Kyg+nc5Y8H7s5s7E8yg+Vh8uhwwuAX4/Ip4FrgG2STpAMd7auif2HyV9k2JH1cZy3oeB/1DOr/rfbIOkozM3io9cp3vddj5GEX6HgT8ADgEzYbQdeFgndyYulG73633AP6UM6oj4HsUwy2xbLTcAv1Nu+V/UMv9hYLrcQfO+9k9deBFxGPg9YE/Zv1+k2IELL3zPdwNLJU1SjIl+teJLPNyyXv4RxaeaZeX8Q+X909X3XYpx7a9RvBdHOLlO3g78OxU7JV/dvoXuqbNvI+IfKIK59TKCD1DsJDzQ5il3UQyPHAb+Gy/8NLgd2N26M7FuPfUV8vIjzvsjYl+3awFQcXjPsoj4x3LF/xJwcRmKPSO3frX5kXReua9iKUXgfCIi7up2XXbmujpGvQicS3GYzjKKscTf7rWQtkXp9yW9hWI8dg/wF12ux+app7aozczORj7Xh5lZ5hzUZmaZc1CbmWXOQW1mljkHtZlZ5v4/d6ORcYGMjX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.boxplot()"
   ]
  },
  {
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sepal Length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sepal Width</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petal Length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Petal Width</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Sepal Length  Sepal Width  Petal Length  Petal Width\n",
       "Sepal Length      1.000000    -0.109369      0.871754     0.817954\n",
       "Sepal Width      -0.109369     1.000000     -0.420516    -0.356544\n",
       "Petal Length      0.871754    -0.420516      1.000000     0.962757\n",
       "Petal Width       0.817954    -0.356544      0.962757     1.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ef2247f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f19ef19fe10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Sepal Length'], data['Sepal Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f19ed906940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Petal Length'], data['Petal Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f19ed8cf278>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Sepal Length'], data['Petal Width'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f19ed84bda0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6SZvQ6uWL+m5HminLGPvXXbwk6P1pxujPMlb4dRcvSTWeerTd4+LWJ+08ad7ba75ogSpqzPY4dagB8xZ0Mxszs9HuzyPAFcCzkdkeAj7S/flq4BEvKA8ZktH9/DXnzyneSSmXkNyu1F/c93/nh85n/SVLTxxdDpklXgTM8v7PrZs/u502C75x7QruWLNi7rwxGfHPrVuZen2i7lizou/39nr/nSnu/Shq36xDDUiTcjmPzgXPITp/AL7q7reb2e3ApLs/1I02fhlYBRwArnX3H/VarnLoIiLZBeXQ3f37dAp1dPpnZvz8C2BdSCdFRCRM6x9wUfUbBSSdsr7HpHbT3mTT9AdFVG3/qlp/8tbqW/+rcqOAhCnre0xq94KlZ8TeyxA9p9z0B0VUbf+qWn/6pQdcJKjDjQIyv7K+x6R244o5zL15pukPiqja/lW1/gxCqwt6HW4UkPmV9T1mXX705pmmPyiiavtX1fozCK0u6HW4UUDmV9b3mHX50Ztnmv6giKrtX1XrzyC0uqDX4UYBmV9Z32NSu3E3psHcm2ea/qCIqu1fVevPILQ65XL8QkiTr3q3QVnfY69206Rckt6fdlrVt9Oq7V9V688gtDrlIiJSN0E3Fom0UZaHRxTRTty0kIdjQPgDKdIoog0oN19epWy7jtBFImLz4QPIgofk0KMPfjgxPfLQjKQ+hj6QIo0i2oBy8+VltK0cukgGsfnwAWTBQ3LoccUc5j40I6mPoQ+kSKOINqDcfHnVsu0q6CIRWXLJIRnmovLPce2EPpAijSLagHLz5VXLtqugi0RkySWHZJiLyj/HtRP6QIo0imgDys2XVy3broIuEhGbDx9AFjwkh56040afUZHUx9AHUqRRRBtQbr68atl2pVxEIrLkw0MufIXm0ENSLscvSg4ygVJEG1Buvrxq2XalXEREakQ5dJEe8h67vC7jfV//pSdmjQyZ9PjFKuWspTcdoUurpc1KF5VND5ElEx0t5sdFi3pTxhBvEuXQRRKkzUoXlU0PkSUTnTRme3R61XLW0psKurRa2qx0Udn0EIPIRFctZy29qaBLq6XNSheVTQ8xiEx01XLW0psKurRa2qx0Udn0EFky0UljtkenVy1nLb2poEur3bFmBesvWXriiHzILHbwqDWrxtm4dgXjoyMYMD46wqZ1K9l09cpZ08q8WBjXx6T+bL3x0jnFOy7lkmWZUj6lXEREakQ5dGmMojLRg2injXnuNq5zkiI+CxV0qY1oJnrq4GE2bN8DkOuOMYh2iup7lbRxnZMU9VnoHLrURlGZ6EG008Y8dxvXOUlRn4UKutRGUZlo5bnz0cZ1TlLUZ6GCLrVRVCZaee58tHGdkxT1WaigS20UlYkeRDttzHO3cZ2TFPVZ6KKo1EZRY08Pop2qjZtdhDauc5KiPgvl0EVEaiQoh25mS4D7gLMABza7+xci81wG/CPw391J29399pBOS/3UNXNc5jjneX9mSctLO+Z7meq6/VTJvEfoZnY2cLa77zaz04FdwBp3f2bGPJcBf+buV6ZtWEfozVLXcbPLHOc8788saXkXLD0jdrjcuCEOylLX7acMQeOhu/tL7r67+/NrwF5An7DMUtfMcZnjnOf9mSUtL2ns86Sx4MtQ1+2najKlXMxsGbAK2Bnz8qVm9j0z+2cz+82E999kZpNmNjk9PZ25s1Jddc0clznOed6fWdb3JY0FX4a6bj9Vk7qgm9lpwIPALe7+auTl3cDb3H0l8NfAjrhluPtmd59w94mxsbF++ywVVNfMcZnjnOf9mWV9X9JY8GWo6/ZTNakKupkN0ynmW919e/R1d3/V3Q91f/4mMGxmZ+baU6m0umaOyxznPO/PLGl5SWOfJ40FX4a6bj9VkyblYsA9wF53vzNhnl8HfurubmYX0flD8XKuPZVKq2vmOKnfcdOqnnfvtbyqp1zquv1UTZqUyzuB7wJ7gGPdyZ8GlgK4+9+Y2SeAjwGvA4eBT7r7v/VarlIuIiLZBeXQ3f0xoOfJNne/G7i7v+6JFDf+OOgoUJpLt/5L6Yoaf/zWbd8D78QS82pHpEo0OJeUrqjxx48c9RPFPK92RKpEBV1KV+T443m3I1IlKuhSuiLHH8+7HZEqUUGX0hU1/vjwkHXGacmxHZEq0UVRKV2R44/n3Y5IlWg8dBGRGgnKoTeJxluurqp9N0Xl4rX9SZ5aU9AHkXWWfFTtuykqF6/tT/LWmouiGm+5uqr23RSVi9f2J3lrTUHXeMvVVbXvpshcvLY/yVNrCrrGW66uqn03Rebitf1JnlpT0DXecnVV7bspKhev7U/y1pqLohpvubqq9t0UmYvX9id5Ug5dRKRGeuXQW3PKRUSk6VpzykUki6JuAmpaO1IuFXSRiKJuAmpaO1I+nXIRiSjqJqCmtSPlU0EXiSjqJqCmtSPlU0EXiSjqJqCmtSPlU0EXiSjqJqCmtSPl00VRkYiibgJqWjtSPt1YJCJSI7qxSESkBVTQRUQaQgVdRKQhVNBFRBpCBV1EpCFU0EVEGkIFXUSkIVTQRUQaYt47Rc1sCXAfcBbgwGZ3/0JkHgO+APwO8H/ADe6+O//uSttpXG+RZGlu/X8d+FN3321mpwO7zOxhd39mxjzvA87p/ncx8MXu/0Vyo3G9RXqb95SLu790/Gjb3V8D9gLRvecDwH3e8SQwamZn595baTWN6y3SW6Zz6Ga2DFgF7Iy8NA7sm/H7i8wt+pjZTWY2aWaT09PT2XoqradxvUV6S13Qzew04EHgFnd/tZ/G3H2zu0+4+8TY2Fg/i5AW07jeIr2lKuhmNkynmG919+0xs0wBS2b8vrg7TSQ3GtdbpLd5C3o3wXIPsNfd70yY7SHg96zjEuAVd38px36KsGbVOBvXrmB8dAQDxkdH2Lh2hS6IinSlSbmsBj4M7DGzp7vTPg0sBXD3vwG+SSey+EM6scWP5t9VkU5RVwEXiTdvQXf3xwCbZx4HPp5Xp0REJDvdKSoi0hAq6CIiDaGCLiLSECroIiINoYIuItIQ1gmolNCw2TTw44BFnAn8LKfulK1J6wLNWp8mrQs0a33aui5vc/fYW+1LK+ihzGzS3SfK7kcemrQu0Kz1adK6QLPWR+syl065iIg0hAq6iEhD1Lmgby67Azlq0rpAs9anSesCzVofrUtEbc+hi4jIbHU+QhcRkRlU0EVEGqJ2Bd3M/tbM9pvZf5Tdl1BmtsTMHjWzZ8zsP83s5rL71C8ze6OZ/buZfa+7Ln9Zdp9CmdmQmT1lZv9Udl9CmdnzZrbHzJ42s8my+xPKzEbNbJuZPWtme83s0rL71A8zO7f7nRz/71Uzu6Xv5dXtHLqZvQs4ROeh1O8ouz8hug/SPtvdd5vZ6cAuYI27P1Ny1zLrPghlobsf6j7h6jHg5u5Dw2vJzD4JTABvcvcry+5PCDN7Hphw90bciGNm9wLfdfctZvYG4Nfc/WDZ/QphZkN0nvR2sbv3ddNl7Y7Q3f07wIGy+5EHd3/J3Xd3f34N2EvMw7XrwDsOdX8d7v5Xr6OFGcxsMfB+YEvZfZHZzOwM4F10nqSGu/+q7sW863LguX6LOdSwoDeVmS0DVgE7y+1J/7qnKJ4G9gMPu3tt1wW4C/gUcKzsjuTEgX81s11mdlPZnQn0dmAa+LvuKbEtZraw7E7l4FrggZAFqKBXgJmdRuch3Le4+6tl96df7n7U3c+n85Dwi8yslqfEzOxKYL+77yq7Lzl6p7tfALwP+Hj31GVdnQJcAHzR3VcBPwf+vNwuhemeNroK+FrIclTQS9Y93/wgsNXdt5fdnzx0//n7KPDesvvSp9XAVd3zzl8B3m1m95fbpTDuPtX9/37g68BF5fYoyIvAizP+BbiNToGvs/cBu939pyELUUEvUfdC4j3AXne/s+z+hDCzMTMb7f48AlwBPFtur/rj7hvcfbG7L6Pzz+BH3H19yd3qm5kt7F50p3tq4reB2qbE3P1/gH1mdm530uVA7YIEEdcReLoFUjwkumrM7AHgMuBMM3sR+Ky731Nur/q2GvgwsKd77hng0+7+zRL71K+zgXu7V+oXAF9199rH/RriLODrneMHTgH+3t3/pdwuBftjYGv3VMWPgI+W3J++df/IXgH8QfCy6hZbFBGReDrlIiLSECroIiINoYIuItIQKugiIg2hgi4i0hAq6CIiDaGCLiLSEP8P0MqLov0xVcQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Petal Length'], data['Sepal Width'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training/Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.428704</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.467571</td>\n",
       "      <td>0.457778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.230018</td>\n",
       "      <td>0.180664</td>\n",
       "      <td>0.299054</td>\n",
       "      <td>0.317984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.222222</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.101695</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>0.567797</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.694915</td>\n",
       "      <td>0.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0           1           2           3\n",
       "count  150.000000  150.000000  150.000000  150.000000\n",
       "mean     0.428704    0.439167    0.467571    0.457778\n",
       "std      0.230018    0.180664    0.299054    0.317984\n",
       "min      0.000000    0.000000    0.000000    0.000000\n",
       "25%      0.222222    0.333333    0.101695    0.083333\n",
       "50%      0.416667    0.416667    0.567797    0.500000\n",
       "75%      0.583333    0.541667    0.694915    0.708333\n",
       "max      1.000000    1.000000    1.000000    1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "X = data[['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]\n",
    "y = data['Class']\n",
    "\n",
    "X_scaled = preprocessing.MinMaxScaler().fit_transform(X)\n",
    "\n",
    "pd.DataFrame(X_scaled).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((105, 4), (45, 4), (105,), (45,))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3)\n",
    "\n",
    "(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0,  0],\n",
       "       [ 0, 18,  0],\n",
       "       [ 0,  2, 11]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import precision_score, \\\n",
    "    recall_score, confusion_matrix, classification_report, \\\n",
    "    accuracy_score, f1_score\n",
    "\n",
    "y_pred_gnb = GaussianNB().fit(X_train, y_train).predict(X_test)\n",
    "\n",
    "confusion_matrix(y_test, y_pred_gnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9555555555555556,\n",
       " 0.9666666666666667,\n",
       " 0.9487179487179488,\n",
       " 0.9546783625730995)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_gnb), \\\n",
    "precision_score(y_test, y_pred_gnb, average='macro'), \\\n",
    "recall_score(y_test, y_pred_gnb, average='macro'), \\\n",
    "f1_score(y_test, y_pred_gnb, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        14\n",
      "Iris-versicolor       0.90      1.00      0.95        18\n",
      " Iris-virginica       1.00      0.85      0.92        13\n",
      "\n",
      "       accuracy                           0.96        45\n",
      "      macro avg       0.97      0.95      0.95        45\n",
      "   weighted avg       0.96      0.96      0.95        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_gnb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0,  0],\n",
       "       [ 0, 15,  3],\n",
       "       [ 0,  2, 11]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "y_pred_lgr = LogisticRegression().fit(X_train, y_train).predict(X_test)\n",
    "\n",
    "confusion_matrix(y_test, y_pred_lgr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        14\n",
      "Iris-versicolor       0.88      0.83      0.86        18\n",
      " Iris-virginica       0.79      0.85      0.81        13\n",
      "\n",
      "       accuracy                           0.89        45\n",
      "      macro avg       0.89      0.89      0.89        45\n",
      "   weighted avg       0.89      0.89      0.89        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lgr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  0,  0],\n",
       "       [ 0, 18,  0],\n",
       "       [ 0,  1, 12]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_pred_knn = KNeighborsClassifier(n_neighbors=18).fit(X_train, y_train).predict(X_test)\n",
    "\n",
    "confusion_matrix(y_test, y_pred_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 precision    recall  f1-score   support\n",
      "\n",
      "    Iris-setosa       1.00      1.00      1.00        14\n",
      "Iris-versicolor       0.95      1.00      0.97        18\n",
      " Iris-virginica       1.00      0.92      0.96        13\n",
      "\n",
      "       accuracy                           0.98        45\n",
      "      macro avg       0.98      0.97      0.98        45\n",
      "   weighted avg       0.98      0.98      0.98        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f19dac55358>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "\n",
    "y_pred_km = KMeans(n_clusters=3).fit_predict(X_train)\n",
    "\n",
    "plt.scatter(X_train[:, 0], X_train[:, 1], c=y_pred_km, cmap='rainbow')"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}