curve_conf_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import RocCurveDisplay\n",
    "from sklearn.metrics import average_precision_score \n",
    "from sklearn.metrics import PrecisionRecallDisplay ,precision_recall_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def final_func(list1,list2):\n",
    "    confusion_matrix(list1,list2)\n",
    "    cm = confusion_matrix(list1,list2)\n",
    "    cm_display = ConfusionMatrixDisplay(cm).plot()\n",
    "    precision=precision_score(list1,list2)\n",
    "    recall=recall_score(list1,list2)\n",
    "    fscore=f1_score(list1,list2)\n",
    "    print(\"precision :- \",precision,\" recall :- \",recall,\" f1_score :- \",fscore)\n",
    "    fpr, tpr, _ = roc_curve(list1,list2)\n",
    "    roc_display = RocCurveDisplay(fpr=fpr, tpr=tpr).plot()\n",
    "    prec, recall, _ = precision_recall_curve(list1,list2)\n",
    "    pr_display = PrecisionRecallDisplay(precision=prec, recall=recall).plot()\n",
    "    avg_precision=average_precision_score(list1,list2)\n",
    "    print(\"average precision (accuracy):-\",avg_precision*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(r'data/ML_Project_G6 - Testing_Meetings .csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "Meeting Scheduled Date\n",
      "Intimation/Confirmed\n",
      "Type of Meeting\n",
      "Unnamed: 4\n",
      "Meeting Scheduled Date.1\n",
      "Intimation/Confirmed.1\n",
      "Type of Meeting.1\n"
     ]
    }
   ],
   "source": [
    "for i in data.columns:\n",
    "    print(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision :-  1.0  recall :-  0.7368421052631579  f1_score :-  0.8484848484848484\n",
      "average precision (accuracy):- 98.68421052631578 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ijKF2nN5j+DLwepI6BkslLYmIp0raTAO+AiyMiOckNRzFsZiZ2REYKhGccpT7Ph9YExFrASTdC1wFPFXS5gbgvoh4DiAiNh3lZ5qZ2WEaatK5o51obhZQWut4A3DBgDatwHhJD5LMcPrFiPjmwB1JWgQsAmhubj7KsMzMrFRZxeuPkAZZFwOWxwHnAm8C3gj8D0mtB7wpYnFEtEdEe319/fBHamaWY+U8WXykNpAMP+0zm2R6ioFttkTETmCnpIeAM4FVGcZlZmYlyroikDRZUtth7nsp0CJpvqQJwHXAkgFt/gW4SNI4SVNIuo6ePszPMTOzo3DIRCDpzcBy4Efp8lmSBp7QDxAR3cDNwAMkJ/d/jognJd0o6ca0zdPpfh8neXDtzohYcaQHY2Zmh6+crqG/JBkB9CBARCyXNK+cnUfE/cD9A9bdMWD5NuC2cvZnZmbDr5yuoe6I2J55JGZmNiLKuSJYIekGYKykFuCDwMPZhmVmZpVSzhXBB0jqFe8B/olkOmrXIzAzGyXKuSJoi4iPAR/LOhgzM6u8cq4IPi/pt5I+Jem0zCMyM7OKOmQiiIhLgIuBzcBiSU9I+njWgZmZWWWU9UBZRGyMiNuBG0meKfhEplGZmVnFlPNA2SmS/lLSCuBLJCOGZmcemZmZVUQ5N4v/HvgW8IaIGDhXkJmZVblDJoKIuLASgZiZ2cg4aCKQ9M8R8XZJT7D/9NFlVSgzM7PqMNQVwZ+mv6+sRCBmZjYyDnqzOCJeTF++PyLWlf4A769MeGZmlrVyho++fpB1lw93IGZmNjKGukfwPpJv/idKerxkUwH4edaBmZlZZQx1j+CfgB8CfwPcWrK+MyK2ZRqVmZlVzFCJICLiWUk3DdwgabqTgZnZ6HCoK4IrgcdIho+qZFsAJ2YYl5mZVchBE0FEXJn+nl+5cMzMrNLKmWvoVZJq0tfvlPR5Sc3Zh2ZmZpVQzvDRrwK7JJ0J/DdgHfAPmUZlZmYVU27x+gCuAr4YEV8kGUJqZmajQDmzj3ZK+u/Au4CLJI0FxmcblpmZVUo5VwTXkhSu/8OI2AjMAm7LNCozM6uYckpVbgTuAaZKuhLYHRHfzDwyMzOriHJGDb0deBR4G/B24JeS3pp1YGZmVhnl3CP4GHBeRGwCkFQP/AT4TpaBmZlZZZRzj2BMXxJIbS3zfWZmVgXKuSL4kaQHSOoWQ3Lz+P7sQjIzs0oqp2bxRyT9V+DVJPMNLY6I72UemZmZVcRQ9QhagM8BJwFPAB+OiOcrFZiZmVXGUH39dwE/AK4hmYH07w5355IWSlopaY2kW4dod56kHo9GMjOrvKG6hgoR8bX09UpJvzqcHadPIH+ZpNTlBmCppCUR8dQg7T4LPHA4+zczs+ExVCKYJOls+usQTC5djohDJYbzgTURsRZA0r0k8xU9NaDdB4DvAucdZuxmZjYMhkoELwKfL1neWLIcwKWH2PcsYH3J8gbggtIGkmYBV6f7OmgikLQIWATQ3OwZsM3MhtNQhWkuOcp9a5B1MWD5C8AtEdEjDda8GMtiYDFAe3v7wH2YmdlRKOc5giO1AZhTsjwbeGFAm3bg3jQJ1AFXSOqOiO9nGJeZmZXIMhEsBVokzQeeB64DbihtUFoGU9LdwA+cBMzMKiuzRBAR3ZJuJhkNNBa4KyKelHRjuv2OrD7bzMzKd8hEoKTf5h3AiRHxybRecVNEPHqo90bE/QyYjuJgCSAi3lNWxGZmNqzKmTzuK8ArgOvT5U6S5wPMzGwUKKdr6IKIOEfSrwEi4iVJEzKOy8zMKqScK4J96dO/AcV6BL2ZRmVmZhVTTiK4Hfge0CDpr4H/BD6daVRmZlYx5UxDfY+kx4DLSB4Se0tEPJ15ZGZmVhHljBpqBnYB/1q6LiKeyzIwMzOrjHJuFv8byf0BAZOA+cBK4LQM4zIzswopp2vo9NJlSecAf5JZRGZmVlGHXYQ+nX7aU0abmY0S5dwj+FDJ4hjgHGBzZhGZmVlFlXOPoFDyupvknsF3swnHzMwqbchEkD5IVhsRH6lQPGZmVmEHvUcgaVxE9JB0BZmZ2Sg11BXBoyRJYLmkJcC3gZ19GyPivoxjMzOzCijnHsF0YCtJXeG+5wkCcCIwMxsFhkoEDemIoRX0J4A+rhtsZjZKDJUIxgK1lFeE3szMqtRQieDFiPhkxSIxM7MRMdSTxYNdCZiZ2SgzVCK4rGJRmJnZiDloIoiIbZUMxMzMRsZhTzpnZmajixOBmVnOORGYmeWcE4GZWc45EZiZ5ZwTgZlZzjkRmJnlnBOBmVnOORGYmeVcpolA0kJJKyWtkXTrINvfIenx9OdhSWdmGY+ZmR0os0SQ1jv+MnA5cCpwvaRTBzR7BnhtRJwBfApYnFU8ZmY2uCyvCM4H1kTE2ojYC9wLXFXaICIejoiX0sVHgNkZxmNmZoPIMhHMAtaXLG9I1x3MHwE/HGyDpEWSlklatnnz5mEM0czMskwEZVc2k3QJSSK4ZbDtEbE4Itojor2+vn4YQzQzs3KK1x+pDcCckuXZwAsDG0k6A7gTuDwitmYYj5mZDSLLK4KlQIuk+ZImANcBS0obSGoG7gPeFRGrMozFzMwOIrMrgojolnQz8AAwFrgrIp6UdGO6/Q7gE8AM4CuSALojoj2rmMzM7EBZdg0REfcD9w9Yd0fJ6z8G/jjLGMzMbGh+stjMLOecCMzMcs6JwMws55wIzMxyzonAzCznnAjMzHLOicDMLOecCMzMcs6JwMws55wIzMxyzonAzCznnAjMzHLOicDMLOecCMzMcs6JwMws55wIzMxyzonAzCznnAjMzHLOicDMLOecCMzMcs6JwMws55wIzMxyzonAzCznnAjMzHLOicDMLOecCMzMcs6JwMws55wIzMxyzonAzCznnAjMzHLOicDMLOcyTQSSFkpaKWmNpFsH2S5Jt6fbH5d0TpbxmJnZgTJLBJLGAl8GLgdOBa6XdOqAZpcDLenPIuCrWcVjZmaDy/KK4HxgTUSsjYi9wL3AVQPaXAV8MxKPANMkzcwwJjMzGyDLRDALWF+yvCFdd7htkLRI0jJJyzZv3nxEwTRNncQVpzdRO3HcEb3fzGy0yvKsqEHWxRG0ISIWA4sB2tvbD9hejnPnHs+5c889kreamY1qWV4RbADmlCzPBl44gjZmZpahLBPBUqBF0nxJE4DrgCUD2iwB3p2OHroQ2B4RL2YYk5mZDZBZ11BEdEu6GXgAGAvcFRFPSrox3X4HcD9wBbAG2AW8N6t4zMxscJneOY2I+0lO9qXr7ih5HcBNWcZgZmZD85PFZmY550RgZpZzTgRmZjnnRGBmlnNK7tdWD0mbgXVH+PY6YMswhlMNfMz54GPOh6M55rkRUT/YhqpLBEdD0rKIaB/pOCrJx5wPPuZ8yOqY3TVkZpZzTgRmZjmXt0SweKQDGAE+5nzwMedDJsecq3sEZmZ2oLxdEZiZ2QBOBGZmOTcqE4GkhZJWSloj6dZBtkvS7en2xyWdMxJxDqcyjvkd6bE+LulhSWeORJzD6VDHXNLuPEk9kt5ayfiyUM4xS7pY0nJJT0r6aaVjHG5l/NueKulfJf0mPeaqnsVY0l2SNklacZDtw3/+iohR9UMy5fXvgBOBCcBvgFMHtLkC+CFJhbQLgV+OdNwVOOZXAsenry/PwzGXtPt/JLPgvnWk467A33ka8BTQnC43jHTcFTjmjwKfTV/XA9uACSMd+1Ec82uAc4AVB9k+7Oev0XhFcD6wJiLWRsRe4F7gqgFtrgK+GYlHgGmSZlY60GF0yGOOiIcj4qV08RGSanDVrJy/M8AHgO8CmyoZXEbKOeYbgPsi4jmAiKj24y7nmAMoSBJQS5IIuisb5vCJiIdIjuFghv38NRoTwSxgfcnyhnTd4bapJod7PH9E8o2imh3ymCXNAq4G7mB0KOfv3AocL+lBSY9JenfFostGOcf8JeAUkjK3TwB/GhG9lQlvRAz7+SvTwjQjRIOsGzhGtpw21aTs45F0CUkieHWmEWWvnGP+AnBLRPQkXxarXjnHPA44F7gMmAz8QtIjEbEq6+AyUs4xvxFYDlwKnAT8WNLPImJH1sGNkGE/f43GRLABmFOyPJvkm8LhtqkmZR2PpDOAO4HLI2JrhWLLSjnH3A7cmyaBOuAKSd0R8f3KhDjsyv23vSUidgI7JT0EnAlUayIo55jfC3wmkg70NZKeAU4GHq1MiBU37Oev0dg1tBRokTRf0gTgOmDJgDZLgHend98vBLZHxIuVDnQYHfKYJTUD9wHvquJvh6UOecwRMT8i5kXEPOA7wPurOAlAef+2/wW4SNI4SVOAC4CnKxzncCrnmJ8juQJCUiPQBqytaJSVNeznr1F3RRAR3ZJuBh4gGXFwV0Q8KenGdPsdJCNIrgDWALtIvlFUrTKP+RPADOAr6Tfk7qjimRvLPOZRpZxjjoinJf0IeBzoBe6MiEGHIVaDMv/OnwLulvQESbfJLRFRtdNTS/oWcDFQJ2kD8BfAeMju/OUpJszMcm40dg2ZmdlhcCIwM8s5JwIzs5xzIjAzyzknAjOznHMisGNSOlvo8pKfeUO07RqGz7tb0jPpZ/1K0iuOYB93Sjo1ff3RAdsePtoY0/30/XdZkc64Oe0Q7c+SdMVwfLaNXh4+asckSV0RUTvcbYfYx93ADyLiO5LeAHwuIs44iv0ddUyH2q+kbwCrIuKvh2j/HqA9Im4e7lhs9PAVgVUFSbWS/m/6bf0JSQfMNCpppqSHSr4xX5Suf4OkX6Tv/bakQ52gHwIWpO/9ULqvFZL+LF1XI+nf0vnvV0i6Nl3/oKR2SZ8BJqdx3JNu60p//5/Sb+jplcg1ksZKuk3SUiVzzP9JGf9ZfkE62Zik85XUmfh1+rstfRL3k8C1aSzXprHflX7Orwf772g5NNJzb/vHP4P9AD0kE4ktB75H8hT8cem2OpKnKvuuaLvS338OfCx9PRYopG0fAmrS9bcAnxjk8+4mrVcAvA34JcnkbU8ANSTTGz8JnA1cA3yt5L1T098Pknz7LsZU0qYvxquBb6SvJ5DMIjkZWAR8PF0/EVgGzB8kzq6S4/s2sDBdPg4Yl75+HfDd9PV7gC+VvP/TwDvT19NI5iCqGem/t39G9mfUTTFho8bLEXFW34Kk8cCnJb2GZOqEWUAjsLHkPUuBu9K234+I5ZJeC5wK/DydWmMCyTfpwdwm6ePAZpIZWi8DvhfJBG5Iug+4CPgR8DlJnyXpTvrZYRzXD4HbJU0EFgIPRcTLaXfUGeqvojYVaAGeGfD+yZKWA/OAx4Afl7T/hqQWkpkoxx/k898A/BdJH06XJwHNVPd8RHaUnAisWryDpPrUuRGxT9KzJCexooh4KE0UbwL+QdJtwEvAjyPi+jI+4yMR8Z2+BUmvG6xRRKySdC7JfC9/I+nfI+KT5RxEROyW9CDJ1MnXAt/q+zjgAxHxwCF28XJEnCVpKvAD4CbgdpL5dv4jIq5Ob6w/eJD3C7gmIlaWE6/lg+8RWLWYCmxKk8AlwNyBDSTNTdt8Dfg6Sbm/R4BXSerr858iqbXMz3wIeEv6nhqSbp2fSToB2BUR/wh8Lv2cgfalVyaDuZdkorCLSCZTI/39vr73SGpNP3NQEbEd+CDw4fQ9U4Hn083vKWnaSdJF1ucB4ANKL48knX2wz7D8cCKwanEP0C5pGcnVwW8HaXMxsFzSr0n68b8YEZtJTozfkvQ4SWI4uZwPjIhfkdw7eJTknsGdEfFr4HTg0bSL5mPAXw3y9sXA4303iwf4d5K6tD+JpPwiJHUingJ+paRo+f/mEFfsaSy/IZma+W9Jrk5+TnL/oM9/AKf23SwmuXIYn8a2Il22nPPwUTOznPMVgZlZzjkRmJnlnBOBmVnOORGYmeWcE4GZWc45EZiZ5ZwTgZlZzv1/mcHTWSOGiM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "final_func(data['Type of Meeting'],data['Type of Meeting.1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "LABELS:\n",
    "\n",
    "Div = Dividend\n",
    "Percent = Dividend percentage (DP)\n",
    "OfferV = Dividend Offer Value\n",
    "FaceV = Share Face Value (‘FV’)\n",
    "DivExDate = Dividend Ex- Date\n",
    "AnnDate = Dividend Announce Date\n",
    "Dec = Divided Decision\n",
    "Ratio = Bonus/ Right Ratio\n",
    "BonusExDate = Bonus/Right Ex-date\n",
    "BonusReq = Bonus Require\n",
    "BonusProv = Bonus Provided \n",
    "BonusDec = Bonus Decision\n",
    "SDate = Meeting Scheduled Date\n",
    "Int = Intimation/Confirmed\n",
    "Type = Type of Meeting \n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.DataFrame({\"entity name\":[\"Meeting Scheduled (SDate) \", \"Intimation/Confirmed (Int)\", \"Type of Meeting (Type)\"],\"precision\":[1.0, 1.0, 1.0],\"recall\":[0.7777777777777778,0.7058823529411765,0.7368421052631579],\"f1_score\":[0.875,0.8275862068965517,0.8484848484848484],\"average precision (accuracy) %\":[97.77,95.58,98.68]}) "
   ]
  },
  {
   "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>entity name</th>\n",
       "      <th>precision</th>\n",
       "      <th>recall</th>\n",
       "      <th>f1_score</th>\n",
       "      <th>average precision (accuracy) %</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Meeting Scheduled (SDate)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>97.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Intimation/Confirmed (Int)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>95.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Type of Meeting (Type)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.736842</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>98.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  entity name  precision    recall  f1_score  \\\n",
       "0  Meeting Scheduled (SDate)         1.0  0.777778  0.875000   \n",
       "1  Intimation/Confirmed (Int)        1.0  0.705882  0.827586   \n",
       "2      Type of Meeting (Type)        1.0  0.736842  0.848485   \n",
       "\n",
       "   average precision (accuracy) %  \n",
       "0                           97.77  \n",
       "1                           95.58  \n",
       "2                           98.68  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ML_Project_G6 - Testing_Bonus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.read_csv(r'data/ML_Project_G6 - Testing_Bonus.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary\n",
      "Bonus Require \n",
      "Bonus Provided\n",
      "Bonus/ Right Ratio\n",
      "Bonus Decision\n",
      "Predicted Bonus Require \n",
      "Predicted Bonus Provided\n",
      "Predicted Bonus Decision\n",
      "Bonus/ Right Ratio.1\n",
      "divdent face value \n",
      "divident offer value\n",
      "divident%\n"
     ]
    }
   ],
   "source": [
    "for i in data2.columns:\n",
    "    print(i)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Summary</th>\n",
       "      <th>Bonus Require</th>\n",
       "      <th>Bonus Provided</th>\n",
       "      <th>Bonus/ Right Ratio</th>\n",
       "      <th>Bonus Decision</th>\n",
       "      <th>Predicted Bonus Require</th>\n",
       "      <th>Predicted Bonus Provided</th>\n",
       "      <th>Predicted Bonus Decision</th>\n",
       "      <th>Bonus/ Right Ratio.1</th>\n",
       "      <th>divdent face value</th>\n",
       "      <th>divident offer value</th>\n",
       "      <th>divident%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ITC Ltd has informed BSE that the Board of Dir...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GM Breweries Ltd has informed BSE that the Boa...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Summary  Bonus Require   \\\n",
       "0  ITC Ltd has informed BSE that the Board of Dir...               1   \n",
       "1  GM Breweries Ltd has informed BSE that the Boa...               0   \n",
       "\n",
       "   Bonus Provided  Bonus/ Right Ratio  Bonus Decision  \\\n",
       "0               1                   0               1   \n",
       "1               0                   1               1   \n",
       "\n",
       "   Predicted Bonus Require   Predicted Bonus Provided  \\\n",
       "0                         1                         1   \n",
       "1                         1                         1   \n",
       "\n",
       "   Predicted Bonus Decision  Bonus/ Right Ratio.1  divdent face value   \\\n",
       "0                         1                     0                  NaN   \n",
       "1                         1                     0                  NaN   \n",
       "\n",
       "   divident offer value  divident%  \n",
       "0                   NaN        NaN  \n",
       "1                   NaN        NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision :-  1.0  recall :-  0.75  f1_score :-  0.8571428571428571\n",
      "average precision (accuracy):- 100.0 %\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/samyak/anaconda3/lib/python3.8/site-packages/sklearn/metrics/_ranking.py:803: UndefinedMetricWarning: No negative samples in y_true, false positive value should be meaningless\n",
      "  warnings.warn(\"No negative samples in y_true, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "#final_func(data2['Bonus Provided'],data2['Predicted Bonus Provided'])\n",
    "#final_func(data2['Bonus/ Right Ratio'],data2['Bonus/ Right Ratio.1'])\n",
    "#-------------------ERROR------------------------------------\n",
    "final_func(data2['Bonus Decision'],data2['Predicted Bonus Decision'])\n",
    "#final_func(data2['Bonus Require'],data2['Predicted Bonus Require'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}