analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a3e20b30-0be3-42c8-88e4-454fe6407ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data successfully loaded!\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Group</th>\n",
       "      <th>Income</th>\n",
       "      <th>Monthly_Housing_Cost</th>\n",
       "      <th>Num_Children</th>\n",
       "      <th>Own_Car</th>\n",
       "      <th>Own_Housing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>288363</th>\n",
       "      <td>1</td>\n",
       "      <td>100000</td>\n",
       "      <td>3199.13</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>64982</th>\n",
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       "      <td>100000</td>\n",
       "      <td>3452.20</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "        Group  Income  Monthly_Housing_Cost  Num_Children  Own_Car  \\\n",
       "288363      1  100000               3199.13             1        1   \n",
       "64982       0  100000               3452.20             0        0   \n",
       "227641      0   80000               3033.16             1        0   \n",
       "137672      0   70000               2507.21             3        0   \n",
       "12758       0   70000               2473.60             1        0   \n",
       "\n",
       "        Own_Housing  \n",
       "288363            1  \n",
       "64982             1  \n",
       "227641            0  \n",
       "137672            1  \n",
       "12758             0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#load data, separate features \n",
    "data = pd.read_csv(\"synthetic_credit_card_approval.csv\")\n",
    "X = data.drop([\"Target\"], axis=1)\n",
    "y = data[\"Target\"]\n",
    "\n",
    "# Break into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)\n",
    "\n",
    "# Preview the data\n",
    "print(\"Data successfully loaded!\\n\")\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af622bae-1d1d-4ade-aacd-733087bfd6ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train model and make preds \n",
    "from sklearn import tree \n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "import matplotlib as plt \n",
    "#classifier\n",
    "clf = tree.DecisionTreeClassifier(random_state=0, max_depth=3)\n",
    "#fit data to clf\n",
    "clf.fit(X_train, y_train)\n",
    "#get predictions from clf \n",
    "preds = clf.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "05129a11-1d7e-4f1b-822e-859afacea55f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total approvals: 69869\n",
      "Group A: 21930 (31.39% of approvals)\n",
      "Group B: 47939 (68.61% of approvals)\n",
      "\n",
      "Overall accuracy: 94.37%\n",
      "Group A: 91.1%\n",
      "Group B: 97.63%\n",
      "\n",
      "Sensitivity / True positive rate:\n",
      "Group A: 83.17%\n",
      "Group B: 97.93%\n",
      "Total approvals: 69869\n",
      "Group A: 60396 (86.44% of approvals)\n",
      "Group B: 9473 (13.56% of approvals)\n",
      "\n",
      "Overall accuracy: 94.37%\n",
      "Group A: 94.34%\n",
      "Group B: 94.57%\n",
      "\n",
      "Sensitivity / True positive rate:\n",
      "Group A: 92.69%\n",
      "Group B: 93.13%\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SmZk1ROX14CjqpP7ldLP7FXAAWR8/IuJZYO9mLJOZWVlE1g+6nKWIyurFERFvr/CkdEnzFMfMrHEKGnvLUk6AflvSHkBIWg04k6wDt5lZq2vr80GfApxGNrHHDLKp+E5rxjKZmZWl3JnsihrDG6xBR8Rs4FstUBYzs0arLmr0LUM5vTg2k/QXSbMkzZQ0WtJmLVE4M7OGNNV0o3lUThPHLcDtZGPVvwTcAdzanIUyMytH1oujvKWIygnQa0bEjRGxOC03Ae2bu2BmZg0qs/Zc1Bp0fXNxdE2r96YXHI4im5vjCBo54YeZWXMpaOwtS30PCSeTBeSa2/9uyb4AzmuuQpmZlauoteNy1DcXx6YtWRAzs8YSUF3UBuYylDWSUNL2ZK8KX9b2HBEjm6tQZmblqtzwXEaAlnQBMJAsQI8FDgQeAxygzaxVSY16J2HhlNOLYygwCPhXRJwA9CF7QaKZWatr0yMJgU8iYqmkxZI6AzNZ/u20Zmatpk0+JCwxKb3t9vdkPTsWAk82Z6HMzMpVwfG5rLk4vpdWr5F0H9A5vWDRzKxVSWqbvTgk9a1vX0Q80zxFMjMrX1tt4vi/evYFsG8Tl8XsC+uy6/DWLoK1knJ6OhRVfQNV9mnJgpiZNZZouzVoM7Pcq+AmaAdoMysuyUO9zcxyq4Ljc1lvVJGkYyT9JG1vLKl/8xfNzKxhlTySsJwHoFcDuwNHpe0FwFXNViIzszJlb1RRWUsRldPEsVtE9JU0BSAi5klavZnLZWZWljbZza7EvyVVk/V9RlJ3YGmzlsrMrEwFrRyXpZwAfSVwN7CepEvIZrc7v1lLZWZWhjY71LtGRNwsaTLZlKMCDo+Il5q9ZGZmZajg+FzWhP0bAx8DfylNi4i3mrNgZmYNqXlIWKnKaeL4K5+/PLY9sCnwCrBdM5bLzKwsFRyfy2ri2KF0O81y9706spuZtRy18SaOFUXEM5J2a47CmJk1lir4tbHljCQ8u2T5vqRbgBktUDYzs3oJaFdV3lLveaSekh6S9KKkaZLOTOldJY2X9Gr67JLSJelKSdMlPVc6f76k41L+VyUdV5K+i6Tn0zFXqoxp+Mrp492pZFmDrE36sDKOMzNrdpLKWhqwGDgnIrYFBgCnSdoW+CHwQET0Bh5I2wAHAr3TcjLw21SWrsAFwG5Af+CCmqCe8pxUctyQhgpVbxNHGqDSKSK+39CJzMxaWtaLY9XPExHvAe+l9QWSXgI2JKuMDkzZbgAmAD9I6SMjIoC/S1pH0gYp7/iImAsgaTwwRNIEstcF/j2ljwQOB+6tr1z1vfKqXUQslrTnF7hfM7Pm17iJkLpJmlSyfW1EXLvSKaVewM7AU0CPFLwB/gX0SOsbAm+XHPZOSqsv/Z1a0utVXw16ItAXmCppDHAH8FHNzoj4U0MnNzNrbo3oBz07IvrVl0HSWsBdwFkR8WFp00hEhKT4wgX9AsrpxdEemEP2DsKa/tABOECbWasSUN1EsyVJWo0sON9cUgF9X9IGEfFeasKYmdLfBXqWHL5RSnuXz5tEatInpPSNaslfr/pubT1JZwMvAM+nz2np84WGTmxm1vxEVZlLvWfJqsrXAS9FxC9Ldo0BanpiHAeMLkkflnpzDAA+SE0h44DBkrqkh4ODgXFp34eSBqRrDSs5V53qq0FXA2tBrXfWotV8M7PaZC+NbZJT7QkcCzwvaWpK+2/gUuB2SScCbwLfTPvGAgcB08mmwjgBICLmSvop8HTKd1HNA0OyAX7XAx3IHg7W+4AQ6g/Q70XEReXcmZlZq2iikYQR8Ri1V0YhmyhuxfwBnFbHuUYAI2pJnwRs35hy1RegK3d4jplVjLY6WdJKfzXMzPKkCZs4cqnOAF3SbmJmllttesJ+M7O8En4noZlZPoly5tkoLAdoMyu0yg3PDtBmVmB+5ZWZWY5Vbnh2gDazQhNV7sVhZpY/7sVhZpZj7sVhZpZTlRueHaDNrMjcD9rMLJ8EVDtAm5nlU+WGZwdoMyu4Cq5AO0CbWXFl3ewqN0I7QJtZobkGbWaWS0KuQZuZ5Y97cZiZ5ZXcxGFmllsO0GZmOeU2aDOzHMom7G/tUjQfB2gzKzS/UcXMLKfcxGG5dcbFNzP+8Wl069KJR285D4B5H3zESedfz1vvzWXjDbryh0tOYJ3Oa/LqG+9zxsU389wrb/PfpxzCad8aBMC778/jtAtvZNbcBUji2MP34LtHDGzFu6pMVVXioZHn8t7MDzjy7GsYe+1ZrNWxPQDdunTimWlvcMx//Z7TjxnENw7cFYB21VVs2Wt9thj8Q+Z/+DGd1+rAlecfzTabb0AEnP7Tm3n6+dc5bNDO/ODkg9iqVw8GHf8Lpr70VmveaotxE8cqkHQ4cDewTUS83JzXaquOPHg3Thy6N8MvumlZ2pUj/8Zeu27JmcP254qR47ly5Hh+Mvww1um8Jj87++uMffj55c5RXV3FhWd8lT5b92ThR4sYdPzPGdh/K7badIOWvp2KdsqR+/CP19+nUwrKB538q2X7brjsO4x9+DkAfn3TA/z6pgcAGLLX9px61D7M//BjAC49ZygPPPkix//wOlZrV02H9qsD8NI/ZzDs3N9z+XlHteAd5UFlD1Rp7rfFHAU8lj5XmSTX+Fewx85b0KXzmsul3fvo8xxxUH8AjjioP2MfyQJy966d2HnbTVit3fK/9vW7rU2frXsCsFbH9mzZqwfvzfygBUrfdnxpvXUY/OXtGDn6iZX2derYnr37bbksQJf6+uB+3HX/ZAA6d2zPHjtvzo2jnwTg34uX8OHCTwD4xxvvM/3Nmc14BzmV+kGXsxRRswVoSWsBXwZOBI5MaQMlPSLpr5JekXSNpKq0b6GkyyVNk/SApO4pfYKkX0maBJwpaZCkKZKelzRC0hqShki6o+TaAyXdk9YHS3pS0jOS7kjlqmiz5i5g/W5rA9Bj3c7Mmrug7GPfmjGH5//xLrtsv0lzFa9N+tnZX+eCK//M0qWx0r6DvrIjDz/9Cgs+WrRceoc1VmPQ7tsw5sGpAGy84brMnr+Qqy44hodv+gFX/Oho1kw16LZMZS5F1Jw16MOA+yLiH8AcSbuk9P7A6cC2wObA11J6R2BSRGwHPAxcUHKu1SOiH3AVcD1wRETsQNZEcyrwN2A3SR1T/iOAUZK6AecD+0VEX2AScHZthZV0sqRJkibNnjVr1e8+JySVXXtY+PGnnHDedVx81tfo1LFD8xasDTngy9sze94Cnn357Vr3Dz1gF+4aN3ml9CF778BTz722rHmjXXU1fbbqyYg7H+Urx1zGx4s+5azj92/WsuddzVDvcpYias4AfRQwKq2P4vNmjokR8VpELAFuJatlAywFbkvrN5WkU5K+FfB6CvoANwB7R8Ri4D7g0NQMcjAwGhhA9ofgcUlTgeOAWquGEXFtRPSLiH7dunf/grecD927duJfs7Mmin/N/oBuXTo1eMy/Fy/hhPOuY+gB/Thknz7NXcQ2Zbc+mzFkrx14dvSFXPezE9hr1y353UXDAOi6dkf6btuL+x9/YaXjvrb/8oF7xsx5zJg5n8nT3gRgzANT6bNVz5a5iTyr4Cp0s7TpSuoK7AvsICmAaiCAv6bPUit/51s5/aMyLjsKGA7MJauJL1D2srLxEdGmnpwM2Wt7bhs7kTOH7c9tYydy4F471Js/IjjrklvYslcPTj163xYqZdtx0VVjuOiqMQDs2bc3px8ziO/+ZCQAhw3amXGPvcCnny1e7pjOHduzZ98t+O5PbliWNnPOAt59fx5bbLIe09+cyd67bsUrr/+r5W4kp/yQsPGGAjdGxCYR0SsiegKvA3sB/SVtmtqejyB7iFhTlqFp/eiS9FKvAL0kbZG2jyVrDiF99gVO4vOa+9+BPWvyS+ooacumusk8OPnH13PgSZcz/c332fHQH3PTmCc5Y9j+PDzxFfoP/SmPTHyFM4btB8D7cz5kx0N/zG9vfYhf/vF+djz0xyz46BOeevY1br/3aR6b9CoDj72MgcdexvgnprXynbUNXxu8C3fdP2ml9IP36cNDT73Mx4s+Wy793F/cwbUXHc9jt5zHDltuyP/9cVyWf+COvHDPT9l1h17cdvkp3HnlaS1S/jyo5IeEiqirArsKJ5UeAi6LiPtK0s4gay+eBSwAtgAeAr4XEUslLQSuBQYDM8namWdJmgB8PyImpfMMAn5BVvt/Gjg1Ij5N+34DHA+sFxEfp7R9gcuANVJRzo+IMfWVv+8u/eKRJyau8s/BWk73AWe0dhGskRZNvWpyerb0hW2zw84xcvSEsvL233ydVb5eS2uWJo6I2KeWtCslPUcWbA+p47iVHuBFxMAVth8Adq7j+OFkzRylaQ8Cu5ZdeDMrloLWjsvhfsVmVlhSZc/F0dwDVZYTERPqqT1XfP9kM2t6TdWJI42rmCnphZK0rpLGS3o1fXZJ6ZJ0paTpkp6T1LfkmONS/lclHVeSvksavzE9HdtgsVo0QJuZNbmm62Z3PTBkhbQfAg9ERG/ggbQNcCDQOy0nA7+FZT3YLgB2IxvzcUFNUE95Tio5bsVrrcQB2swKTGX/15CIeISsm26pw8jGW5A+Dy9JHxmZvwPrSNoAOICsa+/ciJgHjAeGpH2dI+LvkfXMGFlyrjq5DdrMCq0RTdDd0pQRNa6NiGsbOKZHRLyX1v8F9EjrGwKlQ0PfSWn1pb9TS3q9HKDNrLBEowL07FXpZhcRkQbetRg3cZhZoTVVE0cd3k/NE6TPmikD3wVKx9lvlNLqS9+olvR6OUCbWaE180jCMWRz+JA+R5ekD0u9OQYAH6SmkHHAYEld0sPBwcC4tO9DSQNS741hJeeqk5s4zKzQmqoXtKRbgYFkbdXvkPXGuBS4XdKJwJvAN1P2scBBwHTgY+AEgIiYK+mnZKOcAS6KiJoHj98j6ynSAbg3LfVygDaz4mrCmerqmVRtUC15A6h1wpOIGAGMqCV9ErB9Y8rkAG1mhVbJs9k5QJtZYfmlsWZmeeYAbWaWT27iMDPLqQqezM4B2syKrYLjswO0mRVcBUdoB2gzK6xKn7DfAdrMCq1yw7MDtJkVXQVHaAdoMyuwVZqpLvccoM2s0Cq4CdoB2syKq5ET9heOA7SZFZqbOMzMcso1aDOznKrg+OwAbWYFtmqvs8o9B2gzK7jKjdAO0GZWWJ6w38wsx9zEYWaWU+5mZ2aWV5Ubnx2gzazYKjg+O0CbWXHJ3ezMzPJLFRyhHaDNrNAqNzw7QJtZwVVwBdoB2syKzBP2m5nlkueDNjPLMQdoM7OcchOHmVkeuR+0mVk+CXezMzPLrwqO0A7QZlZoboM2M8spT9hvZpZXDtBmZvnkJg4zsxyq9JGEiojWLkPuSJoFvNna5Wgm3YDZrV0Ia5RK/Z1tEhHdV+UEku4j+/mUY3ZEDFmV67U0B+g2RtKkiOjX2uWw8vl31nZVtXYBzMysdg7QZmY55QDd9lzb2gWwRvPvrI1yG7SZWU65Bm1mllMO0GZmOeUAXXCSlkiaKmmapGclnSPJv9dWJulwSSFp69YuixWX/0cuvk8iYqeI2A7YHzgQuKCVy2RwFPBY+lxlkjzqtw1ygK4gETETOBkYrky1pJ9LelrSc5K+CyBpoKQJku6U9LKkm1P+fSX9ueZ8kvaXdHcr3U5hSVoL+DJwInBkShso6RFJf5X0iqRrar7pSFoo6fL0LegBSd1T+gRJv5I0CThT0iBJUyQ9L2mEpDUkDZF0R8m1B0q6J60PlvSkpGck3ZHKZQXiAF1hIuI1oBpYjyxAfBARuwK7AidJ2jRl3Rk4C9gW2AzYE3gI2LomQAAnACNarvQV4zDgvoj4BzBH0i4pvT9wOtnPfHPgaym9IzApfQt6mOW/Aa2eRhFeBVwPHBERO5DNo3Mq8DdgN0kdU/4jgFGSugHnA/tFRF9gEnB2c9ysNR8H6Mo2GBgmaSrwFLAu0DvtmxgR70TEUmAq0CuyPpc3AsdIWgfYHbi3pQtdAY4CRqX1UXzezDExIl6LiCXArWS1bIClwG1p/aaSdErStwJeT0Ef4AZg74hYDNwHHJqaQQ4GRgMDyP4QPJ5+/8cBmzTZHVqLcLtWhZG0GbAEmEk22dfpETFuhTwDgU9Lkpbw+b+FPwJ/ARYBd6QAYGWS1BXYF9hBUpB9mwngr+mzVF2DEErTPyrjsqOA4cBcspr4AkkCxkdEk7SBW+twDbqCpKaJa4DfpNrwOOBUSaul/VuWfBWuVUTMAGaQfT3+YzMXuRINBW6MiE0ioldE9AReB/YC+kvaNLU9H0H2EBGy/w+HpvWjS9JLvQL0krRF2j6WrDmE9NkXOInPa+5/B/asyS+po6Qtm+omrWU4QBdfh5pudmTtkfcDF6Z9fwBeBJ6R9ALwO8r71nQz8HZEvNQcBa5wRwErPli9K6U/DfwGeIksaNfk+4gseL9AVvu+aMWTRsQismcCd0h6nqxZ5Jq0bwlwD1kPnntS2izgeOBWSc8BTwLu8lcwHuptK5H0G2BKRFzX2mWpFKlZ6fsRcUgt+xZGhHtY2ErcBm3LkTSZrEZ3TmuXxaytcw3azCyn3AZtZpZTDtBmZjnlAG1mllMO0PaFlMyi90Ka52HNVTjX9ZKGpvU/SNq2nrwDJe3xBa7xRhr+XFb6CnkWNvJa/yPp+40to9mKHKDti6qZRW974DPglNKdX3T2tYj4TkS8WE+WgUCjA7RZETlAW1N4FNgi1W4flTQGeLGe2fQk6TdpVre/kU3sRNo3QVK/tD4kzcT2bJrlrRfZH4L/TLX3vSR1l3RXusbTkvZMx64r6f40Q9wfyIa910vSnyVNTsecvMK+2mab21zSfemYR+W5n62JuR+0rZJUUz6QbMIeyIYcbx8Rr6cg90FE7CppDbKJe+4nm0lvK7LJfHqQjXYcscJ5uwO/J5sQ6HVJXSNirqRrgIUR8YuU7xbg8oh4TNLGZMPbtyGbEe6xiLhI0sFkM/s15NvpGh2ApyXdFRFz+Hy2uf+U9JN07uFkL3M9JSJelbQbcDXZSECzJuEAbV9UhzRLGmQ16OvImh4mRsTrKX0wsGNN+zKwNtlsensDt6YhyjMkPVjL+QcAj9ScKyLm1lGO/YBts7mBAOic5j3emzSdZ0T8VdK8Mu7pDElfTes9U1nnsPJsc39K19iDbOh1zfFrlHENs7I5QNsX9UlE7FSakAJV6exrdc2md1ATlqMKGJDmqlixLGVLQ7H3A3aPiI8lTQDa15E90nXnr/gzMGtKboO25lTXbHqPAEekNuoNgH1qOfbvwN5KLxhI03gCLAA6leS7n2wSfFK+ndLqI2QzwyHpQKBLA2VdG5iXgvPWZDX4GivNNhcRHwKvS/pGuoYk9WngGmaN4gBtzamu2fTuBl5N+0aSzbS2nDQb28lkzQnP8nkTw1+Ar9Y8JATOAPqlh5Av8nlvkgvJAvw0sqaOtxoo631AO0kvAZeS/YGoUddsc98CTkzlm0b2JhWzJuO5OMzMcso1aDOznHKANjPLKQdoM7OccoA2M8spB2gzs5xygDYzyykHaDOznPr/Cgc/rZef+IkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Function to plot confusion matrix\n",
    "def plot_confusion_matrix(estimator, X, y_true, y_pred, display_labels=[\"Deny\", \"Approve\"],\n",
    "                          include_values=True, xticks_rotation='horizontal', values_format='',\n",
    "                          normalize=None, cmap=plt.cm.Blues):\n",
    "    cm = confusion_matrix(y_true, y_pred, normalize=normalize)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels)\n",
    "    return cm, disp.plot(include_values=include_values, cmap=cmap, xticks_rotation=xticks_rotation,\n",
    "                     values_format=values_format)\n",
    "\n",
    "\n",
    "\n",
    "# Function to evaluate the fairness of the model\n",
    "def get_stats(X, y, model, group_one, preds):\n",
    "        \n",
    "    y_zero, preds_zero, X_zero = y[group_one==False], preds[group_one==False], X[group_one==False]\n",
    "    y_one, preds_one, X_one = y[group_one], preds[group_one], X[group_one]\n",
    "    \n",
    "    print(\"Total approvals:\", preds.sum())\n",
    "    print(\"Group A:\", preds_zero.sum(), \"({}% of approvals)\".format(round(preds_zero.sum()/sum(preds)*100, 2)))\n",
    "    print(\"Group B:\", preds_one.sum(), \"({}% of approvals)\".format(round(preds_one.sum()/sum(preds)*100, 2)))\n",
    "    \n",
    "    print(\"\\nOverall accuracy: {}%\".format(round((preds==y).sum()/len(y)*100, 2)))\n",
    "    print(\"Group A: {}%\".format(round((preds_zero==y_zero).sum()/len(y_zero)*100, 2)))\n",
    "    print(\"Group B: {}%\".format(round((preds_one==y_one).sum()/len(y_one)*100, 2)))\n",
    "    \n",
    "    cm_zero, disp_zero = plot_confusion_matrix(model, X_zero, y_zero, preds_zero)\n",
    "    disp_zero.ax_.set_title(\"Group A\")\n",
    "    cm_one, disp_one = plot_confusion_matrix(model, X_one, y_one, preds_one)\n",
    "    disp_one.ax_.set_title(\"Group B\")\n",
    "    \n",
    "    print(\"\\nSensitivity / True positive rate:\")\n",
    "    print(\"Group A: {}%\".format(round(cm_zero[1,1] / cm_zero[1].sum()*100, 2)))\n",
    "    print(\"Group B: {}%\".format(round(cm_one[1,1] / cm_one[1].sum()*100, 2)))\n",
    "#for group=1     \n",
    "get_stats(X_test, y_test, clf, X_test[\"Group\"] == 1, preds)\n",
    "#for group = numChildren > 1 \n",
    "get_stats(X_test, y_test, clf, X_test[\"Num_Children\"] == 0, preds)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}