diff --git a/P4/Jupyter_Notebook_Setup_P4.ipynb b/P4/Jupyter_Notebook_Setup_P4.ipynb
index 7c52fad9..091e9587 100644
--- a/P4/Jupyter_Notebook_Setup_P4.ipynb
+++ b/P4/Jupyter_Notebook_Setup_P4.ipynb
@@ -321,10 +321,7 @@
    "id": "79228d8e",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "#Eliminating the student id and gender\n",
-    "test_data =test_data.drop(columns=['Student ID', 'Gender'])"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -357,7 +354,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "aa56f0ae",
+   "id": "53ef1752",
    "metadata": {},
    "outputs": [
     {
@@ -404,7 +401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 165,
    "id": "d27a24a9",
    "metadata": {},
    "outputs": [
@@ -415,7 +412,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [152]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Plotting the distribution of the test dataset (Gender)\u001b[39;00m\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGender\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m90\u001b[39m) \n",
+      "Input \u001b[0;32mIn [165]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Plotting the distribution of the test dataset (Gender)\u001b[39;00m\n\u001b[1;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcountplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGender\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_data\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxticks(rotation\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m90\u001b[39m) \n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m     44\u001b[0m     )\n\u001b[1;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/seaborn/categorical.py:3598\u001b[0m, in \u001b[0;36mcountplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)\u001b[0m\n\u001b[1;32m   3595\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3596\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot pass values for both `x` and `y`\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 3598\u001b[0m plotter \u001b[38;5;241m=\u001b[39m \u001b[43m_CountPlotter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3599\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3600\u001b[0m \u001b[43m    \u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mci\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_boot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3601\u001b[0m \u001b[43m    \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpalette\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaturation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3602\u001b[0m \u001b[43m    \u001b[49m\u001b[43merrcolor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcapsize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdodge\u001b[49m\n\u001b[1;32m   3603\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3605\u001b[0m plotter\u001b[38;5;241m.\u001b[39mvalue_label \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   3607\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/seaborn/categorical.py:1584\u001b[0m, in \u001b[0;36m_BarPlotter.__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, estimator, ci, n_boot, units, seed, orient, color, palette, saturation, errcolor, errwidth, capsize, dodge)\u001b[0m\n\u001b[1;32m   1579\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[1;32m   1580\u001b[0m              estimator, ci, n_boot, units, seed,\n\u001b[1;32m   1581\u001b[0m              orient, color, palette, saturation, errcolor,\n\u001b[1;32m   1582\u001b[0m              errwidth, capsize, dodge):\n\u001b[1;32m   1583\u001b[0m     \u001b[38;5;124;03m\"\"\"Initialize the plotter.\"\"\"\u001b[39;00m\n\u001b[0;32m-> 1584\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mestablish_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1585\u001b[0m \u001b[43m                             \u001b[49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhue_order\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munits\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1586\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[1;32m   1587\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestimate_statistic(estimator, ci, n_boot, seed)\n",
@@ -589,6 +586,34 @@
     "plt.show()\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "id": "1833a922",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"['Student ID', 'Gender'] not found in axis\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [164]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m#Eliminating the student id and gender\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m test_data \u001b[38;5;241m=\u001b[39m\u001b[43mtest_data\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mStudent ID\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGender\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/util/_decorators.py:311\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    306\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    307\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39marguments),\n\u001b[1;32m    308\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    309\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mstacklevel,\n\u001b[1;32m    310\u001b[0m     )\n\u001b[0;32m--> 311\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py:4954\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4806\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mself\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m   4807\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[1;32m   4808\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4815\u001b[0m     errors: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   4816\u001b[0m ):\n\u001b[1;32m   4817\u001b[0m     \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4818\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   4819\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4952\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   4953\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4954\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4955\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4956\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4957\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4958\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4959\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4960\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4961\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4962\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/generic.py:4267\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4265\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4266\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4267\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4269\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4270\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/generic.py:4311\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, consolidate, only_slice)\u001b[0m\n\u001b[1;32m   4309\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4310\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4311\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4312\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4314\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4315\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6644\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6642\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   6643\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 6644\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   6645\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   6646\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
+      "\u001b[0;31mKeyError\u001b[0m: \"['Student ID', 'Gender'] not found in axis\""
+     ]
+    }
+   ],
+   "source": [
+    "#Eliminating the student id and gender\n",
+    "test_data =test_data.drop(columns=['Student ID', 'Gender'])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -686,7 +711,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "f8048f5e",
+   "id": "f6c83eba",
    "metadata": {},
    "outputs": [
     {
@@ -733,7 +758,7 @@
   {
    "cell_type": "code",
    "execution_count": 153,
-   "id": "72c1fc78",
+   "id": "53e9599a",
    "metadata": {},
    "outputs": [
     {
@@ -749,21 +774,19 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 154,
-   "id": "59a603ff",
+   "cell_type": "markdown",
+   "id": "15bdf93f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Starting fairness evaluation seciton\n"
-     ]
-    }
-   ],
    "source": [
-    "print (\"Starting fairness evaluation seciton\")"
+    "# Starting fairness evaluation seciton"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd82eb26",
+   "metadata": {},
+   "source": [
+    "# The Classification Report includes precision, recall, and F1-score. The results show that precision for class 1 and 0 are balanced, and the F1(the mean of the precision and recall) is balanced as well for both classes 1 and 0, thus this classification report suggests a well-performing model that has room for further improvments, this conclusion was reached because of the distribution of false positives and false negatives. "
    ]
   },
   {
@@ -789,7 +812,8 @@
     }
    ],
    "source": [
-    "# Classification Report\n",
+    "\n",
+    "\n",
     "from sklearn.metrics import classification_report\n",
     "report = classification_report(Y_true, prediction)\n",
     "print(report)\n",
@@ -864,19 +888,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "3b8f50d5",
+   "cell_type": "markdown",
+   "id": "ac92ce1f",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "\n"
+    "# The True positives comparisons for the features: Gender, Age, Major. (Equality of opportunity)\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 76,
-   "id": "85ec00cd",
+   "id": "1f4f01de",
    "metadata": {},
    "outputs": [
     {
@@ -923,8 +945,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "id": "c5b9baa3",
+   "execution_count": 166,
+   "id": "95c6893f",
    "metadata": {},
    "outputs": [
     {
@@ -949,6 +971,8 @@
     "prediction = model.predict(x_test)\n",
     "\n",
     "# Add the 'Prediction' column to evaluation_using_major\n",
+    "evaluation_using_major = test_data.copy()\n",
+    "\n",
     "evaluation_using_major['Prediction'] = prediction\n",
     "\n",
     "# Filterring rows where 'Good Candidate' is 1 \n",
@@ -961,7 +985,7 @@
   {
    "cell_type": "code",
    "execution_count": 156,
-   "id": "665c2f28",
+   "id": "741f978e",
    "metadata": {},
    "outputs": [
     {
@@ -1000,10 +1024,18 @@
     "print(tpr_age)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "9c0fab85",
+   "metadata": {},
+   "source": [
+    "# The Precision Scores for Gender, Age and Major (The predicitve Parity where the precision of groups is compared)"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 157,
-   "id": "fbd2b374",
+   "execution_count": 169,
+   "id": "905ce3da",
    "metadata": {},
    "outputs": [
     {
@@ -1029,6 +1061,8 @@
     "prediction = model.predict(x_test) \n",
     "\n",
     "# Add the 'Prediction' column to evaluation_using_major\n",
+    "evaluation_using_major = test_data.copy()\n",
+    "\n",
     "evaluation_using_major['Prediction'] = prediction\n",
     "\n",
     "# Calculate precision by 'Major'\n",
@@ -1042,8 +1076,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
-   "id": "13831f9d",
+   "execution_count": 172,
+   "id": "9f4bfbb8",
    "metadata": {},
    "outputs": [
     {
@@ -1053,7 +1087,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [158]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m evaluation_using_major[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m prediction\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# Calculate precision by 'Major'\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m precision_major \u001b[38;5;241m=\u001b[39m \u001b[43mevaluation_using_major\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGender\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: precision_score(x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGood Candidate\u001b[39m\u001b[38;5;124m'\u001b[39m], x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m], zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     12\u001b[0m )\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecision by Gender:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(precision_gender)\n",
+      "Input \u001b[0;32mIn [172]\u001b[0m, in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m evaluation_using_gender[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m prediction\n\u001b[1;32m     11\u001b[0m \u001b[38;5;66;03m# Calculate precision by 'Major'\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m precision_gender \u001b[38;5;241m=\u001b[39m \u001b[43mevaluation_using_gender\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGender\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mapply(\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mlambda\u001b[39;00m x: precision_score(x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGood Candidate\u001b[39m\u001b[38;5;124m'\u001b[39m], x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m], zero_division\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     14\u001b[0m )\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrecision by Gender:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mprint\u001b[39m(precision_gender)\n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py:7718\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[1;32m   7713\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[1;32m   7715\u001b[0m \u001b[38;5;66;03m# https://github.com/python/mypy/issues/7642\u001b[39;00m\n\u001b[1;32m   7716\u001b[0m \u001b[38;5;66;03m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[39;00m\n\u001b[1;32m   7717\u001b[0m \u001b[38;5;66;03m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[39;00m\n\u001b[0;32m-> 7718\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   7719\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7720\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7721\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7722\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7723\u001b[0m \u001b[43m    \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7724\u001b[0m \u001b[43m    \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7725\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7726\u001b[0m \u001b[43m    \u001b[49m\u001b[43msqueeze\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore[arg-type]\u001b[39;49;00m\n\u001b[1;32m   7727\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7728\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   7729\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/groupby/groupby.py:882\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[1;32m    879\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    880\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgroupby\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgrouper\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_grouper\n\u001b[0;32m--> 882\u001b[0m     grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    883\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    884\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    885\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    886\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    887\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    888\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    889\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmutated\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmutated\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    890\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    891\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    893\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m    894\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
       "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/groupby/grouper.py:882\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[1;32m    880\u001b[0m         in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    881\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 882\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m    883\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    884\u001b[0m     \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m    885\u001b[0m     exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
@@ -1068,11 +1102,13 @@
     "x_test = test_data.drop(columns=['Good Candidate'], axis=1)\n",
     "prediction = model.predict(x_test) \n",
     "\n",
-    "# Add the 'Prediction' column to evaluation_using_major\n",
-    "evaluation_using_major['Prediction'] = prediction\n",
+    "# Add the 'Prediction' column\n",
+    "evaluation_using_gender = test_data.copy()\n",
+    "\n",
+    "evaluation_using_gender['Prediction'] = prediction\n",
     "\n",
     "# Calculate precision by 'Major'\n",
-    "precision_major = evaluation_using_major.groupby('Gender').apply(\n",
+    "precision_gender = evaluation_using_gender.groupby('Gender').apply(\n",
     "    lambda x: precision_score(x['Good Candidate'], x['Prediction'], zero_division=0)\n",
     ")\n",
     "\n",
@@ -1083,7 +1119,7 @@
   {
    "cell_type": "code",
    "execution_count": 159,
-   "id": "9f37cb6b",
+   "id": "d7b73c7b",
    "metadata": {},
    "outputs": [
     {
@@ -1125,7 +1161,7 @@
   {
    "cell_type": "code",
    "execution_count": 160,
-   "id": "10609ae7",
+   "id": "be49068f",
    "metadata": {},
    "outputs": [
     {
@@ -1162,17 +1198,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "85ce33a4",
+   "cell_type": "markdown",
+   "id": "24b8485b",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "# First Fairness evaluation Startegy/Metric: Group Unaware --> This metric evaluates if the model is feature-agnostic with regard to groups. This is essential to make sure biases based on these characteristics are not reinforced or amplified by the model."
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
-   "id": "d5ad5823",
+   "execution_count": 173,
+   "id": "c335ceac",
    "metadata": {},
    "outputs": [
     {
@@ -1185,14 +1221,14 @@
    ],
    "source": [
     "age_checking = 'Age' in x_test.columns\n",
-    "group_unaware_status_age = \"Group Unaware\" if not is_age_used else \"Group Aware\"\n",
+    "group_unaware_status_age = \"Group Unaware\" if not age_checking else \"Group Aware\"\n",
     "print(\"The model is \" + group_unaware_status_age + \" for Age attribute.\")\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 162,
-   "id": "7a9a4f21",
+   "id": "fee4e3f6",
    "metadata": {},
    "outputs": [
     {
@@ -1212,7 +1248,7 @@
   {
    "cell_type": "code",
    "execution_count": 163,
-   "id": "1d7a200f",
+   "id": "c198c926",
    "metadata": {},
    "outputs": [
     {
@@ -1229,10 +1265,18 @@
     "print(\"The model is \" + group_unaware_status_major + \" for Gender attribute.\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7676a982",
+   "metadata": {},
+   "source": [
+    "#  Demographic Parity measures how evenly a model's favorable predictions are dispersed among various demographic groupings. It aims for comparable success rates for all groups, irrespective of variables like age or gender. By measuring this, the metric makes sure that the model does not behave biasedly in favor of or against any certain demographic segment. Reaching Demographic Parity promotes inclusivity and equal treatment. For a thorough review, fairness indicators must be balanced with the overall performance of the model."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 135,
-   "id": "abd88986",
+   "id": "6e7ddfd5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1252,7 +1296,7 @@
   {
    "cell_type": "code",
    "execution_count": 136,
-   "id": "f021f671",
+   "id": "8ddca0b2",
    "metadata": {},
    "outputs": [
     {
@@ -1278,7 +1322,7 @@
   {
    "cell_type": "code",
    "execution_count": 137,
-   "id": "8d6135bc",
+   "id": "6a25b821",
    "metadata": {},
    "outputs": [
     {
@@ -1306,7 +1350,7 @@
   {
    "cell_type": "code",
    "execution_count": 138,
-   "id": "422045d2",
+   "id": "a93d3a0f",
    "metadata": {},
    "outputs": [
     {
@@ -1330,53 +1374,17 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 112,
-   "id": "c036b656",
+   "cell_type": "markdown",
+   "id": "fbc4bdb4",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Good candidates for men (predicted): 0.6\n",
-      "Good candidates for women, predicted: 0.6\n"
-     ]
-    }
-   ],
    "source": [
-    "# #Demographic Parity\n",
-    "# import numpy as np\n",
-    "\n",
-    "# # Example: Define predicted and actual values for men\n",
-    "# predicted_men = [1, 0, 1, 1, 0]\n",
-    "# actual_men = [1, 1, 0, 1, 0]\n",
-    "\n",
-    "# # Function to calculate positive rate\n",
-    "# def calculate_positive_rate(predictions, actuals):\n",
-    "#     numpy_predictions = np.array(predictions)\n",
-    "#     positive_count = np.sum(numpy_predictions == 1)\n",
-    "#     total_count = len(predictions)\n",
-    "#     return positive_count / total_count\n",
-    "\n",
-    "# # Calculate and print positive rates for men\n",
-    "# prediction_men = calculate_positive_rate(predicted_men, actual_men)\n",
-    "# print(f\"Good candidates for men (predicted): {prediction_men}\")\n",
-    "\n",
-    "# # Example: Define predicted and actual values for women\n",
-    "# predicted_women = [0, 1, 0, 1, 1]\n",
-    "# actual_women = [1, 0, 1, 0, 1]\n",
-    "\n",
-    "# # Calculate and print positive rates for women\n",
-    "# prediction_women = calculate_positive_rate(predicted_women, actual_women)\n",
-    "# print(f\"Good candidates for women, predicted: {prediction_women}\")\n",
-    "# x\n"
+    "#  The fairness evaluation's equality odds quantify the distribution of real positive rates among the various demographic categories. By taking into account both true positives and false positives, it seeks to guarantee fairly accurate predictions for various subgroups. The metric is designed to address any differences in model performance by focusing on equalizing the probability of good predictions. Aiming for Equality Odds reduces disparities in expected outcomes between different groups, which advances justice. It should be taken into account for a thorough evaluation of fairness together with the model's overall performance."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "545eae10",
+   "id": "7d7f235c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1386,7 +1394,7 @@
   {
    "cell_type": "code",
    "execution_count": 132,
-   "id": "ac95da67",
+   "id": "e8a504b1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1412,7 +1420,7 @@
   {
    "cell_type": "code",
    "execution_count": 133,
-   "id": "fb06baa5",
+   "id": "92116453",
    "metadata": {},
    "outputs": [
     {
@@ -1452,7 +1460,7 @@
   {
    "cell_type": "code",
    "execution_count": 130,
-   "id": "55a667a3",
+   "id": "e44fbf21",
    "metadata": {},
    "outputs": [
     {
@@ -1489,7 +1497,7 @@
   {
    "cell_type": "code",
    "execution_count": 127,
-   "id": "dce69565",
+   "id": "2a267478",
    "metadata": {},
    "outputs": [
     {
@@ -1515,7 +1523,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "4099f31e",
+   "id": "26ff0af4",
    "metadata": {},
    "outputs": [],
    "source": []