diff --git a/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb b/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb
index 7b3b6ff4..2eca3323 100644
--- a/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb
+++ b/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 147,
    "id": "28c7a0ed",
    "metadata": {},
    "outputs": [],
@@ -21,12 +21,12 @@
     "warnings.filterwarnings(action='ignore', category=UserWarning)\n",
     "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
     "# Loading the test dataset\n",
-    "test_data = pd.read_csv('/Users/zainab/student_data.csv')\n"
+    "test_data = pd.read_csv('student_data.csv')\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 148,
    "id": "e911c179",
    "metadata": {
     "scrolled": true
@@ -152,7 +152,7 @@
        "4               0  "
       ]
      },
-     "execution_count": 221,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -163,8 +163,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
-   "id": "a5d76762",
+   "execution_count": 149,
+   "id": "94b15a3d",
    "metadata": {},
    "outputs": [
     {
@@ -295,7 +295,7 @@
        "max                4.000000        1.000000  "
       ]
      },
-     "execution_count": 222,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -306,8 +306,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
-   "id": "9aa9b85b",
+   "execution_count": 150,
+   "id": "d1c29453",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,8 +317,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
-   "id": "4791c7a2",
+   "execution_count": 151,
+   "id": "79228d8e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -328,8 +328,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
-   "id": "3a64f2c7",
+   "execution_count": 38,
+   "id": "412b261b",
    "metadata": {},
    "outputs": [
     {
@@ -356,8 +356,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
-   "id": "127ba006",
+   "execution_count": 45,
+   "id": "4f022baf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Age', 'Major', 'GPA', 'Extra Curricular', 'Num Programming Languages',\n",
+      "       'Num Past Internships', 'Good Candidate'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(test_data.columns)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ba1c9cb2",
    "metadata": {},
    "outputs": [
     {
@@ -384,8 +404,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
-   "id": "1863ba26",
+   "execution_count": 152,
+   "id": "d27a24a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Could not interpret input 'Gender'",
+     "output_type": "error",
+     "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",
+      "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",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/seaborn/categorical.py:153\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    151\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    152\u001b[0m         err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret input \u001b[39m\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\u001b[38;5;241m.\u001b[39mformat(var)\n\u001b[0;32m--> 153\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[1;32m    155\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[1;32m    156\u001b[0m orient \u001b[38;5;241m=\u001b[39m infer_orient(\n\u001b[1;32m    157\u001b[0m     x, y, orient, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequire_numeric\n\u001b[1;32m    158\u001b[0m )\n",
+      "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'Gender'"
+     ]
+    },
+    {
+     "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": [
+    "#Plotting the distribution of the test dataset (Gender)\n",
+    "plt.title('Gender')\n",
+    "sns.countplot(x='Gender', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "57624579",
    "metadata": {},
    "outputs": [
     {
@@ -412,8 +475,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
-   "id": "214dd318",
+   "execution_count": 15,
+   "id": "ccf66e8b",
    "metadata": {},
    "outputs": [
     {
@@ -442,8 +505,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
-   "id": "d3a74385",
+   "execution_count": 16,
+   "id": "83b82ec6",
    "metadata": {},
    "outputs": [
     {
@@ -471,8 +534,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
-   "id": "92d36835",
+   "execution_count": 17,
+   "id": "4c65532f",
    "metadata": {},
    "outputs": [
     {
@@ -500,13 +563,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
-   "id": "8ff8d8a5",
+   "execution_count": 141,
+   "id": "85a840d1",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -520,17 +583,16 @@
    "source": [
     "#Plotting the distribution of the test dataset (Number of Programming Languages)\n",
     "plt.title('Number of Programming Languages')\n",
-    "sns.countplot(x='Num Programming Languages', data=test_data )\n",
+    "sns.countplot(x='Num Programming Languages', data=test_data)\n",
     "plt.ylabel('Distribution')\n",
     "plt.xlabel('Number of Programming Languages')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
+    "plt.show()\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
-   "id": "4b81b623",
+   "execution_count": 19,
+   "id": "0e8bc628",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -542,8 +604,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
-   "id": "e22cb1c5",
+   "execution_count": 51,
+   "id": "a7037861",
    "metadata": {},
    "outputs": [
     {
@@ -553,29 +615,23 @@
       "The accuracy score for this model is: 0.832\n",
       "Confusion matrix\n",
       "[[221  42]\n",
-      " [ 42 195]]\n"
+      " [ 42 195]]\n",
+      "Confusion matrix in percentages\n",
+      "[[33.33333333 16.66666667]\n",
+      " [16.66666667 33.33333333]]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 576x432 with 2 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
       "needs_background": "light"
      },
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Confusion matrix in percentages\n",
-      "[[33.33333333 16.66666667]\n",
-      " [16.66666667 33.33333333]]\n"
-     ]
     }
    ],
    "source": [
@@ -588,35 +644,18 @@
     "accuracy = accuracy_score(Y_true, prediction)\n",
     "print(\"The accuracy score for this model is\" + \":\", accuracy)\n",
     "\n",
-    "conf_matrix= confusion_matrix(Y_true, prediction)\n",
-    "print('Confusion matrix')\n",
-    "print(conf_matrix)\n",
     "\n",
-    "classes = [\"1\", \"2\"]  # Modify this based on your actual class labels\n",
     "\n",
-    "# Calculate the confusion matrix\n",
-    "confusion_matrix = confusion_matrix(y_test, prediction)  # Assuming 'y_test' and 'prediction' are the actual and predicted labels\n",
     "\n",
-    "# Plot the confusion matrix\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "plt.imshow(confusion_matrix, interpolation='nearest', cmap=plt.get_cmap('Blues'))\n",
-    "plt.title('Confusion Matrix')\n",
-    "plt.colorbar()\n",
     "\n",
-    "# Add labels and ticks\n",
-    "tick_marks = np.arange(len(classes))\n",
-    "plt.xticks(tick_marks, classes, rotation=90)\n",
-    "plt.yticks(tick_marks, classes)\n",
+    "conf_matrix= confusion_matrix(Y_true, prediction)\n",
+    "print('Confusion matrix')\n",
+    "print(conf_matrix)\n",
     "\n",
-    "# Display the values on the plot\n",
-    "thresh = confusion_matrix.max() / 2.\n",
-    "for i, j in itertools.product(range(confusion_matrix.shape[0]), range(confusion_matrix.shape[1])):\n",
-    "    plt.text(j, i, format(confusion_matrix[i, j], 'd'), horizontalalignment=\"center\", color=\"white\" if confusion_matrix[i, j] > thresh else \"black\")\n",
+    "classes = [\"1\", \"2\"]  \n",
     "\n",
-    "plt.tight_layout()\n",
-    "plt.ylabel('True label')\n",
-    "plt.xlabel('Predicted label')\n",
-    "plt.show()\n",
+    "# Computing the confusion matrix (Regular numbers)\n",
+    "confusion_matrix = confusion_matrix(Y_true, prediction) \n",
     "\n",
     "\n",
     "# Compute the confusion matrix percentage\n",
@@ -627,36 +666,858 @@
     "Y_true = np.array([0, 1, 1, 0, 1, 0])\n",
     "predictions = np.array([0, 1, 0, 0, 1, 1])\n",
     "conf_matrix = confusion_matrix(Y_true, predictions)\n",
-    "conf_matrix_percent = (conf_matrix / conf_matrix.sum()) * 100\n",
+    "conf_matrix_percentage = (conf_matrix / conf_matrix.sum()) * 100\n",
     "\n",
     "\n",
     "print(\"Confusion matrix in percentages\")\n",
-    "print(conf_matrix_percent)\n",
+    "print(conf_matrix_percentage)\n",
+    "\n",
+    "plt.figure(figsize=(6, 4))\n",
+    "sns.heatmap(conf_matrix_percentage, annot=True, fmt=\"0.2f\", cmap=\"Blues\", cbar=False, square=True)\n",
+    "plt.xlabel(\"Predicted\")\n",
+    "plt.ylabel(\"True\")\n",
+    "plt.title(\"Confusion Matrix in Percentages\")\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
     "      "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
-   "id": "9afef01c",
+   "execution_count": 56,
+   "id": "8f9869f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The accuracy score for this model is: 0.832\n",
+      "Confusion matrix\n",
+      "[[221  42]\n",
+      " [ 42 195]]\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"
+    }
+   ],
+   "source": [
+    "#Performance Evaluation Section\n",
+    "\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "Y_true= test_data['Good Candidate']\n",
+    "accuracy = accuracy_score(Y_true, prediction)\n",
+    "print(\"The accuracy score for this model is\" + \":\", accuracy)\n",
+    "\n",
+    "conf_matrix= confusion_matrix(Y_true, prediction)\n",
+    "print('Confusion matrix')\n",
+    "print(conf_matrix)\n",
+    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
+    "plt.xlabel('Predicted')\n",
+    "plt.ylabel('True')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "091bbc00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done with Performance Evaluation Section\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"done with Performance Evaluation Section\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "59a603ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting fairness evaluation seciton\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Starting fairness evaluation seciton\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "684fd7a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.84      0.84      0.84       263\n",
+      "           1       0.82      0.82      0.82       237\n",
+      "\n",
+      "    accuracy                           0.83       500\n",
+      "   macro avg       0.83      0.83      0.83       500\n",
+      "weighted avg       0.83      0.83      0.83       500\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Classification Report\n",
+    "from sklearn.metrics import classification_report\n",
+    "report = classification_report(Y_true, prediction)\n",
+    "print(report)\n",
+    "      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "283e22ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "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.metrics import roc_curve, auc, precision_recall_curve\n",
+    "#Receiver Operating Characteristic curve plotting \n",
+    "fpr, tpr, _ = roc_curve(Y_true, prediction)\n",
+    "roc_auc = auc(fpr, tpr)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(fpr, tpr, color='darkblue', lw=2, label=f'Receiver Operating Characteristic curve (area = {roc_auc:.2f})')\n",
+    "plt.plot([0, 1], [0, 1], color='green', lw=2, linestyle='--')\n",
+    "plt.xlabel('False Positive')\n",
+    "plt.ylabel('True Positive')\n",
+    "plt.title('Receiver Operating Characteristic')\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "6d44244d",
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "# Precision-Recall Curve\n",
+    "precision, recall, _ = precision_recall_curve(Y_true, prediction)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.plot(recall, precision, color='darkblue', lw=2, label='Precision Recall curve')\n",
+    "plt.xlabel('Recall')\n",
+    "plt.ylabel('Precision')\n",
+    "plt.title('Precision-Recall Curve')\n",
+    "plt.legend(loc=\"lower left\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8270b981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Fairness Evaluation Section\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "cf13e617",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Gender'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3621\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3620\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:136\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx:163\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5198\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5206\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Gender'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [76]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\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      9\u001b[0m \u001b[38;5;66;03m# Ensure 'Gender' column is present in the DataFrame\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m evaluation_using_gender[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mtest_data\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\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Calculate true positive by 'Gender'\u001b[39;00m\n\u001b[1;32m     13\u001b[0m tpr_gender \u001b[38;5;241m=\u001b[39m evaluation_using_gender[evaluation_using_gender[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGood Candidate\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mgroupby(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m)[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/frame.py:3505\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   3504\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3505\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3506\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   3507\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3621\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3622\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3623\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3624\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3625\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3626\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3627\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3628\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Gender'"
+     ]
+    }
+   ],
+   "source": [
+    "# True positives for gender\n",
+    "x_test = test_data.drop(columns=['Good Candidate'], axis=1)\n",
+    "\n",
+    "prediction = model.predict(x_test)\n",
+    "\n",
+    "evaluation_using_gender = test_data.copy()\n",
+    "evaluation_using_gender['Prediction'] = prediction\n",
+    "\n",
+    "# Ensure 'Gender' column is present in the DataFrame\n",
+    "evaluation_using_gender['Gender'] = test_data['Gender']\n",
+    "\n",
+    "# Calculate true positive by 'Gender'\n",
+    "tpr_gender = evaluation_using_gender[evaluation_using_gender['Good Candidate'] == 1].groupby('Gender')['Prediction'].mean()\n",
+    "\n",
+    "print(\"TP classification by Gender:\")\n",
+    "print(tpr_gender)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "c244b83c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP classification by Major:\n",
+      "Major\n",
+      "Business                               0.875000\n",
+      "Computer Science                       0.837838\n",
+      "Electrical and Computer Engineering    0.775000\n",
+      "Information Systems                    0.756098\n",
+      "Math                                   0.844444\n",
+      "Statistics and Machine Learning        0.852941\n",
+      "Name: Prediction, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# True positives for  majors\n",
+    "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",
+    "\n",
+    "# Filterring rows where 'Good Candidate' is 1 \n",
+    "tpr_major = evaluation_using_major[evaluation_using_major['Good Candidate'] == 1].groupby('Major')['Prediction'].mean()\n",
+    "\n",
+    "print(\"TP classification by Major:\")\n",
+    "print(tpr_major)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "63372c1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TP classification by Age\n",
+      "Age\n",
+      "18    0.818182\n",
+      "19    0.869565\n",
+      "20    0.720000\n",
+      "21    0.892857\n",
+      "22    0.891304\n",
+      "23    0.875000\n",
+      "24    0.666667\n",
+      "25    0.250000\n",
+      "Name: Prediction, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# True positives for AGE\n",
+    "\n",
+    "evaluation_using_age = test_data.copy()\n",
+    "\n",
+    "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_age\n",
+    "evaluation_using_age['Prediction'] = prediction\n",
+    "\n",
+    "# Filter rows where 'Good Candidate' is 1 \n",
+    "tpr_age = evaluation_using_age[evaluation_using_age['Good Candidate'] == 1].groupby('Age')['Prediction'].mean()\n",
+    "\n",
+    "print(\"TP classification by Age\")\n",
+    "print(tpr_age)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "ce84922a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision by Major:\n",
+      "Major\n",
+      "Business                               0.897436\n",
+      "Computer Science                       0.775000\n",
+      "Electrical and Computer Engineering    0.861111\n",
+      "Information Systems                    0.794872\n",
+      "Math                                   0.844444\n",
+      "Statistics and Machine Learning        0.763158\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "\n",
+    "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",
+    "\n",
+    "# Calculate precision by 'Major'\n",
+    "precision_major = evaluation_using_major.groupby('Major').apply(\n",
+    "    lambda x: precision_score(x['Good Candidate'], x['Prediction'], zero_division=0)\n",
+    ")\n",
+    "\n",
+    "print(\"Precision by Major:\")\n",
+    "print(precision_major)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a2cb18bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Gender'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [81]\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",
+      "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",
+      "\u001b[0;31mKeyError\u001b[0m: 'Gender'"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "from sklearn.metrics import precision_score\n",
+    "\n",
+    "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",
+    "\n",
+    "# Calculate precision by 'Major'\n",
+    "precision_major = evaluation_using_major.groupby('Gender').apply(\n",
+    "    lambda x: precision_score(x['Good Candidate'], x['Prediction'], zero_division=0)\n",
+    ")\n",
+    "\n",
+    "print(\"Precision by Gender:\")\n",
+    "print(precision_gender)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "221162a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision for Age:\n",
+      "Age\n",
+      "18    0.900000\n",
+      "19    0.909091\n",
+      "20    0.782609\n",
+      "21    0.833333\n",
+      "22    0.732143\n",
+      "23    0.875000\n",
+      "24    1.000000\n",
+      "25    1.000000\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "\n",
+    "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_age\n",
+    "evaluation_using_age['Prediction'] = prediction\n",
+    "\n",
+    "# Calculate precision by 'Age'\n",
+    "precision_age = evaluation_using_age.groupby('Age').apply(\n",
+    "    lambda x: precision_score(x['Good Candidate'], x['Prediction'], zero_division=0)\n",
+    ")\n",
+    "\n",
+    "print(\"Precision for Age:\")\n",
+    "print(precision_age)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "c7292c69",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision for Major:\n",
+      "Major\n",
+      "Business                               0.897436\n",
+      "Computer Science                       0.775000\n",
+      "Electrical and Computer Engineering    0.861111\n",
+      "Information Systems                    0.794872\n",
+      "Math                                   0.844444\n",
+      "Statistics and Machine Learning        0.763158\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import precision_score\n",
+    "\n",
+    "x_test = test_data.drop(columns=['Good Candidate'], axis=1)\n",
+    "\n",
+    "prediction = model.predict(x_test)  \n",
+    "\n",
+    "# Add the 'Prediction' column to evaluation_using_major\n",
+    "evaluation_using_age['Prediction'] = prediction\n",
+    "precision_major = evaluation_using_major.groupby('Major').apply(\n",
+    "    lambda x: precision_score(x['Good Candidate'], x['Prediction'], zero_division=0)\n",
+    ")\n",
+    "\n",
+    "print(\"Precision for Major:\")\n",
+    "print(precision_major)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f18be1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#First Fairness evaluation tool: Group Unaware"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "fe7fafdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is Group Aware for Age attribute.\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_age_used = 'Age' in x_test.columns\n",
+    "group_unaware_status_age = \"Group Unaware\" if not is_age_used else \"Group Aware\"\n",
+    "print(\"The model is \" + group_unaware_status_age + \" for Age attribute.\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "e515a4f4",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "done\n"
+      "The model is Group Aware for Major attribute.\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_major_used = 'Major' in x_test.columns\n",
+    "group_unaware_status_major = \"Group Unaware\" if not is_major_used else \"Group Aware\"\n",
+    "print(\"The model is \" + group_unaware_status_major + \" for Major attribute.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "791586bc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The model is Group Aware for Gender attribute.\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_gender_used = 'Gender' in x_test.columns\n",
+    "group_unaware_status = \"Group Unaware\" if not is_gender_used else \"Group Aware\"\n",
+    "print(\"The model is \" + group_unaware_status_major + \" for Gender attribute.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "76e42835",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2nd : Demographic Parity \n",
+    "\n",
+    "def demographic_parity(data, feature):\n",
+    "\n",
+    "    selection_feature = data.groupby(feature)['Good Candidate'].mean()\n",
+    "\n",
+    "    # Check  demographic parity\n",
+    "    demographic_parity = selection_feature / selection_feature.mean()\n",
+    "\n",
+    "    print(f\"Demographic Parity for {feature}:\")\n",
+    "    print(demographic_parity)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "97a221c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Gender'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [136]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdemographic_parity\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[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\n",
+      "Input \u001b[0;32mIn [135]\u001b[0m, in \u001b[0;36mdemographic_parity\u001b[0;34m(data, feature)\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdemographic_parity\u001b[39m(data, feature):\n\u001b[0;32m----> 5\u001b[0m     selection_feature \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfeature\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGood Candidate\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;66;03m# Check  demographic parity\u001b[39;00m\n\u001b[1;32m      8\u001b[0m     demographic_parity \u001b[38;5;241m=\u001b[39m selection_feature \u001b[38;5;241m/\u001b[39m selection_feature\u001b[38;5;241m.\u001b[39mmean()\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",
+      "\u001b[0;31mKeyError\u001b[0m: 'Gender'"
+     ]
+    }
+   ],
+   "source": [
+    "demographic_parity(test_data, 'Gender')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "id": "dd45687e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Demographic Parity for Age:\n",
+      "Age\n",
+      "18    0.770865\n",
+      "19    0.703334\n",
+      "20    0.757607\n",
+      "21    0.672755\n",
+      "22    0.781483\n",
+      "23    1.055301\n",
+      "24    1.576769\n",
+      "25    1.681887\n",
+      "Name: Good Candidate, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "demographic_parity(test_data, 'Age')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "5537c6e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Demographic Parity for Major:\n",
+      "Major\n",
+      "Business                               1.039182\n",
+      "Computer Science                       0.865119\n",
+      "Electrical and Computer Engineering    1.002068\n",
+      "Information Systems                    1.106129\n",
+      "Math                                   1.007398\n",
+      "Statistics and Machine Learning        0.980105\n",
+      "Name: Good Candidate, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "demographic_parity(test_data, 'Major')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "0ad9f500",
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6007c896",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#equality odds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "67ca6bd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def equality_odds(data, target_variable, sensitive_feature, predicted_prob_column):\n",
+    "\n",
+    "    data['Prediction'] = prediction\n",
+    "\n",
+    "    # Calculate true positive rates\n",
+    "    tpr_by_group = data[data[target_variable] == 1].groupby(sensitive_feature)['Prediction'].mean()\n",
+    "\n",
+    "    # Print true positive rates\n",
+    "    print(f\"\\nTrue Positive Rates by {sensitive_feature}:\")\n",
+    "    print(tpr_by_group)\n",
+    "\n",
+    "    # Calculate false positive rates\n",
+    "    fpr_by_group = data[data[target_variable] == 0].groupby(sensitive_feature)['Prediction'].mean()\n",
+    "\n",
+    "    # Print false positive rates\n",
+    "    print(f\"\\nFalse Positive Rates by {sensitive_feature}:\")\n",
+    "    print(fpr_by_group)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "342a5098",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "True Positive Rates by Age:\n",
+      "Age\n",
+      "18    0.818182\n",
+      "19    0.869565\n",
+      "20    0.720000\n",
+      "21    0.892857\n",
+      "22    0.891304\n",
+      "23    0.875000\n",
+      "24    0.666667\n",
+      "25    0.250000\n",
+      "Name: Prediction, dtype: float64\n",
+      "\n",
+      "False Positive Rates by Age:\n",
+      "Age\n",
+      "18    0.076923\n",
+      "19    0.062500\n",
+      "20    0.163934\n",
+      "21    0.119048\n",
+      "22    0.283019\n",
+      "23    0.210526\n",
+      "24    0.000000\n",
+      "Name: Prediction, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "equality_odds(test_data, 'Good Candidate', 'Age', 'Prediction')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "9f0a7b95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "True Positive Rates by Major:\n",
+      "Major\n",
+      "Business                               0.875000\n",
+      "Computer Science                       0.837838\n",
+      "Electrical and Computer Engineering    0.775000\n",
+      "Information Systems                    0.756098\n",
+      "Math                                   0.844444\n",
+      "Statistics and Machine Learning        0.852941\n",
+      "Name: Prediction, dtype: float64\n",
+      "\n",
+      "False Positive Rates by Major:\n",
+      "Major\n",
+      "Business                               0.097561\n",
+      "Computer Science                       0.169811\n",
+      "Electrical and Computer Engineering    0.113636\n",
+      "Information Systems                    0.216216\n",
+      "Math                                   0.142857\n",
+      "Statistics and Machine Learning        0.230769\n",
+      "Name: Prediction, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "equality_odds(test_data, 'Good Candidate', 'Major', 'Prediction')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "a2d5335d",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Gender'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [127]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mequality_odds\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGood Candidate\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\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mPrediction\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Input \u001b[0;32mIn [124]\u001b[0m, in \u001b[0;36mequality_odds\u001b[0;34m(data, target_variable, sensitive_feature, predicted_prob_column)\u001b[0m\n\u001b[1;32m      3\u001b[0m data[\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      5\u001b[0m \u001b[38;5;66;03m# Calculate true positive rates\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m tpr_by_group \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtarget_variable\u001b[49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43msensitive_feature\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mmean()\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Print true positive rates\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mTrue Positive Rates by \u001b[39m\u001b[38;5;132;01m{\u001b[39;00msensitive_feature\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m)\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",
+      "\u001b[0;31mKeyError\u001b[0m: 'Gender'"
      ]
     }
    ],
    "source": [
-    "print(\"done\") "
+    "equality_odds(test_data, 'Good Candidate', 'Gender', 'Prediction')"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "f9ada5bf",
+   "id": "d0fe341c",
    "metadata": {},
    "outputs": [],
    "source": []