diff --git a/career-model/JupyterNotebook.ipynb b/career-model/JupyterNotebook.ipynb
index 0636f93..910b407 100644
--- a/career-model/JupyterNotebook.ipynb
+++ b/career-model/JupyterNotebook.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 184,
    "metadata": {
     "scrolled": true
    },
@@ -18,17 +18,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: matplotlib in /Users/malika/miniconda3/lib/python3.11/site-packages (3.8.0)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.1.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (4.43.1)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.4.5)\n",
-      "Requirement already satisfied: numpy<2,>=1.21 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (1.24.2)\n",
-      "Requirement already satisfied: packaging>=20.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (23.0)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (10.1.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (3.1.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/malika/miniconda3/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
+      "Requirement already satisfied: matplotlib in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (3.5.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (1.4.2)\n",
+      "Requirement already satisfied: numpy>=1.17 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (1.24.2)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (21.3)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (9.2.0)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (3.0.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
      ]
     }
    ],
@@ -38,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 185,
    "metadata": {
     "scrolled": true
    },
@@ -47,20 +48,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: seaborn in /Users/malika/miniconda3/lib/python3.11/site-packages (0.13.0)\n",
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-      "Requirement already satisfied: pandas>=1.2 in /Users/malika/miniconda3/lib/python3.11/site-packages (from seaborn) (1.5.3)\n",
-      "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/malika/miniconda3/lib/python3.11/site-packages (from seaborn) (3.8.0)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.1.1)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.43.1)\n",
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-      "Requirement already satisfied: packaging>=20.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.0)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.1.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /Users/malika/miniconda3/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n",
-      "Requirement already satisfied: pytz>=2020.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2022.7.1)\n",
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+      "Requirement already satisfied: seaborn in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (0.11.2)\n",
+      "Requirement already satisfied: numpy>=1.15 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from seaborn) (1.24.2)\n",
+      "Requirement already satisfied: scipy>=1.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from seaborn) (1.10.1)\n",
+      "Requirement already satisfied: pandas>=0.23 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from seaborn) (1.5.3)\n",
+      "Requirement already satisfied: matplotlib>=2.2 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from seaborn) (3.5.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (1.4.2)\n",
+      "Requirement already satisfied: packaging>=20.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (21.3)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (9.2.0)\n",
+      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (3.0.4)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from pandas>=0.23->seaborn) (2022.7.1)\n",
+      "Requirement already satisfied: six>=1.5 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib>=2.2->seaborn) (1.16.0)\n",
+      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
      ]
     }
    ],
@@ -70,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 186,
    "metadata": {
     "scrolled": true
    },
@@ -79,17 +82,19 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: joblib==1.2.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 1)) (1.2.0)\r\n",
-      "Requirement already satisfied: numpy==1.24.2 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 2)) (1.24.2)\r\n",
-      "Requirement already satisfied: pandas==1.5.3 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 3)) (1.5.3)\r\n",
-      "Requirement already satisfied: pydantic==1.10.6 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 4)) (1.10.6)\r\n",
-      "Requirement already satisfied: python-dateutil==2.8.2 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 5)) (2.8.2)\r\n",
-      "Requirement already satisfied: pytz==2022.7.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 6)) (2022.7.1)\r\n",
-      "Requirement already satisfied: scikit-learn==1.2.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 7)) (1.2.1)\r\n",
-      "Requirement already satisfied: scipy==1.10.1 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 8)) (1.10.1)\r\n",
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-      "Requirement already satisfied: threadpoolctl==3.1.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 10)) (3.1.0)\r\n",
-      "Requirement already satisfied: typing_extensions==4.5.0 in /Users/malika/miniconda3/lib/python3.11/site-packages (from -r requirements.txt (line 11)) (4.5.0)\r\n"
+      "Requirement already satisfied: joblib==1.2.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 1)) (1.2.0)\n",
+      "Requirement already satisfied: numpy==1.24.2 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (1.24.2)\n",
+      "Requirement already satisfied: pandas==1.5.3 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 3)) (1.5.3)\n",
+      "Requirement already satisfied: pydantic==1.10.6 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 4)) (1.10.6)\n",
+      "Requirement already satisfied: python-dateutil==2.8.2 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 5)) (2.8.2)\n",
+      "Requirement already satisfied: pytz==2022.7.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 6)) (2022.7.1)\n",
+      "Requirement already satisfied: scikit-learn==1.2.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 7)) (1.2.1)\n",
+      "Requirement already satisfied: scipy==1.10.1 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 8)) (1.10.1)\n",
+      "Requirement already satisfied: six==1.16.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 9)) (1.16.0)\n",
+      "Requirement already satisfied: threadpoolctl==3.1.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 10)) (3.1.0)\n",
+      "Requirement already satisfied: typing_extensions==4.5.0 in /Users/SaraAl-Saloos/miniconda3/lib/python3.8/site-packages (from -r requirements.txt (line 11)) (4.5.0)\n",
+      "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
      ]
     }
    ],
@@ -99,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -237,7 +242,7 @@
        "4               0  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {
     "scrolled": true
    },
@@ -383,7 +388,7 @@
        "max                4.000000        1.000000  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,6 +397,90 @@
     "df.describe()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gender Counts:\n",
+      "F    263\n",
+      "M    237\n",
+      "Name: Gender, dtype: int64\n",
+      "Unique Genders:\n",
+      "['F' 'M']\n",
+      "Mode Gender: F\n",
+      "\n",
+      "Major Counts:\n",
+      "Math                                   94\n",
+      "Computer Science                       90\n",
+      "Electrical and Computer Engineering    84\n",
+      "Business                               81\n",
+      "Information Systems                    78\n",
+      "Statistics and Machine Learning        73\n",
+      "Name: Major, dtype: int64\n",
+      "Unique Majors:\n",
+      "['Statistics and Machine Learning' 'Information Systems' 'Math'\n",
+      " 'Computer Science' 'Business' 'Electrical and Computer Engineering']\n",
+      "Mode Major: Math\n",
+      "\n",
+      "Extra Curricular Counts:\n",
+      "Student Theatre               51\n",
+      "Teaching Assistant            50\n",
+      "Sorority                      48\n",
+      "Society of Women Engineers    43\n",
+      "American Football             42\n",
+      "Student Government            42\n",
+      "Women in CS                   42\n",
+      "Buggy                         42\n",
+      "Men's Golf                    38\n",
+      "Men's Basketball              36\n",
+      "Volleyball                    34\n",
+      "Fraternity                    32\n",
+      "Name: Extra Curricular, dtype: int64\n",
+      "Unique Extra Curricular Activities:\n",
+      "['Sorority' 'Fraternity' 'Teaching Assistant' 'Volleyball'\n",
+      " 'Society of Women Engineers' 'American Football' 'Student Government'\n",
+      " \"Men's Basketball\" \"Men's Golf\" 'Student Theatre' 'Women in CS' 'Buggy']\n",
+      "Mode Extra Curricular: Student Theatre\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Count of unique categories\n",
+    "gender_counts = df['Gender'].value_counts()\n",
+    "print(\"Gender Counts:\")\n",
+    "print(gender_counts)\n",
+    "major_counts = df['Major'].value_counts()\n",
+    "print(\"\\nMajor Counts:\")\n",
+    "print(major_counts)\n",
+    "extra_curricular_counts = df['Extra Curricular'].value_counts()\n",
+    "print(\"\\nExtra Curricular Counts:\")\n",
+    "print(extra_curricular_counts)\n",
+    "\n",
+    "# Unique categories\n",
+    "unique_genders = df['Gender'].unique()\n",
+    "print(\"Unique Genders:\")\n",
+    "print(unique_genders)\n",
+    "unique_majors = df['Major'].unique()\n",
+    "print(\"Unique Majors:\")\n",
+    "print(unique_majors)\n",
+    "unique_extra_curricular = df['Extra Curricular'].unique()\n",
+    "print(\"Unique Extra Curricular Activities:\")\n",
+    "print(unique_extra_curricular)\n",
+    "\n",
+    "# Mode\n",
+    "mode_gender = df['Gender'].mode().values[0]\n",
+    "print(\"Mode Gender:\", mode_gender)\n",
+    "mode_major = df['Major'].mode().values[0]\n",
+    "print(\"Mode Major:\", mode_major)\n",
+    "mode_extra_curricular = df['Extra Curricular'].mode().values[0]\n",
+    "print(\"Mode Extra Curricular:\", mode_extra_curricular)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -401,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +501,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -423,28 +512,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/25/pvvjr64x0z5c12gllk64jq880000gn/T/ipykernel_1154/1408825379.py:6: FutureWarning: \n",
-      "\n",
-      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
-      "\n",
-      "  sns.countplot(x='Gender', data=df, palette=custom_palette)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": "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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -467,7 +547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -476,23 +556,24 @@
        "Text(0.5, 1.0, 'Distribution of Major')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
-    "\n",
     "sns.countplot(data = df, y = 'Major', order = df['Major'].value_counts().index, hue = 'Major')\n",
     "plt.title(\"Distribution of Major\")"
    ]
@@ -505,7 +586,7 @@
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='Age', ylabel='Count'>"
+       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
       ]
      },
      "execution_count": 8,
@@ -514,24 +595,25 @@
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Age\n",
-    "#df.groupby('Age').size().plot(kind='bar', title='Distribution of Age', ylabel='No. of Students')\n",
     "sns.histplot(data=df, x=\"Age\", kde=True, bins = 10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -540,30 +622,31 @@
        "[Text(0.5, 1.0, 'Distribution of GPA')]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "# GPA\n",
-    "\n",
     "sns.histplot(data=df, x=\"GPA\", kde=True).set(title='Distribution of GPA')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -572,18 +655,20 @@
        "Text(0.5, 1.0, 'Distribution of Extra-Curriculars')"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -591,37 +676,27 @@
     "# Extra Curricular\n",
     "color = ['black','red','green','orange','blue','limegreen','darkgreen','royalblue','navy','red','pink','orange']\n",
     "\n",
-    "\n",
     "sns.countplot(data = df, y = 'Extra Curricular', order = df['Extra Curricular'].value_counts().index, hue='Extra Curricular')\n",
     "plt.title(\"Distribution of Extra-Curriculars\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 11,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/25/pvvjr64x0z5c12gllk64jq880000gn/T/ipykernel_1154/1277272373.py:4: FutureWarning: \n",
-      "\n",
-      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
-      "\n",
-      "  sns.countplot(x='Num Programming Languages', data=df, palette=custom_palette)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -643,30 +718,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/25/pvvjr64x0z5c12gllk64jq880000gn/T/ipykernel_1154/1628466750.py:5: FutureWarning: \n",
-      "\n",
-      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
-      "\n",
-      "  sns.countplot(x='Num Past Internships', data=df, palette=custom_palette)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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StwD+PJ1FSZL6Md47rx0CXEDzD2WHAOcnWZdLZ0uSZqHxHj56M/DYqloJkGQB8C2a6xZJktYT4/320f2GAqH12wksK0maI8a7p/D1JN8ATmuHDwW+Nj0lSZL6srZ7NP81zc1vXp/kmcDfAgF+AHx2BuqTJM2gtR0C+iBwB0BVnV5Vr6mqV9PsJXxwekuTJM20tYXCoqr68fCRVbWc5tackqT1yNpCYeMxpj1wKguRJPVvbaFwYZKXDB/Z3gjnoukpSZLUl7V9++hVwJeTPI81IbAY2Ah4xjTWJUnqwZihUFU3A09I8g/A7u3os6rq29NemSRpxo33fgrnAudOcy2SpJ75X8mSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnq9BYKSTZIckmSr7bDWyY5O8lV7eMWfdUmSfNVn3sKRwNXDgwfA5xTVbsA57TDkqQZ1EsoJNkeOAD4xMDoA4GT2+cnAwfNcFmSNO/1tafwQeANwD0D47apqhUA7ePWIy2Y5Mgky5MsX7Vq1bQXKknzyYyHQpKnASuralJ3bquqE6tqcVUtXrBgwRRXJ0nz27jupzDFngg8PclTae4B/ZAknwFuTrKwqlYkWQis7KE2SZrXZnxPoaqOrartq2oR8Bzg21X1fOBMYEk72xLgjJmuTZLmu9n0fwonAE9JchXwlHZYkjSD+jh81KmqZcCy9vlvgX36rEeS5rvZtKcgSeqZoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6sx4KCTZIcm5Sa5McnmSo9vxWyY5O8lV7eMWM12bJM13fewprAZeW1W7AnsBL0+yG3AMcE5V7QKc0w5LkmbQjIdCVa2oqovb53cAVwLbAQcCJ7eznQwcNNO1SdJ81+s5hSSLgEcD5wPbVNUKaIID2HqUZY5MsjzJ8lWrVs1YrZI0H/QWCkk2Ab4EvKqqbh/vclV1YlUtrqrFCxYsmL4CJWke6iUUktyfJhA+W1Wnt6NvTrKwnb4QWNlHbZI0n/Xx7aMAnwSurKoPDEw6E1jSPl8CnDHTtUnSfLdhD30+EXgB8JMkl7bj3gScACxNcgRwPXBwD7VJ0rw246FQVd8FMsrkfWayFknSvfkfzZKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSerMulBIsl+SnyW5OskxfdcjSfPJrAqFJBsAHwH2B3YDnptkt36rkqT5Y1aFArAncHVVXVNVfwY+BxzYc02SNG+kqvquoZPk2cB+VfXidvgFwOOq6hUD8xwJHNkOPhz42YwXel9bAb/pu4hZwnWxhutiDdfFGrNhXexUVQtGmrDhTFeyFhlh3L1Sq6pOBE6cmXLGJ8nyqlrcdx2zgetiDdfFGq6LNWb7uphth49uBHYYGN4euKmnWiRp3pltoXAhsEuShybZCHgOcGbPNUnSvDGrDh9V1eokrwC+AWwAfKqqLu+5rPGYVYezeua6WMN1sYbrYo1ZvS5m1YlmSVK/ZtvhI0lSjwwFSVLHUFgHXpJjjSSfSrIyyWV919KnJDskOTfJlUkuT3J03zX1JcnGSS5I8qN2Xbyt75r6lmSDJJck+WrftYzGUJgkL8lxHycB+/VdxCywGnhtVe0K7AW8fB6/L+4CnlxVjwL2APZLsle/JfXuaODKvosYi6EweV6SY0BVfQe4pe86+lZVK6rq4vb5HTQfANv1W1U/qnFnO3j/9mfefrMlyfbAAcAn+q5lLIbC5G0H3DAwfCPz9I9fI0uyCHg0cH7PpfSmPVxyKbASOLuq5u26AD4IvAG4p+c6xmQoTN5aL8mh+SvJJsCXgFdV1e1919OXqrq7qvaguTrBnkl277mkXiR5GrCyqi7qu5a1MRQmz0tyaERJ7k8TCJ+tqtP7rmc2qKrbgGXM3/NOTwSenuRamkPNT07ymX5LGpmhMHlekkP3kSTAJ4Erq+oDfdfTpyQLkmzePn8gsC/w016L6klVHVtV21fVIprPim9X1fN7LmtEhsIkVdVqYOiSHFcCS+fIJTmmRZLTgB8AD09yY5Ij+q6pJ08EXkCzJXhp+/PUvovqyULg3CQ/ptmIOruqZu1XMdXwMheSpI57CpKkjqEgSeoYCpKkjqEgSeoYCpKkjqGgWSNJJXn/wPDrkhw/Df0clmRV+3XRK5K8ZBJtHDTahe6SHJ/kdWtZfo+Z+KpqkmVJ7nOT+CSLk/yv6e5fc4+hoNnkLuCZSbaagb4+315+YW/g3Um2meDyB9FcHXey9gAmFApJpuz2uVW1vKpeOVXtaf1hKGg2WU1z/9pXD5+Q5KQkzx4YvrN93DvJeUmWJvl5khOSPK+9jv9PkjxsrA6raiXwC2CnJP+RZPnwa/+3bV6R5MdJ3pfkCcDTgX9v9zZG7aPdUv+3tp6fJ3lS+x/wbwcObZc/NMmD23tSXNheb//AdvnDknwhyVeAb7bDpyf5epKrkry3nW+Ddh1d1v7eg+vw4MH+B9bbV9vnxyc5Ncm32zZf0o5fmOQ7bY2XDS2r9duUbXlIU+QjwI+HPuzG6VHArjSX7r4G+ERV7dne4OYo4FWjLZhkZ2Bn4GrgzVV1S3uvjHOSPJLmGlfPAP6mqirJ5lV1W5Izga9W1RfHUd+GbT1PBY6rqn2T/CuwuKpe0dbxbppLH7yovTTEBUm+1S7/eOCRbW2H0exlPJpmz+pnST4MbA1sV1W7t+1tPlr/NJebGO6RNPd/eDBwSZKzgOcC36iqd7Xr5EHj+F01x7mnoFmlvaLoKcBEDm1c2N7H4C6arf5vtuN/AiwaZZlD20s6nwa8tKpuAQ5JcjFwCfAImsNDtwN/Aj6R5JnAHyb2GwEwdFG8i8ao5x+BY9qalgEbAzu2085u6xtyTlX9rqr+BFwB7EQThjsn+XCS/dq6J9L/GVX1x6r6DXAuzf1CLgQOb8/r/Lf2/hBazxkKmo0+CBxBs9U6ZDXt+7W96NxGA9PuGnh+z8DwPYy+N/z5qtqjqh5XVV9O8lDgdcA+VfVI4Cxg4/YaV3vSXPX0IODrk/h9huq5e4x6AjyrrWmPqtqxqobu0PX7Udrr2qyqW2n2mJYBL+feN3IZT//Dr3dT7Y2T/g74FXBqkheOsqzWI4aCZp12q3gpTTAMuRb47+3zA2nu4jWVHkLz4fu79qTz/tDdF2GzqvoazWGoPdr57wA2XYf+hi//DeCoNvBI8uiJNNaenL9fVX0JeCvwmAnWc2Caeyr/F5qT7xcm2YnmHgAfp7ny60Tb1BxkKGi2ej8w+C2kjwN/n+QC4HHcd+t5nVTVj2gOG10OfAr4XjtpU+Craa70eR5rToJ/Dnh9e1J4zJPZozgX2G3oRDPwDpqg+3GSy9rhidgOWNYefjoJOHaCy19As3f0Q+AdVXUTTThcmuQS4FnAhybYpuYgr5IqzXPtOYM7q+p9fdei/rmnIEnquKcgSeq4pyBJ6hgKkqSOoSBJ6hgKkqSOoSBJ6vx/H0NZGwK8M9gAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -688,28 +754,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/25/pvvjr64x0z5c12gllk64jq880000gn/T/ipykernel_1154/2427641960.py:6: FutureWarning: \n",
-      "\n",
-      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
-      "\n",
-      "  sns.countplot(x='Good Candidate', data=df, palette=custom_palette)\n"
-     ]
-    },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -739,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -849,7 +906,7 @@
        "4            Sorority                          2                     1  "
       ]
      },
-     "execution_count": 44,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -863,26 +920,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
        "                                   transformers=[(&#x27;One Hot Encode&#x27;,\n",
        "                                                  OneHotEncoder(), [1, 3])])),\n",
        "                (&#x27;classifier&#x27;,\n",
        "                 RandomForestClassifier(max_depth=10, max_features=None,\n",
-       "                                        n_estimators=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                                        n_estimators=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
        "                                   transformers=[(&#x27;One Hot Encode&#x27;,\n",
        "                                                  OneHotEncoder(), [1, 3])])),\n",
        "                (&#x27;classifier&#x27;,\n",
        "                 RandomForestClassifier(max_depth=10, max_features=None,\n",
-       "                                        n_estimators=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
-       "                  transformers=[(&#x27;One Hot Encode&#x27;, OneHotEncoder(), [1, 3])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">One Hot Encode</label><div class=\"sk-toggleable__content\"><pre>[1, 3]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;GPA&#x27;, &#x27;Num Programming Languages&#x27;, &#x27;Num Past Internships&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, max_features=None, n_estimators=1000)</pre></div></div></div></div></div></div></div>"
+       "                                        n_estimators=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;One Hot Encode&#x27;, OneHotEncoder(), [1, 3])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">One Hot Encode</label><div class=\"sk-toggleable__content\"><pre>[1, 3]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">remainder</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Age&#x27;, &#x27;GPA&#x27;, &#x27;Num Programming Languages&#x27;, &#x27;Num Past Internships&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, max_features=None, n_estimators=1000)</pre></div></div></div></div></div></div></div>"
       ],
       "text/plain": [
        "Pipeline(steps=[('preprocessor',\n",
@@ -894,7 +951,7 @@
        "                                        n_estimators=1000))])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -913,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 16,
    "metadata": {
     "scrolled": true
    },
@@ -935,7 +992,7 @@
        "Length: 500, dtype: int64"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -950,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 17,
    "metadata": {
     "scrolled": true
    },
@@ -972,7 +1029,7 @@
        "Name: Good Candidate, Length: 500, dtype: int64"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -991,7 +1048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1008,7 +1065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1025,7 +1082,7 @@
        "       [ 42, 195]])"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1033,7 +1090,7 @@
    "source": [
     "from sklearn import metrics\n",
     "confusion_matrix = metrics.confusion_matrix(y, y_pred)\n",
-    "print(\"The confusion matrix is\")\n",
+    "print(\"Confusion Matrix: \")\n",
     "confusion_matrix"
    ]
   },
@@ -1041,12 +1098,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Confusion Matrix"
+    "### Confusion Matrix and Performance Evaluation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -1054,21 +1111,23 @@
     {
      "data": {
       "text/plain": [
-       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x126b5f650>"
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2848305b0>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
+       "<Figure size 432x288 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1076,11 +1135,876 @@
     "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = [False, True])\n",
     "cm_display.plot()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.83\n",
+      "Precision: 0.82\n",
+      "Recall: 0.82\n",
+      "Classification Report:\n",
+      "              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",
+      "Confusion Matrix:\n",
+      "[[221  42]\n",
+      " [ 42 195]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report\n",
+    "\n",
+    "# Make predictions on the test dataset\n",
+    "y_pred = clf.predict(X)\n",
+    "\n",
+    "# Calculate accuracy\n",
+    "accuracy = accuracy_score(y_true, y_pred)\n",
+    "print(f\"Accuracy: {accuracy:.2f}\")\n",
+    "\n",
+    "# Calculate precision and recall\n",
+    "precision = precision_score(y_true, y_pred)\n",
+    "recall = recall_score(y_true, y_pred)\n",
+    "print(f\"Precision: {precision:.2f}\")\n",
+    "print(f\"Recall: {recall:.2f}\")\n",
+    "\n",
+    "# Generate a classification report (includes precision, recall, and F1-score)\n",
+    "print(\"Classification Report: \")\n",
+    "print(classification_report(y_true, y_pred))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confusion Matrix:\n",
+      "[[221  42]\n",
+      " [ 42 195]]\n",
+      "True Positives: 195\n",
+      "True Negatives: 221\n",
+      "False Positives: 42\n",
+      "False Negatives: 42\n",
+      "Recall (True Positive Rate): 0.82\n",
+      "Precision: 0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, recall_score, precision_score\n",
+    "\n",
+    "y_true = df['Good Candidate']  # True labels (actual values)\n",
+    "y_pred = clf.predict(X)  # Predicted labels\n",
+    "\n",
+    "# Calculate the confusion matrix\n",
+    "confusion = confusion_matrix(y_true, y_pred)\n",
+    "\n",
+    "# Extract true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN)\n",
+    "TP = confusion[1, 1]\n",
+    "TN = confusion[0, 0]\n",
+    "FP = confusion[0, 1]\n",
+    "FN = confusion[1, 0]\n",
+    "\n",
+    "# Calculate Recall (True Positive Rate)\n",
+    "recall = recall_score(y_true, y_pred)\n",
+    "\n",
+    "# Calculate Precision\n",
+    "precision = precision_score(y_true, y_pred)\n",
+    "\n",
+    "# Print the results\n",
+    "print(\"Confusion Matrix:\")\n",
+    "print(confusion)\n",
+    "print(f\"True Positives: {TP}\")\n",
+    "print(f\"True Negatives: {TN}\")\n",
+    "print(f\"False Positives: {FP}\")\n",
+    "print(f\"False Negatives: {FN}\")\n",
+    "print(f\"Recall (True Positive Rate): {recall:.2f}\")\n",
+    "print(f\"Precision: {precision:.2f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-------- Age --------\n",
+      "count    500.000000\n",
+      "mean      20.944000\n",
+      "std        1.455025\n",
+      "min       18.000000\n",
+      "25%       20.000000\n",
+      "50%       21.000000\n",
+      "75%       22.000000\n",
+      "max       25.000000\n",
+      "Name: Age, dtype: float64\n",
+      "median   \t 21.0\n",
+      "mode\n",
+      " 0    21\n",
+      "Name: Age, dtype: int64\n",
+      "-------- GPA --------\n",
+      "count    500.000000\n",
+      "mean       2.905780\n",
+      "std        0.839559\n",
+      "min        0.000000\n",
+      "25%        2.345000\n",
+      "50%        2.990000\n",
+      "75%        3.560000\n",
+      "max        4.000000\n",
+      "Name: GPA, dtype: float64\n",
+      "median   \t 2.99\n",
+      "mode\n",
+      " 0    4.0\n",
+      "Name: GPA, dtype: float64\n",
+      "-------- Num Programming Languages --------\n",
+      "count    500.00000\n",
+      "mean       3.04600\n",
+      "std        1.36073\n",
+      "min        1.00000\n",
+      "25%        2.00000\n",
+      "50%        3.00000\n",
+      "75%        4.00000\n",
+      "max        5.00000\n",
+      "Name: Num Programming Languages, dtype: float64\n",
+      "median   \t 3.0\n",
+      "mode\n",
+      " 0    2\n",
+      "1    3\n",
+      "Name: Num Programming Languages, dtype: int64\n",
+      "-------- Num Past Internships --------\n",
+      "count    500.000000\n",
+      "mean       2.052000\n",
+      "std        1.407572\n",
+      "min        0.000000\n",
+      "25%        1.000000\n",
+      "50%        2.000000\n",
+      "75%        3.000000\n",
+      "max        4.000000\n",
+      "Name: Num Past Internships, dtype: float64\n",
+      "median   \t 2.0\n",
+      "mode\n",
+      " 0    2\n",
+      "Name: Num Past Internships, dtype: int64\n",
+      "-------- Good Candidate --------\n",
+      "count    500.000000\n",
+      "mean       0.474000\n",
+      "std        0.499824\n",
+      "min        0.000000\n",
+      "25%        0.000000\n",
+      "50%        0.000000\n",
+      "75%        1.000000\n",
+      "max        1.000000\n",
+      "Name: Good Candidate, dtype: float64\n",
+      "median   \t 0.0\n",
+      "mode\n",
+      " 0    0\n",
+      "Name: Good Candidate, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "numFields = ['Age', 'GPA', 'Num Programming Languages', 'Num Past Internships', 'Good Candidate']\n",
+    "for field in numFields:\n",
+    "  print(f\"-------- {field} --------\")\n",
+    "  print(df[field].describe())\n",
+    "  print(\"median   \\t\", df[field].median())\n",
+    "  print(\"mode\\n\", df[field].mode())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{\n",
+      "    \"Age\": {\n",
+      "        \"18\": 24,\n",
+      "        \"19\": 55,\n",
+      "        \"20\": 111,\n",
+      "        \"21\": 140,\n",
+      "        \"22\": 99,\n",
+      "        \"23\": 51,\n",
+      "        \"24\": 16,\n",
+      "        \"25\": 4\n",
+      "    },\n",
+      "    \"Extra Curricular\": {\n",
+      "        \"American Football\": 42,\n",
+      "        \"Buggy\": 42,\n",
+      "        \"Fraternity\": 32,\n",
+      "        \"Men's Basketball\": 36,\n",
+      "        \"Men's Golf\": 38,\n",
+      "        \"Society of Women Engineers\": 43,\n",
+      "        \"Sorority\": 48,\n",
+      "        \"Student Government\": 42,\n",
+      "        \"Student Theatre\": 51,\n",
+      "        \"Teaching Assistant\": 50,\n",
+      "        \"Volleyball\": 34,\n",
+      "        \"Women in CS\": 42\n",
+      "    },\n",
+      "    \"Gender\": {\n",
+      "        \"F\": 263,\n",
+      "        \"M\": 237\n",
+      "    },\n",
+      "    \"Major\": {\n",
+      "        \"Business\": 81,\n",
+      "        \"Computer Science\": 90,\n",
+      "        \"Electrical and Computer Engineering\": 84,\n",
+      "        \"Information Systems\": 78,\n",
+      "        \"Math\": 94,\n",
+      "        \"Statistics and Machine Learning\": 73\n",
+      "    },\n",
+      "    \"Num Past Internships\": {\n",
+      "        \"0\": 93,\n",
+      "        \"1\": 97,\n",
+      "        \"2\": 107,\n",
+      "        \"3\": 97,\n",
+      "        \"4\": 106\n",
+      "    },\n",
+      "    \"Num Programming Languages\": {\n",
+      "        \"1\": 82,\n",
+      "        \"2\": 109,\n",
+      "        \"3\": 109,\n",
+      "        \"4\": 104,\n",
+      "        \"5\": 96\n",
+      "    }\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "columns = ['Major', 'Age', 'Gender', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "distribution_counts = dict()\n",
+    "for index, row in df.iterrows():\n",
+    "  for i in range(len(columns)):\n",
+    "    col = columns[i]\n",
+    "    if col not in distribution_counts:\n",
+    "      distribution_counts[col] = dict()\n",
+    "    if row[col] not in distribution_counts[col]:\n",
+    "      distribution_counts[col][row[col]] = 0\n",
+    "    distribution_counts[col][row[col]] += 1\n",
+    "\n",
+    "print(json.dumps(distribution_counts, sort_keys=True, indent=4))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairness - Group Aware"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# creds to Spring '23 team\n",
+    "\n",
+    "import pandas as pd\n",
+    "import joblib\n",
+    "from pydantic import BaseModel, Field\n",
+    "from pydantic.tools import parse_obj_as\n",
+    "\n",
+    "# Pydantic Models\n",
+    "class Student(BaseModel):\n",
+    "    student_id: str = Field(alias=\"Student ID\")\n",
+    "    gender: str = Field(alias=\"Gender\")\n",
+    "    age: str = Field(alias=\"Age\")\n",
+    "    major: str = Field(alias=\"Major\")\n",
+    "    gpa: str = Field(alias=\"GPA\")\n",
+    "    extra_curricular: str = Field(alias=\"Extra Curricular\")\n",
+    "    num_programming_languages: str = Field(alias=\"Num Programming Languages\")\n",
+    "    num_past_internships: str = Field(alias=\"Num Past Internships\")\n",
+    "\n",
+    "    class Config:\n",
+    "        allow_population_by_field_name = True\n",
+    "\n",
+    "class PredictionResult(BaseModel):\n",
+    "    good_employee: int\n",
+    "\n",
+    "def predict(student):\n",
+    "    # Use Pydantic to validate model fields exist\n",
+    "    student = parse_obj_as(Student, student)\n",
+    "    clf = joblib.load('./model.pkl')\n",
+    "    student = student.dict(by_alias=True)\n",
+    "    query = pd.DataFrame(student, index=[0])\n",
+    "    prediction = clf.predict(query)\n",
+    "    # predicts if student would be good employee (either 0)\n",
+    "    return { 'good_employee': prediction[0] }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# Keep track of outputs, predicted vs actual for accuracy metrics to be done later\n",
+    "predicted = []\n",
+    "actual = []\n",
+    "\n",
+    "# Iterate over dataset and predict each student\n",
+    "for index, row in df.iterrows():\n",
+    "  fields = [\"student_id\", \"major\", \"age\", \"gender\", \"gpa\", \"extra_curricular\", \"num_programming_languages\", \"num_past_internships\"]\n",
+    "  names = ['Student ID', 'Major', 'Age', 'Gender', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "\n",
+    "  # Create a student object for predicting\n",
+    "  student = dict()\n",
+    "  for i in range(len(fields)):\n",
+    "    field = fields[i]\n",
+    "    col = names[i]\n",
+    "    student[field] = row[col]\n",
+    "\n",
+    "  # Get the outputs, predicted and actual\n",
+    "  predicted.append(predict(student)['good_employee'])\n",
+    "  actual.append(row['Good Candidate'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Yc9jMuciK38SUJI2XZ2JKUqMscElqlAUuSY2ywCU1Icn6cWc41VjgZ6gkD0+yK8knk9zR3Q52Y48Yd75BJHnPuDPMS/KwJL+V5C+SvHjRvj8cV67FkjwqyR8leUOS85L8cpKPJdmTZOO4881Lsm7R7TzgI0nWJlk37nzzkly8YP3hSd6Y5KNJ3ppkw6hfv6kCTzKV5P1J/jLJY5LsT/KfSa5P8tRx55uX5Nwkv5rkli7fXJIPJ3nZuLMtsAf4AvC9VXVeVZ0HfF839tdjTbZAkqed4PZtwJZx51vgSnpnJ78DuCzJO5I8uNt34fhi3cebgU8AnwHeD/wXcAnwj8Afjy/WfXwOuGHB7QDwaODGbv1U8ZsL1n8HOAo8n955Mn8y6hdv6mOEST4CXAE8Avht4Cer6qokW4Ffr6rvGGe+eUn2AlcDfwe8EDiH3pd9vQb4j6r6hTHGAyDJrVV1wXL3rbYkdwMfoP9XN1xYVWevcqS+ktxcVVsWbP8i8P3AC4D9VfW0cWVbKMlNVfXUbv1IVW1asO9ef4ZxSvIzwLOBn62qj3Vj/15VjxtvsntLcuP8322ffwMj/3mezHehjMMDq+o9AEleW1VXAVTVbJLXjTfavUxW1Zu79dcnub6qfi3Jy+kd/Yy9wIFPJ/k5YKaqjgF0v/K9jN7R2aniIPBjVXVo8Y4kp1LOByd5QFX9L0BV/UaS24B/AM4db7R7Wfhb958v2rdmNYPcn6p6XZK3A7/b/T1fQZ/vWjoFrE/yU/QOMB6WJHXPUfHIZziamkIB/jvJc5JcClSS7QBJvge4e6zJ7u0rSZ4JkOT5wOcBuv+4+x1JjsMPAecBH0jyhSSfB64F1tH7reFU8cuc+N/pK1cxx1L+FnjWwoGqmgF+Gvj6WBL1tzfJuQBV9Zr5wSSPZ/jfFHpSquq2qrqU3lTPfuAhY47Uz58CD6X3P+kZet9ESJJHATeP/NWrqpkb8BR6p+6/B3gC8PvAncAtwHeOO9+CnE8GPtJl+yDwzd34BPCqcedbkPMJ9H5NPXfR+MXjztYn59aGcz533Nla/3kCZwNPOtVzrvbPc+x/+CH+EF8+7gwt5QReRe+I62+Aw8C2BftuHHe+BnO+0pz+va92zrH/AIb4gzwy7gwt5QQ+Nn/EAEzSe2f/1d32TePOZ05zmnPpW1NvYib56Il2ASP/zOWgGsm5pqq+DFBVh5N8L3BVksdy6szTgzmHzZzDNdacrb2JuQF4Kb3PWS6+3THGXIu1kPP2JFvmN7p/hM+j9ybMt44rVB/mHC5zDtdYc7b2OfA3AldW1Qf77HtrVb24z8NWXQs5k5wP3FVVt/fZ94yq+qcxxLoPcw6XOYdr3DmbKnBJ0j1am0KRJHUscElqlAUuSY2ywCWpURa4JDXq/wD1KtyRrRUFqQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "# Information for women\n",
+    "df_women = df[df.Gender != 'M']\n",
+    "\n",
+    "# Distribution of all other fields (besides Gender)\n",
+    "fields = ['Age', 'Major', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships', 'Good Candidate']\n",
+    "for field in fields:\n",
+    "  fig, ax = plt.subplots()\n",
+    "  if field == 'GPA':\n",
+    "    df_women[field].plot(kind='hist', edgecolor='yellow', title=field)\n",
+    "  else:\n",
+    "    df_women[field].value_counts().sort_index().plot(ax=ax, kind='bar', title=field)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_women = []\n",
+    "actual_women = []\n",
+    "\n",
+    "for index, row in df_women.iterrows():\n",
+    "  fields = [\"student_id\", \"major\", \"age\", \"gender\", \"gpa\", \"extra_curricular\", \"num_programming_languages\", \"num_past_internships\"]\n",
+    "  names = ['Student ID', 'Major', 'Age', 'Gender', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "\n",
+    "  student = dict()\n",
+    "  for i in range(len(fields)):\n",
+    "    field = fields[i]\n",
+    "    col = names[i]\n",
+    "    student[field] = row[col]\n",
+    "\n",
+    "  predicted_women.append(predict(student)['good_employee'])\n",
+    "  actual_women.append(row['Good Candidate'])\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for women is  0.870722433460076\n",
+      "Confusion matrix for women: \n",
+      " [[140   2]\n",
+      " [ 32  89]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Accuracy score for women is \", accuracy_score(actual_women, predicted_women))\n",
+    "print(\"Confusion matrix for women: \\n\", confusion_matrix(actual_women, predicted_women))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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lgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElq1JwBnuRdSbYmuXyg7cAk65Nc3d8esLhlSpKGjdIDfzdw/FDbWmBDVR0BbOjnJUlLaM4Ar6pPAl8Zaj4RWNdPrwNOGm9ZkqS5LHQMfEVVbQHobw8eX0mSpFEs+ouYSdYk2ZRk08zMzGIfTpJ2GwsN8JuTrATob7fuaMWqOqOqVlXVqqmpqQUeTpI0bKEBfi6wup9eDZwznnIkSaMa5W2E7wcuAo5MckOSU4HTgeckuRp4Tj8vSVpCe861QlWdsoNFx465FknSPPhJTElqlAEuSY0ywCWpUQa4JDXKAJekRhngktQoA1ySGmWAS1KjDHBJapQBLkmNMsAlqVEGuCQ1ygCXpEYZ4JLUKANckhplgEtSowxwSWqUAS5JjTLAJalRBrgkNcoAl6RGGeCS1CgDXJIaZYBLUqMMcElqlAEuSY0ywCWpUQa4JDXKAJekRu1SgCc5PslVSa5JsnZcRUmS5rbgAE+yB/AXwPOAo4BTkhw1rsIkSTu3Kz3wY4Brquq6qroX+ABw4njKkiTNJVW1sA2TnwKOr6pX9vMvB55eVa8ZWm8NsKafPRK4auHljsVBwC0TrmG58Fps57XYzmux3XK5Fo+vqqnhxj13YYeZpe0hjwZVdQZwxi4cZ6ySbKqqVZOuYznwWmzntdjOa7Hdcr8WuzKEcgNw2MD8ocCNu1aOJGlUuxLgnwOOSPLtSR4JvAQ4dzxlSZLmsuAhlKq6L8lrgH8F9gDeVVVXjK2yxbNshnOWAa/Fdl6L7bwW2y3ra7HgFzElSZPlJzElqVEGuCQ1ygCXpEYZ4LuRJN+V5Ngk+w61Hz+pmiYlyTFJvr+fPirJ65I8f9J1LQdJ/mHSNSwHSX6ov18cN+ladmS3fhEzySuq6sxJ17EUkrwWeDVwJXA0cFpVndMv+3xVPW2C5S2pJG+h+w6fPYH1wNOBjcCzgX+tqt+bXHVLK8nwW38D/BjwbwBV9aIlL2pCkny2qo7pp19F9/fyUeA44LyqOn2S9c1mdw/w66vq8EnXsRSS/BfwA1V1Z5Jp4MPAP1bVnyb5QlU9dbIVLp3+WhwN7A3cBBxaVbcneTRwcVV97yTrW0pJPg/8N/B3dJ+kDvB+us91UFWfmFx1S2vw7yDJ54DnV9VMkscAn6mq75lshQ+1Kx+lb0KSy3a0CFixlLVM2B5VdSdAVW1O8qPAh5M8ntm/FuHh7L6quh+4K8m1VXU7QFXdneSBCde21FYBpwG/Cfx6VV2a5O7dKbgHPCLJAXRDy6mqGYCq+nqS+yZb2uwe9gFOF9LPBb461B7g00tfzsTclOToqroUoO+JvwB4F7DsehaL7N4k+1TVXcD3bWtMsh+wWwV4VT0AvCPJh/rbm9k9cmE2+wGX0GVDJXlcVd3Uv2a0LDs5u8Mv6nxg323BNSjJxiWvZnJ+DnhQL6Kq7gN+LsnfTKakiXlWVd0D3wywbfYCVk+mpMmqqhuAk5OcANw+6Xomoaqmd7DoAeDFS1jKyHbrMXBJaplvI5SkRhngktQoA1ySGmWAS1KjDHBJatT/A/s5WfpgFMhBAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+    "# Men\n",
+    "df_men = df[df.Gender != 'F']\n",
+    "\n",
+    "fields = ['Age', 'Major', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships', 'Good Candidate']\n",
+    "for field in fields:\n",
+    "  fig, ax = plt.subplots()\n",
+    "  if field == 'GPA':\n",
+    "    df_men[field].plot(kind='hist', edgecolor='white', title=field)\n",
+    "  else:\n",
+    "    df_men[field].value_counts().sort_index().plot(ax=ax, kind='bar', title=field)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_men = []\n",
+    "actual_men = []\n",
+    "\n",
+    "for index, row in df_men.iterrows():\n",
+    "  fields = [\"student_id\", \"major\", \"age\", \"gender\", \"gpa\", \"extra_curricular\", \"num_programming_languages\", \"num_past_internships\"]\n",
+    "  names = ['Student ID', 'Major', 'Age', 'Gender', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "\n",
+    "  # Construct a student object to be predicted\n",
+    "  student = dict()\n",
+    "  for i in range(len(fields)):\n",
+    "    field = fields[i]\n",
+    "    col = names[i]\n",
+    "    student[field] = row[col]\n",
+    "\n",
+    "  # Get the outputs, predicted and actual\n",
+    "  predicted_men.append(predict(student)['good_employee'])\n",
+    "  actual_men.append(row['Good Candidate'])\n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy score for men is  0.7890295358649789\n",
+      "Confusion matrix: \n",
+      " [[ 81  40]\n",
+      " [ 10 106]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Accuracy score for men is \", accuracy_score(actual_men, predicted_men))\n",
+    "print(\"Confusion matrix: \\n\", confusion_matrix(actual_men, predicted_men))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True Positive Rate (Recall) for men: 0.91\n",
+      "True Positive Rate (Recall) for women: 0.74\n"
+     ]
+    }
+   ],
+   "source": [
+    "# True positive rates for men and women\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix, recall_score\n",
+    "\n",
+    "# Men\n",
+    "conf_matrix_men = confusion_matrix(actual_men, predicted_men)\n",
+    "TP_men = conf_matrix_men[1, 1]\n",
+    "FN_men = conf_matrix_men[1, 0]\n",
+    "TPR_men = TP_men / (TP_men + FN_men)\n",
+    "\n",
+    "#######\n",
+    "\n",
+    "# Women\n",
+    "conf_matrix_women = confusion_matrix(actual_women, predicted_women)\n",
+    "TP_women = conf_matrix_women[1, 1]\n",
+    "FN_women = conf_matrix_women[1, 0]\n",
+    "TPR_women = TP_women / (TP_women + FN_women)\n",
+    "\n",
+    "#######\n",
+    "\n",
+    "print(f\"True Positive Rate (Recall) for men: {TPR_men:.2f}\")\n",
+    "print(f\"True Positive Rate (Recall) for women: {TPR_women:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairness - Group Unaware"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_men = []\n",
+    "actual_men = []\n",
+    "\n",
+    "for index, row in df_men.iterrows():\n",
+    "    fields = [\"student_id\", \"major\", \"age\", \"gender\", \"gpa\", \"extra_curricular\", \"num_programming_languages\", \"num_past_internships\"]\n",
+    "    names = ['Student ID', 'Major', 'Age', 'Gender', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "    student = {fields[i]: row[names[i]] for i in range(len(fields))}\n",
+    "    predicted_men.append(predict(student)['good_employee'])\n",
+    "    actual_men.append(row['Good Candidate'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicted_women = []\n",
+    "actual_women = []\n",
+    "\n",
+    "for index, row in df_women.iterrows():\n",
+    "    fields = [\"student_id\", \"major\", \"age\", \"gender\", \"gpa\", \"extra_curricular\", \"num_programming_languages\", \"num_past_internships\"]\n",
+    "    names = ['Student ID', 'Major', 'Age', 'Gender', 'GPA', 'Extra Curricular', 'Num Programming Languages', 'Num Past Internships']\n",
+    "    student = {fields[i]: row[names[i]] for i in range(len(fields))}\n",
+    "    predicted_women.append(predict(student)['good_employee'])\n",
+    "    actual_women.append(row['Good Candidate'])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairness - Demographic Parity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Good candidates for men (predicted):  0.6160337552742616\n",
+      "Good candidates for men (actual):  0.48945147679324896\n",
+      "Good candidates for women (predicted):  0.34600760456273766\n",
+      "Good candidates for women (actual):  0.4600760456273764\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy\n",
+    "\n",
+    "numpy_predicted_men = numpy.array(predicted_men)\n",
+    "unique, counts = numpy.unique(numpy_predicted_men, return_counts=True)\n",
+    "d = dict(zip(unique, counts))\n",
+    "positive_men = d[1]\n",
+    "total_men = d[0] + d[1]\n",
+    "prediction_men = positive_men/total_men\n",
+    "# number of predicted good candidates for men\n",
+    "print(f\"Good candidates for men (predicted): {prediction_men}\")\n",
+    "\n",
+    "\n",
+    "numpy_actual_men = numpy.array(actual_men)\n",
+    "unique, counts = numpy.unique(numpy_actual_men, return_counts=True)\n",
+    "d = dict(zip(unique, counts))\n",
+    "positive_men1 = d[1]\n",
+    "total_men1 = d[0] + d[1]\n",
+    "actual_men = positive_men1/total_men1\n",
+    "# number of actual good candidates for men\n",
+    "print(f\"Good candidates for men (actual): {actual_men}\")\n",
+    "\n",
+    "\n",
+    "numpy_predicted_women = numpy.array(predicted_women)\n",
+    "unique, counts = numpy.unique(numpy_predicted_women, return_counts=True)\n",
+    "d = dict(zip(unique, counts))\n",
+    "positive_women = d[1]\n",
+    "total_women = d[0] + d[1]\n",
+    "prediction_women = positive_women/total_women\n",
+    "# number of predicted good candidates for women\n",
+    "print(f\"Good candidates for women (predicted): {prediction_women}\")\n",
+    "\n",
+    "\n",
+    "numpy_actual_women = numpy.array(actual_women)\n",
+    "unique, counts = numpy.unique(numpy_actual_women, return_counts=True)\n",
+    "d = dict(zip(unique, counts))\n",
+    "positive_women1 = d[1]\n",
+    "total_women1 = d[0] + d[1]\n",
+    "actual_women = positive_women1/total_women1\n",
+    "# number of actual good candidates for women\n",
+    "print(f\"Good candidates for women (actual): {actual_women}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fairness Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Disparate Impact Predicted: 0.5616698786395125\n",
+      "Disparate Impact Actual: 0.9399829552904155\n",
+      "Statistical Parity Difference Predicted: -0.2806803968981906\n",
+      "Statistical Parity Difference Actual: -0.0309368927937822\n",
+      "Equal Opportunity Difference Predicted: -0.1782559133656313\n",
+      "Equal Opportunity Difference Actual: -0.1782559133656313\n",
+      "Equalized Odds Difference Predicted: -0.1782559133656313\n",
+      "Equalized Odds Difference Actual: -0.1782559133656313\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Disparate Impact (DI)\n",
+    "DI_predicted = prediction_women / prediction_men\n",
+    "DI_actual = actual_women / actual_men\n",
+    "\n",
+    "TPR_actual_men = TPR_men\n",
+    "TPR_actual_women = TPR_women\n",
+    "\n",
+    "# extra functions for clarity\n",
+    "pr_women = prediction_women / (prediction_women + prediction_men)\n",
+    "pr_men = prediction_men / (prediction_women + prediction_men)\n",
+    "ac_women = actual_women / (actual_women + actual_men)\n",
+    "ac_men = actual_men / (actual_women + actual_men)\n",
+    "\n",
+    "# Statistical Parity Difference (SPD)\n",
+    "SPD_predicted = pr_women - pr_men\n",
+    "SPD_actual = ac_women - ac_men\n",
+    "\n",
+    "# Equal Opportunity Difference (EOD)\n",
+    "EOD_predicted = TPR_actual_women - TPR_actual_men\n",
+    "EOD_actual = TPR_actual_women - TPR_actual_men\n",
+    "EOD_positive_predicted = TPR_actual_women - TPR_actual_men\n",
+    "EOD_positive_actual = TPR_actual_women - TPR_actual_men\n",
+    "\n",
+    "# Equalized Odds Difference Predicted\n",
+    "TNR_actual_men = 1.0 - TPR_actual_men\n",
+    "TNR_actual_women = 1.0 - TPR_actual_women\n",
+    "\n",
+    "print(\"Disparate Impact Predicted:\", DI_predicted)\n",
+    "print(\"Disparate Impact Actual:\", DI_actual)\n",
+    "\n",
+    "print(\"Statistical Parity Difference Predicted:\", SPD_predicted)\n",
+    "print(\"Statistical Parity Difference Actual:\", SPD_actual)\n",
+    "\n",
+    "print(\"Equal Opportunity Difference Predicted:\", EOD_predicted)\n",
+    "print(\"Equal Opportunity Difference Actual:\", EOD_actual)\n",
+    "\n",
+    "print(\"Equalized Odds Difference Predicted:\", EOD_positive_predicted)\n",
+    "print(\"Equalized Odds Difference Actual:\", EOD_positive_actual)\n"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -1094,7 +2018,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.8.8"
   }
  },
  "nbformat": 4,