diff --git a/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb b/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb
new file mode 100644
index 00000000..265f8f76
--- /dev/null
+++ b/P4/.ipynb_checkpoints/Jupyter_Notebook_Setup_P4-checkpoint.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "28c7a0ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import accuracy_score , confusion_matrix\n",
+    "import warnings\n",
+    "from sklearn.exceptions import DataConversionWarning\n",
+    "import joblib\n",
+    "#filter warnings\n",
+    "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "e911c179",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Student ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Major</th>\n",
+       "      <th>GPA</th>\n",
+       "      <th>Extra Curricular</th>\n",
+       "      <th>Num Programming Languages</th>\n",
+       "      <th>Num Past Internships</th>\n",
+       "      <th>Good Candidate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Statistics and Machine Learning</td>\n",
+       "      <td>2.83</td>\n",
+       "      <td>Sorority</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Information Systems</td>\n",
+       "      <td>2.89</td>\n",
+       "      <td>Fraternity</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>Teaching Assistant</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Information Systems</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>Fraternity</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>F</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Statistics and Machine Learning</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>Sorority</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Student ID Gender  Age                            Major   GPA  \\\n",
+       "0           0      F   21  Statistics and Machine Learning  2.83   \n",
+       "1           1      M   20              Information Systems  2.89   \n",
+       "2           2      F   20                             Math  2.66   \n",
+       "3           3      M   20              Information Systems  2.48   \n",
+       "4           4      F   21  Statistics and Machine Learning  3.30   \n",
+       "\n",
+       "     Extra Curricular  Num Programming Languages  Num Past Internships  \\\n",
+       "0            Sorority                          4                     1   \n",
+       "1          Fraternity                          5                     3   \n",
+       "2  Teaching Assistant                          3                     1   \n",
+       "3          Fraternity                          5                     0   \n",
+       "4            Sorority                          2                     1   \n",
+       "\n",
+       "   Good Candidate  \n",
+       "0               0  \n",
+       "1               0  \n",
+       "2               0  \n",
+       "3               0  \n",
+       "4               0  "
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "529f1caa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Student ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>GPA</th>\n",
+       "      <th>Num Programming Languages</th>\n",
+       "      <th>Num Past Internships</th>\n",
+       "      <th>Good Candidate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.00000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>20.944000</td>\n",
+       "      <td>2.905780</td>\n",
+       "      <td>3.04600</td>\n",
+       "      <td>2.052000</td>\n",
+       "      <td>0.474000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>144.481833</td>\n",
+       "      <td>1.455025</td>\n",
+       "      <td>0.839559</td>\n",
+       "      <td>1.36073</td>\n",
+       "      <td>1.407572</td>\n",
+       "      <td>0.499824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>124.750000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>2.345000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>2.990000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>374.250000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>3.560000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>499.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Student ID         Age         GPA  Num Programming Languages  \\\n",
+       "count  500.000000  500.000000  500.000000                  500.00000   \n",
+       "mean   249.500000   20.944000    2.905780                    3.04600   \n",
+       "std    144.481833    1.455025    0.839559                    1.36073   \n",
+       "min      0.000000   18.000000    0.000000                    1.00000   \n",
+       "25%    124.750000   20.000000    2.345000                    2.00000   \n",
+       "50%    249.500000   21.000000    2.990000                    3.00000   \n",
+       "75%    374.250000   22.000000    3.560000                    4.00000   \n",
+       "max    499.000000   25.000000    4.000000                    5.00000   \n",
+       "\n",
+       "       Num Past Internships  Good Candidate  \n",
+       "count            500.000000      500.000000  \n",
+       "mean               2.052000        0.474000  \n",
+       "std                1.407572        0.499824  \n",
+       "min                0.000000        0.000000  \n",
+       "25%                1.000000        0.000000  \n",
+       "50%                2.000000        0.000000  \n",
+       "75%                3.000000        1.000000  \n",
+       "max                4.000000        1.000000  "
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "ce7cabd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Loading the pickle model\n",
+    "model =joblib.load('model.pkl')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "f475f585",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Eliminating the student id and gender\n",
+    "test_data =test_data.drop(columns=['Student ID', 'Gender'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "d730a0da",
+   "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": [
+    "#Plotting the distribution of the test dataset (Good Candidate)\n",
+    "plt.title('Good/non-Good Candidates')\n",
+    "sns.countplot(x='Good Candidate', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "b8bf2891",
+   "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": [
+    "#Plotting the distribution of the test dataset (Age)\n",
+    "plt.title('Age')\n",
+    "sns.countplot(x='Age', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "id": "9e6d9c4c",
+   "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": [
+    "#Plotting the distribution of the test dataset (Majors)\n",
+    "plt.title('Majors')\n",
+    "sns.countplot(x='Major', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "c76e53fd",
+   "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": [
+    "#Plotting the distribution of the test dataset (Number of Past Internships)\n",
+    "plt.title('Number of Past Internships')\n",
+    "sns.countplot(x='Num Past Internships', data=test_data )\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xlabel('Number of Past Internships')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "c6f5a3c3",
+   "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": [
+    "#Plotting the distribution of the test dataset (GPAs)\n",
+    "plt.title('GPAs')\n",
+    "sns.histplot(test_data['GPA'])\n",
+    "plt.xlabel('GPA')\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "ec7447b0",
+   "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": [
+    "#Plotting the distribution of the test dataset (Extra Curricular Activities)\n",
+    "plt.title('Extra Curricular Activities')\n",
+    "sns.countplot(x='Extra Curricular', data=test_data )\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.tight_layout()\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "814c497f",
+   "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": [
+    "#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",
+    "plt.ylabel('Distribution')\n",
+    "plt.xlabel('Number of Programming Languages')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "id": "c778c5b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Predicting\n",
+    "x_test = test_data.drop(columns=['Good Candidate'], axis=1)\n",
+    "\n",
+    "prediction =model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "id": "66728a26",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acuuracy Score for the model is 0.832\n",
+      "Confusion matrix\n",
+      "[[221  42]\n",
+      " [ 42 195]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#computing the accuracy\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "y_test= test_data['Good Candidate']\n",
+    "accuracy = accuracy_score(y_test, prediction)\n",
+    "print(\"Acuuracy Score for the model is\", accuracy)\n",
+    "\n",
+    "confusion_matrix= confusion_matrix(y_test, prediction)\n",
+    "print('Confusion matrix')\n",
+    "print(confusion_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "76d57e16",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"done\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a154513",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/P4/Jupyter_Notebook_Setup_P4.ipynb b/P4/Jupyter_Notebook_Setup_P4.ipynb
new file mode 100644
index 00000000..be4e1494
--- /dev/null
+++ b/P4/Jupyter_Notebook_Setup_P4.ipynb
@@ -0,0 +1,699 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "id": "28c7a0ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.metrics import accuracy_score , confusion_matrix\n",
+    "import warnings\n",
+    "from sklearn.exceptions import DataConversionWarning\n",
+    "import joblib\n",
+    "#filter warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning)\n",
+    "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
+    "# Loading the test dataset\n",
+    "test_data = pd.read_csv('student_data.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "id": "e911c179",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Student ID</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Major</th>\n",
+       "      <th>GPA</th>\n",
+       "      <th>Extra Curricular</th>\n",
+       "      <th>Num Programming Languages</th>\n",
+       "      <th>Num Past Internships</th>\n",
+       "      <th>Good Candidate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>F</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Statistics and Machine Learning</td>\n",
+       "      <td>2.83</td>\n",
+       "      <td>Sorority</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Information Systems</td>\n",
+       "      <td>2.89</td>\n",
+       "      <td>Fraternity</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>2.66</td>\n",
+       "      <td>Teaching Assistant</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>M</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Information Systems</td>\n",
+       "      <td>2.48</td>\n",
+       "      <td>Fraternity</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>F</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Statistics and Machine Learning</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>Sorority</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Student ID Gender  Age                            Major   GPA  \\\n",
+       "0           0      F   21  Statistics and Machine Learning  2.83   \n",
+       "1           1      M   20              Information Systems  2.89   \n",
+       "2           2      F   20                             Math  2.66   \n",
+       "3           3      M   20              Information Systems  2.48   \n",
+       "4           4      F   21  Statistics and Machine Learning  3.30   \n",
+       "\n",
+       "     Extra Curricular  Num Programming Languages  Num Past Internships  \\\n",
+       "0            Sorority                          4                     1   \n",
+       "1          Fraternity                          5                     3   \n",
+       "2  Teaching Assistant                          3                     1   \n",
+       "3          Fraternity                          5                     0   \n",
+       "4            Sorority                          2                     1   \n",
+       "\n",
+       "   Good Candidate  \n",
+       "0               0  \n",
+       "1               0  \n",
+       "2               0  \n",
+       "3               0  \n",
+       "4               0  "
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "id": "94b15a3d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Student ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>GPA</th>\n",
+       "      <th>Num Programming Languages</th>\n",
+       "      <th>Num Past Internships</th>\n",
+       "      <th>Good Candidate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.00000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>20.944000</td>\n",
+       "      <td>2.905780</td>\n",
+       "      <td>3.04600</td>\n",
+       "      <td>2.052000</td>\n",
+       "      <td>0.474000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>144.481833</td>\n",
+       "      <td>1.455025</td>\n",
+       "      <td>0.839559</td>\n",
+       "      <td>1.36073</td>\n",
+       "      <td>1.407572</td>\n",
+       "      <td>0.499824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>18.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>124.750000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>2.345000</td>\n",
+       "      <td>2.00000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>249.500000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>2.990000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>374.250000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>3.560000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>499.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.00000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Student ID         Age         GPA  Num Programming Languages  \\\n",
+       "count  500.000000  500.000000  500.000000                  500.00000   \n",
+       "mean   249.500000   20.944000    2.905780                    3.04600   \n",
+       "std    144.481833    1.455025    0.839559                    1.36073   \n",
+       "min      0.000000   18.000000    0.000000                    1.00000   \n",
+       "25%    124.750000   20.000000    2.345000                    2.00000   \n",
+       "50%    249.500000   21.000000    2.990000                    3.00000   \n",
+       "75%    374.250000   22.000000    3.560000                    4.00000   \n",
+       "max    499.000000   25.000000    4.000000                    5.00000   \n",
+       "\n",
+       "       Num Past Internships  Good Candidate  \n",
+       "count            500.000000      500.000000  \n",
+       "mean               2.052000        0.474000  \n",
+       "std                1.407572        0.499824  \n",
+       "min                0.000000        0.000000  \n",
+       "25%                1.000000        0.000000  \n",
+       "50%                2.000000        0.000000  \n",
+       "75%                3.000000        1.000000  \n",
+       "max                4.000000        1.000000  "
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "id": "d1c29453",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Loading the pickle model\n",
+    "model =joblib.load('model.pkl')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "id": "79228d8e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Eliminating the student id and gender\n",
+    "test_data =test_data.drop(columns=['Student ID', 'Gender'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "id": "412b261b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (Good Candidate)\n",
+    "plt.title('Good/non-Good Candidates')\n",
+    "sns.countplot(x='Good Candidate', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "id": "ba1c9cb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (Age)\n",
+    "plt.title('Age')\n",
+    "sns.countplot(x='Age', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "id": "d27a24a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "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": 227,
+   "id": "57624579",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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p/BERLZPCHxHRMin8EREtk8IfEdEyKfwRES2Twh8R0TIp/BERLZPCHxHRMin8EREtk8IfEdEyKfwRES2Twh8R0TIp/BERLZPCHxHRMin8EREtk8IfEdEyKfwRES1TpfBLeoGkX0r6laTjamSIiGirSS/8klYH/g14IbA9cJik7Sc7R0REW9Vo8T8d+JXt39h+APgqcHCFHBERrSTbk/sDpUOAF9j+6+b4NcAzbP/DiK87CjiqOXwS8MtJjLkRcOck/rzJNsy/3zD/bpDfb9BN9u/3eNszR55cYxIDdGiUc8u8+tg+FTi1/3GWJWmu7Vk1fvZkGObfb5h/N8jvN+imyu9Xo6vn98AWXcePA26pkCMiopVqFP6fANtK2lrSWsArgXMr5IiIaKVJ7+qx/ZCkfwC+C6wOfMH2zyc7xxiqdDFNomH+/Yb5d4P8foNuSvx+k35zNyIi6srM3YiIlknhj4homRT+iIiWqTGOf8qRdB7LziW4C5gLfNb2fZOfqnck7QlcZftuSYcDuwKfsH1j5WgxBkkCXg08wfbJkrYEHmv7isrRYoClxV/8BvgL8LnmYyFwO/DE5njQfQa4R9JOwDuBG4Ev1Y3UW5JWl7SZpC07H7Uz9ci/A3sAhzXHiyhrXQ0NScdImqHiNElXSnpe7Vy9IGmRpIUjPn4n6euSnlArV1r8xS629+46Pk/SD23vLWmqDTVdGQ/ZtqSDKS390yQdUTtUr0g6GjiB8mL9SHPawI7VQvXOM2zvKmk+gO0/NfNfhskbbH9C0vOBmcDrgdOBC+rG6olTKBNUv0JZteCVwGMpS9B8AXh2jVAp/MVMSVvavgmgaS1u1Fx7oF6snlkk6XjgcGDvZoXUNStn6qVjgCfZ/kPtIH3wYPP3ZQBJM1ny4jYsOsu47A+cbvunTRfXMHiB7Wd0HZ8q6bKm2+5dtUKlq6d4O3CJpIskXQz8CHiHpHWB2VWT9cYrgPuBI23fBmwOfKRupJ76HeWezDD6JPB1YGNJ7wcuAT5QN1LPzZN0AaXwf1fSegzPi9sjkg6VtFrzcWjXtWqTqDKBqyFpbWA7SuvjukG/oTsaSTPoepdn+48V46wySW9rHj6FsoLr/1Be4ACwfUqNXL0maTtgX8q/zQttX1s5Uk9JWg3YGfiN7T9L2hB4nO2r6yZbdU0//ico92kMXAa8FbgZ2M32JVVypfAXkp4JbMXShXEoboBK+hvgZOBelrQybLvazaVekHTCCi7b9smTFqZPJO0O/Nz2ouZ4PWB725fXTdY7GXU2+VL4AUlnANsAVwEPN6dt+83VQvWQpOuBPWwP5Trnkl5u+7/GOjeImpu6u7r5j9q0jufa3rVust6RdDWwE+Vm/BnAacBLbT+rarAeaO7JvJFlG5VvqJUJcnO3YxalFTWsr4K/Bu6pHaKPjgdGFvnRzg0idf+7tP2IpGH7fzvMo86+Sbln+H2WNCqrG7Z/QCvrZ5QhVrfWDtInxwM/lnQ5S/eBD/Q7GkkvpNwQ3FzSJ7suzQAeqpOq534j6c2UuRgAf0+ZdzJMOqPOXgP8vyEbdbaO7WNrhxgphb/YCPiFpCtYujAeVC9ST30W+AFwDcMzWgLK+Oi5wEHAvK7ziyg30IbB31JG9rybcn/mQpZsSTosXgG8ijKe/7ZmOPWwjDo7X9L+tr9VO0i39PEDkkbtS7Q9Z7Kz9IOkH9t+Zu0c/SJpTdsP1s4RK0/S44FtbX9f0jrA6p0b2oNM0iJgXUqD8kHKyCzbnlE1Vwr/8GvGf98InMfS72gGejhnh6RtgQ8C2wPTOucHfdQSTN2bg70k6Y2UdzEb2t6m+fv8D9v7Vo42tFrd1SPpEtt7Na/K3a+AU+JVuYde1Xw+vuucgYEvjI3TKUs2/CvwHMqU/2GZ+Tklbw722JuApwOXA9i+XtLGdSOtGknb2b5O0qijr2xfOdmZurW68Nveq/m8Xu0s/WR769oZ+my67QslqRn7faKkH1FeDAbdlLw52GP3236gs0pDM2pp0Lsi3kZ5F/OxUa4Z2Gdy4yyt1YW/WzOSYBOWfjt9U71EvdP0mb4N2NL2Uc1b6SfZPr9ytF65rxnffn2zn/PNwEC3GLtMyZuDPTanWbdmuqT9KCOXzqucaZU0/89WA95t+9LaeUZKHz/LX93R9jCs7oik/6SMenmt7R0kTQf+1/bOdZP1hqSnAdcCGwDvBdYHPmz7spq5eqHr5uADzcewdUN2JqUdCTyP8vt9F/j8MMyrkfS/tveonWOkFH5A0q8oy98O4+qOSJpre5ak+bZ3ac791PZOtbNFDDNJJwFXA+dMpReydPUUw7y6I8ADTSu/M+1/G7pG9wwqSeeu6PowzMNolid+NbC17fdK2gLYdJh24GrW6jkReDylJnXe1QzD4IO3Ud6xPSzpXqbIO7a0+AFJpzHcqzs+D/gnynDHC4A9gdfbvqhqsFUkaQHlRfssyoiQpUbyDMM8DEmfoXQ/7mP7yZIeDVxg+2mVo/WMpOsoE+7m0TVyaVjfgU8FafEXNzUfazUfQ8X2BZLmAbtTiuMxQ7Jg22OB/SjbEr6K8sJ9lu1h2DWtow07cN1l+9u1Q/TDVH3H1vrC34zm2db24bWz9IukC5vJMP8zyrmBZfth4DvAd5r9FA4DLpZ0su1P1U3XM23YgesiSR8BzmHpd9xVx7r3yL/TvGOjDDz4C2XP5Krv2Fpf+G0/LGmmpLVsD8M2i4tJmgasA2zUdBF0ukJmAJtVC9ZDTcE/gFL0t6Ksa3NOzUw9NnIHrkMo6/YMk87WhLO6zlUf694jU/IdW+sLf+MG4NLmZuHdnZND0Mf/N8BbKEV+HksK/0JKq2OgSZoN7AB8GzjJ9s8qR+o522c23XSdHbhePGw7cNl+Tu0MfTQl37Hl5i7L38nJ9kmTnaUfJB09RF0fi0l6hCUv1EO15IakGbYXNtsQLmMY1lmSdLjtL3dtobmUIWh4IenVlNVHdwO+SPOOrfYmQWnxMzwFfgVuk7Se7UWS3k3Z2u59g96Hanu12hn66CvAgZR3asu8qDEc6yyt23we2iVTpuo7trT4Wfz2652UTbu7V3cchj5GJF1te0dJe1FWsfwo8C7bzxjjqRGxipr/d9vaPr2pNY+y/duamYa5xTQRZwLXAVsDJ1H6/H9SM1CPdcZGHwB8xvY3GcJhq8NI0kskrd91vIGkF1eM1HOSPixphqQ1JV0o6U6VTdcHXtONfCxLVsZdE/hyvURFCn/xGNunAQ/antOsdb577VA9dLOkzwKHAt9qRsLk734wnGB78axy239mOFYd7fY82wspXVu/B54IvKNupJ55CWWHuLsBbN/CFOjayn/+orN7062SDpC0C/C4moF67FDKwlcvaArHhgzPf6xhN9r/0WG7N9fZX3d/ygS8gb9x3eWBZo2ezqiedcf4+kkxbP+AVtb7mrfTbwc+RRnnPix7tgKcDJxu+3oA27cyvBvLD5u5kk6hDL81cDRL7y88DM5rlm24F/j7ph/8vsqZeuXs5t32Bs1OY28APlc5U27utoGkv6bsSrUGZbeqs7q7D2LqalqI/ww8lzIq5ALKiKy7V/jEAdNMMFzYTKhcB5hh+7bauXqh2WNg8ZLTtr9XOVIKP4CkJwKfATZp1qvfETjI9vsqR+spSU+ivAAcBlwKfG7QF2prk6Y4/nkqLe/bC5JeO9p521+a7CxtkT7+4nOUu+4PAti+Gnhl1UQ91swe3K75uBP4KfA2SV+tGixGJek9krZrHq8t6QfAr4DbJT23brqee1rXx/+jLNE88EtqA0h6qaTrJd0laaGkRZIW1s6VPv5iHdtXdPb8bDxUK0yvNX3ELwJ+AHyga2XAf5H0y3rJYgVeQVnUC+AISiNtY8qIl9mUzdeHgu2ju4+b+21nVIrTax8GXjQVJm11S+Ev7mw2J+nceT+E4br5+TPKNPF7Rrn29MkOE+PyQFeXzvMp92UeBq5V2Yx8mN0DbFs7RI/cPtWKPqTwd7wJOBXYTtLNwG8pa2gPNEmPp/QJf6E5fg7wYuBG4NO2H8hN3inrfkk7UPaBfg7wj13X1qkTqT8knceSZSlWo2wYdHa9RD01t9nz+hssveR01RVkc3O3SzOCYrVmTZu32P547UyrQtLlwEts3yJpZ0r3wAeBHSmT1f66Zr5YPknPoHTpzAQ+bvu9zfn9gdfYPqxmvl6S9Kyuw4eAG23/vlaeXpJ0+iin3UwSrSaFfzkk3WR7y9o5VkVnjZ7m8UeBR2y/U9JqwFWdaxFThaSNgD8M28ilqSZdPcunsb9kyuv+HfahWS/E9iMjbmRHTDpJuwMfAv5IuZF9BrARsJqk19r+Ts18q0LSO21/WNKnWHp1VQBsv7lCrMVS+JdvGFocP5B0NuVG9aMpo3qQtCkwVLuNxUD6NPAuYH3Kv80X2r6sGcZ6FmVbzUHVuaE7t2qK5Wh1V4+kRYxe4AVMtz3QL4zNRs+vADYFzrZ9c3N+F2Bj29+tmS9WrOmS2932j2tn6QdJV9neuXl8re0nd12bb3uXauGG3EAXtlVlu/oqef3U9JMuM0HL9vwKcWKCmi65jwF71M7SJ91bEN474tpQtEhHjFjquIvyTuCztqusSdTqFn/EVCfpJOBq4Jxhu+Ep6WHKcsUCplPG79McT7O95vKeOygkfYIyMuus5tQrgNsov+8M26+pkmvI/i1FDJWmO3JdymY69zIE+wm3iaQf2t57tHOSfm77KTVytbqrJ2KqG/buyBaYKWlL2zcBSNqSMnIJKg6wSOFvNLNct7X9fUnTgTVsL6qdqxck7UlZ+OrxlL/zTqtxGDbsHmrNDfpXA1vbfq+kLYBNu9Zbiqnt7cAlkn5N+X+3NWXPgXUpE/SqSFcP0GyQcBSwoe1tJG0L/IftfStH64lmk4u3Ujbw6Oy/i+0/VAsV4yLpM5SboPvYfnKzNPMFtp9WOVqMU7PV6XaUwn9drRu63dLiL95EWazscgDb10vauG6knrrL9rdrh4iV8gzbu0qaD2D7T5LWqh0qJmQ3YCtKvd1RUvW9BlL4i/ttP9CZzdqsfjhMb4UukvQR4ByWXijqynqRYpwebPZS6KwcO5Olh0HGFCbpDGAb4CqWvNs2kMI/BcyR9C5gerNN2t8D51XO1EvPaD7P6jpnyjIOMbV9Evg6sLGk9wOHULZijMEwC9h+qg3FTR8/i2dIHknXvpjA56faX1a0U7OEwb6Uf5sXTsX13WN0kv4LeLPtKbW/Rwp/CzQ7Gp0AdMYTzwFOzlr8U5+kM0ZO8hntXExNki4CdgauYOlu1qpbS6arh1YMd/wCZReuQ5vj1wCnAy+tlijGa6kJPk1//26VssTEnVg7wGjS4mf4hzt2L4a1onMxdUg6nrJyZWcpg8462g8Ap9o+vla2GJuk7Wxf1zxe2/b9Xdd2t31ZvXRlm7NohjvavsP2HzoftUP10L2S9uocNO9wRi6KFVOI7Q82s3Y/YnuG7fWaj8ek6A+Er3Q9/t8R1/59MoOMJl09xbAPd/w7YHbT1y/Kxhevq5ooxuvbkvYeedL2D2uEiXHTch6PdjzpUviLoR7uaPsqYCdJM5rjhXUTxQS8o+vxNMpEw3kMyb/NIeblPB7teNKl8AO2n1M7Qz9IOtz2lyW9bcR5AGyfUiVYjJvtF3UfN2v1fLhSnBi/x0n6JKV133lMc7x5vVhFqwv/8gpjxxAUxnWbz6Ot8Fi91REr5ffADrVDxJi636mN3H6x+naMrS78rLgwDjzbn20eft/2pd3Xmhu8McWN2Kx7NcqY8J9WCxTjYrvaypvjkeGcLSDpStu7jnUuph5JR3QdPgTcMPJFPGKi2t7iBxYvfPVGlqygB4DtN9TK1AuS9gCeSdkMors7awawep1UMRG2ZzercW5Hafn/snKkGAIp/MU3gR8B36drAtcQWAt4FOXvubs7ayFlsa+Y4iTtD3wWWLyRh6S/yTLbU18zy/rNtv+1dpaR0tXD8M9ilfR42zfWzhET18wqP9D2r5rjbYD/sb1d3WQxHpIutv3s2jlGSou/OF/S/ra/VTtIn9zTTFB7CmUsOAC2MxZ86rujU/QbvwHuqBUmJuxSSZ8G/hO4u3Oy9uTQVrf4JS2i9JuKMsLnfuBBlizSNqNivJ6RdAHlH94/An8LHAEssH1s1WAxpmbrxccDZ1P+rb6c0s9/KYDtc+qli7E0q3OO5NqNrlYX/raQNM/2bpKutr1jc26O7WfVzhYrJun0FVz2oA9AiDrS1QNIegnwg8769JI2AJ5t+xs1c/XQg83nWyUdANwCPK5inhgn26+vnSFWnqRNgA8Am9l+oaTtgT1sn1Y1V1r8y122eL7tXSpF6ilJB1JGLW0BfIoynPMk2+dWDRZjkrQ1cDTLDjWuupFHjI+kb1P2vvgn2zs1+3nPt/3UmrnS4i9GW556aP5sbJ/fPLwLGMp1iYbYN4DTKHtAZ5P1wbOR7bOb/RWw/ZCk6kPGh6a4raK5kk4B/o1yA+1oygqIQyGtxoF2n+1Pjv1lMUXdLekxNMtuSNqd0gCrKl09gKR1gX8GnksZ0XMB8D7bd6/wiQNC0k8prcZr6Go12p5TLVSMi6RXAdtS/k0O414RQ03SrpTu1R0o25/OBF5uu+p6Syn8LSDpctvPGPsrY6qR9EHKHsm/ZsmLdvXhgDE+ktamrAbwJEqj8pfAat1bMVbJlcK/eK2edzKkE5zSahxczczdHW0/UDtLTNxUXSAxffzFmZQJTgfSNcGpaqLeeiql1bgPXa1GsovTIPgpsAGZrTtQJD2WsuHKdEm7sGS7xRnAOtWCNdLiZ/gnOKXVOLgkXQzsCPyEpd+t5cb8FNYsp/06ynau3RuvLAK+WHvGdVr8xbBPcEqrcXCdUDtATFyzEctsSS+z/d+184yUwl+8T9L6wNtZMsHprXUj9dQmwHWS0mocMLbnNLM/n9acusJ2XsAHxw6SnjLypO2Ta4TpSOGnFROc0mocUJIOBT4CXEzpJ/6UpHfY/lrVYDFef+l6PI1yH/HaSlkWa3Ufv5bsfD8q22+erCz9Imk14Grb2aB7ADVzMPbrtPKbEWjft71T3WSxMprhnefafn7NHG1v8f8tZVLF2ZR+fa34yweP7Uck/VTSlrZvqp0nJmy1EV07f2D0JUZiMKwDPKF2iLYX/k0p65u/grKR9X8C/237T1VT9d6mwM8lXcHSm0Gkj3/q+46k7wJnNcevALLt4oCQdA3Ncg2Ufa5nAlX796HlXT3dJG0OHAa8DTjW9hmVI/WMpFGHpWbJhsEg6aXAXpR3pD+0/fXKkWKcJD2+6/Ah4HbbD9XK05HCz+L1NA4D9qMszvYx27+om6q3MjJksEj6K2AT25eOOL83cLPtX9dJFhPV1Je9KC3/S2zPrxyp3X2Fkk6SNI/Syp8DzLJ95BAW/UOBKyjdWocCl0s6pG6qGMPHKZN9RrqnuRYDQNJ7gNnAY4CNgC9KenfdVC1v8Ut6hLJ59b3Nqc4fRmfP3R2rBOuxjAwZPJJ+tryRWJKuqb2RR4yPpGuBXWzf1xxPB660/eSaudp+c3fr2gEmSUaGDJ5pK7g2fdJSxKq6gfJ3eV9zvDZlpdWqWl34bd9YO8MkGW1kyLcq5omx/UTSG21/rvukpCMZok2CWuB+yoi671F6FPYDLunMIao1V6jVXT3DTtLanXW/MzJksDQ3478OPMCSQj8LWAt4ie3bamWL8WsWa1uuZk2fSZfCP8Q6635LOsP2a2rniYmT9BzK7k0AP7f9g5p5Yji0uqunBdZqWhzPbFr8S6m9NGyMzfZFwEW1c8TKkXQg8F7g8ZR62xk4MqNqrja3+EfMqlvGoI/qkbQX8GrKEM5zR1y27TdMfqqI9pD0K+ClwDWeQsW27S3+A5vPb2o+d2brvpoyXnqg2b6EciNpru3TaueJaKHfAT+bSkUfWt7i75B0qe09xzo3yCQ9E9iKrhd721+qFiiiBSQ9jdLVM4el98I4pVoo0uLvWFfSXk0LuVMk162cqWcknQFsA1wFPNycNpDCH9Ff76esyT+NMiJrSkjhL44EvtDswgXwZ2CY+r9nAdtPtbebES2woe3n1Q4xUgo/YHsesJOkGZTur7tqZ+qxnwGPBW6tHSSiZb4v6Xm2L6gdpFv6+Fm8K87LWLYPvPq62b0g6SJgZ8pCbdlzN2KSSFpE6TZ+AHiwOV19OGda/MU3KfvtzqOrMA6RE2sHiGgj2+vVzjCatPhZ8UqIERGrQtJBwN7N4cW2z6+ZB9Li7/ixpKfavqZ2kF5q3maO9so+JWYPRgw7SR+ibIB0ZnPqmGYE4XEVY6XFDyDpF8BfAb+ldPUM1Xr8EVGHpKuBnW0/0hyvDsyvXVvS4i9eWDtARAytDYA/No/XX8HXTZoUfpasyy9pY1a8AUZExER8EJjfjKwTpa//+LqR0tUDLL758jFgM+AOykp619p+StVgETHwJG1K6ecXcPlU2EshhZ/Fe9LuQ9mHdpdmDfTDbB9VOVpEDCBJzwfWs/21EedfDdxh+3t1khXZd7V40PYfgNUkrdasgb5z5UwRMbhOoizMNtKFQPWJoenjL/4s6VHAD4EzJd0BPFQ5U0QMrnVsLxh50vZtkqovAJkWf3EwZf39twLfAX4NvKhqoogYZNMkLdOwlrQmML1CnqVzpI8/IqK3molbmwD/YPvu5ty6wCeBO20fWzNfWvwREb33buB24EZJ8yTNA24AFjTXqkqLPyKiTyRNp6wKAPAr2/fWzNORwj+CpEcDW9i+unaWiIh+SFcPIOliSTMkbQj8FDhdUtU9MSMi+iWFv1jf9kLgpcDptncDnls5U0REX2Qcf7FGM636UOCfaoeJiMEmadcVXbd95WRlGU0Kf3Ey8F3gEts/kfQE4PrKmSJicH2s+TwNmEXpQhawI3A5sFelXEBu7kZE9I2krwLv72zyJGkH4B9tv65mrvTxA5JmS9qg6/jRkr5QMVJEDIftunf2s/0zpsA6YOnqKXa0/efOge0/SdqlYp6IGA7XSvo88GXKNqiHA9fWjZTC37GapEfb/hNAM6wzfzYRsapeD/wdcExz/EPgM/XiFOnjByS9lrIrTmft7JdT+uXOqJcqIqI/UvgbkranbMYi4ELbv6gcKSIGnKQ9gRMpu/ot7kWw/YRamaDlhV/SDNsLm66dZdj+42jnIyLGQ9J1lOXe5wEPd843Gz9V0/Z+7K8AB1L+UrpfAdUcV31VjoiBd5ftb9cOMVKrW/wREf3UrMu/OnAOcH/nfO2Zuyn8gKQLbe871rmIiImQdNEop217n0kP06XVXT2SpgHrABs1yzGruTQD2KxasIgYCrafUzvDaFpd+IG/Ad5CKfLzWFL4FwL/VilTRAwRSQcAT6Gs2wOA7ZPrJUpXDwCSjrb9qdo5ImK4SPoPSq/Cc4DPA4cAV9g+smaurNVT3CZpPQBJ75Z0zljLqkZEjMMzbb8W+JPtk4A9gC0qZ0rhb/yz7UWS9gKeD8xmCkyrjoiB19lj9x5JmwEPAltXzAOk8Hd0JlYcAHzG9jeBtSrmiYjhcH6z8u9HgCuBG4CzagaC9PEDIOl84GbKdou7UV6lr7C9U9VgETE0JK0NTLN9V/UsKfwgaR3gBcA1tq9vtmF8qu0LKkeLiOi5Vhf+rNUTEW3U9sJ/vu0DJf2WsjaPui679gp6ERH90OrCHxHRD2MNB89aPVNA1uqJiF5azho9HVmrp6as1RMR/TBV1+jpaHXhJ2v1RESfSdoB2J6l1+r5Ur1E6eoBslZPRPSHpBOAZ1MK/7eAFwKX2D6kaq4U/mIqvipHxGCTdA2wEzDf9k6SNgE+b/tFNXO1vasHWP6rMpDCHxGr4l7bj0h6SNIM4A6mwJauWaunOATYF7jN9uspr9Br140UEUNgbrNWz+co9xGvBK6omoh09QAg6QrbT5c0j7Ju9iLgZ7afUjlaRAwJSVsBM2xfXTtLWvzFlHxVjojBJuklktYHsH0DcJOkF1cNRVr8y5hKr8oRMdgkXWV75xHn5tvepVIkIC1+oMzS7Ty2fYPtq7vPRUSspNFqbPVBNdUD1JSZuxHRZ3MlnUKZEGrgaEp3clWtLvxk5m5E9NfRwD8D/0mpLxcAb6qaiPTxA5m5GxHt0urCL+lpwO9s39YcvxZ4GXAjcGI2YomIlSHp47bfIuk8ShfPUmwfVCHWYm0v/FcCz7X9R0l7A1+lvDXbGXhy7fU0ImIwSdrN9jxJzxrtuu05k52pW9sL/087G6pL+jdgge0Tm+NlhmFFRIyXpNWB2bYPr51lpLYP51xdUucG977AD7qutf3Gd0SsAtsPAzMlrVU7y0htL25nAXMk3QncC/wIQNJfAXfVDBYRQ+EG4FJJ5wJ3d07aPqVaIlpe+G2/v5motSlwgZf0e61G6euPiFgVtzQfqwHrNeeq96+3uvAD2L5slHP/VyNLRAydX9j+r+4Tkl5eK8ziDG2+uRsR0U+SrrS961jnJlvrW/wREb0m6YXA/sDmkj7ZdWkG8FCdVEuk8EdE9N4twFzgIJZem2cR8NYqibqkqyciok+a7RbvboZ2dsb2r237npq52j6OPyKiny4ApncdTwe+XynLYin8ERH9M832XzoHzeN1KuYBUvgjIvrpbkmLR/BI2o0yWbSq3NyNiOiftwD/JemW5nhT4BX14hS5uRsR0UeS1gSeRNmI5TrbD1aOlK6eiIh+kbQOcCxwjO1rgK0kHVg5Vgp/REQfnQ48AOzRHP8eeF+9OEUKf0RE/2xj+8PAgwC272XJ3t7VpPBHRPTPA5Km06zIKWkb4P66kTKqJyKin04AvgNsIelMYE/gdVUTkVE9ERF9JekxwO6ULp7LbN9ZOVIKf0REr3VP2hqN7SsnK8toUvgjInpM0kUruGzb+0xamFGk8EdEtExG9URE9Jikd3Y9fvmIax+Y/ERLS+GPiOi9V3Y9Pn7EtRdMZpDRpPBHRPSelvN4tONJl8IfEdF7Xs7j0Y4nXW7uRkT0mKSHgbsprfvpQGerRVE2Z1mzVjZI4Y+IaJ109UREtEwKf0REy6TwR0S0TAp/REOSJZ3RdbyGpAWSzh/jebMkfbL/CSN6I8syRyxxN7CDpOnNhhn7ATeP9STbc4G54/0hktaw/dDKx4xYNWnxRyzt28ABzePDgLM6FyQ9XdKPJc1vPj+pOf/szrsCSRtK+oakqyVdJmnH5vyJkk6VdAHwpcn9lSKWlsIfsbSvAq+UNA3YEbi869p1wN62dwHeA4y25spJwHzbOwLvYukivxtwsO1X9SV5xDilqyeii+2rJW1Fae1/a8Tl9YHZkralzL4cbRLOXsDLmu/1A0mPkbR+c+3cpgspoqq0+COWdS7wUbq6eRrvBS6yvQPwImDaKM8dbR2WzizJu3uWMGIVpPBHLOsLwMm2rxlxfn2W3Ox93XKe+0Pg1VD6/oE7bS/sfcSIlZfCHzGC7d/b/sQolz4MfFDSpcDqI5/WfD4RmCXpauBDwBF9CxqxkrJWT8QqkvQy4CDbKfIxEHJzN2IVSDoIeD/whtpZIsYrLf6IiJZJH39ERMuk8EdEtEwKf0REy6TwR0S0TAp/RETL/H+Iv1OxuoQowgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (Majors)\n",
+    "plt.title('Majors')\n",
+    "sns.countplot(x='Major', data=test_data)\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "ccf66e8b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (Number of Past Internships)\n",
+    "plt.title('Number of Past Internships')\n",
+    "sns.countplot(x='Num Past Internships', data=test_data )\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.xlabel('Number of Past Internships')\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "83b82ec6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (GPAs)\n",
+    "plt.title('GPAs')\n",
+    "sns.histplot(test_data['GPA'])\n",
+    "plt.xlabel('GPA')\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "id": "4c65532f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Plotting the distribution of the test dataset (Extra Curricular Activities)\n",
+    "plt.title('Extra Curricular Activities')\n",
+    "sns.countplot(x='Extra Curricular', data=test_data )\n",
+    "plt.ylabel('Distribution')\n",
+    "plt.tight_layout()\n",
+    "plt.xticks(rotation=90) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "id": "85a840d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "plt.ylabel('Distribution')\n",
+    "plt.xlabel('Number of Programming Languages')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "id": "0e8bc628",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Predicting\n",
+    "x_test = test_data.drop(columns=['Good Candidate'], axis=1)\n",
+    "\n",
+    "prediction =model.predict(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "id": "a7037861",
+   "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",
+      "Confusion matrix in percentages\n",
+      "[[33.33333333 16.66666667]\n",
+      " [16.66666667 33.33333333]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import itertools \n",
+    "\n",
+    "\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",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "conf_matrix= confusion_matrix(Y_true, prediction)\n",
+    "print('Confusion matrix')\n",
+    "print(conf_matrix)\n",
+    "\n",
+    "classes = [\"1\", \"2\"]  \n",
+    "\n",
+    "# Computing the confusion matrix (Regular numbers)\n",
+    "confusion_matrix = confusion_matrix(y_test, prediction) \n",
+    "\n",
+    "\n",
+    "# Compute the confusion matrix percentage\n",
+    "\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "import numpy as np\n",
+    "\n",
+    "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_percentage = (conf_matrix / conf_matrix.sum()) * 100\n",
+    "\n",
+    "\n",
+    "print(\"Confusion matrix in percentages\")\n",
+    "print(conf_matrix_percent)\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",
+    "      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "id": "59a603ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "done\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"done\") "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "684fd7a0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/P4/test_data.pkl b/P4/test_data.pkl
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