Assignment - Mahsa Dorri - 301257460.ipynb

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    "# COMP377: AI for Software Developers\n",
    "## Assignment 1: Linear Regression\n",
    "### Exercise 1\n",
    "\n",
    "Name: -------------------------Mahsa Dorri-------------------------------------\n",
    "\n",
    "Student ID: --------------------------------------------------------\n",
    "\n",
    "Date: --------------------------------------------------------------\n",
    "\n",
    "File Name: MahsaDorri301257460Lab2_Ex2\n",
    "\n",
    "### Instructions\n",
    "\n",
    "1.  Complete the code in the cells provided according to the instructions given in the comments.\n",
    "2.  Ensure that your code runs without errors.\n",
    "3.  Submit your completed notebook via eCentennial.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "import io\n",
    "from zipfile import ZipFile\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_auc_score, roc_curve\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from joblib import dump, load\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "\n",
    "import io"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['school' 'sex' 'age' 'address' 'famsize' 'Pstatus' 'Medu' 'Fedu' 'Mjob'\n",
      " 'Fjob' 'reason' 'guardian' 'traveltime' 'studytime' 'failures'\n",
      " 'schoolsup' 'famsup' 'paid' 'activities' 'nursery' 'higher' 'internet'\n",
      " 'romantic' 'famrel' 'freetime' 'goout' 'Dalc' 'Walc' 'health' 'absences'\n",
      " 'G1' 'G2' 'G3']\n"
     ]
    },
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     "data": {
      "text/plain": [
       "school        object\n",
       "sex           object\n",
       "age            int64\n",
       "address       object\n",
       "famsize       object\n",
       "Pstatus       object\n",
       "Medu           int64\n",
       "Fedu           int64\n",
       "Mjob          object\n",
       "Fjob          object\n",
       "reason        object\n",
       "guardian      object\n",
       "traveltime     int64\n",
       "studytime      int64\n",
       "failures       int64\n",
       "schoolsup     object\n",
       "famsup        object\n",
       "paid          object\n",
       "activities    object\n",
       "nursery       object\n",
       "higher        object\n",
       "internet      object\n",
       "romantic      object\n",
       "famrel         int64\n",
       "freetime       int64\n",
       "goout          int64\n",
       "Dalc           int64\n",
       "Walc           int64\n",
       "health         int64\n",
       "absences       int64\n",
       "G1             int64\n",
       "G2             int64\n",
       "G3             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
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   "source": [
    "\n",
    "\n",
    "## Part 1: Data Loading ##\n",
    "\n",
    "data_url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00320/student.zip\"\n",
    "\n",
    "response = requests.get(data_url)\n",
    "zip_file = ZipFile(io.BytesIO(response.content))\n",
    "csv_file = zip_file.open('student-mat.csv')\n",
    "df_student = pd.read_csv(csv_file, sep=\";\")\n",
    "df_student.head()\n",
    "df_student.head()\n",
    "df_student.shape\n",
    "\n",
    "print(df_student.columns.values)\n",
    "\n",
    "df_student.describe()\n",
    "df_student.dtypes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "#Importing necessary libraries for data analysis and machine learning is the first step. Requests are then used to retrieve the compressed dataset.obtain.\n",
    "Extracting and Loading Data: A pandas DataFrame containing the initial data structure with.head() and.shape is created once the dataset is loaded from the zip file.\n",
    "Data Overview: The outputs give a thorough overview of the qualities and characteristics of the dataset, including column names, a statistical summary, and data types.\n"
   ]
  },
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   "metadata": {},
   "source": [
    "### Part 2: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>passed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.5</td>\n",
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       "      <td>-0.248101</td>\n",
       "      <td>-0.220759</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.043400</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.50</td>\n",
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       "      <td>-0.369304</td>\n",
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       "      <td>-0.220759</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>-0.242315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.057215</td>\n",
       "      <td>-0.244304</td>\n",
       "      <td>-0.142838</td>\n",
       "      <td>-0.020759</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.242315</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>...</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.049451</td>\n",
       "      <td>0.255696</td>\n",
       "      <td>0.172951</td>\n",
       "      <td>0.229241</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.099458</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>...</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.022785</td>\n",
       "      <td>-0.306804</td>\n",
       "      <td>-0.037575</td>\n",
       "      <td>-0.020759</td>\n",
       "      <td>1.0</td>\n",
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       "  </tbody>\n",
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       "<p>5 rows × 34 columns</p>\n",
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      "text/plain": [
       "   school  sex       age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  \\\n",
       "0     0.0  0.0  0.186257      1.0      0.0      0.0  1.00  1.00  0.00  1.00   \n",
       "1     0.0  0.0  0.043400      1.0      0.0      1.0  0.25  0.25  0.00  0.50   \n",
       "2     0.0  0.0 -0.242315      1.0      1.0      1.0  0.25  0.25  0.00  0.50   \n",
       "3     0.0  0.0 -0.242315      1.0      0.0      1.0  1.00  0.50  0.25  0.75   \n",
       "4     0.0  0.0 -0.099458      1.0      0.0      1.0  0.75  0.75  0.50  0.50   \n",
       "\n",
       "   ...  freetime  goout  Dalc  Walc  health  absences        G1        G2  \\\n",
       "0  ...      0.50   0.75  0.00  0.00     0.5  0.003882 -0.369304 -0.248101   \n",
       "1  ...      0.50   0.50  0.00  0.00     0.5 -0.022785 -0.369304 -0.300733   \n",
       "2  ...      0.50   0.25  0.25  0.50     0.5  0.057215 -0.244304 -0.142838   \n",
       "3  ...      0.25   0.25  0.00  0.00     1.0 -0.049451  0.255696  0.172951   \n",
       "4  ...      0.50   0.25  0.00  0.25     1.0 -0.022785 -0.306804 -0.037575   \n",
       "\n",
       "         G3  passed  \n",
       "0 -0.220759     0.0  \n",
       "1 -0.220759     0.0  \n",
       "2 -0.020759     1.0  \n",
       "3  0.229241     1.0  \n",
       "4 -0.020759     1.0  \n",
       "\n",
       "[5 rows x 34 columns]"
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     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Part 2: Data Pre-processing ##\n",
    "\n",
    "# write a code to get passing student also we consider G3>10 passed otherwise they fails\n",
    "df_student['passed'] = df_student['G3'].apply(lambda x: 1 if x >= 10 else 0)\n",
    "\n",
    "#label encong cathegorical \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "def encode_categorical_data_with_labelencoder(df):\n",
    "    label_encoder = LabelEncoder()\n",
    "    categorical_cols = df.select_dtypes(include=['object']).columns\n",
    "    for col in categorical_cols:\n",
    "        df[col] = label_encoder.fit_transform(df[col])\n",
    "        \n",
    "    return df\n",
    "df_student_encoded = encode_categorical_data_with_labelencoder(df_student)\n",
    "df_student_encoded.head()\n",
    "\n",
    "# Function for custom feature scaling\n",
    "def feature_scaling(df):\n",
    "    numerical_cols = df.select_dtypes(include=[np.number]).columns\n",
    "    for col in numerical_cols:\n",
    "        max_val = np.max(df[col])\n",
    "        min_val = np.min(df[col])\n",
    "        mean_val = np.mean(df[col])\n",
    "        if max_val > 6:  \n",
    "            df[col] = (df[col] - mean_val) / (max_val - min_val)\n",
    "        else:  \n",
    "            df[col] = (df[col] - min_val) / (max_val - min_val)\n",
    "    return df\n",
    "x_train.head()\n",
    "#  scaling functions\n",
    "df_student = feature_scaling(df_student_encoded)\n",
    "df_student.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a pass/fail indicator, add a 'passed' column to the DataFrame. Students are designated as passed (1) if they receive a final grade (G3) of 10 or better, and failed (0) if they do not.\n",
    "\n",
    "Categorical Data Encoding: This function uses LabelEncoder to encode all categorical features, transforming text-based categories into numerical values to support machine learning.\n",
    "\n",
    "Feature Scaling: By standardizing feature magnitudes, a custom feature scaling function is applied to the dataset, normalizing numerical features according to their maximum value. Features with a max value larger than 6 are handled differently. This enhances model performance.\n"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "### Part 3: Create and Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X = df_student_encoded.drop([ 'G3', 'passed'], axis=1)  \n",
    "y = df_student_encoded['passed']  \n",
    "x_train,x_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)\n",
    "\n",
    "#  the logistic regression model\n",
    "logistic_reg_model = LogisticRegression()\n",
    "\n",
    "\n",
    "logistic_reg_model.fit(x_train, y_train)\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4: Test Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8861\n",
      "Precision: 0.9057\n",
      "Recall: 0.9231\n",
      "F1 Score: 0.9143\n",
      "Confusion Matrix:\n",
      "[[22  5]\n",
      " [ 4 48]]\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.85      0.81      0.83        27\n",
      "         1.0       0.91      0.92      0.91        52\n",
      "\n",
      "    accuracy                           0.89        79\n",
      "   macro avg       0.88      0.87      0.87        79\n",
      "weighted avg       0.89      0.89      0.89        79\n",
      "\n"
     ]
    }
   ],
   "source": [
    "imputer = SimpleImputer(strategy='mean')\n",
    "feature_names = x_train.columns\n",
    "x_train_imputed = pd.DataFrame(imputer.fit_transform(x_train), columns=feature_names)\n",
    "x_test_imputed = pd.DataFrame(imputer.transform(x_test), columns=feature_names)\n",
    "\n",
    "logistic_reg_model.fit(x_train_imputed, y_train)\n",
    "\n",
    "y_pred = logistic_reg_model.predict(x_test_imputed)\n",
    "\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)\n",
    "\n",
    "class_report = classification_report(y_test, y_pred)\n",
    "print(\"Classification Report:\")\n",
    "print(class_report)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With an accuracy of 88.61%, the Logistic Regression model shows good efficacy in predicting student pass/fail results. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pre-processed dataset is split into training and testing sets, and a Logistic Regression model is initialized to predict student pass/fail outcomes based on their attributes, with the exception of the derived passed column and the final grade (G3).\n",
    "\n",
    "Handling Missing Values & Model Training: To ensure robustness against incomplete data, SimpleImputer is used to impute the mean of each column to fill in missing values in the training and testing sets. The Logistic Regression model is then retrained using this imputed data.\n",
    "\n",
    "Performance Evaluation: Provides a thorough assessment of the model's efficacy in assigning students to pass-or-fail classes based on test set pass/fail outcomes and computes performance metrics (accuracy, precision, recall, F1 score), in addition to a confusion matrix and classification report.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5: Calculate Accuracy & Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression Model: 0.8860759493670886\n",
      "Precision: 0.9057\n",
      "Recall: 0.9231\n",
      "F1 Score: 0.9143\n",
      "Confusion Matrix:\n",
      "[[22  5]\n",
      " [ 4 48]]\n"
     ]
    }
   ],
   "source": [
    "### Part 5: Model Accuracy ###\n",
    "# Additional Model Evaluation Metrics\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy of Logistic Regression Model:\", accuracy)\n",
    "\n",
    "# Precision, Recall, F1 Score\n",
    "precision = precision_score(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "\n",
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(cm)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section evaluates the Logistic Regression model's performance on predicting student outcomes, highlighting accuracy, precision, recall, and F1 score as key metrics for assessing its effectiveness and reliability. The confusion matrix provides a detailed breakdown of true positives, false positives, true negatives, and false negatives, further illustrating the model's predictive accuracy in classifying students as passing or failing.\n",
    "The Logistic Regression model demonstrates high accuracy (88.61%) and balanced performance (precision of 90.57%, recall of 92.31%, F1 score of 91.43%) in predicting student pass/fail outcomes, proving its efficacy in educational settings."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization and Display (Optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Score: 0.9473\n"
     ]
    }
   ],
   "source": [
    "### Visualization & Display ###\n",
    "# Confusion Matrix Visualization\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap='Blues', xticklabels=['Not Passed', 'Passed'], yticklabels=['Not Passed', 'Passed'])\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "\n",
    "# ROC Curve\n",
    "y_prob = logistic_reg_model.predict_proba(x_test_imputed)[:, 1]\n",
    "roc_auc = roc_auc_score(y_test, y_prob)\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_prob)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, label=f'ROC curve (area = {roc_auc:.2f})')\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "print(f\"ROC AUC Score: {roc_auc:.4f}\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Conclusions\n",
    "\n",
    "\n",
    "With an accuracy of 88.61%, the logistic regression model showed great efficacy in forecasting student pass results.\n",
    "The remarkable discriminative performance of the model between passing and failing classes is further demonstrated by its ROC AUC score of 95.44%. Based on these indicators, it appears that the model may be a useful resource for educational institutions trying to identify kids who are at risk early on.\n",
    "\n",
    "A binary classification model's performance is visually represented by the Confusion Matrix, which provides information on the model's accuracy and error rates by displaying 48 true positives, 5 false positives, and 22 true negatives in addition to 4 false negatives."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}