diff --git a/models/svm_classifier_best.pkl b/models/svm_classifier_best.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..70daa8e14fb817028eeecfdaab19eeb60cb66822
Binary files /dev/null and b/models/svm_classifier_best.pkl differ
diff --git a/notebooks/classification/SVM(Classification).ipynb b/notebooks/classification/SVM(Classification).ipynb
index 40258b412e27108a10365e043ec1bc3efb63f01c..28a62f92cfa1380ccb3a9e3fc78509017cddb32b 100644
--- a/notebooks/classification/SVM(Classification).ipynb
+++ b/notebooks/classification/SVM(Classification).ipynb
@@ -1,5 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9a15f6a0-e634-4894-bf5e-ab33a7e47c27",
+   "metadata": {},
+   "source": [
+    "### **Problem Statament**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a47cc090-0904-45e7-9ec0-8a9bee953be5",
+   "metadata": {},
+   "source": [
+    "**Predicting High-Income Individuals Using Support Vector Machines**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -7,15 +23,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Import Necessary Libraires\n",
+    "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "import seaborn as sns\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "from sklearn.svm import SVC\n",
-    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
+    "from sklearn.model_selection import GridSearchCV\n",
     "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_auc_score, roc_curve\n",
     "\n",
     "\n",
@@ -894,7 +912,8 @@
    ],
    "source": [
     "# Target column distribution\n",
-    "target_col = 'Income'  # assumed from common census datasets\n",
+    "\n",
+    "target_col = 'Income'\n",
     "print(\"\\nTarget class distribution:\")\n",
     "print(df[target_col].value_counts())"
    ]
@@ -923,11 +942,21 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b1767072-720c-4ffc-a9fc-7dd39a693f6a",
+   "metadata": {},
+   "source": [
+    "-  The target variable distribution chart reveals a significant class imbalance, with a much larger proportion of individuals earning ≤$50K (class 0) compared to those earning >$50K (class 1). This imbalance presents a challenge for classification models, as they tend to favor the majority class unless specifically tuned or balanced."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "e56f71c4-5b16-44a7-b8f6-4144723b4496",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -989,7 +1018,7 @@
    ],
    "source": [
     "# Compute correlations with the target variable\n",
-    "target_corr = df.corr()['Income'].drop('Income')  # drop self-correlation\n",
+    "target_corr = df.corr()['Income'].drop('Income') \n",
     "\n",
     "# Sort by absolute correlation\n",
     "target_corr_sorted = target_corr.abs().sort_values(ascending=False)\n",
@@ -997,7 +1026,7 @@
     "print(\"Features most correlated with target 'income':\")\n",
     "print(target_corr_sorted)\n",
     "\n",
-    "# Optional: Plot top N\n",
+    "# Plot top N\n",
     "top_n = 10\n",
     "target_corr_sorted[:top_n].plot(kind='barh', title=f\"Top {top_n} Correlated Features with Target\")\n",
     "plt.xlabel(\"Correlation (absolute)\")\n",
@@ -1006,7 +1035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "496c0cec-416b-40a8-b269-ff0f4dae7796",
    "metadata": {},
    "outputs": [
@@ -1035,15 +1064,19 @@
     "plt.tight_layout()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "bfd6af03-8bf6-4ecb-893b-9a7aee7874dc",
+   "metadata": {},
+   "source": []
+  },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "f02fd265-c9fd-4857-8493-897fac290e18",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.preprocessing import StandardScaler\n",
-    "\n",
     "# Separate features and target\n",
     "X = df.drop(columns=['Income'])\n",
     "y = df['Income']\n",
@@ -1055,7 +1088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "66cab099-edad-43fb-a80d-e249e8ee4b95",
    "metadata": {},
    "outputs": [
@@ -1096,17 +1129,13 @@
     }
    ],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "# Split the data (80% train, 20% test)\n",
+    "# Split the data\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
     "    X_scaled, y, test_size=0.2, random_state=42, stratify=y\n",
     ")\n",
     "\n",
     "print(f\"Training size: {X_train.shape}, Test size: {X_test.shape}\")\n",
     "\n",
-    "from sklearn.svm import SVC\n",
-    "\n",
     "# Train basic SVM with RBF kernel\n",
     "svm_clf = SVC(kernel='rbf', random_state=42)\n",
     "svm_clf.fit(X_train, y_train)\n",
@@ -1125,17 +1154,336 @@
     "plt.title(\"Confusion Matrix - Baseline SVM\")\n",
     "plt.show()\n",
     "\n",
-    "# ROC AUC (optional for binary classification)\n",
+    "# ROC AUC\n",
     "y_prob = svm_clf.decision_function(X_test)\n",
     "roc_auc = roc_auc_score(y_test, y_prob)\n",
     "print(f\"ROC AUC Score: {roc_auc:.4f}\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7103955f-5a49-4185-b336-a65e3ddff0b3",
+   "metadata": {},
+   "source": [
+    "-  The confusion matrices provide insights into prediction accuracy. In the baseline model, while the model performs well on the majority class (class 0), it struggles with correctly identifying instances of class 1, as shown by a high number of false negatives."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "2ed11197-a552-467e-82f8-8bb507b4b363",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n",
+      "Best Parameters from GridSearch:\n",
+      "{'C': 0.1, 'gamma': 'scale', 'kernel': 'linear'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Define hyperparameter grid\n",
+    "\n",
+    "param_grid = {\n",
+    "    'C': [0.1, 1],\n",
+    "    'gamma': ['scale', 0.01, 0.1],\n",
+    "    'kernel': ['rbf', 'linear']\n",
+    "}\n",
+    "\n",
+    "# Initialize GridSearchCV\n",
+    "grid_search = GridSearchCV(\n",
+    "    SVC(),\n",
+    "    param_grid,\n",
+    "    cv=5,\n",
+    "    scoring='f1_weighted',\n",
+    "    n_jobs=-1,\n",
+    "    verbose=2\n",
+    ")\n",
+    "\n",
+    "# Fit on training data\n",
+    "grid_search.fit(X_train, y_train)\n",
+    "\n",
+    "# Best parameters\n",
+    "print(\"Best Parameters from GridSearch:\")\n",
+    "print(grid_search.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "93e10a8f-24da-4cb2-b301-2db5932747e4",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Classification Report (Tuned SVM):\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.86      0.93      0.90      6296\n",
+      "           1       0.70      0.52      0.60      1919\n",
+      "\n",
+      "    accuracy                           0.84      8215\n",
+      "   macro avg       0.78      0.73      0.75      8215\n",
+      "weighted avg       0.83      0.84      0.83      8215\n",
+      "\n",
+      "ROC AUC Score (Tuned): 0.8787\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2400x1800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time= 1.3min\n",
+      "[CV] END ....................C=1, gamma=scale, kernel=linear; total time= 1.4min\n",
+      "[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time= 1.2min\n",
+      "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time= 1.5min\n",
+      "[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time= 1.2min\n",
+      "[CV] END ....................C=1, gamma=scale, kernel=linear; total time= 1.5min\n",
+      "[CV] END ..................C=0.1, gamma=scale, kernel=linear; total time= 1.4min\n",
+      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time= 1.3min\n",
+      "[CV] END ..................C=0.1, gamma=scale, kernel=linear; total time= 1.4min\n",
+      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time= 1.3min\n",
+      "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 2.8min\n",
+      "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 2.9min\n",
+      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time= 1.5min\n",
+      "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time= 1.3min\n",
+      "[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time= 1.5min\n",
+      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time= 1.4min\n",
+      "[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time= 1.3min\n",
+      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time= 1.5min\n",
+      "[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time= 1.3min\n",
+      "[CV] END ....................C=1, gamma=scale, kernel=linear; total time= 1.6min\n",
+      "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 2.9min\n",
+      "[CV] END ..................C=0.1, gamma=scale, kernel=linear; total time= 1.2min\n",
+      "[CV] END ....................C=1, gamma=scale, kernel=linear; total time= 1.7min\n",
+      "[CV] END ..................C=0.1, gamma=scale, kernel=linear; total time= 1.2min\n",
+      "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time= 1.7min\n",
+      "[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time= 1.2min\n",
+      "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time= 1.7min\n",
+      "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 2.9min\n",
+      "[CV] END ...................C=0.1, gamma=0.01, kernel=linear; total time= 1.3min\n",
+      "[CV] END ....................C=1, gamma=scale, kernel=linear; total time= 1.7min\n",
+      "[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time= 1.5min\n",
+      "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time= 1.5min\n",
+      "[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time= 1.5min\n",
+      "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time= 1.5min\n",
+      "[CV] END ....................C=0.1, gamma=0.1, kernel=linear; total time= 1.5min\n",
+      "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time= 1.5min\n",
+      "[CV] END ..................C=0.1, gamma=scale, kernel=linear; total time= 1.5min\n",
+      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time= 1.6min\n",
+      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time= 1.7min\n",
+      "[CV] END .....................C=1, gamma=0.01, kernel=linear; total time= 1.4min\n",
+      "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time= 2.1min\n",
+      "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time= 1.1min\n",
+      "[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time= 2.0min\n",
+      "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time= 1.2min\n",
+      "[CV] END .......................C=0.1, gamma=0.1, kernel=rbf; total time= 2.4min\n",
+      "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time=  50.1s\n",
+      "[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time= 2.0min\n",
+      "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time= 1.2min\n",
+      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time= 2.0min\n",
+      "[CV] END ......................C=1, gamma=0.1, kernel=linear; total time= 1.3min\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get best model\n",
+    "best_svm = grid_search.best_estimator_\n",
+    "\n",
+    "# Predictions\n",
+    "y_pred_best = best_svm.predict(X_test)\n",
+    "\n",
+    "print(\"\\nClassification Report (Tuned SVM):\")\n",
+    "print(classification_report(y_test, y_pred_best))\n",
+    "\n",
+    "# Confusion Matrix\n",
+    "cm_best = confusion_matrix(y_test, y_pred_best)\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm_best)\n",
+    "disp.plot()\n",
+    "plt.title(\"Confusion Matrix - Tuned SVM\")\n",
+    "\n",
+    "# ROC AUC\n",
+    "y_prob_best = best_svm.decision_function(X_test)\n",
+    "roc_auc_best = roc_auc_score(y_test, y_prob_best)\n",
+    "print(f\"ROC AUC Score (Tuned): {roc_auc_best:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a87d783-5b0d-4db9-92d0-6a477f996e53",
+   "metadata": {},
+   "source": [
+    "-  After hyperparameter tuning, the confusion matrix for the tuned model shows a slight improvement in correctly classifying high-income individuals, reducing false negatives and increasing true positives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f56a6420-8206-440c-b8f9-bd18e21ae238",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 2400x1500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Combine actual and predicted into a dataframe\n",
+    "comparison_df = pd.DataFrame({\n",
+    "    'Actual': y_test,\n",
+    "    'Predicted': y_pred_best\n",
+    "})\n",
+    "\n",
+    "# Melt the dataframe for side-by-side plotting\n",
+    "melted_df = comparison_df.melt(var_name='Type', value_name='Class')\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "sns.countplot(data=melted_df, x='Class', hue='Type')\n",
+    "plt.title(\"Actual vs Predicted Class Distribution - Tuned SVM\")\n",
+    "plt.xlabel(\"Class (0 = ≤50K, 1 = >50K)\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.grid(True, axis='y')\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e1e7553-2ee1-402c-a0ff-1186c85976c2",
+   "metadata": {},
+   "source": [
+    "-  The actual vs predicted class distribution chart highlights how the tuned model still slightly overpredicts class 0 and underpredicts class 1, though the gap has narrowed compared to the baseline."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "096758bb-3519-451e-b3ac-4d707b5bd855",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2400x1800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time= 1.8min\n",
+      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 1.6min\n",
+      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time= 1.7min\n",
+      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 1.6min\n",
+      "[CV] END ......................C=0.1, gamma=0.01, kernel=rbf; total time= 1.8min\n",
+      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 1.6min\n",
+      "[CV] END .....................C=0.1, gamma=scale, kernel=rbf; total time= 1.9min\n",
+      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 1.4min\n",
+      "[CV] END .......................C=1, gamma=scale, kernel=rbf; total time= 2.0min\n",
+      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time= 1.4min\n"
+     ]
+    }
+   ],
+   "source": [
+    "# ROC Curve Comparison\n",
+    "\n",
+    "fpr_base, tpr_base, _ = roc_curve(y_test, y_prob)\n",
+    "fpr_tuned, tpr_tuned, _ = roc_curve(y_test, y_prob_best)\n",
+    "\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.plot(fpr_base, tpr_base, label='Baseline SVM (AUC = {:.2f})'.format(roc_auc), linestyle='--')\n",
+    "plt.plot(fpr_tuned, tpr_tuned, label='Tuned SVM (AUC = {:.2f})'.format(roc_auc_best))\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 Comparison\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e07f44b-52f5-45a6-8296-2ba05195a9fa",
+   "metadata": {},
+   "source": [
+    "-  Lastly, the ROC curve comparison illustrates the enhanced discriminatory power of the tuned SVM, with its ROC AUC increasing from 0.85 to 0.88, indicating better class separability and improved overall performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "317c2e5c-15cd-4f21-bc8f-ed0a2b003a57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['../../models/svm_classifier_best.pkl']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# Save the best estimator (tuned SVM model)\n",
+    "joblib.dump(best_svm, '../../models/svm_classifier_best.pkl')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b675c149-4825-484f-ab2f-55a3f2604110",
+   "metadata": {},
+   "source": [
+    "### Conclusion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "646b7e07-2b0b-4cf0-9a2e-53ffcfd9a752",
+   "metadata": {},
+   "source": [
+    "-  This SVM Classification model classify income levels using census data. After performing data exploration and preprocessing, a baseline SVM model was trained and evaluated. To improve performance—especially for the minority class—hyperparameter tuning was applied using GridSearchCV. The tuned model showed improved metrics, particularly in recall and ROC AUC. These metrics highlighted the challenges of class imbalance and guided improvements through tuning. The tuned model achieved an accuracy of 84%, an F1-score of 0.60 for the high-income class, and a ROC AUC of 0.88, reflecting better class separation.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0e941c1-5412-4341-8445-67ec1dd33ca6",
+   "metadata": {},
    "outputs": [],
    "source": []
   }